March 2025 arXiv papers — page 207

J. L. Bravo, R. Trinidad-Forte

We characterize global centers (all solutions are periodic) of the piecewise linear equation $x'=a(t)|x| + b(t)$ when the coefficients $a,b$ are trigonometric polynomials, under some generic hypotheses. We prove that the global centers are those determined by the composition condition on $a,b$. That is, the equation has a global center if and only if there e

Evgenia A. Kovaleva, Lyudmila V. Begunovich, Maxim M. Korshunov

We use time-dependent density functional theory-based approaches, TD-DFT and TD-DFTB, to investigate the optical absorption of B800 part of Rhodoblastus acidophilus light-harvesting complex 2 (LH2). Both methods are shown to give qualitative agreement with experimental spectra for a single BChl a molecule and for the optimized structure of B800 complex conta

Elizabeth Bates, Chris Hicks, Vasilios Mavroudis

The last few years have seen an explosion of interest in autonomous cyber defence agents based on deep reinforcement learning. Such agents are typically trained in a cyber gym environment, also known as a cyber simulator, at least 32 of which have already been built. Most, if not all cyber gyms provide dense "scaffolded" reward functions which combine many p

Jorge García-Torres, Øyvind Meinich-Bache, Sara Brunner, Siren Rettedal

Around 10% of newborns require some help to initiate breathing, and 5\% need ventilation assistance. Accurate Time of Birth (ToB) documentation is essential for optimizing neonatal care, as timely interventions are vital for proper resuscitation. However, current clinical methods for recording ToB often rely on manual processes, which can be prone to inaccur

Kaibo Hu, Ting Lin

We provide a finite element discretization of $\ell$-form-valued $k$-forms on triangulation in $\mathbb{R}^{n}$ for general $k$, $\ell$ and $n$ and any polynomial degree. The construction generalizes finite element Whitney forms for the de~Rham complex and their higher-order and distributional versions, the Regge finite elements and the Christiansen--Regge e

Devang Somawanshi, Souradeep Bhattacharya, Manish Kataria, Preetish K. Mishra

Integral field unit (IFU) spectroscopic observations of resolved galaxies provide an optimal experimental setting for determination of stellar population properties, in particular - age, metallicity and $\alpha$-enhancement, which are key to understanding evolution of galaxies across diverse physical environments. We determine these properties for the edge-o

Yue Hou, He Zhu, Ruomei Liu, Yingke Su

With the emerging of huge amount of unlabeled data, unsupervised out-of-distribution (OOD) detection is vital for ensuring the reliability of graph neural networks (GNNs) by identifying OOD samples from in-distribution (ID) ones during testing, where encountering novel or unknown data is inevitable. Existing methods often suffer from compromised performance

Takashi Yamazoe

We study a concept of evasion and prediction associated with slaloms, called slalom prediction. This article collects ZFC-provable properties on the slalom prediction.

Shady Ali, Mahmoud Ashraf, Seif Hegazy, Fatty Salem

Evolutionary Algorithms (EAs) employ random or simplistic selection methods, limiting their exploration of solution spaces and convergence to optimal solutions. The randomness in performing crossover or mutations may limit the model's ability to evolve efficiently. This paper introduces Preference-Aligned Individual Reciprocity (PAIR), a novel selection appr

Alexey A. Margun, Alexey A. Bobtsov, Denis V. Efimov, Alexandr D. Panin

This article focuses on the development of functional unknown input observers for systems with arbitrary relative degree. Two distinct approaches are presented to address this challenge. The first approach is tailored to a class of time-varying systems expressed in a canonical controllable form. This method leverages the Generalized Parameter Estimation-Base

Jiarui Yao, Ruida Wang, Tong Zhang

Large Language Models (LLMs) have displayed astonishing abilities in various tasks, especially in text generation, classification, question answering, etc. However, the reasoning ability of LLMs still faces many debates. The inherent ambiguity of Natural Language (NL) limits LLMs' ability to perform verifiable reasoning, making its answers lack coherence and

Hongyang Zhang, Ju Jing, Jason T. L. Wang, Haimin Wang

We present a transformer model, named DeepHalo, to predict the occurrence of halo coronal mass ejections (CMEs). Our model takes as input an active region (AR) and a profile, where the profile contains a time series of data samples in the AR that are collected 24 hours before the beginning of a day, and predicts whether the AR would produce a halo CME during

Yihan Hou, Xingchen Zeng, Yusong Wang, Manling Yang

Existing approaches for color-concept association typically rely on query-based image referencing, and color extraction from image references. However, these approaches are effective only for common concepts, and are vulnerable to unstable image referencing and varying image conditions. Our formative study with designers underscores the need for primary-acce

Len Bos, Michael A. Slawinski, Raphaël A. Slawinski, Theodore Stanoev

We formulate an optimization of a bicycle ascent time under the constraints of the average, maximum, and minimum powers. In contrast to the first part of this study, we do not restrict the departure to flying starts with an initial speed determined by the model and its optimization. We allow for various initial speeds, from a standstill to a launched start.

Dakarai Crowder, Kojo Vandyck, Xiping Sun, James McCann

Humans are able to convey different messages using only touch. Equipping robots with the ability to understand social touch adds another modality in which humans and robots can communicate. In this paper, we present a social gesture recognition system using a fabric-based, large-scale tactile sensor placed onto the arms of a humanoid robot. We built a social

Nemanja Stefan Perović, Mark F. Flanagan, Le-Nam Tran

Integrated sensing and communication (ISAC) has been recognized as one of the key technologies for future wireless networks, which potentially need to operate in multiple frequency bands to satisfy ever-increasing demands for both communication and sensing services. Motivated by this, we consider the sum sensing rate (SR) optimization for a cooperative ISAC

Ali Jalal-Kamali, Nikolos Gurney, David Pynadath

Understanding how individual traits influence team performance is valuable, but these traits are not always directly observable. Prior research has inferred traits like trust from behavioral data. We analyze conversational data to identify team traits and their correlation with teaming outcomes. Using transcripts from a Minecraft-based search-and-rescue expe

Chengjin Li, Yuqian Chen, Nir A. Sochen, Wei Zhang

Diffusion MRI (dMRI) plays a crucial role in studying brain white matter connectivity. Cortical surface reconstruction (CSR), including the inner whiter matter (WM) and outer pial surfaces, is one of the key tasks in dMRI analyses such as fiber tractography and multimodal MRI analysis. Existing CSR methods rely on anatomical T1-weighted data and map them int

Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Yuuki Yamanaka

Diffusion models are powerful generative models but often generate sensitive data that are unwanted by users, mainly because the unlabeled training data frequently contain such sensitive data. Since labeling all sensitive data in the large-scale unlabeled training data is impractical, we address this problem by using a small amount of labeled sensitive data.

Longshen Ou, Yu Takahashi, Ye Wang

Prior approaches to lead instrument detection primarily analyze mixture audio, limited to coarse classifications and lacking generalization ability. This paper presents a novel approach to lead instrument detection in multitrack music audio by crafting expertly annotated datasets and designing a novel framework that integrates a self-supervised learning mode

Tao Wang, Zhihua Wu, Qiaozhi He, Jiaming Chu

Text-to-motion generation, which translates textual descriptions into human motions, has been challenging in accurately capturing detailed user-imagined motions from simple text inputs. This paper introduces StickMotion, an efficient diffusion-based network designed for multi-condition scenarios, which generates desired motions based on traditional text and

Mohamed AbdulHameed, Benjamin Beeler, Conor O. T. Galvin, Michael W. D. Cooper

Uranium mononitride (UN) is a promising advanced nuclear fuel due to its high thermal conductivity and high fissile density. Yet, many aspects of its mechanical behavior and microstructural features are currently unknown. In this paper, molecular dynamics (MD) simulations are used to study UN's diffusional creep. Nanometer-sized polycrystals are used to simu

Kun Huang, Yifu Wang, Si'ao Zhang, Zhirui Wang

The present paper proposes optimization-based solutions to visual SLAM with a vehicle-mounted surround-view camera system. Owing to their original use-case, such systems often only contain a single camera facing into either direction and very limited overlap between fields of view. Our novelty consist of three optimization modules targeting at practical onli

Jinming Chen, Jingyi Fang, Yuanzhong Zheng, Yaoxuan Wang

Emotion recognition plays a pivotal role in intelligent human-machine interaction systems. Multimodal approaches benefit from the fusion of diverse modalities, thereby improving the recognition accuracy. However, the lack of high-quality multimodal data and the challenge of achieving optimal alignment between different modalities significantly limit the pote

Anagha A G, Arunima Banerjee

About two-thirds of the galactic disks exhibit a central ellipsoidal stellar component called the bar, with or without a gaseous counterpart. However, there are a few dwarf galaxies with purely gaseous bars: NGC3741, NGC2915 and DDO168. This is a puzzle as gas is a collisional medium, and a gaseous bar is expected to be ripped off by shock waves. We study th

Qinglin Liu, Zonglin Li, Xiaoqian Lv, Xin Sun

In this paper, we explore a novel image matting task aimed at achieving efficient inference under various computational cost constraints, specifically FLOP limitations, using a single matting network. Existing matting methods which have not explored scalable architectures or path-learning strategies, fail to tackle this challenge. To overcome these limitatio

C. Grotta-Ragazzo, Lei Liu, Pedro A. S. Salomão

This paper is about the existence of periodic orbits near an equilibrium point of a two-degree-of-freedom Hamiltonian system. The equilibrium is supposed to be a nondegenerate minimum of the Hamiltonian. Every sphere-like component of the energy surface sufficiently close to the equilibrium contains at least two periodic orbits forming a Hopf link (A. Weinst

Yice Zhang, Guangyu Xie, Jingjie Lin, Jianzhu Bao

This paper explores targeted distillation methods for sentiment analysis, aiming to build compact and practical models that preserve strong and generalizable sentiment analysis capabilities. To this end, we conceptually decouple the distillation target into knowledge and alignment and accordingly propose a two-stage distillation framework. Moreover, we intro

Vishakha Agrawal, Archie Chaudhury, Shreya Agrawal

While Large Language Models (LLMs) are fundamentally next-token prediction systems, their practical applications extend far beyond this basic function. From natural language processing and text generation to conversational assistants and software use, LLMs have numerous use-cases, and have already acquired a significant degree of enterprise adoption. To eval

Mahfuz Ahmed Anik, Abdur Rahman, Azmine Toushik Wasi, Md Manjurul Ahsan

Language is a cornerstone of cultural identity, yet globalization and the dominance of major languages have placed nearly 3,000 languages at risk of extinction. Existing AI-driven translation models prioritize efficiency but often fail to capture cultural nuances, idiomatic expressions, and historical significance, leading to translations that marginalize li

Johann Rafelski, Cheng Tao Yang

We explore the Higgs particle in the cosmic quark-gluon plasma (QGP) below the electroweak phase transition temperature $T_\mathrm{EW}\simeq 125\mathrm{\,GeV}$. We show that Higgs is neither in abundance (chemical) nor in momentum distribution equilibrium in certain stages of the Universe evolution. Nonequilibrium originates in: For chemical nonequilibrium i

Yongyun Chen, Qiusheng Gu, Junhui Fan, Xiaotong Guo

Both theoretical models and observational evidence indicate that jets and/or outflows driven by central active supermassive black holes exert a significant feedback effect on the overall properties of their host galaxies. Theoretical models suggest that the spin of supermassive black holes drives relativistic jets. Therefore, we investigate the relationship

Zhumei Wang, Zechen Hu, Ruoxi Guo, Huaijin Pi

Human motion recovery for real-world interaction demands both precise action details and metric-scale trajectories. Recovering absolute human pose from monocular input presents a viable solution, but faces two main challenges: (1) models' reliance on 3D training data from constrained environments limits their out-of-distribution generalization; and (2) the i

Petar Žugec, Davor Horvatić, Ivica Smolić

We examine a logical foundation of depicting a Lorentz contraction of a Coulomb field (an electric field of a point charge in uniform motion) by means of the 'Lorentz contracted' field lines. Two existing arguments for a contraction of field lines sound appealing and lead to very simple calculations yielding the correct results. However, one of them is a vic

Yuchen Zhang, Hui Chen, Pinjun Zheng, Boyu Ning

We investigate joint bistatic positioning (BP) and monostatic sensing (MS) within a multi-input multi-output orthogonal frequency-division system. Based on the derived Cram\'er-Rao Bounds (CRBs), we propose novel beamforming optimization strategies that enable flexible performance trade-offs between BP and MS. Two distinct objectives are considered in this m

Mengke Zhao, Xindi Tang, Keping Qiu, Yuxin He

Filament G37 exhibits a distinctive "caterpillar" shape, characterized by two semicircular structures within its 40\,pc-long body, providing an ideal target to investigate the formation and evolution of filaments. By analyzing multiple observational data, such as CO spectral line, the H$\alpha$\,RRL, and multi-wavelength continuum, we find that the expanding

Takahide Kubota, Kazuya Z. Suzuki, Yoshiyuki Hirayama, Shigeki Takahashi

High-entropy alloys (HEAs) exhibit various physical properties, such as high microhardness for structured materials and high efficiency for catalysis. These features are recognized as a cocktail effect of five or more elements that stabilize a single-phase solid solution due to a high configurational entropy. HEAs may also exhibit short-range orders and micr

Chao Ding, Ebrahim Sarabi, Shiwei Wang

Tilt stability plays a pivotal role in understanding how local solutions of an optimization problem respond to small, targeted perturbations of the objective. Although quadratic bundles are a powerful tool for capturing second-order variational behavior, their characterization remains incomplete beyond well-known polyhedral and certain specialized nonpolyhed

Alejandro Corichi, Juan D. Reyes, Tatjana Vukasinac

The Hamiltonian description of classical gauge theories is a well studied subject. The two best known approaches, namely the covariant and canonical Hamiltonian formalisms have received a lot of attention in the literature. However, in our opinion, a full understanding of the relation between them is not available, specially for gauge theories that are defin

Wentao Li, Congcong Wang, Xiaoxiao Cui, Zhi Liu

Open Source Intelligence (OSINT) requires the integration and reasoning of diverse multimodal data, presenting significant challenges in deriving actionable insights. Traditional approaches, including multimodal large language models (MLLMs), often struggle to infer complex contextual relationships or deliver comprehensive intelligence from unstructured data

Ankush Tyagi, Dhruv Motwani, Vipul K. Dabhi, Harshadkumar B. Prajapati

The rice grain quality can be determined from its size and chalkiness. The traditional approach to measure the rice grain size involves manual inspection, which is inefficient and leads to inconsistent results. To address this issue, an image processing based approach is proposed and developed in this research. The approach takes image of rice grains as inpu

Haiyue Zu, Jun Ge, Heting Xiao, Jile Xie

The reliance on large labeled datasets presents a significant challenge in medical image segmentation. Few-shot learning offers a potential solution, but existing methods often still require substantial training data. This paper proposes a novel approach that leverages the Segment Anything Model 2 (SAM2), a vision foundation model with strong video segmentat

Huy Nguyen, Nhat Ho, Alessandro Rinaldo

Mixture of experts (MoE) has recently emerged as an effective framework to advance the efficiency and scalability of machine learning models by softly dividing complex tasks among multiple specialized sub-models termed experts. Central to the success of MoE is an adaptive softmax gating mechanism which takes responsibility for determining the relevance of ea

Romanshu Garg, G. P. Singh, Ashutosh Singh

We study the cosmological implications of barotropic fluid satisfying affine equation of state (EoS) in the General relativity and $f(Q)$ gravity framework. We describe the impact of affine EoS on the cosmic evolution in the model and derive the observational constraints on the model parameters. The models of General relativity and $f(Q)$ gravity may unify t

Zhangting Lin, Mingfu Xue, Kewei Chen, Wenmao Liu

Currently, deep learning models are easily exposed to data leakage risks. As a distributed model, Split Learning thus emerged as a solution to address this issue. The model is splitted to avoid data uploading to the server and reduce computing requirements while ensuring data privacy and security. However, the transmission of data between clients and server

Shenzhi Yang, Jun Xia, Jingbo Zhou, Xingkai Yao

Aggregating information from neighboring nodes benefits graph neural networks (GNNs) in semi-supervised node classification tasks. Nevertheless, this mechanism also renders nodes susceptible to the influence of their neighbors. For instance, this will occur when the neighboring nodes are imbalanced or the neighboring nodes contain noise, which can even affec

Ki Vin Foo, Burkhard C. Schipper

We extend Kuhn's Theorem to games of the extensive form with unawareness. We prove that if a game of the extensive form with unawareness has perfect recall, then for each mixed strategy there is an equivalent behavior strategy. We show that the converse does not hold under unawareness without restricting the evolution of the player's awareness to constant aw

Hanji Wu, Wei Wang

MAXI J1348-630 as a low-mass black hole binary system in the Galaxy showed an X-ray outburst in 2019. We analyzed the Insight-HXMT spectral data in the low hard state (LHS) and intermediate state (IS) during the outburst from MJD 58510 to 58519 at the energy band from 2 keV to 100 keV. During the entire process, a thin disk extending to the innermost stable

Slim Ibrahim, Ikkei Shimizu

Isolated skyrmion solutions to the two-dimensional Landau-Lifshitz equation with Dzyaloshinskii-Moriya interaction, Zeeman term, and easy-plane anisotropy of various strengths are studied. In the full range of parameter values for which the energy is a positive variation of the Bogomol'nyi case, we construct solutions to the corresponding Euler-Lagrange equa

Han Zheng, Jiale Zhang, Mingyang Jiang, Peiyuan Liu

Autonomous navigation is a fundamental task for robot vacuum cleaners in indoor environments. Since their core function is to clean entire areas, robots inevitably encounter dead zones in cluttered and narrow scenarios. Existing planning methods often fail to escape due to complex environmental constraints, high-dimensional search spaces, and high difficulty

Pei-Wei Chen, Shaokai Lin, Adwait Godbole, Ramneet Singh

Several software systems are polyglot; that is, they comprise programs implemented in a combination of programming languages. Verifiers that directly run on mainstream programming languages are currently customized for single languages. Thus, to verify polyglot systems, one usually translates them into a common verification language or formalism on which the

Binxu Wang, Cengiz Pehlevan

We develop an analytical framework for understanding how the generated distribution evolves during diffusion model training. Leveraging a Gaussian-equivalence principle, we solve the full-batch gradient-flow dynamics of linear and convolutional denoisers and integrate the resulting probability-flow ODE, yielding analytic expressions for the generated distrib

Ruida Wang, Rui Pan, Yuxin Li, Jipeng Zhang

Solving mathematical problems using computer-verifiable languages like Lean has significantly impacted the mathematical and computer science communities. State-of-the-art methods utilize a single Large Language Model (LLM) to generate complete proof or perform tree search, but they fail to balance these tasks. We propose **MA-LoT**: *Model-CollAboration Lean

Setu A. Bhatt, Harshadkumar B. Prajapati, Vipul K. Dabhi, Ankush Tyagi

This paper presents an innovative approach that enables the user to find matching faces based on the user-selected face parameters. Through gradio-based user interface, the users can interactively select the face parameters they want in their desired partner. These user-selected face parameters are transformed into a text prompt which is used by the Text-To-

Parwat Singh Anjana, Matin Amini, Rohit Kapoor, Rahul Parmar

Parallel execution of smart contract transactions in large multicore architectures is critical for higher efficiency and improved throughput. The main bottleneck for maximizing the throughput of a node through parallel execution is transaction conflict resolution: when two transactions interact with the same data, like an account balance, their order matters

Sneh Pillai

Training vision-language models for image-text alignment typically requires large datasets to achieve robust performance. In low-data scenarios, standard contrastive learning can struggle to align modalities effectively due to overfitting and unstable training dynamics. In this paper, we propose a variance-aware loss scheduling approach that dynamically adju

Jizhao Zhu, Akang Shi, Zixuan Li, Long Bai

In this paper, we aim to enhance the robustness of Universal Information Extraction (UIE) by introducing a new benchmark dataset, a comprehensive evaluation, and a feasible solution. Existing robust benchmark datasets have two key limitations: 1) They generate only a limited range of perturbations for a single Information Extraction (IE) task, which fails to

Harry Freeman, George Kantor

In this paper, we present a transformer-based method to spatio-temporally associate apple fruitlets in stereo-images collected on different days and from different camera poses. State-of-the-art association methods in agriculture are dedicated towards matching larger crops using either high-resolution point clouds or temporally stable features, which are bot

Marc R. Schlichting, Vale Rasmussen, Heba Alazzeh, Houjun Liu

In aviation emergencies, high-stakes decisions must be made in an instant. Pilots rely on quick access to precise, context-specific information -- an area where emerging tools like large language models (LLMs) show promise in providing critical support. This paper introduces LeRAAT, a framework that integrates LLMs with the X-Plane flight simulator to delive

Tianyi Zhang, Sicheng Chen, Borui Kang, Dankai Liao

Whole Slide Imaging (WSI) has become a gold standard in cancer diagnosis, inspecting multi-scale information from cellular to tissue levels. Processing an entire WSI directly is infeasible due to GPU memory constraints; thus, Multiple Instance Learning (MIL) has emerged as the standard solution by partitioning WSIs into tiles. While recent two-stage MIL fram

Ziqian Yao, Clarissa Daniel, Eric Stolt, Vakhtang Chulukhadze

We present the first lithium tantalate (LT) thickness-extensional (TE) mode bulk acoustic resonators designed for piezoelectric power conversion, showcasing a low temperature coefficient of frequency (TCF) of -13.56 ppm/K. These resonators also exhibit high quality factors (Q) of 1698, and electromechanical coupling coefficients ($k^2$) of 8.8%, making them

Attila Lischka, Simon Rauch, Oliver Stritzel

In the past years, predictive process monitoring (PPM) techniques based on artificial neural networks have evolved as a method to monitor the future behavior of business processes. Existing approaches mostly focus on interpreting the processes as sequences, so-called traces, and feeding them to neural architectures designed to operate on sequential data such

Zhiyuan Huang, Ziming Cheng, Junting Pan, Zhaohui Hou

Graphical User Interface (GUI) agents show amazing abilities in assisting human-computer interaction, automating human user's navigation on digital devices. An ideal GUI agent is expected to achieve high accuracy, low latency, and compatibility for different GUI platforms. Recent vision-based approaches have shown promise by leveraging advanced Vision Langua

Gibson Nkhata Shi Yin Hong, Susan Gauch

Stance Detection (SD) has become a critical area of interest due to its applications in various contexts leading to increased research within NLP. Yet the subtlety and complexity of texts sourced from online platforms often containing sarcastic language pose significant challenges for SD algorithms in accurately determining the authors stance. This paper add

Sushant Vijayan, Zhe Feng, Swati Padmanabhan, Karthikeyan Shanmugam

We consider the problem of bidding in online advertising, where an advertiser aims to maximize value while adhering to budget and Return-on-Spend (RoS) constraints. Unlike prior work that assumes knowledge of the value generated by winning each impression ({e.g.,} conversions), we address the more realistic setting where the advertiser must simultaneously le

Guangfu Guo, Kai Zhang, Bryan Hoo, Yujun Cai

Medical question-answering (QA) is a critical task for evaluating how effectively large language models (LLMs) encode clinical knowledge and assessing their potential applications in medicine. Despite showing promise on multiple-choice tests, LLMs frequently struggle with open-ended medical questions, producing responses with dangerous hallucinations or lack

Andrew Brahms, Alan Duan, Jesse Geneson, Jacob Greene

A 0-1 matrix $M$ contains a 0-1 matrix $P$ if $M$ has a submatrix $P'$ which can be turned into $P$ by changing some of the ones to zeroes. Matrix $M$ is $P$-saturated if $M$ does not contain $P$, but any matrix $M'$ derived from $M$ by changing a zero to a one must contain $P$. The saturation function $sat(n,P)$ is defined as the minimum number of ones of a

Alexander Thoms, Alan Papalia, Jared Velasquez, David M. Rosen

Reliable simultaneous localization and mapping (SLAM) algorithms are necessary for safety-critical autonomous navigation. In the communication-constrained multi-agent setting, navigation systems increasingly use point-to-point range sensors as they afford measurements with low bandwidth requirements and known data association. The state estimation problem fo

Chao Ning, Wanshui Gan, Weihao Xuan, Naoto Yokoya

Pre-trained encoders are widely employed in dense prediction tasks for their capability to effectively extract visual features from images. The decoder subsequently processes these features to generate pixel-level predictions. However, due to structural differences and variations in input data, only encoders benefit from pre-learned representations from visi

Jonathan Barenboim, Andrei V. Frolov, Gabor Kunstatter

We present a general class of non-singular black holes in semi-classical, two-dimensional dilaton gravity, with a focus on a Bardeen-like model. The equations of motion for an evaporating black hole including backreaction are solved numerically. The apparent horizons evaporate smoothly in finite time to form a compact trapped region. Backreaction effects lea

Jingzhou Luo, Yang Liu, Weixing Chen, Zhen Li

3D Question Answering (3D QA) requires the model to comprehensively understand its situated 3D scene described by the text, then reason about its surrounding environment and answer a question under that situation. However, existing methods usually rely on global scene perception from pure 3D point clouds and overlook the importance of rich local texture deta

Clifford M. Will

We present and discuss the possibility, derived from work carried out 20 years ago, that the equations of motion for compact binary neutron stars at the third post-Newtonian (3PN) order in general relativity might actually depend on the internal structure of the bodies. These effects involve integrals over the density and internal gravitational potentials of

Xia Zhang, Leilei Wei, Ming Liu

In this paper, we first introduce the notion of the Laplace transform for an abstract-valued function from $[0, \infty)$ to a $\mathcal{T}_{\varepsilon, \lambda}$-complete random normed module $S$. Then, combining respective advantages of the $(\varepsilon, \lambda)$-topology and the locally $L^0$-convex topology on $S$, we prove the differentiability, Post-

Chian Yeong Chuah, Zhen-Chuan Liu, Tao Mei

We study the $p=1$ operator Khintchine inequality associated with orthonormal systems and establish two quantitative implications relating its optimal constant $A_1$ to the $Z_2$ property. For an orthonormal system $W$ with finite $Z_2(W)$, we prove $A_1(W)\leq\sqrt{1+Z_2(W)}$. Conversely, there exists an absolute constant $δ>0$ such that, for canonical grou

Ben Hutchinson, Celeste Rodríguez Louro, Glenys Collard, Ned Cooper

In Australia, post-contact language varieties, including creoles and local varieties of international languages, emerged as a result of forced contact between Indigenous communities and English speakers. These contact varieties are widely used, yet are poorly supported by language technologies. This gap presents barriers to participation in civil and economi

Qiang Yin

In the calculation of the decay rate at finite temperature using the saddle point approximation, we identified some inconsistencies in the calculation of the decay rate at zero temperature. These inconsistencies may impact the explanation provided by Callan and Coleman. To address these inconsistencies, we recalculated the decay rate using the shifted-bounce

Idan Attias, Avrim Blum, Keziah Naggita, Donya Saless

One of the most basic lower bounds in machine learning is that in nearly any nontrivial setting, it takes $\textit{at least}$ $1/\epsilon$ samples to learn to error $\epsilon$ (and more, if the classifier being learned is complex). However, suppose that data points are agents who have the ability to improve by a small amount if doing so will allow them to re

Yao Du, Jiaxin Zhuang, Xiaoyu Zheng, Jing Cong

Histopathology image analysis is fundamental to digital pathology, with hematoxylin and eosin (H&E) staining as the gold standard for diagnostic and prognostic assessments. While H&E imaging effectively highlights cellular and tissue structures, it lacks sensitivity to birefringence and tissue anisotropy, which are crucial for assessing collagen organization

Yuanshen Lin, Zhenli Xu, Yusu Zhang, Qi Zhou

Kernel approximation with exponentials is useful in many problems with convolution quadrature and particle interactions such as integral-differential equations, molecular dynamics and machine learning. This paper proposes a weighted balanced truncation to construct an optimal model reduction method for compressing the number of exponentials in the sum-of-exp

Xinyuan Lin, Chenlu Li, Zongle Huang, Chunyu Wang

Larger model sizes and longer sequence lengths have empowered the Large Language Model (LLM) to achieve outstanding performance across various domains. However, this progress brings significant storage capacity challenges for LLM pretraining. High Bandwidth Memory (HBM) is expensive and requires more advanced packaging technologies for capacity expansion, cr

Zhihua Chang, Yongjie Wang

We introduce a Drinfeld presentation for the super-Yangian $\mathrm{Y}(\mathfrak{q}_n)$ associated with the queer Lie superalgebra $\mathfrak{q}_n$. The Drinfeld generators of $\mathrm{Y}(\mathfrak{q}_n)$ are obtained through a block Gauss decomposition of the generator matrix in its RTT presentation, and the Drinfeld relations are explicitly computed by uti

Ashutosh Ghimire, Ghazal Ghajari, Karma Gurung, Love K. Sah

Ensuring the security of critical infrastructure has become increasingly vital with the proliferation of Internet of Things (IoT) systems. However, the heterogeneous nature of IoT data and the lack of human-comprehensible insights from anomaly detection models remain significant challenges. This paper presents a hybrid framework that combines numerical anoma

Chong-Xing Yue, Xin-Yang Li, Mei-Shu-Yu Wang, Yang-Yang Bu

The axion-like particle (ALP) is one of the best motivated particles beyond the Standard Model (SM). We explore the possibility of detecting the couplings of ALP with leptons via three-lepton final state processes $e^- p \to e^- j a~(a \to \ell^+ \ell^-)$ at the LHeC (FCC-eh). For completeness, we investigate the cases where the ALP decays not only into elec

Lei Ke, Haohang Xu, Xuefei Ning, Yu Li

Diffusion models have achieved significant progress in both image and video generation while still suffering from huge computation costs. As an effective solution, flow matching aims to reflow the diffusion process of diffusion models into a straight line for a few-step and even one-step generation. However, in this paper, we suggest that the original traini

Nick Winovich, Mitchell Daneker, Lu Lu, Guang Lin

With the increased prevalence of neural operators being used to provide rapid solutions to partial differential equations (PDEs), understanding the accuracy of model predictions and the associated error levels is necessary for deploying reliable surrogate models in scientific applications. Existing uncertainty quantification (UQ) frameworks employ ensembles

Mingji Chen, Shuai Lu, Wei Gu, Zhaoyang Dong

The flexible loads in power systems, such as interruptible and transferable loads, are critical flexibility resources for mitigating power imbalances. Despite their potential, accurate modeling of these loads is a challenging work and has not received enough attention, limiting their integration into operational frameworks. To bridge this gap, this paper dev

Yusuke Takase

The quest for primordial $B$-mode polarization signatures in the Cosmic Microwave Background (CMB) is a major goal of contemporary cosmology. Detecting these signatures would confirm primordial gravitational waves and allow precise determination of the tensor-to-scalar ratio, $r$, which is crucial for distinguishing between inflationary models. This requires

S. G. Khambholja, A. Abhishek, B. Y. Thakore

Due to the compound forming tendency, some of the liquid metal alloys show anomalous behavior in their physical and chemical properties. Near the compound forming concentration, their electrical resistivity is beyond the metallic values and hence they may be labelled as liquid semiconductors. Lithium-Bismuth is one such system. It shows some interesting feat

Rouzbeh Modarresi-Yazdi, Shuzhe Shi, Charles Gale, Sangyong Jeon

As an important signature of the quark-gluon plasma (QGP), a high-precision energy-loss model is essential to independently verify the QGP properties extracted from soft particles. In this work, we optimize the energy loss modeling in MARTINI by introducing the formation time of the parton shower in the initial hard scattering, which is essential for a simul

Raigul Zheldibayeva

This research studies the impact of AI and peer feedback on the academic writing development of Kazakhstani scholars using the CGScholar platform - a product of research into collaborative learning, big data, and artificial intelligence developed by educators and computer scientists at the University of Illinois at Urbana-Champaign (UIUC). The study aimed to

Jesse Keyes

In $G$-equivariant stable homotopy theory, it is known that the equivariant Eilenberg-Mac Lane spectra representing ordinary equivariant cohomology have nontrivial $RO(G)$-graded homotopy corresponding to the equivariant (co)homology of representation spheres. We will compute the universal case of this ordinary $RO(G)$-graded homotopy in the case of $G=\math

Gibson Nkhata, Susan Gauch

Stance Detection (SD) on social media has emerged as a prominent area of interest with implications for social business and political applications thereby garnering escalating research attention within NLP. The inherent subtlety and complexity of texts procured from online platforms pose challenges for SD algorithms in accurately discerning the authors stanc

Zexin Wang, Dancheng Lu

We compute the Betti numbers of the edge rings of multi-path graphs using the \emph{induced-subgraph approach} introduced in \cite{WL1}. Here, a multi-path graph refers to a simple graph composed of several paths that have the same starting point and the same ending point. Special cases include the graph $G_{r,d}$ introduced in \cite{GHK}, the graph $G_{r,s,

Haokai Ma, Javier Yong, Yunshan Ma, Kuei Chen

Cyber Threat Intelligence (CTI) reports document observations of cyber threats, synthesizing evidence about adversaries' actions and intent into actionable knowledge that informs detection, response, and defense planning. However, the unstructured and verbose nature of CTI reports poses significant challenges for security practitioners to manually extract an

Faranak Bahrami, Matthew P. Bland, Nana Shumiya, Ray D. Chang

Tantalum (Ta) based superconducting circuits have been demonstrated to enable record qubit coherence times and quality factors, motivating a careful study of the microscopic origin of the remaining losses that limit their performance. We have recently shown that the losses in Ta-based resonators are dominated by two-level systems (TLSs) at low microwave powe

Yash Gupta, William Vreeland, Andrew Peterman, Coley Girouard

The transportation sector is the single largest contributor to US emissions and the second largest globally. Electric vehicles (EVs) are expected to represent half of global car sales by 2035, emerging as a pivotal solution to reduce emissions and enhance grid flexibility. The electrification of buildings, manufacturing, and transportation is expected to gro

Madhuka Thisuri De Silva, Jim Smiley, Sarah Goodwin, Leona M Holloway

Dance teachers rely primarily on verbal instructions and visual demonstrations to convey key dance concepts and movement. These techniques, however, have limitations in supporting students who are blind or have low vision (BLV). This work explores the role technology can play in supporting instruction for BLV students, as well as improvisation with their ins

Xing Tang, Yunpeng Weng, Fuyuan Lyu, Dugang Liu

With the rapid growth of online investment platforms, funds can be distributed to individual customers online. The central issue is to match funds with potential customers under constraints. Most mainstream platforms adopt the recommendation formulation to tackle the problem. However, the traditional recommendation regime has its inherent drawbacks when appl

Hailan Ma, Bo Qi, Ian R. Petersen, Re-Bing Wu

The development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation, and sensing. This poses severe challenges in efficient control, calibration, and validation of quantum states and their dynamics. Machine learning methods have emerged as powerful tools owing

Aleese Barron, Yulai Zhang, Neelima Kandula, Matthew Shadwell

Fragmentation and breakage of rocks is essential to iron ore mining and extraction. The breakage energy requirements and resulting ore particle size and mineral distributions are key to understanding and optimising mining and processing practices. This study combines high-speed and experimental X-ray micro-CT (micro-computed tomography) imaging with 3D image