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Omnidirectional (360-degree) video is rapidly gaining popularity due to advancements in immersive technologies like virtual reality (VR) and extended reality (XR). However, real-time streaming of such videos, especially in live mobile scenarios like unmanned aerial vehicles (UAVs), is challenged by limited bandwidth and strict latency constraints. Traditional methods, such as compression and adaptive resolution, help but often compromise video quality and introduce artifacts that degrade the viewer experience. Additionally, the unique spherical geometry of 360-degree video presents challenges not encountered in traditional 2D video. To address these issues, we initiated the 360-degree Video Super Resolution and Quality Enhancement Challenge. This competition encourages participants to develop efficient machine learning solutions to enhance the quality of low-bitrate compressed 360-degree videos, with two tracks focusing on 2x and 4x super-resolution (SR). In this paper, we outline the challenge framework, detailing the two competition tracks and highlighting the SR solutions proposed by the top-performing models. We assess these models within a unified framework, considering quality enhancement, bitrate gain, and computational efficiency. This challenge aims to drive innovation in real-time 360-degree video streaming, improving the quality and accessibility of immersive visual experiences., Comment: 14 pages, 9 figures

The fall 2023 and spring 2024 undergraduate enrollment increases, marking the first growth since the COVID-19 pandemic, show signs of a post-pandemic turnaround for higher education. However, a significant share of current undergraduates will eventually disengage from college before earning a degree or other credential. They will join tens of millions of other adult Americans who are Some College, No Credential (SCNC). The SCNC population has been consistently rising over time. Re-engaging those who stop out remains a persistent challenge and a priority for the forty states that have set ambitious postsecondary attainment goals. This report aims to provide timely insights into the SCNC population, offering state leaders and policymakers accurate data on its current status, along with tracking progress and outcome measures for SCNC students. The first section of this report describes who makes up the SCNC population and how it has changed since the last report. In this section, the authors pay particular attention to Recent Stopouts, who joined the SCNC population after being stopped out between January 2021 and July 2022. In the second section, the authors report on SCNC re-enrollment in the 2022-23 academic year as well as first-year credential earning for re-enrollees. The authors also provide new updates on continued enrollment and second-year credential earning for SCNC re-enrollees in the 2021-22 academic year, whom were first reported on last year.

The evolution of vehicle-to-everything (V2X) communication brings significant challenges, such as data integrity and vulnerabilities stemming from centralized management. This paper presents an innovative integration of decentralized blockchain technology with V2X communication through a multi-layered architecture that combines the Simulation of Urban Mobility (SUMO) traffic simulator and the BlockSim blockchain simulator. In addition, as the Social Internet of Vehicles (SIoV) emerges, efficient resource management becomes indispensable for ensuring seamless communication. We also propose a reference multi-connectivity management method named Enhanced MAX-SINR, designed to advance research in blockchain-specific approaches, taking into account retransmission successfull rates. We evaluate blockchain performance in diverse environments such as urban, suburban, and rural areas, demonstrating that enhancing the success rate of retransmitted blockchain-related messages significantly boosts blockchain transaction performance and provides a foundation for developing intelligent SIoV systems.

We report on an air-spaced virtually imaged phased array (VIPA) spectrometer that resolves the modes of a mid-infrared frequency comb with a repetition rate of 250 MHz, without an optical filter cavity. With a record spectral resolution of 94 MHz for VIPA, the spectrometer enables precision molecular spectroscopy with high resolution, broad spectral coverage and fast data acquisition. We demonstrate the capabilities of the spectrometer by measuring the absorption spectra of molecular species generated in plasmas. Using plasmas of a mixture of nitrogen, hydrogen, and methane at a low pressure of 1.5 mbar, we obtained high-resolution spectra of methane, around 3017 cm$^{-1}$, as well as hydrogen cyanide and ammonia, around 3240 cm$^{-1}$, demonstrating a wide spectral coverage over 290 cm$^{-1}$ (equivalent to 8.7 THz). The spectrometer performance in terms of Allan-Werle deviation and noise-equivalent absorption is discussed. The air-spaced VIPA concept offers a compact and practical spectrometer that harnesses the full potential of a stabilized frequency comb, making it suitable for a wide range of high-precision spectroscopic applications., Comment: 11 pages, 4 figures

Haptic affection plays a crucial role in user experience, particularly in the automotive industry where the tactile quality of components can influence customer satisfaction. This study aims to accurately predict the affective property of a car door by only watching the force or torque profile of it when opening. To this end, a deep learning model is designed to capture the underlying relationships between force profiles and user-defined adjective ratings, providing insights into the door-opening experience. The dataset employed in this research includes force profiles and user adjective ratings collected from six distinct car models, reflecting a diverse set of door-opening characteristics and tactile feedback. The model's performance is assessed using Leave-One-Out Cross-Validation, a method that measures its generalization capability on unseen data. The results demonstrate that the proposed model achieves a high level of prediction accuracy, indicating its potential in various applications related to haptic affection and design optimization in the automotive industry., Comment: 12 pages, 9 figures, 3 tables. Waseem Hassan and Mudassir Ibrahim Awan are equally contributing authors

Dynamic Stackelberg games are a broad class of two-player games in which the leader acts first, and the follower chooses a response strategy to the leader's strategy. Unfortunately, only stylized Stackelberg games are explicitly solvable since the follower's best-response operator (as a function of the control of the leader) is typically analytically intractable. This paper addresses this issue by showing that the \textit{follower's best-response operator} can be approximately implemented by an \textit{attention-based neural operator}, uniformly on compact subsets of adapted open-loop controls for the leader. We further show that the value of the Stackelberg game where the follower uses the approximate best-response operator approximates the value of the original Stackelberg game. Our main result is obtained using our universal approximation theorem for attention-based neural operators between spaces of square-integrable adapted stochastic processes, as well as stability results for a general class of Stackelberg games.

Weight-averaged model-merging has emerged as a powerful approach in deep learning, capable of enhancing model performance without fine-tuning or retraining. However, the underlying mechanisms that explain its effectiveness remain largely unexplored. In this paper, we investigate this technique from three novel perspectives to provide deeper insights into how and why weight-averaged model-merging works: (1) we examine the intrinsic patterns captured by the learning of the model weights, through the visualizations of their patterns on several datasets, showing that these weights often encode structured and interpretable patterns; (2) we investigate model ensemble merging strategies based on averaging on weights versus averaging on features, providing detailed analyses across diverse architectures and datasets; and (3) we explore the impact on model-merging prediction stability in terms of changing the parameter magnitude, revealing insights into the way of weight averaging works as regularization by showing the robustness across different parameter scales. Our findings shed light on the "black box" of weight-averaged model-merging, offering valuable insights and practical recommendations that advance the model-merging process.

Automatically graded programming assignments provide instant feedback to students and significantly reduce manual grading time for instructors. However, creating comprehensive suites of test cases for programming problems within automatic graders can be time-consuming and complex. The effort needed to define test suites may deter some instructors from creating additional problems or lead to inadequate test coverage, potentially resulting in misleading feedback on student solutions. Such limitations may reduce student access to the well-documented benefits of timely feedback when learning programming. In this work, we evaluate the effectiveness of using Large Language Models (LLMs), as part of a larger workflow, to automatically generate test suites for CS1-level programming problems. Each problem's statement and reference solution are provided to GPT-4 to produce a test suite that can be used by an autograder. We evaluate our proposed approach using a sample of 26 problems, and more than 25,000 attempted solutions to those problems, submitted by students in an introductory programming course. We compare the performance of the LLM-generated test suites against the instructor-created test suites for each problem. Our findings reveal that LLM-generated test suites can correctly identify most valid solutions, and for most problems are at least as comprehensive as the instructor test suites. Additionally, the LLM-generated test suites exposed ambiguities in some problem statements, underscoring their potential to improve both autograding and instructional design., Comment: Submitted to Journal of Computer Assisted Learning; updated table refs

Continuous Integration/Continuous Deployment (CI/CD) is fundamental for advanced software development, supporting faster and more efficient delivery of code changes into cloud environments. However, security issues in the CI/CD pipeline remain challenging, and incidents (e.g., DDoS, Bot, Log4j, etc.) are happening over the cloud environments. While plenty of literature discusses static security testing and CI/CD practices, only a few deal with network traffic pattern analysis to detect different cyberattacks. This research aims to enhance CI/CD pipeline security by implementing anomaly detection through AI (Artificial Intelligence) support. The goal is to identify unusual behaviour or variations from network traffic patterns in pipeline and cloud platforms. The system shall integrate into the workflow to continuously monitor pipeline activities and cloud infrastructure. Additionally, it aims to explore adaptive response mechanisms to mitigate the detected anomalies or security threats. This research employed two popular network traffic datasets, CSE-CIC-IDS2018 and CSE-CIC-IDS2017. We implemented a combination of Convolution Neural Network(CNN) and Long Short-Term Memory (LSTM) to detect unusual traffic patterns. We achieved an accuracy of 98.69% and 98.30% and generated log files in different CI/CD pipeline stages that resemble the network anomalies affected to address security challenges in modern DevOps practices, contributing to advancing software security and reliability., Comment: 10 pages

We study the rate-distortion problem for both scalar and vector memoryless heavy-tailed $\alpha$-stable sources ($0 < \alpha < 2$). Using a recently defined notion of ``strength" as a power measure, we derive the rate-distortion function for $\alpha$-stable sources subject to a constraint on the strength of the error, and show it to be logarithmic in the strength-to-distortion ratio. We showcase how our framework paves the way to finding optimal quantizers for $\alpha$-stable sources and more generally to heavy-tailed ones. In addition, we study high-rate scalar quantizers and show that uniform ones are asymptotically optimal under the strength measure. We compare uniform Gaussian and Cauchy quantizers and show that more representation points for the Cauchy source are required to guarantee the same quantization quality. Our findings generalize the well-known rate-distortion and quantization results of Gaussian sources ($\alpha = 2$) under a quadratic distortion measure., Comment: 28 pages, 6 figures

With the wave of high-profile supply chain attacks targeting development and client organizations, supply chain security has recently become a focal point. As a result, there is an elevated discussion on securing the development environment and increasing the transparency of the third-party code that runs in software products to minimize any negative impact from third-party code in a software product. However, the literature on secure software development lacks insight into how the third-party development tools used by every developer affect the security posture of the developer, the development organization, and, eventually, the end product. To that end, we have analyzed 52,880 third-party VS Code extensions to understand their threat to the developer, the code, and the development organizations. We found that ~5.6\% of the analyzed extensions have suspicious behavior, jeopardizing the integrity of the development environment and potentially leaking sensitive information on the developer's product. We also found that the VS Code hosting the third-party extensions lacks practical security controls and lets untrusted third-party code run unchecked and with questionable capabilities. We offer recommendations on possible avenues for fixing some of the issues uncovered during the analysis.

Cepheid masses are particularly necessary to help solving the mass discrepancy, while independent distance determinations provide crucial test of the period-luminosity relation and Gaia parallaxes. We used CHARA/MIRC to measure the astrometric positions of the high-contrast companion orbiting the Cepheid SU Cygni. We also present new radial velocity measurements from the HST. The combination of interferometric astrometry with optical and ultraviolet spectroscopy provides the full orbital elements of the system, in addition to component masses and the distance to the Cepheid system. We measured the mass of the Cepheid, $M_A = 4.859\pm0.058M_\odot$, and its two companions, $M_{Ba} = 3.595 \pm 0.033 M_\odot$ and $M_{Bb} = 1.546 \pm 0.009 M_\odot$. This is the most accurate existing measurement of the mass of a Galactic Cepheid (1.2%). Comparing with stellar evolution models, we show that the mass predicted is higher than the measured mass of the Cepheid, similar to conclusions of our previous work. We also measured the distance to the system to be $926.3 \pm 5.0$pc, i.e. an unprecedented parallax precision of $6\mu$as (0.5%), being the most precise and accurate distance for a Cepheid. Such precision is similar to what is expected by Gaia for the last data release (DR5 in $\sim$ 2030) for single stars fainter than G = 13, but is not guaranteed for stars as bright as SU Cyg. We demonstrated that evolutionary models remain inadequate in accurately reproducing the measured mass, often predicting higher masses for the expected metallicity, even when factors such as rotation or convective core overshooting are taken into account. Our precise distance measurement allowed us to compare prediction period-luminosity relations. We found a disagreement of 0.2-0.5 mag with relations calibrated from photometry, while relations calibrated from direct distance measurement are in better agreement., Comment: Accepted for publication in A&A

Cavities exhibit inherent self-sustaining oscillations driven by the coupling between their hydrodynamic and acoustic properties. In practical applications, cavities are often placed within confinements that introduce compression waves, significantly influencing their primary flow characteristics. The oscillations in cavities have widespread applications, such as in fuel-air mixing, heat exchangers, and landing gears However, when resonance occurs, these oscillations can lead to structural failures. Therefore, understanding cavity oscillations under diverse geometrical configurations and flow conditions is essential. The present study examines the impact of top wall confinement on an open cavity with a length-to-depth ratio (L/D) ratio of 3 at Mach 1.71, along with the effects of varying deflection angles on flow characteristics and the influence of an increased Mach number on configurations with the highest and lowest oscillation frequencies. A three-dimensional numerical investigation is carried out, employing large eddy simulations within the OpenFOAM framework. We analyze the flow fields through the spatial variation of density over time. Fast Fourier Transformation and Wavelet Transformation reveal the frequency content from unsteady pressure signals and illustrate its evolution over time under different conditions. Additionally, reduced-order modeling provides a better understanding of the relationship between frequencies and flow structures of the cavity. Results from these analyses demonstrate that top wall confinement increases oscillation frequency, while greater deflection angles introduce Kelvin-Helmholtz instability in the flow field, reducing the frequency. An increase in the Mach number to 2, further intensifies instability, substantially affecting oscillations.

This paper presents an approach to joint wireless and computing resource management in slice-enabled metaverse networks, addressing the challenges of inter-slice and intra-slice resource allocation in the presence of in-network computing. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and derive an optimal solution using standard optimization techniques. Through extensive simulations, we demonstrate that our proposed method significantly improves system performance by effectively balancing the allocation of radio and computing resources across multiple slices. Our approach outperforms existing benchmarks, particularly in scenarios with high user demand and varying computational tasks.

The advent of high-intensity, high-polarization electron beams led to significantly improved measurements of the ratio of the proton's charge to electric form factors, GEp/GMp. However, high-$Q^2$ measurements yielded significant disagreement with extractions based on unpolarized scattering, raising questions about the reliability of the measurements and consistency of the techniques. Jefferson Lab experiment E01-001 was designed to provide a high-precision extraction of GEp/GMp from unpolarized cross section measurements using a modified version of the Rosenbluth technique to allow for a more precise comparison with polarization data. Conventional Rosenbluth separations detect the scattered electron which requires comparisons of measurements with very different detected electron energy and rate for electrons at different angles. Our Super-Rosenbluth measurement detected the struck proton, rather than the scattered electron, to extract the cross section. This yielded a fixed momentum for the detected particle and dramatically reduced cross section variation, reducing rate- and momentum-dependent corrections and uncertainties. We measure the cross section vs angle with high relative precision, allowing for extremely precise extractions of GEp/GMp at $Q^2$ = 2.64, 3.20, and 4.10 GeV$^2$. Our results are consistent with traditional extractions but with much smaller corrections and systematic uncertainties, comparable to the uncertainties from polarization measurements. Our data confirm the discrepancy between Rosenbluth and polarization extractions of the proton form factor ratio using an improved Rosenbluth extraction that yields smaller and less-correlated uncertainties than typical of previous Rosenbluth extractions. We compare our results to calculations of two-photon exchange effects and find that the observed discrepancy can be relatively well explained by such effects., Comment: 32 pages, 21 figures. arXiv admin note: text overlap with arXiv:nucl-ex/0610006

This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, dialect and low-resource speech translation, and Indic languages. The shared tasks attracted 18 teams whose submissions are documented in 26 system papers. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia., Comment: IWSLT 2024; 59 pages

The superior performance introduced by deep learning approaches in removing atmospheric particles such as snow and rain from a single image; favors their usage over classical ones. However, deep learning-based approaches still suffer from challenges related to the particle appearance characteristics such as size, type, and transparency. Furthermore, due to the unique characteristics of rain and snow particles, single network based deep learning approaches struggle in handling both degradation scenarios simultaneously. In this paper, a global framework that consists of two Generative Adversarial Networks (GANs) is proposed where each handles the removal of each particle individually. The architectures of both desnowing and deraining GANs introduce the integration of a feature extraction phase with the classical U-net generator network which in turn enhances the removal performance in the presence of severe variations in size and appearance. Furthermore, a realistic dataset that contains pairs of snowy images next to their groundtruth images estimated using a low-rank approximation approach; is presented. The experiments show that the proposed desnowing and deraining approaches achieve significant improvements in comparison to the state-of-the-art approaches when tested on both synthetic and realistic datasets.

In this paper we study a principal-agent problem in continuous time with multiple lump-sum payments (contracts) paid at different deterministic times. We reduce the non-zero sum Stackelberg game between the principal and agent to a standard stochastic optimal control problem. We apply our result to a benchmark model for which we investigate how different inputs (payment frequencies, payments' distribution, discounting factors, agent's reservation utility) affect the principal's value and agent's optimal compensations.

Secondary research use of Electronic Health Records (EHRs) is often hampered by the abundance of missing data in this valuable resource. Missingness in EHRs occurs naturally as a result of the data recording practices during routine clinical care, but handling it is crucial to the precision of medical analysis and the decision-making that follows. The literature contains a variety of imputation methodologies based on deep neural networks. Those aim to overcome the dynamic, heterogeneous and multivariate missingness patterns of EHRs, which cannot be handled by classical and statistical imputation methods. However, all existing deep imputation methods rely on end-to-end pipelines that incorporate both imputation and downstream analyses, e.g. classification. This coupling makes it difficult to assess the quality of imputation and takes away the flexibility of re-using the imputer for a different task. Furthermore, most end-to-end deep architectures tend to use complex networks to perform the downstream task, in addition to the already sophisticated deep imputation network. We, therefore ask if the high performance reported in the literature is due to the imputer or the classifier and further ask if an optimised state-of-the-art imputer is used, a simpler classifier can achieve comparable performance. This paper explores the development of a modular, deep learning-based imputation and classification pipeline, specifically built to leverage the capabilities of state-of-the-art imputation models for downstream classification tasks. Such a modular approach enables a) objective assessment of the quality of the imputer and classifier independently, and b) enables the exploration of the performance of simpler classification architectures using an optimised imputer.

Research in vision and language has made considerable progress thanks to benchmarks such as COCO. COCO captions focused on unambiguous facts in English; ArtEmis introduced subjective emotions and ArtELingo introduced some multilinguality (Chinese and Arabic). However we believe there should be more multilinguality. Hence, we present ArtELingo-28, a vision-language benchmark that spans $\textbf{28}$ languages and encompasses approximately $\textbf{200,000}$ annotations ($\textbf{140}$ annotations per image). Traditionally, vision research focused on unambiguous class labels, whereas ArtELingo-28 emphasizes diversity of opinions over languages and cultures. The challenge is to build machine learning systems that assign emotional captions to images. Baseline results will be presented for three novel conditions: Zero-Shot, Few-Shot and One-vs-All Zero-Shot. We find that cross-lingual transfer is more successful for culturally-related languages. Data and code are provided at www.artelingo.org., Comment: 9 pages, Accepted at EMNLP 24, for more details see www.artelingo.org

We present context parallelism for long-context large language model inference, which achieves near-linear scaling for long-context prefill latency with up to 128 H100 GPUs across 16 nodes. Particularly, our method achieves 1M context prefill with Llama3 405B model in 77s (93% parallelization efficiency, 63% FLOPS utilization) and 128K context prefill in 3.8s. We develop two lossless exact ring attention variants: pass-KV and pass-Q to cover a wide range of use cases with the state-of-the-art performance: full prefill, persistent KV prefill and decode. Benchmarks on H100 GPU hosts inter-connected with RDMA and TCP both show similar scalability for long-context prefill, demonstrating that our method scales well using common commercial data center with medium-to-low inter-host bandwidth.

Parenting brings emotional and physical challenges, from balancing work, childcare, and finances to coping with exhaustion and limited personal time. Yet, one in three parents never seek support. AI systems potentially offer stigma-free, accessible, and affordable solutions. Yet, user adoption often fails due to issues with explainability and reliability. To see if these issues could be solved using a co-design approach, we developed and tested NurtureBot, a wellbeing support assistant for new parents. 32 parents co-designed the system through Asynchronous Remote Communities method, identifying the key challenge as achieving a "successful chat". Aspart of co-design, parents role-played as NurturBot, rewriting its dialogues to improve user understanding, control, and outcomes. The refined prototype evaluated by 32 initial and 46 new parents, showed improved user experience and usability, with final CUQ score of 91.3/100, demonstrating successful interaction patterns. Our process revealed useful interaction design lessons for effective AI parenting support.

Classical Wolf-Rayet stars are descendants of massive OB-type stars that have lost their hydrogen-rich envelopes, and are in the final stages of stellar evolution, possibly exploding as type Ib/c supernovae. It is understood that the mechanisms driving this mass-loss are either strong stellar winds and or binary interactions, so intense studies of these binaries including their evolution can tell us about the importance of the two pathways in WR formation. WR 138 (HD 193077) has a period of just over 4 years and was previously reported to be resolved through interferometry. We report on new interferometric data combined with spectroscopic radial velocities in order to provide a three-dimensional orbit of the system. The precision on our parameters tend to be about an order of magnitude better than previous spectroscopic techniques. These measurements provide masses of the stars, namely $M_{\rm WR} = 13.93\pm1.49M_{\odot}$ and $M_{\rm O} = 26.28\pm1.71M_{\odot}$. The derived orbital parallax agrees with the parallax from \textit{Gaia}, namely with a distance of 2.13 kpc. We compare the system's orbit to models from BPASS, showing that the system likely may have been formed with little interaction but could have formed through some binary interactions either following or at the start of a red supergiant phase, but with the most likely scenario occurring as the red supergiant phase starts for a $\sim 40M_\odot$ star., Comment: accepted to ApJ

In this article, the non-Hermitian characteristics of three-dimensional PT-symmetric coupled electronic resonators are theoretically analyzed. First, the concept of non-Hermitian PT symmetry is illustrated in the context of electronics using a pair of coupled electronic resonators. Two typical configurations of parallel-coupled PT-symmetric electronic trimers are then analyzed. The results indicate that, for the planar configuration, the system can exhibit two phase transitions as the coupling coefficient or gain-loss parameter changes, different from the linear configuration. By comparing system equations based on coupled-mode theory and circuit theory, it is shown that high dimensionality alone is not a sufficient condition for the existence of a higher-order exceptional point; an approximation condition is also required. A modified exceptional point is proposed, and the approximation conditions for the mean deviation $D$ for the real part of the three eigenfrequencies, satisfying $D \leq 1\%$ and $D \leq 0.1\%$, are discussed, respectively. The theoretical results presented in this paper not only reveal the unique non-Hermitian characteristics of high-dimensional PT-symmetric electronic systems but also offer theoretical support for wireless power transmission and wireless sensing technologies.

We discuss the impact of eligible top-quark pair production differential cross-section measurements at the LHC with a collision energy of 13 TeV on the parton distribution functions (PDFs) of the proton as well as the impact of approximate next-to-next-to-next-to-leading order (aN$^3$LO) QCD corrections combined with next-to-leading order (NLO) electroweak (EW) corrections on $t\bar t$ observables. We illustrate the effects on the gluon PDF at large $x$ from an optimal baseline selection of data in NNLO global fits, and show comparisons between the theory prediction for $t\bar t$ total and differential cross sections at aN$^3$LO QCD combined with NLO EW and recent measurements from the ATLAS and CMS collaborations at the LHC., Comment: 13 Pages, 3 figures. Contribution to the conference proceedings of LHCP2024, the 12th Edition of the Large Hadron Collider Physics Conference, 3-7 June 2024 Boston, USA. This contribution is based on 2307.11153[hep-ph] and 2306.06166[hep-ph]

The research aims to construct a new type of matrix called the Fibonacci-Hessenberg-Lorentz matrix by multiplying Fibonacci-Hessenberg matrices with Lorentz matrix multiplication. The study will start by examining the properties of Hessenberg and tridiagonal matrices and then focus on developing the Fibonacci-Hessenberg matrix using Fibonacci sequences. By multiplication it with a Lorentz matrix multiplication, the resulting matrix, the Fibonacci-Hessenberg-Lorentz matrix, will be analyzed to obtain special number sequences through its determinants for n>=1. The primary objective is to explore whether the determinants of these matrices can generate new or known number sequences, where the elements are expressed as functions of the matrix parameters. Furthermore, the research will attempt to generalize these sequences of using Fibonacci numbers to establish a generalized formula for their terms. Ultimately, the goal is to derive a mathematical representation that connects the characteristics of the newly defined matrices to well-known special sequences in mathematics., Comment: 18 pages

The manner in which different racial and gender groups are portrayed in news coverage plays a large role in shaping public opinion. As such, understanding how such groups are portrayed in news media is of notable societal value, and has thus been a significant endeavour in both the computer and social sciences. Yet, the literature still lacks a longitudinal study examining both the frequency of appearance of different racial and gender groups in online news articles, as well as the context in which such groups are discussed. To fill this gap, we propose two machine learning classifiers to detect the race and age of a given subject. Next, we compile a dataset of 123,337 images and 441,321 online news articles from New York Times (NYT) and Fox News (Fox), and examine representation through two computational approaches. Firstly, we examine the frequency and prominence of appearance of racial and gender groups in images embedded in news articles, revealing that racial and gender minorities are largely under-represented, and when they do appear, they are featured less prominently compared to majority groups. Furthermore, we find that NYT largely features more images of racial minority groups compared to Fox. Secondly, we examine both the frequency and context with which racial minority groups are presented in article text. This reveals the narrow scope in which certain racial groups are covered and the frequency with which different groups are presented as victims and/or perpetrators in a given conflict. Taken together, our analysis contributes to the literature by providing two novel open-source classifiers to detect race and age from images, and shedding light on the racial and gender biases in news articles from venues on opposite ends of the American political spectrum., Comment: 13 pages, and 11 figures

As the role of variable renewables in electricity markets expands, new market products help system operators manage imbalances caused by uncertainty and variability. Whereas work in the last decade has focused on constructing demand curves for central procurement of those products, little attention has been paid to designing their settlement scheme and understanding the connections between the economic value of these products, the schedule of variable resources, and the cost of flexibility. In this article, we compare a new product called Flexibility Options, which addresses these gaps, with a traditional reserve product using a case study similar to the 2019 Texas (ERCOT) system. Our findings suggest that both products are equally effective in managing imbalances, but Flexibility Options have superior risk management properties and keep the system operator revenue adequate.

Limited public understanding of legal processes and inconsistent verdicts in the Indonesian court system led to widespread dissatisfaction and increased stress on judges. This study addresses these issues by developing a deep learning-based predictive system for court sentence lengths. Our hybrid model, combining CNN and BiLSTM with attention mechanism, achieved an R-squared score of 0.5893, effectively capturing both local patterns and long-term dependencies in legal texts. While document summarization proved ineffective, using only the top 30% most frequent tokens increased prediction performance, suggesting that focusing on core legal terminology balances information retention and computational efficiency. We also implemented a modified text normalization process, addressing common errors like misspellings and incorrectly merged words, which significantly improved the model's performance. These findings have important implications for automating legal document processing, aiding both professionals and the public in understanding court judgments. By leveraging advanced NLP techniques, this research contributes to enhancing transparency and accessibility in the Indonesian legal system, paving the way for more consistent and comprehensible legal decisions., Comment: 11 pages, 7 figures, 6 tables, submitted to Journal of Advances in Information Technology

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Retrieval-Augmented Generation (RAG) systems using large language models (LLMs) often generate inaccurate responses due to the retrieval of irrelevant or loosely related information. Existing methods, which operate at the document level, fail to effectively filter out such content. We propose LLM-driven chunk filtering, ChunkRAG, a framework that enhances RAG systems by evaluating and filtering retrieved information at the chunk level. Our approach employs semantic chunking to divide documents into coherent sections and utilizes LLM-based relevance scoring to assess each chunk's alignment with the user's query. By filtering out less pertinent chunks before the generation phase, we significantly reduce hallucinations and improve factual accuracy. Experiments show that our method outperforms existing RAG models, achieving higher accuracy on tasks requiring precise information retrieval. This advancement enhances the reliability of RAG systems, making them particularly beneficial for applications like fact-checking and multi-hop reasoning.

The use of non-invasive sensors & systems to measure particle beam characteristics is a crucial part of modern accelerator control systems due to their ability to return real time passive measurements without impacting the beam quality. Simulations, which can predict these sensors' behaviour and performance under anticipated accelerator conditions, are valuable tools to ensure confidence in the sensors' functionality prior to a physical bench test. This paper details the design, testing, and results of two sensor models developed using CST Studio Suite software. One model is an elliptical, large-aperture beam position monitor (BPM) for which vertical & horizontal position signal linearity was analyzed. The second model is an AC current transformer (ACCT) beam current monitor (BCM), which was used to search for potential fault cases within the BCM and beam pipe flange gaps. Fermilab Proton Improvement Plan II (PIP-II) accelerator beam conditions were applied, and special focus is given to the discovery of linearity variations within the BPM as well as the use of frequency domain techniques in the BCM fault case analyses., Comment: 5 pages, 9 figures. To be submitted to the Journal of Instrumentation

We simulated the Coulomb explosion dynamics due to the fast ionization induced by high-intensity X-rays in six proteins that share similar atomic content and shape. We followed and projected the trajectory of the fragments onto a virtual detector, providing a unique explosion footprint. After collecting 500 explosion footprints for each protein, we utilized principal component analysis and t-distributed stochastic neighbor embedding to classify these. The results show that the classification algorithms were able to separate proteins on the basis of explosion footprints from structurally similar proteins into distinct groups. The explosion footprints, therefore, provide a unique identifier for each of the proteins. We envision that method could be used concurrently with single particle coherent imaging experiments to provide additional information on shape, mass, or conformation., Comment: 6 pages. 4 figures

Grammatical error correction (GEC) is important for improving written materials for low-resource languages like Zarma -- spoken by over 5 million people in West Africa. Yet it remains a challenging problem. This study compares rule-based methods, machine translation (MT) models, and large language models (LLMs) for GEC in Zarma. We evaluate each approach's effectiveness on our manually-built dataset of over 250,000 examples using synthetic and human-annotated data. Our experiments show that the MT-based approach using the M2M100 model outperforms others, achieving a detection rate of 95.82% and a suggestion accuracy of 78.90% in automatic evaluations, and scoring 3.0 out of 5.0 in logical/grammar error correction during MEs by native speakers. The rule-based method achieved perfect detection (100%) and high suggestion accuracy (96.27%) for spelling corrections but struggled with context-level errors. LLMs like MT5-small showed moderate performance with a detection rate of 90.62% and a suggestion accuracy of 57.15%. Our work highlights the potential of MT models to enhance GEC in low-resource languages, paving the way for more inclusive NLP tools.

Optimally designing molten salt applications requires knowledge of their thermophysical properties, but existing databases are incomplete, and experiments are challenging. Ideal mixing and Redlich-Kister models are computationally cheap but lack either accuracy or generality. To address this, a transfer learning approach using deep neural networks (DNNs) is proposed, combining Redlich-Kister models, experimental data, and ab initio properties. The approach predicts molten salt density with high accuracy ($r^{2}$ > 0.99, MAPE < 1%), outperforming the alternatives., Comment: Manuscript contains 25 pages including references and other information. Manuscript contains 4 figures and 3 tables. To be submitted to ACS Journal of Chemical Theory and Computation

Measurements of the EMC effect in the tritium and helium-3 mirror nuclei are reported. The data were obtained by the MARATHON Jefferson Lab experiment, which performed deep inelastic electron scattering from deuterium and the three-body nuclei, using a cryogenic gas target system and the High Resolution Spectrometers of the Hall A Facility of the Lab. The data cover the Bjorken $x$ range from 0.20 to 0.83, corresponding to a squared four-momentum transfer $Q^2$ range from 2.7 to $11.9\gevsq$, and to an invariant mass $W$ of the final hadronic state greater than 1.84 GeV/${\it c}^2$. The tritium EMC effect measurement is the first of its kind. The MARATHON experimental results are compared to results from previous measurements by DESY-HERMES and JLab-Hall C experiments, as well as with few-body theoretical predictions., Comment: arXiv admin note: text overlap with arXiv:2104.05850

The right to be forgotten mandates that machine learning models enable the erasure of a data owner's data and information from a trained model. Removing data from the dataset alone is inadequate, as machine learning models can memorize information from the training data, increasing the potential privacy risk to users. To address this, multiple machine unlearning techniques have been developed and deployed. Among them, approximate unlearning is a popular solution, but recent studies report that its unlearning effectiveness is not fully guaranteed. Another approach, exact unlearning, tackles this issue by discarding the data and retraining the model from scratch, but at the cost of considerable computational and memory resources. However, not all devices have the capability to perform such retraining. In numerous machine learning applications, such as edge devices, Internet-of-Things (IoT), mobile devices, and satellites, resources are constrained, posing challenges for deploying existing exact unlearning methods. In this study, we propose a Constraint-aware Adaptive Exact Unlearning System at the network Edge (CAUSE), an approach to enabling exact unlearning on resource-constrained devices. Aiming to minimize the retrain overhead by storing sub-models on the resource-constrained device, CAUSE innovatively applies a Fibonacci-based replacement strategy and updates the number of shards adaptively in the user-based data partition process. To further improve the effectiveness of memory usage, CAUSE leverages the advantage of model pruning to save memory via compression with minimal accuracy sacrifice. The experimental results demonstrate that CAUSE significantly outperforms other representative systems in realizing exact unlearning on the resource-constrained device by 9.23%-80.86%, 66.21%-83.46%, and 5.26%-194.13% in terms of unlearning speed, energy consumption, and accuracy., Comment: Accepted to IEEE Symposium on Security and Privacy 2025 (Oakland 2025)

Recent experiments by S. Krishnia et al., Nano Lett. 23, 6785 (2023) reported an unprecedentedly large enhancement of torques upon inserting thin Al layer in Co/Pt heterostructure that suggested the presence of a Rashba-like interaction at the metallic Co/Al interface. Based on first-principles calculations, we reveal the emergence of a large helical orbital texture in reciprocal space at the interfacial Co layer, whose origin is attributed to the orbital Rashba effect due to the formation of the surface states at the Co/Al interface and where spin-orbit coupling is found to produce smaller contributions with a higher-order winding of the orbital momentum. Our results unveil that the orbital texture gives rise to a non-equilibrium orbital accumulation producing large current-induced torques, thus providing an essential theoretical background for the experimental data and advancing the use of orbital transport phenomena in all-metallic magnetic systems with light elements., Comment: Accepted to ACS Nano Letters

Computed Tomography (CT) reconstruction of objects with cylindrical symmetry can be performed with a single projection. When the measured rays are parallel, and the axis of symmetry is perpendicular to the optical axis, the data can be modeled with the so-called Abel Transform. The Abel Transform has been extensively studied and many methods exist for accurate reconstruction. However, most CT geometries are cone-beam rather than parallel-beam. Using Abel methods for reconstruction in these cases can lead to distortions and reconstruction artifacts. Here, we develop analytic and model-based iterative reconstruction (MBIR) methods to reconstruct symmetric objects with an arbitrary axis of symmetry from a cone-beam geometry. The MBIR methods demonstrate superior results relative to the analytic inversion methods by mitigating artifacts and reducing noise while retaining fine image features. We demonstrate the efficacy of our methods using simulated and experimentally-acquired x-ray and neutron projections.

Silicon vacancy ($\mathrm{V_{Si}}$) color centers in 4H-SiC are optically accessible through their zero-phonon line (ZPL) photoluminescence (PL), which is sensitive to the sample temperature. We report the effects of localized laser-induced heating in 4H-SiC by measuring the PL spectra of $\mathrm{V_{Si}}$ color centers. The effects of laser-induced heating manifest as the decrease in the peak height, redshift, and broadening of the ZPLs in the PL spectrum. The local temperature in the sample can be determined from the center energy of the ZPLs by using the Varshni equation. The sample temperature can be modeled as a system in contact with a thermal reservoir while being heated by a laser beam. This work highlights the importance of considering laser-induced heating in the optical properties of color centers in 4H-SiC and their potential applications. The result also suggests that the sharp and bright ZPLs of color centers can be used as local temperature probes in 4H-SiC devices., Comment: 13 pages, 14 figures

Classical Wolf-Rayet stars are the descendants of massive OB stars that have lost their hydrogen envelopes and are burning helium in their cores prior to exploding as type Ib/c supernovae. The mechanisms for losing their hydrogen envelopes are either through binary interactions or through strong stellar winds potentially coupled with episodic mass-loss. Amongst the bright classical WR stars, the binary system WR\,137 (HD\,192641; WC7d + O9e) is the subject of this paper. This binary is known to have a 13-year period and produces dust near periastron. Here we report on interferometry with the CHARA Array collected over a decade of time and providing the first visual orbit for the system. We combine these astrometric measurements with archival radial velocities to measure masses of the stars of $M_{\rm WR} = 9.5\pm3.4 M_\odot$ and $M_{\rm O} = 17.3\pm 1.9 M_\odot$ when we use the most recent \textit{Gaia} distance. These results are then compared to predicted dust distribution using these orbital elements, which match the observed imaging from \textit{JWST} as discussed recently by Lau et al. Furthermore, we compare the system to the BPASS models, finding that the WR star likely formed through stellar winds and not through binary interactions. However, the companion O star did likely accrete some material from the WR's mass-loss to provide the rotation seen today that drives its status as an Oe star., Comment: Accepted to ApJ

High-speed multiplex imaging of fluorescent probes is limited by a combination of spectral resolution, sensitivity, high cost and low light throughput of detectors, and filters. In this work, we present a hyperspectral detection system based on a silicon photomultiplier array that enables high-speed, high-light throughput hyperspectral imaging at low cost. We demonstrate 16 spectral channel imaging at 50 MP/s (800M spectra per second) with a conventional two photon microscope combined with a generalized spectral unmixing model that enables extraction of spectrally overlapping fluorophores. We show that the high spectral resolution combined with high throughput enables the multiplexing of multiple contrast agents over large areas and the detection of subtle spectral shifts associated with molecular binding. Silicon photomultiplier arrays may be a promising method to extend multiplex fluorescence imaging in a variety of scenarios., Comment: 14 pages, 7 figures

We explore two constructions of oligomorphic Jordan permutation groups preserving a `limit of betweenness relations' and a `limit of $D$-relations', from \cite{bhattmacph2006jordan} and \cite{almazaydeh2021jordan} respectively. Several issues left open in \cite{almazaydeh2021jordan} are resolved. In particular it is shown that the `limit of $D$-relations' is not homogeneous in the given language, but is `homogenizable', that is, there is a homogeneous structure over a finite relational language with the same universe and the same automorphism group. The structure is NIP, but not monadically NIP, its age is not well-quasi-ordered under embeddability, and the growth rate of the sequence enumerating orbits on $k$-sets grows faster than exponentially. The automorphism group is maximal-closed in the symmetric group. Similar results are shown for the construction in \cite{bhattmacph2006jordan}., Comment: 21 pages

This paper presents advancements in the functionalities of the Recupera-Reha lower extremity exoskeleton robot. The exoskeleton features a series-parallel hybrid design characterized by multiple kinematic loops resulting in 148 degrees of freedom in its spanning tree and 102 independent loop closure constraints, which poses significant challenges for modeling and control. To address these challenges, we applied an optimal control approach to generate feasible trajectories such as sitting, standing, and static walking, and tested these trajectories on the exoskeleton robot. Our method efficiently solves the optimal control problem using a serial abstraction of the model to generate trajectories. It then utilizes the full series-parallel hybrid model, which takes all the kinematic loop constraints into account to generate the final actuator commands. The experimental results demonstrate the effectiveness of our approach in generating the desired motions for the exoskeleton., Comment: 8 pages, 16 figures, IEEE-RAS International Conference on Humanoid Robots 2024

Neural multi-channel speech enhancement models, in particular those based on the U-Net architecture, demonstrate promising performance and generalization potential. These models typically encode input channels independently, and integrate the channels during later stages of the network. In this paper, we propose a novel modification of these models by incorporating relative information from the outset, where each channel is processed in conjunction with a reference channel through stacking. This input strategy exploits comparative differences to adaptively fuse information between channels, thereby capturing crucial spatial information and enhancing the overall performance. The experiments conducted on the CHiME-3 dataset demonstrate improvements in speech enhancement metrics across various architectures.