May 2023 arXiv papers — page 25

Kai Yi Ji

This paper presents an evaluation of edge detectors using receiver operating characteristic (ROC) analysis on the BIPED dataset. Our study examines the benefits and drawbacks of applying this technique in Matlab. We observed that while ROC analysis is suitable for certain edge filters, but for filters such as Laplacian, Laplacian of Gaussian, and Canny, it p

Magdalena Wysocka, Oskar Wysocki, Maxime Delmas, Vincent Mutel

The paper introduces a framework for the evaluation of the encoding of factual scientific knowledge, designed to streamline the manual evaluation process typically conducted by domain experts. Inferring over and extracting information from Large Language Models (LLMs) trained on a large corpus of scientific literature can potentially define a step change in

Lincoln Huber, Boris Kovalerchuk, Charles Recaido

Understanding black-box Machine Learning methods on multidimensional data is a key challenge in Machine Learning. While many powerful Machine Learning methods already exist, these methods are often unexplainable or perform poorly on complex data. This paper proposes visual knowledge discovery approaches based on several forms of lossless General Line Coordin

Juan Pablo Borthagaray, Wenbo Li, Ricardo H. Nochetto

We prove Besov boundary regularity for solutions of the homogeneous Dirichlet problem for fractional-order quasi-linear operators with variable coefficients on Lipschitz domains $\Omega$ of $\mathbb{R}^d$. Our estimates are consistent with the boundary behavior of solutions on smooth domains and apply to fractional $p$-Laplacians and operators with finite ho

Olukunle Owolabi

Early detection of power outages is crucial for maintaining a reliable power distribution system. This research investigates the use of transfer learning and language models in detecting outages with limited labeled data. By leveraging pretraining and transfer learning, models can generalize to unseen classes. Using a curated balanced dataset of social media

Dixon Vimalajeewa, Chihoon Lee, Brani Vidakovic

A heart murmur is an atypical sound produced by the flow of blood through the heart. It can be a sign of a serious heart condition, so detecting heart murmurs is critical for identifying and managing cardiovascular diseases. However, current methods for identifying murmurous heart sounds do not fully utilize the valuable insights that can be gained by explor

Ryan Kaufman, Theodore White, Mark I. Dykman, Andrea Iorio

We demonstrate a Josephson parametric amplifier design with a band-pass impedance matching network based on a third-order Chebyshev prototype. We measured eight amplifiers operating at 4.6 GHz that exhibit gains of 20 dB with less than 1 dB gain ripple and up to 500 MHz bandwidth. The amplifiers further achieve high output saturation powers around -73 dBm ba

Christian Y. Cardall

Galilei-Newton spacetime $\mathbb{G}$ with its Galilei group can be understood as a `degeneration' as $c \rightarrow \infty$ of Minkowski spacetime $\mathbb{M}$ with its Poincar\'e group. $\mathbb{G}$ does not have a spacetime metric and its Galilei symmetry transformations do not include energy; but Bargmann-Galilei spacetime $B\mathbb{G}$, a 5-dimensional

Jinghao Lyu, Alexander B. Boyd, James P. Crutchfield

When reformulated as a resource theory, thermodynamics can analyze system behaviors in the single-shot regime. In this, the work required to implement state transitions is bounded by {\alpha}-Renyi divergences and so differs in identifying efficient operations compared to stochastic thermodynamics. Thus, a detailed understanding of the difference between sto

Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang

A major challenge in reinforcement learning is to determine which state-action pairs are responsible for future rewards that are delayed. Reward redistribution serves as a solution to re-assign credits for each time step from observed sequences. While the majority of current approaches construct the reward redistribution in an uninterpretable manner, we prop

Akshansh Mishra, Vijaykumar S Jatti

This research paper explores the impact of various input parameters, including Infill percentage, Layer Height, Extrusion Temperature, and Print Speed, on the resulting Tensile Strength in objects produced through additive manufacturing. The main objective of this study is to enhance our understanding of the correlation between the input parameters and Tensi

Richard Brewster, Kieka Mynhardt, Laura Teshima

The independent domination number $i(G)$ of a graph $G$ is the minimum cardinality of a maximal independent set of $G$, also called an $i(G)$-set. The $i$-graph of $G$ is the graph whose vertices correspond to the $i(G)$-sets, and where two $i(G)$-sets are adjacent if and only if they differ by two adjacent vertices. Not all graphs are $i$-graph realizable,

Shahrzad Pour, Amir Masoumi, Niels Skov Dujardin

Decision Support Systems for pavement and maintenance strategies have traditionally been designed as silos led to local optimum systems. Moreover, since big data usage didn't exist as result of Industry 4.0 as of today, DSSs were not initially designed adaptive to the sources of uncertainties led to rigid decisions. Motivated by the vulnerability of the road

S. A. Ahmed, R. Saadeh, A. Qazza, T. M. Elzaki

In this study, we combine two novel methods, the conformable double Laplace-Sumudu transform (CDLST) and the modified decomposition technique. We use the new approach called conformable double Laplace-Sumudu modified decomposition (CDLSMD) method, to solve some nonlinear fractional partial differential equations. We present the essential properties of the CD

Zihan Chen, Lei Nico Zheng, Cheng Lu, Jialu Yuan

ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier. In this research, we introduce a novel framework that leverages ChatGPT's graph inference capabilities

Kevin Jude Concessao, Unnikrishnan Cheramangalath, MJ Ricky Dev, Rupesh Nasre

Graph algorithms are challenging to implement due to their varying topology and irregular access patterns. Real-world graphs are dynamic in nature and routinely undergo edge and vertex additions, as well as, deletions. Typical examples of dynamic graphs are social networks, collaboration networks, and road networks. Applying static algorithms repeatedly on d

Simo Ryu, Seunghyun Seo, Jaejun Yoo

In this paper, we present an efficient method for storing fine-tuned models by leveraging the low-rank properties of weight residuals. Our key observation is that weight residuals in large overparameterized models exhibit even stronger low-rank characteristics. Based on this insight, we propose Efficient Residual Encoding (ERE), a novel approach that achieve

Ziqi Jin, Wei Lu

The chain-of-though (CoT) prompting methods were successful in various natural language processing (NLP) tasks thanks to their ability to unveil the underlying complex reasoning processes. Such reasoning processes typically exhibit implicitly structured steps. Recent efforts also started investigating methods to encourage more explicitly structured reasoning

Denis Allard, Julien Aublin, Bruny Baret, Etienne Parizot

Analysing the available data relative to the anisotropies of the ultra-high-energy cosmic rays (UHECR) at intermediate angular scales, we examine to what extent they can be used to constrain the origin of these particles, and what could be gained from a new generation of observatories with increased exposure. We simulate realistic UHECR sky maps for a wide r

Ajinkya Dusane, Pietro Lenarda, Marco Paggi

The viscoelastic response of backsheet materials significantly affects the durability of the photovoltaic (PV) module. In this study, the viscoelastic response of commercially available backsheet materials is experimentally characterized and computationally modeled. An extensive viscoelastic experimental study on backsheet materials is carried out, consideri

Patrik Okanovic, Roger Waleffe, Vasilis Mageirakos, Konstantinos E. Nikolakakis

Methods for carefully selecting or generating a small set of training data to learn from, i.e., data pruning, coreset selection, and data distillation, have been shown to be effective in reducing the ever-increasing cost of training neural networks. Behind this success are rigorously designed strategies for identifying informative training examples out of la

Alireza Fathollah Pour, Hassan Ashtiani

We consider the class of noisy multi-layered sigmoid recurrent neural networks with $w$ (unbounded) weights for classification of sequences of length $T$, where independent noise distributed according to $\mathcal{N}(0,\sigma^2)$ is added to the output of each neuron in the network. Our main result shows that the sample complexity of PAC learning this class

Ekaterina Kompantseva, Askar Tuganbaev

For an Abelian group $G$, any homomorphism $\mu\colon G\otimes G\rightarrow G$ is called a \textsf{multiplication} on $G$. The set $\text{Mult}\,G$ of all multiplications on an Abelian group $G$ is an Abelian group with respect to addition. An Abelian group $G$ with multiplication, defined on it, is called a \textsf{ring on the group} $G$. Let $\mathcal{A}_0

Renbo Zhao

We present and analyze an away-step Frank-Wolfe method for the convex optimization problem ${\min}_{x\in\mathcal{X}} \; f(\mathsf{A} x) + \langle{c},{x}\rangle$, where $f$ is a $\theta$-logarithmically-homogeneous self-concordant barrier, $\mathsf{A}$ is a linear operator that may be non-invertible, $\langle{c},{\cdot}\rangle$ is a linear function and $\math

Ali Borji

Transformers have emerged as the prevailing standard solution for various AI tasks, including computer vision and natural language processing. The widely adopted Query, Key, and Value formulation (QKV) has played a significant role in this. Nevertheless, no research has examined the essentiality of these three components for transformer performance. Therefor

Meng Jiang, Hy Dang, Lingbo Tong

Language models (LMs) are being scaled and becoming powerful. Improving their efficiency is one of the core research topics in neural information processing systems. Tay et al. (2022) provided a comprehensive overview of efficient Transformers that have become an indispensable staple in the field of NLP. However, in the section of "On Evaluation", they left

A. Burqan, R. Saadeh, A. Qazza, S. Momani

In this article a new approach in solving time fractional partial differential equations is introduced, that is, the ARA-residual power series method. The main idea of this technique, depends on applying the ARA-transform and using Taylor's expansion to construct approximate series solutions. The procedure of getting the approximate solutions for nonlinear t

Marjan Qazvini

Deep neural networks are powerful tools for modelling non-linear patterns and are very effective when the input data is homogeneous such as images and texts. In recent years, there have been attempts to apply neural nets to heterogeneous data, such as tabular and multimodal data with mixed categories. Transformation methods, specialised architectures such as

Felix A. Buot

In contrast to abstract statistical analyses in the literature, we present a concrete physical diagrammatic model of entanglement characterization and measure with its underlying discrete phase-space physics. This paper serves as a pedagogical treatment of this complex subject of entanglement measures. We review the important inherent concurrence property of

Kanwar Bharat Singh, Srikanth Sivaramakrishnan

Despite their widespread use, current tire models have demonstrated a certain level of inaccuracy, primarily due to uncertainties related to unaccounted nonlinearity and disturbance effects resulting from tire operating conditions. Noteworthy factors such as tread depth, inflation pressure, tire temperature, and road surface condition significantly impact ti

Emanuel Tewolde, Caspar Oesterheld, Vincent Conitzer, Paul W. Goldberg

We study single-player extensive-form games with imperfect recall, such as the Sleeping Beauty problem or the Absentminded Driver game. For such games, two natural equilibrium concepts have been proposed as alternative solution concepts to ex-ante optimality. One equilibrium concept uses generalized double halving (GDH) as a belief system and evidential deci

Shaokun Zhang, Yiran Wu, Zhonghua Zheng, Qingyun Wu

In this work, we propose a hyperparameter optimization method named \emph{HyperTime} to find hyperparameters robust to potential temporal distribution shifts in the unseen test data. Our work is motivated by an important observation that it is, in many cases, possible to achieve temporally robust predictive performance via hyperparameter optimization. Based

Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou

Dynamic decision-making under distributional shifts is of fundamental interest in theory and applications of reinforcement learning: The distribution of the environment in which the data is collected can differ from that of the environment in which the model is deployed. This paper presents two novel model-free algorithms, namely the distributionally robust

Zexue He, Marco Tulio Ribeiro, Fereshte Khani

Even when aggregate accuracy is high, state-of-the-art NLP models often fail systematically on specific subgroups of data, resulting in unfair outcomes and eroding user trust. Additional data collection may not help in addressing these weaknesses, as such challenging subgroups may be unknown to users, and underrepresented in the existing and new data. We pro

W. Ronny Huang, Hao Zhang, Shankar Kumar, Shuo-yiin Chang

We propose a method of segmenting long-form speech by separating semantically complete sentences within the utterance. This prevents the ASR decoder from needlessly processing faraway context while also preventing it from missing relevant context within the current sentence. Semantically complete sentence boundaries are typically demarcated by punctuation in

Pablo Valle, Aitor Arrieta, Maite Arratibel

Elevator systems are one kind of Cyber-Physical Systems (CPSs), and as such, test cases are usually complex and long in time. This is mainly because realistic test scenarios are employed (e.g., for testing elevator dispatching algorithms, typically a full day of passengers traveling through a system of elevators is used). However, in such a context, when nee

Pierre Nazé

The analytical expression for shortcuts to adiabaticity for any switching time and any thermally isolated system performing a finite-time and weakly driven process is presented. It is based on the analytical solution of the optimal protocols of weak processes for open systems. The extension to adiabatic processes was made by employing the concept of waiting

Chenyin Gao, Shu Yang

Multiple heterogeneous data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we develop a unified framework of the test-and-pool approach to general parameter estimation by combining gold-standard probability and non-probability samples. We focus on the case w

Lama Alssum, Juan Leon Alcazar, Merey Ramazanova, Chen Zhao

Class-incremental learning is one of the most important settings for the study of Continual Learning, as it closely resembles real-world application scenarios. With constrained memory sizes, catastrophic forgetting arises as the number of classes/tasks increases. Studying continual learning in the video domain poses even more challenges, as video data contai

Shanka Subhra Mondal, Steven Frankland, Taylor Webb, Jonathan D. Cohen

Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of

Abhiroop Bhattacharjee, Abhishek Moitra, Youngeun Kim, Yeshwanth Venkatesha

In-Memory Computing (IMC) platforms such as analog crossbars are gaining focus as they facilitate the acceleration of low-precision Deep Neural Networks (DNNs) with high area- & compute-efficiencies. However, the intrinsic non-idealities in crossbars, which are often non-deterministic and non-linear, degrade the performance of the deployed DNNs. In addition

G. Venkat, D. A. Allwood, T. J. Hayward

Domain walls (DWs) in magnetic nanowires are promising candidates for a variety of applications including Boolean/unconventional logic, memories, in-memory computing as well as magnetic sensors and biomagnetic implementations. They show rich physical behaviour and are controllable using a number of methods including magnetic fields, charge and spin currents

Diab W. Abueidda, Mostafa E. Mobasher

Most currently available methods for modeling multiphysics, including thermoelasticity, using machine learning approaches, are focused on solving complete multiphysics problems using data-driven or physics-informed multi-layer perceptron (MLP) networks. Such models rely on incremental step-wise training of the MLPs, and lead to elevated computational expense

Ismel Martínez-Díaz

In this paper we present the first REST API for the generation and evaluation of bijective S-boxes. We also present the first Single Page Application tool for researchers and students that allows the use of a graphical interface. We give a small dataset of classical S-boxes to test the properties evaluations. We show how to define experiments and we include

Sudarshan Regmi, Bibek Panthi, Sakar Dotel, Prashnna K. Gyawali

Neural networks are notorious for being overconfident predictors, posing a significant challenge to their safe deployment in real-world applications. While feature normalization has garnered considerable attention within the deep learning literature, current train-time regularization methods for Out-of-Distribution(OOD) detection are yet to fully exploit thi

Alexander Koldobsky, Michael Roysdon, Artem Zvavitch

Given two non-negative functions $f$ and $g$ such that the Radon transform of $f$ is pointwise smaller than the Radon transform of $g$, does it follow that the $L^p$-norm of $f$ is smaller than the $L^p$-norm of $g$ for a given $p>0$? We consider this problem for the classical and spherical Radon transforms. In both cases we point out classes of functions fo

Paul E. Lammert

Informed by an abstraction of Kohn-Sham computation called a KS machine, a functional analytic perspective is developed on mathematical aspects of density functional theory. A natural semantics for the machine is bivariate, consisting of a sequence of potentials paired with a ground density. Although the question of when the KS machine can converge to a solu

Johann Brehmer, Pim de Haan, Sönke Behrends, Taco Cohen

Problems involving geometric data arise in physics, chemistry, robotics, computer vision, and many other fields. Such data can take numerous forms, for instance points, direction vectors, translations, or rotations, but to date there is no single architecture that can be applied to such a wide variety of geometric types while respecting their symmetries. In

Orli Herscovici, Galyna V. Livshyts, Liran Rotem, Alexander Volberg

In this paper, we show the stability, and characterize the equality cases in the strong B-inequality of Cordero-Erasquin, Fradelizi and Maurey \cite{B-conj}. As an application, we establish uniqueness of Bobkov's maximal Gaussian measure position from \cite{Bobkov-Mpos}.

Malavika Mukundan, Nikolai Prochorov, Bernhard Reinke

We prove that every postsingularly finite entire map $g$ can be approximated by a sequence of postcritically finite complex polynomials $(g_n)$ such that their postsingular dynamics $g|P_g$ and $g_n|P_{g_n}$ are conjugate for every $n \in \mathbb{N}$. To establish this result, we introduce the notion of combinatorial convergence for sequences of entire Thurs

A. Serra, R. Lichtenthäler, O. C. B. Santos, K. C. C. Pires

A geometric model for the direct contribution of the reaction cross section induced by light ions on different targets is presented. The model separates the total reaction cross section into two components, one for total fusion and another for direct reactions. We show that the direct part scales as $2 \pi Ra$, where $R$ is related to the nuclear radius and

Sai Krishna Prathapaneni, Shvejan Shashank, Srikar Reddy K

This research aims to explore the possibility of designing a neural network architecture that allows for small networks to adopt the properties of huge networks, which have shown success in self-supervised learning (SSL), for all the downstream tasks like image classification, segmentation, etc. Previous studies have shown that using convolutional neural net

Vi Cecilia Erik Kronberg, Stela Andrea Muntean, Nils Hendrik Kröger, Adrian Muntean

The internal structure of adhesive tapes determines the effective mechanical properties. This holds true especially for blended systems, here consisting of acrylate and rubber phases. In this note, we propose a lattice-based model to study numerically the formation of internal morphologies within a four-component mixture (of discrete particles) where the sol

Zied Ammari, Shahnaz Farhat, Vedran Sohinger

This article is concerned with the almost sure existence of global solutions for initial value problems of the form $\dot{\gamma}(t)= v(t,\gamma(t))$ on separable dual Banach spaces. We prove a general result stating that whenever there exists $(\mu_t)_{t\in \mathbb{R}}$ a family of probability measures satisfying a related statistical Liouville equation, th

Sumit Kumar, Varun Gupta, Sankalp Sagar, Sachin Kumar Singh

Today, technological advancement is increasing day by day. Earlier, there was only a computer system in which we could only perform a few tasks. But now, machine learning, artificial intelligence, deep learning, and a few more technologies have made computer systems so advanced that we can perform any type of task. In this era of advancement, if people are s

Haiteng Zhao, Shengchao Liu, Chang Ma, Hannan Xu

Molecule property prediction has gained significant attention in recent years. The main bottleneck is the label insufficiency caused by expensive lab experiments. In order to alleviate this issue and to better leverage textual knowledge for tasks, this study investigates the feasibility of employing natural language instructions to accomplish molecule-relate

Huizong Yang, Yuxin Sun, Ganesh Sundaramoorthi, Anthony Yezzi

We present new insights and a novel paradigm (StEik) for learning implicit neural representations (INR) of shapes. In particular, we shed light on the popular eikonal loss used for imposing a signed distance function constraint in INR. We show analytically that as the representation power of the network increases, the optimization approaches a partial differ

Hua Zheng, Sarah W. Harcum, Jinxiang Pei, Wei Xie

Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a

Michael Shenoda

Real-time object detection is a crucial problem to solve when in comes to computer vision systems that needs to make appropriate decision based on detection in a timely manner. I have chosen the YOLO v1 architecture to implement it using PyTorch framework, with goal to familiarize with entire object detection pipeline I attempted different techniques to modi

Michael Shenoda

Creating an object detector, in computer vision, has some common challenges when initially developed based on Convolutional Neural Network (CNN) architecture. These challenges are more apparent when creating model that needs to adapt to images captured by various camera orientations, lighting conditions, and environmental changes. The availability of the ini

Zixuan Hu, Li Shen, Zhenyi Wang, Baoyuan Wu

Data-free meta-learning (DFML) aims to enable efficient learning of new tasks by meta-learning from a collection of pre-trained models without access to the training data. Existing DFML work can only meta-learn from (i) white-box and (ii) small-scale pre-trained models (iii) with the same architecture, neglecting the more practical setting where the users on

Narjes Nikzad Khasmakhi, Meysam Asgari-Chenaghlu, Nabiha Asghar, Philipp Schaer

Conversational generative vision models (CGVMs) like Visual ChatGPT (Wu et al., 2023) have recently emerged from the synthesis of computer vision and natural language processing techniques. These models enable more natural and interactive communication between humans and machines, because they can understand verbal inputs from users and generate responses in

Homanga Bharadhwaj, Abhinav Gupta, Shubham Tulsiani

Motivated by the intuitive understanding humans have about the space of possible interactions, and the ease with which they can generalize this understanding to previously unseen scenes, we develop an approach for learning visual affordances for guiding robot exploration. Given an input image of a scene, we infer a distribution over plausible future states t

Yu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka

Many event sequence data exhibit mutually exciting or inhibiting patterns. Reliable detection of such temporal dependency is crucial for scientific investigation. The de facto model is the Multivariate Hawkes Process (MHP), whose impact function naturally encodes a causal structure in Granger causality. However, the vast majority of existing methods use dire

Wei Zhou, Eugen Beck, Simon Berger, Ralf Schlüter

Modern public ASR tools usually provide rich support for training various sequence-to-sequence (S2S) models, but rather simple support for decoding open-vocabulary scenarios only. For closed-vocabulary scenarios, public tools supporting lexical-constrained decoding are usually only for classical ASR, or do not support all S2S models. To eliminate this restri

V. L. Oknyansky, C. M. Gaskell

We show that, contrary to simple predictions, most AGNs show at best only a small increase of lags with increasing wavelength in the J, H, K, and L bands . We suggest that a possible cause of this near simultaneity of the variability from the near-IR to the mid-IR is that the hot dust is in a hollow bi-conical outflow of which we preferentially see the near

Lorenzo Furio, Davide Lombardo

Serre's uniformity question asks whether there exists a bound $N>0$ such that, for every non-CM elliptic curve $E$ over $\mathbb{Q}$ and every prime $p>N$, the residual Galois representation $\rho_{E,p}:\operatorname{Gal}(\overline{\mathbb{Q}}/\mathbb{Q}) \to \operatorname{Aut}(E[p])$ is surjective. The work of many authors has shown that, for $p>37$, this r

Griffin Adams, Alexander R. Fabbri, Faisal Ladhak, Kathleen McKeown

Two-step approaches, in which summary candidates are generated-then-reranked to return a single summary, can improve ROUGE scores over the standard single-step approach. Yet, standard decoding methods (i.e., beam search, nucleus sampling, and diverse beam search) produce candidates with redundant, and often low quality, content. In this paper, we design a no

Jinqiu Xia, Yiwen Zhou, Hailong Wang, Wenxin Deng

Metal artifacts caused by the presence of metallic implants tremendously degrade the reconstructed computed tomography (CT) image quality, affecting clinical diagnosis or reducing the accuracy of organ delineation and dose calculation in radiotherapy. Recently, deep learning methods in sinogram and image domains have been rapidly applied on metal artifact re

Wenqi Cui, Yan Jiang, Baosen Zhang, Yuanyuan Shi

We study the optimal control of multiple-input and multiple-output dynamical systems via the design of neural network-based controllers with stability and output tracking guarantees. While neural network-based nonlinear controllers have shown superior performance in various applications, their lack of provable guarantees has restricted their adoption in high

Marc R. Bourgeois, Austin G. Nixon, Matthieu Chalifour, David J. Masiello

Advances in the ability to manipulate free electron phase profiles within the electron microscope have spurred development of quantum-mechanical descriptions of electron energy loss (EEL) processes involving transitions between phase-shaped transverse states. Here, we elucidate an underlying connection between two ostensibly distinct optical polarization ana

A. V. Kotikov

We present the results of studies [1,2] of the gauge covariance of the massless fermion propagator in three-dimensional quenched quantum electrodynamics in the framework of dimensional regularization in d=3-2\ep. Assuming the finiteness of the perturbative expansion, i.e. existence of the limit \ep \to 0, it was shown in [1] that exactly for d=3 all odd pert

Nikhil Vyas, Alexander Atanasov, Blake Bordelon, Depen Morwani

We study the effect of width on the dynamics of feature-learning neural networks across a variety of architectures and datasets. Early in training, wide neural networks trained on online data have not only identical loss curves but also agree in their point-wise test predictions throughout training. For simple tasks such as CIFAR-5m this holds throughout tra

Mugariya Farooq, Shahad Hardan, Aigerim Zhumbhayeva, Yujia Zheng

The need for more usable and explainable machine learning models in healthcare increases the importance of developing and utilizing causal discovery algorithms, which aim to discover causal relations by analyzing observational data. Explainable approaches aid clinicians and biologists in predicting the prognosis of diseases and suggesting proper treatments.

Sergii Kivva

First-order hydrostatic reconstruction (HR) schemes for shallow water equations are highly diffusive whereas high-order schemes can produce entropy-violating solutions. Our goal is to develop a flux correction with maximum antidiffusive fluxes to obtain entropy solutions of shallow water equations with variable bottom topography. For this purpose, we conside

Jamshaid Ul Rahman, Faiza Makhdoom, Dianchen Lu

Activation functions play a decisive role in determining the capacity of Deep Neural Networks as they enable neural networks to capture inherent nonlinearities present in data fed to them. The prior research on activation functions primarily focused on the utility of monotonic or non-oscillatory functions, until Growing Cosine Unit broke the taboo for a numb

Madhav P. Desai

We investigate the utility of augmenting a microprocessor with a single execution pipeline by adding a second copy of the execution pipeline in parallel with the existing one. The resulting dual-hardware-threaded microprocessor has two identical, independent, single-issue in-order execution pipelines (hardware threads) which share a common memory sub-system

Antonios Katsianis, Xiaohu Yang, Matthew Fong, Jie Wang

We present a physical model of the Mass Accretion Histories (MAH) of haloes in concordance with the {\it observed} cosmic star formation rate density (CSFRD). We model the MAHs of dark matter haloes using a Gamma ($\Gamma$) functional form: $M_h(T) = \frac{M_0}{f_{0}} \, \times \frac{\gamma(\alpha_h, ~\beta_h \times (T-Th))}{\Gamma(\alpha_h)}$, where $M_0$ i

Shashank Motepalli, Hans-Arno Jacobsen

Blockchains are decentralized; are they genuinely? We analyze blockchain decentralization's often-overlooked but quantifiable dimension: geospatial distribution of transaction processing. Blockchains bring with them the potential for geospatially distributed transaction processing. They enable validators from geospatially distant locations to partake in cons

Songxue Gao, Chuanqi Jiao, Ruidong Chen, Weijie Wang

Point cloud completion aims to recover raw point clouds captured by scanners from partial observations caused by occlusion and limited view angles. This makes it hard to recover details because the global feature is unlikely to capture the full details of all missing parts. In this paper, we propose a novel approach to point cloud completion task called Poin

Lilit Avetisyan, Aditya Deshmukh, X. Jessie Yang, Feng Zhou

In mixed traffic environments that involve conventional vehicles (CVs) and autonomous vehicles (AVs), it is crucial for CV drivers to maintain an appropriate level of situation awareness to ensure safe and efficient interactions with AVs. This study investigates how AV communication through human-machine interfaces (HMIs) affects CV drivers' situation awaren

Lu Qi, Jason Kuen, Weidong Guo, Jiuxiang Gu

Despite the progress of image segmentation for accurate visual entity segmentation, completing the diverse requirements of image editing applications for different-level region-of-interest selections remains unsolved. In this paper, we propose a new task, All-Inclusive Multi-Level Segmentation (AIMS), which segments visual regions into three levels: part, en

Aaron Küsters, Wil M. P. van der Aalst

The Alpha algorithm was the first process discovery algorithm that was able to discover process models with concurrency based on incomplete event data while still providing formal guarantees. However, as was stated in the original paper, practical applicability is limited when dealing with exceptional behavior and processes that cannot be described as a stru

Nilakshi Das, Amit Adhikary, Rupak Dutta

Recent experimental measurements of several observables in semileptonic B meson decays have pointed towards the possibility of new physics. The LHCb collaboration has reported a significant deviation, exceeding $3.2\sigma$, in the combined measurement of the ratio of branching ratios $R(D)-R(D^\star)$ from the predictions of the standard model. Furthermore,

Tomoyuki Arakawa, Lewis Topley, Juan J. Villarreal

The Feigin--Frenkel theorem states that, over the complex numbers, the centre of the universal affine vertex algebra at the critical level is an infinite rank polynomial algebra. The first author and W.~Wang observed that in positive characteristics, the universal affine vertex algebra contains a large central subalgebra known as the $p$-centre. They conject

Amit Berman, Yaron Shany, Itzhak Tamo

We present $O(m^3)$ algorithms for specifying the support of minimum-weight words of extended binary BCH codes of length $n=2^m$ and designed distance $d(m,s,i):=2^{m-1-s}-2^{m-1-i-s}$ for some values of $m,i,s$, where $m$ may grow to infinity. The support is specified as the sum of two sets: a set of $2^{2i-1}-2^{i-1}$ elements, and a subspace of dimension

Zhixiang Min, Bingbing Zhuang, Samuel Schulter, Buyu Liu

Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature. Estimating 3D coordinates for each pixel on the object surface holds great potential as it provides dense 2D-3D geometric constraints for the underlying PnP problem. However, high-quality ground truth supervision is not available in driving scen

Peiyao Xiao, Hao Ban, Kaiyi Ji

Multi-objective optimization (MOO) has become an influential framework in many machine learning problems with multiple objectives such as learning with multiple criteria and multi-task learning (MTL). In this paper, we propose a new direction-oriented multi-objective problem by regularizing the common descent direction within a neighborhood of a direction th

F. Carnesecchi, B. Sabiu, S. Strazzi, G. Vignola

In this paper, different Silicon PhotoMultiplier (SiPM) sensors have been tested with charged particles to characterize the Cherenkov light produced in the sensor protection layer. A careful position scan of the SiPM response has been performed with different prototypes, confirming the large number of firing cells and proving almost full efficiency, with the

Naweiluo Zhou, Florent Dufour, Vinzent Bode, Peter Zinterhof

Cloud computing provisions computer resources at a cost-effective way based on demand. Therefore it has become a viable solution for big data analytics and artificial intelligence which have been widely adopted in various domain science. Data security in certain fields such as biomedical research remains a major concern when moving their workflows to cloud,

V. V. Bobylev, A. T. Bajkova

An overview of the methods used to estimate the mass of the Galaxy and the results obtained by various authors recently according to modern data is given. In particular, the estimates obtained based on the analysis of the galactic rotation curve, on the kinematics of the Galactic dwarf satellites and globular clusters, on the streams of such dwarf galaxies,

Khanh Nguyen

How do language models "think"? This paper formulates a probabilistic cognitive model called the bounded pragmatic speaker, which can characterize the operation of different variations of language models. Specifically, we demonstrate that large language models fine-tuned with reinforcement learning from human feedback (Ouyang et al., 2022) embody a model of

Iakovos Androulidakis

Alan Weinstein remarked that, working in the framework of diffeology, a construction from Noncommutative Differential Geometry might provide the non-trivial representations required for the geometric quantisation of a symplectic structure which is not integral. In this note we show that the construction we gave with P. Antonini does indeed provide non-trivia

Yuki Okamoto, Kanta Shimonishi, Keisuke Imoto, Kota Dohi

In conventional studies on environmental sound separation and synthesis using captions, datasets consisting of multiple-source sounds with their captions were used for model training. However, when we collect the captions for multiple-source sound, it is not easy to collect detailed captions for each sound source, such as the number of sound occurrences and

Shengchao Liu, Weitao Du, Zhiming Ma, Hongyu Guo

Molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Naturally, molecules can be represented as 2D topological graphs or 3D geometric point clouds. Although most existing pertaining methods focus on merely the single modality, recent research has shown that maximizing the mutual information (MI) betwee

Md. Simul Hasan Talukder, Mohammad Raziuddin Chowdhury, Md Sakib Ullah Sourav, Abdullah Al Rakin

In certain Asian countries, Jute is one of the primary sources of income and Gross Domestic Product (GDP) for the agricultural sector. Like many other crops, Jute is prone to pest infestations, and its identification is typically made visually in countries like Bangladesh, India, Myanmar, and China. In addition, this method is time-consuming, challenging, an

Tullio Traverso, Francesco Coletti, Luca Magri, Tassos G. Karayiannis

The accurate prediction of the two-phase heat transfer coefficient (HTC) as a function of working fluids, channel geometries and process conditions is key to the optimal design and operation of compact heat exchangers. Advances in artificial intelligence research have recently boosted the application of machine learning (ML) algorithms to obtain data-driven

Lata Narayanan, Yasaman Sabbagh, Alexandros A. Voudouris

Recently, strategic games inspired by Schelling's influential model of residential segregation have been studied in the TCS and AI literature. In these games, agents of k different types occupy the nodes of a network topology aiming to maximize their utility, which is a function of the fraction of same-type agents they are adjacent to in the network. As such

Ashik Saha

The Garo Hills have always been fascination to the naked human eyes. The hills are the shelter of the earliest human habitation of Bangladesh. It is a place of ancient cultures and many botanical wonders. It is situated in the most northern part of Durgapur sub-district having border with Meghalaya of India. Durgapur is rich with ethnic diversity with Hajong

Valeri V. Makarov, Megan C. Johnson, Nathan J. Secrest

The fundamental celestial reference frame (CRF) is based on two catalogs of astrometric positions, the third realization of the International Celestial Reference Frame (ICRF3), and the much larger Gaia~CRF, built from the third data release (DR3). The objects in common between these two catalogs are mostly distant AGNs and quasars that are both sufficiently