April 2026 arXiv papers — page 132

Pascal Bégout, Jesús Ildefonso Díaz

We study the complex Ginzburg-Landau equation posed on possibly unbounded domains, including some singular and saturated nonlinear damping terms. This model interpolates between the nonlinear Schr{\"o}dinger equation and dissipative parabolic dynamics through a complex time-derivative prefactor, capturing the interplay between dispersion and dissipation. Und

Alipriyo Hoory, Dhiraj Kumar Hazra, L. Sriramkumar

[Abridged] A variety of model-dependent as well as model-independent approaches suggest that certain localized features in the primordial scalar power spectrum can lead to a significantly better fit to the observed anisotropies in the cosmic microwave background (CMB). In this work, we focus on three types of such features and examine whether these features

Yongil Choi

Conventional neural networks strictly separate learning and inference because if parameters are updated during inference, outputs become unstable and even the inference function itself is not well defined [1, 2, 3]. This paper shows that DynamicGate MLP structurally permits learning inference concurrency [4, 5]. The key idea is to separate routing (gating) p

Bastien Michel, Soeren Albeck Nielsen, Morten Hesse, Kristine Roemer Thomsen

Access to mental health care is often rationed through waiting lists, yet there is limited causal evidence on the consequences of delayed access. We study whether eliminating waiting time for psychological support improves outcomes for young adults who grew up with parental substance misuse. Using a randomized waitlist-controlled trial in Denmark combined wi

Khushbu Gulati, Parameswaran Sankaran

A perforated surface is the complement $\mathring\Sigma:=\Sigma\setminus A$ of a countable dense subset $A$ in a connected paracompact surface $\Sigma$. It is known that the topological type of $\Sigma\setminus A$ is independent of the choice of $A$. Any perforated surface is one-dimensional, connected, locally path connected, and is not semi-locally simply

Mohammad Hasan Ahmadilivani, Levent Aksoy, Mohammad Eslami, Jaan Raik

Long Short-Term Memory (LSTM) neural networks have penetrated healthcare applications where real-time requirements and edge computing capabilities are essential. Gait analysis that detects abnormal steps to prevent patients from falling is a prominent problem for such applications. Given the extremely stringent design requirements in performance, power dissi

Mahyar T Moghaddam, Joakim Leed, Anders Frandsen

The growth of compute-intensive AI tasks highlights the need to mitigate the processing costs and improve performance and energy efficiency. This necessitates the integration of intelligent agents as architectural adaptation supervisors tasked with adaptive scaling of the infrastructure and efficient offloading of computation within the continuum. This paper

Matias Bundgaard-Nielsen, Jake Iles-Smith

While the quantum regression theorem (QRT) is the standard tool for computing multi-time correlation functions in open quantum systems, it relies on system-bath separability and an environment that remains in equilibrium, assumptions that are violated once dynamical correlations develop. Using the projection operator formalism, we derive an extension to the

Yibo Jiang, Tao Wu, Rui Jiang, Yehao Lu

Unified Multimodal Models (UMMs) aim to integrate visual understanding and generation within a single structure. However, these models exhibit a notable capability mismatch, where their understanding capability significantly outperforms their generation. This mismatch indicates that the model's rich internal knowledge, while effective for understanding tasks

Stanley E. Lazic

Debates about juridical proof are often framed as a conflict between probabilistic approaches and relative plausibility theory (RPT). This paper argues that this opposition rests on a level-of-analysis error. Drawing on Marr's distinction between levels of analysis, we show that RPT and probabilistic approaches operate at different conceptual levels and are

Tatsuya Ichinose, Youmi Ma, Masanari Oi, Ryuto Koike

Using responses generated by high-performing large language models (LLMs) for instruction tuning has become a widely adopted approach. However, the existing literature overlooks a property of LLM-generated responses: they conflate world knowledge acquired during pre-training with instruction-following capabilities acquired during post-training. We hypothesiz

Bin Liu, Lele Kang, Jiannan Yang

Open-source large language models have made platforms such as Hugging Face central hubs for decentralized AI innovation. Yet these ecosystems are shaped not only by collaboration, but also by competition for priority and community attention. Drawing on Hill and Stein's Race-to-the-Bottom framework, this study extends the logic of project potential, maturatio

Shawn Wanxiang Zhong, Junxuan Liao, Jing Liu, Mai Zheng

AI coding agents regularly misuse their filesystem access, causing data corruption, loss, and leakage. We conduct the first systematic study of this problem through an analysis of 290 public reports. Our study reveals two fundamental gaps: users and agents have limited information about filesystem effects and insufficient control over them. To close these ga

Marco Bella, Vivian Poulin, Sunny Vagnozzi, Lloyd Knox

We show that the strong constraints placed by Planck NPIPE Cosmic Microwave Background (CMB) data on axion-like early dark energy (EDE) are significantly alleviated in models with multiple fields. We find a $1.5\sigma$ residual tension with the Local Distance Network value of $H_0$ in a 2-field model, with no improvement beyond two fields, and a best-fit val

Conrad Borchers, Olga Viberg, René F. Kizilcec

As AI-mediated learning systems increasingly shape how learners plan, make decisions, and progress through education, learner agency is becoming both more consequential and harder to conceptualize at scale. Existing research often treats agency as a proxy for engagement and self-regulation, leaving unclear who actually holds decision-making authority in larg

Jianzong Wang, Botao Zhao, Yayun He, Junqing Peng

Achieving general-purpose robotics requires empowering robots to adapt and evolve based on their environment and feedback. Traditional methods face limitations such as extensive training requirements, difficulties in cross-task generalization, and lack of interpretability. Prompt learning offers new opportunities for self-evolving robots without extensive tr

Rahul Singh, Ritajit Kundu, Arijit Kundu, Adhip Agarwala

Symmetry-protected topological phases of matter, characterized by non-trivial band topology, are spectrally gapped and show non-trivial boundary phenomena. Here, we show that scattering states when interjected by an array of periodically modulated defects can result in emergent topological phases whose properties can be tuned by modulating the defect strengt

Renqi Chen, Zeyin Tao, Jianming Guo, Jing Wang

Graphical User Interface (GUI) agents show strong capabilities for automating web tasks, but existing interactive benchmarks primarily target benign, predictable consumer environments. Their effectiveness in high-stakes, investigative domains such as authentic e-commerce risk management remains underexplored. To bridge this gap, we present RiskWebWorld, the

Mitchell A. Thornton

We provide a computable criterion for selecting among Fourier, wavelet, and time-frequency analysis by extending the algebraic diversity (AD) framework to Lie groups acting on $L^2(\mathbb{R})$. To our knowledge, there is no other criterion that provides this selection capability. The group-averaged estimator generalizes from a finite sum over group elements

Fumihiko Asano

Rimless wheels are known as the simplest model for passive dynamic walking. It is known that the passive gait generated only by gravity effect always becomes asymptotically stable and 1-period because a rimless wheel automatically achieves the two necessary conditions for guaranteeing the asymptotic stability; one is the constraint on impact posture and the

Rémi Robin, Pierre Rouchon, Lev-Arcady Sellem

We propose and analyze an experimentally accessible Lindblad master equation for a quantum harmonic oscillator, simplifying a previous proposal to alleviate implementation constraints. It approximately stabilizes periodic grid states introduced in 2001 by Gottesman, Kitaev and Preskill (GKP), with applications for quantum error correction and quantum metrolo

Ryandhimas E. Zezario, Dyah A. M. G. Wisnu, Szu-Wei Fu, Sabato Marco Siniscalchi

In this paper, we introduce GatherMOS, a novel framework that leverages large language models (LLM) as meta-evaluators to aggregate diverse signals into quality predictions. GatherMOS integrates lightweight acoustic descriptors with pseudo-labels from DNSMOS and VQScore, enabling the LLM to reason over heterogeneous inputs and infer perceptual mean opinion s

Tatu A. S. Korkiamäki, Tuomas A. Puurtinen, Mikko Kivekäs, Teemu Loippo

Two-dimensional phononic crystals (PnCs) formed by a periodic array of holes in a suspended membrane have previously been used to coherently control thermal conductance at low temperatures by modifying the phonon dispersion, thereby altering the phonon group velocities and the density of states. Here, in contrast, we demonstrate that PnCs formed by a periodi

Wentao Hu, Yanbo Zhai, Xiaohui Hu, Mingkuan Zhao

Sparse Mixture-of-Experts (MoE) models have achieved remarkable scalability, yet they remain vulnerable to hallucinations, particularly when processing long-tail knowledge. We identify that this fragility stems from static Top-$k$ routing: routers tend to favor high-frequency patterns over rare factual associations. Consequently, ``specialist experts'' posse

Kengo Nakamura, Masaaki Nishino

Given a network and a set of vertices called seeds to initially inject information, influence spread is the expected number of vertices that eventually receive the information under a certain stochastic model of information propagation. Under the commonly used independent cascade model, influence spread is equivalent to the expected number of vertices reacha

Biswarup Karmakar, Ratikanta Behera

The robust low-rank tensor completion problem addresses the challenge of recovering corrupted high-dimensional tensor data with missing entries, outliers, and sparse noise commonly found in real-world applications. Existing methodologies have encountered fundamental limitations due to their reliance on uniform regularization schemes, particularly the tensor

Ramanath Garai, Iswari Sahu, S. Chakraverty

In this chapter, we investigate the bending behavior of a perforated nanobeam subjected to sinusoidal loading using an efficient and computationally robust Physics-Informed Functional Link Constrained Framework with Domain Mapping (DFL-TFC) method. Our aim is to determine the relationship between static bending response and dynamic deflection of a perforated

Luan Pham, Huong Ha, Xiuzhen Zhang, Hongyu Zhang

Existing multi-source root cause analysis (RCA) methods for microservice systems assume all services have traces to construct a service call graph. However, this assumption is not practical as microservice systems evolve rapidly and may contain blackbox services without traces, such as compiled software or unsupported services. We refer to these services as

Kenji Kubo, Shunsuke Kamiya, Masanori Koyama, Kohei Hayashi

Neural network models with latent recurrent processing, where identical layers are recursively applied to the latent state, have gained attention as promising models for performing reasoning tasks. A strength of such models is that they enable test-time scaling, where the models can enhance their performance in the test phase without additional training. Mod

Chaoran Zhang, Guangyao Li, Dongxu Ji

Metal-organic frameworks (MOFs) are highly promising for carbon capture, yet navigating their vast design space remains challenging. Recent deep generative models enable de novo MOF design but primarily act as feed-forward structure generators. By heavily relying on predefined building block libraries and non-differentiable post-optimization, they fundamenta

Mintu Dutta, Ritesh Vyas, Mohendra Roy

Self-supervised learning has emerged as a major technique for the task of learning from unlabeled data, where the current methods mostly revolve around alignment of representations and input recon struction. Although such approaches have demonstrated excellent performance in practice, their scope remains mostly confined to learning from observed data and doe

Yeqi Zhou, Songbai Yao, Wenwu Zhang

We present a numerical investigation of kerosene droplet mist film cooling for the thermal protection of the rotating detonation combustor (RDC) and compare its performance with conventional air film cooling and combined mist/air cooling scheme. In the study, the cooling behavior of kerosene droplets injected through wall film holes is numerically examined a

Huayu Dai, Guangtao Zeng

We compute the quasinormal mode spectrum of scalar perturbations on Kaigorodov pp-wave spacetimes, the horizonless gravity duals of zero temperature null fluids. The pp-wave deformation promotes the Poincar\'e horizon at $r=0$ to an irregular singular point of rank $(d+2)/2$, which acts as a geometric absorber for ingoing waves: rank~$0$ corresponds to therm

Xiaole Su, Kasey Zhang, Andy Lyu

Supervised fine-tuning (SFT) followed by Group Relative Policy Optimization (GRPO) is a common post-training recipe. We conduct a controlled ablation over SFT-GRPO data overlap, evaluating Qwen3-8B (thinking disabled) post-trained for Lean 4 autoformalization under six conditions that differ solely in training recipe: a base model, SFT-only, GRPO-only, and t

Hao Shen, Junyu Guo, Junqi Liu, Lihong Zhi

Applying Gr\"obner basis theory to concrete problems in Lean 4 remains difficult since the current formalization of multivariate polynomials is based on a non-computable representation and is therefore not suitable for efficient symbolic computation. As a result, computing Gr\"obner bases directly inside Lean is impractical for realistic examples. To address

Xin Yang, Dongliang Fan, Yunteng Ma, Yuxuan Liao

Cardiovascular diseases account for around 17.9 million deaths per year globally, the treatment of which is challenging considering the confined space and complex topology of the vascular network and high risks during operations. Robots, although promising, still face the dilemma of possessing versatility or maneuverability after decades of development. Insp

Nafis Fuad

Comparison of two probability density/mass functions (PDF/PMFs) is ubiquitous in various forms of scientific analysis, including machine learning, optimization problems, and hypothesis tests. A copious amount of distance metrics have already been proposed and are regularly being used in this regard. In this document, we report a data-driven systematic compar

Keisuke Morita, Federico Ricci-Tersenghi, Masayuki Ohzeki

In many real-world problems, recovering sparse signals from underdetermined linear systems remains a fundamental challenge. Although $\ell_1$ norm minimization is widely used, it suffers from estimation bias that prevents it from reaching the Bayes-optimal reconstruction limit. Nonconvex alternatives, such as the log-sum penalty, have been proposed to promot

Himadri Mukherjee, Askar Ali M

The aim of this paper is to prove a version of Lie's theorem for the supertropical algebra.

Hengye Lyu, Zisu Li, Yue Hong, Yueting Weng

Recent advances in video generation models has significantly accelerated video generation and related downstream tasks. Among these, video stylization holds important research value in areas such as immersive applications and artistic creation, attracting widespread attention. However, existing diffusion-based video stylization methods struggle to maintain s

Sanghyeok Chu, Pyunghwan Ahn, Gwangmo Song, SeungHwan Kim

Sparse Upcycling provides an efficient way to initialize a Mixture-of-Experts (MoE) model from pretrained dense weights instead of training from scratch. However, since all experts start from identical weights and the router is randomly initialized, the model suffers from expert symmetry and limited early specialization. We propose Cluster-aware Upcycling, a

Yike Xu, Mark S. Andersland

Even when providing long-run, worst-case guarantees to competing flows of unit-sized tasks, a slot-timed, constant-capacity server's scheduler may retain significant, short-run, scheduling flexibility. Existing worst-case scheduling frameworks offer only limited opportunities to characterize and exploit this flexibility. We introduce a state-based framework

Liuying Zhang, Wenlong Zhang, Zhidong Zhang

We study an inverse drift problem for a two-dimensional parabolic equation on the unit square with mixed boundary conditions, where the drift coefficient is recovered from terminal observation data $g=u(\cdot,T)$. A monotone operator is constructed whose fixed point coincides with the unknown drift, yielding uniqueness in an admissible class and a constructi

Shentong Mo

Designing effective reward functions is a cornerstone of reinforcement learning (RL), yet it remains a challenging and labor-intensive process due to the inefficiencies and inconsistencies inherent in traditional methods. Existing methods often rely on extensive manual design and evaluation steps, which are prone to redundancy and overlook local uncertaintie

Andrew Atta, Nick Prouse, Shuoyu Chen, Kimihiro Okumura

The forthcoming Hyper-Kamiokande experiment requires substantially larger Monte Carlo datasets than previous experiments to satisfy stringent systematic-uncertainty requirements. While traditional maximum-likelihood reconstruction provides high-quality results, its per-event computational cost makes processing these large samples increasingly impractical. We

Arnab Chowhan, Timothy R. Bedding, Daniel Huber, J. M. Joel Ong

We present a detailed study of the secondary red clump star, $\kappa$ Cyg, by combining long-baseline visible interferometry using the PAVO beam combiner at the CHARA Array with high-precision asteroseismology from TESS. This dual approach allowed for a stringent test of stellar evolutionary models in the core helium-burning phase, which remains a regime of

Ibrahim Aboushehada, Boris Bellalta, Giovanni Geraci, Lorenzo Galati Giordano

Coordinated beamforming (Co-BF) is a key multi-access-point coordination (MAPC) technique for dense Wi-Fi deployments, but its performance can be hindered by the large channel state information (CSI) feedback required through channel sounding across overlapping basic service sets (OBSS). This work proposes an autoencoder (AE)-based CSI compression mechanism

Luís H. Carnevale, Gabriela Niechwiadowicz, Panagiotis E. Theodorakis

The sodium dodecyl sulfate (SDS) surfactant is widely used in various applications, such as household products (e.g., shampoos, toothpaste, detergents, and cleaning products) and food manufacturing (e.g., emulsifiers). To investigate its properties via computer simulation, various models have been developed, including coarse-grained (CG) models that are suit

Joseph Dongho, Joel Fotso Tachago, Franck Tchinda, Elvira Zappale

This paper aims to extend to Orlicz-Sobolev spaces some results of integral representation for the simultaneous homogenization and dimensional reduction of integral energies defined on fields taking values on a differentiable manifold. Since our functional framework goes beyond the classical Sobolev's spaces, we also prove, via $\Gamma$-convergence, a genera

Y. Al Sadi, R. Sbiaa, W. Al Saidi, M. Souier

Magnetic skyrmions and higher-order topological spin textures offer rich opportunities for multi-level information encoding, yet their deterministic stabilization and transformation under geometric confinement at room temperature remain poorly understood. Here, we demonstrate that geometric confinement acts as a robust and universal control parameter that go

Peng Zou, Ali Maatouk, Egemen Erbayat, Suresh Subramaniam

Motivated by cooperative distributed networks in which users dynamically alternate between transmit and receive modes under half-duplex constraints, this paper studies the Age of Information (AoI) in a distributed multi-user network using an ALOHA-based protocol. We derive closed-form expressions for the average AoI and formulate an optimization problem over

Liang Feng, Xiao Tang

In this paper, we investigate the existence of $C^n$, $n\in \mathbb{N}^+$, solutions for a class of second-order iterative functional equations involving iterates of the unknown function and a nonlinear term. Applying the Fiber Contraction Theorem and Fa\`a di Bruno's Formula, we establish the existence of bounded $C^n$ solutions with bounded derivatives of

Juneyong Lee, Geonwoo Baek, Ikbeom Jang

Alzheimer's disease (AD) progresses heterogeneously across individuals, motivating subject-specific synthesis of follow-up magnetic resonance imaging (MRI) to support progression assessment. While Diffusion Transformers (DiT), an emerging transformer-based diffusion model, offer a scalable backbone for image synthesis, longitudinal AD MRI generation with cli

Towheed Ahmad Nengroo, Shafqat Ul Islam, Sushant G. Ghosh

The Event Horizon Telescope (EHT) has guided strong-field gravitational physics by providing the first direct images of the supermassive black holes M87* and Sagittarius A*. The EHT observations offer unprecedented opportunities to test modified gravity theories against general relativity (GR). Motivated by this, we investigate charged rotating black holes i

Yiming Chen

We study whether a uniformly random Boolean function $f : \{-1,1\}^p \to \{-1,1\}$ is determined by its Walsh--Fourier coefficients of degree at most $d$. We show that the threshold lies at $p/2$ up to an $O(\sqrt{p \log p})$ window: if \[ d \le \frac{p}{2} - \sqrt{\frac{p}{2}\bigl(\log p + ω(1)\bigr)}, \] then with probability $1-o(1)$ there exists another

Pou-Chun Kung, Yuan Tian, Zhengqin Li, Yue Liu

Radar is more resilient to adverse weather and lighting conditions than visual and Lidar simultaneous localization and mapping (SLAM). However, most radar SLAM pipelines still rely heavily on frame-to-frame odometry, which leads to substantial drift. While loop closure can correct long-term errors, it requires revisiting places and relies on robust place rec

Leonel Bixano, Tonatiuh Matos

We present a novel class of rotating dilatonic solutions within the framework of Einstein-ModMax-type gravity. The configuration belongs to the nonlinear sector characterized by $\mathcal F/\mathcal G=\mathrm{const}$ and carries nontrivial electric and magnetic potentials, with both $A_t$ and $A_\varphi$ turned on, together with a nontrivial gravitomagnetic

David C. Flynn

We present a unified corpus of 8,963 spatially resolved HI rotation curve measurements across 423 galaxies (438 total catalog entries including 15 metadata-only THINGS galaxies), drawn from four major surveys: SPARC (175), THINGS (34), LITTLE THINGS (26), and WALLABY DR2 (203). The corpus is distributed as a single structured JSON file with nested per-ring k

Ziwei Wang, Junjie Zheng, Leyang Yang, Sheng Zhou

Autonomous Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) enable digital automation on end-user devices. While scaling both parameters and data has yielded substantial gains, advanced methods still suffer from prohibitive deployment costs on resource-constrained devices. When facing complex in-the-wild scenarios, li

Tanmay Singh, Mayuri Sathyanarayana Rao, Ritoban Basu Thakur

The Himalayan plateau region of Ladakh, India, is a potential host for a science-class submillimeter observatory, building on existing astronomical infrastructure near Hanle and Merak. Using the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) data, we analyze precipitable water vapor (PWV) at monthly resolution o

En-Rui Zhou, Yi-Jia Mao, Pei-Lun He, Feng He

Bright squeezed light has entered strong-field atomic physics, creating an immediate need to characterize the quantum field delivered to the target on subcycle timescales. Here we show that attosecond streaking maps the coherent displacement and phase-sensitive covariance of a displaced squeezed field onto distinct harmonics: the mean photoelectron momentum

Sida Liu, Yangzi Guo, Mingyuan Wang

Clustering and dimensionality reduction have been crucial topics in machine learning and computer vision. Clustering high-dimensional data has been challenging for a long time due to the curse of dimensionality. For that reason, a more promising direction is the joint learning of dimension reduction and clustering. In this work, we propose a Manifold Learnin

Kaja Gruntkowska, Hanmin Li, Xun Qian, Peter Richtárik

Most non-convex optimization theory is built around gradient dynamics, leaving global convergence largely unexplored. The dominant paradigm focuses on stationarity, certifying only that the gradient norm vanishes, which is often a weak proxy for actual optimization success. In practice, gradient norms can stagnate or even increase during training, and statio

Young Kwang Kim, Young Sun Lee, Timothy C. Beers

We investigate the progenitors of low-inclination retrograde substructures in the Milky Way (MW) halo, which are remnants of accreted dwarf galaxies on retrograde orbits. Our sample consists of halo stars with low orbital inclinations and eccentricities ($0 \le e \le 0.5$), constructed by combining spectroscopic data with $\it Gaia$ astrometry. We identify s

Kyle J. C. Hall, Maria J. Molina

Here, we describe Monthly Diffusion at 1.5-degree grid spacing (MD-1.5 version 0.9), a climate emulator that leverages a spherical Fourier neural operator (SFNO)-inspired Conditional Variational Auto-Encoder (CVAE) architecture to model the evolution of low-frequency internal atmospheric variability using latent diffusion. MDv0.9 was designed to forward-step

Hongze Li, Raymond L. Orbach, Gregory G. Kenning

Spin glass dynamics is a strong function of spatial dimensionality $D$. The lower critical dimension is close to 2.5, so that, in two dimensions, the condensation temperature $T_\text{g}=0$, and only fluctuations are present at finite temperatures. However, by using thin film multilayers, one can explore the dynamics in both $D=3$ and $D=2$ dimensions. Spin

Lakmali Nadeesha Kumari Rathukohe Mudiyanselage, Sen-Ching Samson Cheung

Frequency-based loss reweighting, the standard remedy for imbalanced histopathology segmentation, implicitly assumes that rare classes are difficult. Yet difficulty also arises from morphological variability, boundary ambiguity, and contextual similarity, all largely orthogonal to class frequency. We propose Dynamic Focal Attention (DFA), a simple, efficient

Junyi Ma, Pei Li, Rui Gan, Kai Cheng

Crash narratives in crash reports provide crucial contextual information for traffic safety analysis. Yet, their broader use is hindered by the presence of personally identifiable information (PII), including names, home addresses, and license plate numbers. Because PII appears sparsely and inconsistently in crash narratives, manual detection is not scalable

Mansur M. Arief

The bullwhip effect remains operationally persistent despite decades of analytical research. Two computational deficiencies hinder progress: the absence of modular open-source simulation tools for multi-echelon inventory dynamics with asymmetric costs, and the lack of a standardized benchmarking protocol for comparing mitigation strategies across shared metr

Xiangji Cai, Yonggang Peng, Yujun Zheng

We theoretically investigate the statistical properties of photon emission of a driven two-level single-molecule system undergoing spectral diffusion induced by nonequilibrium environmental fluctuations. Within the framework of the generating function method and the stochastic Liouville equation, we analyze the influence of the nonequilibrium characteristics

Jiahao Ma, Qiang Zhang, Peiran Liu, Zeran Su

Surround-view perception is increasingly important for robotic navigation and loco-manipulation, especially in human-in-the-loop settings such as teleoperation, data collection, and emergency takeover. However, current robotic visual interfaces are often limited to narrow forward-facing views, or, when multiple on-board cameras are available, require cumbers

Shan Jin, Sai Rahul Rachuri, Yizhen Wang, Anderson C. A. Nascimento

We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering both input and output privacy, for the vertically split scenario in federated learning (FL), where features are split across clients and labels are not shared by all parties. We do so by distributing the role of t

Ilya Davidson, Sandro Claudio Lera, Robert Mahari

Judicial promotions shape the composition of higher courts, yet their determinants remain poorly understood. This paper examines promotion from U.S. District Courts to Courts of Appeals using a discrete-time hazard framework that models annual promotion probability. Using a judge-year panel covering over 36,000 observations from 1930 to present, we incorpora

Sourav Ganguly, Kartik Pandit, Arnob Ghosh

Real-world decision-making systems operate in environments where state transitions depend not only on the agent's actions, but also on \textbf{exogenous factors outside its control}--competing agents, environmental disturbances, or strategic adversaries--formally, $s_{h+1} = f(s_h, a_h, \bar{a}_h)+\omega_h$ where $\bar{a}_h$ is the adversary/external action,

Peter Zhiping Zhang, Jeffrey D. Varner

In silico tools are important for generating novel hypotheses and exploring alternatives in de novo metabolic pathway design. However, while many computational frameworks have been proposed for retrobiosynthesis, few successful examples of algorithm-guided xenobiotic biochemical retrosynthesis have been reported in the literature. Deep learning has improved

Nafiz Ishtiaque, Syed Arefinul Haque, Kazi Ashraful Alam, Fatima Jahara

We prove that conditional diffusion models whose reverse kernels are finite Gaussian mixtures with ReLU-network logits can approximate suitably regular target distributions arbitrarily well in context-averaged conditional KL divergence, up to an irreducible terminal mismatch that typically vanishes with increasing diffusion horizon. A path-space decompositio

Justin Stark, Scott Wilkie

Cloud sovereignty can no longer be defined by data residency or infrastructure location alone. Under conditions of geopolitical disruption, legal exposure, and expanding service boundaries, sovereignty must be understood as enforceable control over how digital services are governed, operated, and recovered. This paper introduces Sovereign 2.0, a control-plan

Thilina Pathirage Don, Aneta Neumann, Frank Neumann

The travelling thief problem (TTP) is a well-known multi-component optimisation problem that captures the interdependence between two components: the tour across cities and the packing of items. The packing while travelling problem (PWT) is an NP-hard subproblem of TTP where the packing of items should be optimised for a given fixed tour. In many solvers, th

Sunkyung Lee, Jihye Back, Donghyeon Jeon, Soonhwan Kwon

Generative information retrieval (GenIR) formulates the retrieval process as a text-to-text generation task, leveraging the vast knowledge of large language models. However, existing works primarily optimize for relevance while often overlooking document trustworthiness. This is critical in high-stakes domains like healthcare and finance, where relying solel

Raphael Grondin

We establish a stability result for the Shannon-McMillan-Breiman theorem on the one-sided finite shift space. For any shift-invariant probability measure P and any data-dependent parsing whose number of blocks is sublinear in N almost surely, we show that the normalized sum of the negative log-likelihoods of the parsing blocks converges almost surely and in

Ahmadreza Eslaminia, Kuan-Chieh Lu, Klara Nahrstedt, Chenhui Shao

Ultrasonic metal welding (UMW) is widely used in industrial applications but is sensitive to tool wear, surface contamination, and material variability, which can lead to unexpected process faults and unsatisfactory weld quality. Conventional monitoring systems typically rely on supervised learning models that assume all fault types are known in advance, lim

Yiheng Xiong, Shiwen Song, Bo Ma, Ting Su

Mobile apps often suffer from functional bugs that do not cause crashes but instead manifest as incorrect behaviors under specific user interactions. Such bugs are difficult to detect automatically because they often lack explicit test oracles. Property-based testing can effectively expose them by checking intended behavioral properties under diverse interac

Eileen Kapel, Jan Lennartz, Luis Cruz, Diomidis Spinellis

Effective IT change management is important for businesses that depend on software and services, particularly in highly regulated sectors such as finance, where operational reliability, auditability, and explainability are essential. A significant portion of IT incidents are caused by changes, making it important to identify high-risk changes before deployme

Harsh Shah

LLM debugging agents that consume cloud logs and execute remediation commands are vulnerable to indirect prompt injection through log content. We present LogJack, a benchmark of 42 payloads across 5 cloud log categories, and evaluate 8 foundation models under 3 prompt conditions with 5 independent trials each (n = 160 per model per condition on 32 attack pay

Andrii Vakhnovskyi

Conventional climate control in Controlled Environment Agriculture (CEA) uses independent PID loops for temperature and humidity, creating cross-coupling conflicts that waste 20-40% of HVAC energy. We propose a cascading architecture that elevates Vapor Pressure Deficit (VPD) from a monitored metric to the primary outer-loop control variable. A 7-3-3 neural

Zonghuan Xu, Xingjun Ma

A central challenge in continual learning is forgetting, the loss of performance on previously learned tasks induced by sequential adaptation to new ones. While forgetting has been extensively studied empirically, rigorous theoretical characterizations remain limited. A notable step in this direction is \citet{evron2022catastrophic}, which analyzes forgettin

Mohammed Ezzaldin Babiker Abdullah

Turbofan engine degradation under sustained operational stress necessitates robust prognostic systems capable of accurately estimating the Remaining Useful Life (RUL) of critical components. Existing deep learning approaches frequently fail to simultaneously capture multi-sensor spatial correlations and long-range temporal dependencies, while standard symmet

Songrun He

In this paper, I present the first comprehensive, around-the-clock analysis of systematic jump risk by combining high-frequency market data with contemporaneous news narratives identified as the underlying causes of market jumps. These narratives are retrieved and classified using a state-of-the-art open-source reasoning LLM. Decomposing market risk into int

Chaitanya Pallerla, Siavash Mahmoudi, Dongyi Wang

Woody Breast (WB) and Spaghetti Meat (SM) myopathies significantly impact poultry meat quality, yet current detection methods rely either on subjective manual evaluation or costly laboratory-grade imaging systems. We address the problem of low-cost, non-destructive multi-class myopathy classification using consumer smartphones. MyoVision is introduced as a m

Mohammed Ezzaldin Babiker Abdullah, Rufaidah Abdallah Ibrahim Mohammed

Accurate Global Horizontal Irradiance (GHI) forecasting is critical for grid stability, particularly in arid regions characterized by rapid aerosol fluctuations. While recent trends favor computationally expensive Transformer-based architectures, this paper challenges the prevailing "complexity-first" paradigm. We propose a lightweight, Physics-Informed Hybr

Tong Lu, Huaizhong Zhao

In this paper, we consider the classical spin systems on unbounded lattices given by infinite-dimensional stochastic differential equations (SDEs). We assume that the stochastic forcing acts only on one particle. The other particles are not subject to stochastic forcing directly, but interact with their nearest neighbouring particles. Under the above highly

Xinjin Li, Jinghan Cao, Mengyue Wang, Yue Wu

Traffic forecasting requires modeling complex temporal dynamics and long-range spatial dependencies over large sensor networks. Existing methods typically face a trade-off between expressiveness and efficiency: Transformer-based models capture global dependencies well but suffer from quadratic complexity, while recent selective state-space models are computa

Ishani Mondal, Yiwen Song, Mihir Parmar, Palash Goyal

Long-form visual storytelling requires maintaining continuity across shots, including consistent characters, stable environments, and smooth scene transitions. While existing generative models can produce strong individual frames, they fail to preserve such continuity, leading to appearance changes, inconsistent backgrounds, and abrupt scene shifts. We intro

Xingen Zheng, Ping Tang, Xuejuan Liu, Zhixiong Li

We predict the emergence of nontrivial topological magnon states in the skyrmionium lattice with zero topological charge. We propose the concept of weighted magnetic flux, which provides a clear physical picture for this anomalous phenomenon. We also map the skyrmionium lattice onto the Haldane model, offering an alternative framework for interpreting this.

Shenghan Gao, Kathy Lüdge, Francesco Da Ros, Nathan Youngblood

Coherent photonic computing uses both the phase and amplitude of light to implement linear operations such as dot products and matrix multiplication but requires phase stability between the interfering paths. This poses a challenge for such strategies when optical data is generated at a remote source due to environmental phase variations in fiber. Convention

Quang Minh Thai, Rajesh Kumar, Justin Rudie, Xiaoxin Wang

Separate absorption charge multiplication germanium tin on silicon avalanche photodiode offers a viable solution to achieve CMOS compatible, high sensitivity detection technology in SWIR or extended SWIR range, leveraging the excellent k-factor of Si as multiplication layer and SWIR or e-SWIR band absorption of GeSn. However, unlike well-established growth o

Lemeng Wang, Qinqian Lei, Vidhi Bakshi, Daniel Yi

Human-object interaction (HOI) detection aims to detect interactions between humans and objects in images. While recent advances have improved performance on existing benchmarks, their evaluations mainly focus on overall prediction accuracy and provide limited insight into the underlying causes of model failures. In particular, modern models often struggle i

Xingting Yan, Yuetao Meng, Nana Bao, Youwen Sun

Toroidal rotation is crucial for maintaining stable and high performance plasmas in tokamak fusion reactors. Among its driving mechanisms, the neoclassical toroidal viscosity (NTV) torque--induced by three-dimensional magnetic perturbations--is particularly significant due to its strong impact and controllability, especially for reactor-scale devices like IT

Tony Wong

Recent U.S. Environmental Protection Agency (EPA) analyses have argued that greenhouse gas emissions from U.S. on-road vehicles contribute negligibly to global mean sea-level rise (GMSLR). Here, I replicate and extend the EPA's modeling framework using the FaIR climate model coupled with the BRICK sea-level model, incorporating a probabilistic weighting appr

Mohammadreza Salehi, Matteo Ciabattoni, Francesco Monticone

For over a decade, photonic time crystals have promised access to novel and exotic optical phenomena, offering fundamentally new ways to manipulate classical and quantum light. Central to these capabilities is the emergence of momentum bandgaps -- the counterpart of the more familiar frequency bandgaps in spatial crystals -- which have proven difficult to ob