February 2024 arXiv papers — page 114

Marco Gallo

In this thesis we investigate how the nonlocalities affect the study of different PDEs coming from physics, and we analyze these equations under almost optimal assumptions of the nonlinearity. In particular, we focus on the fractional Laplacian operator and on sources involving convolution with the Riesz potential, as well as on the interaction of the two, a

Stefano Finelli, Alessio Ciamei, Beatrice Restivo, Maximilian Schemmer

Quantum gases of doubly-polar molecules represent appealing frameworks for a variety of cross-disciplinary applications, encompassing quantum simulation and computation, controlled quantum chemistry and precision measurements. Through a joint experimental and theoretical study, here we explore a novel class of ultracold paramagnetic polar molecules combining

Jonas Brugger, Tim Friede, Florian Klinglmüller, Martin Posch

The log-rank test and the Cox proportional hazards model are commonly used to compare time-to-event data in clinical trials, as they are most powerful under proportional hazards. But there is a loss of power if this assumption is violated, which is the case for some new oncology drugs like immunotherapies. We consider a two-stage test procedure, in which the

Denis Rustand, Janet van Niekerk, Elias Teixeira Krainski, Håvard Rue

This paper introduces the R package INLAjoint, designed as a toolbox for fitting a diverse range of regression models addressing both longitudinal and survival outcomes. INLAjoint relies on the computational efficiency of the integrated nested Laplace approximations methodology, an efficient alternative to Markov chain Monte Carlo for Bayesian inference, ens

David C. Norris, Markus Triska

We present, as a pure Prolog program, the first executable specification of the 3 + 3 dose-escalation protocol commonly used in early-phase oncology drug development. In this program, the imperative operations of the protocol emerge as consequences of clinically meaningful anticipatory-regret scenarios that are declared as CLP(Z) constraints. This 'regret-co

Antoine Habis, Roy Rosman Nathanson, Vannary Meas-Yedid, Elsa D. Angelini

This paper proposes a dynamic interactive and weakly supervised segmentation method with minimal user interactions to address two major challenges in the segmentation of whole slide histopathology images. First, the lack of hand-annotated datasets to train algorithms. Second, the lack of interactive paradigms to enable a dialogue between the pathologist and

Nicolas Bousquet, Clément Dallard, Maël Dumas, Claire Hilaire

A graph $H$ is said to be an induced minor of a graph $G$ if $H$ can be obtained from $G$ by a sequence of vertex deletions and edge contractions. Equivalently, $H$ is an induced minor of $G$ if there exists an induced minor model of $H$ in $G$, that is, a collection of pairwise disjoint subsets of vertices of $G$ labeled by the vertices of $H$, each inducin

Luke Schaeffer, Jeffrey Shallit, Stefan Zorcic

Let $\alpha$ and $\beta$ belong to the same quadratic field. We show that the inhomogeneous Beatty sequence $(\lfloor n \alpha + \beta \rfloor)_{n \geq 1}$ is synchronized, in the sense that there is a finite automaton that takes as input the Ostrowski representations of $n$ and $y$ in parallel, and accepts if and only if $y = \lfloor n \alpha + \beta \rfloo

Jan Homberg, Alexander Weismann, Richard Berndt

Lead phthalocyanine (PbPc), a non-planar molecule, is studied on Pb(100) using scanning tunneling spectroscopy. A rigid shift of the molecular orbitals is found between molecules with the central Pb ion pointing to (PbPc$\downarrow$) or away (PbPc$\uparrow$) from the substrate and understood from the interaction between the molecules and their image charges.

A. Chorozidou, T. Gaitanos

Neutron stars offer a great opportunity to study highly compressed hadronic matter experimentally and theoretically. However, the so-called hyperon-puzzle arises at neutron star densities. The hyperon coexistence with other particles in compressed matter softens the equation of state and many widely-accepted models fail to reproduce precise observations of l

David Strieder, Mathias Drton

Knowledge of the underlying causal relations is essential for inferring the effect of interventions in complex systems. In a widely studied approach, structural causal models postulate noisy functional relations among interacting variables, where the underlying causal structure is then naturally represented by a directed graph whose edges indicate direct cau

Weizhe Lin, Jingbiao Mei, Jinghong Chen, Bill Byrne

Large Multimodal Models (LMMs) excel in natural language and visual understanding but are challenged by exacting tasks such as Knowledge-based Visual Question Answering (KB-VQA) which involve the retrieval of relevant information from document collections to use in shaping answers to questions. We present an extensive training and evaluation framework, M2KR,

V. V. Reshetniak, A. V Aborkin, A. V. Filippov

The paper analyzes theoretically the influence of fullerenes on the characteristics of $\mathrm{Al/C}_{60}$ composites. The molecular dynamics method is used to study the dependences of density and stiffness constants on the concentration of inclusions, and to calculate the values of the bulk and shear moduli for isotropic polycrystalline nanocomposites. The

F. Piscitelli, F. Ghazi Moradi, F. S. Alves, M. J. Christensen

The Multi-Blade (MB) Boron-10-based neutron detector is the chosen technology for three instruments at the European Spallation Source (ESS): the two ESS reflectometers, ESTIA and FREIA, and the Test Beam Line. A fourth MB detector has been built, installed and commissioned for the user operation of the reflectometer Amor at PSI (Switzerland). Amor can be con

Felix Petersen, Aashwin Mishra, Hilde Kuehne, Christian Borgelt

We propose a new approach for propagating stable probability distributions through neural networks. Our method is based on local linearization, which we show to be an optimal approximation in terms of total variation distance for the ReLU non-linearity. This allows propagating Gaussian and Cauchy input uncertainties through neural networks to quantify their

Thilo Hagendorff

The advent of generative artificial intelligence and the widespread adoption of it in society engendered intensive debates about its ethical implications and risks. These risks often differ from those associated with traditional discriminative machine learning. To synthesize the recent discourse and map its normative concepts, we conducted a scoping review o

Gholamreza Ramezan, Ehsan Meamari

This paper introduces the zk-IoT framework, a novel approach to enhancing the security of Internet of Things (IoT) ecosystems through the use of Zero-Knowledge Proofs (ZKPs) on blockchain platforms. Our framework ensures the integrity of firmware execution and data processing in potentially compromised IoT devices. By leveraging the concept of ZKP, we establ

Taira Tsuchiya, Shinji Ito, Junya Honda

Partial monitoring is a generic framework of online decision-making problems with limited feedback. To make decisions from such limited feedback, it is necessary to find an appropriate distribution for exploration. Recently, a powerful approach for this purpose, \emph{exploration by optimization} (ExO), was proposed, which achieves optimal bounds in adversar

Andy Cătrună, Adrian Cosma, Emilian Rădoi

Gait, an unobtrusive biometric, is valued for its capability to identify individuals at a distance, across external outfits and environmental conditions. This study challenges the prevailing assumption that vision-based gait recognition, in particular skeleton-based gait recognition, relies primarily on motion patterns, revealing a significant role of the im

Federico Mazzola, Wojciech Brzezicki, Maria Teresa Mercaldo, Anita Guarino

The relation between crystal symmetries, electron correlations, and electronic structure steers the formation of a large array of unconventional phases of matter, including magneto-electric loop currents and chiral magnetism. Detection of such hidden orders is a major goal in condensed matter physics. However, to date, nonstandard forms of magnetism with chi

Alba Morollon Diaz-Faes, Carla Sofia Ribeiro Murteira, Martin Ruskov

Looking at how social values are represented in fairy tales can give insights about the variations in communication of values across cultures. We study how values are communicated in fairy tales from Portugal, Italy and Germany using a technique called word embedding with a compass to quantify vocabulary differences and commonalities. We study how these thre

Ryo Namiki

A resolution of the identity due to canonical coherent states is often proven in the weak operator topology. However, such a resolution with an integral symbol is typically supposed to hold in the strong operator topology associated with the framework of the spectral theorem. We provide an elementary proof of the strong convergence for the resolution of the

Jonas Hübotter, Bhavya Sukhija, Lenart Treven, Yarden As

We study a generalization of classical active learning to real-world settings with concrete prediction targets where sampling is restricted to an accessible region of the domain, while prediction targets may lie outside this region. We analyze a family of decision rules that sample adaptively to minimize uncertainty about prediction targets. We are the first

Andy Cătrună, Adrian Cosma, Emilian Rădoi

Gaze estimation, the task of predicting where an individual is looking, is a critical task with direct applications in areas such as human-computer interaction and virtual reality. Estimating the direction of looking in unconstrained environments is difficult, due to the many factors that can obscure the face and eye regions. In this work we propose CrossGaz

Dmitri Alekseevsky, Gianni Manno, Giovanni Moreno

We develop a method for describing invariant Monge-Amp\`ere equations in the sense of V. Lychagin and T. Morimoto (MAE) on a homogeneous contact manifold $N$ of a semisimple Lie group $G$, which is the contactification of the homogeneous symplectic manifold $M = G/H = \mathrm{Ad}_G Z \subset \mathfrak{g}$, where $M$ is the adjoint orbit of a splittable close

Jonas Hübotter, Bhavya Sukhija, Lenart Treven, Yarden As

We study the question: How can we select the right data for fine-tuning to a specific task? We call this data selection problem active fine-tuning and show that it is an instance of transductive active learning, a novel generalization of classical active learning. We propose ITL, short for information-based transductive learning, an approach which samples ad

Thomas Archbold, Bart de Keijzer, Carmine Ventre

Bounded rationality in mechanism design aims to ensure incentive-compatibility for agents who are cognitively limited. These agents lack the contingent reasoning skills that traditional mechanism design assumes, and depending on how these cognitive limitations are modelled this alters the class of incentive-compatible mechanisms. In this work we design mecha

Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer, Bernhard C. Geiger

This paper employs physics-informed neural networks (PINNs) to solve Fisher's equation, a fundamental reaction-diffusion system with both simplicity and significance. The focus is on investigating Fisher's equation under conditions of large reaction rate coefficients, where solutions exhibit steep traveling waves that often present challenges for traditional

Théo Mariotte, Anthony Larcher, Silvio Montrésor, Jean-Hugh Thomas

Voice Activity Detection (VAD) and Overlapped Speech Detection (OSD) are key pre-processing tasks for speaker diarization. In the meeting context, it is often easier to capture speech with a distant device. This consideration however leads to severe performance degradation. We study a unified supervised learning framework to solve distant multi-microphone jo

F. Kiefer, M. Bonnefoy, B. Charnay, A. Boccaletti

Molecular mapping is a supervised method exploiting the spectral diversity of integral field spectrographs to detect and characterize resolved exoplanets blurred into the stellar halo. We present an evolution of the method to remove the stellar halo and the nuisance of telluric features in the datacubes and access a continuum-subtracted spectra of the planet

Thomas Pöllabauer, Julius Kühn, Jiayi Li, Arjan Kuijper

Estimating the 3D shape of an object using a single image is a difficult problem. Modern approaches achieve good results for general objects, based on real photographs, but worse results on less expressive representations such as historic sketches. Our automated approach generates a variety of detailed 3D representation from a single sketch, depicting a medi

Daniel Nahmias, Gal Engelberg, Dan Klein, Asaf Shabtai

Spear-phishing attacks present a significant security challenge, with large language models (LLMs) escalating the threat by generating convincing emails and facilitating target reconnaissance. To address this, we propose a detection approach based on a novel document vectorization method that utilizes an ensemble of LLMs to create representation vectors. By

Stephan I. Tzenov

A novel technique to determine invariant curves in nonlinear beam dynamics based on the method of formal series has been developed. It is first shown how the solution of the Hamilton equations of motion describing nonlinear betatron oscillations in the presence of a single sextupole can be represented in a nonperturbative form. Further, the solution of the H

Antoine Aymard, Emilie Delplanque, Davy Dalmas, Julien Scheibert

Many devices, including touchscreens and robotic hands, involve frictional contacts. Optimizing these devices requires fine control of the interface's friction law. We lack systematic methods to create dry contact interfaces whose frictional behavior satisfies preset specifications. We propose a generic surface design strategy to prepare dry rough interfaces

Guillermo A. Mena Marugán, Andrés Mínguez-Sánchez

Recently, there has been a growing interest in investigating homogeneous but anisotropic spacetimes owing to their relation with nonrotating, uncharged black hole interiors. We present a description of axial perturbations for a massless scalar field minimally coupled to this geometry. We truncate the action at the quadratic perturbative order and tailor our

Simon Gottschalk, Lukas Lanza, Karl Worthmann, Kerstin Lux-Gottschalk

We propose a two-component data-driven controller to safely perform docking maneuvers for satellites. Reinforcement Learning is used to deduce an optimal control policy based on measurement data. To safeguard the learning phase, an additional feedback law is implemented in the control unit, which guarantees the evolution of the system within predefined perfo

Daniele Faenzi, Marcos Jardim, Jean Vallès

Saito gave a nice and efficient criterion to determine whether the module of logarithmic derivation associated with a reduced divisor in a complex variety is free or not. The aim of this note is to propose a new proof of this criterion, in the affine space, in the projective space, and for multiderivations, based on straightforward observations concerning fr

Pedro Sarriguren

Interference terms between monopole and quadrupole Coulomb form factors that contribute to the cross-section of electron scattering from polarized nuclei are studied within the plane wave Born approximation. By experimentally exploring the effect of this nuclear response, valuable insights into the sign of the quadrupole deformation can be obtained. The nucl

Yin Fang, Kangwei Liu, Ningyu Zhang, Xinle Deng

As Large Language Models (LLMs) rapidly evolve, their influence in science is becoming increasingly prominent. The emerging capabilities of LLMs in task generalization and free-form dialogue can significantly advance fields like chemistry and biology. However, the field of single-cell biology, which forms the foundational building blocks of living organisms,

Athanase Papadopoulos

These are a few historical remarks, addenda and references with comments on some topics discussed by Thurston in his notes ''The geometry and topology of three-manifolds''. The topics are mainly hyperbolic geometry, geometric structures, volumes of hyperbolic polyhedra and the so-called Koebe--Andreev--Thurston theorem. I discuss in particular some works of

Alexandre Fernandes, José Edson Sampaio, Joserlan Perote da Silva

In this article, we show the H\"older invariance of the Henry-Parusinski invariant. For a single germ $ f$, the Henry-Parusinski invariant of $ f $ is given in terms of the leading coefficients of the asymptotic expansion of $ f $ along the branches of the generic polar curve of $f$. As a consequence, we obtain that the classification problem of polynomial f

Xin Jin, Wu Zhou, Jingyu Wang, Duo Xu

Computational aesthetic evaluation has made remarkable contribution to visual art works, but its application to music is still rare. Currently, subjective evaluation is still the most effective form of evaluating artistic works. However, subjective evaluation of artistic works will consume a lot of human and material resources. The popular AI generated conte

Jean-Baptiste Riccoboni, Tony Monnet, Antoine Eon, Patrick Lacouture

Background: Occupational safety and health institutions report that caregivers areparticularly at risk of developing work-related musculoskeletal disorders (WRMSDs) andpatient handling is often pointed out as one of the main causes. While lots of studiesaddressed the use of assistive devices in order to protect caregivers, it seems that motorlessstand-up lif

Leonard Bradatsch, Oleksandr Miroshkin, Natasa Trkulja, Frank Kargl

Zero Trust security has recently gained attention in enterprise network security. One of its key ideas is making network-level access decisions based on trust scores. However, score-based access control in the enterprise domain still lacks essential elements in our understanding, and in this paper, we contribute with respect to three crucial aspects. First,

Josu Ceberio, Borja Calvo

Experimentation is an intrinsic part of research in artificial intelligence since it allows for collecting quantitative observations, validating hypotheses, and providing evidence for their reformulation. For that reason, experimentation must be coherent with the purposes of the research, properly addressing the relevant questions in each case. Unfortunately

Paolo Carniti, Claudio Gotti, Gianluigi Pessina

The use of silicon photomultipliers (SiPMs) to detect light signals in highly radioactive environments presents several challenges, particularly due to their sensitivity on radiation, temperature, and overvoltage, requiring a proper management of their bias supply. This article presents the design and performance of ALDO2, an application-specific integrated

Matthieu Nastorg, Jean-Marc Gratien, Thibault Faney, Michele Alessandro Bucci

Large-scale numerical simulations often come at the expense of daunting computations. High-Performance Computing has enhanced the process, but adapting legacy codes to leverage parallel GPU computations remains challenging. Meanwhile, Machine Learning models can harness GPU computations effectively but often struggle with generalization and accuracy. Graph N

Nilasis Chaudhuri, Muhammed Ali Mehmood, Charlotte Perrin, Ewelina Zatorska

We introduce the notion of duality solution for the hard-congestion model on the real line, and additionally prove an existence result for this class of solutions. Our study revolves around the analysis of a generalised Aw-Rascle system, where the offset function is replaced by the gradient of a singular function, such as $\rho$ $\gamma$ n , where $\gamma$ $

Manxi Lin, Jakob Ambsdorf, Emilie Pi Fogtmann Sejer, Zahra Bashir

We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements. Working in fetal ultrasound, where ranking is challenging and annotations are noisy, we design a robust coarse-to-fine model that ranks images based on their semantic image quality and endow our predicted rankings with an uncertainty estima

Paolo Edera, Minaspi Bantawa, Stefano Aime, Roger T Bonnecaze

When glassy materials are rapidly quenched from the liquid to the solid state upon flow cessation or cooling, they solidify in an out-of-equilibrium configuration, retaining the memory of the processing conditions for very long times. This is the origin of various phenomena, such as residual stresses and directional memory, which greatly affect their propert

Qinghui Sun, Sharon Xuesong Wang, Luis Welbanks, Johanna Teske

The two prevailing planet formation scenarios, core-accretion and disk instability, predict distinct planetary mass-metallicity relations. Yet, the detection of this trend remains challenging due to inadequate data on planet atmosphere abundance and inhomogeneities in both planet and host stellar abundance measurements. Here we analyze high-resolution spectr

Richard Löscher, Olaf Steinbach, Marco Zank

In this paper, we analyze the discrete inf-sup condition and related error estimates for a modified Hilbert transformation as used in the space-time discretization of time-dependent partial differential equations. It turns out that the stability constant depends linearly on the finite element mesh parameter, but in most cases, we can show optimal convergence

André Artelt, Shubham Sharma, Freddy Lecué, Barbara Hammer

Counterfactual explanations are a widely used approach for examining the predictions of black-box systems. They can offer the opportunity for computational recourse by suggesting actionable changes on how to alter the input to obtain a different (i.e., more favorable) system output. However, recent studies have pointed out their susceptibility to various for

Yun Lu, Dejiang Zheng, Rong Su, Avalpreet Singh Brar

Extensive research has been conducted to explore vehicle lane changes, while the study on cut-ins has not received sufficient attention. The existing studies have not addressed the fundamental question of why studying cut-ins is crucial, despite the extensive investigation into lane changes. To tackle this issue, it is important to demonstrate how cut-ins, a

Pedro Abdalla, Shahar Mendelson

We construct an estimator $\widehat{\Sigma}$ for covariance matrices of unknown, centred random vectors X, with the given data consisting of N independent measurements $X_1,...,X_N$ of X and the wanted confidence level. We show under minimal assumptions on X, the estimator performs with the optimal accuracy with respect to the operator norm. In addition, the

Izumi Hachisu, Mariko Kato, Katsura Matsumoto

The classical nova V339 Del 2013 is characterized by a 1.5 mag dip of the $V$ light curve owing to a dust shell formation, during which soft X-ray emissions coexist. We present Str\"omgren $y$ band light curve, which represents continuum emission, not influenced by strong [O III] emission lines. The $y$ light curve monotonically decreases in marked contrast

Minzhao Lyu, Rahul Dev Tripathi, Vijay Sivaraman

The "metaverse", wherein users can enter virtual worlds to work, study, play, shop, socialize, and entertain, is fast becoming a reality, attracting billions of dollars in investment from companies such as Meta, Microsoft, and Clipo Labs. Further, virtual reality (VR) headsets from entities like Oculus, HTC, and Microsoft are rapidly maturing to provide full

Stanislav Nagy, Petra Laketa

The angular halfspace depth (ahD) is a natural modification of the celebrated halfspace (or Tukey) depth to the setup of directional data. It allows us to define elements of nonparametric inference, such as the median, the inter-quantile regions, or the rank statistics, for datasets supported in the unit sphere. Despite being introduced in 1987, ahD has neve

Takanori Ugai, Yusuke Koyanagi, Fumihito Nishino

XAI (eXplanable AI) techniques that have the property of explaining the reasons for their conclusions, i.e. explainability or interpretability, are attracting attention. XAI is expected to be used in the development of forensic science and the justice system. In today's forensic and criminal investigation environment, experts face many challenges due to larg

Annesha Ghosh, Anil K. Ghosh, Rita SahaRay, Soham Sarkar

We propose a novel semiparametric classifier based on Mahalanobis distances of an observation from the competing classes. Our tool is a generalized additive model with the logistic link function that uses these distances as features to estimate the posterior probabilities of different classes. While popular parametric classifiers like linear and quadratic di

Yudai Kubono

Reasoning abilities of human beings are limited. Logics that treat logical inference for human knowledge should reflect these limited abilities. Logic of awareness is one of those logics. In the logic, what an agent with a limited reasoning ability actually knows at a given moment (explicit knowledge) is distinguished from the ideal knowledge that an agent o

Roman Bezrukavnikov, Simon Riche

In this paper we construct equivalences of monoidal categories relating three geometric or representation-theoretic categorical incarnations of the affine Hecke algebra of a connected reductive algebraic group $G$ over a field of positive characteristic: a category of Harish-Chandra bimodules for the Lie algebra of $G$; the derived category of equivariant co

Antonia Wüst, Wolfgang Stammer, Quentin Delfosse, Devendra Singh Dhami

The challenge in learning abstract concepts from images in an unsupervised fashion lies in the required integration of visual perception and generalizable relational reasoning. Moreover, the unsupervised nature of this task makes it necessary for human users to be able to understand a model's learnt concepts and potentially revise false behaviours. To tackle

Janko Bračič

In this survey paper we present known results about reflexive subspace lattices. We show that every nest and every atomic Boolean subspace lattice in a complex Banach space is reflexive, even strongly reflexive. Our main tool is Ringrose's Lemma about presence of rank-one operators in a reflexive algebra of operators. We consider realizations of small lattic

Shinji Hirano, Masaki Shigemori

We study the map between two descriptions of the $T\bar{T}$ deformation of conformal field theory (CFT): One is the defining description as a deformation of CFT by the $T\bar{T}$-operator. The other is an alternative description as the undeformed CFT on the dynamical $T\bar{T}$-deformed space that backreacts to the state or operator insertions, reminiscent o

Tobias Schimanski, Jingwei Ni, Mathias Kraus, Elliott Ash

Advances towards more faithful and traceable answers of Large Language Models (LLMs) are crucial for various research and practical endeavors. One avenue in reaching this goal is basing the answers on reliable sources. However, this Evidence-Based QA has proven to work insufficiently with LLMs in terms of citing the correct sources (source quality) and truth

Frauke Wilm, Jonas Ammeling, Mathias Öttl, Rutger H. J. Fick

The U-net architecture has significantly impacted deep learning-based segmentation of medical images. Through the integration of long-range skip connections, it facilitated the preservation of high-resolution features. Out-of-distribution data can, however, substantially impede the performance of neural networks. Previous works showed that the trained networ

Michał Malinowski

This paper presents a study on the implementation of the author's Algorithm of Recommendation Sessions (ARS) in an operational e-commerce information system and analyses the basic parameters of the resulting recommendation system. It begins with a synthetic overview of recommendation systems, followed by a presentation of the proprietary ARS algorithm, which

Dror Chawin, Ishay Haviv

For a field $\mathbb{F}$ and integers $d$ and $k$, a set of vectors of $\mathbb{F}^d$ is called $k$-nearly orthogonal if its members are non-self-orthogonal and every $k+1$ of them include an orthogonal pair. We prove that for every prime $p$ there exists a positive constant $\delta = \delta (p)$, such that for every field $\mathbb{F}$ of characteristic $p$

Hisanari Otsu, Killian Herveau, Johannes Hanika, Derek Nowrouzezahrai

The design of the proposal distributions, and most notably the kernel parameters, are crucial for the performance of Markov chain Monte Carlo (MCMC) rendering. A poor selection of parameters can increase the correlation of the Markov chain and result in bad rendering performance. We approach this problem by a novel path perturbation strategy for online-learn

Sholom Schechtman

It is well-known that the convergence of a family of smooth functions does not imply the convergence of its gradients. In this work, we show that if the family is definable in an o-minimal structure (for instance semialgebraic, subanalytic, or any composition of the previous with exp, log), then the gradient's limit admits a variational stratification and, u

Mohammed-Younes Gueddari, Walid Hachem, Jamal Najim

Approximate Message Passing (AMP) algorithmshave recently gathered significant attention across disciplines such as statistical physics, machine learning, and communication systems. This study aims to extend AMP algorithms to non-symmetric (elliptic) matrices, motivated by analyzing equilibrium properties in ecological systems featuring elliptic interaction

Corentin Latappy, Thomas Degueule, Jean-Rémy Falleri, Romain Robbes

Automatic Static Analysis Tools (ASATs) are widely used by software developers to diffuse and enforce coding practices. Yet, we know little about the documentation of ASATs, despite it being critical to learn about the coding practices in the first place. We shed light on this through several contributions. First, we analyze the documentation of more than 10

Joachim Bona-Pellissier, François Malgouyres, François Bachoc

Neural networks with a large number of parameters often do not overfit, owing to implicit regularization that favors \lq good\rq{} networks. Other related and puzzling phenomena include properties of flat minima, saddle-to-saddle dynamics, and neuron alignment. To investigate these phenomena, we study the local geometry of deep ReLU neural networks. We show

Hao Liu, Wilson Yan, Matei Zaharia, Pieter Abbeel

Enabling long-context understanding remains a key challenge in scaling existing sequence models -- a crucial component in developing generally intelligent models that can process and operate over long temporal horizons that potentially consist of millions of tokens. In this paper, we aim to address these challenges by providing a comprehensive exploration of

Kei Iino, Shunsuke Akamatsu, Hiroshi Watanabe, Shohei Enomoto

Image coding for machines (ICM) aims to compress images for machine analysis using recognition models rather than human vision. Hence, in ICM, it is important for the encoder to recognize and compress the information necessary for the machine recognition task. There are two main approaches in learned ICM; optimization of the compression model based on task l

Marek Cúth, Michal Doucha, Tamás Titkos

We describe surjective linear isometries and linear isometry groups of a large class of Lipschitz-free spaces that includes e.g. Lipschitz-free spaces over any graph. We define the notion of a Lipschitz-free rigid metric space whose Lipschitz-free space only admits surjective linear isometries coming from surjective dilations (i.e. rescaled isometries) of th

Shentao Yang, Tianqi Chen, Mingyuan Zhou

Aligning text-to-image diffusion model (T2I) with preference has been gaining increasing research attention. While prior works exist on directly optimizing T2I by preference data, these methods are developed under the bandit assumption of a latent reward on the entire diffusion reverse chain, while ignoring the sequential nature of the generation process. Th

Olivier Hudry, Ville Junnila, Antoine Lobstein

A set $C$ of vertices in a graph $G=(V,E)$ is an identifying code if it is dominating and any two vertices of $V$ are dominated by distinct sets of codewords. This paper presents a survey of Iiro Honkala's contributions to the study of identifying codes with respect to several aspects: complexity of computing an identifying code, combinatorics in binary Hamm

Sachchidanand Das, Abhiram Soori

Crossed Andreev reflection (CAR) is a scattering phenomenon occurring in a superconductor (SC) connected to two metallic leads, where an incident electron on one side of the SC emerges on the opposite side as a hole. Despite its significance, CAR detection is often impeded by the prevalent electron tunneling (ET), wherein the incident electron exits on the o

Andreas Cap, Christoph Harrach, Pierre Julg

The aim of this article is to construct a specific Poisson transform mapping differential forms on the sphere $S^{2n+1}$ endowed with its natural CR structure to forms on complex hyperbolic space. The transforms we construct have values that are harmonic and co-closed and they descend to the BGG (Rumin) complex and intertwine the differential operators in th

Jinyang Li, Ang Li, Weiwen Jiang

Most of the existing quantum neural network models, such as variational quantum circuits (VQCs), are limited in their ability to explore the non-linear relationships in input data. This gradually becomes the main obstacle for it to tackle realistic applications, such as natural language processing, medical image processing, and wireless communications. Recen

Peng Luo, Alexander Schied, Xiaole Xue

The present paper studies a kind of robust optimization problems with constraint. The problem is formulated through Backward Stochastic Differential Equations (BSDEs) with quadratic generators. A necessary condition is established for the optimal solution using a terminal perturbation method and properties of Bounded Mean Oscillation (BMO) martingales. The n

Xuanliang Zhang, Dingzirui Wang, Longxu Dou, Qingfu Zhu

Table reasoning, which aims to generate the corresponding answer to the question following the user requirement according to the provided table, and optionally a text description of the table, effectively improving the efficiency of obtaining information. Recently, using Large Language Models (LLMs) has become the mainstream method for table reasoning, becau

Huanchen Bao, Jinfeng Song

Let $G_k$ be a connected reductive group over an algebraically closed field $k$ of char $\neq 2$. Let $\theta_k$ be an algebraic group involution of $G_k$ and denote the fixed point subgroup by $K_k$. We construct an integral model for the symmetric space $K_k \backslash G_k$ with a natural action of the Chevalley group scheme over integers. We show the coor

Mark E. Turiansky, Kamyar Parto, Galan Moody, Chris G. Van de Walle

Single-photon emitters are an essential component of quantum networks, and defects or impurities in semiconductors are a promising platform to realize such quantum emitters. Here we present a model that encapsulates the essential physics of coupling to phonons, which governs the behavior of real single-photon emitters, and critically evaluate several approxi

Hengnian Gu, Zhiyi Duan, Pan Xie, Dongdai Zhou

The knowledge concept recommendation in Massive Open Online Courses (MOOCs) is a significant issue that has garnered widespread attention. Existing methods primarily rely on the explicit relations between users and knowledge concepts on the MOOC platforms for recommendation. However, there are numerous implicit relations (e.g., shared interests or same knowl

Heinrich van Deventer, Anna Sergeevna Bosman

Continual learning is the sequential learning of different tasks by a machine learning model. Continual learning is known to be hindered by catastrophic interference or forgetting, i.e. rapid unlearning of earlier learned tasks when new tasks are learned. Despite their practical success, artificial neural networks (ANNs) are prone to catastrophic interferenc

Maxim Mornev, Richard Pink

Let $\phi$ be a Drinfeld $A$-module of finite residual characteristic $\bar{\mathfrak{p}}$ over a local field $K$. We study the action of the inertia group of $K$ on a modified adelic Tate module $\smash{T^\circ_{\text{ad}}}(\phi)$ which differs from the usual adelic Tate module only at the $\bar{\mathfrak{p}}$-primary component. After replacing $K$ by a fin

Toru Matsuura, Kazuma Handa, Masakazu Arakawa

Infrared diodes generate electricity from thermal radiation emitted from themselves. The negative process of photovoltaic effect has been expected for application to energy harvesting systems converting from terrestrial radiation. However, its energy conversion efficiency has been known to be very low. In this paper, we investigate energy conversion efficien

Shiqi Zhang, Zheng Qiu, Daiki Takeuchi, Noboru Harada

With the rapid development of neural networks in recent years, the ability of various networks to enhance the magnitude spectrum of noisy speech in the single-channel speech enhancement domain has become exceptionally outstanding. However, enhancing the phase spectrum using neural networks is often ineffective, which remains a challenging problem. In this pa

Minh Dang Tu, Kieu Trang Le, Manh Duong Phung

This work presents a neural network model capable of recognizing small and tiny objects in thermal images collected by unmanned aerial vehicles. Our model consists of three parts, the backbone, the neck, and the prediction head. The backbone is developed based on the structure of YOLOv5 combined with the use of a transformer encoder at the end. The neck incl

Hayata Saitou, Harumi Haraguchi

Swarm intelligence algorithms have traditionally been designed for continuous optimization problems, and these algorithms have been modified and extended for application to discrete optimization problems. Notably, their application in feature selection for machine learning has demonstrated improvements in model accuracy, reduction of unnecessary data, and de

Yifan Yang, Mingquan Lin, Han Zhao, Yifan Peng

Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to recognize that these systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender. Such

Ying Jin, Jiaqi Wang, Dahua Lin

We consider multi-source free domain adaptation, the problem of adapting multiple existing models to a new domain without accessing the source data. Among existing approaches, methods based on model ensemble are effective in both the source and target domains, but incur significantly increased computational costs. Towards this dilemma, in this work, we propo

D. C. Gunawardhana, G. H. J. Lanel

A graph $G$ consists of vertices $V(G)$ and edges $E(G)$. In this paper, we propose four new indices defined and named as first Rehan-Lanel index of $G$ $(RL_1)$, second Rehan-Lanel index of $G$ $(RL_2)$, second Rehan-Lanel index of $G$, third Rehan-Lanel index of $G$, $(RL_3)$ and fourth Rehan-Lanel index of $G$ $(RL_4)$. The degrees of the vertices $u, v \

Ang Li

We define $\Psi$-autoreducible sets given an autoreduction procedure $\Psi$. Then, we show that for any $\Psi$, a measurable class of $\Psi$-autoreducible sets has measure zero. Using this, we show that classes of cototal, uniformly introenumerable, introenumerable, and hyper-cototal enumeration degrees all have measure zero. By analyzing the arithmetical co

Duy Nam Bui, Thuy Ngan Duong, Manh Duong Phung

This paper presents a new swarm intelligence-based approach to deal with the cooperative path planning problem of unmanned aerial vehicles (UAVs), which is essential for the automatic inspection of infrastructure. The approach uses a 3D model of the structure to generate viewpoints for the UAVs. The calculation of the viewpoints considers the constraints rel

Duy Nam Bui, Manh Duong Phung, Hung Pham Duy

This paper presents the design and implementation of a self-reconfigurable V-shape formation controller for multiple unmanned aerial vehicles (UAVs) navigating through narrow spaces in a dense obstacle environment. The selection of the V-shape formation is motivated by its maneuverability and visibility advantages. The main objective is to develop an effecti

Barathi Subramanian, Rathinaraja Jeyaraj, Rakhmonov Akhrorjon Akhmadjon Ugli

Activation function is a pivotal component of deep learning, facilitating the extraction of intricate data patterns. While classical activation functions like ReLU and its variants are extensively utilized, their static nature and simplicity, despite being advantageous, often limit their effectiveness in specialized tasks. The trainable activation functions