October 2025 arXiv papers — page 83

Maksym Deliyergiyev, Morgan Le Delliou, Antonino Del Popolo

We explore the Hubble tension within an anisotropic cosmological framework by revisiting the Bianchi type-I model introduced in Le Delliou et al. 2020. Motivated by ongoing debates surrounding back-reaction effects and observed anomalies in the cosmic microwave background (CMB), we investigate whether a departure from isotropy in the late Universe could reco

Smita Khapre

Cyberattack detection in Critical Infrastructure and Supply Chains has become challenging in Industry 4.0. Intrusion Detection Systems (IDS) are deployed to counter the cyberattacks. However, an IDS effectively detects attacks based on the known signatures and patterns, Zero-day attacks go undetected. To overcome this drawback in IDS, the integration of a De

Shifa Sulaiman, Francesco Schetter, Mohammad Gohari, Fanny Ficuciello

The significance of employing a controller in prosthetic hands cannot be overstated, as it plays a crucial role in enhancing the functionality and usability of these systems. This paper introduces an adaptive neuro-controller specifically developed for a tendon-driven soft continuum wrist of a prosthetic hand. Kinematic and dynamic modeling of the wrist is c

Chloë Hebborn, Carl R. Brune, Daniel R. Phillips

We examine the relationship between the Asymptotic Normalization Coefficient (ANC) of $^6$Li and other low-energy observables in the $\alpha$-deuteron system. Our analysis uses a set of calculations carried out within the {\it ab initio} No Core Shell Model with Continuum (NCSMC) using a variety of inter-nucleon interactions and basis sizes, and yielding ${}

Javad Eshtiyagh, Pei Zhao, Federico Librino, Giovanni Resta

Since the COVID-19 pandemic, online grocery shopping has rapidly reshaped consumer behavior worldwide, fueled by ever-faster delivery promises aimed at maximizing convenience. Yet, this growth has also substantially increased urban traffic congestion, emissions, and pollution. Despite extensive research on urban delivery optimization, little is known about t

Elif Ozturk, Hira Asif, Mehmet Gunay, Mehmet Emre Tasgin

Plasmonic nanoantennas offer new avenues to manipulate the propagation of light in materials due to their near field enhancement and ultrafast response time. Here we investigate the epsilon-near-zero (ENZ) response in an L-shaped nanoantenna structure under the phenomenon of plasmonic analog of enhancement in the index of refraction. Using a quantum mechanic

Jordan Pack, Karl V. Falb, Sanat Ghosh, Xuehao Wu

Many recent advances in our understanding of two-dimensional (2D) electron systems stem from van der Waals (vdW) heterostructures. The assembly process relies on the weak bonding across interfaces between layered vdW compounds, making it possible to construct exceptionally clean heterostructures from chemically and structurally distinct materials - a challen

Michał Szczepanik, Ákos Nagy, Emil Żak

We present a quantum algorithm for simulating rovibrational Hamiltonians on fault-tolerant quantum computers. The method integrates exact curvilinear kinetic energy operators and general-form potential energy surfaces expressed in a hybrid finite-basis/discrete-variable representation. The Hamiltonian is encoded as a unitary quantum circuit using a quantum r

Shouda Wang

We establish a monotonicity property of the deficit associated with the local log-Brunn-Minkowski inequality (LLBM) under addition of line segments. As a corollary, if the LLBM holds for a convex body K, then it also holds for K+Z for any zonoid Z, which in particular yields a new proof of the inequality for zonoids. Moreover, assuming the LLBM is valid, we

Amith Ananthram, Elias Stengel-Eskin, Lorena A. Bradford, Julia Demarest

While vision-language models (VLMs) have advanced into detailed image description, evaluation remains a challenge. Standard metrics (e.g. CIDEr, SPICE) were designed for short texts and tuned to recognize errors that are now uncommon, such as object misidentification. In contrast, long texts require sensitivity to attribute and relation attachments and score

Ali Shakeri Kahnamouei, Saeed Lotfifard

This paper proposes graph analysis methods to fully automate the fault location identification task in power distribution systems. The proposed methods take basic unordered data from power distribution systems as input, including branch parameters, load values, and the location of measuring devices. The proposed data preparation and analysis methods automati

Jindi Wang, Yidi Zhang, Zhaoxing Li, Pedro Bem Haja

The rapid expansion of online courses and social media has generated large volumes of unstructured learner-generated text. Understanding how learners construct knowledge in these spaces is crucial for analysing learning processes, informing content design, and providing feedback at scale. However, existing approaches typically rely on manual coding of well-s

Fausto Vega, Jon Arrizabalaga, Ryan Watson, Zachary Manchester

Conjunction analysis and maneuver planning for spacecraft collision avoidance remains a manual and time-consuming process, typically involving repeated forward simulations of hand-designed maneuvers. With the growing density of satellites in low-Earth orbit (LEO), autonomy is becoming essential for efficiently evaluating and mitigating collisions. In this wo

Anna Cetera, Sima Ghafoori, Ali Rabiee, Mohammad Hassan Farhadi

The vision-based grasping brain network integrates visual perception with cognitive and motor processes for visuomotor tasks. While invasive recordings have successfully decoded localized neural activity related to grasp type planning and execution, macroscopic neural activation patterns captured by noninvasive electroencephalography (EEG) remain far less un

Kuai Yu, Xiaoyu Wu, Peishen Yan, Qingqian Yang

Federated Learning (FL) enables decentralized model training across multiple clients without exposing local data, but its distributed feature makes it vulnerable to backdoor attacks. Despite early FL backdoor attacks modifying entire models, recent studies have explored the concept of backdoor-critical (BC) layers, which poison the chosen influential layers

Brandon James Carone, Iran R. Roman, Pablo Ripollés

Multimodal Large Language Models (MLLMs) have demonstrated capabilities in audio understanding, but current evaluations may obscure fundamental weaknesses in relational reasoning. We introduce the Music Understanding and Structural Evaluation (MUSE) Benchmark, an open-source resource with 10 tasks designed to probe fundamental music perception skills. We eva

Shiyu Liu, Ilija Hadzic, Akshay Gupta, Aliasghar Arab

This paper addresses motion planning and con- trol of an overactuated 4-wheel drive train with independent steering (4WIS) where mechanical constraints prevent the wheels from executing full 360-degree rotations (swerve). The configuration space of such a robot is constrained and contains discontinuities that affect the smoothness of the robot motion. We int

Shaohong Shi, Eric A. Cator, Jacco Heres, Simon H. Tindemans

Electrical grid congestion is a growing challenge in Europe, driving the need for accurate prediction of load, particularly of peak load. Non-time-resolved models of peak load offer the advantages of simplicity and compactness, and among them, Velander's formula (VF) is a traditional method that has been used for decades. Moreover, VF can be adapted into a q

Matthias Busch, Gregor Häfner, Jiayu Xie, Marius Tacke

All simulation approaches eventually face limits in computational scalability when applied to large spatiotemporal domains. This challenge becomes especially apparent in molecular-level particle simulations, where high spatial and temporal resolution leads to rapidly increasing computational demands. To overcome these limitations, hybrid methods that combine

Rekha R Nair, Tina Babu, Alavikunhu Panthakkan, Hussain Al-Ahmad

The proliferation of high-dimensional datasets in fields such as genomics, healthcare, and finance has created an urgent need for machine learning models that are both highly accurate and inherently interpretable. While traditional deep learning approaches deliver strong predictive performance, their lack of transparency often impedes their deployment in cri

Wenqian Ye, Guangtao Zheng, Aidong Zhang

In reinforcement learning from human feedback, preference-based reward models play a central role in aligning large language models to human-aligned behavior. However, recent studies show that these models are prone to reward hacking and often fail to generalize well due to over-optimization. They achieve high reward scores by exploiting shortcuts, that is,

Aaron Bernstein, Jiale Chen

We study the approximate maximum weight matching (MWM) problem in a fully dynamic graph subject to edge insertions and deletions. We design meta-algorithms that reduce the problem to the unweighted approximate maximum cardinality matching (MCM) problem. Despite recent progress on bipartite graphs -- Bernstein-Dudeja-Langley (STOC 2021) and Bernstein-Chen-Dud

Marcelo Cerda Castillo

Short-term forecasting of airport fog (visibility < 1.0 km) presents challenges in geographic generalization because many machine learning models rely on location-specific features and fail to transfer across sites. This study investigates whether fundamental thermodynamic and radiative processes can be encoded in a coordinate-free (location-independent) fea

Ghulam Mudassir, Antinisca Di Marco, Giordano d'Aloisio

Natural disasters always have several effects on human lives. It is challenging for governments to tackle these incidents and to rebuild the economic, social and physical infrastructures and facilities with the available resources (mainly budget and time). Governments always define plans and policies according to the law and political strategies that should

Sagar Sehrawat, Klas Lindfors, Andriy Shevchenko

Local intensity enhancement by plasmonic nanoparticles is widely used in optics and photonics. However, the effect is usually based on dipole resonances in the particles. Recently, it has been shown that quadrupole and octupole resonances can exhibit comparable, or even higher near-field enhancement. In this work, we focus on the near-field enhancement by a

P. Stammer, J. Rivera-Dean, P. Tzallas, M. F. Ciappina

Intense light-matter interaction largely relies on the use of high-power light sources, creating fields comparable to, or even stronger than, the field keeping the electrons bound in atoms. Under such conditions, the interaction induces highly nonlinear processes such as high harmonic generation, in which the low-frequency photons of a driving laser field ar

Claudia Di Cesare, Jorryt Matthee, Rohan P. Naidu, Alberto Torralba

Galaxies exhibit a tight correlation between their star-formation rate and stellar mass over a wide redshift range known as the star-forming main sequence (SFMS). With JWST, we can now investigate the SFMS at high redshifts down to masses of $\sim10^6$ M$_{\odot}$, using sensitive star-formation rate tracers such as H$\alpha$ emission -- which allow us to pr

Jiaxing Ma, Thomas J. Clark, Vincent Dumont, Jack C. Sankey

Modern mechanical sensors increasingly measure motion with precision sufficient to resolve the fundamental thermal noise floor over a broad band. Compared to traditional sensors -- achieving this limit only near resonance -- this capability provides massive gains in acquisition rates along with access to otherwise obscured transient signals. However, these s

Jerome Daquin, Tamas Kovacs

The recurrence-based divergence quantifier ($DIV$), traditionally applied to dissipative systems, is shown here to be an effective finite-time chaos indicator for conservative dynamics. We benchmark its performances against the well-established fast Lyapunov indicator (FLI), focusing on the standard map, a canonical model of Hamiltonian chaos. Through extens

Tobias Ekholm, Pietro Longhi, Sunghyuk Park, Vivek Shende

Given a 3-manifold $M$, and a branched cover arising from the projection of a Lagrangian 3-manifold $L$ in the cotangent bundle of $M$ to the zero-section, we define a map from the skein of $M$ to the skein of $L$, via the skein-valued counting of holomorphic curves. When $M$ and $L$ are products of surfaces and intervals, we show that wall crossings in the

Nakul Upadhya, Eldan Cohen

Decision trees are prized for their interpretability and strong performance on tabular data. Yet, their reliance on simple axis-aligned linear splits often forces deep, complex structures to capture non-linear feature effects, undermining human comprehension of the constructed tree. To address this limitation, we propose a novel generalization of a decision

Junichiro Fukuyama

We show that a family $\mathcal{F}$ of sets each of cardinality $m \in \mathbb{Z}_{>2}$ includes a $k$-sunflower if $ |\mathcal{F}| \ge \left( \frac{c k^2 \ln m}{\ln \ln m} \right)^m$ for some constant $c>0$, where $k$-sunflower means a family of $k$ different sets with a common pairwise intersection. The base of the exponential lower bound is sub-logarithmi

Suswitha Pericharla, Daniel B. Hier, Tayo Obafemi-Ajayi

Effective biomedical data integration depends on automated term normalization, the mapping of natural language biomedical terms to standardized identifiers. This linking of terms to identifiers is essential for semantic interoperability. Large language models (LLMs) show promise for this task but perform unevenly across terminologies. We evaluated both memor

Eranga Bandara, Sachin Shetty, Ravi Mukkamala, Ross Gore

In recent years, blockchain has experienced widespread adoption across various industries, becoming integral to numerous enterprise applications. Concurrently, the rise of generative AI and LLMs has transformed human-computer interactions, offering advanced capabilities in understanding and generating human-like text. The introduction of the MCP has further

Amirreza Hosseini, Amro M. Farid

Within the project management context, project scheduling serves as an indispensable component, functioning as a fundamental tool for planning, monitoring, controlling, and managing projects more broadly. Although the resource-constrained project scheduling problem (RCPSP) lies at the core of project management activities, it remains largely disconnected fro

Pavlo Bulanchuk, Sue Ann Koay, Sandro Romani

Solar flares, email exchanges, and many natural or social systems exhibit bursty dynamics, with periods of intense activity separated by long inactivity. These patterns often follow power- law distributions in inter-event intervals or event rates. Existing models typically capture only one of these features and rely on non-local memory, which complicates ana

Andrew Anderson, Fatima A. Moussaoui, Jimena Noa Guevara, Md Montaser Hamid

While much research has shown the presence of AI's "under-the-hood" biases (e.g., algorithmic, training data, etc.), what about "over-the-hood" inclusivity biases: barriers in user-facing AI products that disproportionately exclude users with certain problem-solving approaches? Recent research has begun to report the existence of such biases -- but what do t

Abeer Badawi, Elahe Rahimi, Md Tahmid Rahman Laskar, Sheri Grach

Evaluating Large Language Models (LLMs) for mental health support is challenging due to the emotionally and cognitively complex nature of therapeutic dialogue. Existing benchmarks are limited in scale, reliability, often relying on synthetic or social media data, and lack frameworks to assess when automated judges can be trusted. To address the need for larg

Akilan Amithasagaran, Sagnik Dakshit, Bhavani Suryadevara, Lindsey Stockton

Simulations constitute a fundamental component of medical and nursing education and traditionally employ standardized patients (SP) and high-fidelity manikins to develop clinical reasoning and communication skills. However, these methods require substantial resources, limiting accessibility and scalability. In this study, we introduce CLiVR, a Conversational

Manish Kumar

We present two new quantum algorithms for reaction-diffusion equations that employ the truncated Chebyshev polynomial approximation. This method is employed to numerically solve the ordinary differential equation emerging from the linearization of the associated nonlinear differential equation. In the first algorithm, we use the matrix exponentiation method

Zackary Rackauckas, Nobuaki Minematsu, Julia Hirschberg

We present Re:Member, a system that explores how emotionally expressive, memory-grounded interaction can support more engaging second language (L2) learning. By drawing on users' personal videos and generating stylized spoken questions in the target language, Re:Member is designed to encourage affective recall and conversational engagement. The system aligns

Ayan Chakravorty, Surajit Adhikari, Priya Johari

Antiperovskite derivatives have emerged as promising candidates for optoelectronic applications. However, due to the significant computational cost, their excitonic and polaronic properties remain underexplored despite being critical for optoelectronic performance. Here, we present the structural, electronic, optical, excitonic, and polaronic properties of a

Eunsu Kim, Junyeong Park, Juhyun Oh, Kiwoong Park

As LLMs are increasingly deployed in real-world interactions, their social reasoning in interpersonal communication becomes critical. To explore their capabilities, we introduce SCRIPTS, a 1.1k-dialogue dataset in English and Korean, sourced from movie scripts and propose a social reasoning task based on SCRIPTS that evaluates the capacity of LLMs to infer t

P. Djorwé, J. -X. Peng, S. Adbel-Khalek, A. -H. Abdel-Aty

Engineering quantum resources that survive against environmental temperature is of great interest for modern quantum technologies. However, it is a tricky task to synthetize such quantum states. Here, we propose a scheme to generate highly resilient tripartite entanglement and quantum coherence against thermal fluctuations. Our benchmark model consists of a

Behnam Rezaei Bezanjani, Seyyed Hamid Ghafouri, Reza Gholamrezaei

In recent years, the rapid integration of Internet of Things (IoT) devices into the healthcare sector has brought about revolutionary advancements in patient care and data management. While these technological innovations hold immense promise, they concurrently raise critical security concerns, particularly in safeguarding medical data against potential cybe

Hamed Jelodar, Samita Bai, Roozbeh Razavi-Far, Ali A. Ghorbani

Dataset availability and quality remain critical challenges in machine learning, especially in domains where data are scarce, expensive to acquire, or constrained by privacy regulations. Fields such as healthcare, biomedical research, and cybersecurity frequently encounter high data acquisition costs, limited access to annotated data, and the rarity or sensi

Jingruo Chen, TungYen Wang, Marie Williams, Natalia Jordan

AI-generated images are increasingly prevalent on social media, raising concerns about trust and authenticity. This study investigates how different levels of label detail (basic, moderate, maximum) and content stakes (high vs. low) influence user engagement with and perceptions of AI-generated images through a within-subjects experimental study with 105 par

Norbert Hauck

A photometric study in combination with existing stellar models has revealed details of this eclipsing post-mass-transfer binary. The shell star has an equatorial/ polar radius of ~2.60/1.90 Rsun at an equatorial rotational velocity of ~430 km s-1, an effective mean temperature Teff of ~13300 K and a mass of ~3.42 Msun. This former accretor star is surrounde

Aritra Bhowmik, Denis Korzhenkov, Cees G. M. Snoek, Amirhossein Habibian

Text-to-video diffusion models have enabled high-quality video synthesis, yet often fail to generate temporally coherent and physically plausible motion. A key reason is the models' insufficient understanding of complex motions that natural videos often entail. Recent works tackle this problem by aligning diffusion model features with those from pretrained v

Laurent Bonnasse-Gahot, Jean-Pierre Nadal

In humans and other animals, category learning enhances discrimination between stimuli close to the category boundary. This phenomenon, called categorical perception, was also empirically observed in artificial neural networks trained on classification tasks. In previous modeling works based on neuroscience data, we show that this expansion/compression is a

Yixuan Florence Wu, Yilun Zhu, Lei Cao, Naichen Shi

We propose a new method for statistical inference in generalized linear models. In the overparameterized regime, Principal Component Regression (PCR) reduces variance by projecting high-dimensional data to a low-dimensional principal subspace before fitting. However, PCR incurs truncation bias whenever the true regression vector has mass outside the retained

F. Javier Garcia Garcia, Pablo Fariñas Alvariño

The present research is a theoretical study about the transient friction created in circular pipe mean flow, whenever an incompressible Newtonian fluid is accelerated through a monotonously-increased mean-pressure gradient. The resulting friction stress is the sum of two components, one laminar and the other purely turbulent, not synchronised between them. E

Paolo Mantero, Vinh Nguyen

Let $\M$ be a matroid, and let $I_{\M}$ be either the Stanley--Reisner or the cover ideal of $\M$. In this paper we prove that for any matroid $\M$ on $[n]$, any $\ell\in \ZZ_+$, and any squarefree monomial $N\in R=\kk[x_1,\ldots,x_n]$, the ideal $I_{\M}^{(\ell)}:N$, which we call a ``slightly mixed symbolic power" of $I_{\M}$, is always Cohen--Macaulay and

Wei Xiang, Yunkai Xu, Yuyang Fang, Zhuyu Teng

Elderly people with speech impairments often face challenges in engaging in meaningful social communication, particularly when using Augmentative and Alternative Communication (AAC) tools that primarily address basic needs. Moreover, effective chats often rely on personal memories, which is hard to extract and reuse. We introduce SocializeChat, an AAC tool t

Marc Bernacki

In mesoscopic scale microstructure evolution modeling, two primary numerical frameworks are used: Front-Capturing (FC) and Front-Tracking (FT) ones. FC models, like phase-field or level-set methods, indirectly define interfaces by tracking field variable changes. On the contrary, FT models explicitly define interfaces using interconnected segments or surface

Kwang-Il Seon, Jongwan Ko, Woowon Byun, Jaehyun Lee

Observations have shown that the optical colors of Galactic cirrus clouds differ significantly from those of extragalactic sources; thus, they can be used to distinguish Galactic cirrus from extragalactic low surface brightness (LSB) features. To understand these properties, we calculate radiative transfer models in dust clouds, where photons are incident fr

Toni Mäkelä

The forward neutrino program at the Large Hadron Collider has entered the era of providing the first measurements and observations. As it has notable connections to astrophysics and will complement the results and projections of key measurements at the LHC and other measurements, it is becoming increasingly important for collider physicists to recognize and

Saman Nessari, Ali Bozorgi-Amiri

Current medical practice depends on standardized treatment frameworks and empirical methodologies that neglect individual patient variations, leading to suboptimal health outcomes. We develop a comprehensive system integrating Large Language Models (LLMs), Conditional Tabular Generative Adversarial Networks (CTGAN), T-learner counterfactual models, and conte

Milos Ertola Urtubey, Daniela Pérez

If a black hole is immersed in a magnetosphere,its rotational energy can be transferred to the electromagnetic field and escape as a Poynting flux to infinity. This process of extraction of rotational energy is known as the Blandford-Znajek mechanism. It relies on the presence of both a magnetosphere and an ergosphere surrounding the black hole. Previously,

Ivan Borodii, Illia Fedorovych, Halyna Osukhivska, Diana Velychko

During the study, the results of a comparative analysis of the process of handling large datasets using the Apache Spark platform in Java, Python, and Scala programming languages were obtained. Although prior works have focused on individual stages, comprehensive comparisons of full ETL workflows across programming languages using Apache Iceberg remain limit

Chibuzor Okocha, Maya Bakri, Christan Grant

Child speech differs from adult speech in acoustics, prosody, and language development, and disfluencies (repetitions, prolongations, blocks) further challenge Automatic Speech Recognition (ASR) and downstream Natural Language Processing (NLP). Recent large audio-language models (LALMs) demonstrate strong cross-modal audio understanding; however, their behav

Elizabeth Cucuzzella, Tria McNeely, Kimberly Wood, Ann B. Lee

Accurate tropical cyclone (TC) short-term intensity forecasting with a 24-hour lead time is essential for disaster mitigation in the Atlantic TC basin. Since most TCs evolve far from land-based observing networks, satellite imagery is critical to monitoring these storms; however, these complex and high-resolution spatial structures can be challenging to qual

Yaoyuan Vincent Tan, Gang Xu, Chenkun Wang

There has been an increasing interest in using cell and gene therapy (CGT) to treat/cure difficult diseases. The hallmark of CGT trials are the small sample size and extremely high efficacy. Due to the innovation and novelty of such therapies, when there is missing data, more scrutiny is exercised, and regulators often request for missing data handling strat

C. B. Adams, A. Archer, P. Bangale, J. T. Bartkoske

Background: Axion-like particles (ALPs) are hypothetical particles that emerge in numerous theoretical extensions to the Standard Model. Their coupling to electromagnetic field implies that ALPs would mix with photons in the presence of external magnetic fields. As ALP phenomenology is governed by the mass and strength of its coupling, there is a subset of t

Karelia Salinas, Victor Barella, Thales Viera, Luis Gustavo Nonato

Graph vertex ordering is widely employed in spatial data analysis, especially in urban analytics, where street graphs serve as spatial discretization for modeling and simulation. It is also crucial for visualization, as many methods require vertices to be arranged in a well-defined order to reveal non-trivial patterns. The goal of vertex ordering methods is

Joydeep Chandra, Satyam Kumar Navneet

Domestic AI agents faces ethical, autonomy, and inclusion challenges, particularly for overlooked groups like children, elderly, and Neurodivergent users. We present the Plural Voices Model (PVM), a novel single-agent framework that dynamically negotiates multi-user needs through real-time value alignment, leveraging diverse public datasets on mental health,

Haofan Dong, Houtianfu Wang, Hanlin Cai, Ozgur B. Akan

Terahertz inter-satellite links enable unprecedented sensing precision for Low Earth Orbit (LEO) constellations, yet face fundamental bounds from hardware impairments, pointing errors, and network interference. We develop a Network Cram\'er-Rao Lower Bound (N-CRLB) framework incorporating dynamic topology, hardware quality factor $\Gamma_{\text{eff}}$, phase

Hamed Jelodar, Mohammad Meymani, Roozbeh Razavi-Far, Ali A. Ghorbani

Generative AI and large language models (LLMs) have shown strong capabilities in code understanding, but their use in cybersecurity, particularly for malware detection and analysis, remains limited. Existing detection systems often fail to generalize to obfuscated or previously unseen threats, underscoring the need for more adaptable and explainable models.

Sophia Xiao Pu, Sitao Cheng, Xin Eric Wang, William Yang Wang

Oversensitivity occurs when language models defensively reject prompts that are actually benign. This behavior not only disrupts user interactions but also obscures the boundary between harmful and harmless content. Existing benchmarks rely on static datasets that degrade overtime as models evolve, leading to data contamination and diminished evaluative powe

A. Leonard Nicusan, Darren Gobby, Kit Windows-Yule

Designing and optimising the geometry of industrial process equipment remains slow and still largely ad hoc: engineers make small tweaks to one standard shape at a time, build prototypes, and hope for gains. We introduce HARPPP, an autonomous design loop that couples a compact, programmable geometry model to power-controlled CFD and evolutionary search. The

Zhengbo Zhou, Dooman Arefan, Margarita Zuley, Shandong Wu

Longitudinal analysis of sequential radiological images is hampered by a fundamental data challenge: how to effectively model a sequence of high-resolution images captured at irregular time intervals. This data structure contains indispensable spatial and temporal cues that current methods fail to fully exploit. Models often compromise by either collapsing s

Javier Cembrano, Felix Fischer, Max Klimm

We study the selection of agents based on mutual nominations, a theoretical problem with many applications from committee selection to AI alignment. As agents both select and are selected, they may be incentivized to misrepresent their true opinion about the eligibility of others to influence their own chances of selection. Impartial mechanisms circumvent th

Assaf Marron, David Harel

An algorithm specification in natural language or pseudocode is expected to be clear and explicit enough to enable mechanical execution. In this position paper we contribute an initial characterization of the knowledge that an executing agent, human or machine, should possess in order to be able to carry out the instructions of a given algorithm specificatio

Seungjun Yu, Junsung Park, Youngsun Lim, Hyunjung Shim

We present a two-phase vision-language QA system for autonomous driving that answers high-level perception, prediction, and planning questions. In Phase-1, a large multimodal LLM (Qwen2.5-VL-32B) is conditioned on six-camera inputs, a short temporal window of history, and a chain-of-thought prompt with few-shot exemplars. A self-consistency ensemble (multipl

Nada Adzic Vukotic, Mimi Dai

We study discretely self-similar solutions for the electron magnetohydrodynamics (MHD) without resistivity. Under several different decay and non-decay conditions, we show the absence of non-trivial discretely self-similar blowup solutions.

Buang Zhang, Tung Kieu, Xiangfei Qiu, Chenjuan Guo

Time series anomaly detection is important in modern large-scale systems and is applied in a variety of domains to analyze and monitor the operation of diverse systems. Unsupervised approaches have received widespread interest, as they do not require anomaly labels during training, thus avoiding potentially high costs and having wider applications. Among the

Fabio Apruzzi, Luca Martucci

We uncover an infinite class of novel zero-form non-invertible symmetries in a broad family of four-dimensional models, studied years ago by Gaillard and Zumino (GZ), which includes several extended supergravities as particular subcases. The GZ models consist of abelian gauge fields coupled to a neutral sector, typically including a set of scalars, whose equ

Susheel Vadakkekuruppath, Herman B. Amundsen, Jason M. O'Kane, Marios Xanthidis

Autonomous navigation in 3D is a fundamental problem for autonomy. Despite major advancements in terrestrial and aerial settings due to improved range sensors including LiDAR, compact sensors with similar capabilities for underwater robots have only recently become available, in the form of 3D sonars. This paper introduces a novel underwater 3D navigation pi

Alexandre Boumezoued, Adel Cherchali, Vincent Lemaire, Gilles Pagès

Estimating risk measures such as large loss probabilities and Value-at-Risk is fundamental in financial risk management and often relies on computationally intensive nested Monte Carlo methods. While Multi-Level Monte Carlo (MLMC) techniques and their weighted variants are typically more efficient, their effectiveness tends to deteriorate when dealing with i

Mayur Mune, Praveen Kumar Dhankar, Safiqul Islam, Behnam Pourhassan

Anisotropic Bianchi type-III cosmology is examined within the framework of f(R,T) gravity, where R denotes the Ricci scalar and T the trace of the energy-momentum tensor. In this work, we investigate the statistical constraints on anisotropic Bianchi type-III cosmology within the framework of f(R,T) gravity. The specific choice $f(R,T)=R+2f(T)$ is considered

Srinivas Kotyada, Lalit Vaishya

Let $sym^{2} f$ denote the symmetric square lift of a Hecke eigenform $f \in S_{k}(\Gamma_{0}(N))$ with the $n^{\rm th}$-Fourier coefficients $ \lambda_{sym^{2}f}(n)$. In this article, we prove an estimate for the first moment of the sequence $\{ \lambda_{sym^{2}f}(\mathcal{Q}(\underline{x}))\}_{\mathcal{Q} \in \mathcal{S}_{D}, \underline{x} \in \mathbb{Z}^{

Deborpita Biswas, Mishko Mitkovski

In~\cite{holub1994bases} Holub introduced the concept of near-Riesz bases, as frames that can be considered Riesz bases for computational purposes or that exhibit certain desirable properties of Riesz bases. In this paper, we introduce a generalization of near-Riesz bases that includes sequences which are not necessarily frames. We demonstrate that this broa

Chinmay Burgul, Yewei Huang, Michalis Chatzispyrou, Ioannis Rekleitis

In the past decade, the adoption of compact 3D range sensors, such as LiDARs, has driven the developments of robust state-estimation pipelines, making them a standard sensor for aerial, ground, and space autonomy. Unfortunately, poor propagation of electromagnetic waves underwater, has limited the visibility-independent sensing options of underwater state-es

Hao Wang, Baojun Ma

In real-world time series forecasting tasks, category information plays a pivotal role in capturing inherent data patterns. This paper introduces QKCV (Query-Key-Category-Value) attention, an extension of the traditional QKV framework that incorporates a static categorical embedding C to emphasize category-specific information. As a versatile plug-in module,

Thomas Hofweber, Jefrey Bergl, Ian Reyes, Amir Sadovnik

We investigate and defend the possibility of causing a stock market crash via small manipulations of individual stock values that together realize an adversarial example to financial forecasting models, causing these models to make the self-fulfilling prediction of a crash. Such a crash triggered by an adversarial example would likely be hard to detect, sinc

Silas Ruhrberg Estévez, Nicolás Astorga, Mihaela van der Schaar

There is growing interest in using machine learning (ML) to support clinical diagnosis, but most approaches rely on static, fully observed datasets and fail to reflect the sequential, resource-aware reasoning clinicians use in practice. Diagnosis remains complex and error prone, especially in high-pressure or resource-limited settings, underscoring the need

Victor Oliveira Ferreira, Wiebke Mainville, Vincent Raymond, Jean-Michel Lamarre

We present an experimental unit that realizes the ``multi-input, multi-output manifold'' thermal management technology proposed by Lamarre & Raymond (2023). The proposed setup can be used for experiments aimed at controlling spatiotemporal temperature distribution. Temperature control is achieved by impinging coolant fluid jets, leveraging a manifold of chan

Alberto Sanchez-Delgado, João Carlos Virgolino Soares, Victor Barasuol, Claudio Semini

Permanently Shadowed Regions (PSRs) near the lunar poles are of interest for future exploration due to their potential to contain water ice and preserve geological records. Their complex, uneven terrain favors the use of legged robots, which can traverse challenging surfaces while collecting in-situ data, and have proven effective in Earth analogs, including

Megan Newsome, Iair Arcavi, K. Decker French, Curtis McCully

Using HST/STIS observations, we present the highest-spatial-resolution spectroscopic study to date of four tidal disruption event (TDE) host galaxies, with the best observed being the post-starburst (PSB) host of ASASSN-14li. The stellar population of ASASSN-14li's host, within 44 pc of the nucleus, reveals a younger recent starburst ($\sim$340 Myr) compared

Artur Zolkowski, Wen Xing, David Lindner, Florian Tramèr

Recent findings suggest that misaligned models may exhibit deceptive behavior, raising concerns about output trustworthiness. Chain-of-thought (CoT) is a promising tool for alignment monitoring: when models articulate their reasoning faithfully, monitors can detect and mitigate harmful behaviors before undesirable outcomes occur. However, a key uncertainty i

Kasturi Ranjan Swain, Rajesh K. Malla, Adolfo del Campo

Intrinsic noise in pre-fault-tolerant quantum devices poses a major challenge to the reliable realization of unitary dynamics in quantum algorithms and simulations. To address this, we present a method for simulating open quantum system dynamics on a quantum computer, including negative dissipation rates in the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) ma

Douglas Finamore, Martin Leguil

We study the spectral rigidity problem for Sinai billiards with finite horizon, specifically asking whether the geometry of the billiard table can be recovered from the lengths of its (marked) periodic trajectories. To address this, we introduce an enriched marked length spectrum EL and prove that two Sinai billiards sharing the same EL must be isometric. Ou

Arpan Mukherjee, Marcello Bullo, Debabrota Basu, Deniz Gündüz

While test-time scaling with verification has shown promise in improving the performance of large language models (LLMs), the role of the verifier and its imperfections remain underexplored. The effect of verification manifests through interactions of three quantities: (i) the generator's coverage, (ii) the verifier's region of convergence (ROC), and (iii) t

Joseph DeRose, Shi-Fan Chen

We present a new formalism to separate large- and small-scale contributions to cosmic shear through \textit{lensing counterterms} (LCT) inspired by effective field theory (EFT). Marginalizing over these LCTs isolates the large-scale cosmological signal in weak lensing power spectra while simultaneously constraining the impact of baryonic feedback or new phys

Laura Ciobanu, Gemma Crowe, Pieter Senden, Corentin Bodart

In this paper we introduce and study the degree of twisted commutativity and the twisted conjugacy ratio of a finitely generated group $G$. The degree of twisted commutativity $\mathrm{tdc}_X(\varphi, G)$ generalises the degree of commutativity of $G$, by measuring the density of pairs of elements with trivial twisted commutators in the ball of radius $n$ of

Gabriele Giannone

We establish sufficient conditions for the local boundedness of weak solutions to a broad class of nonlinear elliptic equations in divergence form, under unbalanced growth conditions on the stress field. Our analysis is carried out in a non-variational setting, with no symmetry or structural assumptions on the operator. The ellipticity and growth are prescri

Tomer Shaked, Philipp del Hougne, George C. Alexandropoulos, Nir Shlezinger

Emerging technologies such as Reconfigurable Intelligent Surfaces (RIS) make it possible to optimize some parameters of wireless channels. Conventional approaches require relating the channel and its programmable parameters via a simple model that supports rapid optimization, e.g., re-tuning the parameters each time the users move. However, in practice such

Yuichiro Takeuchi, Yusuke Imoto, Shunya Kato

In this paper we describe Ninja Codes, neurally generated fiducial markers that can be made to naturally blend into various real-world environments. An encoder network converts arbitrary images into Ninja Codes by applying visually modest alterations; the resulting codes, printed and pasted onto surfaces, can provide stealthy 6-DoF location tracking for a wi

Edward Hardy, Anton Sokolov, Henry Stubbs

We revisit cooling bounds on light Kaluza-Klein (KK) gravitons, as arise in the dark dimension scenario, considering red giants, neutron stars, and supernovae. In addition to bremsstrahlung, we account for two novel production channels: resonant mixing with the in-medium photon and a pion-induced process in supernovae. The strongest limits arise from SN 1987

Alexander Canright, Deepak Iyer, Matthew S. Foster

The magnetization field of a quantum Hall ferromagnet (QHFM) can host a variety of spin textures, including skyrmions and magnons. When projected into the lowest Landau level with $\nu = 1$ filling, the topological (Pontryagin) charge density of the magnetization field is proportional to the electric charge density, allowing for long-range spin-spin interact