October 2025 arXiv papers — page 63

Kou Misaki, Takuya Akiba

We introduce String Seed of Thought (SSoT), a novel prompting method for LLMs that improves Probabilistic Instruction Following (PIF). We define PIF as a task requiring an LLM to select its answer from a predefined set of options, each associated with a specific probability, such that the empirical distribution of the generated answers aligns with the target

Yutaro Akahoshi, Riki Toshio, Jun Fujisaki, Hirotaka Oshima

Runtime optimization of the quantum computing within a given computational resource is important to achieve practical quantum advantage. In this paper, we propose a runtime reduction protocol for the lattice surgery, which utilizes the soft information corresponding to the logical measurement error. Our proposal is a simple two-step protocol: operating the l

Yang Zhao, Pu Wang, Hao Frank Yang

Designing optimal prompts and reasoning processes for large language models (LLMs) on domain-specific tasks is both necessary and challenging in real-world applications. Determining how to integrate domain knowledge, enhance reasoning efficiency, and even provide domain experts with refined knowledge integration hints are particularly crucial yet unresolved

Chujun He, Zhonghao Huang, Xiangguo Li, Ye Luo

We present a multi-agent, AI-driven framework for fundamental investing that integrates macro indicators, industry-level and firm-specific information to construct optimized equity portfolios. The architecture comprises: (i) a Macro agent that dynamically screens and weights sectors based on evolving economic indicators and industry performance; (ii) four fi

James D. Shemilt, Neil J. Balmforth, Duncan R. Hewitt

Airway clearance by coughing is a key mechanism for mucus transport, particularly in obstructive lung diseases associated with altered mucus rheology. We investigate the dynamics of a viscoplastic liquid film driven by flow in a turbulent air layer, which is a model for air-driven mucus transport that incorporates yield-stress effects. Our theoretical analys

Yuansheng Zhao, Kenji Shiraishi, Tetsuo Narita, Atsushi Oshiyama

We present density-functional-theory calculations which provide a microscopic picture of the recombination-enhanced migration of interstitial Mg in GaN. We determine stable structures and migration pathways with accurate HSE approximation to the exchange-correlation energy, and also computed recombination rates using the obtained energy spectrum and wavefunc

Hanyu Zhu, Lance Fiondella, Jiawei Yuan, Kai Zeng

Retrieval-Augmented Generation (RAG) empowers Large Language Models (LLMs) to dynamically integrate external knowledge during inference, improving their factual accuracy and adaptability. However, adversaries can inject poisoned external knowledge to override the model's internal memory. While existing attacks iteratively manipulate retrieval content or prom

Jihyun Lee, Yejin Min, San Kim, Yejin Jeon

Panic attacks are acute episodes of fear and distress, in which timely, appropriate intervention can significantly help individuals regain stability. However, suitable datasets for training such models remain scarce due to ethical and logistical issues. To address this, we introduce PACE, which is a dataset that includes high-distress episodes constructed fr

Ethan Hwang, Hossein Adeli, Wenxuan Guo, Andrew Luo

A fine-grained account of functional selectivity in the cortex is essential for understanding how visual information is processed and represented in the brain. Classical studies using designed experiments have identified multiple category-selective regions; however, these approaches rely on preconceived hypotheses about categories. Subsequent data-driven dis

Haarika Manda, Manshi Sagar, Yogesh, Kartikay Singh

Internet speed tests are indispensable for users, ISPs, and policymakers, but their static flooding-based design imposes growing costs: a single high-speed test can transfer hundreds of MB, and collectively, platforms like Ookla, M-Lab, and Fast.com generate petabytes of traffic each month. Reducing this burden requires deciding when a test can be stopped ea

Ying Ming, Yue Lin, Longfei Zhao, Gengwan Li

Computed Tomography Pulmonary Angiography (CTPA) is the reference standard for diagnosing pulmonary vascular diseases such as Pulmonary Embolism (PE) and Chronic Thromboembolic Pulmonary Hypertension (CTEPH). However, its reliance on iodinated contrast agents poses risks including nephrotoxicity and allergic reactions, particularly in high-risk patients. Thi

Hongjie Dong, Pilgyu Jung, Doyoon Kim

This paper investigates weighted mixed-norm estimates for divergence-type parabolic equations on Reifenberg-flat domains with the conormal derivative boundary condition. The leading coefficients are assumed to be merely measurable in the time variable and to have small mean oscillations in the spatial variables. In deriving the boundary estimates, we overcom

Qi-Ming Ding, Ting Zhang, Hui Li, Da-Jian Zhang

Key quantum features like coherence are the fundamental resources enabling quantum advantages and ascertaining their presence in quantum systems is crucial for developing quantum technologies. This task, however, faces severe challenges in the noisy intermediate-scale quantum era. On one hand, experimental data are typically scarce, rendering full state reco

Guorui Song, Guocun Wang, Zhe Huang, Jing Lin

Generating accurate descriptions of human actions in videos remains a challenging task for video captioning models. Existing approaches often struggle to capture fine-grained motion details, resulting in vague or semantically inconsistent captions. In this work, we introduce the Motion-Augmented Caption Model (M-ACM), a novel generative framework that enhanc

Zhonglun Wang, Yizhe Zhao, Gangming Hu, Yali Zheng

Reconfigurable holographic surface (RHS) enables cost-effective large-scale arrays with high spatial gain. However, its amplitude-controlled holographic beamforming suffers from directional fluctuations, making it difficult to fully exploit the spatial gain of RHS. Fortunately, the promising 6D movable antenna (6DMA) provides a potential solution to this pro

Chengming Lyu, Zhenfei Tan, Xiaoyuan Xu, Chen Fu

With the increasing penetration of behind-the-meter (BTM) resources, it is vital to monitor the components of these resources and deduce their response behavior to external environment. Owing to data privacy, however, the appliance-wise measurement is invisible to the power system operator, which hinders the accurate modeling of load identification. To this

Yuhao Fu, Yinghao Zhang, Yalin Liu, Bishenghui Tao

The Medical Internet of Things (MIoT) demands stringent end-to-end latency guarantees for sequential healthcare workflows deployed over heterogeneous cloud-fog-edge infrastructures. Scheduling these sequential workflows to minimize makespan is an NP-hard problem. To tackle this challenge, we propose a Two-tier DDPG-based scheduling framework that decomposes

Kevin Church, Kevin Constantineau, Jean-Philippe Lessard

In this work, we prove the existence of a 2-cycle in an integrodifference equation with a Laplace kernel and logistic growth function, connecting two non-trivial fixed points of the second iterate of the logistic map in the non-chaotic regime. This model was first studied by Kot (1992), and the 2-cycle we establish corresponds to one numerically observed by

Divyanshu Kumar, Nitin Aravind Birur, Tanay Baswa, Sahil Agarwal

Frontier Large Language Models (LLMs) pose unprecedented dual-use risks through the potential proliferation of chemical, biological, radiological, and nuclear (CBRN) weapons knowledge. We present the first comprehensive evaluation of 10 leading commercial LLMs against both a novel 200-prompt CBRN dataset and a 180-prompt subset of the FORTRESS benchmark, usi

Tatsuki Murakami, Keisuke Ogawa, Hajime Kumazaki, Shun Fujii

We demonstrate low-noise Kerr soliton frequency combs with repetition rates below 1~GHz in ultrahigh-Q crystalline magnesium fluoride resonators. Single soliton states with repetition rates of 0.90 GHz, 1.19 GHz, 1.59 GHz, 2.48 GHz, and 4.10 GHz are observed with continuous-wave laser excitation. The near-GHz soliton repetition frequency exhibits a single-si

Guangxin Su, Hanchen Wang, Jianwei Wang, Wenjie Zhang

Large Language Models (LLMs) have achieved remarkable success in natural language processing through strong semantic understanding and generation. However, their black-box nature limits structured and multi-hop reasoning. In contrast, Text-Attributed Graphs (TAGs) provide explicit relational structures enriched with textual context, yet often lack semantic d

Yuxuan Tang, Yifan Feng

Alignment of large language models (LLMs) has predominantly relied on pairwise preference optimization, where annotators select the better of two responses to a prompt. While simple, this approach overlooks the opportunity to learn from richer forms of human feedback, such as multiway comparisons and top-$k$ rankings. We introduce Ranked Choice Preference Op

Qi Deng, Yinghao Zhang, Yalin Liu, Bishenghui Tao

Autism Spectrum Disorder (ASD) diagnosis systems in school environments increasingly relies on IoT-enabled cameras, yet pure cloud processing raises privacy and latency concerns while pure edge inference suffers from limited accuracy. We propose Confidence-Constrained Cloud-Edge Knowledge Distillation (C3EKD), a hierarchical framework that performs most infe

Linyuan Geng, Linxiao Yang, Xinyue Gu, Liang Sun

This paper presents SolarBoost, a novel approach for forecasting power output in distributed photovoltaic (DPV) systems. While existing centralized photovoltaic (CPV) methods are able to precisely model output dependencies due to uniformity, it is difficult to apply such techniques to DPV systems, as DPVs face challenges such as missing grid-level data, temp

Kshipra Bhawalkar, Yang Cai, Zhe Feng, Christopher Liaw

We consider the problem of maximizing a submodular function with access to a noisy value oracle for the function instead of an exact value oracle. Similar to prior work, we assume that the noisy oracle is persistent in that multiple calls to the oracle for a specific set always return the same value. In this model, Hassidim and Singer (2017) design a $(1-1/e

Nagisa Sugishita, Margarida Carvalho

Bilevel linear programming (LP) is one of the simplest classes of bilevel optimization problems, yet it is known to be NP-hard in general. Specifically, determining whether the optimal objective value of a bilevel LP is at least as good as a given threshold, a standard decision version of the problem, is NP-complete. However, this decision problem becomes tr

Yuta Kochi, Yutaro Kinoshita, Masanari Watanabe, Ryutaro Ide

Photon echo (PE) techniques offer a promising approach to optical quantum memory, yet their implementation in conventional platforms, such as rare-earth-ion-doped crystals, is hindered by limited bandwidths. Semiconductor quantum dot (QD) ensembles, featuring THz-scale inhomogeneous broadening and sub-picosecond dynamics, provide an attractive alternative fo

Gan Zhang, Zhuo Wang, Kevin A Reed, Lucas M Harris

The field of weather and climate science is at a pivotal moment, defined by simultaneous forces of institutional disruption and unprecedented technological advancements. While a shifting research and employment landscape has created career uncertainty, prompting many scientists to consider or pursue opportunities in the private sector, it has simultaneously

Honggang Jia, Nan Cheng, Xiucheng Wang

Radio maps (RMs), which provide location-based pathloss estimations, are fundamental to enabling proactive, environment-aware communication in 6G networks. However, existing deep learning-based methods for RM construction often model dynamic environments as a series of independent static snapshots, thereby omitting the temporal continuity inherent in signal

Dhrupad Bhardwaj, Julia Kempe, Tim G. J. Rudner

To deploy large language models (LLMs) in high-stakes application domains that require substantively accurate responses to open-ended prompts, we need reliable, computationally inexpensive methods that assess the trustworthiness of long-form responses generated by LLMs. However, existing approaches often rely on claim-by-claim fact-checking, which is computa

Haibo Zhao, Yu Qi, Boce Hu, Yizhe Zhu

We present Generalizable Hierarchical Skill Learning (GSL), a novel framework for hierarchical policy learning that significantly improves policy generalization and sample efficiency in robot manipulation. One core idea of GSL is to use object-centric skills as an interface that bridges the high-level vision-language model and the low-level visual-motor poli

Alec Helbling, Shruti Palaskar, Kundan Krishna, Polo Chau

What exactly makes a particular image unsafe? Systematically differentiating between benign and problematic images is a challenging problem, as subtle changes to an image, such as an insulting gesture or symbol, can drastically alter its safety implications. However, existing image safety datasets are coarse and ambiguous, offering only broad safety labels w

Lei Shi, David Arbour, Raghavendra Addanki, Ritwik Sinha

Large Language Models (LLMs) enable a new form of digital experimentation where treatments combine human and model-generated content in increasingly sophisticated ways. The main methodological challenge in this setting is representing these high-dimensional treatments without losing their semantic meaning or rendering analysis intractable. Here, we address t

Qiang Ding, Lvzhou Luo, Yixuan Cao, Ping Luo

Ensuring that Large Language Models (LLMs) generate summaries faithful to a given source document is essential for real-world applications. While prior research has explored LLM faithfulness, existing benchmarks suffer from annotation ambiguity, primarily due to the ill-defined boundary of permissible external knowledge in generated outputs. For instance, co

Agostino Capponi, Alfio Gliozzo, Chunghyun Han, Junkyu Lee

This paper presents a first empirical study of agentic AI as autonomous decision-makers in decentralized governance. Using more than 3K proposals from major protocols, we build an agentic AI voter that interprets proposal contexts, retrieves historical deliberation data, and independently determines its voting position. The agent operates within a realistic

Bolun Liu, Trang Quynh Nguyen, Elizabeth A. Stuart, Bryan Lau

Unobserved effect modifiers can induce bias when generalizing causal effect estimates to target populations. In this work, we extend a sensitivity analysis framework assessing the robustness of study results to unobserved effect modification that adapts to various generalizability scenarios, including multiple (conditionally) randomized trials, observational

Yihan Wu, Georgios Milis, Ruibo Chen, Heng Huang

The rapid advancement of next-token-prediction models has led to widespread adoption across modalities, enabling the creation of realistic synthetic media. In the audio domain, while autoregressive speech models have propelled conversational interactions forward, the potential for misuse, such as impersonation in phishing schemes or crafting misleading speec

Yanguang Sun, Jiawei Lian, Jian Yang, Lei Luo

Large-scale foundation models provide powerful feature representations for downstream object segmentation tasks. However, when adapted to specific tasks through the full-parameter fine-tuning, the enormous parameters being updated often results in significant computational overhead, creating a bottleneck in training efficiency. Although existing methods atte

Maitreyi Swaroop, Tamar Krishnamurti, Bryan Wilder

We study the problem of selecting limited features to observe such that models trained on them can perform well simultaneously across multiple subpopulations. This problem has applications in settings where collecting each feature is costly, e.g. requiring adding survey questions or physical sensors, and we must be able to use the selected features to create

Hezam Albaqami, Haitian Wang, Xinyu Wang, Muhammad Ibrahim

High-definition 3D city maps enable city planning and change detection, which is essential for municipal compliance, map maintenance, and asset monitoring, including both built structures and urban greenery. Conventional Digital Surface Model (DSM) and image differencing are sensitive to vertical bias and viewpoint mismatch, while original point cloud or vox

Weijie Zhou, Xuantang Xiong, Yi Peng, Manli Tao

Visual reasoning in multimodal large language models (MLLMs) has primarily been studied in static, fully observable settings, limiting their effectiveness in real-world environments where information is often incomplete due to occlusion or limited field of view. Humans, in contrast, actively explore and interact with their environment-moving, examining, and

Mingxuan Li, Junzhe Zhang, Elias Bareinboim

A key task in Artificial Intelligence is learning effective policies for controlling agents in unknown environments to optimize performance measures. Off-policy learning methods, like Q-learning, allow learners to make optimal decisions based on past experiences. This paper studies off-policy learning from biased data in complex and high-dimensional domains

Federico Ghimenti, Adithya Sriram, Atsushi Yamamura, Hideo Mabuchi

The coherent Ising machine (CIM) is a nonconventional hardware architecture for finding approximate solutions to large-scale combinatorial optimization problems. It operates by annealing a laser gain parameter to adiabatically deform a high-dimensional energy landscape over a set of soft spins, going from a simple convex landscape to the more complex optimiz

Ninghui Feng, Yiyan Qi

Large language models (LLMs) have recently demonstrated impressive multimodal reasoning capabilities, yet their understanding of purely numerical time-series signals remains limited. Existing approaches mainly focus on forecasting or trend description, without uncovering the latent events that drive numerical changes or explaining the reasoning process behin

Julian Greentree, William Moran, Rob Evans, Andrew Melatos

This paper presents an entropy based adaptive measurement sequence strategy for quantum sensing of magnetic fields. To physically ground our ideas we consider a sensor employing a nitrogen vacancy center in diamond, however our approach is applicable to other quantum sensor arrangements. The sensitivity and accuracy of these sensors typically rely on long se

Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay, Gabor Karsai

In Partially Observable Markov Decision Processes (POMDPs), maintaining and updating belief distributions over possible underlying states provides a principled way to summarize action-observation history for effective decision-making under uncertainty. As environments grow more realistic, belief distributions develop complexity that standard mathematical mod

Zhen Yang, Hongyi Lin, Xiao Yu, Jacky Wai Keung

Code-Comment Synchronization (CCS) aims to synchronize the comments with code changes in an automated fashion, thereby significantly reducing the workload of developers during software maintenance and evolution. While previous studies have proposed various solutions that have shown success, they often exhibit limitations, such as a lack of generalization abi

Nikhat Khan, Nikhil Shukla

For over two decades, the G-set benchmark has remained a cornerstone challenge for combinatorial optimization solvers. Remarkably, it continues to yield new best-known solutions even to the present day. Here, we report a new best-known Max-Cut of 27,047 for the 7000-node G63 instance-one of the two instances in the benchmark with the largest number of edges.

ARA Collaboration, N. Alden, S. Ali, P. Allison

We present the first experimental evidence for in-ice Askaryan radiation -- coherent charge-excess radio emission -- from high-energy particle cascades developing in the Antarctic ice sheet. In 208 days of data recorded with the phased-array instrument of the Askaryan Radio Array, a previous analysis has incidentally identified 13 events with impulsive radio

Zongyang Yuan, Lailong Luo, Qianzhen Zhang, Bangbang Ren

As the number of Internet of Things (IoT) devices continuously grows and application scenarios constantly enrich, the volume of sensor data experiences an explosive increase. However, substantial data demands considerable energy during computation and transmission. Redundant deployment or mobile assistance is essential to cover the target area reliably with

Priya Ranjan

This work studies two types of computer networking models. The primary focus is to understand the different dynamical phenomena observed in practice due to the presence of severe nonlinearities, delays and widely varying operating conditions. The first models considered are of senders running TCP (Transmission Control Protocol) and traffic passing through RE

K. Godbey, F. M. Nunes, M. Albertsson, K. J. Cook

This document summarizes the discussions and outcomes of the Facility for Rare Isotope Beams Theory Alliance (FRIB-TA) topical program "The path to Superheavy Isotopes" held in June 2024 at FRIB. Its content is non-exhaustive, reflecting topics chosen and discussed by the participants. The program aimed to assess the current status of theory in superheavy nu

Hui Han, Haotian Teng, Hailong Xu, Jinquan Huang

Quantum clock synchronization underpins modern secure communications and critical infrastructure, yet its fundamental dependence on channel reciprocity introduces an exploitable vulnerability to asymmetric delay attacks. Current attack strategies rely on static delays, limiting their ability to target application-specific stability requirements. Here, we pro

Jingtian Zhao, Xueli Xie, Jianxiang Xi, Xiaogang Yang

Retinex-based low-light image enhancement methods are widely used due to their excellent performance. However, most of them are time-consuming for large-sized images. This paper extends the Retinex model from the spatial domain to the histogram domain, and proposes a novel histogram-based Retinex model for fast low-light image enhancement, named HistRetinex.

Alvaro Alvarez-Parrilla, Roberto Gutiérrez-Soto, Jesús Muciño-Raymundo

A complex rational function R, of degree n>1, on a compact Riemann surface M provided with a cyclic order of its q critical values, determines an homogeneous tessellation of the Riemann surface M, whose 2n tiles are topological q-gons with alternating colors.The tessellation provides a simple and straighforward visual description of the rational function R.

Nuo Chen, Zehua Li, Keqin Bao, Junyang Lin

Building robust and general reasoning ability is a central goal in the development of large language models (LLMs). Recent efforts increasingly turn to code as a rich training source, given its inherent logical structure and diverse reasoning paradigms such as divide-and-conquer, topological ordering, and enumeration. However, reasoning in code is often expr

Jiashen Guo, Yi Liu, Lin Wang

In this note, we investigate the dynamics of invariant circles in area-preserving twist maps. The invariant circles under consideration lie beyond the applicability of classical KAM theory, as the perturbations involved exceed the scope of standard KAM methods. By integrating both constructive and non-constructive techniques, we establish several results on

Jun-Jie Wei, Qing-Bo Ma, Xue-Feng Wu

High-redshift gamma-ray bursts (GRBs), putative counterparts of massive, low-metallicity Population III (Pop III) stars, are a promising probe of the first stars. We assess the detectability of these Pop III GRBs using a metallicity-based progenitor criterion and cosmological $N$-body/hydrodynamical simulations with three distinct Pop III initial mass functi

Alain M. Dikande

We address the Kramers escape problem for Brownian particles in bistable substrates with deformable double-well shapes. The shape deformability is considered of three distinct forms: in one, the positions of the two degenerate minima can be shifted continuously without affecting the barrier height. In the second the minima positions are kept fix while the ba

James Tian

We study maximum-entropy inference for finite-dimensional quantum states under linear moment constraints. Given expectation values of finitely many observables, the feasible set of states is convex but typically non-unique. The maximum-entropy principle selects the Gibbs state that agrees with the data while remaining maximally unbiased. We prove that conver

Yao Lu, Wanwei Liu, Tanghaoran Zhang, Kang Yang

Code differencing is a fundamental technique in software engineering practice and research. While researchers have proposed text-based differencing techniques capable of identifying line changes over the past decade, existing methods exhibit a notable limitation in identifying edit actions (EAs) that operate on text blocks spanning multiple lines. Such EAs a

Siyong Chen, Jinbo Wen, Jiawen Kang, Tenghui Huang

Recently, large models have shown significant potential for smart healthcare. However, the deployment of Large Vision-Language Models (LVLMs) for clinical services is currently hindered by three critical challenges: a tendency to hallucinate answers not grounded in visual evidence, the inefficiency of fixed-depth reasoning, and the difficulty of multi-instit

Nicolas Lanchier

The contact process with an asymptomatic state, introduced in [Belhadji, Lanchier and Mercer, Stochastic Process. Appl., 176:104417, 2024], is a natural variant of the basic contact process that distinguishes between asymptomatic (state 1) and symptomatic (state 2) individuals. Infected individuals infect their healthy neighbors at rate $\lambda_1$ when asym

Kunwoong Kim, Kyungseon Lee, Jihu Lee, Dongyoon Yang

Algorithmic fairness is a socially crucial topic in real-world applications of AI. Among many notions of fairness, subgroup fairness is widely studied when multiple sensitive attributes (e.g., gender, race, age) are present. However, as the number of sensitive attributes grows, the number of subgroups increases accordingly, creating heavy computational burde

Qingru Zhang, Liang Qiu, Ilgee Hong, Zhenghao Xu

Supervised fine-tuning (SFT) has emerged as a crucial method for aligning large language models (LLMs) with human-annotated demonstrations. However, SFT, being an off-policy approach similar to behavior cloning, often struggles with overfitting and poor out-of-domain generalization, especially in limited-data scenarios. To address these limitations, we propo

Rin Takada

We study electromagnetic leptogenesis (EMLG) in the low-scale, resonant regime within a fully effective-field-theory (EFT)--consistent framework. Starting from a UV Lagrangian and performing a one-loop matching to obtain the gauge-invariant dipole operator $O_{NB}$ with its Wilson coefficient $C_{NB}$, we implement the Pilaftsis--Underwood resummation for th

Xiangyang Xu, Hongyang Gao

Molecular property prediction (MPP) is a cornerstone of drug discovery and materials science, yet conventional deep learning approaches depend on large labeled datasets that are often unavailable. Few-shot Molecular property prediction (FSMPP) addresses this scarcity by incorporating relational inductive bias through a context graph that links molecule nodes

Anubhav Jangra, Smaranda Muresan

LLMs are reshaping education, with students increasingly relying on them for learning. Implemented using general-purpose models, these systems are likely to give away the answers, potentially undermining conceptual understanding and critical thinking. Prior work shows that hints can effectively promote cognitive engagement. Building on this insight, we evalu

Jiaqi Xue, Mayank Kumar, Yuzhang Shang, Shangqian Gao

Federated Learning (FL) enables collaborative model training across institutions without sharing raw data. However, gradient sharing still risks privacy leakage, such as gradient inversion attacks. Homomorphic Encryption (HE) can secure aggregation but often incurs prohibitive computational and communication overhead. Existing HE-based FL methods sit at two

Y. Q. Chai, M. Y. Wang, S. N. Wang, P. H. Ouyang

Thermal noise is one of the physical obstacles that constrain the achievable detection sensitivities of various detectors. Indeed, as we showed in a recent paper (PRB 111, 024501 (2025)), the usual Josephson threshold detector (JTD) operated in an equilibrium state can be utilized to implement a weak microwave signal, just approaching (but not arriving at) i

Zan Li, Kyongmin Yeo, Wesley Gifford, Lara Marcuse

In this study, we present a deep learning framework that learns complex spatio-temporal correlation structures of EEG signals through a Spatio-Temporal Attention Network (STAN) for accurate predictions of onset of seizures for Epilepsy patients. Unlike existing methods, which rely on feature engineering and/or assume fixed preictal durations, our approach si

Youssef Megahed, Atallah Madi, Dina El Demellawy, Adrian D. C. Chan

Hirschsprung's disease is defined as the congenital absence of ganglion cells in some segment(s) of the colon. The muscle cannot make coordinated movements to propel stool in that section, most commonly leading to obstruction. The diagnosis and treatment for this disease require a clear identification of different region(s) of the myenteric plexus, where gan

Jorge Alberto Araujo

Applying complex legal rules characterized by multiple, heterogeneously weighted criteria presents a fundamental challenge in judicial decision-making, often hindering the consistent realization of legislative intent. This challenge is particularly evident in the quantification of non-pecuniary damages in personal injury cases. This paper introduces Soppia,

Zhuojin Li, Marco Paolieri, Leana Golubchik

Deploying deep neural networks on mobile devices is increasingly important but remains challenging due to limited computing resources. On the other hand, their unified memory architecture and narrower gap between CPU and GPU performance provide an opportunity to reduce inference latency by assigning tasks to both CPU and GPU. The main obstacles for such coll

Chen Liu, Dionysis Milesis, Chi-Wang Shu, Xiangxiong Zhang

We introduce effective splitting methods for implementing optimization-based limiters to enforce the invariant domain in gas dynamics in high order accurate numerical schemes. The key ingredients include an easy and efficient explicit formulation of the projection onto the invariant domain set, and also proper applications of the classical Douglas-Rachford s

Guoan Xu, Yang Xiao, Wenjing Jia, Guangwei Gao

While recent semantic segmentation networks heavily rely on powerful pretrained encoders, most employ simplistic decoders, leading to suboptimal trade-offs between semantic context and fine-grained detail preservation. To address this, we propose a novel decoder architecture, WaveSeg, which jointly optimizes feature refinement in spatial and wavelet domains.

Hancheng Min, Zhihui Zhu, René Vidal

Among many mysteries behind the success of deep networks lies the exceptional discriminative power of their learned representations as manifested by the intriguing Neural Collapse (NC) phenomenon, where simple feature structures emerge at the last layer of a trained neural network. Prior works on the theoretical understandings of NC have focused on analyzing

Ruoyu Wu

The multivariate Kendall-$\tau$ statistic, denoted by $K_n$, plays a significant role in robust statistical analysis. This paper establishes the limiting properties of the empirical spectral distribution (ESD) of $K_n$. We demonstrate that the ESD of $\frac{1}{2}pK_n$ converges almost surely to the Mar\v{c}enko--Pastur law with variance parameter $\frac{1}{2

Suhasnadh Reddy Veluru, Sai Teja Erukude, Viswa Chaitanya Marella

In the current digital commerce landscape, user-generated reviews play a critical role in shaping consumer behavior, product reputation, and platform credibility. However, the proliferation of fake or misleading reviews often generated by bots, paid agents, or AI models poses a significant threat to trust and transparency within review ecosystems. Existing d

Xinyi Ma, Liping Yang, Xueshang Feng, Hui Tian

Solar wind backmapping is a critical technique for analyzing the origin of the solar wind and space weather events by correlating in situ measurements with solar remote-sensing observations. This technique typically traces magnetic field lines using a heliospheric magnetic field (HMF) model coupled with a coronal magnetic field (CMF). However, the impact of

Shayan Karimi, Xiaoqi Tan

This paper investigates the computational complexity of reinforcement learning in a novel linear function approximation regime, termed partial $q^{\pi}$-realizability. In this framework, the objective is to learn an $\epsilon$-optimal policy with respect to a predefined policy set $\Pi$, under the assumption that all value functions for policies in $\Pi$ are

Sameer Dambal, Akira Sone, Yu Zhang

Simulating open quantum systems on quantum computers presents a fundamental challenge: open quantum dynamics are intrinsically nonunitary, whereas quantum computers operate through unitary evolution. Conventional approaches overcome this mismatch by encoding nonunitary processes into unitary circuits, but such methods incur substantial overhead in both qubit

Mitchell E. C. Sabbadini, Andrew H. Liu, Joseph Ruan, Tyler S. Wilson

Robots operating in changing environments either predict obstacle changes and/or plan quickly enough to react to them. Predictive approaches require a strong prior about the position and motion of obstacles. Reactive approaches require no assumptions about their environment but must replan quickly and find high-quality paths to navigate effectively. Reactive

Mark J. Ablowitz, Justin T. Cole, Sean D. Nixon

Spiral wave solutions are found in linear and weakly nonlinear irrotational water wave equations. These unsteady spiral waves evolve from suitable initial conditions; they are not induced by external forcing. In the linear case, a long-time asymptotic result is obtained via the method of stationary phase. The asymptotic approximation is found to be in good a

Matías Briceño, Hernán A. González, Marc Henneaux, Alfredo Pérez

We provide a new perspective on the general matching conditions between the future of past null infinity and the past of future null infinity, emphasizing the impact of dominant logarithmic terms in the asymptotic expansion of the fields near null infinity. We explicitly consider the cases of a massless scalar field and of electromagnetism. Key in our deriva

Emilio Barucci, Andrea Gurgone, Giulia Iori, Michele Azzone

We analyse financial stability and welfare impacts associated with the introduction of a Central Bank Digital Currency (CBDC) in a macroeconomic agent-based model. The model considers firms, banks, and households interacting on labour, goods, credit, and interbank markets. Households move their liquidity from deposits to CBDC based on the perceived riskiness

Maksym Mohorian

The origin and evolution of chemical elements in the Universe are governed not only by nucleosynthesis processes in stars, but also by mechanisms that alter observed photospheric compositions. Among these, chemical depletion (underabundance of refractory elements in stellar photospheres) presents a key puzzle in understanding the full chemical lifecycle. Thi

Pranav Saxena, Jimmy Chiun

Understanding and reasoning about complex 3D environments requires structured scene representations that capture not only objects but also their semantic and spatial relationships. While recent works on 3D scene graph generation have leveraged pretrained VLMs without task-specific fine-tuning, they are largely confined to single-view settings, fail to suppor

William Christian, Daniel Adamlu, Adrian Yu, Derwin Suhartono

Question Answering (QA) has seen significant improvements with the advancement of machine learning models, further studies enhanced this question answering system by retrieving external information, called Retrieval-Augmented Generation (RAG) to produce more accurate and informative answers. However, these state-of-the-art-performance is predominantly in Eng

Raul Cavalcante Dinardi, Bruno Yamamoto, Anna Helena Reali Costa, Artur Jordao

Reasoning models represent a significant advance in LLM capabilities, particularly for complex reasoning tasks such as mathematics and coding. Previous studies confirm that parallel test-time compute-sampling multiple solutions and selecting the best one-can further enhance the predictive performance of LLMs. However, strategies in this area often require co

R. Li, D. Verney, C. Delafosse, M. N. Harakeh

The different data-sorting modes of the phoswich detector array PARIS used for detecting high-energy (4$-$10 MeV) $\gamma$ rays are investigated. The characteristics including time resolution, energy resolution and detection efficiency under various modes are studied. The present study shows that PARIS has capabilities of rejecting escape and pileup events w

Daniela Martin, Connor O'Brien, Valmir P Moraes Filho, Jinsu Hong

We present a scalable machine learning framework for analyzing Parker Solar Probe (PSP) solar wind data using distributed processing and the quantum-inspired Kernel Density Matrices (KDM) method. The PSP dataset (2018--2024) exceeds 150 GB, challenging conventional analysis approaches. Our framework leverages Dask for large-scale statistical computations and

Tianyi Hu, Thomas J. R. Hughes, Guglielmo Scovazzi, Hector Gomez

Discontinuity capturing (DC) operators are commonly employed to numerically solve problems involving sharp gradients in the solution. Despite their success, the application of DC operators to the direct van der Waals simulation (DVS) remains challenging. The DVS framework models non-equilibrium phase transitions by admitting interfacial regions in which the

Rishav Koirala, Ben A. Olsen

We describe the design, construction, and characterization of a Bitter-type electromagnet that produces a spatially-dependent magnetic field used for Zeeman slowing in cold-atom experiments. The coil consists of stacked copper arcs separated by PTFE spacers of varying thicknesses, generating a near-optimal field profile using a single power supply. With an e

Abdullah Turer, Yongsheng Bai, Halil Sezen, Alper Yilmaz

Timely assessment of integrity of structures after seismic events is crucial for public safety and emergency response. This study focuses on assessing the structural damage conditions using deep learning methods to detect exposed steel reinforcement in concrete buildings and bridges after large earthquakes. Steel bars are typically exposed after concrete spa

Amit Kumar, Nisheeth K. Vishnoi

We study a two-institution stable matching model in which candidates from two distinct groups are evaluated using partially correlated signals that are group-biased. This extends prior work (which assumes institutions evaluate candidates in an identical manner) to a more realistic setting in which institutions rely on overlapping, but independently processed

Gejia Zhang, Robert Mieth

Distributed energy resources offer a control-based option to improve distribution system reliability by ensuring system states that positively impact component failure rates. This option is an attractive complement to otherwise costly and lengthy physical infrastructure upgrades. However, required models that adequately map operational decisions and environm

Subhajit Sarkar, Amos Sharoni, Oliver L. A. Monti, Yonatan Dubi

The chiral-induced spin selectivity (CISS) effect, whereby chiral molecules preferentially transmit electrons of one spin orientation, remains one of the most intriguing and debated phenomena at the interface of spintronics, molecular electronics, and quantum materials. Despite extensive experimental observations across diverse platforms - including transpor

Mahmud Wasif Nafee, Maiqi Jiang, Haipeng Chen, Yanfu Zhang

Large language models (LLMs) excel at factual recall yet still propagate stale or incorrect knowledge. In-context knowledge editing offers a gradient-free remedy suitable for black-box APIs, but current editors rely on static demonstration sets chosen by surface-level similarity, leading to two persistent obstacles: (i) a quantity-quality trade-off, and (ii)

Charlie Carlson, Yury Makarychev, Ron Mosenzon

We obtain hardness of approximation results for the $\ell_p$-Shortest Path problem, a variant of the classic Shortest Path problem with vector costs. For every integer $p \in [2,\infty)$, we show a hardness of $\Omega(p(\log n / \log^2\log n)^{1-1/p})$ for both polynomial- and quasi-polynomial-time approximation algorithms. This nearly matches the approximat