April 2023 arXiv papers — page 67

Mijanur Islam, Tutul Biswas, Saurabh Basu

We consider a quantum ring of a certain radius R built from a sheet of the $\alpha$-$T_3$ lattice and solve for its spectral properties in presence of an external magnetic field. The energy spectrum consists of a conduction band, a valence band and a zero energy flat band, all having a number of discrete levels therein which can be characterized by the angul

Iulian D. Toader

This paper provides an algebraic reconstruction of Einstein's own argument for the incompleteness of quantum mechanics -- the one that he thought did not make it into the EPR paper -- in order to clarify the assumptions that underlie an understanding of Einstein completeness as categoricity, the sense in which it is a type of descriptive completeness, and so

Natalie A. Webb, Didier Barret, Olivier Godet, Maitrayee Gupta

Tidal disruption events (TDEs) occur when a star passes close to a massive black hole, so that the tidal forces of the black hole exceed the binding energy of a star and cause it to be ripped apart. Part of the matter will fall onto the black hole, causing a strong increase in the luminosity. Such events are often seen in the optical or the X-ray (or both) o

Iminhaji Ablimit, Noam Soker

We present a possible evolutionary pathway to form planetary nebulae (PNe) with close neutron star (NS)-white dwarf (WD) binary central stars. By employing a comprehensive binary population synthesis technique we find that the evolution involves two common envelope evolution (CEE) phases and a core collapse supernova explosion between them that forms the NS.

Jinming Su, Ruihong Yin, Xingyue Chen, Junfeng Luo

Recently, instance segmentation has made great progress with the rapid development of deep neural networks. However, there still exist two main challenges including discovering indistinguishable objects and modeling the relationship between instances. To deal with these difficulties, we propose a novel object mining framework for instance segmentation. In th

R. Casagrande, A. Maistrello, M. Recchia, M. De Nardi

SPIDER operation, started in 2018, pointed out performance-limiting issues caused by the technology employed in RF generators, based on tetrode free-running oscillators. One of these limits, namely the onset of frequency instabilities, prevented operation at the full rated power of 200 kW. In addition, tetrodes require high voltage to operate, which translat

Ali Akil, Qianhang Ding

The accretion of dark matter (DM) into astrophysical black holes slowly increases their mass. The rate of this mass accretion depends on the DM model and the model parameters. If this mass accretion effect can be measured accurately enough, it is possible to rule out some DM models, and, with the sufficient technology and the help of other DM constraints, po

Vésteinn Snæbjarnarson, Annika Simonsen, Goran Glavaš, Ivan Vulić

Multilingual language models have pushed state-of-the-art in cross-lingual NLP transfer. The majority of zero-shot cross-lingual transfer, however, use one and the same massively multilingual transformer (e.g., mBERT or XLM-R) to transfer to all target languages, irrespective of their typological, etymological, and phylogenetic relations to other languages.

Bohan Yang, Congying Sui, Fangxun Zhong, Yun-Hui Liu

Deformable object manipulation (DOM) with point clouds has great potential as non-rigid 3D shapes can be measured without detecting and tracking image features. However, robotic shape control of deformable objects with point clouds is challenging due to: the unknown point-wise correspondences and the noisy partial observability of raw point clouds; the model

Yuwei Yin, Jean Kaddour, Xiang Zhang, Yixin Nie

Data augmentation has been established as an efficacious approach to supplement useful information for low-resource datasets. Traditional augmentation techniques such as noise injection and image transformations have been widely used. In addition, generative data augmentation (GDA) has been shown to produce more diverse and flexible data. While generative ad

Jinming Su, Ruihong Yin, Shuaibin Zhang, Junfeng Luo

In recent years, video semantic segmentation has made great progress with advanced deep neural networks. However, there still exist two main challenges \ie, information inconsistency and computation cost. To deal with the two difficulties, we propose a novel motion-state alignment framework for video semantic segmentation to keep both motion and state consis

Zhuo Jin, Zuo Quan Xu, Bin Zou

We study an optimal reinsurance problem under a diffusion risk model for an insurer who aims to minimize the probability of lifetime ruin. To rule out moral hazard issues, we only consider moral-hazard-free reinsurance contracts by imposing the incentive compatibility constraint on indemnity functions. The reinsurance premium is calculated under an extended

Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn

Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We first pre-train an LDM on images only; then, we turn the image

Pengfei Li, Hua Lu, Rong Zhu, Bolin Ding

Targeting in-memory one-dimensional search keys, we propose a novel DIstribution-driven Learned Index tree (DILI), where a concise and computation-efficient linear regression model is used for each node. An internal node's key range is equally divided by its child nodes such that a key search enjoys perfect model prediction accuracy to find the relevant leaf

L. Ducci, C. Malacaria, P. Romano, E. Bozzo

IGR J17407-2808 is an enigmatic and poorly studied X-ray binary that was recently observed quasi-simultaneously with NuSTAR and XMM-Newton. In this paper we report the results of this observational campaign. During the first 60 ks of observation, the source was caught in a relatively low emission state, characterised by a modest variability and an average fl

Jae Hyuck Lee, Dirk Wulferding, Junkyoung Kim, Dongjoon Song

We present a systematic Raman spectroscopy study on a series of pyrochlore ruthenates, a system which is not yet clearly settled on its magnetic origin and structure. Apart from the Raman-active phonon modes, new peaks that appear in the energy range of 15 - 35 meV below the N\'{e}el temperature are assigned as one-magnon modes. The temperature evolution of

Arianne Meijer - van de Griend

In the NISQ era, where quantum computing is dominated by hybrid quantum algorithms, it is important for quantum circuits to be well-optimized to reduce noise from unnecessary gates. We investigate different phase polynomial-based compilation strategies to determine the current best practices and compare them against the DAG-based Qiskit and TKET compilers. W

Petre Stoica, Prabhu Babu

Factor analysis (FA) or principal component analysis (PCA) models the covariance matrix of the observed data as R = SS' + {\Sigma}, where SS' is the low-rank covariance matrix of the factors (aka latent variables) and {\Sigma} is the diagonal matrix of the noise. When the noise is anisotropic (aka nonuniform in the signal processing literature and heterosced

Boris Latosh, Anton Yachmenev

We discuss an alternative approach to studying the low energy limit of quantum general relativity. We investigate the low energy limit of a scattering cross-section for two massive scalar particles. Unlike calculations involving the reconstruction of the gravitational potential, our approach avoids ambiguities and is applicable in any frame. Our results are

Feng Guo, Zheng Sun, Yuxuan Chen, Lei Ju

In recent years, deep learning (DL) models have achieved significant progress in many domains, such as autonomous driving, facial recognition, and speech recognition. However, the vulnerability of deep learning models to adversarial attacks has raised serious concerns in the community because of their insufficient robustness and generalization. Also, transfe

Lei Wang, Paolo Tozzi, Heng Yu, Massimo Gaspari

We investigate the properties of cool cores in an optimally selected sample of 37 massive and X-ray-bright galaxy clusters, with regular morphologies, observed with Chandra. We measured the density, temperature, and abundance radial profiles of their intracluster medium (ICM). From these independent quantities, we computed the cooling (tcool) free-fall (tff)

Yi Li, Kyle Min, Subarna Tripathi, Nuno Vasconcelos

Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has revealed the strong tendency of video-text models towards frame-based spatial representations, while temporal reasoning remains largely unsolved. In this work, we identify several key ch

Ankit Shukla, Laura Heller, Md Golam Morshed, Laura Rehm

Magnetic tunnel junctions (MTJs), which are the fundamental building blocks of spintronic devices, have been used to build true random number generators (TRNGs) with different trade-offs between throughput, power, and area requirements. MTJs with high-barrier magnets (HBMs) have been used to generate random bitstreams with $\lesssim$ 200~Mb/s throughput and

Hongguang Shi, Shuirong Cao, Cam-Tu Nguyen

This paper studies the task of best counter-argument retrieval given an input argument. Following the definition that the best counter-argument addresses the same aspects as the input argument while having the opposite stance, we aim to develop an efficient and effective model for scoring counter-arguments based on similarity and dissimilarity metrics. We fi

Hideaki Hara, Junseok Han, Yasutaka Imai, Noboru Sasao

We observed periodic optical pulses from an Er:YSO crystal during irradiating with an continuous-wave excitation laser. We refer to this new phenomenon as "periodic super-radiance". This periodicity can be understood qualitatively by a simple model, in which a cyclic process of a continuous supply of population inversion and a sudden burst of super-radiance

Norman Marlier, Julien Gustin, Olivier Brüls, Gilles Louppe

Robotic grasping in highly noisy environments presents complex challenges, especially with limited prior knowledge about the scene. In particular, identifying good grasping poses with Bayesian inference becomes difficult due to two reasons: i) generating data from uninformative priors proves to be inefficient, and ii) the posterior often entails a complex di

Jakob Schoeffer, Johannes Jakubik, Michael Voessing, Niklas Kuehl

In AI-assisted decision-making, a central promise of having a human-in-the-loop is that they should be able to complement the AI system by overriding its wrong recommendations. In practice, however, we often see that humans cannot assess the correctness of AI recommendations and, as a result, adhere to wrong or override correct advice. Different ways of rely

Guoliang Xu, Jianqin Yin

The task of Group Activity Recognition (GAR) aims to predict the activity category of the group by learning the actor spatial-temporal interaction relation in the group. Therefore, an effective actor relation learning method is crucial for the GAR task. The previous works mainly learn the interaction relation by the well-designed GCNs or Transformers. For ex

Stein Stroobants, Julien Dupeyroux, Guido C. H. E. de Croon

Compelling evidence has been given for the high energy efficiency and update rates of neuromorphic processors, with performance beyond what standard Von Neumann architectures can achieve. Such promising features could be advantageous in critical embedded systems, especially in robotics. To date, the constraints inherent in robots (e.g., size and weight, batt

Shivani Kumar, Rishabh Gupta, Md Shad Akhtar, Tanmoy Chakraborty

In conversational settings, individuals exhibit unique behaviors, rendering a one-size-fits-all approach insufficient for generating responses by dialogue agents. Although past studies have aimed to create personalized dialogue agents using speaker persona information, they have relied on the assumption that the speaker's persona is already provided. However

I-Kun Chen, Ping-Han Chuang, Chun-Hsiung Hsia, Daisuke Kawagoe

In this article, we investigate the incoming boundary value problem for the stationary linearized Boltzmann equations in $ \Omega \subseteq \mathbb{R}^{3}$. For a $C^2$ bounded domain with boundary of positive Gaussian curvature, the existence theory is established in $H^{1}(\Omega \times \mathbb{R}^{3})$ provided that the diameter of the domain $\Omega$ is

Siyuan Yang, Jun Liu, Shijian Lu, Er Meng Hwa

3D Skeleton-based human action recognition has attracted increasing attention in recent years. Most of the existing work focuses on supervised learning which requires a large number of labeled action sequences that are often expensive and time-consuming to annotate. In this paper, we address self-supervised 3D action representation learning for skeleton-base

Qu Wang

Large-scale Dynamic Networks (LDNs) are becoming increasingly important in the Internet age, yet the dynamic nature of these networks captures the evolution of the network structure and how edge weights change over time, posing unique challenges for data analysis and modeling. A Latent Factorization of Tensors (LFT) model facilitates efficient representation

Maximilian Engel, Georg A. Gottwald

Canards are a well-studied phenomenon in fast-slow ordinary differential equations implying the delayed loss of stability after the slow passage through a singularity. Recent studies have shown that the corresponding maps stemming from explicit Runge-Kutta discretizations, in particular the forward Euler scheme, exhibit significant distinctions to the contin

Hao Feng, Shaokai Liu, Jiajun Deng, Wengang Zhou

In recent years, tremendous efforts have been made on document image rectification, but existing advanced algorithms are limited to processing restricted document images, i.e., the input images must incorporate a complete document. Once the captured image merely involves a local text region, its rectification quality is degraded and unsatisfactory. Our previ

Tita Alissa Bach, Amna Khan, Harry Hallock, Gabriela Beltrão

User trust in Artificial Intelligence (AI) enabled systems has been increasingly recognized and proven as a key element to fostering adoption. It has been suggested that AI-enabled systems must go beyond technical-centric approaches and towards embracing a more human centric approach, a core principle of the human-computer interaction (HCI) field. This revie

Hanying Deng, Zhihao Qu, Yingji He, Changming Huang

We study the nonlinear optical properties of heterojunctions made of graphene nanoribbons (GNRs) consisting of two segments with either the same or different topological properties. By utilizing a quantum mechanical approach that incorporates distant-neighbor interactions, we demonstrate that the presence of topological interface states significantly enhance

Mark-Oliver Wolf, Tom Ewen, Ivica Turkalj

Classical Monte Carlo algorithms can theoretically be sped up on a quantum computer by employing amplitude estimation (AE). To realize this, an efficient implementation of state-dependent functions is crucial. We develop a straightforward approach based on pretraining parameterized quantum circuits, and show how they can be transformed into their conditional

Shih-Wen Huang, L. Andrew Wray, Yu-Cheng Shao, Cheng-Yau Wu

Using high-resolution extreme ultraviolet resonant inelastic X-ray scattering (EUVRIXS) spectroscopy at Cu M-edge, we observed the doping dependent spectral shifts of inter-orbital (dd) excitations of YBa_2Cu_3O_(7-x) and La_(2-x)Sr_xCuO_4. With increasing hole doping level from undoped to optimally doped superconducting compositions, the leading edge of dd

Genqiang Liu, Yang Li

For any $\mathbf{a}=(a_1,\dots,a_n)\in \mathbb{C}^n$, we introduce a Whittaker category $\mathcal{H}_{\mathbf{a}}$ whose objects are $\mathfrak{sl}_{n+1}$-modules $M$ such that $e_{0i}-a_i$ acts locally nilpotently on $M$ for all $i \in \{1,\dots,n\}$, and the subspace $\mathrm{wh}_{\mathbf{a}}(M)=\{v\in M \mid e_{0i} v=a_iv, \ i=1,\dots,n\}$ is finite dimen

Cuong Le, Tien Mai

We study the assortment optimization problem under general linear constraints, where the customer choice behavior is captured by the Cross-Nested Logit model. In this problem, there is a set of products organized into multiple subsets (or nests), where each product can belong to more than one nest. The aim is to find an assortment to offer to customers so th

Jungyeon Park, Estêvão Alvarenga, Jooyoung Jeon, Ran Li

In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants' strategies. Accurate probabilistic forecasts at low aggregation levels can improve peer-to-peer energy sharing, demand response, and the operatio

Besma Smida, Ashutosh Sabharwal, Gabor Fodor, George C. Alexandropoulos

The use of in-band full-duplex (FD) enables nodes to simultaneously transmit and receive on the same frequency band, which challenges the traditional assumption in wireless network design. The full-duplex capability enhances spectral efficiency and decreases latency, which are two key drivers pushing the performance expectations of next-generation mobile net

Fahim Abdul Gafoor, Chung Suk Cho, Maryam R. Al Shehhi

Due to global warming and its detrimental effect, every country is responsible to join the global effort to reduce carbon emissions. In order to improve the mitigation plan of climate change, accurate es-timates of carbon emissions, population, and electricity consumption are critical. Carbon footprint is significantly linked to the socioeconomic development

L. Serafini, V. Petrillo, A. Bacci, C. Curatolo

This paper explores the transition between Compton Scattering and Inverse Compton Scattering (ICS), which is characterized by an equal exchange of energy and momentum between the colliding particles (electrons and photons). This regime has been called Symmetric Compton Scattering (SCS) and has the unique property of cancelling the energy-angle correlation of

Feng-Lei Liu, Xiang-Yu Wu, Shanshan Cao, Guang-You Qin

In a quasi-particle model of QCD matter at finite temperature with thermal masses for quarks and gluons from hard thermal loops, the equation of state (EOS) can be described by an effective temperature dependence of the strong coupling $g(T)$. Assuming the same effective coupling between the exchanged gluon and thermal partons, the EOS can also be related to

Angel Gómez Nicola, Jacobo Ruiz de Elvira, Andrea Vioque-Rodríguez

We perform a complete calculation of the pion-kaon scattering amplitude in Chiral Perturbation Theory at finite temperature, paying particular attention to the analytic structure of the amplitude and the main differences with respect to the zero temperature case. We also extend the Inverse Amplitude Method at finite temperature for unequal-mass scattering pr

Patrick Dondl, Yongming Luo, Stefan Neukamm, Steve Wolff-Vorbeck

Motivated by an application involving additively manufactured bioresorbable polymer scaffolds supporting bone tissue regeneration, we investigate the impact of uncertain geometry perturbations on the effective mechanical properties of elastic rods. To be more precise, we consider elastic rods modeled as three-dimensional linearly elastic bodies occupying ran

Marie Billaud-Friess, Arthur Macherey, Anthony Nouy, Clémentine Prieur

Probabilistic variants of Model Order Reduction (MOR) methods have recently emerged for improving stability and computational performance of classical approaches. In this paper, we propose a probabilistic Reduced Basis Method (RBM) for the approximation of a family of parameter-dependent functions. It relies on a probabilistic greedy algorithm with an error

Raj G. Patel, Tomas Dominguez, Mohammad Dib, Samuel Palmer

The Cheyette model is a quasi-Gaussian volatility interest rate model widely used to price interest rate derivatives such as European and Bermudan Swaptions for which Monte Carlo simulation has become the industry standard. In low dimensions, these approaches provide accurate and robust prices for European Swaptions but, even in this computationally simple s

Zihui Liang, Bakh Khoussainov, Toru Takisaka, Mingyu Xiao

The paper introduces two player connectivity games played on finite bipartite graphs. Algorithms that solve these connectivity games can be used as subroutines for solving M\"uller games. M\"uller games constitute a well established class of games in model checking and verification. In connectivity games, the objective of one of the players is to visit every

Minrui Xu, Dusit Niyato, Hongliang Zhang, Jiawen Kang

Aiming at achieving artificial general intelligence (AGI) for Metaverse, pretrained foundation models (PFMs), e.g., generative pretrained transformers (GPTs), can effectively provide various AI services, such as autonomous driving, digital twins, and AI-generated content (AIGC) for extended reality. With the advantages of low latency and privacy-preserving,

Ran Li, Chuan Huang, Xiaoqi Qin, Lei Yang

Mobile edge caching (MEC) is a promising technique to improve the quality of service (QoS) for mobile users (MU) by bringing data to the network edge. However, optimizing the crucial QoS aspects of message freshness and service promptness, measured by age of information (AoI) and service delay, respectively, entails a tradeoff due to their competition for sh

Diana Korka, Kavous Salehzadeh Niksirat, Mauro Cherubini

System-generated user-facing notices, dialogs, and warnings in privacy and security interventions present the opportunity to support users in making informed decisions about identified risks. However, too often, they are bypassed, ignored, and mindlessly clicked through, mainly in connection to the well-studied effect of user fatigue and habituation. The con

Dieter Teichrib, Moritz Schulze Darup

Neural network (NN) approximations of model predictive control (MPC) are a versatile approach if the online solution of the underlying optimal control problem (OCP) is too demanding and if an exact computation of the explicit MPC law is intractable. The drawback of such approximations is that they typically do not preserve stability and performance guarantee

Stein Stroobants, Christophe De Wagter, Guido C. H. E. de Croon

Neuromorphic processing promises high energy efficiency and rapid response rates, making it an ideal candidate for achieving autonomous flight of resource-constrained robots. It will be especially beneficial for complex neural networks as are involved in high-level visual perception. However, fully neuromorphic solutions will also need to tackle low-level co

Cong Yi, Xiang-Yu Wu, Di-Lun Yang, Jian-Hua Gao

We investigate the hydrodynamic helicity polarization of $\Lambda$ hyperons, defined as the projection of the spin polarization vector along the directions of particle momenta, at RHIC-BES energies by utilizing the relativistic (3+1)D CLVisc hydrodynamics framework with SMASH initial conditions. As opposed to local spin polarization at high energy collisions

Omar G. Benvenuto, Eduardo Bauer, Isaac Vidaña

We study the properties of hot and dense neutron star matter under the presence of strong magnetic fields using two Skyrme interactions, namely the LNS and the BSk21 ones. Asking for $\beta$--stability and charge neutrality, we construct the equation of state of the system and analyze its composition for a range of densities, temperatures and magnetic field

Li Jiang, Ting Zhang, Qiruyi Zuo, Chenyu Tian

Spatiotemporal (ST) data collected by sensors can be represented as multi-variate time series, which is a sequence of data points listed in an order of time. Despite the vast amount of useful information, the ST data usually suffer from the issue of missing or incomplete data, which also limits its applications. Imputation is one viable solution and is often

Giuseppina Simone

Despite being extremely old concepts, plasmonics and surface plasmon resonance-based biosensors have been increasingly popular in the recent two decades due to the growing interest in nanooptics and are now of relevant significance in regards to applications associated with human health. Plasmonics integration into point-of-care devices for health surveillan

Frank Neumann, Carsten Witt

Evolutionary multi-objective algorithms have successfully been used in the context of Pareto optimization where a given constraint is relaxed into an additional objective. In this paper, we explore the use of 3-objective formulations for problems with chance constraints. Our formulation trades off the expected cost and variance of the stochastic component as

J. Varela, K. Nagasaki, S. Kobayashi, K. Nagaoka

The aim of the present study is to analyze the effect of the electron cyclotron heating (ECH) on the linear stability of Alfven Eigenmodes (AE) and energetic particle modes (EPM) triggered by energetic ions in Heliotron J plasma. The analysis is performed using the FAR3d code that solves a reduced MHD model to describe the thermal plasma coupled with a gyrof

Sofia Hustiu, Joaquin Ezpeleta, Cristian Mahulea, Marius Kloetzer

This paper focuses on designing motion plans for a heterogeneous team of robots that must cooperate to fulfill a global mission. Robots move in an environment that contains some regions of interest, while the specification for the entire team can include avoidance, visits, or sequencing of these regions of interest. The mission is expressed in terms of a Pet

J. Varela, A. S. Brun, P. Zarka, A. Strugarek

The present study aims at characterizing the habitability conditions of exoplanets with an Earth-like magnetosphere inside the habitable zone of M stars and F stars like tau Boo, caused by the direct deposition of the stellar wind on the exoplanet surface if the magnetosphere shielding is inefficient. In addition, the radio emission generated by exoplanets w

Po-Ta Chen, Michal Levo, Benjamin Zoller, Thomas Gregor

Transcription commonly occurs in bursts, with alternating productive (ON) and quiescent (OFF) periods, governing mRNA production rates. Yet, how transcription is regulated through bursting dynamics remains unresolved. Here, we conduct real-time measurements of endogenous transcriptional bursting with single-mRNA sensitivity. Leveraging the diverse transcript

Madhav Khirwar, Karthik S. Gurumoorthy, Ankit Ajit Jain, Shantala Manchenahally

With Reinforcement Learning (RL) for inventory management (IM) being a nascent field of research, approaches tend to be limited to simple, linear environments with implementations that are minor modifications of off-the-shelf RL algorithms. Scaling these simplistic environments to a real-world supply chain comes with a few challenges such as: minimizing the

Vladimir I. Kruglov

We have derived the extended Korteweg-de Vries equation describing the long gravity waves without limitation to surface deviation. The only restriction to the surface deviation is connected with the stability condition for appropriate solutions. The derivation of extended KdV equation is based on the Euler equations for inviscid irrotational and incompressib

Xiaomei Zhang, Zhaoxi Zhang, Qi Zhong, Xufei Zheng

Adversarial attacks are a serious threat to the reliable deployment of machine learning models in safety-critical applications. They can misguide current models to predict incorrectly by slightly modifying the inputs. Recently, substantial work has shown that adversarial examples tend to deviate from the underlying data manifold of normal examples, whereas p

Varsha Jayaprakash, Akilesh K, Ajay kumar, Balamurugan M. S

Cashew is one of the most extensively consumed nuts in the world, and it is also known as a cash crop. A tree may generate a substantial yield in a few months and has a lifetime of around 70 to 80 years. Yet, in addition to the benefits, there are certain constraints to its cultivation. With the exception of parasites and algae, anthracnose is the most commo

Vrinda Agarwal, Aaron George Pichappa, Manideep Ramisetty, Bala Murugan MS

With the increasing need to strengthen vehicle safety and detection, the availability of pre-existing methods of catching criminals and identifying vehicles manually through the various traffic surveillance cameras is not only time-consuming but also inefficient. With the advancement of technology in every field the use of real-time traffic surveillance mode

Zhengping Yuan, Jingwei Long, Zhengde Xu, Yue Xin

The dynamics of a spin torque driven ferrimagnetic (FiM) system is investigated using the two-sublattice macrospin model. We demonstrate an ultrafast switching in the picosecond range. However, we find that the excessive current leads to the magnetic oscillation. Therefore, faster switching cannot be achieved by unlimitedly increasing the current. By systema

Takafumi Kita

Ordered phases realized through broken continuous symmetries embrace long-range order-parameter fluctuations as manifest in the power-law decays of both the transverse and longitudinal correlations, which are similar to those at the second-order transition point. We calculate the transverse one-loop correction to the dispersion relation of nonrelativistic Na

Qianqian Xie, Zheheng Luo, Benyou Wang, Sophia Ananiadou

The exponential growth of biomedical texts such as biomedical literature and electronic health records (EHRs), poses a significant challenge for clinicians and researchers to access clinical information efficiently. To tackle this challenge, biomedical text summarization (BTS) has been proposed as a solution to support clinical information retrieval and mana

Yu-Ze Wang, Wei-Jun Zhang, Guang-Zhao Xu, Jia-Min Xiong

A superconducting microstrip single-photon detector (SMSPD) generally requires a shunt resistor to avoid latching, caused by its high current-carrying capacity and low kinetic inductance. Here, the effect of the shunt resistor on the behaviors of microbridge SMSPDs was investigated. We analyzed the change in equivalent switching current at different shunt re

Alaukik Saxena, Nikita Polin, Navyanth Kusampudi, Shyam Katnagallu

Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of chemical segregation and microstructure in modern multicomponent materials. Yet, the quantitative analysis typically relies on human expertise to define regions of interest. We introduce a computationally efficient, multistage machine learning strategy to identify c

Hsin-Ku Chen

We describe the resolution of singularities of a threefold which has minimal Picard number. We describe the relation between this minimal resolution and an arbitrary resolution of singularities.

Yeongdae Kim, Takane Ueno, Katie Seaborn, Hiroki Oura

Misinformation is a global problem in modern social media platforms with few solutions known to be effective. Social media platforms have offered tools to raise awareness of information, but these are closed systems that have not been empirically evaluated. Others have developed novel tools and strategies, but most have been studied out of context using stat

Shihao Zhang, Junda Wu, Lvzhou Li

We conduct a systematic study of quantum circuits composed of multiple-control $Z$-rotation (MCZR) gates as primitives, since they are widely-used components in quantum algorithms and also have attracted much experimental interest in recent years. Herein, we establish a circuit-polynomial correspondence to characterize the functionality of quantum circuits o

Yiyu Zhuang, Qi Zhang, Xuan Wang, Hao Zhu

Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lighting models such as environment maps to represent non-distant illumination, or using a network to fit indirect light modeling without a solid basis, can lead to an undesirable dec

Yang Liu, Shen Yan, Yuge Zhang, Kan Ren

Vision Transformers have shown great performance in single tasks such as classification and segmentation. However, real-world problems are not isolated, which calls for vision transformers that can perform multiple tasks concurrently. Existing multi-task vision transformers are handcrafted and heavily rely on human expertise. In this work, we propose a novel

Xiang Li, Zhanpeng Gu, Dunyan Yan, Zhongci Hang

In this paper, we will use the conclusions and methods in \cite{1} to obtain the sharp bounds for a class of integral operators with the nonnegative kernels in weighted-type spaces on Heisenberg group. As promotions, the sharp bounds of Hardy operator , Hardy Littlewood-P\'{o}lya operator and Hilbert operator are also obtained.

Srija Santhanam, Kavipriya P, Balamurugan MS, Manoj Kumar Rajagopal

Wellness in trivial terms combines physical, social, and mental wellbeing. While mental health is neglected, long-term success in a person life is mostly determined by his psychological health and contentment. For a person in distress, professional mental health services are quite expensive, unpopular, and invite a lot of hesitation. Hence, it would be effec

Xin Man, Chenghong Zhang, Jin Feng, Changyu Li

Weather forecasting is a long-standing computational challenge with direct societal and economic impacts. This task involves a large amount of continuous data collection and exhibits rich spatiotemporal dependencies over long periods, making it highly suitable for deep learning models. In this paper, we apply pre-training techniques to weather forecasting an

Tsuneo Suzuki

Non-Abelian gauge fields having a line-singularity of the Dirac type lead us to violation of the non-Abelian Bianchi identity. The violation as an operator is equivalent to violation of Abelian-like Bianchi identities corresponding to eight Abelian-like conserved magnetic monopole currents of the Dirac type in $SU(3)$ QCD. It is very interesting to study if

Yunlong Feng, Bohan Li, Libo Qin, Xiao Xu

Cross-domain text classification aims to adapt models to a target domain that lacks labeled data. It leverages or reuses rich labeled data from the different but related source domain(s) and unlabeled data from the target domain. To this end, previous work focuses on either extracting domain-invariant features or task-agnostic features, ignoring domain-aware

Jie Gu

We investigate bounds on speed, non-adiabatic entropy production and trade-off relation between them for classical stochastic processes with time-independent transition rates. Our results show that the time required to evolve from an initial to a desired target state is bounded from below by the informational-theoretic $\infty$-R\'enyi divergence between the

Hadyan Luthfan Prihadi, Freddy Permana Zen, Donny Dwiputra, Seramika Ariwahjoedi

The scrambling time and its delay are calculated using holography in an asymptotically AdS black hole solution of the gauged Einstein-Maxwell-Dilaton-Axion (EMDA) theory, the dyonic Kerr-Sen-AdS$_4$ black hole, perturbed by rotating and charged shock waves along the equator. The leading term of the scrambling time for a black hole with large entropy is logar

Katie Seaborn, Giulia Barbareschi, Shruti Chandra

Critical voices within and beyond the scientific community have pointed to a grave matter of concern regarding who is included in research and who is not. Subsequent investigations have revealed an extensive form of sampling bias across a broad range of disciplines that conduct human subjects research called "WEIRD": Western, Educated, Industrial, Rich, and

Aakash Choudhary, R. K. Sharma

Given $\mathbb{F}_{q^{n}}$, a field with $q^n$ elements, where $q $ is a prime power and $n$ is positive integer. For $r_1,r_2,m_1,m_2 \in \mathbb{N}$, $k_1,k_2 \in \mathbb{N}\cup \{0\}$, a rational function $F = \frac{F_1}{F_2}$ in $\mathbb{F}_{q}[x]$ with deg($F_i$) $\leq m_i$; $i=1,2,$ satisfying some conditions, and $a,b \in \mathbb{F}_{q}$, we construct

Frank Elavsky

In Option-Driven Design, users must interact with options and settings for systems to adapt to their needs. This approach places the burden on both the user and the system to make the interaction between user and system fit. The user must know and find which options they need and then adjust them. In addition, the system must be capable of robust change, sim

Vitaly Shalumov, Harel Haskey

In this paper, we fill in an existing gap in resources available to the Hebrew NLP community by providing it with the largest so far pre-train dataset HeDC4, a state-of-the-art pre-trained language model HeRo for standard length inputs and an efficient transformer LongHeRo for long input sequences. The HeRo model was evaluated on the sentiment analysis, the

Jiaojiao Wang, Zitan Chen

We derive a lower bound on the amount of information accessed to repair failed nodes within a single rack from any number of helper racks in the rack-aware storage model that allows collective information processing in the nodes that share the same rack. Furthermore, we construct a family of rack-aware minimum-storage regenerating (MSR) codes with the proper

Tue M. Cao, Nhat H. Tran, Phi Le Nguyen, Hieu Pham

This work discusses the use of contrastive learning and deep learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals. While the ECG signals usually contain 12 leads (channels), many healthcare facilities and devices lack access to all these 12 leads. This raises the problem of how to use only fewer ECG leads to produce meaningf

Naoto Ohsaka

Given a two-prover game $G$ and its two satisfying labelings $\psi_\mathsf{ini}$ and $\psi_\mathsf{tar}$, the Label Cover Reconfiguration problem asks whether $\psi_\mathsf{ini}$ can be transformed into $\psi_\mathsf{tar}$ by repeatedly changing the label of a single vertex while preserving any intermediate labeling satisfying $G$. We consider its optimizati

Kasper Green Larsen, Huacheng Yu

In this work, we prove a $\tilde{\Omega}(\lg^{3/2} n )$ unconditional lower bound on the maximum of the query time and update time for dynamic data structures supporting reachability queries in $n$-node directed acyclic graphs under edge insertions. This is the first super-logarithmic lower bound for any natural graph problem. In proving the lower bound, we

Christian Hirsch, Moritz Otto, Takashi Owada, Christoph Thäle

We prove a large deviation principle for the point process of large Poisson $k$-nearest neighbor balls in hyperbolic space. More precisely, we consider a stationary Poisson point process of unit intensity in a growing sampling window in hyperbolic space. We further take a growing sequence of thresholds such that there is a diverging expected number of Poisso

Kazumi Kasaura, Shuwa Miura, Tadashi Kozuno, Ryo Yonetani

This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms. In action-constrained RL, each action taken by the learning system must comply with certain constraints. These constraints are crucial for ensuring the feasibility and safety of actions in real-world systems. We evaluate existing algorithms and their nov

Maximilian Du, Suraj Nair, Dorsa Sadigh, Chelsea Finn

Enabling robots to learn novel visuomotor skills in a data-efficient manner remains an unsolved problem with myriad challenges. A popular paradigm for tackling this problem is through leveraging large unlabeled datasets that have many behaviors in them and then adapting a policy to a specific task using a small amount of task-specific human supervision (i.e.

Ryoh Fuji-Hara, Ying Miao

An ideal secret sharing scheme is a method of sharing a secret key in some key space among a finite set of participants in such a way that only the authorized subsets of participants can reconstruct the secret key from their shares which are of the same length as that of the secret key. The set of all authorized subsets of participants is the access structur

Pranava Singhal, Waqar Mirza, Ajit Rajwade, Karthik S. Gurumoorthy

In this paper, we describe a method for estimating the joint probability density from data samples by assuming that the underlying distribution can be decomposed as a mixture of product densities with few mixture components. Prior works have used such a decomposition to estimate the joint density from lower-dimensional marginals, which can be estimated more