March 2024 arXiv papers — page 140

Pan He, Quanyi Li, Xiaoyong Yuan, Bolei Zhou

Traffic signal control (TSC) is crucial for reducing traffic congestion leading to smoother traffic flow, reduced idle time, and mitigated CO2 emissions. In this paper, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. Unlike traditional feature-based approaches, vision-based methods depend much

Maria Kourou, Eleftherios K. Theodosiadis, Konstantinos Zarvalis

We study parabolic semigroups of finite shift in the unit disk with regard to the rate of convergence of their orbits to the Denjoy--Wolff point. We examine this rate in terms of Euclidean distance, hyperbolic distance and harmonic measure. In each case, we provide explicit examples to display the sharpness of the results. We further discuss the correspondin

N. A. Slavnov

We consider a model of a one-dimensional Bose gas with attraction. We study ground state equal-time correlation functions in this model using the algebraic Bethe ansatz. In cases of strong interaction or/and large-volume systems, we obtain very simple explicit formulas for correlations.

Mirko Primc, Goran Trupčević

In this note we prove linear independence of the combinatorial spanning set for standard $C_\ell^{(1)}$-module $L(k\Lambda_0)$ by establishing a connection with the combinatorial basis of Feigin-Stoyanovsky's type subspace $W(k\Lambda_0)$ of $C_{2\ell}^{(1)}$-module $L(k\Lambda_0)$. It should be noted that the proof of linear independence for the basis of $W

Junseok Park, Yoonsung Kim, Hee Bin Yoo, Min Whoo Lee

Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards. Drawing inspiration from this Toddler-Inspired Reward Transition, we set out to explore the implications of varying reward transitions when incorporated into Reinforcement Learning (RL) tasks. Central to our inquiry is th

Emanuele Bacchiocchi, Andrea Bastianin, Toru Kitagawa, Elisabetta Mirto

This paper studies the identification of Structural Vector Autoregressions (SVARs) exploiting a break in the variances of the structural shocks. Point-identification for this class of models relies on an eigen-decomposition involving the covariance matrices of reduced-form errors and requires that all the eigenvalues are distinct. This point-identification,

Pierre Vallet, Jérôme Cayssol

The Anderson-Higgs mode in a superconductor corresponds to a collective and coherent oscillation of the order parameter amplitude. We propose to detect this mode in a tunnel Josephson junction between two singlet s-wave diffusive superconductors. We find a strong enhancement of the tunneling current when the junction is pumped at the equilibrium gap frequenc

Yifu Tao, Yash Bhalgat, Lanke Frank Tarimo Fu, Matias Mattamala

We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photo-realistic textures. This system adapts the state-of-the-art neural radiance field (NeRF) representation to also incorporate lidar data which adds strong geometric constra

Alexandre Benatti, Luciano da F. Costa

Several interesting approaches have been reported in the literature on complex networks, random walks, and hierarchy of graphs. While many of these works perform random walks on stable, fixed networks, in the present work we address the situation in which the connections traversed by each step of a uniformly random walks are progressively removed, yielding a

Agney K. Rajeev, A. V. Anil Kumar

Effective interactions that violate Newton's third law of action-reaction symmetry are common in systems where interactions are mediated by a non-equilibrium environment. Extensive Monte Carlo simulations are carried out on a two-dimensional Ising model, where the interactions are modified non-reciprocally. We demonstrate that the critical temperature decrea

L. E. Hogeweg, R. Gangireddy, D. Brunink, V. J. Kalkman

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper, we focus on the species recognition task in images concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality

Minsu Kim, Sanghyeok Choi, Hyeonah Kim, Jiwoo Son

We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference and parallel stochastic search. Our method first leverages Generative Flow Networks (GFlowNets) to amortize a \emph{multi-modal} prior distribution over combinatorial solution space that encompasses both high-reward and di

Xufeng Cai, Jelena Diakonikolas

Incremental gradient and incremental proximal methods are a fundamental class of optimization algorithms used for solving finite sum problems, broadly studied in the literature. Yet, without strong convexity, their convergence guarantees have primarily been established for the ergodic (average) iterate. Motivated by applications in continual learning, we obt

Nishchal Prasad, Mohand Boughanem, Taoufiq Dkaki

Legal judgment prediction suffers from the problem of long case documents exceeding tens of thousands of words, in general, and having a non-uniform structure. Predicting judgments from such documents becomes a challenging task, more so on documents with no structural annotation. We explore the classification of these large legal documents and their lack of

Yuyang Deng, Junyuan Hong, Jiayu Zhou, Mehrdad Mahdavi

Recent advances in unsupervised learning have shown that unsupervised pre-training, followed by fine-tuning, can improve model generalization. However, a rigorous understanding of how the representation function learned on an unlabeled dataset affects the generalization of the fine-tuned model is lacking. Existing theoretical research does not adequately acc

Martin Menabue, Emanuele Frascaroli, Matteo Boschini, Enver Sangineto

Prompt-tuning methods for Continual Learning (CL) freeze a large pre-trained model and train a few parameter vectors termed prompts. Most of these methods organize these vectors in a pool of key-value pairs and use the input image as query to retrieve the prompts (values). However, as keys are learned while tasks progress, the prompting selection strategy is

Hao Chen, Zihan Wang, Ran Tao, Hongxin Wei

Foundation models are usually pre-trained on large-scale datasets and then adapted to downstream tasks through tuning. However, the large-scale pre-training datasets, often inaccessible or too expensive to handle, can contain label noise that may adversely affect the generalization of the model and pose unexpected risks. This paper stands out as the first wo

Pierre Yves Gaudreau Lamarre, Yier Lin

We consider the Stochastic Heat Equation (SHE) in $(1+1)$ dimensions with delta Dirac initial data and spacetime white noise. We prove exact large-time asymptotics for multi-point correlations of the SHE for strictly sublinear space coordinates. The sublinear condition is optimal, in the sense that different asymptotics are known to occur when the space coor

Antonina F. Zinhailo

We compute precise values of quasinormal modes of a massive scalar field in the background of the Schwarzschild-like brane-localised black holes. It is shown that the quasinormal spectrum of the massive field differs qualitatively from that previously known for other black hole models, due to the presence of two kinds of modes: those whose damping rate vanis

Sergey Kastryulin, Denis Prokopenko, Artem Babenko, Dmitry V. Dylov

This paper introduces a new data-driven, non-parametric method for image quality and aesthetics assessment, surpassing existing approaches and requiring no prompt engineering or fine-tuning. We eliminate the need for expressive textual embeddings by proposing efficient image anchors in the data. Through extensive evaluations of 7 state-of-the-art self-superv

Mohamed El Louadi

In this paper we delve into the historical evolution of data as a fundamental element in communication and knowledge transmission. The paper traces the stages of knowledge dissemination from oral traditions to the digital era, highlighting the significance of languages and cultural diversity in this progression. It also explores the impact of digital technol

Valery V. Ryzhikov

Examples of rigid Poisson suspensions without roots are presented. The discrete rational component in spectrum of an ergodic automorphism S prevents some roots from existing. If S is tensorly multiplied by an ergodic automorphism of the space with a sigma-finite measure, discrete spectrum disappears in this product, but the memory of it can remain in the for

Alfons Van Daele

Let $A$ be a non-degenerate algebra over the complex numbers and $\Delta$ a homomorphism from $A$ to the multiplier algebra $M(A\otimes A)$. Consider the linear maps $T_1$ and $T_2$ from $A\otimes A$ to $M(A\otimes A)$ defined by \begin{equation*} T_1(a\otimes b)=\Delta(a)(1\otimes b) \qquad\text{and}\qquad T_2(c\otimes a)=(c\otimes 1)\Delta(a). \end{equatio

Zhengyi Luo, Jinkun Cao, Rawal Khirodkar, Alexander Winkler

We present SimXR, a method for controlling a simulated avatar from information (headset pose and cameras) obtained from AR / VR headsets. Due to the challenging viewpoint of head-mounted cameras, the human body is often clipped out of view, making traditional image-based egocentric pose estimation challenging. On the other hand, headset poses provide valuabl

Luisa Toledo Tude, Paul R. Eastham

Polariton condensates occur away from thermal equilibrium, in an open system where heat and particles are continually exchanged with reservoirs. These phenomena have been extensively analyzed in terms of kinetic equations. Based on the collection of knowledge about polariton kinetics provided by these simulations and by experimental works, we constructed a f

Ibrahim Salihu Yusuf, Mukhtar Opeyemi Yusuf, Kobby Panford-Quainoo, Arnu Pretorius

Desert locust swarms present a major threat to agriculture and food security. Addressing this challenge, our study develops an operationally-ready model for predicting locust breeding grounds, which has the potential to enhance early warning systems and targeted control measures. We curated a dataset from the United Nations Food and Agriculture Organization'

Balakrishna S. Haridasu, Paolo Salucci, Gauri Sharma

Utilizing the well-established Radial Tully-Fisher (RTF) relation observed in a `large' (843) sample of local galaxies, we report the maximum allowed variance in the Hubble parameter, $H_0$. We estimate the total intrinsic scatter in the magnitude of the RTF relation(s) implementing a cosmological model-independent cosmographic expansion. We find that the ma

Sara Mazzonetto, Paolo Pigato

We consider a simple mean reverting diffusion process, with piecewise constant drift and diffusion coefficients, discontinuous at a fixed threshold. We discuss estimation of drift and diffusion parameters from discrete observations of the process, with a generalized moment estimator and a maximum likelihood estimator. We develop the asymptotic theory of the

Bambang Parmanto, Bayu Aryoyudanta, Wilbert Soekinto, I Made Agus Setiawan

Unlike professional caregivers, family caregivers often assume this role without formal preparation or training. Because of this, there is an urgent need to enhance the capacity of family caregivers to provide quality care. Large language models can potentially be used as a foundation technology for supporting caregivers as educational tools or as adjunct to

Amit Eliav, Sharon Gannot

We present a deep-learning approach for the task of Concurrent Speaker Detection (CSD) using a modified transformer model. Our model is designed to handle multi-microphone data but can also work in the single-microphone case. The method can classify audio segments into one of three classes: 1) no speech activity (noise only), 2) only a single speaker is acti

Áron Samuel Kovács, Pedro Hermosilla, Renata G. Raidou

Mesh texture synthesis is a key component in the automatic generation of 3D content. Existing learning-based methods have drawbacks -- either by disregarding the shape manifold during texture generation or by requiring a large number of different views to mitigate occlusion-related inconsistencies. In this paper, we present a novel surface-aware approach for

Joar Skalse, Alessandro Abate

Inverse reinforcement learning (IRL) aims to infer an agent's preferences (represented as a reward function $R$) from their behaviour (represented as a policy $\pi$). To do this, we need a behavioural model of how $\pi$ relates to $R$. In the current literature, the most common behavioural models are optimality, Boltzmann-rationality, and causal entropy maxi

Deepak Ojha, Christopher Penschke, Peter Saalfrank

In this work, the vibrational dynamics and spectroscopy of deuterated water molecules (D$_{2}$O) mimicking dense water layers at room temperature on the surfaces of two different C/N based materials with different N content and pore size, namely graphitic C$_{3}$N$_{4}$ (g-C$_{3}$N$_{4}$) and C$_{2}$N are studied using Ab Initio Molecular Dynamics (AIMD). In

Alireza Seif, Haoran Liao, Vinay Tripathi, Kevin Krsulich

Coherent errors, and especially those that occur in correlation among a set of qubits, are detrimental for large-scale quantum computing. Correlations in noise can occur as a result of spatial and temporal configurations of instructions executing on the quantum processor. In this paper, we perform a detailed experimental characterization of many of these err

Mohammad Shushtari, Julia Foellmer, Arash Arami

Human-robot physical interaction contains crucial information for optimizing user experience, enhancing robot performance, and objectively assessing user adaptation. This study introduces a new method to evaluate human-robot co-adaptation in lower limb exoskeletons by analyzing muscle activity and interaction torque as a two-dimensional random variable. We i

Alain Bretto, Alain Faisant, Francois Hennecart

This paper is concerned with two conjectures which are intimately related. The first is a generalization to hypergraphs of Vizing's Theorem on the chromatic index of a graph and the second is the well-known conjecture of Erd\H{o}s, Faber and Lov\'asz which deals with the problem of coloring a family of cliques intersecting in at most one vertex. We are led t

Bruno P. Zimmermann

We consider the problem of when a closed hyperbolic surface admits a totally geodesic embedding into a closed hyperbolic 3-manifold, and in particular equivariant versions of such embeddings. In a previous paper we considered orientation-preserving actions on orientable surfaces; in the present paper, we consider large orientation-reversing actions on orient

Vijay Khopkar, Balaram Sahoo

We demonstrate that the solution combustion reaction (SCR) route is suitable for the synthesis of phase pure (x)BaFe0.5Nb0.5O3-(1-x)KNbO3 (x = 0, 0.2, 0.4, 0.6, 0.8, 1) (BFN-KN) solid solutions due to atomic level of mixing of precursors than that of the solid-state reaction (SSR) route. A variation in composition 'x' of our double perovskite samples leads t

Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu

This paper is an extended abstract of our original work published in KDD23, where we won the best research paper award (Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan. All in one: Multi-task prompting for graph neural networks. KDD 23) The paper introduces a novel approach to bridging the gap between pre-trained graph models and the diverse tasks

Wouter Jansen, Jan Steckel

Echolocation is the prime sensing modality for many species of bats, who show the intricate ability to perform a plethora of tasks in complex and unstructured environments. Understanding this exceptional feat of sensorimotor interaction is a key aspect into building more robust and performant man-made sonar sensors. In order to better understand the underlyi

Chao Zhang, Mohan Li, Ignas Budvytis, Stephan Liwicki

Multimodal learning has advanced the performance for many vision-language tasks. However, most existing works in embodied dialog research focus on navigation and leave the localization task understudied. The few existing dialog-based localization approaches assume the availability of entire dialog prior to localizaiton, which is impractical for deployed dial

Guosheng Zhao, Xiaofeng Wang, Zheng Zhu, Xinze Chen

World models have demonstrated superiority in autonomous driving, particularly in the generation of multi-view driving videos. However, significant challenges still exist in generating customized driving videos. In this paper, we propose DriveDreamer-2, which builds upon the framework of DriveDreamer and incorporates a Large Language Model (LLM) to generate

Sihe Chen, Cheng Li

The computational fluid dynamics on a sphere is relevant to global simulations of geophysical fluid dynamics. Using the conventional spherical-polar (or lat-lon) grid results in a singularity at the poles, with orders of magnitude smaller cell sizes at the poles in comparison to the equator. To address this problem, we developed a general circulation model (

Giorgio Leonardi, Clara Maldarizzi, Stefania Montani, Manuel Striani

Nowadays, there is evidence that several factors may increase the risk, for an infant, to require stabilisation or resuscitation manoeuvres at birth. However, this risk factors are not completely known, and a universally applicable model for predicting high-risk situations is not available yet. Considering both these limitations and the fact that the need fo

Viktoriya Nikitina, Alberto De Marchi, Matthias Gerdts

Models involving hybrid systems are versatile in their application but difficult to optimize efficiently due to their combinatorial nature. This work presents a method to cope with hybrid optimal control problems which, in contrast to decomposition techniques, does not require relaxing the integrality constraints. Based on the discretize-then-optimize approa

Boyang Yu, Frederic Cordier, Hyewon Seo

The capability to generate simulation-ready garment models from 3D shapes of clothed humans will significantly enhance the interpretability of captured geometry of real garments, as well as their faithful reproduction in the virtual world. This will have notable impact on fields like shape capture in social VR, and virtual try-on in the fashion industry. To

Yanming Liu, Xinyue Peng, Xuhong Zhang, Weihao Liu

Large language models (LLMs) demonstrate exceptional performance in numerous tasks but still heavily rely on knowledge stored in their parameters. Moreover, updating this knowledge incurs high training costs. Retrieval-augmented generation (RAG) methods address this issue by integrating external knowledge. The model can answer questions it couldn't previousl

Elio Faddoul, Ghassan M. Kraidy, Constantinos Psomas, Ioannis Krikidis

The concept of fluid antennas (FAs) has emerged as a promising solution to enhance the spectral efficiency of wireless networks, achieved by introducing additional degrees of freedom, including reconfigurability and flexibility. In this paper, we investigate the use of index-modulated (IM) transmissions within the framework of FA systems, where an FA positio

Lyuye Zhang, Kaixuan Li, Kairan Sun, Daoyuan Wu

Smart contracts are susceptible to various security issues, among which access control (AC) vulnerabilities are particularly critical. While existing research has proposed multiple detection tools, the automatic and appropriate repair of AC vulnerabilities in smart contracts remains a challenge. Unlike commonly supported vulnerability types by existing repai

Christian Wachinger, Dennis Hedderich, Melissa Thalhammer, Fabian Bongratz

Understanding individual differences in cortical structure is key to advancing diagnostics in neurology and psychiatry. Reference models aid in detecting aberrant cortical thickness, yet site-specific biases limit their direct application to unseen data, and region-wise averages prevent the detection of localized cortical changes. To address these limitation

Sikai Bai, Jie Zhang, Shuaicheng Li, Song Guo

Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target domain) is absent from the decentralized training data (source domains). However, most existing FL methods assume that domain labels are provided during training, and their evaluation

F. Jara-Ferreira, P. B. Tissera, E. Sillero, Y. Rosas-Guevara

The variations in metallicity and spatial patterns within star-forming regions of galaxies result from diverse physical processes unfolding throughout their evolutionary history, with a particular emphasis in recent events. Analysing MaNGA and \textsc{eagle} galaxies, we discovered an additional dependence of the mass-metallicity relation (MZR) on metallicit

Wenting Chen, Pengyu Wang, Hui Ren, Lichao Sun

Data scarcity and privacy concerns limit the availability of high-quality medical images for public use, which can be mitigated through medical image synthesis. However, current medical image synthesis methods often struggle to accurately capture the complexity of detailed anatomical structures and pathological conditions. To address these challenges, we pro

Matteo Stockinger, Masaru Shibata

Tidal disruptions of stars on the equatorial plane orbiting Kerr black holes have been widely studied. However thus far, there have been fewer studies of stars in inclined precessing orbits around a Kerr black hole. In this paper, we use the tensor virial equations to show the presence of possible resonances in these systems for typical physical parameters o

Egor Zverev, Sahar Abdelnabi, Soroush Tabesh, Mario Fritz

Instruction-tuned Large Language Models (LLMs) show impressive results in numerous practical applications, but they lack essential safety features that are common in other areas of computer science, particularly an explicit separation of instructions and data. This makes them vulnerable to manipulations such as indirect prompt injections and generally unsuit

Zhuo Chen, Yin Fang, Yichi Zhang, Lingbing Guo

The rise of Multi-modal Pre-training highlights the necessity for a unified Multi-Modal Knowledge Graph (MMKG) representation learning framework. Such a framework is essential for embedding structured knowledge into multi-modal Large Language Models effectively, alleviating issues like knowledge misconceptions and multi-modal hallucinations. In this work, we

Matteo Cappelli, Giorgio del Castello, Marco Vignati

In many particle physics experiments the data processing is based on the analysis of the digitized waveforms provided by the detector. While the waveform amplitude is usually correlated to the event energy, the shape may carry useful information for event discrimination. Thanks to the high signal to noise ratio they provide, matched filters are often applied

Shuaikang Shang, Xuejing Kang, Anlong Ming

High Dynamic Range (HDR) imaging aims to generate an artifact-free HDR image with realistic details by fusing multi-exposure Low Dynamic Range (LDR) images. Caused by large motion and severe under-/over-exposure among input LDR images, HDR imaging suffers from ghosting artifacts and fusion distortions. To address these critical issues, we propose an HDR Tran

Rita Fioresi, Emanuele Latini, Chiara Pagani

In this paper we develop the theory of reduction of quantum principal bundles over projective bases. We show how the sheaf theoretic approach can be effectively applied to certain relevant examples as the Klein model for the projective spaces; in particular we study in the algebraic setting the reduction of the principal bundle $\mathrm{GL}(n) \to \mathrm{GL

Colin Troisemaine, Vincent Lemaire

This paper proposes a method for the automatic creation of variables (in the case of regression) that complement the information contained in the initial input vector. The method works as a pre-processing step in which the continuous values of the variable to be regressed are discretized into a set of intervals which are then used to define value thresholds.

Max Rang, Paul J. Kelly

The orbital Hall effect generates a current of orbital angular momentum perpendicular to a charge current. Experiments suggest that this orbital current decays on a long length scale that is of the order of the spin flip diffusion length or longer. We examine this suggestion using first-principles quantum mechanical scattering calculations to study the decay

Zhenwen Dai, Federico Tomasi, Sina Ghiassian

In-context learning is a promising approach for online policy learning of offline reinforcement learning (RL) methods, which can be achieved at inference time without gradient optimization. However, this method is hindered by significant computational costs resulting from the gathering of large training trajectory sets and the need to train large Transformer

Huan-Qiang Zhou, Qian-Qian Shi, Ian P. McCulloch, Murray T. Batchelor

Spontaneous symmetry breaking (SSB) with type-B Goldstone modes is investigated in the macroscopically degenerate phase for a quantum spin-1 many-body system with competing dimer and trimer interactions. The SSB involves three distinct patterns. The first occurs at the dimer point, with the pattern from staggered ${\rm SU}(3)$ to ${\rm U}(1)\times{\rm U}(1)$

Yves-Marie Ducimetière, Edouard Boujo, François Gallaire

We consider fluid flows, governed by the Navier-Stokes equations, subject to a steady symmetry-breaking bifurcation and forced by a weak noise acting on a slow time scale. By generalizing the multiple-scale weakly nonlinear expansion technique employed in the literature for the response of the Duffing oscillator, we rigorously derive a stochastically forced

Abdallah El Ali, Karthikeya Puttur Venkatraj, Sophie Morosoli, Laurens Naudts

Advances in Generative Artificial Intelligence (AI) are resulting in AI-generated media output that is (nearly) indistinguishable from human-created content. This can drastically impact users and the media sector, especially given global risks of misinformation. While the currently discussed European AI Act aims at addressing these risks through Article 52's

Mohamed Zbiri, Anne A. Y. Guilbert

The dynamics of the conjugated polymers poly(9,9-dioctylfluorene) (PF8) and poly(9,9-didodecylfluorene) (PF12), differing by the length of their side chains, is investigated in the amorphous phase using the quasielastic neutron scattering (QENS) technique. The measurements are synergistically underpinned by molecular dynamics (MD) simulations. The probe is f

Maria Gualdani, Mikel Ispizua, Nicola Zamponi

We consider the singular limit of a chemotaxis model of bacterial collective motion recently introduced in arXiv:2009.11048 [math.AP]. The equation models aggregation-diffusion phenomena with advection that is discontinuous and depends sharply on the gradient of the density itself. The quasi-linearity of the problem poses major challenges in the construction

Rocco D'Agostino, Orlando Luongo, Stefano Mancini

In this paper, we compute departures in the black hole thermodynamics induced by either geometric or topological corrections to general relativity. Specifically, we analyze the spherically symmetric spacetime solutions of two modified gravity scenarios with Lagrangians $\mathcal{L}\sim R^{1+\epsilon}$ and $\mathcal{L}\sim R+\epsilon\, \mathcal{G}^2$, where $

Moritz Garkisch, Sebastian Lotter, Gui Zhou, Vahid Jamali

Future communication systems are envisioned to employ intelligent reflecting surfaces (IRSs) and the millimeter wave (mmWave) frequency band to provide reliable high-rate services. For mobile users, the time-varying channel state information (CSI) requires adequate adjustment of the reflection pattern of the IRS. We propose a novel codebook-based user tracki

Martin Grohe, Eran Rosenbluth

Graph neural networks (GNN) are deep learning architectures for graphs. Essentially, a GNN is a distributed message passing algorithm, which is controlled by parameters learned from data. It operates on the vertices of a graph: in each iteration, vertices receive a message on each incoming edge, aggregate these messages, and then update their state based on

Gabriel P. Langlois, Jatan Buch, Jérôme Darbon

Maximum entropy (Maxent) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, Maxent models need efficient optimization algorithms to scale well for big data applications. State-of-the-art algorithms for Maxent models, however, were not originally

Manish Kumar, Michael D. Graham

Elastoinertial turbulence (EIT) is a chaotic flow resulting from the interplay between inertia and viscoelasticity in wall bounded shear flows. Understanding EIT is important because it is thought to set a limit on the effectiveness of turbulent drag reduction in polymer solutions. Here, we analyze simulations of two dimensional EIT in channel flow using Spe

Hao-Lun Hsu, Qitong Gao, Miroslav Pajic

Deep Brain Stimulation (DBS) stands as an effective intervention for alleviating the motor symptoms of Parkinson's disease (PD). Traditional commercial DBS devices are only able to deliver fixed-frequency periodic pulses to the basal ganglia (BG) regions of the brain, i.e., continuous DBS (cDBS). However, they in general suffer from energy inefficiency and s

Mohammad Alkhalefi, Georgios Leontidis, Mingjun Zhong

Contrastive instance discrimination methods outperform supervised learning in downstream tasks such as image classification and object detection. However, these methods rely heavily on data augmentation during representation learning, which can lead to suboptimal results if not implemented carefully. A common augmentation technique in contrastive learning is

Yahav Bechavod

We revisit the problem of online learning with individual fairness, where an online learner strives to maximize predictive accuracy while ensuring that similar individuals are treated similarly. We first extend the frameworks of Gillen et al. (2018); Bechavod et al. (2020), which rely on feedback from human auditors regarding fairness violations, as we consi

Igor Tominec, Josefin Ahlkrona

The Shallow Ice Approximation (SIA) model on strong form is commonly used for inferring the flow dynamics of grounded ice sheets. The solution to the SIA model is a closed-form expression for the velocity field. When that velocity field is used to advance the ice surface in time, the time steps have to take small values due to quadratic scaling in terms of t

Yufei Xie

Human action recognition in videos is a critical task with significant implications for numerous applications, including surveillance, sports analytics, and healthcare. The challenge lies in creating models that are both precise in their recognition capabilities and efficient enough for practical use. This study conducts an in-depth analysis of various deep

B. Bayón-Buján, M. Merino

An algorithm to obtain data-driven models of oscillatory phenomena in plasma space propulsion systems is presented, based on sparse regression (SINDy) and Pareto front analysis. The algorithm can incorporate physical constraints, use data bootstrapping for additional robustness, and fine-tuning to different metrics. Standard, weak and integral SINDy formulat

Yangyu Fan, Wenbin Luo, Binggang Qu

Let $k$ be an algebraically closed field of characteristic zero. Let $G$ be a connected reductive group over $k$, $P \subseteq G$ be a parabolic subgroup and $\lambda: P \longrightarrow G$ be a strictly anti-dominant character. Let $C$ be a projective smooth curve over $k$ with function field $K=k(C)$ and $F$ be a principal $G$-bundle on $C$. Then $F/P \long

Jonathan Heek, Emiel Hoogeboom, Tim Salimans

Diffusion models are relatively easy to train but require many steps to generate samples. Consistency models are far more difficult to train, but generate samples in a single step. In this paper we propose Multistep Consistency Models: A unification between Consistency Models (Song et al., 2023) and TRACT (Berthelot et al., 2023) that can interpolate between

Navdeep Kumar, Yashaswini Murthy, Itai Shufaro, Kfir Y. Levy

We present the first finite time global convergence analysis of policy gradient in the context of infinite horizon average reward Markov decision processes (MDPs). Specifically, we focus on ergodic tabular MDPs with finite state and action spaces. Our analysis shows that the policy gradient iterates converge to the optimal policy at a sublinear rate of $O\le

Shakiba Shahbandegan, Emily Dolson

Lexicase and epsilon-lexicase selection are state of the art parent selection techniques for problems featuring multiple selection criteria. Originally, lexicase selection was developed for cases where these selection criteria are unlikely to be in conflict with each other, but preliminary work suggests it is also a highly effective many-objective optimizati

Souhaib Attaiki, Maks Ovsjanikov

We present Shape Non-rigid Kinematics (SNK), a novel zero-shot method for non-rigid shape matching that eliminates the need for extensive training or ground truth data. SNK operates on a single pair of shapes, and employs a reconstruction-based strategy using an encoder-decoder architecture, which deforms the source shape to closely match the target shape. D

Chuangchuang Tan, Ping Liu, RenShuai Tao, Huan Liu

Recently, the proliferation of increasingly realistic synthetic images generated by various generative adversarial networks has increased the risk of misuse. Consequently, there is a pressing need to develop a generalizable detector for accurately recognizing fake images. The conventional methods rely on generating diverse training sources or large pretraine

Olga Kondrateva, Stefan Dietzel, Björn Scheuermann

Small satellites are widely used today as cost effective means to perform Earth observation and other tasks that generate large amounts of high-dimensional data, such as multi-spectral imagery. These satellites typically operate in low earth orbit, which poses significant challenges for data transmission due to short contact times with ground stations, low b

Ibrahim Ethem Hamamci, Sezgin Er, Bjoern Menze

Medical imaging plays a crucial role in diagnosis, with radiology reports serving as vital documentation. Automating report generation has emerged as a critical need to alleviate the workload of radiologists. While machine learning has facilitated report generation for 2D medical imaging, extending this to 3D has been unexplored due to computational complexi

Shu Yang, Yihui Wang, Hao Chen

Multiple Instance Learning (MIL) has emerged as a dominant paradigm to extract discriminative feature representations within Whole Slide Images (WSIs) in computational pathology. Despite driving notable progress, existing MIL approaches suffer from limitations in facilitating comprehensive and efficient interactions among instances, as well as challenges rel

Souvik Sadhukhan, Subhodeep Dey, Smarajit Karmakar, Saroj Kumar Nandi

Dynamics is central to living systems. In the last two decades, experiments have revealed that the dynamics in diverse biological systems - from intracellular cytoplasm to cellular and organismal aggregates - are remarkably similar to that in dense systems of inanimate particles in equilibrium. They show a glass transition from a solid-like jammed state to a

Shuai Li, Xiaoguang Ma, Shancheng Jiang, Lu Meng

Remarkable successes were made in Medical Image Classification (MIC) recently, mainly due to wide applications of convolutional neural networks (CNNs). However, adversarial examples (AEs) exhibited imperceptible similarity with raw data, raising serious concerns on network robustness. Although adversarial training (AT), in responding to malevolent AEs, was r

Adarsh N L, Arun P, Alok Porwal, Malcolm Aranha

Data generated by edge devices has the potential to train intelligent autonomous systems across various domains. Despite the emergence of diverse machine learning approaches addressing privacy concerns and utilizing distributed data, security issues persist due to the sensitive storage of data shards in disparate locations. This paper introduces a potentiall

Michiel van der Meer, Enrico Liscio, Catholijn M. Jonker, Aske Plaat

Large-scale survey tools enable the collection of citizen feedback in opinion corpora. Extracting the key arguments from a large and noisy set of opinions helps in understanding the opinions quickly and accurately. Fully automated methods can extract arguments but (1) require large labeled datasets that induce large annotation costs and (2) work well for kno

Eladio Montero-Porras, Rémi Suchon, Tom Lenaerts, Elias Fernández Domingos

Extracting from shared resources requires making choices to balance personal profit and sustainability. We present the results of a behavioural experiment wherein we manipulate the default extraction from a finite resource. Participants were exposed to two treatments -- pro-social or self-serving extraction defaults -- and a control without defaults. We exam

Julian Talbot, Charles Antoine, Philippe Claudin, Ellák Somfai

The behavior of non-spherical particles in a shear-flow is of significant practical and theoretical interest. These systems have been the object of numerous investigations since the pioneering work of Jeffery a century ago. His eponymous orbits describe the deterministic motion of an isolated, rod-like particle in a shear flow. Subsequently, the effect of ad

Xuan Zhang, Duoyu Feng, Djibrina Barry, Jiajia Li

The reconstruction of brain neural network connections occurs not only during the infancy and early childhood stages of brain development, but also in patients with cognitive impairment in middle and old age under the therapy with stimulated external interference, such as the non-invasive repetitive transcranial magnetic stimulation (rTMS) and the transcrani

Xiaogang Xu, Shu Kong, Tao Hu, Zhe Liu

Pre-trained models with large-scale training data, such as CLIP and Stable Diffusion, have demonstrated remarkable performance in various high-level computer vision tasks such as image understanding and generation from language descriptions. Yet, their potential for low-level tasks such as image restoration remains relatively unexplored. In this paper, we ex

Amanda Phaladi, Thipe Modipa

Speech technology is a field that encompasses various techniques and tools used to enable machines to interact with speech, such as automatic speech recognition (ASR), spoken dialog systems, and others, allowing a device to capture spoken words through a microphone from a human speaker. End-to-end approaches such as Connectionist Temporal Classification (CTC

Jin-Can Liu, Li-Ya Tang, Xiao-Ying Sun, Qi-Rui Qu

Purpose The aim of this study was to explore whether electroacupuncture regulates phenotypic transformation of smooth muscle cells by inhibiting ferroptosis and inhibiting fibrosis, thereby improving bladder urination function after suprasacral spinal cord injury (SSCI). Methods The experiment was divided into sham, model, and electroacupuncture group. After

Jie-Ming Wang

In this paper, we derive explicit sharp two-sided estimates of the Dirichlet heat kernels for a class of symmetric subordinate diffusion processes with diffusive components in $C^{1, \alpha}(\alpha\in (0, 1])$ open sets in $\mathbb R^d$ when the scaling order of the Laplace exponent of purely discontinuous part of the subordinator is between $0$ and $1$ incl

Aditya Dani, Shardul Mangade, Piyush Nimbalkar, Harshad Shirwadkar

The growing value of data as a strategic asset has given rise to the necessity of implementing reliable backup and recovery solutions in the most efficient and cost-effective manner. The data backup methods available today on linux are not effective enough, because while running, most of them block I/Os to guarantee data integrity. We propose and implement N

Carlos Lassance, Hervé Déjean, Thibault Formal, Stéphane Clinchant

A companion to the release of the latest version of the SPLADE library. We describe changes to the training structure and present our latest series of models -- SPLADE-v3. We compare this new version to BM25, SPLADE++, as well as re-rankers, and showcase its effectiveness via a meta-analysis over more than 40 query sets. SPLADE-v3 further pushes the limit of