May 2024 arXiv papers — page 145

Anka Reuel, Devin Ma

While our understanding of fairness in machine learning has significantly progressed, our understanding of fairness in reinforcement learning (RL) remains nascent. Most of the attention has been on fairness in one-shot classification tasks; however, real-world, RL-enabled systems (e.g., autonomous vehicles) are much more complicated in that agents operate in

Rohan Banerjee, Rajat Kumar Jenamani, Sidharth Vasudev, Amal Nanavati

Robot-assisted bite acquisition involves picking up food items with varying shapes, compliance, sizes, and textures. Fully autonomous strategies may not generalize efficiently across this diversity. We propose leveraging feedback from the care recipient when encountering novel food items. However, frequent queries impose a workload on the user. We formulate

Shuyuan Xu, Zelong Li, Kai Mei, Yongfeng Zhang

Since their inception, programming languages have trended towards greater readability and lower barriers for programmers. Following this trend, natural language can be a promising type of programming language that provides great flexibility and usability and helps towards the democracy of programming. However, the inherent vagueness, ambiguity, and verbosity

Guangyuan Jiang, Matthias Hofer, Jiayuan Mao, Lionel Wong

One hallmark of human language is its combinatoriality -- reusing a relatively small inventory of building blocks to create a far larger inventory of increasingly complex structures. In this paper, we explore the idea that combinatoriality in language reflects a human inductive bias toward representational efficiency in symbol systems. We develop a computati

José A. Díaz-García, Francisco J. Caro-Lopera

This paper proposes famillies of multimatricvariate and multimatrix variate distributions based on elliptically contoured laws in the context of real normed division algebras. The work allows to answer the following inference problems about random matrix variate distributions: 1) Modeling of two or more probabilistically dependent random variables in all pos

Zihang Jia, Zhen Zhang, Witold Pedrycz

Efficient and robust data clustering remains a challenging task in the field of data analysis. Recent efforts have explored the integration of granular-ball (GB) computing with clustering algorithms to address this challenge, yielding promising results. However, existing methods for generating GBs often rely on single indicators to measure GB quality and emp

Ruihai Wu, Haoran Lu, Yiyan Wang, Yubo Wang

Garment manipulation (e.g., unfolding, folding and hanging clothes) is essential for future robots to accomplish home-assistant tasks, while highly challenging due to the diversity of garment configurations, geometries and deformations. Although able to manipulate similar shaped garments in a certain task, previous works mostly have to design different polic

Kang Du, Yu Xiang

Causal inference from observational data following the restricted structural causal models (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or non-linearity. This methodology can be adapted to stationary time series, yet inferring causal relationships from nonstationary time

Mamoru Endo, Kazuma Takahashi, Takefumi Nomura, Tatsuki Sonoyama

Photon number-resolving detectors (PNRDs) are the ultimate optical sensors. Superconducting-nanostrip photon detectors (SNSPDs), traditionally known as ON-OFF detectors, have recently been found to have photon number resolving capability without multiplexing. This discovery positions them to become true PNRDs. However, their practical use is limited by the n

Luciano Combi, Huan Yang, Eduardo Gutierrez, Scott C. Noble

We present the first dynamical model of plasma accretion onto traversable wormholes by performing General Relativistic magneto-hydrodynamical (GRMHD) simulations of the flow on both sides of the wormhole. We evolve the ideal MHD equations on a wormhole spacetime described by the spherically symmetric Simpson--Visser metric. The disk is initialized on one sid

Guanlin Mo, Shihong Song, Hu Ding

DBSCAN is a popular density-based clustering algorithm that has many different applications in practice. However, the running time of DBSCAN in high-dimensional space or general metric space ({\em e.g.,} clustering a set of texts by using edit distance) can be as large as quadratic in the input size. Moreover, most of existing accelerating techniques for DBS

Xiao-Ping Chen, Chun-Lei Tang

This paper focuses on optimal constants and optimizers of the second order Caffarelli-Kohn-Nirenberg inequalities. Firstly, we aim to study optimal constants and optimizers for the following second order Caffarelli-Kohn-Nirenberg inequality in radial space: let $N\ge1$, $t\ge p>1$, \begin{equation}\label{0.1} \left(\int_{\mathbb{R}^N} \frac{|\Delta u|^p}{|x|

Jinwei Lin

Front-following is more technically difficult to implement than the other two human following technologies, but front-following technology is more practical and can be applied in more areas to solve more practical problems. Front-following technology has many advantages not found in back-following and side-by-side technologies. In this paper, we will discuss

Chunna Zeng, Yuqi Zhou

All $\textrm{SL}(n)$ contravariant matrix-valued valuations on polytopes in $\mathbb{R}^n$ are completely classified without any continuity assumptions. Moreover, the symmetry assumption of matrices is removed. The general Lutwak-Yang-Zhang matrix turns out to be the only such valuation if $n\geq 4$, while a new function shows up in dimension three. In dimen

Xianghui Cao, Siqi Xu, Yang Li, Guangyao Chen

We compute the gravitational form factors $A_i$, $D_i$ and $\bar c_i$ of the scalar Yukawa theory using both the light-cone and covariant perturbation theory at the one-loop level. The light-cone formalism provides a potential approach to access these form factors beyond the perturbative regime. However, unlike the covariant formulation, the Poincar\'e symme

Hanzhong Cao

The evolution of User Interfaces marks a significant transition from traditional command-line interfaces to more intuitive graphical and touch-based interfaces, largely driven by the emergence of personal computing devices. The advent of spatial computing and Extended Reality technologies further pushes the boundaries, promising a fusion of physical and digi

Chenglin Li, Yuanzhen Xie, Chenyun Yu, Lei Cheng

Inductive representation learning on temporal heterogeneous graphs is crucial for scalable deep learning on heterogeneous information networks (HINs) which are time-varying, such as citation networks. However, most existing approaches are not inductive and thus cannot handle new nodes or edges. Moreover, previous temporal graph embedding methods are often tr

Siddhant Dutta, Hemant Singh, Kalpita Shankhdhar, Sridhar Iyer

Consuming poisonous mushrooms can have severe health consequences, even resulting in fatality and accurately distinguishing edible from toxic mushroom varieties remains a significant challenge in ensuring food safety. So, it's crucial to distinguish between edible and poisonous mushrooms within the existing species. This is essential due to the significant d

Tianchen Yu, Junyi Yang, Wenfa Zhou, Zhongguo Li

A simple and effective single-shot pump-probe technique is reported for studying the ultrafast dynamic processes in various materials. Using only two commercial gratings, a large time window of ~ 95.58 ps is spatially encoded in a single probe pulse, and single-shot time-resolved measurements are implemented. This time window exceeds the maximum reported val

Haonan Wang

This study introduces a novel data augmentation technique, ADLDA, aimed at mitigating the negative impact of data distribution shifts caused by the data augmentation process in computer vision task. ADLDA partitions augmented data into distinct subdomains and incorporates domain labels, combined with domain adaptation techniques, to optimize data representat

Yang Li, Qun Wang, James P. Vary

Everything gravitates and they do so through the energy-momentum tensor (EMT). However, there is no consensus on how to define the EMT of a hadron, e.g. the proton, the fundamental building blocks of the visible world. In this work, we show that the hadronic EMT can be cast into the form of relativistic continua with spin. The fluid-like form allows a clear

Yifan Liu, Haruki Shimizu, Atsushi Ueda, Masaki Oshikawa

We present the finite-size scaling theory of one-dimensional quantum critical systems in the presence of boundaries. While the finite-size spectrum in the conformal limit, namely of a conformal field theory with conformally invariant boundary conditions, is related to the dimensions of boundary operators by Cardy, the actual spectra of lattice models are aff

Yong Guan, Xiaozhi Wang, Lei Hou, Juanzi Li

Event relation extraction (ERE) is a critical and fundamental challenge for natural language processing. Existing work mainly focuses on directly modeling the entire document, which cannot effectively handle long-range dependencies and information redundancy. To address these issues, we propose a cluster-aware compression method for improving event relation

Pan Shang, Lingchen Kong, Yiting Ma

Regularized models have been applied in lots of areas, with high-dimensional data sets being popular. Because tuning parameter decides the theoretical performance and computational efficiency of the regularized models, tuning parameter selection is a basic and important issue. We consider the tuning parameter selection for adaptive nuclear norm regularized t

Qiqing Xia, Qianru Zhu, Huiqin Xie, Li Yang

Many quantum algorithms for attacking symmetric cryptography involve the rank problem of quantum linear equations. In this paper, we first propose two quantum algorithms for solving quantum linear systems of equations with coherent superposition and construct their specific quantum circuits. Unlike previous related works, our quantum algorithms are universal

Armando Meza Gaxiola, Pablo Padilla Longoria

In this work, we compute the universe temperature for the cosmological horizon for the FLRW metric. For this purpose, we consider a scalar field on the cosmological horizon. This scalar field satisfies the Klein-Gordon equation in a curved space-time. Recently, some authors like Barrow, Bekenstein and others have proposed that the fundamental constants might

Jinglin Xu, Sibo Yin, Guohao Zhao, Zishuo Wang

Existing action quality assessment (AQA) methods mainly learn deep representations at the video level for scoring diverse actions. Due to the lack of a fine-grained understanding of actions in videos, they harshly suffer from low credibility and interpretability, thus insufficient for stringent applications, such as Olympic diving events. We argue that a fin

Yong Guan, Dingxiao Liu, Jinchen Ma, Hao Peng

Generative document retrieval, an emerging paradigm in information retrieval, learns to build connections between documents and identifiers within a single model, garnering significant attention. However, there are still two challenges: (1) neglecting inner-content correlation during document representation; (2) lacking explicit semantic structure during ide

Chengyuan Wu, Shuai Zha, Yongzhi Cai, Zhengyang Zhang

Type Icn supernovae (SNe Icn) are a newly detected rare subtype of interacting stripped-envelope supernovae which show narrow P-Cygni lines of highly ionized carbon, oxygen, and neon in their early spectra due to the interactions of the SNe ejecta with dense hydrogen- and helium-deficient circumstellar material (CSM). It has been suggested that SNe Icn may h

Zirou Qiu, Abhijin Adiga, Madhav V. Marathe, S. S. Ravi

Networked dynamical systems are widely used as formal models of real-world cascading phenomena, such as the spread of diseases and information. Prior research has addressed the problem of learning the behavior of an unknown dynamical system when the underlying network has a single layer. In this work, we study the learnability of dynamical systems over multi

Sayantan Choudhury, Ahaskar Karde, Sudhakar Panda, Soumitra SenGupta

We present a complete and consistent exposition of the regularization, renormalization, and resummation procedures in the setup of having a contraction and then non-singular bounce followed by inflation with a sharp transition from slow-roll (SR) to ultra-slow roll (USR) phase for generating primordial black holes (PBHs). We consider following an effective f

Suprio Bhar, Ritwik Mukherjee, Prathmesh Patil

In $1946$, Mark Kac proved a Central Limit type theorem for a sequence of random variables that were not independent. The random variables under consideration were obtained from the angle-doubling map. The idea behind Kac's proof was to show that although the random variables under consideration were not independent, they were what he calls \textit{statistic

Md Mostafijur Rahman, Mustafa Munir, Radu Marculescu

An efficient and effective decoding mechanism is crucial in medical image segmentation, especially in scenarios with limited computational resources. However, these decoding mechanisms usually come with high computational costs. To address this concern, we introduce EMCAD, a new efficient multi-scale convolutional attention decoder, designed to optimize both

Jun-Cen Li, Zhen-Gang Zhu

In this paper, we study the intrinsic contribution of nonlinear magnon thermal Hall Effect. We derive the intrinsic second order thermal Hall conductivity of magnon by the thermal scalar potential (TSP) method and the thermal vector potential (TVP) method. We find that the intrinsic second order magnon thermal Hall conductivity is related to the thermal Berr

Yaobin Tang, Binxiang Dai

This paper is devoted to a nonlocal reaction-diffusion-advection model that describes the spatial dynamics of freshwater organisms in a river with a directional motion. Our goal is to investigate how the advection rate affects the dynamic behaviors of species. We first establish the well-posedness of global solutions, where the regularized problem containing

Ming-Tien Tsai, Chia-Hsuan Tsai

In this note, when the dimension $p$ is large we look into the insight of the Mar$\check{c}$enko-Pastur equation to get an explicit equality relationship, and use the obtained equality to establish a new kind of orthogonally equivariant estimator of the population covariance matrix. Under some regularity conditions, the proposed novel estimators of the popul

Fengzhao Shen, Tao Li, Jingkui He, Shenghui Xie

Organic scintillators are important in advancing nuclear detection and particle physics experiments. Achieving a high signal-to-noise ratio necessitates efficient pulse shape discrimination techniques to accurately distinguish between neutrons, gamma rays, and other particles within scintillator detectors. Although traditional charge comparison methods perfo

Ying Zhao

Anomaly localization is a practical technology for improving industrial production line efficiency. Due to anomalies are manifold and hard to be collected, existing unsupervised researches are usually equipped with anomaly synthesis methods. However, most of them are biased towards structural defects synthesis while ignoring the underlying logical constraint

Yong Liu, Ziyao Xu

Discrete fracture models with reduced-dimensional treatment of conductive and blocking fractures are widely used to simulate fluid flow in fractured porous media. Among these, numerical methods based on interface models are intensively studied, where the fractures are treated as co-dimension one manifolds in a bulk matrix with low-dimensional governing equat

Shu-Yi Lin, Ming-Hui Yu, Xian-Hui Ge, Li-Jun Tian

This study focuses on the examination of the island rule within the context of four-dimensional Reissner-Nordstr\"om-AdS (4D RN-AdS) black holes, illuminating the intricate relationship between the entanglement entropy and phase transitions of black holes. The entanglement entropy of 4D RN-AdS black holes follows the anticipated linear growth pattern before

Jinwoo Jeon, Woontack Woo

We propose a novel edge-assisted multi-user collaborative augmented reality framework in a large indoor environment. In Collaborative Augmented Reality, data communication that synchronizes virtual objects has large network traffic and high network latency. Due to drift, CAR applications without continuous data communication for coordinate system alignment h

Xuda Ye, Zhennan Zhou

We propose a novel discrete Poisson equation approach to estimate the statistical error of a broad class of numerical integrators for the underdamped Langevin dynamics. The statistical error refers to the mean square error of the estimator to the exact ensemble average with a finite number of iterations. With the proposed error analysis framework, we show th

Kok Hao Chen, Duc Tu Dao, Han Mao Kiah, Van Long Phuoc Pham

Motivated by applications in spatial genomics, we revisit group testing (Dorfman~1943) and propose the class of $\lambda$-{\sf ADD}-codes, studying such codes with certain distance $d$ and codelength $n$. When $d$ is constant, we provide explicit code constructions with rates close to $1/2$. When $d$ is proportional to $n$, we provide a GV-type lower bound w

Hengzhe Zhang, Qi Chen, Bing Xue, Wolfgang Banzhaf

In recent years, genetic programming (GP)-based evolutionary feature construction has achieved significant success. However, a primary challenge with evolutionary feature construction is its tendency to overfit the training data, resulting in poor generalization on unseen data. In this research, we draw inspiration from PAC-Bayesian theory and propose using

Carlos E. Sierra, Kathleen Harrington, Shreya Sutariya, Thomas Alford

The Simons Observatory will map the temperature and polarization over half of the sky, at millimeter wavelengths in six spectral bands from the Atacama Desert in Chile. These data will provide new insights into the genesis, content, and history of our Universe; the astrophysics of galaxies and galaxy clusters; objects in our solar system; and time-varying as

Han Hong, Zetian Yan

We prove that nonnegative $3$-intermediate Ricci curvature combined with uniformly positive $k$-triRic curvature implies rigidity of complete noncompact two-sided stable minimal hypersurfaces in a Riemannian manifold $(X^5,g)$ with bounded geometry. The stonger assumption of nonnegative $3$-intermediate Ricci curvature can be replaced by the nonnegativity of

Elynn Chen, Xi Chen, Lan Gao, Jiayu Li

In the evolving landscape of digital commerce, adaptive dynamic pricing strategies are essential for gaining a competitive edge. This paper introduces novel {\em doubly nonparametric random utility models} that eschew traditional parametric assumptions used in estimating consumer demand's mean utility function and noise distribution. Existing nonparametric m

Josephine Passananti, Stanley Wu, Shawn Shan, Haitao Zheng

Generative AI models are often used to perform mimicry attacks, where a pretrained model is fine-tuned on a small sample of images to learn to mimic a specific artist of interest. While researchers have introduced multiple anti-mimicry protection tools (Mist, Glaze, Anti-Dreambooth), recent evidence points to a growing trend of mimicry models using videos as

Wirawat Kokaew, Bohdan Kulchytskyy, Shunji Matsuura, Pooya Ronagh

We investigate the advantages of using autoregressive neural quantum states as ansatze for classical shadow tomography to improve its predictive power. We introduce a novel estimator for optimizing the cross-entropy loss function using classical shadows, and a new importance sampling strategy for estimating the loss gradient during training using stabilizer

Yanqiang Guo, Jianchao Zhang, Jiahui Hou, Xiaomin Guo

Standard weak measurement with an assistant pointer and single weak interaction constrains measurement precision and quantity of interaction parameters, and a compelling characterization of quantum effect featuring weak-value amplification (WVA) remains elusive. Here, we theoretically and experimentally demonstrate an enhanced dual-pointer WVA scheme based o

André Dosea, Cleto B. Miranda-Neto

A conjecture raised in 1990 by C. Huneke predicts that, for a $d$-dimensional Noetherian local ring $R$, local cohomology modules of finitely generated $R$-modules have finitely many associated primes. Although counterexamples do exist, the conjecture has been confirmed in several cases, for instance if $d\leq 3$, and witnessed some progress in special cases

Rafael L. H. Freire, F. Crasto de Lima, Roberto H. Miwa, A. Fazzio

The discovery of the quantum spin Hall effect led to the exploration of the electronic transport for spintronic devices. Here, we theoretically investigated the electronic conductance in large-gap realistic quantum spin Hall system, Pt$_2$HgSe$_3$ nanoribbons. By an ab initio approach, we found that the edge states present a penetration depth of about $0.9$\

Matthew Levy, Joseph Squillace

Given a natural number $n$, let $\omega\left(n\right)$ denote the number of distinct prime factors of $n$, let $Z$ denote a standard normal variable, and let $P_{n}$ denote the uniform distribution on $\left\{ 1,\ldots,n\right\} $. The Erd\H{o}s-Kac Theorem states that if $N\left(n\right)$ is a uniformly distributed variable on $\lbrace 1,\ldots,n \rbrace$,

Parth Patil, Ben Boardley, Jack Gardner, Emily Loiselle

Deep neural networks (DNNs) have been used to create models for many complex analysis problems like image recognition and medical diagnosis. DNNs are a popular tool within machine learning due to their ability to model complex patterns and distributions. However, the performance of these networks is highly dependent on the quality of the data used to train t

Xin-Zhi Li, Zhen-Bo Qi, Quansheng Wu, Wen-Yu He

Topological superconductivity has attracted significant attention due to its potential applications in quantum computation, but its experimental realization remains challenging. Recently, monolayer T$_{\textrm{d}}$-MoTe$_2$ was observed to exhibit gate tunable superconductivity, and its in-plane upper critical field exceeds the Pauli limit. Here, we show tha

Herbert Susmann, Adrian E. Raftery

Estimates of future migration patterns are of broad interest in demography. Forced migration, including refugee and asylum seekers, plays an important role in overall migration patterns, but is notoriously difficult to forecast. Focusing on refugees and asylum seekers, we propose a modeling pipeline based on Bayesian hierarchical time-series modeling for pro

Chengyi Nie, Rodrigo Fonseca, Zhenhua Liu

The demand for large language model (LLM) inference is gradually dominating the artificial intelligence workloads. Therefore, there is an urgent need for cost-efficient inference serving. Existing work focuses on single-worker optimization and lacks consideration of cluster-level management for both inference queries and computing resources. However, placing

Fan Xing, Yan Wei, Zeyang Liao

Continuous-time quantum walks provide an alternative method for quantum search problems. Most of the earlier studies confirmed that quadratic speedup exists in some synthetic Hamiltonians, but whether there is quadratic speedup in real physical systems is elusive. Here, we investigate three physical systems with long-range atom-atom interaction which are pos

Tong Zhou

We prove a microlocal characterisation of character sheaves on a reductive Lie algebra over an algebraically closed field of sufficiently large positive characteristic: a perverse irreducible G-equivariant sheaf is a character sheaf if and only if it has nilpotent singular support and is quasi-admissible. We also present geometric proofs, in positive charact

Tuomas Oikarinen, Tsui-Wei Weng

In recent years many methods have been developed to understand the internal workings of neural networks, often by describing the function of individual neurons in the model. However, these methods typically only focus on explaining the very highest activations of a neuron. In this paper we show this is not sufficient, and that the highest activation range is

C. Escudero, F. Lara, M. Sama

This work focuses on the mathematical study of constant function market makers. We rigorously establish the conditions for optimal trading under the assumption of a quasilinear, but not necessarily convex (or concave), trade function. This generalizes previous results that used convexity, and also guarantees the robustness against arbitrage of so-designed au

Alexander Yue, Rubem Mondaini, Qiujiang Guo, Richard T. Scalettar

Quantum state transfer (QST) describes the coherent passage of quantum information from one node in a network to another. Experiments on QST span a diverse set of platforms and currently report transport across up to tens of nodes in times of several hundred nanoseconds with fidelities that can approach 90% or more. Theoretical studies examine both the lossl

Wesley H. Holliday

In traditional semantics for classical logic and its extensions, such as modal logic, propositions are interpreted as subsets of a set, as in discrete duality, or as clopen sets of a Stone space, as in topological duality. A point in such a set can be viewed as a "possible world," with the key property of a world being primeness--a world makes a disjunction

Francesca Mignacco, Chi-Ning Chou, SueYeon Chung

Understanding how neural systems efficiently process information through distributed representations is a fundamental challenge at the interface of neuroscience and machine learning. Recent approaches analyze the statistical and geometrical attributes of neural representations as population-level mechanistic descriptors of task implementation. In particular,

Aristide Houndetoungan, Cristelle Kouame, Michael Vlassopoulos

Peer influence on effort devoted to some activity is often studied when effort is unobserved, and the researcher instead observes an outcome that combines effort with other shocks. For instance, in education, achievement measures such as GPA reflect both effort and idiosyncratic GPA shocks. We propose an alternative approach that circumvents this approximati

Julius Heitkoetter, Michael Gerovitch, Laker Newhouse

The trustworthiness of highly capable language models is put at risk when they are able to produce deceptive outputs. Moreover, when models are vulnerable to deception it undermines reliability. In this paper, we introduce a method to investigate complex, model-on-model deceptive scenarios. We create a dataset of over 10,000 misleading explanations by asking

Mustafa Munir, William Avery, Md Mostafijur Rahman, Radu Marculescu

Vision graph neural networks (ViG) offer a new avenue for exploration in computer vision. A major bottleneck in ViGs is the inefficient k-nearest neighbor (KNN) operation used for graph construction. To solve this issue, we propose a new method for designing ViGs, Dynamic Axial Graph Construction (DAGC), which is more efficient than KNN as it limits the numb

Tony Tohme, Mohammad Javad Khojasteh, Mohsen Sadr, Florian Meyer

We introduce an Invertible Symbolic Regression (ISR) method. It is a machine learning technique that generates analytical relationships between inputs and outputs of a given dataset via invertible maps (or architectures). The proposed ISR method naturally combines the principles of Invertible Neural Networks (INNs) and Equation Learner (EQL), a neural networ

N. Buzinsky, R. J. Taylor, W. Byron, W. DeGraw

Cyclotron radiation emission spectroscopy (CRES) is a modern technique for high-precision energy spectroscopy, in which the energy of a charged particle in a magnetic field is measured via the frequency of the emitted cyclotron radiation. The He6-CRES collaboration aims to use CRES to probe beyond the standard model physics at the TeV scale by performing hig

Danny Halawi, Aron Sarmasi, Siena Saltzen, Joshua McCoy

In recent years, machine learning approaches have made dramatic advances, reaching superhuman performance in Go, Atari, and poker variants. These games, and others before them, have served not only as a testbed but have also helped to push the boundaries of AI research. Continuing this tradition, we examine the tabletop game Dominion and discuss the properti

James Tang, Shashwat Suri, Daniel Ajisafe, Bastian Wandt

It is now possible to estimate 3D human pose from monocular images with off-the-shelf 3D pose estimators. However, many practical applications require fine-grained absolute pose information for which multi-view cues and camera calibration are necessary. Such multi-view recordings are laborious because they require manual calibration, and are expensive when u

Célia Borlido

We consider monoids equipped with a compatible quantale valued relation, to which we call quantale enriched monoids, and study semidirect products of such structures. It is well-known that semidirect products of monoids are closely related to Schreier split extensions which, in the setting of monoids, play the role of split extensions of groups. We will thus

Sergio Demian Lerner, Ramon Amela, Shreemoy Mishra, Martin Jonas

BitVMX is a new design for a virtual CPU to optimistically execute arbitrary programs on Bitcoin based on a challenge response game introduced in BitVM. Similar to BitVM1 we create a general-purpose CPU to be verified in Bitcoin script. Our design supports common architectures, such as RISC-V or MIPS. Our main contribution to the state of the art is a design

Guanyu Hu, Eleni Papadopoulou, Dimitrios Kollias, Paraskevi Tzouveli

The increasing integration of machine learning algorithms in daily life underscores the critical need for fairness and equity in their deployment. As these technologies play a pivotal role in decision-making, addressing biases across diverse subpopulation groups, including age, gender, and race, becomes paramount. Automatic affect analysis, at the intersecti

Ke Xu, Jialin Sun, Yuchen Hu, Xinwei Fang

The deployment of Large Language Models (LLMs) for code debugging (e.g., C and Python) is widespread, benefiting from their ability to understand and interpret intricate concepts. However, in the semiconductor industry, utilising LLMs to debug Register Transfer Level (RTL) code is still insufficient, largely due to the underrepresentation of RTL-specific dat

Daniel Shy, Richard S. Woolf, Clio Sleator, Bernard Phlips

The ComPair gamma-ray telescope is a technology demonstrator for a future gamma-ray telescope called the All-sky Medium Energy Gamma-ray Observatory (AMEGO). The instrument is composed of four subsystems, a double-sided silicon strip detector, a virtual Frisch grid CdZnTe calorimeter, a CsI:Tl based calorimeter, and an anti-coincidence detector (ACD). The Cs

Matthew T. Calef, Kelly M. Olsen, Piyush S. Agram

Given a collection of points $S \subset \mathbb{R}^N$, which is partitioned into $M$ overlapping subsets $\{S_i\}_{i=1}^M$, and approximate data $\{D_i\}_{i=1}^M$ associated with the subsets, one may seek a consistent merged dataset $D$ that is derived from $\{S_i\}_{i=1}^M$ and $\{D_i\}_{i=1}^M$. This note presents a method for constructing $D$ under the as

Pere Ara, Ken Goodearl, Kevin C. O'Meara, Enrique Pardo

We give a number of constructions where inverse limits seriously degrade properties of regular rings, such as unit-regularity, diagonalisation of matrices, and finite stable rank. This raises the possibility of using inverse limits to answer the long standing Separativity Problem (in the negative).

Salma J. Ahmed, Emad A. Mohammed

Developing new drugs is laborious and costly, demanding extensive time investment. In this paper, we introduce a de-novo drug design strategy, which harnesses the capabilities of language models to devise targeted drugs for specific proteins. Employing a Reinforcement Learning (RL) framework utilizing Proximal Policy Optimization (PPO), we refine the model t

Harsh Patel, Buvaneswari A. Ramanan, Manzoor A. Khan, Thomas Williams

This paper explores the possibilities of the current generation of Large Language Models for incorporating Machine Learning Operations (MLOps) functionalities into ML training code bases. We evaluate the performance of OpenAI (gpt-3.5-turbo) and WizardCoder (open-source, 15B parameters) models on the automated accomplishment of various MLOps functionalities

C. Sipling, Y. -H. Zhang, M. Di Ventra

Time non-locality, or memory, is a non-equilibrium property shared by all physical systems. Here, we show that memory is sufficient to induce a phase of spatial long-range order (LRO) even if the system's primary dynamical variables are coupled locally. This occurs when the memory degrees of freedom have slower dynamics than the primary degrees of freedom. I

Elena Rybalova, Nadezhda Semenova

In this article we consider the influence of a periodic sequence of Gaussian pulses on a chimera state in a ring of coupled FitzHugh-Nagumo systems. We found that on the way to complete spatial synchronization one can observe a number of variations of chimera states that are not typical for the parameter range under consideration. For example, the following

Zhe Li, Fei Xie

JavaScript is prevalent in web and server apps, handling sensitive data. JS testing methods lag behind other languages. Insitu concolic testing for JS is effective but slow and complex. Our method enhances tracing with V8 Sparkplug baseline compiler and remill libraries for assembly to LLVM IR conversion. Evaluation on 160 Node.js libraries reveals comparabl

Reza Hosseini Dolatabadi, Mordedcai J. Golin, Arian Zamani

The problem of constructing optimal AIFV codes is a special case of that of constructing minimum cost Markov Chains. This paper provides the first complete proof of correctness for the previously known iterative algorithm for constructing such Markov chains. A recent work describes how to efficiently solve the Markov Chain problem by first constructing a Mar

Liam Tyler, Ivan De Oliveira Nunes

Cookies maintain state across related web traffic. As such, cookies are commonly used for authentication by storing a user's session ID and replacing the need to re-enter credentials in subsequent traffic. These so-called ``session cookies'' are prime targets for attacks that aim to steal them to gain unauthorized access to user accounts. To mitigate these a

Johannes Brunner, Jens Fortmann, Horst Schulte

This paper presents a novel Model Reference Control (MRC) approach for wind turbine (WT) systems in the full load region employing a fuzzy Parallel Distribution Compensation Controller (PDC-C) derived using a Takagi-Sugeno (TS) fuzzy System approach. Through first-order Taylor series expansion, local linear submodels are generated and combined via triangular

Junfeng Cheng, Tania Stathaki

This paper proposes a novel task named "3D part grouping". Suppose there is a mixed set containing scattered parts from various shapes. This task requires algorithms to find out every possible combination among all the parts. To address this challenge, we propose the so called Gradient Field-based Auto-Regressive Sampling framework (G-FARS) tailored specific

Matthew Bossart, Jose Daniel Lara, Ciaran Roberts, Rodrigo Henriquez-Auba

The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. In this paper, we propose a data-driven su

John M. Li, Jon Aytac, Philip Johnson-Freyd, Amal Ahmed

Currently, there is a gap between the tools used by probability theorists and those used in formal reasoning about probabilistic programs. On the one hand, a probability theorist decomposes probabilistic state along the simple and natural product of probability spaces. On the other hand, recently developed probabilistic separation logics decompose state via

Chandrasheel Bhagwat, Shubham Jaiswal

This article is inspired from the work of M Krithika and P Vanchinathan on Cluster Magnification and the work of Alexander Perlis on Cluster Size. We establish the existence of polynomials for given degree and cluster size over number fields which generalises a result of Perlis. We state the Strong cluster magnification problem and establish an equivalent cr

Shida Wang, Jalal Fadili, Peter Ochs

Quasi-Newton methods refer to a class of algorithms at the interface between first and second order methods. They aim to progress as substantially as second order methods per iteration, while maintaining the computational complexity of first order methods. The approximation of second order information by first order derivatives can be expressed as adopting a

Bo Hui, Haolin Yuan, Neil Gong, Philippe Burlina

Large Language Models (LLMs) enable a new ecosystem with many downstream applications, called LLM applications, with different natural language processing tasks. The functionality and performance of an LLM application highly depend on its system prompt, which instructs the backend LLM on what task to perform. Therefore, an LLM application developer often kee

Luyuan Xie, Manqing Lin, Tianyu Luan, Cong Li

Federated learning is widely used in medical applications for training global models without needing local data access. However, varying computational capabilities and network architectures (system heterogeneity), across clients pose significant challenges in effectively aggregating information from non-independently and identically distributed (non-IID) dat

Federico Zocco, Daniel R. Lake, Seán McLoone, Shahin Rahimifard

The circular economy paradigm is gaining interest as a solution to reducing both material supply uncertainties and waste generation. One of the main challenges in realizing this paradigm is monitoring materials, since in general, something that is not measured cannot be effectively managed. In this paper, we propose a real-time synchronized object detection

C. Gimeno-Amo, E. Martínez-González, R. B. Barreiro

The presence of astrophysical emissions in microwave observations forces us to perform component separation to extract the Cosmic Microwave Background (CMB) signal. However, even in the most optimistic cases, there are still strongly contaminated regions, such as the Galactic plane or those with emission from extragalactic point sources, which require the us

Sheriff Issaka, Zhaoyi Zhang, Mihir Heda, Keyi Wang

Despite comprising one-third of global languages, African languages are critically underrepresented in Artificial Intelligence (AI), threatening linguistic diversity and cultural heritage. Ghanaian languages, in particular, face an alarming decline, with documented extinction and several at risk. This study pioneers a comprehensive survey of Natural Language

Horst Schulte

This paper presents a coherent design of wind turbine controllers with explicit consideration of transitions between operating regions by fuzzy membership functions. In improving the design process of wind turbines, the transitions between partial-load operation by torque control and full-load operation by pitch control need to be systematically considered.

Thai-Hoang Pham, Xueru Zhang, Ping Zhang

Although recent advances in machine learning have shown its success to learn from independent and identically distributed (IID) data, it is vulnerable to out-of-distribution (OOD) data in an open world. Domain generalization (DG) deals with such an issue and it aims to learn a model from multiple source domains that can be generalized to unseen target domain

Daniel Shy, Richard Woolf, Bernard Phlips, Mary Johnson-Rambert

Gamma-ray astrophysics in the MeV band is an exciting field in astronomy due to its potential for multi-messenger astrophysics. It has, however, remained under-explored when compared to other wavelengths. One reason for this observational gap is the difficulties with measuring these high-energy photons and the requirement of large amounts of detection materi

Jialiang Fan, Xinhui Fan, Chengyan Song, Xiaofan Wang

Intracerebral hemorrhage (ICH) is a severe and sudden medical condition caused by the rupture of blood vessels in the brain, leading to permanent damage to brain tissue and often resulting in functional disabilities or death in patients. Diagnosis and analysis of ICH typically rely on brain CT imaging. Given the urgency of ICH conditions, early treatment is