December 2025 arXiv papers — page 39

Zhi-Song Liu, Chenhang He, Roland Maier, Andreas Rupp

Recent advances in generative modeling have demonstrated strong promise for high-quality point cloud upsampling. In this work, we present PUFM++, an enhanced flow-matching framework for reconstructing dense and accurate point clouds from sparse, noisy, and partial observations. PUFM++ improves flow matching along three key axes: (i) geometric fidelity, (ii)

Ziyuan Zheng, Qingqing Wu, Yanze Zhu, Honghao Wang

This paper investigates a low-altitude integrated sensing and communication (ISAC) system that leverages cooperative rotatable active and passive arrays. We consider a downlink scenario where a base station (BS) with an active rotatable array serves multiple communication users and senses low-altitude targets, assisted by a rotatable reconfigurable intellige

Yihan Wang, Huanqi Yang, Shantanu Pal, Weitao Xu

The integration of Large Language Models (LLMs) into wearable sensing is creating a new class of mobile applications capable of nuanced human activity understanding. However, the reliability of these systems is critically undermined by their vulnerability to prompt injection attacks, where attackers deliberately input deceptive instructions into LLMs. Tradit

Wei Wu, Lingyi Wang, Fuhui Zhou, Zhaohui Yang

Artificial intelligence (AI)-native three-dimensional (3D) spectrum maps are crucial in spectrum monitoring for intelligent communication networks. However, it is challenging to obtain and transmit 3D spectrum maps in a spectrum-efficient, computation-efficient, and AI-driven manner, especially under complex communication environments and sparse sampling dat

Oleksii Proniakin, Diego Fajardo, Ruslan Nazarenko, Razvan Marinescu

Large language models (LLMs) are increasingly evaluated in clinical settings using multi-dimensional rubrics which quantify reasoning quality, safety, and patient-centeredness. Yet, replicating specific mistakes in other LLM models is not straightforward and often requires manual effort. We introduce MedMistake, an automatic pipeline that extracts mistakes L

Unhyeon Kang, Jaesang Lee, Seungmin Oh, Hanchan Song

Over the last decade, dendrites within individual biological neurons, which were previously thought to generally perform information pooling and networking, have now been shown to express complex temporal dynamics, Boolean-like logic, arithmetic, signal discrimination, and edge detection for image and sound recognition. Mimicking this rich functional density

Haotian Wu, Gen Li, Pier Luigi Dragotti, Deniz Gündüz

This paper introduces Implicit-JSCC, a novel overfitted joint source-channel coding paradigm that directly optimizes channel symbols and a lightweight neural decoder for each source. This instance-specific strategy eliminates the need for training datasets or pre-trained models, enabling a storage-free, modality-agnostic solution. As a low-complexity alterna

Xinquan Yang, Jinheng Xie, Yawen Huang, Yuexiang Li

Long-tailed pulmonary anomalies in chest radiography present formidable diagnostic challenges. Despite the recent strides in diffusion-based methods for enhancing the representation of tailed lesions, the paucity of rare lesion exemplars curtails the generative capabilities of these approaches, thereby leaving the diagnostic precision less than optimal. In t

Jaydeb Sarkar

This chapter surveys the advances of the past decade arising from the contributions of Indian mathematicians in the broad areas of operator algebras and operator theory. It brings together the work of twenty mathematicians and their collaborators, each writing from the perspective of their respective research fields and beyond. Several problems highlighted h

Ben-Rui Xu, Ao Xu, Heng-Dong Xi

We investigate buoyancy-induced transitions in flow structure and the associated velocity dip in turbulent mixed convection. Numerical simulations are performed for an open Poiseuille--Rayleigh--Bénard system with a heated no-slip lower wall and a cooled free-slip upper boundary over $10^5 \leq Ra \leq 10^8$, $90 \leq Re_b \leq 5700$, $Pr=0.71$, and $0.013 \

Zeqing Song, Zhongmiao Yan, Junyuan Deng, Songpengcheng Xia

Large-scale incremental mapping is fundamental to the development of robust and reliable autonomous systems, as it underpins incremental environmental understanding with sequential inputs for navigation and decision-making. LiDAR is widely used for this purpose due to its accuracy and robustness. Recently, neural LiDAR mapping has shown impressive performanc

Yujin Roh, Inho Jake Park, Chigon Hwang

CCTV-based vehicle tracking systems face structural limitations in continuously connecting the trajectories of the same vehicle across multiple camera environments. In particular, blind spots occur due to the intervals between CCTVs and limited Fields of View (FOV), which leads to object ID switching and trajectory loss, thereby reducing the reliability of r

Jingyang You, Hanna Kurniawati

Bayesian Reinforcement Learning (BRL), a subclass of Meta-Reinforcement Learning (Meta-RL), provides a principled framework for generalisation by explicitly incorporating Bayesian task parameters into transition and reward models. However, classical BRL methods assume known forms of transition and reward models. While recent deep BRL methods incorporate mode

Yihan Xia, Taotao Wang, Wenxin Xu, Shengli Zhang

Autonomous Large Language Model (LLM)-based multi-agent systems have emerged as a promising paradigm for facilitating cross-application and cross-organization collaborations. These autonomous agents often operate in trustless environments, where centralized coordination faces significant challenges, such as the inability to ensure transparent contribution me

Kai Fang, Tianbao Guo, Jinghao Huang, Fedor Sukochev

This is a systematic study of isometries between noncommutative symmetric spaces. Let $\mathcal{M}$ be a semifinite von Neumann algebra (or an atomic von Neumann algebra with all atoms having the same trace) acting on a separable Hilbert space $\mathcal{H}$ equipped with a semifinite faithful normal trace $\tau$. We show that for any noncommutative symmetric

Zengzhao Xu, Ligong Wang, Weige Xi

A factor of a graph is essentially a specific type spanning subgraph. The study of characterizing the existence of $[a, b]$-factors based on eigenvalue conditions can be traced back to the work of Brouwer and Haemers (2005) on perfect matchings. With the advancement of graphs factor theory, the related spectral extremal problems, particularly the study of $[

Fengyuan Xuan, Jiexi Song, Zhiyuan Sun

An ab initio approach is presented for studying the collective excitations in excitonic insulators, charge/spin density waves and superconductors. We derive the Bethe-Salpeter-Equation for the particle-hole excitations in the quasiparticle representation, from which the collective excited states are solved and the corresponding order parameter fluctuations a

Sirui Chen, Jingji Chen, Siqi Zhu, Ziheng Jiang

Distributed attention is essential for scaling large language models (LLMs) to long contexts, yet existing methods either have limited parallelism or incur high communication costs. Ulysses uses efficient all-to-all communication but cannot scale beyond the number of attention heads, whereas Ring-Attention removes this limit at the cost of high per-GPU commu

Linggao Kong, Yuedong Xu, Lei Jiao, Chuan Xu

As foundation models grow in size, fine-tuning them becomes increasingly expensive. While GPU spot instances offer a low-cost alternative to on-demand resources, their volatile prices and availability make deadline-aware scheduling particularly challenging. We tackle this difficulty by using a mix of spot and on-demand instances. Distinctively, we show the p

Timm Strecker, Michael Cantoni

A feedback control system is proposed for balancing the deviations of water levels from set-points along open channels subject to uncertain supply-demand mismatch that exceeds individual pool capacity. Decentralized controllers adjust the gate flows between pools to regulate potentially weighted differences between neighbouring water-level errors to zero in

Guo-Peng Li, Xi-Long Fan

GW241011 and GW241110 both exhibit extremely asymmetric masses, high primary spins, and significant spin-orbit misalignment, which challenge the formation of first-generation binary black hole mergers formed from stellar collapse. This implies that these two gravitational wave events might originate from the hierarchical merger mechanism, with at least one o

Ji Hyuk Jung, Ji Won Yoon

Autonomous Vehicles (AVs) refer to systems capable of perceiving their states and moving without human intervention. Among the factors required for autonomous decision-making in mobility, positional awareness of the vehicle itself is the most critical. Accordingly, extensive research has been conducted on defense mechanisms against GPS spoofing attacks, whic

Shaun Scovil, Bhargav Chickmagalur Nanjundappa

Fungible tokens with time-to-live (TTL) semantics require tracking individual expiration times for each deposited unit. A naive implementation creates a new balance record per deposit, leading to unbounded storage growth and vulnerability to denial-of-service attacks. We present time-bucketed balance records, a data structure that bounds storage to O(k) reco

Lalit Kumar Shukla

The accelerating digitization of economic activity has made information a dominant driver of market expectations, coordination, and systemic risk. Yet contemporary monetary systems remain anchored in architectures designed for material scarcity, institutional authority, or cryptographic constraint, leaving them increasingly misaligned with information-driven

Yifan Yao, Baojuan Wang, Jinhao Duan, Kaidi Xu

Chat-based cybercrime has emerged as a pervasive threat, with attackers leveraging real-time messaging platforms to conduct scams that rely on trust-building, deception, and psychological manipulation. Traditional defense mechanisms, which operate on static rules or shallow content filters, struggle to identify these conversational threats, especially when a

Mohammadreza Daneshvaramoli, Helia Karisani, Mohammad Hajiesmaili, Shahin Kamali

We initiate a formal study of fairness for the $k$-server problem, where the objective is not only to minimize the total movement cost, but also to distribute the cost equitably among servers. We first define a general notion of $(\alpha,\beta)$-fairness, where, for parameters $\alpha \ge 1$ and $\beta \ge 0$, no server incurs more than an $\alpha/k$-fractio

An Luo, Jin Du, Fangqiao Tian, Xun Xian

Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) have significantly automated data science workflows, but a fundamental question persists: Can these agentic AI systems truly match the performance of human data scientists who routinely leverage

R Yadunandan, Nimisha Ghosh

De novo drug design is a crucial component of modern drug development, yet navigating the vast chemical space to find synthetically accessible, high-affinity candidates remains a significant challenge. Reinforcement Learning (RL) enhances this process by enabling multi-objective optimization and exploration of novel chemical space - capabilities that traditi

Xianjin Yang, Matthieu Darcy, Matthew Hudes, Francis J. Alexander

We present an operator learning framework for solving non-perturbative functional renormalization group equations, which are integro-differential equations defined on functionals. Our proposed approach uses Gaussian process operator learning to construct a flexible functional representation formulated directly on function space, making it independent of a pa

M. V. Suslikov, A. I. Kolbin, N. V. Borisov

We performed an optical study of the magnetic period-bouncer candidate IL Leo. Long-term photometric analysis over $\approx 20$ years reveals multiple state transitions. Modelling the ultraviolet and optical spectral energy distribution refined the white dwarf parameters, yielding a mass of $M_\textrm{wd} = 0.74 \pm 0.05 M_{\odot}$ and an effective temperatu

Xiang Zhang, Jiaqi Wei, Yuejin Yang, Zijie Qiu

Chain-of-Thought (CoT) prompting has significantly advanced task-solving capabilities in natural language processing with large language models. Unlike standard prompting, CoT encourages the model to generate intermediate reasoning steps, non-answer tokens, that help guide the model toward more accurate final outputs. These intermediate steps enable more com

Yuxiao Wang, Yuedong Xu, Qingyang Duan, Yuxuan Liu

The rapid growth of large language models (LLMs) and the continuous release of new GPU products have significantly increased the demand for distributed training across heterogeneous GPU environments. In this paper, we present a comprehensive analysis of the challenges involved in implementing 3D parallelism in such environments, addressing critical issues su

Young Ju Ko

The impact of position resolution on the sensitivity of short-baseline reactor neutrino experiments searching for light sterile neutrinos is investigated. Detailed simulations are conducted to evaluate two detector configurations: a segmented detector and an opaque liquid scintillator (OLS) detector, each positioned at two candidate research reactor sites, H

Jiangen He, Wanqi Zhang, Jessica Barfield

As artificial agents increasingly integrate into professional environments, fundamental questions have emerged about how societal biases influence human-robot selection decisions. We conducted two comprehensive experiments (N = 1,038) examining how occupational contexts and stereotype activation shape robotic agent choices across construction, healthcare, ed

Mohammad Mahdi Abootorabi, Alireza Ghahramani Kure, Mohammadali Mohammadkhani, Sina Elahimanesh

This paper presents our system for SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval. In an era where misinformation spreads rapidly, effective fact-checking is increasingly critical. We introduce TriAligner, a novel approach that leverages a dual-encoder architecture with contrastive learning and incorporates both native and En

Huashen Lu, Wensheng Gan, Guoting Chen, Zhichao Huang

Graph neural networks (GNNs) have brought revolutionary advancements to the field of link prediction (LP), providing powerful tools for mining potential relationships in graphs. However, existing methods face challenges when dealing with large-scale sparse graphs and the need for a high degree of alignment between different datasets in transfer learning. Bes

Shize Liang, Hongzhi Wang

Large language models(LLMs) excel at text generation and knowledge question-answering tasks, but they are prone to generating hallucinated content, severely limiting their application in high-risk domains. Current hallucination detection methods based on uncertainty estimation and external knowledge retrieval suffer from the limitation that they still produc

Zhongren Dong, Haotian Guo, Weixiang Xu, Huan Zhao

Neuropsychiatric disorders, such as Alzheimer's disease (AD), depression, and autism spectrum disorder (ASD), are characterized by linguistic and acoustic abnormalities, offering potential biomarkers for early detection. Despite the promise of multi-modal approaches, challenges like multi-lingual generalization and the absence of a unified evaluation framewo

Shunyu Yao, Zhou Wang, Kangkang Zhao, Zhi Zheng

In order to solve the continuous large cooling power supply problem (20 kW) for the radon-removal cryogenic distillation system, which operates at high liquid ffow rate of 856 kg/h (5 LPM) for the dark matter detector PandaX-xT of the next-generation, a throttling refrigeration system based on carbon tetraffuoride (R14) refrigerant for cooling power recovery

Guanqiao Qu, Tao Li, Qian Chen, Xianhao Chen

To support on-device inference, the next-generation mobile networks are expected to support real-time model downloading services to mobile users. However, powerful AI models typically have large model sizes, resulting in excessive end-to-end (E2E) downloading-and-inference (DAI) latency. To address this issue, we propose a simultaneous model downloading and

Chih-Teng Ling, Tomotsugu Goto, Seong Jin Kim, Cossas K. -W. Wu

Mid-infrared (mid-IR) observations are crucial for understanding galaxy evolution, tracing star formation, and active galactic nuclei (AGN) activity via dust emission. This work presents mid-IR galaxy luminosity functions (LFs) at $0.5 < z < 6$, derived from the JWST Systematic Mid-infrared Instrument Legacy Extragalactic Survey (SMILES) program. We combine

Zhongren Dong, Bin Wang, Jing Han, Haotian Guo

Neural Speech Codecs face a fundamental trade-off at low bitrates: preserving acoustic fidelity often compromises semantic richness. To address this, we introduce SACodec, a novel codec built upon an asymmetric dual-quantizer that employs our proposed Semantic Anchoring mechanism. This design strategically decouples the quantization of Semantic and Acoustic

Zhe Wang, Jinghang Li, Yifei Zhu

Free-viewpoint video (FVV) enables immersive viewing experiences by allowing users to view scenes from arbitrary perspectives. As a prominent reconstruction technique for FVV generation, 4D Gaussian Splatting (4DGS) models dynamic scenes with time-varying 3D Gaussian ellipsoids and achieves high-quality rendering via fast rasterization. However, existing 4DG

Sidharth Santhi Nivas, Prem Sagar Pattanshetty Vasanth Kumar, Zhaoxi Zhang, Chenzhi Zhao

Accurate timing and synchronization, typically enabled by GPS, are essential for modern wireless communication systems. However, many emerging applications must operate in GPS-denied environments where signals are unreliable or disrupted, resulting in oscillator drift and carrier frequency impairments. To address these challenges, we present BenchLink, a Sys

Yiren Shen, Juan J. Alonso

Data-driven surrogate models are increasingly adopted to accelerate vehicle design. However, open-source multi-fidelity datasets and empirical guidelines linking dataset size to model performance remain limited. This study investigates the relationship between training data size and prediction accuracy for a graph neural network (GNN) based surrogate model f

Shuhao Ye, Sitong Mao, Yuxiang Cui, Xuan Yu

Vision-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to navigate towards target in continuous environments, following natural language instructions. While current graph-based methods offer an efficient, structured approach by abstracting the environment into a topological map and simplifying the action space to waypoint s

Ilsun Chang

Modern OLAP systems have mitigated I/O bottlenecks via storage-compute separation and columnar layouts, but CPU costs in the execution layer (especially Top-K selection and join probe) are emerging as new bottlenecks at scale. This paper proposes a hybrid architecture that augments existing vectorized execution by selectively offloading only high-impact prim

James Aspnes

Extending well-structured transition systems to incorporate a probabilistic scheduling rule, we define a new class of stochastic well-structured transition systems that includes population protocols, chemical reaction networks, and many common gossip models; as well as augmentations of these systems by an oracle that exposes a total order on agents as in pop

Jing Han, Zhiqiang Gao, Shihao Gao, Jialing Liu

Recent advances in multimodal large language models (MLLMs) have demonstrated remarkable multi- and cross-modal integration capabilities. However, their potential for fine-grained emotion understanding remains systematically underexplored. While open-vocabulary multimodal emotion recognition (MER-OV) has emerged as a promising direction to overcome the limit

Pascal Passigan, Kevin Zhu, Angelina Ning

Understanding how small molecules perturb gene expression is essential for uncovering drug mechanisms, predicting off-target effects, and identifying repurposing opportunities. While prior deep learning frameworks have integrated multimodal embeddings into biomedical knowledge graphs (BKGs) and further improved these representations through graph neural netw

Ruiqi Liu, Yi Han, Zhengbo Zhang, Liwei Yao

The rapid progress of generative models has intensified the need for reliable and robust detection under real-world conditions. However, existing detectors often overfit to generator-specific artifacts and remain highly sensitive to real-world degradations. As generative architectures evolve and images undergo multi-round cross-platform sharing and post-proc

Hongxing Fan, Shuyu Zhao, Jiayang Ao, Lu Sheng

Amodal completion, the task of inferring invisible object parts, faces significant challenges in maintaining semantic consistency and structural integrity. Prior progressive approaches are inherently limited by inference instability and error accumulation. To tackle these limitations, we present a Collaborative Multi-Agent Reasoning Framework that explicitly

Hieu Do Quang, Chien Truong-Quoc, Quoc Van Tran

This paper introduces a diffusion-based planner for leader--follower formation control in cluttered environments. The diffusion policy is used to generate the trajectory of the midpoint of two leaders as a rigid bar in the plane, thereby defining their desired motion paths in a planar formation. While the followers track the leaders and form desired foramtio

K. C. Oliver-Mallory, A. M. Baker, E. Jacquet, T. J. Sumner

We present spectroscopic measurements of xenon luminescence in a time projection chamber operated in a dual-phase (liquid-gas) configuration. Thorium-228 $\alpha$ decays excited the liquid, resulting in the formation of singlet and triplet excimers that emit vacuum ultraviolet (VUV) scintillation. Ionisation electrons were drifted to the liquid surface and e

Shengguang Wu, Xiaohan Wang, Yuhui Zhang, Hao Zhu

Spatial reasoning in 3D scenes requires precise geometric calculations that challenge vision-language models. Visual programming addresses this by decomposing problems into steps calling specialized tools, yet existing methods rely on either fixed toolsets or speculative tool induction before solving problems, resulting in suboptimal programs and poor utiliz

Juan Sebastian Hernandez, Cesar Nieto, Juan Manuel Pedraza, Abhyudai Singh

Multiple cellular processes are triggered when the concentration of a regulatory protein reaches a critical threshold. Previous analyses have characterized timing statistics for single-gene systems. However, many biological timers are based on cascades of genes that activate each other sequentially. Here, we develop an analytical framework to describe the ti

Boyu Li, Mansi Suryawanshi

We establish an Ando-type dilation theorem for a pair of commuting contractions together with a representation of a right LCM monoid via either the Cartesian or the free product. We prove that if each individual contraction together with the monoid representation has $*$-regular dilation, then they can be dilated to commuting isometries and an isometric repr

Sean C. Borneman, Julia Krebs, Ronnie B. Wilbur, Evie A. Malaia

Human language processing relies on the brain's capacity for predictive inference. We present a machine learning framework for decoding neural (EEG) responses to dynamic visual language stimuli in Deaf signers. Using coherence between neural signals and optical flow-derived motion features, we construct spatiotemporal representations of predictive neural dyn

Yoonwoo Jeong, Cheng Sun, Frank Wang, Minsu Cho

Recent advancements in computer vision have successfully extended Open-vocabulary segmentation (OVS) to the 3D domain by leveraging 3D Gaussian Splatting (3D-GS). Despite this progress, efficiently rendering the high-dimensional features required for open-vocabulary queries poses a significant challenge. Existing methods employ codebooks or feature compressi

Prakash Chourasia, Sarwan Ali, Murray Patterson

The rapid advancement of large language models (LLMs) has enabled significant strides in various fields. This paper introduces a novel approach to evaluate the effectiveness of LLM embeddings in the context of inherent geometric properties. We investigate the structural properties of these embeddings through three complementary metrics $\delta$-hyperbolicity

Hanyang Yuan, Ning Tang, Tongya Zheng, Jiarong Xu

Diversified recommendation has attracted increasing attention from both researchers and practitioners, which can effectively address the homogeneity of recommended items. Existing approaches predominantly aim to infer the diversity of user preferences from observed user feedback. Nonetheless, due to inherent data biases, the observed data may not fully refle

Yiqi Hu, Jun-Jie Zhang, Zhou Zhou, Shun Wang

The layered van der Waals (vdW) ferroelectric CuInP2S6 (CIPS) exhibits unique cation hopping-driven phenomena that bring about unconventional properties with intriguing mechanisms and hold promises for advanced applications in nanoelectronics. However, an explicit analysis of its lattice dynamics and vibrational symmetries, pivotal for understanding the mate

Bo Yang, Yinfen Xia, Weisong Sun, Yang Liu

In Text-to-SQL generation, large language models (LLMs) have shown strong generalization and adaptability. However, LLMs sometimes generate hallucinations, i.e.,unrealistic or illogical content, which leads to incorrect SQL queries and negatively impacts downstream applications. Detecting these hallucinations is particularly challenging. Existing Text-to-SQL

Andrew D. Blevins, Ian K. Quigley

Can machine learning models identify which chemist made a molecule from structure alone? If so, models trained on literature data may exploit chemist intent rather than learning causal structure-activity relationships. We test this by linking CHEMBL assays to publication authors and training a 1,815-class classifier to predict authors from molecular fingerpr

Carli Mankowski, Jamie Tayar, Cassidy Martin

Star clusters have long been central to the study of stellar evolution due to their chemically and chronologically homogeneous populations. Asteroseismology, the analysis of stellar oscillations and pulsations, provides precise information about properties such as masses, radii, and ages of stars in the field. However, these stars lack calibration to an abso

Jiarui Sun, Kaiyuan Liu, Xiao-Hua Zhou

Free-response observer performance studies are of great importance for accuracy evaluation and comparison in tasks related to the detection and localization of multiple targets or signals. The free-response receiver operating characteristic (FROC) curve and many similar curves based on the free-response observer performance assessment data are important tool

Tianlong Wang, Tianyu Zhao, Minghui Du, Ziren Luo

The space-based gravitational wave (GW) detectors are expected to observe lensed GW events, offering new opportunities for cosmology and fundamental physics.Across the millihertz band, lensing effects transition from the wave-optics regime at lower frequencies to the geometric-optics approximation at higher frequencies.Although traditional GW identification

Yingying Wang, Rongjin Zhuang, Hui Zheng, Xuanhua He

Image fusion integrates complementary information from different modalities to generate high-quality fused images, thereby enhancing downstream tasks such as object detection and semantic segmentation. Unlike task-specific techniques that primarily focus on consolidating inter-modal information, general image fusion needs to address a wide range of tasks whi

Ningyuan Liu, Jing Yang, Kaitong Cai, Keze Wang

Full parameter fine tuning is a key technique for adapting large language models (LLMs) to downstream tasks, but it incurs substantial memory overhead due to the need to cache extensive intermediate activations for backpropagation. This bottleneck makes full fine tuning of contemporary large scale LLMs challenging in practice. Existing distributed training f

Yaoting Zhou, Weisen Wang, Zhuangzhuang Tian, Bin Huang

Quantum state readout with minimal resources is crucial for scalable quantum information processing. As a leading platform, neutral atom arrays rely on atomic fluorescence imaging for qubit readout, requiring short exposure, low photon count schemes to mitigate heating and atom loss while enabling mid-circuit feedback. However, a fundamental challenge arises

Costas Lambros, Emerson Melo

This paper proposes a framewrok for analyzing how the welfare effects of policy interventions are distributed across individuals when those effects are unobserved. Rather than focusing solely on average outcomes, the approach uses readily available information on average welfare responses to uncover meaningful patterns in how gains and losses are distributed

Rui Meng, Zixuan Huang, Jingshu Yan, Mengying Sun

Radio Access Network (RAN) is a bridge between user devices and the core network in mobile communication systems, responsible for the transmission and reception of wireless signals and air interface management. In recent years, Semantic Communication (SemCom) has represented a transformative communication paradigm that prioritizes meaning-level transmission

Haoyu Wang, Yitong Wang, Jining Wang

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated significant potential in recommendation systems. However, the effective application of MLLMs to multimodal sequential recommendation remains unexplored: A) Existing methods primarily leverage the multimodal semantic understanding capabilities of pre-trained MLLMs to generate it

Bharat Sharman, Elkafi Hassini

This study introduces GCO-HPIF, a general machine-learning-based framework to predict and explain the computational hardness of combinatorial optimization problems that can be represented on graphs. The framework consists of two stages. In the first stage, a dataset is created comprising problem-agnostic graph features and hardness classifications of problem

Ian Bounos, Pablo Groisman, Mariela Sued, Esteban Tabak

A methodology is developed to extract $d$ invariant features $W=f(X)$ that predict a response variable $Y$ without being confounded by variables $Z$ that may influence both $X$ and $Y$. The methodology's main ingredient is the penalization of any statistical dependence between $W$ and $Z$ conditioned on $Y$, replaced by the more readily implementable plain i

Denys Derlian Carvalho Brito, Fernando Valadares, André Jorge Carvalho Chaves

As superconducting circuits emerge as a leading platform for scalable quantum information processing, building comprehensive bridges from the foundational principles of macroscopic quantum phenomena to the architecture of modern quantum devices is increasingly essential for introducing new researchers to the field. This tutorial provides a self-contained, pe

Sahinur Reja, Satoshi Nishimoto

We investigate the ground-state phase diagram of the spin-1 Kitaev--Heisenberg chain in the presence of uniaxial single-ion anisotropy (SIA) $D_z$ by density-matrix renormalization group (DMRG) calculations. By combining energy-curvature diagnostics on periodic $N=24$ clusters with a refined characterization based on order parameters and correlation function

E. Salahinejad, M. Jafari Baghjeghaz

In this research, a novel group of Ca-Mg oxyfluorosilicates containing different levels of fluoride substituting for oxide was synthesized by an inorganic salt coprecipitation process followed by calcination/sintering. The effects of the incorporation of fluoride on the resultant structural characteristics, apatite-forming ability and biodegradability were e

Jing Guo, Xiushan Jiang, Weihai Zhang

This paper investigates a model-free solution to the stochastic linear quadratic regulation (LQR) problem for linear discrete-time systems with both multiplicative and additive noises. We formulate the stochastic LQR problem as a nonconvex optimization problem and rigorously analyze its dual problem structure. By exploiting the inherent convexity of the dual

Ali Zeytoon-Nejad, Barry Goodwin

The conventional functional form of the Constant-Elasticity-of-Substitution (CES) production function is a general production function nesting a number of other forms of production functions. Examples of such functions include Leontief, Cobb-Douglas, and linear production functions. Nevertheless, the conventional form of the CES production specification is s

Ali Zeytoon-Nejad

Purpose: This paper explores gender differences in two distinct forms of risk aversion -- Payoff Risk Aversion (PaRA) and Price Risk Aversion (PrRA) -- in order to provide a more nuanced understanding of how men and women respond to different types of economic uncertainty. Design/methodology/approach: The study employs a laboratory experiment using Multiple-

Kaiyuan Liu, Shaotian Yan, Rui Miao, Bing Wang

Reasoning distillation has attracted increasing attention. It typically leverages a large teacher model to generate reasoning paths, which are then used to fine-tune a student model so that it mimics the teacher's behavior in training contexts. However, previous approaches have lacked a detailed analysis of the origins of the distilled model's capabilities.

Xinglong Ye, Zhibo Zhao, Qian Wang, Jiangnan Li

Emergent behavior, which arises from local interactions between simple elements, is pervasive in nature. It underlies the energy-efficient computing in our brains. However, realizing such dynamics in artificial materials, particularly under low-energy stimuli, remains a fundamental challenge. While dipole-dipole interactions are typically suppressed in magne

Zheng Xing, Weibing Zhao

Unsupervised human motion segmentation (HMS) can be effectively achieved using subspace clustering techniques. However, traditional methods overlook the role of temporal semantic exploration in HMS. This paper explores the use of temporal vision semantics (TVS) derived from human motion sequences, leveraging the image-to-text capabilities of a large language

Haidong Hu, Xiaoyu Zheng, Jin Zhou, Yingxu Wang

Deep clustering methods typically rely on a single, well-defined representation for clustering. In contrast, pretrained diffusion models provide abundant and diverse multi-scale representations across network layers and noise timesteps. However, a key challenge is how to efficiently identify the most clustering-friendly representation in the layer*timestep s

Wei Du, Qing Fang, Ligang Liu, Xiao-Ming Fu

We propose an efficient method to compute a small set of integer-constrained cone singularities, which induce a rotationally seamless conformal parameterization with low distortion. Since the problem only involves discrete variables, i.e., vertex-constrained positions, integer-constrained angles, and the number of cones, we alternately optimize these three t

Siqi Mu, Shuo Wen, Yang Lu, Ruihong Jiang

Due to their inherent flexibility and autonomous operation, unmanned aerial vehicles (UAVs) have been widely used in Internet of Medical Things (IoMT) to provide real-time biomedical edge computing service for wireless body area network (WBAN) users. In this paper, considering the time-varying task criticality characteristics of diverse WBAN users and the du

Yidong Chai, Yanguang Liu, Xuan Tian, Jiaheng Xie

We study investor learning and information acquisition in private markets using a large dataset of expert network calls. We develop a sequential Large Language Model (LLM)-Bayesian framework that treats expert interactions as sequential signals and recovers time-varying beliefs about firm success and associated uncertainty from unstructured conversations, pr

Maria Pia Gualdani, Weiran Sun

We prove the uniqueness of $H$-solutions to the homogeneous Landau-Coulomb equation satisfying $\langle v \rangle^{k_0} f \in C([0, T]; L^{3/2}(\mathbb{R}^3))$ and $\langle v \rangle^{-3/2} \nabla_v ((\langle v \rangle^{k_0} f)^{3/4}) \in L^2((0, T) \times \mathbb{R}^3)$ for any $k_0 \geq 5$. In particular, this shows that the solutions constructed in~\cite{

Xiao Yu, Zhaojie Fang, Guanyu Zhou, Yin Shen

Lung cancer continues to be the leading cause of cancer-related deaths globally. Early detection and diagnosis of pulmonary nodules are essential for improving patient survival rates. Although previous research has integrated multimodal and multi-temporal information, outperforming single modality and single time point, the fusion methods are limited to inef

Matteo Cerutti, Jian-Ping Chen, Umberto D'Alesio, Haiyan Gao

In this paper we present a detailed impact study of semi-inclusive deep inelastic scattering unpolarized cross sections' measurements using the proposed SoLID apparatus at Jefferson Lab. This type of data, collected at large Bjorken $x_{bj}$, moderate values of $Q^2$ and small values of the transverse momentum of produced hadrons, $P_{hT}$, allows to study t

Kazuma Onishi, Katsuhiko Hayashi, Hidetaka Kamigaito

In real-world recommender systems, user-item interactions are Missing Not At Random (MNAR), as interactions with popular items are more frequently observed than those with less popular ones. Missing observations shift recommendations toward frequently interacted items, which reduces the diversity of the recommendation list. To alleviate this problem, Inverse

Xin Feng, Wei Wu

This paper presents a rigorous mathematical analysis for symmetry-based Bound States in the Continuum (BICs) in optical waveguide arrays. Different from existing research, we consider a finite system of horizontally and equidistantly aligned waveguides and transform the wave propagation problem into Nonorthogonal Coupled-Mode Equations (NCME), rather than ad

Jinpeng Huang, Yang Xiang, Lunbing Chen, Suyang Qin

Stationary take-off, without a running start or elevated descent, requires substantial aerodynamic forces to overcome weight, particularly for large birds such as geese exceeding 2 kg. However, the complex wing motion and high-Reynolds-number (Re $\approx$$10^5$) flow dynamics challenge conventional expectations of avian flight aerodynamics, rendering this m

Runqi Lin

With deep neural networks (DNNs) increasingly embedded in modern society, ensuring their safety has become a critical and urgent issue. In response, substantial efforts have been dedicated to the red-blue adversarial framework, where the red team focuses on identifying vulnerabilities in DNNs and the blue team on mitigating them. However, existing approaches

Tingfeng Xian, Wenlve Zhou, Zhiheng Zhou, Zhelin Li

Cross-Modality Ship Re-Identification (CMS Re-ID) is critical for achieving all-day and all-weather maritime target tracking, yet it is fundamentally challenged by significant modality discrepancies. Mainstream solutions typically rely on explicit modality alignment strategies; however, this paradigm heavily depends on constructing large-scale paired dataset

Hiroshi Okada, Yutaro Shoji

We propose a novel realization of linear seesaw model in a non-invertible selection rule with the assistance of $\mathbb Z_3$ symmetry. In our framework, Dirac mass matrices are generated at one-loop level, dynamically breaking the non-invertible symmetry while the symmetry is invariant under the tree-level. In addition to the active neutrino masses, the mod

Masaki J. S. Yang

In this letter, we present an explicit rephasing transformation that maps a general $4\times4$ flavor mixing matrix to the standard parametrization of four-generation models. By combining rephasing-covariant minors and the determinant systematically, we derive expressions for all three Dirac-type CP phases, three Majorana phases, and four unphysical phases b

Yiding Nie, Dongliang Fan, Jiatai Huang, Chunyu Liu

Stretchable sensors indicate promising prospects for soft robotics, medical devices, and human-machine interactions due to the high compliance of soft materials. Discrete sensing strategies, including sensor arrays and distributed sensors, are broadly involved in tactile sensors across versatile applications. However, it remains a challenge to achieve high s

Ryan Ripsman, Brayden Kell, Andreas Hilfinger

Many cellular components are present in such low numbers that individual stochastic production and degradation events lead to significant fluctuations in molecular abundances. Although feedback control can, in principle, suppress such low-copy-number fluctuations, general rules have emerged that suggest fundamental performance constraints on feedback control