November 2024 arXiv papers — page 128

Ziwei Yang, Takeyuki Tamura

When simulating metabolite productions with genome-scale constraint-based metabolic models, gene deletion strategies are necessary to achieve growth-coupled production, which means cell growth and target metabolite production occur simultaneously. Since obtaining gene deletion strategies for large genome-scale models suffers from significant computational ti

Allen Liu, Ankur Moitra

Model stealing, where a learner tries to recover an unknown model via carefully chosen queries, is a critical problem in machine learning, as it threatens the security of proprietary models and the privacy of data they are trained on. In recent years, there has been particular interest in stealing large language models (LLMs). In this paper, we aim to build

Syed W. Shah. Lei Pan, Din Duc Nha Nguyen, Robin Doss, Warren Armstrong

DNSSEC, a DNS security extension, is essential to accurately translating domain names to IP addresses. Digital signatures provide the foundation for this reliable translation; however, the evolution of 'Quantum Computers' has made traditional digital signatures vulnerable. In light of this, NIST has recently selected potential post-quantum digital signatures

Steven Jens Jorgensen, Ravi Bhadeshiya

We present an approach for retartgeting off-the-shelf Virtual Reality (VR) trackers to effectively teleoperate an upper-body humanoid while ensuring self-collision-free motions. Key to the effectiveness was the proper assignment of trackers to joint sets via modified task Jacobians and relaxed barrier functions for self-collision avoidance. The approach was

Linyang He, Ercong Nie, Helmut Schmid, Hinrich Schütze

This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning) by distinguishing two LLM assessment paradigms: psycholinguistic and neurolinguistic. Traditional psycholinguistic evaluations often reflect statistical rules that may not accurately represent LLMs' true linguistic competenc

J. Nicholas Neuberger, Alen Alexanderian, Bart van Bloemen Waanders

We consider goal-oriented optimal design of experiments for infinite-dimensional Bayesian linear inverse problems governed by partial differential equations (PDEs). Specifically, we seek sensor placements that minimize the posterior variance of a prediction or goal quantity of interest. The goal quantity is assumed to be a nonlinear functional of the inversi

Zifan Tang, Yang Luo, Jian-Min Wang

A stellar-mass black hole, embedded within the accretion disk of an active galactic nuclei (AGN), has the potential to accrete gas at a rate that can reach approximately $\sim 10^9$ times the Eddington limit. This study explores the potential for nuclear burning in the rapidly accreting flow towards this black hole and studies how nucleosynthesis affects met

Taichi Nogami, Issei Suzuki, Daiki Motai, Hiroshi Tanimura

Tin sulfide (SnS) has garnered much attention as a promising material for various applications, including solar cells and thermoelectric devices, owing to its favorable optical and electronic properties and the abundant and nontoxic nature of its constituent elements. Herein, we investigated the effect of non-stoichiometry on the morphology and electrical pr

C. Bray, S. Fretwell, L. A. Zepeda-Ruiz, I. Kim

Superconducting sensors doped with rare isotopes have recently demonstrated powerful sensing performance for sub-keV radiation from nuclear decay. Here, we report the first high-resolution recoil spectroscopy of a single, selected nuclear state using superconducting tunnel junction (STJ) sensors. The STJ sensors were used to measure the eV-scale nuclear reco

Minda Li, Bhaskar Krishnamachari

ChatGPT and other large language models (LLMs) promise to revolutionize software development by automatically generating code from program specifications. We assess the performance of ChatGPT's GPT-3.5-turbo model on LeetCode, a popular platform with algorithmic coding challenges for technical interview practice, across three difficulty levels: easy, medium,

Muhammed Fatih Bulut, Yingqi Liu, Naveed Ahmad, Maximilian Turner

Large and Small Language Models (LMs) are typically pretrained using extensive volumes of text, which are sourced from publicly accessible platforms such as Wikipedia, Book Corpus, or through web scraping. These models, due to their exposure to a wide range of language data, exhibit impressive generalization capabilities and can perform a multitude of tasks

Junxi Liu, Yanyan Feng, Jiehai Chen, Yun Xue

The dynamic expansion of social media has led to an inundation of hateful memes on media platforms, accentuating the growing need for efficient identification and removal. Acknowledging the constraints of conventional multimodal hateful meme classification, which heavily depends on external knowledge and poses the risk of including irrelevant or redundant co

Randolph T. Bushman, Tanya M. Tebcherani, Alhassan S. Yasin

In this paper, we introduce and prove QR Sort, a novel non-comparative integer sorting algorithm. This algorithm uses principles derived from the Quotient-Remainder Theorem and Counting Sort subroutines to sort input sequences stably. QR Sort exhibits the general time and space complexity $\mathcal{O}(n+d+\frac{m}{d})$, where $n$ denotes the input sequence l

Zhenjie Liu, Kun Xu, Jun Zhang, Wenting Wang

Xu \& Jing reported a monotonic relationship between host halo mass $M_h$ and the morphology of massive central galaxies, characterized by the S\'ersic index $n$, at fixed stellar mass, suggesting that morphology could serve as a good secondary proxy for halo mass. Since their results were derived using the indirect abundance matching method, we further inve

Jiahao Wu, Yuxin Wu, Xin Li, Guihua Zhang

Prediction of Kelvin-Helmholtz instability (KHI) is crucial across various fields, requiring extensive high-fidelity data. However, experimental data are often sparse and noisy, while simulated data may lack credibility due to discrepancies with real-world configurations and parameters. This underscores the need for field reconstruction and parameter inferen

Raed Al Kontar

We focus on collaborative and federated black-box optimization (BBOpt), where agents optimize their heterogeneous black-box functions through collaborative sequential experimentation. From a Bayesian optimization perspective, we address the fundamental challenges of distributed experimentation, heterogeneity, and privacy within BBOpt, and propose three unify

Haydar Sahin, Hakan Akgün, Zhuo Bin Siu, S. M. Rafi-Ul-Islam

The erratic nature of chaotic behavior is thought to erode the stability of periodic behavior, including topological oscillations. However, we discover that in the presence of chaos, non-trivial topology not only endures but also provides robust protection to chaotic dynamics within a topological lattice hosting non-linear oscillators. Despite the difficulty

Sina Bagheri Nezhad, Sayan Bandyapadhyay, Ameeta Agrawal

Fairness in multi-document summarization of user-generated content remains a critical challenge in natural language processing (NLP). Existing summarization methods often fail to ensure equitable representation across different social groups, leading to biased outputs. In this paper, we introduce two novel methods for fair extractive summarization: FairExtra

Mortan Thomas, Abinash Borah, Anirudh Paranjothi

Connected autonomous vehicles, or Vehicular Ad hoc Networks (VANETs), hold great promise, but concerns persist regarding safety, privacy, and security, particularly in the face of Sybil attacks, where malicious entities falsify neighboring traffic information. Despite advancements in detection techniques, many approaches suffer from processing delays and rel

Muhammed Fatih Bulut, Acar Tamersoy, Naveed Ahmad, Yingqi Liu

This paper introduces TIPS: Threat Actor Informed Prioritization using SecEncoder, a specialized language model for security. TIPS combines the strengths of both encoder and decoder language models to detect and prioritize compromised applications. By integrating threat actor intelligence, TIPS enhances the accuracy and relevance of its detections. Extensive

Yinglin Xie, Xinyi Hou, Yanjie Zhao, Kai Chen

Impersonation tactics, such as app squatting and app cloning, have posed longstanding challenges in mobile app stores, where malicious actors exploit the names and reputations of popular apps to deceive users. With the rapid growth of Large Language Model (LLM) stores like GPT Store and FlowGPT, these issues have similarly surfaced, threatening the integrity

Risako Tanigawa, Kenji Ishikawa, Noboru Harada, Yasuhiro Oikawa

Development of optical technology has enabled imaging of two-dimensional (2D) sound fields. This acousto-optic sensing enables understanding of the interaction between sound and objects such as reflection and diffraction. Moreover, it is expected to be used an advanced measurement technology for sonars in self-driving vehicles and assistive robots. However,

Jialu Li, Manish Kumar Thota, Ruslan Gokhman, Radek Holik

Visual Question Answering (VQA) research seeks to create AI systems to answer natural language questions in images, yet VQA methods often yield overly simplistic and short answers. This paper aims to advance the field by introducing Visual Question Explanation (VQE), which enhances the ability of VQA to provide detailed explanations rather than brief respons

Yang He, Chengchuan An, Jiawei Lu, Yao-Jan Wu

The acquisition of real-time and accurate traffic arrival information is of vital importance for proactive traffic control systems, especially in partially connected vehicle environments. License plate recognition (LPR) data that record both vehicle departures and identities are proven to be desirable in reconstructing lane-based arrival curves in previous w

Ruiquan Huang, Yingbin Liang, Jing Yang

Distributionally robust offline reinforcement learning (RL) aims to find a policy that performs the best under the worst environment within an uncertainty set using an offline dataset collected from a nominal model. While recent advances in robust RL focus on Markov decision processes (MDPs), robust non-Markovian RL is limited to planning problem where the t

S. N. Gladkovskii

The author proposed an elementary derivation of the formula of Yu.V. Nesterenko expansion in continued fraction of a number 2*$\zeta(3)$.

Marta Deysiane Alves Faria Sousa, Raquel Meister Ko. Freitag, Túlio Sousa de Gois

The collection of speech data carried out in Sociolinguistics has the potential to enhance large language models due to its quality and representativeness. In this paper, we examine the ethical considerations associated with the gathering and dissemination of such data. Additionally, we outline strategies for addressing the sensitivity of speech data, as it

Qi Ding

In this paper, we develop a new strategy to study Lagrangain mean curvature equation on open sets of $\mathbb{R}^{n}(n\geq2)$. By establishing an Allard-type regularity theorem, we obtain an interior Hessian estimate of solutions to this equation with prescribed Lipschitz critical and supercritical phases. Here, our condition on the phases is sharp. The proo

Wenwei Xie, Jie Yin, Zihao Chen

To address the issues of insufficient robustness, unstable features, and data noise interference in existing network attack detection and identification models, this paper proposes an attack traffic detection and identification method based on temporal spectrum. First, traffic data is segmented by a sliding window to construct a feature sequence and a corres

Katsuya T. Abe, Masamune Oguri, Simon Birrer, Narayan Khadka

Time delays in both galaxy- and cluster-scale strong gravitational lenses have recently attracted a lot of attention in the context of the Hubble tension. Future wide-field cadenced surveys, such as the LSST, are anticipated to discover strong lenses across various scales. We generate mock catalogs of strongly lensed QSOs and SNe on galaxy-, group-, and clus

Lei Sang, Qiuze Ru, Honghao Li, Yiwen Zhang

Click-Through Rate (CTR) prediction plays a vital role in recommender systems, online advertising, and search engines. Most of the current approaches model feature interactions through stacked or parallel structures, with some employing knowledge distillation for model compression. However, we observe some limitations with these approaches: (1) In parallel s

Mohammad Ful Hossain Seikh

Radio antennas are widely used in the field of particle astrophysics in searches for ultra-high energy cosmic rays (UHECR) and neutrinos (UHEN). It is therefore necessary to properly describe the physics of their response. In this article, we summarize the mathematics underlying parameterizations of radio antennas. As a paradigm, we focus on a half-wave dipo

Yang Hu, Xiao Wang, Zezhen Ding, Lirong Wu

Diffusion-based models have significant achievements in time series generation but suffer from inefficient computation: solving high-dimensional ODEs/SDEs via iterative numerical solvers demands hundreds to thousands of drift function evaluations per sample, incurring prohibitive costs. To resolve this, we propose FlowTS, an ODE-based model that leverages re

Reyan Ahmed, Debajyoti Mondal, Rahnuma Islam Nishat

An \emph{additive +$\beta W$ spanner} of an edge weighted graph $G=(V,E)$ is a subgraph $H$ of $G$ such that for every pair of vertices $u$ and $v$, $d_{H}(u,v) \le d_G(u,v) + \beta W$, where $d_G(u,v)$ is the shortest path length from $u$ to $v$ in $G$. While additive spanners are very well studied in the literature, spanners that are both additive and ligh

He Wei, Yuekui Yang, Yang Zhang, Haiyang Wu

Deep Learning Recommendation Model(DLRM)s utilize the embedding layer to represent various categorical features. Traditional DLRMs adopt unified embedding size for all features, leading to suboptimal performance and redundant parameters. Thus, lots of Automatic Embedding size Search (AES) works focus on obtaining mixed embedding sizes with strong model perfo

Shengqi Chen, Zilin Wang, Jianrong Dai, Shirui Qin

Background and Purpose: Accurate motion tracking in MRI-guided Radiotherapy (MRIgRT) is essential for effective treatment delivery. This study aimed to enhance motion tracking precision in MRIgRT through an automatic real-time markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD) framework with automatic segmentation. Materials and

G. H. Sargsyan, Kazuki Yoshida, Kazuyuki Ogata, K. D. Launey

We report on the first \textit{ab initio} informed $\alpha$ knock-out reaction in the intermediate-mass region, with the aim to probe the underlying chiral potential and its impact on the emergence of alpha clustering in this mass region. The theoretical predictions of the $\alpha+^{16}$O clustering in the $^{20}$Ne ground state, based on the \textit{ab init

Lior Shamir

In the past decade, an asymmetry in the large-scale distribution of galaxy spin directions has been observed in data from all relevant digital sky surveys, all showing a higher number of galaxies rotating in the opposite direction relative to the Milky Way as observed from Earth. Additionally, JWST deep fields have shown that the asymmetry is clear and obvio

Gaurav Menghani, Ravi Kumar, Sanjiv Kumar

One of the core pillars of efficient deep learning methods is architectural improvements such as the residual/skip connection, which has led to significantly better model convergence and quality. Since then the residual connection has become ubiquitous in not just convolutional neural networks but also transformer-based architectures, the backbone of LLMs. I

Jie Zhou, Chao Xiao, Bowen Peng, Tianpeng Liu

The fundamental challenge in SAR target detection lies in developing discriminative, efficient, and robust representations of target characteristics within intricate non-cooperative environments. However, accurate target detection is impeded by factors including the sparse distribution and discrete features of the targets, as well as complex background inter

Virginia Vassilevska Williams, Alek Westover

This work considers the problem of output-sensitive listing of occurrences of $2k$-cycles for fixed constant $k\geq 2$ in an undirected host graph with $m$ edges and $t$ $2k$-cycles. Recent work of Jin and Xu (and independently Abboud, Khoury, Leibowitz, and Safier) [STOC 2023] gives an $O(m^{4/3}+t)$ time algorithm for listing $4$-cycles, and recent work by

Cong Wu, Jing Chen, Ziwei Wang, Ruichao Liang

Smart contracts, self-executing agreements directly encoded in code, are fundamental to blockchain technology, especially in decentralized finance (DeFi) and Web3. However, the rise of Ponzi schemes in smart contracts poses significant risks, leading to substantial financial losses and eroding trust in blockchain systems. Existing detection methods, such as

Hiromi Oginuma, Masato Shinoda

The game of Nim, which has been well known for many years, has numerous variations. One such variation is Circular Nim, where piles of stones are arranged on a circumference, and players take stones from consecutive adjacent piles in one move. In this paper, we propose a new variant called Shrinking Circular Nim, in which the size of the circle decreases as

Ganzhao Yuan

This paper considers a class of structured fractional minimization problems. The numerator consists of a differentiable function, a simple nonconvex nonsmooth function, a concave nonsmooth function, and a convex nonsmooth function composed with a linear operator. The denominator is a continuous function that is either weakly convex or has a weakly convex squ

Shuwei Xing, Mateen Mirzaei, Wenyao Xia, Inaara Ahmed-Fazal

The 2D projective nature of X-ray radiography presents significant limitations in fluoroscopy-guided interventions, particularly the loss of depth perception and prolonged radiation exposure. Integrating magnetic trackers into these workflows is promising; however, it remains challenging and under-explored in current research and practice. To address this, w

Alwin Peng, Julian Michael, Henry Sleight, Ethan Perez

As large language models (LLMs) grow more powerful, ensuring their safety against misuse becomes crucial. While researchers have focused on developing robust defenses, no method has yet achieved complete invulnerability to attacks. We propose an alternative approach: instead of seeking perfect adversarial robustness, we develop rapid response techniques to l

Jian Wang, Xing Wang, Yefan Wang

The Higgs boson decay into bottom quarks is the dominant decay channel contributing to its total decay width, which can be used to measure the bottom quark Yukawa coupling and mass. This decay width has been computed up to $\mathcal{O}(\alpha_s^4)$ for the process induced by the bottom quark Yukawa coupling, assuming massless final states, and the correspond

Yuichiro Ishida, Kenneth C. Wong, Anton T. Jaelani, Anupreeta More

Galaxy-scale strong gravitational lenses are valuable objects for a variety of astrophysical and cosmological applications. Strong lensing galaxies are rare, so efficient search methods, such as convolutional neural networks, are often used on large imaging datasets. In this work, we apply a new technique to improve the performance of supervised neural netwo

Miguel Bacaoco, Max Galettis, James Huang, Denis Ilin

We theoretically investigate the generation of three-photon states with spatial entanglement in cubic nonlinear coupled waveguides using third-order spontaneous parametric down-conversion and quantum walks. Our approach involves independently pumping two coupled waveguides to generate a path-encoded three-photon Greenberger Horne Zeilinger (GHZ) state, which

Jia Wei, Chun Ouyang, Arthur ter Hofstede, Ying Wang

Real-world processes involve multiple object types with intricate interrelationships. Traditional event logs (in XES format), which record process execution centred around the case notion, are restricted to a single-object perspective, making it difficult to capture the behaviour of multiple objects and their interactions. To address this limitation, object-

Lei Yang, Wei He, Sheng Shen, Yucheng He

Reducing the scanning time of very-low field (VLF) magnetic resonance imaging (MRI) scanners, commonly employed for stroke diagnosis, can enhance patient comfort and operational efficiency. The conventional parallel imaging (PI) technique for high-field MRI should be tailored to apply here, considering the differences in the direction of the main magnetic fi

Zhikang Fan, Weiran Shen

A monopolistic seller aims to sell an indivisible item to multiple potential buyers. Each buyer's valuation depends on their private type and the item's quality. The seller can observe the quality but it is unknown to buyers. This quality information is valuable to buyers, so it is beneficial for the seller to strategically design experiments that reveal inf

Dun Tang

In this paper, we first generalize the K-theoretic Ancestor-Descendant (AD) correspondence in \cite{perm7} to allow arbitrary permutative inputs. With this version of AD correspondence, we reconstruct K-theoretical descendant $g=0$ invariants, and $g=1$ invariants with point target space, from $1$-point invariants of the corresponding genus. In the appendix,

Ming Lyu, Hao Chen, Dan Wang, Guangyin Feng

Integrated sensing and communications (ISAC) as one of the key technologies is capable of supporting high-speed communication and high-precision sensing for the upcoming 6G. This paper studies a waveform strategy by designing the orthogonal frequency division multiplexing (OFDM)-based reference signal (RS) for sensing and communication in ISAC system. We der

Ke Ma, Junfei Xie

The rise of delay-sensitive yet computing-intensive Internet of Things (IoT) applications poses challenges due to the limited processing power of IoT devices. Mobile Edge Computing (MEC) offers a promising solution to address these challenges by placing computing servers close to end users. Despite extensive research on MEC, optimizing network topology to im

Yuexin Bian, Jie Feng, Yuanyuan Shi

Real-world control systems require policies that are not only high-performing but also interpretable and robust. A promising direction toward this goal is model-based control, which learns system dynamics and cost functions from historical data and then uses these models to inform decision-making. Building on this paradigm, we introduce DiffOP, a novel frame

Pasan Dissanayake, Faisal Hamman, Barproda Halder, Ilia Sucholutsky

Knowledge distillation deploys complex machine learning models in resource-constrained environments by training a smaller student model to emulate internal representations of a complex teacher model. However, the teacher's representations can also encode nuisance or additional information not relevant to the downstream task. Distilling such irrelevant inform

Jinming Xing, Ruilin Xing, Chang Xue, Dongwen Luo

Link prediction is crucial for understanding complex networks but traditional Graph Neural Networks (GNNs) often rely on random negative sampling, leading to suboptimal performance. This paper introduces Fuzzy Graph Attention Networks (FGAT), a novel approach integrating fuzzy rough sets for dynamic negative sampling and enhanced node feature aggregation. Fu

Xiang-Cheng Meng, Chao-Hui Wang, Shao-Wen Wei

The observed jet precession period of approximately 11 years for M87* strongly suggests the presence of a supermassive rotating black hole with a tilted accretion disk at the center of the galaxy. By modeling the motion of the tilted accretion disk particle with the spherical orbits around a Kerr-Newman black hole, we study the effect of charge on the observ

Yi Wen Heng, Zeyang Ma, Haoxiang Zhang, Zhenhao Li

Reusing third-party libraries increases productivity and saves time and costs for developers. However, the downside is the presence of vulnerabilities in those libraries, which can lead to catastrophic outcomes. For instance, Apache Log4J was found to be vulnerable to remote code execution attacks. A total of more than 35,000 packages were forced to update t

Shehan Edirimannage, Charitha Elvitigala, Asitha Kottahachchi Kankanamge Don, Wathsara Daluwatta

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

Zhihao Liang, Hongdong Li, Kui Jia, Kailing Guo

Recovering the intrinsic physical attributes of a scene from images, generally termed as the inverse rendering problem, has been a central and challenging task in computer vision and computer graphics. In this paper, we present GUS-IR, a novel framework designed to address the inverse rendering problem for complicated scenes featuring rough and glossy surfac

Bing Gu

We present a numerical implementation of the density matrix renormalization group (DMRG) using the discrete variable representation (DVR) basis set. One main advantage of using the local DVR basis sets is that the computations of one-electron integral and two-electron repulsion integrals are drastically simplified. For comparison, we further implemented DVR

André LeClair

We consider $2$ coupled Higgs doublets which transform in the usual way under SU(2). By constructing marginal operators which satisfy an operator product expansion based on the SU(2) Lie algebra, we can obtain a rich pattern of renormalization group (RG) flows which includes lines of fixed points and more interestingly, cyclic RG flows which are unavoidable

Ashley Wang, Peter Chin

The graph alignment problem, which considers the optimal node correspondence across networks, has recently gained significant attention due to its wide applications. There are graph alignment methods suited for various network types, but we focus on the unsupervised geometric alignment algorithms. We propose Degree Matrix Comparison (DMC), a very simple degr

Daria Kryvosheieva, Roger Levy

Language models (LMs) are capable of acquiring elements of human-like syntactic knowledge. Targeted syntactic evaluation tests have been employed to measure how well they form generalizations about syntactic phenomena in high-resource languages such as English. However, we still lack a thorough understanding of LMs' capacity for syntactic generalizations in

Dean Doron, João Ribeiro

Seeded extractors are fundamental objects in pseudorandomness and cryptography, and a deep line of work has designed polynomial-time seeded extractors with nearly-optimal parameters. However, existing constructions of seeded extractors with short seed length and large output length run in time $\Omega(n \log(1/\varepsilon))$ and often slower, where $n$ is th

Anisha Pal, Julia Kruk, Mansi Phute, Manognya Bhattaram

Text-to-image diffusion models have impactful applications in art, design, and entertainment, yet these technologies also pose significant risks by enabling the creation and dissemination of misinformation. Although recent advancements have produced AI-generated image detectors that claim robustness against various augmentations, their true effectiveness rem

Jonathan D. Dunbar, Nathaniel Woltman

In this paper, we explicitly show the various isometries of the plane under the taxicab metric. We then use these isometries to prove that Euclid's proposition I.5 for isoscelese triangles is true under certain circumstances in taxicab geometry.

Justin Ting, Jing Shuang Li

Bimanual tasks performed by human agents present unique optimal control considerations compared to cyberphysical agents. These considerations include minimizing attention, distributing attention across two isolated hands, and coordinating the two hands to reach a broader goal. In this work, we propose a two-layer controller that captures these considerations

Taehun Kim, Philip Lu

Primordial black holes (PBH) can arise in a wide range of scenarios, from inflation to first-order phase transitions. Light PBHs, such as those produced during preheating, in bounce cosmologies, or at the GUT scale, could induce an early matter-dominated phase given a moderate initial abundance. During the early matter domination, the growth of initial PBH d

Arman Riasi, Jorge Guajardo, Thang Hoang

Machine learning has revolutionized data analysis and pattern recognition, but its resource-intensive training has limited accessibility. Machine Learning as a Service (MLaaS) simplifies this by enabling users to delegate their data samples to an MLaaS provider and obtain the inference result using a pre-trained model. Despite its convenience, leveraging MLa

Jesse He, Helen Jenne, Herman Chau, Davis Brown

Machine learning is becoming an increasingly valuable tool in mathematics, enabling one to identify subtle patterns across collections of examples so vast that they would be impossible for a single researcher to feasibly review and analyze. In this work, we use graph neural networks to investigate \emph{quiver mutation} -- an operation that transforms one qu

Kawshik Manikantan, Makarand Tapaswi, Vineet Gandhi, Shubham Toshniwal

Recent evaluations of LLMs on coreference resolution have revealed that traditional output formats and evaluation metrics do not fully capture the models' referential understanding. To address this, we introduce IdentifyMe, a new benchmark for mention resolution presented in a multiple-choice question (MCQ) format, commonly used for evaluating LLMs. Identify

Yi-Ling Zhang, Li-Wei Wang, Yang Liu, Zhao-Xian Chen

Non-Hermitian topology provides a research frontier for exploring topological phenomena, revealing novel topological effects and driving the development of emergent materials and platforms. Here, we explore the non-Hermitian Chern insulator phases and the hybrid skin-topological effects in checkerboard lattices with synthetic gauge fluxes. Such lattices can

J. A. Martínez-Cadena, J. M. Sánchez-Cerritos, A. Marin-Lopez, M. Meraz

In the recent two decades, the Mexico City Metropolitan Area (MCMA) has been plagued by high concentrations of air pollutants, risking the health integrity of its inhabitants. Although some policies have been undertaken, they have been insufficient to deplete high air pollutants. Environmental contingencies are commonly imposed when the ozone concentration o

Shubham Gandhi, Manasi Patwardhan, Lovekesh Vig, Gautam Shroff

Large Language Models (LLMs) excel in diverse applications including generation of code snippets, but often struggle with generating code for complex Machine Learning (ML) tasks. Although existing LLM single-agent based systems give varying performance depending on the task complexity, they purely rely on larger and expensive models such as GPT-4. Our invest

Chika Maduabuchi, Ericmoore Jossou, Matteo Bucci

High-speed video (HSV) phase detection (PD) segmentation is crucial for monitoring vapor, liquid, and microlayer phases in industrial processes. While CNN-based models like U-Net have shown success in simplified shadowgraphy-based two-phase flow (TPF) analysis, their application to complex HSV PD tasks remains unexplored, and vision foundation models (VFMs)

Jiaxuan Chen, Bo Zhang, Qingdong He, Jinlong Peng

Generative image composition aims to regenerate the given foreground object in the background image to produce a realistic composite image. The existing methods are struggling to preserve the foreground details and adjust the foreground pose/viewpoint at the same time. In this work, we propose an effective finetuning strategy for generative image composition

Anas Awadalla, Le Xue, Manli Shu, An Yan

We introduce BLIP3-KALE, a dataset of 218 million image-text pairs that bridges the gap between descriptive synthetic captions and factual web-scale alt-text. KALE augments synthetic dense image captions with web-scale alt-text to generate factually grounded image captions. Our two-stage approach leverages large vision-language models and language models to

Jorge Pinochet

Stephen Hawkings most important scientific contribution was his theoretical discovery that black holes aint so black, since they emit thermal radiation as if they were hot bodies with an absolute temperature known as the Hawking temperature. Although this year marks half a century since Hawking made his discovery, it remains unknown to the vast majority of p

Won-Kwang Park

In this contribution, we consider MUltiple SIgnal Classification (MUSIC)-type algorithm for a non-iterative microwave imaging of small and arbitrary shaped extended anomalies located in a homogeneous media from scattering matrix whose elements are scattering parameters measured at dipole antennas. In order to explain the feasibility of MUSIC in microwave ima

Hannah P. Earnshaw, Gauri Patti, Murray Brightman, Rajath Sathyaprakash

We present preliminary results of a Chandra Large Program to monitor the ultraluminous X-ray source (ULX) populations of three nearby, ULX-rich galaxies over the course of a year, finding the ULX population to show a variety of long-term variability behaviours. Of a sample of 36 ULXs, some show persistent or moderately variable flux, often with a significant

Cheng Jia, Enci Wang, Huiyuan Wang, Hui Li

We investigate size variation with rest-frame wavelength for star-forming galaxies based on the second JWST Advanced Deep Extragalactic Survey data release. Star-forming galaxies are typically smaller at longer wavelength from UV-to-NIR at $z<3.5$, especially for more massive galaxies, indicating the inside-out assembly with in-situ star formation if ignorin

Nan Xu, Xuezhe Ma

While large language models (LLMs) have demonstrated increasing power, they have also called upon studies on their hallucinated outputs that deviate from factually correct statements. In this paper, we focus on one important scenario of false premises, where LLMs are distracted by misaligned claims although the model possesses the required factual knowledge

Wen-Yu He, K. T. Law

The nonlinear Hall effect has recently garnered significant attention as a powerful probe of Fermi surface quantum geometry in metals. While current studies mainly focus on the nonlinear Hall response driven by quasi-static electric fields of low frequencies, the extension of the response to higher frequencies is another promising frontier, which introduces

Liang Dong, Hai Tao Li, Zheng-Yu Li, Jian Wang

The $tW\bar{b}$ production contributes to the real corrections to the $tW$ cross section. It would interfere with the top quark pair production, causing difficulties in a clear definition of the $tW{\bar b}$ events. The subtraction of the $t\bar{t}$ contributions has been performed in the diagram removal or diagram subtraction schemes for the tree-level proc

Bryce Decker, Nathan Dalaklis

We assign every metric space $X$ the value $t_{D}HD(X)$, an ordinal number or one of the symbols $-1$ or $\Omega$, and we call it the $D$-variant of transfinite Hausdorff dimension of $X$. This ordinal assignment is primarily constructed by way of the $D$-dimension, a transfinite dimension function consistent with the large inductive dimension on finite dime

Jiangwen Chen, Siwei Li, Guo Jiang, Cheng Dongzhen

The safe and reliable operation of complex electromechanical systems in nuclear power plants is crucial for the safe production of nuclear power plants and their nuclear power unit. Therefore, accurate and timely fault diagnosis of nuclear power systems is of great significance for ensuring the safe and reliable operation of nuclear power plants. The existin

Thien Udomsrirungruang, Nobuko Yoshida

Multiparty session types provide a type discipline for ensuring communication safety, deadlock-freedom and liveness for multiple concurrently running participants. The original formulation of MPST takes the top-down approach, where a global type specifies a bird's eye view of the intended interactions between participants, and each distributed process is loc

Reuben Luera, Ryan Rossi, Franck Dernoncourt, Alexa Siu

In this work, we research user preferences to see a chart, table, or text given a question asked by the user. This enables us to understand when it is best to show a chart, table, or text to the user for the specific question. For this, we conduct a user study where users are shown a question and asked what they would prefer to see and used the data to estab

Bor Plestenjak

We investigate critical points of eigencurves of bivariate matrix pencils $A+\lambda B +\mu C$. Points $(\lambda,\mu)$ for which $\det(A+\lambda B+\mu C)=0$ form algebraic curves in $\mathbb C^2$ and we focus on points where $\mu'(\lambda)=0$. Such points are referred to as zero-group-velocity (ZGV) points, following terminology from engineering applications

Andreas Floros, Seyed-Mohsen Moosavi-Dezfooli, Pier Luigi Dragotti

Diffusion models have transformed image synthesis through iterative denoising, by defining trajectories from noise to coherent data. While their capabilities are widely celebrated, a critical challenge remains unaddressed: ensuring responsible use by verifying whether an image originates from a model's training set, its novel generations or external sources.

Chadawan Khamdang, Mengen Wang

Sn-based perovskites as low-toxic materials are actively studied for optoelectronic applications. However, their performance is limited by $p$-type self-doping, which can be suppressed by substitutional doping on the cation sites. In this study, we combine density functional theory (DFT) calculations with machine learning (ML) to develop a predictive model a

Cilin Yan, Jingyun Wang, Lin Zhang, Ruihui Zhao

Automatic prompt engineering aims to enhance the generation quality of large language models (LLMs). Recent works utilize feedbacks generated from erroneous cases to guide the prompt optimization. During inference, they may further retrieve several semantically-related exemplars and concatenate them to the optimized prompts to improve the performance. Howeve

Yuanbo Wen, Tao Gao, Ziqi Li, Jing Zhang

Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration. Specific

Siddharth Agarwal, Maria A. Rodriguez, Rajkumar Buyya

In today's Function-as-a-Service offerings, a programmer is usually responsible for configuring function memory for its successful execution, which allocates proportional function resources such as CPU and network. However, right-sizing the function memory force developers to speculate performance and make ad-hoc configuration decisions. Recent research has

Konstantin Sidorov, Koos van der Linden, Gonçalo Homem de Almeida Correia, Mathijs de Weerdt

Modern software for propositional satisfiability problems gives a powerful automated reasoning toolkit, capable of outputting not only a satisfiable/unsatisfiable signal but also a justification of unsatisfiability in the form of resolution proof (or a more expressive proof), which is commonly used for verification purposes. Empirically, modern SAT solvers p

Stevan Gajović, J. Steffen Müller

We describe an algorithm to compute the local Coleman-Gross p-adic height at p on a hyperelliptic curve. Previously, this was only possible using an algorithm due to Balakrishnan and Besser, which was limited to odd degree. While we follow their general strategy, our algorithm is significantly faster and simpler and works for both odd and even degree. We dis

Roland Leißa, Marcel Ulrich, Joachim Meyer, Sebastian Hack

Traditional compilers, designed for optimizing low-level code, fall short when dealing with modern, computation-heavy applications like image processing, machine learning, or numerical simulations. Optimizations should understand the primitive operations of the specific application domain and thus happen on that level. Domain-specific languages (DSLs) fulfil