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155,937 results on '"Guzmán, A."'

1. Alternative Grit Models: Explorations into the Psychometric Properties of Grit-S and Academic Performance

Author

Miguel Eduardo Uribe-Moreno, Iván Felipe Medina-Arboleda, Alfredo Guzmán-Rincón, and Suelen Emilia Castiblanco-Moreno

Abstract

Grit, the passion for achieving long-term goals, has been conceived as a two-dimensional construct (Consistency of interest and Perseverance of effort). The construct is well known for its easy measurement and its relationship with performance, including academic performance. However, there have been different criticisms, such as the overlap of grit with other personality characteristics, the variability in the psychometric structure of the Grit-O and Grit-S tests, and the fact that some work reports a weak influence of grit on academic performance. Within this framework, this study contrasts different psychometric structures of the Grit-S scale and its relationship with the academic performance of higher education students. So, with this purpose, a dichotomous model of high and low grit, a K-medias clustering model, and three structural equation models have been tested. The results indicate that (a) there is a statistically significant relationship between grit--mainly determined by the consistency dimension--and academic performance, although it decreases when controlling for contextual variables, and (b) Consistency mediates the relationship between Perseverance and academic performance. The instability of grit to predict performance is discussed, and the thesis of a two-sub-dimensional structure is supported.

Published
2024

2. TiniScript: A Simplified Language for Educational Robotics

Author

Ramos, Gabriel Gonzalo Guzman and Ramos, Pedro Jesus Guzman

Subjects
Computer Science - Robotics, K.3.2, I.2.9
Abstract

TiniScript is an intermediate programming language designed for educational robotics, aligned with STEM principles to foster integrative learning experiences. With its minimalist single-line syntax, such as F(2, 80) , TiniScript simplifies robotic programming, allowing users to bypass complex code uploading processes and enabling realtime direct instruction transmission. Thanks to its preloaded interpreter, TiniScript decouples programming from hardware, significantly reducing wait times. Instructions can be sent wirelessly from any Bluetooth enabled device, making TiniScript adaptable to various robots. This adaptability optimizes iterative and collaborative learning, allowing students to focus on the creative aspects of robotics. This paper explores TiniScripts design principles, syntax, and practical applications, highlighting its potential to make robotics programming more accessible and effective in developing critical thinking skills., Comment: 10 pages, 5 figures. For associated resources and block-based programming interface, see tinibot.pe. This work explores TiniScripts design for simplified, real-time robotics programming aimed at educational environments, emphasizing accessibility and creative engagement in STEM learning

Published
2024

3. Grand Challenges in the Verification of Autonomous Systems

Author

Leahy, Kevin, Asgari, Hamid, Dennis, Louise A., Feather, Martin S., Fisher, Michael, Ibanez-Guzman, Javier, Logan, Brian, Olszewska, Joanna I., and Redfield, Signe

Subjects
Computer Science - Robotics
Abstract

Autonomous systems use independent decision-making with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their way into an increasing number of applications in an ever wider range of settings. If we are to deploy them to perform safety-critical or mission-critical roles, it is imperative that we have justified confidence in their safe and correct operation. Verification is the process by which such confidence is established. However, autonomous systems pose challenges to existing verification practices. This paper highlights viewpoints of the Roadmap Working Group of the IEEE Robotics and Automation Society Technical Committee for Verification of Autonomous Systems, identifying these grand challenges, and providing a vision for future research efforts that will be needed to address them.

Published
2024

4. Quadrupole-hexadecapole correlations in neutron-rich samarium and gadolinium isotopes

Author

Lotina, L., Nomura, K., Rodríguez-Guzmán, R., and Robledo, L. M.

Subjects
Nuclear Theory, Nuclear Experiment
Abstract

We present an extensive study of quadrupole-hexadecapole correlation effects in even-even Sm and Gd isotopes with neutron number $N=88-106$. The calculations are performed in the framework of the Gogny energy density functional (EDF) with the D1S parametrization and the $sdg$ interacting boson model (IBM). The quadrupole-hexadecapole constrained self-consistent mean-field potential energy surface is mapped onto the expectation value of the $sdg$-boson Hamiltonian. This procedure determines the parameters of the $sdg$-IBM Hamiltonian microscopically. Calculated excitation energies and transition strengths are compared to the ones obtained with a simpler $sd$-IBM, as well as with the experimental data. The Gogny-EDF mapped $sdg$-IBM reproduces spectroscopic properties of the studied nuclei as reasonably as in the case of the previous $sdg$-boson mapping calculations that were based on the relativistic EDF, indicating that the axial quadrupole-hexadecapole method is sound regardless of whether relativistic or nonrelativistic EDF is employed. The mapped $sdg$-IBM improves some of the results in lighter Sm and Gd isotopes compared to the mapped $sd$-IBM, implying the existence of significant hexadecapole correlations in those nuclei. For those nuclei with $N \geq 94$, hexadecapole effects are minor, and the only significant difference between the two boson models can be found in the description of $E0$ monopole transitions., Comment: 12 pages, 12 figures

Published
2024

5. Hereditary First-Order Model Checking

Author

Bodirsky, Manuel and Guzmán-Pro, Santiago

Subjects
Mathematics - Logic, Computer Science - Computational Complexity, Computer Science - Discrete Mathematics, Computer Science - Logic in Computer Science, 03B16, 03B70, 03C13, F.1.3, F.4.1
Abstract

Many computational problems can be modelled as the class of all finite relational structures $\mathbb A$ that satisfy a fixed first-order sentence $\phi$ hereditarily, i.e., we require that every substructure of $\mathbb A$ satisfies $\phi$. In this case, we say that the class is in HerFO. The problems in HerFO are always in coNP, and sometimes coNP-complete. HerFO also contains many interesting computational problems in P, including many constraint satisfaction problems (CSPs). We show that HerFO captures the class of complements of CSPs for reducts of finitely bounded structures, i.e., every such CSP is polynomial-time equivalent to the complement of a problem in HerFO. However, we also prove that HerFO does not have the full computational power of coNP: there are problems in coNP that are not polynomial-time equivalent to a problem in HerFO, unless E=NE. Another main result is a description of the quantifier-prefixes for $\phi$ such that hereditarily checking $\phi$ is in P; we show that for every other quantifier-prefix there exists a formula $\phi$ with this prefix such that hereditarily checking $\phi$ is coNP-complete.

Published
2024

6. Einstein metrics on the full flag $F(n)$

Author

Guzman, Mikhail R.

Subjects
Mathematics - Differential Geometry
Abstract

Let $ M = G/K $ be a full flag manifold. In this work, we investigate the $ G$-stability of Einstein metrics on $M$ and analyze their stability types, including coindices, for several cases. We specifically focus on $F(n) = \mathrm{SU}(n)/T$, emphasizing $n = 5$, where we identify four new Einstein metrics in addition to known ones. Stability data, including coindex and Hessian spectrum, confirms that these metrics on $F(5)$ are pairwise non-homothetic, providing new insights into the finiteness conjecture., Comment: 15

Published
2024

7. Measurement of the emittance of accelerated electron bunches at the AWAKE experiment

Author

Cooke, D. A., Pannell, F., Della Porta, G. Zevi, Farmer, J., Bencini, V., Bergamaschi, M., Mazzoni, S., Ranc, L., Senes, E., Sherwood, P., Wing, M., Agnello, R., Ahdida, C. C., Amoedo, C., Andrebe, Y., Apsimon, O., Apsimon, R., Arnesano, J. M., Blanchard, P., Burrows, P. N., Buttenschön, B., Caldwell, A., Chung, M., Clairembaud, A., Davut, C., Demeter, G., Dexter, A. C., Doebert, S., Fasoli, A., Fonseca, R., Furno, I., van Gils, N. Z., Granados, E., Granetzny, M., Graubner, T., Grulke, O., Gschwendtner, E., Guran, E., Henderson, J., Kedves, M. Á., Kraus, F., Krupa, M., Lefevre, T., Liang, L., Liu, S., Lopes, N., Lotov, K., Calderon, M. Martinez, Mezger, J., Guzmán, P. I. Morales, Moreira, M., Nechaeva, T., Okhotnikov, N., Pakuza, C., Pardons, A., Pepitone, K., Poimendidou, E., Pucek, J., Pukhov, A., Ramjiawan, R. L., Rey, S., Rossel, R., Saberi, H., Schmitz, O., Silva, F., Silva, L., Spear, B., Stollberg, C., Sublet, A., Swain, C., Topaloudis, A., Tuev, N. TorradoP., Velotti, F., Verzilov, V., Vieira, J., Walter, E., Welsch, C., Wendt, M., Wolfenden, J., Woolley, B., Xia, G., Verra, L., Yarygova, V., and Zepp, M.

Subjects
Physics - Accelerator Physics
Abstract

The vertical plane transverse emittance of accelerated electron bunches at the AWAKE experiment at CERN has been determined, using three different methods of data analysis. This is a proof-of-principle measurement using the existing AWAKE electron spectrometer to validate the measurement technique. Large values of the geometric emittance, compared to that of the injection beam, are observed ($\sim \SI{0.5}{\milli\metre\milli\radian}$ compared with $\sim \SI{0.08}{\milli\metre\milli\radian}$), which is in line with expectations of emittance growth arising from plasma density ramps and large injection beam bunch size. Future iterations of AWAKE are anticipated to operate in conditions where emittance growth is better controlled, and the effects of the imaging systems of the existing and future spectrometer designs on the ability to measure the emittance are discussed. Good performance of the instrument down to geometric emittances of approximately $\SI{1e-4}{\milli\metre\milli\radian}$ is required, which may be possible with improved electron optics and imaging., Comment: 20 pages, 9 figures

Published
2024

8. Non-Euclidean High-Order Smooth Convex Optimization

Author

Contreras, Juan Pablo, Guzmán, Cristóbal, and Martínez-Rubio, David

Subjects
Mathematics - Optimization and Control, Computer Science - Data Structures and Algorithms, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We develop algorithms for the optimization of convex objectives that have H\"older continuous $q$-th derivatives with respect to a $p$-norm by using a $q$-th order oracle, for $p, q \geq 1$. We can also optimize other structured functions. We do this by developing a non-Euclidean inexact accelerated proximal point method that makes use of an inexact uniformly convex regularizer. We also provide nearly matching lower bounds for any deterministic algorithm that interacts with the function via a local oracle.

Published
2024

9. Control of the classical dynamics of a particle in the Morse-soft-Coulomb potential

Author

Amici, Gabriel Albertin, Morán, José Andrés Guzmán, and de Lima, Emanuel Fernandes

Subjects
Nonlinear Sciences - Chaotic Dynamics, Physics - Atomic Physics
Abstract

We introduce the one-dimensional Morse-soft-Coulomb (MsC) potential consisting of a Morse repulsive barrier smoothly connected with a soft-core Coulomb potential at the origin. This new potential has a single parameter that controls the softness of the repulsive barrier and the well depth. When this softening-depth parameter tends to zero, the MsC potential approaches the Coulomb potential with an infinite repulsive barrier, a known successful model for the hydrogen atom. We investigate the classical chaotic dynamics of the MsC potential subjected to time-dependent external fields, comparing the results with the Coulomb potential. We show that the MsC potential reproduces the dynamics and the ionization probabilities of the Coulomb potential for sufficiently small values of the softening parameter. We also investigate the role of the softening parameter in the phase-space structures, showing that the increasing of its value leads to the increasing of the chaotic sea and consequently to the rise of the ionization probability. Finally, we address the problem of controlling the dynamics of a particle in the MsC potential from the perspective of optimal control theory, which cannot be easily applied in the case of the Coulomb potential due to the singularity at the origin. We analize a particular optimal solution to the problem of transferring a given amount of energy to the system at minimum cost. Our results show that the MsC potential can be a useful simple model for investigating the hydrogen atom.

Published
2024

10. Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry

Author

Bassily, Raef, Guzmán, Cristóbal, and Menart, Michael

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of $(\epsilon,\delta)$-differential privacy (DP) in both Euclidean and non-Euclidean setups. We first consider Lipschitz convex-concave SSPs in the $\ell_p/\ell_q$ setup, $p,q\in[1,2]$. Here, we obtain a bound of $\tilde{O}\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$ on the strong SP-gap, where $n$ is the number of samples and $d$ is the dimension. This rate is nearly optimal for any $p,q\in[1,2]$. Without additional assumptions, such as smoothness or linearity requirements, prior work under DP has only obtained this rate when $p=q=2$ (i.e., only in the Euclidean setup). Further, existing algorithms have each only been shown to work for specific settings of $p$ and $q$ and under certain assumptions on the loss and the feasible set, whereas we provide a general algorithm for DP SSPs whenever $p,q\in[1,2]$. Our result is obtained via a novel analysis of the recursive regularization algorithm. In particular, we develop new tools for analyzing generalization, which may be of independent interest. Next, we turn our attention towards SVIs with a monotone, bounded and Lipschitz operator and consider $\ell_p$-setups, $p\in[1,2]$. Here, we provide the first analysis which obtains a bound on the strong VI-gap of $\tilde{O}\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$. For $p-1=\Omega(1)$, this rate is near optimal due to existing lower bounds. To obtain this result, we develop a modified version of recursive regularization. Our analysis builds on the techniques we develop for SSPs as well as employing additional novel components which handle difficulties arising from adapting the recursive regularization framework to SVIs.

Published
2024

11. Emulating a quantum Maxwell's demon with non-separable structured light

Author

Medina-Segura, Edgar, Obando, Paola C., Mkhumbuza, Light, Galvez, Enrique J., Rosales-Guzmán, Carmelo, Ruffato, Gianluca, Romanato, Filippo, Forbes, Andrew, and Nape, Isaac

Subjects
Physics - Optics, Quantum Physics
Abstract

Maxwell's demon (MD) has proven an instructive vehicle by which to explore the relationship between information theory and thermodynamics, fueling the possibility of information driven machines. A long standing debate has been the concern of entropy violation, now resolved by the introduction of a quantum MD, but this theoretical suggestion has proven experimentally challenging. Here, we use classical vectorially structured light that is non-separable in spin and orbital angular momentum to emulate a quantum MD experiment. Our classically entangled light fields have all the salient properties necessary of their quantum counterparts but without the experimental complexity of controlling quantum entangled states. We use our experiment to show that the demon's entropy increases during the process while the system's entropy decreases, so that the total entropy is conserved through an exchange of information, confirming the theoretical prediction. We show that our MD is able to extract useful work from the system in the form of orbital angular momentum, opening a path to information driven optical spanners for the mechanical rotation of objects with light. Our synthetic dimensions of angular momentum can easily be extrapolated to other degrees of freedom, for scalable and robust implementations of MDs at both the classical and quantum realms, enlightening the role of a structured light MD and its capability to control and measure information., Comment: 14 pages, 6 Figures

Published
2024

12. First Detection of Molecular Gas in the Giant Low Surface Brightness Galaxy Malin 1

Author

Galaz, Gaspar, González-López, Jorge, Guzmán, Viviana, Messias, Hugo, Junais, Boissier, Samuel, Epinat, Benoît, Weilbacher, Peter M., Puzia, Thomas, Johnston, Evelyn J., Amram, Philippe, Frayer, David, Blaña, Matías, Howk, J. Christopher, Berg, Michelle, Bustos-Espinoza, Roy, Muñoz-Mateos, Juan Carlos, Cortés, Paulo, García-Appadoo, Diego, and Joachimi, Katerine

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

After over three decades of unsuccessful attempts, we report the first detection of molecular gas emission in Malin 1, the largest spiral galaxy observed to date, and one of the most iconic giant low surface brightness galaxies. Using ALMA, we detect significant $^{12}$CO(J=1-0) emission in the galaxy's central region and tentatively identify CO emission across three regions on the disc. These observations allow for a better estimate of the H$_2$ mass and molecular gas mass surface density, both of which are remarkably low given the galaxy's scale. By integrating data on its HI mass, we derive a very low molecular-to-atomic gas mass ratio. Overall, our results highlight the minimal presence of molecular gas in Malin 1, contrasting sharply with its extensive, homogeneous atomic gas reservoir. For the first time, we position Malin 1 on the Kennicutt-Schmidt (K-S) diagram, where it falls below the main sequence for normal spirals, consistent with previous upper limits but now with more accurate figures. These findings are crucial for constraining our understanding of star formation processes in environments characterized by extremely low molecular gas densities and for refining models of galaxy formation, thereby improving predictions concerning the formation, evolution, and distribution of these giant, elusive galaxies., Comment: Accepted for publication in The Astrophysical Journal Letters. Published

Published
2024
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13. Code Collaborate: Dissecting Team Dynamics in First-Semester Programming Students

Author

Berrezueta-Guzman, Santiago, Bassner, Patrick, Wagner, Stefan, and Krusche, Stephan

Subjects
Computer Science - Software Engineering
Abstract

Understanding collaboration patterns in introductory programming courses is essential, as teamwork is a critical skill in computer science. In professional environments, software development relies on effective teamwork, navigating diverse perspectives, and contributing to shared goals. This paper offers a comprehensive analysis of the factors influencing team efficiency and project success, providing actionable insights to enhance the effectiveness of collaborative programming education. By analyzing version control data, survey responses, and performance metrics, the study highlights the collaboration trends that emerge as first-semester students develop a 2D game project. Results indicate that students often slightly overestimate their contributions, with more engaged individuals more likely to acknowledge mistakes. Team performance shows no significant variation based on nationality or gender composition, though teams that disbanded frequently consisted of lone wolves, highlighting collaboration challenges and the need for strengthened teamwork skills. Presentations closely reflected individual project contributions, with active students excelling in evaluative questioning and performing better on the final exam. Additionally, the complete absence of plagiarism underscores the effectiveness of proactive academic integrity measures, reinforcing honest collaboration in educational settings., Comment: The final version accepted at the conference IEEE International Conference on IT in Higher Education and Training (ITHET 2025)

Published
2024

14. Multi-view biomedical foundation models for molecule-target and property prediction

Author

Suryanarayanan, Parthasarathy, Qiu, Yunguang, Sethi, Shreyans, Mahajan, Diwakar, Li, Hongyang, Yang, Yuxin, Eyigoz, Elif, Saenz, Aldo Guzman, Platt, Daniel E., Rumbell, Timothy H., Ng, Kenney, Dey, Sanjoy, Burch, Myson, Kwon, Bum Chul, Meyer, Pablo, Cheng, Feixiong, Hu, Jianying, and Morrone, Joseph A.

Subjects
Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Foundation models applied to bio-molecular space hold promise to accelerate drug discovery. Molecular representation is key to building such models. Previous works have typically focused on a single representation or view of the molecules. Here, we develop a multi-view foundation model approach, that integrates molecular views of graph, image and text. Single-view foundation models are each pre-trained on a dataset of up to 200M molecules and then aggregated into combined representations. Our multi-view model is validated on a diverse set of 18 tasks, encompassing ligand-protein binding, molecular solubility, metabolism and toxicity. We show that the multi-view models perform robustly and are able to balance the strengths and weaknesses of specific views. We then apply this model to screen compounds against a large (>100 targets) set of G Protein-Coupled receptors (GPCRs). From this library of targets, we identify 33 that are related to Alzheimer's disease. On this subset, we employ our model to identify strong binders, which are validated through structure-based modeling and identification of key binding motifs., Comment: 34 pages including supplement. 9 figures, 4 tables

Published
2024

15. Search for gravitational waves emitted from SN 2023ixf

Author

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., Gong, Y., González, G., Goodarzi, P., Goode, S., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Govorkova, K., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, A., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurs, J., Gutierrez, N., Guzman, F., H, H. -Y., Haba, D., Haberland, M., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Hardison, A. R., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hart, J., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, R., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Henderson-Sapir, O., Hendry, M., Heng, I. S., Hennes, E., Henshaw, C., Hertog, T., Heurs, M., Hewitt, A. L., Heyns, J., Higginbotham, S., Hild, S., Hill, S., Himemoto, Y., Hirata, N., Hirose, C., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Holmes, Z. J., Holz, D. E., Honet, L., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huxford, R., Huynh-Dinh, T., Iampieri, L., Iandolo, G. A., Ianni, M., Iess, A., Imafuku, H., Inayoshi, K., Inoue, Y., Iorio, G., Iqbal, M. H., Irwin, J., Ishikawa, R., Isi, M., Ismail, M. A., Itoh, Y., Iwanaga, H., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, C., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Kubisz, J., Johanson, C., Johns, G. R., Johnson, N. A., Johnston, M. C., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Jose, S., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kaku, I., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, T., Katsavounidis, E., Katzman, W., Kaushik, R., Kawabe, K., Kawamoto, R., Kazemi, A., Keitel, D., Kelley-Derzon, J., Kennington, J., Kesharwani, R., Key, J. S., Khadela, R., Khadka, S., Khalili, F. Y., Khan, F., Khan, I., Khanam, T., Khursheed, M., Khusid, N. M., Kiendrebeogo, W., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, S., Kim, Y. -M., Kimball, C., Kinley-Hanlon, M., Kinnear, M., Kissel, J. S., Klimenko, S., Knee, A. M., Knust, N., Kobayashi, K., Obergaulinger, M., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kou, X., Koushik, A., Kouvatsos, N., Kovalam, M., Kozak, D. B., Kranzhoff, S. L., Kringel, V., Krishnendu, N. V., Królak, A., Kruska, K., Kuehn, G., Kuijer, P., Kulkarni, S., Ramamohan, A. Kulur, Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuntimaddi, N., Kuroyanagi, S., Kurth, N. J., Kuwahara, S., Kwak, K., Kwan, K., Kwok, J., Lacaille, G., Lagabbe, P., Laghi, D., Lai, S., Laity, A. H., Lakkis, M. H., Lalande, E., Lalleman, M., Lalremruati, P. C., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rana, A., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Lawrence, J., Lawrence, M. N., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Lecoeuche, Y. K., Lee, H. M., Lee, H. W., Lee, K., Lee, R. -K., Lee, R., Lee, S., Lee, Y., Legred, I. N., Lehmann, J., Lehner, L., Jean, M. Le, Lemaître, A., Lenti, M., Leonardi, M., Lequime, M., Leroy, N., Lesovsky, M., Letendre, N., Lethuillier, M., Levin, S. E., Levin, Y., Leyde, K., Li, A. K. Y., Li, K. L., Li, T. G. F., Li, X., Li, Z., Lihos, A., Lin, C-Y., Lin, C. -Y., Lin, E. T., Lin, F., Lin, H., Lin, L. C. -C., Lin, Y. -C., Linde, F., Linker, S. D., Littenberg, T. B., Liu, A., Liu, G. C., Liu, Jian, Villarreal, F. Llamas, Llobera-Querol, J., Lo, R. K. L., Locquet, J. -P., London, L. T., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Lorenzo-Medina, A., Loriette, V., Lormand, M., Losurdo, G., Lott IV, T. P., Lough, J. D., Loughlin, H. A., Lousto, C. O., Lowry, M. J., Lu, N., Lück, H., Lumaca, D., Lundgren, A. P., Lussier, A. W., Ma, L. -T., Ma, S., Ma'arif, M., Macas, R., Macedo, A., MacInnis, M., Maciy, R. R., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Macri, D., Maeda, K., Maenaut, S., Hernandez, I. Magaña, Magare, S. S., Magazzù, C., Magee, R. M., Maggio, E., Maggiore, R., Magnozzi, M., Mahesh, M., Mahesh, S., Maini, M., Majhi, S., Majorana, E., Makarem, C. N., Makelele, E., Malaquias-Reis, J. A., Mali, U., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mannix, B., Mansell, G. L., Mansingh, G., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Márka, S., Márka, Z., Markosyan, A. S., Markowitz, A., Maros, E., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, B. B., Martinez, M., Martinez, V., Martini, A., Martinovic, K., Martins, J. C., Martynov, D. V., Marx, E. J., Massaro, L., Masserot, A., Masso-Reid, M., Mastrodicasa, M., Mastrogiovanni, S., Matcovich, T., Matiushechkina, M., Matsuyama, M., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McEachin, S., McElhenny, C., McGhee, G. I., McGinn, J., McGowan, K. B. M., McIver, J., McLeod, A., McRae, T., Meacher, D., Meijer, Q., Melatos, A., Mellaerts, S., Menendez-Vazquez, A., Menoni, C. S., Mera, F., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Mérou, J. R., Merritt, J. D., Merzougui, M., Messenger, C., Messick, C., Meyer-Conde, M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Miller, A. L., Miller, S., Millhouse, M., Milotti, E., Milotti, V., Minenkov, Y., Mio, N., Mir, Ll. M., Mirasola, L., Miravet-Tenés, M., Miritescu, C. -A., Mishra, A. K., Mishra, A., Mishra, C., Mishra, T., Mitchell, A. L., Mitchell, J. G., Mitra, S., Mitrofanov, V. P., Mittleman, R., Miyakawa, O., Miyamoto, S., Miyoki, S., Mo, G., Mobilia, L., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. J., Moraru, D., More, A., More, S., Moreno, G., Morgan, C., Morisaki, S., Moriwaki, Y., Morras, G., Moscatello, A., Mourier, P., Mours, B., Mow-Lowry, C. M., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Samanwaya, Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Mundi, J., Mungioli, C. L., Oberg, W. R. Munn, Murakami, Y., Murakoshi, M., Murray, P. G., Muusse, S., Nabari, D., Nadji, S. L., Nagar, A., Nagarajan, N., Nagler, K. N., Nakagaki, K., Nakamura, K., Nakano, H., Nakano, M., Nandi, D., Napolano, V., Narayan, P., Nardecchia, I., Narikawa, T., Narola, H., Naticchioni, L., Nayak, R. K., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Neunzert, A., Ng, S., Quynh, L. Nguyen, Nichols, S. A., Nielsen, A. B., Nieradka, G., Niko, A., Nishino, Y., Nishizawa, A., Nissanke, S., Nitoglia, E., Niu, W., Nocera, F., Norman, M., North, C., Novak, J., Siles, J. F. Nuño, Nuttall, L. K., Obayashi, K., Oberling, J., O'Dell, J., Oertel, M., Offermans, A., Oganesyan, G., Oh, J. J., Oh, K., O'Hanlon, T., Ohashi, M., Ohkawa, M., Ohme, F., Oliveira, A. S., Oliveri, R., O'Neal, B., Oohara, K., O'Reilly, B., Ormsby, N. D., Orselli, M., O'Shaughnessy, R., O'Shea, S., Oshima, Y., Oshino, S., Ossokine, S., Osthelder, C., Ota, I., Ottaway, D. J., Ouzriat, A., Overmier, H., Owen, B. J., Pace, A. E., Pagano, R., Page, M. A., Pai, A., Pal, A., Pal, S., Palaia, M. A., Pálfi, M., Palma, P. P., Palomba, C., Palud, P., Pan, H., Pan, J., Pan, K. C., Panai, R., Panda, P. K., Pandey, S., Panebianco, L., Pang, P. T. H., Pannarale, F., Pannone, K. A., Pant, B. C., Panther, F. H., Paoletti, F., Paolone, A., Papalexakis, E. E., Papalini, L., Papigkiotis, G., Paquis, A., Parisi, A., Park, B. -J., Park, J., Parker, W., Pascale, G., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passenger, L., Passuello, D., Patane, O., Pathak, D., Pathak, M., Patra, A., Patricelli, B., Patron, A. S., Paul, K., Paul, S., Payne, E., Pearce, T., Pedraza, M., Pegna, R., Pele, A., Arellano, F. E. Peña, Penn, S., Penuliar, M. D., Perego, A., Pereira, Z., Perez, J. J., Périgois, C., Perna, G., Perreca, A., Perret, J., Perriès, S., Perry, J. W., Pesios, D., Petracca, S., Petrillo, C., Pfeiffer, H. P., Pham, H., Pham, K. A., Phukon, K. S., Phurailatpam, H., Piarulli, M., Piccari, L., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierra, G., Pierro, V., Pietrzak, M., Pillas, M., Pilo, F., Pinard, L., Pinto, I. M., Pinto, M., Piotrzkowski, B. J., Pirello, M., Pitkin, M. D., Placidi, A., Placidi, E., Planas, M. L., Plastino, W., Poggiani, R., Polini, E., Pompili, L., Poon, J., Porcelli, E., Porter, E. K., Posnansky, C., Poulton, R., Powell, J., Pracchia, M., Pradhan, B. K., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Prasia, P., Pratten, G., Principe, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Puecher, A., Pullin, J., Punturo, M., Puppo, P., Pürrer, M., Qi, H., Qin, J., Quéméner, G., Quetschke, V., Quigley, C., Quinonez, P. J., Raab, F. J., Raabith, S. S., Raaijmakers, G., Raja, S., Rajan, C., Rajbhandari, B., Ramirez, K. E., Vidal, F. A. Ramis, Ramos-Buades, A., Rana, D., Ranjan, S., Ransom, K., Rapagnani, P., Ratto, B., Rawat, S., Ray, A., Raymond, V., Razzano, M., Read, J., Payo, M. Recaman, Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Relton, P., Renzini, A. I., Rettegno, P., Revenu, B., Reyes, R., Rezaei, A. S., Ricci, F., Ricci, M., Ricciardone, A., Richardson, J. W., Richardson, M., Rijal, A., Riles, K., Riley, H. K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S. K., Roy, S., Rozza, D., Ruggi, P., Ruhama, N., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Sah, M. R., Saha, S. S., Saha, S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Santoliquido, F., Saravanan, T. R., Sarin, N., Sasaoka, S., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. L., Sayah, S., Scacco, V., Schaetzl, D., Scheel, M., Schiebelbein, A., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schouteden, K., Schulte, B. W., Schutz, B. F., Schwartz, E., Scialpi, M., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Serra, M., Servignat, G., Sevrin, A., Shaffer, T., Shah, U. S., Shaikh, M. A., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shaw, M. R., Shawhan, P., Shcheblanov, N. S., Sheridan, E., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singh, S., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somiya, K., Song, I., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Sotani, H., Soulard, R., Southgate, A., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Spoon, J. B., Sprague, C. A., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suleiman, L., Sullivan, K. D., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Suzuki, T., Suzuki, Y., Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table

Published
2024

16. Structured Light\'s Applications: A perspective

Author

Rosales-Guzmán, Carmelo and Rodriguez-Fajardo, Valeria

Subjects
Physics - Optics
Abstract

For the past few decades, structured light has been gaining popularity across various research fields. Its fascinating properties have been exploited for both previously unforeseen and established applications from new perspectives. Crucial to this is the several techniques that have been proposed for both their generation and characterisation. On one hand, the former has been boosted by the invention of computer-controlled devices, which combined with a few optical components allow flexible and complete control of the spatial and polarisation degrees of freedom on light, thus enabling a plethora of proof-of-principle experiments for novel and old applications. On the other hand, characterising light beams is important not only for gaining better insights into light's properties but also for potentially being used as metrics. In this perspective, we thus offer our take on a few key applied research fields where structured light is particularly promising, as well as some pivotal generation and characterisation techniques. In addition, we share our vision of where we believe structured light's applications are moving towards., Comment: The following article has been submitted to Applied Physics Letters. After it is published, it will be found at https://publishing.aip.org/resources/librarians/products/journals/

Published
2024

17. Microscopic description of spontaneous fission based on a Gogny energy density functional including tensor contributions

Author

Rodríguez-Guzmán, R., Robledo, L. M., and Bernard, R. N.

Subjects
Nuclear Theory
Abstract

This paper extends previous studies on the impact of tensor forces in fission dynamics of neutron-deficient Thorium isotopes to other isotopic chains of heavy actinides and low-mass super-heavy nuclei. Calculations are carried out within a mean-field framework based on the Gogny-D1S parametrization supplemented with the D1ST2a perturbative tensor term as driving force. Fission barrier heights and spontaneous fission half-lives are used as benchmarks to analyze the impact of the tensor term. A significant reduction of fission barrier heights and half-lives is associated to the tensor component of the force., Comment: 11 pages, 5 figures

Published
2024
Full Text
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18. The GALAH Survey: Stellar parameters and abundances for 800,000 Gaia RVS spectra using GALAH DR4 and The Cannon

Author

Das, Pradosh Barun, Zucker, Daniel B., De Silva, Gayandhi M., Borsato, Nicholas W., Mura-Guzmán, Aldo, Buder, Sven, Ness, Melissa, Nordlander, Thomas, Casey, Andrew R., Martell, Sarah L., Bland-Hawthorn, Joss, de Grijs, Richard, Freeman, Ken C., Kos, Janez, Stello, Dennis, Lewis, Geraint F., Hayden, Michael R., and Sharma, Sanjib

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
Abstract

Analysing stellar parameters and abundances from nearly one million Gaia DR3 Radial Velocity Spectrometer (RVS) spectra poses challenges due to the limited spectral coverage (restricted to the infrared Ca II triplet) and variable signal-to-noise ratios of the data. To address this, we use The Cannon, a data-driven method, to transfer stellar parameters and abundances from the GALAH Data Release 4 (DR4; R ~ 28,000) catalogue to the lower resolution Gaia DR3 RVS spectra (R ~ 11,500). Our model, trained on 14,484 common targets, predicts parameters such as Teff, log g, and [Fe/H], along with several other elements across approximately 800,000 Gaia RVS spectra. We utilise stars from open and globular clusters present in the Gaia RVS catalogue to validate our predicted mean [Fe/H] with high precision (~0.02-0.10 dex). Additionally, we recover the bimodal distribution of [Ti/Fe] versus [Fe/H], reflecting the high and low alpha-components of Milky Way disk stars, demonstrating The Cannon's capability for accurate stellar abundance determination from medium-resolution Gaia RVS spectra. The methodologies and resultant catalogue presented in this work highlight the remarkable potential of the RVS dataset, which by the end of the Gaia mission will comprise spectra of over 200 million stars., Comment: Submitted to MNRAS, 16 pages, 15 figures

Published
2024

19. The gold partition conjecture and the Lexicographic sum of posets

Author

Dolores-Cuenca, Eric R., Guzmán-Sáenz, Aldo, and Kim, Sangil

Subjects
Mathematics - Combinatorics, 06A07
Abstract

If a finite poset $Q$ satisfies the Gold Partition Conjecture, and $P$ is a finite poset, then for any $i$ in $P$ the lexicographic sum of $P$ with $Q$ on the point $i$, satisfies the Gold Partition Conjecture., Comment: 8 pages

Published
2024

20. A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154

Author

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Azrad, D., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R. ., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghonge, S., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., Gong, Y., González, G., Goodarzi, P., Goode, S., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Govorkova, K., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, A., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurs, J., Gutierrez, N., Guzman, F., H, H. -Y., Haba, D., Haberland, M., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Hardison, A. R., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hart, J., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, R., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Henderson-Sapir, O., Hendry, M., Heng, I. S., Hennes, E., Henshaw, C., Hertog, T., Heurs, M., Hewitt, A. L., Heyns, J., Higginbotham, S., Hild, S., Hill, S., Himemoto, Y., Hirata, N., Hirose, C., Ho, W. C. G., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Holmes, Z. J., Holz, D. E., Honet, L., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huxford, R., Huynh-Dinh, T., Iampieri, L., Iandolo, G. A., Ianni, M., Iess, A., Imafuku, H., Inayoshi, K., Inoue, Y., Iorio, G., Iqbal, M. H., Irwin, J., Ishikawa, R., Isi, M., Ismail, M. A., Itoh, Y., Iwanaga, H., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, C., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Kubisz, J., Johanson, C., Johns, G. R., Johnson, N. A., Johnston, M. C., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Jose, S., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kaku, I., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, T., Katsavounidis, E., Katzman, W., Kaushik, R., Kawabe, K., Kawamoto, R., Kazemi, A., Keitel, D., Kelley-Derzon, J., Kennington, J., Kesharwani, R., Key, J. S., Khadela, R., Khadka, S., Khalili, F. Y., Khan, F., Khan, I., Khanam, T., Khursheed, M., Khusid, N. M., Kiendrebeogo, W., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, S., Kim, Y. -M., Kimball, C., Kinley-Hanlon, M., Kinnear, M., Kissel, J. S., Klimenko, S., Knee, A. M., Knust, N., Kobayashi, K., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kou, X., Koushik, A., Kouvatsos, N., Kovalam, M., Kozak, D. B., Kranzhoff, S. L., Kringel, V., Krishnendu, N. V., Królak, A., Kruska, K., Kuehn, G., Kuijer, P., Kulkarni, S., Ramamohan, A. Kulur, Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuntimaddi, N., Kuroyanagi, S., Kurth, N. J., Kuwahara, S., Kwak, K., Kwan, K., Kwok, J., Lacaille, G., Lagabbe, P., Laghi, D., Lai, S., Laity, A. H., Lakkis, M. H., Lalande, E., Lalleman, M., Lalremruati, P. C., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rana, A., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Lawrence, J., Lawrence, M. N., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Lecoeuche, Y. K., Lee, H. M., Lee, H. W., Lee, K., Lee, R. -K., Lee, R., Lee, S., Lee, Y., Legred, I. N., Lehmann, J., Lehner, L., Jean, M. Le, Lemaître, A., Lenti, M., Leonardi, M., Lequime, M., Leroy, N., Lesovsky, M., Letendre, N., Lethuillier, M., Levin, S. E., Levin, Y., Leyde, K., Li, A. K. Y., Li, K. L., Li, T. G. F., Li, X., Li, Z., Lihos, A., Lin, C-Y., Lin, C. -Y., Lin, E. T., Lin, F., Lin, H., Lin, L. C. -C., Lin, Y. -C., Linde, F., Linker, S. D., Littenberg, T. B., Liu, A., Liu, G. C., Liu, Jian, Villarreal, F. Llamas, Llobera-Querol, J., Lo, R. K. L., Locquet, J. -P., London, L. T., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Lorenzo-Medina, A., Loriette, V., Lormand, M., Losurdo, G., Lott IV, T. P., Lough, J. D., Loughlin, H. A., Lousto, C. O., Lowry, M. J., Lu, N., Lück, H., Lumaca, D., Lundgren, A. P., Lussier, A. W., Ma, L. -T., Ma, S., Ma'arif, M., Macas, R., Macedo, A., MacInnis, M., Maciy, R. R., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Macri, D., Maeda, K., Maenaut, S., Hernandez, I. Magaña, Magare, S. S., Magazzù, C., Magee, R. M., Maggio, E., Maggiore, R., Magnozzi, M., Mahesh, M., Mahesh, S., Maini, M., Majhi, S., Majorana, E., Makarem, C. N., Makelele, E., Malaquias-Reis, J. A., Mali, U., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mannix, B., Mansell, G. L., Mansingh, G., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Márka, S., Márka, Z., Markosyan, A. S., Markowitz, A., Maros, E., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, B. B., Martinez, M., Martinez, V., Martini, A., Martinovic, K., Martins, J. C., Martynov, D. V., Marx, E. J., Massaro, L., Masserot, A., Masso-Reid, M., Mastrodicasa, M., Mastrogiovanni, S., Matcovich, T., Matiushechkina, M., Matsuyama, M., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McCormick, S., McCuller, L., McEachin, S., McElhenny, C., McGhee, G. I., McGinn, J., McGowan, K. B. M., McIver, J., McLeod, A., McRae, T., Meacher, D., Meijer, Q., Melatos, A., Mellaerts, S., Menendez-Vazquez, A., Menoni, C. S., Mera, F., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Mérou, J. R., Merritt, J. D., Merzougui, M., Messenger, C., Messick, C., Meyer-Conde, M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Miller, A. L., Miller, S., Millhouse, M., Milotti, E., Milotti, V., Minenkov, Y., Mio, N., Mir, Ll. M., Mirasola, L., Miravet-Tenés, M., Miritescu, C. -A., Mishra, A. K., Mishra, A., Mishra, C., Mishra, T., Mitchell, A. L., Mitchell, J. G., Mitra, S., Mitrofanov, V. P., Mittleman, R., Miyakawa, O., Miyamoto, S., Miyoki, S., Mo, G., Mobilia, L., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. J., Moraru, D., More, A., More, S., Moreno, G., Morgan, C., Morisaki, S., Moriwaki, Y., Morras, G., Moscatello, A., Mourier, P., Mours, B., Mow-Lowry, C. M., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Samanwaya, Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Mundi, J., Mungioli, C. L., Oberg, W. R. Munn, Murakami, Y., Murakoshi, M., Murray, P. G., Muusse, S., Nabari, D., Nadji, S. L., Nagar, A., Nagarajan, N., Nagler, K. N., Nakagaki, K., Nakamura, K., Nakano, H., Nakano, M., Nandi, D., Napolano, V., Narayan, P., Nardecchia, I., Narola, H., Naticchioni, L., Nayak, R. K., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Neunzert, A., Ng, S., Quynh, L. Nguyen, Nichols, S. A., Nielsen, A. B., Nieradka, G., Niko, A., Nishino, Y., Nishizawa, A., Nissanke, S., Nitoglia, E., Niu, W., Nocera, F., Norman, M., North, C., Novak, J., Siles, J. F. Nuño, Nuttall, L. K., Obayashi, K., Oberling, J., O'Dell, J., Oertel, M., Offermans, A., Oganesyan, G., Oh, J. J., Oh, K., O'Hanlon, T., Ohashi, M., Ohkawa, M., Ohme, F., Oliveira, A. S., Oliveri, R., O'Neal, B., Oohara, K., O'Reilly, B., Ormsby, N. D., Orselli, M., O'Shaughnessy, R., O'Shea, S., Oshima, Y., Oshino, S., Ossokine, S., Osthelder, C., Ota, I., Ottaway, D. J., Ouzriat, A., Overmier, H., Owen, B. J., Pace, A. E., Pagano, R., Page, M. A., Pai, A., Pal, A., Pal, S., Palaia, M. A., Pálfi, M., Palma, P. P., Palomba, C., Palud, P., Pan, H., Pan, J., Pan, K. C., Panai, R., Panda, P. K., Pandey, S., Panebianco, L., Pang, P. T. H., Pannarale, F., Pannone, K. A., Pant, B. C., Panther, F. H., Paoletti, F., Paolone, A., Papalexakis, E. E., Papalini, L., Papigkiotis, G., Paquis, A., Parisi, A., Park, B. -J., Park, J., Parker, W., Pascale, G., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passenger, L., Passuello, D., Patane, O., Pathak, D., Pathak, M., Patra, A., Patricelli, B., Patron, A. S., Paul, K., Paul, S., Payne, E., Pearce, T., Pedraza, M., Pegna, R., Pele, A., Arellano, F. E. Peña, Penn, S., Penuliar, M. D., Perego, A., Pereira, Z., Perez, J. J., Périgois, C., Perna, G., Perreca, A., Perret, J., Perriès, S., Perry, J. W., Pesios, D., Petracca, S., Petrillo, C., Pfeiffer, H. P., Pham, H., Pham, K. A., Phukon, K. S., Phurailatpam, H., Piarulli, M., Piccari, L., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierra, G., Pierro, V., Pietrzak, M., Pillas, M., Pilo, F., Pinard, L., Pinto, I. M., Pinto, M., Piotrzkowski, B. J., Pirello, M., Pitkin, M. D., Placidi, A., Placidi, E., Planas, M. L., Plastino, W., Poggiani, R., Polini, E., Pompili, L., Poon, J., Porcelli, E., Porter, E. K., Posnansky, C., Poulton, R., Powell, J., Pracchia, M., Pradhan, B. K., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Prasia, P., Pratten, G., Principe, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Puecher, A., Pullin, J., Punturo, M., Puppo, P., Pürrer, M., Qi, H., Qin, J., Quéméner, G., Quetschke, V., Quigley, C., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raabith, S. S., Raaijmakers, G., Raja, S., Rajan, C., Rajbhandari, B., Ramirez, K. E., Vidal, F. A. Ramis, Ramos-Buades, A., Rana, D., Ranjan, S., Ransom, K., Rapagnani, P., Ratto, B., Rawat, S., Ray, A., Raymond, V., Razzano, M., Read, J., Payo, M. Recaman, Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Relton, P., Renzini, A. I., Rettegno, P., Revenu, B., Reyes, R., Rezaei, A. S., Ricci, F., Ricci, M., Ricciardone, A., Richardson, J. W., Richardson, M., Rijal, A., Riles, K., Riley, H. K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S. K., Roy, S., Rozza, D., Ruggi, P., Ruhama, N., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Sah, M. R., Saha, S. S., Saha, S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Santoliquido, F., Saravanan, T. R., Sarin, N., Sasaoka, S., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. L., Sayah, S., Scacco, V., Schaetzl, D., Scheel, M., Schiebelbein, A., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schouteden, K., Schulte, B. W., Schutz, B. F., Schwartz, E., Scialpi, M., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Serra, M., Servignat, G., Sevrin, A., Shaffer, T., Shah, U. S., Shaikh, M. A., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shaw, M. R., Shawhan, P., Shcheblanov, N. S., Sheridan, E., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singh, S., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somiya, K., Song, I., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Sotani, H., Soulard, R., Southgate, A., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Spoon, J. B., Sprague, C. A., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suleiman, L., Sullivan, K. D., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Suzuki, T., Suzuki, Y., Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zucker, M. E., and Zweizig, J.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables

Published
2024

21. STROBE-X High Energy Modular Array (HEMA)

Author

Hutcheson, Anthony L., Feroci, Marco, Argan, Andrea, Antonelli, Matias, Barbera, Marco, Bayer, Jorg, Bellutti, Pierluigi, Bertuccio, Giuseppe, Bonvicini, Valter, Cadoux, Franck, Campana, Riccardo, Vignali, Matteo Centis, Ceraudo, Francesco, Christophersen, Marc, Cirrincione, Daniela, D'Anca, Fabio, De Angelis, Nicolas, De Rosa, Alessandra, Della Casa, Giovanni, Del Monte, Ettore, Dilillo, Giuseppe, Evangelista, Yuri, Favre, Yannick, Ficorella, Francesco, Fiorini, Mauro, Ford, Jeremy J., Grassi, Marco, Grove, J. Eric, Guzman, Alejandro, Heddermann, Paul, Kole, Merlin R., Cicero, Ugo Lo, Lombardi, Giovanni, Malcovati, Piero, Michalska, Malgorzata, Meuris, Aline, Minervini, Gabriele, Nowosielski, Witold, Nuti, Alessio, Pacciani, Luigi, Pepponi, Giancarlo, Persyn, Steven C., Picciotto, Antonino, Pliego, Samuel, Rachevski, Alexander, Rashevskaya, Irina, Ray, Paul S., Samusenko, Alina, Santangelo, Andrea, Schanne, Stephane, Schwendeman, Carl L., Sleator, Clio, Smith, Jacob R., Sveda, Libor, Svoboda, Jiri, Tenzer, Christoph, Todaro, Michela, Trois, Alessio, Vacchi, Andrea, Xiong, Hao, Wang, Xianqi, Wu, Xin, Wulf, Eric A., Zampa, Gianluigi, Zampa, Nicola, Zdziarski, Andrzej, and Zorzi, Nicola

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The High Energy Modular Array (HEMA) is one of three instruments that compose the STROBE-X mission concept. The HEMA is a large-area, high-throughput non-imaging pointed instrument based on the Large Area Detector developed as part of the LOFT mission concept. It is designed for spectral timing measurements of a broad range of sources and provides a transformative increase in sensitivity to X-rays in the energy range of 2--30 keV compared to previous instruments, with an effective area of 3.4 m$^{2}$ at 8.5 keV and an energy resolution of better than 300 eV at 6 keV in its nominal field of regard., Comment: 16 pages, 10 figures

Published
2024
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22. Crossing Margins: Intersectional Users' Ethical Concerns about Software

Author

Olson, Lauren, Humbert, Tom P., Fischer, Ricarda Anna-Lena, Westerveld, Bob, Kunneman, Florian, and Guzmán, Emitzá

Subjects
Computer Science - Software Engineering, Computer Science - Human-Computer Interaction
Abstract

Many modern software applications present numerous ethical concerns due to conflicts between users' values and companies' priorities. Intersectional communities, those with multiple marginalized identities, are disproportionately affected by these ethical issues, leading to legal, financial, and reputational issues for software companies, as well as real-world harm for intersectional users. Historically, the voices of intersectional communities have been systematically marginalized and excluded from contributing their unique perspectives to software design, perpetuating software-related ethical concerns. This work aims to fill the gap in research on intersectional users' software-related perspectives and provide software practitioners with a starting point to address their ethical concerns. We aggregated and analyzed the intersectional users' ethical concerns over time and developed a prioritization method to identify critical concerns. To achieve this, we collected posts from over 700 intersectional subreddits discussing software applications, utilized deep learning to identify ethical concerns in these posts, and employed state-of-the-art techniques to analyze their content in relation to time and priority. Our findings revealed that intersectional communities report \textit{critical} complaints related to cyberbullying, inappropriate content, and discrimination, highlighting significant flaws in modern software, particularly for intersectional users. Based on these findings, we discuss how to better address the ethical concerns of intersectional users in software development.

Published
2024

23. The ground calibration of the HERMES-Pathfinder payload flight models

Author

Dilillo, G., Marchesini, E. J., Baroni, G., Della Casa, G., Campana., R., Evangelista, Y., Guzmán, A., Hedderman, P., Bellutti, P., Bertuccio, G., Ceraudo, F., Citossi, M., Cirrincione, D., Dedolli, I., Demenev, E., Feroci, M., Ficorella, F., Fiorini, M., Gandola, M., Grassi, M., La Rosa, G., Lombardi, G., Malcovati, P., Mele, F., Nogara, P., Nuti, A., Perri, M., Pliego-Caballero, S., Pirrotta, S., Puccetti, S., Rashevskaya, I., Russo, F., Sottile, G., Tenzer, C., Trenti, M., Trevisan, S., Vacchi, A., Zampa, G., Zampa, N., and Fiore, F.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract

HERMES-Pathfinder is a space-borne mission based on a constellation of six nano-satellites flying in a low-Earth orbit. The 3U CubeSats, to be launched in early 2025, host miniaturized instruments with a hybrid Silicon Drift Detector/scintillator photodetector system, sensitive to both X-rays and gamma-rays. A seventh payload unit is installed onboard SpIRIT, an Australian-Italian nano-satellite developed by a consortium led by the University of Melbourne and launched in December 2023. The project aims at demonstrating the feasibility of Gamma-Ray Burst detection and localization using miniaturized instruments onboard nano-satellites. The HERMES flight model payloads were exposed to multiple well-known radioactive sources for spectroscopic calibration under controlled laboratory conditions. The analysis of the calibration data allows both to determine the detector parameters, necessary to map instrumental units to accurate energy measurements, and to assess the performance of the instruments. We report on these efforts and quantify features such as spectroscopic resolution and energy thresholds, at different temperatures and for all payloads of the constellation. Finally we review the performance of the HERMES payload as a photon counter, and discuss the strengths and the limitations of the architecture.

Published
2024

24. Reductions of Crystalline Representations for Small Weights

Author

Guzman, Anthony

Subjects
Mathematics - Number Theory
Abstract

We compute explicit reductions of crystalline representations of the absolute Galois group $\text{Gal}(\overline{\mathbb{Q}}_p/\mathbb{Q}_{p^f})$ with labeled Hodge-Tate weights in the range $p+2\le k_{0}\le 2p-4$ and $2\le k_i\le p-3$ for $1\le i\le f-1$.

Published
2024

25. Reductions of Some Crystalline Representations in the Unramified Setting

Author

Guzman, Anthony

Subjects
Mathematics - Number Theory
Abstract

We determine semisimple reductions of irreducible, 2-dimensional crystalline representations of the absolute Galois group $\text{Gal}(\overline{\mathbb{Q}_p}/\mathbb{Q}_{p^f})$. To this end, we provide explicit representatives for the isomorphism classes of the associated weakly admissible filtered $\varphi$-modules by concretely describing the strongly divisible lattices which characterize the structure of the aforementioned modules. Using these representatives, we construct Kisin modules canonically associated to Galois stable lattice representations inside our crystalline representations. This allows us to compute the reduction of such crystalline representations for arbitrary labeled Hodge-Tate weights so long as the $p$-adic valuations of certain parameters are sufficiently large. Hence, we provide a Berger-Li-Zhu type bound in the unramified setting.

Published
2024

26. Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity

Author

Nguyen, Quan, Mehta, Nishant A., and Guzmán, Cristóbal

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Mathematics - Optimization and Control
Abstract

The minimax sample complexity of group distributionally robust optimization (GDRO) has been determined up to a $\log(K)$ factor, for $K$ the number of groups. In this work, we venture beyond the minimax perspective via a novel notion of sparsity that we dub $(\lambda, \beta)$-sparsity. In short, this condition means that at any parameter $\theta$, there is a set of at most $\beta$ groups whose risks at $\theta$ all are at least $\lambda$ larger than the risks of the other groups. To find an $\epsilon$-optimal $\theta$, we show via a novel algorithm and analysis that the $\epsilon$-dependent term in the sample complexity can swap a linear dependence on $K$ for a linear dependence on the potentially much smaller $\beta$. This improvement leverages recent progress in sleeping bandits, showing a fundamental connection between the two-player zero-sum game optimization framework for GDRO and per-action regret bounds in sleeping bandits. The aforementioned result assumes having a particular $\lambda$ as input. Perhaps surprisingly, we next show an adaptive algorithm which, up to log factors, gets sample complexity that adapts to the best $(\lambda, \beta)$-sparsity condition that holds. Finally, for a particular input $\lambda$, we also show how to get a dimension-free sample complexity result., Comment: 38 pages

Published
2024

27. Toward a robust physical and chemical characterization of heterogeneous lines of sight: The case of the Horsehead nebula

Author

Ségal, Léontine, Roueff, Antoine, Pety, Jérôme, Gerin, Maryvonne, Roueff, Evelyne, Goicoechea, R. Javier, Bešlic, Ivana, Coud'e, Simon, Einig, Lucas, Mazurek, Helena, Orkisz, H. Jan, Palud, Pierre, Santa-Maria, G. Miriam, Zakardjian, Antoine, Bardeau, S'ebastien, Bron, Emeric, Chainais, Pierre, Demyk, Karine, Magalhaes, Victor de Souza, Gratier, Pierre, Guzman, V. Viviana, Hughes, Annie, Languignon, David, Levrier, François, Bourlot, Jacques Le, Petit, Franck Le, Lis, C. Dariusz, Liszt, S. Harvey, Peretto, Nicolas, Sievers, Albrecht, and Thouvenin, Pierre-Antoine

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Dense cold molecular cores/filaments are surrounded by an envelope of translucent gas. Some of the low-J emission lines of CO and HCO$^+$ isotopologues are more sensitive to the conditions either in the translucent environment or in the dense cold one. We propose a cloud model composed of three homogeneous slabs of gas along each line of sight (LoS), representing an envelope and a shielded inner layer. IRAM-30m data from the ORION-B large program toward the Horsehead nebula are used to demonstrate the method's capability. We use the non-LTE radiative transfer code RADEX to model the line profiles from the kinetic temperature $T_{kin}$, the volume density $n_{H_2}$, kinematics and chemical properties of the different layers. We then use a maximum likelihood estimator to simultaneously fit the lines of the CO and HCO$^+$ isotopologues. We constrain column density ratios to limit the variance on the estimates. This simple heterogeneous model provides good fits of the fitted lines over a large part of the cloud. The decomposition of the intensity into three layers allows to discuss the distribution of the estimated physical/chemical properties along the LoS. About 80$\%$ the CO integrated intensity comes from the envelope, while $\sim55\%$ of that of the (1-0) and (2-1) lines of C$^{18}$O comes from the inner layer. The $N(^{13}CO)/N(C^{18}O)$ in the envelope increases with decreasing $A_v$, and reaches $25$ in the pillar outskirts. The envelope $T_{kin}$ varies from 25 to 40 K, that of the inner layer drops to $\sim 15$ K in the western dense core. The inner layer $n_{H_2}$ is $\sim 3\times10^4\,\text{cm}^{-3}$ toward the filament and it increases by a factor $10$ toward dense cores. The proposed method correctly retrieves the physical/chemical properties of the Horsehead nebula and offers promising prospects for less supervised model fits of wider-field datasets.

Published
2024

28. Sparsity, Regularization and Causality in Agricultural Yield: The Case of Paddy Rice in Peru

Author

Guzman-Lopez, Rita Rocio, Huamanchumo, Luis, Fernandez, Kevin, Cutipa-Luque, Oscar, Tiahuallpa, Yhon, and Rojas, Helder

Subjects
Statistics - Methodology, Computer Science - Machine Learning, Statistics - Applications, Statistics - Machine Learning
Abstract

This study introduces a novel approach that integrates agricultural census data with remotely sensed time series to develop precise predictive models for paddy rice yield across various regions of Peru. By utilizing sparse regression and Elastic-Net regularization techniques, the study identifies causal relationships between key remotely sensed variables-such as NDVI, precipitation, and temperature-and agricultural yield. To further enhance prediction accuracy, the first- and second-order dynamic transformations (velocity and acceleration) of these variables are applied, capturing non-linear patterns and delayed effects on yield. The findings highlight the improved predictive performance when combining regularization techniques with climatic and geospatial variables, enabling more precise forecasts of yield variability. The results confirm the existence of causal relationships in the Granger sense, emphasizing the value of this methodology for strategic agricultural management. This contributes to more efficient and sustainable production in paddy rice cultivation.

Published
2024

29. LLM-CARD: Towards a Description and Landscape of Large Language Models

Author

Tian, Shengwei, Han, Lifeng, Guzman, Erick Mendez, and Nenadic, Goran

Subjects
Computer Science - Computation and Language, Computer Science - Digital Libraries
Abstract

With the rapid growth of the Natural Language Processing (NLP) field, a vast variety of Large Language Models (LLMs) continue to emerge for diverse NLP tasks. As an increasing number of papers are presented, researchers and developers face the challenge of information overload. Thus, it is particularly important to develop a system that can automatically extract and organise key information about LLMs from academic papers (\textbf{LLM model card}). This work is to develop such a pioneer system by using Named Entity Recognition (\textbf{NER}) and Relation Extraction (\textbf{RE}) methods that automatically extract key information about large language models from the papers, helping researchers to efficiently access information about LLMs. These features include model \textit{licence}, model \textit{name}, and model \textit{application}. With these features, we can form a model card for each paper. \textbf{Data-contribution} wise, 106 academic papers were processed by defining three dictionaries - LLMs name, licence, and application. 11,051 sentences were extracted through dictionary lookup, and the dataset was constructed through manual review of the final selection of 129 sentences that have a link between the name and the licence, and 106 sentences that have a link between the model name and the application. Data and code in \textsc{LLM-Card} is openly hosted at \url{https://github.com/shengwei-tian/dependency-parser-visualization}, Comment: ongoing work, 16 pages

Published
2024

30. Design and development of the HERMES Pathfinder payloads

Author

Campana, R., Evangelista, Y., Fiore, F., Guzman, A., Baroni, G., Della Casa, G., Dilillo, G., Hedderman, P., Marchesini, E. J., Bertuccio, G., Ceraudo, F., Demenev, E., Fiorini, M., Grassi, M., Malcovati, P., Mele, F., Nogara, P., Nuti, A., Perri, M., Pirrotta, S., Pliego-Caballero, S., Puccetti, S., Sottile, G., Russo, F., and Trevisan, S.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract

HERMES (High Energy Rapid Modular Ensemble of Satellites) Pathfinder mission aims to observe and localize Gamma Ray Bursts (GRBs) and other transients using a constellation of nanosatellites in low-Earth orbit (LEO). Scheduled for launch in early 2025, the 3U CubeSats will host miniaturized instruments featuring a hybrid Silicon Drift Detector (SDD) and GAGG:Ce scintillator photodetector system, sensitive to X-rays and gamma-rays across a wide energy range. Each HERMES payload contains 120 SDD cells, each with a sensitive area of 45 mm^2, organized into 12 matrices, reading out 60 12.1x6.94x15.0 mm^3 GAGG:Ce scintillators. Photons interacting with an SDD are identified as X-ray events (2-60 keV), while photons in the 20-2000 keV range absorbed by the crystals produce scintillation light, which is read by two SDDs, allowing event discrimination. The detector system, including front-end and back-end electronics, a power supply unit, a chip-scale atomic clock, and a payload data handling unit, fits within a 10x10x10 cm^3 volume, weighs 1.5 kg, and has a maximum power consumption of about 2 W. This paper outlines the development of the HERMES constellation, the design and selection of the payload detectors, and laboratory testing, presenting the results of detector calibrations and environmental tests to provide a comprehensive status update of the mission., Comment: 8 pages, 6 figures. Proceedings of SPIE 13093, Space Telescopes and Instrumentation 2024: Ultraviolet to Gamma Ray

Published
2024
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31. Scattering for the generalized Hartree equation with a potential

Author

Guzmán, Carlos M., Loli, Cristian, and Yapu, Luis P.

Subjects
Mathematics - Analysis of PDEs
Abstract

We consider the focusing generalized Hartree equation in $H^1(\R^3)$ with a potential, \begin{equation*} iu_t + \Delta u - V(x)u + (I_\gamma \ast |u|^p )|u|^{p-2} u=0, \end{equation*} where $I_\gamma = \frac{1}{|x|^{3-\gamma}}$, $p \geq 2$ and $\gamma < 3$. In this paper, we prove scattering for the generalized Hartree equation with a potential in the intercritical case assuming radial initial data. The novelty of our approach lies in the use of a general mass-potential condition, incorporating the potential V, which extends the standard mass-energy framework. To this end, we employ a simplified method inspired by Dodson and Murphy \cite{Dod-Mur}, based on Tao's scattering criteria and Morawetz estimates. This approach provides a more straightforward proof of scattering compared to the traditional concentration-compactness/rigidity method of Kenig and Merle \cite{KENIG}., Comment: 20 pages

Published
2024

32. Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science

Author

Cheng, Austin, Ser, Cher Tian, Skreta, Marta, Guzmán-Cordero, Andrés, Thiede, Luca, Burger, Andreas, Aldossary, Abdulrahman, Leong, Shi Xuan, Pablo-García, Sergio, Strieth-Kalthoff, Felix, and Aspuru-Guzik, Alán

Subjects
Computer Science - Machine Learning, Condensed Matter - Materials Science, Computer Science - Artificial Intelligence, Physics - Chemical Physics
Abstract

Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspective, we first outline current applications across a diversity of problems in chemistry. Then, we discuss how machine learning researchers view and approach problems in the field. Finally, we provide our considerations for maximizing impact when researching machine learning for chemistry.

Published
2024
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33. Effect of pressure on the carbon dioxide hydrate-water interfacial free energy along its dissociation line

Author

Romero-Guzmán, Cristóbal, Zerón, Iván M., Algaba, Jesús, Mendiboure, Bruno, Míguez, José Manuel, and Blas, Felipe J.

Subjects
Condensed Matter - Soft Condensed Matter
Abstract

We investigate the effect of pressure on the carbon dioxide (CO$_{2}$) hydrate-water interfacial free energy along its dissociation line using advanced computer simulation techniques. In previous works, we have determined the interfacial energy of the hydrate at $400 \,\text{bar}$ using the TIP4P/ice and TraPPE molecular models for water and CO$_{2}$, respectively, in combination with two different extensions of the Mold Integration technique [J. Chem. Phys. 141, 134709 (2014)]. Results obtained from computer simulation, $29(2)$ and $30(2)\,\text{mJ/m}^{2}$, are found to be in excellent agreement with the only two measurements that exist in the literature, $28(6)\,\text{mJ/m}^{2}$ determined by Uchida et al. [J. Phys. Chem. B 106, 8202 (2002)] and $30(3)\,\text{mJ/m}^{2}$ by Anderson et al. [J. Phys. Chem. B 107, 3507 (2002)]. Since the experiments do not allow to obtain the variation of the interfacial energy along the dissociation line of the hydrate, we extend our previous studies to quantify the effect of pressure on the interfacial energy at different pressures. Our results suggest that there exists a correlation between the interfacial free energy values and the pressure, i.e., it decreases with the pressure between $100$ and $1000\,\text{bar}$. We expect that the combination of reliable molecular models and advanced simulation techniques could help to improve our knowledge of the thermodynamic parameters that control the interfacial free energy of hydrates from a molecular perspective., Comment: 7 pages, 4 figures

Published
2024
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34. Risk-based Calibration for Probabilistic Classifiers

Author

Pérez, Aritz, Echegoyen, Carlos, and Santafé, Guzmán

Subjects
Computer Science - Machine Learning
Abstract

We introduce a general iterative procedure called risk-based calibration (RC) designed to minimize the empirical risk under the 0-1 loss (empirical error) for probabilistic classifiers. These classifiers are based on modeling probability distributions, including those constructed from the joint distribution (generative) and those based on the class conditional distribution (conditional). RC can be particularized to any probabilistic classifier provided a specific learning algorithm that computes the classifier's parameters in closed form using data statistics. RC reinforces the statistics aligned with the true class while penalizing those associated with other classes, guided by the 0-1 loss. The proposed method has been empirically tested on 30 datasets using na\"ive Bayes, quadratic discriminant analysis, and logistic regression classifiers. RC improves the empirical error of the original closed-form learning algorithms and, more notably, consistently outperforms the gradient descent approach with the three classifiers.

Published
2024

35. On Using Curved Mirrors to Decrease Shadowing in VLC

Author

Guzman, Borja Genoves, Armada, Ana Garcia, and Brandt-Pearce, Maïté

Subjects
Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

Visible light communication (VLC) complements radio frequency in indoor environments with large wireless data traffic. However, VLC is hindered by dramatic path losses when an opaque object is interposed between the transmitter and the receiver. Prior works propose the use of plane mirrors as optical reconfigurable intelligent surfaces (ORISs) to enhance communications through non-line-of-sight links. Plane mirrors rely on their orientation to forward the light to the target user location, which is challenging to implement in practice. This paper studies the potential of curved mirrors as static reflective surfaces to provide a broadening specular reflection that increases the signal coverage in mirror-assisted VLC scenarios. We study the behavior of paraboloid and semi-spherical mirrors and derive the irradiance equations. We provide extensive numerical and analytical results and show that curved mirrors, when developed with proper dimensions, may reduce the shadowing probability to zero, while static plane mirrors of the same size have shadowing probabilities larger than 65%. Furthermore, the signal-to-noise ratio offered by curved mirrors may suffice to provide connectivity to users deployed in the room even when a line-of-sight link blockage occurs., Comment: Accepted to be published in IEEE Globecom 2024

Published
2024

36. The HERMES (High Energy Rapid Modular Ensemble of Satellites) Pathfinder mission

Author

Evangelista, Y., Fiore, F., Campana, R., Baroni, G., Ceraudo, F., Della Casa, G., Demenev, E., Dilillo, G., Fiorini, M., Ghirlanda, G., Grassi, M., Guzmán, A., Hedderman, P., Marchesini, E. J., Morgante, G., Mele, F., Nava, L., Nogara, P., Nuti, A., Caballero, S. Pliego, Rashevskaya, I., Russo, F., Sottile, G., Lavagna, M., Colagrossi, A., Silvestrini, S., Quirino, M., Bechini, M., Brandonisio, A., De Cecio, F., Dottori, A., Troisi, i., Bertacin, R., Bellutti, P., Bertuccio, G., Burderi, L., Chen, T., Citossi, M., Di Salvo, T., Feroci, M., Ficorella, F., Gao, N., Grappasonni, C., Labanti, C., La Rosa, G., Leone, W., Malcovati, P., Negri, B., Pepponi, G., Perri, M., Piazzolla, R., Picciotto, A., Pirrotta, S., Puccetti, S., Rashevsky, A., Riggio, A., Rinaldi, M., Sanna, A., Santangelo, A., Tenzer, C., Tiberia, A., Trenti, M., Trevisan, S., Vacchi, A., Xiong, S., Zampa, G., Zampa, N., Zhang, S., Zorzi, N., Ripa, J., and Werner, N.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

HERMES (High Energy Rapid Modular Ensemble of Satellites) Pathfinder is a space-borne mission based on a constellation of six nano-satellites flying in a low-Earth orbit (LEO). The 3U CubeSats, to be launched in early 2025, host miniaturized instruments with a hybrid Silicon Drift Detector/GAGG:Ce scintillator photodetector system, sensitive to X-rays and gamma-rays in a large energy band. HERMES will operate in conjunction with Australian Space Industry Responsive Intelligent Thermal (SpIRIT) 6U CubeSat, launched in December 2023. HERMES will probe the temporal emission of bright high-energy transients such as Gamma-Ray Bursts (GRBs), ensuring a fast transient localization in a field of view of several steradians exploiting the triangulation technique. HERMES intrinsically modular transient monitoring experiment represents a keystone capability to complement the next generation of gravitational wave experiments. In this paper we outline the scientific case, development and programmatic status of the mission, Comment: 13 pages, 16 figures. Proceedings of SPIE "Astronomical Telescopes and Instrumentation" 2024. arXiv admin note: text overlap with arXiv:2210.13866

Published
2024
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37. TPACK in In-Service Secondary Education Teachers: A Systematic Review of the Literature

Author

Javier Ricardo Guzmán González and Grace Judith Vesga Bravo

Abstract

The TPACK model is an essential framework for appropriately integrating technology in the classroom. The present study aimed to categorize and establish the main research findings around TPACK between 2017 and 2022, specifically in practicing teachers at the secondary education level. A systematic literature review guided by the PRISMA statement was conducted. The search for papers was carried out in the Scopus, Web of Science, Sage Journal, and SciELO databases in September 2022. 37 articles were selected for review. The review of the studies made it possible to establish that the papers were mainly grouped into two categories: the first related to teachers' perception of TPACK, which includes research with self-perception questionnaires, case studies and relationships of self-perception with other variables such as barriers to technology integration in the classroom, pedagogical beliefs, and online education; and the second to developing training courses based on TPACK, complemented with self-perception questionnaires in the form of pretest-posts, classroom observations, and interviews. The results of the first category are dissimilar since TPACK levels differ from one study to another in variables such as gender and teaching experience. From the second, it is established that teacher professional development courses favor improving technological integration in the classroom.

Published
2024

38. Reference equations for DLNO and DLCO in Mexican Hispanics: influence of altitude and race.

Author

Gochicoa-Rangel, Laura, De-Los-Santos-Martínez, Ada, Reyes-García, Alejandro, Martínez-Briseño, David, Vargas, Mario, Lechuga-Trejo, Irma, Guzmán-Valderrábano, Carlos, Torre-Bouscoulet, Luis, and Zavorsky, Gerald

Subjects
Lung Physiology, Respiratory Function Test, Respiratory Measurement, Humans, Altitude, Male, Pulmonary Diffusing Capacity, Female, Middle Aged, Adult, Aged, Mexico, Young Adult, Hispanic or Latino, Nitric Oxide, Adolescent, Aged, 80 and over, Child, Carbon Monoxide, Child, Preschool, Reference Values, White People
Abstract

OBJECTIVES: This study aimed to evaluate pulmonary diffusing capacity for nitric oxide (DLNO) and pulmonary diffusing capacity for carbon monoxide (DLCO) in Mexican Hispanics born and raised at 2240 m altitude (midlanders) compared with those born and raised at sea level (lowlanders). It also aimed to assess the effectiveness of race-specific reference equations for pulmonary diffusing capacity (white people vs Mexican Hispanics) in minimising root mean square errors (RMSE) compared with race-neutral equations. METHODS: DLNO, DLCO, alveolar volume (VA) and gas transfer coefficients (KNO and KCO) were measured in 392 Mexican Hispanics (5 to 78 years) and compared with 1056 white subjects (5 to 95 years). Reference equations were developed using segmented linear regression (DLNO, DLCO and VA) and multiple linear regression (KNO and KCO) and validated with Least Absolute Shrinkage and Selection Operator. RMSE comparisons between race-specific and race-neutral models were conducted using repeated k-fold cross-validation and random forests. RESULTS: Midlanders exhibited higher DLCO (mean difference: +4 mL/min/mm Hg), DLNO (mean difference: +7 mL/min/mm Hg) and VA (mean difference: +0.17 L) compared with lowlanders. The Bayesian information criterion favoured race-specific models and excluding race as a covariate increased RMSE by 61% (DLNO), 18% (DLCO) and 4% (KNO). RMSE values for VA and KCO were comparable between race-specific and race-neutral models. For DLCO and DLNO, race-neutral equations resulted in 3% to 6% false positive rates (FPRs) in Mexican Hispanics and 20% to 49% false negative rates (FNRs) in white subjects compared with race-specific equations. CONCLUSIONS: Mexican Hispanics born and raised at 2240 m exhibit higher DLCO and DLNO compared with lowlanders. Including race as a covariate in reference equations lowers the RMSE for DLNO, DLCO and KNO and reduces FPR and FNR compared with race-neutral models. This study highlights the need for altitude-specific and race-specific reference equations to improve pulmonary function assessments across diverse populations.

Published
2024

39. VLT/MUSE detection of accretion-ejection associated with the close stellar companion in the HT Lup system

Author

Jorquera, Sebastián, Bonnefoy, Mickaël, Pérez, Laura M., Chauvin, Gaël, Aguinaga, Adrian, Dougados, Catherine, Julo, Rémi, Demars, Dorian, Andrews, Sean M., Ricci, Luca, Zhu, Zhaohuan, kurtovic, Nicolas T., Cuello, Nicolás, Bai, Xue-ning, Birnstiel, Til, Dullemond, Cornelis, and Guzmán, Viviana V.

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics
Abstract

The accretion/ejection processes in T-Tauri stars are fundamental to their physical evolution, while also impacting the properties and evolution of the circumstellar material at a time when planet formation takes place. To this date, characterization of ongoing accretion processes in stellar pairs at 5-50\,au scales has been challenging, high angular resolution spectrographs are required to extract the spectral features of each component. We present the analysis of spectroscopic observations of the tight (160mas, 25au) T-Tauri system HT Lup A/B, obtained with MUSE at VLT in March and July of 2021. We focus on constraining the accretion/ejection processes and variability of the secondary component HT Lup B, by searching for accretion tracers applying High-Resolution Spectral Differential Imaging techniques. We retrieve strong (SNR $>$ 5) $H\alpha, H\beta$ and [OI]$\lambda6300$ emission in both epochs. The $H\alpha$ and $H\beta$ line fluxes showcase high variability, with variations up to 400-500\% between epochs. The fluxes are consistent with accretion rates of $8\times10^{-9} M_\odot \, yr^{-1}$ and $2\times10^{-9} M_\odot \, yr^{-1}$ for the first and second epoch, respectively. We attribute the increased accretion activity during the first night to a "burst" like event, followed by a relaxation period more representative of the common accretion activity of the system. The [OI]$\lambda6300$ line profiles remain relatively similar between epochs and suggest ejection rates on the order of $10^{-9}-10^{-10} M_\odot \, yr^{-1}$, compatible with moderate disk winds emission. Our results also indicate that the accretion processes of HT Lup B are compatible with Classical T Tauri Stars, unlike previous classifications, Comment: 28 pages, 13 fgures, Accepted by ApJ

Published
2024

40. How to Measure Human-AI Prediction Accuracy in Explainable AI Systems

Author

Koujalgi, Sujay, Anderson, Andrew, Adenuga, Iyadunni, Soneji, Shikha, Dikkala, Rupika, Nader, Teresita Guzman, Soccio, Leo, Panda, Sourav, Das, Rupak Kumar, Burnett, Margaret, and Dodge, Jonathan

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, D.2.8
Abstract

Assessing an AI system's behavior-particularly in Explainable AI Systems-is sometimes done empirically, by measuring people's abilities to predict the agent's next move-but how to perform such measurements? In empirical studies with humans, an obvious approach is to frame the task as binary (i.e., prediction is either right or wrong), but this does not scale. As output spaces increase, so do floor effects, because the ratio of right answers to wrong answers quickly becomes very small. The crux of the problem is that the binary framing is failing to capture the nuances of the different degrees of "wrongness." To address this, we begin by proposing three mathematical bases upon which to measure "partial wrongness." We then uses these bases to perform two analyses on sequential decision-making domains: the first is an in-lab study with 86 participants on a size-36 action space; the second is a re-analysis of a prior study on a size-4 action space. Other researchers adopting our operationalization of the prediction task and analysis methodology will improve the rigor of user studies conducted with that task, which is particularly important when the domain features a large output space.

Published
2024

41. Simulation of the THF hydrate-water interfacial free energy from computer simulation

Author

Torrejón, Miguel J., Romero-Guzmán, Cristóbal, Piñeiro, Manuel M., Blas, Felipe J., and Algaba, Jesús

Subjects
Condensed Matter - Soft Condensed Matter
Abstract

In this work, the tetrahydrofuran (THF) hydrate-water interfacial free energy is determined at $500\,\text{bar}$, at one point of the univariant two-phase coexistence line of the THF hydrate, by molecular dynamics simulation. The Mold Integration-Host methodology, an extension of the original Mold Integration technique to deal with hydrate-fluid interfaces, is used to calculate the interfacial energy. Water is described using the well-known TIP4P/Ice model and THF is described using a rigid version of the TraPPE model. We have recently used the combination of these two models to accurately describe the univariant two-phase dissociation line of the THF hydrate, in a wide range of pressures, from computer simulation [J. Chem. Phys. 160, 164718 (2024)]. The THF hydrate-water interfacial free energy predicted in this work is compared with the only experimental data available in the literature. The value obtained, $27(2)\,\text{mJ/m}^{2}$, is in excellent agreement with the experimental data taken from the literature, $24(8)\,\text{mJ/m}^{2}$. To the best of our knowledge, this is the first time that the THF hydrate-water interfacial free energy is predicted from computer simulation. This work confirms that the Mold Integration technique can be used with confidence to predict solid-fluid interfaces of complex structures, including hydrates that exhibit sI and sII crystallographic structures., Comment: 11 pages, 6 figures

Published
2024
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42. Quantifying the informativity of emission lines to infer physical conditions in giant molecular clouds. I. Application to model predictions

Author

Einig, Lucas, Palud, Pierre, Roueff, Antoine, Pety, Jérôme, Bron, Emeric, Petit, Franck Le, Gerin, Maryvonne, Chanussot, Jocelyn, Chainais, Pierre, Thouvenin, Pierre-Antoine, Languignon, David, Bešlić, Ivana, Coudé, Simon, Mazurek, Helena, Orkisz, Jan H., Santa-Maria, Miriam G., Ségal, Léontine, Zakardjian, Antoine, Bardeau, Sébastien, Demyk, Karine, Magalhães, Victor de Souza, Goicoechea, Javier R., Gratier, Pierre, Guzmán, Viviana V., Hughes, Annie, Levrier, François, Bourlot, Jacques Le, Lis, Dariusz C., Liszt, Harvey S., Peretto, Nicolas, Roueff, Evelyne, and Sievers, Albrecht

Subjects
Astrophysics - Astrophysics of Galaxies, Statistics - Applications
Abstract

Observations of ionic, atomic, or molecular lines are performed to improve our understanding of the interstellar medium (ISM). However, the potential of a line to constrain the physical conditions of the ISM is difficult to assess quantitatively, because of the complexity of the ISM physics. The situation is even more complex when trying to assess which combinations of lines are the most useful. Therefore, observation campaigns usually try to observe as many lines as possible for as much time as possible. We search for a quantitative statistical criterion to evaluate the constraining power of a (or combination of) tracer(s) with respect to physical conditions in order to improve our understanding of the statistical relationships between ISM tracers and physical conditions and helps observers to motivate their observation proposals. The best tracers are obtained by comparing the mutual information between a physical parameter and different sets of lines. We apply this method to simulations of radio molecular lines emitted by a photodissociation region similar to the Horsehead Nebula that would be observed at the IRAM 30m telescope. We search for the best lines to constrain the visual extinction $A_v^{tot}$ or the far UV illumination $G_0$. The most informative lines change with the physical regime (e.g., cloud extinction). Short integration time of the CO isotopologue $J=1-0$ lines already yields much information on the total column density most regimes. The best set of lines to constrain the visual extinction does not necessarily combine the most informative individual lines. Precise constraints on $G_0$ are more difficult to achieve with molecular lines. They require spectral lines emitted at the cloud surface (e.g., [CII] and [CI] lines). This approach allows one to better explore the knowledge provided by ISM codes, and to guide future observation campaigns.

Published
2024
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43. Acoustic and Optical Phonon Frequencies and Acoustic Phonon Velocities in Silicon-Doped Aluminum Nitride Thin Films

Author

Wright, Dylan, Mudiyanselage, Dinusha Herath, Guzman, Erick, Fu, Xuke, Teeter, Jordan, Da, Bingcheng, Kargar, Fariborz, Fu, Houqiang, and Balandin, Alexander A.

Subjects
Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

We report the results of the study of the acoustic and optical phonons in Si-doped AlN thin films grown by metalorganic chemical vapor deposition on sapphire substrates. The Brillouin - Mandelstam and Raman light scattering spectroscopies were used to measure the acoustic and optical phonon frequencies close to the Brillouin zone center. The optical phonon frequencies reveal non-monotonic changes, reflective of the variations in the thin-film strain and dislocation densities with the addition of Si dopant atoms. The acoustic phonon velocity decreases monotonically with increasing Si dopant concentration, reducing by ~300 m/s at the doping level of 3 x 10^19 1/cm3. Knowledge of the acoustic phonon velocities can be used to optimize ultra-wide bandgap semiconductor heterostructures and minimize the thermal boundary resistance of high-power devices., Comment: 16 pages; 4 figures

Published
2024

44. Chiral homology of the projective line and extensions of vertex algebra modules

Author

Cardoso, Thadeu Henrique, van Ekeren, Jethro, Guzman, Juan, and Heluani, Reimundo

Subjects
Mathematics - Quantum Algebra, Mathematics - Algebraic Geometry, Mathematics - Representation Theory
Abstract

The purpose of this note is to establish an isomorphism from the vector space of extensions between two modules over a vertex algebra, to an appropriate first chiral homology of one dimensional projective space with coefficients in the corresponding chiral algebra., Comment: 15 pages

Published
2024

45. Detection of Dimethyl Ether in the Central Region of the MWC 480 Protoplanetary Disk

Author

Yamato, Yoshihide, Aikawa, Yuri, Guzmán, Viviana V., Furuya, Kenji, Notsu, Shota, Cataldi, Gianni, Öberg, Karin I., Qi, Chunhua, Law, Charles J., Huang, Jane, Teague, Richard, and Gal, Romane Le

Subjects
Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract

Characterizing the chemistry of complex organic molecules (COMs) at the epoch of planet formation provides insights into the chemical evolution of the interstellar medium (ISM) and the origin of organic materials in our Solar System. We report a detection of dimethyl ether (CH$_3$OCH$_3$) in the disk around the Herbig Ae star MWC 480 with the sensitive Atacama Large Millimeter/submillimeter Array observations. This is the first detection of CH$_3$OCH$_3$ in a non-transitional Class II disk. The spatially unresolved, compact (${\lesssim}$25 au in radius) nature, the broad line width ($\sim$30 km s$^{-1}$), and the high excitation temperature (${\sim}$200 K) indicate sublimation of COMs in the warm inner disk. Despite the detection of CH$_3$OCH$_3$, methanol (CH$_3$OH), the most abundant COM in the ISM, has not been detected, from which we constrain the column density ratio of CH$_3$OCH$_3$/CH$_3$OH ${\gtrsim}$7. This high ratio may indicate the reprocessing of COMs during the disk phase, as well as the effect of the physical structure in the inner disk. We also find that this ratio is higher than in COM-rich transition disks recently discovered. This may indicate that, in the full disk of MWC 480, COMs have experienced substantial chemical reprocessing in the innermost region, while the COM emission in the transition disks predominantly traces the inherited ice sublimating at the dust cavity edge located at larger radii (${\gtrsim}$20 au)., Comment: 16 pages, 6 figures, accepted for publication in ApJ

Published
2024

46. Comparative of light propagation in Born-Infeld, Euler-Heisenberg and ModMax nonlinear electrodynamics

Author

Guzman-Herrera, Elda, Montiel, Ariadna, and Breton, Nora

Subjects
General Relativity and Quantum Cosmology, High Energy Physics - Theory
Abstract

We compare light propagation through an intense electromagnetic background as described by three different nonlinear electrodynamics: Born-Infeld (BI), Euler-Heisenberg (EH), and Modified Maxwell (MM). We use the concept of effective metric to determine the phase velocities of a propagating wave from the BI and EH nonlinear electrodynamics and use them to set constraints on the MM nonlinear parameter. In a second part of the paper, we consider the black hole solutions of the nonlinear electrodynamics coupled with General Relativity and determine the shadows cast by the static black holes. Confronting the observations of the shadows of M87$^*$ and Sagittarius A$^*$ with our theoretical results allows us to set restrictions on the nonlinear parameters., Comment: 19 pages, 7 figures, updated to match the version accepted by JCAP

Published
2024
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47. The Llama 3 Herd of Models

Author

Dubey, Abhimanyu, Jauhri, Abhinav, Pandey, Abhinav, Kadian, Abhishek, Al-Dahle, Ahmad, Letman, Aiesha, Mathur, Akhil, Schelten, Alan, Yang, Amy, Fan, Angela, Goyal, Anirudh, Hartshorn, Anthony, Yang, Aobo, Mitra, Archi, Sravankumar, Archie, Korenev, Artem, Hinsvark, Arthur, Rao, Arun, Zhang, Aston, Rodriguez, Aurelien, Gregerson, Austen, Spataru, Ava, Roziere, Baptiste, Biron, Bethany, Tang, Binh, Chern, Bobbie, Caucheteux, Charlotte, Nayak, Chaya, Bi, Chloe, Marra, Chris, McConnell, Chris, Keller, Christian, Touret, Christophe, Wu, Chunyang, Wong, Corinne, Ferrer, Cristian Canton, Nikolaidis, Cyrus, Allonsius, Damien, Song, Daniel, Pintz, Danielle, Livshits, Danny, Esiobu, David, Choudhary, Dhruv, Mahajan, Dhruv, Garcia-Olano, Diego, Perino, Diego, Hupkes, Dieuwke, Lakomkin, Egor, AlBadawy, Ehab, Lobanova, Elina, Dinan, Emily, Smith, Eric Michael, Radenovic, Filip, Zhang, Frank, Synnaeve, Gabriel, Lee, Gabrielle, Anderson, Georgia Lewis, Nail, Graeme, Mialon, Gregoire, Pang, Guan, Cucurell, Guillem, Nguyen, Hailey, Korevaar, Hannah, Xu, Hu, Touvron, Hugo, Zarov, Iliyan, Ibarra, Imanol Arrieta, Kloumann, Isabel, Misra, Ishan, Evtimov, Ivan, Copet, Jade, Lee, Jaewon, Geffert, Jan, Vranes, Jana, Park, Jason, Mahadeokar, Jay, Shah, Jeet, van der Linde, Jelmer, Billock, Jennifer, Hong, Jenny, Lee, Jenya, Fu, Jeremy, Chi, Jianfeng, Huang, Jianyu, Liu, Jiawen, Wang, Jie, Yu, Jiecao, Bitton, Joanna, Spisak, Joe, Park, Jongsoo, Rocca, Joseph, Johnstun, Joshua, Saxe, Joshua, Jia, Junteng, Alwala, Kalyan Vasuden, Upasani, Kartikeya, Plawiak, Kate, Li, Ke, Heafield, Kenneth, Stone, Kevin, El-Arini, Khalid, Iyer, Krithika, Malik, Kshitiz, Chiu, Kuenley, Bhalla, Kunal, Rantala-Yeary, Lauren, van der Maaten, Laurens, Chen, Lawrence, Tan, Liang, Jenkins, Liz, Martin, Louis, Madaan, Lovish, Malo, Lubo, Blecher, Lukas, Landzaat, Lukas, de Oliveira, Luke, Muzzi, Madeline, Pasupuleti, Mahesh, Singh, Mannat, Paluri, Manohar, Kardas, Marcin, Oldham, Mathew, Rita, Mathieu, Pavlova, Maya, Kambadur, Melanie, Lewis, Mike, Si, Min, Singh, Mitesh Kumar, Hassan, Mona, Goyal, Naman, Torabi, Narjes, Bashlykov, Nikolay, Bogoychev, Nikolay, Chatterji, Niladri, Duchenne, Olivier, Çelebi, Onur, Alrassy, Patrick, Zhang, Pengchuan, Li, Pengwei, Vasic, Petar, Weng, Peter, Bhargava, Prajjwal, Dubal, Pratik, Krishnan, Praveen, Koura, Punit Singh, Xu, Puxin, He, Qing, Dong, Qingxiao, Srinivasan, Ragavan, Ganapathy, Raj, Calderer, Ramon, Cabral, Ricardo Silveira, Stojnic, Robert, Raileanu, Roberta, Girdhar, Rohit, Patel, Rohit, Sauvestre, Romain, Polidoro, Ronnie, Sumbaly, Roshan, Taylor, Ross, Silva, Ruan, Hou, Rui, Wang, Rui, Hosseini, Saghar, Chennabasappa, Sahana, Singh, Sanjay, Bell, Sean, Kim, Seohyun Sonia, Edunov, Sergey, Nie, Shaoliang, Narang, Sharan, Raparthy, Sharath, Shen, Sheng, Wan, Shengye, Bhosale, Shruti, Zhang, Shun, Vandenhende, Simon, Batra, Soumya, Whitman, Spencer, Sootla, Sten, Collot, Stephane, Gururangan, Suchin, Borodinsky, Sydney, Herman, Tamar, Fowler, Tara, Sheasha, Tarek, Georgiou, Thomas, Scialom, Thomas, Speckbacher, Tobias, Mihaylov, Todor, Xiao, Tong, Karn, Ujjwal, Goswami, Vedanuj, Gupta, Vibhor, Ramanathan, Vignesh, Kerkez, Viktor, Gonguet, Vincent, Do, Virginie, Vogeti, Vish, Petrovic, Vladan, Chu, Weiwei, Xiong, Wenhan, Fu, Wenyin, Meers, Whitney, Martinet, Xavier, Wang, Xiaodong, Tan, Xiaoqing Ellen, Xie, Xinfeng, Jia, Xuchao, Wang, Xuewei, Goldschlag, Yaelle, Gaur, Yashesh, Babaei, Yasmine, Wen, Yi, Song, Yiwen, Zhang, Yuchen, Li, Yue, Mao, Yuning, Coudert, Zacharie Delpierre, Yan, Zheng, Chen, Zhengxing, Papakipos, Zoe, Singh, Aaditya, Grattafiori, Aaron, Jain, Abha, Kelsey, Adam, Shajnfeld, Adam, Gangidi, Adithya, Victoria, Adolfo, Goldstand, Ahuva, Menon, Ajay, Sharma, Ajay, Boesenberg, Alex, Vaughan, Alex, Baevski, Alexei, Feinstein, Allie, Kallet, Amanda, Sangani, Amit, Yunus, Anam, Lupu, Andrei, Alvarado, Andres, Caples, Andrew, Gu, Andrew, Ho, Andrew, Poulton, Andrew, Ryan, Andrew, Ramchandani, Ankit, Franco, Annie, Saraf, Aparajita, Chowdhury, Arkabandhu, Gabriel, Ashley, Bharambe, Ashwin, Eisenman, Assaf, Yazdan, Azadeh, James, Beau, Maurer, Ben, Leonhardi, Benjamin, Huang, Bernie, Loyd, Beth, De Paola, Beto, Paranjape, Bhargavi, Liu, Bing, Wu, Bo, Ni, Boyu, Hancock, Braden, Wasti, Bram, Spence, Brandon, Stojkovic, Brani, Gamido, Brian, Montalvo, Britt, Parker, Carl, Burton, Carly, Mejia, Catalina, Wang, Changhan, Kim, Changkyu, Zhou, Chao, Hu, Chester, Chu, Ching-Hsiang, Cai, Chris, Tindal, Chris, Feichtenhofer, Christoph, Civin, Damon, Beaty, Dana, Kreymer, Daniel, Li, Daniel, Wyatt, Danny, Adkins, David, Xu, David, Testuggine, Davide, David, Delia, Parikh, Devi, Liskovich, Diana, Foss, Didem, Wang, Dingkang, Le, Duc, Holland, Dustin, Dowling, Edward, Jamil, Eissa, Montgomery, Elaine, Presani, Eleonora, Hahn, Emily, Wood, Emily, Brinkman, Erik, Arcaute, Esteban, Dunbar, Evan, Smothers, Evan, Sun, Fei, Kreuk, Felix, Tian, Feng, Ozgenel, Firat, Caggioni, Francesco, Guzmán, Francisco, Kanayet, Frank, Seide, Frank, Florez, Gabriela Medina, Schwarz, Gabriella, Badeer, Gada, Swee, Georgia, Halpern, Gil, Thattai, Govind, Herman, Grant, Sizov, Grigory, Guangyi, Zhang, Lakshminarayanan, Guna, Shojanazeri, Hamid, Zou, Han, Wang, Hannah, Zha, Hanwen, Habeeb, Haroun, Rudolph, Harrison, Suk, Helen, Aspegren, Henry, Goldman, Hunter, Damlaj, Ibrahim, Molybog, Igor, Tufanov, Igor, Veliche, Irina-Elena, Gat, Itai, Weissman, Jake, Geboski, James, Kohli, James, Asher, Japhet, Gaya, Jean-Baptiste, Marcus, Jeff, Tang, Jeff, Chan, Jennifer, Zhen, Jenny, Reizenstein, Jeremy, Teboul, Jeremy, Zhong, Jessica, Jin, Jian, Yang, Jingyi, Cummings, Joe, Carvill, Jon, Shepard, Jon, McPhie, Jonathan, Torres, Jonathan, Ginsburg, Josh, Wang, Junjie, Wu, Kai, U, Kam Hou, Saxena, Karan, Prasad, Karthik, Khandelwal, Kartikay, Zand, Katayoun, Matosich, Kathy, Veeraraghavan, Kaushik, Michelena, Kelly, Li, Keqian, Huang, Kun, Chawla, Kunal, Lakhotia, Kushal, Huang, Kyle, Chen, Lailin, Garg, Lakshya, A, Lavender, Silva, Leandro, Bell, Lee, Zhang, Lei, Guo, Liangpeng, Yu, Licheng, Moshkovich, Liron, Wehrstedt, Luca, Khabsa, Madian, Avalani, Manav, Bhatt, Manish, Tsimpoukelli, Maria, Mankus, Martynas, Hasson, Matan, Lennie, Matthew, Reso, Matthias, Groshev, Maxim, Naumov, Maxim, Lathi, Maya, Keneally, Meghan, Seltzer, Michael L., Valko, Michal, Restrepo, Michelle, Patel, Mihir, Vyatskov, Mik, Samvelyan, Mikayel, Clark, Mike, Macey, Mike, Wang, Mike, Hermoso, Miquel Jubert, Metanat, Mo, Rastegari, Mohammad, Bansal, Munish, Santhanam, Nandhini, Parks, Natascha, White, Natasha, Bawa, Navyata, Singhal, Nayan, Egebo, Nick, Usunier, Nicolas, Laptev, Nikolay Pavlovich, Dong, Ning, Zhang, Ning, Cheng, Norman, Chernoguz, Oleg, Hart, Olivia, Salpekar, Omkar, Kalinli, Ozlem, Kent, Parkin, Parekh, Parth, Saab, Paul, Balaji, Pavan, Rittner, Pedro, Bontrager, Philip, Roux, Pierre, Dollar, Piotr, Zvyagina, Polina, Ratanchandani, Prashant, Yuvraj, Pritish, Liang, Qian, Alao, Rachad, Rodriguez, Rachel, Ayub, Rafi, Murthy, Raghotham, Nayani, Raghu, Mitra, Rahul, Li, Raymond, Hogan, Rebekkah, Battey, Robin, Wang, Rocky, Maheswari, Rohan, Howes, Russ, Rinott, Ruty, Bondu, Sai Jayesh, Datta, Samyak, Chugh, Sara, Hunt, Sara, Dhillon, Sargun, Sidorov, Sasha, Pan, Satadru, Verma, Saurabh, Yamamoto, Seiji, Ramaswamy, Sharadh, Lindsay, Shaun, Feng, Sheng, Lin, Shenghao, Zha, Shengxin Cindy, Shankar, Shiva, Zhang, Shuqiang, Wang, Sinong, Agarwal, Sneha, Sajuyigbe, Soji, Chintala, Soumith, Max, Stephanie, Chen, Stephen, Kehoe, Steve, Satterfield, Steve, Govindaprasad, Sudarshan, Gupta, Sumit, Cho, Sungmin, Virk, Sunny, Subramanian, Suraj, Choudhury, Sy, Goldman, Sydney, Remez, Tal, Glaser, Tamar, Best, Tamara, Kohler, Thilo, Robinson, Thomas, Li, Tianhe, Zhang, Tianjun, Matthews, Tim, Chou, Timothy, Shaked, Tzook, Vontimitta, Varun, Ajayi, Victoria, Montanez, Victoria, Mohan, Vijai, Kumar, Vinay Satish, Mangla, Vishal, Albiero, Vítor, Ionescu, Vlad, Poenaru, Vlad, Mihailescu, Vlad Tiberiu, Ivanov, Vladimir, Li, Wei, Wang, Wenchen, Jiang, Wenwen, Bouaziz, Wes, Constable, Will, Tang, Xiaocheng, Wang, Xiaofang, Wu, Xiaojian, Wang, Xiaolan, Xia, Xide, Wu, Xilun, Gao, Xinbo, Chen, Yanjun, Hu, Ye, Jia, Ye, Qi, Ye, Li, Yenda, Zhang, Yilin, Zhang, Ying, Adi, Yossi, Nam, Youngjin, Yu, Wang, Hao, Yuchen, Qian, Yundi, He, Yuzi, Rait, Zach, DeVito, Zachary, Rosnbrick, Zef, Wen, Zhaoduo, Yang, Zhenyu, and Zhao, Zhiwei

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition
Abstract

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Published
2024

48. Interactive Learning in Computer Science Education Supported by a Discord Chatbot

Author

Berrezueta-Guzman, Santiago, Parmacli, Ivan, Krusche, Stephan, and Wagner, Stefan

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Emerging Technologies
Abstract

Enhancing interaction and feedback collection in a first-semester computer science course poses a significant challenge due to students' diverse needs and engagement levels. To address this issue, we created and integrated a command-based chatbot on the course communication server on Discord. The DiscordBot enables students to provide feedback on course activities through short surveys, such as exercises, quizzes, and lectures, facilitating stress-free communication with instructors. It also supports attendance tracking and introduces lectures before they start. The research demonstrates the effectiveness of the DiscordBot as a communication tool. The ongoing feedback allowed course instructors to dynamically adjust and improve the difficulty level of upcoming activities and promote discussion in subsequent tutor sessions. The data collected reveal that students can accurately perceive the activities' difficulty and expected results, providing insights not possible through traditional end-of-semester surveys. Students reported that interaction with the DiscordBot was easy and expressed a desire to continue using it in future semesters. This responsive approach ensures the course meets the evolving needs of students, thereby enhancing their overall learning experience., Comment: Revised and accepted paper at the IEEE German Education Conference 2024 (GECon 2024) and to be published in IEEE proceedings

Published
2024

49. The need to implement FAIR principles in biomolecular simulations

Author

Amaro, Rommie, Åqvist, Johan, Bahar, Ivet, Battistini, Federica, Bellaiche, Adam, Beltran, Daniel, Biggin, Philip C., Bonomi, Massimiliano, Bowman, Gregory R., Bryce, Richard, Bussi, Giovanni, Carloni, Paolo, Case, David, Cavalli, Andrea, Chang, Chie-En A., Cheatham III, Thomas E., Cheung, Margaret S., Chipot, Cris, Chong, Lillian T., Choudhary, Preeti, Cisneros, Gerardo Andres, Clementi, Cecilia, Collepardo-Guevara, Rosana, Coveney, Peter, Covino, Roberto, Crawford, T. Daniel, Peraro, Matteo Dal, de Groot, Bert, Delemotte, Lucie, De Vivo, Marco, Essex, Jonathan, Fraternali, Franca, Gao, Jiali, Gelpí, Josep Lluís, Gervasio, Francesco Luigi, Gonzalez-Nilo, Fernando Danilo, Grubmüller, Helmut, Guenza, Marina, Guzman, Horacio V., Harris, Sarah, Head-Gordon, Teresa, Hernandez, Rigoberto, Hospital, Adam, Huang, Niu, Huang, Xuhui, Hummer, Gerhard, Iglesias-Fernández, Javier, Jensen, Jan H., Jha, Shantenu, Jiao, Wanting, Jorgensen, William L., Kamerlin, Shina Caroline Lynn, Khalid, Syma, Laughton, Charles, Levitt, Michael, Limongelli, Vittorio, Lindahl, Erik, Lindorff-Larsen, Kresten, Loverde, Sharon, Lundborg, Magnus, Luo, Yun Lyna, Luque, Francisco Javier, Lynch, Charlotte I., MacKerell, Alexander, Magistrato, Alessandra, Marrink, Siewert J., Martin, Hugh, McCammon, J. Andrew, Merz, Kenneth, Moliner, Vicent, Mulholland, Adrian, Murad, Sohail, Naganathan, Athi N., Nangia, Shikha, Noe, Frank, Noy, Agnes, Oláh, Julianna, O'Mara, Megan, Ondrechen, Mary Jo, Onuchic, José N., Onufriev, Alexey, Osuna, Silvia, Panchenko, Anna R., Pantano, Sergio, Parish, Carol, Parrinello, Michele, Perez, Alberto, Perez-Acle, Tomas, Perilla, Juan R., Pettitt, B. Montgomery, Pietropalo, Adriana, Piquemal, Jean-Philip, Poma, Adolfo, Praprotnik, Matej, Ramos, Maria J., Ren, Pengyu, Reuter, Nathalie, Roitberg, Adrian, Rosta, Edina, Rovira, Carme, Roux, Benoit, Röthlisberger, Ursula, Sanbonmatsu, Karissa Y., Schlick, Tamar, Shaytan, Alexey K., Simmerling, Carlos, Smith, Jeremy C., Sugita, Yuji, Świderek, Katarzyna, Taiji, Makoto, Tao, Peng, Tikhonova, Irina G., Tirado-Rives, Julian, Tunón, Inaki, Van Der Kamp, Marc W., Van der Spoel, David, Velankar, Sameer, Voth, Gregory A., Wade, Rebecca, Warshel, Ariel, Welborn, Valerie Vaissier, Wetmore, Stacey, Wong, Chung F., Yang, Lee-Wei, Zacharias, Martin, and Orozco, Modesto

Subjects
Quantitative Biology - Biomolecules
Abstract

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/)

Published
2024

50. SpIRIT Mission: In-Orbit Results and Technology Demonstrations

Author

Trenti, Michele, del Castillo, Miguel Ortiz, Mearns, Robert, McRobbie, Jack, Therakam, Clint, Chapman, Airlie, Woods, Andrew, Morgan, Jonathan, Barraclough, Simon, Mallo, Ivan Rodriguez, Baroni, Giulia, Fiore, Fabrizio, Evangelista, Yuri, Campana, Riccardo, Guzman, Alejandro, and Hedderman, Paul

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract

The Space Industry Responsive Intelligent Thermal (SpIRIT) 6U CubeSat is a mission led by The University of Melbourne in cooperation with the Italian Space Agency. Launched in a 510 km Polar Sun Synchronous Orbit in December 2023, SpIRIT carries multiple subsystems for scientific and technology demonstration. The main payload is the HERMES instrument for detection of high-energy astrophysics transients (Gamma Ray Bursts), and for studies of their variability at scales below 1 ms. The satellite includes a novel thermal management system for its class, based on a Stirling-cycle cooler and deployable thermal radiator, designed to cool HERMES to reduce instrumental background noise. A low-latency communication subsystem based on a sat-phone network is supporting rapid transmission of time-critical data and telecommands. SpIRIT is also equipped with a set of RGB and thermal IR cameras, connected to an on-board image processing unit with artificial intelligence capabilities for autonomous feature recognition. To effectively manage all interfaces between different subsystems and mission stakeholders, the University of Melbourne developed an instrument control unit (PMS) which operates all payloads. PMS also provides backup uninterruptible power to the HERMES instrument through a supercapacitor-based UPS for safe instrument shutdown in case of platform power interruptions. This paper first presents a mission and payload overview, and early in-orbit results, along with lessons learned throughout the mission. This work not only sheds light on the novelty of some of the on-board technologies onboard and on their potential impact to enable greater utilization of CubeSats for scientific missions, but also offers insights into the practical challenges and accomplishments related to developing and operating a multi-organization CubeSat with a complex array of instruments and systems., Comment: 12 pages, 38th Annual Small Satellite Conference

Published
2024

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