April 2024 arXiv papers — page 21

Mingang Kim, Mikhail N. Koffarnus, Christopher T Franck

Standard nonlinear regression is commonly used when modeling indifference points due to its ability to closely follow observed data, resulting in a good model fit. However, standard nonlinear regression currently lacks a reasonable distribution-based framework for indifference points, which limits its ability to adequately describe the inherent variability i

Nadia Saeed

Accurate representation of medical information is crucial for patient safety, yet artificial intelligence (AI) systems, such as Large Language Models (LLMs), encounter challenges in error-free clinical text interpretation. This paper presents a novel approach submitted to the MEDIQA-CORR 2024 shared task (Ben Abacha et al., 2024a), focusing on the automatic

Konstantin L. Metlov, Andreas Michels

Magnetic hopfions are three-dimensional localized magnetic topological solitons which can exist in the bulk of magnetic materials. Based on a Ritz model for magnetic hopfions in a chiral magnet, the unpolarized magnetic small-angle neutron scattering (SANS) cross section, the spin-flip scattering cross section, and the chiral function (characterizing the imb

Jiuge Ren, David Sweet

Field experiments and computer simulations are effective but time-consuming methods of measuring the quality of engineered systems at different settings. To reduce the total time required, experimenters may employ Bayesian optimization, which is parsimonious with measurements, and take measurements of multiple settings simultaneously, in a batch. In practice

Alexsandro Oliveira Alexandrino

Considering a pair of genomes, the goal of rearrangement distance problems is to estimate how distant these genomes are from each other based on genome rearrangements. Seminal works in genome rearrangements assumed that both genomes being compared have the same set of genes (balanced genomes) and, furthermore, only the relative order of genes and their orien

Damith Chamalke Senadeera, Xiaoyun Yang, Dimitrios Kollias, Gregory Slabaugh

In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. As surveillance systems become more prevalent due to technological advances and decreasing costs, the challenge of efficiently monitoring vast amounts of video data has intensified. CUE-Net addresses this challenge by combining spatial Cr

Thomas G. Brooks

Julius Whiston calculated the maximum size of an irredundant generating set for $S_n$ and $A_n$ by examination of maximal subgroups. Using analogous considerations, we will compute upper bounds to this value for the first two Mathieu groups, $M_{11}$ and $M_{12}$. Computational results gave explicit irredundant generating sets of $M_{11}$ and $M_{12}$ of siz

Nadia Saeed

The MEDIQA-M3G 2024 challenge necessitates novel solutions for Multilingual & Multimodal Medical Answer Generation in dermatology (wai Yim et al., 2024a). This paper addresses the limitations of traditional methods by proposing a weakly supervised learning approach for open-ended medical question-answering (QA). Our system leverages readily available MEDIQA-

Menghua Xia, Huidong Xie, Qiong Liu, Bo Zhou

Deep learning-based positron emission tomography (PET) image denoising offers the potential to reduce radiation exposure and scanning time by transforming low-count images into high-count equivalents. However, existing methods typically blur crucial details, leading to inaccurate lesion quantification. This paper proposes a lesion-perceived and quantificatio

Diana Sungatullina, Tomas Pajdla

We present an approach to backpropagating through minimal problem solvers in end-to-end neural network training. Traditional methods relying on manually constructed formulas, finite differences, and autograd are laborious, approximate, and unstable for complex minimal problem solvers. We show that using the Implicit function theorem (IFT) to calculate deriva

Yash Aggarwal

Presently, it is unclear whether the Eddington ratio and radiative efficiency depend upon a supermassive black hole's (SMBH's) redshift z and mass MBH. We attempt to resolve this issue using published data for 132,000 SMBHs with MBH >1E+7 Msun (solar masses) at ~0.1<z<2.4 covering ~10 billion years of cosmic time, with MBH determined using MgII lines and bol

Lin Ai, Zheng Hui, Zizhou Liu, Julia Hirschberg

Machine Reading Comprehension (MRC) poses a significant challenge in the field of Natural Language Processing (NLP). While mainstream MRC methods predominantly leverage extractive strategies using encoder-only models such as BERT, generative approaches face the issue of out-of-control generation -- a critical problem where answers generated are often incorre

Mohamed Rashad, Zilong Zhao, Jeremie Decouchant, Lydia Y. Chen

Autoencoders are popular neural networks that are able to compress high dimensional data to extract relevant latent information. TabNet is a state-of-the-art neural network model designed for tabular data that utilizes an autoencoder architecture for training. Vertical Federated Learning (VFL) is an emerging distributed machine learning paradigm that allows

Lukas Walter, Clemens Sauerwein, Daniel W. Woods

Security conferences are important venues for information sharing, where academics and practitioners share knowledge about new attacks and state-of-the-art defenses. Despite their importance, researchers have not systematically examined who shares information and which security topics are discussed. To address this gap, our paper characterizes the speakers,

Longke Tang

We show that ind-smoothness of flat ring maps can be tested on constructible stratifications, even for maps of non-Noetherian rings. We prove this by generalizing ind-smoothness and ind-lci to a sequence of conditions on animated ring maps called ind-$d$-smoothness, and showing all at once that they can be tested on constructible stratifications.

Tiago Cruz

The foundations of Ringel duality for split quasi-hereditary algebras over commutative Noetherian rings are strengthened. Several descriptions and properties of the smallest resolving subcategory containing all standard modules over split quasi-hereditary algebras over commutative Noetherian rings are provided. In particular, given two split quasi-hereditary

Arkady Kitover, Mehmet Orhon

We investigate the spectrum and the essential spectra of weighted automorphisms of the polydisc algebra $\mathbb{A}^n$. In the case $n=2$ we provide a detailed and, in most cases, complete description of these spectra.

Radu Ioan Bot, Chiara Schindler

In our pursuit of finding a zero for a monotone and Lipschitz continuous operator $M : \R^n \rightarrow \R^n$ amidst noisy evaluations, we explore an associated differential equation within a stochastic framework, incorporating a correction term. We present a result establishing the existence and uniqueness of solutions for the stochastic differential equati

Milena Pustet, Elisabeth Steffen, Helena Mihaljević

The automated detection of conspiracy theories online typically relies on supervised learning. However, creating respective training data requires expertise, time and mental resilience, given the often harmful content. Moreover, available datasets are predominantly in English and often keyword-based, introducing a token-level bias into the models. Our work a

Ali Reza Ghavamipour, Benjamin Zi Hao Zhao, Fatih Turkmen

Decentralized learning (DL) offers a novel paradigm in machine learning by distributing training across clients without central aggregation, enhancing scalability and efficiency. However, DL's peer-to-peer model raises challenges in protecting against inference attacks and privacy leaks. By forgoing central bottlenecks, DL demands privacy-preserving aggregat

Tiantian Feng, Xuan Shi, Rahul Gupta, Shrikanth S. Narayanan

Automatic Speech Understanding (ASU) aims at human-like speech interpretation, providing nuanced intent, emotion, sentiment, and content understanding from speech and language (text) content conveyed in speech. Typically, training a robust ASU model relies heavily on acquiring large-scale, high-quality speech and associated transcriptions. However, it is oft

Jean-Baptiste Waring, Christophe Pere, Sébastien Le Beux

In the current landscape of noisy intermediate-scale quantum (NISQ) computing, the inherent noise presents significant challenges to achieving high-fidelity long-range entanglement. Furthermore, this challenge is amplified by the limited connectivity of current superconducting devices, necessitating state permutations to establish long-distance entanglement.

Ícaro Rodolfo Soares Coelho Da Paz, Pedro F. A. Silva, Helena Bordini de Lucas, Sérgio H. A. Lira

How the human brain processes information during different cognitive tasks is one of the greatest questions in contemporary neuroscience. Understanding the statistical properties of brain signals during specific activities is one promising way to address this question. Here we analyze freely available data from implanted electrocorticography (ECoG) in five h

Amanda Baran, Jacob Nelson-Slivon, Lewis Tseng, Roberto Palmieri

Remote direct memory access (RDMA) networks are being rapidly adopted into industry for their high speed, low latency, and reduced CPU overheads compared to traditional kernel-based TCP/IP networks. RDMA enables threads to access remote memory without interacting with another process. However, atomicity between local accesses and remote accesses is not guara

Yury Malevich, Said Ergoktas, Gokhan Bakan, Pietro Stainer

Unlocking the potential of terahertz (THz) and millimetre (mm) waves for next generation communications and imaging applications requires reconfigurable intelligent surfaces (RIS) with programmable elements that can manipulate the waves in real-time. Realization of this technology has been hindered by the lack of efficient THz electro-optical materials and T

Michael Majurski, Sumeet Menon, Parniyan Farvardin, David Chapman

Discriminative deep learning models with a linear+softmax final layer have a problem: the latent space only predicts the conditional probabilities $p(Y|X)$ but not the full joint distribution $p(Y,X)$, which necessitates a generative approach. The conditional probability cannot detect outliers, causing outlier sensitivity in softmax networks. This exacerbate

Himanshu Pandey, Akhil Amod, Shivang

Prior Authorization delivers safe, appropriate, and cost-effective care that is medically justified with evidence-based guidelines. However, the process often requires labor-intensive manual comparisons between patient medical records and clinical guidelines, that is both repetitive and time-consuming. Recent developments in Large Language Models (LLMs) have

Rebecca G. Martin, Stephen H. Lubow, Philip J. Armitage, Daniel J. Price

With three-dimensional hydrodynamical simulations we show that the size of the decretion disc and the structure of the accretion flow onto the neutron star in a Be/X-ray binary strongly depends upon the disc aspect ratio, $H/R$. We simulate a Be star disc that is coplanar to the orbit of a circularly or moderately eccentric neutron star companion, thereby ma

Simone Barandoni, Filippo Chiarello, Lorenzo Cascone, Emiliano Marrale

In the rapidly evolving landscape of Natural Language Processing (NLP), Large Language Models (LLMs) have emerged as powerful tools for many tasks, such as extracting valuable insights from vast amounts of textual data. In this study, we conduct a comparative analysis of LLMs for the extraction of travel customer needs from TripAdvisor and Reddit posts. Leve

Shaofan Liu, Junbo Chen, Jianke Zhu

Incremental scene reconstruction is essential to the navigation in robotics. Most of the conventional methods typically make use of either TSDF (truncated signed distance functions) volume or neural networks to implicitly represent the surface. Due to the voxel representation or involving with time-consuming sampling, they have difficulty in balancing speed,

Zhiyan Liu, Qiao Lan, Kaibin Huang

The 6G mobile networks feature two new usage scenarios -- distributed sensing and edge artificial intelligence (AI). Their natural integration, termed integrated sensing and edge AI (ISEA), promises to create a platform that enables intelligent environment perception for wide-ranging applications. A basic operation in ISEA is for a fusion center to acquire a

Paolo De Luca, Marrick Braam, Thaddeus D. Komacek, Assaf Hochman

The emergence of the James Webb Space Telescope and the development of other advanced observatories (e.g., ELTs, LIFE and HWO) marks a pivotal moment in the quest to characterize the atmospheres of Earth-like exoplanets. Motivated by these advancements, we conduct theoretical explorations of exoplanetary atmospheres, focusing on refining our understanding of

Shijun Cheng, Tariq Alkhalifah

Using symbolic regression to discover physical laws from observed data is an emerging field. In previous work, we combined genetic algorithm (GA) and machine learning to present a data-driven method for discovering a wave equation. Although it managed to utilize the data to discover the two-dimensional (x,z) acoustic constant-density wave equation u_tt=v^2(u

Ali Reza Ghavamipour, Benjamin Zi Hao Zhao, Oguzhan Ersoy, Fatih Turkmen

Decentralized machine learning (DL) has been receiving an increasing interest recently due to the elimination of a single point of failure, present in Federated learning setting. Yet, it is threatened by the looming threat of Byzantine clients who intentionally disrupt the learning process by broadcasting arbitrary model updates to other clients, seeking to

Paola Bonizzoni, Clelia De Felice, Rocco Zaccagnino, Rosalba Zizza

The notion of inverse Lyndon word is related to the classical notion of Lyndon word. More precisely, inverse Lyndon words are all and only the nonempty prefixes of the powers of the anti-Lyndon words, where an anti-Lyndon word with respect to a lexicographical order is a classical Lyndon word with respect to the inverse lexicographic order. Each word $w$ adm

Charles Brazier, Jean-Luc Rouas

Neural Machine Translation (NMT) is the task of translating a text from one language to another with the use of a trained neural network. Several existing works aim at incorporating external information into NMT models to improve or control predicted translations (e.g. sentiment, politeness, gender). In this work, we propose to improve translation quality by

Krithika Iyer, Jadie Adams, Shireen Y. Elhabian

Statistical shape modeling (SSM) is a powerful computational framework for quantifying and analyzing the geometric variability of anatomical structures, facilitating advancements in medical research, diagnostics, and treatment planning. Traditional methods for shape modeling from imaging data demand significant manual and computational resources. Additionall

Necip Fazıl Yıldıran, Jeho Oh, Julia Lawall, Paul Gazzillo

The Linux kernel is highly-configurable, with a build system that takes a configuration file as input and automatically tailors the source code accordingly. Configurability, however, complicates testing, because different configuration options lead to the inclusion of different code fragments. With thousands of patches received per month, Linux kernel mainta

N. Brahiti, M. Balli, M. Abbasi Eskandari, A. El Boukili

The effects of sintering temperature (Ts) and chemical substitution on the structural and magnetic properties of manganite compounds La1-xSrxMn1-yFeyO3 (0.025 <= x <= 0.7; y= 0.01,0.15) are explored in a search to optimize their magnetocaloric properties around room temperature. A ferromagnetic (FM) to paramagnetic (PM) phase transition is observed at a Curi

Shengyi Pan, You Wang, Zhongxin Liu, Xing Hu

Forking is a typical way of code reuse, which provides a simple way for developers to create a variant software (denoted as hard fork) by copying and modifying an existing codebase. Despite of the benefits, forking also leads to duplicate efforts in software maintenance. Developers need to port patches across the hard forks to address similar bugs or impleme

C. H. M. Pabst, J. R. Goicoechea, S. Cuadrado, P. Salas

We present a study of hydrogen, helium, and carbon millimeter-wave radio-recombination lines (RRLs) toward ten representative positions throughout the Orion Nebula complex, using the Yebes 40m telescope in the Q band (31.3 GHz to 50.6 GHz) at an angular resolution of about $45\arcsec$ ($\sim$0.09\,pc). The observed positions include the Orion Nebula (M42) wi

Pranav Gokhale, Caitlin Carnahan, William Clark, Teague Tomesh

Recent work has shown the promise of applying deep learning to enhance software processing of radio frequency (RF) signals. In parallel, hardware developments with quantum RF sensors based on Rydberg atoms are breaking longstanding barriers in frequency range, resolution, and sensitivity. In this paper, we describe our implementations of quantum-ready machin

Zelong Zeng, Kaname Tomite

In anomaly segmentation for complex driving scenes, state-of-the-art approaches utilize anomaly scoring functions to calculate anomaly scores. For these functions, accurately predicting the logits of inlier classes for each pixel is crucial for precisely inferring the anomaly score. However, in real-world driving scenarios, the diversity of scenes often resu

Md Robiul Islam, Md Mahamodul Islam, Mst. Suraiya Afrin, Anika Antara

Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and

Vasileios Kalantzis, Mark S. Squillante, Shashanka Ubaru

We study from a theoretical viewpoint the fundamental problem of efficiently computing the stationary distribution of general classes of structured Markov processes. In strong contrast with previous work, we consider this fundamental problem within the context of quantum computational environments from a mathematical perspective and devise the first quantum

Ying Cai, Hailong Guo, Zhimin Zhang

This paper presents an innovative continuous linear finite element approach to effectively solve biharmonic problems on surfaces. The key idea behind this method lies in the strategic utilization of a surface gradient recovery operator to compute the second-order surface derivative of a piecewise continuous linear function defined on the approximate surface,

Xinyu Zhao, Bartosz Protas, Roman Shvydkoy

We consider Euler flows on two-dimensional (2D) periodic domain and are interested in the stability, both linear and nonlinear, of a simple equilibrium given by the 2D Taylor-Green vortex. As the first main result, numerical evidence is provided for the fact that such flows possess unstable eigenvalues embedded in the band of the essential spectrum of the li

Viviana del Barco, Andrei Moroianu

A locally conformally product (LCP) structure on a compact conformal manifold is a closed non-exact Weyl connection (i.e.~a linear connection which is locally but not globally the Levi-Civita connection of Riemannian metrics in the conformal class), with reducible holonomy. A left-invariant LCP structure on a compact quotient $\Gamma\backslash G$ of a simply

Mohamed Bouali

A recently published result states inequalities of the harmonic mean of the digamma function. In this work, we prove among others results that for all positive real numbers $x\neq 1$, $$-\gamma<-\gamma H(x,1/x)<\frac{\gamma^2}{\psi\big(H(x,1/x)\big)}<\psi\Big(1/H(x,1/x)\Big)<H\Big(\psi(x), \psi(1/x)\Big),$$ $$H\Big(\zeta(x),\zeta(1/x)\Big)<-2,$$ and for all

Jia Zeng, Dan Han, Yaling Zhu, Yangzhong Wang

In the current software development environment, third-party libraries play a crucial role. They provide developers with rich functionality and convenient solutions, speeding up the pace and efficiency of software development. However, with the widespread use of third-party libraries, associated security risks and potential vulnerabilities are increasingly a

Giorgos Kritikakis, Ioannis G Tollis

Inquiries such as whether a task A depends on a task B, whether an author A has been influenced by a paper B, whether a certain protein is associated with a specific biological process or molecular function, or whether class A inherits from class B, are just a few examples of inquiries that can be modeled as reachability queries on a network (Directed Graph)

Ayan Bhattacharya, Piotr Dyszewski, Nina Gantert, Zbigniew Palmowski

We study a branching random walk with independent and identically distributed, heavy tailed displacements. The offspring law is supercritical and satisfies the Kesten-Stigum condition. We treat the case when the law of the displacements does not lie in the max-domain of attraction of an extreme value distribution. Hence, the classical extreme value theory, w

Heather Woodhouse, Gerard Hall, Callum Simpson, Csaba Kozma

Background: Understanding healthy human brain function is crucial to identify and map pathological tissue within it. Whilst previous studies have mapped intracranial EEG (icEEG) from non-epileptogenic brain regions, these maps do not consider the effects of age and sex. Further, most existing work on icEEG has often suffered from a small sample size due to t

Shujian Yu, Xi Yu, Sigurd Løkse, Robert Jenssen

The information bottleneck (IB) approach is popular to improve the generalization, robustness and explainability of deep neural networks. Essentially, it aims to find a minimum sufficient representation $\mathbf{t}$ by striking a trade-off between a compression term $I(\mathbf{x};\mathbf{t})$ and a prediction term $I(y;\mathbf{t})$, where $I(\cdot;\cdot)$ re

Carolina Grejo, Fabio Lopes, Fábio Machado, Alejandro Roldán-Correa

We consider a stochastic model for a pathogen population in the presence of an immune response, in which pathogen types are partially ordered by ancestry and the immune system must eliminate ancestor types before it can eliminate their descendants. In this model, pathogens reproduce independently at rate $\lambda>0$ and, at each birth, a mutation occurs with

Chenhao Cui, Yufan Jiang, Shuangzhi Wu, Zhoujun Li

Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct answer from a set of options based on a given passage and question. The existing methods employ the pre-trained language model as the encoder, share and transfer knowledge through fine-tuning.These methods mainly focus on the design of exquisite mechanisms to effectively capture the

G. V. Afonin, J. C. Qiao, A. S. Makarov, N. P. Kobelev

We performed high-frequency (0.4 to 1.7 MHz) measurements of the internal friction (IF) on 14 bulk metallic glasses (MGs). It is found that 12 of these MGs display relaxation IF peaks at temperatures T= 400-500 K, which are weakly affected by heat treatment within the amorphous state. The corresponding relaxation time is about 0.3 microseconds. This fast rel

Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer, Michalis Vazirgiannis

Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in various graph representation learning tasks. Recently, studies revealed their vulnerability to adversarial attacks. In this work, we theoretically define the concept of expected robustness in the context of attributed graphs and relate it to the classical definition of adversarial

Gao Huang, Song Li

In this paper, we first propose a unified framework for analyzing the stability of the phaseless operators for both amplitude and intensity measurement on an arbitrary geometric set, thereby characterizing the robust performance of phase retrieval via the empirical minimization method. We introduce the random embedding of concave lifting operators to charact

Minjung Park, Sirio Belli, Charlie Conroy, Benjamin D. Johnson

Massive quiescent galaxies in the young universe are expected to be quenched rapidly, but it is unclear whether they all experience starbursts before quenching and what physical mechanism drives rapid quenching. We study 14 massive quiescent galaxies ($\log(M_\star/M_\odot) > 10$) at $z\sim2$ selected from a representative sample of the Blue Jay survey. We r

Sohaib Ahmed, Hassan Khalid, Muhammad Hamza, Danyal Farhat

Mobile Edge Computing (MEC) has emerged as a solution to the high latency and suboptimal Quality of Experience (QoE) associated with Mobile Cloud Computing (MCC). By processing data near the source, MEC reduces the need to send information to distant data centers, resulting in faster response times and lower latency. This paper explores the differences betwe

Tony Gracious, Ambedkar Dukkipati

Understanding relations arising out of interactions among entities can be very difficult, and predicting them is even more challenging. This problem has many applications in various fields, such as financial networks and e-commerce. These relations can involve much more complexities than just involving more than two entities. One such scenario is evolving re

Sergej Monavari, Andrea T. Ricolfi

Let $C$ be a smooth projective curve, $E$ a locally free sheaf. Hyperquot schemes on $C$ parametrise flags of coherent quotients of $E$ with fixed Hilbert polynomial, and offer alternative compactifications to the spaces of maps from $C$ to partial flag varieties. Motivated by enumerative geometry, in this paper we construct a perfect obstruction theory (and

Naman Kumar

In this letter, we show that in a particular braneworld scenario with variable brane tension, we obtain matter acting as dark energy while the gravitational constant $G$ promoted to a scalar field on the brane plays the role of matter (both in the sense they have an 'effective' Equation of State equivalent to that of dark energy and matter respectively). Thi

Berat Dogan

Dimensionality reduction methods are employed to decrease data dimensionality, either to enhance machine learning performance or to facilitate data visualization in two or three-dimensional spaces. These methods typically fall into two categories: feature selection and feature transformation. Feature selection retains significant features, while feature tran

Robert Denkert, Huyên Pham, Xavier Warin

We propose a comprehensive framework for policy gradient methods tailored to continuous time reinforcement learning. This is based on the connection between stochastic control problems and randomised problems, enabling applications across various classes of Markovian continuous time control problems, beyond diffusion models, including e.g. regular, impulse a

Payel Mukhopadhyay, Jonah Miller, Gail C. McLaughlin

We postprocess a three-dimensional general relativistic, full transport neutrino radiation magnetohydrodynamics simulation of the black hole--accretion disk--wind system thought to be a potential outcome of the GW170817 merger to investigate the presence of electron lepton number (ELN-XLN) crossings in the neutrino angular distribution. Neutrinos are evolved

Imran Ansari, Charu Sharma, Akshay Agrawal, Niteesh Sahni

This paper highlights the significance of mesoscale structures, particularly the core-periphery structure, in financial networks for portfolio optimization. We build portfolios of stocks belonging to the periphery part of the Planar maximally filtered subgraphs of the underlying network of stocks created from Pearson correlations between pairs of stocks and

Niful Islam

Artificial intelligence is currently a dominant force in shaping various aspects of the world. Machine learning is a sub-field in artificial intelligence. Feature scaling is one of the data pre-processing techniques that improves the performance of machine learning algorithms. The traditional feature scaling techniques are unsupervised where they do not have

Zheng Cheng, Guodong Fan, Jingchun Zhou, Min Gan

Underwater images often suffer from various issues such as low brightness, color shift, blurred details, and noise due to light absorption and scattering caused by water and suspended particles. Previous underwater image enhancement (UIE) methods have primarily focused on spatial domain enhancement, neglecting the frequency domain information inherent in the

Jin-Yu Huo, Li-Cheng Sheng, Rui Chen, Xiang Liu

In the framework of a one-boson-exchange model, we carry out a comprehensive investigation of the $\Xi_cN/\Lambda_c\Sigma/\Xi_c^{\prime}N/\Sigma_c\Lambda/\Xi_c^*N/\Sigma_c^*\Lambda/\Sigma_c\Sigma/\Sigma_c^*\Sigma$ interactions. We consider the $S$-$D$-wave mixing effects and the coupled-channel effects to derive the relevant effective potentials. Our results

Alice Marcotte, Pier Giuseppe Ledda, Valentin Buriasco, Paul Dené

In confined systems, the entrapment of a gas volume with an equivalent spherical diameter greater than the dimension of the channel can form extended bubbles that obstruct fluid circuits and compromise performance. Notably, in sealed vertical tubes, buoyant long bubbles -- called Taylor bubbles -- cannot rise if the inner tube radius is below a critical valu

Andrey Kupavskii, Dmitry Tsarev

Bohn, Faenza, Fiorini, Fisikopoulos, Macchia, and Pashkovich (2015) conjectured that 2-level polytopes cannot simultaneously have many vertices and many facets, namely, that the maximum of the product of the number of vertices and facets is attained on the cube and cross-polytope. This was proved in a recent work by Kupavskii and Weltge. In this paper, we re

Shin Kiriki, Xiaolong Li, Yushi Nakano, Teruhiko Soma

We consider the concept of strong pluripotency of dynamical systems for a hyperbolic invariant set, as introduced in [KNS]. To the best of our knowledge, for the whole hyperbolic invariant set, the existence of robust strongly pluripotent dynamical systems has not been proven in previous studies. In fact, there is an example of strongly pluripotent dynamical

Toshitaka Hayashi, Dalibor Cimr, Hamido Fujita, Richard Cimler

This paper offers a comprehensive review of one-class classification (OCC), examining the technologies and methodologies employed in its implementation. It delves into various approaches utilized for OCC across diverse data types, such as feature data, image, video, time series, and others. Through a systematic review, this paper synthesizes promi-nent strat

Benoît Gérin, Anaïs Halin, Anthony Cioppa, Maxim Henry

In the era of the Internet of Things (IoT), objects connect through a dynamic network, empowered by technologies like 5G, enabling real-time data sharing. However, smart objects, notably autonomous vehicles, face challenges in critical local computations due to limited resources. Lightweight AI models offer a solution but struggle with diverse data distribut

Xiao Wang, Qian Zhu, Jiandong Jin, Jun Zhu

Existing pedestrian attribute recognition (PAR) algorithms are mainly developed based on a static image, however, the performance is unreliable in challenging scenarios, such as heavy occlusion, motion blur, etc. In this work, we propose to understand human attributes using video frames that can fully use temporal information by fine-tuning a pre-trained mul

Evgeny S. Asmolov, Olga I. Vinogradova

A migration of charged particles relative to a solvent, caused by a gradient of salt concentration and termed a diffusiophoresis, is of much interest being exploited in many fields. Existing theories deal with diffusiophoresis of passive inert particles. In this paper, we extend prior models by focusing on a particle, which is both passive and catalytic, by

Ryoma Norose, Yutaro Yamaguchi

Finding a minimum-weight strongly connected spanning subgraph of an edge-weighted directed graph is equivalent to the weighted version of the well-known strong connectivity augmentation problem. This problem is NP-hard, and a simple $2$-approximation algorithm was proposed by Frederickson and J\'aj\'a (1981); surprisingly, it still achieves the best known ap

Xiao Wang, Yuehang Li, Wentao Wu, Jiandong Jin

Existing X-ray based pre-trained vision models are usually conducted on a relatively small-scale dataset (less than 500k samples) with limited resolution (e.g., 224 $\times$ 224). However, the key to the success of self-supervised pre-training large models lies in massive training data, and maintaining high resolution in the field of X-ray images is the guar

Simone Tonini, Andrea Vandin, Francesca Chiaromonte, Daniele Licari

We present a novel, simple and widely applicable semi-supervised procedure for anomaly detection in industrial and IoT environments, SAnD (Simple Anomaly Detection). SAnD comprises 5 steps, each leveraging well-known statistical tools, namely; smoothing filters, variance inflation factors, the Mahalanobis distance, threshold selection algorithms and feature

Catrin Campbell-Moore

Coherent sets of desirable gamble sets is used as a model for representing an agents opinions and choice preferences under uncertainty. In this paper we provide some results about the axioms required for coherence and the natural extension of a given set of desirable gamble sets. We also show that coherent sets of desirable gamble sets can be represented by

Luis Gambarte, Iosif Petrakis

Translating notions and results from category theory to the theory of computability models of Longley and Normann, we introduce the Grothendieck computability model and the first-projection-simulation. We prove some basic properties of the Grothendieck computability model, and we show that the category of computability models is a type-category, in the sense

Laksh Nanwani, Kumaraditya Gupta, Aditya Mathur, Swayam Agrawal

Humans excel at forming mental maps of their surroundings, equipping them to understand object relationships and navigate based on language queries. Our previous work, SI Maps (Nanwani L, Agarwal A, Jain K, et al. Instance-level semantic maps for vision language navigation. In: 2023 32nd IEEE International Conference on Robot and Human Interactive Communicat

Lenny Jones

A monic polynomial $f(x)\in {\mathbb Z}[x]$ of degree $N$ is called monogenic if $f(x)$ is irreducible over ${\mathbb Q}$ and $\{1,\theta,\theta^2,\ldots ,\theta^{N-1}\}$ is a basis for the ring of integers of ${\mathbb Q}(\theta)$, where $f(\theta)=0$. In a series of recent articles, complete classifications of the Galois groups were given for irreducible p

D. Yu. Kazmin, V. D. Esin, A. V. Timonina, N. N. Kolesnikov

We experimentally investigate Josephson current between two 5~$\mu$m spaced superconducting indium leads, coupled to a NiTe$_2$ single crystal flake, which is a type-II Dirac semimetal. Under microwave irradiation, we demonstrate a.c. Josephson effect at millikelvin temperatures as a number of Shapiro steps. In addition to the integer ($n=1,2,3,4...$) steps,

Robert Angarone, Patricia Commins, Trevor Karn, Satoshi Murai

Let $\Omega$ be the {\em superspace ring} of polynomial-valued differential forms on affine $n$-space. The natural action of the symmetric group $\mathfrak{S}_n$ on $n$-space induces an action of $\mathfrak{S}_n$ on $\Omega$. The {\em superspace coinvariant ring} is the quotient $SR$ of $\Omega$ by the ideal generated by $\mathfrak{S}_n$-invariants with vani

Timothee Mickus, Raúl Vázquez, Joseph Attieh

Modularity is a paradigm of machine translation with the potential of bringing forth models that are large at training time and small during inference. Within this field of study, modular approaches, and in particular attention bridges, have been argued to improve the generalization capabilities of models by fostering language-independent representations. In

Mirza Tanzim Sami, Da Yan, Saugat Adhikari, Lyuheng Yuan

Accurate and timely mapping of flood extent from high-resolution satellite imagery plays a crucial role in disaster management such as damage assessment and relief activities. However, current state-of-the-art solutions are based on U-Net, which can-not segment the flood pixels accurately due to the ambiguous pixels (e.g., tree canopies, clouds) that prevent

Chenghao Huang, Xiaolu Chen, Yanru Zhang, Hao Wang

Heterogeneity arising from label distribution skew and data scarcity can cause inaccuracy and unfairness in intelligent communication applications that heavily rely on distributed computing. To deal with it, this paper proposes a novel personalized federated learning algorithm, named Federated Contrastive Shareable Representations (FedCoSR), to facilitate kn

Kolos Csaba Ágoston, Veronika Varga

Since 2016 the operation of insurance companies in the European Union is regulated by the Solvency II directive. According to the EU directive the capital requirement should be calculated as a 99.5\% of Value at Risk. In this study, we examine the impact of this capital requirement constraint on equilibrium premiums and capitals. We discuss the case of the o

Payman Mahmoudi, Atirach Ritboon, Radim Filip

Nonlinear processes with individual quanta beyond bilinear interactions are essential for quantum technology with bosonic systems. Diverse coherent splitting and merging of quanta in them already manifest in the estimation of their nonlinear coupling from observed statistics. We derive non-trivial, but optimal strategies for sensing the basic and experimenta

Zhao-Yang Peng, Jia-Ming Chen, Jirong Mao

GRB 211211A is a peculiar long Gamma-ray burst (GRB) with very high brightness and short burst properties. It's full lightcurve consists of three emission episodes, i.e. a precursor, a main burst and a extended emission. We find a recently detected long-duration GRB 230307A also includes the three consistent emission episodes. Furthermore, the two bursts hav

Manav Nitin Kapadnis, Sohan Patnaik, Abhilash Nandy, Sourjyadip Ray

Radiology Report Generation (R2Gen) demonstrates how Multi-modal Large Language Models (MLLMs) can automate the creation of accurate and coherent radiological reports. Existing methods often hallucinate details in text-based reports that don't accurately reflect the image content. To mitigate this, we introduce a novel strategy, SERPENT-VLM (SElf Refining Ra

Fabrizio Colombo, Francesco Mantovani, Peter Schlosser

In mathematical physics, the gradient operator with nonconstant coefficients encompasses various models, including Fourier's law for heat propagation and Fick's first law, that relates the diffusive flux to the gradient of the concentration. Specifically, consider $n\geq 3$ orthogonal unit vectors $e_1,\dots,e_n\in\mathbb{R}^n$, and let $\Omega\subseteq\math

Farzad Nozarian, Shashank Agarwal, Farzaneh Rezaeianaran, Danish Shahzad

Semi-supervised 3D object detection can benefit from the promising pseudo-labeling technique when labeled data is limited. However, recent approaches have overlooked the impact of noisy pseudo-labels during training, despite efforts to enhance pseudo-label quality through confidence-based filtering. In this paper, we examine the impact of noisy pseudo-labels

S. A. Rukolaine

The heat equation, based on Fourier's law, is commonly used for description of heat conduction. However, Fourier's law is valid under the assumption of local thermodynamic equilibrium, which is violated in very small dimensions and short timescales, and at low temperatures. In the paper R. Kovacs and P. Van, Generalized heat conduction in heat pulse experime

Brandon Engelbrecht, Mario G. Santos, José Fonseca, Yichao Li

Radio Frequency Interference (RFI) is emitted from various sources, terrestrial or orbital, and create a nuisance for ground-based 21cm experiments. In particular, single-dish 21cm intensity mapping experiments will be highly susceptible to contamination from these sources due to its wide primary beam and sensitivity. This work aims to simulate the contamina

Muneo Cho, B. Nachevska Nastovska, Kotaro Tanahashi

In [6] Cho and Tanahashi showe new spectral mapping theorem of the taylor spectrum for doubly commuting pairs of p-hyponormal operators and log-hyponormal operators. In this paper, we will show that same spectral mapping theorem holds for commuting n-tuples.

Ananth Jonnavittula, Sagar Parekh, Dylan P. Losey

Robots can use Visual Imitation Learning (VIL) to learn manipulation tasks from video demonstrations. However, translating visual observations into actionable robot policies is challenging due to the high-dimensional nature of video data. This challenge is further exacerbated by the morphological differences between humans and robots, especially when the vid