May 2024 arXiv papers — page 99

Ayan Banerjee, Aranyak Maity, Payal Kamboj, Sandeep K. S. Gupta

We explore the usage of large language models (LLM) in human-in-the-loop human-in-the-plant cyber-physical systems (CPS) to translate a high-level prompt into a personalized plan of actions, and subsequently convert that plan into a grounded inference of sequential decision-making automated by a real-world CPS controller to achieve a control goal. We show th

Yusheng Jiao, Feng Ling, Sina Heydari, Nicolas Heess

Animals and robots exist in a physical world and must coordinate their bodies to achieve behavioral objectives. With recent developments in deep reinforcement learning, it is now possible for scientists and engineers to obtain sensorimotor strategies (policies) for specific tasks using physically simulated bodies and environments. However, the utility of the

Hong Yen Tran, Jiankun Hu, Wen Hu

Existing fuzzy extractors and similar methods provide an effective way for extracting a secret key from a user's biometric data, but are susceptible to impersonation attack: once a valid biometric sample is captured, the scheme is no longer secure. We propose a novel multi-factor fuzzy extractor that integrates both a user's secret (e.g., a password) and a u

Jordan G. Taqi-Eddin

Claims of soft-handed prosecutorial policies and increases in crime were precipitating factors in the removal of Chesa Boudin as district attorney of the city and county of San Francisco. However, little research has been conducted to empirically investigate the veracity of these indictments on the former district attorney. Using regression discontinuity des

Gargi Sen, Debaprasad Maity, Santabrata Das

We investigate the structure of relativistic, low-angular momentum, inviscid advective accretion flow in a stationary axisymmetric Kerr-like wormhole (WH) spacetime, characterized by the spin parameter ($a_{\rm k}$), the dimensionless parameter ($\beta$), and the source mass ($M_{\rm WH}$). In doing so, we self-consistently solve the set of governing equatio

Yuling Jiao, Yanming Lai, Yang Wang

Machine learning is a rapidly advancing field with diverse applications across various domains. One prominent area of research is the utilization of deep learning techniques for solving partial differential equations(PDEs). In this work, we specifically focus on employing a three-layer tanh neural network within the framework of the deep Ritz method(DRM) to

Thomas Strohm, Karen Wintersperger, Florian Dommert, Daniel Basilewitsch

This is the second paper in a series of papers providing an overview of different quantum computing hardware platforms from an industrial end-user perspective. It follows our first paper on neutral-atom quantum computing. In the present paper, we provide a survey on the current state-of-the-art in trapped-ion quantum computing, taking up again the perspectiv

Tongze Wang, Xiaohui Xie, Wenduo Wang, Chuyi Wang

Network traffic classification is a crucial research area aiming to enhance service quality, streamline network management, and bolster cybersecurity. To address the growing complexity of transmission encryption techniques, various machine learning and deep learning methods have been proposed. However, existing approaches face two main challenges. Firstly, t

Zejun Gu, Zhong-Qiu Zhao, Henghui Ding, Hao Shen

In practical applications of human pose estimation, low-resolution inputs frequently occur, and existing state-of-the-art models perform poorly with low-resolution images. This work focuses on boosting the performance of low-resolution models by distilling knowledge from a high-resolution model. However, we face the challenge of feature size mismatch and cla

Haruki Emori, Masanao Ozawa, Akihisa Tomita

According to the uncertainty principle, every quantum measurement accompanies disturbance. In particular, accurate sequential measurements need the accurate control of disturbance. However, the correct role of disturbance in the uncertainty principle has been known only recently. Understanding the disturbance is crucial for understanding the fundamentals of

Sanchit Sinha, Yuguang Yue, Victor Soto, Mayank Kulkarni

Adapting large language models (LLMs) to unseen tasks with in-context training samples without fine-tuning remains an important research problem. To learn a robust LLM that adapts well to unseen tasks, multiple meta-training approaches have been proposed such as MetaICL and MetaICT, which involve meta-training pre-trained LLMs on a wide variety of diverse ta

Ta Tang, Daniel Jost, Brian Moritz, Thomas P. Devereaux

Quasi-one-dimensional (1D) materials provide a unique platform for understanding the importance and influence of extended interactions on the physics of strongly correlated systems due to their relative structural simplicity and the existence of powerful theoretical tools well-adapted to one spatial dimension. Recently, this was highlighted by anomalous obse

Jonathan Chávez-Casillas, José E. Figueroa-López, Chuyi Yu, Yi Zhang

A novel high-frequency market-making approach in discrete time is proposed that admits closed-form solutions. By taking advantage of demand functions that are linear in the quoted bid and ask spreads with random coefficients, we model the variability of the partial filling of limit orders posted in a limit order book (LOB). As a result, we uncover new patter

Sergey B. Levin, Alexandr S. Bagmutov, Victor O. Toropov

The goal of this paper is to construct an effective model for studying the asymptotic solution of the scattering problem of three one-dimensional quantum particles with finite (short-range) attractive pair potentials. The asymptotic nature of the solution is defined by the rapid decrease in its discrepancy in the Schr\"odinger equation.

Ziyu Zhu, Zhuofan Zhang, Xiaojian Ma, Xuesong Niu

A unified model for 3D vision-language (3D-VL) understanding is expected to take various scene representations and perform a wide range of tasks in a 3D scene. However, a considerable gap exists between existing methods and such a unified model, due to the independent application of representation and insufficient exploration of 3D multi-task training. In th

Zejun Gu, Zhong-Qiu Zhao, Hao Shen, Zhao Zhang

In real-world applications of human pose estimation, low-resolution input images are frequently encountered when the performance of the image acquisition equipment is limited or the shooting distance is too far. However, existing state-of-the-art models for human pose estimation perform poorly on low-resolution images. One key reason is the presence of downs

Chiyu Zhang, Yifei Sun, Minghao Wu, Jun Chen

Content-based recommendation systems play a crucial role in delivering personalized content to users in the digital world. In this work, we introduce EmbSum, a novel framework that enables offline pre-computations of users and candidate items while capturing the interactions within the user engagement history. By utilizing the pretrained encoder-decoder mode

Wei Sun, Bo Gao, Ke Xiong, Yuwei Wang

In federated learning (FL), although the original intention of available but not visible data is to allay data privacy concerns, it potentially brings new security threats, particularly poisoning attacks that target such not visible local data. Intuitively, such data poisoning attacks have great potential in stealthily degrading global FL outcomes, and are e

S. B. Bian, Y. W. Wu, Y. Xu, M. J. Reid

We report measurements of trigonometric parallax and proper motion for two 6.7 GHz methanol and two 22 GHz water masers located in the far portion of the Sagittarius spiral arm as part of the BeSSeL Survey. Distances for these sources are estimated from parallax measurements combined with 3-dimensional kinematic distances. The distances of G033.64$-$00.22, G

Xiaoyu Wang, Nisha Geng, Kyla de Villa, Burkard Militzer

In the last decade, there has been great progress in predicting and synthesizing polyhydrides that exhibit superconductivity when squeezed. Dopants allow these compounds to become metals at pressures lower than those required to metallize elemental hydrogen. Here, we show that by combining the fundamental planetary building blocks of molecular hydrogen and a

Fadila Wendigoundi Douamba, Jianjun Song, Ling Fu, Yuliang Liu

Scene text recognition is essential in many applications, including automated translation, information retrieval, driving assistance, and enhancing accessibility for individuals with visual impairments. Much research has been done to improve the accuracy and performance of scene text detection and recognition models. However, most of this research has been c

Haoyuan Sun, Zihao Wu, Bo Xia, Pu Chang

The success of artificial neural networks (ANNs) hinges greatly on the judicious selection of an activation function, introducing non-linearity into network and enabling them to model sophisticated relationships in data. However, the search of activation functions has largely relied on empirical knowledge in the past, lacking theoretical guidance, which has

Jessica Lemieux, Matteo Lostaglio, Sam Pallister, William Pol

The problems of quantum state preparation and matrix block-encoding are ubiquitous in quantum computing: they are crucial parts of various quantum algorithms for the purpose for initial state preparation as well as loading problem relevant data. We first present an algorithm based on QRS that prepares a quantum state $|\psi_f\rangle \propto \sum^N_{x=1} f(x)

Lin Niu, Yi Wang

Differentially positive systems are the nonlinear systems whose linearization along trajectories preserves a cone field on a smooth Riemannian manifold. One of the embryonic forms for cone fields in reality is originated from the general relativity. By utilizing the Perron-Frobenius vector fields and the $\Gamma$-invariance of cone fields, we show that gener

Thomas Trask, Nicholas Lytle, Michael Boyle, David Joyner

As online courses become the norm in the higher-education landscape, investigations into student performance between students who take online vs on-campus versions of classes become necessary. While attention has been given to looking at differences in learning outcomes through comparisons of students' end performance, less attention has been given in compar

Gaoxia Zhu, Vidya Sudarshan, Jason Fok Kow, Yew Soon Ong

This research investigates distinct human-generative AI collaboration types and students' interaction experiences when collaborating with generative AI (i.e., ChatGPT) for problem-solving tasks and how these factors relate to students' sense of agency and perceived collaborative problem solving. By analyzing the surveys and reflections of 79 undergraduate st

Pintu Debnath

$A$ set is called $IP$-set in a semigroup $\left(S,\cdot \right)$ if it contains finite products of a sequence. A set that intersects with all $IP$-sets is called $IP^\star$-set. It is a well known and established result by Bergelson and Hindman that if $A$ is an $IP^{\star}$-set, then for any sequence $\langle x_{n}\rangle_{n=1}^{\infty}$, there exists a su

Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester

This paper presents a study of robust policy networks in deep reinforcement learning. We investigate the benefits of policy parameterizations that naturally satisfy constraints on their Lipschitz bound, analyzing their empirical performance and robustness on two representative problems: pendulum swing-up and Atari Pong. We illustrate that policy networks wit

Jingyang Wu, Xinyi Zhang, Fangyixuan Huang, Haochen Zhou

There has been much interest in accurate cryptocurrency price forecast models by investors and researchers. Deep Learning models are prominent machine learning techniques that have transformed various fields and have shown potential for finance and economics. Although various deep learning models have been explored for cryptocurrency price forecasting, it is

Jianbo Dai, Jianqiao Lu, Yunlong Feng, Guangtao Zeng

Recent advancements in large language models (LLMs) have greatly improved code generation, specifically at the function level. For instance, GPT-4o has achieved a 91.0\% pass rate on HumanEval. However, this draws into question the adequacy of existing benchmarks in thoroughly assessing function-level code generation capabilities. Our study analyzed two comm

Roni Edwin

In a 1979 paper, Ventevogel and Nijboer showed that classical point particles interacting via the pair potential $\phi(x)=\left(1+x^4\right)^{-1}$ are not equally spaced in their ground states in one dimension when the particle density is high, in contrast with many other potentials such as inverse power laws or Gaussians. In this paper, we explore a broad c

Mohammadreza Soltaninia, Junpeng Zhan

Quantum computing, leveraging principles of quantum mechanics, represents a transformative approach in computational methodologies, offering significant enhancements over traditional classical systems. This study tackles the complex and computationally demanding task of simulating power system transients through solving differential algebraic equations (DAEs

Yi C. Huang

A one-line proof of a minimax theorem due to Steinerberger is given.

Ebrahim Forati, Brandon W. Langley, Ani Nersisyan, Reza Molavi

The precise engineering of electromagnetic couplings is paramount for constructing scalable and highfidelity superconducting quantum processors. While essential for orchestrating qubit operations, these couplings also present significant design challenges, including the mitigation of crosstalk and the management of environmental decoherence. A clear and unif

Alden Walker

We describe some features of the A100 memory architecture. In particular, we give a technique to reverse-engineer some hardware layout information. Using this information, we show how to avoid TLB issues to obtain full-speed random HBM access to the entire memory, as long as we constrain any particular thread to a reduced access window of less than 64GB.

Manuel E. Lladser, Alexander J. Paradise

A subset of points in a metric space is said to resolve it if each point in the space is uniquely characterized by its distance to each point in the subset. In particular, resolving sets can be used to represent points in abstract metric spaces as Euclidean vectors. Importantly, due to the triangle inequality, points close by in the space are represented as

Kevin K. Hardegree-Ullman, Dániel Apai, Sebastiaan Y. Haffert, Martin Schlecker

Biosignature detection in the atmospheres of Earth-like exoplanets is one of the most significant and ambitious goals for astronomy, astrobiology, and humanity. Molecular oxygen is among the strongest indicators of life on Earth, but it will be extremely difficult to detect via transmission spectroscopy. We used the Bioverse statistical framework to assess t

William M. Hayes, Nicolas Yax, Stefano Palminteri

In-context learning enables large language models (LLMs) to perform a variety of tasks, including learning to make reward-maximizing choices in simple bandit tasks. Given their potential use as (autonomous) decision-making agents, it is important to understand how these models perform such reinforcement learning (RL) tasks and the extent to which they are su

Violet Chen, J. N. Hooker, Derek Leben

Statistical parity metrics have been widely studied and endorsed in the AI community as a means of achieving fairness, but they suffer from at least two weaknesses. They disregard the actual welfare consequences of decisions and may therefore fail to achieve the kind of fairness that is desired for disadvantaged groups. In addition, they are often incompatib

Zhao Liu, Bing Liu, Yuefeng Yin, Nikhil V. Medhekar

The integration of intrinsically magnetic and topologically nontrivial two-dimensional materials holds tantalizing prospects for the exotic quantum anomalous Hall insulators and magnetic second-order topological insulators (SOTIs). Compared with the well-studied nonmagnetic counterparts, the pursuit of intrinsic magnetic SOTIs remains limited. In this work,

Meifan Zhang, Xin Liu, Lihua Yin

Join size estimation on sensitive data poses a risk of privacy leakage. Local differential privacy (LDP) is a solution to preserve privacy while collecting sensitive data, but it introduces significant noise when dealing with sensitive join attributes that have large domains. Employing probabilistic structures such as sketches is a way to handle large domain

Yinfeng Li, Fengkui Ju

Classical logics for strategic reasoning, such as Coalition Logic and Alternating-time Temporal Logic, formalize absolute strategic reasoning about the unconditional strategic abilities of agents to achieve their goals. Goranko and Ju, in two recent papers, introduced a Logic for Conditional Strategic Reasoning (CSR). However, its completeness is still an op

Kweiguu Liu, Setareh Maghsudi

In a conventional contextual multi-armed bandit problem, the feedback (or reward) is immediately observable after an action. Nevertheless, delayed feedback arises in numerous real-life situations and is particularly crucial in time-sensitive applications. The exploration-exploitation dilemma becomes particularly challenging under such conditions, as it coupl

Zhe Xu, Ruizhong Qiu, Yuzhong Chen, Huiyuan Chen

Graph is a prevalent discrete data structure, whose generation has wide applications such as drug discovery and circuit design. Diffusion generative models, as an emerging research focus, have been applied to graph generation tasks. Overall, according to the space of states and time steps, diffusion generative models can be categorized into discrete-/continu

Brian Curtin

We describe the Terwilliger algebras of the four-class Latin-square association schemes arising from Cayley tables of Bol loops. We give some necessary conditions involving Terwilliger algebras for a quasigroup to be a Bol loop.

Xiaotian Chang, Xi Chen, Adrian Zahariuc

We proved that the general members of Severi varieties on an Atiyah ruled surface over a general elliptic curve have nodes and ordinary triple points as singularities.

Aivar Sootla

In this paper, we consider the systems with trajectories originating in the nonnegative orthant becoming nonnegative after some finite time transient. First we consider dynamical systems (i.e., fully observable systems with no inputs), which we call eventually positive. We compute forward-invariant cones and Lyapunov functions for these systems. We then exte

M. Salazar-Ramírez, D. Ojeda-Guillén, J. A. Martínez-Nuño, R. I. Ramírez-Espinoza

In this article we investigate and solve exactly the modified Dirac oscillator in curved spacetime with spin and pseudospin symmetries through an algebraic approach. By focusing on the radial part of this problem, we use the Schr\"odinger factorization method to show that this problem possesses an SU(1; 1) symmetry. This symmetry allowed us to obtain the wav

Hiroki Sayama, Shun Cao

This paper reports a case study of an application of high-resolution agent-based modeling and simulation to pandemic response planning on a university campus. In the summer of 2020, we were tasked with a COVID-19 pandemic response project to create a detailed behavioral simulation model of the entire campus population at Binghamton University. We conceptuali

Shreeram Suresh Chandra, Zongyang Du, Berrak Sisman

Many frameworks for emotional text-to-speech (E-TTS) rely on human-annotated emotion labels that are often inaccurate and difficult to obtain. Learning emotional prosody implicitly presents a tough challenge due to the subjective nature of emotions. In this study, we propose a novel approach that leverages text awareness to acquire emotional styles without t

Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin

Recent attention to relational knowledge bases has sparked a demand for understanding how relations change between entities. Petri nets can represent knowledge structure and dynamically simulate interactions between entities, and thus they are well suited for achieving this goal. However, logic programs struggle to deal with extensive Petri nets due to the l

Michael Swann, Pedro Machado, Isibor Kennedy Ihianle, Salisu Yahaya

The SmartAntenna proposes a novel approach to extend wireless communication, focusing on autonomous orientation to extend range and optimize performance. Through meticulous evaluation, various aspects of its functionality were assessed, revealing both strengths and areas for improvement. Notably, the antenna tracking mechanism exhibited remarkable efficacy.

Andrew Stratton, Kris Hauser, Christoforos Mavrogiannis

Social robot navigation algorithms are often demonstrated in overly simplified scenarios, prohibiting the extraction of practical insights about their relevance to real-world domains. Our key insight is that an understanding of the inherent complexity of a social robot navigation scenario could help characterize the limitations of existing navigation algorit

Luis Chahua, Juan Gutierrez

In 1982, Tuza conjectured that the size $\tau(G)$ of a minimum set of edges that intersects every triangle of a graph $G$ is at most twice the size $\nu(G)$ of a maximum set of edge-disjoint triangles of $G$. This conjecture was proved for several graph classes. In this paper, we present three results regarding Tuza's Conjecture for dense graphs. By using a

Boyang Yan

The rapid expansion of cloud services and their unpredictable workload demands present significant challenges in resource management. Traditional resource management approaches, primarily based on static rules and thresholds, often fail to ensure cost-effectiveness and optimal resource utilization. This research introduces a predictive model designed to fore

Nikil Sharan Prabahar Balasubramanian, Sagnik Dakshit

Advancements in deep learning have generated a large-scale interest in the development of foundational deep learning models. The development of Large Language Models (LLM) has evolved as a transformative paradigm in conversational tasks, which has led to its integration and extension even in the critical domain of healthcare. With LLMs becoming widely popula

Jingdong Zhang, Luan Yang, Qunxi Zhu, Wei Lin

In order to stabilize nonlinear systems modeled by stochastic differential equations, we design a Fast Exponentially Stable and Safe Neural Controller (FESSNC) for fast learning controllers. Our framework is parameterized by neural networks, and realizing both rigorous exponential stability and safety guarantees. Concretely, we design heuristic methods to le

Victor Agostinelli, Sanghyun Hong, Lizhong Chen

A promising approach to preserving model performance in linearized transformers is to employ position-based re-weighting functions. However, state-of-the-art re-weighting functions rely heavily on target sequence lengths, making it difficult or impossible to apply them to autoregressive and simultaneous tasks, where the target and sometimes even the input se

Zikun Tan, Ron Dabora, H. Vincent Poor

In this work we study the rate-distortion function (RDF) for lossy compression of asynchronously-sampled continuous-time (CT) wide-sense cyclostationary (WSCS) Gaussian processes with memory. As the case of synchronous sampling, i.e., when the sampling interval is commensurate with the period of the cyclostationary statistics, has already been studied, we fo

Daniel T. Speckhard, Tim Bechtel, Luca M. Ghiringhelli, Martin Kuban

Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what {\it big} means in the context of typical materials-science machine-learning problems. This concerns not only data volume, but also data quality and veracity as much as infrastructure issues. With selected examples, we ask (i) how models general

Md. Ashraful Islam, Mohammed Eunus Ali, Md Rizwan Parvez

Code synthesis, which requires a deep understanding of complex natural language problem descriptions, generation of code instructions for complex algorithms and data structures, and the successful execution of comprehensive unit tests, presents a significant challenge. While large language models (LLMs) demonstrate impressive proficiency in natural language

Kyle Baldes, Diptanil Chaudhuri, Jason M. O'Kane, Dylan A. Shell

Robots incurring component failures ought to adapt their behavior to best realize still-attainable goals under reduced capacity. We formulate the problem of planning with actuators known a priori to be susceptible to failure within the Markov Decision Processes (MDP) framework. The model captures utilization-driven malfunction and state-action dependent like

Luke Bhan, Yuexin Bian, Miroslav Krstic, Yuanyuan Shi

Over the last decade, data-driven methods have surged in popularity, emerging as valuable tools for control theory. As such, neural network approximations of control feedback laws, system dynamics, and even Lyapunov functions have attracted growing attention. With the ascent of learning based control, the need for accurate, fast, and easy-to-use benchmarks h

Venkat Venkatasubramanian, Abhishek Sivaram, N. Sanjeevrajan, Arun Sankar

The motility-induced phase separation (MIPS) phenomenon in active matter has been of great interest for the past decade or so. A central conceptual puzzle is that this behavior, which is generally characterized as a nonequilibrium phenomenon, can yet be explained using simple equilibrium models of thermodynamics. Here, we address this problem using a new the

Shima Dezfulian, Andreas Wächter

We analyze the convergence properties of a modified barrier method for solving bound-constrained optimization problems where evaluations of the objective function and its derivatives are affected by bounded and non-diminishing noise. The only modification compared to a standard barrier method is a relaxation of the Armijo line-search condition. We prove that

Sina Kazemdehbashi, Yanchao Liu

Unmanned aerial vehicles (UAVs) are increasingly utilized in global search and rescue efforts, enhancing operational efficiency. In these missions, a coordinated swarm of UAVs is deployed to efficiently cover expansive areas by capturing and analyzing aerial imagery and footage. Rapid coverage is paramount in these scenarios, as swift discovery can mean the

Amirhossein Saba, Carlo Gigli, Ye Pu, Demetri Psaltis

Optical diffraction tomography (ODT) has emerged as an important label-free tool in biomedicine to measure the three-dimensional (3D) structure of a biological sample. In this paper, we describe ODT using second-harmonic generation (SHG) which is a coherent nonlinear optical process with a strict symmetry selectivity and has several advantages over tradition

Ming Jin

Operating safely and reliably despite continual distribution shifts is vital for high-stakes machine learning applications. This paper builds upon the transformative concept of ``antifragility'' introduced by (Taleb, 2014) as a constructive design paradigm to not just withstand but benefit from volatility. We formally define antifragility in the context of o

Matheus Guedes de Andrade, Jake Navas, Saikat Guha, Inès Montaño

Errors are the fundamental barrier to the development of quantum systems. Quantum networks are complex systems formed by the interconnection of multiple components and suffer from error accumulation. Characterizing errors introduced by quantum network components becomes a fundamental task to overcome their depleting effects in quantum communication. Quantum

Kamel Ourabah

It has recently been demonstrated [A. Giusti, Phys. Rev. D 101, 124029 (2020)] that characteristic traits of Milgrom's modified Newtonian dynamics (MOND) can be replicated from an entirely distinct framework: a fractional variant of Newtonian mechanics. To further assess its validity, this proposal needs to be tested in relevant astrophysical scenarios. Here

Chi-Yuan Chang, Chieh Hsu, Ying Choon Wu, Siwen Wang

Objective: To investigate the effects of different approaches to EEG preprocessing, channel montage selection, and model architecture on the performance of an online-capable stress detection algorithm in a classroom scenario. Methods: This analysis used EEG data from a longitudinal stress and fatigue study conducted among university students. Their self-repo

Noah Ripke

We introduce and prove several new formulas for the Euler-Mascheroni Constant. This is done through the introduction of the defined E-Harmonic function, whose properties, in this paper, lead to two novel formulas, alongside a family of formulas. While the paper does introduce many new approximations, it does not exhaust the possibilities of the E-Harmonic fu

Victor Trinquet, Matthew L. Evans, Cameron J. Hargreaves, Pierre-Paul De Breuck

Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for qu

Yunfei Peng, Pengyu Wei, Wei Wei, Xiaole Xue

We propose a deep learning algorithm for high dimensional optimal stopping problems. Our method is inspired by the penalty method for solving free boundary PDEs. Within our approach, the penalized PDE is approximated using the Deep BSDE framework proposed by \cite{weinan2017deep}, which leads us to coin the term "Deep Penalty Method (DPM)" to refer t

Eric Keto, Wesley Andres Watters

Moving objects have characteristic signatures in multi-spectral images made by Earth observation satellites that use push broom scanning. While the general concept is applicable to all satellites of this type, each satellite design has its own unique imaging system and requires unique methods to analyze the characteristic signatures. We assess the feasibilit

Habtamu Hailemichael, Beshah Ayalew, Andrej Ivanco

Reinforcement learning (RL) can improve control performance by seeking to learn optimal control policies in the end-use environment for vehicles and other systems. To accomplish this, RL algorithms need to sufficiently explore the state and action spaces. This presents inherent safety risks, and applying RL on safety-critical systems like vehicle powertrain

Belle Collaboration, V. Savinov, I. Adachi, J. K. Ahn

We report the results of the first search for Standard Model and baryon-number-violating two-body decays of the neutral $B$ mesons to $\Lambda^{0}$ and $\Omega^{(*)0}_c$ using 711~${\rm fb^{-1}}$ of data collected at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB asymmetric-energy $e^+ e^-$ collider. We observe no evidence of signal from an

Victor Arinde, Liberty Idowu

Security, defined as protection against external threats, is a critical concern for homes and offices. Intrusion, characterized by unauthorized access, presents a significant challenge to maintaining security. This research aims to address this issue by designing and implementing an automated intrusion detection system utilizing a combination of sensors and

Haoze He, Jing Wang, Anna Choromanska

This work focuses on the decentralized deep learning optimization framework. We propose Adjacent Leader Decentralized Gradient Descent (AL-DSGD), for improving final model performance, accelerating convergence, and reducing the communication overhead of decentralized deep learning optimizers. AL-DSGD relies on two main ideas. Firstly, to increase the influen

Habtamu Hailemichael, Beshah Ayalew

Low ambient temperatures significantly reduce Lithium ion batteries' (LIBs') charge/discharge power and energy capacity, and cause rapid degradation through lithium plating. These limitations can be addressed by preheating the LIB with an external heat source or by exploiting the internal heat generation through the LIB's internal impedance. Fast external he

Nimrod Moiseyev

Enhancing chemical reactions, such as $A+B \to [\textit{activated complex}]^\# \to C+D$, in gas phase through its coupling to quantum-electrodynamics (QED) modes in a dark cavity is investigated. The main result is that the enhancement of the reaction rate by a dark cavity is for asymmetric reactions (products different from reactants.) Notice that in additi

Qiyue Wang, Wu Xue, Xiaoke Zhang, Fang Jin

It is critically important to detect the content of liver fat as it is related to cardiac complications and cardiovascular disease mortality. However, existing methods are either associated with high cost and/or medical complications (e.g., liver biopsy, imaging technology) or only roughly estimate the grades of steatosis. In this paper, we propose a deep ne

Mieke Wessel

We give a new graph-theoretic proof of Cobham's Theorem which says that the support of an automatic sequence is either sparse or grows at least like $N^\alpha$ for some $\alpha > 0$. The proof uses the notions of tied vertices and cycle arboressences. With the ideas of the proof we can also give a new interpretation of the rank of a sparse sequence as the he

Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté, Trevor Campbell

Non-reversible parallel tempering (NRPT) is an effective algorithm for sampling from target distributions with complex geometry, such as those arising from posterior distributions of weakly identifiable and high-dimensional Bayesian models. In this work we establish the uniform (geometric) ergodicity of NRPT under a model of efficient local exploration. The

Kun Qian, Mohamed Kheir

The main objective of this paper is to investigate the feasibility of employing Physics-Informed Neural Networks (PINNs) techniques, in particular KolmogorovArnold Networks (KANs), for facilitating Electromagnetic Interference (EMI) simulations. It introduces some common EM problem formulations and how they can be solved using AI-driven solutions instead of

Adrián García Riber, Francisco Serradilla

The exploration of the universe is experiencing a huge development thanks to the success and possibilities of today's major space telescope missions which can generate measurements and images with a resolution 100 times higher than their precedents. This big ecosystem of observations, aimed at expanding the limits of known science, can be analyzed using pers

Saiyang Zhang, Volker Bromm, Boyuan Liu

We examine the effects of massive primordial black holes (PBHs) on cosmic structure formation, employing both a semi-analytical approach and cosmological simulations. Our simulations incorporate PBHs with a monochromatic mass distribution centered around $10^6 \ \rm M_{\odot}$, constituting a fraction of $10^{-2}$ to $10^{-4}$ of the dark matter (DM) in the

Tianhao Wei, Liqian Ma, Rui Chen, Weiye Zhao

The requirements for real-world manipulation tasks are diverse and often conflicting; some tasks require precise motion while others require force compliance; some tasks require avoidance of certain regions, while others require convergence to certain states. Satisfying these varied requirements with a fixed state-action representation and control strategy i

Jeng-Yuan Tsai, Weiyi Gong, Qimin Yan

Drawing on their atomically thin structure, two-dimensional (2D) materials present a groundbreaking avenue for the precision fabrication and systematic manipulation of quantum defects. Through a method grounded in site-symmetry principles, we devise a comprehensive workflow to pinpoint potential native quantum defects across the entire spectrum of known bina

Brenden R. Ortiz, Heda Zhang, Karolina Gornicka, David S. Parker

Here we present the discovery and characterization of the kagome metal TbTi$_3$Bi$_4$ in tandem with a new series of compounds, the Ln$_{2-x}$Ti$_{6+x}$Bi$_9$ (Ln: Tb-Lu) shurikagome metals. We previously reported on the growth of the LnTi$_3$Bi$_4$ (Ln: La-Gd$^{3+}$, Eu$^{2+}$, Yb$^{2+}$) family, a chemically diverse and exfoliable series of kagome metals w

Chenyin Gao, Zhiming Zhang, Shu Yang

This study introduces an innovative method for analyzing the impact of various interventions on customer churn, using the potential outcomes framework. We present a new causal model, the tensorized latent factor block hazard model, which incorporates tensor completion methods for a principled causal analysis of customer churn. A crucial element of our approa

Natan Hoffmann, Amareshwara Sainadh Chamarthi, Steven H. Frankel

In this study, we introduce a robust central Gradient-Based Reconstruction (GBR) scheme for the compressible Navier-Stokes equations. The method leverages transformation to characteristic space, allowing selective treatment of waves from the compressible Euler equations. By averaging left- and right-biased state interpolations, a central scheme is achieved f

Bibek Bhandari, Irwin Huang, Ahmed Hajr, Kagan Yanik

We theoretically explore an alternative circuit for Kerr-cat qubits based on symmetrically threaded Superconducting Quantum Interference Devices (SQUID). The Symmetrically Threaded SQUIDs (STS) architecture employs a simplified flux-pumped design that suppresses two-photon dissipation, a dominant loss mechanism in high-Kerr regimes, by engineering the drive

Jingyin Huang

Let $\Delta$ be the Artin complex of the Artin group of type $D_n$. This complex is also called the spherical Deligne complex of type $D_n$. We show certain types of 6-cycles in the 1-skeleton of $\Delta$ either have a center, which is a vertex adjacent to each vertex of the 6-cycle, or a quasi-center, which is a vertex adjacent to three of the alternating v

Santiago Llorens, Walther González, Gael Sentís, John Calsamiglia

This paper introduces quantum edge detection, aimed at locating boundaries of quantum domains where all particles share the same pure state. Focusing on the 1D scenario of a string of particles, we develop an optimal protocol for quantum edge detection, efficiently computing its success probability through Schur-Weyl duality and semidefinite programming tech

Grzegorz Zakrzewski, Kacper Skonieczka, Mikołaj Małkiński, Jacek Mańdziuk

Electricity price forecasts play a crucial role in making key business decisions within the electricity markets. A focal point in this domain are probabilistic predictions, which delineate future price values in a more comprehensive manner than simple point forecasts. The golden standard in probabilistic approaches to predict energy prices is the Quantile Re

Yutaro Akita

This paper presents a simple proof of Dekel (1986)'s representation theorem for betweenness preferences. The proof is based on the separation theorem.

Mateusz Wiśniewski, Jerzy Łuczka, Jakub Spiechowicz

Analysis of non-Markovian systems and memory induced phenomena poses an everlasting challenge for physics. As a paradigmatic example we consider a classical Brownian particle of mass $M$ subjected to an external force and exposed to correlated thermal fluctuations. We show that the recently developed approach to this system, in which its non-Markovian dynami

Olena Atlasiuk, Arnaud Heibig, Adrien Petrov

This paper deals with a dynamic Gao beam of infinite length subjected to a moving concentrated Dirac mass. Under appropriate regularity assumptions on the initial data, the problem possesses a weak solution which is obtained as the limit of a sequence of solutions of regularized problems.

Michael Molle, Ulrich Raithel, Dirk Kraemer, Norbert Graß

As part of the Internet of Things (IoT) and Industry 4.0 Cloud services are increasingly interacting with low-performance devices that are used in automation. This results in security issues that will be presented in this paper. Particular attention is paid to so-called critical infrastructures. The authors intend to work on the addressed security challenges