April 2026 arXiv papers — page 105

Xiaochen Liu, Ken-Tye Yong

We present a rigorous derivation and numerical validation of a universal projection-law factorization for quartic nonlinear coupling rates across physically distinct platforms. The central result is that observable Kerr-type interactions -- self-Kerr, cross-Kerr, and cross-phase modulation -- factorize into a dimensionless projection coefficient and an intri

Huaxin Wang, Xinge Wu, Jiajun Liu, Ruiqing He

Quantum error mitigation (QEM) provides a practical route for estimating reliable observables on noisy intermediate-scale quantum (NISQ) devices. Traditional QEM strategies, including zero-noise extrapolation (ZNE) and Clifford data regression (CDR), rely on noise scaling or global regression, and their performance is constrained by the exponential growth of

Hamid Sakhouf, Peng Xue

Although non-Hermitian dynamics near exceptional points (EPs) provide a route to accelerated entanglement generation, entanglement can also be generated far from EPs at comparable or even higher rates. However, the behavior of such entanglement in open systems remains largely unexplored, rendering it highly susceptible to environmental noise. Here, we study

Soham Sen

In this thesis, we have used a linearized quantum gravity setting to investigate the effects of gravitons on matter systems. Based on the graviton-matter interaction, we have then proposed detector models that may be able to pick up graviton-induced signatures in a matter of a few years. We start with the simple model of a two-particle model detector system

Keshav Shenoy, Li Yang, Abhay Sheshadri, Sören Mindermann

When model developers or users fine-tune an LLM, this can induce behaviors that are unexpected, deliberately harmful, or hard to detect. It would be far easier to audit LLMs if they could simply describe their behaviors in natural language. Here, we study a scalable approach to rapidly identify learned behaviors of many LLMs derived from a shared base LLM. G

Akash Chowdhury, Surjyasish Mitra, Sushanta K. Mitra

In this work, we perform experiments with spherical polyacrylamide (PAAm) hydrogel drops/spheres, spanning a broad range of shear moduli and impact velocities on hydrophilic (plasma-treated glass) and hydrophobic (silane-coated) substrates, yielding an elastic number El variation of five orders of magnitude. Transient spreading morphology and impact force we

Yifan Yang, Aoyang FANG, Songhan Zhang, Pinjia He

Distributed tracing in microservices is critical for diagnostics but generates overwhelming data volumes, necessitating intelligent sampling. To maximize fidelity, state-of-the-art (SOTA) tail-based samplers analyze complete (or even log-enriched) traces by modeling them as graphs. However, this reliance on computationally expensive graph analysis creates a

Peifeng Gao, Wenyi Fang, Yang Zheng, Difan Zou

Delayed loss spikes have been reported in neural-network training, but existing theory mainly explains earlier non-monotone behavior caused by overly large fixed learning rates. We study one stylized hypothesis: normalization can postpone instability by gradually increasing the effective learning rate during otherwise stable descent. To test this hypothesis

Chen Yang

Referring image segmentation (RIS) requires accurate segmentation of target regions in images according to language descriptions, which is a cross-modal task integrating vision and language. Existing RIS methods typically employ large-scale vision and language encoding models to improve performance, but their enormous parameter size severely restricts deploy

Sumeet Ramesh Motwani, Chuan Du, Aleksander Petrov, Christopher Davis

Optimization problems are central to decision-making in manufacturing, logistics, scheduling, and other industrial settings. Translating complicated descriptions of these problems into solver-ready formulations requires specialized operations research (OR) expertise, making it hard to scale. We present AutoOR, a scalable synthetic data generation and reinfor

Xiang Bin, Liu Liang, Zhu Liying, Shi Xiangdong

Star clusters, as dynamically rich environments, are thought to be important sites for the formation of contact binaries. To investigate this, we conducted a systematic search for contact binaries within two young open clusters, COIN-Gaia 25 and Mamajek 4, and their associated tidal tails. From this search, we identified and confirmed two contact binary syst

Junji Yan, Asrin Efe Yorulmaz, Hanchen Zhou, Tamer Başar

Modern Graphics Processing Unit (GPU)-backed services must satisfy strict latency service-level objectives (SLOs) while controlling spare-capacity costs. In multi-tenant GPU cloud platforms, this trade-off is inherently dynamic because workload demand is endogenous; specifically, pricing shapes the submissions of heterogeneous tenants, which subsequently imp

Ilgar Jabbarov

In this paper, a new method for investigating Dirichlet's divisor problem is developed. For this purpose, integer points under the graph of a hyperbola are studied. Since many investigations in this direction focus on direct estimates of trigonometric sums and are not suitable for studying means, we shall consider shifts with respect to various parameters to

Zilin Li, Weiwei Xu, Xuchun Tong, Xuanbo Lu

While modern representation learning relies heavily on global error signals, decentralized algorithms driven by local interactions offer a fundamental distributed alternative. However, the macroscopic convergence properties of these discrete dynamics on continuous data manifolds remain theoretically unresolved, notoriously suffering from parameter explosion.

Ligen Shi, Zengyu Pang, Chang Liu, Shuchen Sun

Addressing the issues of severe noise and high frequency structural degradation in visible images under low-light conditions, this paper proposes a Near Infrared (NIR) aided low light image restoration method based on Frequency Decoupled Implicit Neural Representation (FDINR). Based on the statistical prior of RGB-NIR cross-modal frequency correlations, spec

Xuan-Quang Bui, Vu Trong Luong, Nguyen Van Minh

We study conditions for the well-posedness of nonautonomous perturbation of evolution equations of the form \[ u'(t)=(A+B(t))u(t), \quad t \in [a,b], \] where $A$ generates a $\mathrm{C}_0$-semigroup $\left (T(t)\right )_{t\ge 0}$ with $\| T(t)\| \le Me^{\omega_0 t}$, $t\ge 0$, in a Banach space $\mathbb{X}$ and $B(t)$ are $t$-dependent (unbounded) linear op

Junfeng Zhou, Geliang Zhu, Lei Xu

A simple framework is presented for unified applications in various fields of colloidal research, with minimal additional concepts & definitions. Several case studies concerning glass transition & crystallization are provided under the minimalist version, upon which adaptations can be made to suit more complicated topics. Major factors influencing accuracy a

Shunpu Tang, Qianqian Yang

Generative semantic communication (SemCom) harnesses pretrained generative priors to improve the perceptual quality of wireless image transmission. Existing generative SemCom receivers, however, rely on maximum a posteriori (MAP) estimation, which fundamentally cannot preserve the data distribution and thus limits achievable perceptual quality. Moreover, cur

Kang Dai, Jian Wang

We study long-time behaviors for branching-diffusion process corresponding to the drifted Schr\"odinger operator $\mathcal{L} = \frac{1}{2} \Delta + \langle \nabla V,\nabla \rangle - K$, where $K$ represents the reduction rate of a population dynamics and $\nabla V$ is a given drift term. In particular, we establish exponential convergence rates for the tota

Kai Cheng, Ruoqi Wang, Qiong Luo

Radio astronomy plays a crucial role in understanding the universe, particularly within the realm of non-thermal astrophysics. Images of celestial objects are derived from the signals (called visibility) measured by radio telescopes. Such imaging results, called dirty images, contain artifacts due to factors such as sparsity and therefore require reconstruct

Omid Vaheb, Sebastien Fabbro, Stark Draper

In astronomical imaging, the low photon count of exposures necessitates extensive post-processing steps, including contamination removal and denoising. This paper evaluates deep-learning denoising methods that can be trained without clean ground-truth images and assesses their utility for detection11 oriented analysis of astronomical data. We adapt and compa

Nguyen Lam, Guozhen Lu, Andrey Russanov

We employ a Markov semigroup approach combined with the $\Gamma$-calculus to establish a generalized Beckner inequality associated with weighted Gaussian measures. As a direct consequence, we derive the corresponding Poincar\'e inequality in the same setting. Subsequently, by means of a duality argument, we investigate gradient and $L^2$ stability estimates

Zixiao Zhao, Amirreza Esmaeili, Fatemeh Fard

Large Language Models are increasingly used as judges to evaluate code artifacts when exhaustive human review or executable test coverage is unavailable. LLM-judge is increasingly relevant in agentic software engineering workflows, where it can help rank candidate solutions and guide patch selection. While attractive for scale, current practice lacks a princ

David Rousso

Many recent efforts at the LHC have been made to search for new particles that do not decay promptly but are instead long-lived. This has been done via many different exotic signatures, including searches performed at ATLAS for displaced vertices (DV), where the new long-lived particle decays into multiple visible tracks after having traveled a certain dista

Xueyao Chen, Jingkai Jia, Tong Yang, Yibo Fu

Robotic manipulation policies often degrade over extended horizons, yet existing benchmarks provide limited insight into why such failures occur. Most prior benchmarks are either simulation-based or report aggregate success, making it difficult to disentangle the distinct sources of temporal difficulty in real-world execution. We introduce LongBench, a real-

Avinash Goutham Aluguvelly

We study how informal surface forms degrade NLI accuracy in ELECTRA-small (14M) and RoBERTa-large (355M) across four transforms applied to SNLI and MultiNLI: slang substitution, emoji replacement, Gen-Z filler tokens, and their combination. Slang substitution (replacing formal words with informal equivalents, e.g., "going to" -> "gonna", "friend" -> "homie")

Ksenia Sosnova, Martin Lichtman, Allison Carter, Nora Crocker

Ion trap quantum computers often store qubits on field-sensitive S_1/2 ground state Zeeman levels of the valence electron, such as in 40Ca+, 88Sr+, and 138Ba+ atomic systems. We experimentally synthesize magnetically insensitive qubit states in multiple metastable electronic D_3/2 Zeeman levels in such an atomic system. We demonstrate coherent operations wit

Hanling Yi, Feng Lin, Mao Luo, Yifan Yang

Recent advances in Multimodal Large Language Models (MLLMs) have enabled open-ended object recognition, yet they struggle with fine-grained tasks. In contrast, CLIP-style models excel at fine-grained recognition but lack broad coverage of general object categories. To bridge this gap, we propose \textbf{HyMOR}, a \textbf{Hy}brid \textbf{M}ulti-granularity op

Da-Wei Luo, Ting Yu

We study the estimation of parameters pertaining to non-Markovian quantum open systems, such as the dissipation rate and environmental memory time. A key challenge is identifying the optimal measurement time, which must allow sufficient time to acquire information about the environment, yet be short enough to avoid dissipation that erases the information. Us

Seungjin Kim, Reza Jafarpourmarzouni, Christopher Neff, Hamed Tabkhi

Vehicle trajectory prediction is central to highway perception, but deployment on roadside edge devices necessitates bounded, deterministic end-to-end latency. We present EdgeVTP, an embedded-first trajectory predictor that combines interaction-aware graph modeling with a lightweight transformer backbone and a one-shot curve decoder. By predicting future mot

Min-Fong Yang

The interplay between quantum geometry and electron correlation has emerged as a compelling paradigm in quantum many-body physics. Recent studies have highlighted the diagnostic utility of quantum geometry in identifying magnetic instabilities within itinerant electron systems. In the present work, we critically re-examine these theoretical proposals. Using

Sandesh Rao Mattu, Nishant Mehrotra, Venkatesh Khammammetti, Robert Calderbank

This tutorial derives the mathematical foundations of what it means for a carrier waveform to be predictable and non-selective. We focus on Zak-OTFS, where each carrier waveform is a pulse in the delay-Doppler (DD) domain, formally a quasi-periodic localized function with specific periods along delay and Doppler. Viewed in the time domain, the Zak-OTFS carri

Ahmed Abdelmuniem Abdalla Mohammed

Standard transformer architectures apply the same number of layers to every token regardless of contextual difficulty. We present Token-Selective Attention (TSA), a learned per-token gate on residual updates between consecutive transformer blocks. Each gate is a lightweight two-layer multi-layer perceptron (MLP) that produces a continuous halting probability

Qiaoyue Tang, Sepidehsadat Hosseini, Mengyao Zhai, Thibaut Durand

This paper presents FairNVT, a lightweight debiasing framework for pretrained transformer-based encoders that improves prediction fairness while preserving task performance. FairNVT is motivated by the intuition that reducing sensitive-attribute information in the representation used by the downstream classifier can facilitate fairer predictions. Our approac

Samrendra Roy, Syed Bahauddin Alam

Quantum feature maps offer expressive embeddings for classical learning tasks, and augmenting sparse identification of nonlinear dynamics (SINDy) with such features is a natural but unexplored direction. We introduce \textbf{Q-SINDy}, a quantum-kernel-augmented SINDy framework, and identify a specific failure mode that arises: \emph{coefficient cannibalizati

Wenshuai Hu, Guanghua Ji, Xiao Li

The modified Landau--de Gennes (mLdG) theory provides a powerful continuum framework for modeling smectic-A (SmA) liquid crystals by coupling the tensorial orientational order parameter $\mathbf{Q}$ with the scalar positional order parameter $u$. In this paper, we develop and analyze a structure-preserving generalized scalar auxiliary variable exponential in

Xin Hu, Ke Qin, Wen Yin, Yuan-Fang Li

Scene Graph Generation (SGG) unifies object localization and visual relationship reasoning by predicting boxes and subject-predicate-object triples. Yet most pipelines treat SGG as a one-shot, deterministic classification problem rather than a genuinely progressive, generative task. We propose FlowSG, which recasts SGG as continuous-time transport on a hybri

Jiahao Li, Jiayi Dong, Peng Ye, Xiaochi Zhou

Modeling single-cell gene expression across diverse biological and technical conditions is crucial for characterizing cellular states and simulating unseen scenarios. Existing methods often treat genes as independent tokens, overlooking their high-level biological relationships and leading to poor performance. We introduce SAVE, a unified generative framewor

Inhyeok Lee, Luke Solo, Michael C. Burkhart, Bashar Ramadan

Every prediction from a generative medical event model is bounded by how clinical events are tokenized, yet input representation is rarely isolated from other system and architectural choices. We evaluate how representation decisions affect downstream prediction after a shared one-epoch pretraining budget. We train 28 matched transformers on MIMIC-IV and eva

Revant Nayar, Dnyanesh Kulkarni, El Mehdi Ainasse

We develop \emph{Topological Risk Parity} (TRP), a tree-based portfolio construction approach intended for long/short, market neutral, factor-aware portfolios. The method is motivated by the dominance of passive/factor flows that naturally create a tree-like structure in markets. We introduce two implementation variants: (i) a rooted minimum-spanning-tree al

Shahin Hossain

Student engagement with large language models (LLMs) in academic writing is not a stable trait, an adoption decision, or a competency level; it is a continuously negotiated process that existing frameworks cannot adequately theorize. Typological models provide categories without mechanisms; technology acceptance models explain adoption but not post-adoption

Qiaohua Yang, Leyuan Yu

We establish sharp Sobolev trace inequalities for conformally invariant fractional powers of the sublaplacian on the Heisenberg group and the CR sphere, extending the corresponding Euclidean results of Einav-Loss, Beckner, and Bez-Machihara-Sugimoto to these non-Euclidean settings. In the limiting case, sharp trace Beckner-Onofri inequalities are also establ

Aakash Sorathiya, Gouri Ginde

Privacy, security, and accessibility, like ethical concerns in mobile applications (a.k.a. apps), commonly subsumed under non-functional requirements, are generally reported by users through app reviews available in app stores. However, these remain unidentified among other types of reviews, such as user experiences, problem reports, and new feature discussi

Chaewan Chun, Delvin Ce Zhang, Dongwon Lee

Audio platforms have evolved beyond entertainment. They have become central to public discourse, from podcasts and radio to WhatsApp voice notes and live streams. With millions of shows and hundreds of millions of listeners, audio platforms are now a major channel for misinformation. Yet existing fact-checking pipelines are mostly designed for written claims

James Henry Atwater, David Lambert, Yuri Rostovtsev

A simple translation between a standard representation of $\mathfrak{sl}_2\mathbb{C}$ and the complex-quaternions ($\mathbb{H}\otimes_\mathbb{R}\mathbb{C}$) is established and exploited to construct a novel hyper-complex description of the Dirac theory, electrodynamics, and ultimately the electroweak sector of the standard model. We find that coupling the co

Lei Cao, Wen Zeng, Xinyue Wu, Eun Cheol Choi

Online political hostility is pervasive, yet it remains unclear how toxicity varies across campaign issues and political ideology, and what psychosocial signals and framing accompany toxic expression online. In this work, we present a large-scale analysis of discourse on X (Twitter) during the five weeks surrounding the 2024 U.S. presidential election. We ca

Zihan Zhou, Bo-Wei Qin, Kai Du, Wei Lin

The Transformer, a breakthrough architecture in artificial intelligence, owes its success to the attention mechanism, which utilizes long-range interactions in sequential data, enabling the emergent coherence between large language models (LLMs) and data distributions. However, temporal attention, that is, different forms of long-range interactions in tempor

Jay Patel, Joel Chan

Across scholarly communities, manuscripts face similar evaluative rituals: editors invite experts to privately assess submissions through formal peer reviews. This closed, loosely structured, and publisher-mediated process is now being supplemented by critiques on open, distributed platforms. We call this practice, a blend of three open peer review variants,

Zhenyu Ma, Chunyi Yang, Yuyang Song, Jingyi Zhu

Molecular dynamics (MD) simulation is a powerful tool for studying biomolecular structural changes, molecular recognition, transmembrane transport, and functional mechanisms. However, its practical bottleneck lies not only in software operation or parameter setup, but in translating experimental questions into executable, interpretable, and reviewable comput

Shutong Jin, Ruiyi Guo, Ray C. C. Cheung

Modern AI agents routinely depend on secrets such as API keys and SSH credentials, yet the dominant deployment model still exposes those secrets directly to the agent process through environment variables, local files, or forwarding sockets. This design fails against prompt injection, tool misuse, and model-controlled exfiltration because the agent can both

Leeroy Makusha, Preston Abadie, Donald J. Docimo

Design, control, and estimation for dynamic systems require accurate and analytically tractable models. However, modern engineered systems contain components that are described with heterogeneous modeling paradigms, as well as subsystems that are challenging to model from physics alone. There have been significant efforts to address this through heterogeneou

Jannatul Ferdous, Rafiqul Islam, Md Zahidul Islam

Ransomware detection systems increasingly rely on behavior-based machine learning to address evolving attack strategies. However, emerging privacy compliance, data governance, and responsible AI deployment demand not only accurate detection but also the ability to efficiently remove the influence of specific training samples without retraining the models fro

Kai Liang, Muxi Li

We give a weak solution for an impartial game we called ``Inverse Treblecross''. We have determined which of its starting positions are $\mathscr{P}$-position and give a reasonable strategy.

Maxwell Shepherd

We present a system for automated detection, localization, and scoring of arrow punctures on 40\,cm indoor archery target faces, trained on only 48 annotated photographs (5{,}084 punctures). Our pipeline combines three components: a color-based canonical rectification stage that maps perspective-distorted photographs into a standardized coordinate system whe

Sree Bhattacharyya, Manas Mehta, Leona Chen, Cristina Salvador

The expression of emotions that serve social purposes, such as asserting independence or fostering interdependence, is central to human interactions and varies systematically across cultures. As LLMs are increasingly used to simulate human behavior in culturally nuanced interactions, it is important to understand whether they faithfully capture human pattern

Mandeep Kaur Saggi, Amandeep Singh Bhatia, Humaira Gowher, Sabre Kais

Quantum machine learning offers a promising new paradigm for computational biology by leveraging quantum mechanical principles to enhance cancer classification, biomarker discovery, and bioinformatics diagnostics. In this study, we apply QML to identify subtype specific biomarkers for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the tw

Francesco Sovrano, Gabriele Dominici, Alberto Bacchelli

Prompt-induced cognitive biases are changes in a general-purpose AI (GPAI) system's decisions caused solely by biased wording in the input (e.g., framing, anchors), not task logic. In software engineering (SE) decision support (where problem statements and requirements are natural language) small phrasing shifts (e.g., popularity hints or outcome reveals) ca

Valentin Kriegmair, Dirk U. Wulff

As large language models (LLMs) are increasingly integrated into daily life, in roles ranging from high-stakes decision support to companionship, understanding their behavioral dispositions becomes critical. A growing literature uses psychometric inventories and cognitive paradigms to profile LLM dispositions. However, these approaches cannot determine wheth

Vaibhava Lakshmi Ravideshik, Mayank Kejriwal

AI Scientist systems capable of autonomous research have the potential to significantly accelerate scientific discovery. However, evaluating and comparing the quality of AI-generated papers remains an open challenge. We propose and implement a rigorous benchmarking protocol using an automated peer-review system that harnesses frontier large language models t

Zeyu Ren, Ling Yue, Ran Li, Yishu Wang

Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episode are usually discarded after execution. Existing skill libraries provide reusable executable routines, but are typically assembled offline and do not grow from the agent's own workflows. We introduce FlowEvo, a train

Abhirup Dasgupta, Fernando Spadea, Oshani Seneviratne

Personal Knowledge Graphs (PKGs) offer a privacy-preserving framework for modeling user preferences, yet constructing them from unstructured, decentralized conversational data remains a challenge. This paper bridges the gap between conversational "strings" and semantic "things" by presenting a reproducible pipeline for extracting structured u

Felipe Carvalho Silva, Geivison Ribeiro, Régis Varão

We focus on the existence of large linear structures within the sets of hypercyclic and frequently hypercyclic vectors. For operators $T$ satisfying Kitai's Criterion or the Frequent Hypercyclicity Criterion, we analyze the fundamental linear space $\{f(T)x | f \in H(\mathbb{C})\}$, studied by Herrero, Bourdon, Bès, Wengenroth, and many others. We show t

Rahul Subramani

Large language models discard critical details when conversation history is compacted to fit within finite context windows. We present LANTERN (Layered Archival aNd Temporal Episodic Retrieval Network), a lightweight memory layer that proactively archives every conversation turn and restores relevant details after compaction via hybrid retrieval -- requiring

Ivo Bueno, Babette Bühler, Philipp Stark, Tim Fütterer

Automated scoring models are increasingly used to assign rubric-based quality ratings to complex language performances, including classroom transcripts, yet they typically provide little insight into why a particular score is produced. We propose a general framework for sentence-level interpretability of rubric-based scoring that combines model-agnostic Shap

Sungmook Woo, Hyungu Kang, Chanwoo Kim

Punctuation restoration improves ASR (Automatic Speech Recognition) readability. However streaming ASR requires online decisions with limited future context. In streaming ASR, the system predicts punctuation incrementally, which makes generation-based approaches prone to latency and alignment failures under boundary-wise evaluation. This paper proposes a non

Ali Holmov, Paul Youssef, Nandi Schoots, Christin Seifert

Knowledge editing methods such as ROME and MEMIT update factual associations in transformer models by modifying MLP weights. While evaluated mainly by output behavior, their internal mechanism remains underexplored. We investigate whether edits rely on a common mechanism, regardless of which fact is modified. Despite fact-specific weight changes, we argue th

Qingyu Meng, Min Chen, Dingming Liu, Yifan Mo

Large language models (LLMs) can generate fluent dialogue, but prior works lack situational grounding, dynamic strategy control, and evaluation aligned with clinical standards in motivational interviewing (MI). We introduce StoryMI, a multi-LLM agent framework for controllable MI dialogue generation, where questionnaire-based client profiles are expanded int

Mike Thelwall

Large Language Models (LLMs) like ChatGPT, DeepSeek and Gemini seem to be increasingly used for knowledge discovery, information retrieval, and knowledge summaries, including for academic topics. This can result in users being misled, such as due to hallucinations. These problems may be exacerbated for academic knowledge if LLMs base their answers on journal

Yifan Zhu, Yu Mi, Yue Lu, Yanchu Guan

Multimodal Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, existing multimodal RAG systems predominantly rely on coarse-grained page-level retrieval, which fails to capture fine-grained semantic and layout structures in visually rich documents, thereby comp

Brandon Dent

Frontier language models are being deployed into clinical workflows faster than the infrastructure to evaluate them safely. Static medical-QA benchmarks miss the failure modes that matter in emergency medicine: trajectory-level safety collapse, tool misuse, and capitulation under sustained clinical pressure. We present HealthCraft, the first public reinforce

Chenjun Shi, Jitao Zhang

Confocal Brillouin microscopy enables high-resolution mechanical imaging but has low acquisition speed, partly due to its pixel-by-pixel mapping strategy. Line-scanning Brillouin microscopy (LSBM) significantly improves imaging speed by utilizing a multiplexing approach. However, current method is limited to a single-stage virtually imaged phased array (VIPA

Arun Kumar, Aswathy Baiju, Radu Timofte, Dmitry Ignatov

Mobile deployment of facial age estimation requires models that balance predictive accuracy with low latency and compact size. In this work, we present MobileAgeNet, a lightweight age-regression framework that achieves an MAE of 4.65 years on the UTKFace held-out test set while maintaining efficient on-device inference with an average latency of 14.4 ms meas

Yunchong Xiao, Yuxiang Zhao, Ziyang Ma, Shuai Wang

The growing reliance on large-scale speech data has made privacy protection a critical concern. However, existing anonymization approaches often degrade data utility, for example by disrupting acoustic continuity or reducing vocal diversity, which compromises the value of speech data for downstream tasks such as Automatic Speech Recognition (ASR), Text-to-Sp

De-Zhang Li, Xin Wang

The square lattice Ising model under the Brascamp-Kunz boundary conditions is a well-known exactly solvable lattice model. The exact solution of this system has been derived within the framework of Pfaffian-type method. In this paper we provide a derivation for the solution by the Schultz-Mattis-Lieb method in the transfer matrix formalism. We set special in

Jiachen Qian

The evolution from static ranking models to Agentic Recommender Systems (Agentic RecSys) empowers AI agents to maintain long-term user profiles and autonomously plan service tasks. While this paradigm shift enhances personalization, it introduces a vulnerability: reliance on Long-term Memory (LTM). In this paper, we uncover a threat termed "Visual Incept

Zhentian Zhang, Yuanhui Wu, Kai-Kit Wong, Hao Jiang

Fluid antenna systems (FASs) have introduced a new paradigm for wireless system design by revealing how mutual correlation can be exploited to harvest inherent spatial diversity. While existing studies have mainly focused on one-sided FAS configurations, i.e., with FAS deployed at either the transmitter or the receiver, this work investigates the ergodic cap

S. Lazarević, R. Brose, L. M. Oskinova, M. Chernyakova

In a search for low surface brightness radio nebulae using the ASKAP-EMU survey, we discovered a faint radio shell, G289.6+5.8, and its central point radio source at the position of the soft gamma-ray source IGR J11187-5438. The central radio source is spatially coincident with a previously known low-mass X-ray binary (LMXB) with an M-type donor star. Howeve

Taketo Yoshida, Tohru Nagao, Yoshiki Toba, Naomichi Yutani

We present our discovery of a complex ionized outflow in SDSS J101034.28+372514.7 (J1010+3725), a dust-obscured galaxy (DOG) at $z=0.282$. The SDSS optical spectrum of J1010+3725 shows five narrow components with one broad component in [O{\,\sc iii}]$λ$5007, which represents one of the most complex outflow structures observed among dusty active galactic nucl

Reggie C. Pantig, Ali Övgün

We develop a reference-renormalized (photon-sphere-free) normalization scheme for Gauss-Bonnet gravitational lensing at finite distance in static, spherically symmetric spacetimes. The method treats the curvature primitive used to reduce the Gauss-Bonnet curvature-area integral as a quantity defined only modulo an additive constant (an additive gauge freedom

Miguel Araya, Santiago Ramírez, Diego Bueso, Braulio J. Solano-Rojas

The Vela supernova remnant (SNR), G263.9-3.3, and its pulsar wind nebula (PWN), Vela X, is one of the closest such systems, and it has been studied using observations across the electromagnetic spectrum. SNRs are known sources of gamma rays with energies from GeV to the TeV range. In the GeV band, a cluster of cataloged unidentified Fermi-LAT point sources a

Y-h. Taguchi, Yoh-ichi Mototake

In this paper, we proposed Bayesian Tucker decomposition (BTuD) in which residual is supposed to obey Gaussian distribution analogous to linear regression. Although we have proposed an algorithm to perform the proposed BTuD, the conventional higher-order orthogonal iteration can generate Tucker decomposition consistent with the present implementation. Using

Chengchao Yuan, Ruo-Yu Liu

This work investigates the origin of the TeV emission detected by the Large High Altitude Air Shower Observatory (LHAASO) from NGC~4278, a galaxy hosting a low-luminosity active galactic nucleus (LLAGN). Considering two plausible scenarios, AGN jets and winds, we model the X-ray, GeV, and TeV emission during both TeV-low (quasi-quiet) and TeV-high (active) s

R. G. M. Rodrigues

Fisher zeros play a central role in the theoretical understanding of phase transitions. However, their computation requires knowledge of the density of states, which limits their practical applicability. Alternative approaches based on the Energy Probability Distribution (EPD) and Moment Generating Function (MGF) alleviate the computational cost but suffer f

Zhaolin Li, Jan Niehues

Automatic speech recognition (ASR) still covers only a small fraction of the world's languages, mainly due to supervised data scarcity. In-context learning (ICL) with large language models (LLMs) addresses this problem, but prior work largely focuses on high-resource languages covered during training and text-only settings. This paper investigates whethe

Avishek Maity, Stephan Rosenkranz, Raymond Osborn, Rolf Heid

We investigated the lattice dynamics of quasi-two-dimensional Pd-intercalated $\mathrm{ErTe}_3$ in relation to its charge-density-wave (CDW) transitions by means of x-ray diffuse and meV-resolution inelastic x-ray scattering. In pristine $\mathrm{ErTe}_3$, CDW order develops at orthogonal in-plane wave vectors $\boldsymbol{\mathrm{q}}_{1}^{c} = (0, 0, 0.29)$

Marta Galbiati, Davide Tornotti, Michele Fumagalli, Matteo Fossati

We investigate the connection between strong, blended Ly$α$ absorption systems (SBLAs) and $\approx1000$ Ly$α$ emitting galaxies (LAEs) at $z\gtrsim3$ in 28 quasar fields from the MUSE Analysis of Gas around Galaxies (MAGG) survey. Selecting SBLAs as spectral regions with transmitted flux $-0.05<F<0.25$ over $\approx138\text{ km s}^{-1}$ bins, we find a stro

Shahriyar Zaman Ridoy, Azmine Toushik Wasi, Koushik Ahamed Tonmoy, Taki Hasan Rafi

As multilingual Large Language Models (LLMs) gain traction across South Asia, their alignment with local ethical norms, particularly for Bengali, spoken by over 285 million people worldwide and among the most widely spoken languages globally, remains underexplored. Existing ethics benchmarks are predominantly English-centric and shaped by Western moral frame

Damien Rudaz, Mathias Broth, Jakub Mlynar

To this day, turn-taking models determining voice agents&#39; conduct have been examined primarily from a technical point of view, while the ways in which they emerge as interactional constraints or resources for human conversationalists in situ remain underexplored. Drawing on a detailed analysis of corpora of naturalistic data, we document how humans&#39;

Dragan Prekrat

We clarify a key point in the geometric reinterpretation of the Grosse$\unicode{x2013}$Wulkenhaar (GW) model proposed in &#34;Geometry of the Grosse-Wulkenhaar model&#34; [JHEP 03 (2010) 053]. Specifically, we show that the analysis in Section 6 was performed not for the actual $Ω$-term in the GW action, which involves both ordinary and star-products, but fo

Fouad Trad, Ali Chehab

The rise of QR code-based phishing (&#34;Quishing&#34;) poses a growing cybersecurity threat, as attackers increasingly exploit QR codes to bypass traditional phishing defenses. Existing detection methods predominantly focus on URL analysis, which requires the extraction of the QR code payload, and may inadvertently expose users to malicious content. Moreove

Carlo Bellingeri, Yvain Bruned

In this work, we study the two main symmetries for the one-dimensional generalised KPZ equation (gKPZ): the chain rule and the Itô Isometry. We consider the equation in the full-subcritical regimes and use multi-indices that avoid an over-parametrization of the renormalised equation to compute the dimension of the two spaces associated with these two symmetr

Linyu Peng, Peter E. Hydon

The difference variational bicomplex, which is the natural setting for systems of difference equations, is constructed and used to examine the geometric and algebraic properties of various systems. Exactness of the bicomplex gives a coordinate-free setting for finite difference variational problems, Euler--Lagrange equations and Noether&#39;s theorem. We als

Eren Unlu

As large language models (LLMs) transition into autonomous agents integrated with extensive tool ecosystems, traditional routing heuristics increasingly succumb to context pollution and "overthinking". We argue that the bottleneck is not a deficit in algorithmic capability or skill diversity, but the absence of disciplined second-order metacognitive governan

Eren Unlu

Current agent evaluations largely reward execution on fully specified tasks, while recent work studies clarification [11, 22, 2], capability awareness [9, 1], abstention [8, 14], and search termination [20, 5] mostly in isolation. This leaves open whether agents can diagnose why a task is blocked before acting. We introduce the Support-State Triage Audit (SS

Marie Wisz, Marc Rafelski, Grecco Oyarzun, Regina Jorgenson

Damped Ly$\alpha$ absorbers (DLAs) are the highest \HI\ column density (\NHI) absorption line systems detected in the spectra of background quasars. DLAs dominate the neutral gas content of the Universe ($\Omega_{\rm HI}$) and are used to measure the metallicity evolution of \HI\ gas. In this work, we introduce a sample of five recently detected DLAs at $z >

Melida Carranza Trejo, Monica Moreno Rocha

We study the dynamics of a parametric family of rational functions of odd degree, where each function is a generalized Blaschke product that maps the unit circle onto itself. The action of the Blaschke product restricted to the unit circle defines a circle map, and the parameter space of the family exhibits Arnold tongues. As the parameter varies over an Arn

Benjamin Chou, Yi Zhu, Surya Koppisetti

Audio deepfakes pose a significant security threat, yet current state-of-the-art (SOTA) detection systems do not generalize well to realistic in-the-wild deepfakes. We introduce a novel \textbf{I}n-\textbf{C}ontext \textbf{L}earning paradigm with comparison-guidance for \textbf{A}udio \textbf{D}eepfake detection (\textbf{ICLAD}). The framework enables the us

Xiang Ao

Time series forecasting plays a pivotal role in critical sectors such as finance, energy, transportation, and meteorology. However, Long-term Time Series Forecasting (LTSF) remains a significant challenge because real-world signals contain highly entangled temporal dynamics that are difficult to fully capture from a purely 1D perspective. To break this repre

Ahmad Droby

Dynamic 3D Gaussian Splatting methods achieve strong training-view PSNR on monocular video but generalize poorly: on the D-NeRF benchmark we measure an average train-test PSNR gap of 6.18 dB, rising to 11 dB on individual scenes. We report two findings that together account for most of that gap. Finding 1 (the role of splitting). A systematic ablation of the

Jnaneshwar Das

The spectral kernel field equation R[k] = T[k] lacks a conservation-law analog. We prove (i) the fixed-point flow is strictly volume-expanding (tr DF > 0), precluding automatic conservation, and (ii) the conservation deficit per mode equals the Hessian stability margin exactly: D_m = -Delta'. Closing the deficit requires a scene-side compensating contributio