April 2026 arXiv papers — page 157

Brian Barch, Daniel Lidar

Non-Hermitian (NH) quantum systems demonstrate striking differences from their Hermitian counterparts, leading to claims of NH advantage in areas ranging from metrology to entanglement generation. We show that in the context of quantum computation, any such NH advantage is unlikely to be scalable as an efficient computational resource: if coherent normalized

Haijin Ding, Guofeng Zhang

In this paper, we investigate non-Markovian quantum dynamics from the perspective of quantum noises in a network of atoms mediated by a waveguide. In such networks, quantum coherent feedback control becomes achievable when coherent fields (or quantum noises) in the format of photons with continuous modes propagate through the waveguide. Different from tradit

Adrien Meyer, Lorenzo Arboit, Giuseppe Massimiani, Shih-Min Yin

Purpose: The Segment Anything Model (SAM) promises to ease the annotation bottleneck in medical segmentation, but overlapping anatomy and blurred boundaries make its point prompts ambiguous, leading to cycles of manual refinement to achieve precise masks. Better prompting strategies are needed. Methods: We propose a structured prompting strategy using 4 poin

Jiangwen Wang, Feida Jiang

In this paper, we study the degenerate or singular fully nonlinear dead-core systems coupled with strong absorption terms. We establish several properties, including improved regularity of viscosity solutions along the free boundary, non-degeneracy, a measure estimate of the free boundary, Liouville-type results, and the behavior of blow-up solution. We also

Mariana Macedo, Ronaldo Menezes, Alessio Cardillo

We investigate how parenthood and marriage (two major life events) reshape urban mobility patterns, an aspect overlooked in traditional `average citizen' mobility models. Leveraging US census data, we analyse whether these life transitions create distinct urban experiences. Parenthood introduces new priorities including caregiving responsibilities, work-

Minghao Wang, Brian R. Williams

Topological field theories and holomorphic field theories naturally appear in both mathematics and physics. However, there exist intriguing hybrid theories that are topological in some directions and holomorphic in others, such as twists of supersymmetric field theories or Costello's 4-dimensional Chern-Simons theory. In this paper, we rigorously prove t

Tong Dou, Shiro Kumara, Josh Burns, Ethan Sigler

The computational demands of deep learning motivate the investigation of alternative approaches to computation. One alternative is physical neural networks~(PNNs), in which learning and inference are performed directly via physical processes. Stochastic PNNs arise when the underlying neurons are realized by the dynamics of a stochastic activation switch. Her

Sandra Cerrai, Qin Li, Anjali Nair, Jaeyoung Yoon

Inverse problems in scientific computing often require optimization over infinite-dimensional Hilbert spaces. A commonly used solver in such settings is stochastic gradient descent (SGD), where gradients are approximated using randomly sampled sub-objective functions. In this work we study the continuous-time limit of SGD in the small step-size regime. We sh

Amir Ziashahabi, Chaoyang He, Salman Avestimehr

Federated learning (FL) has emerged as a practical means for privacy-preserving distributed machine learning. FL's versatile design makes it suitable for various training settings, from IoT edge devices in cross-device FL to powerful servers in cross-silo FL. A key consequence of this versatility is the high level of diversity found in the networking configu

Joppe De Jonghe, Konstantin Usevich, Philippe Dreesen, Mariya Ishteva

The decoupling of multivariate functions is a powerful modeling paradigm for learning multivariate input-output relations from data. For the single-layer case, established CPD-based methods are available, but the multi-layer case remained largely unexplored. This work introduces a tensor-based framework for multi-layer decoupling, which is based on ParaTuck-

Zhiyang Xun, Eric Price

Diffusion models generate samples by iteratively querying learned score estimates. A rapidly growing literature focuses on accelerating sampling by minimizing the number of score evaluations, yet the information-theoretic limits of such acceleration remain unclear. In this work, we establish the first score query lower bounds for diffusion sampling. We prove

Seth Z. Zhao, Luobin Wang, Hongwei Ruan, Yuxin Bao

Open-loop (OL) to closed-loop (CL) gap (OL-CL gap) exists when OL-pretrained policies scoring high in OL evaluations fail to transfer effectively in closed-loop (CL) deployment. In this paper, we unveil the root causes of this systemic failure and propose a practical remedy. Specifically, we demonstrate that OL policies suffer from Observational Domain Shift

Ruitian Chen, Jincong Pang, Lizhong Lang, Jiaze Wu

Halide perovskites are promising optoelectronic materials, but their time-dependent permanent deformation under illumination (i.e., photo-creep) is poorly understood, limiting their mechanical stability. Here we report wavelength-dependent photo-creep phenomena in CsPbBr3 and FAPbBr3 single crystals, studied by constant-load nanoindentation under controlled

Yan Li

It is well known that estimating the expectation of any given bounded random variable with values in $[-B, B]$ has two basic properties: (1) the number of samples needed scales at the order of $O(B^2/ε^2)$, where $ε$ is the prescribed target precision, and (2) such a sample complexity is independent of the underlying probability measure. We show that neither

Shuhei Kashiwamura, Yusuke Kato, Hiroshi Kori, Masato Okada

In many problems of data-driven modeling for dynamical systems, the governing equations are not known a priori and must be selected phenomenologically from a large set of candidate interactions and basis functions. In such situations, point estimates alone can be misleading, because multiple model components may explain the observed data comparably well, esp

Maruf Ahmed Mridul, Rohit Kapa, Oshani Seneviratne

Task-oriented evaluation of knowledge graph (KG) quality increasingly asks whether an ontology-based representation can answer the competency questions that users actually care about, in a manner that is reproducible, explainable, and traceable to evidence. This paper adopts that perspective and focuses on gap and overlap analysis for policy-like documents (

Alicia Golden, Carole-Jean Wu, Gu-Yeon Wei, David Brooks

The push for greater efficiency in AI computation has given rise to an array of accelerator architectures that increasingly challenge the GPU's long-standing dominance. In this work, we provide a quantitative view of this evolving landscape of AI accelerators, including the Cerebras CS-3, SambaNova SN-40, Groq, Gaudi, and TPUv5e platforms, and compare agains

Kaoru Seki, Manisha Vijay, Yasmine Kotturi

Generative AI is reshaping education, yet most university AI policies are written without students and focus on penalizing misuse. This top-down approach sidelines those most affected from decisions that shape their everyday learning, resulting in confusion and fear about acceptable use. We examine how participatory, student-driven AI policy design can addre

Jaime de Miguel Rodriguez, Artjom Vargunin, Brigitta Robin Raudne, David Solis Martin

This study presents a controlled parametric framework for analyzing energy storage planning under uncertainty in a multi-stage model predictive control setting. The framework enables a broad and systematic exploration through parametrized generation of synthetic datasets in the context of energy price arbitrage. It facilitates the study of the joint effects

Jennifer C. Borges, Helenice de O. Florentino, Socorro Rangel

Research on multi-objective combinatorial optimization and on the Cutting Stock Problem (CSP) has been widely developed over the years. In contrast, the multi-objective Cutting Stock Problem has received limited attention and has been explored in only a small number of studies. In this paper a bi-objective study of the one-dimensional and the two-dimensional

Cristiano Mafuz, Rodrigo Silva

Federated learning (FL) performance is highly sensitive to heterogeneity across clients, yet practitioners lack reliable methods to anticipate how a federation will behave before training. We propose readiness indices, derived from Task2Vec embeddings, that quantifies the alignment of a federation prior to training and correlates with its eventual performanc

Francesco D'Angelo, Nicolas Flammarion

Sequence modelling requires determining which past tokens are causally relevant from the context and their importance: a process inherent to the attention layers in transformers, yet whose underlying learned mechanisms remain poorly understood. In this work, we formalize the task of estimating token importance as an in-context learning problem by introducing

Luana Passos-Reis, Elisabete M. de Gouveia Dal Pino, Juan C. Rodríguez-Ramírez, Giovani H. Vicentin

We model the Seyfert II AGN NGC 1068 within a turbulence-induced magnetic reconnection framework to explain its high-energy emission. Observations reveal a neutrino flux excess higher than the observed GeV gamma-ray emission by orders of magnitude, with no detected TeV counterpart, suggesting efficient hadronic acceleration in the nuclear region with strong

Buxin She, Brian Chen, Luanzheng Guo, Fangxing Li

Power system simulation workflows remain expert-intensive. Engineers must translate study intents into code or API calls, execute analyses, and interpret outputs. To automate this workflow, this paper presents PFAgent, a tractable and self-evolving power-flow agent for interactive grid analysis. PFAgent integrates four key capabilities: i) a tractable and in

Yi Yang

We reassess a horizon-inspired description of quarkonium formation in jets, distinguishing momentum redistribution from loss of spin alignment. A normalized energy-sharing prescription, with benchmark inputs for the QCD string tension and quarkonium radii, softens specified $J/ψ$ and $Υ(1S)$ fragmentation baselines without fitting their measured distribution

Bruno Domínguez, Cecilia Mateu, Pau Ramos, Guillaume F. Thomas

Context. Nearly 150 tidal streams are known in the Milky Way, but full phase-space information exists for only a few. RR Lyrae stars (RRL), as standard candles, provide a powerful way to probe these structures, yet they have been identified in less than a dozen streams. Aims. We study the RRL population in all known stellar streams with reported proper motio

Abhishek Dharmaratnakar, Aadheeshwar Vijayakumar, Suchand Dayanand

Retinal Cysts are formed by leakage and accumulation of fluid in the retina due to the incompetence of retinal vasculature. These cystic spaces have significance in several ocular diseases such as age-related macular degeneration, diabetic macular edema, etc. Optical coherence tomography is one of the predominant diagnosing techniques for imaging retinal pat

Hanqing Zhu, Shupeng Ning, Hongjian Zhou, Ziang Yin

The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scale beyond transistor density alone. Integrated photonics is emerging as a candidate for artificial intelligence (AI) acceleration by exploiting optical bandwidth and parallelism to

Maryam Boukor, Pedro Tallón Marrón, Richard Phat The Nguyen, Jérôme Vétel

Flexible structures in an incoming perpendicular flow typically undergo elastic reconfiguration that reduces drag; however, at higher velocities, they are prone to dynamical instabilities that entail complex wake dynamics and fluctuating loads. In this study, we investigate the wake of a thin, flexible plate clamped at its midpoint and oriented normal to an

Kevin Hernández, Elías Castellanos

We study a relativistic scalar field model for self-bound Bose-Einstein condensates (BECs) by analyzing a nonlinear Klein-Gordon equation with cubic and logarithmic interactions. This framework captures essential features of quantum droplets, such as self-trapping and finite energy configurations, which emerge from the interplay between attractive and repuls

Yoshitsugu Sekine

We discuss a no-go theorem for Bose-Einstein condensation (BEC) of quasiparticles (phonons) from the viewpoint of operator algebras, using the van Hove model. The $\beta$-KMS states of the van Hove model satisfy the self-consistency condition of arXiv:1207.4621. However, the self-consistency condition is a constraint concerning the definition of the field, a

Zeqian Long, Ozgur Kara, Haotian Xue, Yongxin Chen

Image-to-video (I2V) generation has the potential for societal harm because it enables the unauthorized animation of static images to create realistic deepfakes. While existing defenses effectively protect against static image manipulation, extending these to I2V generation remains underexplored and non-trivial. In this paper, we systematically analyze why m

Eduardo M. K. Souza, Francisco A. B. F. de Moura, Guilherme M. A. Almeida

We investigate localization properties of the Apollonian network (AN) in the presence of diagonal and off-diagonal disorder. By employing a site-resolved localization measure, we show that the localization degree is strongly dependent on the energy and tied to the hierarchical topology of the network. At the spectral edges, eigenstates are strongly localized

Zerui Chen, Rolandos Alexandros Potamias, Shizhe Chen, Jiankang Deng

Generating realistic 3D hand-object interactions (HOI) is a fundamental challenge in computer vision and robotics, requiring both temporal coherence and high-fidelity physical plausibility. Existing methods remain limited in their ability to learn expressive motion representations for generation and perform temporal reasoning. In this paper, we present HO-Fl

Griffin T. Goodwin, Alison J. March, Jayant Biradar, Christoph Schirninger

The Geostationary Operational Environmental Satellite (GOES) solar soft X-ray (SXR) irradiance in the 1-8{\AA} wavelength range is a long-standing measure of solar activity, used to define the classification of flare strengths. As a result, the flare class, along with the SXR light curves, are routinely used as a primary input for forecasting properties of s

Maria Camporese, Fabio Massacci, Yuanjun Gong

[Background:] Thematic analysis of free-text justifications in human experiments provides significant qualitative insights. Yet, it is costly because reliable annotations require multiple domain experts. Large language models (LLMs) seem ideal candidates to replace human annotators. [Problem:] Coding security-specific aspects (code identifiers mentioned, lin

Hugh Brosnahan, Izabela Lipinska

Recent reports indicate that sustained interaction with conversational artificial intelligence (AI) systems can, in a small subset of users, contribute to the emergence or stabilisation of delusional experience. Existing accounts typically attribute such cases either to individual vulnerability or to failures of safety engineering. These explanations are inc

Ajay Dhakar, Arunesh Sinha, Shamik Sural

In recent years, many countries have started enacting laws to safeguard privacy of personal data of their citizens collected and maintained by various enterprises through websites, mobile apps, and other means. It is imperative that the privacy policies of these enterprises respect the provisions of the applicable law. In this paper, we show how such organiz

Yuwei Hu, Bryce L. Ferguson

In many engineered systems, agents make decisions under incomplete information, creating opportunities for a planner to influence decentralized behavior through signaling. We study how such signaling can be designed in parallel-network, affine latency congestion games when users may not interpret recommendations using the same beliefs assumed by the planner.

Haofan Dong, Houtianfu Wang, Hanlin Cai, O. Tansel Baydas

Rain attenuates Ku-band satellite signals by up to 20~dB, encoding precipitation information along the Earth-space slant path. This paper derives the Bayesian Cram\'{e}r-Rao bound (BCRB) for rain rate estimation from LEO broadband OFDM downlinks. Using corrected ITU-R P.838-3 coefficients, the standard CRB yields a minimum detectable rain rate $R_{\min} \app

Xiaoyan Zhou, Natalia Sempere, Pooria Ghavamian, Asreen Rostami

Micromobility vehicles, such as e-scooters, Segways, skateboards, and unicycles, are increasingly adopted for short-distance travel due to their low weight and low emissions. Despite their growing popularity, we lack controlled, low-risk environments to study rider experiences and performance. While virtual reality (VR) simulators offer a promising approach

Anastasiia Tkachenko, Haitao Wang

For a set $\mathcal{D}$ of disks in the plane, its disk graph $G(\mathcal{D})$ is the graph with vertex set $\mathcal{D}$, where two vertices are adjacent if and only if the corresponding disks intersect. Given a set $\mathcal{D}$ of $n$ weighted disks, computing a maximum independent set of $G(\mathcal{D})$ is NP-hard. In this paper, we present an $O(n^3\lo

Shuhan Ai

This study applies the Causal Fairness Analysis (CFA) framework of Plecko and Bareinboim (2024) to decompose the total variation in STEM outcomes attributable to ADHD status into direct, indirect, and spurious components using Pearl's Structural Causal Model. Drawing on nationally representative data from the High School Longitudinal Study of 2009, this stud

Mohamed Ehab, Ali Hamdi

Plant seedling segmentation supports automated phenotyping in precision agriculture. Standard segmentation models face difficulties due to intricate background images and fine structures in leaves. We introduce UGDA-Net (Uncertainty-Guided Dual Attention Network with Entropy-Weighted Loss and Deep Supervision). Three novel components make up UGDA-Net. The fi

Pinaki Mohanty, Ruqi Zhang

High-dimensional and complex discrete distributions often exhibit multimodal behavior due to inherent discontinuities, posing significant challenges for sampling. Gradient-based discrete samplers, while effective, frequently become trapped in local modes when confronted with rugged or disconnected energy landscapes. This limits their ability to achieve adequ

Oleg Kiriukhin

This paper studies a finite reversible lumpable Markov chain for which relaxed spectral compression yields a larger determinant than partition-constrained compression. For a symmetric six-state lumpable chain and the positive operator $T=P^2$, I compare the relaxed benchmark \begin{equation*} \mathfrak D^{\mathrm{rel}}_3(T):=\sup_{U^*U=I_3}\det(U^*TU) \end{e

Kexin Wang, Haohui He, Ruolin Li

Weaving ramps are critical bottlenecks in highway networks due to conflicting traffic flows and complex interactions among heterogeneous vehicle types. In mixed-autonomy settings, the presence of controllable autonomous vehicles (AVs) introduces new opportunities to influence system-level outcomes, yet the structural impact of such control remains poorly und

Elbert Du, Cynthia Dwork, Pranay Tankala, Linjun Zhang

A recent line of work initiated by Chiesa and Gur and further developed by Herman and Rothblum investigates the sample and communication complexity of verifying properties of distributions with the assistance of a powerful, knowledgeable, but untrusted prover. In this work, we initiate the study of differentially private distribution property verification. A

Kin Ming Tsang

Let $\pi$ be a cuspidal automorphic representation for $\mathrm{GL}(n)$ over a number field. We establish a conditional upper bound on the number of cuspidal isobaric summands in the symmetric $k$-th power lift of $\pi$, assuming that the symmetric $m$-th power lift of $\pi$ is automorphic and cuspidal for all $m \leq k-1$, along with other specified Langlan

Eran Ben-Arosh, Eldar Ragonis, Lev Merensky, Avner Fleischer

A well-known shortcoming of High Harmonic Generation (HHG) is the strong dependence of the broadband HHG spectra (HGS) on the carrier envelope phase (CEP) of the driver. Here we numerically show that compared to the current well-established scalar (linearly polarized) schemes for generating broadband HGS, namely a short driver [Amplitude gating (AG)], Polari

S. V. Bolokhov

We study massive scalar quasinormal spectra of charged Einstein--Maxwell--dilaton black holes by combining high-order WKB--Pad\'e calculations with time-domain evolution. The two approaches show close agreement in the regime where both methods are reliable, allowing controlled tracking of spectral trends across different charges and dilaton couplings. We fin

Aaron Lauve, Anthony Lazzeroni

In the theory of species, the species $\mathbf{L}$ of linear orders and the substitution operation $\boldsymbol{\circ}$ combine for a compelling result: given any positive comonoid $\mathbf{p}$, $\mathbf{L}\boldsymbol{\circ}\mathbf{p}$ carries the structure of Hopf monoid, freely generated by $\mathbf{p}$. Leaving aside the universal property this implies, w

Hongwei Xu

MeloTune is an iPhone-deployed music agent that instantiates the Mesh Memory Protocol (MMP) and Symbolic-Vector Attention Fusion (SVAF) as a production system for affect-aware music curation with peer-to-peer mood coupling. Each device runs two closed-form continuous-time (CfC) networks: a private listener-level CfC that predicts a short-horizon affective tr

Yijin Ni, Xiaoming Huo

We study online covariance matrix estimation for Polyak--Ruppert averaged stochastic gradient descent (SGD). The online batch-means estimator of Zhu, Chen and Wu (2023) achieves an operator-norm convergence rate of $O(n^{-(1-\alpha)/4})$, which yields $O(n^{-1/8})$ at the optimal learning-rate exponent $\alpha \rightarrow 1/2^+$. A rigorous per-block bias an

Dheeraj Mudireddy, Sai Patibandla

Pokemon Red is a long-horizon JRPG with sparse rewards, partial observability, and quirky control mechanics that make it a challenging benchmark for reinforcement learning. While recent work has shown that PPO agents can clear the first two gyms using heavy reward shaping and engineered observations, training remains brittle in practice, with agents often de

X. Hernandez

The colliding galaxy clusters system 1E 0657-56, the "Bullet Cluster", is often presented as a serious objection to modified gravity theories dispensing with dark matter in general, and in particular, to the MOND formalism. The argument centres on the fact that the baryonic matter distribution of this system is dominated by the X-ray emitting gas, wh

Harry Freeman, Chung Hee Kim, George Kantor

Recent advancements in learning from human demonstration have shown promising results in addressing the scalability and high cost of data collection required to train robust visuomotor policies. However, existing approaches are often constrained by a reliance on multiview camera setups, depth sensors, or custom hardware and are typically limited to policy ex

Amin Gino Fabbrucci Barbagli, Jürgen Lerner, Viviana Amati, Domenico De Stefano

Sociological research has framed collective action in science, innovation, and culture as tripartite networks connecting teams of actors, lists of prior works, and sets of labels (e.g., keywords, topics). While methods for multipartite social networks were proposed decades ago, and have received a recent surge in interest, none of the suggested solutions sca

Haofan Dong, Ozgur B. Akan

We propose CisLunarSense, an opportunistic integrated sensing and communication (ISAC) framework that exploits the Lunar Gateway's Ka-band relay for monostatic debris detection, addressing the absence of cislunar space situational awareness infrastructure beyond the reach of ground-based radars. Using NASA/ESA-documented system parameters with author-selecte

Jian Sun, Xiyan Jiang, Xiaocong Zhao, Jie Wang

Human drivers' control quality in the first seconds after a handover is critical to shared-driving safety; potentially unsafe steering or pedal inputs therefore require detection and correction by the automated vehicle's safety-fallback system. Yet performance in this window is vulnerable because cognitive states fluctuate rapidly, causing purely rationality

Hassan Babaei, Mimi Dai

In turbulent flows, the Kolmogorov wavenumber characterizes the smallest scales at which viscous effects dominate. A mathematical analogue of this notion first introduced by Foias and Prodi [8] -- a determining wavenumber -- quantifies the minimal set of modes that uniquely determine the long-time behavior of solutions. Extending this framework from the Navi

Hewei Frederic Jia, Mukund Rangamani

We study analytic properties of thermal spectral functions of holographic CFTs, examining both their (a) exact properties at finite momentum and (b) asymptotics at large momentum. For even-dimensional holographic CFTs on Minkowski spacetime and for scalar primaries with integer dimensions, we demonstrate that the exact spectral function at finite momentum fa

Jean Gillibert, Florence Gillibert, Gabriele Ranieri

We give upper and lower bounds on the Chevalley-Bass number of a field of characteristic zero, whenever this quantity is well-defined. We also describe an algorithm which computes the Chevalley-Bass number of a field, provided its maximal abelian subextension is known. As a primary application, we improve the value of a constant related to exponential diopha

Sophia Heck, Eleni Akrida

Modern networks are highly dynamic, and temporal graphs capture these changes through discrete edge appearances on a fixed vertex set, known in advance up to the graph's lifetime. The Vertex Cover problem extends to the temporal setting as Temporal Vertex Cover (TVC) and Sliding Window Temporal Vertex Cover (SW-TVC). In TVC, each edge is covered by one endpo

Jugal Gajjar

Learned classifiers deployed in agentic pipelines face a fundamental reliability problem: predictions are probabilistic inferences, not verified conclusions, and acting on them without grounding in observable evidence leads to compounding failures across downstream stages. Software vulnerability analysis makes this cost concrete and measurable. We address th

Faizuddin Ahmed, Ahmad Al-Badawi, İzzet Sakallı

We study the geodesic motion and epicyclic oscillations of massive test particles around a static, spherically symmetric black hole (BH) solution of the Einstein--Skyrme (ES) theory in Anti-de Sitter (AdS) spacetime. The lapse function of this BH depends on the Skyrme coupling $\eta$, a charge-like parameter $Q$ inherited from the Skyrme term, and the cosmol

Krzysztof Ociepa, Łukasz Flis, Remigiusz Kinas, Krzysztof Wróbel

The development of the Bielik v3 PL series, encompassing both the 7B and 11B parameter variants, represents a significant milestone in the field of language-specific large language model (LLM) optimization. While general-purpose models often demonstrate impressive multilingual capabilities, they frequently suffer from a fundamental architectural inefficiency

Mohammad Baqar, Raji Rustamov, Alexander Hughes

Code smells and software vulnerabilities both increase maintenance cost, yet they are often handled by separate tools that miss structural context and produce noisy warnings. This paper presents The Code Whisperer, a hybrid framework that combines graph-based program analysis with large language models to detect, explain, and repair maintainability and secur

Hongbin Ni, Ozgur B. Akan

Brain organoid interfaces that seek neuromodulator readout benefit from chemical receivers with molecular specificity and tolerance to drift. This paper presents a receiver-centric theoretical study of a control-referenced tri-channel organic electrochemical transistor (OECT) receiver with dopamine- and serotonin-selective pixels alongside a hydrogel-matched

Xin Tian, Xudong Ma, Tianqi Yang, Alin Achim

We present WBCBench 2026, an ISBI challenge and benchmark for automated WBC classification designed to stress-test algorithms under three key difficulties: (i) severe class imbalance across 13 morphologically fine-grained WBC classes, (ii) strict patient-level separation between training, validation and test sets, and (iii) synthetic scanner- and setting-ind

Norbert Heuer, Antti H. Niemi

We consider an elastic model for a circular arch that incorporates membrane, transverse shear, and bending effects. The central line of the arch is partitioned into elements, and an ultra-weak variational formulation is developed alongside a discontinuous Petrov-Galerkin (DPG) approximation procedure based on so-called optimal test functions. The formulation

Efstratios Koukoutsis, Kyriakos Hizanidis, Lucas I Inigo Gamiz, Oscar Amaro

This work develops a symplectic framework for quantum computing to be applied to classical Hamiltonian systems, exploiting the intrinsic geometric compatibility between unitary quantum evolution and symplectic phase-space dynamics in a two-fold way. The first part is devoted in establishing an exact correspondence between quantum evolution and classical Hami

Adrian Bajraktari, Andreas Vogelsang

Modern research heavily relies on software. A significant challenge researchers face is understanding the complex software used in specific research fields. We target two scenarios in this context, namely long onboarding times for newcomers and conference reviewers evaluating replication packages. We hypothesize that both scenarios can be significantly impro

Oleg Kiriukhin

Variable-length Markov chains on finite quivers provide a natural framework for context-dependent stochastic growth under incidence constraints. I study quiver-valued variable-length Markov chains observed through finite boundary windows and develop a first-order theory of visible-depth identifiability via stationary visible one-step transition laws and thei

Chirag Shinde

We propose two complementary modifications to transformer attention blocks. First, a non-linear pre-projection MLP is inserted between layer norm and Q/K/V projections, constructing richer features in a position-agnostic manner before any positional encoding is applied. Second, a content skip connection routes the pre-projection's features around the attenti

Jørgen Randrup, Roberto Capote, Ramona Vogt

Dynamical fission calculations show that the post-scission configurations resemble two collinear pear-shaped fragments whose juxtaposed surface bulges subside relatively quickly, as the fragments acquire smoother shapes. The associated rapid speed of the healing bulge surface may boost nucleons in the fragment to energies sufficient for emission. The present

Mingyu Dong, Chong Xia, Mingyuan Jia, Weichen Lyu

Humans exhibit an innate capacity to rapidly perceive and segment objects from video observations, and even mentally assemble them into structured 3D scenes. Replicating such capability, termed compositional 3D reconstruction, is pivotal for the advancement of Spatial Intelligence and Embodied AI. However, existing methods struggle to achieve practical deplo

Sarmistha Das, Shreyas Guha, Suvrayan Bandyopadhyay, Salisa Phosit

Idiomatic reasoning, deeply intertwined with metaphor and culture, remains a blind spot for contemporary language models, whose progress skews toward surface-level lexical and semantic cues. For instance, the Bengali idiom \textit{\foreignlanguage{bengali}{\char"0986\char"0999\char"09CD\char"0997\char"09C1 \char"09B0 \char"09AB\char"09B2 \char"099F\char"0995

Beicheng Bei, Hannah Hyesun Chun, Chen Guo, Arwa Saghiri

Narrative understanding requires multidimensional semantic structures. This study investigates whether BERT embeddings encode dimensions of fictional narrative semantics -- time, space, causality, and character. Using an LLM to accelerate annotation, we construct a token-level dataset labeled with these four narrative categories plus "others." A linear probe

Gregory Magarshak

We introduce LAWS (Learning from Actual Workloads Symbolically), a self-certifying inference caching architecture that builds a growing library of certified expert functions from deployment observations. Each expert covers a region of input space defined by a node in the Probabilistic Language Trie (PLT) of the base model and carries a formal error bound hol

Marko Imbrišak, Krešimir Tisanić

Modern radio and multi-instrument astrophysical datasets are increasingly assembled from surveys with different sensitivities and selection effects. In such heterogeneous datasets, published measurement uncertainties are often incomplete, non-uniform across subsets, or missing cross-correlation information altogether. This limits reliable statistical inferen

Said Ohamouddou, Hanaa El Afia, Abdellatif El Afia, Raddouane Chiheb

Three-dimensional (3D) point cloud analysis has become central to applications ranging from autonomous driving and robotics to forestry and ecological monitoring. Although numerous deep learning methods have been proposed for point cloud understanding, including supervised backbones, self-supervised pre-training (SSL), and parameter-efficient fine-tuning (PE

Zijun Li, Aswin Kannan

We consider joint optimization and learning problems arising in real-time decision systems. While most existing work focuses primarily on convex, revenue-based objectives, we extend this line of research to multi-objective formulations. In energy systems, for instance, we incorporate metrics such as renewable penetration and generation costs. Our key focus,

Vineet R. Shenoy, Cheng Peng, Rama Chellappa, Yu Sun

Imaging Photoplethysmography (iPPG), an optical procedure which recovers a human's blood volume pulse (BVP) waveform using pixel readout from a camera, is an exciting research field with many researchers performing clinical studies of iPPG algorithms. While current algorithms to solve the iPPG task have shown outstanding performance on benchmark datasets, no

Bilel Abderrahmane Benziane, Benoit Lardeux, Ayoub Mcharek, Maher Jridi

The use of artificial intelligence in supply chain forecasting has attracted many scientific studies for several decades. However, the process of selecting an appropriate forecasting solution becomes a daunting task. This complexity arises due to the distinct features inherent to each dataset. Research to tackle this issue has been performed since the eighti

Antonio Picano, Marco Schiro

We investigate the nonequilibrium dynamics of the weak-coupling Hubbard-Holstein model after a sudden switch-on of the electron-phonon interaction within nonequilibrium dynamical mean-field theory (DMFT). Using the self-consistent Migdal approximation for the electron-phonon coupling together with second-order perturbation theory for the electron-electron in

Tan H. Cao, Hassan Saoud

We study a catching-up algorithm for a class of differential inclusions driven by maximal monotone operators with continuous perturbations. Using a decomposition of the monotone operator into the closed convex hull of its single-valued part and the normal cone to a closed convex set, we establish existence of solutions and derive global energy bounds under a

Alok Yadav

The macroscopic dynamics of topological defects in magnetic materials are traditionally modeled using pairwise interactions. However, higher-order quantum exchange mechanisms - such as biquadratic and 4-spin ring exchange-play a critical role in strongly correlated systems. In this work, we introduce the "Simplicial Bridge," an exact analytical framework tha

Haiyan Jiang, Deyu Zhang, Dongdong Weng, Weitao Song

3D layout generation and editing play a crucial role in Embodied AI and immersive VR interaction. However, manual creation requires tedious labor, while data-driven generation often lacks diversity. The emergence of large models introduces new possibilities for 3D scene synthesis. We present HOG-Layout that enables text-driven hierarchical scene generation,

Tomasz Kania, Grzegorz Lewicki

Over the real or complex field, we establish a duality formula for projection constants of finite-codimensional subspaces of Banach spaces with the Daugavet property. If \[ Y=\bigcap_{j=1}^n \ker f_j \subset X, \qquad W=\operatorname{span}\{f_1,\dots,f_n\} \subset X^*, \] then \[ \lambda(Y,X)=1+\lambda(W,X^*), \] and minimal projections onto $Y$ correspond e

Lixiong Li

Empirical researchers increasingly use upstream machine-learning (ML) methods to construct proxies for latent target variables from complex, unstructured data. A naive plug-in use of such proxies in downstream econometric models, however, can lead to biased estimation and invalid inference. This paper develops a framework for partial identification and infer

Subir Majumder, Minlan Yu, Le Xie

Artificial intelligence (AI) is driving rapid growth in electricity demand, yet the grid-facing power dynamics of AI data centers remain poorly understood. Here we show that, in shared-GPU systems, the composition of batch and inference workloads decouples aggregate power variability from short-horizon ramping. As the inference share rises, variability becom

Geethanjali S, Katsunori Wakabayashi, Sasmita Mohakud

Vacancy-induced structural reconstruction provides a general microscopic route to $d$-wave altermagnetism in two-dimensional systems. As a concrete realization, reconstructed $\mathrm{V_2X_2}$ ($\mathrm{X}=\mathrm{S}, \mathrm{Se}$) monolayers form an inverse Lieb magnetic network in which two inequivalent edge vanadium sites, related by $C_4$ lattice rotatio

Ming-Yang Ho, Alberto Bartesaghi

Open-set 3D macromolecule detection in cryogenic electron tomography eliminates the need for target-specific model retraining. However, strict VRAM constraints prohibit processing an entire 3D tomogram, forcing current methods to rely on slow sliding-window inference over extracted subvolumes. To overcome this, we propose FullTilt, an end-to-end framework th

Imama Ajmi, Abhishek Das

Lung cancer, the second leading cause of cancer-related deaths, is primarily linked to long-term tobacco smoking (85% of cases). Surprisingly, 10-15% of cases occur in non-smokers. In 2020, approximately 2 million people were affected globally, resulting in 1.5 million deaths. The survival rate, at around 20%, lags behind other cancers, partly due to late-st

Priyanshu Chakraborty

In this paper we study Category $\mcal O$ for the polynomial toroidal Lie algebras and its $S,H$ type subalgebras. We classify irreducible objects of category $\mcal O$ as unique irreducble quotient of standard modules. Surprisingly, costandard objects of category $\mcal O$ arrises from Shen-Larsson type modules. We determine necessary sufficient conditions

Eden Wu, Christos Koutras, Cláudio T. Silva, Juliana Freire

Schema matching remains fundamental to data integration, yet evaluating and comparing matching methods is hindered by limited benchmark diversity and lack of interactive validation frameworks. BDIViz, recently published at IEEE VIS 2025, is an interactive visualization system for schema matching with LLM-assisted validation. Given source and target datasets,

Anna Gabetti, Fabrizio Dolcini, Davide Girolami

We derive a bound on the efficiency of thermal engines that can be sharper than Carnot's limit. It is a function of statistical correlations between the engine internal state and Hamiltonian, can be saturated even in finite-time cycles, and applies to both classical and quantum engines. Specifically, the bound establishes the exact maximal efficiency of engi

Agustín Balestra, Agustín Nolasco, Facundo Molina, Diego Garbervetsky

Contract assertions, such as preconditions, postconditions, and invariants, play a crucial role in software development, enabling applications such as program verification, test generation, and debugging. Despite their benefits, the adoption of contract assertions is limited, due to the difficulty of manually producing such assertions. Dynamic analysis-based

Yunxiang Liao, Andrey Grankin, Archisman Panigrahi, Victor Galitski

Tunable interactions make ultracold quantum gases a unique platform for exploring hydrodynamic properties in the strongly correlated regime. Of particular interest are turbulent flows possible in the regime of high Reynolds numbers. Since the system size and flow velocity are limited in experimentally realistic systems, we propose an alternative approach to