November 2025 arXiv papers — page 101

M. Stocker, W. Małgorzewicz, M. Fontana, S. Ben Taieb

Conformal prediction is a powerful post-hoc framework for uncertainty quantification that provides distribution-free coverage guarantees. However, these guarantees crucially rely on the assumption of exchangeability. This assumption is fundamentally violated in time series data, where temporal dependence and distributional shifts are pervasive. As a result,

Xin Xu, Hao Liu, Wei Liu, Wei Wang

Low-Light Image Enhancement (LLIE) task aims at improving contrast while restoring details and textures for images captured in low-light conditions. HVI color space has made significant progress in this task by enabling precise decoupling of chrominance and luminance. However, for the interaction of chrominance and luminance branches, substantial distributio

Umberto Michelucci

This document offers a concise introduction to the mathematical theory and practical application of the Hausdorff Measure and Dimension. The primary objective is to clarify and rigorously detail the two most common methods used for calculating the dimension of a set, ensuring all calculation details are transparent for the reader. The paper first establishes

Noor-Ines Boudjema, Frank F. Deppisch, Antonio Herrero-Brocal, Chayan Majumdar

Motivated by the observation of non-zero neutrino masses and the potential for discovering physics beyond the Standard Model, numerous experiments are actively searching for neutrinoless double beta $(0\nu\beta\beta)$ decay. In all of these searches, a substantial amount of data on two-neutrino double beta $(2\nu\beta \beta)$ decay has been collected. In thi

Niv Buchbinder, Joseph, Naor, David Wajc

We introduce the \emph{submodular objectives chasing problem}, which generalizes many natural and previously-studied problems: a sequence of constrained submodular maximization problems is revealed over time, with both the objective and available ground set changing at each step. The goal is to maintain solutions of high approximation and low total \emph{rec

Sahil Pontula, Debasmita Banerjee, Marin Soljacic, Yannick Salamin

Frequency combs are multimode photonic systems that underlie countless precision sensing and metrology applications. Since their invention over two decades ago, numerous efforts have pushed frequency combs to broader bandwidths and more stable operation. More recently, quantum squeezing and entanglement have been explored in single frequency comb systems for

Bartosz Uniejewski

Accurate day-ahead electricity price forecasts are critical for power system operation and market participation, yet growing renewable penetration and recent crises have caused unprecedented volatility that challenges standard models. This paper revisits variance stabilizing transformations (VSTs) as a preprocessing tool by introducing a novel parametrizatio

Serkan Gür

This paper presents a novel class of information-theoretic strategies for solving the game of Mastermind, achieving state-of-the-art performance among known heuristic methods. The core contribution is the application of a weighted entropy heuristic, based on the Belis-Guias, u framework, which assigns context-dependent utility values to each of the possible

Jungwoo Ho, Sangun Park, Soyeong Oh

We propose a nonparametric estimator of multivariate joint entropy based on partitioned sample spacing (PSS). The method extends univariate spacing ideas to $\mathbb{R}^{d}$ by partitioning into localized cells and aggregating within-cell statistics, with strong consistency guarantees under mild conditions. In benchmarks across diverse distributions, PSS con

Kai Wang

This paper presents a large-scale computational study on the dimensional properties of twisted Goppa codes. Through the systematic analysis of over 50,000 parameter sets, we uncover a remarkable deterministic regularity: the actual dimension k of a twisted Goppa code is uniquely determined by a set of macro-parameters (q,m,t,b,u). Specifically, when the orde

Claire Y. Yin, Peter M. Kogge

Subgraph Isomorphism uses a small graph as a pattern to identify within a larger graph a set of vertices that have matching edges. This paper addresses a logic program written in Prolog for a specific relatively complex graph pattern for which multiple conventional implementations (including parallel) exist. The goal is to understand the complexity differenc

James Tian

We study how iterated and composed completely positive maps act on operator-valued kernels. Each kernel is realized inside a single Hilbert space where composition corresponds to applying bounded creation operators to feature vectors. This model yields a direct formula for every iterated kernel and allows pointwise limits, contractive behavior, and kernel do

Jiaxiong Tang, Zhengchunmin Dai, Liantao Wu, Peng Sun

Split Federated Learning (SFL) is renowned for its privacy-preserving nature and low computational overhead among decentralized machine learning paradigms. In this framework, clients employ lightweight models to process private data locally and transmit intermediate outputs to a powerful server for further computation. However, SFL is a double-edged sword: w

Olalla A. Castro-Alvaredo, Stefano Negro, Fabio Sailis

In this proceeding contribution, we review a recently proposed method to compute the minimal form factors (MFFs) of diagonal integrable field theories perturbed by irrelevant fields of the $T\bar{T}$ family. Our construction generalizes standard form factor techniques to deal with the deformed two-body scattering amplitudes, which are typical in this setting

Gabriel Fazoli, Paulo Santana

In this work we revisit and extend the method introduced by Lins Neto, Sad and Sc\'{a}rdua for detecting the non-existence of invariant algebraic curves other than some prescribed invariant nodal curve. We prove that, under the existence of a suitable example, the space of polynomial vector fields whose elements have the prescribed curve as their unique inva

Sebastiano Mengozzi, Giovanni B. Esposito, Michelangelo Bin, Andrea Acquaviva

This work addresses the full-information output regulation problem for nonlinear systems, assuming the states of both the plant and the exosystem are known. In this setting, perfect tracking or rejection is achieved by constructing a zero-regulation-error manifold $\pi(w)$ and a feedforward input $c(w)$ that render such manifold invariant. The pair $(\pi(w),

Masaaki Nakamura, Masanori Yamanaka

We derive the condition for the occurrence of the integer quantum Hall effect in two-dimensional lattice systems with interactions, expressed as $\phi\nu-\rho\in\mathbb{Z}$, where $\phi$, $\nu$, and $\rho$ denote the magnetic flux, the Chern number, and the electron density, respectively. By employing spiral boundary conditions, which treat the system as an

Piaohong Wang, Motong Tian, Jiaxian Li, Yuan Liang

Recent advancements in LLM-powered agents have demonstrated significant potential in generating human-like responses; however, they continue to face challenges in maintaining long-term interactions within complex environments, primarily due to limitations in contextual consistency and dynamic personalization. Existing memory systems often depend on semantic

Danielle A. Berg, Rohan P. Naidu, John Chisholm, Hakim Atek

We present the discovery of extreme nitrogen enrichment by Wolf Rayet nitrogen stars (WN) in the metal-poor (~10% Z_odot), lensed, compact (R_eff ~ 20 pc) galaxy RXCJ2248 at z=6.1, revealed by unprecedentedly deep JWST/NIRSpec medium-resolution spectroscopy from the GLIMPSE-D Survey. The exquisite S/N reveals multiple high-ionization nebular lines and broad

Hao Wang, Yuanfeng Song, Xiaoming Yin, Xing Chen

Text-to-SQL datasets are essential for training and evaluating text-to-SQL models, but existing datasets often suffer from limited coverage and fail to capture the diversity of real-world applications. To address this, we propose a novel taxonomy for text-to-SQL classification based on dimensions including core intents, statement types, syntax structures, an

Serte Donderwinkel, Joost Jorritsma, Guillem Perarnau

Although false for general graphs, this note gives an elementary proof of the bunkbed conjecture for any acyclic graph. The argument is short and self-contained, and may be of educational interest.

Haotian Dong, Ye Li, Rongwei Lu, Chen Tang

Visual autoregressive (AR) generation models have demonstrated strong potential for image generation, yet their next-token-prediction paradigm introduces considerable inference latency. Although speculative decoding (SD) has been proven effective for accelerating visual AR models, its "draft one step, then verify one step" paradigm prevents a direct reductio

Pranjal Gupta, Karan Bhukar, Harshit Kumar, Seema Nagar

IT environments typically have logging mechanisms to monitor system health and detect issues. However, the huge volume of generated logs makes manual inspection impractical, highlighting the importance of automated log analysis in IT Software Support. In this paper, we propose a log analytics tool that leverages Large Language Models (LLMs) for log data proc

Yinuo Xu, Yan Cui, Mingyao Li, Zhi Huang

Identifying cell types and subtypes in routine histopathology is fundamental for understanding disease. Existing tile-based models capture nuclear detail but miss the broader tissue context that influences cell identity. Current human annotations are coarse-grained and uneven across studies, making fine-grained, subtype-level classification difficult. In thi

Sara Beck, Pei-Ying Hsieh, Jean Turner

Haro 2 is a blue compact dwarf galaxy and the closest (at 21Mpc) known $Ly\alpha$ emitter. UV and optical observations have found Haro 2 to be immersed in shells of partly ionized gas expanding at $\sim200$km/s. Observations of CO(2-1) (Becket et al 2020; Paper 1) with moderate ($2^{\prime\prime}$) resolution discovered a large-scale one-sided outflow associ

Souvik Das, Luca Schenato, Subhrakanti Dey

This article presents a second-order fully distributed optimization algorithm, HBNET-GIANT, driven by heavy-ball momentum, for $L$-smooth and $\mu$-strongly convex objective functions. A rigorous convergence analysis is performed, and we demonstrate global linear convergence under certain sufficient conditions. Through extensive numerical experiments, we sho

Azam Shirali, Vitalii Stebliankin, Jimeng Shi, Prem Chapagain

Protein-protein docking is crucial for understanding how proteins interact. Numerous docking tools have been developed to discover possible conformations of two interacting proteins. However, the reliability and success of these docking tools rely on their scoring function. Accurate and efficient scoring functions are necessary to distinguish between native

Alfonso Cevallos, Robert Hambrock, Alistair Stewart

Merkle structures are widely used as commitment schemes: they allow a prover to publish a compact commitment to an ordered list $X$ of items, and then efficiently prove to a verifier that $x_i\in X$ is the $i$-th item in it. We compare different Merkle structures and their corresponding properties as commitment schemes in the context of blockchain applicatio

Detlef Lehmann

We apply the methodology of our recent paper 'The Dynamics of the Hubbard Model through Stochastic Calculus and Girsanov Transformation' [1] to thermodynamic correlation functions in the Fermi-Hubbard model. They can be obtained from a stochastic differential equation (SDE) system. To this SDE system, a Girsanov transformation can be applied. This has the ef

Samaneh Baghbani, Uygar Akkoc, Clara Stock, Christiane Werner

The quantum yield efficiency of photosystem II (PhiPSII) is an important parameter for assessing the photosynthetic performance and stress status of plants. Commercial PAM fluorometers can measure this parameter, but they are often expensive, bulky, or lack autonomous operation. This work presents the development of an autonomous PAM fluorometer designed to

Mihai Popa, Kamil Szpojankowski

We investigate commutators of free variables of the form \( i[x, s] \), where \( s \) is a semicircular element. We show that although \( s \) and \( i[x, s] \) are not free, their sum nevertheless satisfies the free additive convolution identity \[ \mu_{s + i[x, s]} = \mu_s \boxplus \mu_{i[x, s]}. \] Furthermore, we prove that the polynomial \( x + i[x, s]

Maxime Brazeau, Mathieu Giroux, Nada Boubrik, Raphael St-Gelais

Measurement of low power infrared light emission spectra from microstructures can be challenging, but is of key importance in several research fields. Fourier transform infrared spectrometers (FTIR) can be used for characterizing such weak light emitters, but this requires additional custom user calibration compared to traditional FTIR measurements of, e.g.,

Anhao Xiang, Weiping Pei, Chuan Yue

With the requirements and emphases on privacy transparency placed by regulations such as GDPR and CCPA, the Google Play Store requires Android developers to more responsibly communicate their apps' privacy practices to potential users by providing the proper information via the data safety, privacy policy, and permission manifest privacy transparency channel

Linhan Zhou, Shuang Li, Neng Dong, Yonghang Tai

Person re-identification (ReID) aims to retrieve target pedestrian images given either visual queries (image-to-image, I2I) or textual descriptions (text-to-image, T2I). Although both tasks share a common retrieval objective, they pose distinct challenges: I2I emphasizes discriminative identity learning, while T2I requires accurate cross-modal semantic align

Yan V. Fyodorov, Jan Meibohm

We develop an analytic approach to evaluating the density $\rho ({\cal E},\Gamma)$ of complex resonance poles with real energies $\mathcal{E}$ and widths $\Gamma$ in the pure reflection problem from a one-dimensional disordered sample with white-noise random potential. We start with establishing a general link between the density of resonances and the distri

Joseph, Naor, Nitya Raju, Abhishek Shetty

Sampling from multiple distributions so as to maximize overlap has been studied by statisticians since the 1950s. Since the 2000s, such correlated sampling from the probability simplex has been a powerful building block in disparate areas of theoretical computer science. We study a generalization of this problem to sampling sets from given vectors in the hyp

Andreas Kamilaris, Chirag Padubidri, Asfa Jamil, Arslan Amin

This paper describes the experiences and lessons learned after the deployment of a country-scale environmental digital twin on the island of Cyprus for three years. This digital twin, called GAEA, contains 27 environmental geospatial services and is suitable for urban planners, policymakers, farmers, property owners, real-estate and forestry professionals, a

Ziyang Huang, Jiagang Chen, Jin Liu, Shunping Ji

3D Gaussian Splatting (3DGS) has emerged as a leading framework for novel view synthesis, yet its core optimization challenges remain underexplored. We identify two key issues in 3DGS optimization: entrapment in suboptimal local optima and insufficient convergence quality. To address these, we propose Opt3DGS, a robust framework that enhances 3DGS through a

Jerry L. Whitten

A method for increasing the accuracy of configuration interaction (CI) calculations of molecules and other electronic systems is proposed. The energy defect of a given calculation is associated with the electron pair origin of configurations not yet generated and this defect is mapped onto the exchange interaction for the corresponding pair of spatial molecu

Simone Bruno, Yi Fu, Felipe A. Campos, Domitilla Del Vecchio

Continuous time Markov chains are commonly used as models for the stochastic behavior of chemical reaction networks. More precisely, these Stochastic Chemical Reaction Networks (SCRNs) are frequently used to gain a mechanistic understanding of how chemical reaction rate parameters impact the stochastic behavior of these systems. One property of interest is m

Daria Sakhanda, Joshué Helí Ricalde-Guerrero

This paper is devoted to developing a unified framework for stochastic growth models with environmental risk, in which rare but catastrophic shocks interact with capital accumulation and pollution. The analysis is based upon a general Poisson point process formulation, leading to non-local Hamilton-Jacobi-Bellman (HJB) equations that admit closed-form candid

Clayton P. Byers, Subrahmanyam Duvvuri

The direction and magnitude of energy transfer between turbulence scale brought about by external forcing on a turbulent boundary layer are uncovered through the bispectrum, bicoherence, and biphase. The bispectrum is a third-order, complex-valued spectrum of the streamwise velocity that preserves the phase information between triadically consistent scales.

Jingyi Zhao, Daqian Shi, Zhengda Wang, Xiongfeng Tang

Intelligent wearable systems are at the forefront of precision medicine and play a crucial role in enhancing human-machine interaction. Traditional devices often encounter limitations due to their dependence on empirical material design and basic signal processing techniques. To overcome these issues, we introduce the concept of Human-Symbiotic Health Intell

Péter L. Erdős, Gábor Lippner, Na'ama Nevo, Lajos Soukup

Let $n>c_1\ge c_2$ and $\Sigma$ be positive integers with $n\cdot c_1\ge \Sigma \ge n\cdot c_2.$ Let $\mD=\dds{n}{\Sigma}{c_1}{c_2}$ denote the set of all degree sequences of length $n$ with the even sum $\Sigma$ and satisfying $c_1\ge d_i\ge c_2.$ We show that if all degree sequences in $\mD$ are graphic, then $\mD$ is $3n^{13}$-stable. (The concept of $P$-

Ivan A. Belkovich, Damir Sadekov, Gleb S. Zverev

We study non-equilibrium initial states of quantum fields in curved space-time and develop a framework for describing global quenches as unitary perturbations of the initial density matrix. Using the Keldysh-Schwinger functional integral, we derive expressions for post-quench correlators in arbitrary geometries and apply the method to both Minkowski and de S

Shihao Dong, Yue Liu, Xiaotong Zhou, Yuhui Zheng

Multi-view clustering (MVC), which aims to separate the multi-view data into distinct clusters in an unsupervised manner, is a fundamental yet challenging task. To enhance its applicability in real-world scenarios, this paper addresses a more challenging task: MVC under multi-source noises, including missing noise and observation noise. To this end, we propo

Saicharan Kolluru

The deployment of Large Language Models (LLMs) in production environments requires efficient inference serving systems that balance throughput, latency, and resource utilization. This paper presents a comprehensive empirical evaluation of two prominent open-source LLM serving frameworks: vLLM and HuggingFace Text Generation Inference (TGI). We benchmark thes

Anya Katsevich, Alexander Katsevich

Understanding rare events is critical across domains ranging from signal processing to reliability and structural safety, extreme-weather forecasting, and insurance. The analysis of rare events is a computationally challenging problem, particularly in high dimensions $d$. In this work, we develop the first asymptotic high-dimensional theory of rare events. F

Asal Y Siavoshani, Cheng Liang, Ming-Chi Wang, Junpeng Wang

We carry out uniaxial continuous and step stretching of various crosslinked polymer networks to demonstrate how characteristics of rupture (from continuous stretching) and delayed rupture (from step stretching) can be used to probe the structure of the emergent kinetic theory of bond dissociation (KTBD) for elastomeric failure. Based on delayed rupture exper

Stefan Kulk, Frederik Zuiderveen Borgesius

Since the Google Spain judgment of the Court of Justice of the European Union, Europeans have, under certain conditions, the right to have search results for their name delisted. This paper examines how the Google Spain judgment has been applied in the Netherlands. Since the Google Spain judgment, Dutch courts have decided on two cases regarding delisting re

Soumendra Kumar Das, Smruti Ranjan Parida, Prasanjit Samal, Brahmananda Chakraborty

Light element based two dimensional (2D) materials are promising photocatalysts for hydrogen production via water splitting. Boron oxide (BO) is a recently synthesized 2D monolayer which has yet to be thoroughly explored for its potential applications. In this article, using first principles calculations, we report, for the first time, the visible-light phot

Evelien Brouwer, Frederik Zuiderveen Borgesius

In the YS. and M. and S. judgment, the Court of Justice of the European Union ruled on three procedures in which Dutch judges asked for clarification on the right of asylum seekers to have access to the documents regarding the decision on asylum applications. The judgment is relevant for interpreting the concept of personal data and the scope of the right of

Eduardo Abi Jaber, Elie Attal, Dimitri Sotnikov

We introduce a novel and efficient simulation scheme for Hawkes processes on a fixed time grid, leveraging their affine Volterra structure. The key idea is to first simulate the integrated intensity and the counting process using Inverse Gaussian and Poisson distributions, from which the jump times can then be easily recovered. Unlike conventional exact algo

Frederik Zuiderveen Borgesius, Axel Arnbak

This paper discusses the regulation of mass metadata surveillance in Europe through the lens of the landmark judgment in which the Court of Justice of the European Union struck down the Data Retention Directive. The controversial directive obliged telecom and Internet access providers in Europe to retain metadata of all their customers for intelligence and l

Sining Chen, Xiao Xiang Zhu

Monocular height estimation plays a critical role in 3D perception for remote sensing, offering a cost-effective alternative to multi-view or LiDAR-based methods. While deep learning has significantly advanced the capabilities of monocular height estimation, these methods remain fundamentally limited by the availability of labeled data, which are expensive a

Hideo Suganuma, Kei Tohme

For SU(3) lattice QCD calculations at finite baryon-number densities, we propose the ``SO(3) real algebra method'', in which the SU(3) gauge variable is divided into the SO(3) and SU(3)/SO(3) parts. In this method, we introduce the ``maximal SO(3) gauge'' by minimizing the SU(3)/SO(3) part of the SU(3) gauge variable. In the Monte Carlo calculation, the SO(3

Irene Cazzaniga, Toni Giorgino

Molecular dynamics (MD) simulations provide atomistic insights into the structure, dynamics, and function of biomolecules by generating time-resolved, high-dimensional trajectories. Analyzing such data benefits from estimating the minimal number of variables required to describe the explored conformational manifold, known as the intrinsic dimension (ID). We

Rayirth Bhat, Justin R. David, Semanti Dutta

We consider single-particle excitations of the massive Chern-Simons field of mass $M$ in $AdS_3$ and evaluate their contribution at the first sub-leading order in $G_N$ to the entanglement entropy across the Ryu-Takayanagi surface. Quantizing the Chern-Simons field in $AdS_3$, we evaluate the corrections to the holographic entanglement entropy using the Faul

Stefan Ecklebe, Frank Woittennek

This contribution develops an algebraic approach to obtain a controller form for a class of linear hyperbolic MIMO systems, bidirectionally coupled with a linear ODE system at the unactuated boundary. After a short summary of established controller forms for SISO and MIMO ODE as well as SISO hyperbolic PDE systems, it is shown that the approach to state a co

Md. Iqbal Hossain, Afia Sajeeda, Neeresh Kumar Perla, Ming Shao

The advent of multimodal deep learning models, such as CLIP, has unlocked new frontiers in a wide range of applications, from image-text understanding to classification tasks. However, these models are not safe for adversarial attacks, particularly backdoor attacks, which can subtly manipulate model behavior. Moreover, existing defense methods typically invo

Arianna Salazar-Miranda

The growth of suburbs in the US has led to significant sustainability challenges; yet, it remains unclear whether these challenges stem from the remoteness of suburbs from city centers or the specific designs used to develop them. This paper examines how Garden City Design (GCD) -- one of the most influential suburban design paradigms since the early 20th ce

Yuning Zhang, K. W. Wang

Modern autonomous systems are driving the critical need for next-generation adaptive materials and structures with embodied intelligence, i.e., the embodiment of memory, perception, learning, and decision-making within the mechanical domain. A fundamental challenge is the seamless and efficient integration of memory with information processing in a physicall

Amirreza Mehrabi, Jason Wade Morphew, Breejha Quezada, N. Sanjay Rebello

Adaptive learning often diagnoses precisely yet intervenes weakly, producing help that is mistimed or misaligned. This study presents evidence supporting an instructor-governed feedback loop that converts concept-level assessment evidence into vetted microinterventions. The adaptive learning algorithm includes three safeguards: adequacy as a hard guarantee o

Yue Hou, Ruomei Liu, Yingke Su, Junran Wu

A key challenge in graph out-of-distribution (OOD) detection lies in the absence of ground-truth OOD samples during training. Existing methods are typically optimized to capture features within the in-distribution (ID) data and calculate OOD scores, which often limits pre-trained models from representing distributional boundaries, leading to unreliable OOD d

Zichong Wang, Zhipeng Yin, Liping Yang, Jun Zhuang

Ensuring fairness in Graph Neural Networks is fundamental to promoting trustworthy and socially responsible machine learning systems. In response, numerous fair graph learning methods have been proposed in recent years. However, most of them assume full access to demographic information, a requirement rarely met in practice due to privacy, legal, or regulato

Yuanchao Wang, Tian Qin, Eduardo Valle, Bruno Abrahao

Out-of-distribution (OOD) detection is critical for deploying image classifiers in safety-sensitive environments, yet existing detectors often struggle when OOD samples are semantically similar to the in-distribution (ID) classes. We present BootOOD, a fully self-supervised OOD detection framework that bootstraps exclusively from ID data and is explicitly de

Nailya Manatova

For the quintic, mass critical generalized Korteweg-de Vries equation, for any $\nu \in (\frac{1}{2}, 1)$, we prove the existence of solutions in the energy space that blow up in finite time $T>0$ with the blow-up rate $\|\partial_x u(t)\|_{L^2} \sim (T-t)^{-\nu}$ (infinite point blow-up). These solutions are constructed arbitrarily close to the family of so

Clemens Brüser, Julian Weigert

In this article we prove that the adjoint polynomial of arbitrary convex polytopes is up to scaling uniquely determined by vanishing to the right order on the polytopes residual arrangement. This answers a problem posed by Kohn and Ranestad and generalizes their main theorem to non-simple polytopes. We furthermore prove that the adjoint polynomial is already

Shai Keidar, Lior Yanovski

We prove a refinement of Quillen's Theorem A, providing necessary and sufficient conditions for a functor to be cofinal with respect to diagrams valued in a fixed $\infty$-category. We deduce this from a general duality phenomenon for weighted colimits, which is of independent interest. As a sample application, due to Betts and Dan-Cohen, we describe a simpl

Farhin Farhad Riya, Shahinul Hoque, Jinyuan Stella Sun, Olivera Kotevska

As machine learning models are increasingly deployed in safety-critical domains, visual explanation techniques have become essential tools for supporting transparency. In this work, we reveal a new class of attacks that compromise model interpretability without affecting accuracy. Specifically, we show that small color perturbations applied by adversarial cl

Luhang Yang, Adrian E. Feiguin, Thomas P. Devereaux, Elbio Dagotto

The Hubbard model has attracted considerable interest due to its prototypical role in describing strongly interacting electronic systems, such as high-critical-temperature superconductors as well as many novel quantum materials. By introducing next-nearest-neighbor (NNN) hoppings to the Hubbard model, the phase diagram becomes richer, and fascinating phenome

Jeffrey Wen, Rizwan Ahmad, Philip Schniter

In ill-posed imaging inverse problems, uncertainty quantification remains a fundamental challenge, especially in safety-critical applications. Recently, conformal prediction has been used to quantify the uncertainty that the inverse problem contributes to downstream tasks like image classification, image quality assessment, fat mass quantification, etc. Whil

Jeongwoo Jae, Changwon Lee, Juzar Thingna, Yeong-Dae Kwon

Dynamical decoupling (DD) is a key technique for suppressing decoherence and preserving the performance of quantum algorithms. We introduce a measurement-based DD (MDD) protocol that determines control unitary gates from partial measurements of noisy subsystems, with measurement overhead scaling linearly with the number of subsystems. We prove that, under lo

Zhen-Peng Xu, Jie Wang, Qi Ye, Gereon Koßmann

A set of Pauli stings is well characterized by the graph that encodes its commutatitivity structure, i.e., by its frustration graph. This graph provides a natural interface between graph theory and quantum information, which we explore in this work. We investigate all aspects of this interface for a special class of graphs that bears tight connections betwee

Vesna Poprcova, Iulia Lefter, Matthias Wieser, Martijn Warnier

Social anxiety is a prevalent condition that affects interpersonal interactions and social functioning. Recent advances in artificial intelligence and social robotics offer new opportunities to examine social anxiety in the human-robot interaction context. Accurate detection of affective states and behaviours associated with social anxiety requires multimoda

Máté Gedeon, Piroska Zsófia Barta, Péter Mihajlik, Tekla Etelka Gráczi

The advancement of automatic speech recognition (ASR) has been largely enhanced by extensive datasets in high-resource languages, while languages such as Hungarian remain underrepresented due to limited spontaneous and conversational corpora. To address this gap, we introduce two new datasets -- BEA-Large and BEA-Dialogue -- constructed from the previously u

Emile Anand

We prove that every graph of rankwidth at least $72r$ contains an induced subgraph whose minimum balanced cutrank is at least $r$, which implies a vertex subset where every balanced separation has $\mathbb{F}_2$-cutrank at least $r$. This implies a novel relation between rankwidth and a well-linkedness measure, defined entirely by balanced vertex cuts. As a

Ihab Asaad, Maha Shadaydeh, Joachim Denzler

Patch-wise multi-label classification provides an efficient alternative to full pixel-wise segmentation on high-resolution images, particularly when the objective is to determine the presence or absence of target objects within a patch rather than their precise spatial extent. This formulation substantially reduces annotation cost, simplifies training, and a

Zhengda Wang, Daqian Shi, Jingyi Zhao, Xiaolei Diao

Artificial intelligence (AI) is reshaping modern healthcare by advancing disease diagnosis, treatment decision-making, and biomedical research. Among AI technologies, large language models (LLMs) have become especially impactful, enabling deep knowledge extraction and semantic reasoning from complex medical texts. However, effective clinical decision support

Zichong Wang, Zhipeng Yin, Roland H. C. Yap, Wenbin Zhang

Fairness in artificial intelligence (AI) has become a growing concern due to discriminatory outcomes in AI-based decision-making systems. While various methods have been proposed to mitigate bias, most rely on complete demographic information, an assumption often impractical due to legal constraints and the risk of reinforcing discrimination. This survey exa

Yuhang Peng, Yizhou Pan, Xinning He, Jihaoyu Yang

As embodied intelligence emerges as a core frontier in artificial intelligence research, simulation platforms must evolve beyond low-level physical interactions to capture complex, human-centered social behaviors. We introduce FreeAskWorld, an interactive simulation framework that integrates large language models (LLMs) for high-level behavior planning and s

Nikita Neveditsin, Pawan Lingras, Salil Patil, Swarup Patil

Digitization of medical records often relies on smartphone photographs of printed reports, producing images degraded by blur, shadows, and other noise. Conventional OCR systems, optimized for clean scans, perform poorly under such real-world conditions. This study evaluates compact multimodal language models as privacy-preserving alternatives for transcribin

Erik van den Eshof, Wytze de Vries, Jorn van Kampen, Mauro Salazar

This paper presents a modeling and optimization framework to compute the minimum-lap-time spatial trajectory and powertrain operation of racing cars in a computationally efficient fashion. Specifically, we first derive a quasi-steady-state model of a racing car, whereby the racing line trajectory is jointly optimized. Next, we frame the minimum-lap-time prob

Michael J. Connolly

We develop a generalized projective gauge theory of gravity and spinorial matter, incorporating both non-metricity and torsion. The work is divided into three parts. Part I provides a thorough review of General Relativity, Metric-Affine gauge theory and Thomas-Whitehead (TW) Gravity. Part II constructs a gauge gravitational theory based on the Projective Gen

Maja Franz, Lukas Schmidbauer, Joshua Ammermann, Ina Schaefer

Quantum simulation is a leading candidate for demonstrating practical quantum advantage over classical computation, as it is believed to provide exponentially more compute power than any classical system. It offers new means of studying the behaviour of complex physical systems, for which conventionally software-intensive simulation codes based on numerical

Ibrahim Sadiek, Simona Di Bernardo, Uwe Macherius, Jean-Pierre H. van Helden

Hydrogen cyanide (HCN) is a well-known product in combustion, astrophysical, and plasma environments, but its isomer, hydrogen isocyanide (HNC), remains unexplored in molecular plasmas. Here, we report on the detection and quantification of both HNC and HCN isomers in low-temperature plasmas generated from N$_2$/H$_2$/CH$_4$ mixtures using quantum cascade la

Antonio Ferreiro, Jose Navarro-Salas, Silvia Pla

This paper offers a historical overview of the origins and enduring significance of gravitational particle creation, a groundbreaking discovery first formulated in Leonard Parker's 1966 doctoral thesis at Harvard University. By tracing the context in which Parker developed this idea and examining its subsequent influence, the paper highlights how the concept

Elodie Mutombo Ngoie, Mike Nkongolo Wa Nkongolo, Peace Azugo, Mahmut Tokmak

Ransomware continues to evolve in complexity, making early and explainable detection a critical requirement for modern cybersecurity systems. This study presents a comparative analysis of three Transformer-based Large Language Models (LLMs) (BERT, RoBERTa, and DeBERTa) for ransomware detection using two structured datasets: UGRansome and Process Memory (PM).

Xiaojie Chen, Bo Yang, Naoki Shinohara, Changjun Liu

To increase the power level and energy utilization rate of injection-locked magnetron sources, a dual way 1-kW S-band magnetron microwave power combining system with high combining efficiency was proposed and validated. A waveguide magic-Tee was used to achieve power combining and to provide a pathway for the reference signal. This system utilizes the power-

Pragatheeswaran Vipulananthan, Kamal Premaratne, Dilip Sarkar, Manohar N. Murthi

Accurate uncertainty quantification is a critical challenge in machine learning. While neural networks are highly versatile and capable of learning complex patterns, they often lack interpretability due to their ``black box'' nature. On the other hand, probabilistic ``white box'' models, though interpretable, often suffer from a significant performance gap w

Leonard Göke, Jan Wohland, Stefano Moret, André Bardow

The climate-driven uncertainty of renewable generation and electricity demand challenges energy security in net-zero energy systems. By introducing a scalable stochastic model that implicitly accounts for 51'840 climate years, this paper identifies multi-year storage of liquid hydrocarbons as a key option for managing climate uncertainty and ensuring energy

Alexandru Chirvasitu

Consider a continuous bundle $\mathcal{E}\to X$ of Banach/Hilbert spaces or Banach/$C^*$-algebras over a paracompact base space, equivariant for a compact Lie group $\mathbb{U}$ operating on all structures involved. We prove that in all cases homogeneous equivariant subbundles extend equivariantly from $\mathbb{U}$-invariant closed subsets of $X$ to closed i

Melanie Schaller, Nick Janssen, Bodo Rosenhahn

This paper presents Naga, a deep State Space Model (SSM) encoding approach inspired by structural concepts from Vedic mathematics. The proposed method introduces a bidirectional representation for time series by jointly processing forward and time-reversed input sequences. These representations are then combined through an element-wise (Hadamard) interaction

Len Bos, Michael A. Slawinski, Raphaël A. Slawinski, Theodore Stanoev

VAM ({\it velocit\`a ascensionale media}) is a measurement that quantifies a cyclist's climbing ability. We show that to minimize the time to attain a given height gain\, -- \,which is tantamount to maximizing VAM\, -- \,a cyclist should climb as steep a constant-grade hill as possible. Apart from the power-to-weight ratio, the limit of steepness is imposed

Wenyu Zhang, Yao Tong, Yiqiu Liu, Rui Cao

Urban villages (UVs), informal settlements embedded within China's urban fabric, have undergone widespread demolition and redevelopment in recent decades. However, there remains a lack of systematic evaluation of whether the demolished land has been effectively reused, raising concerns about the efficacy and sustainability of current redevelopment practices.

Ida C. Skogvoll, Benjamin A. D. Williamson, Sverre M. Selbach

Ferroelastic twin walls in centrosymmetric perovskites can host emergent polar and magnetic properties forbidden in the bulk. We use density functional theory calculations to study the geometry and magnetic properties of ferroelastic domain walls in orthorhombic CaMnO$_3$, which belongs to the most common perovskite space group, $Pnma$. At the wall, the inhe

Elinor Poole-Dayan, Daniel T Kessler, Hannah Chiou, Margaret Hughes

Public Narratives (PNs) are key tools for leadership development and civic mobilization, yet their systematic analysis remains challenging due to their subjective interpretation and the high cost of expert annotation. In this work, we propose a novel computational framework that leverages large language models (LLMs) to automate the qualitative annotation of

Jie Liu, Faiq Raees

We study singularity formation in two one-dimensional nonlinear wave models with quadratic time-derivative nonlinearities. The non-null model violates the null condition and typically develops finite-time blow-up; the null-form model is Lorentz-invariant and enjoys small-data global existence, yet still admits blow-up for large data. Building on our earlier

Ioannis Diamantis

Modern business and economic datasets often exhibit nonlinear, multi-scale structures that traditional linear tools under-represent. Topological Data Analysis (TDA) offers a geometric lens for uncovering robust patterns, such as connected components, loops and voids, across scales. This paper provides an intuitive, figure-driven introduction to persistent ho

Yuyang Xia, Ruixuan Liu, Li Xiong

In-context learning (ICL) allows LLMs to adapt to new tasks via a few demonstrations, but those demonstrations may contain sensitive data. Differentially private (DP) ICL mechanisms mitigate this risk by injecting noise into the aggregation step, but verifying that an implementation actually meets its claimed privacy bound currently requires repeated end-to-