March 2025 arXiv papers — page 41

Tony J. Puthenpurakal

Let $R$ be a regular ring containing a field $k$. Let $\mathbf{x} = x_1, \ldots, x_r$ be a regular sequence in $R$ such that $R/(\mathbf{x})$ is a regular ring. Fix $m \geq 1$. Set $A_m = R/(\mathbf{x})^m$. We show that for any ideal $Q$ of $A_m$ the set $\text{Ass} \ H^i_Q(A_m)$ is a finite set for $i \geq 0$, in the following cases: 1. $\text{char}\ k = p

N. Emil J. Bjerrum-Bohr, Gang Chen, Chenliang Su, Tianheng Wang

We examine a hypersurface model for the classical dynamics of spinning black holes. Under specific rigid geometric constraints, it reveals an intriguing solution resembling expectations for the Kerr Black three-point amplitude. We explore various generalizations of this formalism and outline potential avenues for employing it to analyze spinning black hole a

Ziying Zhang, Xiang Gao, Zhixin Wang, Qiang hu

Diffusion-based methodologies have shown significant potential in blind face restoration (BFR), leveraging their robust generative capabilities. However, they are often criticized for two significant problems: 1) slow training and inference speed, and 2) inadequate recovery of fine-grained facial details. To address these problems, we propose a novel Truncat

Neal Graneau

The purpose of this work was to investigate historical claims of the existence of a longitudinal ElectroMagnetic (EM) force component acting on metallic atomic current elements in a direction parallel to the current flowing through them. This lies outside conventional textbook physics predictions, yet its reality has been indicated previously and if eventual

Yiran Zhang, Ruiyin Li, Peng Liang, Weisong Sun

Architecture design is a critical step in software development. However, creating a high-quality architecture is often costly due to the significant need for human expertise and manual effort. Recently, agents built upon Large Language Models (LLMs) have achieved remarkable success in various software engineering tasks. Despite this progress, the use of agen

K. Krikova, T. M. D. Pereira

Aims. We investigated the potential of using Hepsilon to diagnose small-scale energetic phenomena such as Ellerman bombs, UV bursts, and small-scale flares. Our focus is to understand the formation of the line and how to use its properties to get insight into the dynamics of small-scale energetic phenomena. Methods. We carried out a forward modeling study, c

Víctor Pérez-Valdés

We construct and give a complete classification of all the differential symmetry breaking operators $\mathbb{D}_{\lambda, \nu}^{N,m}: C^\infty(S^3, \mathcal{V}_\lambda^{2N+1}) \rightarrow C^\infty(S^2, \mathcal{L}_{m, \nu})$, between the spaces of smooth sections of a vector bundle of rank $2N+1$ over the $3$-sphere $\mathcal{V}_\lambda^{2N+1} \rightarrow S^

Michela Corvino, Filippo Daffinà, Chiara Francalanci, Paolo Giacomazzi

Maritime AIS (Automatic Identification Systems) data serve as a valuable resource for studying vessel behavior. This study proposes a methodology to analyze route between maritime points of interest and extract geo-referenced standard routes, as maritime patterns of life, from raw AIS data. The underlying assumption is that ships adhere to consistent pattern

Herbert Weigel

Generalizing quantum chromodynamics (QCD) from three to arbitrarily many color degrees of freedom suggests that baryons can be described as solitons in an effective meson theory whose interaction strength decreases with the number of colors. The exact form of that theory is unknown, but at low energies chiral symmetry and its breaking are considered as the c

Yijiong Yu

Recent advances in reasoning models have demonstrated significant improvements in accuracy by employing detailed and comprehensive reasoning processes. However, generating these lengthy reasoning sequences is computationally expensive and time-consuming. To address this inefficiency, we leverage the inherent parallelizability of certain tasks to accelerate t

Masanori Shimono

Neurons encode information in a binary manner and process complex signals. However, predicting or generating diverse neural activity patterns remains challenging. In vitro and in vivo studies provide distinct advantages, yet no robust computational framework seamlessly integrates both data types. We address this by applying the Transformer model, widely used

Chanoknun Sintavanuruk, Yoichiro Mori, Reza Farhadifar, Eleni Katifori

Biological transport networks adapt through dynamic interactions between material transport and structural modification during growth and development. In this work, we present a model of transport network growth driven by local material concentration. Using an advection-diffusion framework on a metric graph with a tip growth rule, we investigate how successi

Masayuki Hayashi

We consider the Cauchy problem for the logarithmic Schr\"odinger equation and prove uniqueness of weak $H^s(\mathbb{R}^d)$ solutions for $s\in(0,1)$, which improves on the previous uniqueness result in $H^1(\mathbb{R}^d)$. The proof is achieved by combining a nontrivial use of integral equations, local smoothing estimates, and quantitative estimates of the s

Luca Marini

Aquatic bodies face numerous environmental threats caused by several marine anomalies. Marine debris can devastate habitats and endanger marine life through entanglement, while harmful algal blooms can produce toxins that negatively affect marine ecosystems. Additionally, ships may discharge oil or engage in illegal and overfishing activities, causing furthe

Yuanjie Lu, Erion Plaku

This paper considers multi-goal motion planning in unstructured, obstacle-rich environments where a robot is required to reach multiple regions while avoiding collisions. The planned motions must also satisfy the differential constraints imposed by the robot dynamics. To find solutions efficiently, this paper leverages machine learning, Traveling Salesman Pr

Yifei Lu, Fanghua Ye, Jian Li, Qiang Gao

Tool invocation significantly enhances the capabilities of Large Language Models (LLMs), yet challenges persist, particularly in complex task scenarios. Current methods, such as instruction-enhanced reasoning and supervised fine-tuning, often result in unnecessarily long reasoning paths and face difficulties in verifying the correctness of intermediate steps

Matthieu Rosenfeld, Alexander Shen

In this article, we consider some simple combinatorial game and a winning strategy in this game. This game is then used to prove several known results about non-repetitive sequences and approximations with denominators from a lacunary sequence. In this way we simplify the proofs, improve the bounds and get for free the computable versions that required a sep

Tomomasa Yamasaki, Zhehui Wang, Tao Luo, Niangjun Chen

Neural Architecture Search (NAS) is an automated technique to design optimal neural network architectures for a specific workload. Conventionally, evaluating candidate networks in NAS involves extensive training, which requires significant time and computational resources. To address this, training-free NAS has been proposed to expedite network evaluation wi

Yeseul Jeon, Rajarshi Guhaniyogi, Aaron Scheffler, Devin Francom

Developing surrogates for computer models has become increasingly important for addressing complex problems in science and engineering. This article introduces an artificial intelligent (AI) surrogate, referred to as the DeepSurrogate, for analyzing functional outputs with vector-valued inputs. The relationship between the functional output and vector-valued

Zhicheng Guo, Sijie Cheng, Yuchen Niu, Hao Wang

The rapid advancement of large language models (LLMs) has spurred significant interest in tool learning, where LLMs are augmented with external tools to tackle complex tasks. However, existing tool environments face challenges in balancing stability, scalability, and realness, particularly for benchmarking purposes. To address this problem, we propose Mirror

Jürgen Dölz, David Ebert

We present an extension of local sensitivity analysis, also referred to as the perturbation approach for uncertainty quantification, to Bayesian inverse problems. More precisely, we show how moments of random variables with respect to the posterior distribution can be approximated efficiently by asymptotic expansions. This is under the assumption that the me

M. Giulietti, I. Prandoni, M. Bonato, L. Bisigello

[Abridged] SFGs are the dominant population in the faint radio sky, corresponding to flux densities at 1.4 GHz $< 0.1$ mJy. A panchromatic approach is essential for selecting SFGs in the radio band and understanding star formation processes over cosmic time. Semi-empirical models are valuable tools to effectively study galaxy formation and evolution, relying

Andrea Chiesa, Karel Svadlenka

We extend the analysis by Esedo\={g}lu and Otto (2015) of thresholding energies for the celebrated multiphase Bence-Merriman-Osher algorithm for computing mean curvature flow of interfacial networks, to the case of differing space-dependent anisotropies. In particular, we address the special setting of an obstacle problem, where anisotropic particles move on

Lloyd Russell, Anthony Hu, Lorenzo Bertoni, George Fedoseev

Generative models offer a scalable and flexible paradigm for simulating complex environments, yet current approaches fall short in addressing the domain-specific requirements of autonomous driving - such as multi-agent interactions, fine-grained control, and multi-camera consistency. We introduce GAIA-2, Generative AI for Autonomy, a latent diffusion world m

Subramanya Bhat K. N., Amita Das, Bhooshan Paradkar, V Ravishankar

Signatures for non-abelian dynamics have long been central to QCD and QGP. Equally important are they in spin systems and laser-plasma interactions, where they emerge as effective interactions. Distinguishing experimentally gauge inequivalent sources (and hence potentials) that produce the same field tensor is one major task in this endeavour. As a step in t

Snigdha Tiwari, Sahil Sharma, Arvind Bagga, Aditi Sinha

Background Telemedicine has the potential to provide secure and cost-effective healthcare at the touch of a button. Nephrotic syndrome is a chronic childhood illness involving frequent relapses and demands long/complex treatment. Hence, developing a remote means of doctor-patient interface will ensure the provision of quality healthcare to patients. Methods

Yuanjie Lu, Tong Xu, Linji Wang, Nick Hawes

Most, if not all, robot navigation systems employ a decomposed planning framework that includes global and local planning. To trade-off onboard computation and plan quality, current systems have to limit all robot dynamics considerations only within the local planner, while leveraging an extremely simplified robot representation (e.g., a point-mass holonomic

Mark Phil Pacot, Jayno Juventud, Gleen Dalaorao

Edge detection remains a fundamental yet challenging task in computer vision, especially under varying illumination, noise, and complex scene conditions. This paper introduces a Hybrid Multi-Stage Learning Framework that integrates Convolutional Neural Network (CNN) feature extraction with a Support Vector Machine (SVM) classifier to improve edge localizatio

Pyae Hein Htet, Eric Lauga

The passive rotation of rigid helical filaments is the propulsion strategy used by flagellated bacteria and some artificial microswimmers to navigate at low Reynolds numbers. In a classical 1976 paper, Lighthill calculated the `optimal' resistance coefficients in a local (logarithmically accurate) resistive-force theory that best approximates predictions fro

Jinnan Chen, Lingting Zhu, Zeyu Hu, Shengju Qian

Recent advances in auto-regressive transformers have revolutionized generative modeling across different domains, from language processing to visual generation, demonstrating remarkable capabilities. However, applying these advances to 3D generation presents three key challenges: the unordered nature of 3D data conflicts with sequential next-token prediction

Liza Darwesh, Jaspreet Singh, Marin Marian, Eduard Alexa

This study investigates the elicitation of empathy toward a third party through interaction with social agents. Participants engaged with either a physical robot or a voice-enabled chatbot, both driven by a large language model (LLM) programmed to exhibit either an empathetic tone or remain neutral. The interaction is focused on a fictional character, Katie

Mingshi Cui, Kunting Qi, Byran Smucker, Durai Sundarmoorthi

This article describes an improved set of solutions to the problems presented in the 2021 Syngenta Crop Challenge in Analytics \citep{Syngenta2021}. In particular, we use multiobjective optimization and predictive modeling methods to determine a corn planting schedule. The problem involves the following objectives: i. minimize the median and maximum absolute

Mahya Nikouei, Bita Baroutian, Shahabedin Nabavi, Fateme Taraghi

Small object detection (SOD) is a critical yet challenging task in computer vision, with applications like spanning surveillance, autonomous systems, medical imaging, and remote sensing. Unlike larger objects, small objects contain limited spatial and contextual information, making accurate detection difficult. Challenges such as low resolution, occlusion, b

Rodolfo Ostilla-Mónico, Ali Arslan

We study convection in a volumetrically heated fluid which is cooled from both plates and is under rotation through the use of direct numerical simulations. The onset of convection matches similar systems and predictions from asymptotic analysis. At low rotation rates, the fluid becomes more organised, enhancing heat transport and increasing boundary layer a

Anna Savelyeva

We discuss the structure of finite subgroups acting on minimal Severi--Brauer varieties and provide a complete description of such groups for the varieties of dimension $p-1$, where $p \geqslant 3$ is prime.

Menghao Wu, Zhigang Yao

RNA structure determination is essential for understanding its biological functions. However, the reconstruction process often faces challenges, such as atomic clashes, which can lead to inaccurate models. To address these challenges, we introduce the principal submanifold (PSM) approach for analyzing RNA data on a torus. This method provides an accurate, lo

Zichong Zhang, Shuze Zhu

Structural superlubricity in van der Waals layered systems holds immense promise for diverse nanoscale contacts devices and energy-efficient applications. While all-direction structural superlubricity has been widely investigated, the understanding towards the more fundamental directional structural superlubricity requires further attentions. In this study,

Ramón Nartallo-Kaluarachchi, Renaud Lambiotte, Alain Goriely

At its core, the physics paradigm adopts a reductionist approach, aiming to understand fundamental phenomena by decomposing them into simpler, elementary processes. While this strategy has been tremendously successful in physics, it has often fallen short in addressing fundamental questions in the biological sciences. This arises from the inherent complexity

Athena Picarelli, Marco Scaratti, Jonathan Tam

We investigate the global numerical approximation of a class of extended mean field control problems (MFC), where the dynamics and costs depend on the joint distribution of the state and the control. We propose a framework to approximate the value function globally over the Wasserstein space, moving beyond the restriction of fixed initial conditions. Our app

Jingxian Zhou, Ziqing Zhu, Linghua Zhu, Siqi Bu

As power systems expand, solving the Unit Commitment Problem (UCP) becomes increasingly challenging due to the dimensional catastrophe, and traditional methods often struggle to balance computational efficiency and solution quality. To tackle this issue, we propose a problem-structure-informed Quantum Approximate Optimization Algorithm (QAOA) framework that

Leonor Barreiros, Isabel Coutinho, Gonçalo M. Correia, Bruno Martins

Clinical coding is a critical task in healthcare, although traditional methods for automating clinical coding may not provide sufficient explicit evidence for coders in production environments. This evidence is crucial, as medical coders have to make sure there exists at least one explicit passage in the input health record that justifies the attribution of

Amr Mousa, Neil Karavis, Michele Caprio, Wei Pan

Quadrupedal locomotion via Reinforcement Learning (RL) is commonly addressed using the teacher-student paradigm, where a privileged teacher guides a proprioceptive student policy. However, key challenges such as representation misalignment between privileged teacher and proprioceptive-only student, covariate shift due to behavioral cloning, and lack of deplo

Mike Y. M. Lau, Ryosuke Hirai, Daniel J. Price, Ilya Mandel

We present 3D radiation hydrodynamics simulations of common-envelope (CE) evolution involving a 12 solar mass red supergiant donor and a 3 solar mass companion. Existing 3D simulations are predominantly adiabatic, focusing strongly on low-mass donors on the red giant and asymptotic giant branches. However, the adiabatic assumption breaks down once the pertur

Jinghui Yuan, Fangyuan Xie, Feiping Nie, Xuelong Li

The indicator matrix plays an important role in machine learning, but optimizing it is an NP-hard problem. We propose a new relaxation of the indicator matrix and prove that this relaxation forms a manifold, which we call the Relaxed Indicator Matrix Manifold (RIM manifold). Based on Riemannian geometry, we develop a Riemannian toolbox for optimization on th

Zehui Liao, Shishuai Hu, Ke Zou, Mengyuan Jin

Vision-language models (VLMs) have great potential for medical image understanding, particularly in Visual Report Generation (VRG) and Visual Question Answering (VQA), but they may generate hallucinated responses that contradict visual evidence, limiting clinical deployment. Although uncertainty-based hallucination detection methods are intuitive and effecti

S. M. Souliou, D. Ishikawa, R. Heid, D. Bessas

The ubiquitous tendency of superconducting cuprates to form charge density waves (CDWs) has reignited interest in the nature of their electron-phonon interaction and its role in shaping their phase diagrams. While pronounced dispersion anomalies were reported in several phonon branches, their precise connection to charge order and superconductivity remains u

Yiwei Ma, Guohai Xu, Xiaoshuai Sun, Jiayi Ji

Visual instruction tuning (VIT) has emerged as a crucial technique for enabling multi-modal large language models (MLLMs) to follow user instructions adeptly. Yet, a significant gap persists in understanding the attributes of high-quality instruction tuning data and frameworks for its automated selection. To address this, we introduce MLLM-Selector, an autom

Zhen Zhang, Rui Zhang

The rotational metric provides an exact solution to Einstein's clock-rate problem in curved spacetime, specifically, whether time flows more slowly at the equator of a compact object such as a neutron star than at its poles. It features a curvature singularity, an event horizon, a potentially evolving ergosphere, a rigidly-rotating normal space, and two stat

Erhan Karakoca, Hüseyin Çevik, İbrahim Hökelek, Ali Görçin

Neural receivers have recently become a popular topic, where the received signals can be directly decoded by data driven mechanisms such as machine learning and deep learning. In this paper, we propose two novel neural network based orthogonal frequency division multiplexing (OFDM) receivers performing channel estimation and equalization tasks and directly p

Hao-Han Guo, Yao Hu, Fei-Yu Shen, Xu Tang

In this work, we upgrade FireRedTTS to a new version, FireRedTTS-1S, a high-quality streaming foundation text-to-speech system. FireRedTTS-1S achieves streaming speech generation via two steps: text-to-semantic decoding and semantic-to-acoustic decoding. In text-to-semantic decoding, a semantic-aware speech tokenizer converts the speech signal into semantic

Minzhao Liu, Ruslan Shaydulin, Pradeep Niroula, Matthew DeCross

While quantum computers have the potential to perform a wide range of practically important tasks beyond the capabilities of classical computers, realizing this potential remains a challenge. One such task is to use an untrusted remote device to generate random bits that can be certified to contain a certain amount of entropy. Certified randomness has many a

Aishwarya Parab, Prakhar Pradhan, Yogesh Simmhan, Arnab K. Paul

The increasing availability of data from diverse sources, including trusted entities such as governments, as well as untrusted crowd-sourced contributors, demands a secure and trustworthy environment for storage and retrieval. Blockchain, as a distributed and immutable ledger, offers a promising solution to address these challenges. This short paper studies

Aishik Mandal, Dana Atzil-Slonim, Thamar Solorio, Iryna Gurevych

Depression is a highly prevalent and disabling condition that incurs substantial personal and societal costs. Current depression diagnosis involves determining the depression severity of a person through self-reported questionnaires or interviews conducted by clinicians. This often leads to delayed treatment and involves substantial human resources. Thus, se

Naveen Gupta, S. Sivananthan

In Learning Theory, the smoothness assumption on the target function (known as source condition) is a key factor in establishing theoretical convergence rates for an estimator. The existing general form of the source condition, as discussed in learning theory literature, has traditionally been restricted to a class of functions that can be expressed as a pro

Anatolii A. Puhalskii

A Large Deviation Principle (LDP) is established for the stationary distribution of the number of customers in a many--server queue in heavy traffic for a moderate deviation scaling akin to the Halfin--Whitt regime. The interarrival and service times are assumed generally distributed. The deviation function is given by a quasipotential. It is related to the

Maarten Vlaswinkel, Duarte Antunes, Frank Willems

Decarbonization of the transport sector sets increasingly strict demands to maximize thermal efficiency and minimize greenhouse gas emissions of Internal Combustion Engines. This has led to complex engines with a surge in the number of corresponding tunable parameters in actuator set points and control settings. Automated calibration is therefore essential t

Fanhu Zeng, Zhen Cheng, Fei Zhu, Xu-Yao Zhang

Reliable prediction by classifiers is crucial for their deployment in high security and dynamically changing situations. However, modern neural networks often exhibit overconfidence for misclassified predictions, highlighting the need for confidence estimation to detect errors. Despite the achievements obtained by existing methods on small-scale datasets, th

Mohamed Amine Ferrag, Norbert Tihanyi, Merouane Debbah

Recent breakthroughs in generative reasoning have fundamentally reshaped how large language models (LLMs) address complex tasks, enabling them to dynamically retrieve, refine, and organize information into coherent multi-step reasoning chains. Techniques such as inference-time scaling, reinforcement learning, supervised fine-tuning, and distillation have bee

Jiale Cheng, Ruiliang Lyu, Xiaotao Gu, Xiao Liu

Video generation models have achieved remarkable progress in text-to-video tasks. These models are typically trained on text-video pairs with highly detailed and carefully crafted descriptions, while real-world user inputs during inference are often concise, vague, or poorly structured. This gap makes prompt optimization crucial for generating high-quality v

Shibo Han, Bonan Hou, Yuhao Zhang, Xiaotong Shi

In this article, a model predictive control (MPC) method is proposed for constrained linear systems to track bounded references with arbitrary dynamics. Besides control inputs to be determined, artificial reference is introduced as additional decision variable, which serves as an intermediate target to cope with sudden changes of reference and enlarges domai

Hristo Sariev

Although regular conditional distributions (r.c.d.) are well-defined and widely used measure-theoretic objects, they can violate our intuition from the classical definition of a conditional probability given an event. For that purpose, the notion of a proper r.c.d. has been introduced. Here, we study how properness, viewed as a property of probability kernel

Haoran Zheng, Renchi Yang, Jianliang Xu

Given a graph $G$ and a seed node $v_s$, the objective of local graph clustering (LGC) is to identify a subgraph $C_s \in G$ (a.k.a. local cluster) surrounding $v_s$ in time roughly linear with the size of $C_s$. This approach yields personalized clusters without needing to access the entire graph, which makes it highly suitable for numerous applications inv

Kateryna Akbash, Ivan Matsak

The article studies the almost surely asymptotics of extreme values $\bar{\xi}_n = \max_{1\leq i \leq n} \xi_i$, where $ \xi , \xi_1 , \xi_2 , \ldots$ are discrete identically distributed random variables. One of the main results on this topic is related to the law of the iterated logarithm for the lim sup (LIL) and a law of the triple logarithm for the lim

Vladimir Gasparian, Peng Guo, Antonio Pérez Garrido, Esther Jódar

We obtain a closed form expression for the energy spectrum of $\mathcal{P}\mathcal{T}$-symmetric superlattice systems with complex potentials of periodic sets of two $\delta$-potentials in the elementary cell. In the presence of periodic gain and loss we analyzed in detail a diatomic crystal model, varying either the scatterer distances or the potential heig

Vidya Sudevan, Fakhreddine Zayer, Rizwana Kausar, Sajid Javed

Underwater image enhancement (UIE) is fundamental for marine applications, including autonomous vision-based navigation. Deep learning methods using convolutional neural networks (CNN) and vision transformers advanced UIE performance. Recently, spiking neural networks (SNN) have gained attention for their lightweight design, energy efficiency, and scalabilit

Qi Si, Bo Wang, Zhao Zhang

The diffusion model has demonstrated superior performance in synthesizing diverse and high-quality images for text-guided image translation. However, there remains room for improvement in both the formulation of text prompts and the preservation of reference image content. First, variations in target text prompts can significantly influence the quality of th

Yingdong Shi, Changming Li, Yifan Wang, Yongxiang Zhao

Diffusion models have demonstrated impressive capabilities in synthesizing diverse content. However, despite their high-quality outputs, these models often perpetuate social biases, including those related to gender and race. These biases can potentially contribute to harmful real-world consequences, reinforcing stereotypes and exacerbating inequalities in v

Idan Haritan, Xiao Wang, Tamar Goldzak

We develop SOS-RILT-MP2, an efficient Gaussian-based periodic scaled opposite-spin second-order M{\o}ller-Plesset perturbation theory (SOS-MP2) algorithm that utilizes the resolution-of-the-identity approximation (RI) combined with the Laplace transform technique (LT). In our previous work [J. Chem. Phys. 157, 174112 (2022)], we showed that SOS-MP2 yields be

Petr Horky, Ales Prokes, Radek Zavorka, Josef Vychodil

This paper introduces an approach to process channel sounder data acquired from Channel Impulse Response (CIR) of 60GHz and 80GHz channel sounder systems, through the integration of Long Short-Term Memory (LSTM) Neural Network (NN) and Fully Connected Neural Network (FCNN). The primary goal is to enhance and automate cluster detection within peaks from noise

Rodrigo Huerta, Mojtaba Abaie Shoushtary, José-Lorenzo Cruz, Antonio González

GPUs are the most popular platform for accelerating HPC workloads, such as artificial intelligence and science simulations. However, most microarchitectural research in academia relies on GPU core pipeline designs based on architectures that are more than 15 years old. This paper reverse engineers modern NVIDIA GPU cores, unveiling many key aspects of its de

Ahmad Fino, Motohiro Sobajima

The paper concerns with the decay property of solutions to the initial-boundary value problem of the semilinear heat equation $\partial_tu-\Delta u+u^p=0$ in exterior domains $\Omega$ in $\mathbb{R}^N$ ($N\geq 2$). The problem for the one-dimensional case is formulated with $\Omega=(0,\infty)$ which is one of the representative of the connected components in

Robert Lupoiu, Yixuan Shao, Tianxiang Dai, Chenkai Mao

Innovation in nanophotonics currently relies on human experts who synergize specialized knowledge in photonics and coding with simulation and optimization algorithms, entailing design cycles that are time-consuming, computationally demanding, and frequently suboptimal. We introduce MetaChat, a multi-agentic design framework that can translate semantically de

Krystian Kazaniecki, Aleksander Pawlewicz, Michał Wojciechowski

The paper studies the factorization and summing properties of the Sobolev embedding operator. We propose two different approaches. One shows that the Sobolev embedding operator $S:W^{1,1}(\mathbb{T}^2)\hookrightarrow L_2(\mathbb{T}^2)$ factorises through the identical embedding $\ell_\Phi\hookrightarrow\ell_2$ for some Young function with Matuszewska-Orlicz

Alexander Stotsky

Credit card fraud causes significant financial losses and frequently occurs as fraud attack, defined as short-term sequence of fraudulent transactions associated with high transaction rates and amounts, business areas historically tied to fraud, unusual transaction times and locations and different types of errors. Confidence interval method in the moving wi

T. Holovatch, Yu. Kozitsky, K. Pilorz, Yu. Holovatch

The emergence of power laws that govern the large-time dynamics of a one-dimensional billiard of $N$ point particles is analysed. In the initial state, the resting particles are placed in the positive half-line $x\geqslant 0$ at equal distances. Their masses alternate between two distinct values. The dynamics is initialized by giving the leftmost particle a

Kevin David J André, Laurent Forthomme, Bernhard Holzer, Krzysztof Piotrzkowski

We discuss a concept of a lower-energy version of the Large Hadron-electron Collider (LHeC), delivering electron-hadron collisions concurrently to the hadron-hadron collisions at the high-luminosity LHC at CERN. Assuming the use of a 20 GeV electron Energy Recovery Linac (ERL), we report the results on the optimised beam dynamics, accelerator technologies, a

Douglas R. Q. Pacheco

Consistent splitting schemes are among the most accurate pressure segregation methods, incurring no splitting errors or spurious boundary conditions. Nevertheless, their theoretical properties are not yet fully understood, especially when finite elements are used for the spatial discretisation. This work proposes a simple scalar auxiliary variable (SAV) tech

Jianhang Ai, Ondřej Kuželka, Christos Pelekis

Let $P=(x_1,\ldots,x_n)$ be a population consisting of $n\ge 2$ real numbers whose sum is zero, and let $k <n$ be a positive integer. We sample $k$ elements from $P$ without replacement and denote by $X_P$ the sum of the elements in our sample. In this article, using ideas from the theory of majorization, we deduce non-asymptotic lower and upper bounds on th

Yucheng Suo, Fan Ma, Linchao Zhu, Tianyi Wang

Multi-modal Large language models (MLLMs) show remarkable ability in video understanding. Nevertheless, understanding long videos remains challenging as the models can only process a finite number of frames in a single inference, potentially omitting crucial visual information. To address the challenge, we propose generating multiple predictions through visu

Maximilian Kratz, Sebastian Ehmes, Philipp Maximilian Menzel, Andy Schürr

Software engineers are faced with the challenge of creating control algorithms for increasingly complex dynamic systems, such as the management of communication network topologies. To support rapid prototyping for these increasingly complex software systems, we have created the GIPS (Graph-Based ILP Problem Specification) framework to derive some or even all

Adwoa Donyina, Reiko Heckel

Visual learners think in pictures rather than words and learn best when they utilize representations based on graphs, tables, charts, maps, colors and diagrams. We propose a new pedagogy for teaching pointers in the C programming language using graph transformation systems to visually simulate pointer manipulation. In an Introduction to C course, the topic o

Maram Albalwe, Blair Archibald, Michele Sevegnani

Bigraphical Reactive Systems (BRSs) are a graph-rewriting formalism describing systems evolving in two dimensions: spatially, e.g. a person in a room, and non-spatially, e.g. mobile phones communicating regardless of location. Despite use in domains including communication protocols, agent programming, biology, and security, there is no support for real-time

T. Gheorghiu, F. Militello, J. Juul Rasmussen

By use of Lagrangian tracers propagated on 2D simulations of Scrape-Off Layer (SOL) turbulence, we are able to determine the non-local fractional-advection, fractional-diffusion equation (FADE) coefficients for a number of equilibrium cases. Solutions of the resultant FADEs shows good agreement with the simulated mean density profiles. We detail how the FADE

Mattia De Rosa, Mark Minas

A recent paper by Drewes, Hoffmann, and Minas (GCM 2023 proceedings) has shown that certain graph languages can be defined and efficiently recognized by finite automata when strings over typed symbols are interpreted as graphs. This approach has been implemented in the tool GrappaRE, which is described in this paper. GrappaRE allows for the convenient specif

Lluís Palma, Alejandro Peraza, David Civantos, Amanda Duarte

Most operational climate services providers base their seasonal predictions on initialised general circulation models (GCMs) or statistical techniques that fit past observations. GCMs require substantial computational resources, which limits their capacity. In contrast, statistical methods often lack robustness due to short historical records. Recent works p

Ziad Ismaili Alaoui, Detlef Plump

We report on a recent breakthrough in rule-based graph programming, which allows us to reach the time complexity of imperative linear-time algorithms. In general, achieving the complexity of graph algorithms in conventional languages using graph transformation rules is challenging due to the cost of graph matching. Previous work demonstrated that with rooted

Ebtihal Althubiti, Michele Sevegnani

Advancements in information technology have led to the sharing of users' data across borders, raising privacy concerns, particularly when destination countries lack adequate protection measures. Regulations like the European General Data Protection Regulation (GDPR) govern international data transfers, imposing significant fines on companies failing to compl

Nikolaus Huber, Wang Yi

Interaction nets constitute a visual programming language grounded in graph transformation. Owing to their distinctive properties, they inherently facilitate parallelism in the rewriting step. This paper showcases a simple and concise approach to encoding interaction nets within the programming language OCaml, emphasising correctness guarantees. To achieve t

Ruoqi Wen, Rongpeng Li, Xing Xu, Zhifeng Zhao

Deep Reinforcement Learning (DRL) holds significant promise for achieving human-like Autonomous Vehicle (AV) capabilities, but suffers from low sample efficiency and challenges in reward design. Model-Based Reinforcement Learning (MBRL) offers improved sample efficiency and generalizability compared to Model-Free Reinforcement Learning (MFRL) in various mult

Hojer Key

Blockchain technology has emerged as a transformative paradigm for decentralized and secure data management across diverse application domains, including healthcare, supply chain management, and the Internet of Things. Its core features, such as decentralization, immutability, and auditability, achieved through distributed consensus algorithms and cryptograp

Ziye Yu, Xin Liu

Extracting meaningful information from large seismic datasets often requires estimating the uncertainty associated with the results for quantitative analysis. This uncertainty arises from both the raw data and the manually labeled annotations. We introduce an uncertainty estimation framework designed to calculate the uncertainty from manually labeled data. T

Rytis Jursenas

The Weyl family of a dual pair $A\subseteq B^c$ of operators in a Krein space determines a minimal boundary triple uniquely up to similarity; if $A=B$, a necessary and sufficient condition in order that the similarity should be unitary is given.

Iwan Praton, Weiran Zeng

Two polygons are amicable if the perimeter of one is equal to the area of the other and vice versa. A polygon is a lattice polygon if its vertices are on the integer lattice $\Z^2$. We show that there is one pair of amicable lattice triangles and five pairs of amicable lattice rectangles.

Christoph Widder, Johannes Zimmer, Tanja Schilling

In statistical physics, the Nakajima-Mori-Zwanzig projection operator formalism is used to derive an integro-differential equation for observables in a Hilbert space, the generalized Langevin equation (GLE). This technique relies on the splitting of the dynamics into a projected and an orthogonal part. However, the well-posedness of the abstract Cauchy probl

Dominic Joyce, Markus Upmeier

To define enumerative invariants in geometry, one often needs orientations on moduli spaces of geometric objects. This monograph develops a new bordism-theoretic point of view on orientations of moduli spaces. Let $X$ be a manifold with geometric structure, and $\cal M$ a moduli space of geometric objects on $X$. Our theory aims to answer the questions: (i)

Giacomo Cherubini, Christos Katsivelos

We consider a local average in the hyperbolic lattice point counting problem for the Picard group $\Gamma$ acting on the three-dimensional hyperbolic space. Compared to the pointwise case, we improve the bounds on the remainder in the counting, conditionally on a quantum variance estimate for Maass cusp forms attached to $\Gamma$. We also use bounds on a spe

Yangqi Feng, Shing-Ho J. Lin, Baoyuan Gao, Xian Wei

Recent research has revealed that high compression of Deep Neural Networks (DNNs), e.g., massive pruning of the weight matrix of a DNN, leads to a severe drop in accuracy and susceptibility to adversarial attacks. Integration of network pruning into an adversarial training framework has been proposed to promote adversarial robustness. It has been observed th

Martin Ulmer

We study an elliptic operator $L:=\mathrm{div}(A\nabla \cdot)$ on the upper half space. It is known that if the matrix $A$ is independent in the transversal $t$-direction, then the regularity boundary value problem is solvable with data in a Sobolev space. In the present paper we improve on the $t$-independence condition by introducing a mixed $L^1-L^\infty$

Dilpreet Kaur, Saikat Panja

If $G$ is a finite group, an irreducible complex-valued character $\chi$ is called rational if $\chi(g)$ is rational for all $g\in G$. Also, a conjugacy class $x^G$ is called rational, if for all irreducible complex-valued character $\chi$, the value $\chi(x^G)$ is rational. We prove that for $q$, a power of prime, the group $\mathrm{PSL}_2(q)$ has same numb

Edward S. Ricemeyer, Rachel A. Carroll, Wesley C. Warren

The AGP format is a tab-separated table format describing how components of a genome assembly fit together. A standard submission format for genome assemblies is a fasta file giving the sequence of contigs along with an AGP file showing how these components are assembled into larger pieces like scaffolds or chromosomes. For this reason, many scaffolding soft