April 2024 arXiv papers — page 117

Raja Gond, Purushottam Kulkarni

The emergence of CXL (Compute Express Link) promises to transform the status of interconnects between host and devices and in turn impact the design of all software layers. With its low overhead, low latency, and memory coherency capabilities, CXL has the potential to improve the performance of existing devices while making viable new operational use cases (

Mikolaj Czerkawski, Christos Ilioudis, Carmine Clemente, Craig Michie

Deep learning techniques are subject to increasing adoption for a wide range of micro-Doppler applications, where predictions need to be made based on time-frequency signal representations. Most, if not all, of the reported applications focus on translating an existing deep learning framework to this new domain with no adjustment made to the objective functi

Maria Kasinidou, Styliani Kleanthous, Matteo Busso, Marcelo Rodas

With the surge in data-centric AI and its increasing capabilities, AI applications have become a part of our everyday lives. However, misunderstandings regarding their capabilities, limitations, and associated advantages and disadvantages are widespread. Consequently, in the university setting, there is a crucial need to educate not only computer science maj

Nikolaos Pantelaios, Alexandros Kapravelos

Introduced over a decade ago, Chrome extensions now exceed 200,000 in number. In 2020, Google announced a shift in extension development with Manifest Version 3 (V3), aiming to replace the previous Version 2 (V2) by January 2023. This deadline was later extended to January 2025. The company's decision is grounded in enhancing three main pillars: privacy, sec

Tianyu Zhang, Zixuan Zhao, Jiaqi Huang, Jingyu Hua

As Large Language Models (LLMs) of Prompt Jailbreaking are getting more and more attention, it is of great significance to raise a generalized research paradigm to evaluate attack strengths and a basic model to conduct subtler experiments. In this paper, we propose a novel approach by focusing on a set of target questions that are inherently more sensitive t

Mayuko Kori, Kazuki Watanabe, Jurriaan Rot, Shin-ya Katsumata

Proving compositionality of behavioral equivalence on state-based systems with respect to algebraic operations is a classical and widely studied problem. We study a categorical formulation of this problem, where operations on state-based systems modeled as coalgebras can be elegantly captured through distributive laws between functors. To prove compositional

Zhiping Bian, Mitsuki Yoshimura, Ahrong Jeong, Haobo Li

Thermal transistors that switch the thermal conductivity (\k{appa}) of the active layers are attracting increasing attention as thermal management devices. For electrochemical thermal transistors, several transition metal oxides (TMOs) have been proposed as active layers. After electrochemical redox treatment, the crystal structure of the TMO is modulated, w

Jan von der Assen, Christian Killer, Alessandro De Carli, Burkhard Stiller

The advent of Decentralized Physical Infrastructure Networks (DePIN) represents a shift in the digital infrastructure of today's Internet. While Centralized Service Providers (CSP) monopolize cloud computing, DePINs aim to enhance data sovereignty and confidentiality and increase resilience against a single point of failure. Due to the novelty of the emergin

Mariarita Murabito, Marco Stangalini, J. Martin Laming, Deborah Baker

We investigate the propagation of Alfv\'en waves in the solar chromosphere, distinguishing between upward and downward propagating waves. We find clear evidence for the reflection of waves in the chromosphere and differences in propagation between cases with waves interpreted to be resonant or nonresonant with the overlying coronal structures. This establish

Cong Xu, Wen Zhou, Qing-Hua Zhang, Shao-Ming Fei

Uncertainty principle reveals the intrinsic differences between the classical and quantum worlds, which plays a significant role in quantum information theory. By using $\rho$-absolute variance, we introduce the uncertainty of quantum channels and explore its properties. By using Cauchy-Schwarz inequality and the parallelogram law, we establish the product a

Idan Amit, Dror G. Feitelson

Context: Motivation is known to improve performance. In software development in particular, there has been considerable interest in the motivation of contributors to open source. Objective: We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self use, etc.), and evaluate their relative effect on motivation. Since

Xin-Li Zhao, You Zhou, Zi-Wei Lin, Chao Zhang

Using the improved string-melting version of a Multi-Phase Transport model, we investigated the impact of nuclear geometry of $^{16}$O on anisotropic flows in O+O collisions at $\sqrt{s_{\rm NN}} = 200$ GeV. To evaluate the influence of nuclear structure and potential alpha clustering, we implemented four candidate configurations: Woods-Saxon, tetrahedron, s

Yosuke Imamura, Masato Inoue

Based on the D5-brane realization of Wilson line operators in anti-symmetric representations, we propose brane expansion formulas for $I_{N,k}$, the Schur index of ${\cal N}=4$ $U(N)$ SYM decorated by line operators in the anti-symmetric representation of rank $k$. For the large $N$ index $I_{\infty,k}$ we propose a double-sum expansion, and for finite $N$ i

Joel S. Jayson

In this paper we introduce a method for resolving multi-parameter likelihoods by fixing all parameter values, but two. Evaluation of those two variables is followed by iteratively cycling through each of the parameters in turn until convergence. We test the technique on the temperature power spectrum of the lensed cosmic microwave background (CMB). That demo

Peijie Sun, Yifan Wang, Min Zhang, Chuhan Wu

With the surge in mobile gaming, accurately predicting user spending on newly downloaded games has become paramount for maximizing revenue. However, the inherently unpredictable nature of user behavior poses significant challenges in this endeavor. To address this, we propose a robust model training and evaluation framework aimed at standardizing spending da

Motohiko Ezawa

Altermagnets are time-reversal broken antiferromagnets, where the $z$ component of the N\'{e}el vector is detectable by anomalous Hall effects. On the other hand, recently proposed $p$-wave magnets are time-reversal preserved antiferromagnets, and it is a highly nontrivial problem how to detect and control a domain wall. We study a one-dimensional hybrid sys

Subhajit Sahu

PageRank is a widely used centrality measure that "ranks" vertices in a graph by considering the connections and their importance. In this report, we first introduce one of the most efficient GPU implementations of Static PageRank, which recomputes PageRank scores from scratch. It uses a synchronous pull-based atomics-free PageRank computation, with the low

Mikolaj Czerkawski, Christos Ilioudis, Carmine Clemente, Craig Michie

The treatment of interfering motion contributions remains one of the key challenges in the domain of radar-based vital sign monitoring. Removal of the interference to extract the vital sign contributions is demanding due to overlapping Doppler bands, the complex structure of the interference motions and significant variations in the power levels of their con

Brayan M. Shali, Henk J. van Waarde

A major problem in system identification is the incorporation of prior knowledge about the physical properties of the given system, such as stability, positivity and passivity. In this paper, we present first steps towards tackling this problem for passive systems. In particular, using ideas from the theory of reproducing kernel Hilbert spaces, we solve the

Kun Yang, Chenggang Bai, Zhikun She, Quan Quan

In recent years, reports of illegal drones threatening public safety have increased. For the invasion of fully autonomous drones, traditional methods such as radio frequency interference and GPS shielding may fail. This paper proposes a scheme that uses an autonomous multicopter with a strapdown camera to intercept a maneuvering intruder UAV. The interceptor

Kazuki Tsurumaki, Chie Hieida, Kazuki Miyazawa

How are emotions formed? Through extensive debate and the promulgation of diverse theories , the theory of constructed emotion has become prevalent in recent research on emotions. According to this theory, an emotion concept refers to a category formed by interoceptive and exteroceptive information associated with a specific emotion. An emotion concept store

Juan-Pablo Ortega, Florian Rossmannek

A probabilistic framework to study the dependence structure induced by deterministic discrete-time state-space systems between input and output processes is introduced. General sufficient conditions are formulated under which output processes exist and are unique once an input process has been fixed, a property that in the deterministic state-space literatur

Ramakrishna Nanduri, Tapas Kumar Roy

In this article, we investigate the strongly robust property of toric ideals associated with weighted oriented graphs. We establish that the toric ideals of a broad class of monomial ideals are strongly robust; this class encompasses the edge ideals of weighted oriented graphs in which every edge is incident to a vertex of degree $2$.

Vamshi Krishna Kancharla, Neelam sinha

This paper focuses on improving object detection performance by addressing the issue of image distortions, commonly encountered in uncontrolled acquisition environments. High-level computer vision tasks such as object detection, recognition, and segmentation are particularly sensitive to image distortion. To address this issue, we propose a novel approach em

Tianyu Ding, Jinxin Zhou, Tianyi Chen, Zhihui Zhu

Existing angle-based contour descriptors suffer from lossy representation for non-starconvex shapes. By and large, this is the result of the shape being registered with a single global inner center and a set of radii corresponding to a polar coordinate parameterization. In this paper, we propose AdaContour, an adaptive contour descriptor that uses multiple l

Mikolaj Czerkawski, Carmine Clemente, Craig Michie, Christos Tachtatzis

Convolutional neural networks have often been proposed for processing radar Micro-Doppler signatures, most commonly with the goal of classifying the signals. The majority of works tend to disregard phase information from the complex time-frequency representation. Here, the utility of the phase information, as well as the optimal format of the Doppler-time in

Thomas Chambrion, Nabile Boussaid, Marco Caponigro

We present sufficient conditions for the exact controllability in projection of the linear Schr{\"o}dinger equations in the case where the spectrum of the free Hamiltonian is pure point. We consider the general case in which the Hamiltonian may be nonlinear with respect to the control. The controllability result applies, in particular, to Schr{\"o}dinger equ

Andrei Agrachev, Cyril Letrouit

There exist many examples of systems which have some symmetries, and which one may monitor with symmetry preserving controls. Since symmetries are preserved along the evolution, full controllability is not possible, and controllability has to be considered inside sets of states with same symmetries. We prove that generic systems with symmetries are controlla

Isa Hafalir, Onur Kesten, Donglai Luo, Katerina Sherstyuk

We examine a unique auction format used in the Istanbul flower market, which could transform into either Dutch or English auction depending on bidders' bidding behaviors. By introducing a time cost that reduces the value of a perishable good as time passes, we explore how this hybrid auction format accommodates the desire for speed via an adaptive starting p

Gerth Stølting Brodal

Rebalancing schemes for dynamic binary search trees are numerous in the literature, where the goal is to maintain trees of low height, either in the worst-case or expected sense. In this paper we study randomized rebalancing schemes for sequences of $n$ insertions into an initially empty binary search tree, under the assumption that a tree only stores the el

Hao Sun, Junting Chen

This paper explores an energy-modified leverage sampling strategy for matrix completion in radio map construction. The main goal is to address potential identifiability issues in matrix completion with sparse observations by using a probabilistic sampling approach. Although conventional leverage sampling is commonly employed for designing sampling patterns,

Jie Wang, Jun Ai, Minyan Lu, Haoran Su

In recent years, there has been significant attention given to the robustness assessment of neural networks. Robustness plays a critical role in ensuring reliable operation of artificial intelligence (AI) systems in complex and uncertain environments. Deep learning's robustness problem is particularly significant, highlighted by the discovery of adversarial

Yin Jin, Wei Luo

A bottleneck of sufficient dimension reduction (SDR) in the modern era is that, among numerous methods, only the sliced inverse regression (SIR) is generally applicable under the high-dimensional settings. The higher-order inverse regression methods, which form a major family of SDR methods that are superior to SIR in the population level, suffer from the di

Rogelio Tomas Garcia

Analytical expressions are derived for the number of fractions with equal numerators in the Farey sequence of order $n$, $F_n$, and in the truncated Farey sequence $F_n^{1/k}$ containing all Farey fractions below $1/k$, with $1\leq k \leq n$. These developments lead to an expression for the rank of $1/k$ in $F_n$, or equivalently $\left|F_n^{1/k}\right|$, an

Pierre-Antoine Comby, Alexandre Vignaud, Philippe Ciuciu

We propose a new, modular, open-source, Python-based 3D+time fMRI data simulation software, \emph{SNAKE-fMRI}, which stands for \emph{S}imulator from \emph{N}eurovascular coupling to \emph{A}cquisition of \emph{K}-space data for \emph{E}xploration of fMRI acquisition techniques.Unlike existing tools, the goal here is to simulate the complete chain of fMRI da

Yichen Yan, Xingjian He, Sihan Chen, Jing Liu

Referring image segmentation aims to segment an object referred to by natural language expression from an image. The primary challenge lies in the efficient propagation of fine-grained semantic information from textual features to visual features. Many recent works utilize a Transformer to address this challenge. However, conventional transformer decoders ca

Xin Wei, Xiande Zhang, Gennian Ge

Set systems with strongly restricted intersections, called $\alpha$-intersecting families for a vector $\alpha$, were introduced recently as a generalization of several well-studied intersecting families including the classical oddtown and eventown. Given a binary vector $\alpha=(a_1, \ldots, a_k)$, a collection $\mathcal F$ of subsets over an $n$ element se

MingXuan Xiao, Yufeng Li, Xu Yan, Min Gao

Breast cancer is a relatively common cancer among gynecological cancers. Its diagnosis often relies on the pathology of cells in the lesion. The pathological diagnosis of breast cancer not only requires professionals and time, but also sometimes involves subjective judgment. To address the challenges of dependence on pathologists expertise and the time-consu

Omar Hagrass, Bharath Sriperumbudur, Krishnakumar Balasubramanian

We explore the minimax optimality of goodness-of-fit tests on general domains using the kernelized Stein discrepancy (KSD). The KSD framework offers a flexible approach for goodness-of-fit testing, avoiding strong distributional assumptions, accommodating diverse data structures beyond Euclidean spaces, and relying only on partial knowledge of the reference

Shubham Tiwari, Yash Sethia, Ritesh Kumar, Ashwani Tanwar

With the advent of social media, fun selfie filters have come into tremendous mainstream use affecting the functioning of facial biometric systems as well as image recognition systems. These filters vary from beautification filters and Augmented Reality (AR)-based filters to filters that modify facial landmarks. Hence, there is a need to assess the impact of

Ronny Jean-Marie-Desiree, Aymane Najah, Cédric Noël, Ludovic de Poucques

Time-resolved electric field strength measurements have been performed, using an electric-field induced second harmonic (E-FISH) diagnostic, in a nanosecond glow discharge of an impulse dielectric barrier discharge (iDBD), in an ammonia gas environment. A temporal resolution of 2 ns and a spatial resolution estimated at 70 $\mu$m (given by laser waist) have

Abdolhalim Torrik, Mahdi Zarif

Active matter systems being in a non-equilibrium state, exhibit complex behaviors such as self-organization and giving rise to emergent phenomena. There are many examples of active particles with biological origins, including bacteria and spermatozoa, or with artificial origins, such as self-propelled swimmers and Janus particles. The ability to manipulate a

J. Philippe, F. Elson, M. P. N. Casati, S. Sanz

Low-dimensional quantum magnets are a versatile materials platform for studying the emergent many-body physics and collective excitations that can arise even in systems with only short-range interactions. Understanding their low-temperature structure and spin Hamiltonian is key to explaining their magnetic properties, including unconventional quantum phases,

Yujie Li, Yanbin Wang, Haitao Xu, Bin Liu

Adversarial attacks induce misclassification by introducing subtle perturbations. Recently, diffusion models are applied to the image classifiers to improve adversarial robustness through adversarial training or by purifying adversarial noise. However, diffusion-based adversarial training often encounters convergence challenges and high computational expense

Zhangyi Yu, Junping Xie, Xingyong Zhang, Wanting Qi

We investigate the multiplicity of solutions for a generalized poly-Laplacian system on weighted finite graphs and a generalized poly-Laplacian system with Dirichlet boundary value on weighted locally finite graphs, respectively, via the variational methods which are based on mountain pass theorem and topological degree theory. We obtain that these two syste

Lars Ullrich, Alex McMaster, Knut Graichen

Trajectory planning in autonomous driving is highly dependent on predicting the emergent behavior of other road users. Learning-based methods are currently showing impressive results in simulation-based challenges, with transformer-based architectures technologically leading the way. Ultimately, however, predictions are needed in the real world. In addition

Johannes Jaerisch, Elaine Rocha, Manuel Stadlbauer

We discuss relations between the amenability of a graph and spectral properties of a random walk driven by a dynamical system. In order to include graphs which are not locally compact, we introduce the concept of amenability of weighted graphs, which generalises the usual notion as the new definition is shown to be equivalent to Foelner's condition. As a fir

D. L. Bashlakov, O. E. Kvitnitskaya, S. Aswartham, G. Shipunov

VS2 and VSe2 have attracted particular attention among the transition metals dichalcogenides because of their promising physical properties concerning magnetic ordering, charge density wave (CDW), emergent superconductivity, etc., which are very sensitive to stoichiometry and dimensionality reduction. Yanson point contact (PC) spectroscopic study reveals met

Giulio Maria Bianco, Gaetano Marrocco

Microfluidic has been an enabling technology for over a decade, particularly in the field of medical and wearable devices, allowing for the manipulation of small amounts of fluid in confined spaces. Micro-channels can also be used for wireless sensing thanks to the variations in antenna properties when the fluid flows near it. However, up to now, microfluidi

Takayuki Tamura

One of the XRISM mission goals is to measure the gas dynamics of galaxy clusters with Resolve spectroscopy. To archive these, we propose an observation of the X-ray bright galaxy cluster Abell 2256 at z=0.06. This hosts 2nd brightest diffuse radio relic. Suzaku revealed a gas bulk motion at 1500 km/s, for the first time i\ n a cluster. This X-ray gas velocit

Kana Moriwaki, Angus Beane, Adam Lidz

The early stages of the Epoch of Reionization, probed by the 21 cm line, are sensitive to the detailed properties and formation histories of the first galaxies. We use 21cmFAST and a simple, self-consistent galaxy model to examine the redshift evolution of the large-scale cross-power spectrum between the 21 cm field and line-emitting galaxies. A key transiti

Yunting Li, Xiaopeng Cui, Zhaoping Xiong, Bowen Liu

Molecular docking (MD) is a crucial task in drug design, which predicts the position, orientation, and conformation of the ligand when bound to a target protein. It can be interpreted as a combinatorial optimization problem, where quantum annealing (QA) has shown promising advantage for solving combinatorial optimization. In this work, we propose a novel qua

Masahiro Yasuda, Noboru Harada, Yasunori Ohishi, Shoichiro Saito

Observations with distributed sensors are essential in analyzing a series of human and machine activities (referred to as 'events' in this paper) in complex and extensive real-world environments. This is because the information obtained from a single sensor is often missing or fragmented in such an environment; observations from multiple locations and modali

Pu Li, Xiaoyan Yu, Hao Peng, Yantuan Xian

Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) based solutions have achieved state-of-the-art performance. However, GNN-based methods often struggle with missing and noisy edges between message

Kosuke Takahashi, Takahiro Omi, Kosuke Arima, Tatsuya Ishigaki

The development of Large Language Models (LLMs) in various languages has been advancing, but the combination of non-English languages with domain-specific contexts remains underexplored. This paper presents our findings from training and evaluating a Japanese business domain-specific LLM designed to better understand business-related documents, such as the n

Wenhao Yuan, Xuehe Wang

Federated Learning (FL) has increasingly been recognized as an innovative and secure distributed model training paradigm, aiming to coordinate multiple edge clients to collaboratively train a shared model without uploading their private datasets. The challenge of encouraging mobile edge devices to participate zealously in FL model training procedures, while

Slavko Moconja, Predrag Tanović

We introduce and study weak o-minimality in the context of complete types in an arbitrary first-order theory. A type $p\in S(A)$ is weakly o-minimal if for some relatively $A$-definable linear order, $<$, on $p(\mathfrak{C})$ every relatively $L_{\mathfrak{C}}$-definable subset of $p(\mathfrak{C})$ has finitely many convex components in $(p(\mathfrak{C}),<)$

Wan-Hua Her, Udo Kruschwitz

Machine Translation has made impressive progress in recent years offering close to human-level performance on many languages, but studies have primarily focused on high-resource languages with broad online presence and resources. With the help of growing Large Language Models, more and more low-resource languages achieve better results through the presence o

Yu Chen, Shengxiang Wu, Shiwu Gao

The damping of surface plasmons generates hot carriers, which holds promise for photoelectric conversion and photocatalysis. Recent studies have revealed the nonequilibrium characters of the plasmonic hot carriers and their nonadiabatic coupling to molecular vibrations. Yet, the precise mechanism of plasmonic photocatalysis remains elusive and controversial.

Eric Ling

Utilizing some of Sbierski's recent $C^0$-inextendibility techniques [18], we prove the $C^0$-inextendibility of a class of spatially flat FLRW spacetimes without particle horizons.

J. Koga, Y. Chiashi, A. Nakamura, T. Akiba

Transition metal dichalcogenides with superperiodic lattice distortions have been widely investigated as the platform of ultrafast structural phase manipulations. Here we performed ultrafast electron diffraction on room-temperature TaTe$_2$, which exhibits peculiar double zigzag chain pattern of Ta atoms. From the time-dependent electron diffraction pattern,

Yifan Shen, Zhengyuan Li, Gang Wang

Segment Anything Models (SAM) have made significant advancements in image segmentation, allowing users to segment target portions of an image with a single click (i.e., user prompt). Given its broad applications, the robustness of SAM against adversarial attacks is a critical concern. While recent works have explored adversarial attacks against a pre-defined

Zeyu Yang, Han Yu, Peikun Guo, Khadija Zanna

Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis. However, current tabular diffusion models tend to inherit bias in the training dataset and generate biased synthetic data, which may influence discriminatory actions. In this research, we introduce a novel tabular diffusion model that incorpora

Subhadarsi Nayak, Hrithwik Shalu, Joseph Stember

Tropospheric ozone, known as a concerning air pollutant, has been associated with health issues including asthma, bronchitis, and impaired lung function. The rates at which peroxy radicals react with NO play a critical role in the overall formation and depletion of tropospheric ozone. However, obtaining comprehensive kinetic data for these reactions remains

Arup Chattopadhyay, Clément Coine, Saikat Giri, Chandan Pradhan

Consider the set of unitary operators on a complex separable Hilbert space $\hilh$, denoted as $\mathcal{U}(\hilh)$. Consider $1<p<\infty$. We establish that a function $f$ defined on the unit circle $\cir$ is $n$ times continuously Fr\'echet $\Sp^p$-differentiable at every point in $\mathcal{U}(\hilh)$ if and only if $f\in C^n(\cir)$. Take a function $U :\R

Yuqun Wu, Jae Yong Lee, Chuhang Zou, Shenlong Wang

The latest regularized Neural Radiance Field (NeRF) approaches produce poor geometry and view extrapolation for large scale sparse view scenes, such as ETH3D. Density-based approaches tend to be under-constrained, while surface-based approaches tend to miss details. In this paper, we take a density-based approach, sampling patches instead of individual rays

Linhao Xiong

We explore the interactions between Argon and extreme ultraviolet (XUV) laser pulses across photon energies of 200 eV, 260 eV, and 315 eV, scrutinizing the influence of photon energy on Argon ion yields and unraveling the associated ionization pathways. Utilizing pulse durations of 10 fs and 30 fs, we spotlight a notable increase in Argon's ionization propen

Yogesh Kumar, P. R. Mishra, Susanta Samanta, Atul Gaur

Maximum distance separable (MDS) matrices play a crucial role not only in coding theory but also in the design of block ciphers and hash functions. Of particular interest are involutory MDS matrices, which facilitate the use of a single circuit for both encryption and decryption in hardware implementations. In this article, we present several characterizatio

H. Kalyankar, L. Taubert, I. Wygnanski

This study examines flow over a cranked {\lambda}-wing model with a sweep of {\Lambda}=60{\deg} of the inboard leading edge (LE) that changed to {\Lambda}=30{\deg} outboard of the crank. The study focuses on the liftoff of the inboard Leading-Edge Vortex (LEV) and its influence over the flow on the outer wing. Stereoscopic Particle Image Velocimetry (SPIV) i

Volodymyr M. Lashkin

We study the dynamics of two-dimensional nonlinear ion-ion hybrid waves propagating perpendicular to an external magnetic field in plasmas with two ion species. We derive nonlinear equations for the envelope of electrostatic potential at the ion-ion hybrid frequency to describe the interaction of ion-ion hybrid waves with low frequency acoustic-type disturba

Chong-Chong He, Benjamin D. Wibking, Mark R. Krumholz

Numerical radiation-hydrodynamics (RHD) for non-relativistic flows is a challenging problem because it encompasses processes acting over a very broad range of timescales, and where the relative importance of these processes often varies by orders of magnitude across the computational domain. Here we present a new implicit-explicit (IMEX) method for numerical

Jiayi Li, Linqi Ye, Yi Cheng, Houde Liu

The remarkable athletic intelligence displayed by humans in complex dynamic movements such as dancing and gymnastics suggests that the balance mechanism in biological beings is decoupled from specific movement patterns. This decoupling allows for the execution of both learned and unlearned movements under certain constraints while maintaining balance through

Riccardo Ceccaroni, Lorenzo Di Rocco, Umberto Ferraro Petrillo, Pierpaolo Brutti

Persistent homology (PH) is a powerful mathematical method to automatically extract relevant insights from images, such as those obtained by high-resolution imaging devices like electron microscopes or new-generation telescopes. However, the application of this method comes at a very high computational cost, that is bound to explode more because new imaging

Souradeep Sengupta, Somendra M. Bhattacharjee, Garima Mishra

We investigate the melting transition of non-supercoiled circular DNA of different lengths, employing Brownian dynamics simulation. In the absence of supercoiling, we find that melting of circular DNA is driven by a large bubble, which agrees with the previous predictions of circular DNA melting in the presence of supercoiling. By analyzing sector-wise chang

Hao Chen, Di Wu, Meng-Yao Zhang, Hassan Hassanabadi

In this work, we explore the thermodynamic topology of phantom AdS black holes in the context of massive gravity. To this end, we evaluate these black holes in two distinct ensembles: the canonical and grand canonical ensembles (GCE). We begin by examining the topological charge linked to the critical point and confirming the existence of a conventional crit

Hongqiao Lian, Zeyuan Ma, Hongshu Guo, Ting Huang

Solving multimodal optimization problems (MMOP) requires finding all optimal solutions, which is challenging in limited function evaluations. Although existing works strike the balance of exploration and exploitation through hand-crafted adaptive strategies, they require certain expert knowledge, hence inflexible to deal with MMOP with different properties.

Soo Yee Lim, Sidhartha Agrawal, Xueyuan Han, David Eyers

Monolithic operating systems, where all kernel functionality resides in a single, shared address space, are the foundation of most mainstream computer systems. However, a single flaw, even in a non-essential part of the kernel (e.g., device drivers), can cause the entire operating system to fall under an attacker's control. Kernel hardening techniques might

Xinkun Ai

Plasma disruption presents a significant challenge in tokamak fusion, where it can cause severe damage and economic losses. Current disruption predictors mainly rely on data-driven methods, requiring extensive discharge data for training. However, future tokamaks require disruption prediction from the first shot, posing challenges of data scarcity during the

Olga Tanaka, Yosuke Honda, Masahiro Yamamoto, Tomohiro Yamada

A compact accelerator design for irradiation purposes is being proposed at KEK. This design targets an energy of 10 MeV and a current of 50 mA. Current design includes a 100 kV thermionic DC electron gun with an RF grid, 1-cell normal-conducting buncher cavity, and Nb$_{3}$Sn superconducting cavities to accelerate the beam to the final energy of 10 MeV. The

Zeyuan Ma, Jiacheng Chen, Hongshu Guo, Yining Ma

Evolutionary computation (EC) algorithms, renowned as powerful black-box optimizers, leverage a group of individuals to cooperatively search for the optimum. The exploration-exploitation tradeoff (EET) plays a crucial role in EC, which, however, has traditionally been governed by manually designed rules. In this paper, we propose a deep reinforcement learnin

Vidya Sunil, Renu M Rameshan

Eyes serve as our primary sensory organs, responsible for processing up to 80\% of our sensory input. However, common visual aberrations like myopia and hyperopia affect a significant portion of the global population. This paper focuses on simulating a Vision Correction Display (VCD) to enhance the visual experience of individuals with various visual impairm

Yuhang Qiu, Honghui Chen, Xingbo Dong, Zheng Lin

Determining dense feature points on fingerprints used in constructing deep fixed-length representations for accurate matching, particularly at the pixel level, is of significant interest. To explore the interpretability of fingerprint matching, we propose a multi-stage interpretable fingerprint matching network, namely Interpretable Fixed-length Representati

Rahul Kumar Gautam, Anjeneya Swami Kare, S. Durga Bhavani

Nowadays, organizations use viral marketing strategies to promote their products through social networks. It is expensive to directly send the product promotional information to all the users in the network. In this context, Kempe et al. \cite{kempe2003maximizing} introduced the Influence Maximization (IM) problem, which identifies $k$ most influential nodes

Junichi Inoguchi, Shimpei Kobayashi

We classify weakly complete constant Gaussian curvature $-1<K<0$ surfaces in the hyperbolic three-space in terms of holomorphic quadratic differentials. For this purpose, we first establish a loop group method for constant Gaussian curvature surfaces with $K>-1$ and $K \neq 0$ via the harmonicity of the Lagrangian and Legendrian Gauss maps. We then show that

Hao Wu, Haibo Yuan, Yilun Wang, Zexi Niu

During the early merger of the Milky Way, intermediate-mass black holes in merged dwarf galaxies may have been ejected from the center of their host galaxies due to gravitational waves, carrying some central stars along. This process can lead to the formation of hyper-compact star clusters, potentially hosting black holes in the mass range of $10^4$ to $10^5

Hui Bai, Ran Cheng

Hyperparameter optimization plays a key role in the machine learning domain. Its significance is especially pronounced in reinforcement learning (RL), where agents continuously interact with and adapt to their environments, requiring dynamic adjustments in their learning trajectories. To cater to this dynamicity, the Population-Based Training (PBT) was intro

Yaser Baseri, Vikas Chouhan, Abdelhakim Hafid

The emergence of quantum computing poses a formidable security challenge to network protocols traditionally safeguarded by classical cryptographic algorithms. This paper provides an exhaustive analysis of vulnerabilities introduced by quantum computing in a diverse array of widely utilized security protocols across the layers of the TCP/IP model, including T

Yaser Baseri, Vikas Chouhan, Ali Ghorbani, Aaron Chow

The rise of large-scale quantum computing poses a significant threat to traditional cryptographic security measures. Quantum attacks undermine current asymmetric cryptographic algorithms, rendering them ineffective. Even symmetric key cryptography is vulnerable, albeit to a lesser extent, suggesting longer keys or extended hash functions for security. Thus,

Khadija Zanna, Akane Sano

Bias originates from both data and algorithmic design, often exacerbated by traditional fairness methods that fail to address the subtle impacts of protected attributes. This study introduces an approach to mitigate bias in machine learning by leveraging model uncertainty. Our approach utilizes a multi-task learning (MTL) framework combined with Monte Carlo

Jiewen Sheng, Xiaolei Fang

This article introduces differentially private log-location-scale (DP-LLS) regression models, which incorporate differential privacy into LLS regression through the functional mechanism. The proposed models are established by injecting noise into the log-likelihood function of LLS regression for perturbed parameter estimation. We will derive the sensitivitie

Maged Shoman, Dongdong Wang, Armstrong Aboah, Mohamed Abdel-Aty

This paper introduces our solution for Track 2 in AI City Challenge 2024. The task aims to solve traffic safety description and analysis with the dataset of Woven Traffic Safety (WTS), a real-world Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding. Our solution mainly focuses on the following points: 1) To solve dense v

Ghassem Jaberipur, Bardia Nadimi, Jeong-A Lee

Augmenting the balanced residue number system moduli-set $\{m_1=2^n,m_2=2^n-1,m_3=2^n+1\}$, with the co-prime modulo $m_4=2^{2n}+1$, increases the dynamic range (DR) by around 70%. The Mersenne form of product $m_2 m_3 m_4=2^{4n}-1$, in the moduli-set $\{m_1,m_2,m_3,m_4\}$, leads to a very efficient reverse convertor, based on the New Chinese remainder theor

Sandra Q. Liu, Edward H. Adelson

Many robotic hands currently rely on extremely dexterous robotic fingers and a thumb joint to envelop themselves around an object. Few hands focus on the palm even though human hands greatly benefit from their central fold and soft surface. As such, we develop a novel structurally compliant soft palm, which enables more surface area contact for the objects t

Lianyu Hu, Tongkai Shi, Liqing Gao, Zekang Liu

The increase of web-scale weakly labelled image-text pairs have greatly facilitated the development of large-scale vision-language models (e.g., CLIP), which have shown impressive generalization performance over a series of downstream tasks. However, the massive model size and scarcity of available data limit their applications to fine-tune the whole model i

Zongbin Chen

According to Laumon, an affine Springer fiber is homeomorphic to the universal abelian covering of the compactified Jacobian of a spectral curve. We construct equivariant deformations $f_{n}:\overline{\mathcal{P}}_{n}\to \mathcal{B}_{n}$ of the finite abelian coverings of this compactified Jacobian, and decompose the complex $Rf_{n,*}\mathbf{Q}_{\ell}$ as di

Haili Sun, Yan Huang, Lansheng Han, Cai Fu

Multivariate Time Series (MTS) anomaly detection focuses on pinpointing samples that diverge from standard operational patterns, which is crucial for ensuring the safety and security of industrial applications. The primary challenge in this domain is to develop representations capable of discerning anomalies effectively. The prevalent methods for anomaly det

Zhaodong Xu, Zhiqiang Sheng

We present a subspace method based on neural networks (SNN) for solving the partial differential equation with high accuracy. The basic idea of our method is to use some functions based on neural networks as base functions to span a subspace, then find an approximate solution in this subspace. We design two special algorithms in the strong form of partial di

Xiang-Long Zeng, Wen-Xi Lai, Yi-Wen Wei, Yu-Quan Ma

We investigate the quantum metric and topological Euler number in a cyclically modulated Su-Schrieffer-Heeger (SSH) model with long-range hopping terms. By computing the quantum geometry tensor, we derive exactly expressions for the quantum metric and Berry curvature of the energy band electrons, and we obtain the phase diagram of the model marked by the fir

Daniel J. Korchinski, Jörg Rottler

Using mean field theory and a mesoscale elastoplastic model, we analyze the steady state shear rheology of thermally activated amorphous solids. At sufficiently high temperature and driving rates, flow is continuous and described by well-established rheological flow laws such as Herschel-Bulkley and logarithmic rate dependence. However, we find that these fl

Kokila Kasuni Perera, Aneta Neumann

Evolutionary algorithms are particularly effective for optimisation problems with dynamic and stochastic components. We propose multi-objective evolutionary approaches for the knapsack problem with stochastic profits under static and dynamic weight constraints. The chance-constrained problem model allows us to effectively capture the stochastic profits and a