December 2024 arXiv papers — page 158

Qi Zeng, Yimin Deng, Xinyue Yang, Wei Cao

High-order harmonic generation (HHG), characterized by its highly nonlinear nature, often exhibits a complex spatio-temporal profile that poses challenges for practical applications. In this study, we unveil a method for manipulating the spatio-spectral distribution of HHG by guiding the recollision electron trajectory in the spatio-temporal domain using a c

Amit Zrihan, Eitan Yaakobi, Zohar Yakhini

Storing data in DNA is being explored as an efficient solution for archiving and in-object storage. Synthesis time and cost remain challenging, significantly limiting some applications at this stage. In this paper we investigate efficient synthesis, as it relates to cyclic synchronized synthesis technologies, such as photolithography. We define performance m

Rui Li, Kangfei Zhao, Jeffrey Xu Yu, Guoren Wang

Query-driven learned estimators are accurate, flexible, and lightweight alternatives to traditional estimators in query optimization. However, existing query-driven approaches struggle with the Out-of-distribution (OOD) problem, where the test workload distribution differs from the training workload, leading to performancedegradation. In this paper, we prese

Zexing Xu, Zhuang Luo, Yichuan Li, Kyumin Lee

In the realm of software development, providing accurate and personalized code explanations is crucial for both technical professionals and business stakeholders. Technical professionals benefit from enhanced understanding and improved problem-solving skills, while business stakeholders gain insights into project alignments and transparency. Despite the pote

Uttiya Sarkar

Identification of hadronic jets originating from heavy-flavor quarks is extremely important to several physics analyses in High Energy Physics, such as studies of the properties of the top quark and the Higgs boson, and searches for new physics. Recent algorithms used in the CMS experiment were developed using state-of-the-art machine-learning techniques to

Aman Kassahun Wassie, Mahdi Molaei, Yasmin Moslem

In this work, we compare the domain-specific translation performance of open-source autoregressive decoder-only large language models (LLMs) with task-oriented machine translation (MT) models. Our experiments focus on the medical domain and cover four language directions with varied resource availability: English-to-French, English-to-Portuguese, English-to-

Sultan Ahmed, Salman Rakin, Mohammad Washeef Ibn Waliur, Nuzhat Binte Islam

Emotion artificial intelligence is a field of study that focuses on figuring out how to recognize emotions, especially in the area of text mining. Today is the age of social media which has opened a door for us to share our individual expressions, emotions, and perspectives on any event. We can analyze sentiment on social media posts to detect positive, nega

Tony J. Puthenpurakal, Samarendra Sahoo

Let $(A,\mathfrak{m})$ be a complete intersection ring of codimension $c\geq 2$ and dimension $d\geq 1$. Let $M$ be a finitely generated maximal Cohen-Macaulay $A$-module. Set $M_i=\text{Syz}^A_{i}(M)$. Let $e^{\mathfrak{m}}_i(M)$ be the $i$-th Hilbert coefficient of $M$ with respect to $\mathfrak{m}$. We prove for all $i\gg0$, the function $i\mapsto e^{\mat

Kylie Yui Dan, Jerome Seebeck, Sylvain Veilleux, David Rupke

We present new James Webb Space Telescope (JWST) Mid-Infrared Instrument (MIRI) Medium-Resolution Spectrometer (MRS) observations of the nearby ultra-luminous infrared galaxy (ULIRG) F08572+3915 NW. These integral field spectroscopic (IFS) data reveal a kpc-scale warm-molecular rotating disk and biconical outflow traced by the H$_2$ $\nu$ = 0$-$0 S(1), S(2),

Alon Agin, Barak Weiss

Akhunzhanov and Shatskov defined the Dirichlet spectrum, corresponding to $m \times n$ matrices and to norms on $\mathbb{R}^m$ and $\mathbb{R}^n$. In case $(m,n) = (2,1)$ and using the Euclidean norm on $\mathbb{R}^2$, they showed that the spectrum is an interval. We generalize this result to arbitrary $(m,n) \neq (1,1)$ and arbitrary norms, improving previo

Anay Aggarwal, Felix Gotti, Susie Lu

In the first part of this paper, we establish a variation of a recent result by Bienvenu and Geroldinger on the (almost) non-existence of absolute irreducibles in (restricted) power monoids of numerical monoids: we argue the (almost) non-existence of primal elements in the same class of power monoids. The second part of this paper, devoted to the study of th

Ebrahim Khaleghian, Arash Fath Lipaei, Abolfazl Bahrampour, Morteza Nikaeen

In this paper, we introduce a neural network to generate optimal control pulses for general single-qubit quantum logic gates, within a Nuclear Magnetic Resonance (NMR) quantum computer. By utilizing a neural network, we can efficiently implement any single-qubit quantum logic gates within a reasonable time scale. The network is trained by control pulses gene

Pavol Quittner

We consider a priori estimates of possibly sign-changing solutions to superlinear parabolic problems and their applications (blow-up rates, energy blow-up, continuity of blow-up time, existence of nontrivial steady states etc). Our estimates are based mainly on energy, interpolation and bootstrap arguments, but we also use the Pohozaev identity, for example.

Yan Chang, Yukun Guo, Yue Zhao

This paper presents a method for reconstructing an acoustic source located in a two-layered medium from multi-frequency phased or phaseless far-field patterns measured on the upper hemisphere. The interface between the two media is assumed to be flat and infinite, while the source is buried in the lower half-space. In the phased case, a Fourier method is pro

Qing Wu, Hongjiang Wei, Jingyi Yu, Yuyao Zhang

Ring artifacts are prevalent in 3D cone-beam computed tomography (CBCT) due to non-ideal responses of X-ray detectors, substantially affecting image quality and diagnostic reliability. Existing state-of-the-art (SOTA) ring artifact reduction (RAR) methods rely on supervised learning with large-scale paired CT datasets. While effective in-domain, supervised m

Dinesh Parthasarathy, Wayne Bradford Mitchell, Harald Köstler

Multigrid methods despite being known to be asymptotically optimal algorithms, depend on the careful selection of their individual components for efficiency. Also, they are mostly restricted to standard cycle types like V-, F-, and W-cycles. We use grammar rules to generate arbitrary-shaped cycles, wherein the smoothers and their relaxation weights are chose

Jie Zhang, Tingting Ye, Guo Chen, Deyuan Yao

The synthesis of hexazine N_{6} ring is another milestone in nitrogen chemistry after that of aromatic [N_{5}]^{-} anion. However, due to the diversity of carried charges, realizing compounds entirely composed of aromatic hexazine N_{6} ring potentially with high-stability is a challenge. The first reported hexazine N_{6} ring is [N_{6}]^{2-} anion in K_{2}N

Zhiguang Wu, Fengbin Zhu, Xuequn Shang, Yupei Zhang

Text-to-SQL task aims to automatically yield SQL queries according to user text questions. To address this problem, we propose a Cooperative SQL Generation framework based on Multi-functional Agents (CSMA) through information interaction among large language model (LLM) based agents who own part of the database schema seperately. Inspired by the collaboratio

Yichen Qin, Christian Sevenheck, Peter Spacek

Frenkel and Gross constructed a family of connections on $\mathbb{P}^1\backslash\{0,\infty\}$, for almost simple groups $\check{G}$ and their representations. In this article, we calculate the irregular Hodge numbers of these Frenkel--Gross connections, and, as an application, we prove a conjecture of Katzarkov--Kontsevich--Pantev for mirror Landau-Ginzburg

Shuwei Shi, Biao Gong, Xi Chen, Dandan Zheng

The image-to-video (I2V) generation is conditioned on the static image, which has been enhanced recently by the motion intensity as an additional control signal. These motion-aware models are appealing to generate diverse motion patterns, yet there lacks a reliable motion estimator for training such models on large-scale video set in the wild. Traditional me

Ye Yang, Shuya Zhang, Yongkun Zhou, Xinji Zeng

Whether a photon exhibits wavelike or particlelike behaviour depends on the observation method, as clearly demonstrated by Wheeler's delayed choice (DC) experiments. A key aspect of such experiments is the random determination of the observation device's status, typically controlled by a random number generator or a quantum-controlling apparatus. Here, we pr

Yongxuan Chen, Dianhui Wang

Stochastic configuration networks (SCNs), as a class of randomized learner models, are featured by its way of random parameters assignment in the light of a supervisory mechanism, resulting in the universal approximation property at algorithmic level. This paper presents a kernel version of SCNs, termed KSCNs, aiming to enhance model's representation learnin

Yahan Li, Keith Harrigian, Ayah Zirikly, Mark Dredze

Large language models with a transformer-based encoder/decoder architecture, such as T5, have become standard platforms for supervised tasks. To bring these technologies to the clinical domain, recent work has trained new or adapted existing models to clinical data. However, the evaluation of these clinical T5 models and comparison to other models has been l

Deepak Patel, Praveen C. Srivastava

In this work, we present the systematic study of $2\nu$ECEC process in the $^{78}$Kr using large-scale shell-model calculations with the GWBXG effective interaction. We first validate the efficiency of the utilized interaction by comparing the theoretical low-lying energy spectra, the kinematic moment of inertia, and reduced transition probabilities with the

Hao Chen, Hui Guo, Baochen Hu, Shu Hu

The rapid growth of social media has resulted in an explosion of online news content, leading to a significant increase in the spread of misleading or false information. While machine learning techniques have been widely applied to detect fake news, the scarcity of labeled datasets remains a critical challenge. Misinformation frequently appears as paired tex

Rongqing Li, Jiaqi Yu, Changsheng Li, Wenhan Luo

Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes (e.g., the number of convolutional layers) of a target black-box model can be exposed through a sequence of queries. There is a crucial limitation: these works assume the training dataset of the target model is known beforehand

Desire Guel, Flavien Herve Somda, Boureima Zerbo, Oumarou Sie

The rapid development of 5G New Radio (NR) and millimeter-wave (mmWave) communication systems highlights the critical importance of maintaining accurate phase synchronization to ensure reliable and efficient communication. This study focuses on evaluating phase noise models and implementing Minimum Mean Square Error (MMSE) algorithms for Common Phase Error (

Yue Ma, Huantao Ren, Boyu Wang, Jingang Jin

Continual learning aims to update a model so that it can sequentially learn new tasks without forgetting previously acquired knowledge. Recent continual learning approaches often leverage the vision-language model CLIP for its high-dimensional feature space and cross-modality feature matching. Traditional CLIP-based classification methods identify the most s

Juwon Kim, Hogyun Kim, Seokhwan Jeong, Youngsik Shin

We encounter large-scale environments where both structured and unstructured spaces coexist, such as on campuses. In this environment, lighting conditions and dynamic objects change constantly. To tackle the challenges of large-scale mapping under such conditions, we introduce DiTer++, a diverse terrain and multi-modal dataset designed for multi-robot SLAM i

Aniruddha Salve, Saba Attar, Mahesh Deshmukh, Sayali Shivpuje

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external, domain-specific data into the generative process. While LLMs are highly capable, they often rely on static, pre-trained datasets, limiting their ability to integrate dynamic or private data. Traditional RAG systems typically use a single-agent architecture t

Haoran Zhu, Chang Xu, Ruixiang Zhang, Fang Xu

Tiny objects, with their limited spatial resolution, often resemble point-like distributions. As a result, bounding box prediction using point-level supervision emerges as a natural and cost-effective alternative to traditional box-level supervision. However, the small scale and lack of distinctive features of tiny objects make point annotations prone to noi

R. J. Cossins

We propose the relation $M^*_{Higgs} = ({M_{\Lambda} \ M_{I}})^{\frac{1}{2}}$ where $ M^*_{Higgs}, M_{\Lambda}$ and $M_{I}$ denote the mass scale associated with the Higgs boson, the cosmological constant and the inflaton respectively. We demonstrate how this seesaw-like (geometric mean) relation perfectly matches observations and the unified scenario of hol

Sandip K Pal, Arnab Koley, Pritam Ranjan, Debasis Kundu

In recent years, the requirement for real-time understanding of machine behavior has become an important objective in industrial sectors to reduce the cost of unscheduled downtime and to maximize production with expected quality. The vast majority of high-end machines are equipped with a number of sensors that can record event logs over time. In this paper,

Patricio Peralta-Braz, Mehrisadat Makki Alamdari, Mahbub Hassan, Elena Atroshchenko

Metastructures are engineered systems composed of periodic arrays of identical components, called resonators, designed to achieve specific dynamic effects, such as creating a band gap-a frequency range where waves cannot propagate through the structure. When equipped with patches of piezoelectric material, these metastructures exhibit an additional capabilit

Xiuji Chen, Zipeng Liu, Si Chen, Duan Gu

The injector for ERL-FEL has been widely researched. Unlike traditional linacs, the bunch in the injector for ERLs requires additional deflection and matching section at lower energies. It makes the bunch more susceptible to the effects of the Space Charge. This will lead to a degradation in beam quality. In this paper, we comprehensively analyze the impact

Karishma Yasmin

Background: This study examines cardiometabolic (CM) risk factors in an urban South Asian population, integrating medical and Anthropological perspectives to explore the effects of socio-economic, lifestyle, gender-specific factors, and cultural norms on health outcomes. Results: Analysis indicates a high prevalence of MetS and Pre-MetS, particularly among f

Elay Dahan, Hedda Cohen Indelman, Angeles M. Perez-Agosto, Carmit Shiran

The use of synthetic images in medical imaging Artificial Intelligence (AI) solutions has been shown to be beneficial in addressing the limited availability of diverse, unbiased, and representative data. Despite the extensive use of synthetic image generation methods, controlling the semantics variability and context details remains challenging, limiting the

Ugur Kursuncu, Aaron Baird, Yusen Xia

Predictive machine learning (ML) models are computational innovations that can enhance medical decision-making, including aiding in determining optimal timing for discharging patients. However, societal biases can be encoded into such models, raising concerns about inadvertently affecting health outcomes for disadvantaged groups. This issue is particularly p

Shanti Stewart, Gouthaman KV, Lie Lu, Andrea Fanelli

Content creators often use music to enhance their videos, from soundtracks in movies to background music in video blogs and social media content. However, identifying the best music for a video can be a difficult and time-consuming task. To address this challenge, we propose a novel framework for automatically retrieving a matching music clip for a given vid

Faqian Guan, Tianqing Zhu, Wenhan Chang, Wei Ren

Graph Neural Networks (GNNs), specifically designed to process the graph data, have achieved remarkable success in various applications. Link stealing attacks on graph data pose a significant privacy threat, as attackers aim to extract sensitive relationships between nodes (entities), potentially leading to academic misconduct, fraudulent transactions, or ot

Naizhu Jin, Zhong Li, Yinggang Guo, Chao Su

Recent studies have proposed integrating Chain-of-Thought (CoT) reasoning to further enhance the reliability of Code Language Models (CLMs) in generating code, a step-by-step approach that breaks down complex programming tasks into manageable sub-problems. Advances in this area have introduced CoT models, specifically designed to integrate CoT reasoning effe

Sanmei Wang, Yong Zhou, Chunyang Ne, Hengxin Fang

As the extremely-sized nanocrystals and nanopores, an adatom M and atomic vacancy V exhibit extraordinary capability of catalysis with however little knowledge about the catalyst-reactant interfacial bonding dynamics. With the aid of DFT calculations, we examined the dehydrogenization of a single CH4 molecule catalyzed using the Rh(111,100), W(110), Ru(0001)

Tiancheng Li, Weijian Luo, Zhiyang Chen, Liyuan Ma

Proper guidance strategies are essential to achieve high-quality generation results without retraining diffusion and flow-based text-to-image models. Existing guidance either requires specific training or strong inductive biases of diffusion model networks, which potentially limits their ability and application scope. Motivated by the observation that artifa

Sohom Ghosh, Arnab Maji, N Harsha Vardhan, Sudip Kumar Naskar

With consistent growth in Indian Economy, Initial Public Offerings (IPOs) have become a popular avenue for investment. With the modern technology simplifying investments, more investors are interested in making data driven decisions while subscribing for IPOs. In this paper, we describe a machine learning and natural language processing based approach for es

Yuanbo Xiangli, Ruojin Cai, Hanyu Chen, Jeffrey Byrne

Accurate 3D reconstruction is frequently hindered by visual aliasing, where visually similar but distinct surfaces (aka, doppelgangers), are incorrectly matched. These spurious matches distort the structure-from-motion (SfM) process, leading to misplaced model elements and reduced accuracy. Prior efforts addressed this with CNN classifiers trained on curated

Junha Lee, Sojung An, Sujeong You, Namik Cho

Numerical weather prediction (NWP) models are fundamental in meteorology for simulating and forecasting the behavior of various atmospheric variables. The accuracy of precipitation forecasts and the acquisition of sufficient lead time are crucial for preventing hazardous weather events. However, the performance of NWP models is limited by the nonlinear and u

Shixun Wu, Yujia Zhai, Jinyang Liu, Jiajun Huang

GPU-based fast Fourier transform (FFT) is extremely important for scientific computing and signal processing. However, we find the inefficiency of existing FFT libraries and the absence of fault tolerance against soft error. To address these issues, we introduce TurboFFT, a new FFT prototype co-designed for high performance and online fault tolerance. For FF

Yongzhe Jia, Xuyun Zhang, Hongsheng Hu, Kim-Kwang Raymond Choo

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the prese

Haotong Yang, Xiyuan Wang, Qian Tao, Shuxian Hu

Recent research on integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) typically follows two approaches: LLM-centered models, which convert graph data into tokens for LLM processing, and GNN-centered models, which use LLMs to encode text features into node and edge representations for GNN input. LLM-centered models often struggle to ca

Habib Esmaili, Hosein Mohammadzadeh, Mehdi Biderang, Morteza NattaghNajafi

We present a comprehensive quantum many body theory for kq deformed particles, offering a novel framework that relates particle statistics directly to effective interaction strength. Deformed by the parameters k and q, these particles exhibit statistical behaviors that interpolate between conventional bosonic and fermionic systems, enabling us to model compl

Qing Zhang, Haocheng Lv, Jie Liu, Zhiyun Chen

With the rise of large-scale language models (LLMs), it is currently popular and effective to convert multimodal information into text descriptions for multimodal multi-hop question answering. However, we argue that the current methods of multi-modal multi-hop question answering still mainly face two challenges: 1) The retrieved evidence containing a large a

Xiaohui Zhang, Xingming Li, Songnan Yang, Wenqi Bai

This paper proposes a geomagnetic and inertial combined navigation approach based on the flexible correction-model predictive control algorithm (Fc-MPC). This approach aims to overcome the limitations of existing combined navigation methods that require prior geomagnetic maps and the inertial navigation drift of long-range missions. The proposed method uses

Ao Wang, Fengyuan Sun, Hui Chen, Zijia Lin

Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance across a wide range of vision-language tasks, garnering significant attention in the computer vision. However, their efficient deployment remains a substantial challenge due to high computational costs and memory requirements. Recognizing the redundancy of information with

Leigang Qu, Haochuan Li, Wenjie Wang, Xiang Liu

Large Multimodal Models (LMMs) have demonstrated impressive capabilities in multimodal understanding and generation, pushing forward advancements in text-to-image generation. However, achieving accurate text-image alignment for LMMs, particularly in compositional scenarios, remains challenging. Existing approaches, such as layout planning for multi-step gene

Tareq Alodat, Quoc T. Le Gia

This paper examines the temporal evolution of a two-stage stochastic model for spherical random fields. The model uses a time-fractional stochastic hyperbolic diffusion equation, which describes the evolution of spherical random fields on $\bS^2$ in time. The diffusion operator incorporates a time-fractional derivative in the Caputo sense. In the first stage

Chenming Duan, Zhitao Shu, Jingsi Zhang, Feng Xue

Understanding and predicting athletes' mental states is crucial for optimizing sports performance. This study introduces a hybrid BERT-XGBoost model to analyze psychological factors such as emotions, anxiety, and stress, and predict their impact on performance. By combining BERT's bidirectional contextual learning with XGBoost's classification efficiency, th

Jean-Sébastien Sauvé, Madelaine Hare, Geoff Krause, Constance Poitras

The aim of the Canadian publications in Library and Information Science (LIS) database is to help break down the silos in which the two main target audiences - LIS faculty members and academic librarians - conduct their research. As part of a larger project entitled "Breaking down research silos", we created a database of research contributions by Canadian L

Roozbeh Hazrat, Tran Giang Nam

In this article, we establish the relations between a sandpile graph, its sandpile monoid and the weighted Leavitt path algebra associated with it. Namely, we show that the lattice of all idempotents of the sandpile monoid $\text{SP}(E)$ of a sandpile graph $E$ is both isomorphic to the lattice of all nonempty saturated hereditary subsets of $E$, the lattice

Tasfin Mahmud, Tayab Uddin Wara, Chironjeet Das Joy

This study aims to understand the factors that resulted in under-five children's malnutrition from the Multiple Indicator Cluster (MICS-2019) nationwide surveys and classify different malnutrition stages based on the four well-established machine learning algorithms, namely - Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Multi-lay

Alba Rico, A. Domínguez, P. Peñil, M. Ajello

A majority of blazars exhibit variable emission across the entire electromagnetic spectrum, observed over various time scales. In particular, discernible periodic patterns are detected in the $\gamma$-ray light curves of a few blazars, such as PG 1553+113, S5 1044+71, and PKS 0426-380. The presence of trends, flares, and noise complicates periodicity detecti

Sheng-Wei Wang, Shan-Ping Wu, Shao-Wen Wei

Regular black holes, which avoid the essential center singularities, can be constructed through various methods, including nonlinear electrodynamics and quantum corrections. Recently, it was shown that via an infinite tower of higher-curvature corrections, one can obtain different regular black hole solutions in any spacetime dimension $D\geq 5$. Utilizing t

Lyudmila Korobenko, Florian Meister, Olive Ross

This note is a companion paper to arXiv:1608.01630 [math.CA]. Here we generalize some of the geometric results of arXiv:1608.01630 [math.CA] to the case of a $3\times 3$ matrix function $A(x)\approx \mathrm{diag}\{1,f(x_1), g(x_1)\}$. More precisely, we make explicit calculations of the geodesics in the Carnot-Carath\'{e}odory space associated to $A$, and pr

Yifan Wang, Wladimir A. Benalcazar

In two dimensions, Hermitian lattices with non-zero Chern numbers and non-Hermitian lattices with a higher-order skin effect (HOSE) bypass the constraints of the Nielsen-Ninomiya no-go theorem at their one-dimensional boundaries. This allows the realization of topologically-protected one-dimensional edges with nonreciprocal dynamics. However, unlike the edge

Swapnaneel Bhattacharyya, Srijan Chattopadhyay, Sevantee Basu

This review article provides an overview of random matrix theory (RMT) with a focus on its growing impact on the formulation and inference of statistical models and methodologies. Emphasizing applications within high-dimensional statistics, we explore key theoretical results from RMT and their role in addressing challenges associated with high-dimensional da

Shuzhao Xie, Jiahang Liu, Weixiang Zhang, Shijia Ge

Recent advances in 3D Gaussian Splatting (3DGS) have greatly improved 3D reconstruction. However, its substantial data size poses a significant challenge for transmission and storage. While many compression techniques have been proposed, they fail to efficiently adapt to fluctuating network bandwidth, leading to resource wastage. We address this issue from t

David P. Woodruff, Samson Zhou

In the adversarial streaming model, the input is a sequence of adaptive updates that defines an underlying dataset and the goal is to approximate, collect, or compute some statistic while using space sublinear in the size of the dataset. In 2022, Ben-Eliezer, Eden, and Onak showed a dense-sparse trade-off technique that elegantly combined sparse recovery wit

Yuan-Sen Ting

A persistent challenge in astronomical machine learning is a systematic bias where predictions compress the dynamic range of true values-high values are consistently predicted too low while low values are predicted too high. Understanding this bias has important consequences for astronomical measurements and our understanding of physical processes in astrono

Nima Alibabaei

Previous work has shown that the Hausdorff dimension of sofic affine-invariant sets is expressed as a limit involving intricate matrix products. This limit has typically been regarded as incalculable. However, in several highly non-trivial cases, we demonstrate that the dimension can in fact be calculated explicitly. Specifically, the dimension is expressed

Jiali Chen, Xusen Hei, Yuqi Xue, Yuancheng Wei

Large multimodal models (LMMs) have shown remarkable performance in the visual commonsense reasoning (VCR) task, which aims to answer a multiple-choice question based on visual commonsense within an image. However, the ability of LMMs to correct potential visual commonsense errors in the distractor upon their occurrence is yet under-explored. Drawing inspira

Hanzhang Chen, Xiangzhi Zhang, Shufeng Gong, Feng Yao

Path planning is a fundamental problem in road networks, with the goal of finding a path that optimizes objectives such as shortest distance or minimal travel time. Existing methods typically use graph indexing to ensure the efficiency of path planning. However, in real-world road networks, road segments may impose restrictions in terms of height, width, and

Jian Song, Fatemeh Pourahmadian, Todd W. Murray, Venkatalakshmi V. Narumanchi

This study investigates the imaging ability of the time-domain linear sampling method (TLSM) when applied to laser ultrasonic (LU) tomography of subsurface defects from limited-aperture measurements. In this vein, the TLSM indicator and it spectral counterpart known as the multifrequency LSM are formulated within the context of LU testing. The affiliated ima

Sheikh Mannan, Nikhil Krishnaswamy

We present a real-time system that enables bidirectional human-AI learning and teaching in a balancing task that is a realistic analogue of disorientation during piloting and spaceflight. A human subject and autonomous AI model of choice guide each other in maintaining balance using a visual inverted pendulum (VIP) display. We show how AI assistance changes

Deniz Kerimoglu, Eloise Marteau, Daniel Soto, Daniel I. Goldman

Intrusions into granular media are common in natural and engineered settings (e.g. during animal locomotion and planetary landings). While intrusion of complex shapes in dry non-cohesive granular materials is well studied, less is known about intrusion in cohesive powders. Granular resistive force theory (RFT) -- a reduced-order frictional fluid model -- qua

Siyuan Guo, Lexuan Wang, Chang Jin, Jinxian Wang

This paper introduces M$^{3}$-20M, a large-scale Multi-Modal Molecule dataset that contains over 20 million molecules, with the data mainly being integrated from existing databases and partially generated by large language models. Designed to support AI-driven drug design and discovery, M$^{3}$-20M is 71 times more in the number of molecules than the largest

Paolo Amore, Ricardo A. Sáenz

We present a new proof (based on spectral decomposition) of a bound originally proved by Sidelnikov~\, for the frame potentials $\sum_{ij} \left( {\bf P}_i \cdot {\bf P}_j \right)^\ell $ on a unit--sphere in $d$ dimensions. Sidelnikov's bound is a special case of the lower bound for the weighted sums $\sum_{ij} f_i f_j \left( {\bf P}_i \cdot {\bf P}_j \right

Hongwei Jin, Siran Chen, Shaowu Huang, Predrag S. Stanimirović

We study extensions of the GD tensor inverse using the M-product. The aim of current research is threefold. In the first place, the tensor GD inverse under the M-product is introduced and considered. We give the several properties and representations of the GD inverse using the core nilpotent decomposition and then establish the reverse-order law rules for t

Zhenyu Cheng, Li Yang, Xiang Hu, Hantao Lu

Using the time-dependent Lanczos method, we study the non-equilibrium dynamics of the half-filled one-dimensional ionic Hubbard model, deep within the Mott insulating regime, under the influence of a transient laser pulse. In equilibrium, increasing the staggered potential in the Mott regime reduces the Mott gap and broadens the Hubbard bands, creating favor

Derek Palmer, Yifan Zhu, Kenneth Lai, Hannah VanderHoeven

Our goal is to develop an AI Partner that can provide support for group problem solving and social dynamics. In multi-party working group environments, multimodal analytics is crucial for identifying non-verbal interactions of group members. In conjunction with their verbal participation, this creates an holistic understanding of collaboration and engagement

Kaiwen Zha, Lijun Yu, Alireza Fathi, David A. Ross

Image tokenization, the process of transforming raw image pixels into a compact low-dimensional latent representation, has proven crucial for scalable and efficient image generation. However, mainstream image tokenization methods generally have limited compression rates, making high-resolution image generation computationally expensive. To address this chall

Fumiyasu Makinoshima, Saki Yotsui, Shosuke Sato, Fumihiko Imamura

On 1 January 2024, devastating tsunamis caused by the Noto Peninsula earthquake hit coastal areas within several minutes, but only two tsunami casualties were officially reported. Despite its importance, the cause of this unexpectedly low human loss was unclear because of the limited access to the peninsula and the presence of many visitors during the holida

Tao Sun

This paper proposes a Bayesian factor-augmented bundle choice model to estimate joint consumption as well as the substitutability and complementarity of multiple goods in the presence of endogenous regressors. The model extends the two primary treatments of endogeneity in existing bundle choice models: (1) endogenous market-level prices and (2) time-invarian

Minghong Xie, Saiguo Xu, Yinghui Zhang

We investigate weak Serrin-type blowup criterion of the three-dimensional full compressible Navier-Stokes equations for the Cauchy problem, Dirichlet problem and Navier-slip boundary condition. It is shown that the strong or smooth solution exists globally if the density is bounded from above, and either the absolute temperature or velocity satisfies the wea

Deke Zhao

The article is concerned with the Foulkes characters of wreath products, which are block characters of wreath products, i.e., the positive-definite class functions depending only on the length of its elements. Inspired by the works of Gnedin--Gorin--Kerov and Miller, we introduce two specializations of the Schur--Weyl--Sergeev duality for wreath products and

Anastasiia Svanidze, Sudarshan Kundu, Olena Iadlovska, Anil K. Thakur

Azo-containing liquid crystal elatomers are photomechanical materials which can be actuated by illumination. The photomechanical response is a result of the photoisomerization of the azo moiety, which produces bulk stresses in the material. These stresses arise via two distinct and competing mechanisms: order parameter change induced stress and direct contra

Shashank Pathak, Guohui Lin

Motivation: Codon optimization of Open Reading Frame (ORF) sequences is essential for enhancing mRNA stability and expression in applications like mRNA vaccines, where codon choice can significantly impact protein yield which directly impacts immune strength. In this work, we investigate the use of a pre-trained protein language model (PPLM) for getting a ri

Pengzhen Ren, Min Li, Zhen Luo, Xinshuai Song

Realizing scaling laws in embodied AI has become a focus. However, previous work has been scattered across diverse simulation platforms, with assets and models lacking unified interfaces, which has led to inefficiencies in research. To address this, we introduce InfiniteWorld, a unified and scalable simulator for general vision-language robot interaction bui

James Matthew Young, O. Deniz Akyildiz

With the advent of diffusion models, new proteins can be generated at an unprecedented rate. The motif scaffolding problem requires steering this generative process to yield proteins with a desirable functional substructure called a motif. While models have been trained to take the motif as conditional input, recent techniques in diffusion posterior sampling

Liuxin Zhao, Honglei Li, Zhi-Long Han, Fei Huang

The study of extra charged gauge boson beyond the Standard Model has always been of great interest. Future muon colliders will have a significant advantage in discovering exotic particles. In this paper, by studying the $\mu^+ \mu^- \to W^{\prime +} W^{\prime -} \to e^+ e^- n_e \bar{n}_e$ process, we explore the properties of $W^\prime$ in the alternative le

Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson

The Breakthrough Listen program is, to date, the most extensive search for technological life beyond Earth. As part of this goal, over the past nine years it has surveyed thousands of nearby stars, close to 100 nearby galaxies, and a variety of exotic and solar system objects with telescopes around the world, including the Robert C. Byrd Green Bank Telescope

Zhijiao Peng, Zhaosheng Li, Yuanyue Pan, Tao Fu

We report the superburst from 4U 1820--30 in 2021 observed by the Monitor of All-sky X-ray Image and Neutron star Interior Composition Explorer (NICER). During the tail of the superburst, we found that the NICER light curve unexpectedly increased from 1080 to 2204 ${\rm counts~s^{-1}}$ over 6.89 hr. From the time-resolved superburst spectra, we estimated the

Yang Shen, Min Xie, Wenzhe Zhang, Tao Wu

Intercepting system calls is crucial for tools that aim to modify or monitor application behavior. However, existing system call interception tools on the ARM platform still suffer from limitations in terms of performance and completeness. This paper presents an efficient and comprehensive binary rewriting framework, ASC-Hook, specifically designed for inter

Shuguang Yu, Shuxing Fang, Ruixin Peng, Zhengling Qi

This paper studies off-policy evaluation (OPE) in the presence of unmeasured confounders. Inspired by the two-way fixed effects regression model widely used in the panel data literature, we propose a two-way unmeasured confounding assumption to model the system dynamics in causal reinforcement learning and develop a two-way deconfounder algorithm that devise

Yongjie Deng, Tianbo Liu, Ya-jin Zhou

The transverse single-spin asymmetry for $\rho^0$ production in semi-inclusive deep inelastic scattering was recently reported by the COMPASS Collaboration. Using the Sivers functions extracted from pion and kaon productions, we perform a calculation of the Sivers asymmetry within the transverse momentum dependent factorization. Our results are consistent wi

Jingxu Ng, Cheng Lv, Pu Zhao, Wei Niu

Stable diffusion plays a crucial role in generating high-quality images. However, image generation is time-consuming and memory-intensive. To address this, stable-diffusion.cpp (Sdcpp) emerges as an efficient inference framework to accelerate the diffusion models. Although it is lightweight, the current implementation of ggml_conv_2d operator in Sdcpp is sub

Qinchan Li, Kenneth Chen, Changyue Su, Qi Sun

Diffusion models have shown unprecedented success in the task of text-to-image generation. While these models are capable of generating high-quality and realistic images, the complexity of sequential denoising has raised societal concerns regarding high computational demands and energy consumption. In response, various efforts have been made to improve infer

Tao Fu, Zhaosheng Li, Yuanyue Pan, Long Ji

We report an analysis of Insight-HXMT observations of the newly discovered accreting millisecond pulsar SRGA J144459.2$-$604207. During the outburst, detected in 2024 February by SRG/ART-XC, the broadband persistent spectrum was well fitted by an absorbed Comptonization model. We detected 60 type I X-ray bursts in the Insight-HXMT medium energy (ME) data, an

Takeshi Yamazaki, Ken-ichi Ishikawa, Naruhito Ishizuka, Yoshinobu Kuramashi

We calculate the form factors for the kaon semileptonic decay process using the PACS10 configurations, whose physical volume is more than (10 fm)$^4$ very close to the physical point. The configurations were generated with the Iwasaki gauge action and $N_f=2+1$ stout-smeared nonperturbatively $O(a)$-improved Wilson quark action at the three lattice spacings,

Fang Tang, Han Wang, Maria Laura Delle Monache

As natural disasters become increasingly frequent, the need for efficient and equitable evacuation planning has become more critical. This paper proposes a data-driven, reinforcement learning-based framework to optimize bus-based evacuations with an emphasis on improving both efficiency and equity. We model the evacuation problem as a Markov Decision Process

Roman Smirnov

The paper describes LLM unlearning without a retaining dataset, using the ORPO reinforcement learning method with inference enhanced by modified classifier-free guidance. Significant improvement in unlearning, without degradation of the model, is achieved through direct training on synthetic replacement data in CFG-aware training regime, with classifier-free

Azwad Tamir, Jiann-Shiun Yuan

Recent developments in next generation sequencing technology have led to the creation of extensive, open-source protein databases consisting of hundreds of millions of sequences. To render these sequences applicable in biomedical applications, they must be meticulously annotated by wet lab testing or extracting them from existing literature. Over the last fe