March 2025 arXiv papers — page 159

Soumya Shamarao Jahagirdar, Jayasree Saha, C V Jawahar

Learning multimodal video understanding typically relies on datasets comprising video clips paired with manually annotated captions. However, this becomes even more challenging when dealing with long-form videos, lasting from minutes to hours, in educational and news domains due to the need for more annotators with subject expertise. Hence, there arises a ne

Yinghua Li, Yuanxiang Yan, Xijun Yin

In this paper, we study a diffuse interface model for two-phase immiscible flows coupled by Navier-Stokes equations and mass-conserving Allen-Cahn equations. The contact line (the intersection of the fluid-fluid interface with the solid wall) moves along the wall when one fluid replaces the other, such as in liquid spreading or oil-water displacement. The sy

Alessio Quercia, Zhuo Cao, Arya Bangun, Richard D. Paul

Parameter-Efficient Fine-Tuning (PEFT) methods have transformed the approach to fine-tuning large models for downstream tasks by enabling the adjustment of significantly fewer parameters than those in the original model matrices. In this work, we study the "very low rank regime", where we fine-tune the lowest amount of parameters per linear layer for each co

Daniel DeAlcala, Aythami Morales, Julian Fierrez, Gonzalo Mancera

We present the Membership Inference Test Demonstrator, to emphasize the need for more transparent machine learning training processes. MINT is a technique for experimentally determining whether certain data has been used during the training of machine learning models. We conduct experiments with popular face recognition models and 5 public databases containi

Jean-Paul Martischang, Benjamin Reichert, Germain Rousseaux, Alexis Duchesne

Modern telescopes provide breathtaking images of nebulae, clouds and galaxies shaped by gravity-driven interactions between complex bodies. While such structures are prevalent on an astrophysical scale, they are rarely observed at the human scale. In this letter, we report the observations of the complex orbits, collision, and coalescence of droplets on a so

Shibo Huang, Chenfan Shi, Jian Yang, Hanlin Dong

Autonomous navigation in open-world outdoor environments faces challenges in integrating dynamic conditions, long-distance spatial reasoning, and semantic understanding. Traditional methods struggle to balance local planning, global planning, and semantic task execution, while existing large language models (LLMs) enhance semantic comprehension but lack spat

Daniele Lotito

Associative networks theory is increasingly providing tools to interpret update rules of artificial neural networks. At the same time, deriving neural learning rules from a solid theory remains a fundamental challenge. We make some steps in this direction by considering general energy-based associative networks of continuous neurons and synapses that evolve

Sean J. Perkins

Elongated bubble centring$\unicode{x2013}$an obscure counter-buoyant phenomenon encountered in horizontal gas-liquid slug flow$\unicode{x2013}$is correlated with liquid viscosity and their connection is theorized. Extracting from three sets of high-viscosity liquid (HVL) photographic data with $\mu_{\scriptscriptstyle L}$$\in$[1,960]mPa-s and $D$$\in$[20,50.

Liang Yu, Lai Tu, Xiang Bai

Multivariate time-series forecasting holds immense value across diverse applications, requiring methods to effectively capture complex temporal and inter-variable dynamics. A key challenge lies in uncovering the intrinsic patterns that govern predictability, beyond conventional designs, focusing on network architectures to explore latent relationships or tem

José Pombal, Nuno M. Guerreiro, Ricardo Rei, André F. T. Martins

As automatic metrics become increasingly stronger and widely adopted, the risk of unintentionally "gaming the metric" during model development rises. This issue is caused by metric interference (MINT), i.e., the use of the same or related metrics for both model tuning and evaluation. MINT can misguide practitioners into being overoptimistic about the perform

Jan Kristian Haugland

The generalized Petersen graph $G(n, k)$ is a cubic graph with vertex set $V(G(n, k)) = \{v_i\}_{0 \leq i < n} \cup \{w_i\}_{0 \leq i < n}$ and edge set $E(G(n, k)) = \{v_i v_{i+1}\}_{0 \leq i < n} \cup \{w_i w_{i+k}\}_{0 \leq i < n} \cup \{v_i w_i\}_{0 \leq i < n}$ where the indices are taken modulo $n$. Schwenk found the number of Hamiltonian cycles in $G(

Lele Qi, Mengna Liu, Xu Cheng, Fan Shi

Wind farms, typically in high-latitude regions, face a high risk of blade icing. Traditional centralized training methods raise serious privacy concerns. To enhance data privacy in detecting wind turbine blade icing, traditional federated learning (FL) is employed. However, data heterogeneity, resulting from collections across wind farms in varying environme

Benjamin Yadin, Matteo Fadel

The notion of a macroscopic quantum state must be pinned down in order to assess how well experiments probe the large-scale limits of quantum mechanics. However, the issue of quantifying so-called quantum macroscopicity is fraught with multiple approaches having varying interpretations and levels of computability and measurability. Here, we introduce two mea

Morteza Rohanian, Tarun Mehra, Nicola Miglino, Farhad Nooralahzadeh

Clinical oncology generates vast, unstructured data that often contain inconsistencies, missing information, and ambiguities, making it difficult to extract reliable insights for data-driven decision-making. General-purpose large language models (LLMs) struggle with these challenges due to their lack of domain-specific reasoning, including specialized clinic

Hector Kohler, Quentin Delfosse, Waris Radji, Riad Akrour

There exist applications of reinforcement learning like medicine where policies need to be ''interpretable'' by humans. User studies have shown that some policy classes might be more interpretable than others. However, it is costly to conduct human studies of policy interpretability. Furthermore, there is no clear definition of policy interpretabiliy, i.e.,

Ivica Obadic, Dmitry Kangin, Adrian Höhl, Dario Oliveira

Vision graph neural networks have emerged as a popular approach for modeling the global and spatial context for image recognition. However, a significant drawback of these methods is that they do not offer an inherent interpretation of the relevant spatial interactions for their prediction. We address this problem by introducing i-WiViG, an approach that ena

I. J. Lima, G. Tovmassian, C. V. Rodrigues, A. S. Oliveira

We report on the discovery of circular polarization modulated with a period of 1.943 +- 0.002 h in the cataclysmic variable V1082 Sgr. These findings unambiguously reveal the rotation of a magnetic white dwarf and establish its intermediate polar (IP) nature. Along with its extraordinary long orbital period, Porb, of 20.8 h, the spin period (Pspin) places th

Qing-Shou Tan, Ya-Feng Jiao, Yunlan Zuo, Lan Xu

We propose a novel optomechanical gyroscope architecture based on a spinning cavity optomechanical resonator (COM) evanescently coupled to a tapered optical fiber without relying on costly quantum light sources. Our study reveals a striking dependence of the gyroscope's sensitivity on the propagation direction of the driving optical field, manifesting robust

Sayan Mondal, Pei-Hao Fu, Jorge Cayao

Superconductor-semiconductor hybrids are useful for realizing the Josephson diode effect, where nonreciprocity in the supercurrents occurs due to the interplay of the Josephson effect and applied magnetic fields. These junctions can host Andreev and Majorana states with the same ingredients, though their interplay with the Josephson diode effect is unclear.

Zikang Yuan, Yuechuan Pu, Hongcheng Luo, Fengtian Lang

Ensuring the safety of autonomous vehicles necessitates comprehensive simulation of multi-sensor data, encompassing inputs from both cameras and LiDAR sensors, across various dynamic driving scenarios. Neural rendering techniques, which utilize collected raw sensor data to simulate these dynamic environments, have emerged as a leading methodology. While NeRF

Georgios Katranis, Frederik Plahl, Joachim Grimstadt, Ilshat Mamaev

Human-robot collaboration (HRC) introduces significant safety challenges, particularly in protecting human operators working alongside collaborative robots (cobots). While current ISO standards emphasize risk assessment and hazard identification, these procedures are often insufficient for addressing the complexity of HRC environments, which involve numerous

Cheng-Yong Zhang, Zehong Zhang, Ruifeng Zheng

Recently, it has been discovered that the nonlinear self-interaction of matter can induce energy extraction from black holes beyond superradiant instability. This process is closely associated with the occurrence of a dynamical first-order transition between different types of static black holes. To explore whether first-order phase transitions invariably le

Shivani Soni, Akhilesh Kumar, Edward Prabu Amaladass, Jegadeesan P

We report the charge compensation in topological insulator thin films due to the laser fluence-induced segregation of the Sb2Se3 phase. Sb doped Bi2Se3 films were deposited on commercial Si substrates coated with 300 nm of amorphous SiO2 using the Pulsed Laser Deposition technique at different laser fluence of 1.25 J-cm-2, 1.87 J-cm-2, 2.75 J-cm-2, and 3.25

Shile Chen, Jiaxing Zhao, Pengfei Zhuang

The strongest electromagnetic fields in nature are created in high energy nuclear collisions and expected to change the dynamic scattering processes in the early stage. The magnetic field effect on the Drell-Yan process is investigated in this work. The single photon decay into quark pairs and lepton pairs in an external magnetic field leads to a significant

Aleksander Ivanov, Frédéric Jaffrennou

We study the Ramsey property for vector spaces over finite fields with bilinear forms. We prove that symplectic spaces over finite fields do not have the Ramsey property. We also describe vector spaces with skew symmetric bilinear forms and radicals of finite codimension, where the Ramsey property does not hold. Some direct connections with generalized affin

Pol G. Recasens, Ferran Agullo, Yue Zhu, Chen Wang

Large language models have been widely adopted across different tasks, but their auto-regressive generation nature often leads to inefficient resource utilization during inference. While batching is commonly used to increase throughput, performance gains plateau beyond a certain batch size, especially with smaller models, a phenomenon that existing literatur

Vincent Liu, Chris Manzie, Peter M. Dower

This paper develops an algorithm for upper- and lower-bounding the value function for a class of linear time-varying games subject to convex control sets. In particular, a two-player zero-sum differential game is considered where the respective players aim to minimise and maximise a convex terminal state cost. A collection of solutions of a single-player dyn

Denis Brazke, Gianna Götzmann, Hans Knüpfer

We investigate the asymptotic behavior as $\varepsilon \to 0$ of singularly perturbed phase transition models of order $n \geq 2$, given by \begin{align} G_\varepsilon^{\lambda,n}[u] := \int_I \frac 1\varepsilon W(u) -\lambda\varepsilon^{2n-3} (u^{(n-1)})^2 + \varepsilon^{2n-1} (u^{(n)})^2 \ dx, \quad u \in W^{n,2}(I), \end{align} where $\lambda >0$ is fixed

Zhuo Zhi, Chen Feng, Adam Daneshmend, Mine Orlu

Multimodal large language models (MLLMs) show promise in tasks like visual question answering (VQA) but still face challenges in multimodal reasoning. Recent works adapt agentic frameworks or chain-of-thought (CoT) reasoning to improve performance. However, CoT-based multimodal reasoning often demands costly data annotation and fine-tuning, while agentic app

Alex Ergasti, Giuseppe Gabriele Tarollo, Filippo Botti, Tomaso Fontanini

Joint audio-video (AV) generation is still a significant challenge in generative AI, primarily due to three critical requirements: quality of the generated samples, seamless multimodal synchronization and temporal coherence, with audio tracks that match the visual data and vice versa, and limitless video duration. In this paper, we present $^R$-FLAV, a novel

Steeven Janny, Hervé Poirier, Leonid Antsfeld, Guillaume Bono

Progress in Embodied AI has made it possible for end-to-end-trained agents to navigate in photo-realistic environments with high-level reasoning and zero-shot or language-conditioned behavior, but benchmarks are still dominated by simulation. In this work, we focus on the fine-grained behavior of fast-moving real robots and present a large-scale experimental

Jonas Elsborg, Luca Thiede, Alán Aspuru-Guzik, Tejs Vegge

We present the Electronic Tensor Reconstruction Algorithm (ELECTRA) - an equivariant model for predicting electronic charge densities using floating orbitals. Floating orbitals are a long-standing concept in the quantum chemistry community that promises more compact and accurate representations by placing orbitals freely in space, as opposed to centering all

Antoine Papillon, Mathieu Olivier

This study examines the performance of two flapping flat-plate foils interacting with each other while generating thrust at a Reynolds number of 800 through two-dimensional numerical simulations. These fluid dynamics simulations were conducted with a commercial computational fluid dynamics solver implementing a finite-volume method and an overset mesh capabi

Thang N. Dinh, Cao P. Cong

Quantum computing offers a promising route for tackling hard optimization problems by encoding them as Ising models. However, sparse qubit connectivity requires the use of minor-embedding, mapping logical qubits onto chains of physical qubits, which necessitates stronger intra-chain coupling to maintain consistency. This elevated coupling strength forces a r

Ji Zhao, Xiao Lin

The emergence of large language models (LLMs) opens new frontiers for unmanned aerial vehicle (UAVs), yet existing systems remain confined to predefined tasks due to hardware-software co-design challenges. This paper presents the first aerial intelligent agent capable of open-world task execution through tight integration of LLM-based reasoning and robotic a

Xian-Rong Zhang, Yue-Jiao Gong, Yuan-Ting Zhong, Ting Huang

In many-task optimization scenarios, surrogate models are valuable for mitigating the computational burden of repeated fitness evaluations across tasks. This study proposes a novel meta-surrogate framework to assist many-task optimization, by leveraging the knowledge transfer strengths and emergent capabilities of large language models (LLMs). We formulate a

Xinyi Liu, Feiyu Tan, Qi Xie, Qian Zhao

Burst image processing (BIP), which captures and integrates multiple frames into a single high-quality image, is widely used in consumer cameras. As a typical BIP task, Burst Image Super-Resolution (BISR) has achieved notable progress through deep learning in recent years. Existing BISR methods typically involve three key stages: alignment, upsampling, and f

Qiang Zhang, Gang Han, Jingkai Sun, Wen Zhao

In recent years, humanoid robots have garnered significant attention from both academia and industry due to their high adaptability to environments and human-like characteristics. With the rapid advancement of reinforcement learning, substantial progress has been made in the walking control of humanoid robots. However, existing methods still face challenges

Jakub Maciejewski, Konstantinos Nikoletos, George Papadakis, Yannis Velegrakis

Entity Resolution (ER) is typically implemented as a batch task that processes all available data before identifying duplicate records. However, applications with time or computational constraints, e.g., those running in the cloud, require a progressive approach that produces results in a pay-as-you-go fashion. Numerous algorithms have been proposed for Prog

Rong Du, Qingqing Ye, Yue Fu, Haibo Hu

Local Differential Privacy (LDP) has emerged as a widely adopted privacy-preserving technique in modern data analytics, enabling users to share statistical insights while maintaining robust privacy guarantees. However, current LDP applications assume a single service gathering perturbed information from users. In reality, multiple services may be interested

Alain Sarlette, Cyril Elouard, Pierre Rouchon

Quantum systems subjected to a continuous weak measurement process evolve according to stochastic differential equations (SDE). Depending on the outcomes of these stochastic measurements, the quantum state may diffuse in various directions across the state space. This note points out that in many scenarios relevant to quantum engineering, this diffusion is e

Umberto Borso, Davide Paglieri, Jude Wells, Tim Rocktäschel

Diffusion models have achieved state-of-the-art performance across multiple domains, with recent advancements extending their applicability to discrete data. However, aligning discrete diffusion models with task-specific preferences remains challenging, particularly in scenarios where explicit reward functions are unavailable. In this work, we introduce Disc

Abylaikhan Tlemissov, Bobir Toshmatov, Jiří Kovář

We have investigated the polarized images of synchrotron emission from magnetically charged, spherically symmetric regular Bronnikov black hole (in general relativity coupled to nonlinear electrodynamics) and the singular Reissner-Nordstr\"{o}m black hole (in general relativity coupled to Maxwell electrodynamics). By taking into account the fact that within

Alberto Miguel-Diez, Adrián Campazas-Vega, Claudia Álvarez-Aparicio, Gonzalo Esteban-Costales

The constant increase of devices connected to the Internet, and therefore of cyber-attacks, makes it necessary to analyze network traffic in order to recognize malicious activity. Traditional packet-based analysis methods are insufficient because in large networks the amount of traffic is so high that it is unfeasible to review all communications. For this r

Lei Zhang, Federico Abbate, Di Li, Andrea Possenti

Only one globular cluster (GC), 47 Tuc, has been found to contain intracluster medium, with an electron density 100 times higher than that of the ISM in its vicinity. The characteristics of this intracluster medium are closely related to GC evolution and the compact objects within. However, significant knowledge gaps remain regarding the ionized gas content

Sachin Verma, Frank Lindseth, Gabriel Kiss

Semantic segmentation is essential for analyzing highdefinition remote sensing images (HRSIs) because it allows the precise classification of objects and regions at the pixel level. However, remote sensing data present challenges owing to geographical location, weather, and environmental variations, making it difficult for semantic segmentation models to gen

Ranjith Mudimadugula, Federico Schianchi, Anna Neuweiler, Thibeau Wouters

The detection of GW170817, together with its electromagnetic counterparts, has proven that binary neutron star mergers are of central importance to the field of nuclear astrophysics, e.g., through a better understanding of the formation of elements and novel constraints on the supranuclear dense equation of state governing the matter inside neutron stars. Es

Quanshui Wu, Bojuan Yi

We study numerical regularities for complexes over noncommutative noetherian locally finite $\mathbb{N}$-graded algebras $A$ such as CM (cm)-regularity, Tor (tor)-regularity (Ext (ext)-regularity) and Ex (ex)-regularity, which are the supremum or infimum degrees of some associated canonical complexes. We show that for any right bounded complex $X$ with finit

Ryan Donnelly, Zi Li

We study a multi-agent setting in which brokers transact with an informed trader. Through a sequential Stackelberg-type game, brokers manage trading costs and adverse selection with an informed trader. In particular, supplying liquidity to the informed traders allows the brokers to speculate based on the flow information. They simultaneously attempt to minim

Alexis L. Quintana, Nicholas J. Wright, Juan Martínez García

OB stars are crucial for our understanding of Galactic structure, star formation, stellar feedback and multiplicity. In this paper we have compiled a census of all OB stars within 1 kpc of the Sun. We performed evolutionary and atmospheric model fits to observed spectral energy distributions (SEDs) compiled from astro-photometric survey data. We have charact

Lapo Cioni, Luca Ferrari, Rebecca Smith

We introduce a new sorting device for permutations which makes use of a pop stack augmented with a bypass operation. This results in a sorting machine, which is more powerful than the usual Popstacksort algorithm and seems to have never been investigated previously. In the present paper, we give a characterization of sortable permutations in terms of forbidd

Victoria Magdalena López Madejska, Sergio López Bernal, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Brain-Computer Interfaces (BCIs) are systems traditionally used in medicine and designed to interact with the brain to record or stimulate neurons. Despite their benefits, the literature has demonstrated that invasive BCIs focused on neurostimulation present vulnerabilities allowing attackers to gain control. In this context, neural cyberattacks emerged as t

Dario Klingenberg, Rich R. Kerswell

We investigate the energy transfer from the mean profile to velocity fluctuations in channel flow by calculating nonlinear optimal disturbances,i.e. the initial condition of a given finite energy that achieves the highest possible energy growth during a given fixed time horizon. It is found that for a large range of time horizons and initial disturbance ener

Prathan Srivilai, Tawan Thongsuk, Pipat Harata

We calculate the linear-response conductance of a metallic single-electron pump using the path-integral Monte Carlo (PIMC) method. The Coulomb oscillations of the conductance are calculated to illustrate the influence of the Coulomb blockade effect on the system. Furthermore, the experimental conductance is compared with the calculated conductance of various

Guillermo A. Mena Marugán, Andrés Mínguez-Sánchez

Increasing attention has been recently devoted to the study of Kantowski-Sachs spacetime as a way to explore the interior of a Schwarzschild black hole. In this work, we construct a Hamiltonian formulation for polar perturbations of this spacetime in the presence of a perturbative massless scalar field. Our analysis is based on a truncated action at quadrati

Zhenxiong Tan, Qiaochu Xue, Xingyi Yang, Songhua Liu

Fine-grained control of text-to-image diffusion transformer models (DiT) remains a critical challenge for practical deployment. While recent advances such as OminiControl and others have enabled a controllable generation of diverse control signals, these methods face significant computational inefficiency when handling long conditional inputs. We present Omi

Andrzej Cichocki, Toshihisa Tanaka, Frank Nielsen, Sergio Cruces

This paper introduces a broad class of Mirror Descent (MD) and Generalized Exponentiated Gradient (GEG) algorithms derived from trace-form entropies defined via deformed logarithms. Leveraging these generalized entropies yields MD \& GEG algorithms with improved convergence behavior, robustness to vanishing and exploding gradients, and inherent adaptability

Debora Impera, Stefano Pigola, Michele Rimoldi, Giona Veronelli

We establish the validity of the isoperimetric inequality (or equivalently, an $L^1$ Euclidean-type Sobolev inequality) on manifolds with asymptotically non-negative sectional curvature. Unlike previous results in the literature, our approach does not require the negative part of the curvature to be globally small. Furthermore, we derive a Michael-Simon ineq

Feng Gao, Jianmin Shen, Shanshan Wang, Wei Li

Neural network methods are increasingly applied to solve phase transition problems, particularly in identifying critical points in non-equilibrium phase transitions, offering more convenience compared to traditional methods. In this paper, we analyze the (1+1)-dimensional and (2+1)-dimensional directed percolation models using an autoencoder network. We demo

Maryam Roushan, Narges Rashidi, Kourosh Nozari

We investigate anisotropic inflation within the single-field model featuring an intermediate scale factor. Our analysis reveals that the anisotropic nature of the Friedmann equations in this framework affects the slow-roll parameters, which in turn influence key perturbation parameters. Using a numerical approach, we derive constraints on the intermediate pa

Jun Yin, Yangfan He, Miao Zhang, Pengyu Zeng

Learning and improving large language models through human preference feedback has become a mainstream approach, but it has rarely been applied to the field of low-light image enhancement. Existing low-light enhancement evaluations typically rely on objective metrics (such as FID, PSNR, etc.), which often result in models that perform well objectively but la

Ruibin Xiong, Yimeng Chen, Dmitrii Khizbullin, Mingchen Zhuge

Long-form writing agents require flexible integration and interaction across information retrieval, reasoning, and composition. Current approaches rely on predefined workflows and rigid thinking patterns to generate outlines before writing, resulting in constrained adaptability during writing. In this paper we propose WriteHERE, a general agent framework tha

Anzor Khelashvili, Teimuraz Nadareishvili

In the paper, in the scattering problem for the valence electron model potential a self-adjoint extension is performed and Rutherford formula is modified. The scattering of slow particles for this potential is also discussed and the changes caused by the self-adjoint extension in the differential and integral cross-sections of the scattering are studied.

Ajay Kumar, Dikshit Gautam, Surender Verma

In the present work we have investigated some patterns of broken ``scaling" ansatz of the neutrino mass matrix. The scaling neutrino mass matrix is disallowed by the current neutrino oscillation data as, among others, it predicts vanishing reactor angle ($\theta_{13}=0$). We study its possible breaking scenarios in light of the large mixing angle (LMA) and D

Wenzhe Niu, Zongxia Xie, Yanru Sun, Wei He

Recent research has shown an increasing interest in utilizing pre-trained large language models (LLMs) for a variety of time series applications. However, there are three main challenges when using LLMs as foundational models for time series forecasting: (1) Cross-domain generalization. (2) Cross-modality alignment. (3) Error accumulation in autoregressive f

Chengjun Yu, Wei Zhai, Yuhang Yang, Yang Cao

Human reaction generation represents a significant research domain for interactive AI, as humans constantly interact with their surroundings. Previous works focus mainly on synthesizing the reactive motion given a human motion sequence. This paradigm limits interaction categories to human-human interactions and ignores emotions that may influence reaction ge

Junying Wang, Hongyuan Zhang, Yuan Yuan

Recent Customized Portrait Generation (CPG) methods, taking a facial image and a textual prompt as inputs, have attracted substantial attention. Although these methods generate high-fidelity portraits, they fail to prevent the generated portraits from being tracked and misused by malicious face recognition systems. To address this, this paper proposes a Cust

Virginia del Campo, Iker Malaina

Cognitive delegation to artificial intelligence (AI) systems is transforming scientific research by enabling the automation of analytical processes and the discovery of new patterns in large datasets. This study examines the ability of AI to complement and expand knowledge in the analysis of breast cancer using dynamic contrast-enhanced magnetic resonance im

Andrew Bartholomew, Roger Fenn, Louis Kauffman

We generalise the finite biquandle colouring invariant to a polynomial invariant based on labelling a knot diagram with a finite birack that reduces to the biquandle colouring invariant in that case. The polynomial is an invariant of a class of knot theories amenable to a generalisation of theorem of Trace on regular homotopy. We take the opportunity to repr

Asmaa Abdallah, Abdulkadir Celik, Ahmed Alkhateeb, Ahmed M. Eltawil

This paper introduces a novel neural network (NN) structure referred to as an ``Auto-hybrid precoder'' (Auto-HP) and an unsupervised deep learning (DL) approach that jointly designs \ac{mmWave} probing beams and hybrid precoding matrix design for mmWave multi-user communication system with minimal training pilots. Our learning-based model capitalizes on prio

Alessandro Zambon, Enrico M. Malatesta, Guido Tiana, Riccardo Zecchina

The weight space of an artificial neural network can be systematically explored using tools from statistical mechanics. We employ a combination of a hybrid Monte Carlo algorithm which performs long exploration steps, a ratchet-based algorithm to investigate connectivity paths, and coupled replica models simulations to study subdominant flat regions. Our anal

Salvador Hernández

The Bohr compactification is a well known construction for (topological) groups and semigroups. Recently, this notion has been investigated for arbitrary structures in \cite{har_kun:bohr_discrete} where the Bohr compactification is defined, using a set-theoretical approach, as the maximal compactification which is compatible with the structure involved. Here

Thomas Heap, Sam Bowyer, Laurence Aitchison

Bayesian inference for hierarchical models can be very challenging. MCMC methods have difficulty scaling to large models with many observations and latent variables. While variational inference (VI) and reweighted wake-sleep (RWS) can be more scalable, they are gradient-based methods and so often require many iterations to converge. Our key insight was that

Alexandros Tsakpinis, Alexander Pretschner

Software systems rely heavily on open source software (OSS) libraries, which offer benefits but also pose risks. When vulnerabilities arise, the OSS community may struggle to address them due to inactivity or lack of resources. Research highlights the link between OSS maintenance and financial support. To sustain the OSS ecosystem, maintainers should registe

P. A. Mosharev, Choon-Meng Lee, Xu Shu, Xiaoshan Zhang

The search for the optimal pair of active and protection paths in a network with Shared Risk Link Groups (SRLG) is a challenging but high-value problem in the industry that is inevitable in ensuring reliable connections on the modern Internet. We propose a new approach to solving this problem, with a novel use of statistical analysis of the distribution of p

Sanjit Biswas

In this article, we prove the existence of at least two positive weak solutions for a mixed local-nonlocal singular problem in the presence of critical exponential nonlinearity in dimension two. The novelty of this work is the inclusion of a variable singular exponent in the context of mixed operator and critical exponential nonlinearity in R^2. Our approach

Jozefien D'haeseleer, Jonathan Mannaert, Leo Storme

We investigate Cameron-Liebler sets of planes in the Klein quadric $Q^+(5,q)$ in PG$(5,q)$. We prove that there are many examples of such Cameron-Liebler sets of planes in the Klein quadric. More specifically, we provide an incomplete list of examples of such Cameron-Liebler sets of planes. By doing so, we also provide some characteristic results regarding t

Yu Tang Liu, Afonso Vale, Aamir Ahmad, Rodrigo Ventura

Quadcopter attitude control involves two tasks: smooth attitude tracking and aggressive stabilization from arbitrary states. Although both can be formulated as tracking problems, their distinct state spaces and control strategies complicate a unified reward function. We propose a multitask deep reinforcement learning framework that leverages parallel simulat

Amador Martin-Pizarro, Daniel Palacín

The main motivation for this article is to explore the connections between the existence of certain combinatorial patterns (as in van der Corputs's theorem on arithmetic progressions of length $3$) with well-known tools and theorems for definable groups in simple theories. In the last sections of this article, we apply our model-theoretic results to bound th

Yiming Zhong, Qi Jiang, Jingyi Yu, Yuexin Ma

A dexterous hand capable of grasping any object is essential for the development of general-purpose embodied intelligent robots. However, due to the high degree of freedom in dexterous hands and the vast diversity of objects, generating high-quality, usable grasping poses in a robust manner is a significant challenge. In this paper, we introduce DexGrasp Any

Jana Eisoldt, Anna Galanou, Andrey Ruzhanskiy, Nils Küchenmeister

Confidential computing in the public cloud intends to safeguard workload privacy while outsourcing infrastructure management to a cloud provider. This is achieved by executing customer workloads within so called Trusted Execution Environments (TEEs), such as Confidential Virtual Machines (CVMs), which protect them from unauthorized access by cloud administra

Farooq Aslam, Muhammad Farooq Haydar, Suhail Akhtar

This paper proposes a novel geometric nonlinear filter for attitude and bias estimation on the Special Orthogonal Group $SO(3)$ using matrix measurements. The structure of the proposed filter is similar to that of the continuous-time deterministic multiplicative extended Kalman filter (MEKF). The main difference with the MEKF is the inclusion of curvature co

Masahiro Tsujimoto, Teruaki Enoto, María Díaz Trigo, Natalie Hell

High-resolution X-ray spectroscopy is a key to understanding the mass inflow and outflow of compact objects. Spectral lines carry information about the ionization, density, and velocity structures through their intensity ratios and profiles. They are formed in non-local thermodynamic equilibrium conditions under the intense radiation field from the compact o

Hesen Chen, Junyan Wang, Zhiyu Tan, Hao Li

Modern diffusion models encounter a fundamental trade-off between training efficiency and generation quality. While existing representation alignment methods, such as REPA, accelerate convergence through patch-wise alignment, they often fail to capture structural relationships within visual representations and ensure global distribution consistency between p

Marco Scutari, Samir Salah, Delphine Kerob, Jean Krutmann

Environmental and mental conditions are known risk factors for dermatitis and symptoms of skin inflammation, but their interplay is difficult to quantify; epidemiological studies rarely include both, along with possible confounding factors. Infodemiology leverages large online data sets to address this issue, but fusing them produces strong patterns of spati

Pedro V. Guillaumon, Iuda D. Goldman, Eric B. Norman, Keenan J. Thomas

Seventeen representative samples of volcanic origin were collected from Ecuador (Pichincha Volcano), Iceland (Eyjafjallaj\"okull Volcano), India (Deccan Traps), Hawaii, Kilimanjaro, Mt. Etna, Rwanda (Virunga Mountains), and Uganda (Virunga Mountains). Neutron activation analysis (NAA) was performed to determine the concentration of 33 chemical elements, incl

Arshia Afzal, Volkan Cevher, Mahsa Shoaran

Enhancing the accuracy and efficiency of machine learning algorithms employed in neural interface systems is crucial for advancing next-generation intelligent therapeutic devices. However, current systems often utilize basic machine learning models that do not fully exploit the natural structure of brain signals. Additionally, existing learning models used f

Jaa-Yeon Lee, Byunghee Cha, Jeongsol Kim, Jong Chul Ye

While recent advancements in generative modeling have significantly improved text-image alignment, some residual misalignment between text and image representations still remains. Some approaches address this issue by fine-tuning models in terms of preference optimization, etc., which require tailored datasets. Orthogonal to these methods, we revisit the cha

Bo Zhao, Hongbin Zhang

Based on high-throughput density functional theory calculations, we evaluate the local magnetic moments and M\"ossbauer properties for Fe-based intermetallic compounds and employ machine learning to map the local crystalline environments to such properties. It is observed that magnetic moments and M\"ossbauer parameters provide complementary insights into th

Mustafa Gündogan, Mehmet Emre Tasgin

Room temperature microwave and low-THz links exhibit large thermal occupations, making phase sensitive signal-idler correlations difficult to recover after loss. We introduce a work-extraction-based quantum-illumination receiver in which the returned mode $\hat{a}_R$ is measured via heterodyne detection and the outcome is fed forward to a locally stored, pos

Bohua Chen, Lucia Chantal Schneider, Christian Röver, Emmanuelle Comets

In the context of clinical research, computational models have received increasing attention over the past decades. In this systematic review, we aimed to provide an overview of the role of so-called in silico clinical trials (ISCTs) in medical applications. Exemplary for the broad field of clinical medicine, we focused on in silico (IS) methods applied in d

Linda Mauron, Giuseppe Carleo

Recent demonstrations of D-Wave's annealing-based quantum simulators have established new benchmarks for quantum computational advantage [arXiv:2403.00910]. However, the precise location of the classical-quantum computational frontier remains an open question, as classical simulation strategies continue to evolve. Here, we demonstrate that time-dependent var

Seiyun Shin, Ilan Shomorony, Peter Macgregor

We propose a fast and dynamic algorithm for Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that efficiently supports online updates. Traditional DBSCAN algorithms, designed for batch processing, become computationally expensive when applied to dynamic datasets, particularly in large-scale applications where data continuously evolves. To

Vincent P. H. Goverse, Ale Jan Homburg, Jeroen S. W. Lamb

We establish the existence of intermittent two-point dynamics and infinite stationary measures for a class of random circle endomorphisms with zero Lyapunov exponent, as a dynamical characterisation of the transition from synchronisation (negative Lyapunov exponent) to chaos (positive Lyapunov exponent).

Rostislav Arkhipov, Mikhail Arkhipov, Nikolay Rosanov

Unipolar light pulses with a non-zero electric area due to the unidirectional action on charged particles can be used for the ultrafast control of the properties of quantum systems. To control atomic properties in an efficient way, it is necessary to vary the temporal shape of the pulses used. This has led to the problem of obtaining pulses of an unusual sha

Jihong Wu, Chuan Liu, Daniel Bulmash, Wen Wei Ho

We introduce a geometrical framework to construct a large class of time-dependent quantum systems, in which the position of a classical particle moving autonomously on a smooth connected manifold is used to steer a quantum Hamiltonian over time. This results in quantum drives with structured temporal profiles and properties dependent on the local and global

Tristan Tomilin, Meng Fang, Mykola Pechenizkiy

Advancing safe autonomous systems through reinforcement learning (RL) requires robust benchmarks to evaluate performance, analyze methods, and assess agent competencies. Humans primarily rely on embodied visual perception to safely navigate and interact with their surroundings, making it a valuable capability for RL agents. However, existing vision-based 3D

Lachlan Simpson, Federico Costanza, Kyle Millar, Adriel Cheng

Integrated gradients is prevalent within machine learning to address the black-box problem of neural networks. The explanations given by integrated gradients depend on a choice of base-point. The choice of base-point is not a priori obvious and can lead to drastically different explanations. There is a longstanding hypothesis that data lies on a low dimensio

Saad Sohail, Muhammad Usama, Usman Ghous, Manuel Mazzara

Hyperspectral imaging (HSI) provides rich spectral-spatial information across hundreds of contiguous bands, enabling precise material discrimination in applications such as environmental monitoring, agriculture, and urban analysis. However, the high dimensionality and spectral variability of HSI data pose significant challenges for feature extraction and cla

Martijn F. S. Zwanenburg, Siddharth Singh, Eugene Y. Huang, Figen Yilmaz

Single-qubit gates are in many quantum platforms applied using a linear drive resonant with the qubit transition frequency which is often theoretically described within the rotating-wave approximation (RWA). However, for fast gates on low-frequency qubits, the RWA may not hold and we need to consider the contribution from counter-rotating terms to the qubit