December 2023 arXiv papers — page 86

Georgios Chatzigeorgakidis, Kostas Patroumpas, Dimitrios Skoutas, Spiros Athanasiou

Efficiently computing spatio-textual queries has become increasingly important in various applications that need to quickly retrieve geolocated entities associated with textual information, such as in location-based services and social networks. To accelerate such queries, several works have proposed combining spatial and textual indices into hybrid index st

Ning Guo, Xudong Han, Shuqiao Zhong, Zhiyuan Zhou

This paper presents a novel vision-based proprioception approach for a soft robotic finger that can estimate and reconstruct tactile interactions in both terrestrial and aquatic environments. The key to this system lies in the finger's unique metamaterial structure, which facilitates omni-directional passive adaptation during grasping, protecting delicate ob

Xuhui Zhang, Jose Blanchet, Youssef Marzouk, Viet Anh Nguyen

We present the first minimax risk bounds for estimators of the spectral measure in multivariate linear factor models, where observations are linear combinations of regularly varying latent factors. Non-asymptotic convergence rates are derived for the multivariate Peak-over-Threshold estimator in terms of the $p$-th order Wasserstein distance, and information

V. V. Klekovkina, N. M. Abishev

We present the results of simulations of the spectral and thermodynamic properties of frustrated $\text{Pr}_2 \text{Zr}_2 \text{O}_7$ crystals using the distribution function of random strains induced by point defects in an elastically anisotropic continuum.

Waïss Azizian, Guillaume Baudart, Marc Lelarge

Exact Bayesian inference on state-space models~(SSM) is in general untractable, and unfortunately, basic Sequential Monte Carlo~(SMC) methods do not yield correct approximations for complex models. In this paper, we propose a mixed inference algorithm that computes closed-form solutions using belief propagation as much as possible, and falls back to sampling

Michel Caffarel

In this work an approximate analytic expression for the quantum partition function of the quartic oscillator described by the potential $V(x) = \frac{1}{2} \omega^2 x^2 + g x^4$ is presented. Using a path integral formalism, the exact partition function is approximated by the partition function of a harmonic oscillator with an effective frequency depending b

Javier F. Pena, Juan C. Vera, Luis F. Zuluaga

We show that a suitable Slater condition implies a duality inequality between the Hoffman constants of the following feasibility problems: $$ \begin{array}{r} Ax-b \in S\\ x \in R \end{array} \qquad\text{ and }\qquad \begin{array}{r} c-A^T y \in R^*\\ y \in S^*. \end{array} $$ where $A\in \mathbb{R}^{m\times n}$, and $R\subseteq \mathbb{R}^n$ and $S\subseteq

Hassan Ismail Fawaz, Ganesh Del Grosso, Tanguy Kerdoncuff, Aurelie Boisbunon

Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to train models for unlabeled target data. Despite extensive research in domains like computer vision and natural language processing, UDA remains underexplored for time series data, which has widespread real-world applications ranging from medicine and manufacturing to earth observatio

Mehrdad Elyasi, Kei Yamamoto, Tomosato Hioki, Takahiko Makiuchi

Magnets are interesting materials for classical and quantum information technologies. However, the short decoherence and dephasing times that determine the scale and speed of information networks, severely limit the appeal of employing the ferromagnetic resonance. Here we show that the lifetime and coherence of the uniform Kittel mode can be enhanced by 3-ma

J. E. Marquardt, N. Hafen, M. J. Krause

This study addresses the challenge of simulating realistic particle systems by proposing a novel particle decomposition scheme that improves the parallel performance of surface resolved particle simulations. Realistic particle systems often involve large numbers of particles and complex particle shapes. The resulting need to account for shape factors require

Arvind Bhaskar, Arijit Das, Tanumoy Mandal, Subhadip Mitra

The scalar-leptoquark (sLQ) parameter space is well explored experimentally. The direct pair production searches at the LHC have excluded light sLQs almost model agnostically, and the high-$p_{\rm T}$ dilepton tail data have put strong bounds on the leptoquark-quark-lepton Yukawa couplings for a wide range of sLQ masses. However, these do not show the comple

Pietro Bonazzi, Yawei Li, Sizhen Bian, Michele Magno

This paper addresses the growing interest in deploying deep learning models directly in-sensor. We present "Q-Segment", a quantized real-time segmentation algorithm, and conduct a comprehensive evaluation on a low-power edge vision platform with an in-sensors processor, the Sony IMX500. One of the main goals of the model is to achieve end-to-end image segmen

YoungJoon Yoo, Jongwon Choi

This paper introduces a novel approach for topic modeling utilizing latent codebooks from Vector-Quantized Variational Auto-Encoder~(VQ-VAE), discretely encapsulating the rich information of the pre-trained embeddings such as the pre-trained language model. From the novel interpretation of the latent codebooks and embeddings as conceptual bag-of-words, we pr

Jeffrey Kuntz, Andreas Trautner

We discuss an extra-dimensional braneworld with a 5th dimension compactified on a circle. As a characteristic feature, the warp factor is hyperbolic and separates the hidden and visible branes by a bulk horizon without a singularity. The two most widely separated scales of 4D physics - the 4D Planck mass and 4D cosmological constant - are determined by two p

Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich

We propose Manifold Free-Form Flows (M-FFF), a simple new generative model for data on manifolds. The existing approaches to learning a distribution on arbitrary manifolds are expensive at inference time, since sampling requires solving a differential equation. Our method overcomes this limitation by sampling in a single function evaluation. The key innovati

Jesper Glückstad, Andreas Erik Gejl Madsen

HoloTile is a patented computer generated holography approach with the aim of reducing the speckle noise caused by the overlap of the non-trivial physical extent of the point spread function in Fourier holographic systems from adjacent frequency components. By combining tiling of phase-only of rapidly generated sub-holograms with a PSF-shaping phase profile,

Natalie Grasser, Magda Arnaboldi, Carlos Eduardo Barbosa, Chiara Spiniello

Aims. We aim to investigate the stellar population properties, ages, and metal content of the globular clusters (GCs) in NGC 3311, the central galaxy of the Hydra I cluster, to better constrain its evolution history. Methods. We used integral-field spectroscopic data from the Multi-Unit Spectroscopic Explorer (MUSE) to identify 680 sources in the central reg

Samit Dasgupta, Mahesh Kakde, Jesse Silliman

Let $F$ be a totally real field and $K$ a finite abelian CM extension of $F$. Using class field theory, we show that our previous result giving a strong form of the Brumer-Stark conjecture implies the minus part of the equivariant Tamagawa number conjecture for the Tate motive associated to $K/F$. We work integrally over $\mathbf{Z}$, in particular the prime

Marvin Röhrle, Jens Benary, Erik Bernhart, Herwig Ott

Dissipative phase transitions are characteristic features in open quantum systems. Key signatures are the dynamical switching between different states in the vicinity of the phase transition and the appearance of hysteresis. Here, we experimentally study dynamic sweeps across a first order dissipative phase transition in a multi-mode driven-dissipative syste

Grant Rydquist, Mahdi Esmaily

Existing hemolysis algorithms are often constructed for laminar flows that expose red blood cells to a constant rate of shear. It remains an open question whether such models are applicable to turbulent flows, where there is a significant variation in shear rate along cell trajectories. To evaluate the effect of turbulence on hemolysis, we perform cell-resol

Hosameldin Awadalla Omer Mohamed, Gabriele Nava, Punith Reddy Vanteddu, Francesco Braghin

High force/torque (F/T) sensor calibration accuracy is crucial to achieving successful force estimation/control tasks with humanoid robots. State-of-the-art affine calibration models do not always approximate correctly the physical phenomenon of the sensor/transducer, resulting in inaccurate F/T measurements for specific applications such as thrust estimatio

Martin Burger, Samira Kabri

In this chapter we provide a theoretically founded investigation of state-of-the-art learning approaches for inverse problems from the point of view of spectral reconstruction operators. We give an extended definition of regularization methods and their convergence in terms of the underlying data distributions, which paves the way for future theoretical stud

Girolamo Macaluso, Alessandro Sestini, Andrew D. Bagdanov

Offline reinforcement learning leverages pre-collected datasets of transitions to train policies. It can serve as effective initialization for online algorithms, enhancing sample efficiency and speeding up convergence. However, when such datasets are limited in size and quality, offline pre-training can produce sub-optimal policies and lead to degraded onlin

Stefano Bianchini, Moritz Müller, Pierre Pelletier

New technologies have the power to revolutionize science. It has happened in the past and is happening again with the emergence of new computational tools, such as artificial intelligence and machine learning. Despite the documented impact of these technologies, there remains a significant gap in understanding the process of their adoption within the scienti

Nagaraj Adiga, Jinhwan Park, Chintigari Shiva Kumar, Shatrughan Singh

Recently, the cascaded two-pass architecture has emerged as a strong contender for on-device automatic speech recognition (ASR). A cascade of causal and shallow non-causal encoders coupled with a shared decoder enables operation in both streaming and look-ahead modes. In this paper, we propose shallow cascaded model by combining various model compression tec

Kenny Peng, Nikhil Garg

Algorithmic monoculture arises when many decision-makers rely on the same algorithm to evaluate applicants. An emerging body of work investigates possible harms of this kind of homogeneity, but has been limited by the challenge of incorporating market effects in which the preferences and behavior of many applicants and decision-makers jointly interact to det

Samuel Teuber, Bernhard Beckert

This work presents insights gained by investigating the relationship between algorithmic fairness and the concept of secure information flow. The problem of enforcing secure information flow is well-studied in the context of information security: If secret information may "flow" through an algorithm or program in such a way that it can influence the program'

Lorenzo Mauro, Esteban A. Rodríguez-Mena, Marion Bassi, Vivien Schmitt

The dephasing time of spin-orbit qubits is limited by the coupling with electrical and charge noise. However, there may exist "dephasing sweet spots" where the qubit decouples (to first order) from the noise so that the dephasing time reaches a maximum. Here we discuss the nature of the dephasing sweet spots of a spin-orbit qubit electrically coupled to some

Maximilian E. Merkel, Aria Mansouri Tehrani, Claude Ederer

We investigate the interplay of spin-orbit coupling, electronic correlations, and lattice distortions in the $5d^1$ double perovskite Ba$_2$MgReO$_6$. Combining density-functional theory (DFT) and dynamical mean-field theory (DMFT), we establish the Mott-insulating character of Ba$_2$MgReO$_6$ in both its cubic and tetragonal paramagnetic phases. Despite sub

Jacobus Conradi, Benedikt Kolbe, Ioannis Psarros, Dennis Rohde

We present algorithms for the computation of $\varepsilon$-coresets for $k$-median clustering of point sequences in $\mathbb{R}^d$ under the $p$-dynamic time warping (DTW) distance. Coresets under DTW have not been investigated before, and the analysis is not directly accessible to existing methods as DTW is not a metric. The three main ingredients that allo

Artjom Joosen, Ahmed Hassan, Martin Asenov, Rajkarn Singh

This paper releases and analyzes two new Huawei cloud serverless traces. The traces span a period of over 7 months with over 1.4 trillion function invocations combined. The first trace is derived from Huawei's internal workloads and contains detailed per-second statistics for 200 functions running across multiple Huawei cloud data centers. The second trace i

Alessandro Ferreri, David Edward Bruschi, Frank K. Wilhelm, Franco Nori

The dynamical Casimir effect is the physical phenomenon where the mechanical energy of a movable wall of a cavity confining a quantum field can be converted into quanta of the field itself. This effect has been recognized as one of the most astonishing predictions of quantum field theory. At the quantum scale, the energy conversion can also occur incoherentl

Javier Hermosa-Muñoz, Aurelio Hierro-Rodríguez, Andrea Sorrentino, José I. Martín

Bloch points in magnetic materials are attractive entities in view of magnetic information transport. Here, Bloch point configuration has been investigated and experimentally determined in a magnetic trilayer ($Gd_{12}Co_{88}/Nd_{17}Co_{83}/Gd_{24}Co_{76}$) with carefully adjusted composition within the ferrimagnetic $Gd_{x}Co_{1-x}$ alloys in order to engin

Niek Bollemeijer, Phil Uttley, Arkadip Basak, Adam Ingram

In X-ray observations of hard state black hole X-ray binaries, rapid variations in accretion disc and coronal power-law emission are correlated and show Fourier-frequency-dependent time lags. On short (~0.1 s) time-scales, these lags are thought to be due to reverberation and therefore may depend strongly on the geometry of the corona. Low-frequency quasi-pe

Emanuel Laude, Panagiotis Patrinos

In this paper we study a nonlinear dual space preconditioning approach for the relaxed Proximal Point Algorithm (PPA) with application to monotone and relatively cohypomonotone inclusions, called anisotropic PPA. The algorithm is an instance of Luque's nonlinear PPA wherein the nonlinear preconditioner is chosen as the gradient of a Legendre convex function.

Bergfinnur Durhuus, Thordur Jonsson, John Wheater

We characterize the spectrum of the transition matrix for simple random walk on graphs consisting of a finite graph with a finite number of infinite Cayley trees attached. We show that there is a continuous spectrum identical to that for a Cayley tree and, in general, a non-empty pure point spectrum. We apply our results to studying continuous time quantum w

Gul Sena Altintas, Gregor Bachmann, Lorenzo Noci, Thomas Hofmann

Linear mode-connectivity (LMC) (or lack thereof) is one of the intriguing characteristics of neural network loss landscapes. While empirically well established, it unfortunately still lacks a proper theoretical understanding. Even worse, although empirical data points are abound, a systematic study of when networks exhibit LMC is largely missing in the liter

Maxim I. Bolotov, Vyacheslav O. Munyayev, Lev A. Smirnov, Grigory V. Osipov

Cyclops states are intriguing cluster patterns observed in oscillator networks, including neuronal ensembles. The concept of cyclops states formed by two distinct, coherent clusters and a solitary oscillator was introduced in [Munyayev {\it et al.}, Phys. Rev. Lett. 130, 107021 (2023)], where we explored the surprising prevalence of such states in repulsive

June Moh Goo

This research proposes a model to predict the location of the most deprived areas in a city using data from the census. Census data is very high-dimensional and needs to be simplified. We use the diffusion map algorithm to reduce dimensionality and find patterns. Features are defined by eigenvectors of the Laplacian matrix that defines the diffusion map. The

Martina Lanini, Hal Schenck, Julianna Tymoczko

This survey gives an overview of three central algebraic themes related to the study of splines: duality, group actions, and homology. Splines are piecewise polynomial functions of a prescribed order of smoothness on some subdivided domain D in R^k, and appear in applications ranging from approximation theory to geometric modeling to numerical analysis. Alte

Lars Fritsche, Jens Kosiol, Alexander Lauer, Adrian Möller

Sequential model synchronisation is the task of propagating changes from one model to another correlated one to restore consistency. It is challenging to perform this propagation in a least-changing way that avoids unnecessary deletions (which might cause information loss). From a theoretical point of view, so-called short-cut (SC) rules have been developed

Olivier Barrois, Thomas Gastine, Christopher C. Finlay

We present investigations of rapidly-rotating convection in a thick spherical shell geometry relevant to planetary cores, comparing results from Quasi-Geostrophic, 3D and hybrid QG-3D models. The 170 reported calculations span Ekman numbers, $Ek$, between $10^{-4}$ and $10^{-10}$, Rayleigh numbers, $Ra$, between $2$ and $150$ times supercritical, and Prandtl

L. M. André, R. Campbell, E. D'Arcy, A. Farrell

To capture the extremal behaviour of complex environmental phenomena in practice, flexi\-ble techniques for modelling tail behaviour are required. In this paper, we introduce a variety of such methods, which were used by the Lancopula Utopiversity team to tackle the EVA (2023) Conference Data Challenge. This data challenge was split into four challenges, lab

Hossein Fathi, Mikko Narhi, Regina Gumenyuk

We experimentally demonstrate the power scaling of optical vortices by the coherent beam combining, encompassing topological charges ranging from l=1 to l=5 realized on the basis of a Yb-doped fiber short-pulsed laser system. The combining efficiency varies from 83.2 to 96.9% depending on the topological charge and beam pattern quality generated by the spati

Sweta Agrawal, Marine Carpuat

Automatic text simplification (TS) aims to automate the process of rewriting text to make it easier for people to read. A pre-requisite for TS to be useful is that it should convey information that is consistent with the meaning of the original text. However, current TS evaluation protocols assess system outputs for simplicity and meaning preservation withou

Jonas Berx

The dynamics of a generic class of scalar active matter exhibiting a diffusivity edge is studied in a confining potential where the amplitude is governed by a time-dependent protocol. For such non-equilibrium systems, the diffusion coefficient vanishes when the single-particle density field reaches a critical threshold, inducing a condensation transition tha

Fang Wan, Chaoyang Song

Sensory substitution enables biological systems to perceive stimuli that are typically perceived by another organ, which is inspirational for physical agents. Multimodal perception of intrinsic and extrinsic interactions is critical in building an intelligent robot that learns. This study presents a Vision-based See-Through Perception (VBSeeThruP) architectu

Chandresh Pravin, Ivan Martino, Giuseppe Nicosia, Varun Ojha

We propose a systematic analysis of deep neural networks (DNNs) based on a signal processing technique for network parameter removal, in the form of synaptic filters that identifies the fragility, robustness and antifragility characteristics of DNN parameters. Our proposed analysis investigates if the DNN performance is impacted negatively, invariantly, or p

Ximeng Ye, Hongyu Li, Jingjie Huang, Guoliang Qin

This paper launches a thorough discussion on the locality of local neural operator (LNO), which is the core that enables LNO great flexibility on varied computational domains in solving transient partial differential equations (PDEs). We investigate the locality of LNO by looking into its receptive field and receptive range, carrying a main concern about how

Abdul Hannan Faruqi, Anindya Chatterjee

We consider stabilization of linear time-invariant (LTI) and single input single output (SISO) plants in the frequency domain from a fresh perspective. Compensators that are themselves stable are sometimes preferred because they make starting the system easier. Such starting remains easy if there is a stable compensator in parallel with the plant rather than

Lee Hyun, Kim Sung-Bin, Seungju Han, Youngjae Yu

Despite the recent advances of the artificial intelligence, building social intelligence remains a challenge. Among social signals, laughter is one of the distinctive expressions that occurs during social interactions between humans. In this work, we tackle a new challenge for machines to understand the rationale behind laughter in video, Video Laugh Reasoni

Federica Capellino, Andrea Dubla, Stefan Floerchinger, Eduardo Grossi

Heavy quarks (i.e. charm and beauty) in heavy-ion collisions are initially produced out of kinetic equilibrium via hard partonic scattering processes. However, recent measurements of anisotropic flow of charmed hadrons pose the question regarding the thermalization of heavy quarks in the quark-gluon plasma (QGP). Exploiting a mapping between transport theory

Mohsin Hasan, Guojun Zhang, Kaiyang Guo, Xi Chen

Federated Learning (FL) involves training a model over a dataset distributed among clients, with the constraint that each client's dataset is localized and possibly heterogeneous. In FL, small and noisy datasets are common, highlighting the need for well-calibrated models that represent the uncertainty of predictions. The closest FL techniques to achieving s

Anand Devarajan, Erkan Karabulut

Blockchain technology has been revolutionizing many fields since last decade. Its true potential is not practically utilized yet. In a very short period of time, it has evolved twice - Smart contracts and Directed Acyclic Graph (DAG). DAG based blockchains currently referred to as Blockchain 3.0 solves many issues in the current conventional blockchain techn

Volker Branding

This article provides an overview on various conservation laws for polyharmonic maps between Riemannian manifolds. Besides recalling that the variation of the energy for polyharmonic maps with respect to the domain metric gives rise to the stress-energy tensor, we also show how the presence of a Killing vector field on the target manifold leads to a conserva

Ali Rida Khalife, Maryam Bahrami Zanjani, Silvia Galli, Sven Günther

We present an updated analysis of eleven cosmological models that may help reduce the Hubble tension, which now reaches the $6\sigma$ level when considering the latest SH0ES measurement versus recent CMB and BAO data, assuming $\Lambda$CDM. Specifically, we look at five classical extensions of $\Lambda$CDM (with massive neutrinos, spatial curvature, free-str

John Y. H. Soo, Ishaq Y. K. Alshuaili, Imdad Mahmud Pathi

Machine learning has rose to become an important research tool in the past decade, its application has been expanded to almost if not all disciplines known to mankind. Particularly, the use of machine learning in astrophysics research had a humble beginning in the early 1980s, it has rose and become widely used in many sub-fields today, driven by the vast av

Xiao Wang, Wentao Wu, Chenglong Li, Zhicheng Zhao

Understanding vehicles in images is important for various applications such as intelligent transportation and self-driving system. Existing vehicle-centric works typically pre-train models on large-scale classification datasets and then fine-tune them for specific downstream tasks. However, they neglect the specific characteristics of vehicle perception in d

Alessandro Conigli, Julien Frison, Patrick Fritzsch, Antoine Gérardin

In a somewhat forgotten paper [1] it was shown how to perform interpolations between relativistic and static computations in order to obtain results for heavy-light observables for masses from, say, $m_{\rm charm}$ to $m_{\rm bottom}$. All quantities are first continuum extrapolated and then interpolated in $1/m_h=1/m_{\rm heavy}$. Large volume computations

Sophia K. Stuber, Jerome Pety, Eva Schinnerer, Frank Bigiel

We present the first results from "Surveying the Whirlpool at Arcseconds with NOEMA" (SWAN), an IRAM Northern Extended Millimetre Array (NOEMA)+30m large program that maps emission from several molecular lines at 90 and 110 GHz in the iconic nearby grand-design spiral galaxy M~51 at cloud-scale resolution ($\sim$3\arcsec=125\,pc). As part of this work, we ha

Barthélémy Neyra

For any symmetric monoidal category $\mathcal{D}$, Lauda and Pfeiffer showed the equivalence between the $\mathcal{D}$-valued open-closed 2-dimensional TQFTs and the so-called knowledgeable Frobenius algebras (KFAs) in $\mathcal{D}$. Each KFA in $\mathcal{D}=\mathbf{Vec}_{\mathbb{K}}$ provides a sequence of scalars indexed by the set $\mathbb{N}^2$ of diffeo

Ashwin Devaraj, Rahul Sharma, Shwetha Nagesh, Biswajit Paul

XTE J1946+274 is a Be/X-ray binary with a 15.8s spin period and 172 d orbital period. Using $\textit{RXTE/PCA}$ data of the 1998 outburst, a cyclotron line around 37 keV was reported. The presence of this line, its dependence on the pulse phase, and its variation with luminosity have been of some debate since. In this work, we present the reanalysis of two $

L. M. Pomârjanschi

The method of using neural networks (NNs) for turbulent transport prediction in a simplified model of tokamak plasmas is explored. The NNs are trained on a database obtained via test-particle simulations of a transport model in the slab-geometrical approximation. It consists of a five-dimensional input of transport model parameters and the radial diffusion c

Zhenxi Lin, Ziheng Zhang, Xian Wu, Yefeng Zheng

Biomedical entity linking (BioEL) has achieved remarkable progress with the help of pre-trained language models. However, existing BioEL methods usually struggle to handle rare and difficult entities due to long-tailed distribution. To address this limitation, we introduce a new scheme $k$NN-BioEL, which provides a BioEL model with the ability to reference s

Nick Abboud, Enrico Speranza, Jorge Noronha

We construct the general theory of first-order relativistic hydrodynamics for a fluid exhibiting a chiral anomaly, including all possible viscous terms allowed by symmetry. Using standard techniques, we compute the necessary and sufficient conditions for this theory to be relativistically causal in the nonlinear regime and for thermal equilibria to be linear

Felix Karbstein

We study the Heisenberg-Euler effective action in constant electromagnetic fields $\bar{F}$ for QED with $N$ charged particle flavors of the same mass and charge $e$ in the large $N$ limit characterized by sending $N\to\infty$ while keeping $Ne^2\sim e\bar{F}\sim N^0$ fixed. This immediately implies that contributions that scale with inverse powers of $N$ ca

Keith M. Davis, Michiel Spapé, Tuukka Ruotsalo

We investigate inferring individual preferences and the contradiction of individual preferences with group preferences through direct measurement of the brain. We report an experiment where brain activity collected from 31 participants produced in response to viewing images is associated with their self-reported preferences. First, we show that brain respons

Xiran Qu, Xuequn Shang, Yupei Zhang

This paper studies the problem of CPRP, concept prerequisite relation prediction, which is a fundamental task in using AI for education. CPRP is usually formulated into a link-prediction task on a relationship graph of concepts and solved by training the graph neural network (GNN) model. However, current directed GNNs fail to manage graph isomorphism which r

Tosin Adewumi, Lama Alkhaled, Claudia Buck, Sergio Hernandez

We introduce a novel writing method called Probing Chain-of-Thought (ProCoT), which potentially prevents students from cheating using a Large Language Model (LLM), such as ChatGPT, while enhancing their active learning. LLMs have disrupted education and many other fields. For fear of students cheating, many have resorted to banning their use. These LLMs are

Simon Klenk, Marvin Motzet, Lukas Koestler, Daniel Cremers

Event cameras offer the exciting possibility of tracking the camera's pose during high-speed motion and in adverse lighting conditions. Despite this promise, existing event-based monocular visual odometry (VO) approaches demonstrate limited performance on recent benchmarks. To address this limitation, some methods resort to additional sensors such as IMUs, s

Yu-Han Sun, Chiang Lo-Hsuan Lee, Tian-Sheuan Chang

Image prefiltering with just noticeable distortion (JND) improves coding efficiency in a visual lossless way by filtering the perceptually redundant information prior to compression. However, real JND cannot be well modeled with inaccurate masking equations in traditional approaches or image-level subject tests in deep learning approaches. Thus, this paper p

Shang Gao, Chenyang Yu, Pingping Zhang, Huchuan Lu

Occluded person re-identification (ReID) is a very challenging task due to the occlusion disturbance and incomplete target information. Leveraging external cues such as human pose or parsing to locate and align part features has been proven to be very effective in occluded person ReID. Meanwhile, recent Transformer structures have a strong ability of long-ra

Sven Neth

How do we ascribe subjective probability? In decision theory, this question is often addressed by representation theorems, going back to Ramsey (1926), which tell us how to define or measure subjective probability by observable preferences. However, standard representation theorems make strong rationality assumptions, in particular expected utility maximizat

Giuseppe Genovese, Renato Lucà, Riccardo Montalto

We study the Gibbs measure associated to the periodic cubic nonlinear Schr\"odinger equation. We establish a change of variable formula for this measure under the first step of the Birkhoff normal form reduction. We also consider the case of fractional dispersion.

Emanuele Bagnaschi, Gennaro Corcella, Roberto Franceschini, Dibyashree Sengupta

We discuss the possibility that light new physics in the top quark sample at the LHC can be found by investigating with greater care well known kinematic distributions, such as the invariant mass $m_{b\ell}$ of the $b$-jet and the charged lepton in fully leptonic $t\bar{t}$ events. We demonstrate that new physics can be probed in the rising part of the alrea

Deividas Eringis, John Leth, Zheng-Hua Tan, Rafal Wisniewski

In this paper, we derive a PAC-Bayes bound on the generalisation gap, in a supervised time-series setting for a special class of discrete-time non-linear dynamical systems. This class includes stable recurrent neural networks (RNN), and the motivation for this work was its application to RNNs. In order to achieve the results, we impose some stability constra

Pedro Osorio, Guillermo Jimenez-Perez, Javier Montalt-Tordera, Jens Hooge

Artificial Intelligence (AI) based image analysis has an immense potential to support diagnostic histopathology, including cancer diagnostics. However, developing supervised AI methods requires large-scale annotated datasets. A potentially powerful solution is to augment training data with synthetic data. Latent diffusion models, which can generate high-qual

Marcin Waniek, Talal Rahwan

Group centrality measures are a generalization of standard centrality, designed to quantify the importance of not just a single node (as is the case with standard measures) but rather that of a group of nodes. Some nodes may have an incentive to evade such measures, i.e., to hide their actual importance, in order to conceal their true role in the network. A

Christian Oswald, Mate Toth, Paul Meissner, Franz Pernkopf

In this paper we propose a new method for training neural networks (NNs) for frequency modulated continuous wave (FMCW) radar mutual interference mitigation. Instead of training NNs to regress from interfered to clean radar signals as in previous work, we train NNs directly on object detection maps. We do so by performing a continuous relaxation of the cell-

Veronica Piccialli, Jan Schwiddessen, Antonio M. Sudoso

Support vector machines (SVMs) are well-studied supervised learning models for binary classification. In many applications, large amounts of samples can be cheaply and easily obtained. What is often a costly and error-prone process is to manually label these instances. Semi-supervised support vector machines (S3VMs) extend the well-known SVM classifiers to t

Jose Aurelio Medina-Garrido, Jose Maria Biedma-Ferrer, Antonio Rafael Ramos-Rodriguez

Purpose: To assess the impact of the existence of and access to different work-family policies on employee well-being and job performance. Design-methodology-approach: Hypothesis testing was performed using a structural equation model based on a PLS-SEM approach applied to a sample of 1,511 employees of the Spanish banking sector. Findings: The results obtai

Yasser Benigmim, Subhankar Roy, Slim Essid, Vicky Kalogeiton

Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not con

Federica Caforio, Francesco Regazzoni, Stefano Pagani, Elias Karabelas

The development of biophysical models for clinical applications is rapidly advancing in the research community, thanks to their predictive nature and their ability to assist the interpretation of clinical data. However, high-resolution and accurate multi-physics computational models are computationally expensive and their personalisation involves fine calibr

Václav Pritzl, Matouš Vrba, Yurii Stasinchuk, Vít Krátký

Reliable deployment of Unmanned Aerial Vehicles (UAVs) in cluttered unknown environments requires accurate sensors for Global Navigation Satellite System (GNSS)-denied localization and obstacle avoidance. Such a requirement limits the usage of cheap and micro-scale vehicles with constrained payload capacity if industrial-grade reliability and precision are r

Shiwei Lyu, Chenfei Chi, Hongbo Cai, Lei Shi

We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models (LLMs) and medical-specific LLM applications. RJUA-QA is derived from realistic clinical scenarios and aims to facilitate LLMs in generating reliable diagnostic and advice. The da

Xiaoxue Yu, Rongpeng Li, Chengchao Liang, Zhifeng Zhao

Multi-Agent Reinforcement Learning (MARL) has emerged as a foundational approach for addressing diverse, intelligent control tasks in various scenarios like the Internet of Vehicles, Internet of Things, and Unmanned Aerial Vehicles. However, the widely assumed existence of a central node for centralized, federated learning-assisted MARL might be impractical

Peter Brearley, Sylvain Laizet

A quantum algorithm for solving the advection equation by embedding the discrete time-marching operator into Hamiltonian simulations is presented. One-dimensional advection can be simulated directly since the central finite difference operator for first-order derivatives is anti-Hermitian. Here, this is extended to industrially relevant, multi-dimensional fl

Tom Nuno Wolf, Fabian Bongratz, Anne-Marie Rickmann, Sebastian Pölsterl

Explaining predictions of black-box neural networks is crucial when applied to decision-critical tasks. Thus, attribution maps are commonly used to identify important image regions, despite prior work showing that humans prefer explanations based on similar examples. To this end, ProtoPNet learns a set of class-representative feature vectors (prototypes) for

M. Burrows, R. B. Baker, S. Bacca, K. D. Launey

Using the \textit{ab initio} symmetry-adapted no-core shell model, we compute sum rules and response functions for light to medium-mass nuclei, starting from interactions that are derived in the chiral effective field theory. We investigate electromagnetic transitions of monopole, dipole and quadrupole nature for symmetric nuclei such as $^4$He, $^{16}$O, $^

Min-Han Shih, Ho-Lam Chung, Yu-Chi Pai, Ming-Hao Hsu

In recent advancements in spoken question answering (QA), end-to-end models have made significant strides. However, previous research has primarily focused on extractive span selection. While this extractive-based approach is effective when answers are present directly within the input, it falls short in addressing abstractive questions, where answers are no

Benno Buschmann, Andreea Dogaru, Elmar Eisemann, Michael Weinmann

Learning-based scene representations such as neural radiance fields or light field networks, that rely on fitting a scene model to image observations, commonly encounter challenges in the presence of inconsistencies within the images caused by occlusions, inaccurately estimated camera parameters or effects like lens flare. To address this challenge, we intro

Benjamin Jourdain, Gilles Pagès

In this paper, we are interested in the propagation of convexity by the strong solution to a one-dimensional Brownian stochastic differential equation with coefficients Lipschitz in the spatial variable uniformly in the time variable and in the convex ordering between the solutions of two such equations. We prove that while these properties hold without furt

Bohan Tang, Siheng Chen, Xiaowen Dong

Hypergraphs are vital in modelling data with higher-order relations containing more than two entities, gaining prominence in machine learning and signal processing. Many hypergraph neural networks leverage message passing over hypergraph structures to enhance node representation learning, yielding impressive performances in tasks like hypergraph node classif

Parikshit Dutta, Arghya Chattopadhyay

We study the Weyl formula for the asymptotic number of eigenvalues of the Laplace-Beltrami operator with Dirichlet boundary condition on a Riemannian manifold in the context of geometric flows. Assuming the eigenvalues to be the energies of some associated statistical system, we show that geometric flows are directly related with the direction of increasing

Olger Siebinga, Arkady Zgonnikov, David Abbink

One of the bottlenecks of automated driving technologies is safe and socially acceptable interactions with human-driven vehicles, for example during merging. Driver models that provide accurate predictions of joint and individual driver behaviour of high-level decisions, safety margins, and low-level control inputs are required to improve the interactive cap

Zi-Yu Khoo, Jonathan Sze Choong Low, Stéphane Bressan

Many functions characterising physical systems are additively separable. This is the case, for instance, of mechanical Hamiltonian functions in physics, population growth equations in biology, and consumer preference and utility functions in economics. We consider the scenario in which a surrogate of a function is to be tested for additive separability. The

Thomas Mordant

In this note, we give sufficient conditions for the (semi)stability of a hypersurface $H$ of $\mathbb{P}^N_k$ in terms of its degree $d$, the maximal multiplicity $\delta$ of its singularities, and the dimension $s$ of its singular locus. For instance, we show that $H$ is semistable when $d \geq \delta \min (N+1, s+3)$. The proof relies in particular on Beno

Sara Maria Brancato, Davide Salzano, Francesco De Lellis, Davide Fiore

A key problem toward the use of microorganisms as bio-factories is reaching and maintaining cellular communities at a desired density and composition so that they can efficiently convert their biomass into useful compounds. Promising technological platforms for the real time, scalable control of cellular density are bioreactors. In this work, we developed a

Alexander Rothkopf, Jan Nordström

In a recently developed variational discretization scheme for second order initial value problems ( J. Comput. Phys. 498, 112652 (2024) ), it was shown that the Noether charge associated with time translation symmetry is exactly preserved in the interior of the simulated domain. The obtained solution also fulfils the naively discretized equations of motions

N. M. Ivanova, C. A. Pallikaros

We determine the complete degeneration picture inside the variety of nilpotent associative algebras of dimension 3 over an algebraically closed field of characteristic not equal to 2. Comparing with the discussion in [Ivanova N.M. and Pallikaros C.A., Degenerations of complex associative algebras of dimension three via Lie and Jordan algebras, {\it Advances