May 2024 arXiv papers — page 130

Mukul Dwivedi, Tanmay Sarkar

The main purpose of this paper is to design a fully discrete local discontinuous Galerkin (LDG) scheme for the generalized Benjamin-Ono equation. First, we proved the $L^2$-stability for the proposed semi-discrete LDG scheme and obtained a sub-optimal order of convergence for general nonlinear flux. We develop a fully discrete LDG scheme using the Crank-Nico

Georges Gagneré

Andersen's tale The Shadow offers a theatrical situation confronting a Scholar to his Shadow. I program specific creatures that I called shadow avatar to stage the story with five of them and a physical narrator. Echoing Edmond Couchot's ideas about virtual people helping human beings to adapt to technological evolutions, I describe dynamics of coevolution c

Murad Mehrab Abrar, Amal Youssef, Raian Islam, Shalaka Satam

Autonomous Vehicles (AVs) heavily rely on sensors and communication networks like Global Positioning System (GPS) to navigate autonomously. Prior research has indicated that networks like GPS are vulnerable to cyber-attacks such as spoofing and jamming, thus posing serious risks like navigation errors and system failures. These threats are expected to intens

Alessandro Ottazzi, Federico Santagati

We provide a characterization of Carleson measures on locally finite trees. This characterization establishes the connection between Carleson measures and the boundedness of a suitable Poisson integral between $L^p$-spaces. Additionally, when the tree has bounded degree, we investigate the relationship between Carleson measures and BMO functions defined on t

Qian Zhao, Kaitong Sun, Junchao Xia, Hai-Feng Li

Adjusting chemical pressure through doping is a highly effective method for customizing the chemical and physical properties of materials, along with their respective phase diagrams, thereby uncovering novel quantum phenomena. Here, we successfully synthesized Sr-doped Eu$_{1-x}$Sr$_x$AgP $(x = 0.0-0.6)$ and conducted a comprehensive investigation involving

Sebastian Rehms, Stefan Köpsell, Verena Klös, Florian Tschorsch

This work introduces Information Inference Diagrams (I2Ds), a modeling framework aiming to complement existing approaches for privacy and security analysis of distributed systems. It is intended to support established threat modeling processes. Our approach is designed to be compatible with Data Flow Diagrams~(DFDs), which form the basis of many established

Mengsong Wu, Tong Zhu, Han Han, Chuanyuan Tan

This paper presents a new tool learning dataset Seal-Tools, which contains self-instruct API-like tools. Seal-Tools not only offers a large number of tools, but also includes instances which demonstrate the practical application of tools. Seeking to generate data on a large scale while ensuring reliability, we propose a self-instruct method to generate tools

Z. Yousaf, Kazuharu Bamba, Bander Almutairi, M. Z. Bhatti

Fuzzy dark matter wormhole solutions coupled with anisotropic matter distribution are explored in higher-order curvature gravity. We derive the shape function for fuzzy wormholes and explore their possible stability. We study the embedding diagrams of the active gravitational mass associated with fuzzy dark matter wormholes by taking a certain shape function

Adrien Banse, Licio Romao, Alessandro Abate, Raphaël M. Jungers

Abstractions of dynamical systems enable their verification and the design of feedback controllers using simpler, usually discrete, models. In this paper, we propose a data-driven abstraction mechanism based on a novel metric between Markov models. Our approach is based purely on observing output labels of the underlying dynamics, thus opening the road for a

Amedeo Roberto Esposito, Michael Gastpar, Ibrahim Issa

Information measures can be constructed from Rényi divergences much like mutual information from Kullback-Leibler divergence. One such information measure is known as Sibson $α$-mutual information and has received renewed attention recently in several contexts: concentration of measure under dependence, statistical learning, hypothesis testing, and estimatio

Oliver Karras

In the last two decades, several researchers provided snapshots of the "current" state and evolution of empirical research in requirements engineering (RE) through literature reviews. However, these literature reviews were not sustainable, as none built on or updated previous works due to the unavailability of the extracted and analyzed data. KG-EmpiRE is a

Alexis Amouretti, Marion Harmand, Bruno Albertazzi, Antoine Boury

We report here the first equation of state measurements of Fe$_2$O$_3$ obtained with laser-driven shock compression. The data are in excellent agreement with previous dynamic and static compression measurements at low pressure, and extend the known Hugoniot up to 700 GPa. We observe a large volume drop of $\sim$10% at 86 GPa, which could be associated, accor

Yang Hou, Haitao Fu, Chuankai Chen, Zida Li

With the rapid advancement of generative AI, multimodal deepfakes, which manipulate both audio and visual modalities, have drawn increasing public concern. Currently, deepfake detection has emerged as a crucial strategy in countering these growing threats. However, as a key factor in training and validating deepfake detectors, most existing deepfake datasets

Mazen Alamir, Raphaël Dion

In this paper, a new model-free anomaly detection framework is proposed for time-series induced by industrial dynamical systems.The framework lies in the category of conventional approaches which enable appealing features such as a learning with reduced amount of training data, a high potential for explainability as well as a compatibility with incremental l

Vilhelm Sjöberg, Kinnari Dave, Daniel Britten, Maria A Schett

Programs executed on a blockchain - smart contracts - have high financial stakes; their correctness is crucial. We argue, that this correctness needs to be foundational: correctness needs to be based on the operational semantics of their execution environment. In this work we present a foundational system - the DeepSEA system - targeting the Ethereum blockch

Eva-Maria Hainzl, Élie de Panafieu

It is a classic result in spectral theory that the limit distribution of the spectral measure of random graphs G(n, p) converges to the semicircle law in case np tends to infinity with n. The spectral measure for random graphs G(n, c/n) however is less understood. In this work, we combine and extend two combinatorial approaches by Bauer and Golinelli (2001)

Jingzhou Sun

We continue our study on the logarithmic balanced model metric initiated in our previous work. By a non-trivial refinement of the set of tools developed in our previous work, we are able to confirm partially a conjecture we made in our previous work on the asymptotic behavior of the balanced metric near infinity.

Kun Song, Gaoming Chen, Wenhang Liu, Zhenhua Xiong

Rendezvous aims at gathering all robots at a specific location, which is an important collaborative behavior for multi-robot systems. However, in an unknown environment, it is challenging to achieve rendezvous. Previous researches mainly focus on special scenarios where communication is not allowed and each robot executes a random searching strategy, which i

Yingjie Zhai, Wenshuo Li, Yehui Tang, Xinghao Chen

Current architectures for video understanding mainly build upon 3D convolutional blocks or 2D convolutions with additional operations for temporal modeling. However, these methods all regard the temporal axis as a separate dimension of the video sequence, which requires large computation and memory budgets and thus limits their usage on mobile devices. In th

J. R. Ziehn, M. Ruf, M. Roschani, J. Beyerer

Models for vehicle dynamics play an important role in maneuver planning for automated driving. They are used to derive trajectories from given control inputs, or to evaluate a given trajectory in terms of constraint violation or optimality criteria such as safety, comfort or ecology. Depending on the computation process, models with different assumptions and

Whenty Ariyanti, Kai-Chun Liu, Kuan-Yu Chen, Yu Tsao

Respiratory disease, the third leading cause of deaths globally, is considered a high-priority ailment requiring significant research on identification and treatment. Stethoscope-recorded lung sounds and artificial intelligence-powered devices have been used to identify lung disorders and aid specialists in making accurate diagnoses. In this study, audio-spe

Anthony Poëls

In his seminal 1961 paper, Wirsing studied how well a given transcendental real number $\xi$ can be approximated by algebraic numbers $\alpha$ of degree at most $n$ for a given positive integer $n$, in terms of the so-called naive height $H(\alpha)$ of $\alpha$. He showed that the infimum $\omega^*_n(\xi)$ of all $\omega$ for which infinitely many such $\alp

Yuchen Wang, Xingyu Zhu, Guanhui Ye, Shiyao Zhang

DNN-based watermarking methods are rapidly developing and delivering impressive performances. Recent advances achieve resolution-agnostic image watermarking by reducing the variant resolution watermarking problem to a fixed resolution watermarking problem. However, such a reduction process can potentially introduce artifacts and low robustness. To address th

Zhichu Chen, Bart Faatz

In this paper, we describe a way to automatically adjust the quadrupole focusing along the undulator to avoid the instabilities, taking into account energy change and undulator focusing. The procedure is more generalized and applicable to any strongly focusing (planar) undulator.

Ilya Zavolokin

In this paper we study holomorphically homogeneous model submanifolds CR-type (1, 3) complex space $\mathbb C^4$. One finds moduli space of five-dimensional model surfaces Bloom-Graham type ((2, 1), (3, 1), (4, 1)). It is shown that there exists unique model surface of this type with property of holomorphical homogeneous, which is equivalent to tube surface

Benjamin Sinclair, Lucy Vivash, Jasmine Moses, Miranda Lynch

Perivascular spaces(PVSs) form a central component of the brain\'s waste clearance system, the glymphatic system. These structures are visible on MRI images, and their morphology is associated with aging and neurological disease. Manual quantification of PVS is time consuming and subjective. Numerous deep learning methods for PVS segmentation have been devel

Ayisha M Ashruf, Ankush Bhaskar, C Vineeth, Tarun Kumar Pant

The rapid increase in the number of space debris represents a substantial threat to the sustained viability of space operations and underscores the importance of understanding long-term drivers of orbital decay. This first of its kind study examines the long-term impact of solar activity on the orbital decay of 17 LEO debris objects across Solar Cycles 22, 2

Erik Fransson, Julia Wiktor, Paul Erhart

Two-dimensional (2D) halide perovskites (HPs) are promising materials for various optoelectronic applications, yet a comprehensive understanding of their dynamics is still elusive. Here, we offer insight into the dynamics of prototypical 2D HPs based on MAPbI$_3$ as a function of linker molecule and the number of perovskite layers using atomic scale simulati

Amee Bhatt

The recent observations of the galactic center of the M87 galaxy have made the field of observing black holes and calculating its shadow much more intriguing. Approaching the question of calculating shadows, many approximations are made in order to simplify the equations which makes the considered case less realistic. Understanding the shadow of different si

Jiaqing Xie, Ziheng Chi

Pretrained language models (LMs) showcase significant capabilities in processing molecular text, while concurrently, message passing neural networks (MPNNs) demonstrate resilience and versatility in the domain of molecular science. Despite these advancements, we find there are limited studies investigating the bidirectional interactions between molecular str

Kouji Kashiwa, Hiroaki Kouno

In this paper, we discuss the role of Roberge-Weiss periodicity in the thermodynamics of quantum chromodynamics at moderately high temperature, where the semi-quark-gluon plasma is expected. From the construction of the grand canonical partition function at zero and also at finite density via the canonical approach, we are able to discuss the relation betwee

Meena Sanjay Babulal, Sunil Kumar Gauttam, Aditya Maheshwari

In 1990, Jakeman (see \cite{jakeman1990statistics}) defined the binomial process as a special case of the classical birth-death process, where the probability of birth is proportional to the difference between a fixed number and the number of individuals present. Later, a fractional generalization of the binomial process was studied by Cahoy and Polito (2012

Rahul Bhadani, Satyaki Banik, Hao Tu, Srdjan Lukic

Cyber-physical power systems, such as grids, integrate computational and communication components with physical systems to introduce novel functions and improve resilience and fault tolerance. These systems employ computational components and real-time controllers to meet power demands. Microgrids, comprising interconnected components, energy resources withi

Uwe Peters, Ignacio Ojea Quintana

Generics (unquantified generalizations) are thought to be pervasive in communication and when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) tech

Ryutaro Nishimura, Noriyuki Igarashi, Daisuke Wakabayashi, Yuki Shibazaki

The silicon-on-insulator pixel (SOIPIX) detector is a unique monolithic-structure imaging device currently being developed by the SOIPIX group led by the High Energy Accelerator Research Organization (KEK). The detector team at KEK Photon Factory (PF) is also developing an X-ray camera using INTPIX4NA with a 14.1 $\times$ 8.7 $\mathsf{mm^2}$ sensitive area a

Clément Playout, Farida Cheriet

Identifying lesions in fundus images is an important milestone toward an automated and interpretable diagnosis of retinal diseases. To support research in this direction, multiple datasets have been released, proposing groundtruth maps for different lesions. However, important discrepancies exist between the annotations and raise the question of generalizati

Yaju Liu, Xi Lin, Siyuan Li, Gaolei Li

As an emerging paradigm of content creation, AI-Generated Content (AIGC) has been widely adopted by a large number of edge end users. However, the requests for generated content from AIGC users have obvious diversity, and there remains a notable lack of research addressing the variance in user demands for AIGC services. This gap underscores a critical need f

Yuan-Pei Yang, Xiangkun Liu, Yu Pan, Xinzhong Er

SN 2023ixf, recently reported in the nearby galaxy M101 at a distance of $6.85~{\rm Mpc}$, was one of the closest and brightest core-collapse supernovae (CCSNe) in the last decade. In this work, we present multi-wavelength photometric observation of SN 2023ixf with the Multi-channel Photometric Survey Telescope (Mephisto) in $uvgr$ bands and with the twin 50

Xin-Ming Feng, Yuan-Pei Yang, Qiao-Chu Li

Fast radio bursts (FRBs) are cosmological radio transients with millisecond durations and extremely high brightness temperatures. One FRB repeater, FRB 180916.J0158+65 (FRB 180916B), was confirmed to appear 16.35-day periodic activities with 5-day activity window. Another FRB repeater, FRB 121102, and two soft gamma-ray repeaters (SGRs), SGR 1935+2154 and SG

Yaping Yang, Daihao Zeng

The universal enveloping algebra $U(\mathfrak{g} )$ of a current (super)algebra or loop (super)algebra $\mathfrak{g} $ is considered over an algebraically closed field $\mathbb{K} $ with characteristic $p\ge 0$. This paper focuses on the structure of the center $Z(\mathfrak{g} )$ of $U(\mathfrak{g} )$. In the case of zero characteristic, $Z(\mathfrak{g} )$ i

Agung Budiyono

Kirkwood-Dirac (KD) quasiprobability is a quantum analog of classical phase space probability. It offers an informationally complete representation of quantum state wherein the quantumness associated with quantum noncommutativity manifests in its nonclassical values, i.e., the nonreal and/or negative values of the real part. This naturally raises a question:

Takahiko Makiuchi, Takashi Kikkawa, Thanaporn Sichanugrist, Junki Numata

Hybridized nuclear and electron spin excitation in a MnCO$_3$ crystal, a weakly-anisotropic antiferromagnet, has been investigated. In this material, the hyperfine interaction is strong enough to form a nuclear spin wave. We measure the microwave absorption by a bulk MnCO$_3$ and observe the dispersion representing strong frequency repulsion between electron

Dasith de Silva Edirimuni, Xuequan Lu, Gang Li, Lei Wei

Point cloud filtering is a fundamental 3D vision task, which aims to remove noise while recovering the underlying clean surfaces. State-of-the-art methods remove noise by moving noisy points along stochastic trajectories to the clean surfaces. These methods often require regularization within the training objective and/or during post-processing, to ensure fi

Ryutaro Nishimura, Shunji Kishimoto, Toshihiko Sasaki, Shingo Mitsui

Residual stress is an important factor governing evaluating and controlling the quality of metal materials in industrial products. X-ray measurements provide one of the most effective means of evaluating residual stress without destruction. In such measurements, the effects of residual stress on the crystal structure can be observed through the Debye ring de

Hao Tian, Junqiu Liu, Alaina Attanasio, Anat Siddharth

Recent decades have seen significant advancements in integrated photonics, driven by improvements in nanofabrication technology. This field has developed from integrated semiconductor lasers and low-loss waveguides to optical modulators, enabling the creation of sophisticated optical systems on a chip scale capable of performing complex functions like optica

Luis Mantilla Calderón, Robert Raussendorf, Polina Feldmann, Dmytro Bondarenko

Quantum machine learning (QML) leverages quantum computing for classical inference, furnishes the processing of quantum data with machine-learning methods, and provides quantum algorithms adapted to noisy devices. Typically, QML proposals are framed in terms of the circuit model of quantum computation. The alternative measurement-based quantum computing (MBQ

Eli Yablonovitch, Zunaid Omair

It has gradually been recognized that incoming sunlight can be trapped within a high refractive index semiconductor, n~3.5, owing to the narrow 16degree escape cone. The solar light inside a semiconductor is 4n^2 times brighter than incident sunlight. This is called light trapping and has increased the theoretical and practical efficiency of solar panels. Bu

Minbiao Han, Fengxue Zhang, Yuxin Chen

This paper investigates the challenge of learning in black-box games, where the underlying utility function is unknown to any of the agents. While there is an extensive body of literature on the theoretical analysis of algorithms for computing the Nash equilibrium with complete information about the game, studies on Nash equilibrium in black-box games are le

Raghuveer Peri, Sai Muralidhar Jayanthi, Srikanth Ronanki, Anshu Bhatia

Integrated Speech and Large Language Models (SLMs) that can follow speech instructions and generate relevant text responses have gained popularity lately. However, the safety and robustness of these models remains largely unclear. In this work, we investigate the potential vulnerabilities of such instruction-following speech-language models to adversarial at

Yoshihiko Abe, Komei Goto, Testutaro Higaki, Tatsuo Kobayashi

We study vacua of moduli potential consisting of multiple contribution of modular forms in a finite modular symmetry. If the potential is given by a single modular form, the Minkowski vacuum is realized at the fixed point of the modular symmetry. We show that the de Sitter vacuum is realized with a multiple modular form case and obtain a non-trivial vacuum w

Daichi Amagata

Many applications require efficient management of large sets of intervals because many objects are associated with intervals (e.g., time and price intervals). In such interval management systems, range search is a primitive operator for retrieving and analysis tasks. As dataset sizes are growing nowadays, range search results are also becoming larger, which

Yu-Ling Chang, Ting-Wen Lan, J. Xavier Prochaska, Lucas Napolitano

We explore the influence of radio-mode feedback on the properties of the cool circumgalactic medium (CGM). To this end, we assemble a statistical sample of approximately 30,000 radio galaxies with background quasars by combining optical spectroscopic measurements of luminous red galaxies (LRGs) and quasars from the year 1 dataset of Dark Energy Spectroscopic

Asma Yamani, Haifa Alshammare, Malak Baslyman

Machine learning (ML) tools with graphical user interfaces (GUI) are facing demand from novice users who do not have the background of their underlying concepts. These tools are frequently complex and pose unique challenges in terms of interaction and comprehension by novice users. There is yet to be an established set of usability heuristics to guide and as

Chih-Chun Hsu, Jason J. Wang, Jerry W. Xuan, Jean-Baptiste Ruffio

We present the projected rotational velocity and molecular abundances for HD 33632 Ab obtained via Keck Planet Imager and Characterizer high-resolution spectroscopy. HD 33632 Ab is a nearby benchmark brown dwarf companion at a separation of $\sim$20 au that straddles the L/T transition. Using a forward-modeling framework with on-axis host star spectra, self-

Yao Wang, Xin Liu, Weikun Kong, Hai-Tao Yu

Named Entity Recognition and Relation Extraction are two crucial and challenging subtasks in the field of Information Extraction. Despite the successes achieved by the traditional approaches, fundamental research questions remain open. First, most recent studies use parameter sharing for a single subtask or shared features for both two subtasks, ignoring the

Rina Wu, Tianqiang Zhu, Xiangbo Lin, Yi Sun

Generating grasps for a dexterous hand often requires numerous grasping annotations. However, annotating high DoF dexterous hand poses is quite challenging. Especially for functional grasps, requiring the hand to grasp the object in a specific pose to facilitate subsequent manipulations. This prompts us to explore how people achieve manipulations on new obje

Jiayu Xie, Bing Tang

Dynamics of neutral test particles in the spacetime of a Bardeen AdS black hole surrounded by quintessence dark energy is studied. First, we analyze the properties of the black hole and possible values of the monopole charge and quintessential parameters that allows the existence of the event horizon. The effects of the parameters on the effective potential

Taoyong Cui, Chenyu Tang, Dongzhan Zhou, Yuqiang Li

Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physical science. However, distribution shift between training and test data causes deterioration of the test performance of MLIPs, and even leads to collapse of MD simulations. In this

Carlos del-Castillo-Negrete, Rylan Spence, Troy Butler, Clint Dawson

We present a novel method for generating sequential parameter estimates and quantifying epistemic uncertainty in dynamical systems within a data-consistent (DC) framework. The DC framework differs from traditional Bayesian approaches due to the incorporation of the push-forward of an initial density, which performs selective regularization in parameter direc

Xi Cheng, Gaofeng Su, Siyuan Feng, Ke Liu

Parking challenges escalate significantly during large events such as concerts or sports games, yet few studies address dynamic parking lot assignments for such occasions. This paper introduces a smart navigation system designed to optimize parking assignments swiftly during large events, utilizing a mixed search algorithm that accounts for the heterogeneous

Gaofeng Su, Xi Cheng, Siyuan Feng, Ke Liu

This paper is based on a crucial issue in the aviation world: how to optimize the trajectory and controls given to the aircraft in order to optimize flight time and fuel consumption. This study aims to provide elements of a response to this problem and to define, under certain simplifying assumptions, an optimal response, using Constrained Finite Time Optima

Bretislav Hajek, Daniel Reijsbergen, Anwitaman Datta, Jussi Keppo

Stablecoins - crypto tokens whose value is pegged to a real-world asset such as the US Dollar - are an important component of the DeFi ecosystem as they mitigate the impact of token price volatility. In crypto-backed stablecoins, the peg is founded on the guarantee that in case of system shutdown, each stablecoin can be exchanged for a basket of other crypto

Iris Oved, Nikhil Krishnaswamy, James Pustejovsky, Joshua Hartshorne

We offer philosophical motivations for a method we call Virtual World Cognitive Science (VW CogSci), in which researchers use virtual embodied agents that are embedded in virtual worlds to explore questions in the field of Cognitive Science. We focus on questions about mental and linguistic representation and the ways that such computational modeling can add

Wei Wang, John Grundy, Hourieh Khalajzadeh, Anuradha Madugalla

Mobile Health (mHealth) applications have demonstrated considerable potential in supporting chronic disease self-management; however, they remain under-utilised due to low engagement, limited accessibility, and poor long-term adherence. These issues are particularly prominent among users with chronic disease, whose needs and capabilities vary widely. To addr

Ryan Song, Kareem M. Attiah, Wei Yu

This paper considers a massive connectivity setting in which a base-station (BS) aims to communicate sources $(X_1,\cdots,X_k)$ to a randomly activated subset of $k$ users, among a large pool of $n$ users, via a common message in the downlink. Although the identities of the $k$ active users are assumed to be known at the BS, each active user only knows wheth

Hongzhi You, Yijun Cao, Wei Yuan, Fanjun Wang

From a perspective of feature matching, optical flow estimation for event cameras involves identifying event correspondences by comparing feature similarity across accompanying event frames. In this work, we introduces an effective and robust high-dimensional (HD) feature descriptor for event frames, utilizing Vector Symbolic Architectures (VSA). The topolog

Jie Fu, Yuan Hong, Xinpeng Ling, Leixia Wang

In recent years, privacy and security concerns in machine learning have promoted trusted federated learning to the forefront of research. Differential privacy has emerged as the de facto standard for privacy protection in federated learning due to its rigorous mathematical foundation and provable guarantee. Despite extensive research on algorithms that incor

Ke Liu, Fan Hu, Hui Lin, Xi Cheng

This paper explores the optimization of Ground Delay Programs (GDP), a prevalent Traffic Management Initiative used in Air Traffic Management (ATM) to reconcile capacity and demand discrepancies at airports. Employing Reinforcement Learning (RL) to manage the inherent uncertainties in the national airspace system-such as weather variability, fluctuating flig

Yacine Izza, Xuanxiang Huang, Antonio Morgado, Jordi Planes

The uses of machine learning (ML) have snowballed in recent years. In many cases, ML models are highly complex, and their operation is beyond the understanding of human decision-makers. Nevertheless, some uses of ML models involve high-stakes and safety-critical applications. Explainable artificial intelligence (XAI) aims to help human decision-makers in und

Eric Kim, Dohyun Kwon

We study the motion of sets by anisotropic curvature under a volume constraint in the plane. We establish the exponential convergence of the area-preserving anisotropic flat flow to a disjoint union of Wulff shapes of equal area, the critical point of the anisotropic perimeter functional. This is an anisotropic analogue of the results in the isotropic case s

Nilaksh Das, Saket Dingliwal, Srikanth Ronanki, Rohit Paturi

Large language models (LLMs) have shown incredible proficiency in performing tasks that require semantic understanding of natural language instructions. Recently, many works have further expanded this capability to perceive multimodal audio and text inputs, but their capabilities are often limited to specific fine-tuned tasks such as automatic speech recogni

Shuyang Xiao, Adrian Valadkhani, Sarshad Rommel, Paul C. Canfield

ThCr2Si2-type intermetallic compounds are known to exhibit superelasticity associated with structural transitions through lattice collapse and expansion. These transitions occur via the formation and breaking of Si-type bonds, respectively, under uniaxial loading along the [0 0 1] direction. Unlike most ThCr2Si2-type intermetallic compounds, which have eithe

Ke Liu, Kaijing Ding, Xi Cheng, Guanhao Xu

Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research. However, previous delay predictions are often categorical and at a highly aggregated level. To improve that, this study proposes to apply the

Vivek Mohan, Wee Peng Tay, Arindam Basu

This work introduces two novel neural spike detection schemes intended for use in next-generation neuromorphic brain-machine interfaces (iBMIs). The first, an Event-based Spike Detector (Ev-SPD) which examines the temporal neighborhood of a neural event for spike detection, is designed for in-vivo processing and offers high sensitivity and decent accuracy (9

Valeriy G. Bardakov, Igor M. Nikonov, Viktor N. Zhelaybin

If $A$ is an associative algebra, then we can define the adjoint Lie algebra $A^{(-)}$ and Jordan algebra $A^{(+)}$. It is easy to see that any associative Rota--Baxter operator on $A$ induces a Lie and Jordan Rota--Baxter operator on $A^{(-)}$ and $A^{(+)}$ respectively. Are there Lie (Jordan) Rota--Baxter operators, which are not associative Rota--Baxter o

Andrew Chin, Akihiko Nishimura

Piecewise-deterministic Markov process (PDMP) samplers constitute a state-of-the-art Markov chain Monte Carlo paradigm in Bayesian computation, with examples including the zig-zag and bouncy particle sampler (bps). Recent work on the zig-zag has indicated its connection to Hamiltonian Monte Carlo (HMC), a version of the Metropolis algorithm that exploits Ham

Yaoqi Yang, Hongyang Du, Geng Sun, Zehui Xiong

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative A

G. Chen, A. J. Stanier, L. Chacón, S. E. Anderson

Coulomb collisions in particle simulations for weakly coupled plasmas are modeled by the Landau-Fokker-Planck equation, which is typically solved by Monte-Carlo (MC) methods. One of the main disadvantages of MC is the timestep accuracy constraint {\nu}<<1 to resolve the collision frequency {\nu}. The constraint becomes extremely stringent for self-collisions

Yiyan Ma, Akram Shafie, Jinhong Yuan, Guoyu Ma

The orthogonal delay-Doppler (DD) division multiplexing(ODDM) modulation has been recently proposed as a promising modulation scheme for next-generation communication systems with high mobility. Despite its benefits, ODDM modulation and other DD domain modulation schemes face the challenge of excessive equalization complexity. To address this challenge, we p

Johannes Mohr, Tyler Hennen, Daniel Bedau, Rainer Waser

The pressure driven Mott-transition in Chromium doped V2O3 films is investigated by direct electrical measurements on polycrystalline films with thicknesses down to 10 nm, and doping concentrations of 2%, 5% and 15%. A change in resistivity of nearly two orders of magnitude is found for 2% doping. A simulation model based on a scaling law description of the

Hanchen Liu, Xiao Chen

We present a class of two-dimensional randomized plaquette models, where the multi-spin interaction term, referred to as the plaquette term, is replaced by a single-site spin term with a probability of $1-p$. By varying $p$, we observe a ground state phase transition, or equivalently, a phase transition of the symmetry operator. We find that as we vary $p$,

Lázaro Lugo, Carlos Segura, Gara Miranda

The way heuristic optimizers are designed has evolved over the decades, as computing power has increased. Such has been the case for the Linear Ordering Problem (LOP), a field in which trajectory-based strategies led the way during the 1990s, but which have now been surpassed by memetic schemes.This paper focuses on understanding how the design of LOP optimi

Yiluan Xing, Chao Yan, Cathy Chang Xie

Forecasting stock prices remains a considerable challenge in financial markets, bearing significant implications for investors, traders, and financial institutions. Amid the ongoing AI revolution, NVIDIA has emerged as a key player driving innovation across various sectors. Given its prominence, we chose NVIDIA as the subject of our study.

Yang Lu, Weijia Yao, Yongqian Xiao, Xinglong Zhang

In obstacle-dense scenarios, providing safe guidance for mobile robots is critical to improve the safe maneuvering capability. However, the guidance provided by standard guiding vector fields (GVFs) may limit the motion capability due to the improper curvature of the integral curve when traversing obstacles. On the other hand, robotic system dynamics are oft

Rajiv Thummala, Shristi Sharma, Matteo Calabrese, Gregory Falco

Spacecraft are among the earliest autonomous systems. Their ability to function without a human in the loop have afforded some of humanity's grandest achievements. As reliance on autonomy grows, space vehicles will become increasingly vulnerable to attacks designed to disrupt autonomous processes-especially probabilistic ones based on machine learning. This

Eshant English

Machine Learning algorithms are notorious for providing point predictions but not prediction intervals. There are many applications where one requires confidence in predictions and prediction intervals. Stringing together, these intervals give rise to joint prediction regions with the desired significance level. It is an easy task to compute Joint Prediction

Lucas Aronson, Ruben Ngnitewe Massaa, Syed Jamal Safdar Gardezi, Andrew L. Wentland

Objective: Automated segmentation tools are useful for calculating kidney volumes rapidly and accurately. Furthermore, these tools have the power to facilitate large-scale image-based artificial intelligence projects by generating input labels, such as for image registration algorithms. Prior automated segmentation models have largely ignored non-contrast co

Michael J. Mossinghoff

Mahler's problem asks for the largest possible value of the Mahler measure, normalized by the $L_2$ norm, of a polynomial with $\pm1$ coefficients and large degree. We establish a new record value in this problem exceeding $0.95$ by analyzing certain Turyn polynomials, which are defined by cyclically shifting the coefficients of a Fekete polynomial by a pres

Xian-Ming Gu, Jun Liu, Cornelis W. Oosterlee

For pricing American options, %after suitable discretization in space and time, a sequence of discrete linear complementarity problems (LCPs) or equivalently Hamilton-Jacobi-Bellman (HJB) equations need to be solved in a sequential time-stepping manner. In each time step, the policy iteration or its penalty variant is often applied due to their fast converge

Iman Taghavi, Omid Esmaeeli, Sheri Jahan Chowdhury, Kashif Awan

Organic electro-optic materials have demonstrated promising performance in developing electro-optic phase shifters. Their integration with other silicon photonic processes, nanofabrication complexities, and durability remains to be developed. While the required poling step in electro-optic polymers limits their potential and utilization on a large scale, dev

Salvador Bará

Monitoring the evolution of the anthropogenic light emissions is a priority task in light pollution research. Among the complementary approaches that can be adopted to achieve this goal stand out those based on measuring the direct radiance of the sources at ground level or from low Earth orbit satellites, and on measuring the scattered radiance (known as ar

Jiajun Zhou, Xuanze Chen, Shengbo Gong, Chenkai Hu

Ethereum has become one of the primary global platforms for cryptocurrency, playing an important role in promoting the diversification of the financial ecosystem. However, the relative lag in regulation has led to a proliferation of malicious activities in Ethereum, posing a serious threat to fund security. Existing regulatory methods usually detect maliciou

Muhammad Usama, Yunkyung Hwang, Jaehong Kim

This paper proposed a straightforward and efficient current control solution for induction machines employing deep symbolic regression (DSR). The proposed DSR-based control design offers a simple yet highly effective approach by creating an optimal control model through training and fitting, resulting in an analytical dynamic numerical expression that charac

Kejin Wu, Dimitris N. Politis

Deep neural networks (DNN) has received increasing attention in machine learning applications in the last several years. Recently, a non-asymptotic error bound has been developed to measure the performance of the fully connected DNN estimator with ReLU activation functions for estimating regression models. The paper at hand gives a small improvement on the c

Katherine Henneberger, Jing Qin

Tensors serve as a crucial tool in the representation and analysis of complex, multi-dimensional data. As data volumes continue to expand, there is an increasing demand for developing optimization algorithms that can directly operate on tensors to deliver fast and effective computations. Many problems in real-world applications can be formulated as the task

Kanta Kato, Ryosuke Sakagami, Satoru Okayasu, Ataru Ichinose

We investigated the magnetic field dependence of the critical current density (Jc) in Ba0.6K0.4Fe2As2 with various configurations of columnar defects (CDs) introduced by 2.6 GeV U or 320 MeV Au irradiations. Splayed CDs are introduced by crossing CDs at a specific angle with respect to the c-axis, while tilted CDs are introduced by irradiating the sample fro

Yu-Min Hu, Biao Lian

The one-dimensional quantum breakdown model, which features spatially asymmetric fermionic interactions simulating the electrical breakdown phenomenon, exhibits an exponential U(1) symmetry and a variety of dynamical phases including many-body localization and quantum chaos with quantum scar states. We investigate the minimal quantum breakdown model with the

Zhen Chen, Xingjian Luo, Jinlin Wu, Danny T. M. Chan

The surgical intervention is crucial to patient healthcare, and many studies have developed advanced algorithms to provide understanding and decision-making assistance for surgeons. Despite great progress, these algorithms are developed for a single specific task and scenario, and in practice require the manual combination of different functions, thus limiti

Masahiro N. Machida, Shantanu Basu

We perform a long-term simulation of star and disk formation using three-dimensional non-ideal magnetohydrodynamics. The simulation starts from a prestellar cloud and proceeds through the long-term evolution of the circumstellar disk until $\sim 1.5\times10^5$ yr after protostar formation. The disk has size $\lesssim 50$ au and little substructure in the mai

Shishuai Hu, Zehui Liao, Zeyou Liu, Yong Xia

Deep learning-based medical image segmentation models often face performance degradation when deployed across various medical centers, largely due to the discrepancies in data distribution. Test Time Adaptation (TTA) methods, which adapt pre-trained models to test data, have been employed to mitigate such discrepancies. However, existing TTA methods primaril