May 2024 arXiv papers — page 126

Sijia Wang, Lifu Huang

Addressing the challenge of low-resource information extraction remains an ongoing issue due to the inherent information scarcity within limited training examples. Existing data augmentation methods, considered potential solutions, struggle to strike a balance between weak augmentation (e.g., synonym augmentation) and drastic augmentation (e.g., conditional

Tom Burkart, Benedikt J. Müller, Erwin Frey

Intracellular protein patterns regulate many vital cellular functions, such as the processing of spatiotemporal information or the control of shape deformations. To do so, pattern-forming systems can be sensitive to the cell geometry by means of coupling the protein dynamics on the cell membrane to dynamics in the cytosol. Recent studies demonstrated that mo

Alexander W. Levis, Eli Ben-Michael, Edward H. Kennedy

Optimal treatment rules are mappings from individual patient characteristics to tailored treatment assignments that maximize mean outcomes. In this work, we introduce a conditional potential benefit (CPB) metric that measures the expected improvement under an optimally chosen treatment compared to the status quo, within covariate strata. The potential benefi

Hernán R. Sánchez, Javier Garcia

In this work, we propose estimators for the uncertainty in mean residual times that require, for their evaluation, statistically independent individual residence times obtained from a discrete time process. We examine their performance through numerical experiments involving well-known probability distributions, and an application example using molecular dyn

Yashuai Yan, Esteve Valls Mascaro, Tobias Egle, Dongheui Lee

Humanoid robots have the potential to mimic human motions with high visual fidelity, yet translating these motions into practical, physical execution remains a significant challenge. Existing techniques in the graphics community often prioritize visual fidelity over physics-based feasibility, posing a significant challenge for deploying bipedal systems in pr

Georgii Kormakov, Nikolay Koshev

Electrical Impedance Tomography (EIT) is a non-invasive imaging modality that has earned significant attention for its potential in real-time monitoring of various physiological parameters. Despite its promise, the sensitivity and resolution of EIT remain areas of active research. This article presents a new way to analyse the sensitivity of EIT systems usin

Michael J. Curran

In previous work, the author gave upper bounds for the shifted moments of the zeta function \[ M_{{\alpha},{\beta}}(T) = \int_T^{2T} \prod_{k = 1}^m |\zeta(\tfrac{1}{2} + i (t + \alpha_k))|^{2 \beta_k} dt \] introduced by Chandee, where ${\alpha} = {\alpha}(T) = (\alpha_1, \ldots, \alpha_m)$ and ${\beta} = (\beta_1 \ldots , \beta_m)$ satisfy $|\alpha_k| \leq

Pouya Bakhti, Min-Gwa Park, Meshkat Rajaee, Chang Sub Shin

For the first time, we comprehensively examine the potential of a neutral-current interaction of reactor neutrino with $^{13}$C emitting a 3.685 MeV photon to identify the origin of the 5 MeV bump in reactor antineutrino spectra observed through the inverse beta decay (IBD) process. This anomaly may be due to new physics, reactor antineutrino flux inaccuraci

Gunter Malle, Noelia Rizo

If G is a finite group and p is a prime number, we investigate the relationship between the p-modular decomposition numbers of characters of height zero in the principal p-block of G and the p-local structure of G.

Hao Wang, Nao Li

Most current click-through rate prediction(CTR)models create explicit or implicit high-order feature crosses through Hadamard product or inner product, with little attention to the importance of feature crossing; only few models are either limited to the second-order explicit feature crossing, implicitly to high-order feature crossing, or can learn the impor

Koung Hee Leem, Jun Liu, George Pelekanos

The recently developed data-driven eigenmatrix method shows very promising reconstruction accuracy in sparse recovery for a wide range of kernel functions and random sample locations. However, its current implementation can lead to numerical instability if the threshold tolerance is not appropriately chosen. To incorporate regularization techniques, we propo

Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann

Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However, recent work has demonstrated that model misspecification can compromise the reliability of SBI, preventing its adoption in important applications where only misspecified simulator

N. S. Bassler, M. Reitz, R. Holzinger, A. Vibók

Non-adiabatic molecular phenomena, arising from the breakdown of the Born-Oppenheimer approximation, govern the fate of virtually all photo-physical and photochemical processes and limit the quantum efficiency of molecules and other solid-state embedded quantum emitters. A simple and elegant description, the energy gap law, was derived five decades ago, pred

John W. Barrett

It shown that if a vector space carries commuting actions of two Clifford algebras, then the quadratic monomials using generators from either Clifford algebra determine a spinor representation of an orthogonal Lie algebra. Examples of this construction have applications to high energy physics, particularly to the standard model and unification. It is shown h

Daniel Elander, Ali Fatemiabhari, Maurizio Piai

We develop a bottom-up holographic model that provides the dual description of a strongly coupled field theory, in which the spontaneous breaking of an approximate global symmetry yields the SO(5)/SO(4) coset relevant to minimal composite-Higgs models. The gravity background is completely regular and smooth, and has an end of space that mimics confinement on

Derong Xu, Ferdinand Willeke, Michael M. Blaskiewicz, Yun Luo

The Electron-Ion Collider (EIC) will use swap-out injection scheme for the Electron Storage Ring (ESR) to overcome limitations in polarization lifetime. However, the pursuit of highest luminosity with the required 28 nC electron bunches encounters stability challenges in the Rapid Cycling Synchrotron (RCS). One method is to inject multiple RCS bunches into a

Samuel Tesfazgi, Markus Keßler, Emilio Trigili, Armin Lederer

Ensuring safety and adapting to the user's behavior are of paramount importance in physical human-robot interaction. Thus, incorporating elastic actuators in the robot's mechanical design has become popular, since it offers intrinsic compliance and additionally provide a coarse estimate for the interaction force by measuring the deformation of the elastic co

Victor M. Baez, Nikhil Navkar, Aaron T. Becker

We present an analytic solution to the 3D Dubins path problem for paths composed of an initial circular arc, a straight component, and a final circular arc. These are commonly called CSC paths. By modeling the start and goal configurations of the path as the base frame and final frame of an RRPRR manipulator, we treat this as an inverse kinematics problem. T

Amirreza Zamani, Sajad Daei, Tobias J. Oechtering, Mikael Skoglund

We study a multi-task private semantic communication problem, in which an encoder has access to an information source arbitrarily correlated with some latent private data. A user has $L$ tasks with priorities. The encoder designs a message to be revealed which is called the semantic of the information source. Due to the privacy constraints the semantic can n

Matthias Lienau

The generalised random graph contains $n$ vertices with positive i.i.d. weights. The probability of adding an edge between two vertices is increasing in their weights. We require the weight distribution to have finite second moments and study the point process $\mathcal{C}_n$ on $\{3,4,\dots\}$, which counts how many cycles of the respective length are prese

Xueyan Niu, Bo Bai, Lei Deng, Wei Han

Increasing the size of a Transformer does not always lead to enhanced performance. This phenomenon cannot be explained by the empirical scaling laws. Furthermore, the model's enhanced performance is closely associated with its memorization of the training samples. We present a theoretical framework that sheds light on the memorization during pre-training of

Tingyu Shui, Walid Saad

Connected vehicular platoons provide a promising solution to improve traffic efficiency and ensure road safety. Vehicles in a platoon utilize on-board sensors and wireless vehicle-to-vehicle (V2V) links to share traffic information for cooperative adaptive cruise control. To process real-time control and alert information, there is a need to ensure clock syn

Aritram Dhar

In 1797, Pfaff gave a simple proof of a ${}_3F_2$ hypergeometric series summation formula which was much later reproved by Andrews in 1996. In the same paper, Andrews also proved other well-known hypergeometric identities using Pfaff's method. In this paper, we prove a number of terminating $q$-hypergeometric series-product identities using Pfaff's method th

Anton Semenkin, Vitaliy Bibaev, Yaroslav Sokolov, Kirill Krylov

In recent years, several industrial solutions for the problem of multi-token code completion appeared, each making a great advance in the area but mostly focusing on cloud-based runtime and avoiding working on the end user's device. In this work, we describe our approach for building a multi-token code completion feature for the JetBrains' IntelliJ Platform,

P. Mas-Buitrago, A. González-Marcos, E. Solano, V. M. Passegger

Deep learning (DL) techniques are a promising approach among the set of methods used in the ever-challenging determination of stellar parameters in M dwarfs. In this context, transfer learning could play an important role in mitigating uncertainties in the results due to the synthetic gap (i.e. difference in feature distributions between observed and synthet

Guido Granda-Muñoz, Enrique Vázquez-Semadeni, Gilberto C. Gómez

We investigate the alignment mechanism between the magnetic field and interstellar clouds formed by the collision of warm atomic gas. We find that the magnetic field, initially oriented parallel to the flow, is perturbed by a fast MHD shock, which amplifies magnetic field fluctuations parallel to the shock front. Behind the shock, the compressive downstream

Ankit Mahajan, Paul J. Robinson, Joonho Lee, David R. Reichman

In this work, we use neural quantum states (NQS) to describe the high-dimensional wave functions of electron-phonon coupled systems. We demonstrate that NQS can accurately and systematically learn the underlying physics of such problems through a variational Monte Carlo optimization of the energy with minimal incorporation of physical information even in hig

Paul J. Robinson, Joonho Lee, Ankit Mahajan, David R. Reichman

In this work we demonstrate that accurate ground state wave functions may be constructed for polarons in a fully ab initio setting across the wide range of couplings associated with both the large and small polaron limits. We present a single general unitary transformation approach which encompasses an ab initio version of the Lee-Low-Pines theory at weak co

Wenrui Li, Wei Zhang, Qinghao Zhang, Xuegong Zhang

Causal discovery based on observational data is important for deciphering the causal mechanism behind complex systems. However, the effectiveness of existing causal discovery methods is limited due to inferior prior knowledge, domain inconsistencies, and the challenges of high-dimensional datasets with small sample sizes. To address this gap, we propose a no

Yue Xia, Christoph Hofmeister, Maximilian Egger, Rawad Bitar

Federated learning (FL) shows great promise in large scale machine learning, but brings new risks in terms of privacy and security. We propose ByITFL, a novel scheme for FL that provides resilience against Byzantine users while keeping the users' data private from the federator and private from other users. The scheme builds on the preexisting non-private FL

Jeremy Hansen, Kirill Tuchin

We introduce and investigate the Color Chiral Cherenkov effect which consists in radiation of the circularly polarized gluons by a fast color charge moving with constant velocity in the presence of the Chiral Magnetic current. We derive the transition rates for all gluon polarizations. We compute the contribution of the Color Chiral Cherenkov effect to the p

Wolfgang Klassen, Shomi Ahmed, Kiera Pond Grehan, Chris Hovde

We report the performance of a magnetically silent optically pumped cesium magnetometer with a statistical sensitivity of 3.5 pT/rtHz at 1~Hz and a stability of 90 fT over 150 seconds of measurement. Optical pumping with coherent, linearly-polarized, resonant light leads to a relatively long-lived polarized ground state of the cesium vapour contained in a me

Carmela Calabrese, Stefano Berti, Giulia Pasquale, Lorenzo Natale

Addressing multi-label action recognition in videos represents a significant challenge for robotic applications in dynamic environments, especially when the robot is required to cooperate with humans in tasks that involve objects. Existing methods still struggle to recognize unseen actions or require extensive training data. To overcome these problems, we pr

Sven Danz, Mario Berta, Stefan Schröder, Pascal Kienast

We study the problem of estimating frequency response functions of systems of coupled, classical harmonic oscillators using a quantum computer. The functional form of these response functions can be mapped to a corresponding eigenproblem of a Hermitian matrix $H$, thus suggesting the use of quantum phase estimation. Our proposed quantum algorithm operates in

Brieuc Le Dé, Simon Huppert, Riccardo Spezia, Alex W. Chin

Excited state proton transfer is an ubiquitous phenomenon in biology and chemistry, spanning from the ultrafast reactions of photo-bases and acids to light-driven, enzymatic catalysis and photosynthesis. However, the simulation of such dynamics involves multiple challenges, since high-dimensional, out-of-equilibrium vibronic states play a crucial role, while

Jasna Prezelj, Fabio Vlacci

We present the topological foundations for the solvability of Multiplicative Cousin problems formulated on an axially symmetric domain $\Omega \subset \mathbb H.$ In particular, we provide a geometric construction of quaternionic Cartan coverings, which are generalizations of (complex) Cartan coverings as presented in Section 4 of [FP]. Because of the requir

Jan-Hendrik Ewers, Sarah Swinton, David Anderson, Euan McGookin

This study investigates the computational speed and accuracy of two numerical integration methods, cubature and sampling-based, for integrating an integrand over a 2D polygon. Using a group of rovers searching the Martian surface with a limited sensor footprint as a test bed, the relative error and computational time are compared as the area was subdivided t

Qiumei Huang, Qiao Zhu

This paper presents the double-activation neural network (DANN), a novel network architecture designed for solving parabolic equations with time delay. In DANN, each neuron is equipped with two activation functions to augment the network's nonlinear expressive capacity. Additionally, a new parameter is introduced for the construction of the quadratic terms i

Arefur Rahman, Daniel J. Egger, Christian Arenz

Current quantum computers suffer from noise that stems from interactions between the quantum system that constitutes the quantum device and its environment. These interactions can be suppressed through dynamical decoupling to reduce computational errors. However, the performance of dynamical decoupling depends on the type of the system-environment interactio

Nils Caci, Dag-Björn Hering, Matthias R. Walther, Kai P. Schmidt

The quantitative description of long-range order remains a challenge in quantum many-body physics. We provide zero-temperature results from two complementary methods for the ground-state energy per site, the sublattice magnetization, the spin gap, and the transverse spin correlation length for the spin-1/2 anisotropic quantum Heisenberg antiferromagnet on th

Junho Choi, Ryo Okui

This paper concerns the estimation of linear panel data models with endogenous regressors and a latent group structure in the coefficients. We consider instrumental variables estimation of the group-specific coefficient vector. We show that direct application of the Kmeans algorithm to the generalized method of moments objective function does not yield uniqu

Aissam Outchakoucht, Hamza Es-Samaali

Darija Open Dataset (DODa) represents an open-source project aimed at enhancing Natural Language Processing capabilities for the Moroccan dialect, Darija. With approximately 100,000 entries, DODa stands as the largest collaborative project of its kind for Darija-English translation. The dataset features semantic and syntactic categorizations, variations in s

Zichen Lian, Yongchao Wang, Yongqian Wang, Yang Feng

The interplay between nontrivial band topology and layered antiferromagnetism in MnBi2Te4 has opened up a new avenue for exploring topological phases of matter. Representative examples include the quantum anomalous Hall effect and axion insulator state observed in odd and even number layers of MnBi2Te4, when the top and bottom surfaces have parallel and anti

Althea V. Moorhead, Katie Milbrandt, Aaron Kingery

NASA's Meteoroid Engineering Model (MEM) is designed to provide aerospace engineers with an accurate description of potentially hazardous meteoroids. It accepts a spacecraft trajectory as input and its output files describe the flux, speed, directionality, and density of microgram- to gram-sized meteoroids relative to the provided trajectory. MEM provides th

Zheng-Wei Liao, Xin-Wei Yi, Jing-Yang You, Bo Gu

A recent experiment has reported a nitrogen-doped lutetium hydride acheving a remarkable Tc of 294 K at just 1 GPa, significantly reducing the required pressure for obtaining room temperature superconductivity. However, subsequent experimental and theoretical investigations have encountered difficulties in replicating these results, leaving the structure of

Yujie Liu, Ishtiaque Ahmed Navid, Zetian Mi, Emmanouil Kioupakis

Dilute concentrations of antimony (Sb) incorporation into GaN induce strong band-gap bowing and tunable room-temperature photoluminescence from the UV to the green spectral regions. However, the atomistic details of the incorporation of Sb into the GaN host remain unclear. In this work, we use first-principles calculations to understand the thermodynamics of

Bogdan Nica

We obtain asymptotic estimates for the $\ell^p$-operator norm of spherical averaging operators associated to certain geometric group actions. The motivating example is the case of Gromov hyperbolic groups, for which we obtain asymptotically sharp estimates. We deduce asymptotic lower bounds for the combinatorial expansion of spheres.

Qingpeng Kong, Ching-Hao Chiu, Dewen Zeng, Yu-Jen Chen

Numerous studies have revealed that deep learning-based medical image classification models may exhibit bias towards specific demographic attributes, such as race, gender, and age. Existing bias mitigation methods often achieve high level of fairness at the cost of significant accuracy degradation. In response to this challenge, we propose an innovative and

Gaurav Bhandari, S. D. Pathak, Manabendra Sharma, Anzhong Wang

In this paper, we invoke a generalized uncertainty principle (GUP) in the symmetry-reduced cosmological Hamiltonian for a universe driven by a quintessence scalar field with potential. Our study focuses on semi-classical regime. In particular, we derive the GUP-distorted Friedmann, Raychaudhuri, and the Klein-Gordon equation. This is followed by a systematic

Junfeng Jiao, Saleh Afroogh, Yiming Xu, Connor Phillips

This study addresses ethical issues surrounding Large Language Models (LLMs) within the field of artificial intelligence. It explores the common ethical challenges posed by both LLMs and other AI systems, such as privacy and fairness, as well as ethical challenges uniquely arising from LLMs. It highlights challenges such as hallucination, verifiable accounta

Alain Riou, Stefan Lattner, Gaëtan Hadjeres, Geoffroy Peeters

This paper addresses the problem of self-supervised general-purpose audio representation learning. We explore the use of Joint-Embedding Predictive Architectures (JEPA) for this task, which consists of splitting an input mel-spectrogram into two parts (context and target), computing neural representations for each, and training the neural network to predict

Takahiro Hiraki, Koichi Okai, Michael Bartokos, Kjeld Beeks

The radioisotope Th-229 is renowned for its extraordinarily low-energy, long-lived nuclear first-excited state. This isomeric state can be excited by VUV lasers and the transition from the ground state has been proposed as a reference transition for ultra-precise nuclear clocks. Such nuclear clocks will find multiple applications, ranging from fundamental ph

Robert Fulsche, Franz Luef, Reinhard F. Werner

We investigate Wiener's Tauberian theorem from the perspective of limit functions, which results in several new versions of the Tauberian theorem. Based on this, we formulate and prove analogous Tauberian theorems for operators in the sense of quantum harmonic analysis. Using these results, we characterize the class of slowly oscillating operators and show t

Yongqian Wang, Bohan Fu, Yongchao Wang, Zicheng Lian

The quantum anomalous Hall effect in layered antiferromagnet MnBi$_2$Te$_4$ harbors a rich interplay between magnetism and topology, holding a significant promise for low-power electronic devices and topological antiferromagnetic spintronics. In recent years, MnBi$_2$Te$_4$ has garnered considerable attention as the only known material to exhibit the antifer

Derong Xu, Yun Luo, Daniel Marx, Christoph Montag

The Electron-Ion Collider (EIC) plans to utilize the local crabbing crossing scheme. This paper explores the feasibility of adopting a single crab cavity with adjusted voltage, inspired by the successful global crabbing scheme in KEKB, to restore effective head-on collisions. Using weak-strong simulations, the study assesses the potential of this global crab

Alex Luedtke

We introduce an algorithm that simplifies the construction of efficient estimators, making them accessible to a broader audience. 'Dimple' takes as input computer code representing a parameter of interest and outputs an efficient estimator. Unlike standard approaches, it does not require users to derive a functional derivative known as the efficient influenc

Bingdong Li, Zixiang Di, Yongfan Lu, Hong Qian

Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling complex distributions of the Pareto optimal solutions is difficult with limited function evaluations. Existing Pareto set learning algorithms may exhibit considerable instability in suc

S. Mironov, M. Valencia-Villegas

We show that the full Horndeski theory with both curvature and torsion can support nonsingular, stable and subluminal cosmological solutions at all times. Thus, with torsion, the usual No-Go theorem that holds in a curved spacetime is avoided. In particular, it is essential to include the nonminimal derivative couplings of the $\mathcal{L}_{5}$ part of the H

Beilei Cui, Mobarakol Islam, Long Bai, An Wang

Depth estimation plays a crucial role in various tasks within endoscopic surgery, including navigation, surface reconstruction, and augmented reality visualization. Despite the significant achievements of foundation models in vision tasks, including depth estimation, their direct application to the medical domain often results in suboptimal performance. This

Ernesto Lax, Giancarlo Rinaldo, Francesco Romeo

We prove that wheels and block graphs have sequentially Cohen-Macaulay binomial edge ideals. Moreover, we provide a construction of new families of sequentially Cohen-Macaulay graphs by cones.

Francesco Alessio, Michele Arzano

We argue that gravitational interactions between particles require a departure from the conventional picture of the quantum state of a multiparticle system in terms of tensor products of one-particle states. This modification is essential in order to accommodate the existence of a new boost-like relativistic angular momentum charge which pairs of particles m

E. Dinesh Kumar, Steven H. Frankel

We propose a quantum algorithm for the Lattice Boltzmann (LB) method to simulate fluid flows in the low Reynolds number regime. First, we encode the particle distribution functions (PDFs) as probability amplitudes of the quantum state and demonstrate the need to control the state of the ancilla qubit during the initial state preparation. Second, we express t

Qinglong Cao, Yuntian Chen, Lu Lu, Hao Sun

Large-scale Vision-Language Models (VLMs) have demonstrated exceptional performance in natural vision tasks, motivating researchers across domains to explore domain-specific VLMs. However, the construction of powerful domain-specific VLMs demands vast amounts of annotated data, substantial electrical energy, and computing resources, primarily accessible to i

Naomi Davis, Jan-Hendrik Arling, Marta Baselga, Leena Diehl

In high-energy physics, there is a need to investigate alternative silicon sensor concepts that offer cost-efficient, large-area coverage. Sensors based on CMOS imaging technology present such a silicon sensor concept for tracking detectors. The CMOS Strips project investigates passive CMOS strip sensors fabricated by LFoundry in a 150nm technology. By emplo

Hashem Hamedivafa

We use spectro-polarimetric data recorded by Hinode to analyze the magnetic field configuration of a part of a sunspot (AR 10923) where a bundle of penumbral filaments are intruding into its umbra. We want to explore the role of the sunspot magnetic configuration in the formation and kinematics of the fine-structures, such as umbral dots and light bridges, i

Sergey Khrapak

A modified shoving model is applied to estimate the location of the glass transition in a one-component plasma. The estimated value of the coupling parameter $\Gamma\simeq 570$ at the glass transition is compared with other predictions available in the literature.

Vincent Viau

We consider a variant of the classical Erd\H{o}s-R\'enyi random graph, where components with surplus are slowed down to prevent the apparition of complex components. The sizes of the components of this process undergo a similar phase transition to that of the classical model, and in the critical window the scaling limit of the sizes of the components is a "f

Dachao Yu, Jiayuan Ma, Hao Xu

The protocols of distributed consensus normally aim to tolerate different types of faults including crash faults and byzantine faults that occur in the distributed systems. However, the dynamic network topology and stochastic wireless channels may cause the same trustworthy system to suffer both crash fault and byzantine fault. This article proposes the conc

Yuta Kozakai, Cindy Tsang

According to Letourmy and Vendramin, a representation of a skew brace is a pair of representations on the same vector space, one for the additive group and the other for the multiplicative group, that satisfies a certain compatibility condition. Following their definition, we shall explain how some of the results from representation theory of groups, such as

Ronan Keane

It seems that in the current age, computers, computation, and data have an increasingly important role to play in scientific research and discovery. This is reflected in part by the rise of machine learning and artificial intelligence, which have become great areas of interest not just for computer science but also for many other fields of study. More genera

Xin Jin, Vincenzo Aglieri, Young-Gyun Jeong, Atiye Pezeshki

Two-dimensional materials, including transition metal dichalcogenides, are attractive for a variety of applications in electronics as well as photonics and have recently been envisioned as an appealing platform for phonon polaritonics. However, their direct characterization in the terahertz spectral region, of interest for retrieving, e.g., their phonon resp

P B Jones

The linear relationship between pulsar micro-pulse widths and rotation period is consistent with the existence of a physical length L on the neutron-star surface and seen on the observer arc of transit across the polar cap. Within the ion-proton model it is the width of the minimum area of surface that can support the critical growth rate of the unstable two

Armand Coudray

We construct the conformal scattering operator for the scalar wave equation on the Vaidya spacetime using vector field methods. The spacetime we consider is Schwarzschild, near both past and future timelike infinities, in order to use existing decay results for the scalar field, ensuring our energy estimates. These estimates guarantee the injectivity of the

Mateusz Cedro, Marcin Chlebus

The increasing complexity of Artificial Intelligence models poses challenges to interpretability, particularly in the healthcare sector. This study investigates the impact of deep learning model complexity and Explainable AI (XAI) efficacy, utilizing four ResNet architectures (ResNet-18, 34, 50, 101). Through methodical experimentation on 4,369 lung X-ray im

Jue Jiang, Aneesh Rangnekar, Harini Veeraraghavan

Self-supervised learning (SSL) is an approach to extract useful feature representations from unlabeled data, and enable fine-tuning on downstream tasks with limited labeled examples. Self-pretraining is a SSL approach that uses the curated task dataset for both pretraining the networks and fine-tuning them. Availability of large, diverse, and uncurated publi

Boqian Zhang, Juanmian Lei

Machine learning techniques have been applied to enhance turbulence modeling in recent years. However, the "black box" nature of most machine learning techniques poses significant interpretability challenges in improving turbulence models. This paper introduces a novel unit-constrained turbulence modeling framework using symbolic regression to overcome these

Matteo Cederle, Marco Fabris, Gian Antonio Susto

Autonomous intersection management (AIM) poses significant challenges due to the intricate nature of real-world traffic scenarios and the need for a highly expensive centralised server in charge of simultaneously controlling all the vehicles. This study addresses such issues by proposing a novel distributed approach to AIM utilizing multi-agent reinforcement

Khoi Tran Dang, Kevin Delmas, Jérémie Guiochet, Joris Guérin

With the increasing use of neural networks in critical systems, runtime monitoring becomes essential to reject unsafe predictions during inference. Various techniques have emerged to establish rejection scores that maximize the separability between the distributions of safe and unsafe predictions. The efficacy of these approaches is mostly evaluated using th

Chong Zheng

Given two discrete Morse functions on a simplicial complex, we introduce the {\em connectedness homomorphism} between the corresponding discrete Morse complexes. This concept leads to a novel framework for studying the connectedness in discrete Morse theory at the chain complex level. In particular, we apply it to describe a discrete analogy to `cusp-degener

Damir Kinzebulatov

We obtain Sobolev regularity estimates for solutions of non-local parabolic equations with locally unbounded drift satisfying some minimal assumptions. These results yield Krylov bound for the corresponding Feller stable process as well as some a priori regularity estimates on solutions of McKean-Vlasov equations. A key element of our arguments is a paraboli

Qi Shi, Jingyi Sun, Hanwei Fu, Peizhe Fu

This paper introduces a novel blockchain-enabled authentication and communications network for scalable Internet of Vehicles, which aims to bolster security and confidentiality, diminish communications latency, and reduce dependence on centralised infrastructures like Certificate Authorities and Public Key Infrastructures by leveraging Blockchain-enabled Dom

Vishesh Jain, Huy Tuan Pham, Mehtaab Sawhney, Dmitrii Zakharov

We present an explicit subset $A\subseteq \mathbb{N} = \{0,1,\ldots\}$ such that $A + A = \mathbb{N}$ and for all $\varepsilon > 0$, \[\lim_{N\to \infty}\frac{\big|\big\{(n_1,n_2): n_1 + n_2 = N, (n_1,n_2)\in A^2\big\}\big|}{N^{\varepsilon}} = 0.\] This answers a question of Erd\H{o}s.

David Doty, Ben Heckmann

Chemical reaction networks (CRNs) model systems where molecules interact according to a finite set of reactions such as $A + B \to C$, representing that if a molecule of $A$ and $B$ collide, they disappear and a molecule of $C$ is produced. CRNs can compute Boolean-valued predicates $\phi:\mathbb{N}^d \to \{0,1\}$ and integer-valued functions $f:\mathbb{N}^d

Shah Mohammad Bahauddin, Stephen J Bradshaw

The study examines the heating profile of hot solar transition region loops, particularly focusing on transient brightenings observed in IRIS 1400{\AA} slit-jaw images. The findings challenge the adequacy of simplistic, singular heating mechanisms, revealing that the heating is temporally impulsive and requires a spatially complex profile with multiple heati

Rick Koenders, Joshua Moerman

We present an active automata learning algorithm which learns a decomposition of a finite state machine, based on projecting onto individual outputs. This is dual to a recent compositional learning algorithm by Labbaf et al. (2023). When projecting the outputs to a smaller set, the model itself is reduced in size. By having several such projections, we do no

Evgeny Smirnov

As shown by A. Melnikov, the orbits of a Borel subgroup acting by conjugation on upper-triangular matrices with square zero are indexed by involutions in the symmetric group. The inclusion relation among the orbit closures defines a partial order on involutions. We observe that the same order on involutive permutations also arises while describing the inclus

Boqi Chen, Kristóf Marussy, Oszkár Semeráth, Gunter Mussbacher

Graph convolutional neural networks (GCNs) are powerful tools for learning graph-based knowledge representations from training data. However, they are vulnerable to small perturbations in the input graph, which makes them susceptible to input faults or adversarial attacks. This poses a significant problem for GCNs intended to be used in critical applications

David Herel, Tomas Mikolov

How much is 56 times 37? Language models often make mistakes in these types of difficult calculations. This is usually explained by their inability to perform complex reasoning. Since language models rely on large training sets and great memorization capability, naturally they are not equipped to run complex calculations. However, one can argue that humans a

Felix Hekhorn

We discuss the positivity of parton distribution functions using the common $\overline{MS}$ factorization scheme. We find that in the perturbative regime $\overline{MS}$ PDFs inherit the strict positivity of physical PDFs. We explicitly discuss the scheme transformation by using suitable physical observables and find that $\overline{MS}$ PDFs are positive ab

Valerii K. Kozin, Even Thingstad, Daniel Loss, Jelena Klinovaja

We consider a two-dimensional electron gas interacting with a quantized cavity mode. We find that the coupling between the electrons and the photons in the cavity enhances the superconducting gap. Crucially, all terms in the Peierls phase are kept, in contrast to more naive approaches, which may result in spurious superradiant phase transitions. We use a mea

Elisabetta Colombo, Paola Frediani, Gian Pietro Pirola

In this paper we study asymptotic directions in the tangent bundle of the moduli space ${\mathcal M}_g$ of curves of genus $g$, namely those tangent directions that are annihilated by the second fundamental form of the Torelli map. We give examples of asymptotic directions for any $g \geq 4$. We prove that if the rank $d$ of a tangent direction $\zeta \in H^

Antoine Lotz

Multivariate Hawkes processes (MHP) are a class of point processes in which events at different coordinates interact through mutual excitation. The weighted adjacency matrix of the MHP encodes the strength of the relations, and shares its support with the causal graph of interactions of the process. We consider the problem of testing for causal relationships

Calliope Ryan-Smith, Jonathan Schilhan, Yujun Wei

It is sometimes desirable in choiceless constructions of set theory that one iteratively extends some ground model without adding new sets of ordinals after the first extension. Pushing this further, one may wish to have models $V \subseteq M \subseteq N$ of $\mathsf{ZF}$ such that $N$ contains no subsets of $V$ that do not already appear in $M$. We isolate,

Yiwen Zhu, Jinyi Liu, Wenya Wei, Qianyi Fu

Reinforcement Learning (RL) is a widely employed technique in decision-making problems, encompassing two fundamental operations -- policy evaluation and policy improvement. Enhancing learning efficiency remains a key challenge in RL, with many efforts focused on using ensemble critics to boost policy evaluation efficiency. However, when using multiple critic

Maxime Fuccellaro, Laurent Simon, Akka Zemmari

Recent research yielded a wide array of drift detectors. However, in order to achieve remarkable performance, the true class labels must be available during the drift detection phase. This paper targets at detecting drift when the ground truth is unknown during the detection phase. To that end, we introduce Gaussian Split Detector (GSD) a novel drift detecto

Bartosz Barzdajn, Christopher P. Race

Data-driven, machine learning (ML) models of atomistic interactions are often based on flexible and non-physical functions that can relate nuanced aspects of atomic arrangements into predictions of energies and forces. As a result, these potentials are as good as the training data (usually results of so-called ab initio simulations) and we need to make sure

F. J. Aragón-Artacho, W. Cai, Y. Censor, A. Gibali

In this paper we propose an approach for solving systems of nonlinear equations without computing function derivatives. Motivated by the application area of tomographic absorption spectroscopy, which is a highly-nonlinear problem with variables coupling, we consider a situation where straightforward translation to a fixed point problem is not possible becaus

Jean Auriol

In this paper, we design a stabilizing state-feedback control law for a system represented by a general class of integral delay equations subject to a pointwise and distributed input delay. The proposed controller is defined in terms of integrals of the state and input history over a fixed-length time window. We show that the closed-loop stability is guarant

J. Y. Nie, C. C. Zhao, C. Q. Xu, B. Li

Recent penetration depth measurement claimed the observation of unconventional superconductivity in the miassite Rh$_{17}$S$_{15}$ single crystals, evidenced by the linear-in-temperature penetration depth at low temperatures, thereby arguing for the presence of the lines of node in its superconducting gap structure. Here we measure the thermal conductivity o

Johannes Königs, Carsten Bockelmann, Armin Dekorsy

This paper presents an approach for instantaneous bandwidth estimation from level-crossing (LC) samples using a long short-term memory (LSTM) encoder-decoder architecture. LC sampling is a nonuniform sampling technique that is particularly useful for energy-efficient acquisition of signals with sparse spectra. Especially in combination with fully analog wire