February 2024 arXiv papers — page 143

Clément de Chaisemartin, Xavier D'Haultfœuille, Gonzalo Vazquez-Bare

Many treatments or policy interventions are continuous in nature. Examples include prices, taxes or temperatures. Empirical researchers have usually relied on two-way fixed effect regressions to estimate treatment effects in such cases. However, such estimators are not robust to heterogeneous treatment effects in general; they also rely on the linearity of t

Meng Cao, Yu Wang

In this paper, we establish a dynamical quantum state tomography framework. Under this framework, it is feasible to obtain complete knowledge of any unknown state of a $d$-level system via only an arbitrary operator of certain types of IC-POVMs in dimension $d$. We show that under the time-dependent average channel, we can acquire a collection of projective

Aseem Rajan Kshirsagar, Sven Reichardt

BiFeO$_3$ is a multiferroic material featuring ferroelectricity and noncollinear antiferromagnetism. Definitive and efficient control of the characteristic spin texture of BiFeO$_3$ is attractive for emerging quantum devices. In this regard, crystal-field $d\rightarrow d$ excitations localized on Fe atomic sites in BiFeO$_3$ provide an avenue for manipulatio

Simon Brendle, Michael Eichmair

We discuss several classical and recent proofs of the isoperimetric inequality and the Sobolev inequality.

Narendhar Gugulothu, Sanjay P. Bhat, Tejas Bodas

While mixture density networks (MDNs) have been extensively used for regression tasks, they have not been used much for classification tasks. One reason for this is that the usability of MDNs for classification is not clear and straightforward. In this paper, we propose two MDN-based models for classification tasks. Both models fit mixtures of Gaussians to t

Hemanth Saratchandran, Sameera Ramasinghe, Violetta Shevchenko, Alexander Long

Implicit Neural Representations (INRs) have gained popularity for encoding signals as compact, differentiable entities. While commonly using techniques like Fourier positional encodings or non-traditional activation functions (e.g., Gaussian, sinusoid, or wavelets) to capture high-frequency content, their properties lack exploration within a unified theoreti

Kristopher Cooper, Iain G. Hannah, Lindsay Glesener, Brian W. Grefenstette

Microflares are energetically smaller versions of solar flares, demonstrating the same processes of plasma heating and particle acceleration. However, it remains unclear down to what energy scales this impulsive energy release continues, which has implications for how the solar atmosphere is heated. The heating and particle acceleration in microflares can be

Yamato Suda, Daisuke Jido

We compute the vacuum energy density as a function of the quark condensate in the interacting instanton liquid model (IILM) and examine the pattern of dynamical chiral symmetry breaking from its behavior around the origin. This evaluation is performed by using simulation results of the IILM. We find that chiral symmetry is broken in the U(1)_A anomaly assist

Vincent Abbott

Diagrams matter. Unfortunately, the deep learning community has no standard method for diagramming architectures. The current combination of linear algebra notation and ad-hoc diagrams fails to offer the necessary precision to understand architectures in all their detail. However, this detail is critical for faithful implementation, mathematical analysis, fu

Chengzhi Liu, Zheng Tao, Zihong Luo, Chenghao Liu

Time series analysis and modelling constitute a crucial research area. Traditional artificial neural networks struggle with complex, non-stationary time series data due to high computational complexity, limited ability to capture temporal information, and difficulty in handling event-driven data. To address these challenges, we propose a Multi-modal Time Ser

Jyothi Rikhab Chand, Mathews Jacob

End-to-End (E2E) unrolled optimization frameworks show promise for Magnetic Resonance (MR) image recovery, but suffer from high memory usage during training. In addition, these deterministic approaches do not offer opportunities for sampling from the posterior distribution. In this paper, we introduce a memory-efficient approach for E2E learning of the poste

Weikang Wan, Ziyu Wang, Yufei Wang, Zackory Erickson

This paper introduces DiffTORI, which utilizes Differentiable Trajectory Optimization as the policy representation to generate actions for deep Reinforcement and Imitation learning. Trajectory optimization is a powerful and widely used algorithm in control, parameterized by a cost and a dynamics function. The key to our approach is to leverage the recent pro

Kien C. Huynh, Joseph S. B. Mitchell, Linh Nguyen, Valentin Polishchuk

Given a geometric domain $P$, visibility-based search problems seek routes for one or more mobile agents ("watchmen") to move within $P$ in order to be able to see a portion (or all) of $P$, while optimizing objectives, such as the length(s) of the route(s), the size (e.g., area or volume) of the portion seen, the probability of detecting a target distribute

Yuting Feng, Puragra Guhathakurta, Eric W. Peng, Stephen D. J. Gwyn

RR Lyrae stars are standard candles with characteristic photometric variability and serve as powerful tracers of Galactic structure, substructure, accretion history, and dark matter content. Here we report the discovery of distant RR Lyrae stars, including some of the most distant stars known in the Milky Way halo, with Galactocentric distances of approximat

Vaibhav Khatavkar, Makarand Velankar, Sneha Petkar

Captcha are widely used to secure systems from automatic responses by distinguishing computer responses from human responses. Text, audio, video, picture picture-based Optical Character Recognition (OCR) are used for creating captcha. Text-based OCR captcha are the most often used captcha which faces issues namely, complex and distorted contents. There are a

Varsha Subramanyan, Arinnia Anto, Moitrayee Bhattacharyya, Smitha Vishveshwara

We investigate connectivity properties of the SARS-CoV2 spike protein-human ACE2-receptor complex employing a protein side chain-based network method that allows us to span a range from atomic to global protein scales. We analyze network topology in terms of clusters and cliques obtained from averaging over snapshots of MD simulations (from D.E. Shaw Researc

Jiulin Wang, Xu Shi, Rujun Jiang

This paper studies a class of simple bilevel optimization problems where we minimize a composite convex function at the upper-level subject to a composite convex lower-level problem. Existing methods either provide asymptotic guarantees for the upper-level objective or attain slow sublinear convergence rates. We propose a bisection algorithm to find a soluti

Aditya N. Singh, David T. Limmer

The committor constitutes the primary quantity of interest within chemical kinetics as it is understood to encode the ideal reaction coordinate for a rare reactive event. We show the generative utility of the committor, in that it can be used explicitly to produce a reactive trajectory ensemble that exhibits numerically exact statistics as that of the origin

Erhan Bayraktar, Hongyi Zhou

We consider the graphon mean-field system introduced in the work of Bayraktar, Chakraborty, and Wu. It is the large-population limit of a heterogeneously interacting diffusive particle system, where the interaction is of mean-field type with weights characterized by an underlying graphon function. Through observation of continuous-time trajectories within th

Ze Hu, Ka Wing Chan, Ziqing Zhu, Xiang Wei

The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the operational optimization of ICES is hindered by the physical constraints of heterogeneous networks including electricity, natural gas, and heat. These chall

Aron C. Wall, Zihan Yan

Expanding the work of arXiv:1504.08040, we show that black holes obey a second law for linear perturbations to bifurcate Killing horizons, in any covariant higher curvature gravity coupled to scalar and vector fields. The vector fields do not need to be gauged, and (like the scalars) can have arbitrary non-minimal couplings to the metric. The increasing entr

Qianchen Mao, Qiang Li, Bingshu Wang, Yongjun Zhang

In recent years, the detection of infrared small targets using deep learning methods has garnered substantial attention due to notable advancements. To improve the detection capability of small targets, these methods commonly maintain a pathway that preserves high-resolution features of sparse and tiny targets. However, it can result in redundant and expensi

Yuta Hirasaki, Shunsuke Daimon, Naoki Kanazawa, Toshinari Itoko

We have investigated temporal fluctuation of superconducting qubits via the time-resolved measurement for an IBM Quantum system. We found that the qubit error rate abruptly changes during specific time intervals. Each high error state persists for several tens of seconds, and exhibits an on-off behavior. The observed temporal instability can be attributed to

Dewei Zhou, You Li, Fan Ma, Xiaoting Zhang

We present a Multi-Instance Generation (MIG) task, simultaneously generating multiple instances with diverse controls in one image. Given a set of predefined coordinates and their corresponding descriptions, the task is to ensure that generated instances are accurately at the designated locations and that all instances' attributes adhere to their correspondi

Xinyi Hu, Nikolaos Pappas, Howard H. Yang

Federated Learning (FL) has emerged as a privacy-preserving machine learning paradigm facilitating collaborative training across multiple clients without sharing local data. Despite advancements in edge device capabilities, communication bottlenecks present challenges in aggregating a large number of clients; only a portion of the clients can update their pa

Steven Kolawole, Lucio Dery, Jean-François Kagy, Virginia Smith

Structured pruning is a promising approach to create smaller, faster large language models. However, existing methods typically rely on computing the gradient via backward passes, which can inflate memory requirements and compute costs. In this work we introduce Bonsai, a gradient-free structured pruning method that eliminates the need for backpropagation, s

Di Zhang, Fangxin She, Jiaxiang Chen, Li Wei

Single-atom catalysts (SACs) with metal-nitrogen-carbon (M-N-C) structures are widely recognized as promising candidates in oxygen reduction reactions (ORR). According to the classical Sabatier principle, optimal 3d metal catalysts, such as Fe/Co-N-C, achieve superior catalytic performance due to the moderate binding strength. However, the substantial ORR ac

Chen Wang, Junchi Ma, Chao Wang, Defu Lin

Finite cellular automata (FCA) are widely used in simulating nonlinear complex systems, and their reversibility is closely related to information loss during the evolution. However, only a relatively small portion of their reversibility problems has been solved. In this paper, we perform calculations on two graphs and discover that the reversibility of any o

Hongyi Chen, Jingtao Ding, Yong Li, Yue Wang

Crowd simulation holds crucial applications in various domains, such as urban planning, architectural design, and traffic arrangement. In recent years, physics-informed machine learning methods have achieved state-of-the-art performance in crowd simulation but fail to model the heterogeneity and multi-modality of human movement comprehensively. In this paper

Tianjun Zhang, Aman Madaan, Luyu Gao, Steven Zheng

In-context learning (ICL, also known as few-shot prompting) has been the standard method of adapting LLMs to downstream tasks, by learning from a few input-output examples. Nonetheless, all ICL-based approaches only learn from correct input-output pairs. In this paper, we revisit this paradigm, by learning more from the few given input-output examples. We in

Yonghwi Kim, Hyung-Joo Moon, Hanju Yoo, Byoungnam

Full-duplex (FD) technology is gaining popularity for integration into a wide range of wireless networks due to its demonstrated potential in recent studies. In contrast to half-duplex (HD) technology, the implementation of FD in networks necessitates considering inter-node interference (INI) from various network perspectives. When deploying FD technology in

Farhad Pourkamali-Anaraki, Tahamina Nasrin, Robert E. Jensen, Amy M. Peterson

A pivotal aspect in the design of neural networks lies in selecting activation functions, crucial for introducing nonlinear structures that capture intricate input-output patterns. While the effectiveness of adaptive or trainable activation functions has been studied in domains with ample data, like image classification problems, significant gaps persist in

Kelsey Lieberman, Shuai Yuan, Swarna Kamlam Ravindran, Carlo Tomasi

Although binary classification is a well-studied problem in computer vision, training reliable classifiers under severe class imbalance remains a challenging problem. Recent work has proposed techniques that mitigate the effects of training under imbalance by modifying the loss functions or optimization methods. While this work has led to significant improve

Md Abir Hossen, Sonam Kharade, Jason M. O'Kane, Bradley Schmerl

Robotic systems are typically composed of various subsystems, such as localization and navigation, each encompassing numerous configurable components (e.g., selecting different planning algorithms). Once an algorithm has been selected for a component, its associated configuration options must be set to the appropriate values. Configuration options across the

Ritambhara Singh, Abhishek Jain, Pietro Perona, Shivani Agarwal

High-resolution semantic segmentation requires substantial computational resources. Traditional approaches in the field typically downscale the input images before processing and then upscale the low-resolution outputs back to their original dimensions. While this strategy effectively identifies broad regions, it often misses finer details. In this study, we

Zi-Qiang Chen, Long-Bin Chen, Cong-Feng Qiao

The $B_c$ meson pair, including pairs of pseudoscalar states and vector states, productions in proton-proton collisions are investigated at the next-to-leading order (NLO) accuracy in the nonrelativistic quantum chromodynamics factorization formalism. The corresponding cross sections at the Large Hadron Collider (LHC) with $\sqrt{s}=14\; \text{TeV}$ are eval

Gangda Deng, Hongkuan Zhou, Hanqing Zeng, Yinglong Xia

Recently, Temporal Graph Neural Networks (TGNNs) have demonstrated state-of-the-art performance in various high-impact applications, including fraud detection and content recommendation. Despite the success of TGNNs, they are prone to the prevalent noise found in real-world dynamic graphs like time-deprecated links and skewed interaction distribution. The no

Changyu Liu, Wen Su, Kin-Yat Liu, Guosheng Yin

We propose a functional accelerated failure time model to characterize effects of both functional and scalar covariates on the time to event of interest, and provide regularity conditions to guarantee model identifiability. For efficient estimation of model parameters, we develop a sieve maximum likelihood approach where parametric and nonparametric coeffici

Mingjie Wang, Jun Zhou, Yong Dai, Eric Buys

Recently, Class-Agnostic Counting (CAC) problem has garnered increasing attention owing to its intriguing generality and superior efficiency compared to Category-Specific Counting (CSC). This paper proposes a novel ExpressCount to enhance zero-shot object counting by delving deeply into language-guided exemplar learning. Specifically, the ExpressCount is com

Indranil Ghosh, Robert I. McLachlan, David J. W. Simpson

We study two-dimensional, two-piece, piecewise-linear maps having two saddle fixed points. Such maps reduce to a four-parameter family and are well known to have a chaotic attractor throughout open regions of parameter space. The purpose of this paper is to determine where and how this attractor undergoes bifurcations. We explore the bifurcation structure nu

Minyong Jung, Santi Roca-Fàbrega, Ji-hoon Kim, Anna Genina

We analyze and compare the satellite halo populations at $z\sim2$ in the high-resolution cosmological zoom-in simulations of a $10^{12}\,{\rm M}_{\odot}$ target halo ($z=0$ mass) carried out on eight widely-used astrophysical simulation codes ({\sc Art-I}, {\sc Enzo}, {\sc Ramses}, {\sc Changa}, {\sc Gadget-3}, {\sc Gear}, {\sc Arepo-t}, and {\sc Gizmo}) for

Zhuo Chen, Yichi Zhang, Yin Fang, Yuxia Geng

Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for innovation. In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs suppor

Zhiqing Wei, Yucong Du, Qixun Zhang, Wangjun Jiang

Intelligent machines (IMs), including industrial machines, unmanned aerial vehicles (UAVs), and unmanned vehicles, etc., could perform effective cooperation in complex environment when they form IM network. The efficient environment sensing and communication are crucial for IM network, enabling the real-time and stable control of IMs. With the emergence of i

Ling-Fang Lin, Yang Zhang, Gonzalo Alvarez, Adriana Moreo

Quasi-one-dimensional correlated electronic multi-orbital systems with either ladder or chain geometries continue attracting considerable interest due to their complex electronic phases arising from the interplay of the hopping matrix, the crystal-fields splitting, the electronic correlations, and strong quantum fluctuations. Recently, the intriguing cobalt

Amy X. Zhang, Michael S. Bernstein, David R. Karger, Mark S. Ackerman

Social media systems are as varied as they are pervasive. They have been almost universally adopted for a broad range of purposes including work, entertainment, activism, and decision making. As a result, they have also diversified, with many distinct designs differing in content type, organization, delivery mechanism, access control, and many other dimensio

Y. Qiu, D. W, Y. Zeng

This letter considers a two-panel massive multiple-input multiple-output (MIMO) communication system, where the base station (BS) is equipped with two antenna panels that may use different frequency bands for communication. By exploiting the geometric relationships between antenna panels, efficient channel inference methods across antenna panels are proposed

Chih-Teng Ling, Tomotsugu Goto, Seong Jin Kim, Cossas K. -W. Wu

Mid-infrared (MIR) light from galaxies is sensitive to dust-obscured star-formation activities because it traces the characteristic emission of dust heated by young, massive stars. By constructing the MIR luminosity functions (LFs), we are able to quantify the overall dusty star formation history and the evolution of galaxies over cosmic time. In this work,

Tianyu Wang, Zicheng Wang, Jiajia Yu

A zeroth-order Hessian estimator aims to recover the Hessian matrix of an objective function at any given point, using minimal finite-difference computations. This paper studies zeroth-order Hessian estimation for low-rank Hessians, from a matrix recovery perspective. Our challenge lies in the fact that traditional matrix recovery techniques are not directly

Jiawei Shan, Wei Li, Chunrong Ai

Mediation analysis is widely used for exploring treatment mechanisms; however, it faces challenges when nonignorable missing confounders are present. Efficient inference of mediation effects and the efficiency loss due to nonignorable missingness have been rarely studied in the literature because of the difficulties arising from the ill-posed inverse problem

Daya Bay Collaboration, F. P. An, W. D. Bai, A. B. Balantekin

Daya Bay presents the first measurement of cosmogenic $^8$He isotope production in liquid scintillator, using an innovative method for identifying cascade decays of $^8$He and its child isotope, $^8$Li. We also measure the production yield of $^9$Li isotopes using well-established methodology. The results, in units of 10$^{-8}\mu^{-1}$g$^{-1}$cm$^{2}$, are 0

Zhili Liu, Kai Chen, Jianhua Han, Lanqing Hong

Masked Autoencoder~(MAE) is a prevailing self-supervised learning method that achieves promising results in model pre-training. However, when the various downstream tasks have data distributions different from the pre-training data, the semantically irrelevant pre-training information might result in negative transfer, impeding MAE's scalability. To address

Shiyu Wang, Yihao Feng, Tian Lan, Ning Yu

Natural language serves as a common and straightforward signal for humans to interact seamlessly with machines. Recognizing the importance of this interface, the machine learning community is investing considerable effort in generating data that is semantically coherent with textual instructions. While strides have been made in text-to-data generation spanni

J. Gonzalez-Ayala, F. Angulo-Brown, A. Calvo Hernández, S. Velasco

In this work we analyze the deviations of reversible cycles (for both heat engines and refrigerators) from the corresponding Carnot cycle operating between the same extreme temperatures and deviations of irreversible cycles from its corresponding reversible realization while putting emphasis on the corresponding losses. The endoreversible models fit in the p

Jamie Simpson

A palindromic periodicity is a factor of an infinite word $(ps)^\omega$ where $p$ and $s$ are palindromes and the factor has length at least $|ps|$, for example, $accabaccab$. In this paper we describe several ways in which a palindromic periodicity may arise through the interaction of palindromes and periodicity, the simplest case being when a palindrome is

Jia Choon Lee, Sukjoo Lee

We prove that the pure part of the cohomology ring of the moduli space of irregular $\underline{\xi}$-parabolic Higgs bundles is generated by the K\"{u}nneth components of the Chern classes of a universal bundle and the Chern classes of the successive quotients of a universal flag of subbundles. As an application, in the regular full-flag case, we demonstrat

Alexander Soen, Ke Sun

The Fisher information matrix can be used to characterize the local geometry of the parameter space of neural networks. It elucidates insightful theories and useful tools to understand and optimize neural networks. Given its high computational cost, practitioners often use random estimators and evaluate only the diagonal entries. We examine two popular estim

Tharaka Perera, Saman Atapattu, Yuting Fang, Jamie Evans

This paper explores Physical-Layer Security (PLS) in Flexible Duplex (FlexD) networks, considering scenarios involving eavesdroppers. Our investigation revolves around the intricacies of the sum secrecy rate maximization problem, particularly when faced with coordinated and distributed eavesdroppers employing a Minimum Mean Square Error (MMSE) receiver. Our

Guanbo Wang, Xingpeng Di

Background Urinary incontinence (UI) is a common health problem that affects the life and health quality of millions of people in the US. We aimed to investigate the association between sitting time and UI. Methods Across-sectional survey of adult participants of National Health and Nutrition Examination Survey 2007-2018 was performed. Weighted multivariable

Feihu Jin, Yifan Liu, Ying Tan

Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks and exhibited impressive reasoning abilities by applying zero-shot Chain-of-Thought (CoT) prompting. However, due to the evolving nature of sentence prefixes during the pre-training phase, existing zero-shot CoT prompting methods that employ identical CoT prompting acr

Senmao Li, Joost van de Weijer, Taihang Hu, Fahad Shahbaz Khan

The success of recent text-to-image diffusion models is largely due to their capacity to be guided by a complex text prompt, which enables users to precisely describe the desired content. However, these models struggle to effectively suppress the generation of undesired content, which is explicitly requested to be omitted from the generated image in the prom

Youngsik Yun, Jihie Kim

Image Captioning generates descriptive sentences from images using Vision-Language Pre-trained models (VLPs) such as BLIP, which has improved greatly. However, current methods lack the generation of detailed descriptive captions for the cultural elements depicted in the images, such as the traditional clothing worn by people from Asian cultural groups. In th

Mingxin Liu, Yunzan Liu, Pengbo Xu, Jiquan Ma

The histopathology analysis is of great significance for the diagnosis and prognosis of cancers, however, it has great challenges due to the enormous heterogeneity of gigapixel whole slide images (WSIs) and the intricate representation of pathological features. However, recent methods have not adequately exploited geometrical representation in WSIs which is

Rakesh Halder, Mohammadmehdi Ataei, Hesam Salehipour, Krzysztof Fidkowski

The use of deep learning has become increasingly popular in reduced-order models (ROMs) to obtain low-dimensional representations of full-order models. Convolutional autoencoders (CAEs) are often used to this end as they are adept at handling data that are spatially distributed, including solutions to partial differential equations. When applied to unsteady

Pierre Schumacher, Lorenz Krause, Jan Schneider, Dieter Büchler

Recent studies have demonstrated the immense potential of exploiting muscle actuator morphology for natural and robust movement -- in simulation. A validation on real robotic hardware is yet missing. In this study, we emulate muscle actuator properties on hardware in real-time, taking advantage of modern and affordable electric motors. We demonstrate that ou

PeiSong Niu, Tian Zhou, Xue Wang, Liang Sun

Time series forecasting is essential for many practical applications, with the adoption of transformer-based models on the rise due to their impressive performance in NLP and CV. Transformers' key feature, the attention mechanism, dynamically fusing embeddings to enhance data representation, often relegating attention weights to a byproduct role. Yet, time s

Huayu Chen, Guande He, Lifan Yuan, Ganqu Cui

User intentions are typically formalized as evaluation rewards to be maximized when fine-tuning language models (LMs). Existing alignment methods, such as Direct Preference Optimization (DPO), are mainly tailored for pairwise preference data where rewards are implicitly defined rather than explicitly given. In this paper, we introduce a general framework for

Sanjukta Krishnagopal, Mason A. Porter

We generalize bounded-confidence models (BCMs) of opinion dynamics by incorporating neighborhood effects. In a BCM, interacting agents influence each other through dyadic influence if their opinions are sufficiently similar to each other. In our "neighborhood BCMs" (NBCMs), interacting agents are influenced both by each other's opinions and by the opinions o

Wenjie Xu, Wenbin Wang, Yuning Jiang, Bratislav Svetozarevic

We study the problem of preferential Bayesian optimization (BO), where we aim to optimize a black-box function with only preference feedback over a pair of candidate solutions. Inspired by the likelihood ratio idea, we construct a confidence set of the black-box function using only the preference feedback. An optimistic algorithm with an efficient computatio

Xiaoqing Wang, Nick Scholand, Zhengguo Tan, Daniel Mackner

Purpose: To develop a model-based nonlinear reconstruction for simultaneous water-specific $T_{1}$, $R_{2}^{*}$, $B_{0}$ field and/or fat fraction (FF) mapping using single-shot inversion-recovery (IR) multi-echo radial FLASH. Methods: The proposed model-based reconstruction jointly estimates water-specific $T_{1}$, $R_{2}^{*}$, $B_{0}$ field and/or FF maps,

XueGuang Zhang

In this manuscript, clues are provided to support globally negative AGN feedback on star formation in the host galaxies of the local low-redshift SDSS Type-2 AGN, based on the different dependence of narrow H$\alpha$ line luminosity $L_{H\alpha}$ on optical continuum luminosity $\lambda L_{cont}$ between the starforming galaxies and the Type-2 AGN. Through t

M. Mijaíl Martínez-Ramos, Parisa Majari, Andres R. Cruz-Hernández, Hirdesh K. Pharasi

We analyze correlation structures in financial markets by coarse graining the Pearson correlation matrices according to market sectors to obtain Guhr matrices using Guhr's correlation method according to Ref. [P. Rinn {\it et. al.}, Europhysics Letters 110, 68003 (2015)]. We compare the results for the evolution of market states and the corresponding transit

Toshiyuki Kobayashi, Michael Pevzner

Motivated by the concept of "generating operators" for a countable family of operators introduced in the recent paper (arXiv:2306.16800), we find a method to reconstruct the Rankin--Cohen brackets from a very simple multivariable contour integral, and obtain a new proof of their covariance. We also establish a closed formula of the "generating operator" for

José Fernando Thuorst, Luciana Ebani, Thalis José Girardi

This study seeks a better comprehension of anomalies by exploring (n+1)-point perturbative amplitudes in a 2n-dimensional framework. The involved structures combine axial and vector vertices into odd tensors. This configuration enables diverse expressions, considered identities at the integrand level. However, connecting them is not automatic after loop inte

Kentaro Kubo, Yinghao Ho, Hayato Goto

Tunable couplers in superconducting quantum computers have enabled fast and accurate two-qubit gates, with reported high fidelities over 99% in various architectures and gate implementation schemes. However, there are few tunable couplers whose performance in multi-qubit systems is clarified, except for the most widely used one: single-transmon coupler (STC)

Anirban Sen, Riddhick Birbonshi, Kallol Paul

In this paper we explore the relation between the $A$-numerical range and the $A$-spectrum of $A$-bounded operators in the setting of semi-Hilbertian structure. We introduce a new definition of $A$-normal operator and prove that closure of the $A$-numerical range of an $A$-normal operator is the convex hull of the $A$-spectrum. We further prove Anderson's th

Yizhou Zhang, Lun Du, Defu Cao, Qiang Fu

Foundation models, such as Large language Models (LLMs), have attracted significant amount of interest due to their large number of applications. However, when handling tasks involving repetitive sub-tasks and/or deceptive contents, such as arithmetic calculation and article-level fake news detection, simple instructional prompts suffer from inaccurate respo

Minxi He, Muzi Hong, Kyohei Mukaida

We show that various types of scalaron-induced inflation, including the Starobinsky inflation, can be realized in the Einstein-Cartan gravity with the Nieh-Yan term and/or the Holst term. Einstein-Cartan $f(R)$ theory is known not to induce an additional scalar degree of freedom, the scalaron, contrary to the case in the metric formalism. However, there exis

Timothy M. Chan, Zhengcheng Huang

We present the first fully dynamic connectivity data structures for geometric intersection graphs achieving constant query time and sublinear amortized update time for most types of geometric objects in 2D. Our data structures can answer connectivity queries between two objects, as well as "global" connectivity queries (e.g., deciding whether the entire grap

Wenyu Jiang, Zhenlong Liu, Zejian Xie, Songxin Zhang

The ever-increasing fine-tuning cost of large-scale pre-trained models gives rise to the importance of dataset pruning, which aims to reduce dataset size while maintaining task performance. However, existing dataset pruning methods require training on the entire dataset, which is impractical for large-scale pre-trained models. In this paper, we propose a str

Tianyi Zhao, Liangliang Zhang, Yao Ma, Lu Cheng

In the rapidly evolving landscape of artificial intelligence, multimodal learning systems (MMLS) have gained traction for their ability to process and integrate information from diverse modality inputs. Their expanding use in vital sectors such as healthcare has made safety assurance a critical concern. However, the absence of systematic research into their

Xiao-Yun Wang, Jingxuan Bu

In this work, under the framework of vector meson dominance model, the trace anomaly contribution value inside neutrons are extracted for the first time based on vector meson photoproduction data. Furthermore, we systematically compare and analyze the trace anomaly contributions of protons and neutrons. The results show that the trace anomaly contributions o

Taehyeon Kim, Donggyu Kim, Se-Young Yun

In the evolving landscape of federated learning (FL), addressing label noise presents unique challenges due to the decentralized and diverse nature of data collection across clients. Traditional centralized learning approaches to mitigate label noise are constrained in FL by privacy concerns and the heterogeneity of client data. This paper revisits early-lea

Dustin Keys, Jan Wehr

Unravelings provide a probabilistic representation of solutions of master equations and a method of computation of the density operator dynamics. The trajectories generated by unravelings may also be treated as real -- as in the stochastic collapse models. While averages of linear functionals of the unraveling trajectories can be calculated from the master e

Xiaoquan Yu, P. B. Blakie

We investigate the dynamical stability and real time dynamics of the two-types of ferrodark solitons (FDSs) which occur as topological magnetic domain walls in the easy-plane phase of a quasi-two-dimensional (2D) ferromagnetic spin-1 Bose-Einstein condensate. The type-I FDS has positive inertial mass and exhibits a single dynamical instability that generates

Junghun Cha, Ali Haider, Seoyun Yang, Hoeyeong Jin

A significant volume of analog information, i.e., documents and images, have been digitized in the form of scanned copies for storing, sharing, and/or analyzing in the digital world. However, the quality of such contents is severely degraded by various distortions caused by printing, storing, and scanning processes in the physical world. Although restoring h

Mateo Lostanlen, Nicolas Isla, Jose Guillen, Renzo Zanca

Early wildfire detection (EWD) is of the utmost importance to enable rapid response efforts, and thus minimize the negative impacts of wildfire spreads. To this end, we present PYRONEAR-2025, a new dataset composed of both images and videos, allowing for the training and evaluation of smoke plume detection models, including sequential models. The data is sou

MD Romael Haque, Devansh Saxena, Katy Weathington, Joseph Chudzik

Research into recidivism risk prediction in the criminal legal system has garnered significant attention from HCI, critical algorithm studies, and the emerging field of human-AI decision-making. This study focuses on algorithmic crime mapping, a prevalent yet underexplored form of algorithmic decision support (ADS) in this context. We conducted experiments a

Daniel Appelö, Yingda Cheng

In this work, we develop implicit rank-adaptive schemes for time-dependent matrix differential equations. The dynamic low rank approximation (DLRA) is a well-known technique to capture the dynamic low rank structure based on Dirac-Frenkel time-dependent variational principle. In recent years, it has attracted a lot of attention due to its wide applicability.

Arun Kumar, Paul Schrater

Interactions are central to intelligent reasoning and learning abilities, with the interpretation of abstract knowledge guiding meaningful interaction with objects in the environment. While humans readily adapt to novel situations by leveraging abstract knowledge acquired over time, artificial intelligence systems lack principled mechanisms for incorporating

Callie E. Hood, Sagnick Mukherjee, Jonathan J. Fortney, Michael R. Line

We present observations of the T8 dwarf 2MASS 0415-0935 with JWST's NIRSpec spectrograph using the G395H grating ($\sim$ 2.87 - 5.14 $\mu$m). We perform the first atmospheric retrieval analysis at the maximum spectral resolution of NIRSpec (R$\sim$2700) and combine the spectrum with previous observations to study the 0.9-20 $\mu$m spectral energy distributio

Yassine Sekhmani, A. A. Araújo Filho, Ratbay Myrzakulov, Adam Z. Kaczmarek

This paper investigates the contribution of the nonsingular Yukawa-modified potential in the context of four-dimensional Einstein-Gauss-Bonnet (EGB) gravity modeling by a static and spherically symmetric black hole solution. These Yukawa-type corrections are essentially described along two parameters, $\beta$ and $\lambda$, affecting Newton's law of gravity

Minjoon Kim, Jinsu Kim

In this paper, we present criteria for non-exponential ergodicity of continuous-time Markov chains on a countable state space. These criteria can be verified by examining the ratio of transition rates over certain paths. We applied this path method to explore the non-exponential convergence of microscopic biochemical interacting systems. Using reaction netwo

Hsin-Hsiung Huang, Qing He

Nonlinear regression analysis is a popular and important tool for scientists and engineers. In this article, we introduce theories and methods of nonlinear regression and its statistical inferences using the frequentist and Bayesian statistical modeling and computation. Least squares with the Gauss-Newton method is the most widely used approach to parameters

María de los Santos Bruzón, Almudena del Pilar Márquez, Tamara María Garrido, Elena Recio

In this paper, by applying the multiplier method we obtain a complete classification of low-order local conservation laws for a generalized seventh-order KdV equation depending on seven arbitrary nonzero parameters. We apply the Lie method in order to classify all point symmetries admitted by the equation in terms of the arbitrary parameters. We find that th

Maria Teresa Baldassarre, Domenico Gigante, Marcos Kalinowski, Azzurra Ragone

In the ever-expanding landscape of Artificial Intelligence (AI), where innovation thrives and new products and services are continuously being delivered, ensuring that AI systems are designed and developed responsibly throughout their entire lifecycle is crucial. To this end, several AI ethics principles and guidelines have been issued to which AI systems sh

Allysson Allex Araújo, Marcos Kalinowski, Maria Teresa Baldassarre

[Background] Hackathons are increasingly gaining prominence in Software Engineering (SE) education, lauded for their ability to elevate students' skill sets. [Objective] This paper investigates whether hackathons can impact the motivation of SE students. [Method] We conducted an evaluative case study assessing students' motivations before and after a hackath

Jim Fuller, Tristan Guillot, Stephane Mathis, Carl Murray

Tidal interactions between moons and planets can have major effects on the orbits, spins, and thermal evolution of the moons. In the Saturn system, tidal dissipation in the planet transfers angular momentum from Saturn to the moons, causing them to migrate outwards. The rate of migration is determined by the mechanism of dissipation within the planet, which

Raphael Cabral, Marcos Kalinowski, Maria Teresa Baldassarre, Hugo Villamizar

[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly hold for Machine Learning (ML) projects, which involve iterative experimentation with data, models, and algorithms. However, ML components are often developed by data scientists wit

Yu Zhu, Zehang Richard Li, Yang Su, Zhenyu Zhao

The evolving landscape of online multiplayer gaming presents unique challenges in assessing the causal impacts of game features. Traditional A/B testing methodologies fall short due to complex player interactions, leading to violations of fundamental assumptions like the Stable Unit Treatment Value Assumption (SUTVA). Unlike traditional social networks with

Gabriel Haeser, Daiana Oliveira dos Santos

In the seminal book M\'echanique analitique, Lagrange, 1788, the notion of a Lagrange multiplier was first introduced in order to study a smooth minimization problem subject to equality constraints. The idea is that, under some regularity assumption, at a solution of the problem, one may associate a new variable (Lagrange multiplier) to each constraint such