May 2024 arXiv papers — page 40

Maria Andrade, Allan Freitas, Diego A. Marín

In this work, we are interested in studying Serrin's overdetermined problems in Riemannian manifolds. For manifolds endowed with a conformal vector field, we prove a Pohozoaev-type identity to show a Serrin's type rigidity result using the P-function approach introduced by Weinberger. We proceed with a conformal change to achieve this goal, starting from a g

Chendi Qian, Andrei Manolache, Christopher Morris, Mathias Niepert

Message-passing graph neural networks (MPNNs) have emerged as a powerful paradigm for graph-based machine learning. Despite their effectiveness, MPNNs face challenges such as under-reaching and over-squashing, where limited receptive fields and structural bottlenecks hinder information flow in the graph. While graph transformers hold promise in addressing th

Alexander Teretenkov, Oleg Lychkovskiy

We address dissipative dynamics of the one-dimensional nearest-neighbour $XX$ spin-$1/2$ chain governed by the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) equation. In the absence of dissipation the model is integrable. We identify a broad class of dissipative terms that generically destroy integrability but leave the operator space of the model fragmented

Sabarish Krishna Moorthy, Jithin Jagannath

The exponential increase in Internet of Things (IoT) devices coupled with 6G pushing towards higher data rates and connected devices has sparked a surge in data. Consequently, harnessing the full potential of data-driven machine learning has become one of the important thrusts. In addition to the advancement in wireless technology, it is important to efficie

Harry Lane, Martin Mourigal

In two dimensional magnets, the interplay of thermal fluctuations and spin anisotropy control the existence of long-range magnetic order. In the van der Waals antiferromagnets FePX$_3$, orbital degeneracy in the $t2g$ levels of the Fe$^{2+}$ ions in octahedral coordination yields strong uniaxial anisotropy, which stabilizes magnetic order up to T$\approx$100

Miti Mazmudar, Shannon Veitch, Rasoul Akhavan Mahdavi

The InterPlanetary File System (IPFS) is a peer-to-peer network for storing data in a distributed file system, hosting over 190,000 peers spanning 152 countries. Despite its prominence, the privacy properties that IPFS offers to peers are severely limited. Any query within the network leaks the queried content to other peers. We address IPFS' privacy leakage

Qiang Wang, Minghua Liu, Junjun Hu, Fan Jiang

Generating realistic animated videos from static images is an important area of research in computer vision. Methods based on physical simulation and motion prediction have achieved notable advances, but they are often limited to specific object textures and motion trajectories, failing to exhibit highly complex environments and physical dynamics. In this pa

Wojciech J. Jankowski, Arthur S. Morris, Zory Davoyan, Adrien Bouhon

We discuss a class of three-band non-Abelian topological insulators in three dimensions that carry a single bulk Hopf index protected by spatiotemporal ($\mathcal{PT}$) inversion symmetry. These phases may also host subdimensional topological invariants given by the Euler characteristic class, resulting in real Hopf-Euler insulators. Such systems naturally r

Alessandro Abate, Mirco Giacobbe, Diptarko Roy

We present for the first time a supermartingale certificate for $\omega$-regular specifications. We leverage the Robbins & Siegmund convergence theorem to characterize supermartingale certificates for the almost-sure acceptance of Streett conditions on general stochastic processes, which we call Streett supermartingales. This enables effective verification a

Weilin Teng, Yingna Su, Rui Liu, Jialin Chen

Minifilaments are widespread small-scale structures in the solar atmosphere. To better understand their formation and eruption mechanisms, we investigate the entire life of a sigmoidal minifilament located below a large quiescent filament observed by BBSO/GST on 2015 August 3. The H{\alpha} structure initially appears as a group of arched threads, then trans

Maria Rosselló-Sastre, Sascha Husa, Sayantani Bera

In this paper we describe a model for the $(\ell=2, m=0)$ spherical harmonic mode of the gravitational wave signal emitted by the coalescence of binary black holes, in particular, spin-aligned systems. This mode can be viewed as consisting of two components, gravitational wave memory and quasi-normal ringdown, which are both included in our model. Depending

Gerhard Kalusche, Dale Lawlor, Jon-Ivar Skullerud

We study the impact of Gribov copies on the quark propagator in lattice 2-colour QCD. We find that the Gribov noise is comparable to the gauge noise for smaller volumes but becomes less significant for larger spatial volumes. The Gribov noise in the quark propagator is found to be comparable to, but smaller than in the gluon propagator on the same ensembles.

R. A. Caetano, R. M. Angelo

With basis on (i) the physical principle of local causality and (ii) a certain notion of elements of reality, Einstein, Podolsky, and Rosen (EPR) put forward an argument showing that physical instances may exist in which two non-commuting observables can be joint elements of the physical reality. Here, we introduce a new criterion of joint reality. We demons

Nikita Tsoy, Nikola Konstantinov

Simplicity bias, the propensity of deep models to over-rely on simple features, has been identified as a potential reason for limited out-of-distribution generalization of neural networks (Shah et al., 2020). Despite the important implications, this phenomenon has been theoretically confirmed and characterized only under strong dataset assumptions, such as l

Pablo García-Arias

We study the asymptotics of the expected Wasserstein distance between the empirical measure of a Point Process and the background volume form. The main DPP studied is the harmonic ensemble, where we get the optimal rate of convergence for homogeneous manifolds of dimension $d\geq 3$, and for two-point homogeneous manifolds. We also discuss some variations of

Lifan Xu, Shunqiao Sun, A. Lee Swindlehurst

Automotive radar emerges as a crucial sensor for autonomous vehicle perception. As more cars are equipped radars, radar interference is an unavoidable challenge. Unlike conventional approaches such as interference mitigation and interference-avoiding technologies, this paper introduces an innovative collaborative sensing scheme with multiple automotive radar

Airat Kamaletdinov, Natalia G. Berloff

We introduce DOMINO, a light-matter computing platform that exploits the full complex amplitude of coupled condensate networks to solve maximum-weight clique problems and reveal hidden indirect correlations in large graphs. By embedding network structure directly into a gain-controlled polaritonic (or photonic) oscillator array, DOMINO performs analog optimi

Guihua Zhao, Yating Peng, Jiaxin Zhu, Xin Tang

This letter proposes an in-sensor computing multiply-and-accumulate (MAC) circuit based on capacitance. The MAC circuits can constitute an artificial neural network(ANN) layer and be operated as ANN classifiers and autoencoders. The proposed circuit is a promising scheme for capacitive ANN image sensors, showing competitively high efficiency and lower power.

Munazza K. Alam, James Kirk, Leonardo A. Dos Santos, Patrick McCreery

We search for excess in-transit absorption of neutral helium at 1.083 $\mu$m in the atmospheres of the young (<800 Myr) sub-Jovian (0.2-0.5 $\rm R_{J}$) planets HD 63433b, K2-100b, and V1298 Tau c using high-resolution (R~25,000) transit observations taken with Keck II/NIRSPEC. Our observations do not show evidence of helium absorption for any of the planets

Junwei Deng, Ting-Wei Li, Shichang Zhang, Jiaqi Ma

Training data attribution (TDA) methods aim to quantify the influence of individual training data points on the model predictions, with broad applications in data-centric AI, such as mislabel detection, data selection, and copyright compensation. However, existing methods in this field, which can be categorized as retraining-based and gradient-based, have st

Ying Chen, Cezar Joiţa, Mihai Tibăr

For studying the local topology of maps, one uses deformations which split the singularities into simpler ones while preserving the general fibres. We give conditions under which such conservation holds.

Franco Coltraro, Marc Ruiz, Luis Velasco

The advent of digital twins (DT) for the control and management of communication networks requires accurate and fast methods to estimate key performance indicators (KPI) needed for autonomous decision-making. Among several alternatives, queuing theory can be applied to model a real network as a queue system that propagates entities representing network traff

Subhankar Maity, Aniket Deroy, Sudeshna Sarkar

Grammatical error correction (GEC) tools, powered by advanced generative artificial intelligence (AI), competently correct linguistic inaccuracies in user input. However, they often fall short in providing essential natural language explanations, which are crucial for learning languages and gaining a deeper understanding of the grammatical rules. There is li

Yaqian Zhang, Xudong Zhang, Jianzhong Zhang, Fujin Deng

The bridge arm of the hybrid modular multilevel converter (MMC) is composed of half-bridge and full-bridge sub-modules cascaded together. Compared with the half-bridge MMC, it can operate in the boost-AC mode, where the modulation index can be higher than 1, and the DC voltage and the AC voltage level are no longer mutually constrained; compared with the ful

Aristide Houndetoungan

This paper develops a peer effect model for count responses under rational expectations. The model accounts for heterogeneity in peer effects across groups based on observed characteristics. Identification is based on the linear model condition that requires the presence of friends of friends who are not direct friends. I show that this identification condit

Katharina Hopf, Michael Kniely, Alexander Mielke

Electro-energy-reaction-diffusion systems are thermodynamically consistent continuum models for reaction-diffusion processes that account for temperature and electrostatic effects in a way that total charge and energy are conserved. The question of the long-time asymptotic behavior of electro-energy-reaction-diffusion systems motivates the characterization o

Feng Wang

A high-order flux reconstruction solver has been developed and validated to perform implicit large-eddy simulations of industrially representative turbomachinery flows. The T106c low-pressure turbine and VKI LS89 high-pressure turbine cases are studied. The solver uses the Rusanov Riemann solver to compute the inviscid fluxes on the wall boundaries, and HLLC

Kyanna Dagenais, Istvan David

Human guidance is often desired in reinforcement learning to improve the performance of the learning agent. However, human insights are often mere opinions and educated guesses rather than well-formulated arguments. While opinions are subject to uncertainty, e.g., due to partial informedness or ignorance about a problem, they also emerge earlier than hard ev

Fabian Gundlach, Béranger Seguin

We answer various questions concerning the distribution of extensions of a given central simple algebra $K$ over a number field. Specifically, we give asymptotics for the count of inner Galois extensions $L/K$ of fixed degree and center with bounded discriminant. We also relate the distribution of outer extensions of $K$ to the distribution of field extensio

Jian Zhang, Jacques Giacomoni, Vicentiu Radulescu, Minbo Yang

In this paper, we consider the following nonlocal parabolic equation \begin{equation*} u_{t}-\Delta u=\left( \int_{\Omega}\frac{|u(y,t)|^{2^{\ast}_{\mu}}}{|x-y|^{\mu}}dy\right) |u|^{2^{\ast}_{\mu}-2}u,\ \text{in}\ \Omega\times(0,\infty), \end{equation*} where $\Omega$ is a bounded domain in $\mathbb{R}^{N}$, $0<\mu<N$ and $2^{\ast}_{\mu}=(2N-\mu)/(N-2)$ deno

Gregory Camilli

Natural language processing (NLP) is rapidly developing for applications in educational assessment. In this paper, I describe an NLP-based procedure that can be used to support subject matter experts in establishing a crosswalk between item specifications and content standards. This paper extends recent work by proposing and demonstrating the use of multivar

Anand Gopalakrishnan, Aleksandar Stanić, Jürgen Schmidhuber, Michael Curtis Mozer

Current state-of-the-art synchrony-based models encode object bindings with complex-valued activations and compute with real-valued weights in feedforward architectures. We argue for the computational advantages of a recurrent architecture with complex-valued weights. We propose a fully convolutional autoencoder, SynCx, that performs iterative constraint sat

Li Sun, Jingbin Hu, Mengjie Li, Hao Peng

Information diffusion prediction is fundamental to understand the structure and organization of the online social networks, and plays a crucial role to blocking rumor spread, influence maximization, political propaganda, etc. So far, most existing solutions primarily predict the next user who will be informed with historical cascades, but ignore an important

George Miltos Maragkakis, Sotiris Psilodimitrakopoulos, Leonidas Mouchliadis, Abdus Salam Sarkar

The in-plane anisotropic properties of two-dimensional (2D) group IV monochalcogenides provide an additional degree of freedom which can be used in future optoelectronic devices. Here, it is shown that the third harmonic generation (THG) signal produced by ultrathin tin (II) sulfide (SnS) is in-plane anisotropic with respect to the incident linear polarizati

Silvia García-Méndez, Milagros Fernández-Gavilanes, Enrique Costa-Montenegro, Jonathan Juncal-Martínez

In this article we present a novel system for natural language generation (NLG) of Spanish sentences from a minimum set of meaningful words (such as nouns, verbs and adjectives) which, unlike other state-of-the-art solutions, performs the NLG task in a fully automatic way, exploiting both knowledge-based and statistical approaches. Relying on its linguistic

Yifan Xu, Qianwei Wang, Vineet Kamat, Carol Menassa

As the number of Persons with Disabilities (PWD), particularly those with one or more physical impairments, increases, there is an increasing demand for assistive robotic technologies that can support independent mobility in the built environment and reduce the burden on caregivers. Current assistive mobility platforms (e.g., robotic wheelchairs) often fail

Shaoan Wang, Zhanhua Xin, Yaoqing Hu, Dongyue Li

Event camera, a bio-inspired asynchronous triggered camera, offers promising prospects for fusion with frame-based cameras owing to its low latency and high dynamic range. However, calibrating stereo vision systems that incorporate both event and frame-based cameras remains a significant challenge. In this letter, we present EF-Calib, a spatiotemporal calibr

Nicholas Krämer, Pablo Moreno-Muñoz, Hrittik Roy, Søren Hauberg

Tuning scientific and probabilistic machine learning models $-$ for example, partial differential equations, Gaussian processes, or Bayesian neural networks $-$ often relies on evaluating functions of matrices whose size grows with the data set or the number of parameters. While the state-of-the-art for evaluating these quantities is almost always based on L

Thierry Tran, Anthony Ferté, Morgane Vacher

Attochemistry aims to exploit the properties of coherent electronic wavepackets excited via attosecond pulses, to control the formation of photoproducts. Such molecular processes can in principle be simulated with various nonadiabatic dynamics methods, yet the impact of the approximations underlying the methods is rarely assessed. The performances of widely

Valeriano Aiello

We extend a central limit theorem, recently established for the Thompson group $F=F_2$ by Krishnan, to the Brown-Thompson groups $F_p$, where $p$ is any integer greater than or equal to $2$. The non-commutative probability space considered is the group algebra $\mathbb{C}[F_p]$, equipped with the canonical trace. The random variables in question are $a_n:= (

Hang Zhou, Yuezhou Ma, Haixu Wu, Haowen Wang

Deep models have recently emerged as promising tools to solve partial differential equations (PDEs), known as neural PDE solvers. While neural solvers trained from either simulation data or physics-informed loss can solve PDEs reasonably well, they are mainly restricted to a few instances of PDEs, e.g. a certain equation with a limited set of coefficients. T

Joshua Lackman

We derive the geometric quantization program of symplectic manifolds, in the sense of both Kostant-Souriau and Weinstein, from Feynman's path integral formulation on phase space. The state space we use contains states with negative norm and polarized sections determine a Hilbert space. We discuss ambiguities in the definition of path integrals arising from t

Zhi Zheng, Shunyu Yao, Zhenkun Wang, Xialiang Tong

The min-max vehicle routing problem (min-max VRP) traverses all given customers by assigning several routes and aims to minimize the length of the longest route. Recently, reinforcement learning (RL)-based sequential planning methods have exhibited advantages in solving efficiency and optimality. However, these methods fail to exploit the problem-specific pr

Thomas V. Papineau, Denis Jacquemin, Morgane Vacher

Non-adiabatic dynamics simulations have become a standard approach to explore photochemical reactions. Such simulations require underlying potential energy surfaces and couplings between them, calculated at a chosen level of theory, yet this aspect is rarely assessed. Here, in combination with the popular trajectory surface hopping dynamics method, we use a

Yuchuan Jin, Theodor Stenhammar, David Bejmer, Axel Beauvisage

Accurate and timely determination of a vehicle's current lane within a map is a critical task in autonomous driving systems. This paper utilizes an Early Time Series Classification (ETSC) method to achieve precise and rapid ego-lane identification in real-world driving data. The method begins by assessing the similarities between map and lane markings percei

Alfonso Maiellaro, Mattia Trama, Jacopo Settino, Claudio Guarcello

The superconducting diode effect, reminiscent of the unidirectional charge transport in semiconductor diodes, is characterized by a nonreciprocal, dissipationless flow of Cooper pairs. This remarkable phenomenon arises from the interplay between symmetry constraints and the inherent quantum behavior of superconductors. Here, we explore the geometric control

Hsuan-Wei Lee, Colin Cleveland, Attila Szolnoki

In a social dilemma situation, where individual and collective interests are in conflict, it sounds a reasonable assumption that the presence of super or smart players, who simultaneously punish defection and reward cooperation without allowing exploitation, could solve the basic problem. The behavior of such a multi-strategy system, however, is more subtle

Yuting Ma, Lechao Cheng, Yaxiong Wang, Zhun Zhong

Federated learning (FL) is a popular privacy-preserving paradigm that enables distributed clients to collaboratively train models with a central server while keeping raw data locally. In practice, distinct model architectures, varying data distributions, and limited resources across local clients inevitably cause model performance degradation and a slowdown

Lina Fransén, Thierry Tran, Saikat Nandi, Morgane Vacher

Photoionized and electronically excited ethylene \ce{C2H4+} can undergo \ce{H}-loss, \ce{H2}-loss, and ethylene-ethylidene isomerization, where the latter entails a hydrogen migration. Recent pioneering experiments with few-femtosecond extreme ultraviolet pulses and complementary theoretical studies have shed light on the photodynamics of this prototypical o

Luca Scrucca

Entropy estimation plays a crucial role in various fields, such as information theory, statistical data science, and machine learning. However, traditional entropy estimation methods often struggle with complex data distributions. Mixture-based estimation of entropy has been recently proposed and gained attention due to its ease of use and accuracy. This pap

Hongfu Gao, Feipeng Zhang, Wenyu Jiang, Jun Shu

Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples. Recent works claim that in-context learning is robust to noisy demonstrations in text classification. In this work, we show that, on text genera

Adrian-Cristian Nicolaescu, Spyridon Mastorakis, Md Washik Al Azad, David Griffin

This paper investigates an edge computing system where requests are processed by a set of replicated edge servers. We investigate a class of applications where similar queries produce identical results. To reduce processing overhead on the edge servers we store the results of previous computations and return them when new queries are sufficiently similar to

Shengjie Liu, Lu Zhang

Remote sensing data provide a low-cost solution for large-scale monitoring of air pollution via the retrieval of aerosol optical depth (AOD), but is often limited by cloud contamination. Existing methods for AOD reconstruction rely on temporal information. However, for remote sensing data at high spatial resolution, multi-temporal observations are often unav

Denis Kuznedelev, Valerii Startsev, Daniil Shlenskii, Sergey Kastryulin

There is a prevalent opinion that diffusion-based models outperform GAN-based counterparts in the Image Super Resolution (ISR) problem. However, in most studies, diffusion-based ISR models employ larger networks and are trained longer than the GAN baselines. This raises the question of whether the high performance stems from the superiority of the diffusion

Yoeri Poels, Koen Minartz, Harshit Bansal, Vlado Menkovski

Simulation is a powerful tool to better understand physical systems, but generally requires computationally expensive numerical methods. Downstream applications of such simulations can become computationally infeasible if they require many forward solves, for example in the case of inverse design with many degrees of freedom. In this work, we investigate and

Anders Munch, Thomas A. Gerds

Risk prediction models are widely used to guide real-world decision-making in areas such as healthcare and economics, and they also play a key role in estimating nuisance parameters in semiparametric inference. The super learner is a machine learning framework that combines a library of prediction algorithms into a meta-learner using cross-validated loss. In

Runqian Wang, Soumya Ghosh, David Cox, Diego Antognini

Low-rank adapters (LoRA) and their variants are popular parameter-efficient fine-tuning (PEFT) techniques that closely match full model fine-tune performance while requiring only a small number of additional parameters. These additional LoRA parameters are specific to the base model being adapted. When the base model needs to be deprecated and replaced with

Franco Coltraro, Jaume Amorós, Maria Alberich-Carramiñana, Carme Torras

In this work, we study the perception problem for sampled surfaces (possibly with boundary) using tools from computational topology, specifically, how to identify their underlying topology starting from point-cloud samples in space, such as those obtained with 3D scanners. We present a reconstruction algorithm based on a careful topological study of the poin

Tengbo Wang, Yu Bai

How to extract instance-level masks without instance-level supervision is the main challenge of weakly supervised instance segmentation (WSIS). Popular WSIS methods estimate a displacement field (DF) via learning inter-pixel relations and perform clustering to identify instances. However, the resulting instance centroids are inherently unstable and vary sign

Martin A. Bees, Prasad Perlekar

Bioconvection is the prototypical active matter system for hydrodynamic instabilities and pattern formation in suspensions of biased swimming microorganisms, particularly at the dilute end of the concentration spectrum where cell-cell interactions typically are neglected. Confinement is an inherent characteristic of such systems, including those that are nat

Hjalte Frellesvig, Roger Morales, Matthias Wilhelm

We use the loop-by-loop Baikov representation to investigate the geometries in Feynman integrals contributing to the classical dynamics of a black-hole two-body system in the post-Minkowskian expansion of general relativity. These geometries determine the spaces of functions to which the corresponding Feynman diagrams evaluate. As a proof of principle, we pr

Clément de Chaisemartin, Antoine Deeb

In multi-site randomized trials with many sites and few randomization units per site, an Empirical-Bayes estimator can be used to estimate the variance of the treatment effect across sites. When this estimator indicates that treatment effects do vary, we propose estimators of the coefficients from regressions of site-level effects on site-level characteristi

Raphaël Romero, Jefrey Lijffijt, Riccardo Rastelli, Marco Corneli

Representing the nodes of continuous-time temporal graphs in a low-dimensional latent space has wide-ranging applications, from prediction to visualization. Yet, analyzing continuous-time relational data with timestamped interactions introduces unique challenges due to its sparsity. Merely embedding nodes as trajectories in the latent space overlooks this sp

J. Blümlein, M. Saragnese

In many calculations involving polarized twist-2 parton densities to higher order in the strong coupling constant one uses the Larin scheme to describe chiral effects in dimensional regularization. Upon forming observables, the scheme dependence cancels. Still one needs a corresponding regularization scheme to compute the contributing building blocks, like m

Junyoung Seo, Kazumi Fukuda, Takashi Shibuya, Takuya Narihira

Generating novel views from a single image remains a challenging task due to the complexity of 3D scenes and the limited diversity in the existing multi-view datasets to train a model on. Recent research combining large-scale text-to-image (T2I) models with monocular depth estimation (MDE) has shown promise in handling in-the-wild images. In these methods, a

Haohua Que, Wenbin Pan, Jie Xu, Hao Luo

In recent years, various intelligent autonomous robots have begun to appear in daily life and production. Desktop-level robots are characterized by their flexible deployment, rapid response, and suitability for light workload environments. In order to meet the current societal demand for service robot technology, this study proposes using a miniaturized desk

Vasile Ionut Remus Iga, Gheorghe Cosmin Silaghi

Recent work has shown the capability of Large Language Models (LLMs) to solve tasks related to Knowledge Graphs, such as Knowledge Graph Completion, even in Zero- or Few-Shot paradigms. However, they are known to hallucinate answers, or output results in a non-deterministic manner, thus leading to wrongly reasoned responses, even if they satisfy the user's d

Aaron T. Wang, William Convertino, Xiang Cheng, Ricardo Henao

In-context learning based on attention models is examined for data with categorical outcomes, with inference in such models viewed from the perspective of functional gradient descent (GD). We develop a network composed of attention blocks, with each block employing a self-attention layer followed by a cross-attention layer, with associated skip connections.

Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay, Alexander C. Li

Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significan

Thomas Schnappinger, Markus Kowalewski

As pioneering experiments have shown, strong vibrational coupling between molecular vibrations and light modes in an optical cavity can significantly alter molecular properties and even affect chemical reactivity. However, the current theoretical description is limited and far from complete. To explore the origin of this exciting observation, we investigate

Shengyuan Ye, Jiangsu Du, Liekang Zeng, Wenzhong Ou

Transformer-based models have unlocked a plethora of powerful intelligent applications at the edge, such as voice assistant in smart home. Traditional deployment approaches offload the inference workloads to the remote cloud server, which would induce substantial pressure on the backbone network as well as raise users' privacy concerns. To address that, in-s

C. V. da Silva, F. V. Alencar Filho, J. P. Bravo, D. B. de Freitas

We investigate the flux intensities spanning from radio waves to $\gamma$-rays across 36 light curves of Quasar 3C 273, utilizing publicly available data collected by the Integral Science Data Centre (ISDC) database. Our analysis reveals a consistent adherence of all light curves from this quasar to $q$-Gaussian distribution. This compelling finding strongly

Adriana Hugessen, Roger Creus Castanyer, Faisal Mohamed, Glen Berseth

Both entropy-minimizing and entropy-maximizing (curiosity) objectives for unsupervised reinforcement learning (RL) have been shown to be effective in different environments, depending on the environment's level of natural entropy. However, neither method alone results in an agent that will consistently learn intelligent behavior across environments. In an ef

Guilherme Cerqueira-Gonçalves

In this paper, we study the modulus of continuity of solutions to Dirichlet problems for complex Monge-Amp\`ere equations with $L^p$ densities on Stein spaces with isolated singularities. In particular, we prove such solutions are H\"older continuous outside singular points if the boundary data is H\"older continuous.

Yisi Luo, Xile Zhao, Kai Ye, Deyu Meng

Recently, we have witnessed the success of total variation (TV) for many imaging applications. However, traditional TV is defined on the original pixel domain, which limits its potential. In this work, we suggest a new TV regularization defined on the neural domain. Concretely, the discrete data is implicitly and continuously represented by a deep neural net

Zhaoyang Sun, Shengwu Xiong, Yaxiong Chen, Yi Rong

The absence of real targets to guide the model training is one of the main problems with the makeup transfer task. Most existing methods tackle this problem by synthesizing pseudo ground truths (PGTs). However, the generated PGTs are often sub-optimal and their imprecision will eventually lead to performance degradation. To alleviate this issue, in this pape

Sara Santoni, Marco De Petris, Gustavo Yepes, Antonio Ferragamo

Galaxy clusters are located in the densest areas of the universe and are intricately connected to larger structures through the filamentary network of the Cosmic Web. In this scenario, matter flows from areas of lower density to higher density. As a result, the properties of galaxy clusters are deeply influenced by the filaments that are attached to them, wh

Ziyang Li, Saikat Dutta, Mayur Naik

Software is prone to security vulnerabilities. Program analysis tools to detect them have limited effectiveness in practice due to their reliance on human labeled specifications. Large language models (or LLMs) have shown impressive code generation capabilities but they cannot do complex reasoning over code to detect such vulnerabilities especially since thi

Maximilian Schröder

Assessing the contribution of various risk factors to future inflation risks was crucial for guiding monetary policy during the recent high inflation period. However, existing methodologies often provide limited insights by focusing solely on specific percentiles of the forecast distribution. In contrast, this paper introduces a comprehensive framework that

Xiaoyu Li, Jonathan Benjamin, Xin Zhang

Artificial intelligence is revolutionizing architecture through text-to-image synthesis, converting textual descriptions into detailed visual representations. We explore AI-assisted floor plan design, focusing on technical background, practical methods, and future directions. Using tools like, Stable Diffusion, AI leverages models such as Generative Adversar

J. L. Rizos, E. Fernández-Valenzuela, J. L. Ortiz, F. L. Rommel

Centaurs, distinguished by their volatile-rich compositions, play a pivotal role in understanding the formation and evolution of the early solar system, as they represent remnants of the primordial material that populated the outer regions. Stellar occultations offer a means to investigate their physical properties, including shape, rotational state, or the

Fan Wang, Chuan Lin, Yang Cao, Yu Kang

In-context learning (ICL) empowers generative models to address new tasks effectively and efficiently on the fly, without relying on any artificially crafted optimization techniques. In this paper, we study extending ICL to address a broader range of tasks with an extended learning horizon and higher improvement potential, namely General Purpose In-Context L

Haoyu Wang, Bei Liu, Hang Shao, Bo Xiao

Parameter quantization for Large Language Models (LLMs) has attracted increasing attentions recently in reducing memory costs and improving computational efficiency. Early approaches have been widely adopted. However, the existing methods suffer from poor performance in low-bit (such as 2 to 3 bits) scenarios. In this paper, we present a novel and effective

Vincent Guedj, Tat Dat Tô

In a remarkable series of works, Guo, Phong, Song, and Sturm have obtained key uniform estimates for the Green's functions associated with certain K\"ahler metrics. In this note, we broaden the scope of their techniques by removing one of their assumptions and allowing the complex structure to vary. We apply our results to various families of canonical K\"ah

Sanath Narayan, Yasser Abdelaziz Dahou Djilali, Ankit Singh, Eustache Le Bihan

This work presents an extensive and detailed study on Audio-Visual Speech Recognition (AVSR) for five widely spoken languages: Chinese, Spanish, English, Arabic, and French. We have collected large-scale datasets for each language except for English, and have engaged in the training of supervised learning models. Our model, ViSpeR, is trained in a multi-ling

Xin Huang, Héctor González-Herrero, Orlando J. Silveira, Shawulienu Kezilebieke

Van der Waals heterostructures have emerged as an ideal platform for creating engineered artificial electronic states. While vertical heterostructures have been extensively studied, realizing high-quality lateral heterostructures with atomically sharp interfaces remains a major experimental challenge. Here, we advance a one-pot two-step molecular beam latera

Yanjun Ji, Ilia Polian

Dynamical decoupling (DD) is a promising technique for mitigating errors in near-term quantum devices. However, its effectiveness depends on both hardware characteristics and algorithm implementation details. This paper explores the synergistic effects of dynamical decoupling and optimized circuit design in maximizing the performance and robustness of algori

Guozheng Li, Peng He, Xinyu Wang, Runfei Li

Embedding visual representations within original hierarchical tables can mitigate additional cognitive load stemming from the division of users' attention. The created hierarchical table visualizations can help users understand and explore complex data with multi-level attributes. However, because of many options available for transforming hierarchical table

L. K. Eraso-Hernandez, A. P. Riascos

A system is called antifragile when damage acts as a constructive element improving the performance of a global function. In this paper, we analyze the emergence of antifragility in the movement of random walkers on networks with modular structures or communities. The random walker hops considering the capacity of transport of each link, whereas the links ar

Shangqun Yu, Nisal Perera, Daniel Marew, Donghyun Kim

This paper addresses the challenge of terrain-adaptive dynamic locomotion in humanoid robots, a problem traditionally tackled by optimization-based methods or reinforcement learning (RL). Optimization-based methods, such as model-predictive control, excel in finding optimal reaction forces and achieving agile locomotion, especially in quadruped, but struggle

Tito Medina

We show the existence of some special coordinate systems for expressing maps with branch points. These coordinates allow obtaining an explicit representation formula of branch immersions and understanding the regularity of some fundamental elements in the theory of R. D. Gulliver, R. Osserman and H. L. Royden. For conformal maps, we prove that these coordina

Daniel Vebman

This paper introduces a framework for measuring how much black-box decision-makers rely on variables of interest. The framework adapts a permutation-based measure of variable importance from the explainable machine learning literature. With an emphasis on applicability, I present some of the framework's theoretical and computational properties, explain how r

Grayson L. Baird, Stephen L. Bieber

It is often asserted that to control for the effects of confounders, one should include the confounding variables of concern in a statistical model as a covariate. Conversely, it is also asserted that control can only be concluded by design, where the results from an analysis can only be interpreted as evidence of an effect because the design controlled for

M. T. Devine, V. Bertsch

To meet carbon emission targets, governments around the world seek electricity consumers to invest in self-sufficiency technologies such as solar photovoltaic and battery storage. Such behaviour is sought in markets typically characterised by an oligopoly amongst generating firms. In this work, we study the interactions between investment decisions on the de

Cedric Kulbach, Lucas Cazzonelli, Hoang-Anh Ngo, Minh-Huong Le-Nguyen

Machine learning algorithms have become indispensable in today's world. They support and accelerate the way we make decisions based on the data at hand. This acceleration means that data structures that were valid at one moment could no longer be valid in the future. With these changing data structures, it is necessary to adapt machine learning (ML) systems

Nicolas Fortier, Sangyong Jeon, Charles Gale

In this work, we perform a model-to-data comparison for U+U and Au+Au collisions performed at RHIC at $\sqrt{s_{\rm NN}}$ = $193$ GeV and $200$ GeV, using a multistage framework. Model calculations for various configurations of $^{238}$U and $^{197}$Au are used to compare with measurements of $\rho(v_n\{2\}^2,\left\langle p_T \right\rangle)$, the elliptic-fl

Tianyu Yu, Haoye Zhang, Qiming Li, Qixin Xu

Traditional feedback learning for hallucination reduction relies on labor-intensive manual labeling or expensive proprietary models. This leaves the community without foundational knowledge about how to build high-quality feedback with open-source MLLMs. In this work, we introduce RLAIF-V, a novel framework that aligns MLLMs in a fully open-source paradigm.

Hector Socas-Navarro

This paper investigates the likelihood that the CNEOS 2014-01-08 superbolide (CNEOS14) was caused by an interstellar object. This issue has remained controversial due to lack of information on the capabilities of the classified satellite sensors that recorded the fireball. We critically evaluate previous studies, specifically addressing the reliability of th

Matthias Hamann

We prove that every locally finite quasi-transitive graph that does not contain $K_\infty$ as a minor is quasi-isometric to some planar quasi-transitive locally finite graph. This solves a problem of Esperet and Giocanti and improves their recent result that such graphs are quasi-isometric to some planar graph of bounded degree.

Daniel Buschek

This essay proposes and explores the concept of Collage for the design of AI writing tools, transferred from avant-garde literature with four facets: 1) fragmenting text in writing interfaces, 2) juxtaposing voices (content vs command), 3) integrating material from multiple sources (e.g. text suggestions), and 4) shifting from manual writing to editorial and