October 2023 arXiv papers — page 98

Diego Rincon-Yanez, Chahinez Ounoughi, Bassem Sellami, Tarmo Kalvet

Knowledge representation (KR) is vital in designing symbolic notations to represent real-world facts and facilitate automated decision-making tasks. Knowledge graphs (KGs) have emerged so far as a popular form of KR, offering a contextual and human-like representation of knowledge. In international economics, KGs have proven valuable in capturing complex int

Xueyao Zhang, Zihao Fang, Yicheng Gu, Haopeng Chen

Singing Voice Conversion (SVC) is a technique that enables any singer to perform any song. To achieve this, it is essential to obtain speaker-agnostic representations from the source audio, which poses a significant challenge. A common solution involves utilizing a semantic-based audio pretrained model as a feature extractor. However, the degree to which the

Sebastián V. Romero, Xi Chen, Gloria Platero, Yue Ban

The Su-Schrieffer-Heeger (SSH) chain, which serves as a paradigmatic model for comprehending topological phases and their associated edge states, plays an essential role in advancing our understanding of quantum materials and quantum information processing and technology. In this paper, we introduce a hybrid analog-digital protocol designed for the nonadiaba

Honghua Chen, Nai Ding

Large language models possess remarkable capacity for processing language, but it remains unclear whether these models can further generate creative content. The present study aims to investigate the creative thinking of large language models through a cognitive perspective. We utilize the divergent association task (DAT), an objective measurement of creativ

Jannes Münchmeyer

Seismic phase association is an essential task for characterising seismicity: given a collection of phase picks, identify all seismic events in the data. In recent years, machine learning pickers have lead to a rapid growth in the number of seismic phase picks. Even though new associators have been suggested, these suffer from long runtimes and sensitivity i

Qixuan Li, Xiaoshuang Zhong, Liang Sun, Liang Dai

Melittin, a natural antimicrobial peptide comprising 26 amino acid residues, can kill bacteria by inducing pores in cell membranes. Clinical applications of melittin as an antibiotic require a thorough understanding of its poration mechanism and mutations that enhance its antimicrobial activity. Previous experiments showed Melp5, a variant of melittin with f

Eric Collet

The chiral nature of a badminton shuttlecock is responsible for its anti-clockwise spinning as it naturally propagates through the air. This induces a dissymmetry between left-handed and right-handed players and the resulting trajectories of the shuttlecock, which were captured in real condition on the badminton court and in slow-motion at 3700 fps. The vide

Neville K Kitson, Anthony C Constantinou

Causal Bayesian Networks (CBNs) are an important tool for reasoning under uncertainty in complex real-world systems. Determining the graphical structure of a CBN remains a key challenge and is undertaken either by eliciting it from humans, using machine learning to learn it from data, or using a combination of these two approaches. In the latter case, human

Guoxin Wang, Qingyuan Wang, Ganesh Neelakanta Iyer, Avishek Nag

Unsupervised learning methods have become increasingly important in deep learning due to their demonstrated large utilization of datasets and higher accuracy in computer vision and natural language processing tasks. There is a growing trend to extend unsupervised learning methods to other domains, which helps to utilize a large amount of unlabelled data. Thi

Gurjit Kaur, Aakriti Bagai, Gulsheen Ahuja, Manmohan Gupta

Using unitarity, unlike the approaches available in the literature, we have constructed 9 independent representations of CKM matrix starting with each of the 9 elements of the matrix. The relationship of these independently constructed representations with the already available ones in the literature has been compared and discussed. Further, the implications

Volha Lazuka, Annika Elwert

Using the introduction of comprehensive sex education in Sweden as a natural experiment, we explore how educational curricula can shape social norms and impact personal well-being. Inspired by liberal values, the curriculum taught more than just biology. It instilled lessons on abstinence, rational family planning, and the importance of taking social respons

Leonardo Barreto, Fabio M. Canedo, Maria M. M. Paulino, Jacquelyn Noronha-Hostler

The $R$-dependence of jet observables provides a new tool in understanding the interplay between the jet energy-loss mechanism and medium response in heavy-ion collisions. This work applies the Monte Carlo events generator JEWEL and PYTHIA, coupled with $\rm T_{R}ENTo$ initial conditions and the state-of-the-art (2+1)D v-USPhydro, for the simulation of jet d

Dileep Sivaraman, Branesh M. Pillai, Jackrit Suthakorn, Songpol Ongwattanakul

A modified Lagrange Polynomial is introduced for polynomial extrapolation, which can be used to estimate the equally spaced values of a polynomial function. As an example of its application, this article presents a prime-generating algorithm based on a 1-degree polynomial that can generate prime numbers from consecutive primes. The algorithm is based on the

Yuji Tanaka, Kimitaka Sumi, Itsuki Iwata, Takuya Sakamoto

The topology method is an algorithm for accurate estimation of instantaneous heartbeat intervals using millimeter-wave radar signals. In this model, feature points are extracted from the skin displacement waveforms generated by heartbeats and a complex number is assigned to each feature point. However, these numbers have been assigned empirically and without

Chunyan Li, Yaroslav V. Kartashov

In this work, using binary Bose-Einstein condensate we propose a new type of topological insulator that does not explicitly use specially designed potential landscape, but instead utilizes spatially inhomogeneous Rabi coupling between two components, in the form of one- or two-dimensional Su-Schrieffer-Heeger (SSH) structure, combined with Zeeman splitting.

Esther Lagemann, Steven L. Brunton, Christian Lagemann

Friction drag from a turbulent fluid moving past or inside an object plays a crucial role in domains as diverse as transportation, public utility infrastructure, energy technology, and human health. As a direct measure of the shear-induced friction forces, an accurate prediction of the wall-shear stress can contribute to sustainability, conservation of resou

Evelina Leivada, Vittoria Dentella, Elliot Murphy

In the field of Artificial (General) Intelligence (AI), the several recent advancements in Natural language processing (NLP) activities relying on Large Language Models (LLMs) have come to encourage the adoption of LLMs as scientific models of language. While the terminology employed for the characterization of LLMs favors their embracing as such, it is not

O. Grover, T. Eich, P. Manz, W. Zholobenko

A recently developed reduced model of H-mode sustainment based on interchange-drift-Alfv\'en turbulence description in the vicinity of the separatrix matching experimental observations in ASDEX Upgrade has been extended to experiments with the unfavorable $\nabla B$ drift. The combination with the theory of the magnetic-shear-induced Reynolds stress offers a

Jerónimo García-Mejía, Claudio Llosa Isenrich, Gabriel Pallier

We determine the Dehn functions of central products of two families of filiform nilpotent Lie groups of arbitrary dimension with all simply connected nilpotent Lie groups with cyclic centre and strictly lower nilpotency class. We also determine the Dehn functions of all central products of nilpotent Lie groups of dimension at most $5$ with one-dimensional ce

Eric Petermann, Peter Bossew, Joachim Kemski, Valeria Gruber

Accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon-prone areas. Indoor radon concentration at the national scale is usually estimated on the basis of extensive measurement campaigns. However, characteristics of the sampled households often differ from the characteristics of the target p

Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li

Diffusion models have impressive image generation capability, but low-quality generations still exist, and their identification remains challenging due to the lack of a proper sample-wise metric. To address this, we propose BayesDiff, a pixel-wise uncertainty estimator for generations from diffusion models based on Bayesian inference. In particular, we deriv

Peter Polák

Simultaneous speech translation (SST) aims to provide real-time translation of spoken language, even before the speaker finishes their sentence. Traditionally, SST has been addressed primarily by cascaded systems that decompose the task into subtasks, including speech recognition, segmentation, and machine translation. However, the advent of deep learning ha

Inês Koch, Carla Teixeira Lopes, Cristina Ribeiro

Archives are facing numerous challenges. On the one hand, archival assets are evolving to encompass digitized documents and increasing quantities of born-digital information in diverse formats. On the other hand, the audience is changing along with how it wishes to access archival material. Moreover, the interoperability requirements of cultural heritage rep

Wen-Tao Lyu, Yun-He Lyu, Man-Yu Duan, Guan-Ying Wang

Recently, the LHCb Collaboration has measured the processes $B^0\to\bar{D}^0D_s^+\pi^-$ and $B^+\to\bar{D}^0D_s^+\pi^+$, where the $D_s^+\pi^-$ and $D_s^+\pi^+$ invariant mass distributions show the significant signals of two new open-flavor tetraquark states $T_{c\bar{s}}(2900)^0$ and $T_{c\bar{s}}(2900)^{++}$, as the two of the isospin triplet. In this wor

Chao Li, Chen Gong, Qiang He, Xinwen Hou

The combination of deep reinforcement learning (DRL) with ensemble methods has been proved to be highly effective in addressing complex sequential decision-making problems. This success can be primarily attributed to the utilization of multiple models, which enhances both the robustness of the policy and the accuracy of value function estimation. However, th

Peter W. Glynn, Royi Jacobovic, Michel Mandjes

Let $J(\cdot)$ be a compound Poisson process with rate $\lambda>0$ and a jumps distribution $G(\cdot)$ concentrated on $(0,\infty)$. In addition, let $V$ be a random variable which is distributed according to $G(\cdot)$ and independent from $J(\cdot)$. Define a new process $W(t)\equiv W_V(t)\equiv V+J(t)-t$, $t\geqslant 0$ and let $\tau_V$ be the first time

Mauricio Ayala-Rincon, David M. Cerna, Andres Felipe Gonzalez Barragan, Temur Kutsia

Interest in anti-unification, the dual problem of unification, is on the rise due to applications within the field of software analysis and related areas. For example, anti-unification-based techniques have found uses within clone detection and automatic program repair methods. While syntactic forms of anti-unification are enough for many applications, some

Shakti Singh, Manan Dholakia, Sharat Chandra

Iron phosphate glasses (IPG) have been proposed as futuristic glass material for nuclear waste immobilization, anode material for lithium batteries and also as bioactive glass. In the last decade, there have been attempts to propose atomistic models of IPG to explain their properties from atomistic viewpoint and to predict their behavior in radioactive envir

I. V. Anikin

We present the further development of the vacuum massless integrations. In particular, in the Gorishny-Isaev formula, it has been shown that the delta function representing UV-regime should be treated within the sequential approach. It allows us to resolve the problem of vacuum integrations related to the analytical continuation of diagram indices.

Hao Wu, Qiuye Wang, Bai Xue, Naijun Zhan

Constraint-solving-based program invariant synthesis takes a parametric invariant template and encodes the (inductive) invariant conditions into constraints. The problem of characterizing the set of all valid parameter assignments is referred to as the strong invariant synthesis problem, while the problem of finding a concrete valid parameter assignment is c

Oana-Iuliana Popescu, Andreas Gerhardus, Jakob Runge

Conditional independence testing (CIT) is a common task in machine learning, e.g., for variable selection, and a main component of constraint-based causal discovery. While most current CIT approaches assume that all variables are numerical or all variables are categorical, many real-world applications involve mixed-type datasets that include numerical and ca

Ioannis Mavromatis, Stefano De Feo, Pietro Carnelli, Robert J. Piechocki

The Open Radio Access Network (O-RAN) is a burgeoning market with projected growth in the upcoming years. RAN has the highest CAPEX impact on the network and, most importantly, consumes 73% of its total energy. That makes it an ideal target for optimisation through the integration of Machine Learning (ML). However, the energy consumption of ML is frequently

Ekin Ergen, Moritz Grillo

We study the expressivity of ReLU neural networks in the setting of a binary classification problem from a topological perspective. Recently, empirical studies showed that neural networks operate by changing topology, transforming a topologically complicated data set into a topologically simpler one as it passes through the layers. This topological simplific

Ákos K. Matszangosz, Matthias Wendt

We study the structure of mod 2 cohomology rings of oriented Grassmannians $\tilde{\operatorname{Gr}}_k(n)$ of oriented $k$-planes in $\mathbb{R}^n$. Our main focus is on the structure of the cohomology ring ${\rm H}^*(\tilde{\operatorname{Gr}}_k(n);\mathbb{F}_2)$ as a module over the characteristic subring $C$, which is the subring generated by the Stiefel-

Pipat Harata, Wipada Hongthong, Prathan Srivilai

We present a novel approach for calculating the Coulomb Blockade Phase Diagram (CBPD) in the experimentally accessible strong coupling regime of a single-electron transistor (SET). Our method utilizes the Path Integral Monte Carlo (PIMC) technique to accurately compute the Coulomb oscillation of the Differential Capacitance (DC). Furthermore, we investigate

Seohyeon Cha, Honggu Kang, Joonhyuk Kang

Accurate uncertainty quantification in graph neural networks (GNNs) is essential, especially in high-stakes domains where GNNs are frequently employed. Conformal prediction (CP) offers a promising framework for quantifying uncertainty by providing $\textit{valid}$ prediction sets for any black-box model. CP ensures formal probabilistic guarantees that a pred

Roman Novikov

We consider a plane wave, a radiation solution, and the sum of these solutions (total solution) for the Helmholtz equation in an exterior region in $\R^3$. We consider a ray in this region, such that its direction is different from the propagation direction of the plane wave. We show that the restriction of the radiation solution to this ray is uniquely dete

Emmanuele Battista, Harold C. Steinacker

We study cosmological solutions of the IKKT model with $k=-1$ FLWR geometry, taking into account one-loop corrections. A previously discussed covariant quantum spacetime is found to be stabilized through one-loop effects at early times, without adding a mass term to the model. At late times, this background is modified and approaches a solution of the classi

L. Marra, M. Brigitte, N. Rodriguez Cavero, S. Chun

We present the results of the first X-ray polarimetric observation of the low-mass X-ray binary 4U 1957+115, performed with the Imaging X-ray Polarimetry Explorer in May 2023. The binary system has been in a high-soft spectral state since its discovery and is thought to host a black hole. The $\sim$571 ks observation reveals a linear polarisation degree of $

Leon Würsching, Matthias Hollick

Disaster scenarios can disconnect entire cities from the core network (CN), isolating base stations (BSs) and disrupting the Internet connection of app services for many users. Such a disruption is particularly disastrous when it affects critical app services such as communication, information, and navigation. Deploying local app servers at the network edge

Adolfo Ballester-Bolinches, Maria Ferrara, Vicent Pérez-Calabuig, Marco Trombetti

The aim of this short note is to completely answer Questions 2.34 and 2.35 of arXiv:1806.01127. In particular, we show that a finite strong-nil skew brace $B$ of abelian type need not be right-nilpotent, but that this is the case if~$B$ is of nilpotent type and $b\ast b=0$ for all $b\in B$ (our examples show that this is the best possible result).

Lasse Elsemüller, Hans Olischläger, Marvin Schmitt, Paul-Christian Bürkner

Sensitivity analyses reveal the influence of various modeling choices on the outcomes of statistical analyses. While theoretically appealing, they are overwhelmingly inefficient for complex Bayesian models. In this work, we propose sensitivity-aware amortized Bayesian inference (SA-ABI), a multifaceted approach to efficiently integrate sensitivity analyses i

Mintu Karmakar

In the realm of pandemic dynamics, understanding the intricate interplay between disease transmission, interventions, and immunity is pivotal for effective control strategies. Through a rigorous agent-based computer simulation, we embarked on a comprehensive exploration, traversing unmitigated spread, lockdown scenarios, and the transformative potential of v

JuHyeon Lee, Johannes Bischoff, A. O. Hernandez-Castillo, Elahe Abdiha

We report a combined experimental and theoretical study on the influence of microwave pulse durations on enantiomer-specific state transfer. Two triads of rotational states within a chiral molecule (1-indanol) are selected to address the possible scenarios. In the triad connected to the absolute ground state, the simplest triad that exists for all chiral mol

F. Heiße, M. Door, T. Sailer, P. Filianin

We present the measurements of individual bound electron $g$ factors of $^{20}\text{Ne}^{9+}$ and $^{22}\text{Ne}^{9+}$ on the relative level of $0.1\,\text{parts}$ per billion. The comparison with theory represents the most stringent test of bound-state QED in strong electric fields. A dedicated mass measurement results in $m\left(^{20}\text{Ne}\right)=19.9

Andrea Bisterzo, Giona Veronelli

The aim of this paper is to prove a qualitative property, namely the preservation of positivity, for Schr\"odinger-type operators acting on $L^p$ functions defined on (possibly incomplete) Riemannian manifolds. A key assumption is a control of the behaviour of the potential of the operator near the Cauchy boundary of the manifolds. As a by-product, we establ

Huan Yuan, Chao Liao, Jianchao Tan, Peng Yao

Visual Transformers have achieved great success in almost all vision tasks, such as classification, detection, and so on. However, the model complexity and the inference speed of the visual transformers hinder their deployments in industrial products. Various model compression techniques focus on directly compressing the visual transformers into a smaller on

Shayan Alipour, Alessandro Galeazzi, Emanuele Sangiorgio, Michele Avalle

The role of social media in information dissemination and agenda-setting has significantly expanded in recent years. By offering real-time interactions, online platforms have become invaluable tools for studying societal responses to significant events as they unfold. However, online reactions to external developments are influenced by various factors, inclu

Sebastian Andres, David A. Croydon, Takashi Kumagai

We present on-diagonal heat kernel estimates and quantitative homogenization statements for the one-dimensional Bouchaud trap model. The heat kernel estimates are obtained using standard techniques, with key inputs coming from a careful analysis of the volume growth of the invariant measure of the process under study. As for the quantitative homogenization r

Minkush Kansal, Vincent Bertin, Charu Datt, Jens Eggers

The classical Cox-Voinov theory of contact line motion provides a relation between the macroscopically observable contact angle, and the microscopic wetting angle as a function of contact line velocity. Here we investigate how viscoelasticity, specifically the normal stress effect, modifies wetting dynamics. Using the thin film equation for the second-order

Ting Zhang, Ivana Clairine Irsan, Ferdian Thung, David Lo

Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and for

Elad Yoshai, Gil Goldinger, Miki Haifler, Natan T. Shaked

Histopathology plays a pivotal role in medical diagnostics. In contrast to preparing permanent sections for histopathology, a time-consuming process, preparing frozen sections is significantly faster and can be performed during surgery, where the sample scanning time should be optimized. Super-resolution techniques allow imaging the sample in lower magnifica

Isabeau Birindelli, Giulio Galise, Yannick Sire

We show that bounded viscosity solutions of some nonlocal degenerate Isaacs type operators of order $2s$ are H\"older continuous, provided $s$ is sufficiently close to 1. As an application we obtain a Liouville theorem.

Nicolas Heintz, Tom Francart, Alexander Bertrand

Linear Discriminant Analysis (LDA) is one of the oldest and most popular linear methods for supervised classification problems. In this paper, we demonstrate that it is possible to compute the exact projection vector from LDA models based on unlabelled data, if some minimal prior information is available. More precisely, we show that only one of the followin

Tessa Nogatz, Claudia Redenbach, Katja Schladitz

We present a novel algorithm explicitly tailored to estimate motion from time series of 3D images of concrete. Such volumetric images are usually acquired by Computed Tomography and can contain for example in situ tests, or more complex procedures like self-healing. Our algorithm is specifically designed to tackle the challenge of large scale in situ investi

C. M. Raiteri, M. Villata, M. I. Carnerero, S. S. Savchenko

In 2022 the BL Lac object S4 0954+65 underwent a major variability phase, reaching its historical maximum brightness in the optical and $\gamma$-ray bands. We present optical photometric and polarimetric data acquired by the Whole Earth Blazar Telescope (WEBT) Collaboration from 2022 April 6 to July 6. Many episodes of unprecedented fast variability were det

Sebastian Andres, David Croydon, Takashi Kumagai

It is well-known that stochastic processes on fractal spaces or in certain random media exhibit anomalous heat kernel behaviour. One manifestation of such irregular behaviour is the presence of fluctuations in the short- or long-time asymptotics of the on-diagonal heat kernel. In this note we review some examples for which such fluctuations are known to occu

Yulong Dou, Lanzhuju Mei, Dinggang Shen, Zhiming Cui

Orthodontics focuses on rectifying misaligned teeth (i.e., malocclusions), affecting both masticatory function and aesthetics. However, orthodontic treatment often involves complex, lengthy procedures. As such, generating a 2D photograph depicting aligned teeth prior to orthodontic treatment is crucial for effective dentist-patient communication and, more im

Arno Candel, Jon McKinney, Philipp Singer, Pascal Pfeiffer

Large Language Models (LLMs) represent a revolution in AI. However, they also pose many significant risks, such as the presence of biased, private, copyrighted or harmful text. For this reason we need open, transparent and safe solutions. We introduce a complete open-source ecosystem for developing and testing LLMs. The goal of this project is to boost open

Sherko R. HmaSalah, Aras Asaad

Automatic border control systems are wide spread in modern airports worldwide. Morphing attacks on face biometrics is a serious threat that undermines the security and reliability of face recognition systems deployed in airports and border controls. Therefore, developing a robust Machine Learning (ML) system is necessary to prevent criminals crossing borders

Yoshio Ebihara, Xin Dai, Victor Magron, Dimitri Peaucelle

This paper is concerned with the computation of the local Lipschitz constant of feedforward neural networks (FNNs) with activation functions being rectified linear units (ReLUs). The local Lipschitz constant of an FNN for a target input is a reasonable measure for its quantitative evaluation of the reliability. By following a standard procedure using multipl

Peng Yao, Chao Liao, Jiyuan Jia, Jianchao Tan

Deep neural networks have gained great success due to the increasing amounts of data, and diverse effective neural network designs. However, it also brings a heavy computing burden as the amount of training data is proportional to the training time. In addition, a well-behaved model requires repeated trials of different structure designs and hyper-parameters

Haruki Takanashi, Kaoru Teranishi, Kiminao Kogiso

This study aims to develop an encrypted motion-copying system using homomorphic encryption for secure motion preservation and reproduction. A novel concept of encrypted motion-copying systems is introduced, realizing the preservation, edition, and reproduction of the motion over encrypted data. The developed motion-copying system uses the conventional encryp

Yulan Hu, Zhirui Yang, Sheng Ouyang, Yong Liu

Self-Supervised Learning (SSL) has shown significant potential and has garnered increasing interest in graph learning. However, particularly for generative SSL methods, its potential in Heterogeneous Graph Learning (HGL) remains relatively underexplored. Generative SSL utilizes an encoder to map the input graph into a latent representation and a decoder to r

Loren Coquille, Christof Kuelske, Arnaud Le Ny

We prove the continuity of the extremal decomposition measure of the free state of low temperature Potts models, and more generally of ferromagnetic finite-spin models, on a regular tree, including general clock models. The decomposition is supported on uncountably many inhomogeneous extremal states, that we call glassy states. The method of proof provides e

Mentzelos Melistas

Let $L$ be a number field and let $\ell$ be a prime number. Rasmussen and Tamagawa conjectured, in a precise sense, that abelian varieties whose field of definition of the $\ell$-power torsion is both a pro-$\ell$ extension of $L(\mu_\ell)$ and unramified away from $\ell$ are quite rare. In this paper, we formulate an analogue of the Rasmussen--Tamagawa conj

Till Koebe, Zinnya del Villar, Brahmani Nutakki, Nursulu Sagimbayeva

Child pornography represents a severe form of exploitation and victimization of children, leaving the victims with emotional and physical trauma. In this study, we aim to analyze local patterns of child pornography consumption across 1341 French communes in 20 metropolitan regions of France using fine-grained mobile traffic data of Tor network-related web se

Ju-Feng Wu

We prove a comparison theorem between Greenberg--Benois $\mathcal{L}$-invariants and Fontaine--Mazur $\mathcal{L}$-invariants. Such a comparison theorem supplies an affirmative answer to a speculation of Besser--de Shalit.

Johannes Liem, Jakob Kusnick, Samuel Beck, Florian Windhager

Stories are as old as human history - and a powerful means for the engaging communication of information, especially in combination with visualizations. The InTaVia project is built on this intersection and has developed a platform which supports the workflow of cultural heritage experts to create compelling visualization-based stories: From the search for r

Lorenzo Canale, Alberto Messina

The Italian Digital Media Observatory (IDMO) project, part of a European initiative, focuses on countering disinformation and fake news. This report outlines contributions from Rai-CRITS to the project, including: (i) the creation of novel datasets for testing technologies (ii) development of an automatic model for categorizing Pagella Politica verdicts to f

Hongxiang Fan, Stylianos I. Venieris, Alexandros Kouris, Nicholas D. Lane

Running multiple deep neural networks (DNNs) in parallel has become an emerging workload in both edge devices, such as mobile phones where multiple tasks serve a single user for daily activities, and data centers, where various requests are raised from millions of users, as seen with large language models. To reduce the costly computational and memory requir

Leontine Alkema, Thomas Brendan Murphy, Adrian E. Raftery

Professor Adrian E. Raftery is the Boeing International Professor of Statistics and Sociology, and an adjunct professor of Atmospheric Sciences, at the University of Washington in Seattle. He was born in Dublin, Ireland, and obtained a B.A. in Mathematics and an M.Sc. in Statistics and Operations Research at Trinity College Dublin. He obtained a doctorate in

Uri Stern, Daphna Weinshall

The infrequent occurrence of overfit in deep neural networks is perplexing. On the one hand, theory predicts that as models get larger they should eventually become too specialized for a specific training set, with ensuing decrease in generalization. In contrast, empirical results in image classification indicate that increasing the training time of deep mod

Zige Wang, Yonggang Zhang, Zhen Fang, Long Lan

Adapting models deployed to test distributions can mitigate the performance degradation caused by distribution shifts. However, privacy concerns may render model parameters inaccessible. One promising approach involves utilizing zeroth-order optimization (ZOO) to train a data adaptor to adapt the test data to fit the deployed models. Nevertheless, the data a

Jun Wu, Sicheng Li, Sihui Ji, Yifei Yang

Recovering 3D geometry and textures of individual objects is crucial for many robotics applications, such as manipulation, pose estimation, and autonomous driving. However, decomposing a target object from a complex background is challenging. Most existing approaches rely on costly manual labels to acquire object instance perception. Recent advancements in 2

S. Senthamarai Kannan, Arpita Nayek

Let $r$ and $q$ be positive integers and $n=qr+1.$ Let $G = SL(n, \mathbb{C})$ and $T$ be a maximal torus of $G.$ Let $P^{\alpha_r}$ be the maximal parabolic subgroup of $G$ corresponding to the simple root $\alpha_r.$ Let $\omega_r$ be the fundamental weight corresponding to $\alpha_r.$ Let $W$ be the Weyl group of $G$ and $W_{P^{\alpha_r}}$ be the Weyl gro

Amar Kumar Banerjee, Indrajit Debnath

In this paper we have introduced the notion of $\mathcal{I}_{(s)}$-density point corresponding to the family of unbounded and $\mathcal{I}$-monotonic increasing positive real sequences, where $\mathcal{I}$ is the ideal of subsets of the set of natural numbers. We have studied the corresponding topology in the space of reals and have investigated several prop

M. Benyounes, T. Levasseur, E. Loubeau, E. Vergara-Diaz

We consider normal almost contact structures on a Riemannian manifold and, through their associated sections of an ad-hoc twistor bundle, study their harmonicity, as sections or as maps. We rewrite these harmonicity equations in terms of the curvature tensor and find conditions relating the harmonicity of the almost contact metric and almost complex structur

Xin Su, Yao Zhou, Zifei Shan, Qian Chen

It is a long-standing challenge in modern recommender systems to effectively make recommendations for new users, namely the cold-start problem. Cross-Domain Recommendation (CDR) has been proposed to address this challenge, but current ways to represent users' interests across systems are still severely limited. We introduce Personal Knowledge Graph (PKG) as

Qinrui Tang, Hao Cheng

The widespread utilization of smartphones has provided extensive availability to Inertial Measurement Units, providing a wide range of sensory data that can be advantageous for the detection of transportation modes. The objective of this study is to propose a novel end-to-end approach to effectively explore a reduced amount of sensory data collected from a s

Mentzelos Melistas

In this article, we investigate the possible torsion subgroups of twists of abelian varieties with good reduction. As an application, we prove a theorem concerning ramified primes over any quadratic extension where odd-order torsion growth is achieved. In particular, we show that for every rational elliptic curve and every imaginary quadratic field not equal

Dipak Wani, Samuel Ackerman, Eitan Farchi, Xiaotong Liu

Logs enable the monitoring of infrastructure status and the performance of associated applications. Logs are also invaluable for diagnosing the root causes of any problems that may arise. Log Anomaly Detection (LAD) pipelines automate the detection of anomalies in logs, providing assistance to site reliability engineers (SREs) in system diagnosis. Log patter

Yilmazcan Ozyurt, Stefan Feuerriegel, Ce Zhang

Document-level relation extraction aims at inferring structured human knowledge from textual documents. State-of-the-art methods for this task use pre-trained language models (LMs) via fine-tuning, yet fine-tuning is computationally expensive and cannot adapt to new relation types or new LMs. As a remedy, we leverage the generalization capabilities of pre-tr

Risheng Pei, Joé Petrazoller, Achraf Atila, Simon Arnoldi

Solute segregation at low-angle grain boundaries (LAGBs) critically affects the microstructure and mechanical properties of magnesium (Mg) alloys. In modern alloys containing multiple substitutional elements, understanding solute-solute interactions at microstructural defects becomes essential for alloy design. This study investigates the co-segregation mech

Zeyu Zhang, Lu Li, Xingyu Ji, Kaiqi Zhao

Signed graphs are powerful models for representing complex relations with both positive and negative connections. Recently, Signed Graph Neural Networks (SGNNs) have emerged as potent tools for analyzing such graphs. To our knowledge, no prior research has been conducted on devising a training plan specifically for SGNNs. The prevailing training approach fee

Wenjin Li

The autonomous valet parking (AVP) functionality in self-driving vehicles is currently capable of handling most simple parking tasks. However, further training is necessary to enable the AVP algorithm to adapt to complex scenarios and complete parking tasks in any given situation. Training algorithms with real-world data is time-consuming and labour-intensiv

Xusheng Zhao, Hao Liu, Qiong Dai, Hao Peng

Synthetic lethality (SL) prediction is used to identify if the co-mutation of two genes results in cell death. The prevalent strategy is to abstract SL prediction as an edge classification task on gene nodes within SL data and achieve it through graph neural networks (GNNs). However, GNNs suffer from limitations in their message passing mechanisms, including

Javier Huertas-Tato, Alejandro Martin, David Camacho

Online Social Networks serve as fertile ground for harmful behavior, ranging from hate speech to the dissemination of disinformation. Malicious actors now have unprecedented freedom to misbehave, leading to severe societal unrest and dire consequences, as exemplified by events such as the Capitol assault during the US presidential election and the Antivaxx m

Yiqi Chen, Tobias Oechtering, Mikael Skoglund, Yuan Luo

The integrated sensing and communication (ISAC) problem with general state and channel distributions is investigated. General formulas of the capacity-distortion tradeoff for the ISAC problem under maximal and average distortion constraints are provided. The results cover some existing communication models such as the general point-to-point channel and Gel'f

Hsuan Su, Cheng-Chu Cheng, Hua Farn, Shachi H Kumar

Recently, researchers have made considerable improvements in dialogue systems with the progress of large language models (LLMs) such as ChatGPT and GPT-4. These LLM-based chatbots encode the potential biases while retaining disparities that can harm humans during interactions. The traditional biases investigation methods often rely on human-written test case

Oscar Jarrín, Gastón Vergara-Hermosilla

We consider the stationary (time-independent) Navier-Stokes equations in the whole threedimensional space, under the action of a source term and with the fractional Laplacian operator (--$\Delta$) $\alpha$/2 in the diffusion term. In the framework of Lebesgue and Lorentz spaces, we find some natural sufficient conditions on the external force and on the para

Uri Stern, Daniel Shwartz, Daphna Weinshall

Deep neural networks have become the method of choice for solving many classification tasks, largely because they can fit very complex functions defined over raw data. The downside of such powerful learners is the danger of overfit. In this paper, we introduce a novel ensemble classifier for deep networks that effectively overcomes overfitting by combining m

Marion Ribout, Batoul Attar, Carole Hénaux, Jean-François Llibre

The torque motor is the most common technology used in electrohydraulic two-stage servovalves to drive the hydraulic pilot stage. As it is a key component in these valves, its performance considerably affects the overall performance of servovalve systems. Modeling accurately the magnetic behavior of the torque motor will help to get a more realistic performa

Thomas Chaffre, Jonathan Wheare, Andrew Lammas, Paulo Santos

Learning-based adaptive control methods hold the premise of enabling autonomous agents to reduce the effect of process variations with minimal human intervention. However, its application to autonomous underwater vehicles (AUVs) has so far been restricted due to 1) unknown dynamics under the form of sea current disturbance that we can not model properly nor

María Claudia Ramirez-Tannus, Arjan Bik, Lars Cuijpers, Rens Waters

We present the first results of the eXtreme UV Environments (XUE) James Webb Space Telescope (JWST) program, that focuses on the characterization of planet forming disks in massive star forming regions. These regions are likely representative of the environment in which most planetary systems formed. Understanding the impact of environment on planet formatio

Andreas Savin, Jacek Karwowski

The adiabatic connection formalism, usually based on the first-order perturbation theory, has been generalized to an arbitrary order. The generalization stems from the observation that the formalism can be derived from a properly arranged Taylor expansion. The second-order theory is developed in detail and applied to the description of two electrons in a par

Nick Huggett, Karim P. Y. Thébault

We conduct a case study analysis of a proposal for the emergence of time based upon the approximate derivation of three grades of temporal structure within an explicit quantum cosmological model which obeys a Wheeler-DeWitt type equation without an extrinsic time parameter. Our main focus will be issues regarding the consistency of the approximations and der

Jiahui Zhang, Li Deng, Yueping Niu, Shangqing Gong

Chainwise stimulated Raman adiabatic passage (C-STIRAP) in M-type molecular system is a good alternative in creating ultracold deeply-bound molecules when the typical STIRAP in {\Lambda}-type system does not work due to weak Frank-Condon factors between states. However, its creation efficiency under the smooth evolution is generally low. During the process,

Felix Obite, Ali Krayani, Atm S. Alam, Lucio Marcenaro

Future wireless networks will need to improve adaptive resource allocation and decision-making to handle the increasing number of intelligent devices. Unmanned aerial vehicles (UAVs) are being explored for their potential in real-time decision-making. Moreover, cognitive non-orthogonal multiple access (Cognitive-NOMA) is envisioned as a remedy to address spe

Abdul Waheed, Bashar Talafha, Peter Sullivan, AbdelRahim Elmadany

Arabic is a complex language with many varieties and dialects spoken by over 450 millions all around the world. Due to the linguistic diversity and variations, it is challenging to build a robust and generalized ASR system for Arabic. In this work, we address this gap by developing and demoing a system, dubbed VoxArabica, for dialect identification (DID) as