March 2024 arXiv papers — page 146

Sankarasubramanian Ragunathan, Håkon Andreas Hoel

A higher-order change-of-measure multilevel Monte Carlo (MLMC) method is developed for computing weak approximations of the invariant measures of SDE with drift coefficients that do not satisfy the contractivity condition. This is achieved by introducing a spring term in the pairwise coupling of the MLMC trajectories employing the order 1.5 strong It\^o--Tay

Arindam Dutta, Anirban Pathak

Nash equilibrium is employed to find a game theoretic robust security bound on quantum bit error rate (QBER) for DL04 protocol which is a scheme for quantum secure direct communication that has been experimentally realized recently. The receiver, sender and eavesdropper (Eve) are considered to be quantum players (players having the capability to perform quan

Corentin Vazia, Alexandre Bousse, Béatrice Vedel, Franck Vermet

Using recent advances in generative artificial intelligence (AI) brought by diffusion models, this paper introduces a new synergistic method for spectral computed tomography (CT) reconstruction. Diffusion models define a neural network to approximate the gradient of the log-density of the training data, which is then used to generate new images similar to th

Youness Mellak, Konstantinos Chatzipapas, Alexandre Bousse, Catherine Chez-Le Rest

In recent years, the use of Monte Carlo (MC) simulations in the domain of Medical Physics has become a state-of-the-art technology that consumes lots of computational resources for the accurate prediction of particle interactions. The use of generative adversarial network (GAN) has been recently proposed as an alternative to improve the efficiency and extend

George F R Ellis

Quantum physics is a linear theory, so it is somewhat puzzling that it can underlie very complex systems such as digital computers and life. This paper investigates how this is possible. Physically, such complex systems are necessarily modular hierarchical structures, with a number of key features. Firstly, they cannot be described by a single wave function:

Amani S. Jayakody, Joseph Budnick, Jason N. Hancock, Daniela Morales

Bulk Scandium trifluoride ($\mathrm{ScF_3}$) is known for a pronounced negative thermal expansion (NTE) over a wide range of temperature, from $10~\mathrm{K}~\text{to}~ 1100~\mathrm{K}$. The structure of $\mathrm{ScF_3}$ can be described as an $\mathrm{ABX_3}$ perovskite with an empty A-site and a space group of Pm-3m. Growing thin films of $\mathrm{ScF_3}$

Yufei Zhao, Yiyang Jiang, Hyeonhu Bae, Kamal Das

The exploration of magnetic topological insulators is instrumental in exploring axion electrodynamics and intriguing transport phenomena, such as the quantum anomalous Hall effect. Here, we report that a family of magnetic compounds Eu$_{2n+1}$In$_{2}$(As,Sb)$_{2n+2}$ ($n=0,1,2$) exhibit both gapless Dirac surface states and chiral hinge modes. Such a hybrid

Marie-Claude Vachon, Anne Mackay

We propose a unifying framework for the pricing of debt securities under general time-inhomogeneous short-rate diffusion processes. The pricing of bonds, bond options, callable/putable bonds, and convertible bonds (CBs) is covered. Using continuous-time Markov chain (CTMC) approximations, we obtain closed-form matrix expressions to approximate the price of b

Yuda Shao, Shan Yu, Tianshu Feng

Automatic Differentiation Variational Inference (ADVI) is efficient in learning probabilistic models. Classic ADVI relies on the parametric approach to approximate the posterior. In this paper, we develop a spline-based nonparametric approximation approach that enables flexible posterior approximation for distributions with complicated structures, such as sk

Xiaomeng Zhu, Robert Frank

Discourse Entity (DE) recognition is the task of identifying novel and known entities introduced within a text. While previous work has found that large language models have basic, if imperfect, DE recognition abilities (Schuster and Linzen, 2022), it remains largely unassessed which of the fundamental semantic properties that govern the introduction and sub

Artem M. Ponomarenko

In this article, we present a new two-dimensional generalization of the gamma function based on the product of the one-dimensional generalized beta function and the one-dimensional generalized gamma function. As will become clear later, this generalization is also a generalization the famous formula that gives the connection between the classical gamma and b

Ranindya Paramitha, Yuan Feng, Fabio Massacci, Carlos E. Budde

Context: Software of different functional categories, such as text processing vs. networking, has different profiles in terms of metrics like security and updates. Using popularity to compare e.g. Java vs. Python libraries might give a skewed perspective, as the categories of the most popular software vary from one ecosystem to the next. How can one compare

Quanyan Zhu, Tamer Basar

This article explains the distinctions between robustness and resilience in control systems. Resilience confronts a distinct set of challenges, posing new ones for designing controllers for feedback systems, networks, and machines that prioritize resilience over robustness. The concept of resilience is explored through a three-stage model, emphasizing the ne

A. Jung

A key challenge in federated learning applications is the statistical heterogeneity of local datasets. Clustered federated learning addresses this challenge by identifying clusters of local datasets that are approximately homogeneous. One recent approach to clustered federated learning is generalized total variation minimization (GTVMin). This approach requi

Quancheng Liu, Klaus Ziegler

The evolution of non-interacting bosons in the presence of repeated projective measurements is studied. Following the established approach, this monitored evolution is characterized by the first detected return and the first detected transition probabilities. We show that these quantities are directly related to the entanglement entropy and to the entangleme

Cheolhee Han, Doron Cohen, Eran Sela

Non-equilibrium fluctuation theorems (NFTs) relate work performed on a system as its Hamiltonian varies with time, to equilibrium data of the initial and final states. In a classical context the system energy can be directly measured, while a quantum implementation requires the incorporation of a work-agent. We demonstrate that the uncertainty principle impo

Thang Doan, Sima Behpour, Xin Li, Wenbin He

Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior knowledge while learning from limited new data streams, all without overfitting. The rise of Vision-Language models (VLMs) has unlocked numerous applications, leveraging their existing knowledge to fine-tune on custom data. However, training the whole model is computationally

Shengxin Hong, Liang Xiao, Xin Zhang, Jianxia Chen

There are two main barriers to using large language models (LLMs) in clinical reasoning. Firstly, while LLMs exhibit significant promise in Natural Language Processing (NLP) tasks, their performance in complex reasoning and planning falls short of expectations. Secondly, LLMs use uninterpretable methods to make clinical decisions that are fundamentally diffe

V. N. Obridko, A. S. Shibalova, D. D. Sokoloff

The solar cycle is a complex phenomenon, a comprehensive understanding of which requires the study of various tracers. Here, we consider the solar cycle as manifested in the harmonics of the solar large-scale surface magnetic field, including zonal, sectorial and tesseral harmonics, divided into odd and even relative to the solar equator. In addition to cons

Debolena Basak, P. K. Srijith, Maunendra Sankar Desarkar

In several real-world scenarios like autonomous navigation and mobility, to obtain a better visual understanding of the surroundings, image captioning and object detection play a crucial role. This work introduces a novel multitask learning framework that combines image captioning and object detection into a joint model. We propose TICOD, Transformer-based I

Kennedy Cano, Emily Gutknecht, Gautham Kappaganthula, George Miller

We say that a graph $G$ is chromatic-choosable when its list chromatic number $\chi_{\ell}(G)$ is equal to its chromatic number $\chi(G)$. Chromatic-choosability is a well-studied topic, and in fact, some of the most famous results and conjectures related to list coloring involve chromatic-choosability. In 2002 Ohba showed that for any graph $G$ there is an

Nhat Thanh Tran, Jack Xin, Guofa Zhou

Dengue fever is one of the most deadly mosquito-born tropical infectious diseases. Detailed long range forecast model is vital in controlling the spread of disease and making mitigation efforts. In this study, we examine methods used to forecast dengue cases for long range predictions. The dataset consists of local climate/weather in addition to global clima

R. Krithika, V. K. Kutty Malu, Prafullkumar Tale

The Path Contraction and Cycle Contraction problems take as input an undirected graph $G$ with $n$ vertices, $m$ edges and an integer $k$ and determine whether one can obtain a path or a cycle, respectively, by performing at most $k$ edge contractions in $G$. We revisit these NP-complete problems and prove the following results. Path Contraction admits an al

Zijun Long, Lipeng Zhuang, George Killick, Richard McCreadie

Human-annotated vision datasets inevitably contain a fraction of human mislabelled examples. While the detrimental effects of such mislabelling on supervised learning are well-researched, their influence on Supervised Contrastive Learning (SCL) remains largely unexplored. In this paper, we show that human-labelling errors not only differ significantly from s

Justin Yang, Zhihao Duan, Andrew Peng, Yuning Huang

Image compression emerges as a pivotal tool in the efficient handling and transmission of digital images. Its ability to substantially reduce file size not only facilitates enhanced data storage capacity but also potentially brings advantages to the development of continual machine learning (ML) systems, which learn new knowledge incrementally from sequentia

Jorge A. Lizarraga

The Schr\"odinger equation for an electron under the influence of an electromagnetic field is analyzed based on the conserved operators of the system when the magnetic field is described by Landau's gauge. It is shown that the Lorentz force can be recovered only if two conserved generalized momentum operators are considered: one along the $x$-axis and the se

Sayani Maity, Himanshu Chaudhary, Ujjal Debnath, S. K. Maurya

This study examines accelerated cosmic expansion using the Viscous Modified Chaplygin Gas (VMMG) and Generalized Cosmic Chaplygin Gas (GCCM) within Horava-Lifshitz gravity. Our aim is to constrain essential cosmological parameters, such as the Hubble Parameter ($H_{0}$) and Sound Horizon ($r_{d}$). We utilize late-time datasets: 17 Baryon Acoustic Oscillatio

Ming Li, Zhiyong Sun, Patrick J. W. Koelewijn, Siep Weiland

Sontag's universal formula is a widely used technique for stabilizing control through control Lyapunov functions. Recently, it has been extended to address safety-critical control by incorporating control barrier functions (CBFs). However, deriving a universal formula that satisfies requirements on essential properties, including safety, smoothness, and robu

Aaron Hu

Learning difficulties pose significant challenges for students, impacting their academic performance and overall educational experience. These difficulties could sometimes put students into a downward spiral that lack of educational resources for personalized support consistently led to under-accommodation of students special needs, and the student lose oppo

Marcelo Lapola, Luiz Antonio Barreiro

We examine the analogue gravity model within the context of f(R,T) gravity applied to graphene. The derivation of the Lagrangian density in two dimensions (2D) is undertaken, accounting for the altered gravitational effects as characterized by the function f(R,T). The Lagrangian encompasses the quasiparticle field $\psi(x)$, its adjoint $\overline{\psi}$, th

Xiongbang Yang, Gang Cao

We explore the multiwavelength radiation properties of the light curves and energy spectra in the dissipative magnetospheres of pulsars. The dissipative magnetospheres are simulated by the pseudo-spectral method with the combined force-free and Aristotelian electrodynamics, which can produce self-consistent accelerating electric fields mainly distributed in

Lukas Waas

This paper is part of a series of three articles with the objective of investigating a stratified version of the homotopy hypothesis in terms of semi-model structures that interact well with classical examples of stratified spaces, such as Whitney stratified spaces. To this end, we prove the existence of several combinatorial simplicial model structures in t

Wenpin Tang, Fuzhong Zhou

This paper aims to develop and provide a rigorous treatment to the problem of entropy regularized fine-tuning in the context of continuous-time diffusion models, which was recently proposed by Uehara et al. (arXiv:2402.15194, 2024). The idea is to use stochastic control for sample generation, where the entropy regularizer is introduced to mitigate reward col

Miguel Alcobendas, Eric Bax

One method to offer some bidders a discount in a first-price auction is to augment their bids when selecting a winner but only charge them their original bids should they win. Another method is to use their original bids to select a winner, then charge them a discounted price that is lower than their bid should they win. We show that the two methods have equ

Yakov Kononov, Woonam Lim, Miguel Moreira, Weite Pi

We initiate a systematic study on the cohomology rings of the moduli stack $\mathfrak{M}_{d,\chi}$ of semistable one-dimensional sheaves on the projective plane. We introduce a set of tautological relations of geometric origin, including Mumford-type relations, and prove that their ideal is generated by certain primitive relations via the Virasoro operators.

Fotis Koutroulis, Eugenio Megias, Stefan Pokorski, Mariano Quiros

We propose a setup for the origin of dark matter based on spacetime with a warped extra dimension and three branes: the Planck brane, the TeV brane, at a (few) TeV scale $\rho_T$, and a dark brane, at a (sub)-GeV scale $\rho_1\lesssim 100$ GeV $\ll\rho_T$. The Standard Model is localized in the TeV brane, thus solving the Higgs hierarchy problem, while the d

Kwanyoung Kim, Jaa-Yeon Lee, Jong Chul Ye

Nakagami imaging holds promise for visualizing and quantifying tissue scattering in ultrasound waves, with potential applications in tumor diagnosis and fat fraction estimation which are challenging to discern by conventional ultrasound B-mode images. Existing methods struggle with optimal window size selection and suffer from estimator instability, leading

S. G. Alekseev, N. I. Polzikova, V. A. Luzanov, S. A. Nikitov

In this work, we present the results of a systematic experimental study of linear and parametric spin wave resonant excitation accompanied by spin currents (spin pumping) in a multifrequency composite bulk acoustic wave resonator with a ZnO-YIG-GGG-YIG/Pt structure. The features of magnetic dynamics excitation in YIG films due to magnetoelastic coupling with

A. K. Alekseev, A. E. Bondarev

The solution in sense of Prager&Synge is the alternative to the commonly used notion of the numerical solution, which is considered as a limit of grid functions at mesh refinement. Prager&Synge solution is defined as a hypersphere containing the projection of the true solution of the system of partial differentiation equations (PDE) onto the computational gr

Lukas Waas

Homotopy links have proven to be one of the most powerful tools of stratified homotopy theory. In previous work, we described combinatorial models for the generalized homotopy links of a stratified simplicial set. For many purposes, in particular to investigate the stratified homotopy hypothesis, a more general version of this result pertaining to stratified

A. E. Atamuratov, B. O. Jabbarova, M. M. Khalilloev, A. Yusupov

In this work we study the self-heating (1) effect (SHE) in nanoscale Silicon on Insulator Junctionless (SOI JL) FinFET transistor with fi cross section in rectangular, trapeze and triangle form. The lattice temperature dependence on the channel length as well as on buried oxide thickness is considered. It is shown that for considered transistor structure the

Jurij Volčič

Hilbert's Nullstellensatz is one of the most fundamental correspondences between algebra and geometry, and has inspired a plethora of noncommutative analogs. In last two decades, there has been an increased interest in understanding vanishing sets of polynomials in several matrix variables without restricting the matrix size, prompted by developments in nonc

Youyuan Zhang, Xuan Ju, James J. Clark

Diffusion models have demonstrated remarkable capabilities in text-to-image and text-to-video generation, opening up possibilities for video editing based on textual input. However, the computational cost associated with sequential sampling in diffusion models poses challenges for efficient video editing. Existing approaches relying on image generation model

Arun Sharma, Shashi Shekhar

Given trajectories with gaps (i.e., missing data), we investigate algorithms to identify abnormal gaps in trajectories which occur when a given moving object did not report its location, but other moving objects in the same geographic region periodically did. The problem is important due to its societal applications, such as improving maritime safety and reg

Hanfang Lyu, Yuanchen Bai, Xin Liang, Ujaan Das

Preference-based learning aims to align robot task objectives with human values. One of the most common methods to infer human preferences is by pairwise comparisons of robot task trajectories. Traditional comparison-based preference labeling systems seldom support labelers to digest and identify critical differences between complex trajectories recorded in

Tim van Beeck, Umberto Zerbinati

It is well known that the quasi-optimality of the Galerkin finite element method for the Helmholtz equation is dependent on the mesh size and the wave-number. In the literature, different criteria have been proposed to ensure uniform quasi-optimality of the discretisation. In the present work, we study the uniform quasi-optimality of $H^1$ conforming and non

Omer Goldman, Avi Caciularu, Matan Eyal, Kris Cao

Despite it being the cornerstone of BPE, the most common tokenization algorithm, the importance of compression in the tokenization process is still unclear. In this paper, we argue for the theoretical importance of compression, that can be viewed as 0-gram language modeling where equal probability is assigned to all tokens. We also demonstrate the empirical

Atrisha Sarkar, Gillian K. Hadfield

AI-based social media platforms has already transformed the nature of economic and social interaction. AI enables the massive scale and highly personalized nature of online information sharing that we now take for granted. Extensive attention has been devoted to the polarization that social media platforms appear to facilitate. However, a key implication of

Genki Shibukawa

We give a generalization of a Ramanujan's exercise for high school students. Our results can be regarded as a variation of the factorization formula of $x^{n} - 1$.

Svetlana Gorokhova

We give orthonormal characterizations of collectively compact (limited) sets of linear operators from a Hilbert space to a Banach space.

Xiaobo Ma, Pengyu Pan, Jianfeng Li, Wei Wang

Establishing efficient and robust covert channels is crucial for secure communication within insecure network environments. With its inherent benefits of decentralization and anonymization, blockchain has gained considerable attention in developing covert channels. To guarantee a highly secure covert channel, channel negotiation should be contactless before

Amit Meghanani, Thomas Hain

There is a growing interest in cost-effective self-supervised fine-tuning (SSFT) of self-supervised learning (SSL)-based speech models to obtain task-specific representations. These task-specific representations are used for robust performance on various downstream tasks by fine-tuning on the labelled data. This work presents a cost-effective SSFT method nam

Xiaohan Wang, Shengyu Mao, Ningyu Zhang, Shumin Deng

Recently, there has been a growing interest in knowledge editing for Large Language Models (LLMs). Current approaches and evaluations merely explore the instance-level editing, while whether LLMs possess the capability to modify concepts remains unclear. This paper pioneers the investigation of editing conceptual knowledge for LLMs, by constructing a novel b

Xinhao Cai, Qiuxia Lai, Yuwei Wang, Wenguan Wang

Object detection in remote sensing images (RSIs) often suffers from several increasing challenges, including the large variation in object scales and the diverse-ranging context. Prior methods tried to address these challenges by expanding the spatial receptive field of the backbone, either through large-kernel convolution or dilated convolution. However, th

Mohammed Alhashim, Kaylie Hausknecht, Michael Brenner

Inverse design of complex flows is notoriously challenging because of the high cost of high dimensional optimization. Usually, optimization problems are either restricted to few control parameters, or adjoint-based approaches are used to convert the optimization problem into a boundary value problem. Here, we show that the recent advances in automatic differ

Markus Dablander, Thierry Hanser, Renaud Lambiotte, Garrett M. Morris

Extended-connectivity fingerprints (ECFPs) are a ubiquitous tool in current cheminformatics and molecular machine learning, and one of the most prevalent molecular feature extraction techniques used for chemical prediction. Atom features learned by graph neural networks can be aggregated to compound-level representations using a large spectrum of graph pooli

Sudip Mukherjee, Abhik Basu

We set up and study the hydrodynamic theory for inversion-symmetric active fluid and tethered membranes. For some choices of the activity parameter, such membranes are stable and described by linear hydrodynamic equations, which are exact in the asymptotic long wavelength limit, giving stable flat phases with translational quasi long range orders. For other

Van-Giang Trinh, Belaid Benhamou, Loïc Paulevé

The tool mpbn offers a Python programming interface for an easy interactive editing of Boolean networks and the efficient computation of elementary properties of their dynamics, including fixed points, trap spaces, and reachability properties under the Most Permissive update mode. Relying on Answer-Set Programming logical framework, we show that mpbn is scal

Bruno Pereira Cipriano, Pedro Alves

Large Language Models (LLMs) have emerged as promising tools to assist students while solving programming assignments. However, object-oriented programming (OOP), with its inherent complexity involving the identification of entities, relationships, and responsibilities, is not yet mastered by these tools. Contrary to introductory programming exercises, there

Rishabh Gupta, Ewa A. Drzazga-Szczȩśniak, Sabre Kais, Dominik Szczȩśniak

The geometric Brownian motion (GBM) is widely employed for modeling stochastic processes, yet its solutions are characterized by the log-normal distribution. This comprises predictive capabilities of GBM mainly in terms of forecasting applications. Here, entropy corrections to GBM are proposed to go beyond log-normality restrictions and better account for in

Kihoon Son, DaEun Choi, Tae Soo Kim, Juho Kim

Despite the growing demand for professional graphic design knowledge, the tacit nature of design inhibits knowledge sharing. However, there is a limited understanding on the characteristics and instances of tacit knowledge in graphic design. In this work, we build a comprehensive set of tacit knowledge characteristics through a literature review. Through int

Bin Duan, Yuzhang Shang, Dawen Cai, Yan Yan

In this paper, we propose an online multi-spectral neuron tracing method with uniquely designed modules, where no offline training are required. Our method is trained online to update our enhanced discriminative correlation filter to conglutinate the tracing process. This distinctive offline-training-free schema differentiates us from other training-dependen

Apurba Das

This paper considers averaging operators on various algebraic structures and studies the induced structures. We first introduce the notion of an averaging operator on a group $G$ and show that it induces a rack structure. Moreover, the given group structure and the induced rack structure form a group-rack. We observe that any pointed group-rack can be embedd

Gang Hu, Ke Qin, Chenhan Yuan, Min Peng

While the progression of Large Language Models (LLMs) has notably propelled financial analysis, their application has largely been confined to singular language realms, leaving untapped the potential of bilingual Chinese-English capacity. To bridge this chasm, we introduce ICE-PIXIU, seamlessly amalgamating the ICE-INTENT model and ICE-FLARE benchmark for bi

Simina Brânzei, Nicholas J. Recker

Local search is a powerful heuristic in optimization and computer science, the complexity of which has been studied in the white box and black box models. In the black box model, we are given a graph $G = (V,E)$ and oracle access to a function $f : V \to \mathbb{R}$. The local search problem is to find a vertex $v$ that is a local minimum, i.e. with $f(v) \l

Mingyu Lee, Jongwon Choi

We propose a text-guided variational image generation method to address the challenge of getting clean data for anomaly detection in industrial manufacturing. Our method utilizes text information about the target object, learned from extensive text library documents, to generate non-defective data images resembling the input image. The proposed framework ens

Deshun Yang, Luhui Hu, Yu Tian, Zihao Li

Several text-to-video diffusion models have demonstrated commendable capabilities in synthesizing high-quality video content. However, it remains a formidable challenge pertaining to maintaining temporal consistency and ensuring action smoothness throughout the generated sequences. In this paper, we present an innovative video generation AI agent that harnes

Degui Li, Oliver Linton, Haoxuan Zhang

We propose a new estimator of high-dimensional spot volatility matrices satisfying a low-rank plus sparse structure from noisy and asynchronous high-frequency data collected for an ultra-large number of assets. The noise processes are allowed to be temporally correlated, heteroskedastic, asymptotically vanishing and dependent on the efficient prices. We defi

Priyamvada Natarajan, Liliya L. Williams, Marusa Bradac, Claudio Grillo

Galaxy clusters as gravitational lenses play a unique role in astrophysics and cosmology: they permit mapping the dark matter distribution on a range of scales; they reveal the properties of high and intermediate redshift background galaxies that would otherwise be unreachable with telescopes; they constrain the particle nature of dark matter and are a power

Zhenbang Zuo, Gongxiang Liu

The aim of this paper is to introduce a tensor structure for the Serre quotient category of an abelian monoidal category with biexact tensor product to make the canonical functor a monoidal functor. In this tensor product, the Serre quotient category of a multiring category (resp. a multitensor category) by a two-sided Serre tensor-ideal is still a multiring

Fei Wang, Haoyu Liu, Haoyang Bi, Xiangzhuang Shen

For the purpose of efficient and cost-effective large-scale data labeling, crowdsourcing is increasingly being utilized. To guarantee the quality of data labeling, multiple annotations need to be collected for each data sample, and truth inference algorithms have been developed to accurately infer the true labels. Despite previous studies having released pub

Xinmin Qiu, Congying Han, Zicheng Zhang, Bonan Li

Developing blind video deflickering (BVD) algorithms to enhance video temporal consistency, is gaining importance amid the flourish of image processing and video generation. However, the intricate nature of video data complicates the training of deep learning methods, leading to high resource consumption and instability, notably under severe lighting flicker

Demetris Gerogiannis, Anastasios Arsenos, Dimitrios Kollias, Dimitris Nikitopoulos

Computer-aided diagnosis (CAD) systems stand out as potent aids for physicians in identifying the novel Coronavirus Disease 2019 (COVID-19) through medical imaging modalities. In this paper, we showcase the integration and reliable and fast deployment of a state-of-the-art AI system designed to automatically analyze CT images, offering infection probability

Areefa Khatoon, Abdur Raheem, Asma Afreen

This paper deals with the controllability for a class of non-autonomous neutral differential equations of fractional order with infinite delay in an abstract space. The semi-group theory of bounded linear operators, fractional calculus, and stochastic analysis techniques have been implemented to achieve the main result. We prove the existence of mild solutio

Xin Liu, Yuxiang Zhang, Meng Wu, Mingyu Yan

Edge perturbation is a basic method to modify graph structures. It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i.e., graph data augmentation and attack. Surprisingly, both veins of edge perturbation methods employ the same operations, yet yield opposite effects on GNNs' accuracy. A distinct bou

Yasuko Kawahata

This study delves into the intricate electronic and optical behaviors of two-dimensional (2D) honeycomb materials, such as Stannen, arsenene, antimonene, silicene, and bismuthene(bismuth molecule), through the lens of first-principles calculations(AB Initio Calculations) based on the Kohn-Sham equations. Focusing on the exchange-correlation potential approxi

Terrence L. Stahl, Alexander Yu. Sokolov

Algebraic diagrammatic construction (ADC) is a computationally efficient approach for simulating excited electronic states, absorption spectra, and electron correlation. Due to their origin in perturbation theory, the single-reference ADC methods may be susceptible to spin contamination when applied to molecules with unpaired electrons. In this work, we deve

Jhon Yana Galarza, Thiago Ferreira, Diego Lorenzo-Oliveira, Joshua D. Simon

Among Neptunian mass exoplanets ($20-50$ M$_\oplus$), puffy hot Neptunes are extremely rare, and their unique combination of low mass and extended radii implies very low density ($\rho < 0.3$~g~cm$^{-3}$). Over the last decade, only a few puffy planets have been detected and precisely characterized with both transit and radial velocity observations, most not

Carlo Santambrogio, Monica Pragliola, Alessandro Lanza, Marco Donatelli

We consider an unsupervised bilevel optimization strategy for learning regularization parameters in the context of imaging inverse problems in the presence of additive white Gaussian noise. Compared to supervised and semi-supervised metrics relying either on the prior knowledge of reference data and/or on some (partial) knowledge on the noise statistics, the

Linan Yue, Qi Liu, Ye Liu, Weibo Gao

Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) data. Several approaches have been proposed to address this problem. Among them, one solution is to diversify training distributions in vanilla classification by modifying the data

Subhasree Bhattacharjee, Soumyadip Sarkar, Kunal Das, Bikramjit Sarkar

In the emergent realm of quantum computing, the Variational Quantum Eigensolver (VQE) stands out as a promising algorithm for solving complex quantum problems, especially in the noisy intermediate-scale quantum (NISQ) era. However, the ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes. This research int

Benjamin Lu, Jia Wan, Derek Ouyang, Jacob Goldin

Measuring average differences in an outcome across racial or ethnic groups is a crucial first step for equity assessments, but researchers often lack access to data on individuals' races and ethnicities to calculate them. A common solution is to impute the missing race or ethnicity labels using proxies, then use those imputations to estimate the disparity. C

V. Arun Kumar, A. N. Madhavanunni, S. Nivetha, Mahesh Raveendranatha Panicker

Natural starch-based blood-mimicking fluid (BMF) has been used as an alternative to commercially available BMFs for in-vitro Doppler investigations in low-resource settings. Most reported works in the literature have used corn starch-based BMF. Evaluation of other natural starches for potential BMF and their characterization have relatively been unexplored i

Debarshi Banerjee, Khatereh Azizi, Colin K. Egan, Edward Danquah Donkor

The use of computer simulations to study the properties of aqueous systems is, today more than ever, an active area of research. In this context, during the last decade there has been a tremendous growth in the use of data-driven approaches to develop more accurate potentials for water as well as to characterize its complexity in chemical and biological cont

Pedro Zuidberg Dos Martires

Probabilistic circuits (PCs) have gained prominence in recent years as a versatile framework for discussing probabilistic models that support tractable queries and are yet expressive enough to model complex probability distributions. Nevertheless, tractability comes at a cost: PCs are less expressive than neural networks. In this paper we introduce probabili

Wenbin Hu, Mingxian Huang, Yutong Wu, Yana Jia

Lack of nonreciprocity is one of the major drawbacks of solid-state acoustic devices, which has hindered the development of microwave-frequency acoustic isolators and circulators. Here we report giant nonreciprocal transmission of shear-horizontal surface acoustic waves (SH-SAWs) on a LiTaO3 substrate coated with a negative-positive magnetostrictive bilayer

Lin Zhu, Xianzhang Chen, Xiao Wang, Hua Huang

As a bio-inspired vision sensor, the spike camera emulates the operational principles of the fovea, a compact retinal region, by employing spike discharges to encode the accumulation of per-pixel luminance intensity. Leveraging its high temporal resolution and bio-inspired neuromorphic design, the spike camera holds significant promise for advancing computer

Yajiang Chen, Ke-Ji Chen, Jia-Ji Zhu, A. A. Shanenko

Superconducting-proximity topological insulators (STIs) have garnered significant research attention over the past two decades. In this Letter, we demonstrate that a low-dimensional STI in the topological-nontrivial phase (TP) exhibits an interference-induced surface (boundary) superconductivity with the surface critical temperature $T_{cs}$ significantly hi

Shuaibo Ding, Yunfei Bai, A. A. Bulekov, Wenhui Zhang

Significant research has recently been conducted into the Yu-Shiba-Rusinov (YSR) states in kagome superconductors through theoretical modeling and experimental investigations. However, additional efforts are still needed to further understand the local superconductivity near magnetic impurities in the kagome lattice and clarify how relevant quantities depend

Yun-Ang Wu, Yun-Da Tsai, Shou-De Lin

In this study, we delve into the Thresholding Linear Bandit (TLB) problem, a nuanced domain within stochastic Multi-Armed Bandit (MAB) problems, focusing on maximizing decision accuracy against a linearly defined threshold under resource constraints. We present LinearAPT, a novel algorithm designed for the fixed budget setting of TLB, providing an efficient

Hannah Kleine-Pollmann, Guido Homann, Ludwig Mathey

We present optimal control strategies for the DC transport across a Josephson junction. Specifically, we consider a junction in which the Josephson coupling is driven parametrically, with either a bichromatic or a trichromatic driving protocol, and optimize the prefactor of the 1/$\omega$ divergence of the imaginary part of the conductivity. We demonstrate t

Shiroman Prakash, Tanay Saha

We show that using qutrits rather than qubits leads to a substantial reduction in the overhead cost associated with an approach to fault-tolerant quantum computing known as magic state distillation. We construct a family of $[[9m-k, k, 2]]_3$ triorthogonal qutrit error-correcting codes for any positive integers $m$ and $k$ with $k \leq 3m-2$ that are suitabl

Peirong Liu, Oula Puonti, Annabel Sorby-Adams, William T. Kimberly

Remarkable progress has been made by data-driven machine-learning methods in the analysis of MRI scans. However, most existing MRI analysis approaches are crafted for specific MR pulse sequences (MR contrasts) and usually require nearly isotropic acquisitions. This limits their applicability to diverse real-world clinical data, where scans commonly exhibit v

S. Autti, R. P. Haley, A. Jennings, G. R. Pickett

Vibrating probes when immersed in a fluid can provide powerful tools for characterising the surrounding medium. In superfluid 3He-B, a condensate of Cooper pairs, the dissipation arising from the scattering of quasiparticle excitations from a mechanical oscillator provides the basis of extremely sensitive thermometry and bolometry at sub-millikelvin temperat

Boeun Kim, Jungho Kim, Hyung Jin Chang, Jin Young Choi

While existing motion style transfer methods are effective between two motions with identical content, their performance significantly diminishes when transferring style between motions with different contents. This challenge lies in the lack of clear separation between content and style of a motion. To tackle this challenge, we propose a novel motion style

Shicheng Ma, Heng Lin, Jinghui Pi

We investigate imaginary gap-closed (IGC) points and their associated dynamics in dissipative systems. In a general non-Hermitian model, we derive the equation governing the IGC points of the energy spectrum, establishing that these points are only determined by the Hermitian part of the Hamiltonian. Focusing on a class of one-dimensional dissipative chains,

Animesh Chattopadhyay, Subrat Kar

Our work delves into user behaviour at Electric Vehicle(EV) charging stations during peak times, particularly focusing on how impatience drives balking (not joining queues) and reneging (leaving queues prematurely). We introduce an Agent-based simulation framework that incorporates user optimism levels (pessimistic, standard, and optimistic) in the queue dyn

Xihao Fang, Fangzhong Chen, Jiarui Sun

In this paper, we extend the surface growth scheme in AdS spacetime with a boundary in the AdS/BCFT correspondence. We show that the entanglement wedge with a boundary can be constructed from the direct growth of bulk extremal surfaces layer by layer. In addition, we find that the surface growth configuration in BCFT can be connected with the defect multi sc

Jian Zhou, Yulong Gao, Ola Johansson, Björn Olofsson

Safe motion planning for robotic systems in dynamic environments is nontrivial in the presence of uncertain obstacles, where estimation of obstacle uncertainties is crucial in predicting future motions of dynamic obstacles. The worst-case characterization gives a conservative uncertainty prediction and may result in infeasible motion planning for the ego rob

Ruiwen Zhou, Yingxuan Yang, Muning Wen, Ying Wen

Numerous large language model (LLM) agents have been built for different tasks like web navigation and online shopping due to LLM's wide knowledge and text-understanding ability. Among these works, many of them utilize in-context examples to achieve generalization without the need for fine-tuning, while few of them have considered the problem of how to selec