February 2025 arXiv papers — page 19

Cheng Li, Jiexiong Liu, Yixuan Chen, Yanqin Jia

Large language models have demonstrated remarkable performance across various tasks, yet they face challenges such as low computational efficiency, gradient vanishing, and difficulties in capturing complex feature interactions. To address these limitations, a novel framework has been proposed. This framework incorporates a learnable dense residual skip conne

Junji Hisano, Moe Kuroda

In this paper we extend the supersymmetric SU(5) GUTs with the Nelson-Barr (NB) models. The NB models are a candidate for the solution of the strong CP problem. We show that the parameters in the CKM matrix are reproduced while the mass ratios of the down-type quarks and leptons in the second and third generations are explained in the minimal extension.

A. A. Glutsyuk, Yu. S. Ilyashenko

We prove that a completely non-degenerate B-group is uniquely determined by its factor: two such groups with conformally equivalent factors are M\"obius conjugate. A similar property is inherent to the quasi-Fuchsian groups but not to degenerate B-groups. We also study the factor of a B-group as a triple: the main factor, the marked characteristic complex, a

Yoo Yeon Sung, Eve Fleisig, Yu Hou, Ishan Upadhyay

Language models are often miscalibrated, leading to confidently incorrect answers. We introduce GRACE, a benchmark for language model calibration that incorporates comparison with human calibration. GRACE consists of question-answer pairs, in which each question contains a series of clues that gradually become easier, all leading to the same answer; models m

Xiongfei Su, Tianyi Zhu, Lina Liu, Zheng Chen

The domain of non-line-of-sight (NLOS) imaging is advancing rapidly, offering the capability to reveal occluded scenes that are not directly visible. However, contemporary NLOS systems face several significant challenges: (1) The computational and storage requirements are profound due to the inherent three-dimensional grid data structure, which restricts pra

Tianjiao Dai, Weichan Liu, Xin Zhang

In an $r$-partite graph, an independent transversal of size $s$ (ITS) consists of $s$ vertices from each part forming an independent set. Motivated by a question from Bollob\'as, Erd\H{o}s, and Szemer\'edi (1975), Di Braccio and Illingworth (2024) inquired about the minimum degree needed to ensure an $n \times \cdots \times n$ $r$-partite graph contains $K_r

Yao-Jen Liang

By the definition of an angle matrix, we investigate the inverse of the Hadamard product of a full rank and an angle matrices. Our proof involves standard matrix analysis. It enriches the algebra of Hadamard products.

Kai Hu, Feng Gao, Xiaohan Nie, Peng Zhou

Recent advances in Multi-Modal Large Language Models (M-LLMs) show promising results in video reasoning. Popular Multi-Modal Large Language Model (M-LLM) frameworks usually apply naive uniform sampling to reduce the number of video frames that are fed into an M-LLM, particularly for long context videos. However, it could lose crucial context in certain perio

Linzhuo li

Large Language Models, despite their power, have a fundamental architectural vulnerability stemming from their causal transformer design -- order sensitivity. This architectural constraint may distorts classification outcomes when prompt elements like label options are reordered, revealing a theoretical gap between accuracy metrics and true model reliability

Hao-Hui Zhang, Wang-Chen Xue, Xiao-Bo Li, Shuang-Nan Zhang

The density of the Earth's middle and upper atmosphere is an important question in Earth science and is a critical factor in the design, operation, and orbital determination of low Earth orbit spacecraft. In this study, we employ the Earth Occultation Technique (EOT) combined with Maximum Likelihood Estimation to estimate the neutral atmospheric density by m

Hu Gao, Depeng Dang

Image deblurring aims to restore high-quality images by removing undesired degradation. Although existing methods have yielded promising results, they either overlook the varying degrees of degradation across different regions of the blurred image, or they approximate nonlinear function properties by stacking numerous nonlinear activation functions. In this

Zhangdie Yuan, Zifeng Ding, Andreas Vlachos

Forecasting is an important task in many domains, such as technology and economics. However existing forecasting benchmarks largely lack comprehensive confidence assessment, focus on limited question types, and often consist of artificial questions that do not align with real-world human forecasting needs. To address these gaps, we introduce FOReCAst (Future

Yiyang Zhu, Jiayi Zhang, Enyu Shi, Ziheng Liu

Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems offer high spectral efficiency (SE) through multiple distributed access points (APs). However, the large number of antennas increases power consumption. We propose incorporating stacked intelligent metasurfaces (SIM) into CF mMIMO systems as a cost-effective, energy-efficient solution. Thi

Tong Zhang, Shu Shen, C. L. Philip Chen

Multimodal learning enhances the performance of various machine learning tasks by leveraging complementary information across different modalities. However, existing methods often learn multimodal representations that retain substantial inter-class confusion, making it difficult to achieve high-confidence predictions, particularly in real-world scenarios wit

Shubhankar Borse, Kartikeya Bhardwaj, Mohammad Reza Karimi Dastjerdi, Hyojin Park

Diffusion models are increasingly popular for generative tasks, including personalized composition of subjects and styles. While diffusion models can generate user-specified subjects performing text-guided actions in custom styles, they require fine-tuning and are not feasible for personalization on mobile devices. Hence, tuning-free personalization methods

Chenhe Gu, Jindong Gu, Andong Hua, Yao Qin

Multimodal Large Language Models (MLLMs), built upon LLMs, have recently gained attention for their capabilities in image recognition and understanding. However, while MLLMs are vulnerable to adversarial attacks, the transferability of these attacks across different models remains limited, especially under targeted attack setting. Existing methods primarily

Ju-Hyeon Nam, Sang-Chul Lee

Generalizable medical image segmentation is essential for ensuring consistent performance across diverse unseen clinical settings. However, existing methods often overlook the capability to generalize effectively across arbitrary unseen modalities. In this paper, we introduce a novel Test-Time Modality Generalization (TTMG) framework, which comprises two cor

Yeonjun In, Kanghoon Yoon, Sukwon Yun, Kibum Kim

In real-world applications, node features in graphs often contain noise from various sources, leading to significant performance degradation in GNNs. Although several methods have been developed to enhance robustness, they rely on the unrealistic assumption that noise in node features is independent of the graph structure and node labels, thereby limiting th

Kohei Tsuji, Tatsuya Hiraoka, Yuchang Cheng, Eiji Aramaki

This paper investigates how LLMs encode inputs with typos. We hypothesize that specific neurons and attention heads recognize typos and fix them internally using local and global contexts. We introduce a method to identify typo neurons and typo heads that work actively when inputs contain typos. Our experimental results suggest the following: 1) LLMs can fix

Mingsheng Cai, Jiuming Jiang, Wenhao Huang, Che Liu

Cardiovascular diseases are a leading cause of death and disability worldwide. Electrocardiogram (ECG) is critical for diagnosing and monitoring cardiac health, but obtaining large-scale annotated ECG datasets is labor-intensive and time-consuming. Recent ECG Self-Supervised Learning (eSSL) methods mitigate this by learning features without extensive labels

Zinan Zhao, Wenguang Sun

This article presents a Conformalized Locally Adaptive Weighting (CLAW) approach to multiple testing with side information. The proposed method employs innovative data-driven strategies to construct pairwise exchangeable scores, which are integrated into a generic algorithm that leverages a mirror process for controlling the false discovery rate (FDR). By co

Wang Penghui, Wang Shan, Zhao Shengkai

In this paper, we investigate the closed-loop solvability of the quantum stochastic linear quadratic optimal control problem. We derive the Pontryagin maximum principle for the linear quadratic control problem of infinite-dimensional quantum stochastic systems. The equivalence between unique closed-loop solvability for quantum stochastic linear quadratic opt

Yuanchao Wang, Zhao-Rong Lai, Tianqi Zhong

Invariant risk minimization is an important general machine learning framework that has recently been interpreted as a total variation model (IRM-TV). However, how to improve out-of-distribution (OOD) generalization in the IRM-TV setting remains unsolved. In this paper, we extend IRM-TV to a Lagrangian multiplier model named OOD-TV-IRM. We find that the auto

Zihao Huo, Peng Zhang, Haoliang Shi, Xiaochun Yan

Recently, the Pr-doped Ruddlesden-Popper phase of bilayer nickelate La3Ni2O7 has been reported to exhibit a superconducting transition temperature (Tc) of 82.5 K and superconducting volume fraction of about 57 % at high pressure. However, the effect of Pr-doping on La3Ni2O7 remains unclear. Here, we studied the crystal structures and electronic properties of

Faizhal Arif Santosa, Barbara S. Lancho Barrantes

English is widely used as a lingua franca in scholarly communication, yet preserving local languages is vital to reaching a broader audience. Disseminating research in multiple languages can help ensure equitable access, a responsibility shared by both publishers and authors. This study examines the practices of both groups to identify any notable difference

Rohan Juneja, Shivam Aggarwal, Safeen Huda, Tulika Mitra

Quantization is critical for efficiently deploying large language models (LLMs). Yet conventional methods remain hardware-agnostic, limited to bit-width constraints, and do not account for intrinsic circuit characteristics such as the timing behaviors and energy profiles of Multiply-Accumulate (MAC) units. This disconnect from circuit-level behavior limits t

Aoi Yamauchi, Rihito Nagase, Kaixin Li, Takahiro Sagawa

Understanding the fundamental constraint on work far beyond the adiabatic regime is crucial to investigating fast and efficient energy extraction or consumption processes. In this study, we derive thermodynamic speed limits for non-adiabatic work and quantify the fundamental costs of non-adiabatic work extraction or consumption processes in open quantum syst

Zikuan Li, Qiaoyun Wu, Jialin Zhang, Kaijun Zhang

Spiking neural networks (SNNs), inspired by the spiking computation paradigm of the biological neural systems, have exhibited superior energy efficiency in 2D classification tasks over traditional artificial neural networks (ANNs). However, the regression potential of SNNs has not been well explored, especially in 3D point cloud processing. In this paper, we

Annika Camehl, Tomasz Woźniak

We propose a novel Bayesian heteroskedastic Markov-switching structural vector autoregression with data-driven time-varying identification. The model selects among alternative patterns of exclusion restrictions to identify structural shocks within the Markov process regimes. We implement the selection through a multinomial prior distribution over these patte

Yu Jin, Jinsoo Park, Marquis M. McMillan, Daniel Donghyon Ohm

Optically active spin defects in solids are promising platforms for quantum technologies. Here, we present a first-principles framework to investigate intersystem crossing processes, which represent crucial steps in the optical spin-polarization cycle used to address spin defects. Considering the nitrogen-vacancy center in diamond as a case study, we demonst

Pavel Rumiantsev, Mark Coates

Neural Architecture Search (NAS) is a powerful automatic alternative to manual design of a neural network. In the zero-shot version, a fast ranking function is used to compare architectures without training them. The outputs of the ranking functions often vary significantly due to different sources of randomness, including the evaluated architecture's weight

Jackson A. Mickley, Derek B. Leinweber, Luis E. Oxman

The structure of center vortices is studied in SU(4) Yang-Mills theory for the first time to illuminate the interplay between elementary (center charge $\pm 1$) and doubly charged vortices. Unlike in SU(3), where charge $+2$ vortices are simply elementary vortices with reversed orientations in spacetime, these possibilities are physically distinct in SU(4).

Sheng Zhang, Qianchu Liu, Guanghui Qin, Tristan Naumann

Reinforcement learning from verifiable rewards (RLVR) has recently gained attention for its ability to elicit self-evolved reasoning capabilitie from base language models without explicit reasoning supervisions, as demonstrated by DeepSeek-R1. While prior work on RLVR has primarily focused on mathematical and coding domains, its applicability to other tasks

Ben Zhu, Menglong Zhao, Xue-Qiao Xu, Anchal Gupta

The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster, but less accurate empiric

Sharaf K. Magableh, Oraib Dawaghreh, Xuesong Wang, Caisheng Wang

Traditional monofacial photovoltaic (mPV) systems are commonly adopted and well-documented because of their lower upfront costs in comparison to bifacial photovoltaic (bPV) systems. This study investigates how PV technologies impact energy storage in grid-scale hybrid renewable systems, focusing on optimizing and assessing the performance of mPV and bPV tech

Shangding Gu, Laixi Shi, Muning Wen, Ming Jin

Driven by inherent uncertainty and the sim-to-real gap, robust reinforcement learning (RL) seeks to improve resilience against the complexity and variability in agent-environment sequential interactions. Despite the existence of a large number of RL benchmarks, there is a lack of standardized benchmarks for robust RL. Current robust RL policies often focus o

Yuanyuan Xu, Wenjie Zhang, Ying Zhang, Xuemin Lin

Dynamic Text-Attributed Graphs (DyTAGs) are a novel graph paradigm that captures evolving temporal events (edges) alongside rich textual attributes. Existing studies can be broadly categorized into TGNN-driven and LLM-driven approaches, both of which encode textual attributes and temporal structures for DyTAG representation. We observe that DyTAGs inherently

Channprit Kaur, Aria Hajiahmadi, Benjamin R. Ecclestone, James E. D. Tweel

The mechanical properties of micro-scale bio-entities are fundamental for understanding their functions and pathological states. However, current methods for assessing elastic properties at single-particle level such as Brillouin and atomic force microscopies exhibit intrinsic limitations, including being often slow, having poor resolution, or involving comp

Jan Wehner, Sahar Abdelnabi, Daniel Tan, David Krueger

Representation Engineering (RepE) is a novel paradigm for controlling the behavior of LLMs. Unlike traditional approaches that modify inputs or fine-tune the model, RepE directly manipulates the model's internal representations. As a result, it may offer more effective, interpretable, data-efficient, and flexible control over models' behavior. We present the

Hyunmo Kang, Abdulkadir Canatar, SueYeon Chung

Understanding representational similarity between neural recordings and computational models is essential for neuroscience, yet remains challenging to measure reliably due to the constraints on the number of neurons that can be recorded simultaneously. In this work, we apply tools from Random Matrix Theory to investigate how such limitations affect similarit

Ju-Hyung Lee, Andreas F. Molisch

This paper introduces AutoBS, a reinforcement learning (RL)-based framework for optimal base station (BS) deployment in 6G radio access networks (RAN). AutoBS leverages the Proximal Policy Optimization (PPO) algorithm and fast, site-specific pathloss predictions from PMNet-a generative model for digital network twins (DNT). By efficiently learning deployment

Mukaram Shahid, Kunal Das, Hadia Ushaq, Hongwei Zhang

Accurately mapping the radio environment (e.g., identifying wireless signal strength at specific frequency bands and geographic locations) is crucial for efficient spectrum sharing, enabling secondary users (SUs) to access underutilized spectrum bands while protecting primary users (PUs). However, current models are either not generalizable due to shadowing,

Moo Jin Kim, Chelsea Finn, Percy Liang

Recent vision-language-action models (VLAs) build upon pretrained vision-language models and leverage diverse robot datasets to demonstrate strong task execution, language following ability, and semantic generalization. Despite these successes, VLAs struggle with novel robot setups and require fine-tuning to achieve good performance, yet how to most effectiv

Kanglei Zhou, Zikai Hao, Liyuan Wang, Xiaohui Liang

Virtual Reality Video Quality Assessment (VR-VQA) aims to evaluate the perceptual quality of 360-degree videos, which is crucial for ensuring a distortion-free user experience. Traditional VR-VQA methods trained on static datasets with limited distortion diversity struggle to balance correlation and precision. This becomes particularly critical when generali

Umair Zakir, Evan Daykin, Amssatou Diagne, Jacob Faile

This study presents a comparative analysis of deep learning methodologies such as BERT, FinBERT and ULMFiT for sentiment analysis of earnings call transcripts. The objective is to investigate how Natural Language Processing (NLP) can be leveraged to extract sentiment from large-scale financial transcripts, thereby aiding in more informed investment decisions

Ruiqi Wang, Pinjun Zheng, Vijith Varma Kotte, Sakandar Rauf

This paper presents the concept, design, channel modeling, beamforming algorithm development, prototype fabrication, and experimental measurement of an electromagnetically reconfigurable fluid antenna system (ER-FAS), in which each FAS array element features electromagnetic (EM) reconfigurability. Unlike most existing FAS works that investigate spatial recon

Micha Livne

Learning representations that generalize well to unknown downstream tasks is a central challenge in representation learning. Existing approaches such as contrastive learning, self-supervised masking, and denoising auto-encoders address this challenge with varying trade-offs. In this paper, we introduce the {contrastive Mutual Information Machine} (cMIM), a p

Xiaolei Zhang

We study categoricity of the additive closure $\operatorname{Add}(M)$, consisting of all direct summands of arbitrary direct sums of copies of a fixed module $M$. For an $η$-generated module $M$, we prove that categoricity in a single cardinal $λ\ge θ= \max\{|R|,η\}$ is equivalent to the stabilization condition $P^{(η)} \cong M^{(η)}$ for every nonzero $η$-g

Miguel Cuellar, Hyunseok Jung

Using foot traffic data for over 150,000 points of interest (POIs) near the sites of 42 mass shootings (2018-2022, U.S.), we evaluate the spatial-temporal impact of the tragic events on community mobility and relocation of economic activities. Visits to nearby POIs decrease, while farther away POIs experience increased foot traffic, implying that communities

Alexandros Koukras, Daniel W. Savin, Michael Hahn

The origin of the slow solar wind is not well understood, unlike the fast solar wind which originates from coronal holes. In-situ elemental abundances of the slow solar wind suggest that it originates from initially closed field lines that become open. Coronal hole boundary regions are a potential source of slow solar wind as there open field lines interact

Aoyan Liang, Nicolas Bertin, Xinran Zhou, Sylvie Aubry

Used for centuries to enhance mechanical properties of materials, solid solution strengthening (SSS) is a classical metallurgical method in which small amounts of impurity elements are added to a base metal. Developed for dilute alloys, classical theories of SSS are presently challenged by the ongoing explosive development of complex concentrated alloys (CCA

Artem Chebotarenko

For irrational $\theta$ and 1-periodic function $f$ we consider sums $\sum_0^{Q-1}f(k\theta+\varphi)$ where $\varphi \in \mathbb R$. Sidorov proved that if $f$ is absolutely continuous function, then $\liminf_{Q \to \infty} |\sum_0^{Q-1}f(k\theta+\varphi)| = 0$ for any irrational $\theta$ and any $\varphi \in \mathbb R$. The article shows that this property

Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal

Missing data is a pervasive challenge spanning diverse data types, including tabular, sensor data, time-series, images and so on. Its origins are multifaceted, resulting in various missing mechanisms. Prior research in this field has predominantly revolved around the assumption of the Missing Completely At Random (MCAR) mechanism. However, Missing At Random

David Elkouss, Ananda G. Maity, Aditya Nema, Sergii Strelchuk

The majorization relation has found numerous applications in mathematics, quantum information and resource theory, and quantum thermodynamics, where it describes the allowable transitions between two physical states. In many cases, when state vector $x$ does not majorize state vector $y$, it is nevertheless possible to find a catalyst - another vector $z$ su

Anantharaman Sekharipuram Viswanathan, Dipankar Bhattacharya

A glitch is a rare and sudden increase in the otherwise steadily decreasing rotation rate of a pulsar. Its cause is widely attributed to the transfer of angular momentum to the crust of the star from the array of superfluid vortices enclosed within. The magnitude of such an increase defines the size of the glitch. The distribution of glitch sizes in individu

Yue Liu, Yuhang Zhang, Zouyouwei Lu, Dong Li

Bulk two dimensional (2D) superconductivity has gained considerable attention due to its intricate interplay between symmetry breaking, nontrivial topology, 2D phase fluctuations, and unconventional superconductivity. However, certain intercalated layered superconductors, despite their short c-axis superconducting coherence length, have been misclassified as

Roberto Capecelatro, Marco Marciani, Gabriele Campagnano, Procolo Lucignano

We investigate the transport properties of open Josephson junctions (JJs) through a minimal effective non-Hermitian (NH) approach derived from the equilibrium Green's function (GF) formalism. Specifically, we consider a JJ with a quantum dot barrier coupled to a normal metal reservoir. The coupling introduces an imaginary self-energy term in the JJ Hamil

Sergei O. Ivanov, Xiaomeng Xu

We study the concepts of the $\ell_p$-Vietoris-Rips simplicial set and the $\ell_p$-Vietoris-Rips complex of a metric space, where $1\leq p \leq \infty.$ This theory unifies two established theories: for $p=\infty,$ this is the classical theory of Vietoris-Rips complexes, and for $p=1,$ this corresponds to the blurred magnitude homology theory. We prove seve

Arijit Ghosh, Chandrima Kayal, Soumi Nandi

Let $\mathbb{F}$ be a field, and consider the hypercube $\{ 0, 1 \}^{n}$ in $\mathbb{F}^{n}$. Sziklai and Weiner (Journal of Combinatorial Theory, Series A 2022) showed that if a polynomial $P ( X_{1}, \dots, X_{n} ) \in \mathbb{F}[ X_{1}, \dots, X_{n}]$ vanishes on every point of the hypercube $\{0,1\}^{n}$ except those with at most $r$ many ones then the d

Bingbing Zhu, Qingnan Cai, Yaxin Liu, Sheng Zhang

All-dielectric optical nano-resonators, exhibiting exotic near-field distributions upon excitations, have emerged as low-loss, versatile and highly adaptable components in nanophotonic structures for manipulating electromagnetic waves and enhancing light-matter interactions. However, achieving experimental full three-dimensional characterization of near-fiel

James Morgan, Jonathan Spreer

We reprove a necessary condition for the Sakuma-Weeks triangulation of a 2-bridge link complement to be minimal in terms of the mapping class describing its alternating 4-string braid construction. For the 2-bridge links satisfying this condition we construct explicit angle structures on the Sakuma-Weeks triangulations and compute both multiplicative and add

Mojtaba Shahbazi, Mehdi Sadeghi

In this work the effect of anisotropy on computational complexity is considered by CA proposal in holographic two-sided black brane dual of a strongly coupled gauge theory. It is shown that due to confinement-deconfinement phase transition there are two different behaviors: by increase in anisotropy there would be an increase in complexity growth rate in sma

Anne Quéguiner-Mathieu, Jean-Pierre Tignol

The Witt group of skew hermitian forms over a division algebra $D$ with symplectic involution is shown to be canonically isomorphic to the Witt group of symmetric bilinear forms over the Severi-Brauer variety of $D$ with values in a suitable line bundle. In the special case where $D$ is a quaternion algebra we extend previous work by Pfister and by Parimala

Jiazhen Pan, Che Liu, Junde Wu, Fenglin Liu

Reasoning is a critical frontier for advancing medical image analysis, where transparency and trustworthiness play a central role in both clinician trust and regulatory approval. Although Medical Visual Language Models (VLMs) show promise for radiological tasks, most existing VLMs merely produce final answers without revealing the underlying reasoning. To ad

Christopher W. Curtis, Erik M. Bollt

In this work, we quantify the time scales and information flow associated with multiscale energy transfer in a weakly turbulent system. This is done through a greedy optimization algorithm which finds the maximum conditional-mutual information across lagged embeddings of time series localized by wavenumber. For our chosen weakly turbulent system, the algorit

T. Martin, D. Milisavljevic, T. Temim, S. Mandal

We present extensive proper motion measurements of the Crab Nebula made from Canada-France-Hawaii Telescope MegaPrime/MegaCam images taken in 2007, 2016, and 2019. A total of 19974 proper motion vectors with uncertainty $<10$\,mas\,yr$^{-1}$ located over the majority of the Crab Nebula are used to map the supernova remnant's two-dimensional expansion propert

Wen-Han Zhou, Patrick Michel, Marco Delbo, Wenchao Wang

The rotational distribution of asteroids as a function of their size is used {as a diagnostic of} their physical properties and evolution. Recent photometric surveys from the Gaia mission, allowing observation of asteroids with long spin periods (for example $\geq 24$h), found an excessive group of slow rotators and a gap separating them from faster rotators

Hoonhee Cho, Jae-young Kang, Youngho Kim, Kuk-Jin Yoon

Detecting 3D objects in point clouds plays a crucial role in autonomous driving systems. Recently, advanced multi-modal methods incorporating camera information have achieved notable performance. For a safe and effective autonomous driving system, algorithms that excel not only in accuracy but also in speed and low latency are essential. However, existing al

Tiffany J. Callahan, Nathaniel H. Park, Sara Capponi

The vast and complex materials design space demands innovative strategies to integrate multidisciplinary scientific knowledge and optimize materials discovery. While large language models (LLMs) have demonstrated promising reasoning and automation capabilities across various domains, their application in materials science remains limited due to a lack of ben

Mingdai Yang, Fan Yang, Yanhui Guo, Shaoyuan Xu

User modeling in large e-commerce platforms aims to optimize user experiences by incorporating various customer activities. Traditional models targeting a single task often focus on specific business metrics, neglecting the comprehensive user behavior, and thus limiting their effectiveness. To develop more generalized user representations, some existing work

Greg A. Kimmel, Megan K. Dunlap, Kirill Gurdumov, R. Scott Smith

The properties of amorphous solid water at and near the calorimetric glass transition temperature, $T_{g}$, of 136 K have been debated for years. One hypothesis is that water turns into a "true" liquid at $T_{g}$ (i.e., it becomes ergodic) and exhibits all the characteristics of an ergodic liquid, including translational diffusion. A competing hypothesis is

Lydia A. Kanari-Naish, Jack Clarke, Sofia Qvarfort, Michael R. Vanner

A key requirement of any separable quantum state is that its density matrix has a positive partial transpose. For continuous bipartite quantum states, violation of this condition may be tested via the hierarchy of negative-partial-transpose (NPT) based entanglement criteria introduced by Shchukin and Vogel [Phys. Rev. Lett. 95, 230502 (2005)]. However, a pro

Hongkun Yu, Syed Jamal Safdar Gardezi, E. Jason Abel, Daniel Shapiro

Purpose: This study aims to develop and validate a method for synthesizing 3D nephrographic phase images in CT urography (CTU) examinations using a diffusion model integrated with a Swin Transformer-based deep learning approach. Materials and Methods: This retrospective study was approved by the local Institutional Review Board. A dataset comprising 327 pati

Yanan Chen, Ali Pesaranghader, Tanmana Sadhu

Recent advances in Large Language Models (LLMs) have raised interest in their formal reasoning capabilities, particularly in mathematics. While closed LLMs like GPT-4 perform well on mathematical benchmarks, e.g., GSM8K, it remains unclear whether small to medium-sized open LLMs can achieve similar performance, questioning their reliability. To close this ga

Shamnad Mohamed Shaffi

Enterprises today face increasing cybersecurity threats that necessitate robust digital forensics and risk mitigation strategies. This paper explores these challenges through an imaginary case study of an organization, a global identity management and data analytics company handling vast customer data. Given the critical nature of its data assets, EP has est

Laura Caron

Triple difference designs have become increasingly popular in empirical economics. The advantage of a triple difference design is that, within a treatment group, it allows for another subgroup of the population -- potentially less impacted by the treatment -- to serve as a control for the subgroup of interest. While literature on difference-in-differences ha

Paul Honore Takam, Ralf Wunderlich

In this paper, we consider a residential heating system with renewable and non-renewable heat generation and different consumption units and investigate a stochastic optimal control problem for its cost-optimal management. As a special feature, the heating system is equipped with a geothermal storage that enables the intertemporal transfer of thermal energy

Francesc Castella

We prove a conjecture of Kundu--Ray, following from the $p$-adic Birch--Swinnerton-Dyer conjecture for supersingular primes by Bernardi--Perrin-Riou and Kato's Main Conjecture, predicting an expression for the leading term (up to a $p$-adic unit) of a characteristic power series of Kobayashi's signed Selmer groups attached to elliptic curves $E/\mathbb{Q}$ w

Sreejani Chatterjee, Abhinav Gandhi, Berk Calli, Constantinos Chamzas

This work presents a motion planning framework for robotic manipulators that computes collision-free paths directly in image space. The generated paths can then be tracked using vision-based control, eliminating the need for an explicit robot model or proprioceptive sensing. At the core of our approach is the construction of a roadmap entirely in image space

Daniel McLoughlin, Rachel A Rosen

We find explicit de Sitter shockwave solutions in arbitrary spacetime dimensions. We use these to determine the dimensional-dependent "stretching" of the de Sitter Penrose diagram in the presence of a shock or black hole. This stretching sets the scale at which superluminalities in de Sitter can be considered resolvable. We then consider an RFF coupling for

Yan Zhang, Lin Chen, Yixiang Tian

Interpretability analysis methods for artificial intelligence models, such as LIME and SHAP, are widely used, though they primarily serve as post-model for analyzing model outputs. While it is commonly believed that the transparency and interpretability of AI models diminish as their complexity increases, currently there is no standardized method for assessi

Sungduk Yu, Man Luo, Avinash Madasu, Vasudev Lal

Peer review is a critical process for ensuring the integrity of published scientific research. Confidence in this process is predicated on the assumption that experts in the relevant domain give careful consideration to the merits of manuscripts which are submitted for publication. With the recent rapid advancements in large language models (LLMs), a new ris

Wei Xiong, Hanning Zhang, Chenlu Ye, Lichang Chen

We study self-rewarding reasoning large language models (LLMs), which can simultaneously generate step-by-step reasoning and evaluate the correctness of their outputs during the inference time-without external feedback. This integrated approach allows a single model to independently guide its reasoning process, offering computational advantages for model dep

Anwar Hossain Zahid, Monoshi Kumar Roy, Swarna Das

The proliferation of hate speech on social media is one of the serious issues that is bringing huge impacts to society: an escalation of violence, discrimination, and social fragmentation. The problem of detecting hate speech is intrinsically multifaceted due to cultural, linguistic, and contextual complexities and adversarial manipulations. In this study, w

Kanishk Bhatia, Felix Koehler, Nils Thuerey

The physics solvers employed for neural network training are primarily iterative, and hence, differentiating through them introduces a severe computational burden as iterations grow large. Inspired by works in bilevel optimization, we show that full accuracy of the network is achievable through physics significantly coarser than fully converged solvers. We p

Matthew Toles, Nikhil Balwani, Rattandeep Singh, Valentina Giulia Sartori Rodriguez

Many real-world eligibility problems, ranging from medical diagnosis to tax planning, can be mapped to decision problems expressed in natural language, wherein a model must make a binary choice based on user features. Large-scale domains such as legal codes or frequently updated funding opportunities render human annotation (e.g., web forms or decision trees

Carlos A. Gonzalez, Rahul Agrawal, Xiaohua Wu

We present direct numerical simulations (DNSs) of bypass transition over a flat plate with inlet freestream turbulence intensity levels of 0.75%, 1.5%, 2.25%, 3.0%, and 6.0%, respectively. A new definition of the transition intermittency is proposed based on the mean skin friction. Based on these, we develop an intermittency correlation to predict flow trans

Frank A. Cowell, Emmanuel Flachaire

We examine whether mobility measures appropriately represent changes in individual status, like income or ranks. We suggest three elementary principles for mobility comparisons and show that many commonly used indices violate one or more of them. These principles are used to characterise two classes of measures that have a natural interpretation in terms of

Luise Ge, Michael Lanier, Anindya Sarkar, Bengisu Guresti

Many dynamic decision problems, such as robotic control, involve a series of tasks, many of which are unknown at training time. Typical approaches for these problems, such as multi-task and meta reinforcement learning, do not generalize well when the tasks are diverse. On the other hand, approaches that aim to tackle task diversity, such as using task embedd

Yash Mahajan, Matthew Freestone, Sathyanarayanan Aakur, Santu Karmaker

Large Language Models (LLMs) have recently shown remarkable advancement in various NLP tasks. As such, a popular trend has emerged lately where NLP researchers extract word/sentence/document embeddings from these large decoder-only models and use them for various inference tasks with promising results. However, it is still unclear whether the performance imp

James Fullwood

Given two parties performing experiments in separate laboratories, we provide a diagrammatic formulation of what it means for the joint statistics of their experiments to satisfy local realism. In particular, we show that the principles of locality and realism are both captured by a single commutative diagram in the category of probability-preserving maps be

M. E. J. Newman

We describe and analyze a broad class of mixture models for real-valued multivariate data in which the probability density of observations within each component of the model is represented as an arbitrary combination of basis functions. Fits to these models give us a way to cluster data with distributions of unknown form, including strongly non-Gaussian or m

Sharan Mourya, Hannes Leipold, Bibhas Adhikari

In this paper, we apply quantum machine learning (QML) to predict the stock prices of multiple assets using a contextual quantum neural network. Our approach captures recent trends to predict future stock price distributions, moving beyond traditional models that focus on entire historical data, enhancing adaptability and precision. Utilizing the principles

Ilaria Beechey-Newman, Natalya Kizilova, Andreas Andersen Hennig, Eirik Grude Flekkøy

During drying, particle-laden sessile droplets will leave so-called coffee-stain rings behind. This phenomenon is well-known and well-understood (Deegan et al., Nature 389, 827-829 (1997)). Here we show that when particle-laden droplets confined in a slit are allowed to evaporate very slowly, they do not deposit coffee rings, but form a surprisingly intricat

Pian Yu, Yong Li, David Parker, Marta Kwiatkowska

This work studies the planning problem for robotic systems under both quantifiable and unquantifiable uncertainty. The objective is to enable the robotic systems to optimally fulfill high-level tasks specified by Linear Temporal Logic (LTL) formulas. To capture both types of uncertainty in a unified modelling framework, we utilise Markov Decision Processes w

Gaurav Arwade, Sigurdur Olafsson

Decision-making in complex systems often relies on machine learning models, yet highly accurate models such as XGBoost and neural networks can obscure the reasoning behind their predictions. In operations research applications, understanding how a decision is made is often as crucial as the decision itself. Traditional interpretable models, such as decision

Minh Hoang, Azza Fadhel, Aryan Deshwal, Janardhan Rao Doppa

Offline design optimization problem arises in numerous science and engineering applications including material and chemical design, where expensive online experimentation necessitates the use of in silico surrogate functions to predict and maximize the target objective over candidate designs. Although these surrogates can be learned from offline data, their

Shaibal Saha, Lanyu Xu

In recent years, vision transformers (ViTs) have emerged as powerful and promising techniques for computer vision tasks such as image classification, object detection, and segmentation. Unlike convolutional neural networks (CNNs), which rely on hierarchical feature extraction, ViTs treat images as sequences of patches and leverage self-attention mechanisms.

Leo Goldmakher, Greg Martin, Paul Péringuey

A famous conjecture of Artin asserts that any integer $a$ that is neither $-1$ nor a square should be a primitive root (mod $p$) for a positive proportion of primes $p$. Moreover, using a heuristic argument, Artin guessed an explicit formula for the proportion; this formula is well-supported by computations and is known to hold on a generalized Riemann hypot

Qiao He, Baiqing Zhu

We compute the arithmetic intersections of Hecke correspondences on the product of integral model of modular curve $\mathcal{X}_0(N)$ and relate it to the derivatives of certain Siegel Eisenstein series when $N$ is odd and squarefree. We prove this by establishing a precise identity between the arithmetic intersection numbers on the Rapoport--Zink space asso