October 2024 arXiv papers — page 84

Arne Beines, Michael Kaibel, Philip Mayer, Petra Mutzel

We present parametric breadth-first search (PBFS), a new algorithm for solving the parametric minimum cut problem in a network with source-sink-monotone capacities. The objective is to find the set of breakpoints, i.e., the points at which the minimum cut changes. It is well known that this problem can be solved in the same asymptotic runtime as the static m

Lingao Xiao, Yang He

In ImageNet-condensation, the storage for auxiliary soft labels exceeds that of the condensed dataset by over 30 times. However, are large-scale soft labels necessary for large-scale dataset distillation? In this paper, we first discover that the high within-class similarity in condensed datasets necessitates the use of large-scale soft labels. This high wit

Krishna P. B., Adithya P S, Titus K. Mathew

Expansion of the universe is caused by the departure from the holographic equipartition. This principle, the law of emergence, first postulated in the context of Einstein's gravity has been extended successfully to more general gravity theories like Gauss-Bonnet and Lovelock gravity. We derive the law of emergence for braneworld models of gravity, starting f

M. C. D'Arpa, G. Guilluy, G. Mantovan, F. Biassoni

Atmospheric characterization is key to understanding exoplanetary systems, offering insights into the planets current and past conditions. By analyzing key lines like H alpha and the He I triplet, we can trace the evolution of planets through atmospheric photo-evaporation. While ultra-hot Jupiters have been the focus for years, attention is shifting toward s

Xinze Li, Runlin Huang, Zhenghao Wu, Bohan Yang

Semi-supervised learning (SSL) has been widely used to learn from both a few labeled images and many unlabeled images to overcome the scarcity of labeled samples in medical image segmentation. Most current SSL-based segmentation methods use pixel values directly to identify similar features in labeled and unlabeled data. They usually fail to accurately captu

Zhong Du, Mingke Qin, Lei Liu

In this work, we study the asymptotic behaviors and dynamics of degenerate and mixed solitons for the coupled Hirota system with strong coherent coupling effects in the isotropic nonlinear medium. Using the binary Darboux transformation, we derive the solutions to represent the degenerate solitons with two eigenvalues that are conjugate to each other. We obt

Dharmendra Kumar Singh

The object of this paper is to study and develop a Poisson distribution in generalized Wright function form.

Liam Moroy, Guillaume Bourmaud, Frédéric Champagnat, Jean-François Giovannelli

Plug&Play (PnP) diffusion models are state-of-the-art methods in computed tomography (CT) reconstruction. Such methods usually consider applications where the sinogram contains a sufficient amount of information for the posterior distribution to be concentrated around a single mode, and consequently are evaluated using image-to-image metrics such as PSNR/SSI

Szymon Bobek, Paloma Korycińska, Monika Krakowska, Maciej Mozolewski

This paper introduces a dataset that is the result of a user study on the comprehensibility of explainable artificial intelligence (XAI) algorithms. The study participants were recruited from 149 candidates to form three groups representing experts in the domain of mycology (DE), students with a data science and visualization background (IT) and students fro

Qi-Lao Gu, Tie Liu, Zhi-Qiang Shen, Sihan Jiao

We present 850 {\mu}m thermal dust polarization observations with a resolution of 14.4"(~ 0.13 pc) towards an infrared dark cloud G16.96+0.27 using JCMT/POL-2. The average magnetic field orientation, which roughly agrees with the larger-scale magnetic field orientation traced by the Planck 353 GHz data, is approximately perpendicular to the filament structur

Zhengming Wang, Junli Wang, Pengfei Li, Zhaohan Li

While the capabilities of autonomous driving have advanced rapidly, merging into dense traffic remains a significant challenge, many motion planning methods for this scenario have been proposed but it is hard to evaluate them. Most existing closed-loop simulators rely on rule-based controls for other vehicles, which results in a lack of diversity and randomn

Urja Khurana, Eric Nalisnick, Antske Fokkens

When building a predictive model, it is often difficult to ensure that application-specific requirements are encoded by the model that will eventually be deployed. Consider researchers working on hate speech detection. They will have an idea of what is considered hate speech, but building a model that reflects their view accurately requires preserving those

Hanlin Yang, Jian Yao, Weiming Liu, Qing Wang

Recovering a spectrum of diverse policies from a set of expert trajectories is an important research topic in imitation learning. After determining a latent style for a trajectory, previous diverse policies recovering methods usually employ a vanilla behavioral cloning learning objective conditioned on the latent style, treating each state-action pair in the

Fabien Poirier

In this paper, we propose a new architecture for real-time anomaly detection in video data, inspired by human behavior combining spatial and temporal analyses. This approach uses two distinct models: (i) for temporal analysis, a recurrent convolutional network (CNN + RNN) is employed, associating VGG19 and a GRU to process video sequences; (ii) regarding spa

Karan Ruparell, Robert J. Marks, Andy Wood, Kieran M. R. Hunt

Long Short Term Memory networks (LSTMs) are used to build single models that predict river discharge across many catchments. These models offer greater accuracy than models trained on each catchment independently if using the same data. However, the same data is rarely available for all catchments. This prevents the use of variables available only in some ca

Natalia Vesselinova, Matti Harjula, Pauliina Ilmonen

Accurate predictions of base stations' traffic load are essential to mobile cellular operators and their users as they support the efficient use of network resources and allow delivery of services that sustain smart cities and roads. Traditionally, cellular network time-series have been considered for this prediction task. More recently, exogenous factors su

Chengsong Tan, Alastair F. Donaldson, John Wickerson

We report our experience formally modelling and verifying CXL.cache, the inter-device cache coherence protocol of the Compute Express Link standard. We have used the Isabelle proof assistant to create a formal model for CXL.cache based on the prose English specification. This led to us identifying and proposing fixes to several problems we identified as uncl

Yuchen Wang, Ruihuan Wang

Seismic phase picking, which aims to determine the arrival time of P- and S-waves according to seismic waveforms, is fundamental to earthquake monitoring. Generally, manual phase picking is trustworthy, but with the increasing number of worldwide stations and seismic monitors, it becomes more challenging for human to complete the task comprehensively. In thi

Robin Hirsch, Marcel Jackson, Jaš Šemrl

A semigroup of binary relations (under composition) on a set $X$ is \emph{complemented} if it is closed under the taking of complements within $X\times X$. We resolve a 1991 problem of Boris Schein by showing that the class of finite unary semigroups that are representable as complemented semigroups of binary relations is undecidable, so composition with com

Daniel Jezierski, Jose Lorenzana, Wojciech Grochala

Based on the Density Functional Theory calculations, we propose a new pathway toward compounds featuring flat [AgF2] layers which mimic [CuO2] layers in high-temperature oxocuprate superconductor precursors. Calculations predict the dynamic (phonon) and energetic stability of the new phases over diverse substrates. For some compounds with ferro orbital order

Ramin Hassannejad, Gaetano Lambiase, Fabio Scardigli, Fatimah Shojai

In this paper we study an Oppenheimer-Snyder (OS)-like gravitational collapse in the general framework of scale-dependent gravity. We explore the collapse in spherically symmetric solutions suggested both by asymptotically safe gravity (characterized by a positive $\om$-parameter) and by scale-dependent gravity (negative $\om$-parameter), when a singularity

Marvin Dippell, Chiara Esposito, Jonas Schnitzer, Stefan Waldmann

We construct explicit global homotopies for differential Hochschild cochains in differential geometry, thereby upgrading the classical Hochschild-Kostant-Rosenberg map to a deformation retract. Our approach combines two key techniques: a symbol calculus from differential geometry and a coalgebraic version of the van Est theorem. To demonstrate its effectiven

Lukas Scheiderer, Zach Marin, Jonas Ries

Optical super-resolution microscopy is a key technology for structural biology that offers high imaging contrast and live-cell compatibility. Minimal (fluorescence) photons flux microscopy, or MINFLUX, is an emerging super-resolution technique that localizes single fluorophores with high spatiotemporal precision by targeted scanning of a patterned excitation

Luis A. Anchordoqui, Ignatios Antoniadis, Karim Benakli, Jules Cunat

Experimental searches for supersymmetry (SUSY) are entering a new era. The failure to observe signals of sparticle production at the Large Hadron Collider (LHC) has eroded the central motivation for SUSY breaking at the weak scale. However, String Theory requires SUSY at the fundamental scale $M_s$ and hence SUSY could be broken at some high scale below $M_s

N. A. Anjita, J. Indu, P. Thiruvengadam, Vishal Dixit

Desert locusts are notorious agriculture pests prompting billions in losses and global food scarcity concerns. With billions of these locusts invading agrarian lands, this is no longer a thing of the past. This study taps into the existing doppler weather radar (DWR) infrastructure which was originally deployed for meteorological applications. This study dem

Keerti Pandey, Aman Prasad

Nanocrystalline pure FCC metals and some alloys are known to exhibit abnormal grain growth. Addition of solutes, such as W, has led to improved grain size stability in nanocrystalline Ni. While several groups have investigated grain growth behavior in Ni-based binary alloys, grain sizes greater than 15 nm were primarily investigated. In the present study, gr

Yuncheng Yuan, Péter Scheepers, Lydia Tasiou, Yunus Can Gültekin

This paper analyzes the design and competitiveness of four neural network (NN) architectures recently proposed as decoders for forward error correction (FEC) codes. We first consider the so-called single-label neural network (SLNN) and the multi-label neural network (MLNN) decoders which have been reported to achieve near maximum likelihood (ML) performance.

Arpita Mondal, Amruta Mishra

We estimate the binding energies of charmonium ($J/\psi$, $\psi(2S)$, $\psi(1D)$, $\chi_{c0}$, $\chi_{c1}$, $\chi_{c2}$) and bottomonium ($\Upsilon(1S)$, $\Upsilon(2S)$, $\Upsilon_2(1D)$, $\chi_{b0}$, $\chi_{b1}$, $\chi_{b2}$) states bound in various nuclei (${\rm{^{4}He}}$, ${\rm{^{12}C}}$, ${\rm{^{16}O}}$, ${\rm{^{40}Ca}}$, ${\rm{^{90}Zr}}$, and ${\rm{^{20

Ole Lübke

Recently, a novel, MaxSAT-based method for error correction in quantum computing has been proposed that requires both incremental MaxSAT solving capabilities and support for XOR constraints, but no dedicated MaxSAT solver fulfilling these criteria existed yet. We alleviate that and introduce IGMaxHS, which is based on the existing solvers iMaxHS and GaussMax

Lingxiao Lei, Heng Huang, Pingxing Chen, Mingtang Deng

In the pursuit of quantum computing, solid-state quantum systems, particularly superconducting ones, have made remarkable advancements over the past two decades. However, achieving fault-tolerant quantum computing for next-generation applications necessitates the integration of several million qubits, which presents significant challenges in terms of interco

Yiwei Yang, Aibo Hu, Yusheng Zheng, Brian Zhao

The rise of AI agents powered by Large Language Models (LLMs) presents critical challenges: how to securely execute and migrate these agents across heterogeneous environments while protecting sensitive user data, maintaining availability during network failures, minimizing response latency for time-critical decisions, and ensuring output safety in mission-cr

Fabian Seiler, Nima TaheriNejad

Since performance improvements of computers are stagnating, new technologies and computer paradigms are hot research topics. Memristor-based In-Memory Computing is one of the promising candidates for the post-CMOS era, which comes in many flavors. Processing In memory Array (PIA) or using memory, is on of them which is a relatively new approach, and substant

Harikrishnan K J, Amit Kumar Pal

We propose a scalable and deterministic protocol for growing large multi-qubit states starting from two-qubit non-maximally entangled pure states, where the bipartite entanglement in the resultant state is higher than the maximum of the available entangled qubit-pairs. This is achieved via a truncation of the Hilbert space corresponding to a subsystem of qub

Ziqiang Dang, Wenqi Dong, Zesong Yang, Bangbang Yang

In this paper, we present TexPro, a novel method for high-fidelity material generation for input 3D meshes given text prompts. Unlike existing text-conditioned texture generation methods that typically generate RGB textures with baked lighting, TexPro is able to produce diverse texture maps via procedural material modeling, which enables physically-based ren

Shao-Feng Ge, Ui Min, Zhuoni Qian

We propose practical ways of differentiating the various (Breit-Wigner, theoretical, and energy-dependent) resonance schemes of unstable particles at lepton colliders. First, the energy-dependent scheme can be distinguished from the other two by fitting the $Z$ lineshape scan and forward-backward asymmetries at LEP and future lepton colliders with the $Z$ ma

Kirill Lukyanov, Andrew Perminov, Denis Turdakov, Mikhail Pautov

The vulnerability of artificial neural networks to adversarial perturbations in the black-box setting is widely studied in the literature. The majority of attack methods to construct these perturbations suffer from an impractically large number of queries required to find an adversarial example. In this work, we focus on knowledge distillation as an approach

Ferran de Cabrera, Marc Vilà-Insa, Jaume Riba

The problem of measuring conditional dependence between two random phenomena arises when a third one (a confounder) has a potential influence on the amount of information between them. A typical issue in this challenging problem is the inversion of ill-conditioned autocorrelation matrices. This paper presents a novel measure of conditional dependence based o

Marc Vilà-Insa, Jaume Riba

This paper proposes a general analysis of codeword detection in noncoherent communications. Motivated by the existence of error floors in various regimes, fundamental characteristics of signal design are investigated. In particular, the necessary and sufficient conditions for asymptotically singular detection (i.e. error-free in the limit) are derived from c

Pablo Meseguer, Rocío del Amor, Adrian Colomer, Valery Naranjo

Since the emergence of the ImageNet dataset, the pretraining and fine-tuning approach has become widely adopted in computer vision due to the ability of ImageNet-pretrained models to learn a wide variety of visual features. However, a significant challenge arises when adapting these models to domain-specific fields, such as digital pathology, due to substant

Zuojin Tang, Bin Hu, Chenyang Zhao, De Ma

Recent large pretrained models such as LLMs (e.g., GPT series) and VLAs (e.g., OpenVLA) have achieved notable progress on multimodal tasks, yet they are built upon a multi-input single-output (MISO) paradigm. We show that this paradigm fundamentally limits performance in multi-input multi-output (MIMO) scenarios, where parallel task execution is required. In

Bohdan M. Pavlyshenko

The paper considers an approach of using Google Search API and GPT-4o model for qualitative and quantitative analyses of news through retrieval-augmented generation (RAG). This approach was applied to analyze news about the 2024 US presidential election process. Different news sources for different time periods have been analyzed. Quantitative scores generat

Chen Hu, Hanchi Ren, Jingjing Deng, Xianghua Xie

Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in dynamic, data-rich environments for applications such as environmental monitoring and surveillance. These scenarios demand efficient data processing while maintaining privacy and security, making Federated Learning (FL) a promising solution. FL allows UAVs to collaboratively train global model

Pablo Meseguer, Rocío del Amor, Valery Naranjo

Vision-language supervision has made remarkable strides in learning visual representations from textual guidance. In digital pathology, vision-language models (VLM), pre-trained on curated datasets of histological image-captions, have been adapted to downstream tasks, such as region of interest classification. Zero-shot transfer for slide-level prediction ha

Shahriar Iravanian

Polynomial factorization over $ZZ$ is of great historical and practical importance. Currently, the standard technique is to factor the polynomial over finite fields first and then to lift to integers. Factorization over finite fields can be done in polynomial time using Berlekamp or Cantor-Zassenhaus algorithms. Lifting from the finite field to $ZZ$ requires

Yiming Li, Hanchi Ren, Yue Yang, Jingjing Deng

Reliable object grasping is one of the fundamental tasks in robotics. However, determining grasping pose based on single-image input has long been a challenge due to limited visual information and the complexity of real-world objects. In this paper, we propose Triplane Grasping, a fast grasping decision-making method that relies solely on a single RGB-only i

José I. Cogolludo-Agustín, Tamás Lászlo, Jorge Martín-Morales, András Némethi

We describe those Weil divisors of cyclic quotient surface singularities which are (abstract) $r$--tuple curve singularities.

Junjun Xie, Liang Hu, Jiahu Qin, Jun Yang

The control barrier function (CBF) has become a fundamental tool in safety-critical systems design since its invention. Typically, the quadratic optimization framework is employed to accommodate CBFs, control Lyapunov functions (CLFs), other constraints and nominal control design. However, the constrained optimization framework involves hyper-parameters to t

Woosung Koh, Wonbeen Oh, Siyeol Kim, Suhin Shin

Multi-agent reinforcement learning has demonstrated significant potential in addressing complex cooperative tasks across various real-world applications. However, existing MARL approaches often rely on the restrictive assumption that the number of entities (e.g., agents, obstacles) remains constant between training and inference. This overlooks scenarios whe

Olivier Graffeuille, Yun Sing Koh, Joerg Wicker, Moritz Lehmann

Knowledge transfer in multi-task learning is typically viewed as a dichotomy; positive transfer, which improves the performance of all tasks, or negative transfer, which hinders the performance of all tasks. In this paper, we investigate the understudied problem of asymmetric task relationships, where knowledge transfer aids the learning of certain tasks whi

Wataru Urasaki, Go Kawamitsu, Tomoyuki Nakagawa, Kouji Tahata

A measure of asymmetry is a quantification method that allows for the comparison of categorical evaluations before and after treatment effects or among different target populations, irrespective of sample size. We focus on square contingency tables that summarize survey results between two time points or cohorts, represented by the same categorical variables

Anna Meyer, André Kaup

Learned wavelet video coders provide an explainable framework by performing discrete wavelet transforms in temporal, horizontal, and vertical dimensions. With a temporal transform based on motion-compensated temporal filtering (MCTF), spatial and temporal scalability is obtained. In this paper, we introduce variable rate support and a mechanism for quality a

Tara Kalsi, Alessandro Romito, Henning Schomerus

We develop an analytical approach to the spectral form factor and out-of-time ordered correlators in zero-dimensional Brownian models of quantum chaos. The approach expresses these spectral correlations as part of a closed hierarchy of differential equations that can be formulated for all system sizes and in each of the three standard symmetry classes (unita

Jithin G. Krishnan, Chandrima B. Pushpan, Amit Kumar Pal

We design a quantum thermal device that can simultaneously and dynamically cool multiple target qubits. Using a setup with three bosonic heat baths, we propose an engineering of interaction Hamiltonian using operators on different subspaces of the full Hilbert space of the system labelled by different magnetizations. We demonstrate, using the local as well a

Xiaodi Li

Verifying whether a quantum device produces a specific state is a fundamental task in many applications of modern quantum technologies. In the conventional framework of quantum state verification, designing an optimal or efficient protocol for each type of state often requires intricate customization. Recently, Hsin-Yuan Huang et al. introduced a novel appro

Tongxing Jin, Thien-Minh Nguyen, Xinhang Xu, Yizhuo Yang

Loop closure is an important task in robot navigation. However, existing methods mostly rely on some implicit or heuristic features of the environment, which can still fail to work in common environments such as corridors, tunnels, and warehouses. Indeed, navigating in such featureless, degenerative, and repetitive (FDR) environments would also pose a signif

Bahare M. Khorsandi, Mohammad Asif Habibi

The end-to-end (E2E) architecture for the 6th generation of mobile network (6G) necessitates a comprehensive design, considering emerging use cases (UCs), requirements, and key value Indicators (KVIs). These UCs collectively share stringent requirements of extreme connectivity, inclusivity, and flexibility imposed on the architecture and its enablers. Furthe

A. Elmwafy, José J. Oliveira, César M. Silva

In this paper, we investigate the convergence of asymptotic systems in non-autonomous Cohen--Grossberg neural network models, which include both infinite discrete time-varying and distributed delays. We derive stability results under conditions where the non-delay terms asymptotically dominate the delay terms. Several examples and a numerical simulation are

Adam Phillips, Daniel Grandes Rodriguez, Miriam Sánchez-Manzano, Alan Salvadó

In cinema, visual motifs are recurrent iconographic compositions that carry artistic or aesthetic significance. Their use throughout the history of visual arts and media is interesting to researchers and filmmakers alike. Our goal in this work is to recognise and classify these motifs by proposing a new machine learning model that uses a custom dataset to th

Emily Cheng, Francesca Franzon

Grammatical features such as number and gender serve two central functions in human languages. While they encode salient semantic attributes like numerosity and animacy, they also offload sentence processing cost by predictably linking words together via grammatical agreement. Grammars exhibit consistent organizational patterns across diverse languages, inva

Rahul V, S. Aravinda

Multipartite entanglement is a fundamental aspect of quantum mechanics, crucial to advancements in quantum information processing and quantum computation. Within this field, Genuinely Multipartite Entanglement (GME), being entangled in all bipartitions, and Absolutely Maximally Entanglement (AME), maximally entangled in all bipartitions, represent two signif

Nurhan Bulus Guran, Hanchi Ren, Jingjing Deng, Xianghua Xie

Vision Language Models (VLMs) play a crucial role in robotic manipulation by enabling robots to understand and interpret the visual properties of objects and their surroundings, allowing them to perform manipulation based on this multimodal understanding. Accurately understanding spatial relationships remains a non-trivial challenge, yet it is essential for

Fabio Bersano, Michele Aldeghi, Niccolò Martinolli, Victor Boureau

The rise of electron spin qubit architectures for quantum computing processors has led to a strong interest in designing and integrating ferromagnets to induce stray magnetic fields for electron dipole spin resonance (EDSR). The integration of nanomagnets imposes however strict layout and processing constraints, challenging the arrangement of different gatin

Shamim Homaei, Simon Roussanaly, Asgeir Tomasgard

In power markets, understanding the cost dynamics of electricity generation is crucial. The complexity of price formation in the power system arises from its diverse attributes, such as various generator types, each characterized by its specific fixed and variable costs as well as different lifetimes. In this paper, we adopt an approach that investigates bot

E. Abad, S. B. Yuste

We review some representative results for first-passage problems involving so-called mortal or evanescent walkers, i.e., walkers with a finite lifetime. The mortality constraint plays a key role in the modeling of many real scenarios, as it filters out long Brownian trajectories, thereby drastically modifying space exploration properties. Among such scenario

Xin Ma, Yang Liu, Jingjing Liu, Xiaoxu Ma

Large language models (LLMs), although having revolutionized many fields, still suffer from the challenging extrapolation problem, where the inference ability of LLMs sharply declines beyond their max training lengths. In this work, we conduct a theoretical analysis to better understand why No Position Encoding (NoPE) fails outside its effective range, as we

Aleksandra I. Nowak, Otniel-Bogdan Mercea, Anurag Arnab, Jonas Pfeiffer

Parameter-efficient transfer learning (PETL) aims to adapt pre-trained models to new downstream tasks while minimizing the number of fine-tuned parameters. Adapters, a popular approach in PETL, inject additional capacity into existing networks by incorporating low-rank projections, achieving performance comparable to full fine-tuning with significantly fewer

Dana Kuvatova, Taras Panamarev, Maryna Ishchenko, Anton Gluchshenko

In this study, we investigate the contribution of millisecond pulsars (MSPs) to the gamma-ray excess observed in the Galactic Center by analyzing data from high-resolution direct N-body simulations of six globular clusters (GCs) that experience close encounters with the nuclear star cluster. Using the {\phi}-GPU code, we tracked the orbits of individual neut

G. F. Burgio, H. C. Das, I. Vidana

We investigate possible correlations between the stiffness of the symmetry energy at saturation density, the so-called $L$ parameter, and the neutron skin thickness of ${^{48}}$Ca and ${^{208}}$Pb, for which the recent measurements from the CREX and PREX I+II experiments at the Thomas Jefferson Laboratory became available. We choose an ensemble of nucleonic

Patrick van Meurs, Mark A. Peletier, Thomas Slangen

We study a system of stochastic differential equations with singular drift which describes the dynamics of signed particles in two dimensions interacting by the Coulomb potential. In contrast to the well-studied cases of identical particles that either all repel each other or all attract each other, this system contains both `positive' and `negative' particl

Hugh Greatorex, Ole Richter, Michele Mastella, Madison Cotteret

Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited

Alexander Weber, Holger Eichelberger, Jobst Hildebrand

Real-time measurements are important for in-depth control of manufacturing processes, which, for modern AI methods, need integration with high-level languages. In our last SSP paper we investigated the performance of a Python and a Java-JNA based approach to integrate the Beckhoff ADS protocol for real-time edge communication into an Industry 4.0 platform. T

Pankaj Jyoti Mahanta, Manjil P. Saikia

Recently, Andrews and El Bachraoui (2024) proved three very interesting $q$-series identities, from which three simple looking identities involving certain restricted partitions into distinct even parts and $4$-regular partitions follow. In this short note, we give combinatorial proofs of these identities. We also prove the counterpart identities for the res

Sandeep Nagar, Mark Hasegawa-Johnson, David G. Beiser, Narendra Ahuja

The COVID-19 pandemic has underscored the need for low-cost, scalable approaches to measuring contactless vital signs, either during initial triage at a healthcare facility or virtual telemedicine visits. Remote photoplethysmography (rPPG) can accurately estimate heart rate (HR) when applied to close-up videos of healthy volunteers in well-lit laboratory set

Doghonay Arjmand, Filip Marttala

An accurate approximation of solutions to elliptic problems in infinite domains is challenging from a computational point of view. This is due to the need to replace the infinite domain with a sufficiently large and bounded computational domain, and posing artificial boundary conditions on the boundary of the truncated computational geometry, which will then

Shikhar Vashistha, Neetesh Kumar

Traditional graph neural networks (GNNs) lack scalability and lose individual node characteristics due to over-smoothing, especially in the case of deeper networks. This results in sub-optimal feature representation, affecting the model's performance on tasks involving dynamically changing graphs. To address this issue, we present Graph Selective States Focu

Clemens Hofstadler, Manuel Kauers, Martina Seidl

Symmetries have been exploited successfully within the realms of SAT and QBF to improve solver performance in practical applications and to devise more powerful proof systems. As a first step towards extending these advancements to the class of dependency quantified Boolean formulas (DQBFs), which generalize QBF by allowing more nuanced variable dependencies

Jingyu Guo, Christos Matsoukas, Fredrik Strand, Kevin Smith

In multi-view medical diagnosis, deep learning-based models often fuse information from different imaging perspectives to improve diagnostic performance. However, existing approaches are prone to overfitting and rely heavily on view-specific features, which can lead to trivial solutions. In this work, we introduce Random Token Fusion (RTF), a novel technique

Tailai Song, Paolo Garza, Michela Meo, Maurizio Matteo Munafò

The Real-time Transport Protocol (RTP)-based real-time communications (RTC) applications, exemplified by video conferencing, have experienced an unparalleled surge in popularity and development in recent years. In pursuit of optimizing their performance, the prediction of Quality of Service (QoS) metrics emerges as a pivotal endeavor, bolstering network moni

Gianpaolo Perelli, Andrea Panzino, Roberto Casula, Marco Micheletto

The impact of voice disorders is becoming more widely acknowledged as a public health issue. Several machine learning-based classifiers with the potential to identify disorders have been used in recent studies to differentiate between normal and pathological voices and sounds. In this paper, we focus on analyzing the vulnerabilities of these systems by explo

Yizheng Wang, Jinshuai Bai, Zhongya Lin, Qimin Wang

In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Science: Artificial intelligence for science), which has attracted widespread attention. In AI for Science, using artificial intelligence algorithms to solve partial differential equati

Sourav Kundu, Siddhartha Lal

In the field of DNA nanotechnology, it is common wisdom that charge transport occurs through the {\pi} stacked bases of double-stranded DNA. However, recent experimental findings by Zhuravel et. al. [Nat. Nanotech. 15, 836 (2020)] suggest that electronic transport happens through the backbone channels instead of {\pi}-{\pi} interaction of the nitrogen bases.

Cyril Mergny, Frédéric Schmidt

A multiphysics simulation model incorporating a sintering model coupled with the MultIHeaTS thermal solver was developed to study the evolution of icy moons' microstructure. The sintering process is highly dependent on temperature, and this study represents the first attempt in planetary science to examine the coupled interaction between heat transfer and si

Lucas Cantinelli Sevillano, Milan Stanko, Sigbjoern Sangesland

This paper outlines a novel subsea flushing system which uses a subsea tool to improve the performance of the flushing operation. The new method outlined in this paper uses a small-diameter, high-pressure supply line and a subsea deployed tool containing a pump which recirculates the cleaning fluid through the component or system to be retrieved. The main be

Takahide Adachi, Osamu Iyama, Idun Reiten

We give a brief introduction to $\tau$-tilting theory [AIR]. In particular, we will see how our theory unifies two different branches of tilting theory, namely, silting theory and cluster tilting theory. We also introduce the history and recent developments.

Wenzhe Fan, Zishun Yu, Chengdong Ma, Changye Li

In multi-agent reinforcement learning, a commonly considered paradigm is centralized training with decentralized execution. However, in this framework, decentralized execution restricts the development of coordinated policies due to the local observation limitation. In this paper, we consider the cooperation among neighboring agents during execution and form

Anika Hannemann, Arjhun Swaminathan, Ali Burak Ünal, Mete Akgün

Medical imaging is key in modern medicine. From magnetic resonance imaging (MRI) to microscopic imaging for blood cell detection, diagnostic medical imaging reveals vital insights into patient health. To predict diseases or provide individualized therapies, machine learning techniques like kernel methods have been widely used. Nevertheless, there are multipl

Fatemeh Khaledian, Reza Asvadi, Elsa Dupraz, Tad Matsumoto

We explore the problem of distributed Hypothesis Testing (DHT) against independence, focusing specifically on Binary Symmetric Sources (BSS). Our investigation aims to characterize the optimal quantizer among binary linear codes, with the objective of identifying optimal error probabilities under the Neyman-Pearson (NP) criterion for short code-length regime

M. Mousavi, J. Benáček

The various distribution functions can encompass the diverse characteristics of the magnetospheric plasma of surrounding neutron stars in both hot and cold environments; however, the Maxwell-J\"uttner distribution is so far widely used to characterize these plasmas. We aim to analyze the linear dispersion properties yielded from the relativistic kinetic disp

Wenqi Bai, Xiaohui Zhang, Shiliang Zhang, Songnan Yang

Geomagnetic navigation has drawn increasing attention with its capacity in navigating through complex environments and its independence from external navigation services like global navigation satellite systems (GNSS). Existing studies on geomagnetic navigation, i.e., matching navigation and bionic navigation, rely on pre-stored map or extensive searches, le

Ioannis Gavras, George C. Alexandropoulos

Hybrid Reconfigurable Intelligent Surfaces (HRISs) constitute a new paradigm of truly smart metasurfaces with the additional features of signal reception and processing, which have been primarily considered for channel estimation and self-reconfiguration. In this paper, leveraging the simultaneous tunable reflection and signal absorption functionality of HRI

Simone Tentori

New physics and SM parameters can be studied and constrained by looking at the modifications to top-pair differential kinematical distributions due to off-shell effects. I present here three case studies: the determination of the Higgs couplings to the top-quark, a search for generic BSM scalar and pseudoscalar states, and a search for Axion Like Particles (

Cyril Mergny, Frédéric Schmidt

A fully implicit scheme is proposed for solving the heat equation in 1D heterogeneous media, available as a computationally efficient open-source Python code. The algorithm uses finite differences on an irregular grid and is unconditionally stable due to the implicit formulation. The thermal solver is validated against a stiff analytical solution, demonstrat

Thomas Kreutz, Jens Lemke, Max Mühlhäuser, Alejandro Sanchez Guinea

In this paper, we propose LiOn-XA, an unsupervised domain adaptation (UDA) approach that combines LiDAR-Only Cross-Modal (X) learning with Adversarial training for 3D LiDAR point cloud semantic segmentation to bridge the domain gap arising from environmental and sensor setup changes. Unlike existing works that exploit multiple data modalities like point clou

Wenhan Cao, Tianyi Zhang, Zeju Sun, Chang Liu

Practical Bayes filters often assume the state distribution of each time step to be Gaussian for computational tractability, resulting in the so-called Gaussian filters. When facing nonlinear systems, Gaussian filters such as extended Kalman filter (EKF) or unscented Kalman filter (UKF) typically rely on certain linearization techniques, which can introduce

Yijian Liu, Rodrigo Laigner, Yongluan Zhou

The actor model has gained increasing popularity. However, it lacks support for complex state management tasks, such as enforcing foreign key constraints and ensuring data replication consistency across actors. These are crucial properties in partitioned application designs, such as microservices. To fill this gap, we start by analyzing the key impediments i

Saad Eddine Baddis, Adil Belhaj, Hajar Belmahi

In this work, we approach certain black hole issues, including remnants, by providing a statistical description based on the weak gravity conjecture in the swampland program. Inspired by the Pauli exclusion principle in the context of the Fermi sphere, we derive an inequality which can be exploited to verify the instability manifestation of non-supersymmetri

Bryn Davies, Angelica Yu Xiao

In this work, we characterise the statistics of Markov chains by constructing an associated sequence of periodic differential operators. Studying the density of states of these operators reveals the absolutely continuous invariant measure of the Markov chain. This approach also leads to a direct proof of convergence to the invariant measure, along with expli

Tejumade Afonja, Ivaxi Sheth, Ruta Binkyte, Waqar Hanif

Gene regulatory networks (GRNs) represent the causal relationships between transcription factors (TFs) and target genes in single-cell RNA sequencing (scRNA-seq) data. Understanding these networks is crucial for uncovering disease mechanisms and identifying therapeutic targets. In this work, we investigate the potential of large language models (LLMs) for GR

D. Y. C. Wang, Lars Arne Jordanger, Jerry Chun-Wei Lin

Customer churn, particularly in the telecommunications sector, influences both costs and profits. As the explainability of models becomes increasingly important, this study emphasizes not only the explainability of customer churn through machine learning models, but also the importance of identifying multivariate patterns and setting soft bounds for intuitiv

Ignasi Rosell, Antonio Pich, Juan Jose Sanz-Cillero

Experiments have confirmed the presence of a mass gap between the Standard Model and potential New Physics. Consequently, the exploration of effective field theories to detect signals indicative of Physics Beyond the Standard Model is of great interest. In this study, we examine a non-linear realization of the electroweak symmetry breaking, wherein the Higgs