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

Author: Digital Promise, Britney Jacobs, Kate Babineau, and Daniel Parker
Abstract: The expansion of early childhood education (ECE) and increased spending have benefited children and supported families. However, these investments have not addressed inequities within the ECE workforce. ECE providers face economic insecurity, earning an average of $14 per hour, which is below a living wage. In rural communities, this median wage drops to $11.42, and in Tennessee, it is even lower at under $10 per hour. Women of color, especially in rural areas, are disproportionately affected by poor compensation and benefits. To address these issues, this project partners with an organization called Tennessee Early Childhood Training Alliance (TECTA) to understand the experiences of ECE providers in an effort to raise awareness of: (1) the benefits of the TECTA program and the resources they provide; (2) the key challenges and barriers they navigate on the pathway to their education; and (3) the need for program expansion to enable opportunities for social and economic mobility. This study underscores the need for systemic changes to support ECE providers, particularly in rural areas and other marginalized communities. By addressing economic insecurity, professional recognition, training disparities, and policy inconsistencies, we can create a more equitable and effective ECE workforce.
Published: 2024

11. An Analysis of Environmental Education in Indonesian EFL Elementary School Textbooks

Author: Ingrid A. Gavilan Tatin, Sulis Triyono, George M. Jacobs, Sara Trett, Aditya A. Soeta Bangsa, and Chenghao Zhu
Abstract: The worsening global environmental crisis highlights the urgency of integrating Environmental Education (EE) throughout the curriculum including in English as a Foreign Language (EFL) curricula. However, challenges exist, such as the development of appropriate curriculum documents and educators' understanding of approaches to EE. This study looked at Indonesian EFL textbooks for elementary schools, focusing mainly on the images and how they can portray environmental themes. These images and some brief texts that accompany the images were examined using tools from ecolinguistics literature, including Stibbe's (2021) nine stories and the United Nations' EE objectives (UNESCO-UNEP, 1975). This study intends to fill a gap in the EE literature by analyzing images in elementary school textbooks in the specific context of Indonesia, generalizing the idea of EE to other contexts. Findings revealed that while some EE efforts were evident, the majority of the material leaned towards an anthropocentric (human centered) perspective, indicating a need for greater incorporation of insights from ecolinguistics to enhance the content. Moreover, in terms of EE objectives, the textbooks provided ecological knowledge but lacked emphasis on developing skills and encouraging student participation in solving environmental problems. Examples demonstrating the ability to evaluate and address environmental issues were also absent. Suggestions are made for future EE content in EFL materials.
Published: 2024

12. Pitting the Working Class against Itself: Solidarity, Strikebreaking, and Strike Outcomes in the Early US Labor Movement

Author: Isaac, Larry W., McKane, Rachel G., and Jacobs, Anna W.
Published: 2022

13. Helping Young Writers Find Their Voice: A Conversation with Dory Manor

Author: Jacobs, Adriana X.
Published: 2022
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14. Prose, Poetry, and the Heresy of Normalcy: New Voices in Contemporary Hebrew Literature

Author: Cohen, Jessica, Jacobs, Adriana X., and Rovner, Adam
Published: 2022
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15. Reimagining a Framework for Parent Involvement in South Africa: Preparing Preservice Teachers

Author: Carmelita Jacobs
Abstract: Background: School-family engagement significantly influences educational outcomes, yet South African teachers notice limited involvement from parents, particularly in impoverished communities. Teacher education can play a significant role in preparing teachers to work with parents and communities. Aim: This article promotes Community Cultural Wealth theory as a community-based approach to educational support that contrasts with the conventional view of parent involvement, which often overlooks collectivist African cultures. Setting: Teacher education in South African tertiary institutions. Methods: Drawing from a decade of literature, this conceptual study utilised EBSCOhost, and Google Scholar databases, as well as reference mining to select peer-reviewed English articles relevant to teacher preparation for school -family partnerships. Results: The analysis highlights how the concept of parent involvement should be decolonised and reimagined through the lens of Community Cultural Wealth and offers examples from the Global South and pedagogical tools for teacher education. Conclusion: This article makes the assertion that as long as poverty remains unaddressed, the perception of the uninvolved parent will endure as a consequence of systemic economic challenges. However, by embracing the framework suggested in this article, teacher educators can equip preservice teachers with the skills and perspectives necessary to foster meaningful collaboration with families and communities. The article concludes by highlighting the transformative potential of Community Cultural Wealth theory in promoting equitable and inclusive educational practices. Contribution: This study underscores the importance of cultivating a holistic understanding of family engagement among preservice teachers and challenges the classification of impoverished families as 'uninvolved,' advocating for a broader examination of their assets beyond traditional metrics.
Published: 2024

16. Entangled Co-Design with a Trickster: Speculative Framing and Reframing

Author: Vanessa Svihla, Megan Jacobs, Tim Castillo, Mary Tsiongas, Leah Buechley, Drew Trujillo, Amy Traylor, Megan Tucker, Reuben Fresquez, Jaziel Cervantes-Carreon, and Sydney Nesbit
Abstract: Speculative design, as a diverse set of methods that aim to offer critique, can be challenging to engage productively. In this design case, we share how a prior, stalled design project--an ambitious vision of interdisciplinary design education partnered with business and housing development projects in Santa Fe, New Mexico--provided compelling precedent as we sought to reframe during the COVID-19 pandemic. We recognized that solution-focused ways of working in the prior project left the design problem undefined. As we began the design work detailed in this case, we leveraged the perspectives and design knowledge of our interdisciplinary team of faculty and students. While design cases often emphasize the designed training or program, we focus on our reframing process, sharing vignettes as we prepared to and participated in activities at a design workshop, and then used our own design practices to engage in problem framing workshops. In sharing these accounts, we characterize the pandemic as a trickster and speculative co-designer, who revealed much about how our efforts were entangled with institutional structures. Across these punctuated vignettes of design work, we highlight how an initial broad problem frame invited this trickster to participate and how the application of problem framing tools wrested framing agency from the trickster. Collectively, this anchored our attention to systemic inequities in ways that troubled notions of sustainability.
Published: 2024

17. Gender, Conversion, and the End of Empire in the Teaching of Jacob, Newly Baptized

Author: Jacobs, Andrew S.
Published: 2021
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18. CyberDep: Towards the Analysis of Cyber-Physical Power System Interdependencies Using Bayesian Networks and Temporal Data

Author: Homoud, Leen Al, Davis, Katherine, Hossain-McKenzie, Shamina, and Jacobs, Nicholas
Subjects: Electrical Engineering and Systems Science - Systems and Control
Abstract: Modern-day power systems have become increasingly cyber-physical due to the ongoing developments to the grid that include the rise of distributed energy generation and the increase of the deployment of many cyber devices for monitoring and control, such as the Supervisory Control and Data Acquisition (SCADA) system. Such capabilities have made the power system more vulnerable to cyber-attacks that can harm the physical components of the system. As such, it is of utmost importance to study both the physical and cyber components together, focusing on characterizing and quantifying the interdependency between these components. This paper focuses on developing an algorithm, named CyberDep, for Bayesian network generation through conditional probability calculations of cyber traffic flows between system nodes. Additionally, CyberDep is implemented on the temporal data of the cyber-physical emulation of the WSCC 9-bus power system. The results of this work provide a visual representation of the probabilistic relationships within the cyber and physical components of the system, aiding in cyber-physical interdependency quantification., Comment: Accepted and Presented at the 2024 Kansas Power and Energy Conference (KPEC 2024)
Published: 2024

19. Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer

Author: Yao, Jinghan, Jacobs, Sam Ade, Tanaka, Masahiro, Ruwase, Olatunji, Shafi, Aamir, Subramoni, Hari, and Panda, Dhabaleswar K.
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Large Language Models (LLMs) with long context capabilities are integral to complex tasks in natural language processing and computational biology, such as text generation and protein sequence analysis. However, training LLMs directly on extremely long contexts demands considerable GPU resources and increased memory, leading to higher costs and greater complexity. Alternative approaches that introduce long context capabilities via downstream finetuning or adaptations impose significant design limitations. In this paper, we propose Fully Pipelined Distributed Transformer (FPDT) for efficiently training long-context LLMs with extreme hardware efficiency. For GPT and Llama models, we achieve a 16x increase in sequence length that can be trained on the same hardware compared to current state-of-the-art solutions. With our dedicated sequence chunk pipeline design, we can now train 8B LLM with 2 million sequence length on only 4 GPUs, while also maintaining over 55% of MFU. Our proposed FPDT is agnostic to existing training techniques and is proven to work efficiently across different LLM models.
Published: 2024

20. A characterisation of graphs quasi-isometric to $K_4$-minor-free graphs

Author: Albrechtsen, Sandra, Jacobs, Raphael W., Knappe, Paul, and Wollan, Paul
Subjects: Mathematics - Combinatorics, Mathematics - Metric Geometry, 51F30, 05C83, 05C10
Abstract: We prove that there is a function $f$ such that every graph with no $K$-fat $K_4$ minor is $f(K)$-quasi-isometric to a graph with no $K_4$ minor. This solves the $K_4$-case of a general conjecture of Georgakopoulos and Papasoglu. Our proof technique also yields a new short proof of the respective $K_4^-$-case, which was first established by Fujiwara and Papasoglu., Comment: 29 pages, 11 figures, SVG files available in TeX Source
Published: 2024

21. Mask in the Mirror: Implicit Sparsification

Author: Jacobs, Tom and Burkholz, Rebekka
Subjects: Computer Science - Machine Learning
Abstract: Sparsifying deep neural networks to reduce their inference cost is an NP-hard problem and difficult to optimize due to its mixed discrete and continuous nature. Yet, as we prove, continuous sparsification has already an implicit bias towards sparsity that would not require common projections of relaxed mask variables. While implicit rather than explicit regularization induces benefits, it usually does not provide enough flexibility in practice, as only a specific target sparsity is obtainable. To exploit its potential for continuous sparsification, we propose a way to control the strength of the implicit bias. Based on the mirror flow framework, we derive resulting convergence and optimality guarantees in the context of underdetermined linear regression and demonstrate the utility of our insights in more general neural network sparsification experiments, achieving significant performance gains, particularly in the high-sparsity regime. Our theoretical contribution might be of independent interest, as we highlight a way to enter the rich regime and show that implicit bias is controllable by a time-dependent Bregman potential., Comment: 20 pages, 5 figures
Published: 2024

22. Bayesian Inference analysis of jet quenching using inclusive jet and hadron suppression measurements

Author: Ehlers, R., Chen, Y., Mulligan, J., Ji, Y., Kumar, A., Mak, S., Jacobs, P. M., Majumder, A., Angerami, A., Arora, R., Bass, S. A., Datta, R., Du, L., Elfner, H., Fries, R. J., Gale, C., He, Y., Jacak, B. V., Jeon, S., Jonas, F., Kasper, L., Kordell II, M., Kunnawalkam-Elayavalli, R., Latessa, J., Lee, Y. -J., Lemmon, R., Luzum, M., Mankolli, A., Martin, C., Mehryar, H., Mengel, T., Nattrass, C., Norman, J., Parker, C., Paquet, J. -F., Putschke, J. H., Roch, H., Roland, G., Schenke, B., Schwiebert, L., Sengupta, A., Shen, C., Singh, M., Sirimanna, C., Soeder, D., Soltz, R. A., Soudi, I., Tachibana, Y., Velkovska, J., Vujanovic, G., Wang, X. -N., Wu, X., and Zhao, W.
Subjects: High Energy Physics - Phenomenology, Nuclear Experiment, Nuclear Theory
Abstract: The JETSCAPE Collaboration reports a new determination of the jet transport parameter $\hat{q}$ in the Quark-Gluon Plasma (QGP) using Bayesian Inference, incorporating all available inclusive hadron and jet yield suppression data measured in heavy-ion collisions at RHIC and the LHC. This multi-observable analysis extends the previously published JETSCAPE Bayesian Inference determination of $\hat{q}$, which was based solely on a selection of inclusive hadron suppression data. JETSCAPE is a modular framework incorporating detailed dynamical models of QGP formation and evolution, and jet propagation and interaction in the QGP. Virtuality-dependent partonic energy loss in the QGP is modeled as a thermalized weakly-coupled plasma, with parameters determined from Bayesian calibration using soft-sector observables. This Bayesian calibration of $\hat{q}$ utilizes Active Learning, a machine--learning approach, for efficient exploitation of computing resources. The experimental data included in this analysis span a broad range in collision energy and centrality, and in transverse momentum. In order to explore the systematic dependence of the extracted parameter posterior distributions, several different calibrations are reported, based on combined jet and hadron data; on jet or hadron data separately; and on restricted kinematic or centrality ranges of the jet and hadron data. Tension is observed in comparison of these variations, providing new insights into the physics of jet transport in the QGP and its theoretical formulation., Comment: 20 pages, 10 figures, 2 tables, submitted to PRC; updated acknowledgements
Published: 2024

23. Low Thermal Resistance of Diamond-AlGaN Interfaces Achieved Using Carbide Interlayers

Author: Aller, Henry T., Pfeifer, Thomas W., Mamun, Abdullah, Huynh, Kenny, Tadjer, Marko, Feygelson, Tatyana, Hobart, Karl, Anderson, Travis, Pate, Bradford, Jacobs, Alan, Lundh, James Spencer, Goorsky, Mark, Khan, Asif, Hopkins, Patrick, and Graham, Samuel
Subjects: Condensed Matter - Materials Science, Physics - Atomic Physics
Abstract: This study investigates thermal transport across nanocrystalline diamond/AlGaN interfaces, crucial for enhancing thermal management in AlGaN/AlGaN-based devices. Chemical vapor deposition growth of diamond directly on AlGaN resulted in a disordered interface with a high thermal boundary resistance (TBR) of 20.6 m^2-K/GW. We employed sputtered carbide interlayers (e.g., $B_4C$, $SiC$, $B_4C/SiC$) to reduce thermal boundary resistance in diamond/AlGaN interfaces. The carbide interlayers resulted in record-low thermal boundary resistance values of 3.4 and 3.7 m^2-K/GW for Al$_{0.65}$Ga$_{0.35}$N samples with $B_4C$ and $SiC$ interlayers, respectively. STEM imaging of the interface reveals interlayer thicknesses between 1.7-2.5 nm, with an amorphous structure. Additionally, Fast-Fourier Transform (FFT) characterization of sections of the STEM images displayed sharp crystalline fringes in the AlGaN layer, confirming it was properly protected from damage from hydrogen plasma during the diamond growth. In order to accurately measure the thermal boundary resistance we develop a hybrid technique, combining time-domain thermoreflectance and steady-state thermoreflectance fitting, offering superior sensitivity to buried thermal resistances. Our findings underscore the efficacy of interlayer engineering in enhancing thermal transport and demonstrate the importance of innovative measurement techniques in accurately characterizing complex thermal interfaces. This study provides a foundation for future research in improving thermal properties of semiconductor devices through interface engineering and advanced measurement methodologies.
Published: 2024

24. PSM: Learning Probabilistic Embeddings for Multi-scale Zero-Shot Soundscape Mapping

Author: Khanal, Subash, Xing, Eric, Sastry, Srikumar, Dhakal, Aayush, Xiong, Zhexiao, Ahmad, Adeel, and Jacobs, Nathan
Subjects: Computer Science - Sound, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: A soundscape is defined by the acoustic environment a person perceives at a location. In this work, we propose a framework for mapping soundscapes across the Earth. Since soundscapes involve sound distributions that span varying spatial scales, we represent locations with multi-scale satellite imagery and learn a joint representation among this imagery, audio, and text. To capture the inherent uncertainty in the soundscape of a location, we design the representation space to be probabilistic. We also fuse ubiquitous metadata (including geolocation, time, and data source) to enable learning of spatially and temporally dynamic representations of soundscapes. We demonstrate the utility of our framework by creating large-scale soundscape maps integrating both audio and text with temporal control. To facilitate future research on this task, we also introduce a large-scale dataset, GeoSound, containing over $300k$ geotagged audio samples paired with both low- and high-resolution satellite imagery. We demonstrate that our method outperforms the existing state-of-the-art on both GeoSound and the existing SoundingEarth dataset. Our dataset and code is available at https://github.com/mvrl/PSM., Comment: Accepted at ACM MM 2024
Published: 2024

25. Parameterized Verification of Systems with Precise (0,1)-Counter Abstraction

Author: Eichler, Paul, Jacobs, Swen, and Weil-Kennedy, Chana
Subjects: Computer Science - Formal Languages and Automata Theory
Abstract: We introduce a new framework for verifying systems with a parametric number of concurrently running processes. The systems we consider are well-structured with respect to a specific well-quasi order. This allows us to decide a wide range of verification problems, including control-state reachability, coverability, and target, in a fixed finite abstraction of the infinite state-space, called a 01-counter system. We show that several systems from the parameterized verification literature fall into this class, including reconfigurable broadcast networks (or systems with lossy broadcast), disjunctive systems, synchronizations and systems with a fixed number of shared finite-domain variables. Our framework provides a simple and unified explanation for the properties of these systems, which have so far been investigated separately. Additionally, it extends and improves on a range of the existing results, and gives rise to other systems with similar properties.
Published: 2024

26. Parallel Distributional Deep Reinforcement Learning for Mapless Navigation of Terrestrial Mobile Robots

Author: Kich, Victor Augusto, Kolling, Alisson Henrique, de Jesus, Junior Costa, Heisler, Gabriel V., Jacobs, Hiago, Bottega, Jair Augusto, Kelbouscas, André L. da S., Ohya, Akihisa, Grando, Ricardo Bedin, Drews-Jr, Paulo Lilles Jorge, and Gamarra, Daniel Fernando Tello
Subjects: Computer Science - Robotics
Abstract: This paper introduces novel deep reinforcement learning (Deep-RL) techniques using parallel distributional actor-critic networks for navigating terrestrial mobile robots. Our approaches use laser range findings, relative distance, and angle to the target to guide the robot. We trained agents in the Gazebo simulator and deployed them in real scenarios. Results show that parallel distributional Deep-RL algorithms enhance decision-making and outperform non-distributional and behavior-based approaches in navigation and spatial generalization., Comment: Paper accepted at the 24th International Conference on Control, Automation and Systems (ICCAS)
Published: 2024

27. Parameterized Verification of Timed Networks with Clock Invariants

Author: André, Étienne, Jacobs, Swen, Karra, Shyam Lal, and Sankur, Ocan
Subjects: Computer Science - Formal Languages and Automata Theory, D.2.4, F.1.2
Abstract: We consider parameterized verification problems for networks of timed automata (TAs) that communicate via disjunctive guards or lossy broadcast. To this end, we first consider disjunctive timed networks (DTNs), i.e., networks of TAs that communicate via location guards that enable a transition only if there is another process in a certain location. We solve for the first time the general case with clock invariants, and establish the decidability of the parameterized verification problem for local trace properties and for reachability of global configurations; Moreover, we prove that, surprisingly and unlike in other settings, this model is equivalent to lossy broadcast networks., Comment: 31 pages, 7 figures
Published: 2024

28. MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas

Author: Qiao, Feng, Xiong, Zhexiao, Zhu, Xinge, Ma, Yuexin, He, Qiumeng, and Jacobs, Nathan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We introduce Multi-Cylindrical Panoramic Depth Estimation (MCPDepth), a two-stage framework for omnidirectional depth estimation via stereo matching between multiple cylindrical panoramas. MCPDepth uses cylindrical panoramas for initial stereo matching and then fuses the resulting depth maps across views. A circular attention module is employed to overcome the distortion along the vertical axis. MCPDepth exclusively utilizes standard network components, simplifying deployment to embedded devices and outperforming previous methods that require custom kernels. We theoretically and experimentally compare spherical and cylindrical projections for stereo matching, highlighting the advantages of the cylindrical projection. MCPDepth achieves state-of-the-art performance with an 18.8% reduction in mean absolute error (MAE) for depth on the outdoor synthetic dataset Deep360 and a 19.9% reduction on the indoor real-scene dataset 3D60.
Published: 2024

29. Accelerating Domain-Aware Electron Microscopy Analysis Using Deep Learning Models with Synthetic Data and Image-Wide Confidence Scoring

Author: Lynch, Matthew J., Jacobs, Ryan, Bruno, Gabriella, Patki, Priyam, Morgan, Dane, and Field, Kevin G.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Condensed Matter - Materials Science
Abstract: The integration of machine learning (ML) models enhances the efficiency, affordability, and reliability of feature detection in microscopy, yet their development and applicability are hindered by the dependency on scarce and often flawed manually labeled datasets and a lack of domain awareness. We addressed these challenges by creating a physics-based synthetic image and data generator, resulting in a machine learning model that achieves comparable precision (0.86), recall (0.63), F1 scores (0.71), and engineering property predictions (R2=0.82) to a model trained on human-labeled data. We enhanced both models by using feature prediction confidence scores to derive an image-wide confidence metric, enabling simple thresholding to eliminate ambiguous and out-of-domain images resulting in performance boosts of 5-30% with a filtering-out rate of 25%. Our study demonstrates that synthetic data can eliminate human reliance in ML and provides a means for domain awareness in cases where many feature detections per image are needed.
Published: 2024

30. Mitigating calibration errors from mutual coupling with time-domain filtering of 21 cm cosmological radio observations

Author: Charles, N., Kern, N. S., Pascua, R., Bernardi, G., Bester, L., Smirnov, O., Acedo, E. d. L., Abdurashidova, Z., Adams, T., Aguirre, J. E., Baartman, R., Beardsley, A. P., Berkhout, L. M., Billings, T. S., Bowman, J. D., Bull, P., Burba, J., Byrne, R., Carey, S., Chen, K., Choudhuri, S., Cox, T., DeBoer, D. R., Dexter, M., Dillon, J. S., Dynes, S., Eksteen, N., Ely, J., Ewall-Wice, A., Fritz, R., Furlanetto, S. R., Gale-Sides, K., Garsden, H., Gehlot, B. K., Ghosh, A., Gorce, A., Gorthi, D., Halday, Z., Hazelton, B. J., Hewitt, J. N., Hickish, J., Huang, T., Jacobs, D. C., Josaitis, A., Kerrigan, J., Kittiwisit, P., Kolopanis, M., Lanman, A., Liu, A., Ma, Y. -Z., MacMahon, D. H. E., Malan, L., Malgas, K., Malgas, C., Marero, B., Martinot, Z. E., McBride, L., Mesinger, A., Mohamed-Hinds, N., Molewa, M., Morales, M. F., Murray, S., Nikolic, B., Nuwegeld, H., Parsons, A. R., Patra, N., Plante, P. L., Qin, Y., Rath, E., Razavi-Ghods, N., Riley, D., Robnett, J., Rosie, K., Santos, M. G., Sims, P., Singh, S., Storer, D., Swarts, H., Tan, J., Wilensky, M. J., Williams, P. K. G., Wyngaarden, P. v., and Zheng, H.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionisation (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Array (HERA), aimed at systematically mapping this emission for the first time. Precision calibration, however, is a requirement in 21 cm radio observations. Due to the spatial compactness of HERA, the array is prone to the effects of mutual coupling, which inevitably lead to non-smooth calibration errors that contaminate the data. When unsmooth gains are used in calibration, intrinsically spectrally-smooth foreground emission begins to contaminate the data in a way that can prohibit a clean detection of the cosmological EoR signal. In this paper, we show that the effects of mutual coupling on calibration quality can be reduced by applying custom time-domain filters to the data prior to calibration. We find that more robust calibration solutions are derived when filtering in this way, which reduces the observed foreground power leakage. Specifically, we find a reduction of foreground power leakage by 2 orders of magnitude at k=0.5.
Published: 2024

31. Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficent-KAN and WAV-KAN

Author: Patra, Subhajit, Panda, Sonali, Parida, Bikram Keshari, Arya, Mahima, Jacobs, Kurt, Bondar, Denys I., and Sen, Abhijit
Subjects: Computer Science - Machine Learning
Abstract: Physics-informed neural networks have proven to be a powerful tool for solving differential equations, leveraging the principles of physics to inform the learning process. However, traditional deep neural networks often face challenges in achieving high accuracy without incurring significant computational costs. In this work, we implement the Physics-Informed Kolmogorov-Arnold Neural Networks (PIKAN) through efficient-KAN and WAV-KAN, which utilize the Kolmogorov-Arnold representation theorem. PIKAN demonstrates superior performance compared to conventional deep neural networks, achieving the same level of accuracy with fewer layers and reduced computational overhead. We explore both B-spline and wavelet-based implementations of PIKAN and benchmark their performance across various ordinary and partial differential equations using unsupervised (data-free) and supervised (data-driven) techniques. For certain differential equations, the data-free approach suffices to find accurate solutions, while in more complex scenarios, the data-driven method enhances the PIKAN's ability to converge to the correct solution. We validate our results against numerical solutions and achieve $99 \%$ accuracy in most scenarios.
Published: 2024

32. A soft-hard framework with exact four momentum conservation for small systems

Author: Soudi, I., Zhao, W., Majumder, A., Shen, C., Putschke, J. H., Boudreaux, B., Angerami, A., Arora, R., Bass, S. A., Chen, Y., Datta, R., Du, L., Ehlers, R., Elfner, H., Fries, R. J., Gale, C., He, Y., Jacak, B. V., Jacobs, P. M., Jeon, S., Ji, Y., Kasper, L., Kelsey, M., Kordell II, M., Kumar, A., Kunnawalkam-Elayavalli, R., Latessa, J., Lee, Y. -J., Lemmon, R., Luzum, M., Mak, S., Mankolli, A., Martin, C., Mehryar, H., Mengel, T., Nattrass, C., Norman, J., Parker, C., Paquet, J. -F., Roch, H., Roland, G., Schenke, B., Schwiebert, L., Sengupta, A., Singh, M., Sirimanna, C., Soeder, D., Soltz, R. A., Tachibana, Y., Velkovska, J., Vujanovic, G., Wang, X. -N., and Wu, X.
Subjects: High Energy Physics - Phenomenology, High Energy Physics - Experiment, High Energy Physics - Theory, Nuclear Theory
Abstract: A new framework, called x-scape, for the combined study of both hard and soft transverse momentum sectors in high energy proton-proton ($p$-$p$) and proton-nucleus ($p$-$A$) collisions is set up. A dynamical initial state is set up using the 3d-Glauber model with transverse locations of hotspots within each incoming nucleon. A hard scattering that emanates from two colliding hotspots is carried out using the Pythia generator. Initial state radiation from the incoming hard partons is carried out in a new module called I-matter, which includes the longitudinal location of initial splits. The energy-momentum of both the initial hard partons and their associated beam remnants is removed from the hot spots, depleting the energy-momentum available for the formation of the bulk medium. Outgoing showers are simulated using the matter generator, and results are presented for both cases, allowing for and not allowing for energy loss. First comparisons between this hard-soft model and single inclusive hadron and jet data from $p$-$p$ and minimum bias $p$-$Pb$ collisions are presented. Single hadron spectra in $p$-$p$ are used to carry out a limited (in number of parameters) Bayesian calibration of the model. Fair comparisons with data are indicative of the utility of this new framework. Theoretical studies of the correlation between jet $p_T$ and event activity at mid and forward rapidity are carried out., Comment: 18 pages, 15 figures
Published: 2024

33. Determination of $|V_{ub}|$ from simultaneous measurements of untagged $B^0\to\pi^- \ell^+ \nu_{\ell}$ and $B^+\to\rho^0 \ell^+\nu_{\ell}$ decays

Author: Belle II Collaboration, Adachi, I., Aggarwal, L., Aihara, H., Akopov, N., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Bauer, M., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Corona, L., Cui, J. X., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dorner, D., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Eliachevitch, M., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Granderath, S., Greenwald, D., Gruberová, Z., Gu, T., Gudkova, K., Haide, I., Halder, S., Han, Y., Hara, T., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jang, E. -J., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Konno, T., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, J., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Lautenbach, K., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Leo, P., Lemettais, C., Levit, D., Lewis, P. M., Li, L. K., Li, S. X., Li, Y., Li, Y. B., Libby, J., Liptak, Z., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Mizuk, R., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Neu, M., Niiyama, M., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, J., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Prudiiev, I., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Reuter, L., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schneider, S., Schnepf, M., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Uchida, M., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yang, S. B., Yelton, J., Yin, J. H., Yook, Y. M., Yoshihara, K., Yuan, C. Z., Zani, L., Zeng, F., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhou, X. Y., Zhukova, V. I., and Žlebčík, R.
Subjects: High Energy Physics - Experiment
Abstract: We present a measurement of $|V_{ub}|$ from a simultaneous study of the charmless semileptonic decays $B^0\to\pi^- \ell^+ \nu_{\ell}$ and $B^+\to\rho^0 \ell^+\nu_{\ell}$, where $\ell = e, \mu$. This measurement uses a data sample of 387 million $B\overline{B}$ meson pairs recorded by the Belle~II detector at the SuperKEKB electron-positron collider between 2019 and 2022. The two decays are reconstructed without identifying the partner $B$ mesons. We simultaneously measure the differential branching fractions of $B^0\to\pi^- \ell^+ \nu_{\ell}$ and $B^+\to\rho^0 \ell^+\nu_{\ell}$ decays as functions of $q^2$ (momentum transfer squared). From these, we obtain total branching fractions $B(B^0\to\pi^- \ell^+ \nu_{\ell}) = (1.516 \pm 0.042 (\mathrm{stat}) \pm 0.059 (\mathrm{syst})) \times 10^{-4}$ and $B(B^+\to\rho^0 \ell^+\nu_{\ell}) = (1.625 \pm 0.079 (\mathrm{stat}) \pm 0.180 (\mathrm{syst})) \times 10^{-4}$. By fitting the measured $B^0\to\pi^- \ell^+ \nu_{\ell}$ partial branching fractions as functions of $q^2$, together with constraints on the non-perturbative hadronic contribution from lattice QCD calculations, we obtain $|V_{ub}|$ = $(3.93 \pm 0.09 \pm 0.13 \pm 0.19) \times 10^{-3}$. Here, the first uncertainty is statistical, the second is systematic, and the third is theoretical.
Published: 2024

34. First Comparative Exoplanetology Within a Transiting Multi-planet System: Comparing the atmospheres of V1298 Tau b and c

Author: Barat, Saugata, Désert, Jean-Michel, Goyal, Jayesh M., Vazan, Allona, Kawashima, Yui, Fortney, Jonathan J., Bean, Jacob L., Line, Michael R., Panwar, Vatsal, Jacobs, Bob, Shivkumar, Hinna, Sikora, James, Baeyens, Robin, Oklopcić, Antonija, David, Trevor J., and Livingston, John H.
Subjects: Astrophysics - Earth and Planetary Astrophysics
Abstract: The V1298 Tau system (20-30Myr), is a benchmark young multi-planet system that provides the opportunity to perform comparative exoplanetology between planets orbiting the same star right after their formation. We present the first atmospheric comparison between two planets in the same transiting system: V1298 Tau b and V1298 Tau c. We derive constraints on the mass of planet b and c (<20M$_\oplus$ at 3$\sigma$ confidence level and $17_{-6}^{+13} M_{\oplus}$ respectively) and atmospheric metallicity (logZ/Z$_\odot$=-2.04$_{-0.59}^{0.69}$, -0.16$_{-0.94}^{1.15}$ respectively) from atmospheric retrievals. The V1298 Tau planets, are likely to be similar in terms of mass at the current age, implying that both planets are potential sub-Neptune/super-Earth progenitors. However, planet c is expected to lose a higher fraction of its mass compared to planet b given its close proximity to the host star. Alternatively, the observed spectrum of planet c can be explained by atmospheric hazes, which is in contrast to planet b where efficient haze formation can be ruled out. Higher haze formation efficiency in planet c could be due to differences in atmospheric composition, temperature and higher UV flux incident compared to planet b., Comment: Submitted to Astronomy ad Astrophysics
Published: 2024

35. Mixed-View Panorama Synthesis using Geospatially Guided Diffusion

Author: Xiong, Zhexiao, Xing, Xin, Workman, Scott, Khanal, Subash, and Jacobs, Nathan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We introduce the task of mixed-view panorama synthesis, where the goal is to synthesize a novel panorama given a small set of input panoramas and a satellite image of the area. This contrasts with previous work which only uses input panoramas (same-view synthesis), or an input satellite image (cross-view synthesis). We argue that the mixed-view setting is the most natural to support panorama synthesis for arbitrary locations worldwide. A critical challenge is that the spatial coverage of panoramas is uneven, with few panoramas available in many regions of the world. We introduce an approach that utilizes diffusion-based modeling and an attention-based architecture for extracting information from all available input imagery. Experimental results demonstrate the effectiveness of our proposed method. In particular, our model can handle scenarios when the available panoramas are sparse or far from the location of the panorama we are attempting to synthesize.
Published: 2024

36. Measurement of $CP$ asymmetries in $B^0 \to K^0_S \pi^0 \gamma$ decays at Belle II

Author: Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Bilokin, S., Biswas, D., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dorner, D., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Eliachevitch, M., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Halder, S., Han, Y., Hara, K., Hara, T., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Joo, K. K., Junkerkalefeld, H., Kaleta, M., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, J., Kumar, M., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Leo, P., Levit, D., Li, C., Li, L. K., Li, S. X., Li, Y., Li, Y. B., Libby, J., Lin, Y. -R., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Luo, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martel, L., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Molina-Gonzalez, N., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakamura, K. R., Nakao, M., Nakazawa, H., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Onuki, Y., Oskin, P., Otani, F., Pakhlov, P., Pakhlova, G., Panta, A., Pardi, S., Parham, K., Park, H., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Prudiiev, I., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Ravindran, K., Reif, M., Reiter, S., Remnev, M., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sanders, D. A., Sandilya, S., Sangal, A., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schwanda, C., Schwartz, A. J., Schwickardi, M., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sumihama, M., Sumisawa, K., Sutcliffe, W., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Uematsu, Y., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xie, Y., Xu, X. P., Yabsley, B. D., Yamada, S., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yusa, Y., Zani, L., Zeng, F., Zhang, B., Zhang, Y., Zhilich, V., Zhou, Q. D., Zhou, X. Y., Zhukova, V. I., and Žlebčík, R.
Subjects: High Energy Physics - Experiment
Abstract: We report measurements of time-dependent $CP$ asymmetries in $B^0 \to K^0_S \pi^0 \gamma$ decays based on a data sample of $(388\pm6)\times10^6$ $B\bar{B}$ events collected at the $\Upsilon(4S)$ resonance with the Belle II detector. The Belle II experiment operates at the SuperKEKB asymmetric-energy $e^+e^-$ collider. We measure decay-time distributions to determine $CP$-violating parameters $S$ and $C$. We determine these parameters for two ranges of $K^0_S \pi^0$ invariant mass: $m(K^0_S \pi^0)\in (0.8, 1.0)$ $GeV/c^2$, which is dominated by $B^0 \to K^{*0} (\to K^0_S \pi^0) \gamma$ decays, and a complementary region $m(K^0_S \pi^0)\in (0.6, 0.8)\cup(1.0, 1.8)$ $GeV/c^2$. Our results have improved precision as compared to previous measurements and are consistent with theory predictions., Comment: 10 pages, 4 figures
Published: 2024

37. Measurement of branching fractions, CP asymmetry, and isospin asymmetry for $\boldsymbol{B\rightarrow\rho\gamma}$ decays using Belle and Belle II data

Author: Belle II Collaboration, Adachi, I., Adamczyk, K., Aggarwal, L., Aihara, H., Akopov, N., Aloisio, A., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Bilokin, S., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Choi, S. -K., Choudhury, S., Corona, L., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Dong, T. V., Dorigo, M., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dujany, G., Ecker, P., Eliachevitch, M., Epifanov, D., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Halder, S., Han, Y., Hara, T., Hayashii, H., Hazra, S., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Joo, K. K., Junkerkalefeld, H., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kuhr, T., Kumar, J., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Lautenbach, K., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Levit, D., Lewis, P. M., Li, C., Li, L. K., Li, Y., Li, Y. B., Libby, J., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martel, L., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Miller, C., Mirra, M., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Molina-Gonzalez, N., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakamura, K. R., Nakao, M., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Oskin, P., Otani, F., Pakhlov, P., Pakhlova, G., Panta, A., Pardi, S., Parham, K., Park, H., Park, S. -H., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schwanda, C., Schwartz, A. J., Schwickardi, M., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sumihama, M., Sumisawa, K., Sutcliffe, W., Svidras, H., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Uematsu, Y., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanuki, S., Wessel, C., Wiechczynski, J., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Zani, L., Zhang, B., Zhang, Y., Zhilich, V., Zhou, Q. D., Zhou, X. Y., and Zhukova, V. I.
Subjects: High Energy Physics - Experiment
Abstract: We present measurements of $B^{+}\rightarrow\rho^{+}\gamma$ and $B^{0}\rightarrow\rho^{0}\gamma$ decays using a combined data sample of $772 \times 10^6$ $B\overline{B}$ pairs collected by the Belle experiment and $387\times 10^6$ $B\overline{B}$ pairs collected by the Belle II experiment in $e^{+}e^{-}$ collisions at the $\Upsilon (4S)$ resonance. After an optimized selection, a simultaneous fit to the Belle and Belle II data sets yields $114\pm 12$ $B^{+}\rightarrow\rho^{+}\gamma$ and $99\pm 12$ $B^{0}\rightarrow\rho^{0}\gamma$ decays. The measured branching fractions are $(13.1^{+2.0 +1.3}_{-1.9 -1.2})\times 10^{-7}$ and $(7.5\pm 1.3^{+1.0}_{-0.8})\times 10^{-7}$ for $B^{+}\rightarrow\rho^{+}\gamma$ and $B^{0}\rightarrow\rho^{0}\gamma$ decays, respectively, where the first uncertainty is statistical and the second is systematic. We also measure the isospin asymmetry $A_{\rm I}(B\rightarrow\rho\gamma)=(10.9^{+11.2 +7.8}_{-11.7 -7.3})\%$ and the direct CP asymmetry $A_{CP}(B^{+}\rightarrow\rho^{+}\gamma)=(-8.2\pm 15.2^{+1.6}_{-1.2})\%$., Comment: 12 pages, 4 figures
Published: 2024

38. Helios: An extremely low power event-based gesture recognition for always-on smart eyewear

Author: Bhattacharyya, Prarthana, Mitton, Joshua, Page, Ryan, Morgan, Owen, Menzies, Ben, Homewood, Gabriel, Jacobs, Kemi, Baesso, Paolo, Trickett, David, Mair, Chris, Muhonen, Taru, Clark, Rory, Berridge, Louis, Vigars, Richard, and Wallace, Iain
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract: This paper introduces Helios, the first extremely low-power, real-time, event-based hand gesture recognition system designed for all-day on smart eyewear. As augmented reality (AR) evolves, current smart glasses like the Meta Ray-Bans prioritize visual and wearable comfort at the expense of functionality. Existing human-machine interfaces (HMIs) in these devices, such as capacitive touch and voice controls, present limitations in ergonomics, privacy and power consumption. Helios addresses these challenges by leveraging natural hand interactions for a more intuitive and comfortable user experience. Our system utilizes a extremely low-power and compact 3mmx4mm/20mW event camera to perform natural hand-based gesture recognition for always-on smart eyewear. The camera's output is processed by a convolutional neural network (CNN) running on a NXP Nano UltraLite compute platform, consuming less than 350mW. Helios can recognize seven classes of gestures, including subtle microgestures like swipes and pinches, with 91% accuracy. We also demonstrate real-time performance across 20 users at a remarkably low latency of 60ms. Our user testing results align with the positive feedback we received during our recent successful demo at AWE-USA-2024., Comment: Accepted at ECCV-Integrating Computer Vision in Smart Eyewear, 2024. 18 pages, 10 figures. First three authors contributed equally to this paper
Published: 2024

39. Search for the baryon number and lepton number violating decays $\tau^-\to \Lambda\pi^-$ and $\tau^-\to \bar{\Lambda}\pi^-$ at Belle II

Author: Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Borah, J., Boschetti, A., Bozek, A., Branchini, P., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dort, K., Dossett, D., Dubey, S., Dujany, G., Ecker, P., Epifanov, D., Eppelt, J., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Gironella, P., Glazov, A., Gobbo, B., Godang, R., Goldenzweig, P., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Gudkova, K., Haide, I., Halder, S., Hara, K., Harris, C., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Junkerkalefeld, H., Kandra, J., Kang, K. H., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kumar, R., Kumara, K., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Lautenbach, K., Leboucher, R., Lee, M. J., Leo, P., Levit, D., Lewis, P. M., Li, C., Li, L. K., Li, W. Z., Li, Y., Li, Y. B., Libby, J., Lin, J., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuda, T., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Miller, C., Mirra, M., Mitra, S., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Nishida, S., Ogawa, S., Ono, H., Pakhlov, P., Paoloni, E., Pardi, S., Park, J., Park, K., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Reuter, L., Ripp-Baudot, I., Rizzo, G., Roney, J. M., Rout, N., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schnepf, M., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Song, W., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Ueda, I., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, Z., Warburton, A., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Zani, L., Zhang, B., Zhou, J. S., Zhou, Q. D., Zhukova, V. I., and Žlebčík, R.
Subjects: High Energy Physics - Experiment
Abstract: We present a search for the baryon number $B$ and lepton number $L$ violating decays $\tau^- \rightarrow \Lambda \pi^-$ and $\tau^- \rightarrow \bar{\Lambda} \pi^-$ produced from the $e^+e^-\to \tau^+\tau^-$ process, using a 364 fb$^{-1}$ data sample collected by the Belle~II experiment at the SuperKEKB collider. No evidence of signal is found in either decay mode, which have $|\Delta(B-L)|$ equal to $2$ and $0$, respectively. Upper limits at 90\% credibility level on the branching fractions of $\tau^- \rightarrow \Lambda\pi^-$ and $\tau^- \rightarrow \bar{\Lambda}\pi^-$ are determined to be $4.7 \times 10^{-8}$ and $4.3 \times 10^{-8}$, respectively., Comment: 8 pages, 4 figures
Published: 2024

40. Evidence of $h_{b}(\text{2P}) \to \Upsilon(\text{1S})\eta$ decay and search for $h_{b}(\text{1P,2P}) \to \Upsilon(\text{1S})\pi^0$ with the Belle detector

Author: Belle Collaboration, Kovalenko, E., Adachi, I., Aihara, H., Asner, D. M., Aushev, T., Ayad, R., Babu, V., Banerjee, Sw., Belous, K., Bennett, J., Bessner, M., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bondar, A., Bozek, A., Bračko, M., Branchini, P., Browder, T. E., Budano, A., Campajola, M., Chang, M. -C., Cheon, B. G., Chilikin, K., Cho, H. E., Cho, K., Cho, S. -J., Choi, S. -K., Choi, Y., Choudhury, S., Dash, N., De Nardo, G., De Pietro, G., Dhamija, R., Di Capua, F., Doležal, Z., Dong, T. V., Dubey, S., Ecker, P., Epifanov, D., Ferlewicz, D., Fulsom, B. G., Garg, R., Gaur, V., Garmash, A., Giri, A., Goldenzweig, P., Graziani, E., Gu, T., Guan, Y., Gudkova, K., Hadjivasiliou, C., Hara, T., Hayasaka, K., Hazra, S., Hou, W. -S., Hsu, C. -L., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jin, Y., Kawasaki, T., Kiesling, C., Kim, C. H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kinoshita, K., Kodyš, P., Korobov, A., Korpar, S., Križan, P., Krokovny, P., Kuhr, T., Kumar, R., Kumara, K., Kuzmin, A., Kwon, Y. -J., Lai, Y. -T., Lam, T., Levit, D., Li, L. K., Gioi, L. Li, Libby, J., Liventsev, D., Ma, Y., Martini, A., Masuda, M., Matsuda, T., Matvienko, D., Meier, F., Merola, M., Miyabayashi, K., Mizuk, R., Mohanty, G. B., Mussa, R., Nakamura, I., Nakao, M., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Niiyama, M., Nishida, S., Ogawa, S., Ono, H., Pakhlova, G., Pardi, S., Park, J., Park, S. -H., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Pestotnik, R., Piilonen, L. E., Podobnik, T., Prencipe, E., Prim, M. T., Purohit, M. V., Rout, N., Russo, G., Sandilya, S., Santelj, L., Savinov, V., Schnell, G., Schwanda, C., Seino, Y., Senyo, K., Sevior, M. E., Shan, W., Sharma, C., Shiu, J. -G., Shwartz, B., Sokolov, A., Solovieva, E., Starič, M., Sumihama, M., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Tiwary, R., Uchida, M., Unno, Y., Uno, S., Usov, Y., Vinokurova, A., Wang, D., Wang, E., Wang, M. -Z., Wang, X. L., Won, E., Yabsley, B. D., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yook, Y., Yuan, C. Z., Zhang, Z. P., and Zhilich, V.
Subjects: High Energy Physics - Experiment
Abstract: We report the first evidence for the $h_{b}(\text{2P}) \to \Upsilon(\text{1S})\eta$ transition with a significance of $3.5$ standard deviations. The decay branching fraction is measured to be $\mathcal{B}[h_{b}(\text{2P}) \to \Upsilon(\text{1S})\eta]=(7.1 ~^{+3.7} _{-3.2}\pm 0.8)\times10^{-3}$, which is noticeably smaller than expected. We also set upper limits on $\pi^0$ transitions of $\mathcal{B}[h_{b}(\text{2P}) \to \Upsilon(\text{1S})\pi^0] < 1.8\times10^{-3}$, and $\mathcal{B}[h_{b}(\text{1P})\to \Upsilon(\text{1S})\pi^0] < 1.8\times10^{-3}$, at the $90\%$ confidence level. These results are obtained with a $131.4$~fb$^{-1}$ data sample collected near the $\Upsilon(\text{5S})$ resonance with the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider., Comment: to be submitted to PRL
Published: 2024

41. An Update on the External Calibrator for Hydrogen Observatories (ECHO)

Author: Zhao, Yifan, Jacobs, Daniel C., Samson, Titu, Krishna, Mrudula Gopal, Horn, Michael, Lalonde, Marc-Olivier R., Braithwaite, Raven, and Skabelund, Logan
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Precision measurements of the beam pattern response are needed to predict the response of a radio telescope. Mapping the beam of a low frequency radio array presents a unique challenge and science cases such as the observation of the 21\,cm line at high redshift have demanding requirements. Drone-based systems offer the unique potential for a measurement which is entirely under experimenter control, but progress has been paced by practical implementation challenges. Previously, a prototype drone system, called the External Calibrator for Hydrogen Observatories (ECHO), demonstrated good performance in making a complete hemispherical beam measurement. This paper reports updates to the system focusing on performance of a new drone platform, minimizing interference from the drone, and a new transmitter.
Published: 2024

42. Magic Insert: Style-Aware Drag-and-Drop

Author: Ruiz, Nataniel, Li, Yuanzhen, Wadhwa, Neal, Pritch, Yael, Rubinstein, Michael, Jacobs, David E., and Fruchter, Shlomi
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Graphics, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract: We present Magic Insert, a method for dragging-and-dropping subjects from a user-provided image into a target image of a different style in a physically plausible manner while matching the style of the target image. This work formalizes the problem of style-aware drag-and-drop and presents a method for tackling it by addressing two sub-problems: style-aware personalization and realistic object insertion in stylized images. For style-aware personalization, our method first fine-tunes a pretrained text-to-image diffusion model using LoRA and learned text tokens on the subject image, and then infuses it with a CLIP representation of the target style. For object insertion, we use Bootstrapped Domain Adaption to adapt a domain-specific photorealistic object insertion model to the domain of diverse artistic styles. Overall, the method significantly outperforms traditional approaches such as inpainting. Finally, we present a dataset, SubjectPlop, to facilitate evaluation and future progress in this area. Project page: https://magicinsert.github.io/, Comment: Project page: https://magicinsert.github.io/
Published: 2024

43. Measurement of the integrated luminosity of data samples collected during 2019-2022 by the Belle II experiment

Author: The Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Ahn, J. K., Aihara, H., Akopov, N., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Borah, J., Boschetti, A., Bozek, A., Branchini, P., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., Das, S., De La Cruz-Burelo, E., De La Motte, S. A., de Marino, G., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dort, K., Dossett, D., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Epifanov, D., Eppelt, J., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironella, P., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Gudkova, K., Haide, I., Halder, S., Han, Y., Hara, K., Hara, T., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaleta, M., Kalita, D., Kandra, J., Kang, K. H., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kim, Y. J., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kumar, R., Kumara, K., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Lautenbach, K., Leboucher, R., Lee, M. J., Lemettais, C., Leo, P., Levit, D., Lewis, P. M., Li, C., Li, L. K., Li, S. X., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Pakhlov, P., Pakhlova, G., Paoloni, E., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Reuter, L., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schnepf, M., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sfienti, C., Shan, W., Sharma, C., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Song, W., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Ueda, I., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, Z., Warburton, A., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Zani, L., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhukova, V. I., and Žlebčík, R.
Subjects: High Energy Physics - Experiment
Abstract: A series of data samples was collected with the Belle II detector at the SuperKEKB collider from March 2019 to June 2022. We determine the integrated luminosities of these data samples using three distinct methodologies involving Bhabha ($e^+e^- \to e^+e^-(n\gamma)$), digamma ($e^+e^- \to \gamma\gamma(n\gamma)$), and dimuon ($e^+e^- \to \mu^+ \mu^- (n\gamma)$) events. The total integrated luminosity obtained with Bhabha, digamma, and dimuon events is (426.52 $\pm$ 0.03 $\pm$ 2.48)~fb$^{-1}$, (427.32 $\pm$ 0.03 $\pm$ 2.56)~fb$^{-1}$, and (424.84 $\pm$ 0.04 $\pm$ 3.88)~fb$^{-1}$, where the first uncertainties are statistical and the second are systematic. The resulting total integrated luminosity obtained from the combination of the three methods is (426.88 $\pm$ 1.93)~fb$^{-1}$., Comment: 12 pages, 3 figures
Published: 2024

44. "This Piece of Parchment Will Shake the World": The Mystery of Mar Saba and the Evangelical Prototype of a Secular Fiction Genre

Author: Jacobs, Andrew S.
Published: 2020

45. Gender Composition in Contentious Collective Action: "Girl Strikers" in Gilded Age America—Harmful, Helpful, or Both?

Author: Jacobs, Anna W. and Isaac, Larry W.
Published: 2019

46. Community-Guided, Autism-Adapted Group Cognitive Behavioral Therapy for Depression in Autistic Youth (CBT-DAY): Preliminary Feasibility, Acceptability, and Efficacy

Author: Jessica M. Schwartzman, Marissa C. Roth, Ann V. Paterson, Alexandra X. Jacobs, and Zachary J. Williams
Abstract: This study examined the preliminary feasibility, acceptability, and efficacy of an autism-adapted cognitive behavioral therapy for depression in autistic youth, CBT-DAY. Twenty-four autistic youth (11-17 years old) participated in the pilot non-randomized trial including 5 cisgender females, 14 cisgender males, and 5 non-binary youth. Youth participated in 12 weeks of, CBT-DAY and youth depressive symptoms (i.e., primary clinical outcome) and emotional reactivity and self-esteem (i.e., intervention mechanisms) were assessed through self-report and caregiver report at four timepoints: baseline (week 0), midpoint (week 6), post-treatment (week 12), and follow-up (week 24). Results suggested that CBT-DAY may be feasible (16.67% attrition) in an outpatient setting and acceptable to adolescents and their caregivers. Bayesian linear mixed-effects models showed that CBT-DAY may be efficacious in targeting emotional reactivity [[beta][subscript T1-T3] = -2.53, CrI[subscript 95%] (-4.62, -0.58), P[subscript d] = 0.995, d = -0.35] and self-esteem [[beta][subscript T1-T3] = -3.57, CrI[subscript 95%] (-5.17, -2.00), P[subscript d] > 0.999, d = -0.47], as well as youth depressive symptom severity [[beta] = -2.72, CrI[subscript 95%] (-3.85, -1.63), P[subscript d] > 0.999]. Treatment gains were maintained at follow-up. A cognitive behavioral group therapy designed for and with autistic people demonstrates promise in targeting emotional reactivity and self-esteem to improve depressive symptom severity in youth. Findings can be leveraged to implement larger, more controlled trials of CBT-DAY. The trial was registered at Clinicaltrials.gov (Identifier: NCT05430022; https://beta.clinicaltrials.gov/study/NCT05430022).
Published: 2024
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47. Coronavirus Disease 2019 Vaccination by Gender and Age in a Sample of Black Adults in Chicago

Author: Jana L. Hirschtick, Wayne DiFranceisco, Bijou Hunt, Jacquelyn Jacobs, Jesus Valencia, Jennifer L. Walsh, and Katherine Quinn
Abstract: Although vaccine behaviors differ greatly by gender and age, few studies have examined vaccination at the intersection of gender and age within the Black community. We examined COVID-19 vaccination by gender and age using a survey of over 500 Black adults in Chicago, Illinois, fielded from September 2021 to March 2022. Although 54% had received at least one COVID-19 vaccine, the proportion vaccinated was considerably lower for Black men (28%) and women (37%) under 40 years old than Black men (92%) and women (86%) over 40 years (p < 0.001). Concern about vaccine side effects was the most reported barrier for unvaccinated women (56%) and men (38%) under 40 years. Our results suggest that targeted efforts to improve COVID-19 vaccine uptake in the Black community in Chicago after the initial rollout should have focused on young adults, particularly young Black men, with emphasis on addressing concern about vaccine side effects.
Published: 2024
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48. Believing in Public Education: A Demographic and State-Level Analysis of Public Charter School and District Public School Enrollment Trends

Author: National Alliance for Public Charter Schools, Drew Jacobs, and Debbie Veney
Abstract: "Believing in Public Education: A Demographic and State-level Analysis of Public Charter School and District Public School Enrollment Trends" is a new data analysis that examines enrollment trends during the last four school years (2019-2023). Over the last four school years (2019-20 to 2022-23), charter schools have gained more than 300,000 students, an increase of 9%. Meanwhile, district public schools have been unable to recover the 1.5 million students they lost during the pandemic, a 3.5% loss. The report examined data for White, Black, and Hispanic students in 26 of 42 states included in the analysis. All three groups are continuing to choose charter schools--and, in some cases, charter school enrollment growth is even outpacing expected numbers based on population growth trends.
Published: 2023

49. Soil organic carbon certificates – potential and limitations for private and public climate action

Author: Jacobs, Anna, Heidecke, Claudia, Jumshudzade, Zaur, Osterburg, Bernhard, Paulsen, Hans Marten, and Poeplau, Christopher
Subjects: soil organic matter, carbon footprint, carbon farming, carbon neutral crop production, climate change mitigation, sustainable soil management, Agriculture (General), S1-972
Published: 2020
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50. Menstrual cycle-driven hormone concentrations co-fluctuate with white and gray matter architecture changes across the whole brain.

Author: Rizor, Elizabeth, Babenko, Viktoriya, Dundon, Neil, Beverly-Aylwin, Renee, Stump, Alexandra, Hayes, Margaret, Herschenfeld-Catalan, Luna, Jacobs, Emily, and Grafton, Scott
Subjects: brain structure, brain volume, cortical thickness, diffusion imaging, hormones, magnetic resonance imaging, menstrual cycle, Humans, Female, White Matter, Adult, Menstrual Cycle, Estradiol, Young Adult, Gray Matter, Luteinizing Hormone, Brain, Follicle Stimulating Hormone, Progesterone, Magnetic Resonance Imaging, Diffusion Magnetic Resonance Imaging
Abstract: Cyclic fluctuations in hypothalamic-pituitary-gonadal axis (HPG-axis) hormones exert powerful behavioral, structural, and functional effects through actions on the mammalian central nervous system. Yet, very little is known about how these fluctuations alter the structural nodes and information highways of the human brain. In a study of 30 naturally cycling women, we employed multidimensional diffusion and T1-weighted imaging during three estimated menstrual cycle phases (menses, ovulation, and mid-luteal) to investigate whether HPG-axis hormone concentrations co-fluctuate with alterations in white matter (WM) microstructure, cortical thickness (CT), and brain volume. Across the whole brain, 17β-estradiol and luteinizing hormone (LH) concentrations were directly proportional to diffusion anisotropy (μFA; 17β-estradiol: β1 = 0.145, highest density interval (HDI) = [0.211, 0.4]; LH: β1 = 0.111, HDI = [0.157, 0.364]), while follicle-stimulating hormone (FSH) was directly proportional to CT (β1 = 0 .162, HDI = [0.115, 0.678]). Within several individual regions, FSH and progesterone demonstrated opposing relationships with mean diffusivity (Diso) and CT. These regions mainly reside within the temporal and occipital lobes, with functional implications for the limbic and visual systems. Finally, progesterone was associated with increased tissue (β1 = 0.66, HDI = [0.607, 15.845]) and decreased cerebrospinal fluid (CSF; β1 = -0.749, HDI = [-11.604, -0.903]) volumes, with total brain volume remaining unchanged. These results are the first to report simultaneous brain-wide changes in human WM microstructure and CT coinciding with menstrual cycle-driven hormone rhythms. Effects were observed in both classically known HPG-axis receptor-dense regions (medial temporal lobe, prefrontal cortex) and in other regions located across frontal, occipital, temporal, and parietal lobes. Our results suggest that HPG-axis hormone fluctuations may have significant structural impacts across the entire brain.
Published: 2024

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