November 2022 arXiv papers — page 66

Md Hasibul Amin, Harika Madanu, Sahithi Lavu, Hadi Mansourifar

Since the beginning of the vaccination trial, social media has been flooded with anti-vaccination comments and conspiracy beliefs. As the day passes, the number of COVID- 19 cases increases, and online platforms and a few news portals entertain sharing different conspiracy theories. The most popular conspiracy belief was the link between the 5G network sprea

Zahra Shamsi, Isaac Reid, Drew Bryant, Jacob Wilson

We present a machine learning method capable of accurately detecting chromosome abnormalities that cause blood cancers directly from microscope images of the metaphase stage of cell division. The pipeline is built on a series of fine-tuned Vision Transformers. Current state of the art (and standard clinical practice) requires expensive, manual expert analysi

Micah Carroll, Orr Paradise, Jessy Lin, Raluca Georgescu

Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same idea also applies naturally to sequential decision-making, where many well-studied tasks like behavior cloning, offline reinforcement learning, inverse dynamics, and waypoint cond

Zhuotao Xie, Ming Zhao, Hantao Lu, Zhongbing Huang

Using the time-dependent Lanczos method, we study the non-equilibrium dynamics of the one-dimensional ionic-mass imbalanced Hubbard chain driven by a quantum quench of the on-site Coulomb interaction, where the system is prepared in the ground state of the Hamiltonian with a different Hubbard interaction. A full exact diagonalization is adopted to study the

Ziming Wan, Deqing Wang, Xuehua Ming, Fuzhen Zhuang

Heterogeneous graph neural networks (HGNNs) have been widely applied in heterogeneous information network tasks, while most HGNNs suffer from poor scalability or weak representation when they are applied to large-scale heterogeneous graphs. To address these problems, we propose a novel Relation-aware Heterogeneous Graph Neural Network with Contrastive Learni

Girish Sundaram, Daniel Berleant

Objectives: An SLR is presented focusing on text mining based automation of SLR creation. The present review identifies the objectives of the automation studies and the aspects of those steps that were automated. In so doing, the various ML techniques used, challenges, limitations and scope of further research are explained. Methods: Accessible published lit

Xiuding Cai, Yaoyao Zhu, Dong Miao, Linjie Fu

In an unpaired setting, lacking sufficient content constraints for image-to-image translation (I2I) tasks, GAN-based approaches are usually prone to model collapse. Current solutions can be divided into two categories, reconstruction-based and Siamese network-based. The former requires that the transformed or transforming image can be perfectly converted bac

Venkatachalam Natchiappan, Shrihari Vasudevan, Thalanayar Muthukumar

This paper proposes a data and Machine Learning-based forecasting solution for the Telecommunications network-rollout planning problem. Milestone completion-time estimation is crucial to network-rollout planning; accurate estimates enable better crew utilisation and optimised cost of materials and logistics. Using historical data of milestone completion time

Cristian Sbrolli, Paolo Cudrano, Matteo Frosi, Matteo Matteucci

In recent years, Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated exceptional performance in various 2D generative tasks. Following this success, DDPMs have been extended to 3D shape generation, surpassing previous methodologies in this domain. While many of these models are unconditional, some have explored the potential of using guidance

Santiago Camara, Maximo Sangiacomo

Borrowing constraints are a key component of modern international macroeconomic models. The analysis of Emerging Markets (EM) economies generally assumes collateral borrowing constraints, i.e., firms access to debt is constrained by the value of their collateralized assets. Using credit registry data from Argentina for the period 1998-2020 we show that less

Filippo Anzuini, José A. Pons, Antonio Gómez-Bañón, Paul D. Lasky

The coupling between axions and photons modifies Maxwell's equations, introducing a dynamo term in the magnetic induction equation. In neutron stars, for critical values of the axion decay constant and axion mass, the magnetic dynamo mechanism increases the total magnetic energy of the star. We show that this generates substantial internal heating due to enh

Daniel Younis, Joseph H. Eberly

We present an ab initio study of the nonsequential strong-field ionization dynamics of a model two-electron atom with helium character. Single- and double-ionization events are characterized and displayed using detector signals extracted at different points in the two-electron two-dimensional space. The double photoelectron momentum distribution is calculate

Zhizhou Ren, Anji Liu, Yitao Liang, Jian Peng

Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning transferable policies that support few-shot adaptation to unseen tasks. Despite recent advances in meta-RL, most existing methods require the access to the environmental reward func

Wang Tang

In this paper, we study the coupled Higgs equation and its multi-component generalization based on the Hirota's direct method. One and two-soliton solutions of the coupled Higgs equation are derived by the perturbation approach. We express the N-soliton solutions in the form of Pfaffians and demonstrate that the N-component coupled Higgs equation turns out t

Nassim Dehouche, Richard Blythman

Intelligent human inputs are required both in the training and operation of AI systems, and within the governance of blockchain systems and decentralized autonomous organizations (DAOs). This paper presents a formal definition of Human Intelligence Primitives (HIPs), and describes the design and implementation of an Ethereum protocol for their on-chain colle

Futian Weng, Yuanting Ma, Jinghan Sun, Shijun Shan

Dermatological diseases are among the most common disorders worldwide. This paper presents the first study of the interpretability and imbalanced semi-supervised learning of the multiclass intelligent skin diagnosis framework (ISDL) using 58,457 skin images with 10,857 unlabeled samples. Pseudo-labelled samples from minority classes have a higher probability

Dongdong Liu Ting Hua nd Xifu Liu

Multilinear systems play an important role in scientific calculations of practical problems. In this paper, we consider a tensor splitting method with a relaxed Anderson acceleration for solving multilinear systems. The new method preserves nonnegativity for every iterative step and improves the existing ones. Furthermore, the convergence analysis of the pro

Bao Duong, Thin Nguyen

Mutual Information (MI) and Conditional Mutual Information (CMI) are multi-purpose tools from information theory that are able to naturally measure the statistical dependencies between random variables, thus they are usually of central interest in several statistical and machine learning tasks, such as conditional independence testing and representation lear

Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous, Karl H. Johansson

In this paper, we consider the unconstrained distributed optimization problem, in which the exchange of information in the network is captured by a directed graph topology, thus, nodes can only communicate with their neighbors. Additionally, in our problem, the communication channels among the nodes have limited bandwidth. In order to alleviate this limitati

Jiuding Yang, Jinwen Luo, Weidong Guo, Jerry Chen

Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition. NNER is where one entity may be part of a longer entity, and this may happen on multiple levels, as the term nested suggests. These nested structures make traditional sequence labeling methods unable to properly recognize

Stephen Whitelam

We use Monte Carlo and genetic algorithms to train neural-network feedback-control protocols for simulated fluctuating nanosystems. These protocols convert the information obtained by the feedback process into heat or work, allowing the extraction of work from a colloidal particle pulled by an optical trap and the absorption of entropy by an Ising model unde

Ari Mizel

In the design and investigation of superconducting qubits and related devices, a lumped element circuit model is the standard theoretical approach. However, many important physical questions lie beyond its scope, e.g. the behavior of circuits with strong Josephson junctions carrying substantial currents and the properties of small superconducting devices. By

Thomas J. Ringstrom

Reinforcement Learning views the maximization of rewards and avoidance of punishments as central to explaining goal-directed behavior. However, over a life, organisms will need to learn about many different aspects of the world's structure: the states of the world and state-vector transition dynamics. The number of combinations of states grows exponentially

Xuzhong Hu, Zaipeng Duan, Jie Ma

For 3D object detection, labeling lidar point cloud is difficult, so data augmentation is an important module to make full use of precious annotated data. As a widely used data augmentation method, GT-sample effectively improves detection performance by inserting groundtruths into the lidar frame during training. However, these samples are often placed in un

Daniel Domínguez-Vázquez, Qi Wang, Gustaaf B. Jacobs

The forcing of particles in turbulent environments influences dynamical properties pertinent to many fundamental applications involving particle-flow interactions. Current study explores the determination of forcing for one-way coupled passive particles, under the assumption that the ambient velocity fields are known. When measurements regarding particle loc

Xi Wang, Shu-Min Zhao, Xin-Xin Long, Yi-Tong Wang

In the framework of the MSSM extension with local gauged baryon and lepton numbers (BLMSSM), we calculate the muon anomalous magnetic dipole moment (MDM) and lepton $(e, \mu, \tau)$ electric dipole moment (EDM), and discuss how the muon MDM and lepton EDM depend on the parameters within the mass insertion approximation. Among many parameters, $\tan{\beta}$,~

Yiqin He

Let $L$ be a finite extension of $\mathbf{Q}_p$. Let $\rho_L$ be a potentially semi-stable non-crystalline $p$-adic Galois representation such that the associated $F$-semisimple Weil-Deligne representation is absolutely indecomposable. In this paper, we study Fontaine-Mazur parabolic simple $\mathscr{L}$-invariants of $\rho_L$, which was previously only know

Somrath Kanoksirirath

An unconventional approach is applied to solve the one-dimensional Burgers' equation. It is based on spline polynomial interpolations and Hopf-Cole transformation. Taylor expansion is used to approximate the exponential term in the transformation, then the analytical solution of the simplified equation is discretized to form a numerical scheme, involving var

Ross DeMott, Alex Flournoy

In this work, we develop a perturbative method to describe the behavior of a chiral condensate around a spherical black hole whose mass is astrophysically realistic. We use the inverse mass as the expansion parameter for our perturbative series. We test this perturbative method in the case of a Schwarzschild black hole, and we find that it agrees well with p

Zheng Xu, Maxwell Collins, Yuxiao Wang, Liviu Panait

Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user-level DP for large image-to-embedding f

Amirmohammad Pasdar, Young Choon Lee, Seok-Hee Hong

The vulnerability of smartphones to cyberattacks has been a severe concern to users arising from the integrity of installed applications (\textit{apps}). Although applications are to provide legitimate and diversified on-the-go services, harmful and dangerous ones have also uncovered the feasible way to penetrate smartphones for malicious behaviors. Thorough

Bo Hou, Jun Zhao

In this paper, we introduce the notions of crossed module of associative conformal algebras, 2-term strongly homotopy associative conformal algebras, and discuss the relationship between them and the 3-th Hochschild cohomology of associative conformal algebras. We classify the non-abelian extensions by introducing the non-abelian cohomology. We show that non

Junying Chen, Qingcai Chen, Dongfang Li, Yutao Huang

Recently, Dense Retrieval (DR) has become a promising solution to document retrieval, where document representations are used to perform effective and efficient semantic search. However, DR remains challenging on long documents, due to the quadratic complexity of its Transformer-based encoder and the finite capacity of a low-dimension embedding. Current DR m

Byoung S. Ham

Quantum entanglement is known as a unique feature of quantum mechanics, which cannot be obtained from classical physics. Recently, a coherence interpretation has been conducted for the delayed-choice quantum eraser using coherent photon pairs, where phase-locked symmetric frequency detuning between paired photons plays an essential role for selective measure

Samuel Anyaso-Samuel, Dipankar Bandyopadhyay, Somnath Datta

Multistate current status (CS) data presents a more severe form of censoring due to the single observation of study participants transitioning through a sequence of well-defined disease states at random inspection times. Moreover, these data may be clustered within specified groups, and informativeness of the cluster sizes may arise due to the existing laten

Naomi Sweeting

The theory of endoscopy predicts the existence of large families of Tate classes on certain products of Shimura varieties, and it is natural to ask in what cases one can construct algebraic cycles giving rise to these Tate classes. This paper takes up the case of Tate classes arising from the Yoshida lift: these are Tate cycles in middle degree on the Shimur

Wei Deng, Qian Zhang, Qi Feng, Faming Liang

Parallel tempering (PT), also known as replica exchange, is the go-to workhorse for simulations of multi-modal distributions. The key to the success of PT is to adopt efficient swap schemes. The popular deterministic even-odd (DEO) scheme exploits the non-reversibility property and has successfully reduced the communication cost from $O(P^2)$ to $O(P)$ given

Justin Eilertsen, Santiago Schnell, Sebastian Walcher

We consider reaction networks that admit a singular perturbation reduction in a certain parameter range. The focus of this paper is on deriving "small parameters" (briefly for small perturbation parameters), to gauge the accuracy of the reduction, in a manner that is consistent, amenable to computation and permits an interpretation in chemical or biochemical

Ionut-Gabriel Farcas, Benjamin Peherstorfer, Tobias Neckel, Frank Jenko

Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for variance reduction to make tractable uncertainty quantification even when numerically simulating the physical systems of interest with high-fidelity models is computationally expensive. This work proposes a context-aware multi-fidelity Monte Carlo method that optimally balances

Xiangming Meng, Yoshiyuki Kabashima

With the rapid development of diffusion models and flow-based generative models, there has been a surge of interests in solving noisy linear inverse problems, e.g., super-resolution, deblurring, denoising, colorization, etc, with generative models. However, while remarkable reconstruction performances have been achieved, their inference time is typically too

Tao Guo, Jianing Li, Jiaxing Zhao, Lianyi He

In the framework of the improved chromomagnetic interaction model, we complete a systematic study of the $S$-wave tetraquark states $Qq\bar{Q}\bar{q}$ ($Q=c,b$, and $q=u,d,s$) with different quantum numbers, $J^{PC}=0^{+(+)}$, $1^{+(\pm)}$, and $2^{+(+)}$. The mass spectra of tetraquark states are predicted and the possible decay channels are analyzed by con

Sadek Belamfedel Alaoui, Alejandro J. Rojas, Abdelaziz Hmamed, El Houssaine Tissir

This work proposes a new mathematical model for the TCP/AQM system that aims to improve the accuracy of existing fluid models, especially with respect to the sequential events that occur in the network. The analysis is based on the consideration of two time bases, one at the queue's router level and the other at the congestion window level, which leads to th

Sepanta Zeighami, Cyrus Shahabi, Vatsal Sharan

Range aggregate queries (RAQs) are an integral part of many real-world applications, where, often, fast and approximate answers for the queries are desired. Recent work has studied answering RAQs using machine learning (ML) models, where a model of the data is learned to answer the queries. However, there is no theoretical understanding of why and when the M

Vlad Sobal, Jyothir S, Siddhartha Jalagam, Nicolas Carion

Many common methods for learning a world model for pixel-based environments use generative architectures trained with pixel-level reconstruction objectives. Recently proposed Joint Embedding Predictive Architectures (JEPA) offer a reconstruction-free alternative. In this work, we analyze performance of JEPA trained with VICReg and SimCLR objectives in the fu

Yana Lishkova, Paul Scherer, Steffen Ridderbusch, Mateja Jamnik

By one of the most fundamental principles in physics, a dynamical system will exhibit those motions which extremise an action functional. This leads to the formation of the Euler-Lagrange equations, which serve as a model of how the system will behave in time. If the dynamics exhibit additional symmetries, then the motion fulfils additional conservation laws

Xiao-Ji Weng, Jie Bai, Jingyu Hou, Yi Zhu

Large-area single-crystal surface structures were successfully prepared on Cu(111) substrate with boron deposition, which is critical for prospective applications. However, the proposed borophene structures do not match the scanning tunneling microscopy (STM) results very well, while the proposed copper boride is at odds with the traditional knowledge that o

Daniel Lopez-Martinez, Alex Yakubovich, Martin Seneviratne, Adam D. Lelkes

While it has been well known in the ML community that deep learning models suffer from instability, the consequences for healthcare deployments are under characterised. We study the stability of different model architectures trained on electronic health records, using a set of outpatient prediction tasks as a case study. We show that repeated training runs o

Jiazheng Chen, Wanchun Liu, Daniel E. Quevedo, Yonghui Li

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, by leveraging the theoretical results of structural properties of optimal scheduling policies, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of a multi-sensor remote est

Yu Deng, Alexandru D. Ionescu, Fabio Pusateri

Our goal in this paper is to initiate the rigorous investigation of wave turbulence and derivation of wave kinetic equations (WKE) for water waves models. This problem has received intense attention in recent years in the context of semilinear models, such as semilinear Schr\"odinger equations or multi-dimensional KdV-type equations. However, our situation h

Eduardo Mapurunga, Michel Gevers, Alexandre S. Bazanella

The paper [1] presented the first results on generic identifiability of dynamic networks with partial excitation and partial measurements, i.e. networks where not all nodes are excited or not all nodes are measured. One key contribution of that paper was to establish a set of necessary conditions on the excitation and measurement pattern (EMP) that guarantee

Yixiao Chen, Linfeng Zhang, Weinan E, Roberto Car

We propose a quantum Monte Carlo approach to solve the ground state many-body Schrodinger equation for the electronic ground state. The method combines optimization from variational Monte Carlo and propagation from auxiliary field quantum Monte Carlo, in a way that significantly alleviates the sign problem. In application to molecular systems, we obtain high

Chaitanya Goswami, Pulkit Grover

There has been tremendous interest in designing stimuli (e.g. electrical currents) that produce desired neural responses, e.g., for inducing therapeutic effects for treatments. Traditionally, the design of such stimuli has been model-driven. Due to challenges inherent in modeling neural responses accurately, data-driven approaches offer an attractive alterna

Marc Hallin, Gilles Mordant

Based on measure transportation ideas and the related concepts of center-outward quantile functions, we propose multiple-output center-outward generalizations of the traditional univariate concepts of Lorenz and concentration functions, and the related Gini and Kakwani coefficients. These new concepts have a natural interpretation, either in terms of contrib

Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang

Analogical reasoning is the process of discovering and mapping correspondences from a target subject to a base subject. As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a correspo

Svetlana Andrusenko, Daniil Krichevskiy, Valentin Rudenko

The problem of the event horizon in relativistic gravity is discussed. Singular solutions in general relativity are well known. The Schwarschild metric of a spherical mass is singular at zero ($r = 0$) and at the event horizon ($r = r_g$). Both features reflect the existence of the phenomenon of collapse in general relativity for compact masses exceeding $3M

Riccardo Cantini, Fabrizio Marozzo, Alessio Orsino, Domenico Talia

The extensive use of HPC infrastructures and frameworks for running dataintensive applications has led to a growing interest in data partitioning techniques and strategies. In fact, application performance can be heavily affected by how data are partitioned, which in turn depends on the selected size for data blocks, i.e. the block size. Therefore, finding a

Russell B. Cosgrove

We derive a steady-state, electromagnetic solution for collisional plasma and apply it to computing the total conductance for a vertically stratified ionosphere interrogated by a 3D signal with finite transverse wavelength, which we compare to the field-line-integrated conductivity, finding significant differences on all scales investigated. The approximate

Stéphane Bouka, Kowir Pambo Bello, Guy Martial Nkiet

We tackle estimation and prediction at non-visted sites in a spatial semi-functional linear regression model with derivatives that combines a functional linear model with a nonparametric regression one. The parametric part is estimated by a method of moments and the other one by a local linear estimator. We establish the convergence rate of the resulting est

Filomena Barbosa Rodrigues Mendes, Lesly Daiana Barbosa Sobrado, Fredy Maglorio Sobrado Suárez

The article, presents the study of the regularity of two thermoelastic beam systems defined by the Timoshenko beam model coupled with the heat conduction of Green-Naghdiy theory of type III, both mathematical models are differentiated by their coupling terms that arise as a consequence of the constitutive laws initially considered. The systems presented in t

Yuhao Ding, Ming Jin, Javad Lavaei

We study risk-sensitive reinforcement learning (RL) based on an entropic risk measure in episodic non-stationary Markov decision processes (MDPs). Both the reward functions and the state transition kernels are unknown and allowed to vary arbitrarily over time with a budget on their cumulative variations. When this variation budget is known a prior, we propos

Jaya Sagar, Elliott Hastings, Piede Zhang, Milan Stefko

Satellite based Quantum Key Distribution (QKD) in Low Earth Orbit (LEO) is currently the only viable technology to span thousands of kilometres. Since the typical overhead pass of a satellite lasts for a few minutes, it is crucial to increase the the signal rate to maximise the secret key length. For the QUARC CubeSat mission due to be launched within two ye

Juan de Dios Hernández Velázquez, Gregorio Sánchez-Balderas, Armando Gama Goicochea, Elías Pérez

The effective solid liquid interfacial tension (SL IFT) between pure liquids and rough solid surfaces is studied through coarse grained simulations. Using the dissipative particle dynamics method, we design solid liquid interfaces, confining a pure liquid between two explicit solid surfaces with different roughness degrees. The roughness of the solid phase w

Truong Vu, Kien Do, Khang Nguyen, Khoat Than

We propose a novel high-fidelity face swapping method called "Arithmetic Face Swapping" (AFS) that explicitly disentangles the intermediate latent space W+ of a pretrained StyleGAN into the "identity" and "style" subspaces so that a latent code in W+ is the sum of an "identity" code and a "style" code in the corresponding subspaces. Via our disentanglement,

A. Mishra, G. Mamatsashvili, F. Stefani

Magnetorotational instability (MRI) is the most likely mechanism driving angular momentum transport in astrophysical disks. However, despite many efforts, a conclusive experimental evidence of MRI is still missing. Recently, performing 1D linear analysis of the standard MRI (SMRI) in a cylindrical Taylor-Couette (TC) flow with an axial magnetic field, we sho

Estela Mayoral, Juan de Dios Hernández Velázquez, Armando Gama Goicochea

In this work, a comprehensive study about the influence on shear viscosity of polyelectrolyte concentration, persistence length, salt concentration and solvent quality is reported, using numerical simulations of confined solutions under stationary Poiseuille flow. Various scaling regimes for the viscosity are reproduced, both under good solvent and theta sol

José Brito, Gustavo Semaan, Leonardo de Lima, Augusto Fadel

In sampling theory, stratification corresponds to a technique used in surveys, which allows segmenting a population into homogeneous subpopulations (strata) to produce statistics with a higher level of precision. In particular, this article proposes a heuristic to solve the univariate stratification problem - widely studied in the literature. One of its vers

David Adams, Gandharv Suri, Yllias Chali

In Natural Language Processing, multi-document summarization (MDS) poses many challenges to researchers above those posed by single-document summarization (SDS). These challenges include the increased search space and greater potential for the inclusion of redundant information. While advancements in deep learning approaches have led to the development of se

Gayda Mutahar, Tim Miller

This paper evaluates whether training a decision tree based on concepts extracted from a concept-based explainer can increase interpretability for Convolutional Neural Networks (CNNs) models and boost the fidelity and performance of the used explainer. CNNs for computer vision have shown exceptional performance in critical industries. However, it is a signif

Alexandros Zacharis, Constantinos Patsakis

Content generation that is both relevant and up to date with the current threats of the target audience is a critical element in the success of any Cyber Security Exercise (CSE). Through this work, we explore the results of applying machine learning techniques to unstructured information sources to generate structured CSE content. The corpus of our work is a

Matias D. Cattaneo, Jason M. Klusowski, Peter M. Tian

Decision tree learning is increasingly being used for pointwise inference. Important applications include causal heterogenous treatment effects and dynamic policy decisions, as well as conditional quantile regression and design of experiments, where tree estimation and inference is conducted at specific values of the covariates. In this paper, we call into q

Nina Vaidya, Olav Solgaard

Immersion optics enable creation of systems with improved optical concentration and coupling by taking advantage of the fact that the luminance of light is proportional to the square of the refractive index in a lossless optical system. Immersion graded index optical concentrators, that do not need to track the source, are described in terms of theory, simul

Masahiro Tsujimoto, Takayuki Hayashi, Kumiko Morihana, Yuki Moritani

Gamma Cas analog sources are a subset of Be stars that emit intense and hard X-ray emission. Two competing ideas for their X-ray production mechanism are (a) the magnetic activities of the Be star and its disk and (b) the accretion from the Be star to an unidentified compact object. Among such sources, Pi Aqr plays a pivotal role as it is one of the only two

David Leffler, Wilco Burghout, Oded Cats, Erik Jenelius

Over the past decade, there has been a surge of interest in the transport community in the application of agent-based simulation models to evaluate flexible transit solutions characterized by different degrees of short-term flexibility in routing and scheduling. A central modeling decision in the development of an agent-based simulation model for the evaluat

Zhenglun Kong, Haoyu Ma, Geng Yuan, Mengshu Sun

Vision transformers (ViTs) have recently obtained success in many applications, but their intensive computation and heavy memory usage at both training and inference time limit their generalization. Previous compression algorithms usually start from the pre-trained dense models and only focus on efficient inference, while time-consuming training is still una

Lingyao Xie

We use Koll\'ar's gluing theory to prove the contraction theorem for generalized pairs. In particular, we show that we can run the MMP for any generalized log canonical pairs.

Andrzej Gajewski

This dissertation is dedicated to investigating quantum optical effects involving single photons in tailored structures. It consists of six chapters and an appendix. The central part of the thesis starts with a chapter titled "Theoretical background" where the author introduces key quantum optical concepts needed for understanding the subsequent chapters. Th

Torbjørn Cunis Ilya Kolmanovsky

This paper studies convergence properties of inexact iterative solution schemes for bilevel optimization problems. Bilevel optimization problems emerge in control-aware design optimization, where the system design parameters are optimized in the outer loop and a discrete-time control trajectory is optimized in the inner loop, but also arise in other domains

Yixuan Su, Jialu Xu

In the study, we empirically compare the two recently proposed decoding methods, i.e. Contrastive Search (CS) and Contrastive Decoding (CD), for open-ended text generation. The automatic evaluation results suggest that, while CS performs worse than CD on the MAUVE metric, it substantially surpasses CD on the diversity and coherence metrics. More notably, ext

Hrishikesh Viswanath, Andrey Shor, Yoshimasa Kitaguchi

This paper discusses a crowdsourcing based method that we designed to quantify the importance of different attributes of a dataset in determining the outcome of a classification problem. This heuristic, provided by humans acts as the initial weight seed for machine learning models and guides the model towards a better optimal during the gradient descent proc

João Saraiva, Carlos Alemparte, Daniel Belver, Alberto Blanco

A muon telescope equipped with four Resistive Plate Chambers of 2 m$^{2}$ per plane was tested with the muon scattering tomography technique. The telescope was operated during several hours with high atomic number materials located at its center with two detector planes on each side. With an intrinsic efficiency above 98%, spatial resolution around 1 cm and

Xiaohui Chen, Yukun Li, Aonan Zhang, Li-Ping Liu

Learning to generate graphs is challenging as a graph is a set of pairwise connected, unordered nodes encoding complex combinatorial structures. Recently, several works have proposed graph generative models based on normalizing flows or score-based diffusion models. However, these models need to generate nodes and edges in parallel from the same process, who

Stanislav R. Kirpichenko, Lev V. Utkin, Andrei V. Konstantinov

A method for estimating the conditional average treatment effect under condition of censored time-to-event data called BENK (the Beran Estimator with Neural Kernels) is proposed. The main idea behind the method is to apply the Beran estimator for estimating the survival functions of controls and treatments. Instead of typical kernel functions in the Beran es

Amir Ghorbani

This paper will present a model for pedestrian motion by defining a spacetime metric. This model considers the factors that are effective in the movement of pedestrians (such as obstacles, walls and other pedestrians) by defining a proper metric. In fact, the surrounding environment that affects the motion of pedestrians changes the flat(Euclidean) spacetime

Hrishikesh Viswanath, Md Ashiqur Rahman, Rashmi Bhaskara, Aniket Bera

We present, AdaFNIO - Adaptive Fourier Neural Interpolation Operator, a neural operator-based architecture to perform video frame interpolation. Current deep learning based methods rely on local convolutions for feature learning and suffer from not being scale-invariant, thus requiring training data to be augmented through random flipping and re-scaling. On

Omer Gokalp Serbetci, Ju-Hyung Lee, Daoud Burghal, Andreas F. Molisch

Indoor localization is a challenging task. Compared to outdoor environments where GPS is dominant, there is no robust and almost-universal approach. Recently, machine learning (ML) has emerged as the most promising approach for achieving accurate indoor localization. Nevertheless, its main challenge is requiring large datasets to train the neural networks. T

Shenghang Luo, Sunish Kumar Orappanpara Soman, Lutz Lampe, Jeebak Mitra

Fiber nonlinearity effects cap achievable rates and ranges in long-haul optical fiber communication links. Conventional nonlinearity compensation methods, such as perturbation theory-based nonlinearity compensation (PB-NLC), attempt to compensate for the nonlinearity by approximating analytical solutions to the signal propagation over optical fibers. However

Hsin-Yun Ching, Rigoberto Flórez, F. Luca, Antara Mukherjee

In this paper, we look at numbers of the form $H_{r,k}:=F_{k-1}F_{r-k+2}+F_{k}F_{r-k}$. These numbers are the entries of a triangular array called the \emph{determinant Hosoya triangle} which we denote by ${\mathcal H}$. We discuss the divisibility properties of the above numbers and their primality. We give a small sieve of primes to illustrate the density

Ida Sadeghi, Jack Van Sambeek, Tigran Simonian, Michael Xu

Optoelectronic technologies are based on families of semiconductor alloys. It is rare that a new semiconductor alloy family is developed to the point where epitaxial growth is possible; since the 1950s, this has happened approximately once per decade. Here we demonstrate epitaxial thin film growth of semiconducting chalcogenide perovskite alloys in the Ba-Zr

Diana V. Shopova

We propose a thermodynamic model of free energy expansion up to eighth order of magnetisation to describe the complex magnetic phase transitions in ferromagnetic superconductor UGe$_2$. The model successfully describes transitions between ordered phases which take place without changing of magnetic structure but only of magnetisation which is the case of UGe

Aleksander Łukasz Lenart, Giada Bargiacchi, Maria Giovanna Dainotti, Shigehiro Nagataki

Cosmological models and their parameters are widely debated because of theoretical and observational mismatches of the standard cosmological model, especially the current discrepancy between the value of the Hubble constant, $H_{0}$, obtained by Type Ia supernovae (SNe Ia), and the Cosmic Microwave Background Radiation (CMB). Thus, considering high-redshift

Ana C. Cebrián, Jesús Asín, Jorge Castillo-Mateo, Alan E. Gelfand

There is continuing interest in the investigation of change in temperature over space and time. We offer a set of tools to illuminate such change temporally, at desired temporal resolution, and spatially, according to region of interest, using data generated from suitable space-time models. These tools include predictive spatial probability surfaces and spat

Søren Debois, Fritz Henglein, Morten C. Nielsen, Christian Olesen

We characterize digital cash as the digital equivalent of physical cash: secure, fungible, decentralized, directly controlled, privacy-preserving; but enhanced with qualitatively new functionality. It is extremely efficiently transferable and, most importantly, transactional or, more generally, contract-backed. This facilitates fully automated, guaranteed tr

Cameron R. Taylor, Seong Ho Yeon, William H. Clark, Ellen G. Clarrissimeaux

Muscle tissue drives nearly all movement in the animal kingdom, providing power, mobility, and dexterity. Technologies for measuring muscle tissue motion, such as sonomicrometry, fluoromicrometry, and ultrasound, have significantly advanced our understanding of biomechanics. Yet, the field lacks the ability to monitor muscle tissue motion for animal behavior

Aleksandr Lobanov, Belal Alashqar, Darina Dvinskikh, Alexander Gasnikov

This paper studies non-smooth problems of convex stochastic optimization. Using the smoothing technique based on the replacement of the function value at the considered point by the averaged function value over a ball (in $l_1$-norm or $l_2$-norm) of small radius with the center in this point, the original problem is reduced to a smooth problem (whose Lipsch

Mingxuan Ju, Yujie Fan, Chuxu Zhang, Yanfang Ye

Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous security standards, such as product recommendation and user behavior modeling. Under these scenarios, exploiting GNN's vulnerabilities and further downgrading its performance become extremely

Oksana Bezushchak, Anatoliy Petravchuk, Efim Zelmanov

We prove analogs of A.~Selberg's result for finitely generated subgroups of $\text{Aut}(A)$ and of Engel's theorem for subalgebras of $\text{Der}(A)$ for a finitely generated associative commutative algebra $A$ over an associative commutative ring. We prove also an analog of the theorem of W.~Burnside and I.~Schur about locally finiteness of torsion subgroup

Youssef Mohamed, Mohamed Abdelfattah, Shyma Alhuwaider, Feifan Li

This paper introduces ArtELingo, a new benchmark and dataset, designed to encourage work on diversity across languages and cultures. Following ArtEmis, a collection of 80k artworks from WikiArt with 0.45M emotion labels and English-only captions, ArtELingo adds another 0.79M annotations in Arabic and Chinese, plus 4.8K in Spanish to evaluate "cultural-transf

Salman Ahamad Khan, Binoy Krishna Patra

We have studied the thermoelectric response of a hot and magnetized QCD medium created in the noncentral events at heavy-ion collider experiments. The collisional aspects of the medium have been embedded in the relativistic Boltzmann transport equation (RBTE) using Bhatnagar-Gross-Krook (BGK) collision integral, which insures the particle number conservation

G. Mustafa, Faisal Javed, S. K. Maurya, Saibal Ray

In this work, we present the Einstein field equations (EFE) in the framework of a modified matter source and thus find out the solutions for the Wormhole. To obtain characteristic solutions set, we employ two kinds of equations of states (EOS), i.e., the van der Waals and polytropic EOS to the EFE. We adopt embedding class as a general technique for the syst

Nicolo' Michelusi

Implementing Decentralized Gradient Descent (DGD) in wireless systems is challenging due to noise, fading, and limited bandwidth, necessitating topology awareness, transmission scheduling, and the acquisition of channel state information (CSI) to mitigate interference and maintain reliable communications. These operations may result in substantial signaling