Author: "Schwartz, P" - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Schwartz, P"' showing total 55,932 results

Start Over Author "Schwartz, P"

55,932 results on '"Schwartz, P"'

1. Communities of Practice and Acculturation: How International Students in American Colleges Use Social Media to Manage Homesickness

Author: Michael Schwartz and Kikuko Omori
Abstract: Homesickness, a distinctly human phenomenon, is common among college students, domestic or international and is the focus of this research. In this study, we focused on international students in American institutions to better understand the relationship among homesickness, acculturation, and social media use. Through focus group interviews, international students shared their lived experiences of homesickness, use of social media, and acculturation. Four themes (i.e.1. Social media as conflict, 2. Social media as distraction, 3. Social media as frenemy, and 4. Social media as functional) surfaced in the data to describe the relationships among social media use, homesickness, and acculturation. Our participants used social media mainly to communicate with people back home when they felt homesick, yet the use of social media did not help their homesickness. The results are discussed through the lens of communities of practice. The researchers offer practical implications for institutions and people directly involved with international and study-abroad education programs.
Published: 2024

2. Attend First, Consolidate Later: On the Importance of Attention in Different LLM Layers

Author: Artzy, Amit Ben and Schwartz, Roy
Subjects: Computer Science - Computation and Language
Abstract: In decoder-based LLMs, the representation of a given layer serves two purposes: as input to the next layer during the computation of the current token; and as input to the attention mechanism of future tokens. In this work, we show that the importance of the latter role might be overestimated. To show that, we start by manipulating the representations of previous tokens; e.g. by replacing the hidden states at some layer k with random vectors. Our experimenting with four LLMs and four tasks show that this operation often leads to small to negligible drop in performance. Importantly, this happens if the manipulation occurs in the top part of the model-k is in the final 30-50% of the layers. In contrast, doing the same manipulation in earlier layers might lead to chance level performance. We continue by switching the hidden state of certain tokens with hidden states of other tokens from another prompt; e.g., replacing the word "Italy" with "France" in "What is the capital of Italy?". We find that when applying this switch in the top 1/3 of the model, the model ignores it (answering "Rome"). However if we apply it before, the model conforms to the switch ("Paris"). Our results hint at a two stage process in transformer-based LLMs: the first part gathers input from previous tokens, while the second mainly processes that information internally.
Published: 2024

3. LIGO Detector Characterization in the first half of the fourth Observing run

Author: Soni, S., Berger, B. K., Davis, D., Renzo, F. Di., Effler, A., Ferreira, T. A., Glanzer, J., Goetz, E., González, G., Helmling-Cornell, A., Hughey, B., Huxford, R., Mannix, B., Mo, G., Nandi, D., Neunzert, A., Nichols, S., Pham, K., Renzini, A. I., Schofield, R. M. S., Stuver, A, Trevor, M., Álvarez-López, S., Beda, R., Berry, C. P. L., Bhuiyan, S., Bruntz, R., Christensen, N., Blagg, L., Chan, M., Charlton, P., Connolly, G., Dhatri, R., Ding, J., Garg, V., Holley-Bockelmann, K., Hourihane, S., Jani, K., Janssens, K., Jarov, S., Knee, A. M., Lattal, A., Lecoeuche, Y., Littenberg, T., Liyanage, A., Lott, B., Macas, R., Malakar, D., McGowan, K., McIver, J., Millhouse, M., Nuttall, L., Nykamp, D., Ota, I., Rawcliffe, C., Scully, B., Tasson, J., Tejera, A., Thiele, S., Udall, R., Winborn, C., Yarbrough, Z., Zhang, Z., Abbott, R., Abouelfettouh, I., Adhikari, R. X., Ananyeva, A., Appert, S., Arai, K., Aritomi, N., Aston, S. M., Ball, M., Ballmer, S. W., Barker, D., Barsotti, L., Betzwieser, J., Billingsley, G., Biscans, S., Bode, N., Bonilla, E., Bossilkov, V., Branch, A., Brooks, A. F., Brown, D. D., Bryant, J., Cahillane, C., Cao, H., Capote, E., Clara, F., Collins, J., Compton, C. M., Cottingham, R., Coyne, D. C., Crouch, R., Csizmazia, J., Cullen, T. J., Dartez, L. P., Demos, N., Dohmen, E., Driggers, J. C., Dwyer, S. E., Ejlli, A., Etzel, T., Evans, M., Feicht, J., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fulda, P., Fyffe, M., Ganapathy, D., Gateley, B., Giaime, J. A., Giardina, K. D., Goetz, R., Goodwin-Jones, A. W., Gras, S., Gray, C., Griffith, D., Grote, H., Guidry, T., Hall, E. D., Hanks, J., Hanson, J., Heintze, M. C., Holland, N. A., Hoyland, D., Huang, H. Y., Inoue, Y., James, A. L., Jennings, A., Jia, W., Karat, S., Karki, S., Kasprzack, M., Kawabe, K., Kijbunchoo, N., King, P. J., Kissel, J. S., Komori, K., Kontos, A., Kumar, Rahul, Kuns, K., Landry, M., Lantz, B., Laxen, M., Lee, K., Lesovsky, M., Llamas, F., Lormand, M., Loughlin, H. A., MacInnis, M., Makarem, C. N., Mansell, G. L., Martin, R. M., Mason, K., Matichard, F., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McRae, T., Mera, F., Merilh, E. L., Meylahn, F., Mittleman, R., Moraru, D., Moreno, G., Mullavey, A., Nakano, M., Nelson, T. J. N., Notte, J., Oberling, J., O'Hanlon, T., Osthelder, C., Ottaway, D. J., Overmier, H., Parker, W., Pele, A., Pham, H., Pirello, M., Quetschke, V., Ramirez, K. E., Reyes, J., Richardson, J. W., Robinson, M., Rollins, J. G., Romel, C. L., Romie, J. H., Ross, M. P., Ryan, K., Sadecki, T., Sanchez, A., Sanchez, E. J., Sanchez, L. E., Savage, R. L., Schaetzl, D., Schiworski, M. G., Schnabel, R., Schwartz, E., Sellers, D., Shaffer, T., Short, R. W., Sigg, D., Slagmolen, B. J. J., Soike, C., Srivastava, V., Sun, L., Tanner, D. B., Thomas, M., Thomas, P., Thorne, K. A., Torrie, C. I., Traylor, G., Ubhi, A. S., Vajente, G., Vanosky, J., Vecchio, A., Veitch, P. J., Vibhute, A. M., von Reis, E. R. G., Warner, J., Weaver, B., Weiss, R., Whittle, C., Willke, B., Wipf, C. C., Xu, V. A., Yamamoto, H., Zhang, L., and Zucker, M. E.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, General Relativity and Quantum Cosmology
Abstract: Progress in gravitational-wave astronomy depends upon having sensitive detectors with good data quality. Since the end of the LIGO-Virgo-KAGRA third Observing run in March 2020, detector-characterization efforts have lead to increased sensitivity of the detectors, swifter validation of gravitational-wave candidates and improved tools used for data-quality products. In this article, we discuss these efforts in detail and their impact on our ability to detect and study gravitational-waves. These include the multiple instrumental investigations that led to reduction in transient noise, along with the work to improve software tools used to examine the detectors data-quality. We end with a brief discussion on the role and requirements of detector characterization as the sensitivity of our detectors further improves in the future Observing runs., Comment: 35 pages, 18 figures
Published: 2024

4. Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven Approach

Author: Nascimento, Valfride, Laroca, Rayson, Ribeiro, Rafael O., Schwartz, William Robson, and Menotti, David
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Despite significant advancements in License Plate Recognition (LPR) through deep learning, most improvements rely on high-resolution images with clear characters. This scenario does not reflect real-world conditions where traffic surveillance often captures low-resolution and blurry images. Under these conditions, characters tend to blend with the background or neighboring characters, making accurate LPR challenging. To address this issue, we introduce a novel loss function, Layout and Character Oriented Focal Loss (LCOFL), which considers factors such as resolution, texture, and structural details, as well as the performance of the LPR task itself. We enhance character feature learning using deformable convolutions and shared weights in an attention module and employ a GAN-based training approach with an Optical Character Recognition (OCR) model as the discriminator to guide the super-resolution process. Our experimental results show significant improvements in character reconstruction quality, outperforming two state-of-the-art methods in both quantitative and qualitative measures. Our code is publicly available at https://github.com/valfride/lpsr-lacd, Comment: Accepted for presentation at the Conference on Graphics, Patterns and Images (SIBGRAPI) 2024
Published: 2024

5. Leveraging external data in the analysis of randomized controlled trials: a comparative analysis

Author: Kotecha, Gopal, Schwartz, Daniel E., Ventz, Steffen, and Trippa, Lorenzo
Subjects: Statistics - Methodology
Abstract: The use of patient-level information from previous studies, registries, and other external datasets can support the analysis of single-arm and randomized clinical trials to evaluate and test experimental treatments. However, the integration of external data in the analysis of clinical trials can also compromise the scientific validity of the results due to selection bias, study-to-study differences, unmeasured confounding, and other distortion mechanisms. Therefore, leveraging external data in the analysis of a clinical trial requires the use of appropriate methods that can detect, prevent or mitigate the risks of bias and potential distortion mechanisms. We review several methods that have been previously proposed to leverage external datasets, such as matching procedures or random effects modeling. Different methods present distinct trade-offs between risks and efficiencies. We conduct a comparative analysis of statistical methods to leverage external data and analyze randomized clinical trials. Multiple operating characteristics are discussed, such as the control of false positive results, power, and the bias of the treatment effect estimates, across candidate statistical methods. We compare the statistical methods through a broad set of simulation scenarios. We then compare the methods using a collection of datasets with individual patient-level information from several glioblastoma studies in order to provide recommendations for future glioblastoma trials.
Published: 2024

6. Jamba-1.5: Hybrid Transformer-Mamba Models at Scale

Author: Jamba Team, Lenz, Barak, Arazi, Alan, Bergman, Amir, Manevich, Avshalom, Peleg, Barak, Aviram, Ben, Almagor, Chen, Fridman, Clara, Padnos, Dan, Gissin, Daniel, Jannai, Daniel, Muhlgay, Dor, Zimberg, Dor, Gerber, Edden M, Dolev, Elad, Krakovsky, Eran, Safahi, Erez, Schwartz, Erez, Cohen, Gal, Shachaf, Gal, Rozenblum, Haim, Bata, Hofit, Blass, Ido, Magar, Inbal, Dalmedigos, Itay, Osin, Jhonathan, Fadlon, Julie, Rozman, Maria, Danos, Matan, Gokhman, Michael, Zusman, Mor, Gidron, Naama, Ratner, Nir, Gat, Noam, Rozen, Noam, Fried, Oded, Leshno, Ohad, Antverg, Omer, Abend, Omri, Lieber, Opher, Dagan, Or, Cohavi, Orit, Alon, Raz, Belson, Ro'i, Cohen, Roi, Gilad, Rom, Glozman, Roman, Lev, Shahar, Meirom, Shaked, Delbari, Tal, Ness, Tal, Asida, Tomer, Gal, Tom Ben, Braude, Tom, Pumerantz, Uriya, Cohen, Yehoshua, Belinkov, Yonatan, Globerson, Yuval, Levy, Yuval Peleg, and Shoham, Yoav
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: We present Jamba-1.5, new instruction-tuned large language models based on our Jamba architecture. Jamba is a hybrid Transformer-Mamba mixture of experts architecture, providing high throughput and low memory usage across context lengths, while retaining the same or better quality as Transformer models. We release two model sizes: Jamba-1.5-Large, with 94B active parameters, and Jamba-1.5-Mini, with 12B active parameters. Both models are fine-tuned for a variety of conversational and instruction-following capabilties, and have an effective context length of 256K tokens, the largest amongst open-weight models. To support cost-effective inference, we introduce ExpertsInt8, a novel quantization technique that allows fitting Jamba-1.5-Large on a machine with 8 80GB GPUs when processing 256K-token contexts without loss of quality. When evaluated on a battery of academic and chatbot benchmarks, Jamba-1.5 models achieve excellent results while providing high throughput and outperforming other open-weight models on long-context benchmarks. The model weights for both sizes are publicly available under the Jamba Open Model License and we release ExpertsInt8 as open source., Comment: Webpage: https://www.ai21.com/jamba
Published: 2024

7. Iterative Object Count Optimization for Text-to-image Diffusion Models

Author: Zafar, Oz, Wolf, Lior, and Schwartz, Idan
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Graphics, Computer Science - Machine Learning
Abstract: We address a persistent challenge in text-to-image models: accurately generating a specified number of objects. Current models, which learn from image-text pairs, inherently struggle with counting, as training data cannot depict every possible number of objects for any given object. To solve this, we propose optimizing the generated image based on a counting loss derived from a counting model that aggregates an object\'s potential. Employing an out-of-the-box counting model is challenging for two reasons: first, the model requires a scaling hyperparameter for the potential aggregation that varies depending on the viewpoint of the objects, and second, classifier guidance techniques require modified models that operate on noisy intermediate diffusion steps. To address these challenges, we propose an iterated online training mode that improves the accuracy of inferred images while altering the text conditioning embedding and dynamically adjusting hyperparameters. Our method offers three key advantages: (i) it can consider non-derivable counting techniques based on detection models, (ii) it is a zero-shot plug-and-play solution facilitating rapid changes to the counting techniques and image generation methods, and (iii) the optimized counting token can be reused to generate accurate images without additional optimization. We evaluate the generation of various objects and show significant improvements in accuracy. The project page is available at https://ozzafar.github.io/count_token., Comment: Pre-print
Published: 2024

8. Retina-inspired Object Motion Segmentation

Author: Clerico, Victoria, Snyder, Shay, Lohia, Arya, Kaiser, Md Abdullah-Al, Schwartz, Gregory, Jaiswal, Akhilesh, and Parsa, Maryam
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Dynamic Vision Sensors (DVS) have emerged as a revolutionary technology with a high temporal resolution that far surpasses RGB cameras. DVS technology draws biological inspiration from photoreceptors and the initial retinal synapse. Our research showcases the potential of additional retinal functionalities to extract visual features. We provide a domain-agnostic and efficient algorithm for ego-motion compensation based on Object Motion Sensitivity (OMS), one of the multiple robust features computed within the mammalian retina. We develop a framework based on experimental neuroscience that translates OMS' biological circuitry to a low-overhead algorithm. OMS processes DVS data from dynamic scenes to perform pixel-wise object motion segmentation. Using a real and a synthetic dataset, we highlight OMS' ability to differentiate object motion from ego-motion, bypassing the need for deep networks. This paper introduces a bio-inspired computer vision method that dramatically reduces the number of parameters by a factor of 1000 compared to prior works. Our work paves the way for robust, high-speed, and low-bandwidth decision-making for in-sensor computations.
Published: 2024

9. On the Asymptotic Rate of Optimal Codes that Correct Tandem Duplications for Nanopore Sequencing

Author: Yu, Wenjun, Ye, Zuo, and Schwartz, Moshe
Subjects: Computer Science - Information Theory
Abstract: We study codes that can correct backtracking errors during nanopore sequencing. In this channel, a sequence of length $n$ over an alphabet of size $q$ is being read by a sliding window of length $\ell$, where from each window we obtain only its composition. Backtracking errors cause some windows to repeat, hence manifesting as tandem-duplication errors of length $k$ in the $\ell$-read vector of window compositions. While existing constructions for duplication-correcting codes can be straightforwardly adapted to this model, even resulting in optimal codes, their asymptotic rate is hard to find. In the regime of unbounded number of duplication errors, we either give the exact asymptotic rate of optimal codes, or bounds on it, depending on the values of $k$, $\ell$ and $q$. In the regime of a constant number of duplication errors, $t$, we find the redundancy of optimal codes to be $t\log_q n+O(1)$ when $\ell|k$, and only upper bounded by this quantity otherwise.
Published: 2024

10. PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasound

Author: Li, Hao, Oguz, Baris, Arenas, Gabriel, Yao, Xing, Wang, Jiacheng, Pouch, Alison, Byram, Brett, Schwartz, Nadav, and Oguz, Ipek
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Placenta volume measured from 3D ultrasound (3DUS) images is an important tool for tracking the growth trajectory and is associated with pregnancy outcomes. Manual segmentation is the gold standard, but it is time-consuming and subjective. Although fully automated deep learning algorithms perform well, they do not always yield high-quality results for each case. Interactive segmentation models could address this issue. However, there is limited work on interactive segmentation models for the placenta. Despite their segmentation accuracy, these methods may not be feasible for clinical use as they require relatively large computational power which may be especially prohibitive in low-resource environments, or on mobile devices. In this paper, we propose a lightweight interactive segmentation model aiming for clinical use to interactively segment the placenta from 3DUS images in real-time. The proposed model adopts the segmentation from our fully automated model for initialization and is designed in a human-in-the-loop manner to achieve iterative improvements. The Dice score and normalized surface Dice are used as evaluation metrics. The results show that our model can achieve superior performance in segmentation compared to state-of-the-art models while using significantly fewer parameters. Additionally, the proposed model is much faster for inference and robust to poor initial masks. The code is available at https://github.com/MedICL-VU/PRISM-placenta.
Published: 2024

11. Deterministic remote entanglement using a chiral quantum interconnect

Author: Almanakly, Aziza, Yankelevich, Beatriz, Hays, Max, Kannan, Bharath, Assouly, Reouven, Greene, Alex, Gingras, Michael, Niedzielski, Bethany M., Stickler, Hannah, Schwartz, Mollie E., Serniak, Kyle, Wang, Joel I-J., Orlando, Terry P., Gustavsson, Simon, Grover, Jeffrey A., and Oliver, William D.
Subjects: Quantum Physics
Abstract: Quantum interconnects facilitate entanglement distribution between non-local computational nodes. For superconducting processors, microwave photons are a natural means to mediate this distribution. However, many existing architectures limit node connectivity and directionality. In this work, we construct a chiral quantum interconnect between two nominally identical modules in separate microwave packages. We leverage quantum interference to emit and absorb microwave photons on demand and in a chosen direction between these modules. We optimize the protocol using model-free reinforcement learning to maximize absorption efficiency. By halting the emission process halfway through its duration, we generate remote entanglement between modules in the form of a four-qubit W state with 62.4 +/- 1.6% (leftward photon propagation) and 62.1 +/- 1.2% (rightward) fidelity, limited mainly by propagation loss. This quantum network architecture enables all-to-all connectivity between non-local processors for modular and extensible quantum computation., Comment: 25 pages, 9 figures, 5 tables
Published: 2024

12. Learning the Simplicity of Scattering Amplitudes

Author: Cheung, Clifford, Dersy, Aurélien, and Schwartz, Matthew D.
Subjects: High Energy Physics - Theory, Computer Science - Machine Learning, High Energy Physics - Phenomenology
Abstract: The simplification and reorganization of complex expressions lies at the core of scientific progress, particularly in theoretical high-energy physics. This work explores the application of machine learning to a particular facet of this challenge: the task of simplifying scattering amplitudes expressed in terms of spinor-helicity variables. We demonstrate that an encoder-decoder transformer architecture achieves impressive simplification capabilities for expressions composed of handfuls of terms. Lengthier expressions are implemented in an additional embedding network, trained using contrastive learning, which isolates subexpressions that are more likely to simplify. The resulting framework is capable of reducing expressions with hundreds of terms - a regular occurrence in quantum field theory calculations - to vastly simpler equivalent expressions. Starting from lengthy input expressions, our networks can generate the Parke-Taylor formula for five-point gluon scattering, as well as new compact expressions for five-point amplitudes involving scalars and gravitons. An interactive demonstration can be found at https://spinorhelicity.streamlit.app ., Comment: 25+15 pages, 9+6 figures
Published: 2024

13. Hardware-Algorithm Re-engineering of Retinal Circuit for Intelligent Object Motion Segmentation

Author: Sinaga, Jason, Clerico, Victoria, Kaiser, Md Abdullah-Al, Snyder, Shay, Lohia, Arya, Schwartz, Gregory, Parsa, Maryam, and Jaiswal, Akhilesh
Subjects: Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Recent advances in retinal neuroscience have fueled various hardware and algorithmic efforts to develop retina-inspired solutions for computer vision tasks. In this work, we focus on a fundamental visual feature within the mammalian retina, Object Motion Sensitivity (OMS). Using DVS data from EV-IMO dataset, we analyze the performance of an algorithmic implementation of OMS circuitry for motion segmentation in presence of ego-motion. This holistic analysis considers the underlying constraints arising from the hardware circuit implementation. We present novel CMOS circuits that implement OMS functionality inside image sensors, while providing run-time re-configurability for key algorithmic parameters. In-sensor technologies for dynamical environment adaptation are crucial for ensuring high system performance. Finally, we verify the functionality and re-configurability of the proposed CMOS circuit designs through Cadence simulations in 180nm technology. In summary, the presented work lays foundation for hardware-algorithm re-engineering of known biological circuits to suit application needs.
Published: 2024

14. Universal New Physics Latent Space

Author: Hallin, Anna, Kasieczka, Gregor, Kraml, Sabine, Lessa, André, Moureaux, Louis, von Schwartz, Tore, and Shih, David
Subjects: High Energy Physics - Phenomenology, Computer Science - Machine Learning, High Energy Physics - Experiment, Physics - Data Analysis, Statistics and Probability
Abstract: We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space while conserving information about the relationship between the underlying theories. We apply our method to three examples of new physics at the LHC of increasing complexity, showing that models can be clustered according to their LHC phenomenology: different models are mapped to distinct regions in latent space, while indistinguishable models are mapped to the same region. This opens interesting new avenues on several fronts, such as model discrimination, selection of representative benchmark scenarios, and identifying gaps in the coverage of model space., Comment: 25 pages, 17 figures
Published: 2024

15. Galois scaffolds for extraspecial p-extensions in characteristic 0

Author: Keating, Kevin and Schwartz, Paul
Subjects: Mathematics - Number Theory
Abstract: Let $K$ be a local field of characteristic 0 with residue characteristic $p$. Let $G$ be an extraspecial $p$-group and let $L/K$ be a totally ramified $G$-extension. In this paper we find sufficient conditions for $L/K$ to admit a Galois scaffold. This leads to sufficient conditions for the ring of integers $\mathfrak{O}_L$ to be free of rank 1 over its associated order $\mathfrak{A}_{L/K}$, and to stricter conditions which imply that $\mathfrak{A}_{L/K}$ is a Hopf order in the group ring $K[G]$.
Published: 2024

16. IDA: Breaking Barriers in No-code UI Automation Through Large Language Models and Human-Centric Design

Author: Shlomov, Segev, Yaeli, Avi, Marreed, Sami, Schwartz, Sivan, Eder, Netanel, Akrabi, Offer, and Zeltyn, Sergey
Subjects: Computer Science - Human-Computer Interaction, 68T01
Abstract: Business users dedicate significant amounts of time to repetitive tasks within enterprise digital platforms, highlighting a critical need for automation. Despite advancements in low-code tools for UI automation, their complexity remains a significant barrier to adoption among non-technical business users. However, recent advancements in large language models (LLMs) have created new opportunities to overcome this barrier by offering more powerful, yet simpler and more human-centric programming environments. This paper presents IDA (Intelligent Digital Apprentice), a novel no-code Web UI automation tool designed specifically to empower business users with no technical background. IDA incorporates human-centric design principles, including guided programming by demonstration, semantic programming model, and teacher-student learning metaphor which is tailored to the skill set of business users. By leveraging LLMs, IDA overcomes some of the key technical barriers that have traditionally limited the possibility of no-code solutions. We have developed a prototype of IDA and conducted a user study involving real world business users and enterprise applications. The promising results indicate that users could effectively utilize IDA to create automation. The qualitative feedback indicates that IDA is perceived as user-friendly and trustworthy. This study contributes to unlocking the potential of AI assistants to enhance the productivity of business users through no-code user interface automation.
Published: 2024

17. Universal Facial Encoding of Codec Avatars from VR Headsets

Author: Bai, Shaojie, Wang, Te-Li, Li, Chenghui, Venkatesh, Akshay, Simon, Tomas, Cao, Chen, Schwartz, Gabriel, Wrench, Ryan, Saragih, Jason, Sheikh, Yaser, and Wei, Shih-En
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Faithful real-time facial animation is essential for avatar-mediated telepresence in Virtual Reality (VR). To emulate authentic communication, avatar animation needs to be efficient and accurate: able to capture both extreme and subtle expressions within a few milliseconds to sustain the rhythm of natural conversations. The oblique and incomplete views of the face, variability in the donning of headsets, and illumination variation due to the environment are some of the unique challenges in generalization to unseen faces. In this paper, we present a method that can animate a photorealistic avatar in realtime from head-mounted cameras (HMCs) on a consumer VR headset. We present a self-supervised learning approach, based on a cross-view reconstruction objective, that enables generalization to unseen users. We present a lightweight expression calibration mechanism that increases accuracy with minimal additional cost to run-time efficiency. We present an improved parameterization for precise ground-truth generation that provides robustness to environmental variation. The resulting system produces accurate facial animation for unseen users wearing VR headsets in realtime. We compare our approach to prior face-encoding methods demonstrating significant improvements in both quantitative metrics and qualitative results., Comment: SIGGRAPH 2024 (ACM Transactions on Graphics (TOG))
Published: 2024
Full Text: View/download PDF

18. Effect of spin-dependent tunneling in a MoSe$_2$/Cr$_2$Ge$_2$Te$_6$ van der Waals heterostructure on exciton and trion emission

Author: Bergmann, Annika, Deb, Swarup, Schneidt, Veronika, Hemaid, Mustafa, Watanabe, Kenji, Taniguchi, Takashi, Schwartz, Rico, and Korn, Tobias
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract: We study van der Waals heterostructures consisting of monolayer MoSe$_2$ and few-layer Cr$_2$Ge$_2$Te$_6$ fully encapsulated in hexagonal Boron Nitride using low-temperature photoluminescence and polar magneto-optic Kerr effect measurements. Photoluminescence characterization reveals a partial quenching and a change of the exciton-trion emission ratio in the heterostructure as compared to the isolated MoSe$_2$ monolayer. Under circularly polarized excitation, we find that the exciton-trion emission ratio depends on the relative orientation of excitation helicity and Cr$_2$Ge$_2$Te$_6$ magnetization, even though the photoluminescence emission itself is unpolarized. This observation hints at an ultrafast, spin-dependent interlayer charge transfer that competes with exciton and trion formation and recombination.
Published: 2024

19. A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m Predictions per Second

Author: Škrlj, Blaž, Ben-Shalom, Benjamin, Gašperšič, Grega, Schwartz, Adi, Hoseisi, Ramzi, Ziporin, Naama, Kopič, Davorin, and Tori, Andraž
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract: Field-aware Factorization Machines (FFMs) have emerged as a powerful model for click-through rate prediction, particularly excelling in capturing complex feature interactions. In this work, we present an in-depth analysis of our in-house, Rust-based Deep FFM implementation, and detail its deployment on a CPU-only, multi-data-center scale. We overview key optimizations devised for both training and inference, demonstrated by previously unpublished benchmark results in efficient model search and online training. Further, we detail an in-house weight quantization that resulted in more than an order of magnitude reduction in bandwidth footprint related to weight transfers across data-centres. We disclose the engine and associated techniques under an open-source license to contribute to the broader machine learning community. This paper showcases one of the first successful CPU-only deployments of Deep FFMs at such scale, marking a significant stride in practical, low-footprint click-through rate prediction methodologies., Comment: 6p, KDD2024 - AdKDD workshop
Published: 2024

20. Swift-BAT GUANO follow-up of gravitational-wave triggers in the third LIGO-Virgo-KAGRA observing run

Author: Raman, Gayathri, Ronchini, Samuele, Delaunay, James, Tohuvavohu, Aaron, Kennea, Jamie A., Parsotan, Tyler, Ambrosi, Elena, Bernardini, Maria Grazia, Campana, Sergio, Cusumano, Giancarlo, D'Ai, Antonino, D'Avanzo, Paolo, D'Elia, Valerio, De Pasquale, Massimiliano, Dichiara, Simone, Evans, Phil, Hartmann, Dieter, Kuin, Paul, Melandri, Andrea, O'Brien, Paul, Osborne, Julian P., Page, Kim, Palmer, David M., Sbarufatti, Boris, Tagliaferri, Gianpiero, Troja, Eleonora, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., 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., Anand, S., 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., 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., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., 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., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., 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., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Bogaert, G., 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., Boumerdassi, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., 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., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., 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., Castaldi, G., 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, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, R. -J., Chanial, P., Chao, S., Chapman-Bird, C., 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, K. H., Chen, X., Chen, Yi-Ru, Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Chia, H. Y., Chiadini, F., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., 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., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crouch, R., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., 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., Daw, E. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., 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., De Simone, R., Dhani, A., Dhurandhar, S., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, F., 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., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., 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., Emma, M., Engelby, E., Engl, A. J., 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., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Ferrante, I., Ferreira, T. A., Fidecaro, F., 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., Fukunaga, I., Fulda, P., Fyffe, M., Gabella, W. E., Gadre, B., Gair, J. R., Galaudage, S., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Gaonkar, S. G., Garaventa, B., Garcia-Bellido, J., García-Núñez, C., 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., George, J., George, R., Gerberding, O., Gergely, L., Ghadiri, N., Ghosh, Archisman, 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., Gleckl, A. E., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., González, G., Goodarzi, P., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Argianas, L. Granda, Gras, S., Grassia, P., 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., Gruson, A. S., 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., Gurav, R., Gurs, J., Gutierrez, N., Guzman, F., Haba, D., Haberland, M., Haegel, L., Hain, G., 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., Harder, T., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Healy, J., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J. -S., Hennig, M., Henshaw, C., Hernandez, A., Hertog, T., Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Hill, S., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, J., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Hollows, I. J., Holmes, Z. J., Holz, D. E., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hoyland, D., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, S. -C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huang, Y., Huang, Y. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Hur, R., Husa, S., Huxford, R., Huynh-Dinh, T., Iakovlev, A., Iandolo, G. A., Iess, A., Inayoshi, K., Inoue, Y., Iorio, G., Irwin, J., Isi, M., Ismail, M. A., Itoh, Y., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Jan, A. Z., Jani, K., Janiurek, L., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jasal, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Jin, H. -B., Johansmeyer, K., Johns, G. R., Johnson, N. A., 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., 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., Karki, S., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, J., Kato, T., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaur, T., Kaushik, R., Kawabe, K., Keitel, D., Kelley-Derzon, J., Kennington, J., Kesharwani, R., Key, J. S., Khadka, S., Khalili, F. Y., Khan, F., Khan, I., Khanam, T., Khazanov, E. A., Khursheed, M., Kiendrebeogo, W., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, S., Kim, W. S., Kim, Y. -M., Kimball, C., Kimura, N., Kinley-Hanlon, M., Kinnear, M., Kissel, J. S., Kiyota, T., Klimenko, S., Klinger, T., Knee, A. M., Knust, N., 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., Koyama, N., Kozak, D. B., Kranzhoff, S. L., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kuijer, P., Kulkarni, S., Ramamohan, A. Kulur, Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuroyanagi, S., Kuwahara, S., Kwak, K., Kwan, K., Lacaille, G., Lagabbe, P., Laghi, D., Lai, S., Laity, A. H., Lakkis, M. H., Lalande, E., Lalleman, M., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rana, A., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Lawrence, J., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., LeBohec, S., Lecoeuche, Y. K., Lee, H. M., Lee, H. W., Lee, K., Lee, R. -K., Lee, R., Lee, S., Lee, Y., Legred, I. N., Lehmann, J., Lehner, L., Lemaître, A., Lenti, M., Leonardi, M., Leonova, E., Lequime, M., Leroy, N., Lesovsky, M., Letendre, N., Lethuillier, M., Levesque, C., Levin, Y., Leyde, K., Li, A. K. Y., Li, K. L., Li, T. G. F., Li, X., Lin, Chien-Yu, Lin, Chun-Yu, Lin, E. T., Lin, F., Lin, H., Lin, L. C. -C., Linde, F., Linker, S. D., Littenberg, T. B., Liu, A., Liu, G. C., Liu, Jian, Llamas, F., Llobera-Querol, J., Lo, R. K. L., Locquet, J. -P., London, L., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lott IV, T. P., Lough, J. D., Loughlin, H. A., Lousto, C. O., Lowry, M. J., Lück, H., Lumaca, D., Lundgren, A. P., Lussier, A. W., Ma, L. -T., Ma, S., Ma'arif, M., Macas, R., MacInnis, M., Maciy, R. R., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Macri, D., Maeda, K., Maenaut, S., Hernandez, I. Magaña, Magare, S. S., Magazzù, C., Magee, R. M., Maggio, E., Maggiore, R., Magnozzi, M., Mahesh, M., Mahesh, S., Maini, M., Majhi, S., Majorana, E., Makarem, C. N., Malaquias-Reis, J. A., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mannix, B., Mansell, G. L., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, B. B., Martinez, M., Martinez, V., Martini, A., Martinovic, K., Martins, J. C., Martynov, D. V., Marx, E. J., Massaro, L., Masserot, A., Masso-Reid, M., Mastrodicasa, M., Mastrogiovanni, S., Mateu-Lucena, M., Matiushechkina, M., Matsuyama, M., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McGhee, G. I., McGowan, K. B. M., Mchedlidze, M., McIsaac, C., McIver, J., McKinney, K., McLeod, A., McRae, T., McWilliams, S. T., Meacher, D., Mehta, A. K., Meijer, Q., Melatos, A., Mellaerts, S., Menendez-Vazquez, A., Menoni, C. S., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Mérou, J. R., Merritt, J. D., Merzougui, M., Messenger, C., Messick, C., Meyer-Conde, M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Miller, A. L., Miller, S., Millhouse, M., Milotti, E., Minenkov, Y., Mio, N., Mir, Ll. M., Mirasola, L., Miravet-Tenés, M., Miritescu, C. -A., Mishra, A. K., Mishra, A., Mishra, C., Mishra, T., Mitchell, A. L., Mitchell, J. G., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Miyakawa, O., Miyamoto, S., Miyoki, S., Mo, G., Mobilia, L., Modafferi, L. M., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. J., Morales, M., Moraru, D., Morawski, F., More, A., More, S., Moreno, C., Moreno, G., Morisaki, S., Moriwaki, Y., Morras, G., Moscatello, A., Mourier, P., Mours, B., Mow-Lowry, C. M., Mozzon, S., Muciaccia, F., Mukherjee, D., Mukherjee, Samanwaya, Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Mungioli, C. L., Munn, M., Oberg, W. R. Munn, Murakoshi, M., Murray, P. G., Muusse, S., Nadji, S. L., Nagar, A., Nagarajan, N., Nagler, K. N., Nakamura, K., Nakano, H., Nakano, M., Nandi, D., Napolano, V., Narayan, P., Nardecchia, I., Narola, H., Naticchioni, L., Nayak, R. K., Neil, B. F., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Neunzert, A., Ng, S., Nguyen, C., Nguyen, P., Quynh, L. Nguyen, Nichols, S. A., Nielsen, A. B., Nieradka, G., Niko, A., Nishino, Y., Nishizawa, A., Nissanke, S., Nitoglia, E., Niu, W., Nocera, F., Norman, M., North, C., Novak, J., Siles, J. F. Nuño, Nurbek, G., Nuttall, L. K., Obayashi, K., Oberling, J., O'Dell, J., Oertel, M., Offermans, A., Oganesyan, G., Oh, J. J., Oh, K., Oh, S. H., O'Hanlon, T., Ohashi, M., Ohkawa, M., Ohme, F., Ohta, H., Oliveira, A. S., Oliveri, R., Oloworaran, V., O'Neal, B., Oohara, K., O'Reilly, B., Ormsby, N. D., Orselli, M., O'Shaughnessy, R., Oshima, Y., Oshino, S., Ossokine, S., Osthelder, C., Ottaway, D. J., Ouzriat, A., Overmier, H., Owen, B. J., Pace, A. E., Pagano, R., Page, M. A., Pai, A., Pai, S. A., Pal, A., Pal, S., Palaia, M. A., Palashov, O., Pálfi, M., Palma, P. P., Palomba, C., Pan, K. C., Panda, P. K., Panebianco, L., Pang, P. T. H., Pannarale, F., Pant, B. C., Panther, F. H., Panzer, C. D., Paoletti, F., Paoli, A., Paolone, A., Papalexakis, E. E., Papalini, L., Papigkiotis, G., Parisi, A., Park, J., Parker, W., Pascale, G., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patane, O., Patel, M., Pathak, D., Pathak, M., Patra, A., Patricelli, B., Patron, A. S., Paul, S., Payne, E., Pearce, T., Pedraza, M., Pegna, R., Pele, A., Arellano, F. E. Peña, Penn, S., Penuliar, M. D., Perego, A., Pereira, A., Perez, J. J., Périgois, C., Perkins, C. C., Perna, G., Perreca, A., Perret, J., Perriès, S., Perry, J. W., Pesios, D., Petrillo, C., Pfeiffer, H. P., Pham, H., Pham, K. A., Phukon, K. S., Phurailatpam, H., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierra, G., Pierro, V., Pietrzak, M., Pillas, M., Pilo, F., Pinard, L., Pineda-Bosque, C., Pinto, I. M., Pinto, M., Piotrzkowski, B. J., Pirello, M., Pitkin, M. D., Placidi, A., Placidi, E., Planas, M. L., Plastino, W., Poggiani, R., Polini, E., Pompili, L., Poon, J., Porcelli, E., Portell, J., Porter, E. K., Posnansky, C., Poulton, R., Powell, J., Pracchia, M., Pradhan, B. K., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Prasia, P., Pratten, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Prudenzi, L., Puecher, A., Pullin, J., Punturo, M., Puosi, F., Puppo, P., Pürrer, M., Qi, H., Qin, J., Quéméner, G., Quetschke, V., Quigley, C., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raaijmakers, G., Radulesco, N., Raffai, P., Rail, S. X., Raja, S., Rajan, C., Rajbhandari, B., Ramirez, D. S., Ramirez, K. E., Vidal, F. A. Ramis, Ramos-Buades, A., Rana, D., Randel, E., Ranjan, S., Rapagnani, P., Ratto, B., Rawat, S., Ray, A., Raymond, V., Razzano, M., Read, J., Payo, M. Recaman, Regimbau, T., Rei, L., Reid, S., Reid, S. W., Reitze, D. H., Relton, P., Renzini, A., Rettegno, P., Revenu, B., Reza, A., Rezac, M., Rezaei, A. S., Ricci, F., Ricci, M., Richards, D., Richardson, C. J., Richardson, J. W., Rijal, A., Riles, K., Riley, H. K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romanelli, M., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S. K., Roy, S., Rozza, D., Ruggi, P., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Sah, M. R., Saha, S. S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sako, T., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Saravanan, T. R., Sarin, N., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, S., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. L., Sayah, S., Schaetzl, D., Scheel, M., Scheuer, J., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schouteden, K., Schuler, H., Schulte, B. W., Schutz, B. F., Schwartz, E., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Sergeev, A., Serra, M., Servignat, G., Setyawati, Y., Shaffer, T., Shah, U. S., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shawhan, P., Shcheblanov, N. S., Shen, B., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somala, S. N., Somiya, K., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Southgate, A., Sowell, E., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Sullivan, A. G., Sullivan, K. D., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takatani, K., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tanasijczuk, A. J., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., 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., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., 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., Veske, D., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, D., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, L. -C., Yang, Y., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables
Published: 2024

21. Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review

Author: Ghasemi, Amirehsan, Hashtarkhani, Soheil, Schwartz, David L, and Shaban-Nejad, Arash
Subjects: Quantitative Biology - Quantitative Methods, Electrical Engineering and Systems Science - Image and Video Processing, 68T01
Abstract: With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is not trustworthy for clinicians and is considered a black-box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer-reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model-agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP model primarily explained tree-based ensemble machine learning models. The most common reason is that SHAP is model agnostic, which makes it both popular and useful for explaining any model prediction. Additionally, it is relatively easy to implement effectively and completely suits performant models, such as tree-based models. Explainable AI improves the transparency, interpretability, fairness, and trustworthiness of AI-enabled health systems and medical devices and, ultimately, the quality of care and outcomes., Comment: 22 Pages, 6 Figures, 13 Tables
Published: 2024
Full Text: View/download PDF

22. Open-Canopy: A Country-Scale Benchmark for Canopy Height Estimation at Very High Resolution

Author: Fogel, Fajwel, Perron, Yohann, Besic, Nikola, Saint-André, Laurent, Pellissier-Tanon, Agnès, Schwartz, Martin, Boudras, Thomas, Fayad, Ibrahim, d'Aspremont, Alexandre, Landrieu, Loic, and Ciais, Philippe
Subjects: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Estimating canopy height and canopy height change at meter resolution from satellite imagery has numerous applications, such as monitoring forest health, logging activities, wood resources, and carbon stocks. However, many existing forest datasets are based on commercial or closed data sources, restricting the reproducibility and evaluation of new approaches. To address this gap, we introduce Open-Canopy, the first open-access and country-scale benchmark for very high resolution (1.5 m) canopy height estimation. Covering more than 87,000 km$^2$ across France, Open-Canopy combines SPOT satellite imagery with high resolution aerial LiDAR data. We also propose Open-Canopy-$\Delta$, the first benchmark for canopy height change detection between two images taken at different years, a particularly challenging task even for recent models. To establish a robust foundation for these benchmarks, we evaluate a comprehensive list of state-of-the-art computer vision models for canopy height estimation. The dataset and associated codes can be accessed at https://github.com/fajwel/Open-Canopy., Comment: 22 pages, 8 figures, Submitted to NeurIPS 2024 Datasets and Benchmarks Track
Published: 2024

23. 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

24. 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

25. Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images

Author: Li, Hao, Oguz, Baris, Arenas, Gabriel, Yao, Xing, Wang, Jiacheng, Pouch, Alison, Byram, Brett, Schwartz, Nadav, and Oguz, Ipek
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Placenta volume measurement from 3D ultrasound images is critical for predicting pregnancy outcomes, and manual annotation is the gold standard. However, such manual annotation is expensive and time-consuming. Automated segmentation algorithms can often successfully segment the placenta, but these methods may not consistently produce robust segmentations suitable for practical use. Recently, inspired by the Segment Anything Model (SAM), deep learning-based interactive segmentation models have been widely applied in the medical imaging domain. These models produce a segmentation from visual prompts provided to indicate the target region, which may offer a feasible solution for practical use. However, none of these models are specifically designed for interactively segmenting 3D ultrasound images, which remain challenging due to the inherent noise of this modality. In this paper, we evaluate publicly available state-of-the-art 3D interactive segmentation models in contrast to a human-in-the-loop approach for the placenta segmentation task. The Dice score, normalized surface Dice, averaged symmetric surface distance, and 95-percent Hausdorff distance are used as evaluation metrics. We consider a Dice score of 0.95 a successful segmentation. Our results indicate that the human-in-the-loop segmentation model reaches this standard. Moreover, we assess the efficiency of the human-in-the-loop model as a function of the amount of prompts. Our results demonstrate that the human-in-the-loop model is both effective and efficient for interactive placenta segmentation. The code is available at \url{https://github.com/MedICL-VU/PRISM-placenta}.
Published: 2024

26. LLMCloudHunter: Harnessing LLMs for Automated Extraction of Detection Rules from Cloud-Based CTI

Author: Schwartz, Yuval, Benshimol, Lavi, Mimran, Dudu, Elovici, Yuval, and Shabtai, Asaf
Subjects: Computer Science - Cryptography and Security, Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: As the number and sophistication of cyber attacks have increased, threat hunting has become a critical aspect of active security, enabling proactive detection and mitigation of threats before they cause significant harm. Open-source cyber threat intelligence (OS-CTI) is a valuable resource for threat hunters, however, it often comes in unstructured formats that require further manual analysis. Previous studies aimed at automating OSCTI analysis are limited since (1) they failed to provide actionable outputs, (2) they did not take advantage of images present in OSCTI sources, and (3) they focused on on-premises environments, overlooking the growing importance of cloud environments. To address these gaps, we propose LLMCloudHunter, a novel framework that leverages large language models (LLMs) to automatically generate generic-signature detection rule candidates from textual and visual OSCTI data. We evaluated the quality of the rules generated by the proposed framework using 12 annotated real-world cloud threat reports. The results show that our framework achieved a precision of 92% and recall of 98% for the task of accurately extracting API calls made by the threat actor and a precision of 99% with a recall of 98% for IoCs. Additionally, 99.18% of the generated detection rule candidates were successfully compiled and converted into Splunk queries.
Published: 2024

27. Flexible Stellarator Physics Facility

Author: Parra, F. I., Baek, S. -G., Churchill, M., Demers, D. R., Dudson, B., Ferraro, N. M., Geiger, B., Gerhardt, S., Hammond, K. C., Hudson, S., Jorge, R., Kolemen, E., Kriete, D. M., Kumar, S. T. A., Landreman, M., Lowe, C., Maurer, D. A., Nespoli, F., Pablant, N., Pueschel, M. J., Punjabi, A., Schwartz, J. A., Swanson, C. P. S., and Wright, A. M.
Subjects: Physics - Plasma Physics
Abstract: We propose to build a Flexible Stellarator Physics Facility to explore promising regions of the vast parameter space of disruption-free stellarator solutions for Fusion Pilot Plants (FPPs)., Comment: White paper submitted to FESAC subcommittee on Facilities, 8 pages
Published: 2024
Full Text: View/download PDF

28. STRIDE: Simple Type Recognition In Decompiled Executables

Author: Green, Harrison, Schwartz, Edward J., Goues, Claire Le, and Vasilescu, Bogdan
Subjects: Computer Science - Cryptography and Security
Abstract: Decompilers are widely used by security researchers and developers to reverse engineer executable code. While modern decompilers are adept at recovering instructions, control flow, and function boundaries, some useful information from the original source code, such as variable types and names, is lost during the compilation process. Our work aims to predict these variable types and names from the remaining information. We propose STRIDE, a lightweight technique that predicts variable names and types by matching sequences of decompiler tokens to those found in training data. We evaluate it on three benchmark datasets and find that STRIDE achieves comparable performance to state-of-the-art machine learning models for both variable retyping and renaming while being much simpler and faster. We perform a detailed comparison with two recent SOTA transformer-based models in order to understand the specific factors that make our technique effective. We implemented STRIDE in fewer than 1000 lines of Python and have open-sourced it under a permissive license at https://github.com/hgarrereyn/STRIDE.
Published: 2024

29. 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

30. Reading Skills and Background Noise in Autistic and Non-Autistic Children: A Pilot Study

Author: Maryellen Brunson McClain, Sarah E. Yoho, Rochelle B. Drill, Cassity R. Haverkamp, Sarah E. Schwartz, Brittan A. Barker, David N. Longhurst, and Shelley R. Upton
Abstract: Classrooms are often noisy environments, which can result in unfavorable learning conditions for students. However, research has insufficiently addressed how noisy classrooms affect autistic students. This preliminary study examined differences in, and the impact of, background noise on reading performance for elementary-aged autistic and non-autistic (NA) children (N = 49). Autistic (n = 13) and NA children (n = 36) between the ages of 6 and 13 years participated in the current study. We employed a repeated measures design where each participant read four, grade-appropriate reading curriculum-based measurement (CBM) passages and subsequently completed comprehension (i.e., retell and recall) tasks in the presence of four different listening conditions (i.e., experimental condition): (1) quiet, (2) a single talker, (3) classroom noise, and (4) white noise. Using multi-level modeling (MLM), we found that listening condition differentially impacted reading fluency for all children. Children's reading fluency was more negatively impacted by the single talker in comparison to white noise and quiet. The performance of all children to retell story components (a measure of reading comprehension) was moderated by age with older children recalling more story components in the presence of white noise. Recalling story components correctly was not impacted by listening condition or disability. Regardless of disability, environments that include a single talker were not optimal for children's reading fluency skills. Moreover, preferred environments for children's reading comprehension skills--specifically the retelling of key story components--depend on student age, with background white noise being ideal for older children. Notably, no differences in how background noise impacts reading performance were found between autistic and NA children.
Published: 2024
Full Text: View/download PDF

31. Families' Experiences with Online Instruction and Behavior Support during COVID-19

Author: Elizabeth M. Kelly, Shawna G. Harbin, and Ilene S. Schwartz
Abstract: In the Spring of 2020, COVID-19 forced school buildings to close across the United States. As a result, many early learning programs and elementary schools moved their services online. Families of young children with challenging behaviors receiving complex educational and behavioral services in traditional brick-and-mortar classrooms were suddenly required to work closely with educators to support their children's academic, social-emotional, and behavioral progress. This study used a qualitative approach to examine families' experiences with children's challenging behavior, online instruction, and behavior support during COVID-19 school building closures. Findings underscore important themes related to families' perceptions of child challenging behavior at home, challenges with children's meaningful participation in online instruction, families' perceived responsibilities and priorities, and future recommendations. Implications for educators are discussed.
Published: 2024
Full Text: View/download PDF

32. Districts' Pandemic Recovery Efforts as COVID-Relief Aid Expires: Selected Findings from the Spring 2024 American School District Panel Survey. Data Note. RR-A956-23

Author: RAND Education and Labor, Arizona State University (ASU), Center on Reinventing Public Education (CRPE), Melissa Kay Diliberti, and Heather L. Schwartz
Abstract: In spring 2024, the authors surveyed 190 American School District Panel member districts about what interventions (e.g., tutoring, additional staff, additional instruction time) they were still using during the 2023-2024 school year to assist with students' learning recovery from coronavirus disease 2019 (COVID-19) pandemic-related setbacks. Districts were also asked about their expected revenues in future school years and what cuts, if any, they plan to make because of the expiration of federal stimulus funds. This series is intended to provide brief analyses of educator survey results of immediate interest to policymakers, practitioners, and researchers.
Published: 2024
Full Text: View/download PDF

33. Technical Documentation for the Ninth American School District Panel Survey. Research Report. RR-A956-24

Author: RAND Education and Labor, David Grant, Claude Messan Setodji, Gerald P. Hunter, Melissa Kay Diliberti, and Heather L. Schwartz
Abstract: The American School District Panel (ASDP) is a nationally representative set of more than 1,000 school district leaders who agree to take surveys over time. RAND recruits ASDP members using probabilistic sampling methods, which allow researchers to weight survey results to generalize to the national population of school districts and charter management organizations. This report provides technical information about the spring 2024 ASDP survey of district leaders. The authors describe the survey administration and weighting processes they used to produce nationally representative estimates.
Published: 2024
Full Text: View/download PDF

34. Participation in the US Department of Agriculture's Summer Meal Programs: 2019-2021

Author: Kara Burkholder, Brooke L. Bennett, Sarah L. McKee, Juliana F.W. Cohen, Ran Xu, and Marlene B. Schwartz
Abstract: BACKGROUND: The US Department of Agriculture's (USDA) summer meal programs are designed to provide meals at no cost while school is out of session. In response to the COVID-19 pandemic, several regulatory waivers were enacted to facilitate meal distribution. The aim of this study was to assess the rates of meal distribution before and after these waivers were in effect. METHODS: Meal distribution patterns for 2019, 2020, and 2021 were examined through (1) a descriptive comparison of the number of participating districts, sponsors, meal sites, and meals distributed statewide; and (2) repeated measures ANOVAs to examine changes among districts in operation all years. RESULTS: The waivers were associated with an increase in the total number of participating districts, sponsors, and meal sites; an increase in the total number of meals distributed to children during the summer months; and an increase in meal distribution among sponsors that had been in place since 2019. Conclusion: Expanding the area eligibility criteria and enabling flexibility in meal distribution methods increased the number of meals provided. This study provides important preliminary evidence to suggest that the USDA should consider permanent regulatory changes to this program to maximize its reach.
Published: 2024
Full Text: View/download PDF

35. Integrated Learning, Integrated Lives: Highlighting Opportunities for Transformative SEL within Academic Instruction. Social and Emotional Learning Innovations Series

Author: Collaborative for Academic, Social, and Emotional Learning (CASEL), Heather N. Schwartz, Ally Skoog-Hoffman, Joe Polman, Olivia Kelly, Josefina Bañales, and Rob Jagers
Abstract: The SEL Innovations series aims to help the field imagine new, more expansive and equitable approaches to social and emotional learning (SEL) and wellness to ensure that all children, adolescents, and adults feel safe, supported, and seen so that they can thrive. This is the second report in a series exploring innovations in SEL. The purpose of the report is to highlight the importance of systemic, integrated SEL in classrooms, where the goal is to foster supportive classroom environments wherein educators teach explicit SEL and integrate SEL throughout academic instruction by weaving deep academic learning with opportunities for students to understand their own emotions, empathize with diverse perspectives, solve problems constructively, and make decisions while considering the needs of others. The authors briefly explore the Collaborative for Academic, Social, and Emotional Learning (CASEL) model for SEL before critically examining the literature around inquiry-based learning opportunities. The focus is on two inquiry-based approaches to learning, Project-Based Learning (PBL) and Youth Participatory Action Research (YPAR), which show promise as examples of integrated, systemic approaches to SEL by allowing students to be authors and co-designers in their own learning. Next, conditions necessary to enact inquiry-based approaches in the classroom are explored. Lastly, they share a set of case studies illustrating what inquiry-based learning opportunities, specifically PBL and YPAR, can look like in practice. In this way, the goal is to provide both inspiration and practical application for educators, program providers, and researchers looking to continue to move the conversation forward, integrate new strategies into their programs or practices, and expand their research agendas.
Published: 2023

36. How Solvation Alters the Thermodynamics of Asymmetric Bond-Breaking: Quantum Simulation of NaK+ in Liquid Tetrahydrofuran.

Author: Mei, Kenneth and Schwartz, Benjamin
Abstract: Gas-phase potential energy surfaces (PESs) are often used to provide an intuitive understanding of molecular chemical reactivity. Most chemical reactions, however, take place in solution, and it is unclear whether gas-phase PESs accurately represent chemical processes in solvent environments. In this work we use quantum simulations to investigate the dissociation energetics of NaK+ in liquid tetrahydrofuran (THF) to understand the degree to which solvent interactions alter the gas-phase picture. Using umbrella sampling and thermodynamic integration techniques, we construct condensed-phase free energy surfaces of NaK+ on THF in both the ground and electronic excited states. We find that solvation by THF completely alters the nature of the NaK+ bond by reordering the thermodynamic dissociation products. Reaching the thermodynamic dissociation limit in THF also requires a long-range charge transfer process that has no counterpart in the gas phase. Gas-phase PESs, even with perturbations, cannot adequately describe the reactivity of simple asymmetric molecules in solution.
Published: 2024

37. Quality improvement in the era of boarding and burnout: A postpandemic blueprint.

Author: Schwartz, Hope, Huen, William, Kanzaria, Hemal, and Peabody, Christopher
Subjects: COVID‐19, burnout, operations, quality, triage, wait times
Abstract: The COVID-19 pandemic led to unprecedented challenges to healthcare quality in the emergency department, including directly impacting quality metrics and worsening barriers to the quality improvement process such as burnout, staff turnover, and boarding. We aimed to develop a blueprint for postpandemic quality improvement to address these specific barriers, focused on prioritizing frontline staff engagement from idea generation to implementation and assessment. Drawing from teamwork literature, we constructed a process that emphasized egalitarian conversations, psychological safety, and creating an environment where staff could feel heard at every step of the process. We applied this blueprint to improving rates of patients who leave without being seen and achieved a four percentage point reduction (9% vs. 5%, p
Published: 2024

38. Enhancing emergency department charting: Using Generative Pre‐trained Transformer‐4 (GPT‐4) to identify laceration repairs

Author: Bains, Jaskaran, Williams, Christopher YK, Johnson, Drake, Schwartz, Hope, Sabbineni, Naina, Butte, Atul J, and Kornblith, Aaron E
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Emergency Care, Health Services, Public Health and Health Services, Emergency & Critical Care Medicine, Clinical sciences
Published: 2024

39. Esophageal Obstruction from Food Bolus Impaction Successfully Managed with the “Upright Posture, Chin Tuck, Double Swallow” Maneuver: A Case Report

Author: Barden, Matthias and Schwartz, Michael E
Subjects: gastrointestinal, esophageal obstruction, Meat Impaction, Food Bolus Impaction
Abstract: Introduction: An attempt at medical management is often the initial step in addressing esophageal obstruction from an impacted food bolus. Medical management, however, has limited success and often requires urgent endoscopy. We present a case in which standard medical treatment failed, but a swallowing augmentation maneuver resolved the obstruction. Case Report: A 67-year-old female presented with esophageal obstruction after eating steak. Transfer to higher level of care for endoscopy was initiated; however, the receiving gastroenterologist suggested an “upright posture, chin tuck, double swallow” maneuver. This immediately resolved the patient’s symptoms, and she was discharged home. Conclusion: This case suggests a novel, non-endoscopic technique for esophageal obstruction from food bolus impaction.
Published: 2024

40. Grid Resilience Plans: State Requirements, Utility Practices, and Utility Plan Template

Author: Schellenberg, Josh A and Schwartz, Lisa C
Abstract: As of June 2024, 14 states and one city require jurisdictional electric utilities to file resilience plans. Drawing on these requirements and filed plans, this report offers a standard template that states and utilities can consider to improve utility filings for grid resilience plans, either as part of a distribution system plan or as a separate filing. Key elements include a vulnerability assessment, description of proposed resilience programs, and projected costs and rate impacts. While many of these requirements and plans focus on extreme weather hazards, the template also can be used to address additional threats, including cyber and physical attacks and seismic events.
Published: 2024

41. Sizing Electric Service Panels and Utility Infrastructure for Residential Electrification and Distributed Energy Resources Adoption

Author: Davis, Cody and Schwartz, Lisa C
Abstract: This Berkeley Lab Technical Brief summarizes key considerations for electric service equipment and utility infrastructure to support residential customer electrification and adoption of distributed energy resources such as photovoltaic solar and energy storage.
Published: 2024

42. Safety of the PCSK9 inhibitor alirocumab: insights from 47 296 patient-years of observation

Author: Goodman, Shaun G, Steg, Philippe Gabriel, Szarek, Michael, Bhatt, Deepak L, Bittner, Vera A, Diaz, Rafael, Harrington, Robert A, Jukema, J Wouter, White, Harvey D, Zeiher, Andreas M, Manvelian, Garen, Pordy, Robert, Poulouin, Yann, Stipek, Wanda, Garon, Genevieve, Schwartz, Gregory G, Steg, Ph Gabriel, Tricoci, Pierluigi, Roe, Matthew T, Mahaffey, Kenneth W, Edelberg, Jay M, Hanotin, Corinne, Lecorps, Guillaume, Moryusef, Angèle, Sasiela, William J, Tamby, Jean-François, Aylward, Philip E, Drexel, Heinz, Sinnaeve, Peter, Dilic, Mirza, Lopes, Renato D, Gotcheva, Nina N, Prieto, Juan-Carlos, Yong, Huo, López-Jaramillo, Patricio, Pećin, Ivan, Reiner, Zeljko, Ostadal, Petr, Poulsen, Steen Hvitfeldt, Viigimaa, Margus, Nieminen, Markku S, Danchin, Nicolas, Chumburidze, Vakhtang, Marx, Nikolaus, Liberopoulos, Evangelos, Valdovinos, Pablo Carlos Montenegro, Tse, Hung-Fat, Kiss, Robert Gabor, Xavier, Denis, Zahger, Doron, Valgimigli, Marco, Kimura, Takeshi, Kim, Hyo Soo, Kim, Sang-Hyun, Erglis, Andrejs, Laucevicius, Aleksandras, Kedev, Sasko, Yusoff, Khalid, López, Gabriel Arturo Ramos, Alings, Marco, Halvorsen, Sigrun, Flores, Roger M Correa, Sy, Rody G, Budaj, Andrzej, Morais, Joao, Dorobantu, Maria, Karpov, Yuri, Ristic, Arsen D, Chua, Terrance, Murin, Jan, Fras, Zlatko, Dalby, Anthony J, Tuñón, José, de Silva, H Asita, Hagström, Emil, Landmesser, Ulf, Chiang, Chern-En, Sritara, Piyamitr, Guneri, Sema, Parkhomenko, Alexander, Ray, Kausik K, Moriarty, Patrick M, Chaitman, Bernard, Kelsey, Sheryl F, Olsson, Anders G, and Rouleau, Jean-Lucien
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Clinical Trials and Supportive Activities, Patient Safety, Clinical Research, 6.1 Pharmaceuticals, Good Health and Well Being, Humans, Antibodies, Monoclonal, Humanized, Anticholesteremic Agents, Biomarkers, Cardiovascular Diseases, Cholesterol, LDL, Dyslipidemias, PCSK9 Inhibitors, Proprotein Convertase 9, Randomized Controlled Trials as Topic, Serine Proteinase Inhibitors, Time Factors, Treatment Outcome, ODYSSEY OUTCOMES Investigators, Alirocumab, Cholesterol, PCSK9, Safety, Cardiorespiratory Medicine and Haematology, Pharmacology and Pharmaceutical Sciences, Cardiovascular medicine and haematology, Pharmacology and pharmaceutical sciences
Abstract: The ODYSSEY OUTCOMES trial, comprising over 47 000 patient-years of placebo-controlled observation, demonstrated important reductions in the risk of recurrent ischaemic cardiovascular events with the monoclonal antibody to proprotein convertase subtilisin/kexin type 9 alirocumab, as well as lower all-cause death. These benefits were observed in the context of substantial and persistent lowering of low-density lipoprotein cholesterol with alirocumab compared with that achieved with placebo. The safety profile of alirocumab was indistinguishable from matching placebo except for a ∼1.7% absolute increase in local injection site reactions. Further, the safety of alirocumab compared with placebo was evident in vulnerable groups identified before randomization, such as the elderly and those with diabetes mellitus, previous ischaemic stroke, or chronic kidney disease. The frequency of adverse events and laboratory-based abnormalities was generally similar to that in placebo-treated patients. Thus, alirocumab appears to be a safe and effective lipid-modifying treatment over a duration of at least 5 years.
Published: 2024

43. Physiological Adaptations to Progressive Endurance Exercise Training in Adult and Aged Rats: Insights from the Molecular Transducers of Physical Activity Consortium (MoTrPAC)

Author: Schenk, Simon, Sagendorf, Tyler J, Many, Gina M, Lira, Ana K, de Sousa, Luis GO, Bae, Dam, Cicha, Michael, Kramer, Kyle S, Muehlbauer, Michael, Hevener, Andrea L, Rector, R Scott, Thyfault, John P, Williams, John P, Goodyear, Laurie J, Esser, Karyn A, Newgard, Christopher B, Bodine, Sue C, Adkins, Joshua N, Albertson, Brent G, Amar, David, Amper, Mary Anne S, Ashley, Euan, Bamman, Marcas M, Barnes, Jerry, Bergman, Bryan C, Bessesen, Daniel H, Buford, Thomas W, Burant, Charles F, Cutter, Gary R, De Sousa, Luis Gustavo Oliveria, Fernández, Facundo M, Gaul, David A, Ge, Yongchao, Goodpaster, Bret H, Guevara, Kristy, Hirshman, Michael F, Huffman, Kim M, Jackson, Bailey E, Jankowski, Catherine M, Jimenez-Morales, David, Kohrt, Wendy M, Kraus, William E, Lessard, Sarah J, Lester, Bridget, Lindholm, Malene E, Many, Gina, Marjanovic, Nada, Marshall, Andrea G, Melanson, Edward L, Miller, Michael E, Moreau, Kerrie L, Nair, Venugopalan D, Ortlund, Eric A, Qian, Wei-Jun, Rasmussen, Blake B, Richards, Collyn Z-T, Rushing, Scott, Sanford, James A, Schauer, Irene E, Schwartz, Robert S, Sealfon, Stuart C, Seenarine, Nitish, Sparks, Lauren M, Stowe, Cynthia L, Talton, Jennifer W, Teng, Christopher, Tesfa, Nathan D, Thalacker-Mercer, Anna, Trappe, Scott, Trappe, Todd A, Vasoya, Mital, Wheeler, Matthew T, Walkup, Michael P, Yan, Zhen, and Zhen, Jimmy
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Physical Activity, Cardiovascular, Prevention, Behavioral and Social Science, Animals, Male, Rats, Inbred F344, Female, Physical Conditioning, Animal, Adaptation, Physiological, Rats, Aging, Physical Endurance, Muscle, Skeletal, Endurance Training, training, treadmill, maximal oxygen uptake, body composition, citrate synthase, skeletal muscle, biorepository, aging, MoTrPAC Study Group, Medical physiology
Abstract: While regular physical activity is a cornerstone of health, wellness, and vitality, the impact of endurance exercise training on molecular signaling within and across tissues remains to be delineated. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to characterize molecular networks underlying the adaptive response to exercise. Here, we describe the endurance exercise training studies undertaken by the Preclinical Animal Sites Studies component of MoTrPAC, in which we sought to develop and implement a standardized endurance exercise protocol in a large cohort of rats. To this end, Adult (6-mo) and Aged (18-mo) female (n = 151) and male (n = 143) Fischer 344 rats were subjected to progressive treadmill training (5 d/wk, ∼70%-75% VO2max) for 1, 2, 4, or 8 wk; sedentary rats were studied as the control group. A total of 18 solid tissues, as well as blood, plasma, and feces, were collected to establish a publicly accessible biorepository and for extensive omics-based analyses by MoTrPAC. Treadmill training was highly effective, with robust improvements in skeletal muscle citrate synthase activity in as little as 1-2 wk and improvements in maximum run speed and maximal oxygen uptake by 4-8 wk. For body mass and composition, notable age- and sex-dependent responses were observed. This work in mature, treadmill-trained rats represents the most comprehensive and publicly accessible tissue biorepository, to date, and provides an unprecedented resource for studying temporal-, sex-, and age-specific responses to endurance exercise training in a preclinical rat model.
Published: 2024

44. Insulin and leptin oscillations license food-entrained browning and metabolic flexibility

Author: Mattar, Pamela, Reginato, Andressa, Lavados, Christian, Das, Debajyoti, Kalyani, Manu, Martinez-Lopez, Nuria, Sharma, Mridul, Skovbjerg, Grethe, Skytte, Jacob Lercke, Roostalu, Urmas, Subbarayan, Rajasekaran, Picarda, Elodie, Zang, Xingxing, Zhang, Jinghang, Guha, Chandan, Schwartz, Gary, Rajbhandari, Prashant, and Singh, Rajat
Subjects: Biochemistry and Cell Biology, Biological Sciences, Diabetes, Obesity, Nutrition, 2.1 Biological and endogenous factors, Metabolic and endocrine, Animals, Leptin, Insulin, Adipose Tissue, Brown, Mice, Mice, Inbred C57BL, Energy Metabolism, Adipose Tissue, White, Male, Feeding Behavior, CP: Metabolism, ILC2, browning, circadian, insulin, intermittent fasting, leptin, oscillations, time-restricted feeding, Medical Physiology, Biological sciences
Abstract: Timed feeding drives adipose browning, although the integrative mechanisms for the same remain unclear. Here, we show that twice-a-night (TAN) feeding generates biphasic oscillations of circulating insulin and leptin, representing their entrainment by timed feeding. Insulin and leptin surges lead to marked cellular, functional, and metabolic remodeling of subcutaneous white adipose tissue (sWAT), resulting in increased energy expenditure. Single-cell RNA-sequencing (scRNA-seq) analyses and flow cytometry demonstrate a role for insulin and leptin surges in innate lymphoid type 2 (ILC2) cell recruitment and sWAT browning, since sWAT depot denervation or loss of leptin or insulin receptor signaling or ILC2 recruitment each dampens TAN feeding-induced sWAT remodeling and energy expenditure. Consistently, recreating insulin and leptin oscillations via once-a-day timed co-injections is sufficient to favorably remodel innervated sWAT. Innervation is necessary for sWAT remodeling, since denervation of sWAT, but not brown adipose tissue (BAT), blocks TAN-induced sWAT remodeling and resolution of inflammation. In sum, reorganization of nutrient-sensitive pathways remodels sWAT and drives the metabolic benefits of timed feeding.
Published: 2024

45. Comparison of estimated GFR using cystatin C versus creatinine in pediatric kidney transplant recipients.

Author: Pizzo, Helen, Nguyen, John, Schwartz, George, Wesseling-Perry, Katherine, Ettenger, Robert, Chambers, Eileen, and Weng, Patricia
Subjects: Accuracy, Bias, Estimating equations, Kidney function, Precision, Humans, Cystatin C, Glomerular Filtration Rate, Child, Male, Female, Kidney Transplantation, Creatinine, Adolescent, Child, Preschool, Infant, Iohexol, Renal Insufficiency, Chronic, Kidney, Biomarkers, Transplant Recipients
Abstract: BACKGROUND: An accurate, rapid estimate of glomerular filtration rate (GFR) in kidney transplant patients affords early detection of transplant deterioration and timely intervention. This study compared the performance of serum creatinine (Cr) and cystatin C (CysC)-based GFR equations to measured GFR (mGFR) using iohexol among pediatric kidney transplant recipients. METHODS: CysC, Cr, and mGFR were obtained from 45 kidney transplant patients, 1-18 years old. Cr- and CysC-estimated GFR (eGFR) was compared against mGFR using the Cr-based (Bedside Schwartz, U25-Cr), CysC-based (Gentian CysC, CAPA, U25-CysC), and Cr-CysC combination (CKiD Cr-CysC, U25 Cr-CysC) equations in terms of bias, precision, and accuracy. Bland-Altman plots assessed the agreement between eGFR and mGFR. Secondary analyses evaluated the formulas in patients with biopsy-proven histological changes, and K/DOQI CKD staging. RESULTS: Bias was small with Gentian CysC (0.1 ml/min/1.73 m2); 88.9% and 37.8% of U25-CysC estimations were within 30% and 10% of mGFR, respectively. In subjects with histological changes on biopsy, Gentian CysC had a small bias and U25-CysC were more accurate-both with 83.3% of and 41.7% of estimates within 30% and 10% mGFR, respectively. Precision was better with U25-CysC, CKiD Cr-CysC, and U25 Cr-CysC. Bland-Altman plots showed the Bedside Schwartz, Gentian CysC, CAPA, and U25-CysC tend to overestimate GFR when > 100 ml/min/1.72 m2. CAPA misclassified CKD stage the least (whole cohort 24.4%, histological changes on biopsy 33.3%). CONCLUSIONS: In this small cohort, CysC-based equations with or without Cr may have better bias, precision, and accuracy in predicting GFR.
Published: 2024

46. Enantiospecificity in NMR Enabled by Chirality-Induced Spin Selectivity

Author: Georgiou, T., Palma, J. L., Mujica, V., Varela, S., Galante, M., Garcıa, V. Santamarıa, Mboning, L., Schwartz, R. N., Cuniberti, G., and Bouchard, L. -S.
Subjects: Condensed Matter - Soft Condensed Matter, Physics - Biological Physics, Physics - Chemical Physics
Abstract: Spin polarization in chiral molecules is a magnetic molecular response associated with electron transport and enantioselective bond polarization that occurs even in the absence of an external magnetic field. An unexpected finding by Santos and co-workers reported enantiospecific NMR responses in solid-state cross-polarization (CP) experiments, suggesting a possible additional contribution to the indirect nuclear spin-spin coupling in chiral molecules induced by bond polarization in the presence of spin-orbit coupling. Herein we provide a theoretical treatment for this phenomenon, presenting an effective spin-Hamiltonian for helical molecules like DNA and density functional theory (DFT) results on amino acids that confirm the dependence of J-couplings on the choice of enantiomer. The connection between nuclear spin dynamics and chirality could offer insights for molecular sensing and quantum information sciences. These results establish NMR as a potential tool for chiral discrimination without external agents., Comment: 102 pages, 16 figures, 40 tables
Published: 2024

47. Imaging of single barium atoms in a second matrix site in solid xenon for barium tagging in a $^{136}$Xe double beta decay experiment

Author: Yvaine, M., Fairbank, D., Soderstrom, J., Taylor, C., Stanley, J., Walton, T., Chambers, C., Iverson, A., Fairbank, W., Kharusi, S. Al, Amy, A., Angelico, E., Anker, A., Arnquist, I. J., Atencio, A., Bane, J., Belov, V., Bernard, E. P., Bhatta, T., Bolotnikov, A., Breslin, J., Breur, P. A., Brodsky, J. P., Brown, E., Brunner, T., Caden, E., Cao, G. F., Cesmecioglu, D., Chambers, E., Chana, B., Chernyak, D., Chiu, M., Collister, R., Cvitan, M., Daniels, T., Darroch, L., DeVoe, R., di Vacri, M. L., Dolinski, M. J., Eckert, B., Elbeltagi, M., Elmansali, R., Fatemighomi, N., Foust, B., Fu, Y. S., Gallacher, D., Gallice, N., Giacomini, G., Gillis, W., Gingras, C., Gornea, R., Gratta, G., Hardy, C. A., Hedges, S., Hein, E., Holt, J. D., Hoppe, E. W., Karelin, A., Keblbeck, D., Kotov, I., Kuchenkov, A., Kumar, K. S., Kwiatkowski, A. A., Larson, A., Latif, M. B., Leach, K. G., Lennarz, A., Leonard, D. S., Lewis, H., Li, G., Li, Z., Licciardi, C., Lindsay, R., MacLellan, R., Majidi, S., Malbrunot, C., Masbou, J., McMichael, K., Peregrina, M. Medina, Moe, M., Mong, B., Moore, D. C., Natzke, C. R., Ngwadla, X. E., Ni, K., Nolan, A., Nowicki, S. C., Ondze, J. C. Nzobadila, Odian, A., Orrell, J. L., Ortega, G. S., Overman, C. T., Pagani, L., Smalley, H. Peltz, Perna, A., Pocar, A., Radeka, V., Raguzin, E., Rasiwala, H., Ray, D., Rescia, S., Richardson, G., Ross, R., Rowson, P. C., Saldanha, R., Sangiorgio, S., Schwartz, S., Sekula, S., Si, L., Soma, A. K., Spadoni, F., Stekhanov, V., Sun, X. L., Thibado, S., Tidball, A., Totev, T., Triambak, S., Tsang, T., Tyuka, O. A., van Bruggen, E., Vidal, M., Walent, M., Wamba, K., Wang, H. W., Wang, Q. D., Wang, W., Wang, Y. G., Watts, M., Wehrfritz, M., Wen, L. J., Wichoski, U., Wilde, S., Worcester, M., Xu, H., Yang, L., Yu, M., and Zeldovich, O.
Subjects: Physics - Atomic Physics, High Energy Physics - Experiment, Nuclear Experiment
Abstract: Neutrinoless double beta decay is one of the most sensitive probes for new physics beyond the Standard Model of particle physics. One of the isotopes under investigation is $^{136}$Xe, which would double beta decay into $^{136}$Ba. Detecting the single $^{136}$Ba daughter provides a sort of ultimate tool in the discrimination against backgrounds. Previous work demonstrated the ability to perform single atom imaging of Ba atoms in a single-vacancy site of a solid xenon matrix. In this paper, the effort to identify signal from individual barium atoms is extended to Ba atoms in a hexa-vacancy site in the matrix and is achieved despite increased photobleaching in this site. Abrupt fluorescence turn-off of a single Ba atom is also observed. Significant recovery of fluorescence signal lost through photobleaching is demonstrated upon annealing of Ba deposits in the Xe ice. Following annealing, it is observed that Ba atoms in the hexa-vacancy site exhibit antibleaching while Ba atoms in the tetra-vacancy site exhibit bleaching. This may be evidence for a matrix site transfer upon laser excitation. Our findings offer a path of continued research toward tagging of Ba daughters in all significant sites in solid xenon., Comment: 9 pages, 8 figures
Published: 2024

48. BYORP and Dissipation in Binary Asteroids: Lessons from DART

Author: Ćuk, Matija, Agrusa, Harrison, Cueva, Rachel H., Ferrari, Fabio, Hirabayashi, Masatoshi, Jacobson, Seth A., McMahon, Jay, Michel, Patrick, Sánchez, Paul, Scheeres, Daniel J., Schwartz, Stephen, Walsh, Kevin J., and Zhang, Yun
Subjects: Astrophysics - Earth and Planetary Astrophysics
Abstract: The Near-Earth binary asteroid Didymos was the target of a planetary defense demonstration mission DART in September 2022. The smaller binary component, Dimorphos, was impacted by the spacecraft in order to measure momentum transfer in kinetic impacts into rubble piles. DART and associated Earth-based observation campaigns have provided a wealth of scientific data on the Didymos-Dimorphos binary. DART revealed a largely oblate and ellipsoidal shape of Dimorphos before the impact, while the post-impact observations suggest that Dimorphos now has a prolate shape. Here we add those data points to the known properties of small binary asteroids and propose new paradigms of the radiative binary YORP (BYORP) effect as well as tidal dissipation in small binaries. We find that relatively spheroidal bodies like Dimorphos made of small debris may experience a weaker and more size-dependent BYORP effect than previously thought. This could explain the observed values of period drift in several well-characterized binaries. We also propose that energy dissipation in small binaries is dominated by relatively brief episodes of large-scale movement of (likely surface) materials, rather than long-term steady-state tidal dissipation. We propose that one such episode was triggered on Dimorphos by the DART impact. Depending on the longevity of this high-dissipation regime, it is possible that Dimorphos will be more dynamically relaxed in time for the Hera mission than it was in the weeks following the impact., Comment: Accepted for PSJ
Published: 2024

49. The Belle II Detector Upgrades Framework Conceptual Design Report

Author: Aihara, H., Aloisio, A., Auguste, D. P., Aversano, M., Babeluk, M., Bahinipati, S., Banerjee, Sw., Barbero, M., Baudot, J., Beaubien, A., Becherer, F., Bergauer, T., Bernlochner., F. U., Bertacchi, V., Bertolone, G., Bespin, C., Bessner, M., Bettarini, S., Bevan, A. J., Bhuyan, B., Bona, M., Bonis, J. F., Borah, J., Bosi, F., Boudagga, R., Bozek, A., Bračko, M., Branchini, P., Breugnon, P., Browder, T. E., Buch, Y., Budano, A., Campajola, M., Casarosa, G., Cecchi, C., Chen, C., Choudhury, S., Corona, L., de Marino, G., De Nardo, G., De Pietro, G., de Sangro, R., Dey, S., Dingfelder, J. C., Dong, T. V., Dorokhov, A., Dujany, G., Epifanov, D., Federici, L., Ferber, T., Fillinger, T., Finck, Ch., Finocchiaro, G., Forti, F., Frey, A., Friedl, M., Gabrielli, A., Gaioni, L., Gao, Y., Gaudino, G., Gaur, V., Gaz, A., Giordano, R., Giroletti, S., Gobbo, B., Godang, R., Haide, I., Han, Y., Hara, K., Hayasaka, K., Hearty, C., Heidelbach, A., Higuchi, T., Himmi, A., Hoferichter, M., Howgill, D. A., Hu-Guo, C., Iijima, T., Inami, K., Irmler, C., Ishikawa, A., Itoh, R., Iyer, D., Jacobs, W. W., Jaffe, D. E., Jin, Y., Junginger, T., Kandra, J., Kojima, K., Koga, T., Korobov, A. A., Korpar, S., Križan, P., Krüger, H., Kuhr, T., Kumar, A., Kumar, R ., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lacasta, C., Lai, Y. -T., Lalwani, K., Lam, T., Lanceri, L., Lee, M. J., Leonidopoulos, C., Levit, D., Lewis, P. M., Libby, J. F., Liu, Q. Y., Liu, Z. Y., Liventsev, D., Longo, S., Mancinelli, G., Manghisoni, M., Manoni, E., Marinas, C., Martellini, C., Martens, A., Massa, M., Massaccesi, L., Mawas, F., Mazorra, J., Merola, M., Miller, C., Minuti, M., Mizuk, R., Modak, A., Moggi, A., Mohanty, G. B., Moneta, S., Muller, Th., Na, I., Nakamura, K. R., Nakao, M., Natochii, A., Niebuhr, C., Nishida, S., Novosel, A., Pangaud, P., Parker, B., Passeri, A., Pedlar, T. K., Peinaud, Y., Peng, Y., Peschke, R., Pestotnik, R., Pham, T. H., Piccolo, M., Piilonen, L. E., Prell, S., Purohit, M. V., Ratti, L., Re, V., Reuter, L., Riceputi, E., Ripp-Baudot, I., Rizzo, G., Roney, J. M., Russo, A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schall, L., Schnell, G., Schwanda, C., Schwartz, A. J., Schwenker, B., Schwickardi, M., Seljak, A., Serrano, J., Shiu, J. -G., Shwartz, B., Simon, F., Soffer, A., Song, W. M., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Tanaka, S., Taniguchi, N., Teotia, V., Tessema, N., Thalmeier, R., Torassa, E., Trabelsi, K., Trantou, F. F., Traversi, G., Urquijo, P., Vahsen, S. E., Valin, I., Varner, G. S., Varvell, K. E., Vitale, L., Vobbilisetti, V., Wang, X. L., Wessel, C., Wienands, H. U., Won, E., Xu, D., Yamada, S., Yin, J. H., Yoshihara, K., Yuan, C. Z., Zani, L., Zong, Z., and Zou, S.
Subjects: High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: We describe the planned near-term and potential longer-term upgrades of the Belle II detector at the SuperKEKB electron-positron collider operating at the KEK laboratory in Tsukuba, Japan. These upgrades will allow increasingly sensitive searches for possible new physics beyond the Standard Model in flavor, tau, electroweak and dark sector physics that are both complementary to and competitive with the LHC and other experiments., Comment: Editor: F. Forti 170 pages
Published: 2024

50. An Automated SQL Query Grading System Using An Attention-Based Convolutional Neural Network

Author: Schwartz, Donald R. and Rivas, Pablo
Subjects: Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Databases, Computer Science - Machine Learning, I.2.6, H.2.3, K.3.2
Abstract: Grading SQL queries can be a time-consuming, tedious and challenging task, especially as the number of student submissions increases. Several systems have been introduced in an attempt to mitigate these challenges, but those systems have their own limitations. This paper describes our novel approach to automating the process of grading SQL queries. Unlike previous approaches, we employ a unique convolutional neural network architecture that employs a parameter-sharing approach for different machine learning tasks that enables the architecture to induce different knowledge representations of the data to increase its potential for understanding SQL statements., Comment: 12 pages, 8 figures, paper accepted at "The 18th International Conference on Frontiers in Education: Computer Science and Computer Engineering"
Published: 2024

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Region

Database

Publisher

55,932 results on '"Schwartz, P"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources