November 2025 arXiv papers — page 95

Ankit, Abhishek Sarkar

In this paper, we study the existence of nonnegative solutions for a class of multivalued $(p,N)$-Laplace problems having discontinuous nonlinearity with critical exponential growth in $\mathbb{R}^N$. To demonstrate the existence results, we utilized variational methods for non-differentiable functions.

Yixing Peng, Licheng Zhang, Shancheng Fang, Yi Liu

Generating with citations is crucial for trustworthy Large Language Models (LLMs), yet even advanced LLMs often produce mismatched or irrelevant citations. Existing methods over-optimize citation fidelity while overlooking relevance to the user query, which degrades answer quality and robustness in real-world settings with noisy or irrelevant retrieved conte

Richard Hoppe, Maria Bergemann, Philipp Eitner, Momo Ellwarth

The spectral lines of the CH molecule are a key carbon (C) abundance diagnostic in FGKM-type stars. These lines are detectable in metal-rich and, in contrast to atomic C lines, also in metal-poor late-type stars. However, only 3D LTE analyses of the CH lines have been performed so far. We test the formation of CH lines in the solar spectrum, using for the fi

Huanzhu Lyu, Xiao Yang, Xintong Ji

Overtourism poses severe challenges to popular destinations worldwide, threatening natural environments and local communities. This paper develops a decision-making model integrating system dynamics with multi-objective evolutionary algorithms (NSGA-II) to balance economic returns, environmental protection, and social satisfaction. We collect multi-source da

Moritz Reintjes, Ruochen Xia

We give the first general construction of solutions of the static spherically symmetric Einstein-Euler equations, the Tolman-Oppenheimer-Volkoff (TOV-)equation, with prescribed density functions allowed to be discontinuous and non-uniform; these solutions describe stellar phase transitions in General Relativity. Boundedness of the resulting pressure function

Marcel Goossens, Iñigo Arregui, Roberto Soler, Jaume Terradas

Non-uniformity plays an important role for MHD waves. For a uniform plasma of infinite extent the MHD waves can be subdivided in two classes with distinct properties. The first class contains the Alfv\'en waves. The Alfv\'en waves are incompressible and propagate parallel vorticity. They do not have a parallel component of displacement, they do not cause var

Ran Tamir, Nir Weinberger, Albert Guillén i Fàbregas

We study the amount of reliable information that can be stored in a DNA-based storage system composed of short DNA molecules. In this regime, Shomorony and Heckel (2022) put forward a conjecture on the scaling of the number of information bits that can be reliably stored. In this paper, we complete the proof of this conjecture. We analyze a random-coding sch

Zi-Chen Xi, Jiahui Huang, Hao-Xiang Chen, Francis Williams

We proposed a generalized method, NeuralSSD, for reconstructing a 3D implicit surface from the widely-available point cloud data. NeuralSSD is a solver-based on the neural Galerkin method, aimed at reconstructing higher-quality and accurate surfaces from input point clouds. Implicit method is preferred due to its ability to accurately represent shapes and it

Jia-Qi Li, Anton Frisk Kockum, Xin Wang

Strongly correlated multi-photon states are indispensable resources for advanced quantum technologies, yet their deterministic generation remains challenging due to the inherent weak nonlinearity in most optical systems. Here, we propose a scalable architecture for producing correlated few-photon entangled states via cascaded inelastic scattering in a nonlin

Tong Zhang, Yifan Zhao, Liangyu Wang, Jia Li

Cross-Domain Few-Shot Learning (CDFSL) endeavors to transfer generalized knowledge from the source domain to target domains using only a minimal amount of training data, which faces a triplet of learning challenges in the meantime, i.e., semantic disjoint, large domain discrepancy, and data scarcity. Different from predominant CDFSL works focused on generali

Mathis Hardion, Hugo Lavenant

We analyze the gradient flow of a potential energy in the space of probability measures when we substitute the optimal transport geometry with a geometry based on Sinkhorn divergences, a debiased version of entropic optimal transport. This gradient flow appears formally as the limit of the minimizing movement scheme, a.k.a. JKO scheme, when the squared Wasse

Vladimir Vankov

We construct uncountably many finitely generated, pairwise non-isomorphic torsion-free groups, all of which fall into the same quasi-isometry class. This is done by considering Schur covering groups and group cohomology, with the necessary geometric ingredient coming from the theory of bounded-valued cohomology.

Frederik Hoppe, Lars Kleinemeier, Astrid Franz, Udo Göbel

Recent foundation models for tabular data achieve strong task-specific performance via in-context learning. Nevertheless, they focus on direct prediction by encapsulating both representation learning and task-specific inference inside a single, resource-intensive network. This work specifically focuses on representation learning, i.e., on transferable, task-

Youssef Azouzi

Let E be a Dedekind complete Riesz space with weak unit e, equipped with a conditional expectation operator T. We prove that the spaces Lp(T), with their natural vector-valued norms, are strongly complete, extending the p=2 case of Kuo, Kalauch, and Watson. This resolves a question that has remained open for several years. We begin by studying a general type

Ante Wang, Weizhi Ma, Yang Liu

Knowing the reliability of a model's response is essential in practical applications. Given the strong generation capabilities of large language models (LLMs), research has focused on generating verbalized confidence. This approach is further enhanced by integrating chain-of-thought reasoning, which provides logical and transparent estimates. However, how re

Jean-Philippe Chancelier, Pierre Carpentier, Guy Cohen, Thierry Dargent

The problem under consideration is to drive a spatial vehicle to a target at a given final time while minimizing fuel consumption. This is a classical optimal control problem in a deterministic setting. However temporary stochastic failures of the engine may prevent reaching the target after the engine usage is recovered. Therefore, a stochastic optimal cont

Samgeeth Puliyil, Leevi Leppäjärvi, Mário Ziman

We look into the task of differentiating between any two quantum channels and reconstructing them from the obtained measurement statistics with possibly limited information about the experimental set-up. We employ the communication matrix formalism where the measurement statistics of a prepare-and-measure scenario is represented as a stochastic communication

Yiming Zeng, Xi-Le Zhao, Wei-Hao Wu, Teng-Yu Ji

Tensor singular value decomposition (t-SVD) is a promising tool for multi-dimensional image representation, which decomposes a multi-dimensional image into a latent tensor and an accompanying transform matrix. However, two critical limitations of t-SVD methods persist: (1) the approximation of the latent tensor (e.g., tensor factorizations) is coarse and fai

Zhenchuan Ma, Qizhi Teng, Pengcheng Yan, Lindong Li

Heterogeneous porous materials play a crucial role in various engineering systems. Microstructure characterization and reconstruction provide effective means for modeling these materials, which are critical for conducting physical property simulations, structure-property linkage studies, and enhancing their performance across different applications. To achie

David Patrick Duys Montealegre, Alexander Fulton, Mahta Haghighat Ghahfarokhi, Abicumaran Uthamacumaran

We explore the hyperparameters and introduce a methodological framework to convert disease patterns from time series data of blood test results into correlation graphs for causal hypothesis exploration. The networks represent hypotheses that can then be validated or rejected both for causal discovery and causal analysis (under intervention). We synthetically

Jeet Amrit Pattnaik, S. K. Patra

We examine whether quarkyonic equations of state (EOS) can account for compact objects in the $2.5$-$4.5\,M_\odot$ mass range reported for the GW230529 gravitational-wave event. The pressure-energy density and mass-radius (M-R) relations obtained from quarkyonic EOS models indicate a significant stiffening at high densities, allowing stable configurations be

Lo Kai Shun John, Riya Nagar, Abicumaran Uthamacumaran, Hector Zenil

Early detection of cancer and cardiovascular diseases is fundamental to improving patient outcomes and reducing healthcare expenditure. Current cancer screening programs are targeted towards specific cancers and are often inaccessible to large parts of the population, particularly in remote regions. This project aimed to develop digital blood twins: machine

Rui Zhang, Chao Li, Kezhong Liu, Chen Wang

Vessel trajectory prediction is fundamental to intelligent maritime systems. Within this domain, short-term prediction of rapid behavioral changes in complex maritime environments has established multimodal trajectory prediction (MTP) as a promising research area. However, existing vessel MTP methods suffer from limited scenario applicability and insufficien

Steven R. Costenoble, Thomas Hudson

In this, the last of three papers about $C_2$-equivariant complex quadrics, we complete the calculation of the equivariant ordinary cohomology of smooth symmetric quadrics in the cases where the fixed sets have more than two components. These calculations imply one for a $C_2$-equivariant Grassmannian, which we use to prove an equivariant refinement of the r

Pietro Sittoni, Francesco Tudisco

Recent work in deep learning has opened new possibilities for solving classical algorithmic tasks using end-to-end learned models. In this work, we investigate the fundamental task of solving linear systems, particularly those that are ill-conditioned. Existing numerical methods for ill-conditioned systems often require careful parameter tuning, precondition

Yosuke Nishimoto, Takashi Matsubara

World models have been developed to support sample-efficient deep reinforcement learning agents. However, it remains challenging for world models to accurately replicate environments that are high-dimensional, non-stationary, and composed of multiple objects with rich interactions since most world models learn holistic representations of all environmental co

Konstantinos Steiakakis, Alan Pichard, Maxime Vassaux

Collagen fibrils are the building block of many biological tissues, which viability depend on the fibrils properties. Altered properties of collagen fibrils are central to the appearance of many diseases, while physiological or native properties must be reproduced for tissue engineering. Yet, the self-assembly, the structure, and therefore the properties of

Zitong Xu, Huiyu Duan, Xiaoyu Wang, Zhaolin Cai

With the rapid advancement of generative models, powerful image editing methods now enable diverse and highly realistic image manipulations that far surpass traditional deepfake techniques, posing new challenges for manipulation detection. Existing image manipulation detection and localization (IMDL) benchmarks suffer from limited content diversity, narrow g

Hourun Zhu, Yang Gao, Wenlong Fei, Jiawei Li

Large reasoning models have demonstrated remarkable performance on complex reasoning tasks, yet the excessive length of their chain-of-thought outputs remains a major practical bottleneck due to high computation cost and poor deployability. Existing compression methods have achieved partial success but overlook a crucial phenomenon in the training process --

Leonardo Rossi, Bruno Rodrigues

Triathlon training, which involves high-volume swimming, cycling, and running, places athletes at substantial risk for overuse injuries due to repetitive physiological stress. Current injury prediction approaches primarily rely on training load metrics, often neglecting critical factors such as sleep quality, stress, and individual lifestyle patterns that si

Bannanje Ananthamoorthy, Debbijoy Bhattacharya, P. Sreekumar

Observational evidence regarding the impact of AGN feedback on star formation (SF) in non-jetted galaxies is limited. With the available high-resolution UV observations from AstroSat-UVIT, complemented by GALEX, we studied the SF properties in the outskirts ($>0.5R_{25}$) of six AGN-host galaxies and compared them with four non-AGN galaxies of similar morpho

Yu Liu, Xixun Lin, Yanmin Shang, Yangxi Li

Knowledge graph reasoning (KGR) is the task of inferring new knowledge by performing logical deductions on knowledge graphs. Recently, large language models (LLMs) have demonstrated remarkable performance in complex reasoning tasks. Despite promising success, current LLM-based KGR methods still face two critical limitations. First, existing methods often ext

Gabrial Zencha Ashungafac, Mardhiyah Sanni, Busayo Awobade, Alex Gichamba

Recent advances in speech-enabled AI, including Google's NotebookLM and OpenAI's speech-to-speech API, are driving widespread interest in voice interfaces globally. Despite this momentum, there exists no publicly available application-specific model evaluation that caters to Africa's linguistic diversity. We present AfriSpeech-MultiBench, the first domain-sp

B. Ananthamoorthy, Debbijoy Bhattacharya, P. Sreekumar, Swathi B

We present an improved approach for constructing the UV source catalogs using observations from the UltraViolet Imaging Telescope (UVIT) onboard AstroSat, by considering the Poisson distribution of the UV background. The method is tested extensively using fields that are not crowded, the Small Magellanic Cloud (SMC) and M31 (Field 13). The results are compar

A. I. Krivchikov, A. Jezowski, M. S. Barabashko, A. V. Dolbin

We present a comprehensive investigation of the low-temperature heat capacity of thermally reduced graphene oxide (trGO) as a function of compaction pressure and annealing temperature. Graphene oxide was synthesized using a modified Hummers method and subsequently thermally reduced at 300\,{\deg}C, 500\,{\deg}C, and 700\,{\deg}C under vacuum to systematicall

Bannanje Ananthamoorthy, Debbijoy Bhattacharya, Dipanjan Mukherjee, P. Sreekumar

High-resolution AstroSat-UltraViolet Imaging Telescope (UVIT) observations revealed a knot of UV emission, $\sim 1.7$ kpc away from the centre of NGC 315, a nearby elliptical galaxy hosting a giant (Mpc scale) radio source with a jet. We suggest that this patchy and spatially extended UV emission is likely due to ongoing star formation (SF) in the galaxy. Th

Steven R. Costenoble, Thomas Hudson

In this, the second of three papers about $C_2$-equivariant complex quadrics, we calculate the equivariant ordinary cohomology of smooth symmetric quadrics graded on the representation ring of $\Pi BU(1)$ and with coefficients in the Burnside Mackey functor. These calculations exhibit various interesting properties, including the first naturally occurring ex

Jonathan Yaffe, Ben Maman, Meinard Müller, Amit H. Bermano

Automatic Music Transcription (AMT) converts audio recordings into symbolic musical representations. Training deep neural networks (DNNs) for AMT typically requires strongly aligned training pairs with precise frame-level annotations. Since creating such datasets is costly and impractical for many musical contexts, weakly aligned approaches using segment-lev

Rui Liu, Yuan Zhao, Zhenqi Jia

The automatic movie dubbing model generates vivid speech from given scripts, replicating a speaker's timbre from a brief timbre prompt while ensuring lip-sync with the silent video. Existing approaches simulate a simplified workflow where actors dub directly without preparation, overlooking the critical director-actor interaction. In contrast, authentic work

Hongju Lee, Youngjun Park, Jisun An, Dongman Lee

The expansion of short-term rental platforms, such as Airbnb, has significantly disrupted local housing markets, often leading to increased rental prices and housing affordability issues. Accurately forecasting regional Airbnb market trends can thus offer critical insights for policymakers and urban planners aiming to mitigate these impacts. This study propo

Wenkai Lin, Qiming Xia, Wen Li, Xun Huang

Multi-agents rely on accurate poses to share and align observations, enabling a collaborative perception of the environment. However, traditional GNSS-based localization often fails in GNSS-denied environments, making consistent feature alignment difficult in collaboration. To tackle this challenge, we propose a robust GNSS-free collaborative perception fram

Dragos-Patru Covei

We investigate a coupled system of elliptic equations of Lane--Emden--Fowler type on a bounded domain $\Omega \subset \mathbb{R}^n$ ($n \geq 1$) with homogeneous Dirichlet boundary conditions. The system is characterized by sublinear power-law reaction terms $0 < \alpha, \beta < 1$ and includes a fidelity regularization component. Due to the non-gradient str

Kai Tian, Yirong Mao, Wendong Bi, Hanjie Wang

Large language models perform strongly on general tasks but remain constrained in specialized settings such as music, particularly in the music-entertainment domain, where corpus scale, purity, and the match between data and training objectives are critical. We address this by constructing a large, music-related natural language corpus (40B tokens) that comb

Daniel Gibor

We present a randomized polynomial-time simplex algorithm with higher probability and tighter bounds for linear programming by applying improved quasi-convex properties, a logarithmic rounding on a given polytope and its logarithmic perturbation. We base our work on the first randomized polynomial-time simplex method by Jonathan A. Kelner and Daniel A. Spiel

Aditya Sneh, Nilesh Kumar Sahu, Snehil Gupta, Haroon R. Lone

Accurately recognizing human context from smartphone sensor data remains a significant challenge, especially in sedentary settings where activities such as studying, attending lectures, relaxing, and eating exhibit highly similar inertial patterns. Furthermore, social context plays a critical role in understanding user behavior, yet is often overlooked in mo

T. Liimets, D. P. K. Banerjee, M. Santander-García, J. Alcolea

R Aquarii (R Aqr) is a well-known symbiotic binary that has attracted renewed interest during its recent periastron passage, an event that occurs only once every about 40 years. This passage marks the first to be observed with modern, state-of-the-art instruments. We investigate the inner, sub-arcsecond active region of R Aqr during this recent periastron pa

Yuan Li, Xinyue Gui, Ding Xia, Mark Colley

Automated vehicles (AVs) are gradually becoming part of our daily lives. However, effective communication between road users and AVs remains a significant challenge. Although various external human-machine interfaces (eHMIs) have been developed to facilitate interactions, psychological factors, such as a lack of trust and inadequate emotional signaling, may

Kai-Xuan Zhang, Seungbok Lee, Woonghee Cho, Je-Geun Park

Magnetic topology is central to modern quantum magnet, where spin chirality governs exotic spin winding, real-space Berry phase, and topological Hall effect. A key unresolved challenge is how to electrically switch topological spin chirality and its associated gauge flux, an essential requirement for manipulating its topological quantum properties. In this w

Changjian Fu, Haicheng Zhang

We provide a homomorphism of algebras from the quantum group $\mathbf{U}^+_v(\mathfrak{g})$ to the corresponding quantum cluster algebra $\mathcal {A}_q$ with principal coefficients. As a by-product, we show that the quantum cluster variables arising from one-step mutations from the initial cluster variables satisfy the (high order) quantum Serre relations i

Sourav Ghosh

In this note, we develop a parallel theory of the classical Sz.-Nagy--Foias dilation and model theory for a single contraction operator in the setting of pairs of \em{{$q$-commuting}} contraction operators for a unimodular complex number $q$.

Yan Huang, Yongyi Su, Xin Lin, Le Zhang

The emergence of foundation models has substantially advanced zero-shot generalization in monocular depth estimation (MDE), as exemplified by the Depth Anything series. However, given access to some data from downstream tasks, a natural question arises: can the performance of these models be further improved? To this end, we propose WeSTAR, a parameter-effic

Juncheng Hu, Zijian Zhang, Zeyu Wang, Guoyu Wang

Forecasting 3D human motion is an important embodiment of fine-grained understanding and cognition of human behavior by artificial agents. Current approaches excessively rely on implicit network modeling of spatiotemporal relationships and motion characteristics, falling into the passive learning trap that results in redundant and monotonous 3D coordinate in

Linh Van Ma, Unse Fatima, Tepy Sokun Chriv, Haroon Imran

Accurate 3D ground truth estimation is critical for applications such as autonomous navigation, surveillance, and robotics. This paper introduces a novel method that uses an Unscented Kalman Filter (UKF) to fuse 2D bounding box or pose keypoint ground truth annotations from multiple calibrated cameras into accurate 3D ground truth. By leveraging human-annota

Jorn van Kampen, Chun-Cheng Huang, Mauro Salazar

This study presents a framework for optimizing the two-dimensional (2D) placement of electric motorcycle powertrain elements, accounting for the position, the orientation and geometric irregularities. Specifically, we construct a 2D placement model at the component level in which we include near-continuous rotation of components and allow for irregular subsy

Pradosh Keshav MV, NS Kavya, Kenath Arun

Persistent tensions in the Hubble constant (H0) and the matter clustering parameter (S8) motivate late-time new physics that suppresses structure growth without significantly altering the background expansion history of the LambdaCDM model. We study a class of dark-sector dynamics in which a scalar dark energy field, governed by a Z2-symmetric quartic potent

Michael Cranston, Mariia Khodiakova

In this paper we produce precise large deviation estimates through the lens of mod-Poisson convergence. We apply a general result to various examples from number theory, Dedekind domains and polynomials over finite fields when an element is selected using a distribution based on a Dirichlet series.

Wei Xiang, Ziyue Lei, Jie Wang, Yingying Huang

Drivers' perception of risky situations has always been a challenge in driving. Existing risk-detection methods excel at identifying collisions but face challenges in assessing the behavior of road users in non-collision situations. This paper introduces Visionary Co-Driver, a system that leverages large language models to identify non-collision roadside ris

Pierre-Antoine Guihéneuf

We develop a rotational hyperbolic theory for surface homeomorphisms. We use the equivalence relation on ergodic measures that have nontrivial rotational behaviour defined in [arXiv:2312.06249] to define a rotational counterpart of homoclinic classes. These allows to produce a network of horseshoes representing the whole rotational behaviour f the homeomorph

Huram Konjen

This study examines the evolving impact of algorithmic management on human resource management (HRM) practices, with a focus on employee autonomy, procedural transparency, and the sociotechnical dynamics of performance evaluation. Rather than adopting a qualitative or empirical approach, the paper develops a conceptual integration of insights from HRM, human

Ahlam Alrehili, Areej Alhothali

Grammatical Error Correction (GEC) is an important aspect of natural language processing. Arabic has a complicated morphological and syntactic structure, posing a greater challenge than other languages. Even though modern neural models have improved greatly in recent years, the majority of previous attempts used individual models without taking into account

Jim Broadbent, Felix Cohen, Frederik Hvilshøj, Eric Landau

We simplify space binding by focusing on two core components, a single encoder per modality and high-quality data; enabling training state-of-the-art models on a single GPU in a few hours as opposed to multiple days. We present EBind, an Easy, data-centric, and parameter-efficient method to Bind the embedding spaces of multiple contrastive models. We demonst

Junkil Park, Junyoung Choi, Yousung Jung

Recent advances in generative models have introduced a new paradigm for the inverse design of inorganic materials, enabling the discovery of new crystalline structures with desired properties. However, existing generative models focus solely on structural aspects of materials during generation, while overlooking the underlying electronic behavior that fundam

Yuxiang Wang, Siwen Wang, Haowei Han, Ao Wang

Operation recommendation for IoT devices refers to generating personalized device operations for users based on their context, such as historical operations, environment information, and device status. This task is crucial for enhancing user satisfaction and corporate profits. Existing recommendation models struggle with complex operation logic, diverse user

Pierre-Antoine Guihéneuf, Fábio Armando Tal

This article follows and completes [arXiv:2511.14222], where we study the problem of bounded deviations for homeomorphisms of closed surfaces of genus $\ge 2$. This second part deals with bounded deviations relative to geodesic minimal laminations that are not reduced to a closed geodesic. The combination of both articles generalises to the higher genus case

Johannes Seifert, Sid C. Wright, Boris G. Sartakov, Giacomo Valtolina

We experimentally show that an electric dipole moment of more than 1 Debye can be induced in the dysprosium (Dy) atom, in a long-lived state that is about 17513 cm$^{-1}$ above the ground state. This metastable state is part of a strongly coupled opposite-parity doublet. Using optically detected microwave spectroscopy in an atomic beam, we determine the appr

Pierre-Antoine Guihéneuf, Fábio Armando Tal

This is the first article of a series of two where we study the problem of bounded deviations for homeomorphisms of closed surfaces of genus $\ge 2$. This first part studies bounded deviations with respect to closed geodesics. As a byproduct of our proofs, we also get a criterion of existence of periodic orbits in terms of big deviation with respect to some

Hao Jiang, Guoquan Wang, Donglin Zhou, Sheng Yu

Recent advances in Large Language Models (LLMs) have enhanced text-based recommendation by enriching traditional ID-based methods with semantic generalization capabilities. Text-based methods typically encode item textual information via prompt design and generate discrete semantic IDs through item tokenization. However, in domain-specific tasks such as loca

Yunlong Guo, John Canning, Zenon Chaczko, Gang-Ding Peng

A robust and compact magneto-optical rotary encoder for the characterisation of robotic rotary joints is demonstrated. The system employs magnetic field-induced optical attenuation in a double-pass configuration using rotating nonuniform magnets around an optical circulator operating in reflection. The encoder tracks continuous 360{\deg} rotation with rotati

Hyakka Nakada, Yoshiyasu Tanaka

Optical Character Recognition (OCR) for data extraction from documents is essential to intelligent informatics, such as digitizing medical records and recognizing road signs. Multi-modal Large Language Models (LLMs) can solve this task and have shown remarkable performance. Recently, it has been noticed that the accuracy of data extraction by multi-modal LLM

Kumud Tripathi, Aditya Srinivas Menon, Aman Gaurav, Raj Prakash Gohil

The Whisper model, an open-source automatic speech recognition system, is widely adopted for its strong performance across multilingual and zero-shot settings. However, it frequently suffers from hallucination errors, especially under noisy acoustic conditions. Previous works to reduce hallucinations in Whisper-style ASR systems have primarily focused on aud

Xinlei Xiong, Wenbo Hu, Shuxun Zhou, Kaifeng Bi

Weather forecasting is fundamentally challenged by the chaotic nature of the atmosphere, necessitating probabilistic approaches to quantify uncertainty. While traditional ensemble prediction (EPS) addresses this through computationally intensive simulations, recent advances in Bayesian Deep Learning (BDL) offer a promising but often disconnected alternative.

Jaeyeon Park, Dongjoon Kim, Seungjun Lee, Shenghui Cui

Nearest level modulation (NLM) is an attractive modulation method for its implementation simplicity in modular multilevel converter (MMC). However, it introduces significant voltage and current distortion when the number of submodules (SMs) per arm is small, as in medium-voltage applications. While indirect modulation offers fully decoupled control of ac-sid

YunLiang Zheng, Xiaohu Yang, Teng Liu, Shijiang Chen

We use the first eROSITA all-sky survey (eRASS1) to investigate the contributions of AGN and extended gas to the total X-ray luminosity ($L_X$) of galaxy groups with different halo masses ($M_h$) at different redshifts. The presence of AGN in their central galaxies is identified using multi-wavelength catalogs, including the X-ray counterparts, the ASKAP rad

Pattaraphon Kenny Wongchamcharoen, Paul Glasserman

Large language models (LLMs) are increasingly used in finance and economics, where prompt-based attempts against look-ahead bias implicitly assume that models understand chronology. We test this fundamental question with a series of chronological ordering tasks with increasing complexities over facts the model already knows from pre-training. Our tasks cover

Wenjie Li, Yulun Zhang, Guangwei Gao, Heng Guo

Blind face restoration (BFR) may correspond to multiple plausible high-quality (HQ) reconstructions under extremely low-quality (LQ) inputs. However, existing methods typically produce deterministic results, struggling to capture this one-to-many nature. In this paper, we propose a Measurement-Constrained Sampling (MCS) approach that enables diverse LQ face

Morteza Shokrani, Xinlu Wu, Ebbo Krahmer, Martijn Kemerink

Charge transport in QD solids is typically understood as thermally activated tunneling or hopping between states that are localized on individual QDs. Here, we show that the slow relaxation that is associated with the disorder-broadened density of (localized) states leads to a strong electric field F dependence of the charge carrier mobility. We interpret th

Yixin Guo

We consider the mass-imbalance effect on the clustering in a one-dimensional two-component Fermi gas with coexistent even- and odd-wave interactions resulting in different configurations of clustering phases. We obtain the solutions of both stable two- and three-body cluster states with different mass ratios and configurations by solving the corresponding va

N Dinesh Reddy, Dylan Snyder, Lona Kiragu, Mirajul Mohin

We introduce Orion, a visual agent that integrates vision-based reasoning with tool-augmented execution to achieve powerful, precise, multi-step visual intelligence across images, video, and documents. Unlike traditional vision-language models that generate descriptive outputs, Orion orchestrates a suite of specialized computer vision tools, including object

Davood Keshavarzi, Alexander Koehler, Wolfram H. Wellssow, Stefan M. Goetz

An increasing penetration of renewable energy resources, electric vehicle chargers, and energy storage systems into low-voltage power grids causes several power management and stability problems, such as reverse power flow, (local) overload lines, and over- / under-voltage. Previous power-flow and soft-open-point solutions are bulky and expensive. They need

Weimin Bai, Suzhe Xu, Yiwei Ren, Jinhua Hao

Video inverse problems are fundamental to streaming, telepresence, and AR/VR, where high perceptual quality must coexist with tight latency constraints. Diffusion-based priors currently deliver state-of-the-art reconstructions, but existing approaches either adapt image diffusion models with ad hoc temporal regularizers - leading to temporal artifacts - or r

Antonio De Felice, Shinji Tsujikawa

In k-essence theories within general relativity, where the matter Lagrangian depends on a real scalar field $\phi$ and its kinetic term $X$, static and spherically symmetric compact objects with a positive-definite energy density cannot exist without introducing ghosts. We show that this no-go theorem can be evaded when the k-essence Lagrangian is extended t

Alessio Zanga, Marco Scutari, Fabio Stella

Causal discovery combines data with knowledge provided by experts to learn the DAG representing the causal relationships between a given set of variables. When data are scarce, bagging is used to measure our confidence in an average DAG obtained by aggregating bootstrapped DAGs. However, the aggregation step has received little attention from the specialized

Minkwan Kim, Yoonsang Lee

We propose FreeMusco, a motion-free framework that jointly learns latent representations and control policies for musculoskeletal characters. By leveraging the musculoskeletal model as a strong prior, our method enables energy-aware and morphology-adaptive locomotion to emerge without motion data. The framework generalizes across human, non-human, and synthe

Mark Hindmarsh, Asier Lopez-Eiguren, Riikka Seppä, David J. Weir

Magnetic monopoles are an inevitable feature of post-inflation symmetry-breaking phase transitions in grand unified theories. Analytic estimates of their density indicate that they are compatible with standard cosmology only if their mass is less than $10^{11}$ GeV. We initiate a programme of numerical studies of monopole dynamics by simulating a gas of 't H

Yunhe Liu

Maritime vessel re-identification (Re-ID) plays a crucial role in advancing maritime monitoring and intelligent situational awareness systems. However, some existing vessel Re-ID methods are directly adapted from pedestrian-focused algorithms, making them ill-suited for mitigating the unique problems present in vessel images, particularly the greater intra-i

Weiping Yang, Shilin Zhou, Hui Xu, Yujiao Nie

Compute-in-Memory (CIM) and weight sparsity are two effective techniques to reduce data movement during Neural Network (NN) inference. However, they can hardly be employed in the same accelerator simultaneously because CIM requires structural compute patterns which are disrupted in sparse NNs. In this paper, we partially solve this issue by proposing a bit l

Ye Liu, Xu Chen, Wuji Chen, Mang Li

In cross-border e-commerce, search relevance modeling faces the dual challenge of extreme linguistic diversity and fine-grained semantic nuances. Existing approaches typically rely on scaling up a single monolithic Large Language Model (LLM). However, our empirical analysis reveals that single models suffer from uneven capability distributions across regions

Ruichao Dong, Xiaoqing Hu, Owen Dennis McGinnis, Xincheng Wang

Ultrafast charge transfer (CT) processes redistribute electronic charge within and between molecular units and play a central role in many physical, chemical, and biological phenomena. However, the microscopic pathways of multiple CT events, including the coupled structural evolution and energy redistribution, are challenging to disentangle experimentally in

Shoou-Ren Hsiau, Yi-Ching Yao

Let $(S_n^p)_{n\geq 0}$ be a Bernoulli random walk where each of the independent increments is either $1$ or $-1$ with probabilities $p$ and $1-p$. For $p'$ and $p'' \in [0,1]$ with $|p'-1/2|>|p''-1/2|$, we show that $(|S_n^{p''}|)_{n\geq 0}$ is stochastically smaller than $(|S_n^{p'}|)_{n\geq 0}$. In other words, $(|S_n^{p}|)_{n\geq 0}$ is stochastically de

Ningling Ge, Sicheng Dai, Yu Zhu, Shan Yu

Understanding brain function represents a fundamental goal in neuroscience, with critical implications for therapeutic interventions and neural engineering applications. Computational modeling provides a quantitative framework for accelerating this understanding, but faces a fundamental trade-off between computational efficiency and high-fidelity modeling. T

Jiazhuo Tian, Yachao Yuan

In modern communication networks driven by 5G and the Internet of Things (IoT), effective network traffic flow classification is crucial for Quality of Service (QoS) management and security. Traditional centralized machine learning struggles with the distributed data and privacy concerns in these heterogeneous environments, while existing federated learning

Wendy Wong, Yuchao Jiang, Yuekang Li

Although CS programs are booming, introductory courses like CS1 still adopt a one-size-fits-all formats that can exacerbate cognitive load and discourage learners with autism, ADHD, dyslexia and other neurological conditions. These call for compassionate pedagogies and Universal Design For Learning (UDL) to create learning environments and materials where co

Zitang Sun, Masakazu Yoshimura, Junji Otsuka, Atsushi Irie

High-quality data has become a primary driver of progress under scale laws, with curated datasets often outperforming much larger unfiltered ones at lower cost. Online data curation extends this idea by dynamically selecting training samples based on the model's evolving state. While effective in classification and multimodal learning, existing online sampli

Xuan-Hao Liu, Yan-Kai Liu, Tianyi Zhou, Bao-Liang Lu

Reconstructing video from brain signals is an important brain decoding task. Existing brain decoding frameworks are primarily built on a subject-dependent paradigm, which requires large amounts of brain data for each subject. However, the expensive cost of collecting brain-video data causes severe data scarcity. Although some cross-subject methods being intr

Zheyu Lin, Jirui Yang, Yukui Qiu, Hengqi Guo

Evaluating the safety robustness of LLMs is critical for their deployment. However, mainstream Red Teaming methods rely on online generation and black-box output analysis. These approaches are not only costly but also suffer from feedback latency, making them unsuitable for agile diagnostics after training a new model. To address this, we propose N-GLARE (A

Veronika Adolfs, Dominik A. Rudolph, Simon Spelthann, Artsiom Antanovich

Colloidal nanocrystals are unique optical gain materials due to their high intrinsic absorption, excellent quantum yield, and tunable emission. However, integration of colloidal nanocrystal solutions into photonic systems for lasing applications is challenging since high concentration levels are required for optical amplification. Here, we address this chall

Vishal Goyal, Subhra Datta

Lubricant-impregnated surfaces (LIS) and superhydrophobic surfaces (SHSs) are known to passively reduce drag over a surface, which, with a suitable design such as the ribbed texture, can also steer flows anisotropically. Analytical predictions are developed for ribbed textures using an eigenfunction expansion approach. Compared to currently available analyti

Göktuğ Karpat

Entropic uncertainty relations quantify the limits on the predictability of quantum measurements. When the measured system is correlated with a quantum memory, these limits are described by the memory-assisted entropic uncertainty relation (MA-EUR). We examine the behavior of MA-EUR when the memory qubit undergoes noisy dynamics implemented via high-order co

T. V. Smirnova, D. J. Zhou, M. A. Kitaeva, S. A. Andrianov

PSR J1951+2837 is a nearby pulsar with a period of 7.334 s and dispersion measure of DM = 2.9 $\pm$ 0.6 pc cm$^{-3}$, located about 200 or 300 pc from the Sun. It occasionally radiates bright pulses and has been observed by the Large Phased Array (LPA) radio telescope at 110 MHz and by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) at 1250

Haonan Hou, Kai Peng, Yangkai Wang, Jiarui Wang

As time can be introduced as an additional degree of freedom, temporal metamaterials nowadays open up new avenues for wave control and manipulation. Among these advancements, temporal metamaterial-based antireflection coatings have recently emerged as an innovative method that inherently avoids additional spatial insertions. However, prior temporal antirefle