February 2024 arXiv papers — page 144

Gabriel Busquim, Hugo Villamizar, Maria Julia Lima, Marcos Kalinowski

In recent years, Machine Learning (ML) components have been increasingly integrated into the core systems of organizations. Engineering such systems presents various challenges from both a theoretical and practical perspective. One of the key challenges is the effective interaction between actors with different backgrounds who need to work closely together,

Eduardo Zimelewicz, Marcos Kalinowski, Daniel Mendez, Görkem Giray

[Context] Systems incorporating Machine Learning (ML) models, often called ML-enabled systems, have become commonplace. However, empirical evidence on how ML-enabled systems are engineered in practice is still limited, especially for activities surrounding ML model dissemination. [Goal] We investigate contemporary industrial practices and problems related to

Abdurrahman Elmaghbub, Bechir Hamdaoui

Next-generation networks aim for comprehensive connectivity, interconnecting humans, machines, devices, and systems seamlessly. This interconnectivity raises concerns about privacy and security, given the potential network-wide impact of a single compromise. To address this challenge, the Zero Trust (ZT) paradigm emerges as a key method for safeguarding netw

Prabhat Kumar, Michael Hoffmann, Andrew Nonaka, Sayeef Salahuddin

HfO$_2$- and ZrO$_2$-based ferroelectric thin films have emerged as promising candidates for the gate oxides of next generation electronic devices. Recent work has experimentally demonstrated that a tetragonal/orthorhombic (t/o-) phase mixture with partially in-plane polarization can lead to negative capacitance (NC) stabilization. However, there is a discre

Luca Masserano, Alex Shen, Michele Doro, Tommaso Dorigo

An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different between train and target data. We refer to this type of distributional shift as generalized label shift (GLS). Direct cl

Arnaud Dufays, Aristide Houndetoungan, Alain Coën

Change-point processes are one flexible approach to model long time series. We propose a method to uncover which model parameter truly vary when a change-point is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent

Samuel Epstein

Due to M\"{u}ller's theorem, the Kolmogorov complexity of a string was shown to be equal to its quantum Kolmogorov complexity. Thus there are no benefits to using quantum mechanics to compress classical information. The quantitative amount of information in classical sources is invariant to the physical model used. These consequences make this theorem arguab

Abbas Raboonik, Lucas Tarr, David Pontin

In this paper we introduce a new method for exact decomposition of propagating, nonlinear magnetohydrodynamic (MHD) disturbances into their component eigenenergies associated with the familiar slow, Alfv\'en, and fast wave eigenmodes, and the entropy and field-divergence pseudo-eigenmodes. First the mathematical formalism is introduced, where it is illustrat

Saini Jatin Rao, Siddhant Jain, Saptarshi Basu

Bubbles have always captivated our curiosity with their aesthetics and complexities alike. While the act of blowing bubbles is familiar to everyone, the underlying physics of these fleeting spheres often eludes reasoning. In this letter, we discuss the dynamics of inflating a soap bubble using controlled airflow through a film-coated nozzle. We assess and pr

Antoine Cornet, Remy Molherac, Bernard Champagnon, Christine Martinet

Vitreous GeO$_2$ has been compressed at high temperature, to investigate the effect of thermal activation on the structural reorganization during compression. The measurements were performed in-situ using micro Raman spectroscopy under pressure up to 6 GPa and temperature up to 400$^\circ$C. The evolution of the Raman shift of the main band (400-500 cm$^{-1}

ALOHA 2 Team, Jorge Aldaco, Travis Armstrong, Robert Baruch

Diverse demonstration datasets have powered significant advances in robot learning, but the dexterity and scale of such data can be limited by the hardware cost, the hardware robustness, and the ease of teleoperation. We introduce ALOHA 2, an enhanced version of ALOHA that has greater performance, ergonomics, and robustness compared to the original design. T

Beihan Chen, Linu Malakkal, Marat Khafizov, David H. Hurley

Defects can significantly degrade the thermal conductivity of ThO2, an advanced nuclear fuel material as well as a surrogate for other fluorite-structured materials. We investigate how point defects in ThO2 impact phonon mode-resolved thermal transport. By incorporating phonon modes from lattice dynamics, we decompose the trajectory and heat flux to phonon n

Elona Agora, Jorge Antezana, Sergi Baena-Miret, María J. Carro

We prove a pointwise estimate for the decreasing rearrangement of $Tf$, where $T$ is any sublinear operator satisfying the weak-type boundedness $$ T:L^{p,1}(\mu) \to L^{p,\infty}(\nu), \quad \forall p: 1<p_0 < p\leq p_1<\infty, $$ with norm controlled by $C\varphi\left(\left[{p_0^{-1}} - p^{-1}\right]^{-1}\right)$ and $\varphi$ satisfies some admissibility

Elona Agora, Jorge Antezana, Sergi Baena-Miret, María J. Carro

We shall prove pointwise estimates for the decreasing rearrangement of $Tf$, where $T$ covers a wide range of interesting operators in Harmonic Analysis such as operators satisfying a Fefferman-Stein inequality, the Bochner-Riesz operator, rough operators, sparse operators, Fourier multipliers, etc. In particular, our main estimate is of the form $$ (Tf)^*(t

Ciyuan Peng, Jiayuan He, Feng Xia

Multimodal data pervades various domains, including healthcare, social media, and transportation, where multimodal graphs play a pivotal role. Machine learning on multimodal graphs, referred to as multimodal graph learning (MGL), is essential for successful artificial intelligence (AI) applications. The burgeoning research in this field encompasses diverse g

Antoine Cornet, Christine Martinet, Valerie Martinez, Dominique de Ligny

In-situ X-ray scattering monitoring is carried out during temperature annealing on different densified SiO$_2$ glasses. Density fluctuations and intermediate range coherence from x-ray scattering (SAXS) and diffraction (WAXS) evidence a maximum in their evolution at the same relaxation time. These extrema confirm the existence of an intermediate transitory d

Wei Qiao, Tushar Dogra, Otilia Stretcu, Yu-Han Lyu

Large language models (LLMs) are powerful tools for content moderation, but their inference costs and latency make them prohibitive for casual use on large datasets, such as the Google Ads repository. This study proposes a method for scaling up LLM reviews for content moderation in Google Ads. First, we use heuristics to select candidates via filtering and d

Lukas Unglehrt, Michael Manhart

We review models for unsteady porous media flow in the volume-averaging framework and we discuss the theoretical relations between the models and the definition of the model coefficients (and the uncertainty therein). The different models are compared against direct numerical simulations of oscillatory flow through a hexagonal sphere pack. The model constant

Carmen Delgado, Jeroen Famaey

Today's IoT devices rely on batteries, which offer stable energy storage but contain harmful chemicals. Having billions of IoT devices powered by batteries is not sustainable for the future. As an alternative, batteryless devices run on long-lived capacitors charged using energy harvesters. The small energy storage capacity of capacitors results in intermitt

Dipankar Sarkar

Information retrieval is a rapidly evolving field of information retrieval, which is characterized by a continuous refinement of techniques and technologies, from basic hyperlink-based navigation to sophisticated algorithm-driven search engines. This paper aims to provide a comprehensive overview of the evolution of Information Retrieval Technology, with a p

Bianca Minetto Napoleão, Ritika Sarkar, Sylvain Hallé, Fabio Petrillo

Context: The constant growth of primary evidence and Systematic Literature Reviews (SLRs) publications in the Software Engineering (SE) field leads to the need for SLR Updates. However, searching and selecting evidence for SLR updates demands significant effort from SE researchers. Objective: We present emerging results on an automated approach to support se

Macarena Lagos, Leah Jenks, Maximiliano Isi, Kenta Hotokezaka

Extensions to General Relativity (GR) allow the polarization of gravitational waves (GW) from astrophysical sources to suffer from amplitude and velocity birefringence, which respectively induce changes in the ellipticity and orientation of the polarization tensor. We introduce a multi-messenger approach to test this polarization behavior of GWs during their

Antoine Cornet, Valerie Martinez, Dominique de Ligny, Bernard Champagnon

Densified SiO2 glasses, obtained from different pressure and temperature routes have been annealed over a wide range of temperature far below the glass transition temperature (500$^\circ$C-900$^\circ$C). Hot and cold compressions were useful to separate the effects of pressure and the compression temperature. In-situ micro-Raman spectroscopy was used to foll

Craig Goodwin, Sandra Woolley

This extended paper contributes a methodology and a detailed analysis of app installation and functionality on a 'vintage' device. Experimental results are presented that demonstrate barriers to the reuse of vintage Apple devices. and solutions are posited. 230 apps across 23 unique app categories were tested to determine if they could be downloaded, install

Thomas Olsen

We present a perspective on the status of antiferromagnetism in two-dimensional (2D) materials. Various types of spin-compensated orders are discussed and include non-collinear order, spin spirals and altermagnetism. Spin-orbit effects ultimately determine, whether compounds exhibit long range order, Kosterlitz-Thouless physics, or multiferroic properties an

Hejing Li, Praneeth Balasubramanian, Marvin Meiers, Jialin Li

When physical testbeds are out of reach for evaluating a networked system, we frequently turn to simulation. In today's datacenter networks, bottlenecks are rarely at the network protocol level, but instead in end-host software or hardware components, thus current protocol-level simulations are inadequate means of evaluation. End-to-end simulations covering

Christine Martinet, Assia Kassir-Bodon, Thierry Deschamps, Antoine Cornet

Densified silica can be obtained by different pressure and temperature paths and for different stress conditions, hydrostatic or including shear. The density is usually the macroscopic parameter used to characterize the different compressed silica samples. The aim of our present study is to compare structural modifications for silica glass, densified from se

Jiawei Yao, Juhua Hu

Multiple clustering has gathered significant attention in recent years due to its potential to reveal multiple hidden structures of the data from different perspectives. Most of multiple clustering methods first derive feature representations by controlling the dissimilarity among them, subsequently employing traditional clustering methods (e.g., k-means) to

Lazar Atanackovic, Emmanuel Bengio

Generative Flow Networks (GFlowNets, GFNs) are a generative framework for learning unnormalized probability mass functions over discrete spaces. Since their inception, GFlowNets have proven to be useful for learning generative models in applications where the majority of the discrete space is unvisited during training. This has inspired some to hypothesize t

Maria Fedorova, M. V. Sivaselvan

An algorithm for three-dimensional dynamic vehicle-track-structure interaction (VTSI) analysis is described in this paper. The algorithm is described in terms of bridges and high-speed trains, but more generally applies to multibody systems coupled to deformable structures by time-varying kinematic constraints. Coupling is accomplished by a kinematic constra

Peter Graf, Patrick Emami

Neurosymbolic AI combines the interpretability, parsimony, and explicit reasoning of classical symbolic approaches with the statistical learning of data-driven neural approaches. Models and policies that are simultaneously differentiable and interpretable may be key enablers of this marriage. This paper demonstrates three pathways to implementing such models

Yuan Tian, Wenqi Zhou, Michele Viscione, Hao Dong

Symbolic Regression (SR) holds great potential for uncovering underlying mathematical and physical relationships from observed data. However, the vast combinatorial space of possible expressions poses significant challenges for both online search methods and pre-trained transformer models. Additionally, current state-of-the-art approaches typically do not co

Wanli Ma, Oktay Karakus, Paul L. Rosin

The advancement of knowledge distillation has played a crucial role in enabling the transfer of knowledge from larger teacher models to smaller and more efficient student models, and is particularly beneficial for online and resource-constrained applications. The effectiveness of the student model heavily relies on the quality of the distilled knowledge rece

Elona Agora, Jorge Antezana, María J. Carro, Javier Soria

We prove the Lorentz-Shimogaki and Boyd theorems for the spaces $\Lambda^p_u(w)$. As a consequence, we give the complete characterization of the strong boundedness of $H$ on these spaces in terms of some geometric conditions on the weights $u$ and $w$, whenever $p>1$. For these values of $p$, we also give the complete solution of the weak-type boundedness of

Jeremy Watson, Ioannis Lestas

This paper considers the control of AC-AC inter-linking converters (ILCs) in a multi-grid network. We overview the control schemes in the literature and propose a passivity framework for the stabilization of multi-grid networks, considering both AC grid-following and AC grid-forming behavior for the ILC connections. We then analyze a range of AC/AC interlink

Rohit Sarma Sarkar, Bibhas Adhikari

In this paper, we develop a Lie group theoretic approach for parametric representation of unitary matrices. This leads to develop a quantum neural network framework for quantum circuit approximation of multi-qubit unitary gates. Layers of the neural networks are defined by product of exponential of certain elements of the Standard Recursive Block Basis, whic

Chengyi Nie, Jessica Maghakian, Zhenhua Liu

Adjusting batch sizes and adaptively tuning other hyperparameters can significantly speed up deep neural network (DNN) training. Despite the ubiquity of heterogeneous clusters, existing adaptive DNN training techniques solely consider homogeneous environments. Optimizing distributed DNN training over heterogeneous clusters is technically challenging, and dir

Mi Wu

Collaborator recommendation is an important task in academic domain. Most of the existing approaches have the assumption that the recommendation system only need to recommend a specific researcher for the task. However, academic successes can be owed to productive collaboration of a whole academic team. In this work, we propose a new task: academic team work

Lyle Regenwetter, Yazan Abu Obaideh, Amin Heyrani Nobari, Faez Ahmed

This paper introduces a public dataset of 1.4 million procedurally-generated bicycle designs represented parametrically, as JSON files, and as rasterized images. The dataset is created through the use of a rendering engine which harnesses the BikeCAD software to generate vector graphics from parametric designs. This rendering engine is discussed in the paper

Mevan Wijewardena, Michael. J Neely

This paper considers an online multi-player resource-sharing game with bandit feedback. Multiple players choose from a finite collection of resources in a time slotted system. In each time slot, each resource brings a random reward that is equally divided among the players who choose it. The reward vector is independent and identically distributed over the t

Elona Agora, María J. Carro, Javier Soria

We study the boundedness of the Hilbert transform $H$ and the Hilbert maximal operator $H^*$ on weighted Lorentz spaces $\Lambda^p_u(w)$. We start by giving several necessary conditions that, in particular, lead us to the complete characterization of the weak-type boundedness of both $H$ and $H^*$, whenever $u\in A_1$. For the strong-type case, we also get t

Antoine Cornet, Alberto Ronca, Jie Shen, Federico Zontone

A new experimental setup combining X-Ray Photon Correlation Spectroscopy (XPCS) in the hard x-ray regime and a high-pressure sample environment is developed to monitor the pressure dependence of the internal motion of complex systems down to the atomic scale in the multi-gigapascal range, from room temperature to 600K. The high flux of coherent high energy x

Daniel McDuff, Tim Korjakow, Scott Cambo, Jesse Josua Benjamin

Growing concerns over negligent or malicious uses of AI have increased the appetite for tools that help manage the risks of the technology. In 2018, licenses with behaviorial-use clauses (commonly referred to as Responsible AI Licenses) were proposed to give developers a framework for releasing AI assets while specifying their users to mitigate negative appl

M. T. García-Ordás, E. Alegre-Gutiérrez, V. González-Castro, R. Alaiz-Rodríguez

In this paper, a new system based on combinations of a shape descriptor and a contour descriptor has been proposed for classifying inserts in milling processes according to their wear level following a computer vision based approach. To describe the wear region shape we have proposed a new descriptor called ShapeFeat and its contour has been characterized us

M. T. García-Ordás, E. Alegre-Gutiérrez, R. Alaiz-Rodríguez, V. González-Castro

In this work we propose a new online, low cost and fast approach based on computer vision and machine learning to determine whether cutting tools used in edge profile milling processes are serviceable or disposable based on their wear level. We created a new dataset of 254 images of edge profile cutting heads which is, to the best of our knowledge, the first

Alberto Acevedo, Janek Wehr

Given a mixture of states, finding a way to optimally discriminate its elements is a prominent problem in quantum communication theory. In this paper, we will address mixtures of density operators that are unitarily equivalent via elements of a one-parameter unitary group, and the corresponding quantum state discrimination (QSD) problems. We will be particul

A. Joshi, E. Fidalgo, E. Alegre, R. Alaiz-Rodriguez

In this paper, we propose Ranksum, an approach for extractive text summarization of single documents based on the rank fusion of four multi-dimensional sentence features extracted for each sentence: topic information, semantic content, significant keywords, and position. The Ranksum obtains the sentence saliency rankings corresponding to each feature in an u

F. Janez-Martino, R. Alaiz-Rodriguez, V. Gonzalez-Castro, E. Fidalgo

Spam emails are unsolicited, annoying and sometimes harmful messages which may contain malware, phishing or hoaxes. Unlike most studies that address the design of efficient anti-spam filters, we approach the spam email problem from a different and novel perspective. Focusing on the needs of cybersecurity units, we follow a topic-based approach for addressing

Alaiz-Rodriguez, R., Parnell, A. C

Feature selection is a key step when dealing with high dimensional data. In particular, these techniques simplify the process of knowledge discovery from the data by selecting the most relevant features out of the noisy, redundant and irrelevant features. A problem that arises in many of these practical applications is that the outcome of the feature selecti

Pramit Saha, Divyanshu Mishra, Felix Wagner, Konstantinos Kamnitsas

Multimodal Federated Learning (MMFL) utilizes multiple modalities in each client to build a more powerful Federated Learning (FL) model than its unimodal counterpart. However, the impact of missing modality in different clients, also called modality incongruity, has been greatly overlooked. This paper, for the first time, analyses the impact of modality inco

Rishabh Goel

The advent of Large Language Models (LLMs) is promising and LLMs have been applied to numerous fields. However, it is not trivial to implement LLMs in the medical field, due to the high standards for precision and accuracy. Currently, the diagnosis of medical ailments must be done by hand, as it is costly to build a sufficiently broad LLM that can diagnose a

N. Cueto-López, M. T. García-Ordás, V. Dávila-Batista, V. Moreno

Background and objective Risk prediction models aim at identifying people at higher risk of developing a target disease. Feature selection is particularly important to improve the prediction model performance avoiding overfitting and to identify the leading cancer risk (and protective) factors. Assessing the stability of feature selection/ranking algorithms

Shreyas Ramachandran, Simao Joao, Hanwen Jin, Johannes Lischner

Hot electrons and holes generated from the decay of localized surface plasmons in metallic nanoparticles can be harnessed for applications in solar energy conversion and sensing. In this paper, we study the generation of hot carriers in large spherical gold-silver alloy nanoparticles using a recently developed atomistic modelling approach that combines a sol

Maryam Rahnemoonfar, Younghyun Koo

The Ice-sheet and Sea-level System Model (ISSM) provides numerical solutions for ice sheet dynamics using finite element and fine mesh adaption. However, considering ISSM is compatible only with central processing units (CPUs), it has limitations in economizing computational time to explore the linkage between climate forcings and ice dynamics. Although seve

Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D'Oro

A natural approach for reinforcement learning is to predict future rewards by unrolling a neural network world model, and to backpropagate through the resulting computational graph to learn a policy. However, this method often becomes impractical for long horizons since typical world models induce hard-to-optimize loss landscapes. Transformers are known to e

Hanna Furmańczyk, Vahan Mkrtchyan

In this paper, we consider some general properties of block graphs as well as the equitable coloring problem in this class of graphs. In the first part we establish the relation between two structural parameters for general block graphs. We also give complete characterization of block graphs with given value of parameter $\alpha_{\min}$. In the next part of

Griffin T. Goodwin, Viacheslav M. Sadykov, Petrus C. Martens

This study explores the behavior of machine learning-based flare forecasting models deployed in a simulated operational environment. Using Georgia State University's Space Weather Analytics for Solar Flares benchmark dataset (Angryk et al. 2020a,b), we examine the impacts of training methodology and the solar cycle on decision tree, support vector machine, a

Jinge Wang, Sangjoon Lee, Philip S. Marcus

The remarkable robustness of isolated columnar vortices suggests the existence of fundamental constraints that prevent spontaneous disintegration. In this work, we investigate the weakly nonlinear stability of such flows, demonstrating that the triadic resonance of wave modes is governed by a set of hydrodynamic ``selection rules''. By employing a multi-scal

Pavel Pudlák, Vojtěch Rödl

For $0<\delta\leq 1$, let $R_k(m;\delta)$ denote the smallest $N$ such that every coloring of $k$-element subsets by two colors yields an $m$-element set $M$ with relative discrepancy $\delta$, which means that one color class has at least $(\frac{1+\delta}2){m\choose k}$ elements. The number $R_k(m;\delta)$ may be viewed as an extension of the usual $k$-hyp

Yang Zhang, Ling-Fang Lin, Adriana Moreo, Thomas A. Maier

Motivated by the recently reported signatures of superconductivity in trilayer La$_4$Ni$_3$O$_{10}$ under pressure, we comprehensively study this system using {\it ab initio} and random-phase approximation techniques. Without electronic interactions, the Ni $d_{3z^2-r^2}$ orbitals show a bonding-antibonding and nonbonding splitting behavior via the O $p_z$ o

Davide Corsi, Guy Amir, Guy Katz, Alessandro Farinelli

In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-world and complex systems. However, even the state-of-the-art DRL models have been shown to suffer from reliability concerns -- for example, their susceptibility to adversarial inputs, i.e., small and abundant input

Fangzhou Ai, Oleg Shpyrko, Vitaliy Lomakin

Coherent X-ray Diffraction Imaging (CXDI) technique offers unique insights into the nanoscale world, enabling the reconstruction of 3D structures with a nanoscale resolution achieved through computational phase reconstruction from measured scattered intensity maps. Computational demands of 3D CXDI, however, limit its real-time application in experimental set

Ran Zmigrod, Zhiqiang Ma, Armineh Nourbakhsh, Sameena Shah

Visually Rich Form Understanding (VRFU) poses a complex research problem due to the documents' highly structured nature and yet highly variable style and content. Current annotation schemes decompose form understanding and omit key hierarchical structure, making development and evaluation of end-to-end models difficult. In this paper, we propose a novel F1 m

Tanmoy Mondal, Ricardo Mendoza, Lucas Drumetz

In general, underwater images suffer from color distortion and low contrast, because light is attenuated and backscattered as it propagates through water (differently depending on wavelength and on the properties of the water body). An existing simple degradation model (similar to atmospheric image "hazing" effects), though helpful, is not sufficient to prop

Meysam Alishahi, Jeff M. Phillips

We refine and generalize what is known about coresets for classification problems via the sensitivity sampling framework. Such coresets seek the smallest possible subsets of input data, so one can optimize a loss function on the coreset and ensure approximation guarantees with respect to the original data. Our analysis provides the first no dimensional cores

Will Lavanakul, Jason J. Choi, Koushil Sreenath, Claire J. Tomlin

Learning-based approaches are emerging as an effective approach for safety filters for black-box dynamical systems. Existing methods have relied on certificate functions like Control Barrier Functions (CBFs) and Hamilton-Jacobi (HJ) reachability value functions. The primary motivation for our work is the recognition that ultimately, enforcing the safety cons

Thomas Britton, Michael Goodrich, Naomi Jarvis, Torri Jeske

Final report for the AI Assisted Experiment Control and Calibration project. This project integrated AI/ML into the controls and calibration of a production detector system in the GlueX spectrometer, a large scale Nuclear Physics detector in experimental Hall-D at Jefferson Lab. The AI/ML model predicted calibration constants for a Central Drift Chamber usin

Wolfgang Lueck

We investigate the relative assembly map from the family of finite subgroups to the family of virtually cyclic subgroups for the algebraic $K$-theory of twisted group rings of a group G with coefficients in a regular ring R or, more generally, with coefficients in a regular additive category. They are known to be isomorphisms rationally. We show that it suff

Hossein Rastgoftar

This paper studies the problem of safe humanuncrewed aerial system (UAS) collaboration in a shared work environment. By considering human and UAS as co-workers, we use Petri Nets to abstractly model evolution of shared tasks assigned to human and UAS co-workers. Particularly, the Petri Nets places represent work stations; therefore, the Petri Nets transition

Yu Awaya, Vijay Krishna

An informed planner wishes to spread information among a group of agents in order to induce efficient coordination -- say the adoption of a new technology with positive externalities. The agents are connected via a social network. The planner informs a seed and then the information spreads via the network. While the structure of the network affects the rate

Celal Alagoz

The comparative performance of hierarchical classification (HC) and flat classification (FC) methodologies in the realm of time series data analysis is investigated in this study. Dissimilarity measures, including Jensen-Shannon Distance (JSD), Task Similarity Distance (TSD), and Classifier Based Distance (CBD), are leveraged alongside various classifiers su

Isaac Grosof, Siva Theja Maguluri, R. Srikant

A wide variety of queueing systems can be naturally modeled as infinite-state Markov Decision Processes (MDPs). In the reinforcement learning (RL) context, a variety of algorithms have been developed to learn and optimize these MDPs. At the heart of many popular policy-gradient based learning algorithms, such as natural actor-critic, TRPO, and PPO, lies the

Ta-seen Reaz Niloy, Saurav Kumar, Aniruddha Hore, Zoheb Hassan

This paper introduces ASCENT (context Aware Spectrum Coexistence Design and Implementation) toolset, an advanced context-aware terrestrial satellite spectrum sharing toolset designed for researchers, policymakers, and regulators. It serves two essential purposes (a) evaluating the potential for harmful interference to primary users in satellite bands and (b)

Yizhan Shu, Chenyu Yu, John M. Mulvey

This article investigates a regime-switching investment strategy aimed at mitigating downside risk by reducing market exposure during anticipated unfavorable market regimes. We highlight the statistical jump model (JM) for market regime identification, a recently developed robust model that distinguishes itself from traditional Markov-switching models by enh

Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala

Understanding the mechanisms through which neural networks extract statistics from input-label pairs through feature learning is one of the most important unsolved problems in supervised learning. Prior works demonstrated that the gram matrices of the weights (the neural feature matrices, NFM) and the average gradient outer products (AGOP) become correlated

Mohamed Ali Zormati, Hicham Lakhlef, Sofiane Ouni

The Internet of Things (IoT) is an emerging technology that aims to connect heterogeneous and constrained objects to each other and to the Internet. It has grown significantly in a wide variety of applications such as smart homes, smart cities, smart vehicles, etc. The huge number of connected devices increases the challenges, as IoT provides diverse and com

Audrey Thirouin, Scott S. Sheppard

The 5:3 and 7:4 mean motion resonances of Neptune are at 42.3 and 43.7 au, respectively, and overlap with objects in the Classical trans-neptunian belt (Kuiper belt). We report the complete/partial lightcurves of 13 and 14 trans-Neptunian objects (TNOs) in the 5:3 and 7:4 resonances, respectively. We report a most likely contact binary in the 7:4 resonance,

Shih-Wei Chou, Bo-Chih Huang, Yun-guang Lu, Naoki Tsuge

Our goal in this paper is to prove the global existence of a classical solution for the isentropic nozzle flow. Regarding this problem, there exist some global existence theorems of weak solutions. However, that of classical solutions does not have much attention until now. When we consider the present problem, the main difficulty is to obtain the uniform bo

Giovanni Giacomin, Armin Schikorra

We consider the scaling-invariant nonlocal Willmore energy, defined via the nonlocal mean curvature by Caffarelli, Roquejoffre and Savin. Our main result is the existence of minimizers in the class of convex $C^1$-curves.

Sacha Sokoloski, Jure Majnik, Philipp Berens

Animal vision is thought to optimize various objectives from metabolic efficiency to discrimination performance, yet its ultimate objective is to facilitate the survival of the animal within its ecological niche. However, modeling animal behavior in complex environments has been challenging. To study how environments shape and constrain visual processing, we

Nima Rasekh, Niels van der Weide, Benedikt Ahrens, Paige Randall North

Category theory unifies mathematical concepts, aiding comparisons across structures by incorporating objects and morphisms, which capture their interactions. It has influenced areas of computer science such as automata theory, functional programming, and semantics. Certain objects naturally exhibit two classes of morphisms, leading to the concept of a double

Petr Ostroukhov, Aigerim Zhumabayeva, Chulu Xiang, Alexander Gasnikov

This paper presents a novel adaptation of the Stochastic Gradient Descent (SGD), termed AdaBatchGrad. This modification seamlessly integrates an adaptive step size with an adjustable batch size. An increase in batch size and a decrease in step size are well-known techniques to tighten the area of convergence of SGD and decrease its variance. A range of studi

Ernesto Vazquez-Sanchez, Jaime Gomez-Gil, Jose-Carlos Gamazo-Real, Jose Fernando Diez-Higuera

Currently, for many applications, it is necessary to know the speed and position of motors. This can be achieved using mechanical sensors coupled to the motor shaft or using sensorless techniques. The sensorless techniques in brushed dc motors can be classified into two types: 1) techniques based on the dynamic brushed dc motor model and 2) techniques based

Jose-Carlos Gamazo-Real, Raul Torres Fernandez, Adrian Murillo Armas

The large increase in the number of Internet of Things (IoT) devices have revolutionised the way data is processed, which added to the current trend from cloud to edge computing has resulted in the need for efficient and reliable data processing near the data sources using energy-efficient devices. Two methods based on low-cost edge-IoT architectures are pro

Jose-Carlos Gamazo-Real, Ernesto Vazquez-Sanchez, Jaime Gomez-Gil

This paper provides a technical review of position and speed sensorless methods for controlling Brushless Direct Current (BLDC) motor drives, including the background analysis using sensors, limitations and advances. The performance and reliability of BLDC motor drivers have been improved because the conventional control and sensing techniques have been impr

A. Alfaro, L. X. Gutiérrez Guerrero, L. Albino, A. Raya

In this study, we present a perturbative analysis of the three-gluon vertex for a kinematical symmetric configuration in dimension $n=4-2\epsilon$ and different covariant gauges.Our study can describe the form factors of the three gluon vertex in a wide range of momentum. We employ a momentum subtraction (MOM) scheme to define the renormalized vertex. We giv

Julio César Pérez-Pedraza, José Eduardo Barrios-Vargas, Alfredo Raya

We study the Su-Schrieffer-Hegger model, perhaps the simplest realization of a topological insulator, in the presence of an embedded impurity superlattice. We consider the impact of the said impurity by changing the hopping amplitudes between them and their nearest neighbors in the topological boundaries and the edge state localization in the chain of atoms.

Syed Mekael Wasti, Ken Q. Pu, Ali Neshati

The evolution of Large Language Models (LLMs) has showcased remarkable capacities for logical reasoning and natural language comprehension. These capabilities can be leveraged in solutions that semantically and textually model complex problems. In this paper, we present our efforts toward constructing a framework that can serve as an intermediary between a u

Y. Alexanian, J. Saugnier, C. Decorse, J. Robert

The possibilities of combining several degrees of freedom inside a unique material have recently been highlighted in their dynamics and proposed as information carriers in quantum devices where their cross-manipulation by external parameters such as electric and magnetic fields could enhance their functionalities. An emblematic example is that of electromagn

Mohamed Ali Zormati, Hicham Lakhlef

The Internet of Things (IoT) is becoming a part of everyday life through its various sensing devices that collect valuable information. The huge number of interconnected heterogeneous IoT devices poses immense challenges, and network softwarization techniques are an adequate solution to these concerns. Software Defined Networking (SDN) and Network Function V

G. Perez-Palomino, J. E. Page, M. Arrebola, J. A. Encinar

A new approach to designing FSS-based LP-CP converters is presented. It is based on the use of FSSs which exhibit dual diagonal symmetry, and a novel 4-port equivalent circuit able to describe the electrical behavior of the cells for the two linear incident polarizations at the same time. The equivalent circuit allows the use of standardized branch line coup

Yuyang Rong, Zhanghan Yu, Zhenkai Weng, Stephen Neuendorffer

Modern compilers, such as LLVM, are complex pieces of software. Due to their complexity, manual testing is unlikely to suffice, yet formal verification is difficult to scale. End-to-end fuzzing can be used, but it has difficulties in achieving high coverage of some components of LLVM. In this paper, we implement IRFuzzer to investigate the effectiveness of s

Mohamed Ali Zormati, Hicham Lakhlef

The Internet of Things (IoT) has evolved from a novel technology to an integral part of our everyday lives. It encompasses a multitude of heterogeneous devices that collect valuable data through various sensors. The sheer volume of these interconnected devices poses significant challenges as IoT provides complex network services with diverse requirements on

Devansh R Agrawal, Rajiv Govindjee, Jiangbo Yu, Anurekha Ravikumar

This paper proposes two new algorithms for certified perception in safety-critical robotic applications. The first is a Certified Visual Odometry algorithm, which uses a RGBD camera with bounded sensor noise to construct a visual odometry estimate with provable error bounds. The second is a Certified Mapping algorithm which, using the same RGBD images, const

F. Conde-Pumpido, G. Perez-Palomino, J. R Montejo-Garai, J. E. Page

This work presents, for the first time, a generalized bimode Fosters equivalent circuit for characterization of 2-D Planar Periodic Structures (PPSs) with arbitrary geometry at oblique incidence. It considers the interactions between the fundamental TE and TM modes without any restriction within the bimode bandwidth of the geometry. The proposed circuit is o

Yushu Pan, Elias Bareinboim

Counterfactual image editing is an important task in generative AI, which asks how an image would look if certain features were different. The current literature on the topic focuses primarily on changing individual features while remaining silent about the causal relationships between these features, as present in the real world. In this paper, we formalize

My H. Dinh, James Kotary, Ferdinando Fioretto

Learning to Rank (LTR) is one of the most widely used machine learning applications. It is a key component in platforms with profound societal impacts, including job search, healthcare information retrieval, and social media content feeds. Conventional LTR models have been shown to produce biases results, stimulating a discourse on how to address the dispari

Maria Sol Vidal-Saez, Oscar Vilarroya, Jordi Garcia-Ojalvo

To survive in ever-changing environments, living organisms need to continuously combine the ongoing external inputs they receive, representing present conditions, with their dynamical internal state, which includes influences of past experiences. It is still unclear in general, however, (i) how this happens at the molecular and cellular levels, and (ii) how

Serena Dipierro, Matteo Novaga, Enrico Valdinoci

We show by a formal asymptotic expansion that level sets of solutions of a time-fractional Allen-Cahn equation evolve by a geometric flow whose normal velocity is a positive power of the mean curvature. This connection is quite intriguing, since the original equation is nonlocal and the evolution of its solutions depends on all previous states, but the assoc

Robert Kudelić

Quantum computing exposes the brilliance of quantum mechanics through computer science and, as such, gives oneself a marvelous and exhilarating journey to go through. This article leads along that journey with a historical and current outlook on quantum computation that is geared toward computer experts but also to experts from other disciplines as well. It