March 2025 arXiv papers — page 32

Ahmed Khalifa, Roberto Gallotta, Matthew Barthet, Antonios Liapis

This paper introduces the Procedural Content Generation Benchmark for evaluating generative algorithms on different game content creation tasks. The benchmark comes with 12 game-related problems with multiple variants on each problem. Problems vary from creating levels of different kinds to creating rule sets for simple arcade games. Each problem has its own

Jhonathan Navott, Daniel Jenson, Seth Flaxman, Elizaveta Semenova

Gaussian Processes (GPs) provide a flexible and statistically principled foundation for modelling spatiotemporal phenomena, but their $O(N^3)$ scaling makes them intractable for large datasets. Approximate methods such as variational inference (VI), inducing-point (sparse) GPs, low-rank kernel approximations (e.g., Nystrom methods and random Fourier features

Oleksii Kolupaiev

We study the joint spectral properties of two coupled random matrices $H^{(1)}$ and $H^{(2)}$, which are either real symmetric or complex Hermitian. The entries of these matrices exhibit polynomially decaying correlations, both within each matrix and between them. Surprisingly, we find that under extremely weak decorrelation condition, permitting $H^{(1)}$ a

Loc Tan Nguyen, Tin T. Tran

Graph Neural Networks (GNNs) have opened up a potential line of research for collaborative filtering (CF). The key power of GNNs is based on injecting collaborative signal into user and item embeddings which will contain information about user-item interactions after that. However, there are still some unsatisfactory points for a CF model that GNNs could hav

R. Brands, L. Fuchs, J. M. Seyffer, N. Bajcinca

The pharmaceutical industry is moving from off-line quality testing to real-time release testing (RTRT) to improve drug quality while reducing costs. The implementation of RTRT requires advanced in-line process analytics, where UV/Vis spectroscopy has proven its suitability. However, quantification of the sample size requires detailed knowledge of the penetr

Tin T. Tran, V. Snasel

Graph Neural Networks have been extensively applied in the field of machine learning to find features of graphs, and recommendation systems are no exception. The ratings of users on considered items can be represented by graphs which are input for many efficient models to find out the characteristics of the users and the items. From these insights, relevant

Marco Petrovich, Eric Numkam Fokoua, Yong Chen, Hesham Sakr

Throughout history, the development of novel technologies for long-distance communications has had profound influences on societal progress. Landmark scientific discoveries have enabled the transition from short message transmissions via single-wire electrical telegraphs to voice communications through coaxial cables, and ultimately to the optical fibres tha

Jutta Rath, Roswitha Rissner

It is known that for a monomial ideal $I$, the number of minimal generators, $\mu(I^n)$, eventually follows a polynomial pattern for increasing $n$. In general, little is known about the power at which this pattern emerges. Even less is known about the exact form of the minimal generators after this power. Let $s\ge \mu(I)(d^2-1)+1$, where $d$ is a constant

Deependra Singh, Saksham Agarwal, Subhankar Mishra

Our research is motivated by the urgent global issue of a large population affected by retinal diseases, which are evenly distributed but underserved by specialized medical expertise, particularly in non-urban areas. Our primary objective is to bridge this healthcare gap by developing a comprehensive diagnostic system capable of accurately predicting retinal

Alessandro Berti, Wil M. P. van der Aalst

Colored Petri Nets (CPNs) are an established formalism for modeling processes where tokens carry data. Although tools like CPN Tools and CPN IDE excel at CPN-based simulation, they are often separate from modern data science ecosystems. Meanwhile, Python has become the de facto language for process mining, machine learning, and data analytics. In this paper,

Ryan Marinelli, Josef Pichlmeier, Tamas Bisztray

In this work, we propose a metric called Number of Thoughts (NofT) to determine the difficulty of tasks pre-prompting and support Large Language Models (LLMs) in production contexts. By setting thresholds based on the number of thoughts, this metric can discern the difficulty of prompts and support more effective prompt routing. A 2% decrease in latency is a

Junhao Wu, Yixin Yang, Chengxiang Jin, Silu Mu

With the widespread adoption of Ethereum, financial frauds such as Ponzi schemes have become increasingly rampant in the blockchain ecosystem, posing significant threats to the security of account assets. Existing Ethereum fraud detection methods typically model account transactions as graphs, but this approach primarily focuses on binary transactional relat

Jinzhao Pan, Ye Tian

We characterize the distribution of 2-Selmer ranks of quadratic twists of elliptic curves over $\mathbb{Q}$ with full rational 2-torsion. We propose a new type of random alternating matrix model $M_{*,\mathbf t}^{\mathrm{Alt}}(\mathbb{F}_2)$ over $\mathbb{F}_2$ with 0, 1 or 2 ``holes'', with associated Markov chains, described by parameter $\mathbf t=(t_1,\c

Johannes Erdmann, Jonas Kann, Florian Mausolf, Peter Wissmann

We study whether machine-learning models for fast calorimeter simulations can learn meaningful representations of calorimeter signatures that account for variations in the full particle detector's configuration. This may open new opportunities in high-energy physics measurements, for example in the assessment of systematic uncertainties that are related to t

Junyu Luo, Weizhi Zhang, Ye Yuan, Yusheng Zhao

The era of intelligent agents is upon us, driven by revolutionary advancements in large language models. Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence. This survey systematically deconstructs LLM agent systems through a methodo

Chirag Parikh, Deepti Rawat, Rakshitha R. T., Tathagata Ghosh

We introduce RoadSocial, a large-scale, diverse VideoQA dataset tailored for generic road event understanding from social media narratives. Unlike existing datasets limited by regional bias, viewpoint bias and expert-driven annotations, RoadSocial captures the global complexity of road events with varied geographies, camera viewpoints (CCTV, handheld, drones

Jinwen Chen, Jiannan Guo, Dazhuo Qiu, Yawen Li

With the rapid advancement of mobile networks and the widespread use of mobile devices, spatial crowdsourcing, which involves assigning location-based tasks to mobile workers, has gained significant attention. However, most existing research focuses on task assignment at the current moment, overlooking the fluctuating demand and supply between tasks and work

Xiaoqin Wang, Xusen Ma, Xianxu Hou, Meidan Ding

Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in various tasks. However, effectively evaluating these MLLMs on face perception remains largely unexplored. To address this gap, we introduce FaceBench, a dataset featuring hierarchical multi-view and multi-level attributes specifically designed to assess the comprehensive fa

Luis Irastorza-Valera, Ricardo Larraínzar-Garijo, Javier Montoya-Adárraga, Luis Saucedo-Mora

The need for optimized structures with good mechanical performance for the minimum weight is common in industry. Solid Isotropic Material with Penalization (SIMP) is a Topology Optimization (TO) method offering a trade-off between minimum compliance (i.e., maximum stiffness) and a fixed material amount for a given set of boundary conditions. Since TO is a no

Pavlína Wurzel Gonçalves, Pooja Rani, Margaret-Anne Storey, Diomidis Spinellis

Despite the popularity and importance of modern code review, the understanding of the cognitive processes that enable reviewers to analyze code and provide meaningful feedback is lacking. To address this gap, we observed and interviewed ten experienced reviewers while they performed 25 code reviews from their review queue. Since comprehending code changes is

Cătălin Paşcu Moca, Balázs Dóra, Doru Sticlet, Angelo Valli

The Family-Vicsek scaling is a fundamental framework for understanding surface growth in non-equilibrium classical systems, providing a universal description of temporal surface roughness evolution. While universal scaling laws are well established in quantum systems, the applicability of Family-Vicsek scaling in quantum many-body dynamics remains largely un

Ritesh Chandra, Sonali Agarwal, Shashi Shekhar Kumar, Navjot Singh

The exponential expansion of real-time data streams across multiple domains needs the development of effective event detection, correlation, and decision-making systems. However, classic Complex Event Processing (CEP) systems struggle with semantic heterogeneity, data interoperability, and knowledge driven event reasoning in Big Data environments. To solve t

Vladislav Byankin, Aleksandr Tynda, Denis Sidorov, Aliona Dreglea

Volterra's integral equations with local and nonlocal loads represent the novel class of integral equations that have attracted considerable attention in recent years. These equations are a generalisation of the classic Volterra integral equations, which were first introduced by Vito Volterra in the late 19th century. The loaded Volterra integral equations a

Fernando Rodriguez

A recently published article in Inorganic Chemistry (DOI: 10.1021/acs.inorgchem.4c00599) offers theoretical calculations on the effects of pressure on the structural, optical, and magnetic behavior of CsMnF_{4}. Although a thorough theoretical and experimental understanding of this material is certainly warranted, there are previously published experimental

Lucas Nunes, Rodrigo Marcuzzi, Jens Behley, Cyrill Stachniss

Semantic scene understanding is crucial for robotics and computer vision applications. In autonomous driving, 3D semantic segmentation plays an important role for enabling safe navigation. Despite significant advances in the field, the complexity of collecting and annotating 3D data is a bottleneck in this developments. To overcome that data annotation limit

Alejandro García-Fernández, Jose Antonio Parejo, Antonio Ruiz-Cortés

Software as a Service (SaaS) has seen rapid growth in recent years, thanks to its ability to adapt to diverse user needs through subscription-based models. However, as pricing models enhance the customization of subscriptions, managing the associated constraints within a system's codebase becomes increasingly challenging. In response, Pricing-driven Developm

Andreas Fring, Takano Taira, Bethan Turner

We investigate an exactly solvable two-dimensional Lorentzian coupled quantum system that in a certain parameter regime can be transformed to a higher time derivative theory (HTDT) with preserved symplectic structure. By transforming the system's Lagrangian, we explicitly map it onto the standard Pais-Uhlenbeck formulation, revealing a direct correspondence

Xiaozhe Wang, Ruobing Wang, Suyang Sun, Ding Xu

Spontaneous structural relaxation is intrinsic to glassy materials due to their metastable nature. For phase-change materials (PCMs), the resultant temporal change in electrical resistance seriously hamper in-memory computing (IMC) applications. Here, we report an ab-initio-calculation-informed design of amorphous PCM composed of robust "molecule-like" motif

Hamed Ghaemi-Dizicheh, Shahram Dehdashti, Andreas Hanke, Ahmed Touhami

Non-Hermitian systems have attracted significant interest because of their intriguing and useful properties, including exceptional points (EPs), where eigenvalues and the corresponding eigenstates of non-Hermitian operators become degenerate. In particular, quantum photonic systems with EPs exhibit an enhanced sensitivity to external perturbations, which inc

Alejandro García-Fernández, José Antonio Parejo, Pablo Trinidad, Antonio Ruiz-Cortés

Software as a Service (SaaS) pricing models, encompassing features, usage limits, plans, and add-ons, have grown exponentially in complexity, evolving from offering tens to thousands of configuration options. This rapid expansion poses significant challenges for the development and operation of SaaS-based Information Systems (IS), as manual management of suc

Felix Terhag, Philipp Knechtges, Achim Basermann, Anja Bach

Cardiac real-time magnetic resonance imaging (MRI) is an emerging technology that images the heart at up to 50 frames per second, offering insight into the respiratory effects on the heartbeat. However, this method significantly increases the number of images that must be segmented to derive critical health indicators. Although neural networks perform well o

Qiyu Dai, Xingyu Ni, Qianfan Shen, Wenzheng Chen

We consider the problem of adding dynamic rain effects to in-the-wild scenes in a physically-correct manner. Recent advances in scene modeling have made significant progress, with NeRF and 3DGS techniques emerging as powerful tools for reconstructing complex scenes. However, while effective for novel view synthesis, these methods typically struggle with chal

Cameron Seth

We give nearly optimal bounds on the sample complexity of $(\widetilde{\Omega}(\epsilon),\epsilon)$-tolerant testing the $\rho$-independent set property in the dense graph setting. In particular, we give an algorithm that inspects a random subgraph on $\widetilde{O}(\rho^3/\epsilon^2)$ vertices and, for some constant $c,$ distinguishes between graphs that ha

Nikolay Kolomeec, Denis Bykov

The closure $\mathcal{M}_{m}^{\#}$ and the extension $\widehat{\mathcal{M}}_{m}$ of the Maiorana--McFarland class $\mathcal{M}_{m}$ in $m = 2n$ variables relative to the extended-affine equivalence and the bent function construction $f \oplus \mathrm{Ind}_{U}$ are considered, where $U$ is an affine subspace of $\mathbb{F}_{2}^{m}$ of dimension $m/2$. We obta

Sumit Adak, Carsten Witt

Recent research in the runtime analysis of estimation of distribution algorithms (EDAs) has focused on univariate EDAs for multi-valued decision variables. In particular, the runtime of the multi-valued cGA (r-cGA) and UMDA on multi-valued functions has been a significant area of study. Adak and Witt (PPSN 2024) and Hamano et al. (ECJ 2024) independently per

Anis Ur Rahman, Einari Heinaro, Mete Ahishali, Samuli Junttila

Mapping standing dead trees is critical for assessing forest health, monitoring biodiversity, and mitigating wildfire risks, for which aerial imagery has proven useful. However, dense canopy structures, spectral overlaps between living and dead vegetation, and over-segmentation errors limit the reliability of existing methods. This study introduces a hybrid

Eline de Jong, Sebastian De Haro

Technological understanding is not a singular concept but varies depending on context. Building on De Jong and De Haro's (2025) notion of technological understanding as the ability to realise an aim through the use of a technological artefact, this paper refines the concept as an ability that differs by context and degree. We extend the original specificatio

Isabelle Aguilar, Luis Fernando Herbozo Contreras, Omid Kavehei

The ability to learn continuously in artificial neural networks (ANNs) is often limited by catastrophic forgetting, a phenomenon in which new knowledge becomes dominant. By taking mechanisms of memory encoding in neuroscience (aka. engrams) as inspiration, we propose a novel approach that integrates stochastically-activated engrams as a gating mechanism for

Qihang Ai, Ruizhou Li, Menghui Wang, Haiyun Jiang

Recent advances in Vision-Language Models (VLMs) have shown promising capabilities in interpreting visualized graph data, offering a new perspective for graph-structured reasoning beyond traditional Graph Neural Networks (GNNs). However, existing studies focus primarily on single-graph reasoning, leaving the critical challenge of multi-graph joint reasoning

Chad Nester

We introduce Elgot categories, a sort of distributive monoidal category with additional structure in which the partial recursive functions are representable. Moreover, we construct an initial Elgot category, the morphisms of which coincide with a lightly modified version of Lambek's abacus programs. The partial functions that are strongly representable in th

Marko Maljkovic, Nikolas Geroliminis

Efficient traffic monitoring is crucial for managing urban transportation networks, especially under congested and dynamically changing traffic conditions. Drones offer a scalable and cost-effective alternative to fixed sensor networks. However, deploying fleets of low-cost drones for traffic monitoring poses challenges in adaptability, scalability, and real

Satsuki Nishimura, Hajime Otsuka, Haruki Uchiyama

We propose a method to explore the flavor structure of leptons using diffusion models, which are known as one of generative artificial intelligence (generative AI). We consider a simple extension of the Standard Model with the type I seesaw mechanism and train a neural network to generate the neutrino mass matrix. By utilizing transfer learning, the diffusio

David P. Hofmeyr

A novel and intuitive nearest neighbours based clustering algorithm is introduced, in which a cluster is defined in terms of an equilibrium condition which balances its size and cohesiveness. The formulation of the equilibrium condition allows for a quantification of the strength of alignment of each point to a cluster, with these cluster alignment strengths

P. Wcisło, N. Stolarczyk, M. Słowiński, H. Jóźwiak

Parameters associated with the collisional perturbation of spectral lines are essential for modeling the absorption of electromagnetic radiation in gas media. The HITRAN molecular spectroscopic database provides these parameters, although originally they were associated only with the Voigt profile parameterization. However, in the HITRAN2016 and HITRAN2020 e

José F. Alves, João S. Matias

We establish the existence of Young structures for a broad class of partially hyperbolic diffeomorphisms with a splitting $TM = E^{cs} \oplus E^{uu}$, under exactly the same conditions that ensure the existence of SRB measures in a previous work by Bonatti and Viana. This extends the applicability of statistical techniques to systems where statistical proper

Hachem Saddiki, Joshua L. Warren, Corina Lesseur, Elena Colicino

Environmental mixture approaches do not accommodate compositional outcomes, consisting of vectors constrained onto the unit simplex. This limitation poses challenges in effectively evaluating the associations between multiple concurrent environmental exposures and their respective impacts on this type of outcomes. As a result, there is a pressing need for th

Han-Ze Li, Jian-Xin Zhong, Xue-Jia Yu

Measurement-induced entanglement phase transitions (MIET) highlight how local measurements drive quantum systems between area-law and volume-law entangled states. This review surveys MIET in free fermion models, focusing on how unitary hopping competes with measurement-induced non-unitarity. We discuss controversies regarding the existence of MIET in one dim

Sen Zhang, Qingqing Ye, Haibo Hu, Jianliang Xu

The skip-gram model (SGM), which employs a neural network to generate node vectors, serves as the basis for numerous popular graph embedding techniques. However, since the training datasets contain sensitive linkage information, the parameters of a released SGM may encode private information and pose significant privacy risks. Differential privacy (DP) is a

Yongxu Wang, Xu Cao, Weiyun Yi, Zhaoxin Fan

Simultaneous Localization and Mapping (SLAM) is a critical task in robotics, enabling systems to autonomously navigate and understand complex environments. Current SLAM approaches predominantly rely on geometric cues for mapping and localization, but they often fail to ensure semantic consistency, particularly in dynamic or densely populated scenes. To addre

Suyang Zhong, Manuel Rigger

Recently, various automated testing approaches have been proposed that use specialized test oracles to find hundreds of logic bugs in mature, widely-used Database Management Systems (DBMSs). These test oracles require database and query generators, which must account for the often significant differences between the SQL dialects of these systems. Since it ca

Mojca Tancer Verboten, Dean Korošak

This article investigates the dynamics of academic publishing resilience and volatility at Slovenia's University of Maribor (UM) from 2004 to 2023. This period was marked by significant economic pressures and policy shifts, including changes to higher education legislation and university funding. Using UM's employment data and OpenAlex publication records, t

Bokai Cao, Saizhuo Wang, Xinyi Lin, Xiaojun Wu

Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. Recent advances in deep learning and large language models (LLMs) for quant finance have improved predictive modeling and enabled agent-based automation, suggesting a potential paradigm shift in this f

Bart P. G. van Parys, Bert Zwart

We consider the problem of constructing a least conservative estimator of the expected value $\mu$ of a non-negative heavy-tailed random variable. We require that the probability of overestimating the expected value $\mu$ is kept appropriately small; a natural requirement if its subsequent use in a decision process is anticipated. In this setting, we show it

Andreea-Iulia Lefterovici, Michael Perk, Debora Ramacciotti, Antonio F. Rotundo

Solving systems of linear equations is a key subroutine in many quantum algorithms. In the last 15 years, many quantum linear solvers (QLS) have been developed, competing to achieve the best asymptotic worst-case complexity. Most QLS assume fault-tolerant quantum computers, so they cannot yet be benchmarked on real hardware. Because an algorithm with better

Yupei Li, Manuel Milling, Björn W. Schuller

Artificial Intelligence (AI) has achieved new levels of performance and spread in public usage with the rise of deep neural networks (DNNs). Initially inspired by human neurons and their connections, NNs have become the foundation of AI models for many advanced architectures. However, some of the most integral processes in the human brain, particularly neuro

Sylwia Czach, Jakub Rydzewski, Wiesław Nowak

Photoactive proteins absorb light and undergo structural changes that enable them to perform essential biological functions. These proteins are critical for understanding light-induced biological processes, making them important in biophysics, biotechnology, and medicine. One effective approach to uncovering photoactive processes is through computational met

Arthur Tolley

This thesis presents advancements in the detection of gravitational waves from compact binary coalescences, utilising the most sensitive observatories constructed to date. The research focuses on enhancing gravitational-wave signal searches through the development of new tools and the application of existing methodologies to increase the sensitivity of live

Prerna Singh, Kuldeep Singh Yadav, Lalan Kumar, Tapan Kumar Gandhi

This study investigated age-related changes in functional connectivity using resting-state fMRI and explored the efficacy of traditional deep learning for classifying brain developmental stages (BDS). Functional connectivity was assessed using Seed-Based Phase Synchronization (SBPS) and Pearson correlation across 160 ROIs. Clustering was performed using t-SN

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

By analysing $e^+e^-$ annihilation data corresponding to an integrated luminosity of 368.48~pb$^{-1}$ collected at the centre-of-mass energies of $\sqrt{s} = 4.918$ and $4.951$~GeV with the BESIII detector, we report the first observation of $\Lambda_{c}(2595)^{+}$ and $\Lambda_{c}(2625)^{+}\to \Lambda^{+}_{c}\pi^0\pi^0$ with statistical significances of 7.9

Wanli Ni, Haofeng Sun, Huiqing Ao, Hui Tian

Large artificial intelligence (AI) models exhibit remarkable capabilities in various application scenarios, but deploying them at the network edge poses significant challenges due to issues such as data privacy, computational resources, and latency. In this paper, we explore federated fine-tuning and collaborative reasoning techniques to facilitate the imple

Tong Nie, Jian Sun, Wei Ma

Modern transportation systems face pressing challenges due to increasing demand, dynamic environments, and heterogeneous information integration. The rapid evolution of Large Language Models (LLMs) offers transformative potential to address these challenges. Extensive knowledge and high-level capabilities derived from pretraining evolve the default role of L

Hamadi Chihaoui, Paolo Favaro

Zero-shot image restoration (IR) methods based on pretrained diffusion models have recently achieved significant success. These methods typically require at least a parametric form of the degradation model. However, in real-world scenarios, the degradation may be too complex to define explicitly. To handle this general case, we introduce the Diffusion Image

Xiaotian Zhou, Ahad N. Zehmakan, Zhongzhi Zhang

The Kirchhoff index, which is the sum of the resistance distance between every pair of nodes in a network, is a key metric for gauging network performance, where lower values signify enhanced performance. In this paper, we study the problem of minimizing the Kirchhoff index by adding edges. We first provide a greedy algorithm for solving this problem and giv

Marshall Thomas, Edward Fish, Richard Bowden

Lip Reading, or Visual Automatic Speech Recognition (V-ASR), is a complex task requiring the interpretation of spoken language exclusively from visual cues, primarily lip movements and facial expressions. This task is especially challenging due to the absence of auditory information and the inherent ambiguity when visually distinguishing phonemes that have o

Mojan Wegener, Marcel A. Mross, Eduard A. Jorswieck

In this paper, we investigate multi-connectivity schemes in the context of status update systems with short payloads. As the performance metric, we use the Age of Information (AoI). Due to short payloads, transmission errors must be taken into account. In addition to the well-known schemes of packet duplication, message splitting, and multiplexing, we propos

Leon Keller, Daniel Tanneberg, Jan Peters

Imitation learning is a popular method for teaching robots new behaviors. However, most existing methods focus on teaching short, isolated skills rather than long, multi-step tasks. To bridge this gap, imitation learning algorithms must not only learn individual skills but also an abstract understanding of how to sequence these skills to perform extended tas

F. Daem, A. Matzkin

Recent interest in the studies of structured states obtained in relativistic electron beams has highlighted the use of two alternative descriptions, each based on a different wavefunction and the related space-time density. Although both wavefunctions obey the Dirac equation (one directly and the other through a Foldy-Wouthuysen transformation) they lead to

Reinhard Bürger

Population genetic processes, such as the adaptation of a quantitative trait to directional selection, may occur on longer time scales than the sweep of a single advantageous mutation. To study such processes in finite populations, approximations for the time course of the distribution of a beneficial mutation were derived previously by branching process met

Shohreh Askari, Guillem Saldo Rubio, Anagha Datar, Hedi Harjunpää

T-cells are a crucial subset of white blood cells that play a central role in the immune system. When T-cells bind antigens, it leads to cell activation and the induction of an immune response. If T-cells are activated by antigens in vivo or artificially in vitro, they form multicellular aggregates. The mechanical properties of such clusters provide valuable

Tianyu Xu, Yaoyu Cheng, Pinxi Shen, Lin Zhao

Quadrupedal robots can learn versatile locomotion skills but remain vulnerable when one or more joints lose power. In contrast, dogs and cats can adopt limping gaits when injured, demonstrating their remarkable ability to adapt to physical conditions. Inspired by such adaptability, this paper presents Action Learner (AcL), a novel teacher-student reinforceme

Divesh Aggarwal, Shashwat Agrawal, Rajendra Kumar

We explore the computational implications of a superposition of spacetimes, a phenomenon hypothesized in quantum gravity theories. This was initiated by Shmueli (2024) where the author introduced the complexity class $\mathbf{BQP^{OI}}$ consisting of promise problems decidable by quantum polynomial time algorithms with access to an oracle for computing order

Sarah Veronica

Cybersecurity demands rigorous and scalable techniques to ensure system correctness, robustness, and resilience against evolving threats. Automated reasoning, encompassing formal logic, theorem proving, model checking, and symbolic analysis, provides a foundational framework for verifying security properties across diverse domains such as access control, pro

Uffe Høgsbro Thygesen

We recently proposed a method for estimation of states and parameters in stochastic differential equations, which included intermediate time points between observations and used the Laplace approximation to integrate out these intermediate states. In this paper, we establish a Laplace approximation for the transition probabilities in the continuous-time limi

Ş. Kuru, J. Negro, S. Salamanca

We investigate the influence of a different effective mass inside and outside an electric quantum dot on in its energy spectrum. Depending on the different values we give to the mass, we have found quite different spectra. Specifically, when the mass is positive but lighter inside the dot than outside it, the spectrum increases and splits into two types of s

Erik Wallin, Fredrik Kahl, Lars Hammarstrand

Out-of-distribution (OOD) detection in deep learning has traditionally been framed as a binary task, where samples are either classified as belonging to the known classes or marked as OOD, with little attention given to the semantic relationships between OOD samples and the in-distribution (ID) classes. We propose a framework for detecting and classifying OO

Rafael A. Lara, N. Sharadhi, Anna A. L. Huttunen, Lotta Ansas

Swimming is ubiquitous in nature and crucial for the survival of a wide range of organisms. The physics of swimming at the viscosity-dominated microscale and inertia-dominated macroscale is well studied. However, in between lies a complicated mesoscale with swimmers affected by non-linear and time-dependent fluid mechanics. The intricate motility strategies,

Tsanimir Angelov, Rasim Bekir, Galin Gyulchev, Petya Nedkova

We study the linear polarization of the accretion disk around black holes with a dark matter halo. The interaction of the black hole with the dark matter is modelled by considering an exact solution to the Einstein equations which describes a superposition of the Schwarzschild black hole with a Hernquist-type matter distribution. We simulate the observable p

Rifat Mehreen Amin, Oliver Hans Kühle, Daniel Buschek, Andreas Butz

Generative AI models offer many possibilities for text creation and transformation. Current graphical user interfaces (GUIs) for prompting them lack support for iterative exploration, as they do not represent prompts as actionable interface objects. We propose the concept of a composable prompting canvas for text exploration and iteration using dynamic widge

Rohitash Chandra, Aryan Chaudhari, Yeshwanth Rayavarapu

Large Language models (LLMs) have been prominent for language translation, including low-resource languages. There has been limited study on the assessment of the quality of translations generated by LLMs, including Gemini, GPT, and Google Translate. This study addresses this limitation by using semantic and sentiment analysis of selected LLMs for Indian lan

Khoa Tran, Bao Huynh, Tri Le, Lam Pham

Accurate prediction of the Remaining Useful Life (RUL) in Lithium ion battery (LIB) health management systems is essential for ensuring operational reliability and safety. However, many existing methods assume that training and testing data follow the same distribution, limiting their ability to generalize to unseen target domains. To address this, we propos

Mohammad Alipourzadeh, Yaser Hajati

Motivated by the orientation-dependent properties of d-wave superconductors (SCs), we investigate Andreev bound states (ABSs) and Josephson current in s-wave SC/altermagnet/d-wave SC (S/AM/D) and d-wave SC/altermagnet/d-wave SC (D/AM/D) junctions. The asymmetric S/AM/D junction exhibits a node-less ABSs spectrum with distinct spin states, arising from AM man

Markus Upmeier

Vertex $F$-algebras are a deformation of the concept of an ordinary vertex algebra in which the additive formal group law is replaced by an arbitrary formal group law $F$. The main theorem of this paper constructs a Lie algebra from a vertex $F$-algebra - for the additive formal group law, this extends Borcherds' well-known construction for ordinary vertex a

I. A. Makarov, A. A. Slobodchikov, I. A. Nekrasov, Yu. S. Orlov

The electronic structure of the CuO monolayer is investigated taking into account the intra- and interatomic Coulomb interactions on copper and oxygen atoms. Local Coulomb interactions and covalence effects are treated exactly when constructing quasiparticle excitations using the generalized tight-binding method (GTB). The electronic system is described in t

Iain R. Timmins, Fatemeh Torabi, Christopher H. Jackson, Paul C. Lambert

There is increasing interest in flexible parametric models for the analysis of time-to-event data, yet Bayesian approaches that offer incorporation of prior knowledge remain underused. A flexible Bayesian parametric model has recently been proposed that uses M-splines to model the hazard function. We conducted a simulation study to assess the statistical per

Sergio Carrolo, Howard E. Haber, Luis Lourenco, João P. Silva

There are only six independent types of symmetry-constrained (renormalizable) scalar potentials in the two Higgs doublet model (2HDM). For example, the scalar sector symmetry known as $Z_2\otimes\Pi_2$, generated by the simultaneous application of two independent symmetries acting on the scalar fields, and the generalized CP symmetry known as GCP2 yield equi

Alex Altland, Francisco Divi, Tobias Micklitz, Silvia Pappalardi

The spectral form factor of random matrix theory plays a key role in the description of disordered and chaotic quantum systems. While its moments are known to be approximately Gaussian, corrections subleading in the matrix dimension, $D$, have recently come to attention, with conflicting results in the literature. In this work, we investigate these departure

Stefan Hollands

We establish an inequality restricting the evolution of states in quantum field theory with respect to the modular flow of a wedge, $\Delta^{is}$, for large $|s|$. Our bound is related to the quantum null energy condition, QNEC. In one interpretation, it can be seen as providing a ``chaos-bound'' $\le 2\pi$ on the Lyapunov exponent with respect to Rindler ti

Yu Guo, Rion Morishita, Ibrahim Abd El-Sadek, Kohei Yamazaki

In vivo application of dynamic optical coherence tomography (DOCT) is hindered by bulk motion of the sample. We demonstrate DOCT imaging of \invivo human skin by adopting a sample-fixation attachment to suppress bulk motion and a subsequent software motion correction to further reduce the effect of sample motion. The performance of the motion-correction meth

Chengxing Jia, Ziniu Li, Pengyuan Wang, Yi-Chen Li

Adapting Large Language Models (LLMs) to downstream tasks using Reinforcement Learning (RL) has proven to be an effective approach. However, LLMs do not inherently define the structure of an agent for RL training, particularly in terms of defining the action space. This paper studies learning a compact latent action space to enhance the controllability and e

Martina Zündel

We address the question whether hard-core bosons, equivalent to the XX-model, remain integrable once the system is no longer closed. We consider the lattice version under incoherent local pump and loss and show, using random matrix theory, that the statistics of the complex spacial ratios indicate that the system is chaotic. Further, we show that the model b

Haoxiang Sun, Yingqian Min, Zhipeng Chen, Wayne Xin Zhao

The rapid advancement of large reasoning models has saturated existing math benchmarks, underscoring the urgent need for more challenging evaluation frameworks. To address this, we introduce OlymMATH, a rigorously curated, Olympiad-level math benchmark comprising 350 problems, each with parallel English and Chinese versions. OlymMATH is the first benchmark t

Antonio Acuaviva

We give a characterization of the operators on the injective tensor product $E \hat{\otimes}_\varepsilon X$ for any separable Banach space $E$ and any (non-separable) Banach space $X$ with few operators, in the sense that any operator $T: X \to X$ takes the form $T = \lambda I + S$ for a scalar $\lambda \in \mathbb{K}$ and an operator $S$ with separable rang

Kota Dohi, Tomoya Nishida, Harsh Purohit, Takashi Endo

Effectively searching time-series data is essential for system analysis; however, traditional methods often require domain expertise to define search criteria. Recent advancements have enabled natural language-based search, but these methods struggle to handle differences between time-series data. To address this limitation, we propose a natural language que

Hamadi Chihaoui, Paolo Favaro

Supervised training for real-world denoising presents challenges due to the difficulty of collecting large datasets of paired noisy and clean images. Recent methods have attempted to address this by utilizing unpaired datasets of clean and noisy images. Some approaches leverage such unpaired data to train denoisers in a supervised manner by generating synthe

Sangeeta Sharma, Deepika Gill, John Kay Dewhurst, Peter Elliott

Low energy valleys in the band structure of 2d materials represent a potential route to the ultrafast writing of information in quantum matter by laser light, with excited charge at the K or K$^\ast$ valleys representing the fundamental states of 1 and 0. Here we demonstrate that a second electronic feature, the saddle point, is endowed with lightwave contro

Yuki Nakata

We determine the finite group $\mathcal S$ parametrizing a packet in the local Langlands correspondence for a Brylinski-Deligne covering group of an algebraic torus, under some assumption on ramification. Especially, this work generalizes Weissman's result on covering groups of tori that split over an unramified extension of the base field.

Hanyan Cao, Feng Pan, Dongyang Feng, Yijia Wang

Efficient and accurate decoding of quantum error-correcting codes is essential for fault-tolerant quantum computation, however, it is challenging due to the degeneracy of errors, the complex code topology, and the large space for logical operators in high-rate codes. In this work, we propose a decoding algorithm utilizing generative modeling in machine learn

Kazuya Haremaki, Takumitsu Kida, Yusuke Koide, Takashi Uneyama

This paper examines the evolution of microplastic (MP) size distributions fragmented from photo-aged polypropylene (PP) in stirred water. PP specimens fragmented into MPs with their size of 1-30 um after UV irradiation and stirring in laboratory settings. These laboratory-fragmented MPs were dispersed into the water during the stirring process. A series of M

Orgest Zaka

This paper introduces advances in the geometry of the transforms for cross ratio of four points in a line in the Desargues affine plane. The results given here have a clean, based Desargues affine plan axiomatic and definitions of addition and multiplication of points on a line in this plane, and for skew field properties. In this paper are studied, four typ

Chandan Mondal, Siqi Xu, Yiping Liu, Jiangshan Lan

We present our recent progress in applying the basis light-front quantization approach to investigate the nucleon's structure. We solve its wave functions from the eigenstates of the light-front QCD Hamiltonian using a fully relativistic, nonperturbative approach without an explicit confining potential. These eigenstates are determined for the three-quark, t