February 2024 arXiv papers — page 8

Klaus Werner, Helge Todt, Howard E. Bond, Gregory R. Zeimann

We present spectroscopy of three hydrogen-deficient central stars of faint planetary nebulae, with effective temperatures ($T_\mathrm{eff}$) in excess of 100,000 K. The nucleus of RaMul 2 is a Population II Wolf-Rayet star of spectral type [WC], and the central stars of Abell 25 and StDr 138 are two new members of the PG1159 class. Our spectral analyses reve

Zi-Kai Xiao, Guo-Ye Yang, Xue Yang, Tai-Jiang Mu

Considerable efforts have been devoted to Oriented Object Detection (OOD). However, one lasting issue regarding the discontinuity in Oriented Bounding Box (OBB) representation remains unresolved, which is an inherent bottleneck for extant OOD methods. This paper endeavors to completely solve this issue in a theoretically guaranteed manner and puts an end to

Giorgia Minello, Alessandro Bicciato, Luca Rossi, Andrea Torsello

In this paper, we present GGSD, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process. Specifically, we propose to use a denoising model to sample eigenvectors and eigenvalues from which we can reconstruct the graph Laplacian and adjacency matrix. Using the Laplacian spectrum allows us

Ana-Maria Comeaga, Iuliana Marin

In today's life, more and more people tend to opt for a smart house. In this way, the idea of including technology has become popular worldwide. Despite this concept's many benefits, managing security remains an essential problem due to the shared activities. The Internet of Things system behind a smart house is based on several sensors to measure temperatur

V. V. Ryazanov

A multifractal model of neutron evolution in a reactor is considered. For chain reactions, the dimension of the multifractal carrier, information and correlation dimensions, the entropy of the fractal set, the maximum and minimum values of the dimension, the multifractal spectrum function and other characteristics of multifractal neutron behavior are found.

Giacomo Della Posta, Emanuele Martelli, Francesco Salvadore, Matteo Bernardini

Microvortex generators (MVGs) are a promising solution to control shock wave/turbulent boundary layer interactions (SBLIs). This study examines the effects of a microramp VG on an SBLI generated by an oblique shock wave and a turbulent boundary layer using direct numerical simulations (DNSs). Two cases, with and without MVGs, are compared at free-stream Mach

Mayar Elfares, Pascal Reisert, Zhiming Hu, Wenwu Tang

Latest gaze estimation methods require large-scale training data but their collection and exchange pose significant privacy risks. We propose PrivatEyes - the first privacy-enhancing training approach for appearance-based gaze estimation based on federated learning (FL) and secure multi-party computation (MPC). PrivatEyes enables training gaze estimators on

Xiaozheng Zheng, Chao Wen, Zhuo Su, Zeran Xu

In this paper, we delve into the creation of one-shot hand avatars, attaining high-fidelity and drivable hand representations swiftly from a single image. With the burgeoning domains of the digital human, the need for quick and personalized hand avatar creation has become increasingly critical. Existing techniques typically require extensive input data and m

Bar Shaybet, Anurag Kumar, Vladimir Tourbabin, Boaz Rafaely

Ambisonics, a popular format of spatial audio, is the spherical harmonic (SH) representation of the plane wave density function of a sound field. Many algorithms operate in the SH domain and utilize the Ambisonics as their input signal. The process of encoding Ambisonics from a spherical microphone array involves dividing by the radial functions, which may a

Anna Chiara Alfano, Salvatore Capozziello, Orlando Luongo, Marco Muccino

The redshift $z_t$ and the jerk parameter $j_t$ of the transition epoch are constrained by using two model-independent approaches involving the direct expansion of the Hubble rate and the expansion of the deceleration parameter around $z=z_t$. To extend our analysis to high-redshifts, we employ the \emph{Amati}, \emph{Combo}, \emph{Yonetoku} and \emph{Dainot

André Kowacs, Michael Ruzhansky

We prove sufficient conditions in order to obtain a sharp G\aa rding inequality for pseudo-differential operators acting on vector-valued functions on compact Lie groups. As a consequence, we obtain a sharp G\aa rding inequality for compact homogeneous vector bundles and compact homogeneous manifolds. The sharp G\aa rding inequality is the strongest lower bo

Guillem Domènech, Shi Pi, Ao Wang, Jianing Wang

We thoroughly study the induced gravitational wave interpretation of the possible gravitational wave background reported by PTA collaborations, considering the unknown equation of state $w$ of the early universe. We perform a Bayesian analysis of the NANOGrav data using the publicly available \textsc{PTArcade} code together with \textsc{SIGWfast} for the num

K. O. Nikolaev, S. R. Lake, G. Schmidt, S. O. Demokritov

Generation of second-harmonic waves is one of the universal nonlinear phenomena that have found numerous technical applications in many modern technologies, in particular, in photonics. This phenomenon also has great potential in the field of magnonics, which considers the use of spin waves in magnetic nanostructures to implement wave-based signal processing

Ishmael N. Amartey, Andreas A. Linninger, Thomas Ventimiglia

Convolution and deconvolution are essential techniques in various fields, notably in medical imaging, where they play a crucial role in analyzing dynamic processes such as blood flow. This paper explores the convolution and deconvolution of arterial and microvascular signals for determining impulse and residue functions from in vivo or simulated data and the

Petra Hozzová, Laura Kovács, Chase Norman, Andrei Voronkov

We present an automated reasoning framework for synthesizing recursion-free programs using saturation-based theorem proving. Given a functional specification encoded as a first-order logical formula, we use a first-order theorem prover to both establish validity of this formula and discover program fragments satisfying the specification. As a result, when de

Libo Jiang, Daniel R. Terno, Oscar Dahlsten

We consider how to describe Hamiltonian mechanics in generalised probabilistic theories with the states represented as quasi-probability distributions. We give general operational definitions of energy-related concepts. We define generalised energy eigenstates as the purest stationary states. Planck's constant plays two different roles in the framework: the

Yungang Lu

Motivated by the creation-annihilation operators in a newly defined interacting Fock space, we initiate the introduction and the study of the Quon algebra. This algebra serves as an extension of the conventional quon algebra, where the traditional constant parameter $q$ found in the $q$--commutation relation is replaced by a specific operator. Importantly, o

Jennie Karlsson, Marisa Wodrich, Niels Christian Overgaard, Freja Sahlin

Deep learning has shown to have great potential in medical applications. In critical domains as such, it is of high interest to have trustworthy algorithms which are able to tell when reliable assessments cannot be guaranteed. Detecting out-of-distribution (OOD) samples is a crucial step towards building a safe classifier. Following a previous study, showing

Zhuangwei Shi

The stock market plays a pivotal role in economic development, yet its intricate volatility poses challenges for investors. Consequently, research and accurate predictions of stock price movements are crucial for mitigating risks. Traditional time series models fall short in capturing nonlinearity, leading to unsatisfactory stock predictions. This limitation

Boxuan Zhang, Zengmao Wang, Bo Du

The lack of object-level annotations poses a significant challenge for object detection in remote sensing images (RSIs). To address this issue, active learning (AL) and semi-supervised learning (SSL) techniques have been proposed to enhance the quality and quantity of annotations. AL focuses on selecting the most informative samples for annotation, while SSL

K. Ye, M. Menahem, T. Salzillo, F. Knoop

We report a comparative study of temperature-dependent photoluminescence and structural dynamics of two perovskite semiconductors, the chalcogenide BaZrS$_3$ (BZS) and the halide CsPbBr$_3$ (CPB). These materials have similar crystal structures and direct band gaps, but we find that they have quite distinct optoelectronic and vibrational properties. Both mat

Yong Hyun Ahn, Hyeon Bae Kim, Seong Tae Kim

Recent advancements in neural networks have showcased their remarkable capabilities across various domains. Despite these successes, the "black box" problem still remains. Addressing this, we propose a novel framework, WWW, that offers the 'what', 'where', and 'why' of the neural network decisions in human-understandable terms. Specifically, WWW utilizes ada

Dmitrii Pavlov, Simon Telen

The Santal\'o point of a convex polytope is the interior point which leads to a polar dual of minimal volume. This minimization problem is relevant in interior point methods for convex optimization, where the logarithm of the dual volume is known as the universal barrier function. When translating the facet hyperplanes, the Santal\'o point traces out a semi-

Márton Hajdu, Petra Hozzová, Laura Kovács, Giles Reger

Induction in saturation-based first-order theorem proving is a new exciting direction in the automation of inductive reasoning. In this paper we survey our work on integrating induction directly into the saturation-based proof search framework of first-order theorem proving. We describe our induction inference rules proving properties with inductively define

Kevin Lively, Tim Bode, Jochen Szangolies, Jian-Xin Zhu

Quantum computing allows for the manipulation of highly correlated states whose properties quickly go beyond the capacity of any classical method to calculate. Thus one natural problem which could lend itself to quantum advantage is the study of ground-states of condensed matter models, and the transitions between them. However, current levels of hardware no

Sin-Ei Takahasi, Kiyoshi Shirayanagi, Makoto Tsukada

In this paper, we present a complete classification of 2-dimensional endo-commutative straight algebras of type II$_1$ over any field. An endo-commutative algebra is a non-associative algebra in which the square mapping preserves multiplication. A 2-dimensional straight algebra satisfies the condition that there exists an element $x$ such that $x$ and $x^2$

David Dong

Let $A(n,m)$ denote the Eulerian numbers, which count the number of permutations on $[n]$ with exactly $m$ descents, or, due to the Foata transform, the number of permutations on $[n]$ with exactly $m$ excedances. Friends-and-seats graphs, also known as friends-and-strangers graphs, are a seemingly unrelated recent construction in graph theory. In this paper

Boyu Chen, Siran Chen, Kunchang Li, Qinglin Xu

Open-world video recognition is challenging since traditional networks are not generalized well on complex environment variations. Alternatively, foundation models with rich knowledge have recently shown their generalization power. However, how to apply such knowledge has not been fully explored for open-world video recognition. To this end, we propose a gen

Erxin Yu, Jing Li, Chunpu Xu

Social media platforms are daily exhibiting millions of events. To preliminarily predict the mainstream public reaction to these events, we study trendy response prediction to automatically generate top-liked user replies to social media events. While previous works focus on generating responses without factoring in popularity, we propose Popularity-Aligned

Zexi Li, Jie Lin, Zhiqi Li, Didi Zhu

Federated learning (FL) involves multiple heterogeneous clients collaboratively training a global model via iterative local updates and model fusion. The generalization of FL's global model has a large gap compared with centralized training, which is its bottleneck for broader applications. In this paper, we study and improve FL's generalization through a fu

Jonathan Bowden, Sebastian Hensel, Richard Webb

The fine curve graph was introduced as a geometric tool to study homeomorphisms of surfaces. In this paper we study the Gromov boundary of this space and the local topology near points associated with certain foliations and laminations. We then give several applications including finding dynamically explicit elements with positive stable commutator length, a

Duco van Buuren, Pallav Kant, Jochem G. Meijer, Christian Diddens

A uniform solidification front undergoes non-trivial deformations when encountering an insoluble dispersed particle in a melt. For solid particles, the overall deformation characteristics are primarily dictated by heat transfer between the particle and the surroundings, remaining unaffected by the rate of approach of the solidification front. In this Letter,

Yu Zhang, Long Wen, Xiangtong Yao, Zhenshan Bing

This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consists of two phases. The initial phase is centered on a novel sparse Gaussian process (GP) framework. We first integrate a forgetting factor to refine a variational sparse GP algorit

Pengzhou Cheng, Wei Du, Zongru Wu, Fengwei Zhang

Although pre-training achieves remarkable performance, it suffers from task-agnostic backdoor attacks due to vulnerabilities in data and training mechanisms. These attacks can transfer backdoors to various downstream tasks. In this paper, we introduce $\mathtt{maxEntropy}$, an entropy-based poisoning filter that mitigates such risks. To overcome the limitati

Shivani Kumar, Md Shad Akhtar, Erik Cambria, Tanmoy Chakraborty

We present SemEval-2024 Task 10, a shared task centred on identifying emotions and finding the rationale behind their flips within monolingual English and Hindi-English code-mixed dialogues. This task comprises three distinct subtasks - emotion recognition in conversation for code-mixed dialogues, emotion flip reasoning for code-mixed dialogues, and emotion

Yao Zhang, Zezhou Li, Xing Tong, Zhiheng Xie

Metal-oxide interfaces with poor coherency have unique properties comparing to the bulk materials and offer broad applications in the fields of heterogeneous catalysis, battery, and electronics. However, current understanding of the three-dimensional (3D) atomic metal-oxide interfaces remains limited because of their inherent structural complexity and limita

Adi Nusser

The evolution of halos with masses around $M_\textrm{h} \approx 10^{11}\; \textrm{M}_\odot$ and $M_\textrm{h} \approx 10^{12}\; \textrm{M}_\odot$ at redshifts $z>9$ is examined using constrained N-body simulations. {The average specific mass accretion rates, $\dot{M}_\textrm{h} / M_\textrm{h}$, exhibit minimal mass dependence and generally agree with existin

Milajiguli Rexiti, Samad Khabbazi Oskouei, Stefano Mancini

We consider discrete time feedback aimed at reclaiming quantum information after a channel action. We compare Bayesian and Markovian strategies. We show that the former does not offer any advantage for qubit channels, while its superior performance can appear in higher dimensional channels. This is witnessed by cases study for qutrit channels.

Xiao-Fan Xu, Cheng Xue, Xi-Ning Zhuang, Yun-Jie Wang

Achieving a practical quantum speedup for deep neural networks (DNNs) remains a central yet elusive goal, hindered by the dual challenges of constructing deep architectures and the prohibitive overhead of data loading and measurement. We introduce a framework to overcome these barriers, specifically targeting an asymptotic speedup with respect to the large i

Swati Bhardwaj, Leetika Kathuria, Madhu Raka

Let $Q(x_1, \cdots,x_n)$ be a real indefinite quadratic form of the type $(r,s)$, $n=r+s$, signature $\sigma=r-s$ and determinant $D\neq 0$. Let $\Gamma_{r,n-r}$ denote the infimum of all numbers $\Gamma$ such that for any real numbers $c_1, c_2 ,\cdots, c_n$ there exist integers $x_1, x_ 2,\cdots, x_n$ satisfying $$0< Q(x_1+c_1,x_2+c_2,\cdots,x_n+c_n)\leq (

A. Srinivasa Rao

Optical skyrmions formed in terms of polarization are topological quasi-particles and have garnered much interest in the optical community owing to their unique inhomogeneous polarization structure and simplicity in their experimental realization. These structures belong to the Poincar\'e beams satisfying the stable topology. We theoretically investigated th

Arpit Babbar, Praveen Chandrashekar

ADER (Arbitrary high order by DERivatives) and Lax-Wendroff (LW) schemes are two high order single stage methods for solving time dependent partial differential equations. ADER is based on solving a locally implicit equation to obtain a space-time predictor solution while LW is based on an explicit Taylor's expansion in time. We cast the corrector step of AD

Jialiuyuan Li, Jiayuan Chen, Changyan Yi, Tong Zhang

In this paper, the energy-efficient unmanned aerial vehicle (UAV) swarm assisted mobile edge computing (MEC) with dynamic clustering and scheduling is studied. In the considered system model, UAVs are divided into multiple swarms, with each swarm consisting of a leader UAV and several follower UAVs to provide computing services to end-users. Unlike existing

Menghan Tian, Baolei Liu, Zelin Lu, Yao Wang

Miniaturized on-chip spectrometers with small footprints, lightweight, and low cost are in great demand for portable optical sensing, lab-on-chip systems, and so on. Such miniaturized spectrometers are usually based on engineered spectral response units and then reconstruct unknown spectra with algorithms. However, due to the limited footprints of computatio

Zhiqiang Chen, Hongbo Chen, Yuhua Qi, Shipeng Zhong

LiDAR-based localization is valuable for applications like mining surveys and underground facility maintenance. However, existing methods can struggle when dealing with uninformative geometric structures in challenging scenarios. This paper presents RELEAD, a LiDAR-centric solution designed to address scan-matching degradation. Our method enables degeneracy-

Tony C. W. Mok, Zi Li, Yunhao Bai, Jianpeng Zhang

Establishing dense anatomical correspondence across distinct imaging modalities is a foundational yet challenging procedure for numerous medical image analysis studies and image-guided radiotherapy. Existing multi-modality image registration algorithms rely on statistical-based similarity measures or local structural image representations. However, the forme

Libo Jiang, Daniel R. Terno, Oscar Dahlsten

Hamiltonian mechanics describes the evolution of a system through its Hamiltonian. The Hamiltonian typically also represents the energy observable, a Noether-conserved quantity associated with the time-invariance of the law of evolution. In both quantum and classical mechanics, Hamiltonian mechanics demands a precise relationship between time evolution and o

Zeyuan Qu, Tiange Huang, Yuxin Ji, Yongjun Li

Fall detection based on embedded sensor is a practical and popular research direction in recent years. In terms of a specific application: fall detection methods based upon physics sensors such as [gyroscope and accelerator] have been exploited using traditional hand crafted features and feed them in machine learning models like Markov chain or just threshol

Takaaki Saeki, Gary Wang, Nobuyuki Morioka, Isaac Elias

Collecting high-quality studio recordings of audio is challenging, which limits the language coverage of text-to-speech (TTS) systems. This paper proposes a framework for scaling a multilingual TTS model to 100+ languages using found data without supervision. The proposed framework combines speech-text encoder pretraining with unsupervised training using unt

Ravi Dwivedi, Vivek Sahai

In this paper, we study the Appell function $F_4$ from discrete point of view. In particular, we obtain regions of convergence, difference-differential equations, finite and infinite summation formulas and a list of recursion relations satisfied by the discrete analogues of Appell function $F_4$.

Fatih Kamisli, Fabien Racape, Hyomin Choi

Achieving successful variable bitrate compression with computationally simple algorithms from a single end-to-end learned image or video compression model remains a challenge. Many approaches have been proposed, including conditional auto-encoders, channel-adaptive gains for the latent tensor or uniformly quantizing all elements of the latent tensor. This pa

Hongjun Wang, Jiyuan Chen, Yinqiang Zheng, Tieyong Zeng

Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR) performances in recent years. %Despite the substantial advancement% While most existing work assumes a simple and fixed degradation model (e.g., bicubic downsampling), the research of Blind SR seeks to improve model generalization ability with unknown degradation. Recently, Kong

Jiakun Jin, Xiaoxia Ren, Lei Wang

In this paper, we obtain the low order global well-posedness and the asymptotic behavior of solution of 2D MHD problem with partial dissipation in half space with non-slip boundary condition. When magnetic field equal zero, the system be reduced to partial dissipation Navier-Stokes equation, so this result also implies the stabilizing effects of magnetic fie

Xiang Chen, Wenjie Zhu, Jiayuan Chen, Tong Zhang

This paper proposes a novel edge computing enabled real-time video analysis system for intelligent visual devices. The proposed system consists of a tracking-assisted object detection module (TAODM) and a region of interesting module (ROIM). TAODM adaptively determines the offloading decision to process each video frame locally with a tracking algorithm or t

Rui Li, Kentaro Kubo, Yinghao Ho, Zhiguang Yan

Striving for higher gate fidelity is crucial not only for enhancing existing noisy intermediate-scale quantum (NISQ) devices but also for unleashing the potential of fault-tolerant quantum computation through quantum error correction. A recently proposed theoretical scheme, the double-transmon coupler (DTC), aims to achieve both suppressed residual interacti

Haotian Liu, Sanqing Qu, Fan Lu, Zongtao Bu

Event cameras can record scene dynamics with high temporal resolution, providing rich scene details for monocular depth estimation (MDE) even at low-level illumination. Therefore, existing complementary learning approaches for MDE fuse intensity information from images and scene details from event data for better scene understanding. However, most methods di

Armen Sargsyan, David Sarkisyan, Aram Papoyan

Interest in magnetically induced (MI) transitions of alkali metal atoms is caused by the fact that their intensities can exceed the intensities of regular atomic transitions in a wide range of magnetic field (200 - 4000 G). The goal of this work was to form and study, for the first time, an electromagnetically induced transparency (EIT) resonance in a strong

Jeehyun Lee, Yerin Choi, Tae-Jin Song, Myoung-Wan Koo

Dysarthria, a common issue among stroke patients, severely impacts speech intelligibility. Inappropriate pauses are crucial indicators in severity assessment and speech-language therapy. We propose to extend a large-scale speech recognition model for inappropriate pause detection in dysarthric speech. To this end, we propose task design, labeling strategy, a

Chao Hao, Zitong Yu, Xin Liu, Jun Xu

Camouflaged object detection (COD) and salient object detection (SOD) are two distinct yet closely-related computer vision tasks widely studied during the past decades. Though sharing the same purpose of segmenting an image into binary foreground and background regions, their distinction lies in the fact that COD focuses on concealed objects hidden in the im

Ilmun Kim, Larry Wasserman, Sivaraman Balakrishnan, Matey Neykov

Semi-supervised datasets are ubiquitous across diverse domains where obtaining fully labeled data is costly or time-consuming. The prevalence of such datasets has consistently driven the demand for new tools and methods that exploit the potential of unlabeled data. Responding to this demand, we introduce semi-supervised U-statistics enhanced by the abundance

Dongliang Cao, Marvin Eisenberger, Nafie El Amrani, Daniel Cremers

Although 3D shape matching and interpolation are highly interrelated, they are often studied separately and applied sequentially to relate different 3D shapes, thus resulting in sub-optimal performance. In this work we present a unified framework to predict both point-wise correspondences and shape interpolation between 3D shapes. To this end, we combine the

Fahimeh Hosseini Noohdani, Parsa Hosseini, Aryan Yazdan Parast, Hamidreza Yaghoubi Araghi

While standard Empirical Risk Minimization (ERM) training is proven effective for image classification on in-distribution data, it fails to perform well on out-of-distribution samples. One of the main sources of distribution shift for image classification is the compositional nature of images. Specifically, in addition to the main object or component(s) dete

Yi Feng, Yu Ma, Qijun Chen, Ioannis Pitas

Feature-fusion networks with duplex encoders have proven to be an effective technique to solve the freespace detection problem. However, despite the compelling results achieved by previous research efforts, the exploration of adequate and discriminative heterogeneous feature fusion, as well as the development of fallibility-aware loss functions remains relat

Aadirupa Saha, Pierre Gaillard

We address the problem of active online assortment optimization problem with preference feedback, which is a framework for modeling user choices and subsetwise utility maximization. The framework is useful in various real-world applications including ad placement, online retail, recommender systems, fine-tuning language models, amongst many. The problem, alt

Nozomu Sekino

We consider the JSJ-decomposition of the 3-manifold obtained by 0-surgery along a classical pretzel knot of genus one. We use the classification of exceptional fillings of minimally twisted five-chain links by B. Martelli, C. Petronio and F. Roukema.

Rahul Bhagat, B. Mishra

Using the cosmological date sets, the cosmological parameters are constrained in this paper, with some well known form of Hubble parameter. To understand the dynamics of the Weyl type $f(Q,T)$, functional form $f(Q,T)$ has been introduced, where $Q$ and $T$ respectively represents the nonmetricity scalar and trace of energy-momentum tensor. Using the constra

Samik Basu, Ramesh Kasilingam, Ankur Sarkar

This paper explores various differentiable structures on the product manifold $M \times \mathbb{S}^k$, where $M$ is either a 4-dimensional closed, oriented, smooth manifold or a simply connected 5-dimensional closed, smooth manifold. We identify the possible stable homotopy types of $M$ and use it to calculate the concordance inertia group and the concordanc

Yiran Zhao, Wenxuan Zhang, Huiming Wang, Kenji Kawaguchi

As an effective alternative to the direct fine-tuning on target tasks in specific languages, cross-lingual transfer addresses the challenges of limited training data by decoupling ''task ability'' and ''language ability'' by fine-tuning on the target task in the source language and another selected task in the target language, respectively. However, they fai

Debashree Sen, Hana Gil, Chang Ho Hyun

Employing the Korea-IBS-Daegu-SKKU (KIDS) density functional for the hadron phase and the MIT bag model with vector (vBag) model for the quark phase, we obtain hadron-quark phase transition in neutron stars considering Maxwell construction. The structural properties of the resultant hybrid stars are computed for three different values of bag constant ($B$) i

Dmitry Yu. Usachov, Sarah Krebber, Kirill A. Bokai, Artem V. Tarasov

The mechanism of the peculiar transport properties around the magnetic ordering temperature of semiconducting antiferromagnetic EuCd$_2$P$_2$ is not yet understood. With a huge peak in the resistivity observed above the N\'eel temperature, $T_{\rm N}=10.6\,\rm K$, it exhibits a colossal magnetoresistance effect. Recent reports on observations of ferromagneti

Yang Chen, Yitao Liang, Zhouchen Lin

Causality has been combined with machine learning to produce robust representations for domain generalization. Most existing methods of this type require massive data from multiple domains to identify causal features by cross-domain variations, which can be expensive or even infeasible and may lead to misidentification in some cases. In this work, we make a

Xiaobao Wu, Liangming Pan, William Yang Wang, Anh Tuan Luu

Knowledge editing injects knowledge updates into language models to keep them correct and up-to-date. However, its current evaluations deviate significantly from practice: their knowledge updates solely consist of structured facts derived from meticulously crafted datasets, instead of practical sources -- unstructured texts like news articles, and they often

Yu He, Alexander Lam, Minming Li

We take the classic facility location problem and consider a variation, in which each agent's individual cost function is equal to their distance from the facility multiplied by a scaling factor which is determined by the facility placement. In addition to the general class of continuous scaling functions, we also provide results for piecewise linear scaling

Li Wang, Qiang Xu

We investigate boundary estimates for elliptic operators with stationary random coefficients exhibiting integrable correlations, arising from stochastic homogenization theory. As practical applications, we establish decay estimates for Green functions in both quenched and annealed senses. Furthermore, we derive notable annealed estimates for boundary correct

Hrishi Bora, Ng. K. Francis, Bikash Thapa, Shawan Kumar Jha

The current work involves augmenting the $\Delta(54)$ discrete flavor model by incorporating two Standard Model Higgs particles into the Inverse Seesaw mechanism. We introduced Weyl fermions and Vector like fermions, which are gauge singlets in the Standard Model and produces Majorana mass terms in our lagrangian. The resulting mass matrix deviates from the

Shuqi Ke, Charlie Hou, Sewoong Oh, Giulia Fanti

We show that differentially private full fine-tuning (DP-FFT) can distort pre-trained backbone features based on both theoretical and empirical results. We identify the cause of the distortion as the misalignment between the pre-trained backbone and the randomly initialized linear head. We prove that a sequential fine-tuning strategy can mitigate the feature

Kazuharu Harada, Masataka Taguri

While data-driven confounder selection requires careful consideration, it is frequently employed in observational studies. Widely recognized criteria for confounder selection include the minimal-set approach, which involves selecting variables relevant to both treatment and outcome, and the union-set approach, which involves selecting variables associated wi

Weiting Liu, Yunqi Luo, Yugang Yu

Known for its dynamic utilization of destroy and repair operators, the Adaptive Large Neighborhood Search (ALNS) seeks to unearth high-quality solutions and has thus gained widespread acceptance as a meta-heuristic tool for tackling complex Combinatorial Optimization Problems (COPs). However, challenges arise when applying uniform parameters and acceptance c

R. -Z. Lin, C. -H. Hsu, E. -P. Liu, W. -T. Chen

The search for the ferromagnetic quantum critical point (FM QCP) has always been a captivating research topic in the scientific community. In pursuit of this goal, we introduced nonmagnetic transition metals to alloy with elemental nickel, and studied the magnetic properties of nickel binary alloys Ni1-xMox and Ni1-yCuy as a function of x and y up to the cri

U. Özdem

We systematically study the electromagnetic properties of controversial states whose internal structure is not elucidated and we try to offer a different point of view to unravel the internal structure of these states. Inspired by the $\Omega_c$ states observed by the LHCb Collaboration, we study the electromagnetic properties of the $\Omega_c$ states as the

Yunfan Li, Arman Sabbaghi, Jonathan R. Walsh, Charles K. Fisher

Randomized controlled trials (RCTs) with binary primary endpoints introduce novel challenges for inferring the causal effects of treatments. The most significant challenge is non-collapsibility, in which the conditional odds ratio estimand under covariate adjustment differs from the unconditional estimand in the logistic regression analysis of RCT data. This

Yuxuan Lei, Jianxun Lian, Jing Yao, Mingqi Wu

This paper addresses the gap between general-purpose text embeddings and the specific demands of item retrieval tasks. We demonstrate the shortcomings of existing models in capturing the nuances necessary for zero-shot performance on item retrieval tasks. To overcome these limitations, we propose generate in-domain dataset from ten tasks tailored to unlockin

Wei Hao, Ruilin Zhu

We systematically study the mass spectra and their two-body hadronic decays of the beauty-charm meson family considering the coupled channel effects. Our results can good explain the observed $B_c$ meson spectrum and the prediction of the mass spectrum for unobserved beauty-charm mesons can be tested in future experiments. For the coupled channel components,

Yongpeng Jiang, Mingrui Yu, Xinghao Zhu, Masayoshi Tomizuka

Dexterous in-hand manipulation is an essential skill of production and life. However, the highly stiff and mutable nature of contacts limits real-time contact detection and inference, degrading the performance of model-based methods. Inspired by recent advances in contact-rich locomotion and manipulation, this paper proposes a novel model-based approach to c

Geyang Wang, Qi Wang

Non-overlapping codes are a set of codewords such that the prefix of each codeword is not a suffix of any codeword in the set, including itself. If the lengths of the codewords are variable, it is additionally required that every codeword is not contained in any other codeword as a subword. Let $C(n,q)$ be the maximum size of $q$-ary fixed-length non-overlap

Andrés Vallejo, Alejandro Romanelli, Virginia Feldman, Raúl Donangelo

We derive a generalization of Ehrenfest theorem valid for open quantum systems. From this result, we identify three contributions to the evolution of expected values: i) the explicit time dependence of the observable, ii) the incompatibility between the observable and an operator which plays the role of an effective Hamiltonian, and iii) entropy changes. Con

Markus Ackermann, Klaus Helbing

Many instruments for astroparticle physics are primarily geared towards multi-messenger astrophysics, to study the origin of cosmic rays (CR) and to understand high-energy astrophysical processes. Since these instruments observe the Universe at extreme energies and in kinematic ranges not accessible at accelerators these experiments provide also unique and c

Jiajun Zhang, Zhixun Li, Qiang Liu, Shu Wu

With the rapid development of social media, the wide dissemination of fake news on social media is increasingly threatening both individuals and society. One of the unique challenges for fake news detection on social media is how to detect fake news on future events. Recently, numerous fake news detection models that utilize textual information and the propa

Balaji Venkatesan, Syu-You Guan, Jen-Te Chang, Shiang-Bin Chiu

Electronic liquid crystal (ELC) phases are spontaneous symmetry breaking states believed to arise from strong electron correlation in quantum materials such as cuprates and iron pnictides. Here, we report a direct observation of a smectic phase in a weakly correlated nonsymmorphic square-net semimetal GdSbxTe2-x. Incommensurate smectic charge modulation and

Nuo Xu, Wen Wang, Rong Yang, Mengjie Qin

Object-goal navigation is a challenging task that requires guiding an agent to specific objects based on first-person visual observations. The ability of agent to comprehend its surroundings plays a crucial role in achieving successful object finding. However, existing knowledge-graph-based navigators often rely on discrete categorical one-hot vectors and vo

Sasaank Bandi, Chao Jiang, Chris A. Marianetti

Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, Machine learning interatomic potentials (MLIP) can accurately reproduce first-principles data at a cost similar to that of conventional interatomic potential approaches. While MLIP have been extensively tested

Ting Li, Yanfang Zheng, Xuefeng Li, Yijun Hou

Until now, how the magnetic fields in M/X-class flaring active regions (ARs) differ from C-class flaring ARs remains unclear. Here, we calculate the key magnetic field parameters within the area of high photospheric free energy density (HED region) for 323 ARs (217 C- and 106 M$/$X-flaring ARs), including total photospheric free magnetic energy density E$_{f

Tianyi Zhang, Yu Cao, Dianbo Liu

Federated learning (FL), aimed at leveraging vast distributed datasets, confronts a crucial challenge: the heterogeneity of data across different silos. While previous studies have explored discrete representations to enhance model generalization across minor distributional shifts, these approaches often struggle to adapt to new data silos with significantly

Shaojie Hu, Xiaomin Cui, Zengji Yue, Pangpang Wang

We have experimentally investigated the mechanism of the exchange bias in the 2D van der Waals (vdW) ferromagnets by means of the anomalous Hall effect (AHE) together with the dynamical magnetization property. The temperature dependence of the AC susceptibility with its frequency response indicates a glassy transition of the magnetic property for the Te-rich

Lingfeng Li, Xue-Cheng Tai, Raymond Chan

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, with blood pressure serving as a crucial indicator. Arterial blood pressure (ABP) waveforms provide continuous pressure measurements throughout the cardiac cycle and offer valuable diagnostic insights. Consequently, there is a significant demand for non-invasive and cuff-less methods to

Shaoxiong Li, Hiroki Saito

Motivated by the recent experimental realization of a Bose-Einstein condensate (BEC) of europium atoms, we investigate the self-bound droplet state of a europium BEC with spin degrees of freedom. Under a sufficiently weak magnetic field, the droplet has a torus shape with circulating spin vectors, which is referred to as a magnetic vortex. The ground state t

Tina Behnia, Christos Thrampoulidis

Recent findings reveal that over-parameterized deep neural networks, trained beyond zero training-error, exhibit a distinctive structural pattern at the final layer, termed as Neural-collapse (NC). These results indicate that the final hidden-layer outputs in such networks display minimal within-class variations over the training set. While existing research

Chia-Yang Hung, Chih-Ya Shen

Dense subgraph extraction is a fundamental problem in graph analysis and data mining, aimed at identifying cohesive and densely connected substructures within a given graph. It plays a crucial role in various domains, including social network analysis, biological network analysis, recommendation systems, and community detection. However, extracting a subgrap

Anirban Chatterjee, Abhijit Bandyopadhyay, Debasish Majumdar

We investigate an interacting quintessence dark energy - dark matter scenario and its impact on structure formation by analyzing the evolution of scalar perturbations. The interaction is introduced by incorporating a non-zero source term into the continuity equations of the two sectors (with opposite signs), modeled as $\bar{Q}_0 \equiv \alpha\bar{\rho}_{\rm

Viraj Nadkarni, D. Manjunath, Sharayu Moharir

We consider a non stationary multi-armed bandit in which the population preferences are positively and negatively reinforced by the observed rewards. The objective of the algorithm is to shape the population preferences to maximize the fraction of the population favouring a predetermined arm. For the case of binary opinions, two types of opinion dynamics are