November 2025 arXiv papers — page 87

Mingyue Huo, Wei-Cheng Tseng, Yiwen Shao, Hao Zhang

Human voice encodes both identity and paralinguistic cues, yet encoders in large audio-language models (LALMs) rarely balance both aspects. In this work, we present a study toward building a general-purpose voice encoder that captures nuanced voice cues. Through a comprehensive evaluation, we find that multi-task training yields the most balanced representat

Snigdh Sabharwal, Matthias Gohlke, Paul Skrzypczyk, Nic Shannon

Quantum spin liquids (QSL) are phases of matter which are distinguished not by the symmetries they break, but rather by the patterns of entanglement within them. Although these entanglement properties have been widely discussed for ground states, the way in which QSL form at finite temperature remains an open question. Here we introduce a method of character

R. Ariniello, V. Lee, D. Storey, C. Emma

A collider based on beam-driven plasma wakefield acceleration will require the drive beam to transfer 10-100s of Joules to the plasma in each stage, with a drive-to-wake energy transfer efficiency exceeding 70%. Using an all-optical plasma source, we demonstrate significant progress towards these parameters, transferring at least 5.6 J from a 1.52 nC, 10 GeV

Manal Rahal, Bestoun S. Ahmed, Christoph A. Bauer, Johan Ulander

Antisense oligonucleotides (ASOs) are nucleic acid molecules with transformative therapeutic potential, especially for diseases that are untreatable by traditional drugs. However, the production and purification of ASOs remain challenging due to the presence of unwanted impurities. One tool successfully used to separate an ASO compound from the impurities is

Vincent Cohen-Addad, Tommaso d'Orsi, Anupam Gupta, Guru Guruganesh

In linear combinatorial optimization, we aim to find $S^* = \arg\min_{S \in \mathcal{F}} \langle w,\mathbf{1}_S \rangle$ for a family $\mathcal{F} \subseteq 2^U$ over a ground set $U$ of $n$ elements. Traditionally, $w$ is known or accessible via a value oracle. Motivated by practical applications involving pairwise preferences, we study the weaker and more

Sunwoo Kim, Geon Lee, Kyungho Kim, Jaemin Yoo

Recently, large language models (LLMs) have been widely used as recommender systems, owing to their reasoning capability and effectiveness in handling cold-start items. A common approach prompts an LLM with a target user's purchase history to recommend items from a candidate set, often enhanced with retrieval-augmented generation (RAG). Most existing RAG app

Amit Kumar, Maninder Kaur, Raghvendra Mall, Sukrit Gupta

Spatial Transcriptomics enables mapping of gene expression within its native tissue context, but current platforms measure only a limited set of genes due to experimental constraints and excessive costs. To overcome this, computational models integrate Single-Cell RNA Sequencing data with Spatial Transcriptomics to predict unmeasured genes. We propose CASPER

Hyo-Jeong Jang, Hye-Bin Shin, Kang Yin

Deep learning models perform best with abundant, high-quality labels, yet such conditions are rarely achievable in EEG-based emotion recognition. Electroencephalogram (EEG) signals are easily corrupted by artifacts and individual variability, while emotional labels often stem from subjective and inconsistent reports-making robust affective decoding particula

Bestoun S. Ahmed, Tommaso Azzalin, Andreas Kassler, Andreas Thore

We explore a Digital Twin-Based Approach for Smart Manufacturing to improve Sustainability, Efficiency, and Cost-Effectiveness for a steel production plant. Our system is based on a micro-service edge-compute platform that ingests real-time sensor data from the process into a digital twin over a converged network infrastructure. We implement agile machine le

Xiaoxuan Wang, Bo Liu, Song Jiang, Jingzhou Liu

The reasoning capabilities of large language models (LLMs) have been significantly improved through reinforcement learning (RL). Nevertheless, LLMs still struggle to consistently verify their own reasoning traces. This raises the research question of how to enhance the self-verification ability of LLMs and whether such an ability can further improve reasonin

Andrew Amos, Joanne Lee, Tarun Sen Gupta, Bunmi S. Malau-Aduli

Past efforts to map the Medline database have been limited to small subsets of the available data because of the exponentially increasing memory and processing demands of existing algorithms. We designed a novel algorithm for sparse matrix multiplication that allowed us to apply a self-organizing map to the entire Medline dataset, allowing for a more complet

Terence Tao

The dimension of Kakeya sets can be bounded using sum-difference exponents $\SD(R;s)$ for various sets of rational slopes $R$ and output slope $s$; the arithmetic Kakeya conjecture, which implies the Kakeya conjecture in all dimensions, asserts that the infimum of such exponents is $1$. The best upper bound on this infimum currently is $1.67513\dots$. In thi

Wenhao Dong, Andrew Melatos, Nicholas J. O'Neill, Patrick M. Meyers

Crust-superfluid coupling plays an important role in neutron star rotation, particularly with respect to timing noise and glitches. Here, we present new timing-noise-based estimates of the crust-superfluid coupling time-scale \(\tau\) for 105 radio pulsars in the UTMOST dataset, by Kalman filtering the pulse times of arrival. The 105 objects are selected bec

Seon-Hong Kim, Kenneth B. Stolarsky

We present a generalization of the classical Nicomachus' identity for the sum of the first $n$ cubes. Unlike previous generalizations, it has three rather than two terms, and involves not just one, but two distinct triangular numbers, and each term is of degree $4$ in $\lfloor n/2 \rfloor$. The asymptotic behavior for large $n$ leads to continued fractions w

Nishchala Thakur, Swati Kochhar, Deepti R. Bathula, Sukrit Gupta

Active learning reduces annotation costs in medical imaging by strategically selecting the most informative samples for labeling. However, individual acquisition strategies often exhibit inconsistent behavior across different stages of the active learning cycle. We propose Cyclical and Performance-Adaptive Multi-Strategy Active Learning (WaveFuse-AL), a nove

Hokuto Munakata, Takehiro Imamura, Taichi Nishimura, Tatsuya Komatsu

We introduce CASTELLA, a human-annotated audio benchmark for the task of audio moment retrieval (AMR). Although AMR has various useful potential applications, there is still no established benchmark with real-world data. The initial study of AMR trained the models solely on synthetic datasets. Moreover, the evaluation is based on an annotated dataset of fewe

Hanzhi Yan, Qin Lu, Xianqiao Wang, Xiaoming Zhai

While large language models (LLMs) have demonstrated remarkable versatility across a wide range of general tasks, their effectiveness often diminishes in domain-specific applications due to inherent knowledge gaps. Moreover, their performance typically declines when addressing complex problems that require multi-step reasoning and analysis. In response to th

Bingjun Wei, Xuemei Cao, Jiafen Liu, Haoyang Liang

Traditional Federated Multi-View Clustering assumes uniform views across clients, yet practical deployments reveal heterogeneous view completeness with prevalent incomplete, redundant, or corrupted data. While recent approaches model view heterogeneity, they neglect semantic conflicts from dynamic view combinations, failing to address dual uncertainties: vie

Shi-Qiang Zhou, Li-Rong Xie, Dong Wang, Cheng Lian

This paper presents a novel pixel chip readout scheme: the Region-of-Interest Readout Circuit (ROIRC), which is designed for large area, large array pixel chips and Gas Pixel Detector (GPD). This design employs a sentinel pixel detection strategy, enabling rapid identification and prioritized readout of the pixel regions containing signal events. During the

Kimikazu Taniguchi

The norm operator method, which was recently proposed as a new formulation of the boson expansion theory (BET), is used to show that the subspace mapping is properly derived from the whole space mapping. This derivation requires the appropriate renormalization of the contribution of phonons that are not adopted as boson excitations in the subspace mapping. T

Zhiqiang Wang

Let $(\mu, \Lambda)$ be the canonical spectral pair generated by a Hadamard triple $(N,B,L)$ in $\mathbb{R}$ with $0\in B \cap L$, which means that the family $\big\{ e_\lambda(x)=e^{2\pi \mathrm{i} \lambda x}: \lambda \in \Lambda \big\}$ forms an orthonormal basis in $L^2(\mu)$.We prove that if $\#B < N^{0.677}$, then there are infinitely many primes $p$ su

Zhi-zhong Xing

We derive general formulas for three flavor fractions $(\eta^{}_e , \eta^{}_\mu , \eta^{}_\tau)$ of the high-energy neutrinos originating from a remote astrophysical source by using their flavor ratios $(f^{}_e , f^{}_\mu , f^{}_\tau)$ observed at a neutrino telescope, and diagnose a potential divergence associated with $\eta^{}_\mu$ and $\eta^{}_\tau$ as an

Xiaohuan Long, Yibin Wang, Xiangdong Wu, Ru Yi

Let $K$ be a complete non-archimedean field over $\mathbb{Q}_p$, $G$ be a rigid group over $K$, and $X$ be a perfectoid space over $K$. We consider the natural morphism of sites $\nu: X_v \to X_{\mathrm{\acute{e}t}}$. It is known from work of Heuer that the direct image functor $\nu_*$ induces an equivalence of the categories of $G$-torsors. In this article,

Huifan Zhang, Pingqiang Zhou

Conventional radio frequency (RF) passive components modeling based on machine learning requires extensive electromagnetic (EM) simulations to cover geometric and frequency design spaces, creating computational bottlenecks. In this paper, we introduce an uncertainty-aware Bayesian online learning framework for efficient parametric modeling of RF passive comp

Paul Goldsmith-Pinkham, Tianshu Lyu

Financial event studies, ubiquitous in finance research, typically use linear factor models with known factors to estimate abnormal returns and identify causal effects of information events. This paper demonstrates that when factor models are misspecified -- an almost certain reality -- traditional event study estimators produce inconsistent estimates of tre

Fuwei Zhang, Xiaoyu Liu, Dongbo Xi, Jishen Yin

Generative Recommendation (GR) has emerged as a new paradigm in recommender systems. This approach relies on quantized representations to discretize item features, modeling users' historical interactions as sequences of discrete tokens. Based on these tokenized sequences, GR predicts the next item by employing next-token prediction methods. The challenges of

Shahed Shayan Arani

The Gaia space telescope has transformed our understanding of random stellar encounters with the Solar System. This study examines how such perturbations influence the most eccentric comets in the Oort Cloud (OC), a thermalized reservoir of $\sim 10^{12}$ icy bodies extending from $10^{4}$ to $10^{5}$ AU. Recent Gaia-based analyses indicate about 20 stellar

Bohan Zhang, Zihao Wang, Hengyu Fu, Jason D. Lee

In deep learning, a central issue is to understand how neural networks efficiently learn high-dimensional features. To this end, we explore the gradient descent learning of a general Gaussian Multi-index model $f(\boldsymbol{x})=g(\boldsymbol{U}\boldsymbol{x})$ with hidden subspace $\boldsymbol{U}\in \mathbb{R}^{r\times d}$, which is the canonical setup to s

Velimir Todorovski, Kwang Hak Kim, Alessandro Astolfi, Miroslav Krstic

It has been known in the robotics literature since about 1995 that, in polar coordinates, the nonholonomic unicycle is asymptotically stabilizable by smooth feedback, even globally. We introduce a modular design framework that selects the forward velocity to decouple the radial coordinate, allowing the steering subsystem to be stabilized independently. Withi

Jin Wang, Bingfeng Zhang, Jian Pang, Weifeng Liu

Few-shot segmentation has garnered significant attention. Many recent approaches attempt to introduce the Segment Anything Model (SAM) to handle this task. With the strong generalization ability and rich object-specific extraction ability of the SAM model, such a solution shows great potential in few-shot segmentation. However, the decoding process of SAM hi

Jun-Yi Liu, Chung-Hao Chen, Ya-Chi Tsao, Ssu-Yao Wu

In the study, the physical state and mental state of elders are both considered, and an event-triggered system has developed to detect events: watch dog, danger notice and photo link. By adopting GMM background modeling, the motion behavior of visitors and elders can be detected in the watch dog event and danger notice event respectively. Experiments set in

Chung-Yang Wang, Zeming Sun, Thomas Oseroff, Matthias U. Liepe

We use a near-field magnetic microwave microscope to investigate and compare rf vortex nucleation in two superconducting radio-frequency (SRF)-quality Nb3Sn films fabricated by different methods: a conventional vapor-diffused film and an electrochemically plated film followed by thermal annealing, both of which are deposited on Nb substrates. The microscope

Vasiliki Fragkou, Quentin A. Parker, Denise R. Gonçalves

Planetary nebulae (PNe) studies are essential for understanding late stellar evolution of low-to-intermediate mass stars. PNe in open clusters (OC) are rare but valuable since their study directly links their properties to those of their progenitors, something that cannot be achieved for field PNe. Here, we report the identification of one more OC-PN associa

Yin-Fa Shen, Alfredo Gurrola

Broad resonances challenge the standard Monte-Carlo treatment of unstable particles, which introduces a Breit-Wigner width into leading-order matrix elements and can generate unphysical gauge artifacts. We develop a gauge-consistent framework that combines a Dyson-resummed propagator with Slavnov-Taylor-identity-implied resummed vertices, enabling a consiste

Andrés Rubiano

In this paper, we investigate the differential smoothness of graded skew Clifford algebras.

Wei-hsiang Yen, Lyn Chao-ling Chen

The innovation of the study is that the deep learning method and sentiment analysis are integrated in traditional business model analysis and forecasting, and the research subject is TSMC for industry trend prediction of semiconductor industry in Taiwan. For the rapid market changes and development of wafer technologies of semiconductor industry, traditional

Michael Fodroci, Teruyuki Kitabayashi

As measurements of neutrino mixing angles continue to become more precise, it is increasingly likely that in the very near future a realistic neutrino mixing model will be required to precisely reproduce their best-fit values. In this study, a modified TM$_2$ mixing model which reproduces the best-fit values of all three neutrino mixing angles is proposed. T

Ting-An Lin, Pei-Lin Tsai, Yi-An Chen, Feng-Yu Chen

In the study, the device of social robot was designed for visually impaired users, and along with a mobile application for provide functions to assist their lives. Both physical and mental conditions of visually impaired users are considered, and the mobile application provides functions: photo record, mood lift, greeting guest and today highlight. The appli

Devin Chugh, Bhagath Sreenarayanan, Steven Suwito, Ganesh Raghavendran

Machine Learning (ML) and Deep Learning (DL) based framework have evolved rapidly and generated considerable interests for predicting the properties of materials. In this work, we utilize ML-DL framework to predict the electrochemical lithiation state and associated electrical conductivity of spinel Li4Ti5O12 (LTO) thin films using Raman spectroscopy data. R

Liujia Yao, Changsheng You, Chao Zhou, Beixiong Zheng

Movable antenna (MA) is a promising technology for improving the performance of wireless communication systems by providing new degrees-of-freedom (DoFs) in antenna position optimization. However, existing works on MA systems have mostly considered element-wise single-layer MA (SL-MA) arrays, where all the MAs move within the given movable region, hence inev

Yuan Jiang, Zehao Li, Shan Huang, Christoph Treude

Membership inference attacks (MIAs) on code completion models offer an effective way to assess privacy risks by inferring whether a given code snippet was part of the training data. Existing black- and gray-box MIAs rely on expensive surrogate models or manually crafted heuristic rules, which limit their ability to capture the nuanced memorization patterns e

Angshu Adhya, Cindy Yang, Emily Wu, Rishad Hasan

In this work we present AURA, a framework for synergistic human-artist painting. We developed a robot arm that collaboratively paints with a human artist. The robot has an awareness of the artist's heartbeat through the EmotiBit sensor, which provides the arousal levels of the painter. Given the heartbeat detected, the robot decides to increase proximity to

Zhihao Qi, Weibing Deng, Fuhai Zhu

This paper investigates a class of non-autonomous highly oscillatory ordinary differential equations characterized by a linear component inversely proportional to a small parameter $\varepsilon$, with purely imaginary eigenvalues, and an $\varepsilon$-independent nonlinear part. When $0<\varepsilon\ll 1$, the rapidly oscillatory nature of the solution impose

Wenliang Pei, Chonghao Deng

We consider the Choquard system with both linear and nonlinear couplings $-\Delta u + \mu_1 u =\lambda_1 ( I_\alpha * |u|^{r_1} ) |u|^{r_1-2} u + \beta p( I_\alpha * |v|^q)|u|^{p-2} u + \kappa v,$ $-\Delta v + \mu_2 v =\lambda_2 ( I_\alpha * |v|^{r_2} ) |v|^{r_2-2} v + \beta q( I_\alpha * |u|^p)|v|^{q-2} v + \kappa u , $ $\int_{\mathbb{R}^N} u^2 = \rho_1^2\,

Junseo Koo, Jinseo Jeong, Gunhee Kim

The recent introduction of 3D Gaussian Splatting (3DGS) has significantly advanced novel view synthesis. Several studies have further improved the rendering quality of 3DGS, yet they still exhibit noticeable visual discrepancies when synthesizing views at sampling rates unseen during training. Specifically, they suffer from (i) erosion-induced blurring artif

Ashutosh Srivastava, Nikhilesh Maiity, Abhishek Kumar Singh

The significantly weak interlayer coupling strength and puckered structure provide the novel layer-tolerant and anisotropic features in two-dimensional (2D) ReS2. These unique features offer an opportunity to modulate the optoelectronic, vibrational, and transport properties along different lattice directions in ReS2. Here, using first-principles density fun

Fouad Chahrour, Şahin K. Ozdemir, Kurt Busch, Ramy El-Ganainy

Thermalization of isolated quantum systems is an intriguing phenomenon at the forefront of contemporary physics. In this work, we demonstrate that nonlinear multimode optical platforms can be harnessed to induce effective quantum interactions between photons. Through numerical experiments where quantum beams propagate alongside classical light within multimo

Ethan Ashby, Dean Follmann, Holly Janes, Peter B. Gilbert

Understanding how vaccine effectiveness (VE) changes over time can provide evidence-based guidance for public health decision making. While commonly reported by practitioners, time-varying VE estimates obtained using Cox regression are vul- nerable to hidden biases. To address these limitations, we describe how to leverage vaccine-irrelevant infections to id

Vineeth Sai Narajala, Manish Bhatt, Idan Habler, Ronald F. Del Rosario

The AI trustworthiness crisis threatens to derail the artificial intelligence revolution, with regulatory barriers, security vulnerabilities, and accountability gaps preventing deployment in critical domains. Current AI systems operate on opaque data structures that lack the audit trails, provenance tracking, or explainability required by emerging regulation

Weijie Xiong, Kai Zhong, Zhiling Xiao, Jingran Lin

Movable antennas (MA) are a novel technology that allows for the flexible adjustment of antenna positions within a specified region, thereby enhancing the performance of wireless communication systems. In this paper, we explore the use of MA to improve physical layer security in an analog beamforming (AB) communication system. Our goal is to maximize the sec

Weijie Xiong, Jingran Lin, Zhiling Xiao, Qiang Li

Physical layer security (PLS) is an important technology in wireless communication systems to safeguard communication privacy and security between transmitters and legitimate users. The integration of large-scale antenna arrays (LSAA) and intelligent reflecting surfaces (IRS) has emerged as a promising approach to enhance PLS. However, LSAA requires a dedica

Peter L. Guo

Richardson tableaux are a remarkable subfamily of standard Young tableaux introduced by Karp and Precup in order to index the irreducible components of Springer fibers equal to Richardson varieties. We show that the set of insertion tableaux of noncrossing partial matchings on $\{1,2,,\ldots, n\}$ by applying the Robinson--Schensted algorithm coincides with

Weijie Xiong, Jingran Lin, Cunhua Pan, Yilong Zeng

Reconfigurable intelligent surfaces (RISs) hold significant promise for enhancing physical layer security (PLS). However, conventional RISs are typically modeled using diagonal scattering matrices, capturing only independent reflections from each reflecting element, which limits their flexibility in channel manipulation. In contrast, beyond-diagonal RISs (BD

Aneesh Srivatsa, Shruti Tandon, Andrea Elizabeth Biju, Norbert Marwan

Turbulent reacting flows confined to ducts are plagued by thermoacoustic instability, a state in which a positive feedback between flow, flame and acoustic perturbations leads to the emergence of catastrophically high-amplitude oscillatory dynamics in the sound and global heat release rate fluctuations. Modeling the interdependence between local interactions

Chengyu Xie, Zhi Gong, Junchi Ren, Linkun Yu

Pose-guided human image generation is limited by incomplete textures from single reference views and the absence of explicit cross-view interaction. We present jointly conditioned diffusion model (JCDM), a jointly conditioned diffusion framework that exploits multi-view priors. The appearance prior module (APM) infers a holistic identity preserving prior fro

T. Kobashi, R. C. Mouli, J. Liu, S. Chang

Urban decarbonization is central to meeting global climate goals, yet progress toward integrated low-carbon energy systems remains slow. The SolarEV City Concept, linking rooftop photovoltaics with electric vehicles as mobile storage offers a technically robust pathway for deep CO2 reduction, potentially meeting 60-95 percent of municipal electricity demand

Wenhan Yu, Zhaoxi Zhang, Wang Chen, Guanqiang Qi

Scientific documents contain complex multimodal structures, which makes evidence localization and scientific reasoning in Document Visual Question Answering particularly challenging. However, most existing benchmarks evaluate models only at the page level without explicitly annotating the evidence regions that support the answer, which limits both interpreta

Amelia Fernández Seguel, Alejandro I. Maass

The independent control of multiple magnetic microrobots under a shared global signal presents critical challenges in biomedical applications such as targeted drug delivery and microsurgeries. Most existing systems only allow all agents to move synchronously, limiting their use in applications that require differentiated actuation. This research aims to desi

Partha S. Dey, S. Rasoul Etesami, Aditya S. Gopalan

We consider an infinite-dimensional stochastic clustering model on $\mathbb{R}$. In discrete time, each point of a unit-intensity simple point process moves halfway toward either of its left or right neighbors, chosen uniformly at random. Co-located points are merged into a single point, and the resulting simple point process is rescaled to unit intensity. W

Hernán R. Sánchez

A time-domain representation of chromatographic peak shapes is presented as an analytic expression designed for high computational efficiency, which can be used for direct time-domain peak fitting with parameters that represent physical quantities. The underlying model integrates the effects of axial diffusion (molecular and multipath/eddy), finite initial s

Lu Chen, Peiyuan Xu, Guohao Li, Yang Han

Dark energy plays a crucial role in the evolution of cosmic expansion. In most studies, dark energy is considered a single dynamic component. In fact, multi-component dark energy models may theoretically explain the accelerated expansion of the universe as well. In our previous research, we constructed the $w_{\rm{n}}$CDM ($n=2, 3, 5$) models and conducted n

Molla Basir Ahamed, Rajesh Hossain, Xiaoyuan Wang

Let $\mathcal{A}$ denote the class of analytic functions $f$ on the unit disk $\mathbb{D}=\{z\in\mathbb{C} : |z|<1\}$, normalized by $f(0)=0$ and $f^{\prime}(0)=1$. For $-\pi/2<\alpha<\pi/2$, let $\mathcal{S}_{\alpha}$ be the subclass of $\mathcal{A}$ consisting of functions $f$ that satisfy the relation $\mathrm{Re}\{e^{i\alpha}\left(1+zf^{\prime\prime}(z)/

Soumitra Daptari, Koki Igarashi, Jumpei Nakamura, Ryotaro Tanaka

In this paper, we study three types of Birkhoff-James orthogonality in Hilbert $C^*$-modules, that is, the strong, quasi-strong, and original Birkhoff-James orthogonality. In general, the strong Birkhoff-James orthogonality is stronger than the quasi-strong Birkhoff-James orthogonality, and the quasi-strong Birkhoff-James orthogonality is stronger than the o

Wen Yin, Siyu Zhan, Cencen Liu, Xin Hu

Multimodal Emotion Recognition (MER) aims to accurately identify human emotional states by integrating heterogeneous modalities such as visual, auditory, and textual data. Existing approaches predominantly rely on unified emotion labels to supervise model training, often overlooking a critical challenge: inter-modal emotion conflicts, wherein different modal

Masaaki Tokieda

The second law of thermodynamics sets a lower bound on the work required to drive a system between thermal equilibrium states, with equality attained in the quasistatic limit. For finite-time processes, part of the extractable work is inevitably dissipated, motivating the search for driving protocols that minimize the work. While classical stochastic systems

Weikang Zhang, Alison Watts

Crypto enthusiasts claim that buying and holding crypto assets yields high returns, often citing Bitcoin's past performance to promote other tokens and fuel fear of missing out. However, understanding the real risk-return trade-off and what factors affect future crypto returns is crucial as crypto becomes increasingly accessible to retail investors through m

Xiancheng Wang, Lin Wang, Rui Wang, Zhibo Zhang

Time-series anomaly detection plays a critical role in numerous real-world applications, including industrial monitoring and fault diagnosis. Recently, Mamba-based state-space models have shown remarkable efficiency in long-sequence modeling. However, directly applying Mamba to anomaly detection tasks still faces challenges in capturing complex temporal patt

Kazuki Hirota, Takahiro Kashiwazaki, Gyeongmin Ha, Taichi Yamashima

We report generation of 10.1\pm0.2-dB squeezed light from a broadband periodically poled lithium niobate (PPLN) waveguide optical parametric amplifier (OPA). Based on our previous report where a similar PPLN waveguide shows 8.3-dB squeezing [T. Kashiwazaki et al., Appl. Phys. Lett. 122, 234003 (2023)], we reduce phase fluctuations and overall optical losses

Ruth Appel, Peter McCrory, Alex Tamkin, Miles McCain

In this report, we document patterns of Claude usage over time, in 150+ countries, across US states, and among businesses deploying Claude through the API. Based on a privacy-preserving analysis of 1 million conversations on Claude.ai and 1 million API transcripts, we have four key findings: (1) Users increasingly entrust Claude with more autonomy, with dire

Dongting Yang, Hong-Li Liu, Shengli Qin, Tie Liu

Although frequently reported in observations, the definitive confirmation of high-mass prestellar cores has remained elusive, presenting a persistent challenge in star formation studies. Using two-band observational data from the 3mm ATOMS and 1.3mm QUARKS surveys, we report a high-mass prestellar core candidate, C2, located on the side of the bright-rimmed

Ángel Rodríguez--López

For any Legendrian link in $\displaystyle \mathbb{R}^{3}$ given by the rainbow closure of a positive braid word, we develop an explicit and computable description of a Legendrian isotopy invariant associated with it, namely the cohomological category of compactly supported, microlocal rank-one sheaves with singular support on the Legendrian link. In particul

Shengjing Tian, Yinan Han, Xiantong Zhao, Xuehu Liu

Dynamic outdoor environments with high temporal variation (HTV) pose significant challenges for 3D single object tracking in LiDAR point clouds. Existing memory-based trackers often suffer from quadratic computational complexity, temporal redundancy, and insufficient exploitation of geometric priors. To address these issues, we propose MambaTrack3D, a novel

Khaled Hamidouche, John Bachan, Pak Markthub, Peter-Jan Gootzen

Modern AI workloads, especially Mixture-of-Experts (MoE) architectures, increasingly demand low-latency, fine-grained GPU-to-GPU communication with device-side control. Traditional GPU communication follows a host-initiated model, where the CPU orchestrates all communication operations - a characteristic of the CUDA runtime. Although robust for collective op

Max Sampson, Kung-Sik Chan

Individualized treatments are crucial for optimal decision making and treatment allocation, specifically in personalized medicine based on the estimation of an individual's dose-response curve across a continuum of treatment levels, e.g., drug dosage. Current works focus on conditional mean and median estimates, which are useful but do not provide the full p

Henrik Bradland, Morten Goodwin, Vladimir I. Zadorozhny, Per-Arne Andersen

The performance of machine learning models on tabular data is critically dependent on high-quality feature engineering. While Large Language Models (LLMs) have shown promise in automating feature extraction (AutoFE), existing methods are often limited by monolithic LLM architectures, simplistic quantitative feedback, and a failure to systematically integrate

Youwei Xiao, Zizhang Luo, Weijie Peng, Yuyang Zou

Hardware design faces a fundamental challenge: raising abstraction to improve productivity while maintaining control over low-level details like cycle accuracy. Traditional RTL design in languages like SystemVerilog composes modules through wiring-style connections that provide weak guarantees for behavioral correctness. While high-level synthesis (HLS) and

Ashwin Karthikeyan, Hengyu Liu, Kuldeep S. Meel, Ning Luo

Efficient zero-knowledge proofs (ZKPs) have been restricted to NP statements so far, whereas they exist for all statements in PSPACE. This work presents the first practical zero-knowledge (ZK) protocols for PSPACE-complete statements by enabling ZK proofs of QBF (Quantified Boolean Formula) evaluation. The core idea is to validate quantified resolution proof

Ruarai J Tobin, James M McCaw, Freya M Shearer

In temperate regions, respiratory virus epidemics recur on a yearly basis, primarily during the winter season. This is believed to be induced by seasonal forcing, where the rate at which the virus can be transmitted varies cyclically across the course of each year. Seasonal epidemics can place substantial burden upon the healthcare system, with large numbers

Haoyong Wu, Yongmei Liu

In this paper, we propose ProRAC (Progression-based Reasoning about Actions and Change), a neuro-symbolic framework that leverages LLMs to tackle RAC problems. ProRAC extracts fundamental RAC elements including actions and questions from the problem, progressively executes each action to derive the final state, and then evaluates the query against the progre

Zisong Wang, Xuanyu Wang, Hang Chen, Haizhou Wang

Precise prognostic stratification of colorectal cancer (CRC) remains a major clinical challenge due to its high heterogeneity. The conventional TNM staging system is inadequate for personalized medicine. We aimed to develop and validate a novel multiple instance learning model TDAM-CRC using histopathological whole-slide images for accurate prognostic predic

Yachuan Huang, Xianrui Luo, Qiwen Wang, Liao Shen

Bokeh rendering simulates the shallow depth-of-field effect in photography, enhancing visual aesthetics and guiding viewer attention to regions of interest. Although recent approaches perform well, rendering controllable bokeh without additional depth inputs remains a significant challenge. Existing classical and neural controllable methods rely on accurate

Cheng Yang, Haiyuan Wan, Yiran Peng, Xin Cheng

Video Models have achieved remarkable success in high-fidelity video generation with coherent motion dynamics. Analogous to the development from text generation to text-based reasoning in language modeling, the development of video models motivates us to ask: Can video models reason via video generation? Compared with the discrete text corpus, video grounds

Eros Kuikel, Sidrah Javed, Baha Eddine Youcef Belmekki, Yunfei Chen

The growing demands of ubiquitous and resilient global coverage have pushed existing networks to their operational limits, making it increasingly difficult to meet all requirements on their own. Integrating \emph{Terrestrial Base Stations (TBS), High Altitude Platform Stations (HAPS)} and \emph{Low-Earth-Orbit (LEO)} satellites is envisioned as a promising s

Yun Kwan Kim

Clinical decision support systems (CDSSs) have been widely utilized to support the decisions made by cardiologists when detecting and classifying arrhythmia from electrocardiograms. However, forming a CDSS for the arrhythmia classification task is challenging due to the varying lengths of arrhythmias. Although the onset time of arrhythmia varies, previously

Haodong Chen, Guido Zuccon, Teerapong Leelanupab

Genomic question answering often requires complex reasoning and integration across diverse biomedical sources. GeneGPT addressed this challenge by combining domain-specific APIs with OpenAI's code-davinci-002 large language model to enable natural language interaction with genomic databases. However, its reliance on a proprietary model limits scalability, in

Keito Sasagawa, Shuhei Kurita, Daisuke Kawahara

Multimodal Large Language Models (MLLMs) have seen rapid advances in recent years and are now being applied to visual document understanding tasks. They are expected to process a wide range of document images across languages, including Japanese. Understanding documents from images requires models to read what are written in them. Since some Japanese documen

Jorn Zimmerling, Mikhail Zaslavsky, Alexander V. Mamonov, Vladimir Druskin

We consider one-dimensional inverse scattering in attenuating media where both the reflectivity and loss distributions are unknown. Mathematically, this corresponds to recovering the coefficients of a damped wave operator, or equivalently, a quadratic operator pencil in the frequency domain. The Lippmann-Schwinger equation maps the unknown reflectivity and l

Yaxiong Chen, Qicong Wang, Chunlei Li, Jingliang Hu

Existing approaches for the problem of ultrasound image segmentation, whether supervised or semi-supervised, are typically specialized for specific anatomical structures or tasks, limiting their practical utility in clinical settings. In this paper, we pioneer the task of universal semi-supervised ultrasound image segmentation and propose ProPL, a framework

Yun-Feng Wei, Tong Liu, Bao-Quan Huang

The observable characteristics of gamma-ray bursts (GRBs) embedded in the accretion disk of active galactic nuclei (AGNs) are mainly determined by the jet propagation within the disk. In the massive collapsar scenario, we consider that the mass and metallicity of progenitor stars can significantly affect the jet durations and luminosities, which in turn infl

Jian-Ting Guo, Yu-Cheng Chen, Ping-Chun Hsieh, Kuo-Hao Ho

Human-like agents have long been one of the goals in pursuing artificial intelligence. Although reinforcement learning (RL) has achieved superhuman performance in many domains, relatively little attention has been focused on designing human-like RL agents. As a result, many reward-driven RL agents often exhibit unnatural behaviors compared to humans, raising

Srijan Ray, Bikesh K. Nirala, Jason T. Yustein, Sundaresh Ram

Accurate nuclei segmentation in microscopy whole slide images (WSIs) remains challenging due to variability in staining, imaging conditions, and tissue morphology. We propose CellGenNet, a knowledge distillation framework for robust cross-tissue cell segmentation under limited supervision. CellGenNet adopts a student-teacher architecture, where a capacity te

Yue Wen, Kunjing Yang, Minru Bai

The fusion of hyperspectral image (HSI) with multispectral image (MSI) provides an effective way to enhance the spatial resolution of HSI. However, due to different acquisition conditions, there may exist spectral variability and spatially localized changes between HSI and MSI, referred to as inter-image variability, which can significantly affect the fusion

Pengcheng Su, Haibo Cheng, Ping Wang

The shuffle model enhances privacy by anonymizing users' reports through random permutation. This paper presents the first systematic study of the single-message shuffle model from an information-theoretic perspective. We analyze two regimes: the shuffle-only setting, where each user directly submits its message ($Y_i=X_i$), and the shuffle-DP setting, where

Wei-Xi Li, Zhan Xu, Ping Zhang

We investigate the three-dimensional (3D) incompressible anisotropic Navier-Stokes system with dissipation only in the horizontal variables, posed in a strip domain. To overcome the difficulties arising from the boundary terms and the absence of vertical dissipation, we impose a Gevrey-class regularity condition in the vertical direction. For the remaining d

Zachariah Farahany, Jiawei Wu, K M Sajjadul Islam, Praveen Madiraju

COVID-19 is a respiratory disease that caused a global pandemic in 2019. It is highly infectious and has the following symptoms: fever or chills, cough, shortness of breath, fatigue, muscle or body aches, headache, the new loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea. These symptoms vary in severity; some pe

Panqi Yang, Haodong Jing, Nanning Zheng, Yongqiang Ma

In the field of human-object interaction (HOI), detection and generation are two dual tasks that have traditionally been addressed separately, hindering the development of comprehensive interaction understanding. To address this, we propose UniHOI, which jointly models HOI detection and generation via a unified token space, thereby effectively promoting know

Nele Albers, Esra Cemre Su de Groot, Loes Keijsers, Manon H. Hillegers

Personalizing digital applications for health behavior change is a promising route to making them more engaging and effective. This especially holds for approaches that adapt to users and their specific states (e.g., motivation, knowledge, wants) over time. However, developing such approaches requires making many design choices, whose effectiveness is diffic

Kirsten E. Landsiedel, Rachael V. Phillips, Maya L. Petersen, Mark J. van der Laan

Survival is a key metric for evaluating standards of care for people living with HIV. In resource-limited settings, high rates of loss to follow-up (LTFU) often result in underestimation of mortality when only observed deaths are considered. Resampling, which tracks a subset of LTFU patients to ascertain their outcomes, mitigates bias and improves survival e

Gang Wang, Wenjie Liu, Yifei Li, Xin Wang

Over the past decades, network systems have surged in significance, driven by merging technological advancements. These systems play pivotal roles in diverse applications ranging from autonomous driving to smart grids, yet they confront complexities arising from network imperfections and intricate interconnections, which challenge system identification, cont

R. Cárdenas-Sabando, M. G. Cosenza, J. C. González-Avella

We investigate structural transitions in adaptive networks where node states remain fixed and only the connections evolve via state-dependent rewiring. Using a general framework characterized by probabilistic rules for disconnection and reconnection based on node similarity, we systematically explore how homophilic and heterophilic interactions influence net