October 2024 arXiv papers — page 98

Jiajing Wu, Kaixin Lin, Dan Lin, Bozhao Zhang

Cross-chain bridges are essential decentralized applications (DApps) to facilitate interoperability between different blockchain networks. Unlike regular DApps, the functionality of cross-chain bridges relies on the collaboration of information both on and off the chain, which exposes them to a wider risk of attacks. According to our statistics, attacks on c

Saeed Noori Gashti, İzzet Sakallı, Behnam Pourhassan

In this paper, we explore the Weak Gravity Conjecture (WGC) within the context of photon spheres in charged black holes, framed by Perfect Fluid in Rastall Theory. We aim to validate the WGC by identifying the extremality states of these black holes. We highlight the interplay between quantum dynamics and gravitational forces, opening new avenues in high-ene

L. Marques Fagundes, P. H. Souto Ribeiro, R. Medeiros de Araújo

A triangular optical cavity can be used to distinguish between two beams with the same intensity profile but different wavefronts. This is what we show in this paper, both theoretically and experimentally, in the case of beams with a doughnut-like intensity profile: one of them having a helical wavefront (vortex beam with orbital angular momentum) and the ot

Haoming Wang

We derive the distribution of the ratio of a non-central mean matrix and a sample covariance matrix. This aligns with the confluent term ${}_1F_1$ in the non-central uni-variate Student's $t$. Some extensions of matrix-variate distributions are considered.

Dure Adan Ammara, Jianguo Ding, Kurt Tutschku

The fidelity and utility of synthetic network traffic are critically compromised by architectural mismatch across heterogeneous network datasets and prevalent scalability failure. This study addresses this challenge by establishing an Architectural Selection Framework that empirically quantifies how data structure compatibility dictates the optimal fidelity-

Maksuda Akter, Rabea Khatun, Md. Alamin Talukder, Md. Manowarul Islam

Skin cancer is a serious and potentially fatal disease caused by DNA damage. Early detection significantly increases survival rates, making accurate diagnosis crucial. In this groundbreaking study, we present a hybrid framework based on Deep Learning (DL) that achieves precise classification of benign and malignant skin lesions. Our approach begins with data

Hamed Fayyaz, Raphael Poulain, Rahmatollah Beheshti

Large language models (LLMs) have shown impressive potential in helping with numerous medical challenges. Deploying LLMs in high-stakes applications such as medicine, however, brings in many concerns. One major area of concern relates to biased behaviors of LLMs in medical applications, leading to unfair treatment of individuals. To pave the way for the resp

Seunghan Lee, Kibok Lee, Taeyoung Park

Advances in diffusion models for generative artificial intelligence have recently propagated to the time series (TS) domain, demonstrating state-of-the-art performance on various tasks. However, prior works on TS diffusion models often borrow the framework of existing works proposed in other domains without considering the characteristics of TS data, leading

Xiang-Ping Jiang, Mingdi Xu, Lei Pan

We provide approximate solutions for the mobility edge (ME) that demarcates localized and extended states within a specific class of one-dimensional non-Hermitian (NH) quasicrystals. These NH quasicrystals exhibit a combination of nonreciprocal hopping terms and complex quasiperiodic on-site potentials. Our analytical approach is substantiated by rigorous nu

Nick Vannieuwenhoven

A numerical algorithm to decompose an exact low-rank skew-symmetric tensor into a sum of elementary (rank-$1$) skew-symmetric tensors is introduced. The algorithm uncovers this Grassmann decomposition based on linear relations that are encoded by the kernel of the differential of the natural action of the general linear group on the tensor, following the ide

Matthew J. Vowels, Mathieu Rochat, Sina Akbari

Artificial Neural Networks (ANNs), including fully-connected networks and transformers, are highly flexible and powerful function approximators, widely applied in fields like computer vision and natural language processing. However, their inability to inherently respect causal structures can limit their robustness, making them vulnerable to covariate shift a

Kavinayan P. Sivakumar, Yan Zhang, Zachary Bell, Scott Nivison

In this paper, we consider a transfer reinforcement learning problem involving agents with different action spaces. Specifically, for any new unseen task, the goal is to use a successful demonstration of this task by an expert agent in its action space to enable a learner agent learn an optimal policy in its own different action space with fewer samples than

Clément Staelen, Jean-Marc Huré, Anaïs Meunier, Pauline Noé

We report the existence of hydrostatic equilibrium states for a composite body made of two rigidly rotating, homogeneous layers bounded by spheroidal surfaces, where the core has a prolate shape. These new configurations require an oblate envelope that spins faster than the core. No solution with a prolate envelope is found. For some parameters, the prolate

Junjie Wu, Xuming Fang, Dusit Niyato, Jiacheng Wang

With the rapid advancements in wireless communication fields, including low-altitude economies, 6G, and Wi-Fi, the scale of wireless networks continues to expand, accompanied by increasing service quality demands. Traditional deep reinforcement learning (DRL)-based optimization models can improve network performance by solving non-convex optimization problem

James Vo

As large language models (LLMs) continue to advance, the need for precise and efficient evaluation metrics becomes more pressing. Traditional approaches, while informative, often face limitations in computational demands and interpretability. In this paper, we introduce a novel hybrid evaluation method that integrates two established techniques: entropy deri

Fabian Slonimczyk

I propose a relatively simple way to deploy pre-trained large language models (LLMs) in order to extract sentiment and other useful features from text data. The method, which I refer to as prompt-based sentiment extraction, offers multiple advantages over other methods used in economics and finance. In particular, it accepts the text input as is (without pre

Subhankar Maity, Aniket Deroy

As artificial intelligence (AI) becomes more integrated into educational environments, how can we ensure that these systems are both understandable and trustworthy? The growing demand for explainability in AI systems is a critical area of focus. This paper explores Human-Centric eXplainable AI (HCXAI) in the educational landscape, emphasizing its role in enh

Cody Clop, Yannick Teglia

Large Language Models (LLMs) have demonstrated remarkable capabilities in generating coherent text but remain limited by the static nature of their training data. Retrieval Augmented Generation (RAG) addresses this issue by combining LLMs with up-to-date information retrieval, but also expand the attack surface of the system. This paper investigates prompt i

Kevin Sewell, Felipe Murphy-Armando

We use first-principles electronic-structure theory to determine the intra- and inter-valley electron-alloy scattering parameters in n-type GeSn alloys. These parameters are used to determine the alloy scattering contributions to the n-type electron mobility of GeSn at $300K$ and $15K$ using a first iteration of the Boltzmann transport equation in the relaxa

Vladimir R. Kostic, Karim Lounici, Hélène Halconruy, Timothée Devergne

Markov processes serve as a universal model for many real-world random processes. This paper presents a data-driven approach for learning these models through the spectral decomposition of the infinitesimal generator (IG) of the Markov semigroup. The unbounded nature of IGs complicates traditional methods such as vector-valued regression and Hilbert-Schmidt

Jannatun Nayeem Pinky, Ramya Akula

Cryptocurrencies, as decentralized digital assets, have experienced rapid growth and adoption, with over 23,000 cryptocurrencies and a market capitalization nearing \$1.1 trillion (about \$3,400 per person in the US) as of 2023. This dynamic market presents significant opportunities and risks, highlighting the need for accurate price prediction models to man

Markus Kuster, Robert Hartmann, Steffen Hauf, Peter Holl

Structure determination is a key application of XFELs and 4th generation synchrotron sources, particularly using the coherent and pulsed X-ray radiation from X-ray free-electron lasers (XFEL). Scientific interest focuses on understanding the physical, biological, and chemical properties of samples at the nanometer scale. The X-rays from XFELs enable Coherent

Cristian Cobeli, Alexandru Zaharescu

For any odd prime $p$ and any integer $N\ge 0$, let $\mathcal{V}(p,N)$ be the set of vertices of the cyclotomic box $\mathscr{B} = \mathscr{B}(p,N)$ of edge size $2N$ and centered at the origin $O$ of the ring of integers $\mathbb{Z}[\omega]$ of the cyclotomic field $\mathbb{Q}(\omega)$, where $\omega=\exp\big(\frac{2\pi i}{p}\big)$. Cyclotomic boxes represe

Alessandro Falco, Giacomo De Palma

We prove the multimode conditional quantum Entropy Power Inequality for bosonic quantum systems. This inequality determines the minimum conditional von Neumann entropy of the output of the most general linear mixing of bosonic quantum modes among all the input states of the modes with given conditional entropies. Bosonic quantum systems constitute the mathem

Paul Gavrikov, Shashank Agnihotri, Margret Keuper, Janis Keuper

Not all learnable parameters (e.g., weights) contribute equally to a neural network's decision function. In fact, entire layers' parameters can sometimes be reset to random values with little to no impact on the model's decisions. We revisit earlier studies that examined how architecture and task complexity influence this phenomenon and ask: is this phenomen

Bianca Turini, Sedighe Salimian, Matteo Carrega, Federico Paolucci

InSb nanoflags, due to their intrinsic spin-orbit interactions, are an interesting platform in the study of planar Josephson junctions. Ballistic transport, combined with high transparency of the superconductor/semiconductor interfaces, was reported to lead to interesting phenomena such as the Josephson diode effect. The versatility offered by the planar geo

Rongliang Zhou, Jiakun Huang, Mingjun Li, Hepeng Li

A safe and efficient decision-making system is crucial for autonomous vehicles. However, the complexity of driving environments limits the effectiveness of many rule-based and machine learning approaches. Reinforcement Learning (RL), with its robust self-learning capabilities and environmental adaptability, offers a promising solution to these challenges. Ne

Linfeng Piao, Anne Juel

We study experimentally the onset of Faraday waves near the endwalls of rectangular vessel containing two stably-stratified fluid layers, subject to horizontal oscillations. These subharmonic waves (SWs) are excited, because the horizontal inertial forcing drives a harmonic propagating wave which displaces the interface in the vertical direction at the endwa

Andrea Bulgarelli, Elia Cellini, Karl Jansen, Stefan Kühn

We introduce a novel technique to numerically calculate R\'enyi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network architecture around a lattice defect connecting two replicas. Numerical tests for the $\phi^4$ scalar field t

Anton Kirch, So-Ra Park, Joan Ràfols-Ribé, Johannes A. Kassel

Light-emitting electrochemical cells (LECs) are promising candidates for fully solution-processed lighting applications because they can comprise a single active-material layer and air-stable electrodes. While their performance is often claimed to be independent of the electrode material selection due to the in-situ formation of electric double layers (EDLs)

Deke Li, Yuan Li, Qingxuan Wang

We investigate the thermodynamic limit for the cubic-quintic Schr\"{o}dinger model as the size of the domain tends to infinity with fixed density $\rho= N/|\mathcal{D}|$, where $N$ denotes particle number and $|\mathcal{D}|$ denotes the volume of the bounded domain $\mathcal{D}\subset\mathbb{R}^d$ ($d=1,2,3$). We firstly prove the existence of thermodynamic

Jialu Tang, Tong Xia, Yuan Lu, Cecilia Mascolo

Electrocardiogram (ECG) interpretation requires specialized expertise, often involving synthesizing insights from ECG signals with complex clinical queries posed in natural language. The scarcity of labeled ECG data coupled with the diverse nature of clinical inquiries presents a significant challenge for developing robust and adaptable ECG diagnostic system

Elena Kulinskaya, David C. Hoaglin

We consider a three-level meta-analysis of standardized mean differences. The standard method of estimation uses inverse-variance weights and REML/PL estimation of variance components for the random effects. We introduce new moment-based point and interval estimators for the two variance components and related estimators of the overall mean. Similar to tradi

Axel Muller, Alain Giorgetti

This article delves into the concept of quantum contextuality, specifically focusing on proofs of the Kochen-Specker theorem obtained by assigning Pauli observables to hypergraph vertices satisfying a given commutation relation. The abstract structure composed of this hypergraph and the graph of anticommutations is named a hypergram. Its labelings with Pauli

Juliette Marrie, Romain Menegaux, Michael Arbel, Diane Larlus

We address the problem of extending the capabilities of vision foundation models such as DINO, SAM, and CLIP, to 3D tasks. Specifically, we introduce a novel method to uplift 2D image features into Gaussian Splatting representations of 3D scenes. Unlike traditional approaches that rely on minimizing a reconstruction loss, our method employs a simpler and mor

Igor Katrichek

This paper introduces a scalar numerical differentiator, represented as a system of nonlinear differential equations of any high order. We derive the explicit solution for this system and demonstrate that, with a suitable choice of differentiator order, the error converges to zero for polynomial signals with additive white noise. In more general cases, the e

Nandi Schoots, Alex Jackson, Ali Kholmovaia, Peter McBurney

Does the process of training a neural network to solve a task tend to use all of the available weights even when the task could be solved with fewer weights? To address this question we study the effects of pruning fully connected, convolutional and residual models while varying their widths. We find that the proportion of weights that can be pruned without

Pedro Nora, Jurriaan Rot, Lutz Schröder, Paul Wild

While behavioural equivalences among systems of the same type, such as Park/Milner bisimilarity of labelled transition systems, are an established notion, a systematic treatment of relationships between systems of different type is currently missing. We provide such a treatment in the framework of universal coalgebra, in which the type of a system (nondeterm

Fabio Vieira, Hongwei Zhao, Joris Mulder

Random effects are a flexible addition to statistical models to capture structural heterogeneity in the data, such as spatial dependencies, individual differences, temporal dependencies, or non-linear effects. Testing for the presence (or absence) of random effects is an important but challenging endeavor however, as testing a variance component, which must

Gregor Rauw, Ronny Blomme, Yaël Nazé, Delia Volpi

The O-type long-period binary HD 168112 and triple HD 167971 star systems have been known for several decades for their non-thermal synchrotron radio emission. This emission arises from relativistic electrons accelerated in the hydrodynamic shocks of the wind collisions in these systems. Such wind collisions are expected to produce a strong X-ray emission th

A. Zuleta, T. Birnstiel, R. Teague

Increasing evidence shows that warped disks are common, challenging the methods used to model their velocity fields. Molecular line emission of these disks is characterized by a twisted pattern, similar to the signal from radial flows, complicating the study of warped disk kinematics. Previous attempts to model these features have encountered difficulties in

Brian Street

Let $(\mathfrak{M},\rho,\mu)$ be a metric measure space satisfying a doubling condition, $p_0\in (1,\infty)$, and $T(t):L^{p_0}(\mathfrak{M},\mu)\rightarrow L^{p_0}(\mathfrak{M},\mu)$, $t\geq 0$, a strongly continuous semi-group. We provide sufficient conditions under which $T(t)$ is given by integration against an integral kernel satisfying higher-order Gau

Roman Kessler, Alexander Enge, Michael A. Skeide

EEG preprocessing varies widely between studies, but its impact on classification performance remains poorly understood. To address this gap, we analyzed seven experiments with 40 participants drawn from the public ERP CORE dataset. We systematically varied key preprocessing steps, such as filtering, referencing, baseline interval, detrending, and multiple a

Yuming Xu, Hengyu Liang, Jin Li, Shuotao Xu

Approximate Nearest Neighbor Search (ANNS) is now widely used in various applications, ranging from information retrieval, question answering, and recommendation, to search for similar high-dimensional vectors. As the amount of vector data grows continuously, it becomes important to support updates to vector index, the enabling technique that allows for effi

Athanasios Bakopoulos, Thanasis Karakasis, Eleftherios Papantonopoulos

Using Euclidean methods we investigate the thermodynamics of stealth black hole solutions, which are solutions that geometrically are indistinguishable from those of general relativity, however they are accompanied by a non-trivial additional field that does not back-react to the metric. We find that in general, one can observe shifts in the mass and/or the

Gregor Rauw

Many massive binary systems undergo mass and angular momentum transfer over the course of their evolution. This kind of interaction is expected to deeply affect the properties of the mass donor and mass gainer and to leave various observational signatures. The most common smoking guns of a past mass transfer episode are notably rapid rotation of the mass gai

Johannes Brötzner, Herbert Biber, Paul Stefan Szabo, Noah Jäggi

Sputtering by solar wind ions is a key process driving the ejection of high-energy particles into the exospheres of airless bodies like asteroids, Mercury and the Moon. In view of upcoming missions which will deliver new in-situ data on these exospheres like the Artemis program at the Moon and BepiColombo at Mercury, a deeper understanding of sputtering effe

Sara Yasmine Ouerk, Olivier Vo Van, Mouadh Yagoubi

Predictive maintenance of railway infrastructure, especially railroads, is essential to ensure safety. However, accurate prediction of crack evolution represents a major challenge due to the complex interactions between intrinsic and external factors, as well as measurement uncertainties. Effective modeling requires a multidimensional approach and a comprehe

André Giesecke, Mike Wilbert, Ján Šimkanin, Rainer Grauer

We numerically examine dynamo action generated by a flow of an electrically conducting fluid in a precessing cylindrical cavity. We compare a simplified kinematic approach based on the solution of the magnetic induction equation with a prescribed velocity field with the results from a self-consistent three-dimensional simulation of the complete set of magnet

Francesco Calogero

In this paper a procedure is described which allows to identify new systems of nonlinear recursions whose solutions are controllable and which may be asymptotically isochronous as functions of the independent variable (considered a ticking time).

Alberto Espuny Díaz, Richarlotte Valérà Razafindravola

We study Hamiltonicity in the union of an $n$-vertex graph $H$ with high minimum degree and a binomial random graph on the same vertex set. In particular, we consider the case when $H$ has minimum degree close to $n/2$. We determine the perturbed threshold for Hamiltonicity in this setting. To be precise, let $\eta:= n/2-\delta(H)$. For $\eta=\omega(1)$, we

Georg Lehner

We outline how the group $K_{-1}( \mathbb{Z}[G] )$ for a finite group $G$ can be computed using the computer language $GAP$ and compile a table of all groups $G$ of order less than $100$ that have torsion in $K_{-1}( \mathbb{Z}[G] )$.

Xiangtao Kong, Kexin Huang, Ping Li, Lei Zhang

Visual brain decoding aims to decode visual information from human brain activities. Despite the great progress, one critical limitation of current brain decoding research lies in the lack of generalization capability to unseen subjects. Prior works typically focus on decoding brain activity of individuals based on the observation that different subjects exh

Patrick Ciarlet, Erell Jamelot

Using the T-coercivity theory as advocated in [Chesnel, Ciarlet, T -coercivity and continuous Galerkin methods: application to transmission problems with sign changing coefficients (2013)], we propose a new variational formulation of the Stokes problem which does not involve nonlocal operators. With this new formulation, unstable finite element pairs are sta

Guillaume Derval, Damien Ernst, Quentin Louveaux, Bardhyl Miftari

Understanding how the optimal value of an optimisation problem changes when its input data is modified is an old question in mathematical optimisation. This paper investigates the computation of the optimal values of a family of (possibly mixed-integer) linear optimisation problems in which the constraint matrix is subject to linear perturbations controlled

You Wu, Haoyi Wu, Kewei Tu

Recently, sharing key-value (KV) cache across layers has been found effective in efficient inference of large language models (LLMs). To systematically investigate different techniques of cross-layer KV sharing, we propose a unified framework that covers several recent methods and their novel variants. We conduct comprehensive experiments on all the configur

J. Greiner, T. Krühler, J. Bolmer, S. Klose

A dedicated gamma-ray burst (GRB) afterglow observing program was performed between 2007 and 2016 with GROND, a seven-channel optical and near-infrared imager at the 2.2m telescope of the Max-Planck Society at ESO/La Silla. In this first of a series of papers, we describe the GRB observing plan, providing first readings of all so far unpublished GRB afterglo

Sergey Goncharov, Dirk Hofmaan, Pedro Nora, Lutz Schröder

Generic notions of bisimulation for various types of systems (nondeterministic, probabilistic, weighted etc.) rely on identity-preserving (normal) lax extensions of the functor encapsulating the system type, in the paradigm of universal coalgebra. It is known that preservation of weak pullbacks is a sufficient condition for a functor to admit a normal lax ex

Shuang shuang Li, Peihao Dong

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is one of the key technologies for next-generation wireless communication systems. However, acquiring the accurate high-dimensional channel matrix of XL-MIMO remains a pressing challenge due to the intractable channel property and the high complexity. In this paper, a Mixed Attention Tran

Swarnak Ray, Arun Roy

Active droplets undergoing micellar solubilization can self-propel themselves by emitting filled micelles from their surface and are by nature anti-chemotactic. These droplets get repelled by their own trail of filled micelles or by other neighbouring droplets. We experimentally study the various types of scattering events between two such active droplets. W

A. V. Lunkin, K. S. Tikhonov

We present an analytical calculation of the local density of states correlation function $ \beta(\omega) $ in the L\'evy-Rosenzweig-Porter random matrix ensemble at energy scales larger than the level spacing but smaller than the bandwidth. The only relevant energy scale in this limit is the typical level width $\Gamma_0$. We show that $\beta(\omega \ll \Gam

Peiwen Li, Menghua Wu

Perturbation experiments allow biologists to discover causal relationships between variables of interest, but the sparsity and high dimensionality of these data pose significant challenges for causal structure learning algorithms. Biological knowledge graphs can bootstrap the inference of causal structures in these situations, but since they compile vastly d

Rita B. Neves

The standard model of cosmology assumes a homogeneous and isotropic universe that undergoes a period of exponential expansion very early on, named inflation. This stretches quantum fluctuations from the onset of inflation to cosmological scales, which seed the temperature, polarization and matter anisotropies that we observe. However, this paradigm ignores p

Zongyun Chen, Steven J. Miller, Chenghan Wu

This paper presents geometric proofs for the irrationality of square roots of select integers, extending classical approaches. Building on known geometric methods for proving the irrationality of sqrt(2), the authors explore whether similar techniques can be applied to other non-square integers. They begin by reviewing well-known results, such as Euclid's pr

Rabea Khatun, Wahia Tasnim, Maksuda Akter, Md Manowarul Islam

Gallbladder cancer (GBC) is the most frequent cause of disease among biliary tract neoplasms. Identifying the molecular mechanisms and biomarkers linked to GBC progression has been a significant challenge in scientific research. Few recent studies have explored the roles of biomarkers in GBC. Our study aimed to identify biomarkers in GBC using machine learni

J. Dominski, P. Maget, O. Manas, J. Morales

Tungsten peaking is predicted in the core of a WEST plasma with total-f gyrokinetic simulations, including both collisional and turbulent transport. This prediction is validated with a synthetic diagnostic of the bolometry. Although nitrogen impurities are shown to reduce the neoclassical peaking of tungsten on-axis, the overall tungsten peaking increases wh

Mourad Oulghelou, Soufiane Cherroud, Xavier Merle, Paola Cinnella

We present a machine learning-based framework for blending data-driven turbulent closures in the Reynolds-Averaged Navier-Stokes (RANS) equations, aimed at improving their generalizability across diverse flow regimes. Specialized models (hereafter referred to as experts) are trained via sparse Bayesian learning and symbolic regression for distinct flow class

Chris Ormel

Planet formation encompasses processes that span a remarkable 40 magnitudes in mass, ranging from collisions between micron-sized grains inherited from the ISM to the accretion of gas by giant planets. The planet formation process takes place in the interior of dusty disks, which offer us only limited observational constraints. Historically, the two main par

Rui Hu, Qian He, Gaofeng He, Jiedong Zhuang

Modeling and producing lifelike clothed human images has attracted researchers' attention from different areas for decades, with the complexity from highly articulated and structured content. Rendering algorithms decompose and simulate the imaging process of a camera, while are limited by the accuracy of modeled variables and the efficiency of computation. G

Zhaoyang Zhang, Ismaël Septembre, Zhenzhi Liu, Pavel Kokhanchik

Topological physics has broadened its scope from the study of topological insulating phases to include nodal phases containing band structure singularities. The geometry of the corresponding quantum states is described by the quantum metric which provides a theoretical framework for explaining phenomena that conventional approaches fail to address. The field

Santanu Rathod, Pietro Lio, Xiao Zhang

We develop a novel data-driven framework as an alternative to dynamic flux balance analysis, bypassing the demand for deep domain knowledge and manual efforts to formulate the optimization problem. The proposed framework is end-to-end, which trains a structured neural ODE process (SNODEP) model to estimate flux and balance samples using gene-expression time-

Shuai Zhao, Xiaobao Wu, Cong-Duy Nguyen, Yanhao Jia

Parameter-efficient fine-tuning (PEFT) can bridge the gap between large language models (LLMs) and downstream tasks. However, PEFT has been proven vulnerable to malicious attacks. Research indicates that poisoned LLMs, even after PEFT, retain the capability to activate internalized backdoors when input samples contain predefined triggers. In this paper, we i

Ádám Bácsi, Balázs Dóra

We study the non-equilibrium dynamics of the superconducting order parameter in the Hatsugai-Kohmoto (HK) model. In the absence of superconductivity, its ground state is a non-Fermi liquid, whose properties are controlled by the HK interaction. Our protocol involves quantum quenching the HK interaction but leaving the interaction responsible for superconduct

Cynthia Maldonado-Garcia, Arezoo Zakeri, Alejandro F Frangi, Nishant Ravikumar

Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life. This study demonstrates the potential of retinal optical coherence tomography (OCT) imaging combined with fundus photographs for identifying future adverse cardiac events. We

Shuyang Zhang, Chang Gao, Qingfu Zhang, Tianyi Jia

When tracking maneuvering targets, model-driven approaches encounter difficulties in comprehensively delineating complex real-world scenarios and are prone to model mismatch when the targets maneuver. Meanwhile, contemporary data-driven methods have overlooked measurements' confidence, markedly escalating the challenge of fitting a mapping from measurement s

Harish Chandramouleeswaran, Prajakta Nimbhorkar, Nidhi Rathi

We study fair division of indivisible items under a variable input setting, where the set of agents or items may change over time. Starting from an arbitrary allocation, the goal is to restore envy-freeness up to one item (EF1) through item transfers while causing as little disruption as possible. We formalize this via `valid transfers' and introduce the EF1

Benoit Oriol

We compute asymptotic non-linear shrinkage formulas for covariance and precision matrix estimators for weighted sample covariances, and the joint sample-population eigenvector overlap distribution, in the spirit of Ledoit and P\'ech\'e. We detail explicitly the formulas for exponentially-weighted sample covariances. We propose an algorithm to numerically com

Mohammad Okour, Raphael Falque, Alen Alempijevic

Understanding the well-being of cattle is crucial in various agricultural contexts. Cattle's body shape and joint articulation carry significant information about their welfare, yet acquiring comprehensive datasets for 3D body pose estimation presents a formidable challenge. This study delves into the construction of such a dataset specifically tailored for

Nhat A. Nghiem

We propose a new method for simulating certain type of time-dependent Hamiltonian $H(t) = \sum_{i=1}^m \gamma_i(t) H_i$ where $\gamma_i(t)$ (and its higher order derivatives) is bounded, computable function of time $t$, and each $H_i$ is time-independent, and could be efficiently simulated. Our quantum algorithms are based on high-order Runge-Kutta method an

Yeow Meng Chee, Son Hoang Dau, Tuvi Etzion, Han Mao Kiah

Motivated by a repair problem for fractional repetition codes in distributed storage, each block of any Steiner quadruple system (SQS) of order $v$ is partitioned into two pairs. Each pair in such a partition is called a nested design pair and its multiplicity is the number of times it is a pair in this partition. Such a partition of each block is considered

Pauline Béraud, Margaux Rioux, Michel Babany, Philippe de La Chevasnerie

Electricity forecasting has been a recurring research topic, as it is key to finding the right balance between production and consumption. While most papers are focused on the national or regional scale, few are interested in the household level. Desegregated forecast is a common topic in Machine Learning (ML) literature but lacks explainability that househo

Simon Beelen, Marten Nijhof, Christ de Jong, Leen van Wijngaarden

Bubble curtains are widely used to protect marine life from exposure to noise during offshore construction. However, operating a bubble curtain is costly. Therefore optimizing the acoustic effect of the available air is important. An interesting approach is to split the airflow rate into two separate bubble curtains, rather than one single curtain. This conc

Niayesh Afshordi, Abhay Ashtekar, Enrico Barausse, Emanuele Berti

The gravitational physics landscape is evolving rapidly, driven by our ability to study strong-field regions, in particular black holes. Black Holes Inside and Out gathered world experts to discuss the status of the field and prospects ahead. We hope that the ideas and perspectives are a source of inspiration. Structure: Black Hole Evaporation - 50 Years by

Benoit Oriol

We address the issue of computing the non-linear shrinkage formulas for the weighted sample covariance in high dimension. We use theoretical properties of the asymptotic sample spectrum in order to derive the \textit{WeSpeR} algorithm and significantly speed up non-linear shrinkage in dimension higher than $1000$. Empirical tests confirm the good properties

Imran Khan, Vikrant Saxena

The cutoff energy and the divergence of the protons generated by the target normal sheath acceleration mechanism are known to be significantly influenced by micrometer and nanometer-size structures on the target front and rear surfaces. Specifically, the cutoff energy is significantly enhanced by creating a central rectangular groove on the target front surf

Hubert Siuzdak, Florian Grötschla, Luca A. Lanzendörfer

Neural audio codecs have recently gained popularity because they can represent audio signals with high fidelity at very low bitrates, making it feasible to use language modeling approaches for audio generation and understanding. Residual Vector Quantization (RVQ) has become the standard technique for neural audio compression using a cascade of VQ codebooks.

Piotr Szańkowski, Davide Lonigro, Fattah Sakuldee, Łukasz Cywiński

We propose an exercise in which one attempts to deduce the formalism of quantum mechanics solely from phenomenological observations. The only assumed inputs are obtained through sequential probing of quantum systems; no presuppositions about the underlying mathematical structures are permitted. We demonstrate that it is indeed possible to derive, on this bas

Andreas Galanis, Leslie Ann Goldberg, Paulina Smolarova

We consider the problem of sampling from the ferromagnetic $q$-state Potts model on the random $d$-regular graph with parameter $\beta>0$. A key difficulty that arises in sampling from the model is the existence of a metastability window $(\beta_u,\beta_u')$ where the distribution has two competing modes, the so-called disordered and ordered phases, causing

Benoit Oriol

We propose another proof of the high dimensional spectrum convergence of the weighted sample covariance, more concise and self-sufficient but with stronger, but reasonable assumptions. We explain and illustrates this theorem for different weight distributions and show how the spectrum behaves in finite samples with heavy tails. The general purpose is to prov

Xueming Liu, Dengyu Zhang, Qingrui Zhang, Tianjiang Hu

This paper proposes an integrated framework for coordinating multiple unmanned aerial vehicles (UAVs) in a distributed manner to persistently enclose and track a moving target without relying on external localization systems. The proposed framework consists of three modules: cooperative state estimators, circular formation pattern generators, and formation t

Jahir Argote-Gerald, Genki Miyauchi, Paul Trodden, Roderich Gross

This paper studies how groups of robots can effectively navigate through a crowd of agents. It quantifies the performance of platooning and less constrained, greedy strategies, and the extent to which these strategies disrupt the crowd agents. Three scenarios are considered: (i) passive crowds, (ii) counter-flow crowds, and (iii) perpendicular-flow crowds. T

Denitsa Saynova, Lovisa Hagström, Moa Johansson, Richard Johansson

Language models (LMs) can make a correct prediction based on many possible signals in a prompt, not all corresponding to recall of factual associations. However, current interpretations of LMs fail to take this into account. For example, given the query "Astrid Lindgren was born in" with the corresponding completion "Sweden", no difference is made between wh

G. Isopi, V. Capalbo, A. D. Hincks, L. Di Mascolo

According to CMB measurements, baryonic matter constitutes about $5\%$ of the mass-energy density of the universe. A significant population of these baryons, for a long time referred to as `missing', resides in a low density, warm-hot intergalactic medium (WHIM) outside galaxy clusters, tracing the ``cosmic web'', a network of large scale dark matter filamen

N. Osborne, K. Verhaegh, D. Moulton, H. Reimerdes

During detachment, a buffer of neutral atoms and molecules builds up between the target and the ionising plasma. Collisions between the plasma and the molecules play an important role in the detachment process. Studies of plasma-molecular kinetics indicate that the gas temperature is increased during detachment for a wide range of conditions on the MAST-U an

Matthew. N. Crowe, Georgi. G. Sutyrin

We study the evolution of eastward propagating dipoles (modons) in a two-layer quasi-geostrophic $\beta$-plane model using high-resolution numerical simulations. Various combinations of background gradients of potential vorticity in the upper and lower layer (which may include sloping topography) shed light on the recently discovered breakdown mechanisms and

P. Alsina-Bolívar, J. Casanova

Chemical shifts and J-couplings are fundamental parameters in NMR spectroscopy as they provide structural information about molecules. Extracting these quantities from isotopes such as carbon or nitrogen results in reduced sensitivity due to their low gyromagnetic ratios. In this work, we present a method for detecting chemical shifts and J-couplings at the

Kang Chen, Shijun Yan, Aiwen Jiang, Han Li

Bokeh rendering is one of the most popular techniques in photography. It can make photographs visually appealing, forcing users to focus their attentions on particular area of image. However, achieving satisfactory bokeh effect usually presents significant challenge, since mobile cameras with restricted optical systems are constrained, while expensive high-e

Magdalena Wysocka, Danilo Carvalho, Oskar Wysocki, Marco Valentino

Syllogistic reasoning is crucial for Natural Language Inference (NLI). This capability is particularly significant in specialized domains such as biomedicine, where it can support automatic evidence interpretation and scientific discovery. This paper presents SylloBio-NLI, a novel framework that leverages external ontologies to systematically instantiate div

Felix Koulischer, Johannes Deleu, Gabriel Raya, Thomas Demeester

Negative Prompting (NP) is widely utilized in diffusion models, particularly in text-to-image applications, to prevent the generation of undesired features. In this paper, we show that conventional NP is limited by the assumption of a constant guidance scale, which may lead to highly suboptimal results, or even complete failure, due to the non-stationarity a

Dennis Willsch, Philipp Hanussek, Georg Hoever, Madita Willsch

We report on the current state of factoring integers on both digital and analog quantum computers. For digital quantum computers, we study the effect of errors for which one can formally prove that Shor's factoring algorithm fails. For analog quantum computers, we experimentally test three factorisation methods and provide evidence for a scaling performance