Author: "A Dietrich" - Searchworks@Jio Institute Digital Library Search Results

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272,282 results on '"A Dietrich"'

1. Training Hamiltonian neural networks without backpropagation

Author: Rahma, Atamert, Datar, Chinmay, and Dietrich, Felix
Subjects: Computer Science - Machine Learning, Mathematics - Numerical Analysis
Abstract: Neural networks that synergistically integrate data and physical laws offer great promise in modeling dynamical systems. However, iterative gradient-based optimization of network parameters is often computationally expensive and suffers from slow convergence. In this work, we present a backpropagation-free algorithm to accelerate the training of neural networks for approximating Hamiltonian systems through data-agnostic and data-driven algorithms. We empirically show that data-driven sampling of the network parameters outperforms data-agnostic sampling or the traditional gradient-based iterative optimization of the network parameters when approximating functions with steep gradients or wide input domains. We demonstrate that our approach is more than 100 times faster with CPUs than the traditionally trained Hamiltonian Neural Networks using gradient-based iterative optimization and is more than four orders of magnitude accurate in chaotic examples, including the H\'enon-Heiles system., Comment: 5 pages, 2 figures and 2 tables in the main text, includes an Appendix section, accepted to NeurIPS 2024 Workshop ML4PS
Published: 2024

2. Cohomology of perfect Lie algebras

Author: Burde, Dietrich and Wagemann, Friedrich
Subjects: Mathematics - Representation Theory, 17B10, 17B56
Abstract: We study the adjoint cohomology of perfect Lie algebras over the complex numbers. For the family of perfect Lie algebras $\mathfrak{g}=\mathfrak{sl}_2(\Bbb C)\ltimes V_m$ we obtain some explicit results for $H^k(\mathfrak{g},\mathfrak{g})$ with $k\ge 0$. Here $V_m$ is the irreducible representation of $\mathfrak{sl}_2(\Bbb C)$ of dimension $m+1$. For the computation of the cohomology we use the Hochschild-Serre formula, a long exact sequence in the cohomology and explicit formulas for the multiplicities of $V_k$ in the exterior product $\Lambda^j(V_m)$ for $j\le 4$. In general we cannot determine the total adjoint cohomology for $\mathfrak{sl}_2(\Bbb C)\ltimes V_m$, but for some small $m$ this is possible. We also give a classification of complex perfect Lie algebras $\mathfrak{g}$ of dimension $n\le 9$ and explicitly compute the cohomology spaces $H^k(\mathfrak{g},\mathfrak{g})$ with $k=0,1,2$ for all Lie algebras from the classification list.
Published: 2024

3. Detecting electromagnetic counterparts to LIGO/Virgo/KAGRA gravitational wave events with DECam: Neutron Star Mergers

Author: Kunnumkai, Keerthi, Palmese, Antonella, Farah, Amanda M, Bulla, Mattia, Dietrich, Tim, Pang, Peter T H, Anand, Shreya, Andreoni, Igor, Cabrera, Tomas, and Connor, Brendan O
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: With GW170817 being the only multimessenger gravitational wave (GW) event with an associated kilonova (KN) detected so far, there exists a pressing need for realistic estimation of the GW localization uncertainties and rates, as well as optimization of available telescope time to enable the detection of new KNe. For this purpose, we simulate GW events assuming a data-driven, GW-motivated distribution of binary parameters for the LIGO/Virgo/KAGRA (LVK) fourth and fifth observing runs (O4 and O5). For our particular case of estimating KN detection rates in the O4 and O5 runs, we map the binary neutron star (BNS) and neutron star-black hole (NSBH) properties to the optical light curves arising from r-process nucleosynthesis in the ejecta. We use the simulated population of KNe to generate follow-up observing plans, with the primary goal of optimizing detection with the Gravitational Wave Multi-Messenger Astronomy DECam Survey (GW-MMADS). We explore KN detectability as a function of mass, distance, and spin of the binaries. Out of the mergers that produce a KN in our simulations, we expect detectable KNe for DECam-like instruments at a per-year rate of: $1-16$ ($0-1$) for BNS (NSBH) in O4, and $16-165$ ($1-12$) for BNS (NSBH) in O5, using a fiducial exposure time and conditional on the uncertainty on the equation of state (EOS) and volumetric rates of the mergers. Taking into account observability constraints, our scheduler covers the location of the KN $\sim 30-38\%$ of the times for our fiducial EOS. We provide the depths needed to detect a significant fraction of our simulated mergers for the astronomical community to use in their follow-up campaigns., Comment: 18 pages, 10 figures, 10 tables
Published: 2024

4. Explicit construction of the maximal subgroups of the Monster

Author: Dietrich, Heiko, Lee, Melissa, Pisani, Anthony, and Popiel, Tomasz
Subjects: Mathematics - Group Theory, 20D08, 20E28
Abstract: Seysen's Python package mmgroup provides functionality for fast computations within the sporadic simple group $\mathbb{M}$, the Monster. The aim of this work is to present an mmgroup database of maximal subgroups of $\mathbb{M}$: for each conjugacy class $C$ of maximal subgroups in $\mathbb{M}$, we construct explicit group elements in mmgroup and prove that these elements generate a group in $C$. Our generators and the computations verifying correctness are available in accompanying code. The maximal subgroups of $\mathbb{M}$ have been classified in a number of papers spanning several decades; our work constitutes an independent verification of these constructions. We also correct the claim that $\mathbb{M}$ has a maximal subgroup $\mathrm{PSL}_2({59})$, and hence identify a new maximal subgroup $59{:}29$., Comment: 22 pages
Published: 2024

5. On differentiability and mass distributions of topologically typical multivariate Archimedean copulas

Author: Dietrich, Nicolas and Trutschnig, Wolfgang
Subjects: Mathematics - Probability, 26A27, 26A30, 28A50, 54E52
Abstract: Copulas, in particular Archimedean copulas are commonly viewed as analytically nice and regular objects. Motivated by a recently established result sta\-ting that the first partial derivatives of bivariate copulas can exhibit surprisingly pathological behavior, we focus on the class of $d$-dimensional Archimedean copulas denoted by $\mathcal{C}_{ar}^d$ and show that partial derivatives of order $(d-1)$ can be sur\-pri\-singly irregular as well. In fact, we prove the existence of Archimedean copulas $C \in \mathcal{C}_{ar}^d$ whose $(d-1)$-st order partial derivatives are pathological in the sense that for almost every $\mathbf{x} \in [0,1]^{d-1}$ the derivative $\partial_1...\partial_{d-1}C(\mathbf{x},y)$ does not exist on a dense set of $y \in (0,1)$. \\ Since the existence of mixed partial derivatives of order $(d-1)$ of a copula $C$ is closely related to the existence of a discrete component, we also study mass distributions of Archimedean copulas. Building upon the interplay between Archimedean copulas and so-called Williamson measures we show that absolute continuity, discreteness and singularity of the Williamson measure propagates to the associated Archimedean copula and vice versa. Moreover, we prove the fact that the sub-family of $\mathcal{C}_{ar}^d$ consisting of copulas whose absolutely continuous, discrete and singular component have full support is dense in $\mathcal{C}_{ar}^d$. \\ Finally, viewing $\mathcal{C}_{ar}^d$ in the light of Baire categories, we show that, in contrast to the space of bivariate copulas, a topologically typical $d$-dimensional Archimedean copula $C$ is not absolutely continuous but has degenerated discrete component, implying that pathological elements are rare in $\mathcal{C}_{ar}^d$.
Published: 2024

6. ProverbEval: Exploring LLM Evaluation Challenges for Low-resource Language Understanding

Author: Azime, Israel Abebe, Tonja, Atnafu Lambebo, Belay, Tadesse Destaw, Chanie, Yonas, Balcha, Bontu Fufa, Abadi, Negasi Haile, Ademtew, Henok Biadglign, Nerea, Mulubrhan Abebe, Yadeta, Debela Desalegn, Geremew, Derartu Dagne, tesfau, Assefa Atsbiha, Slusallek, Philipp, Solorio, Thamar, and Klakow, Dietrich
Subjects: Computer Science - Computation and Language
Abstract: With the rapid development of evaluation datasets to assess LLMs understanding across a wide range of subjects and domains, identifying a suitable language understanding benchmark has become increasingly challenging. In this work, we explore LLM evaluation challenges for low-resource language understanding and introduce ProverbEval, LLM evaluation benchmark for low-resource languages based on proverbs to focus on low-resource language understanding in culture-specific scenarios. We benchmark various LLMs and explore factors that create variability in the benchmarking process. We observed performance variances of up to 50%, depending on the order in which answer choices were presented in multiple-choice tasks. Native language proverb descriptions significantly improve tasks such as proverb generation, contributing to improved outcomes. Additionally, monolingual evaluations consistently outperformed their cross-lingual counterparts. We argue special attention must be given to the order of choices, choice of prompt language, task variability, and generation tasks when creating LLM evaluation benchmarks.
Published: 2024

7. EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis

Author: Friederich, Nils, Sitcheu, Angelo Jovin Yamachui, Nassal, Annika, Pesch, Matthias, Yildiz, Erenus, Beichter, Maximilian, Scholtes, Lukas, Akbaba, Bahar, Lautenschlager, Thomas, Neumann, Oliver, Kohlheyer, Dietrich, Scharr, Hanno, Seiffarth, Johannes, Nöh, Katharina, and Mikut, Ralf
Subjects: Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail. However, obtaining these continuous data over extended periods is challenging, particularly in achieving accurate and consistent real-time event classification at the intersection of imaging and stochastic biology. To address this issue, we introduce the Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis (EAP4EMSIG). In particular, we present initial zero-shot results from the real-time segmentation module of our approach. Our findings indicate that among four State-Of-The- Art (SOTA) segmentation methods evaluated, Omnipose delivers the highest Panoptic Quality (PQ) score of 0.9336, while Contour Proposal Network (CPN) achieves the fastest inference time of 185 ms with the second-highest PQ score of 0.8575. Furthermore, we observed that the vision foundation model Segment Anything is unsuitable for this particular use case., Comment: Proceedings - 34. Workshop Computational Intelligence
Published: 2024

8. Gradient-free training of recurrent neural networks

Author: Bolager, Erik Lien, Cukarska, Ana, Burak, Iryna, Monfared, Zahra, and Dietrich, Felix
Subjects: Computer Science - Machine Learning, Mathematics - Numerical Analysis
Abstract: Recurrent neural networks are a successful neural architecture for many time-dependent problems, including time series analysis, forecasting, and modeling of dynamical systems. Training such networks with backpropagation through time is a notoriously difficult problem because their loss gradients tend to explode or vanish. In this contribution, we introduce a computational approach to construct all weights and biases of a recurrent neural network without using gradient-based methods. The approach is based on a combination of random feature networks and Koopman operator theory for dynamical systems. The hidden parameters of a single recurrent block are sampled at random, while the outer weights are constructed using extended dynamic mode decomposition. This approach alleviates all problems with backpropagation commonly related to recurrent networks. The connection to Koopman operator theory also allows us to start using results in this area to analyze recurrent neural networks. In computational experiments on time series, forecasting for chaotic dynamical systems, and control problems, as well as on weather data, we observe that the training time and forecasting accuracy of the recurrent neural networks we construct are improved when compared to commonly used gradient-based methods.
Published: 2024

9. The Auger-Meitner Radioisotope Microscope: an instrument for characterization of Auger electron multiplicities and energy distributions

Author: Stollenwerk, Patrick R., Southworth, Stephen H., Granato, Francesco, Renne, Amy, Mustapha, Brahim, Bailey, Kevin G., Mueller, Peter, Nolen, Jerry, O'Connor, Thomas P., Xie, Junqi, Young, Linda, and Dietrich, Matthew R.
Subjects: Physics - Instrumentation and Detectors, Nuclear Experiment, Physics - Atomic Physics, Physics - Medical Physics
Abstract: We describe a new instrument, the Argonne Auger-Meitner Radioisotope Microscope (ARM), capable of characterizing the Auger-Meitner electron emission of radionuclides, including candidates relevant in nuclear medicine. Our approach relies on event-by-event coincidence ion, electron time-of-flight and spatial readout measurement to determine correlated electron multiplicity and energy distributions of Auger-Meitner decays. We present a proof-of-principle measurement with the ARM using X-ray photoionization of stable krypton beyond the K-edge and identify a bifurcation in the electron multiplicity distribution depending on the emission of K-LX electrons. Extension of the ARM to the characterization of radioactive sources of Auger-Meitner electron emissions is enabled by the combination of two recent developments: (1) cryogenic buffer gas beam technology, which enables well-defined initial conditions, gas-phase, high activity introduction of Auger-Meitner emitters into the detection region, and (2) large-area micro-channel plate detectors with multi-hit detection capabilities, which enables the simultaneous detection of many electrons emitted in a single decay. The ARM will generate new experimental data on Auger-Meitner multiplicities that can be used to benchmark atomic relaxation and decay models. As the multiplicities are binned by energy, this data will provide insight into the low-energy regime of Auger-Meitner electrons where intensity calculations are most challenging and experimental data is limited. In particular, accurate multiplicity data of the low-energy regime can be used to inform oncological dosimetry models, where electron energies less than 500 eV are known to be effective in damaging DNA and cell membranes., Comment: 28 pages, 12 figures
Published: 2024

10. A data-driven approach for modeling the temporal and spectral evolution of kilonova systematic uncertainties

Author: Jhawar, Sahil, Wouters, Thibeau, Pang, Peter T. H., Bulla, Mattia, Coughlin, Michael W., and Dietrich, Tim
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: Kilonovae, possible electromagnetic counterparts to neutron star mergers, provide important information about high-energy transient phenomena and, in principle, also allow us to obtain information about the source properties responsible for powering the kilonova. Unfortunately, numerous uncertainties exist in kilonova modeling that, at the current stage, hinder accurate predictions. Hence, one has to account for possible systematic modeling uncertainties when interpreting the observed transients. In this work, we provide a data-driven approach to account for time-dependent and frequency-dependent uncertainties in kilonova models. Through a suite of tests, we find that the most reliable recovery of the source parameters and description of the observational data can be obtained through a combination of kilonova models with time- and frequency-dependent systematic uncertainties. We apply our new method to analyze AT2017gfo. While recovering a total ejecta mass consistent with previous studies, our approach gives insights into the temporal and spectral evolution of the systematic uncertainties of this kilonova. We consistently find a systematic error below $1$ mag between $1$ to $5$ days after the merger. Our work addresses the need for early follow-up of kilonovae at earlier times, and improved modeling of the kilonova at later times, to reduce the uncertainties outside of this time window.
Published: 2024

11. Accelerating Privacy-Preserving Medical Record Linkage: A Three-Party MPC Approach

Author: Mağara, Şeyma Selcan, Dietrich, Noah, Ünal, Ali Burak, and Akgün, Mete
Subjects: Computer Science - Cryptography and Security, Computer Science - Databases
Abstract: Record linkage is a crucial concept for integrating data from multiple sources, particularly when datasets lack exact identifiers, and it has diverse applications in real-world data analysis. Privacy-Preserving Record Linkage (PPRL) ensures this integration occurs securely, protecting sensitive information from unauthorized access. This is especially important in sectors such as healthcare, where datasets include private identity information (IDAT) governed by strict privacy laws. However, maintaining both privacy and efficiency in large-scale record linkage poses significant challenges. Consequently, researchers must develop advanced methods to protect data privacy while optimizing processing performance. This paper presents a novel and efficient PPRL method based on a secure 3-party computation (MPC) framework. Our approach allows multiple parties to compute linkage results without exposing their private inputs and significantly improves the speed of linkage process compared to existing privacy-preserving solutions. We demonstrated that our method preserves the linkage quality of the state-of-the-art PPRL method while achieving up to 14 times faster performance. For example, linking a record against a database of 10,000 records takes just 8.74 seconds in a realistic network with 700 Mbps bandwidth and 60 ms latency. Even on a slower internet connection with 100 Mbps bandwidth and 60 ms latency, the linkage completes in 28 seconds, highlighting the scalability and efficiency of our solution.
Published: 2024

12. Hall signal-dominated microwave transmission through graphene-loaded waveguides

Author: Dietrich, Scott, Akbari-Sharbaf, Arash, Edgar, James H., Roubos, Alex, Freeman, Matthew, and Engel, Lloyd W.
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science, Condensed Matter - Quantum Gases
Abstract: Microwave transmission line spectroscopy is used to observe the integer quantum Hall effect in two samples of monolayer graphene with different geometries that are resistively-coupled to a coplanar waveguide. We find plateaus in transmitted power that do not vary significantly with microwave frequency but are significantly different for two samples due to their shape. With each drop in transmitted power corresponding to an additional quantum Hall edge mode that short the transmission line to ground, these well-known quanta of conductance allow us to calibrate the sensitivity of the devices. One sample with short contact regions matched the sensitivity expected when considering only the quantum Hall conductance of $\nu e^2/h$; another sample with long contact regions demonstrated a nearly three-fold enhancement in sensitivity. We model this result with a purely resistive circuit that introduces an additional resistance to explain the increased sensitivity.
Published: 2024

13. 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. 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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. 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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

14. Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis

Author: Sellner, Jan, Studier-Fischer, Alexander, Qasim, Ahmad Bin, Seidlitz, Silvia, Schreck, Nicholas, Tizabi, Minu, Wiesenfarth, Manuel, Kopp-Schneider, Annette, Knödler, Samuel, Haney, Caelan Max, Salg, Gabriel, Özdemir, Berkin, Dietrich, Maximilian, Michel, Maurice Stephan, Nickel, Felix, Kowalewski, Karl-Friedrich, and Maier-Hein, Lena
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Novel optical imaging techniques, such as hyperspectral imaging (HSI) combined with machine learning-based (ML) analysis, have the potential to revolutionize clinical surgical imaging. However, these novel modalities face a shortage of large-scale, representative clinical data for training ML algorithms, while preclinical animal data is abundantly available through standardized experiments and allows for controlled induction of pathological tissue states, which is not ethically possible in patients. To leverage this situation, we propose a novel concept called "xeno-learning", a cross-species knowledge transfer paradigm inspired by xeno-transplantation, where organs from a donor species are transplanted into a recipient species. Using a total of 11,268 HSI images from humans as well as porcine and rat models, we show that although spectral signatures of organs differ across species, shared pathophysiological mechanisms manifest as comparable relative spectral changes across species. Such changes learnt in one species can thus be transferred to a new species via a novel "physiology-based data augmentation" method, enabling the large-scale secondary use of preclinical animal data for humans. The resulting ethical, monetary, and performance benefits of the proposed knowledge transfer paradigm promise a high impact of the methodology on future developments in the field., Comment: Jan Sellner and Alexander Studier-Fischer contributed equally to this work
Published: 2024

15. What to expect: kilonova light curve predictions via equation of state marginalization

Author: Toivonen, Andrew, Mansingh, Gargi, Griffin, Holton, Kazemi, Armita, Kerkow, Frank, Mahanty, Stephen K., Markus, Jacob, Tsukamoto, Seiya, Chaudhary, Sushant Sharma, Antier, Sarah, Coughlin, Michael W., Chatterjee, Deep, Essick, Reed, Ghosh, Shaon, Dietrich, Tim, and Landry, Philippe
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Efficient multi-messenger observations of gravitational-wave candidates from compact binary coalescence candidate events rely on data products reported in low-latency by the International Gravitational-wave Network (IGWN). While data products such as $\texttt{HasNS}$, the probability of at least one neutron star, and $\texttt{HasRemnant}$, the probability of remnant matter forming after merger, exist, these are not direct observables for a potential kilonova. Here, we present new kilonova light curve and ejecta mass data products derived from merger quantities measured in low latency, by marginalizing over our uncertainty in our understanding of the neutron star equation of state and using measurements of the source properties of the merger, including masses and spins. Two additional types of data products are proposed. The first is the probability of a candidate event having mass ejecta ($m_{\mathrm{ej}}$) greater than $10^{-3} M_\odot$, which we denote as $\texttt{HasEjecta}$. The second are $m_{\mathrm{ej}}$ estimates and accompanying $\texttt{ugrizy}$ and $\texttt{HJK}$ kilonova light curves predictions produced from a surrogate model trained on a grid of kilonova light curves from $\texttt{POSSIS}$, a time-dependent, three-dimensional Monte Carlo radiative transfer code. We are developing these data products in the context of the IGWN low-latency alert infrastructure, and will be advocating for their use and release for future detections.
Published: 2024

16. Users' Perception on Appropriateness of Robotic Coaching Assistant's Disclosure Behaviors

Author: Nilgar, Atikkhan Faridkhan, Dietrich, Manuel, and Van Laerhoven, Kristof
Subjects: Computer Science - Human-Computer Interaction
Abstract: Social robots have emerged as valuable contributors to individuals' well-being coaching. Notably, their integration into long-term human coaching trials shows particular promise, emphasizing a complementary role alongside human coaches rather than outright replacement. In this context, robots serve as supportive entities during coaching sessions, offering insights based on their knowledge about users' well-being and activity. Traditionally, such insights have been gathered through methods like written self-reports or wearable data visualizations. However, the disclosure of people's information by a robot raises concerns regarding privacy, appropriateness, and trust. To address this, we conducted an initial study with [n = 22] participants to quantify their perceptions of privacy regarding disclosures made by a robot coaching assistant. The study was conducted online, presenting participants with six prerecorded scenarios illustrating various types of information disclosure and the robot's role, ranging from active on-demand to proactive communication conditions., Comment: IEEE ICRA 2024 Workshop on Robot Trust for Symbiotic Societies (RTSS)
Published: 2024

17. Improving semantic understanding in speech language models via brain-tuning

Author: Moussa, Omer, Klakow, Dietrich, and Toneva, Mariya
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Speech language models align with human brain responses to natural language to an impressive degree. However, current models rely heavily on low-level speech features, indicating they lack brain-relevant semantics which limits their utility as model organisms of semantic processing in the brain. In this work, we address this limitation by inducing brain-relevant bias directly into the models via fine-tuning with fMRI recordings of people listening to natural stories, a process we name brain-tuning. After testing it on 3 different pretrained model families, we show that brain-tuning not only improves overall alignment with new brain recordings in semantic language regions, but also reduces the reliance on low-level speech features for this alignment. Excitingly, we further show that brain-tuning leads to 1) consistent improvements in performance on a range of downstream tasks and 2) a representational space with increased semantic preference. Our results provide converging evidence, for the first time, that incorporating brain signals into the training of language models improves the models' semantic understanding., Comment: Under Review at ICLR 2025
Published: 2024

18. 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. 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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

19. Measuring the Groundedness of Legal Question-Answering Systems

Author: Trautmann, Dietrich, Ostapuk, Natalia, Grail, Quentin, Pol, Adrian Alan, Bonifazi, Guglielmo, Gao, Shang, and Gajek, Martin
Subjects: Computer Science - Computation and Language
Abstract: In high-stakes domains like legal question-answering, the accuracy and trustworthiness of generative AI systems are of paramount importance. This work presents a comprehensive benchmark of various methods to assess the groundedness of AI-generated responses, aiming to significantly enhance their reliability. Our experiments include similarity-based metrics and natural language inference models to evaluate whether responses are well-founded in the given contexts. We also explore different prompting strategies for large language models to improve the detection of ungrounded responses. We validated the effectiveness of these methods using a newly created grounding classification corpus, designed specifically for legal queries and corresponding responses from retrieval-augmented prompting, focusing on their alignment with source material. Our results indicate potential in groundedness classification of generated responses, with the best method achieving a macro-F1 score of 0.8. Additionally, we evaluated the methods in terms of their latency to determine their suitability for real-world applications, as this step typically follows the generation process. This capability is essential for processes that may trigger additional manual verification or automated response regeneration. In summary, this study demonstrates the potential of various detection methods to improve the trustworthiness of generative AI in legal settings., Comment: to appear NLLP @ EMNLP 2024
Published: 2024

20. Levels of Binary Equivalence for the Comparison of Binaries from Alternative Builds

Author: Dietrich, Jens, White, Tim, Hassanshahi, Behnaz, and Krishnan, Paddy
Subjects: Computer Science - Cryptography and Security, Computer Science - Software Engineering, D.2.13, D.3.4, F.3.2
Abstract: In response to challenges in software supply chain security, several organisations have created infrastructures to independently build commodity open source projects and release the resulting binaries. Build platform variability can strengthen security as it facilitates the detection of compromised build environments. Furthermore, by improving the security posture of the build platform and collecting provenance information during the build, the resulting artifacts can be used with greater trust. Such offerings are now available from Google, Oracle and RedHat. The availability of multiple binaries built from the same sources creates new challenges and opportunities, and raises questions such as: 'Does build A confirm the integrity of build B?' or 'Can build A reveal a compromised build B?'. To answer such questions requires a notion of equivalence between binaries. We demonstrate that the obvious approach based on bitwise equality has significant shortcomings in practice, and that there is value in opting for alternative notions. We conceptualise this by introducing levels of equivalence, inspired by clone detection types. We demonstrate the value of these new levels through several experiments. We construct a dataset consisting of Java binaries built from the same sources independently by different providers, resulting in 14,156 pairs of binaries in total. We then compare the compiled class files in those jar files and find that for 3,750 pairs of jars (26.49%) there is at least one such file that is different, also forcing the jar files and their cryptographic hashes to be different. However, based on the new equivalence levels, we can still establish that many of them are practically equivalent. We evaluate several candidate equivalence relations on a semi-synthetic dataset that provides oracles consisting of pairs of binaries that either should be, or must not be equivalent., Comment: 20 pages, 1 figure, 10 tables
Published: 2024

21. Human Stone Toolmaking Action Grammar (HSTAG): A Challenging Benchmark for Fine-grained Motor Behavior Recognition

Author: Liu, Cheng, Yan, Xuyang, Zhang, Zekun, Ding, Cheng, Zhao, Tianhao, Jannati, Shaya, Martinez, Cynthia, and Stout, Dietrich
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Action recognition has witnessed the development of a growing number of novel algorithms and datasets in the past decade. However, the majority of public benchmarks were constructed around activities of daily living and annotated at a rather coarse-grained level, which lacks diversity in domain-specific datasets, especially for rarely seen domains. In this paper, we introduced Human Stone Toolmaking Action Grammar (HSTAG), a meticulously annotated video dataset showcasing previously undocumented stone toolmaking behaviors, which can be used for investigating the applications of advanced artificial intelligence techniques in understanding a rapid succession of complex interactions between two hand-held objects. HSTAG consists of 18,739 video clips that record 4.5 hours of experts' activities in stone toolmaking. Its unique features include (i) brief action durations and frequent transitions, mirroring the rapid changes inherent in many motor behaviors; (ii) multiple angles of view and switches among multiple tools, increasing intra-class variability; (iii) unbalanced class distributions and high similarity among different action sequences, adding difficulty in capturing distinct patterns for each action. Several mainstream action recognition models are used to conduct experimental analysis, which showcases the challenges and uniqueness of HSTAG https://nyu.databrary.org/volume/1697., Comment: 8 pages, 4 figures, accepted by the 11th IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Published: 2024

22. Observation of disorder-free localization and efficient disorder averaging on a quantum processor

Author: Gyawali, Gaurav, Cochran, Tyler, Lensky, Yuri, Rosenberg, Eliott, Karamlou, Amir H., Kechedzhi, Kostyantyn, Berndtsson, Julia, Westerhout, Tom, Asfaw, Abraham, Abanin, Dmitry, Acharya, Rajeev, Beni, Laleh Aghababaie, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Astrakhantsev, Nikita, Atalaya, Juan, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bengtsson, Andreas, Bilmes, Alexander, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Zijun, Chiaro, Ben, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Das, Sayan, Debroy, Dripto M., De Lorenzo, Laura, Barba, Alexander Del Toro, Demura, Sean, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Earle, Clint, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Fatemi, Reza, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gasca, Robert, Giang, William, Gidney, Craig, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Hill, Gordon, Hilton, Jeremy, Hoffmann, Markus R., Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Ioffe, Lev B., Isakov, Sergei V., Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kieferová, Mária, Kim, Seon, Klimov, Paul V., Klots, Andrey R., Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Joonho, Lee, Kenny, Lester, Brian J., Li, Wing Yan, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Steven, Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Mi, Xiao, Miao, Kevin C., Mieszala, Amanda, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Neill, Charles, Nersisyan, Ani, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Yuezhen, Oliver, William D., Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rocque, Charles, Rubin, Nicholas C., Saei, Negar, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szalay, Marco, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torunbalci, M. Mert, Vaishnav, Abeer, Vdovichev, Sergey, Vidal, Guifré, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, Zobrist, Nicholas, Boixo, Sergio, Kelly, Julian, Lucero, Erik, Chen, Yu, Smelyanskiy, Vadim, Neven, Hartmut, Kovrizhin, Dmitry, Knolle, Johannes, Halimeh, Jad C., Aleiner, Igor, Moessner, Roderich, and Roushan, Pedram
Subjects: Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Strongly Correlated Electrons, High Energy Physics - Lattice
Abstract: One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.
Published: 2024

23. Integrating Trauma-Informedness within a Multi-Tiered System of Support: The Critical Role of Administrators

Author: Lindsay Dietrich Wolf and Maria B. Scuichetti
Abstract: Meeting the needs of students in schools has been challenging for many years. Consequently, some schools have responded with comprehensive, multi-tiered intervention systems (e.g., MTSS) aimed at addressing students' academic, behavioral, and social-emotional needs. However, most educators have a rudimentary understanding of these systems and the impact of trauma and traumatic experiences on students' response to interventions. Currently, there is limited research on how building level-administrators integrate trauma-informedness into these tiered systems. This conceptual article outlines the importance of administrators' perception, understanding, and implementation of using a trauma-informed lens in educational decision making, specifically regarding MTSS implementation. This article presents an overview of the literature and an approach to trauma-informed approach that administrators can utilize when making educational decisions, specifically within multi-tiered systems of intervention and student supports.
Published: 2024

24. AfriHuBERT: A self-supervised speech representation model for African languages

Author: Alabi, Jesujoba O., Liu, Xuechen, Klakow, Dietrich, and Yamagishi, Junichi
Subjects: Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: In this work, we present AfriHuBERT, an extension of mHuBERT-147, a state-of-the-art (SOTA) and compact self-supervised learning (SSL) model, originally pretrained on 147 languages. While mHuBERT-147 was pretrained on 16 African languages, we expand this to cover 39 African languages through continued pretraining on 6,500+ hours of speech data aggregated from diverse sources, including 23 newly added languages. We evaluate AfriHuBERT on two key speech tasks: Language Identification (LID) and Automatic Speech Recognition (ASR) using FLEURS dataset. Our results show a +4% F1 score improvement on average for LID and a -1.2% average Word Error Rate (WER) reduction for ASR. Further analysis shows that ASR models trained on AfriHuBERT exhibit improved cross-corpus generalization. Additionally, the analysis indicates that the FLEURS have data quality limitations that may affect their suitability for evaluating low-resource African languages, suggesting the need for better evaluation benchmarks for these languages., Comment: 14 pages
Published: 2024

25. Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging

Author: Weiser, Paul, Langs, Georg, Bogner, Wolfgang, Motyka, Stanislav, Strasser, Bernhard, Golland, Polina, Singh, Nalini, Dietrich, Jorg, Uhlmann, Erik, Batchelor, Tracy, Cahill, Daniel, Hoffmann, Malte, Klauser, Antoine, and Andronesi, Ovidiu C.
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning
Abstract: Introduction: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian compressed-sense acquisition enables fast high-resolution metabolic imaging but has lengthy reconstruction times that limits throughput and needs expert user interaction. Here, we present a robust and efficient Deep Learning reconstruction to obtain high-quality metabolic maps. Methods: Fast high-resolution whole-brain metabolic imaging was performed at 3.4 mm$^3$ isotropic resolution with acquisition times between 4:11-9:21 min:s using ECCENTRIC pulse sequence on a 7T MRI scanner. Data were acquired in a high-resolution phantom and 27 human participants, including 22 healthy volunteers and 5 glioma patients. A deep neural network using recurring interlaced convolutional layers with joint dual-space feature representation was developed for deep learning ECCENTRIC reconstruction (Deep-ER). 21 subjects were used for training and 6 subjects for testing. Deep-ER performance was compared to conventional iterative Total Generalized Variation reconstruction using image and spectral quality metrics. Results: Deep-ER demonstrated 600-fold faster reconstruction than conventional methods, providing improved spatial-spectral quality and metabolite quantification with 12%-45% (P<0.05) higher signal-to-noise and 8%-50% (P<0.05) smaller Cramer-Rao lower bounds. Metabolic images clearly visualize glioma tumor heterogeneity and boundary. Conclusion: Deep-ER provides efficient and robust reconstruction for sparse-sampled MRSI. The accelerated acquisition-reconstruction MRSI is compatible with high-throughput imaging workflow. It is expected that such improved performance will facilitate basic and clinical MRSI applications.
Published: 2024

26. Visualizing Dynamics of Charges and Strings in (2+1)D Lattice Gauge Theories

Author: Cochran, Tyler A., Jobst, Bernhard, Rosenberg, Eliott, Lensky, Yuri D., Gyawali, Gaurav, Eassa, Norhan, Will, Melissa, Abanin, Dmitry, Acharya, Rajeev, Beni, Laleh Aghababaie, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Atalaya, Juan, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bengtsson, Andreas, Bilmes, Alexander, Bourassa, Alexandre, Bovaird, Jenna, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Zijun, Chiaro, Ben, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Curtin, Ben, Das, Sayan, Demura, Sean, De Lorenzo, Laura, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gasca, Robert, Genois, Élie, Giang, William, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heu, Paula, Higgott, Oscar, Hilton, Jeremy, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Karamlou, Amir H., Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Klimov, Paul V., Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Lau, Kim-Ming, Ledford, Justin, Lee, Kenny, Lester, Brian J., Guevel, Loïck Le, Li, Wing Yan, Lill, Alexander T., Livingston, William P., Locharla, Aditya, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Miao, Kevin C., Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Movassagh, Ramis, Neill, Charles, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Yuezhen, Oliver, William D., Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Vdovichev, Sergey, Vidal, Guifre, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., Ware, Brayden, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, Zobris, Nicholas, Boixo, Sergio, Kelly, Julian, Lucero, Erik, Chen, Yu, Smelyanskiy, Vadim, Neven, Hartmut, Gammon-Smith, Adam, Pollmann, Frank, Knap, Michael, and Roushan, Pedram
Subjects: Quantum Physics, Condensed Matter - Strongly Correlated Electrons, High Energy Physics - Lattice
Abstract: Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.
Published: 2024

27. Multi-physics framework for fast modeling of gamma-ray burst afterglows

Author: Nedora, Vsevolod, Menegazzi, Ludovica Crosato, Peretti, Enrico, Dietrich, Tim, and Shibata, Masaru
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: In this paper, we present PyBlastAfterglow, a modular C++ code with a Python interface to model light curves and sky maps of gamma-ray burst afterglows. The code is open-source, modular, and sufficiently fast to perform parameter grid studies. PyBlastAfterglow is designed to be easily extendable and used as a testing bed for new physics and methods related to gamma-ray burst afterglows. For the dynamical evolution of relativistic ejecta, a thin-shell approximation is adopted, where both forward and reverse shocks are included self-consistently, as well as lateral structure, lateral spreading, and radiation losses. Several models of the shock microphysics are implemented, including a fully numerical model of the downstream electron distribution evolution, synchrotron emission, self-absorption, and synchrotron self-Compton emission under the one-zone approximation. Thus, the code is designed to be able to model complex afterglows that include emission from reverse shock, very high energy emission, structured jets, and off-axis observations., Comment: 28 pages, 23 figures
Published: 2024

28. Hadamard matrices in centraliser algebras of monomial representations

Author: Acevedo, Santiago Barrera, Catháin, Padraig Ó, Dietrich, Heiko, and Egan, Ronan
Subjects: Mathematics - Combinatorics, Mathematics - Group Theory, Mathematics - Representation Theory, 05B20, 20B25
Abstract: Centraliser algebras of monomial representations of finite groups may be constructed and studied using methods similar to those employed in the study of permutation groups. Guided by results of D. G. Higman and others, we give an explicit construction for a basis of the centraliser algebra of a monomial representation. The character table of this algebra is then constructed via character sums over double cosets. We locate the theory of group-developed and cocyclic-developed Hadamard matrices within this framework. We apply Gr\"obner bases to produce a new classification of highly symmetric complex Hadamard matrices., Comment: 20 pages
Published: 2024

29. HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios

Author: Theisen, Nick, Bartsch, Robin, Paulus, Dietrich, and Neubert, Peer
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Semantic segmentation is an essential step for many vision applications in order to understand a scene and the objects within. Recent progress in hyperspectral imaging technology enables the application in driving scenarios and the hope is that the devices perceptive abilities provide an advantage over RGB-cameras. Even though some datasets exist, there is no standard benchmark available to systematically measure progress on this task and evaluate the benefit of hyperspectral data. In this paper, we work towards closing this gap by providing the HyperSpectral Semantic Segmentation benchmark (HS3-Bench). It combines annotated hyperspectral images from three driving scenario datasets and provides standardized metrics, implementations, and evaluation protocols. We use the benchmark to derive two strong baseline models that surpass the previous state-of-the-art performances with and without pre-training on the individual datasets. Further, our results indicate that the existing learning-based methods benefit more from leveraging additional RGB training data than from leveraging the additional hyperspectral channels. This poses important questions for future research on hyperspectral imaging for semantic segmentation in driving scenarios. Code to run the benchmark and the strong baseline approaches are available under https://github.com/nickstheisen/hyperseg., Comment: Accepted for publication at 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)
Published: 2024

30. SPORES-HWO. II. Limits on Planetary Companions of Future High-contrast Imaging Targets from $>$20 Years of HIRES and HARPS Radial Velocities

Author: Harada, Caleb K., Dressing, Courtney D., Kane, Stephen R., Blunt, Sarah, Dietrich, Jamie, Hinkel, Natalie R., Li, Zhexing, Mamajek, Eric, Rice, Malena, Tuchow, Noah W., Turtelboom, Emma V., and Wittenmyer, Robert A.
Subjects: Astrophysics - Earth and Planetary Astrophysics
Abstract: Future large, space-based observatories with starlight suppression technology, e.g., the Habitable Worlds Observatory (HWO), will directly image and characterize nearby Earth-like exoplanets. Prior limits on planet masses and system architectures from radial velocity (RV) measurements of potential exo-Earth hosts are critical to the success of HWO's science goals. Here, we present a uniform analysis of archival RVs from HIRES/Keck and HARPS/ESO of the most promising targets for the HWO exo-Earth survey. We analyze RVs and stellar activity indicators of 90 stars in the NASA ExEP Mission Star List and SPORES-HWO Catalog, finding 33 Keplerian signals associated with known planets and 12 signals associated with stellar activity. We also identify 5 new RV signals that we classify as either planet candidates or sources requiring confirmation, noting that the RV observations are biased toward cooler and less active stars. Assessing the sensitivity of the HIRES and HARPS data, we calculate RV limits ranging from $K_{\rm RV} = 0.6 \,{\rm m\,s}^{-1}$ (HD 10700) to $371 \,{\rm m\,s}^{-1}$ (HD 17925) in the middle of the conservative habitable zone (HZ), corresponding to projected planet masses of $5.4 \,{\rm M_\oplus}$ and $10.6 \,{\rm M_{Jup}}$ for those stars. The median HZ sensitivity limit of our sample is $M_{\rm p} \sin i \simeq 66 \,{\rm M_\oplus}$. This work demonstrates the need for future extreme precision radial velocity (EPRV) monitoring of high-priority targets for the next generation of DI missions that will search for habitable extrasolar systems. We advocate for the use of these results in developing future EPRV strategies., Comment: 55 pages, 12 figures, 9 tables, submitted to AAS Journals
Published: 2024

31. Kilonova emission from GW230529 and mass gap neutron star-black hole mergers

Author: Kunnumkai, Keerthi, Palmese, Antonella, Bulla, Mattia, Dietrich, Tim, Farah, Amanda M., and Pang, Peter T. H.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The detection of the gravitational-wave event GW230529, presumably a neutron star-black hole (NSBH) merger, by the LIGO-Virgo-KAGRA (LVK) Collaboration is an exciting discovery for multimessenger astronomy. The black hole (BH) has a high probability of falling within the ''mass gap'' between the peaks of the neutron star (NS) and the BH mass distributions. Because of the low primary mass, the binary is more likely to produce an electromagnetic counterpart than previously detected NSBH mergers. We investigate the possible kilonova (KN) emission from GW230529, and find that if it was an NSBH, there is a $\sim$ 2-41% probability (depending on the assumed equation of state) that GW230925 produced a KN with magnitude peaking at $\sim 1-2$ day post merger at $g \lesssim 23.5$, $i<23$. Hence, it could have been detected by ground-based telescopes. If it was a binary neutron star (BNS) merger, we find $\sim$ 0-12% probability that it produced a KN. Motivated by these numbers, we simulated a broader population of mgNSBH mergers that may be detected in O4, and we obtained a 9-21% chance of producing a KN, which would be detectable with $g\lesssim 25$ and $ i \lesssim 24$, typically fainter than what is expected from GW230529. Based on these findings, DECam-like instruments may be able to detect up to 80% of future mgNSBH KNe, thus up to $\sim1$ multimessenger mgNSBH per year may be discoverable at the current level of sensitivity (O4)., Comment: 13 pages, 4 figures, 2 tables
Published: 2024

32. Critical Casimir levitation of colloids above a bull's-eye pattern

Author: Nowakowski, Piotr, Bafi, Nima Farahmand, Volpe, Giovanni, Kondrat, Svyatoslav, and Dietrich, S.
Subjects: Condensed Matter - Soft Condensed Matter
Abstract: Critical Casimir forces emerge among particles or surfaces immersed in a near-critical fluid, with the sign of the force determined by surface properties and with its strength tunable by minute temperature changes. Here, we show how such forces can be used to trap a colloidal particle and levitate it above a substrate with a bull's-eye pattern consisting of a ring with surface properties opposite to the rest of the substrate. Using the Derjaguin approximation and mean-field calculations, we find a rich behavior of spherical colloids at such a patterned surface, including sedimentation towards the ring and levitation above the ring (ring levitation) or above the bull's-eye's center (point levitation). Within the Derjaguin approximation, we calculate a levitation diagram for point levitation showing the depth of the trapping potential and the height at which the colloid levitates, both depending on the pattern properties, the colloid size, and the solution temperature. Our calculations reveal that the parameter space associated with point levitation shrinks if the system is driven away from a critical point, while, surprisingly, the trapping force becomes stronger. We discuss the application of critical Casimir levitation for sorting colloids by size and for determining the thermodynamic distance to criticality. Our results show that critical Casimir forces provide rich opportunities for controlling the behavior of colloidal particles at patterned surfaces.
Published: 2024

33. Social Mediation through Robots -- A Scoping Review on Improving Group Interactions through Directed Robot Action using an Extended Group Process Model

Author: Weisswange, Thomas H., Javed, Hifza, Dietrich, Manuel, Jung, Malte F., and Jamali, Nawid
Subjects: Computer Science - Robotics, Computer Science - Human-Computer Interaction
Abstract: Group processes refer to the dynamics that occur within a group and are critical for understanding how groups function. With robots being increasingly placed within small groups, improving these processes has emerged as an important application of social robotics. Social Mediation Robots elicit behavioral change within groups by deliberately influencing the processes of groups. While research in this field has demonstrated that robots can effectively affect interpersonal dynamics, there is a notable gap in integrating these insights to develop coherent understanding and theory. We present a scoping review of literature targeting changes in social interactions between multiple humans through intentional action from robotic agents. To guide our review, we adapt the classical Input-Process-Output (I-P-O) models that we call "Mediation I-P-O model". We evaluated 1633 publications, which yielded 89 distinct social mediation concepts. We construct 11 mediation approaches robots can use to shape processes in small groups and teams. This work strives to produce generalizable insights and evaluate the extent to which the potential of social mediation through robots has been realized thus far. We hope that the proposed framework encourages a holistic approach to the study of social mediation and provides a foundation to standardize future reporting in the domain.
Published: 2024

34. WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Case

Author: Gautam, Vagrant, Steuer, Julius, Bingert, Eileen, Johns, Ray, Lauscher, Anne, and Klakow, Dietrich
Subjects: Computer Science - Computation and Language
Abstract: While measuring bias and robustness in coreference resolution are important goals, such measurements are only as good as the tools we use to measure them. Winogender Schemas (Rudinger et al., 2018) are an influential dataset proposed to evaluate gender bias in coreference resolution, but a closer look reveals issues with the data that compromise its use for reliable evaluation, including treating different pronominal forms as equivalent, violations of template constraints, and typographical errors. We identify these issues and fix them, contributing a new dataset: WinoPron. Using WinoPron, we evaluate two state-of-the-art supervised coreference resolution systems, SpanBERT, and five sizes of FLAN-T5, and demonstrate that accusative pronouns are harder to resolve for all models. We also propose a new method to evaluate pronominal bias in coreference resolution that goes beyond the binary. With this method, we also show that bias characteristics vary not just across pronoun sets (e.g., he vs. she), but also across surface forms of those sets (e.g., him vs. his)., Comment: Workshop on Computational Models of Reference, Anaphora and Coreference at EMNLP 2024
Published: 2024

35. On the Role of Muons in Binary Neutron Star Mergers: First Simulations

Author: Gieg, Henrique, Schianchi, Federico, Ujevic, Maximiliano, and Dietrich, Tim
Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: In this work we present a set of binary neutron star (BNS) merger simulations including the net muon fraction as an additional degree-of-freedom in the equation of state (EoS) and hydrodynamics evolution using the numerical-relativity code BAM. Neutrino cooling is modeled via a neutrinos leakage scheme, including in-medium corrections to the opacities and emission rates of semi-leptonic charged-current reactions. We show that, for our particular choice of baseline baryonic EoS, the presence of muons delays the gravitational collapse of the remnant compared to the case where muons are neglected. Furthermore, when muons and muon-driven neutrino reactions are considered, no gravitational collapse occurs within our simulation timespan and muons are confined in the densest portions of the remnant, while the disk is effectively colder, less protonized and de-muonized. Accordingly, ejecta properties are affected, e.g., ejecta masses are systematically smaller for the muonic setups and exhibit a larger fraction of neutron-rich, small velocity material. Overall, our results suggest that the inclusion of muons and muon-flavored neutrino reactions in the context of BNS merger simulations should not be neglected, thus representing an important step towards more realistic modelling of such systems., Comment: 19 pages, 8 figures
Published: 2024

36. Searching for Additional Planets in TESS Multi-Planet Systems: Testing Empirical Models Based on Kepler Data

Author: Turtelboom, Emma V., Dietrich, Jamie, Dressing, Courtney D., and Harada, Caleb K.
Subjects: Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Multi-planet system architectures are frequently used to constrain possible formation and evolutionary pathways of observed exoplanets. Therefore, understanding the predictive and descriptive power of empirical models of these systems is critical to understanding their formation histories. Additionally, if empirical models can reproduce architectures over a range of scales, transit and radial velocity observations can be more easily and effectively used to inform future microlensing, astrometric, and direct imaging surveys. We analyze 52 TESS multi-planet systems previously studied using Dynamite (Dietrich & Apai 2020), who used TESS data alongside empirical models based on Kepler planets to predict additional planets in each system. We analyze additional TESS data to search for these predicted planets. We thereby evaluate the degree to which these models can be used to predict planets in TESS multi-planet systems. Specifically, we study whether a period ratio method or clustered period model is more predictive. We find that the period ratio model predictions are most consistent with the planets discovered since 2020, accounting for detection sensitivity. However, neither model is highly predictive, highlighting the need for additional data and nuanced models to describe the full population. Improved eccentricity and dynamical stability prescriptions incorporated into Dynamite provide a modest improvement in the prediction accuracy. We also find that the current sample of 183 TESS multi-planet systems are are highly dynamically packed, and appear truncated relative to detection biases. These attributes are consistent with the Kepler sample, and suggest a highly efficient formation process., Comment: Submitted to AJ on 05 August 2024
Published: 2024

37. Dichroic mirror pulses for optimized higher-order atomic Bragg diffraction

Author: Pfeiffer, Dominik, Dietrich, Maximilian, Schach, Patrik, Birkl, Gerhard, and Giese, Enno
Subjects: Quantum Physics
Abstract: Increasing the sensitivity of light-pulse atom interferometers progressively relies on large-momentum transfer techniques. Precise control of such methods is imperative to exploit the full capabilities of these quantum sensors. One key element is the mitigation of deleterious effects such as parasitic paths deteriorating the interferometric signal. In this work, we present the experimental realization of dichroic mirror pulses for atom interferometry specifically designed for higher-order Bragg diffraction. Our approach selectively reflects resonant atom paths into the detected interferometer output, ensuring that these contribute to the signal with intent. Simultaneously, parasitic paths are efficiently transmitted by the mirror and not directed to the relevant interferometer outputs. This method effectively isolates the desired interferometric signal from noise induced by unwanted paths. It not only demonstrates enhanced control over the atomic trajectories but also represents a significant step forward in optimizing the performance of light-pulse atom interferometers for high-precision applications.
Published: 2024

38. The impact of dark matter on tidal signatures in neutron star mergers with Einstein Telescope

Author: Koehn, Hauke, Giangrandi, Edoardo, Kunert, Nina, Somasundaram, Rahul, Sagun, Violetta, and Dietrich, Tim
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology, Nuclear Theory
Abstract: If dark matter (DM) accumulates inside neutron stars (NS), it changes their internal structure and causes a shift of the tidal deformability from the value predicted by the dense-matter equation of state (EOS). In principle, this shift could be observable in the gravitational-wave (GW) signal of binary neutron star (BNS) mergers. We investigate the effect of fermionic, non-interacting DM when observing a large number of GW events from DM-admixed BNSs with the precision of the proposed Einstein telescope (ET). Specifically, we study the impact on the recovery of the baryonic EOS and whether DM properties can be constrained. For this purpose, we create event catalogues of BNS mock events with DM fraction up to 1%, from which we reconstruct the posterior uncertainties with the Fisher matrix approach. Using this data, we perform joint Bayesian inference on the baryonic EOS, DM particle mass, and DM particle fraction in each event. Our results reveal that when falsely ignoring DM effects, the EOS posterior is biased towards softer EOSs, though the offset is rather small. Further, we find that within our assumptions of our DM model and population, ET will likely not be able to test the presence of DM in BNSs, even when combining many events and adding Cosmic Explorer (CE) to the next-generation detector network. Likewise, the potential constraints on the DM particle mass will remain weak because of degeneracies with the fraction and EOS., Comment: 15 pages, 6 figures, published in PRD 110, 103033
Published: 2024
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39. Quantum error correction below the surface code threshold

Author: Acharya, Rajeev, Aghababaie-Beni, Laleh, Aleiner, Igor, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Astrakhantsev, Nikita, Atalaya, Juan, Babbush, Ryan, Bacon, Dave, Ballard, Brian, Bardin, Joseph C., Bausch, Johannes, Bengtsson, Andreas, Bilmes, Alexander, Blackwell, Sam, Boixo, Sergio, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Yu, Chen, Zijun, Chiaro, Ben, Chik, Desmond, Chou, Charina, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Curtin, Ben, Das, Sayan, Davies, Alex, De Lorenzo, Laura, Debroy, Dripto M., Demura, Sean, Devoret, Michel, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Earle, Clint, Edlich, Thomas, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Farhi, Edward, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Garcia, Gonzalo, Gasca, Robert, Genois, Élie, Giang, William, Gidney, Craig, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hall, John, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heras, Francisco J. H., Heslin, Stephen, Heu, Paula, Higgott, Oscar, Hill, Gordon, Hilton, Jeremy, Holland, George, Hong, Sabrina, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Ioffe, Lev B., Isakov, Sergei V., Iveland, Justin, Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Karamlou, Amir H., Kechedzhi, Kostyantyn, Kelly, Julian, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Klimov, Paul V., Klots, Andrey R., Kobrin, Bryce, Kohli, Pushmeet, Korotkov, Alexander N., Kostritsa, Fedor, Kothari, Robin, Kozlovskii, Borislav, Kreikebaum, John Mark, Kurilovich, Vladislav D., Lacroix, Nathan, Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Kenny, Lensky, Yuri D., Leon, Shannon, Lester, Brian J., Li, Wing Yan, Li, Yin, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lucero, Erik, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Malone, Fionn D., Maloney, Ashley, Mandrá, Salvatore, Martin, Leigh S., Martin, Steven, Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Mi, Xiao, Miao, Kevin C., Mieszala, Amanda, Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Mruczkiewicz, Wojciech, Naaman, Ofer, Neeley, Matthew, Neill, Charles, Nersisyan, Ani, Neven, Hartmut, Newman, Michael, Ng, Jiun How, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, O'Brien, Thomas E., Oliver, William D., Opremcak, Alex, Ottosson, Kristoffer, Petukhov, Andre, Pizzuto, Alex, Platt, John, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rosenberg, Eliott, Rosenfeld, Emma, Roushan, Pedram, Rubin, Nicholas C., Saei, Negar, Sank, Daniel, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Senior, Andrew W., Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Singh, Shraddha, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smelyanskiy, Vadim, Smith, W. Clarke, Somma, Rolando D., Springer, Sofia, Sterling, George, Strain, Doug, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Vdovichev, Sergey, Vidal, Guifre, Villalonga, Benjamin, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., Ware, Brayden, Weber, Kate, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, and Zobrist, Nicholas
Subjects: Quantum Physics
Abstract: Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information
Published: 2024

40. Boolean basis, formula size, and number of modal operators

Author: Berkholz, Christoph, Kuske, Dietrich, and Schwarz, Christian
Subjects: Computer Science - Logic in Computer Science, Mathematics - Logic, 03B45, F.4.1, I.2.4
Abstract: Is it possible to write significantly smaller formulae when using Boolean operators other than those of the De Morgan basis (and, or, not, and the constants)? For propositional logic, a negative answer was given by Pratt: formulae over one set of operators can always be translated into an equivalent formula over any other complete set of operators with only polynomial increase in size. Surprisingly, for modal logic the picture is different: we show that elimination of bi-implication is only possible at the cost of an exponential number of occurrences of the modal operator $\lozenge$ and therefore of an exponential increase in formula size, i.e., the De Morgan basis and its extension by bi-implication differ in succinctness. Moreover, we prove that any complete set of Boolean operators agrees in succinctness with the De Morgan basis or with its extension by bi-implication. More precisely, these results are shown for the modal logic $\mathrm{T}$ (and therefore for $\mathrm{K}$). We complement them showing that the modal logic $\mathrm{S5}$ behaves as propositional logic: the choice of Boolean operators has no significant impact on the size of formulae.
Published: 2024

41. A machine-learning classifier for the postmerger remnant of binary neutron stars

Author: Puecher, Anna and Dietrich, Tim
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: Knowing the kind of remnant produced after the merger of a binary neutron star system, e.g., if a black hole forms or not, would not only shed light on the equation of state describing the extremely dense matter inside neutron stars, but also help understand the physical processes involved in the postmerger phase. Moreover, in the event of a gravitational-wave detection, predicting the presence of a neutron star remnant is crucial in order to advise potential electromagnetic follow-up campaigns. In this work, we use Gradient Boosted Decision Trees and publicly available data from numerical-relativity simulations to construct a classifier that predicts the outcome of binary neutron star mergers, based on the binary's parameters inferred from gravitational-wave inspiral signals: total mass, mass-weighted tidal deformability, mass ratio, and effective inspiral spin. Employing parameters that can be estimated from the inspiral part of the signal only allows us to predict the remnant independently on the detection of a postmerger gravitational-wave signal. We build three different classifiers to distinguish between various potential scenarios, we estimate their accuracy and the confidence of their predictions. Finally, we apply the developed classifiers to real events data, finding that GW170817 most likely lead to the formation of a hypermassive neutron star, while GW190425 to a prompt collapse to a black hole.
Published: 2024

42. Characteristically nilpotent Lie groups with flat coadjoint orbits

Author: Burde, Dietrich and van Velthoven, Jordy Timo
Subjects: Mathematics - Representation Theory, Mathematics - Rings and Algebras, 17A36, 22E27, 17B30
Abstract: We study the existence of certain characteristically nilpotent Lie algebras with flat coadjoint orbits. Their connected, simply connected Lie groups admit square-integrable representations modulo the center. There are many examples of nilpotent Lie groups admitting families of dilations and square-integrable representations, but so far no examples admitting square-integrable representations for which the quotient by the center does not admit a family of dilations. In this paper we construct a two-parameter family of characteristically nilpotent Lie groups $G(\alpha,\beta)$ in dimension $11$, admitting square-integrable representations modulo the center $Z$, such that $G(\alpha,\beta)/Z$ does not admit a family of dilations.
Published: 2024

43. New spectral imaging biomarkers for sepsis and mortality in intensive care

Author: Seidlitz, Silvia, Hölzl, Katharina, von Garrel, Ayca, Sellner, Jan, Katzenschlager, Stephan, Hölle, Tobias, Fischer, Dania, von der Forst, Maik, Schmitt, Felix C. F., Weigand, Markus A., Maier-Hein, Lena, and Dietrich, Maximilian
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: With sepsis remaining a leading cause of mortality, early identification of septic patients and those at high risk of death is a challenge of high socioeconomic importance. The driving hypothesis of this study was that hyperspectral imaging (HSI) could provide novel biomarkers for sepsis diagnosis and treatment management due to its potential to monitor microcirculatory alterations. We conducted a comprehensive study involving HSI data of the palm and fingers from more than 480 patients on the day of their intensive care unit (ICU) admission. The findings demonstrate that HSI measurements can predict sepsis with an area under the receiver operating characteristic curve (AUROC) of 0.80 (95 % confidence interval (CI) [0.76; 0.84]) and mortality with an AUROC of 0.72 (95 % CI [0.65; 0.79]). The predictive performance improves substantially when additional clinical data is incorporated, leading to an AUROC of up to 0.94 (95 % CI [0.92; 0.96]) for sepsis and 0.84 (95 % CI [0.78; 0.89]) for mortality. We conclude that HSI presents novel imaging biomarkers for the rapid, non-invasive prediction of sepsis and mortality, suggesting its potential as an important modality for guiding diagnosis and treatment., Comment: Markus A. Weigand, Lena Maier-Hein and Maximilian Dietrich contributed equally
Published: 2024

44. On differentiability and mass distributions of typical bivariate copulas

Author: Dietrich, Nicolas and Trutschnig, Wolfgang
Subjects: Mathematics - Statistics Theory, Mathematics - Probability, 62G32, 54E52, 28A50, 26A30
Abstract: Despite the fact that copulas are commonly considered as analytically smooth/regular objects, derivatives of copulas have to be handled with care. Triggered by a recently published result characterizing multivariate copulas via $(d-1)$-increasingness of their partial derivative we study the bivariate setting in detail and show that the set of non-differentiability points of a copula may be quite large. We first construct examples of copulas $C$ whose first partial derivative $\partial_1C(x,y)$ is pathological in the sense that for almost every $x \in (0,1)$ it does not exist on a dense subset of $y \in (0,1)$, and then show that the family of these copulas is dense. Since in commonly considered subfamilies more regularity might be typical, we then focus on bivariate Extreme Value copulas (EVC) and show that a topologically typical EVC is not absolutely continuous but has degenerated discrete component, implying that in this class typically $\partial_1C(x,y)$ exists in full $(0,1)^2$. Considering that regularity of copulas is closely related to their mass distributions we then study mass distributions of topologically typical copulas and prove the surprising fact that topologically typical bivariate copulas are mutually completely dependent with full support. Furthermore, we use the characterization of EVCs in terms of their associated Pickands dependence measures $\vartheta$ on $[0,1]$, show that regularity of $\vartheta$ carries over to the corresponding EVC and prove that the subfamily of all EVCs whose absolutely continuous, discrete and singular component has full support is dense in the class of all EVCs.
Published: 2024

45. Human Speech Perception in Noise: Can Large Language Models Paraphrase to Improve It?

Author: Chingacham, Anupama, Zhang, Miaoran, Demberg, Vera, and Klakow, Dietrich
Subjects: Computer Science - Computation and Language
Abstract: Large Language Models (LLMs) can generate text by transferring style attributes like formality resulting in formal or informal text. However, instructing LLMs to generate text that when spoken, is more intelligible in an acoustically difficult environment, is an under-explored topic. We conduct the first study to evaluate LLMs on a novel task of generating acoustically intelligible paraphrases for better human speech perception in noise. Our experiments in English demonstrated that with standard prompting, LLMs struggle to control the non-textual attribute, i.e., acoustic intelligibility, while efficiently capturing the desired textual attributes like semantic equivalence. To remedy this issue, we propose a simple prompting approach, prompt-and-select, which generates paraphrases by decoupling the desired textual and non-textual attributes in the text generation pipeline. Our approach resulted in a 40% relative improvement in human speech perception, by paraphrasing utterances that are highly distorted in a listening condition with babble noise at a signal-to-noise ratio (SNR) -5 dB. This study reveals the limitation of LLMs in capturing non-textual attributes, and our proposed method showcases the potential of using LLMs for better human speech perception in noise., Comment: Accepted at HuCLLM @ ACL 2024
Published: 2024

46. Accelerating Full Waveform Inversion By Transfer Learning

Author: Singh, Divya Shyam, Herrmann, Leon, Sun, Qing, Bürchner, Tim, Dietrich, Felix, and Kollmannsberger, Stefan
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Full waveform inversion (FWI) is a powerful tool for reconstructing material fields based on sparsely measured data obtained by wave propagation. For specific problems, discretizing the material field with a neural network (NN) improves the robustness and reconstruction quality of the corresponding optimization problem. We call this method NN-based FWI. Starting from an initial guess, the weights of the NN are iteratively updated to fit the simulated wave signals to the sparsely measured data set. For gradient-based optimization, a suitable choice of the initial guess, i.e., a suitable NN weight initialization, is crucial for fast and robust convergence. In this paper, we introduce a novel transfer learning approach to further improve NN-based FWI. This approach leverages supervised pretraining to provide a better NN weight initialization, leading to faster convergence of the subsequent optimization problem. Moreover, the inversions yield physically more meaningful local minima. The network is pretrained to predict the unknown material field using the gradient information from the first iteration of conventional FWI. In our computational experiments on two-dimensional domains, the training data set consists of reference simulations with arbitrarily positioned elliptical voids of different shapes and orientations. We compare the performance of the proposed transfer learning NN-based FWI with three other methods: conventional FWI, NN-based FWI without pretraining and conventional FWI with an initial guess predicted from the pretrained NN. Our results show that transfer learning NN-based FWI outperforms the other methods in terms of convergence speed and reconstruction quality.
Published: 2024

47. Block-Operations: Using Modular Routing to Improve Compositional Generalization

Author: Dietz, Florian and Klakow, Dietrich
Subjects: Computer Science - Machine Learning
Abstract: We explore the hypothesis that poor compositional generalization in neural networks is caused by difficulties with learning effective routing. To solve this problem, we propose the concept of block-operations, which is based on splitting all activation tensors in the network into uniformly sized blocks and using an inductive bias to encourage modular routing and modification of these blocks. Based on this concept we introduce the Multiplexer, a new architectural component that enhances the Feed Forward Neural Network (FNN). We experimentally confirm that Multiplexers exhibit strong compositional generalization. On both a synthetic and a realistic task our model was able to learn the underlying process behind the task, whereas both FNNs and Transformers were only able to learn heuristic approximations. We propose as future work to use the principles of block-operations to improve other existing architectures.
Published: 2024

48. Comgra: A Tool for Analyzing and Debugging Neural Networks

Author: Dietz, Florian, Fellenz, Sophie, Klakow, Dietrich, and Kloft, Marius
Subjects: Computer Science - Machine Learning
Abstract: Neural Networks are notoriously difficult to inspect. We introduce comgra, an open source python library for use with PyTorch. Comgra extracts data about the internal activations of a model and organizes it in a GUI (graphical user interface). It can show both summary statistics and individual data points, compare early and late stages of training, focus on individual samples of interest, and visualize the flow of the gradient through the network. This makes it possible to inspect the model's behavior from many different angles and save time by rapidly testing different hypotheses without having to rerun it. Comgra has applications for debugging, neural architecture design, and mechanistic interpretability. We publish our library through Python Package Index (PyPI) and provide code, documentation, and tutorials at https://github.com/FlorianDietz/comgra.
Published: 2024

49. General Relativistic Magneto-Hydrodynamic Simulations with BAM: Implementation and Code Comparison

Author: Neuweiler, Anna, Dietrich, Tim, Brügmann, Bernd, Giangrandi, Edoardo, Kiuchi, Kenta, Schianchi, Federico, Mösta, Philipp, Shankar, Swapnil, Giacomazzo, Bruno, and Shibata, Masaru
Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena
Abstract: Binary neutron star mergers are among the most energetic events in our Universe, with magnetic fields significantly impacting their dynamics, particularly after the merger. While numerical-relativity simulations that correctly describe the physics are essential to model their rich phenomenology, the inclusion of magnetic fields is crucial for realistic simulations. For this reason, we have extended the BAM code to enable general relativistic magneto-hydrodynamic (GRMHD) simulations employing a hyperbolic `divergence cleaning' scheme. We present a large set of standard GRMHD tests and compare the BAM code to other GRMHD codes, SPRITZ, GRaM-X, and SACRA$_{\rm KK22}$, which employ different schemes for the evolution of the magnetic fields. Overall, we find that the BAM code shows a good performance in simple special-relativistic tests. In addition, we find good agreement and consistent results when comparing GRMHD simulation results between BAM and SACRA$_{\rm KK22}$., Comment: 21 pages, 21 figures
Published: 2024

50. Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning

Author: Lin, Pin-Jie, Zhang, Miaoran, Mosbach, Marius, and Klakow, Dietrich
Subjects: Computer Science - Computation and Language
Abstract: Identifying beneficial tasks to transfer from is a critical step toward successful intermediate-task transfer learning. In this work, we experiment with 130 source-target task combinations and demonstrate that the transfer performance exhibits severe variance across different source tasks and training seeds, highlighting the crucial role of intermediate-task selection in a broader context. We compare four representative task selection methods in a unified setup, focusing on their effectiveness and consistency. Compared to embedding-free methods and text embeddings, task embeddings constructed from fine-tuned weights can better estimate task transferability by improving task prediction scores from 2.59% to 3.96%. Despite their strong performance, we observe that the task embeddings do not consistently demonstrate superiority for tasks requiring reasoning abilities. Furthermore, we introduce a novel method that measures pairwise token similarity using maximum inner product search, leading to the highest performance in task prediction. Our findings suggest that token-wise similarity is better predictive for predicting transferability compared to averaging weights., Comment: Accepted to ACL SRW 2024
Published: 2024

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