March 2025 arXiv papers — page 143

Jiacheng Xie, Hua-Chieh Shao, Yunxiang Li, Shunyu Yan

Deformable liver motion tracking using a single X-ray projection enables real-time motion monitoring and treatment intervention. We introduce a conditional point cloud diffusion model-based framework for accurate and robust liver motion tracking from arbitrarily angled single X-ray projections (PCD-Liver), which estimates volumetric liver motion by solving d

Kaiming Shen, Wei Yu

Fractional programming (FP) is a branch of mathematical optimization that deals with the optimization of ratios. It is an invaluable tool for signal processing and machine learning, because many key metrics in these fields are fractionally structured, e.g., the signal-to-interference-plus-noise ratio (SINR) in wireless communications, the Cram\'{e}r-Rao boun

Mingyu Liu, Xian Shi

Quantifying the entanglement and coherence of quantum systems is a topic of significant theoretical and practical interest. In this paper, we propose a method to evaluate lower bounds for several widely used coherence measures and genuine multipartite entanglement (GME) measures. Our approach, which is resource-efficient and computationally feasible, provide

Joonhyung Lee, Shmulik Markovich-Golan, Daniel Ohayon, Yair Hanani

Low-precision data types are essential in modern neural networks during both training and inference as they enhance throughput and computational capacity by better exploiting available hardware resources. Despite the incorporation of FP8 in commercially available neural network accelerators, a comprehensive exposition of its underlying mechanisms, along with

Jiaqi Wu, Junbiao Pang, Qingming Huang

Current Semi-supervised Learning (SSL) adopts the pseudo-labeling strategy and further filters pseudo-labels based on confidence thresholds. However, this mechanism has notable drawbacks: 1) setting the reasonable threshold is an open problem which significantly influences the selection of the high-quality pseudo-labels; and 2) deep models often exhibit the

Yu-Ying He, Xin-Jian Wen

The surface tension of quark matter in a strong magnetic field is investigated using a geometric approach. The interface between the hadronic phase and quark phase is determined by the Maxwell construction of the first-order transition. When surface tension is included, the free energy per baryon is no longer a monotonic function of the chemical potential. S

Sergi Elizalde

It is known that, when $n$ is even, the number of permutations of $\{1,2,\dots,n\}$ all of whose cycles have odd length equals the number of those all of whose cycles have even length. Adin, Heged\H{u}s and Roichman recently found a surprising refinement of this identity. They showed that, for any fixed set $J$, the equality still holds when restricting to p

Yifan Zheng, Paulo Montero-Camacho, Zheng Cai, Yi Mao

Cosmic reionization of HI leaves enduring relics in the post-reionization intergalactic medium, potentially influencing the Lyman-$\alpha$ (Ly$\alpha$) forest down to redshifts as low as $z \approx 2$, which is the so-called ''memory of reionization'' effect. Here, we re-analyze the baryonic acoustic oscillation (BAO) measurements from Ly$\alpha$ absorption

Sachin Vaidya, André Grossi Fonseca, Mark R. Hirsbrunner, Taylor L. Hughes

We introduce new classes of gapped topological phases characterized by quantized crystalline-electromagnetic responses, termed "multipolar Chern insulators". These systems are characterized by nonsymmorphic momentum-space symmetries and mirror symmetries, leading to quantization of momentum-weighted Berry curvature multipole moments. We construct lattice mod

Nathan Drenkow, Mitchell Pavlak, Keith Harrigian, Ayah Zirikly

Artificial Intelligence (AI) is now firmly at the center of evidence-based medicine. Despite many success stories that edge the path of AI's rise in healthcare, there are comparably many reports of significant shortcomings and unexpected behavior of AI in deployment. A major reason for these limitations is AI's reliance on association-based learning, where n

Zihao Zhang, Aming Wu, Yahong Han

Recently, a task of Single-Domain Generalized Object Detection (Single-DGOD) is proposed, aiming to generalize a detector to multiple unknown domains never seen before during training. Due to the unavailability of target-domain data, some methods leverage the multimodal capabilities of vision-language models, using textual prompts to estimate cross-domain in

Jiahua Tian, Xin Wang

In this work we uncover a connection that relates the 1-form and the 2-group symmetries of 5D SCFTs derived from geometric engineering methods to monodromies of the corresponding B-models via mirror symmetry. Viewing defects as branes wrapping relative cycles in a non-compact CY3, we find that the defect groups can be read off from the VEVs of the correspond

Tatsuo Kobayashi, Yume Nishioka, Hajime Otsuka, Morimitsu Tanimoto

We study the coupling selection rules associated with non-group symmetries, i.e., $\mathbb{Z}_2$ gauging of $\mathbb{Z}_M$ symmetries. We clarify which Yukawa textures can be derived by our selection rules for $M=3, 4$, and 5, and obtain various textures including the the nearest neighbor interaction type and its extension. Some of them cannot be realized by

Min Ju, Xuhao Wu, Hong Shen

We study how the nuclear symmetry energy slope affects the properties of hadron-quark pasta phases and their sizes in massive hybrid stars ($> 2 M_{\odot}$). We utilize the relativistic mean-field model with a density-dependent isovector coupling constant fitted by varying values of the symmetry energy slope $L$ to describe the hadronic matter, while the qua

Bhavik Chandna, Mariam Aboujenane, Usman Naseem

Large Multimodal Models (LMMs) are increasingly vulnerable to AI-generated extremist content, including photorealistic images and text, which can be used to bypass safety mechanisms and generate harmful outputs. However, existing datasets for evaluating LMM robustness offer limited exploration of extremist content, often lacking AI-generated images, diverse

Lin Tian, Jonathan Williams-Ramirez, Dina Zemlyanker, Lucas J. Deden-Binder

Correlation of neuropathology with MRI has the potential to transfer microscopic signatures of pathology to in vivo scans. There is increasing interest in building these correlations from 3D reconstructed stacks of slab photographs, which are routinely taken during dissection at brain banks. These photographs bypass the need for ex vivo MRI, which is not wid

Jiayu Jiang, Changxing Ding, Wentao Tan, Junhong Wang

Text-to-image person re-identification (ReID) aims to retrieve the images of an interested person based on textual descriptions. One main challenge for this task is the high cost in manually annotating large-scale databases, which affects the generalization ability of ReID models. Recent works handle this problem by leveraging Multi-modal Large Language Mode

Xin Zhu, Hongyi Pan, Ahmet Enis Cetin

The large volume of electroencephalograph (EEG) data produced by brain-computer interface (BCI) systems presents challenges for rapid transmission over bandwidth-limited channels in Internet of Things (IoT) networks. To address the issue, we propose a novel multi-channel asymmetrical variational discrete cosine transform (DCT) network for EEG data compressio

Muhammad Hassan Jamal, Abdulwahab Alazeb, Shahid Allah Bakhsh, Wadii Boulila

Fire safety practices are important to reduce the extent of destruction caused by fire. While smoke alarms help save lives, firefighters struggle with the increasing number of false alarms. This paper presents a precise and efficient Weighted ensemble model for decreasing false alarms. It estimates the density, computes weights according to the high and low-

Jiansheng Li, Haotian Song, Jinni Zhou, Qiang Nie

The six-degree-of-freedom (6-DOF) robotic arm has gained widespread application in human-coexisting environments. While previous research has predominantly focused on functional motion generation, the critical aspect of expressive motion in human-robot interaction remains largely unexplored. This paper presents a novel real-time motion generation planner tha

Zhenyu Liu, Dongfang Li, Xinshuo Hu, Xinping Zhao

Recent studies have explored the working mechanisms of In-Context Learning (ICL). However, they mainly focus on classification and simple generation tasks, limiting their broader application to more complex generation tasks in practice. To address this gap, we investigate the impact of demonstrations on token representations within the practical alignment ta

Mingjia Zhu, Lechuan Wang, Julien Sebot, Bijan Arbab

We explore the causal relationship between COVID-19 lockdown policies and changes in personal computer usage. In particular, we examine how lockdown policies affected average daily computer usage, as well as how it affected usage patterns of different groups of users. This is done through a merging of the Oxford Policy public data set, which describes the ti

Yu Qiao, Phuong-Nam Tran, Ji Su Yoon, Loc X. Nguyen

Reinforcement learning (RL)-based large language models (LLMs), such as ChatGPT, DeepSeek, and Grok-3, have attracted widespread attention for their remarkable capabilities in multimodal data understanding. Meanwhile, the rapid expansion of information services has led to a growing demand for AI-enabled wireless networks. The open-source DeepSeek models are

Enrico Da Ronche

In this article we prove a version of Kolyvagin's conjecture for modular forms at non-ordinary primes. In particular, we generalize the work of Wang on a converse to a higher weight Gross-Zagier-Kolyvagin theorem in order to prove the conjecture under the hypothesis that some Selmer group has rank one. The main ingredients that we use in non-ordinary setting

Erin Morissette, Peiyu Qin, K. Watanabe, T. Taniguchi

Spontaneous symmetry breaking provides a powerful window into the nature of underlying electronic orders. In strongly correlated systems, multiple symmetry-breaking orders can arise simultaneously. and their interplay generates an intricate landscape of quantum phases that has remained a central focus of condensed-matter research. In this work, we report a p

Hansa Ahsan, Safee Ullah, Jawad Ahmad, Aizaz Ahmad Khattak

In this digital age, ensuring the security of digital data, especially the image data is critically important. Image encryption plays an important role in securing the online transmission/storage of images from unauthorized access. In this regard, this paper presents a novel diffusion-confusion-based image encryption algorithm named as X-CROSS. The diffusion

Nobuhiro Okabe, Thomas H. Reiprich, Sebastian Grandis, I-Non Chiu

We performed individual weak-lensing (WL) mass measurements for 78 eROSITA's first All-Sky Survey (eRASS1) clusters in the footprint of Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) S19A. We did not adopt priors on the eRASS1 X-ray quantities or assumption of the mass and concentration relation. In the sample, we found three clusters are misassociated

Xinglong Sun, Haijiang Sun, Shan Jiang, Jiacheng Wang

The trackers based on lightweight neural networks have achieved great success in the field of aerial remote sensing, most of which aggregate multi-stage deep features to lift the tracking quality. However, existing algorithms usually only generate single-stage fusion features for state decision, which ignore that diverse kinds of features are required for id

Yuxiang Fu, Qi Yan, Lele Wang, Ke Li

In this paper, we address the problem of human trajectory forecasting, which aims to predict the inherently multi-modal future movements of humans based on their past trajectories and other contextual cues. We propose a novel motion prediction conditional flow matching model, termed MoFlow, to predict K-shot future trajectories for all agents in a given scen

Yuanxin Liu, Rui Zhu, Shuhuai Ren, Jiacong Wang

With the rapid growth of video generative models (VGMs), it is essential to develop reliable and comprehensive automatic metrics for AI-generated videos (AIGVs). Existing methods either use off-the-shelf models optimized for other tasks or rely on human assessment data to train specialized evaluators. These approaches are constrained to specific evaluation a

Xiaobo Xia, Xiaofeng Liu, Jiale Liu, Kuai Fang

Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning offers transformative potential for large-scale water quality prediction and scientific insights generation. However, their widespread adoption in high-stakes operational decision-making, such as pollution mitigation and equitable resource al

Shiryo Owa, Derek B. Leinweber, Anthony W. Thomas

The study of the spectrum of excited states of the nucleon is vital to our understanding of how QCD is realized in the baryon spectrum. Using a simultaneous analysis of the pion nucleon scattering data up to 2 GeV as well as the results of lattice QCD calculations, we obtain new insight into the nature of the Roper resonance as well as the excited states aro

Adolfo Holguin, Hiroki Kawai

We study various aspects of half-BPS surface defect operators in $\mathcal{N}=4$ SYM. For defects on generic points on the moduli space we use superconformal symmetry to fix the form of one-point and two-point functions of half-BPS operators and solve the superconformal Ward identities in terms of superconformal blocks, emphasizing the role of the broken rot

D. Liu

We give the converse to Dirichlet's theorem on primes in arithmetic progressions by generalizing an old result of Guinand.

Yasheng Sun, Zhiliang Xu, Hang Zhou, Jiazhi Guan

Co-speech gesture video synthesis is a challenging task that requires both probabilistic modeling of human gestures and the synthesis of realistic images that align with the rhythmic nuances of speech. To address these challenges, we propose Cosh-DiT, a Co-speech gesture video system with hybrid Diffusion Transformers that perform audio-to-motion and motion-

Mu Chen, Wenyu Chen, Mingchuan Yang, Yuan Zhang

3D semantic occupancy has rapidly become a research focus in the fields of robotics and autonomous driving environment perception due to its ability to provide more realistic geometric perception and its closer integration with downstream tasks. By performing occupancy prediction of the 3D space in the environment, the ability and robustness of scene underst

Xitao Ji, Wenjie He, Junda Chen, Mingming Zhang

Artificial intelligence-driven (AI-driven) data centres, which require high-performance, scalable, energy-efficient, and secure infrastructure, have led to unprecedented data traffic demands. These demands involve low latency, high bandwidth connections, low power consumption, and data confidentiality. However, conventional optical interconnect solutions, su

Muhammad Ali, Jawad Ahmad, Muhammad Abdullah Hussain Khan, Safee Ullah

In this digital era, ensuring the security of digital data during transmission and storage is crucial. Digital data, particularly image data, needs to be protected against unauthorized access. To address this, this paper presents a novel image encryption scheme based on a confusion diffusion architecture. The diffusion module introduces a novel geometric blo

Sen Wang, Dongliang Zhou, Liang Xie, Chao Xu

Vision-and-language navigation (VLN) tasks require agents to navigate three-dimensional environments guided by natural language instructions, offering substantial potential for diverse applications. However, the scarcity of training data impedes progress in this field. This paper introduces PanoGen++, a novel framework that addresses this limitation by gener

Yi-Liang Yin, Wen-Bo Dong, Cong Yi, Qun Wang

We calculate the spin density matrix for neutral $\rho$ mesons from the spectral function and thermal shear tensor by Kubo formula in the linear response theory, which contributes to the $\gamma$ correlator for the CME search. We derive the spectral function of neutral $\rho$ mesons with $\rho\pi\pi$ and $\rho\rho\pi\pi$ interactions using the Dyson-Schwinge

Charles Baylis, Douglas Sicker, Austin Egbert, Andrew Clegg

A significant movement from rigid use of the wireless spectrum toward adaptive and reconfigurable spectrum use has been prompted by increasing spectral crowding. Some bands have moved to an adaptive sharing model, and proposals are growing for this approach to be applied to additional bands. The process of moving from a fixed, rigid spectrum paradigm to adap

E. M. Fernandes, L. Sanz, F. M. Souza

We examine the impact of time-dependent gate voltages on entanglement generation in two capacitively coupled charge qubits, with single-electron injection triggered on demand. The gate voltage modulates the tunnel coupling between the qubits and electronic reservoirs, initiating charge transport into the system. The formation of entangled states arises from

Lawrence W. Abrams

It is time to move on from attempts to make the pharmacy benefit manager (PBM) reseller business model more transparent. Time and time again the Big 3 PBMs have developed opaque alternatives to piece-meal 100% pass-through mandates. Time and time again PBMs have demonstrated expertise in finding loopholes in state government disclosure laws. The purpose of t

Lixiu Wang, Lueling Jia, Zijian Cao, Huiyuan Li

In this paper, we propose fast solvers for Maxwell's equations in rectangular domains. We first discretize the simplified Maxwell's eigenvalue problems by employing the lowest-order rectangular N\'ed\'elec elements and derive the discrete eigen-solutions explicitly, providing a Hodge-Helmholtz decomposition framework at the discrete level. Based on exact eig

Xu-Yao Hu, Vladimir Rosenhaus

We study the kinetic theory of a weakly interacting quantum field. Assuming a state that is close to homogeneous and stationary, we derive a closed kinetic equation for the rate of change of the occupation numbers, perturbatively in the coupling. For a dilute gas, this reproduces the quantum Boltzmann equation, which only accounts for two-to-two scattering p

Ibrahim Nali, Attila Dénes

We present a mathematical model for within host viral infections that incorporates the Crowley Martin functional response, focusing on the dynamics influenced by periodic effects. This study establishes key properties of the model, including the existence, uniqueness, positivity, and boundedness of periodic orbits within the non-autonomous system. We demonst

Hussein N. Naser, Hashim A. Hashim, Mojtaba Ahmadi

This paper presents a nonlinear control strategy for an aerial cooperative payload transportation system consisting of two quadrotor UAVs rigidly connected to a payload. The system includes human physical interaction facilitated by an admittance control. The proposed control framework integrates an adaptive Backstepping controller for the position subsystem

Weiwei Zhou, Chenkun Ling, Zefeng Cai

Human emotion recognition plays a crucial role in facilitating seamless interactions between humans and computers. In this paper, we present our innovative methodology for tackling the Valence-Arousal (VA) Estimation Challenge, the Expression Recognition Challenge, and the Action Unit (AU) Detection Challenge, all within the framework of the 8th Workshop and

Elden Elmanto, Dmitry Kubrak, Vladimir Sosnilo

Given a compact Lie group $G$ acting on a space $X$, the classical Atiyah-Segal completion theorem identifies topological $K$-theory of the homotopy quotient $X/G$ with an explicit completion of $G$-equivariant topological $K$-theory of $X$. We prove an analog of this result for algebraic $K$-theory over a field of characteristic 0. In our setting $G$ is a r

Julia Ive, Olatomiwa Olukoya, Jonathan P. Funnell, James Booker

Introduction: Timely care in a specialised neuro-intensive therapy unit (ITU) reduces mortality and hospital stays, with planned admissions being safer than unplanned ones. However, post-operative care decisions remain subjective. This study used artificial intelligence (AI), specifically natural language processing (NLP) to analyse electronic health records

Siyang Zhang, Harry Yang, Ser-Nam Lim

Long video generation remains a challenging and compelling topic in computer vision. Diffusion based models, among the various approaches to video generation, have achieved state of the art quality with their iterative denoising procedures. However, the intrinsic complexity of the video domain renders the training of such diffusion models exceedingly expensi

Mahmoud Srewa, Tianyu Zhao, Salma Elmalaki

Ensuring Large Language Models (LLMs) align with diverse human preferences while preserving privacy and fairness remains a challenge. Existing methods, such as Reinforcement Learning from Human Feedback (RLHF), rely on centralized data collection, making them computationally expensive and privacy-invasive. We introduce PluralLLM a federated learning-based ap

Yifei Yan, Juan Sosa, Carlos Martínez

This analysis derives the maximum likelihood estimator and applies Bayesian inference to model geometric Brownian motion, incorporating jump diffusion to account for sudden market shifts. The Bayesian approach is implemented using Markov Chain Monte Carlo simulations on S\&P 500 stock data from 2009 to 2014, providing a robust framework for analyzing stock d

Yiming Liu, Kareem M. Attiah, Wei Yu

This paper studies an uplink dual-functional sensing and communication system aided by a reconfigurable intelligent surface (RIS), whose reflection pattern is configured to trade-off sensing and communication functionalities. Specifically, the Bayesian Cram\'{e}r-Rao lower bound (BCRLB) for estimating the azimuth angle of a sensing user is minimized while en

Ruben Mamani Velasco, Akaki Tikaradze

Let $H(R, \phi, z)$ be a generalized Weyl algebra associated with a ring $R$, its central element $z\in Z(R)$ and an automorphism $\phi,$ such that for some $l \geq 1$, $\phi^l(z)-z$ is nilpotent and $(z,\phi^i(z))=R$ for all $0<i<l$. We prove that the category $\mathcal{O}$ over $H(R, z,\phi)$ is equivalent to the category $\mathcal{O}$ over its $l$-th twis

Carlos F. S. Pereira, Marcos V. de S. Silva, H. Belich, Denis C. Rodrigues

In the present study, we analyze the effects of violation of Lorentz symmetry for black-bounce solutions in a $k$-essence theory that has the form of a power law for the configuration $n=1/3$. We perform such analysis for a known model explored in previous work $\Sigma^2=x^2+a^2$ and complement the proposal with a new black-bounce model for the area function

Alex Davies, Prateek Gupta, Sebastien Racaniere, Grzegorz Swirszcz

We provide a family of $5$-dimensional prismatoids whose width grows linearly in the number of vertices. This provides a new infinite family of counter-examples to the Hirsch conjecture whose excess width grows linearly in the number of vertices, and answers a question of Matschke, Santos and Weibel.

Michele Lenzi, Arnau Montava Agudo, Carlos F. Sopuerta

Quasi-normal modes (QNMs) and greybody factors are some of the most characteristic features of the dynamics of black holes (BHs) and represent the basis for a number of fundamental physics tests with gravitational wave observations. It is therefore important to understand the properties of these quantities, naturally introduced within BH perturbation theory,

Fabio Banchelli, Marta Garcia-Gasulla, Filippo Mantovani, Joan Vinyals

MareNostrum5 is a pre-exascale supercomputer at the Barcelona Supercomputing Center (BSC), part of the EuroHPC Joint Undertaking. With a peak performance of 314 petaflops, MareNostrum5 features a hybrid architecture comprising Intel Sapphire Rapids CPUs, NVIDIA Hopper GPUs, and DDR5 and high-bandwidth memory (HBM), organized into four partitions optimized fo

Jiaqi Sun, Yujia Zheng, Xinshuai Dong, Haoyue Dai

Knowledge graphs serve as critical resources supporting intelligent systems, but they can be noisy due to imperfect automatic generation processes. Existing approaches to noise detection often rely on external facts, logical rule constraints, or structural embeddings. These methods are often challenged by imperfect entity alignment, flexible knowledge graph

M. M. Akbar, S. M. Modumudi

We analyze the near-horizon symmetries of static, axisymmetric, four-dimensional black holes with spherical and toroidal horizon topologies in vacuum general relativity. These black hole solutions, collectively referred to as local/distorted black holes, are known in closed form and are not asymptotically flat. Building on earlier works in the literature tha

Andries E. Brouwer, Sergey Goryainov, Leonid Shalaginov, Chi Hoi Yip

We study maximal cliques in the collinearity graphs of Desarguesian nets, give some structural results and some numerical information. In particular, we show for Desarguesian nets that the set consisting of a point $x$ together with all its neighbors on a line $L$ (with $x$ not on $L$) is contained in a unique maximal clique $C_{x,L}$ and determine the sizes

Tomonari Michiyama, Toshiki Saito, Kouichiro Nakanishi, Daisuke Iono

We report the detection of [C I] $^3P_1$--$^3P_0$ emission in the nearby galaxy NGC 7679 using the Atacama Compact Array (ACA) of the Atacama Large Millimeter/submillimeter Array (ALMA). In Michiyama et al. (2021), [C I] $^3P_1$--$^3P_0$ emission in NGC 7679 was reported as undetected based on ACA observations conducted in 2019 (ALMA Cycle 6). These observat

Tiantian Yang, Dongwei Chen

Wind speed distribution has many applications, such as the assessment of wind energy and building design. Applying an appropriate statistical distribution to fit the wind speed data, especially on its heavy right tail, is of great interest. In this study, we introduce a novel four-parameter class of generalized Lindley distribution, called the beta-generaliz

Kohei Hayashi, Masanori Koyama, Julian Jorge Andrade Guerreiro

Various world model frameworks are being developed today based on autoregressive frameworks that rely on discrete representations of actions and observations, and these frameworks are succeeding in constructing interactive generative models for the target environment of interest. Meanwhile, humans demonstrate remarkable generalization abilities to combine ex

Stephen Wormald, David Koblah, Matheus Kunzler Maldaner, Domenic Forte

Constraining deep neural networks (DNNs) to learn individual logic types per node, as performed using the DiffLogic network architecture, opens the door to model-specific explanation techniques that quell the complexity inherent to DNNs. Inspired by principles of circuit analysis from computer engineering, this work presents an algorithm (eXpLogic) for produ

Rahul Hoskeri, Hua Huang

Rotation speed is a key metric for many applications, such as calibrating electric motors in a factory, monitoring a car&#39;s engine health, detecting faults in electrical appliances, and more. However, existing measurement techniques have several drawbacks, including the need for line-of-sight visibility, intrusive machine modification, fixed sensor deploy

Léo Noirot, Cécile X. Yu, José C. Abadillo-Uriel, Étienne Dumur

The entanglement of microwave photons and spin qubits in silicon represents a pivotal step forward for quantum information processing utilizing semiconductor quantum dots. Such hybrid spin circuit quantum electrodynamics (cQED) has been achieved by granting a substantial electric dipole moment to a spin by de-localizing it in a double quantum dot under spin-

Rajiv Krishnakumar, Julien Baglio, Frederik F. Flöther, Christian Ruiz

Whole-slide image classification represents a key challenge in computational pathology and medicine. Attention-based multiple instance learning (MIL) has emerged as an effective approach for this problem. However, the effect of attention mechanism architecture on model performance is not well-documented for biomedical imagery. In this work, we compare differ

Alceu Bissoto, Trung-Dung Hoang, Tim Flühmann, Susu Sun

Machine learning (ML) models may suffer from significant performance disparities between patient groups. Identifying such disparities by monitoring performance at a granular level is crucial for safely deploying ML to each patient. Traditional subgroup analysis based on metadata can expose performance disparities only if the available metadata (e.g., patient

Wilhelm Kerle-Malcharek, Karsten Klein, Martin Wikelski, Falk Schreiber

Bio-loggers, electronic devices used to track animal behaviour through various sensors, have become essential in wildlife research. Despite continuous improvements in their capabilities, bio-loggers still face significant limitations in storage, processing, and data transmission due to the constraints of size and weight, which are necessary to avoid disturbi

Kingshook Biswas, Arkajit Pal Choudhury

The relation between negatively curved spaces and their boundaries is important for various rigidity problems. In \cite{biswas2024quasi}, the class of Gromov hyperbolic spaces called maximal Gromov hyperbolic spaces was introduced, and the boundary functor $X \mapsto \partial X$ was shown to give an equivalence of categories between maximal Gromov hyperbolic

Harsh Gupta, Yuchen Mo, Shengmiao Jin, Wenzhen Yuan

High-resolution tactile sensors have become critical for embodied perception and robotic manipulation. However, a key challenge in the field is the lack of transferability between sensors due to design and manufacturing variations, which result in significant differences in tactile signals. This limitation hinders the ability to transfer models or knowledge

Qi Wang, Yingying Zhang

We show that trivial extensions of gentle tree algebras are exactly Brauer tree algebras without exceptional vertex. We also give a characterization for the algebras whose trivial extensions are Brauer line/star/cycle algebras. As a consequence, the number of support $τ$-tilting modules over the trivial extension $T(A)$ of a gentle tree algebra $A$ depends o

Yoshinori Kanamura, Hyuga Yoshizaki

In 2021, Brock, Elkies, and Jordan generalized the theory of periodic continued fractions (PCFs) over $\mathbb{Z}$ to the ring of integers in a number field. In particular, they considered the case where the number field is an intermediate field of the $\mathbb{Z}_2$-extension over $\mathbb{Q}$ and asked whether a $(N, \ell)$-type PCF for $X_n = 2\cos(2\pi/2

Guy E. Blelloch, Andrew C. Brady

We present a work optimal algorithm for parallel fully batch-dynamic maximal matching against an oblivious adversary. It processes batches of updates (either insertions or deletions of edges) in constant expected amortized work per edge update, and in $O(\log^3 m)$ depth per batch whp, where $m$ is the maximum number of edges in the graph over time. This gre

Aditya Ghosh, Guido Imbens, Stefan Wager

Regression discontinuity designs have become one of the most popular research designs in empirical economics. We argue, however, that the widely used approaches to building confidence intervals in regression discontinuity designs often exhibit suboptimal behavior in practice. We propose a new estimator, the partially linear regression discontinuity (PLRD) es

Haaris Mehmood, Karthikeyan Saravanan, Pablo Peso Parada, David Tuckey

Automatic Speech Recognition (ASR) is widely used within consumer devices such as mobile phones. Recently, personalization or on-device model fine-tuning has shown that adaptation of ASR models towards target user speech improves their performance over rare words or accented speech. Despite these gains, fine-tuning on user data (target domain) risks the pers

Allison Andreyev

Automated speech recognition (ASR) models have gained prominence for applications such as captioning, speech translation, and live transcription. This paper studies Whisper and two model variants: one optimized for live speech streaming and another for offline transcription. Notably, these models have been found to generate hallucinated content, reducing tra

Zhenping Guo, Xiaowen Su, Kai Sun, Byungkwon Park

In studies on complex network systems using graph theory, eigen-analysis is typically performed on an undirected graph model of the network. However, when analyzing cascading failures in a power system, the interactions among failures suggest the need for a directed graph beyond the topology of the power system to model directions of failure propagation. To

Ti Ti Nguyen, Thanh-Dung Le, Vu Nguyen Ha, Hong-fu Chou

The integration of machine learning (ML) has significantly enhanced the capabilities of Earth Observation (EO) systems by enabling the extraction of actionable insights from complex datasets. However, the performance of data-driven EO applications is heavily influenced by the data collection and transmission processes, where limited satellite bandwidth and l

Zahra Abbasiantaeb, Simon Lupart, Leif Azzopardi, Jeffery Dalton

The rise of personalized conversational search systems has been driven by advancements in Large Language Models (LLMs), enabling these systems to retrieve and generate answers for complex information needs. However, the automatic evaluation of responses generated by Retrieval Augmented Generation (RAG) systems remains an understudied challenge. In this paper

Rama Adithya Varanasi, Batia Mishan Wiesenfeld, Oded Nov

Generative AI (GAI) technologies are disrupting professional writing, challenging traditional practices. Recent studies explore GAI adoption experiences of creative practitioners, but we know little about how these experiences evolve into established practices and how GAI resistance alters these practices. To address this gap, we conducted 25 semi-structured

Mahmut Pekedis

This study proposes a simple and practical approach based on in-vitro measurements and in-silico simulation using the likelihood-free Bayesian inference with the finite element method simultaneously for stochastic calibration and uncertainty quantification of the mechanical response of biological materials. We implement the approach for distal, middle, and p

Zahra Abbasiantaeb, Chuan Meng, Leif Azzopardi, Mohammad Aliannejadi

Incomplete relevance judgments limit the reusability of test collections. When new systems are compared to previous systems that contributed to the pool, they often face a disadvantage. This is due to pockets of unjudged documents (called holes) in the test collection that the new systems return. The very nature of Conversational Search (CS) means that these

Kaiyang Huang, Yang Liu, Kai Sun, Feng Qiu

Dynamic simulation plays a crucial role in power system transient stability analysis, but traditional numerical integration-based methods are time-consuming due to the small time step sizes. Other semi-analytical solution methods, such as the Differential Transformation method, often struggle to select proper orders and steps, leading to slow performance and

J. Nosowitz, M. A. Cordiner, C. A. Nixon, A. E. Thelen

Titan, Saturn's largest satellite, maintains an atmosphere composed primarily of nitrogen (N$_2$) and methane (CH$_4$) that leads to a complex organic chemistry. Some of the nitriles (CN-bearing organics) on Titan are known to have substantially enhanced $^{15}$N abundances compared to Earth and to Titan's dominant nitrogen (N$_2$) reservoir. The $^{14}$N/$^

Ping Chen, David Hinote, Guoqing Chen

Objective: The aim of this study was to build an effective co-reference resolution system tailored for the biomedical domain. Materials and Methods: Experiment materials used in this study is provided by the 2011 i2b2 Natural Language Processing Challenge. The 2011 i2b2 challenge involves coreference resolution in medical documents. Concept mentions have bee

Nirmal Baishnab, Ethan Herron, Aditya Balu, Soumik Sarkar

The ability to generate 3D multiphase microstructures on-demand with targeted attributes can greatly accelerate the design of advanced materials. Here, we present a conditional latent diffusion model (LDM) framework that rapidly synthesizes high-fidelity 3D multiphase microstructures tailored to user specifications. Using this approach, we generate diverse t

Konstantin Ottnad, Simone Bacchio, Jacob Finkenrath, Bartosz Kostrzewa

We determine masses and mixing parameters of the $\eta$ and $M_{\eta^\prime}$ meson in lattice QCD. The calculations are carried out on a set of 13 ETMC gauge ensembles with $N_f=2+1+1$ (maximally) twisted-mass Clover-improved quarks. These ensemble cover four values of the lattice spacing $a=0.057\mathrm{fm},...,0.092\mathrm{fm}$ and pion masses from $140\m

Abhipsha Das, Nicholas Lourie, Siavash Golkar, Mariel Pettee

The scientific literature's exponential growth makes it increasingly challenging to navigate and synthesize knowledge across disciplines. Large language models (LLMs) are powerful tools for understanding scientific text, but they fail to capture detailed relationships across large bodies of work. Unstructured approaches, like retrieval augmented generation,

Anton Chudaykin, Martin Kunz, Julien Carron

We present updated constraints on modified gravity by including the Integrated Sachs-Wolfe (ISW) effect from CMB lensing-CMB temperature cross-correlations, based on the latest Planck PR4 maps. Utilizing the Effective Field Theory of dark energy approach and adopting the $w_0w_a$CDM background cosmological model, we find that including the CMB ISW lensing cr

Kaiyang Huang, Min Xiong, Yang Liu, Kai Sun

As inverter-based resources (IBRs) penetrate power systems, the dynamics become more complex, exhibiting multiple timescales, including electromagnetic transient (EMT) dynamics of power electronic controllers and electromechanical dynamics of synchronous generators. Consequently, the power system model becomes highly stiff, posing a challenge for efficient s

Zack B. Hall, Dimitra A. Pefkou, Aaron S. Meyer, Thomas R. Richardson

We study finite-volume (FV) corrections to determinations of $g_A$ via lattice quantum chromodynamics (QCD) using analytic results and numerical analysis. We observe that $SU(2)$ Heavy Baryon Chiral Perturbation Theory does not provide an unambiguous prediction for the sign of the FV correction, which is not surprising when one also considers large-$N_c$ con

Juan Carlos D'Olivo, José Arnulfo Herrera Lara, Ismael Romero, Matias Reynoso

Many geophysical and geochemical phenomena in the Earth's interior are related to physical and chemical processes in the outer core and the core-mantle boundary, which are directly linked to the isotopic composition. Determining the composition using standard geophysical methods has been challenging. Atmospheric neutrino oscillations, influenced by their wea

Aadirupa Saha, Vinod Raman, Hilal Asi

We design differentially private algorithms for the problem of prediction with expert advice under dynamic regret, also known as tracking the best expert. Our work addresses three natural types of adversaries, stochastic with shifting distributions, oblivious, and adaptive, and designs algorithms with sub-linear regret for all three cases. In particular, und

Aidan Lindberg, Jenna Rajchgot

The K-theoretic quiver component formula expresses the K-polynomial of a type A quiver locus as an alternating sum of products of double Grothendieck polynomials. This formula was conjectured by A. Buch and R. Rim\'anyi and later proved by R. Kinser, A. Knutson, and the second author. We provide a new proof of this formula which replaces Gr\"obner degenerati

O. Deniz Akyildiz, Pierre del Moral, Joaquin Miguez

We develop a novel stability theory for Sinkhorn semigroups based on Lyapunov techniques and quantitative contraction coefficients, and establish exponential convergence of Sinkhorn iterations on weighted Banach spaces. This operator-theoretic framework yields explicit exponential decay rates of Sinkhorn iterates toward Schr\"odinger bridges with respect to

Thomas Strobl, Rafał R. Suszek

We present a novel generalisation of principal bundles -- principaloid bundles: These are fibre bundles $\pi:P\to B$ where the typical fibre is the arrow manifold $G$ of a Lie groupoid $G\rightrightarrows M$ and the structure group is reduced to the latter's group of bisections. Each such bundle canonically comes with a bundle map $D:P\to F$ to another fibre

Daniel Syomichev, Padmini Gopinath, Guang-Lin Wei, Eric Chang

Analyzing CT scans, MRIs and X-rays is pivotal in diagnosing and treating diseases. However, detecting and identifying abnormalities from such medical images is a time-intensive process that requires expert analysis and is prone to interobserver variability. To mitigate such issues, machine learning-based models have been introduced to automate and significa