March 2026 arXiv papers — page 27

Zizhan Tang, Yao Liu, Jessica Liu

We present a safety-oriented framework for autonomous underwater vehicles (AUVs) that improves localization accuracy, enhances trajectory prediction, and supports efficient search operations during communication loss. Acoustic signals emitted by the AUV are detected by a network of fixed buoys, which compute Time-Difference-of-Arrival (TDOA) range-difference

Guilin Zhang, Wulan Guo, Ziqi Tan, Chuanyi Sun

Deep learning applications at the network edge lead to a significant growth in AI-related carbon emissions, presenting a critical sustainability challenge. The existing edge computing frameworks optimize for latency and throughput, but they largely ignore the environmental impact of inference workloads. This paper introduces CarbonEdge, a carbon-aware deep l

Zoltán Vörös, Owen Wyn Roberts, Yasuhito Narita, Emiliya Yordanova

Dynamo action refers to energy exchange processes through which magnetic fields are generated at the expense of kinetic energy of the plasma flows. Dynamos can generate magnetic fields across scales larger or smaller than the flows themselves. Multi-scale dynamo processes underpin magnetic phenomena from planetary cores to stellar and galactic environments,

Shivam Pandey

Can drivers' situation awareness during automated driving be maintained using haptic cues that provide information about road and traffic scenarios while the drivers are engaged in a secondary task? And can this be done without disengaging them from the secondary task? Multiple Resource Theory predicts that using different sensory channels can improve multip

Yuxuan Ren, Shijie Deng

This paper presents a novel centroid-based heuristic algorithm, termed Kempe Swap K-Means, for constrained clustering under rigid must-link (ML) and cannot-link (CL) constraints. The algorithm employs a dual-phase iterative process: an assignment step that utilizes Kempe chain swaps to refine current clustering in the constrained solution space and a centroi

Nimesh Khandelwal, Shakti S. Gupta

This paper documents a case study in agent-driven autonomous reinforcement learning research for quadruped locomotion. The setting was not a fully self-starting research system. A human provided high-level directives through an agentic coding environment, while an agent carried out most of the execution loop: reading code, diagnosing failures, editing reward

Yitao Li

Classical optimization algorithms--hill climbing, simulated annealing, population-based methods--generate candidate solutions via random perturbations. We replace the random proposal generator with an LLM agent that reasons about evaluation diagnostics to propose informed candidates, and ask: does the classical optimization machinery still help when the prop

Charlie Cowen-Breen, Alekh Agarwal, Stephen Bates, William W. Cohen

Statistical estimation often involves tradeoffs between expensive, high-quality measurements and a variety of lower-quality proxies. We introduce Multiple-Prediction-Powered Inference (MultiPPI): a general framework for constructing statistically efficient estimates by optimally allocating resources across these diverse data sources. This work provides theor

Andreas Jungherr, Adrian Rauchfleisch

As AI-enabled systems become available for political campaign outreach, an important question has received little empirical attention: how do people evaluate the communicative practices these systems represent, and what consequences do those evaluations carry? Most research on AI-enabled persuasion examines attitude change under enforced exposure, leaving as

Isaac Llorente-Saguer

We present LatentBiopsy, a training-free method for detecting harmful prompts by analysing the geometry of residual-stream activations in large language models. Given 200 safe normative prompts, LatentBiopsy computes the leading principal component of their activations at a target layer and characterises new prompts by their radial deviation angle $\theta$ f

Mengqi Shi

The rapid advancement of AI companionship systems has positioned them as scalable interventions for addressing social isolation. Current design approaches emphasize maximizing user engagement and satisfaction, treating effective alignment between AI capabilities and user needs as an unqualified success. However, this framing may overlook a critical dimension

Lance Ying, Aydan Y. Huang, Aviv Netanyahu, Andrei Barbu

People infer rich social information from others' actions. These inferences are often constrained by the physical world: what agents can do, what obstacles permit, and how the physical actions of agents causally change an environment and other agents' mental states and behavior. We propose that such rich social perception is more than visual pattern matching

P. Kràl, S. S. Islam, Andrea N. Capa Salinas, J. N. Graham

Understanding the phase diagram of kagome superconductors from a microscopic perspective is crucial for clarifying the interplay between charge order and superconductivity. Ti-doped CsV$_{3}$Sb$_{5}$ exhibits a nonmonotonic temperature-doping phase diagram in which both $T_{\rm c}$ and the charge-order temperature initially decrease with doping, followed by

Kuo-Ho Yang, Robert D. Nevels

We derive analytic solutions for the longitudinal and the transverse components of the vector potential in the Lorenz gauge for an arbitrary time-dependent charge-current distribution.

O. Gerguri, P. Kràl, M. Spitaler, M. Salamin

Kagome materials provide a powerful platform for exploring how flat electronic bands promote symmetry-breaking quantum states, yet studies have so far focused mainly on kagome-derived $d$-electron flat bands. In this paper, we introduce CeRu$_{3}$Si$_{2}$, a kagome superconductor in which our first-principles calculations show the coexistence of Ru $d$-orbit

Jianming Wen

We develop a differential source-encoding protocol for local parameter estimation in a time-reversed Young interferometer, where the source plane is used not merely as a scan coordinate but as a programmable measurement basis. Two sequential positive-only source patterns implement an antisymmetric differential probe about a chosen operating point, converting

Peter J. F. Lucas, Eleonora Zullo, Fabio Stella

In this paper, the relationship between probabilistic graphical models, in particular Bayesian networks, and causal diagrams, also called structural causal models, is studied. Structural causal models are deterministic models, based on structural equations or functions, that can be provided with uncertainty by adding independent, unobserved random variables

Jakub Masłowski, Jarosław A. Chudziak

Large Language Models (LLMs) are being increasingly used as autonomous agents in complex reasoning tasks, opening the niche for dialectical interactions. However, Multi-Agent systems implemented with systematically unconstrained systems systematically undergo semantic drift and logical deterioration and thus can hardly be used in providing ethical tutoring w

Kai Ye, Qingtao Pan, Shuo Li

Large language models (LLMs) need reliable test-time control of hallucinations. Existing conformal methods for LLMs typically provide only \emph{marginal} guarantees and rely on a single global threshold, which can under-cover hard prompts, over-cover easy ones, and produce oversized prediction sets. We propose \emph{Conditional Factuality Control} (CFC), a

Ahmet Yusuf Salim, Jianan Wu, Soner Seçkiner, Eslam Elmitwalli

A 64-spin all-to-all current-mode coupling Ising machine is implemented in 65 nm CMOS. The design supports 31 coefficient levels in 0.943 mm2 and achieves Energy-to-Solution (ETS) of 2.28 nJ/edge-bit. Continuous programming refresh not only mitigates leakage but also provides a mechanism for deterministic energy landscape perturbation, which consistently imp

Shtefan V. Sanduleanu, Peter Yu. Shlykov, Alexei N. Bolgar, Daria A. Kalacheva

Lasing - an effect of orthodox quantum mechanics - was discovered in 1955 and recognized by the Nobel Prize in 1964 due to its fundamentality. Nowadays, lasers and masers routinely work with electromagnetic waves and consist of a resonator with an active medium - usually a system of atoms with population inversion mechanism. Amazingly, quantum mechanics rema

Dwait Bhatt, Shih-Chieh Chou, Nikolay Atanasov

Several approaches have been proposed to improve the sample efficiency of online reinforcement learning (RL) by leveraging demonstrations collected offline. The offline data can be used directly as transitions to optimize RL objectives, or offline policy and value functions can first be learned from the data and then used for online finetuning or to provide

Ching Christie Pang, Xuetong Wang, Yuk Hang Tsui, Pan Hui

Online knowledge communities (OKC) such as Stack Exchange, Reddit, and Zhihu have long functioned as socio technical infrastructures for collective problem solving. The rapid adoption of Generative AI (GenAI) introduces both complementarity and substitution. Large language models (LLMs) offer faster, more accessible drafts, yet divert traffic and contributio

Albert Tang

Autism Spectrum Disorder (ASD) affects more than 75 million people worldwide. However, scalable support for practicing everyday conversation is scarce: Low-cost activities such as story reading yield limited improvement. At the same time, effective role-play therapy demands expensive, in-person sessions with specialists. SocialWise bridges this gap through a

Anu V S Nath, Pijush Patra, Anubhab Roy

In a dilute non-Brownian suspension undergoing simple shear, pairwise hydrodynamic interactions are fore-aft symmetric at zero Reynolds number and produce no net cross-streamline displacement. A weak central repulsive force between particles breaks this symmetry, deflecting trajectories and generating irreversible transverse displacements that cumulatively y

Jhonathan Barrios, Yásser Echávez, Carlos F. Álvarez

We propose a topological framework for detecting Hopf-type dynamical transitions directly from scalar time series. The method combines delay-coordinate reconstruction with persistent homology and uses the maximum persistence of one-dimensional homology classes as a scalar descriptor of cyclic structure. For the supercritical Hopf setting, we derive finite-re

Pavel A. Andreev

The small amplitude perturbations of spins are considered in the four component antiferromagnetic materials with the equilibrium state of form up-up-down-down (uniaxial samples). Other configurations for the four component antiferromagnetic materials and two component antiferromagnetic materials are briefly considered for the comparison with main regime. Dis

Karl Svozil

Partition logics -- non-Boolean event structures obtained by pasting Boolean algebras -- provide a natural language for situations in which a system has a definite latent state but can be accessed and resolved only through mutually incompatible coarse-grained modes of observation. We show that this structure arises in a range of social-science settings by co

Magdalena Mittermeier, Christian M. Grams, Urs Beyerle, Laura Suarez-Gutierrez

Omega-blocks can trigger spatially compounding heat-precipitation extremes with severe societal impacts, as seen in September 2023 when a heatwave over France coincided with devastating floods in the Iberian Peninsula and Greece. Although blocking in general has been linked to moist processes in upstream warm conveyor belts (WCBs), it has remained unexplored

Jiaxing Zhao, Taesoo Song, Joerg Aichelin, Elena Bratkovskaya

We investigate charmonium production and the charmonium-nucleon correlation function in pp collisions using the EPOS4+CATS framework. For the first time, the emission source of charmonium-proton pairs is dynamically generated and found to be non-Gaussian. This enables a femtoscopic extraction of the charmonium-proton interaction directly from experimental co

Naveen Mysore

Reinforcement learning algorithms assume that observations satisfy the Markov property, yet real-world sensors frequently violate this assumption through correlated noise, latency, or partial observability. Standard performance metrics conflate Markov breakdowns with other sources of suboptimality, leaving practitioners without tools to detect such violation

Weimin Han, Shengda Zeng

This paper is devoted to the well-posedness analysis of a nonstationary Stokes hemivariational inequality for an incompressible fluid flow described by the Stokes equations subject to a nonsmooth boundary condition of friction type described by the Clarke subdifferential. In a recent paper [19], well-posedness of the nonstationary Stokes hemivariational ineq

Marino P. Lenzarini, Diogo O. Soares-Pinto

How quantum coherence influences thermodynamic behavior remains an open question in quantum thermodynamics. Here we investigate this relation within the pure dephasing framework, where a central qubit interacts with a finite Ising-like spin environment. Although the system's internal energy remains constant, the interaction induces decoherence and gives rise

Wilailuck Treerath, Fabrizio Pittorino

Active learning (AL) reduces labeling cost by querying informative samples, but in tabular settings its cold-start gains are often limited because uncertainty estimates are unreliable when models are trained on very few labels. Tabular foundation models such as TabPFN provide calibrated probabilistic predictions via in-context learning (ICL), i.e., without t

Rachid El Aitouni, Clarence Cortes, David Laroze, Ahmed Jellal

We investigate the influence of a magnetic field on the electronic properties of WS$e_2$ with a focus on spin-orbit coupling, spin and valley polarization, and conductance. We solve the eigenvalue equation analytically and use the continuity equation to determine the transmission probability based on current densities. We calculate the conductance using B\"u

Nazia Tasnim, Shrimai Prabhumoye, Bryan A. Plummer

Parameter Recombination (PR) methods aim to efficiently compose the weights of a neural network for applications like Parameter-Efficient FineTuning (PEFT) and Model Compression (MC), among others. Most methods typically focus on one application of PR, which can make composing them challenging. For example, when deploying a large model you may wish to compre

Mayur Sawant, Abdelhamid Tayebi

We consider the constrained stabilization problem of second-order systems evolving on the n-sphere. We propose a control strategy with a constraint proximity-based dynamic damping mechanism that ensures safe and almost global asymptotic stabilization of the target point in the presence of star-shaped constraints on the n-sphere. It is also shown that the pro

Md Jueal Mia, Joaquin Molto, Yanzhao Wu, M. Hadi Amini

Small Language Models (SLMs) are emerging as efficient and economically viable alternatives to Large Language Models (LLMs), offering competitive performance with significantly lower computational costs and latency. These advantages make SLMs suitable for resource-constrained and efficient deployment on edge devices. However, existing jailbreak defenses show

Rostyslav Hnatyshyn, Danny Perez

Large-scale atomistic simulations can produce extreme volumes of information in the form of long trajectories. Reliably and automatically extracting key information from such datasets remains a formidable challenge, especially as it pertains to the analysis of the structural transitions affecting the system. We present a novel approach to characterize and co

M. H. M. Rashid

This work resolves the open problem of strong singularity ($\alpha(z)> 1$) in nonlocal Kirchhoff-type equations with variable exponents through five original theorems that collectively establish a comprehensive theory. Beginning with weighted Sobolev spaces and existence via truncation, we develop comparison principles, optimal regularity results, and when c

Albert Fannjiang, Weilin Li

We develop a multidimensional version of Gradient-MUSIC for estimating the frequencies of a nonharmonic signal from noisy samples. The guiding principle is that frequency recovery should be based only on the signal subspace determined by the data. From this viewpoint, the MUSIC functional is an economical nonconvex objective encoding the relevant information

D. V. Belskiy

New determinant equalities were obtained based on the Wronskian formulas for a particular solution of the Volterra chain. Using the relationship between the Toda and Volterra chains, new first integrals for the Volterra chain are calculated using the first integrals for the Toda chain. Using the first integrals, a periodic Volterra chain with a period of fiv

Apoorv Kumar Masta, Srinjoy Ganguly, Shalini Devendrababu, Farina Riaz

Quantum machine learning has faced growing scrutiny over its practical advantages compared to classical approaches, particularly following dequantization results and large scale benchmarking studies that have challenged earlier optimistic claims. This work presents a systematic empirical evaluation of non unitary quantum machine learning implemented via the

Aayushi Dangol, Robert Wolfe, Nisha Devasia, Mitsuka Kiyohara

Two of the most socially consequential issues facing today's children are the rise of artificial intelligence (AI) and the rapid changes to the earth's climate. Both issues are complex and contested, and they are linked through the notable environmental costs of AI use. Using a systems thinking framework, we developed an interactive system called Ecoprompt t

Yuhang Han, Yuyang Wu, Zhengbo Jiao, Yiyu Wang

Reinforcement Learning from Verifiable Rewards (RLVR) has substantially enhanced the reasoning capabilities of large language models in abstract reasoning tasks. However, its application to Large Vision-Language Models (LVLMs) remains constrained by a structural representational bottleneck. Existing approaches generally lack explicit modeling and effective u

Spencer Schutz, Charlott Vallon, Francesco Borrelli

In adaptive-sampling control, the control frequency can be adjusted during task execution. Ensuring that these changes do not jeopardize the safety of the system being controlled requires attention. We introduce robust M-step hold model predictive control (MPC) to address this. Our formulation provides robust constraint satisfaction for an uncertain discrete

Wahab Khawaja, Nestor J Hernandez Marcano, Rune Hylsberg Jacobsen

Digital Video Broadcasting Satellite, Second Generation and its extension DVBS2X are widely used in modern satellite communications, where synchronization relies on physical layer headers, pilot symbols, and optional superframe structures but lacks defined implementation methods. This work explores the use of external synchronization to enhance DVBS2 perform

Susnata Seth

The DEAP-3600 experiment, with an approximately 3.3 tonne liquid argon (LAr) target, is currently the world's largest single-phase LAr dark matter detector. It is located 2 km underground at SNOLAB, Canada, one of the most radiopure underground laboratories. With excellent pulse-shape discrimination against low-energy beta decays and precise position reconst

Ke Li, Tianjia Yang, Kaidi Liang, Xianbiao Hu

Video prediction is a useful function for autonomous driving, enabling intelligent vehicles to reliably anticipate how driving scenes will evolve and thereby supporting reasoning and safer planning. However, existing models are constrained by multi-stage training pipelines and remain insufficient in modeling the diverse motion patterns in real driving scenes

Bogdan Grechuk, Anton Malandii, Terry Rockafellar, Stan Uryasev

This paper revisits and extends the 2013 development by Rockafellar and Uryasev of the Risk Quadrangle (RQ) as a unified scheme for integrating risk management, optimization, and statistical estimation. The RQ features four stochastics-oriented functionals -- risk, deviation, regret, and error, along with an associated statistic, and articulates their reveal

Jiguang Yu, Louis Shuo Wang, Ye Liang

In this paper, we formulate and analyze an original infinite-horizon bioeconomic optimal control problem for a nonlinear, size-structured fish population. Departing from standard endogenous reproduction frameworks, we model population dynamics using a McKendrick--von Foerster partial differential equation characterized by strictly exogenous lower-boundary re

Yotam Katzman, Mor Elgarisi, Amos A. Hari, Jonathan Ericson

The conventional manufacturing of ophthalmic lenses is an inefficient subtractive process where up to 97% of the material is discarded through grinding, polishing, and edging. Fluidic Shaping has emerged as a powerful alternative, utilizing surface tension to form optical-quality surfaces. While the approach enabled the creation of ophthalmic lenses without

Aviraj Bevli, Sofian Chaybouti, Yasser Dahou, Hakim Hacid

Perception-centric systems are typically implemented with a modular encoder-decoder pipeline: a vision backbone for feature extraction and a separate decoder (or late-fusion module) for task prediction. This raises a central question: is this architectural separation essential or can a single early-fusion stack do both perception and task modeling at scale?

Narges Golmohammadi, Madan Mohan Rayguru, Sabur Baidya

This work proposes a novel learning driven bandwidth optimization framework called DRASTIC (Dynamic Resource Allocation for Slicing in Task aware Closed loop tactile Internet applications). The proposed framework dynamically allocates resources among network slices supporting both enhanced Mobile Broadband (eMBB) and high reliable low latency communication (

Badal Nyalang, Biman Debbarma

We present KokborokMT, a high-quality neural machine translation (NMT) system for Kokborok (ISO 639-3), a Tibeto-Burman language spoken primarily in Tripura, India with approximately 1.5 million speakers. Despite its status as an official language of Tripura, Kokborok has remained severely under-resourced in the NLP community, with prior machine translation

Lishan Li, Jun Hu, Yi Qi, Yu Sun

In this paper, we apply the classical Perron method to give a proof of the existence and uniqueness/rigidity result of a circle pattern on a closed surface equipped with conical spherical metric when prescribed measures of the angles of intersecting circles stay in the range (0,{\pi}/2] and total geodesic curvatures are assigned to the circles, which is rece

Chi Cheuk Tsang

We show that every pseudo-Anosov flow on a graph manifold is almost equivalent, i.e. orbit equivalent in the complement of a finite collection of closed orbits, to a totally periodic pseudo-Anosov flow or a suspension Anosov flow. The proof is via a hands-on construction of a partial Birkhoff section with genus one components that misses finitely many closed

Jyotsana Khatri, Manasi Patwardhan

Rebuttal generation is a critical component of the peer review process for scientific papers, enabling authors to clarify misunderstandings, correct factual inaccuracies, and guide reviewers toward a more accurate evaluation. We observe that Large Language Models (LLMs) often struggle to perform targeted refutation and maintain accurate factual grounding whe

Amir Zeldes, Katherine Conhaim, Lauren Levine

Despite a long tradition of work on extractive summarization, which by nature aims to recover the most important propositions in a text, little work has been done on operationalizing graded proposition salience in naturally occurring data. In this paper, we adopt graded summarization-based salience as a metric from previous work on Salient Entity Extraction

Noa Kraicer, Erez Yosef, Raja Giryes

Polarization imaging captures the polarization state of light, revealing information invisible to the human eye yet valuable in domains such as biomedical diagnostics, autonomous driving, and remote sensing. However, conventional polarization cameras are often expensive, bulky, or both, limiting their practical use. Lensless imaging offers a compact, low-cos

Maziar Kianimoghadam Jouneghani

Recognizing information disorder is difficult because judgments about manipulation depend on cultural and linguistic context. Yet current Large Language Models (LLMs) often behave as monocultural, English-centric "black boxes," producing fluent rationales that overlook localized framing. Preliminary evidence from the multilingual Information Disorder (InDor)

Joon-Hwi Kim, Sangmin Lee

We establish an equivalence between massive spinning particle models in four spacetime dimensions coupled to electromagnetism or gravity, within the spin-magnitude-preserving sector. Four representative models in the literature are shown to describe exactly the same physics in their free and interacting theories: vector oscillator, spinor oscillator, spheric

Kejun Liu

Maxwell counting predicts an isostatic threshold at $\langle r\rangle = 2.4$ for covalent network glasses, but which structural correlations actually produce rigidity near this point is still unclear. In this work, we test four candidates: enthalpic stress, chemical defects, geometric interlocking, and medium-range order (MRO). We use a locally tree-like con

Gian-Luca Geuken, Patrick Kurzeja, David Wiedemann, Martin Zlatić

This work investigates different sufficient and necessary criteria for hyperelastic, isotropic polyconvex material models, focusing on neural network implementations for compressible and incompressible materials. Furthermore, the expressiveness, accuracy, simplicity as well as the efficiency of those models is analyzed. This also enables an assessment of the

Caroline Wagner, Xiaojing Cai

The global network of scientific cooperation has undergone major restructuring over the past two decades, with important implications for geopolitics and science policy. China's integration into this network has redistributed positions of influence in ways that challenge zero-sum views of national competition and security. Drawing on structural holes theory

Amartya Bhattacharya

Vision-language models (VLMs) excel at image-text retrieval yet persistently fail at compositional reasoning, distinguishing captions that share the same words but differ in relational structure. We present, a unified evaluation and augmentation framework benchmarking four architecturally diverse VLMs,CLIP, BLIP, LLaVA, and Qwen3-VL-8B-Thinking,on the Winogr

Lynn Vonderhaar, Timothy Elvira, Tyler Thomas Procko, Omar Ochoa

With the ubiquity of computer vision in industry, the importance of image provenance is becoming more apparent. Provenance provides information about the origin and derivation of some resource, e.g., an image dataset, enabling users to trace data changes to better understand the expected behaviors of downstream models trained on such data. Provenance may als

Javier Rodríguez, Raquel Giménez, Jesús Malo

Quantitative estimation of information flow in early vision with psychophysically realistic networks is still an open issue. This is because, up to date, the necessary elements (general and plausible network, accurate noise, and reliable information measures) have not been put together. As a result, previous works made different approximations that limit the

Yu Zhang, Karl Mason

Robotic manipulation remains challenging for reinforcement learning due to contact-rich dynamics, long horizons, and training instability. Although off-policy actor-critic algorithms such as SAC and TD3 perform well in simulation, they often suffer from policy oscillations and performance collapse in realistic settings, partly due to experience replay strate

Olena Atlasiuk, Vladimir Mikhailets, Jari Taskinen

We study a wide class of linear inhomogeneous boundary-value problems for $r$th order ODE-systems depending on a parameter $\mu$ belonging to a general metric space $\mathcal M$. The solutions belong to the Sobolev spaces $(W^{n+r}_p)^m$, $n\in\mathbb{N}\cup\{0\}$, $m, r \in \mathbb{N}$, $1\leq p\leq \infty$. The boundary conditions are of a most general for

Ted Lentsch, Santiago Montiel-Marín, Holger Caesar, Dariu M. Gavrila

LiDAR perception is fundamental to robotics, enabling machines to understand their environment in 3D. A crucial task for LiDAR-based scene understanding and navigation is ground segmentation. However, existing methods are either handcrafted for specific sensor configurations or rely on costly per-point manual labels, severely limiting their generalization an

Dengzhe Hou, Lingyu Jiang, Deng Li, Zirui Li

Existing large language models (LLMs) evaluations use fixed-difficulty benchmarks that cannot adapt as models improve, and rarely isolate specific cognitive processes. We introduce Working Memory Fidelity-Active Manipulation (WMF-AM), a probe of cumulative state tracking, the ability to maintain and update intermediate results across K sequential operations

Yhonatan Gayer

Spherical Harmonics ROOM), an open-source Python library for room acoustics simulation using Ambisonics, available at https://github.com/Yhonatangayer/shroom and installable via \texttt{pip install pyshroom}. \textbf{shroom} projects image-source contributions onto a Spherical Harmonics (SH) basis, yielding a composable pipeline for binaural decoding, spheri

Pengcheng Pan, Yonekura Shogo, Kuniyoshi Yasuo

Optimizing vision models purely for classification accuracy can impose an alignment tax, degrading human-like scanpaths and limiting interpretability. We introduce EVA, a neuroscience-inspired hard-attention mechanistic testbed that makes the performance-human-likeness trade-off explicit and adjustable. EVA samples a small number of sequential glimpses using

Claus Gerhardt

We quantize the exterior region of a Schwarzschild-AdS black hole using our model of quantum gravity. The resulting hyperbolic equation is solved by products of temporal eigenfunctions $w_i$, the eigenvalues of which all have multiplicity one, and spatial eigendistributions $v_{ij}$ having the same eigenvalues but with multiplicities $1\le m_i$, where the $m

Michael Ripa, Jim Davies

Recent advancements in language model technology have significantly enhanced the ability to edit factual information. Yet, the modification of moral judgments, a crucial aspect of aligning models with human values, has garnered less attention. In this work, we introduce CounterMoral, a benchmark dataset crafted to assess how well current model editing techni

Joonhyung Bae

The global landscape of art-technology institutions, including festivals, biennials, research labs, conferences, and hybrid organizations, has grown increasingly diverse, yet systematic frameworks for analyzing their multidimensional characteristics remain scarce. This paper proposes ASTRA (Art-technology Institution Spatial Taxonomy and Relational Analysis)

Fabio Lokwani Di Matteo, Pier Luigi Sacco

Why do some national music markets sustain a rich musical diversity whereas others converge on mostly formulaic output? The existing models of cultural consumption (superstar economics, rational addiction, Bayesian social learning) each capture part of the answer, but none can explain how exposure, social influence, institutional gatekeeping, and algorithmic

Afreen Islam

In this work, Global Position System (GPS) data from a flock of homing pigeons are analysed. The flocking behaviour of the considered homing pigeons is formulated as a swarm optimal trajectory tracking control problem. The swarm problem in this work is modeled with the idea that one or two pigeons at the forefront lead the flock. Each follower pigeon is assu

Yiqing Zhang, Xiaozhong Liu, Fabricio Murai

Trustworthy biomedical question answering (QA) systems must not only provide accurate answers but also justify them with current, verifiable evidence. Retrieval-augmented approaches partially address this gap but lack mechanisms to iteratively refine poor queries, whereas self-reflection methods kick in only after full retrieval is completed. In this context

Andreas Pyka, Violetta Weger

The homogeneous metric can be viewed as a natural extension of the Hamming metric to finite chain rings. It distinguishes between three types of elements: zero, non-zero elements in the socle, and elements outside the socle. Since the Singleton bound is one of the most fundamental and widely studied bounds in classical coding theory, we investigate its analo

Jia Li, Zeyang Zhuang, Zhuangbin Chen, Yuxin Su

Compilation errors pose pervasive and critical challenges in software development, significantly hindering productivity. Therefore, Automated Compilation Error Repair (ACER) techniques are proposed to mitigate these issues. Despite recent advancements in ACER, its real-world performance remains poorly evaluated. This can be largely attributed to the limitati

Kaishen Wang, Heng Huang

Recent advances in Large Language Models (LLMs) and Text-to-Image (T2I) models have led to the emergence of Unified Multimodal Models (UMMs), where multimodal understanding and image generation are tightly integrated within a shared architecture. Prior studies suggest that such reciprocity enhances cross-functionality performance through shared representatio

Qinghao Guan, Yuchen Pan, Donghao Li, Zishi Zhang

In religion and theology studies, spirituality has garnered significant research attention for the reason that it not only transcends culture but offers unique experience to each individual. However, social scientists often rely on limited datasets, which are basically unavailable online. In this study, we collaborated with social scientists to develop a hig

João Areias, Jorge Picado

We study four adjoint situations in pointfree topology that interchange images and preimages with closure and interior operators and establish with them a number of characterisations for meet-preserving maps, localic maps, open maps (in a broad sense) and open localic maps between locales. The principal and most attractive feature of these adjunctions is tha

Pina Romaniello, J. Arjan Berger

We present the screened multichannel Dyson equation for the simulation of both direct and inverse photoemission spectra from first principles. The screened multichannel Dyson equation improves upon the standard multichannel Dyson equation by correctly including the screening of all particle-particle and electron-hole interactions due to the presence of the o

Hussein Naser, Hashim A. Hashim, Mojtaba Ahmadi

This paper introduces an advanced Quaternion-based Unscented Kalman Filter (QUKF) for real-time, robust estimation of system states and external wrenches in assistive aerial payload transportation systems that engage in direct physical interaction. Unlike conventional filtering techniques, the proposed approach employs a unit-quaternion representation to inh

Debarshi Majumder, Binod Sreenivasan

Two-component convection driven by both compositional and thermal buoyancy within the fluid core of a rapidly rotating planet produces a predominantly axial dipole field. In a dynamo driven by strong compositional buoyancy that by itself destabilizes the axial dipole, the addition of relatively weak thermal buoyancy establishes the dipole field through the s

Amitabh Chakravorty, Matthew Price, Nelly Elsayed, Zag ElSayed

Phishing attacks remain among the most prevalent cybersecurity threats, causing significant financial losses for individuals and organizations worldwide. This paper presents a machine learning-based phishing email detection system that analyzes email body content using natural language processing (NLP) techniques. Unlike existing approaches that primarily fo

Youcef Soufiane Gheffari, Oussama Mustapha Benouddane, Samiya Silarbi

Recognizing emotions from speech using machine learning has become an active research area due to its importance in building human-centered applications. However, while many studies have been conducted in English, German, and other European and Asian languages, research in Arabic remains scarce because of the limited availability of annotated datasets. In th

Jinwu Yang, Jiaan Wu, Zedong Liu, Xinyang Ma

The rapid scaling of Large Language Models presents significant challenges for their deployment and inference, particularly on resource-constrained specialized AI hardware accelerators such as Huawei's Ascend NPUs, where weight data transfer has become a critical performance bottleneck. While lossless compression can preserve model accuracy and reduce data v

Mehedi Hasan Tusar, Fateme Fayyazbakhsh, Igor Melnychuk, Ming C. Leu

Accurate wound classification and boundary segmentation are essential for guiding clinical decisions in both chronic and acute wound management. However, most existing AI models are limited, focusing on a narrow set of wound types or performing only a single task (segmentation or classification), which reduces their clinical applicability. This study present

D. Ambrosino, R. Colalillo, V. M. Grieco, F. Guarino

Water Cherenkov Detectors (WCDs) are pivotal in various scientific fields, including neutrino physics, gamma-ray astronomy, and cosmic-ray research. The detection sensitivity and precision of these detectors crucially rely on photomultiplier tubes (PMTs) to capture Cherenkov radiation produced by charged particles moving faster than the speed of light in wat

Didier Alain Njamen Njomen, Fidel Djongreba Ndikwa

The aim of this article is to determine a new six-parameter Beta Weibull distribution and its various associated functions, namely the cumulative distribution, survival, probability density and hazard functions. Next, we determine the sub-distributions of the new distribution and show that the latter generalizes those of the literature. Finally, numerical si

Ana Luiza Souza Tavares, Henrique Santos Lima, Artur Pedro Martins Neto, Bruno Duarte Gomes

We characterize the amplitude statistics of intraoperative microelectrode recordings (MERs) obtained during deep brain stimulation (DBS) surgery in 46 patients with Parkinson's disease, using 184 recordings equally balanced between inside and outside the subthalamic nucleus (STN). The probability density of every recording is quantitatively well described by

Soyeon Park, Doohee Chung, Charmgil Hong

Forecasting multivariate time series remains challenging due to complex cross-variable dependencies and the presence of heterogeneous external influences. This paper presents Spectrogram-Enhanced Multimodal Fusion (SEMF), which combines spectral and temporal representations for more accurate and robust forecasting. The target time series is transformed into

Juraj Bodik

Retrospective causal questions ask what would have happened to an observed individual had they received a different treatment. We study the problem of estimating $\mu(x,y)=\mathbb{E}[Y(1)\mid X=x,Y(0)=y]$, the expected counterfactual outcome for an individual with covariates $x$ and observed outcome $y$, and constructing valid prediction intervals under the

Bruno Mera, José M. Mourão, João P. Nunes, Carolina Paiva

In this paper, we apply techniques of geometric quantization to study the response of the integer and fractional quantum Hall effects to toroidal geometry deformation. The main method is that of using complex time Hamiltonian evolution to induce the geometry change and then the associated generalized coherent state transforms (gCST) to find the evolution of

Simon WS Fischer, Hanna Schraffenberger, Serge Thill, Pim Haselager

Many generative AI systems as well as decision-support systems (DSSs) provide operators with predictions or recommendations. Various studies show, however, that people can mistakenly adopt the erroneous results presented by those systems. Hence, it is crucial to promote critical thinking and reflection during interaction. One approach we are focusing on invo

Leixin Chang, Xinchen Yao, Ben Liu, Liangjing Yang

On-policy reinforcement learning (RL) algorithms have demonstrated great potential in robotic control, where effective exploration is crucial for efficient and high-quality policy learning. However, how to encourage the agent to explore the better trajectories efficiently remains a challenge. Most existing methods incentivize exploration by maximizing the po