July 2023 arXiv papers — page 161

Yueen Ma, Dafeng Chi, Jingjing Li, Kai Song

The natural language generation domain has witnessed great success thanks to Transformer models. Although they have achieved state-of-the-art generative quality, they often neglect generative diversity. Prior attempts to tackle this issue suffer from either low model capacity or over-complicated architectures. Some recent methods employ the VAE framework to

Fabian Göttsch, Noboru Osawa, Issei Kanno, Takeo Ohseki

We consider a user-centric cell-free massive MIMO wireless network with $L$ remote radio units, each with $M$ antennas, serving $K_{\rm tot}$ user equipments (UEs). Most of the literature considers the regime $LM \gg K_{\rm tot}$, where the $K$ UEs are active on each time-frequency slot, and evaluates the system performance in terms of ergodic rates. In this

Matthias Reitner, Lorenzo Crippa, Dominik Robert Fus, Jan Carl Budich

At thermal equilibrium, we find that generalized susceptibilities encoding the static physical response properties of Hermitian many-electron systems possess inherent non-Hermitian (NH) matrix symmetries. This leads to the generic occurrence of exceptional points (EPs), i.e., NH spectral degeneracies, in the generalized susceptibilities of prototypical Fermi

Ryotaku Suzuki, Shinya Tomizawa

We investigate the nonlinear dynamics of D=2N+3 Myers-Perry black holes with almost equal angular momenta, which have N equal spins out of possible N+1 spins. In particular, we study the ultraspinning instability and the fate of its nonlinear evolution using the large D effective theory approach. We find that every stationary phase can be mapped to the count

Kala Agbo Bidi, Luís Almeida, Jean-Michel Coron

This work concerns feedback global stabilization of the sterile insect technique dynamics. The Sterile Insect Technique (SIT) is presently one of the most ecological methods for controlling insect pests responsible for crop destruction and disease transmission worldwide. This technique consists in releasing sterile males among the insect pest population, the

Tim Schreiter, Lucas Morillo-Mendez, Ravi T. Chadalavada, Andrey Rudenko

Robots are increasingly used in shared environments with humans, making effective communication a necessity for successful human-robot interaction. In our work, we study a crucial component: active communication of robot intent. Here, we present an anthropomorphic solution where a humanoid robot communicates the intent of its host robot acting as an "Anthrop

Kaushani Majumder, SibiRaj B. Pillai, Satish Mulleti

Simultaneous operation of all sensors in a large-scale sensor network is power-consuming and computationally expensive. Hence, it is desirable to select fewer sensors. A greedy algorithm is widely used for sensor selection in homogeneous networks with a theoretical worst-case performance of (1-1/e) ~ 63% of the optimal performance when optimizing submodular

Antoine Prouff

We consider the Schr{\"o}dinger equation in $\mathbf{R}^d$, $d \ge 1$, with a confining potential growing at most quadratically. Our main theorem characterizes open sets from which observability holds, provided they are sufficiently regular in a certain sense. The observability condition involves the Hamiltonian flow associated with the Schr{\"o}dinger opera

Igor Plokhikh, Vladimir Pomjakushin, Dariusz Jakub Gawryluk, Oksana Zaharko

LnSbTe (Ln - lanthanide) group of materials, belonging to ZrSiS/PbFCl (P4/nmm) structure type, is a platform to study the phenomena originating from the interplay between the electronic correlations, magnetism, structural instabilities and topological electronic structure. Here we report a systematic study of magnetic properties and magnetic structures of Ln

Xinwei Shen, Nicolai Meinshausen

Distributional regression aims to estimate the full conditional distribution of a target variable, given covariates. Popular methods include linear and tree-ensemble based quantile regression. We propose a neural network-based distributional regression methodology called `engression'. An engression model is generative in the sense that we can sample from the

Benedikt Bollig, Arnaud Sangnier, Olivier Stietel

We study first-order logic over unordered structures whose elements carry a finite number of data values from an infinite domain. Data values can be compared wrt.\ equality. As the satisfiability problem for this logic is undecidable in general, we introduce a family of local fragments. They restrict quantification to the neighbourhood of a given reference p

Nicholas Hu, Rowan Killip, Monica Visan

We demonstrate that in three space dimensions, the scattering behaviour of semilinear wave equations with quintic-type nonlinearities uniquely determines the nonlinearity. The nonlinearity is permitted to depend on both space and time.

Shengbo Wang, Ke Li, Yin Yang, Yuting Cao

Breaking safety constraints in control systems can lead to potential risks, resulting in unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even among similar tasks. In this paper, we develop a novel adaptive safe control framework that integrates meta learning, Bayesian models, and control barrier function (CBF) method. Specif

Luis Miguel Vieira da Silva, Aljosha Köcher, Milapji Singh Gill, Marco Weiss

In order to react efficiently to changes in production, resources and their functions must be integrated into plants in accordance with the plug and produce principle. In this context, research on so-called capabilities and skills has shown promise. However, there are currently two incompatible approaches to modeling capabilities and skills. On the one hand,

Ping-Rui Tsai, Hong-Yue Huang, Ying-Pin Tsai, Chih-Jung Lin

Fascinating in art and science, the ability to float is also captivating and relevant in practical applications, such as Penning and ion traps that are fundamental to quantum computing. In this work, we first reproduce the classic water bridge by glycerol and, as it breaks down due to thermal agitation, observe that a lump of glycerol with mass~2.5 g can flo

E. Basor, T. Ehrhardt, J. A. Virtanen

We determine the asymptotics of the block Toeplitz determinants $\det T_n(\phi)$ as $n\to\infty$ for $N\times N$ matrix-valued piecewise continuous functions $\phi$ with a finitely many jumps under mild additional conditions. In particular, we prove that $$ \det T_n(\phi) \sim G^n n^\Omega E\quad {\rm as}\ n\to \infty, $$ where $G$, $E$, and $\Omega$ are con

Chongzhi Wang, Haibin Shao, Ying Tan, Dewei Li

Recent advancements in bipartite consensus, a scenario where agents are divided into two disjoint sets with agents in the same set agreeing on a certain value and those in different sets agreeing on opposite or specifically related values, have highlighted its potential applications across various fields. Traditional research typically relies on the presence

Akshay Mehra, Yunbei Zhang, Jihun Hamm

The growing popularity of transfer learning, due to the availability of models pre-trained on vast amounts of data, makes it imperative to understand when the knowledge of these pre-trained models can be transferred to obtain high-performing models on downstream target tasks. However, the exact conditions under which transfer learning succeeds in a cross-dom

Biswajit Khara, Kumar Saurabh, Robert Dyja, Anupam Sharma

We present a full space-time numerical solution of the advection-diffusion equation using a continuous Galerkin finite element method on conforming meshes. The Galerkin/least-square method is employed to ensure stability of the discrete variational problem. In the full space-time formulation, time is considered another dimension, and the time derivative is i

Qianni Cao, Chen Shen

Under frequency load shedding (UFLS) constitutes the very last resort for preventing total blackouts and cascading events. Fluctuating operating conditions and weak resilience of the future grid require UFLS strategies adapt to various operating conditions and non-envisioned faults. This paper develops a novel data-enabled predictive control algorithm KLS to

Jing Lin, Ailing Zeng, Shunlin Lu, Yuanhao Cai

In this paper, we present Motion-X, a large-scale 3D expressive whole-body motion dataset. Existing motion datasets predominantly contain body-only poses, lacking facial expressions, hand gestures, and fine-grained pose descriptions. Moreover, they are primarily collected from limited laboratory scenes with textual descriptions manually labeled, which greatl

George Papadakis, Nishadi Kirielle, Peter Christen, Themis Palpanas

Entity resolution (ER) is the process of identifying records that refer to the same entities within one or across multiple databases. Numerous techniques have been developed to tackle ER challenges over the years, with recent emphasis placed on machine and deep learning methods for the matching phase. However, the quality of the benchmark datasets typically

Albert Piwonski, Julien Dular, Rodrigo Silva Rezende, Rolf Schuhmann

Power cables have complex geometries in order to reduce their ac resistance. Although there are many different cable designs, most have in common that their inner conductors' cross-section is divided into several electrically insulated conductors, which are twisted over the cable's length (helicoidal symmetry). In previous works, we presented how to exploit

Chuan Qin, Le Zhang, Yihang Cheng, Rui Zha

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and manage

Dongwei Wang, Zhi Han, Yanmei Wang, Xiai Chen

Reviewing plays an important role when learning knowledge. The knowledge acquisition at a certain time point may be strongly inspired with the help of previous experience. Thus the knowledge growing procedure should show strong relationship along the temporal dimension. In our research, we find that during the network training, the evolution of feature map f

Lucas Huysmans, Edriss S. Titi

We study selection by vanishing viscosity for the transport of a passive scalar $f(x,t)\in\mathbb{R}$ advected by a bounded, divergence-free vector field $u(x,t)\in\mathbb{R}^2$. This is described by the initial value problem to the PDE $\frac{\partial f}{\partial t} + \nabla\cdot (u f) = 0$, or with positive viscosity/diffusivity $\nu>0$, to the PDE $\frac{

Michele Buzzicotti, Alessandro De Santis, Nazario Tantalo

We present a new supervised deep-learning approach to the problem of the extraction of smeared spectral densities from Euclidean lattice correlators. A distinctive feature of our method is a model-independent training strategy that we implement by parametrizing the training sets over a functional space spanned by Chebyshev polynomials. The other distinctive

Zhongjin Luo, Dong Du, Heming Zhu, Yizhou Yu

Modeling 3D avatars benefits various application scenarios such as AR/VR, gaming, and filming. Character faces contribute significant diversity and vividity as a vital component of avatars. However, building 3D character face models usually requires a heavy workload with commercial tools, even for experienced artists. Various existing sketch-based tools fail

Zhong-Yu Wang, Zhi-Feng Sun

With the chiral unitary approach, we evaluate the hidden strange $B_{c}$-like molecular states of $b\bar{c}s\bar{s}$ systems $\bar{B}_{s}\bar{D}_{s}$, $\bar{B}_{s}^{*}\bar{D}_{s}$, $\bar{B}_{s}\bar{D}_{s}^{*}$, and $\bar{B}_{s}^{*}\bar{D}_{s}^{*}$ coupled to the non-strange channels. The $S$-wave scattering amplitudes are calculated based on the vector meson

Jacob Fox, Tung Nguyen, Alex Scott, Paul Seymour

A well-known theorem of R\"odl says that for every graph $H$, and every $\epsilon>0$, there exists $\delta>0$ such that if $G$ does not contain an induced copy of $H$, then there exists $X\subseteq V(G)$ with $|X|\ge \delta|G|$ such that one of $G[X],\overline{G}[X]$ has edge-density at most $\epsilon$. But how does $\delta$ depend on $\epsilon$? Fox and Sud

Paul Fischer, Emil Lundt Larsen, Carsten Witt

We consider a simple setting in neuroevolution where an evolutionary algorithm optimizes the weights and activation functions of a simple artificial neural network. We then define simple example functions to be learned by the network and conduct rigorous runtime analyses for networks with a single neuron and for a more advanced structure with several neurons

Song Li, Wen-Biao Han

In this work, we present a non-GR full waveform for general parametrization of axisymmetric black holes by extending our previous PSI model. Our model comprises two main components: an inspiral part obtained by using phenomenological method in frequency-domain and a ringdown part derived from quasinormal modes associated with photon motion. For quantitativel

Sujoy Chakraborty, Arjun Paul

Let $D$ be a reduced effective strict normal crossing divisor on a smooth complex variety $X$, and let $\mathfrak{X}_D$ be an associated root stack over $\mathbb C$. Suppose that $X$ admits an anti-holomorphic involution (real structure) that keeps $D$ invariant. We show that the root stack $\mathfrak{X}_D$ naturally admits a real structure compatible with $

Woonyoung Chang, Arun Kumar Kuchibhotla, Alessandro Rinaldo

We consider the problem of inference for projection parameters in linear regression with increasing dimensions. This problem has been studied under a variety of assumptions in the literature. The classical asymptotic normality result for the least squares estimator of the projection parameter only holds when the dimension $d$ of the covariates is of a smalle

Fengyuan Liu, Bedoor AlShebli, Talal Rahwan

Research-active editors face a potential conflict of interest (COI) when handling submissions from authors who share the same affiliation or those who recently collaborated with the editor. Since perception of COIs arising from such editor-author associations may erode trust in science, some policies recommend, and others demand, recusal in such incidents. H

Baoshuang Shang

Anelasticity, as an intrinsic property of amorphous solids, plays a significant role in understanding their relaxation and deformation mechanism. However, due to the lack of long-range order in amorphous solids, the structural origin of anelasticity and its distinction from plasticity remain elusive. In this work, we study the transition from anelasticity to

Adam Chojecki, Paweł Morgen, Bartosz Kołodziejek

The study of hidden structures in data presents challenges in modern statistics and machine learning. We introduce the $\mathbf{gips}$ package in R, which identifies permutation subgroup symmetries in Gaussian vectors. $\mathbf{gips}$ serves two main purposes: exploratory analysis in discovering hidden permutation symmetries and estimating the covariance mat

Adrian P. C. Lim

We construct a 4-dimensional quantum field theory on a Hilbert space, dependent on a simple Lie Algebra of a compact Lie group, that satisfies Wightman's axioms. This Hilbert space can be written as a countable sum of non-separable Hilbert spaces, each indexed by a non-trivial, inequivalent irreducible representation of the Lie Algebra. In each component Hil

Daniel Tubbenhauer

We classify rank one 2-representations of SL2, GL2 and SO3 web categories. The classification is inspired by similar results about quantum groups, given by reducing the problem to the classification of bilinear and trilinear forms, and is formulated such that it can be adapted to other web categories.

Anthony Onwuli, Ashish V. Hegde, Kevin Nguyen, Keith T. Butler

The traditional display of elements in the periodic table is convenient for the study of chemistry and physics. However, the atomic number alone is insufficient for training statistical machine learning models to describe and extract composition-structure-property relationships. Here, we assess the similarity and correlations contained within high-dimensiona

Yujia Xiao, Shaofei Zhang, Xi Wang, Xu Tan

While state-of-the-art Text-to-Speech systems can generate natural speech of very high quality at sentence level, they still meet great challenges in speech generation for paragraph / long-form reading. Such deficiencies are due to i) ignorance of cross-sentence contextual information, and ii) high computation and memory cost for long-form synthesis. To addr

Hanyang Li, Ying Cui

We investigate a class of composite nonconvex functions, where the outer function is the sum of univariate extended-real-valued convex functions and the inner function is the limit of difference-of-convex functions. A notable feature of this class is that the inner function may fail to be locally Lipschitz continuous. It covers a range of important yet chall

Orgest Zaka, Armend Salihu

In this paper, in continuation of our work, on the determinants of cubic -matrix of order 2 and order 3, we have analyzed the possibilities of developing the concept of determinant of cubic-matrix with three indexes, studying the possibility of their calculation according the Laplace expansion method's. We have noted that the concept of permutation expansion

Jason Atnip, Gary Froyland, Cecilia Gonzalez-Tokman, Sandro Vaienti

We develop a quenched thermodynamic formalism for open random dynamical systems generated by finitely branched, piecewise-monotone mappings of the interval. The openness refers to the presence of holes in the interval, which terminate trajectories once they enter. Our random driving is generated by an invertible, ergodic, measure-preserving transformation on

Weimin Tan, Siyuan Chen, Bo Yan

Diffusion models have demonstrated excellent performance in image generation. Although various few-shot semantic segmentation (FSS) models with different network structures have been proposed, performance improvement has reached a bottleneck. This paper presents the first work to leverage the diffusion model for FSS task, called DifFSS. DifFSS, a novel FSS p

Ning Lin, Shaocong Wang, Yi Li, Bo Wang

The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Har

Chenfeng Cao, Hiroshi Yano, Yuya O. Nakagawa

The variational quantum eigensolver (VQE) stands as a prominent quantum-classical hybrid algorithm for near-term quantum computers to obtain the ground states of molecular Hamiltonians in quantum chemistry. However, due to the non-commutativity of the Pauli operators in the Hamiltonian, the number of measurements required on quantum computers increases signi

Xudong Wang, Shufan Li, Konstantinos Kallidromitis, Yusuke Kato

Open-vocabulary image segmentation aims to partition an image into semantic regions according to arbitrary text descriptions. However, complex visual scenes can be naturally decomposed into simpler parts and abstracted at multiple levels of granularity, introducing inherent segmentation ambiguity. Unlike existing methods that typically sidestep this ambiguit

Akiyoshi Yamada, Yuki Fuseya

The three-dimensional magneto-conductivity tensor was derived in a gauge invariant form based on the Kubo formula considering the quantum effect under a magnetic field, such as the Landau quantization and the quantum oscillations. We analytically demonstrated that the quantum formula of the magneto-conductivity can be obtained by adding a quantum oscillation

James Lawrence, Yuanyuan He, Haipeng Wei, Jie Su

The atomic doping of open-shell nanographenes enables the precise tuning of their electronic and magnetic state, which is crucial for their promising potential applications in optoelectronics and spintronics. Among this intriguing class of molecules, triangulenes stand out with their size-dependent electronic properties and spin states, which can also be inf

Yanhui Guo, Fangzhou Luo, Xiaolin Wu

Image signal processing (ISP) pipeline plays a fundamental role in digital cameras, which converts raw Bayer sensor data to RGB images. However, ISP-generated images usually suffer from imperfections due to the compounded degradations that stem from sensor noises, demosaicing noises, compression artifacts, and possibly adverse effects of erroneous ISP hyperp

Wenwen Liu, Hanyu Wang, Biao Yang, Shuang Zhang

With their non-Abelian topological charges, real multi-bandgap systems challenge the conventional topological phase classifications. As the minimal sector of multi-bandgap systems, real triple degeneracies (RTPs), which serve as real 'Weyl points', lay the foundation for the research on real topological phases. However, experimental demonstration of

Yuhang Chen, Chaoyun Zhang, Minghua Ma, Yudong Liu

Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges e

Can Cui, Yaohong Wang, Shunxing Bao, Yucheng Tang

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even th

Tzula B. Propp, Sayonee Ray, John B. DeBrota, Tameem Albash

We study how decoherence increases the efficiency with which we can simulate the quantum dynamics of an anharmonic oscillator, governed by the Kerr effect. As decoherence washes out the fine-grained subPlanck structure associated with phase-space quantum interference in the closed quantum system, open quantum dynamics can be more efficiently simulated using

Sangrok Lee, Ha Young Kim

Traffic forecasting is a highly challenging task owing to the dynamical spatio-temporal dependencies of traffic flows. To handle this, we focus on modeling the spatio-temporal dynamics and propose a network termed Edge Squeeze Graph Convolutional Network (ESGCN) to forecast traffic flow in multiple regions. ESGCN consists of two modules: W-module and ES modu

Bo-Qiang Lu, Cheng-Wei Chiang, Tianjun Li

The recent Pulsar Timing Arrays (PTAs) nano-Hz gravitational wave (GW) background signal can be naturally induced by the annihilation of domain walls (DWs) formed at a symmetry-breaking scale $f\simeq 200$~TeV in the clockwork axion framework. Based on our first successful and precise prediction, we for the first time suggest that the recent PTA observations

Weijie Xu, Xiaoyu Jiang, Srinivasan H. Sengamedu, Francis Iannacci

Recently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real world due to the challenge of incorporating human knowledge. This work presents a semi-supervised neural topic modeling method, vONTSS, which uses von Mises-Fisher (vMF) based varia

Yu-Peng Zhang, Xun Chen, Xiao-Hua Li, Akira Watanabe

The differential cross sections of elastic proton-proton (pp) and proton-antiproton (pp\bar) scattering are studied in a holographic QCD model, considering the strong and Coulomb interaction in the Regge regime. Based on previous studies of strong interactions described in terms of Pomeron and Reggeon exchange, we add the contribution of Coulomb interaction

Ching-Lung Lin, Hongyu Liu, Catharine W. K. Lo

In this work, we are concerned with inverse problems involving poly-fractional operators, where the poly-fractional operator is of the form \[P( (-\Delta_g)^s)u := \sum_{i=1}^M \alpha_i(-\Delta_{g_i})^{s_i}u\] for $s=(s_1,\dots,s_M)$, $0<s_1<\cdots<s_M<\infty$, $s_M\in\mathbb{R}_+\backslash\mathbb{Z}$, $g=(g_1,\dots,g_M)$. There are three major contributions

Rongen Dong, Feng Shu, Yongzhao Li, Jun Li

To boost the secrecy rate (SR) of the conventional directional modulation (DM) network and overcome the double fading effect of the cascaded channels of passive intelligent reflecting surface (IRS), a novel active IRS-assisted DM system with a power adjusting strategy between transmitter and active IRS is proposed in this paper. Then, a joint optimization of

Patricia Sorya

A slope $p/q$ is said to be characterizing for a knot $K$ if the homeomorphism type of the $p/q$-Dehn surgery along $K$ determines the knot up to isotopy. Extending previous work of Lackenby and McCoy on hyperbolic and torus knots respectively, we study satellite knots to show that for a knot $K$, any slope $p/q$ is characterizing provided $|q|$ is sufficien

Chengxing He, Yubo Wang, Carlo Waldfried, Guangcanlan Yang

UV and visible photonics enable applications ranging from spectroscopic sensing to communication and quantum information processing. Photonics structures in these wavelength regimes, however, tend to experience higher loss than their IR counterpart. Particularly in the near-UV band, on-chip optical microresonators have not yet achieved a quality factor beyon

Yaozhong Hu, Haiyi Shi

We obtain the almost sure strong consistency and the Berry-Esseen type bound for the maximum likelihood estimator Ln of the ensemble L for determinantal point processes (DPPs), strengthening and completing previous work initiated in Brunel, Moitra, Rigollet, and Urschel [BMRU17]. Numerical algorithms of estimating DPPs are developed and simulation studies ar

Chen-Chen Miao, Victoria Blackmon, Wei-Wei Zhu, Dong-Zi Li

We report the radio observations of the eclipsing black widow pulsar J1720-0534, a 3.26 ms pulsar in orbit with a low mass companion of mass 0.029 to 0.034 M$_{\odot}$. We obtain the phase-connected timing ephemeris and polarization profile of this millisecond pulsar (MSP) using the Five-hundred-meter Aperture Spherical Radio Telescope (FAST), the Green Bank

Mingwu Li, Bálint Kaszás, George Haller

Approximate streamsurfaces of a 3D velocity field have recently been constructed as isosurfaces of the closest first integral of the velocity field. Such approximate streamsurfaces enable effective and efficient visualization of vortical regions in 3D flows. Here we propose a variational construction of these approximate streamsurfaces to remove the limitati

Benoit Assi, Stefan Höche

We present an algorithm for massive parton evolution which is based on the differentially accurate simulation of soft-gluon radiation by means of a non-trivial azimuthal angle dependence of the splitting functions. The kinematics mapping is chosen such as to to reflect the symmetry of the final state in soft-gluon radiation and collinear splitting processes.

Bushra Sabir, Yansong Gao, Alsharif Abuadbba, M. Ali Babar

Transformer-based text classifiers such as BERT, RoBERTa, T5, and GPT have shown strong performance in natural language processing tasks but remain vulnerable to adversarial examples. These vulnerabilities raise significant security concerns, as small input perturbations can cause severe misclassifications. Existing robustness methods often require heavy com

Tomoki Matsuoka, Ryo Sawada

Recent observations of supernovae (SNe) have indicated that a fraction of massive stars possess dense circumstellar medium (CSM) at the moment of their core collapses. They suggest the presence of additional activities of the SN progenitor driving the enhancement of the mass-loss rate, and some physical processes attributing to single star&#39;s activities h

Roberto da Silva, Tânia Tomé, Mário José de Oliveira

An interesting concept that has been underexplored in the context of time-dependent simulations is the correlation of total magnetization, $C(t)$%. One of its main advantages over directly studying magnetization is that we do not need to meticulously prepare initial magnetizations. This is because the evolutions are computed from initial states with spins th

Kai Xu

We prove a new existence theorem for proper solutions of Huisken and Ilmanen's weak inverse mean curvature flow, assuming certain non-degeneracy conditions on the isoperimetric profile. In particular, no curvature assumption is imposed in our existence theorem.

Weiyi Xiong, Jianan Liu, Tao Huang, Qing-Long Han

As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion with other modalities are lacking. On t

Yan-Chen Bi, Yu-Mei Wu, Zu-Cheng Chen, Qing-Guo Huang

NANOGrav, EPTA, PPTA, and CPTA have announced the evidence for a stochastic signal from their latest data sets. Supermassive black hole binaries (SMBHBs) are supposed to be the most promising gravitational-wave (GW) sources of pulsar timing arrays. Assuming an astro-informed formation model, we use the NANOGrav 15-year data set to constrain the gravitational

Koji Hashimoto, Tomoya Naito, Hisashi Naito

We find that simple neural networks with ReLU activation generate polytopes as an approximation of a unit sphere in various dimensions. The species of polytopes are regulated by the network architecture, such as the number of units and layers. For a variety of activation functions, generalization of polytopes is obtained, which we call neural polytopes. They

Daniel Jost, Hsiao-Yu Huang, Matteo Rossi, Amol Singh

Polarons - fermionic charge carriers bearing a strong companion lattice deformation - exhibit a natural tendency for self-localization due to the recursive interaction between electrons and the lattice. While polarons are ubiquitous in insulators, how they evolve in transitions to metallic and superconducting states in quantum materials remains an open quest

Keqiang Sun, Junting Pan, Yuying Ge, Hao Li

While recent advancements in vision-language models have had a transformative impact on multi-modal comprehension, the extent to which these models possess the ability to comprehend generated images remains uncertain. Synthetic images, in comparison to real data, encompass a higher level of diversity in terms of both content and style, thereby presenting sig

William M. Campbell, Maxim Goryachev, Michael E. Tobar

The Multi-mode Acoustic Gravitational wave Experiment (MAGE) is a high frequency gravitational wave detection experiment. In its first stage, the experiment features two near-identical quartz bulk acoustic wave resonators that act as strain antennas with spectral sensitivity as low as $6.6\times 10^{-21} \left[\textrm{strain}\right]/\sqrt{\textrm{Hz}}$ in mu

Deyi Ji, Feng Zhao, Hongtao Lu

Most existing ultra-high resolution (UHR) segmentation methods always struggle in the dilemma of balancing memory cost and local characterization accuracy, which are both taken into account in our proposed Guided Patch-Grouping Wavelet Transformer (GPWFormer) that achieves impressive performances. In this work, GPWFormer is a Transformer ($\mathcal{T}$)-CNN

Yuki Isobe, Masami Ouchi, Nozomu Tominaga, Kuria Watanabe

We present chemical abundance ratios of 70 star-forming galaxies at $z\sim4$-10 observed by the JWST/NIRSpec ERO, GLASS, and CEERS programs. Among the 70 galaxies, we have pinpointed 2 galaxies, CEERS_01019 at $z=8.68$ and GLASS_150008 at $z=6.23$, with extremely low C/N ([C/N]$\lesssim -1$), evidenced with CIII]$λλ$1907,1909, NIII]$λ$1750, and NIV]$λλ$1483,

Rose Baunach

I explore whether distinguishing features of phase transitions and/or the inflationary energy scale can be imprinted on cosmological observables due to entanglement during inflation, given a spectator scalar field with a Higgs-like potential. As a consequence of this analysis, I also present results that illustrate the variety of features a Higgs-like specta

Fengyan Yang, Fengyan Yang, Ding Wang, Ping Wang

ScAlN is an emerging ferroelectric material that possesses large band gap, strong piezoelectricity, and holds great promises for enhanced χ^{(2)} nonliearity. In this study, we demonstrate high-fidelity ferroelectric domain switching and periodic poling of Al-polar ScAlN thin film epitaxially grown on on c-axis sapphire substrate using gallium nitride as a b

Koshi Oishi, Yasushi Amano, Jimbo Tomohiko

Achieving cooperative transportation by aerial robot teams ensures flexibility regarding payloads and robustness against failures, which has garnered significant attention in recent years. This study proposes a flexible decentralized controller for robots and the shapes of payloads in a cooperative transport task using multiple single-rotor robots. The propo

L. H. Fowler-Gerace, Zhiwen Zhou, E. A. Szwed, D. J. Choksy

Long lifetimes of spatially indirect excitons (IXs), also known as interlayer excitons, allow implementing both quantum exciton systems and long-range exciton transport. Van der Waals heterostructures (HS) composed of atomically thin layers of transition-metal dichalcogenides (TMD) offer the opportunity to explore IXs in moir\'e superlattices. The moir\'e IX

Shu-Min Wu, Chun-Xu Wang, Rui-Di Wang, Jin-Xuan Li

Here, we study quantum coherence of N-partite GHZ (Greenberger-Horne-Zeilinger) and W states in the multiverse consisting of N causally disconnected de Sitter spaces. Interestingly, N-partite coherence increases monotonically as the curvature increases, while the Unruh effect destroys multipartite coherence in Rindler spacetime. Conversely, the curvature eff

Sharif Ahmed, Nasir U. Eisty

Effective peer code review in collaborative software development necessitates useful reviewer comments and supportive automated tools. Code review comments are a central component of the Modern Code Review process in the industry and open-source development. Therefore, it is important to ensure these comments serve their purposes. This paper reflects the evo

Bivek Gupta, Amit K. Verma, Ravi P. Agarwal

In this paper, we extend the coupled fractional Fourier transform of a complex valued functions to that of the quaternion valued functions on $\mathbb{R}^4$ and call it the quaternion coupled fractional Fourier transform (QCFrFT). We obtain the sharp Hausdorff-Young inequality for QCFrFT and obtain the associated Rènyi uncertainty principle. We also define t

Swagata Bhaumik, Sawant Omkar Deepak

Here, variation of the dispersion characteristics of 3D linearised compressible Navier-Stokes equation with respect to bulk viscosity ratio $κ/μ$, specific heat ratio $γ$ and Prandtl number $Pr$ is presented. The 3D compressible NSE supports two vortical, one entropic and two acoustic modes. While the vortical and entropic modes are non-dispersive in nature,

Alexandre J. H. de O. Luna, Marcelo L. M. Marinho

Context: Agile Governance Theory (AGT) has emerged as a potential model for organizational chains of responsibility across business units and teams. Objective: This study aims to assess how AGT is reflected in practice. Method: AGT was operationalized down into 16 testable hypotheses. All hypotheses were tested by arranging eight theoretical scenarios with 1

Dilshan Senerath, Sanuja Tharinda, Maduka Vishwajith, Sanka Rasnayaka

Continuous Authentication (CA) using behavioural biometrics is a type of biometric identification that recognizes individuals based on their unique behavioural characteristics, like their typing style. However, the existing systems that use keystroke or touch stroke data have limited accuracy and reliability. To improve this, smartphones&#39; Inertial Measur

Yudong Gao, Honglong Chen, Peng Sun, Junjian Li

Backdoor attacks pose serious security threats to deep neural networks (DNNs). Backdoored models make arbitrarily (targeted) incorrect predictions on inputs embedded with well-designed triggers while behaving normally on clean inputs. Many works have explored the invisibility of backdoor triggers to improve attack stealthiness. However, most of them only con

Kahraman Kostas, Mike Just, Michael A. Lones

In the era of rapid IoT device proliferation, recognizing, diagnosing, and securing these devices are crucial tasks. The IoTDevID method (IEEE Internet of Things 2022) proposes a machine learning approach for device identification using network packet features. In this article we present a validation study of the IoTDevID method by testing core components, n

Jianping Pan, Jinwei Zhao, Lin Cai

Starlink and alike have attracted a lot of attention recently, however, the inner working of these low-earth-orbit (LEO) satellite networks is still largely unknown. This paper presents an ongoing measurement campaign focusing on Starlink, including its satellite access networks, gateway and point-of-presence structures, and backbone and Internet connections

Abdoljalil Addeh, Fernando Vega, Rebecca J Williams, Ali Golestani

In many fMRI studies, respiratory signals are unavailable or do not have acceptable quality. Consequently, the direct removal of low-frequency respiratory variations from BOLD signals is not possible. This study proposes a one-dimensional CNN model for reconstruction of two respiratory measures, RV and RVT. Results show that a CNN can capture informative fea

Haifa Almutairi, Ghulam Mubashar Hassan, Amitava Datta

Classification of sleep stages plays an essential role in diagnosing sleep-related diseases including Sleep Disorder Breathing (SDB) disease. In this study, we propose an end-to-end deep learning architecture, named SSNet, which comprises of two deep learning networks based on Convolutional Neuron Networks (CNN) and Long Short Term Memory (LSTM). Both deep l

Jun Ma, Huanfeng Shen, Menghui Jiang, Liupeng Lin

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained

Simon Scheider, Harm Bartholomeus, Judith Verstegen

The recent success of large language models and AI chatbots such as ChatGPT in various knowledge domains has a severe impact on teaching and learning Geography and GIScience. The underlying revolution is often compared to the introduction of pocket calculators, suggesting analogous adaptations that prioritize higher-level skills over other learning content.

Guojun Huang, Jiaxing Zhao, Pengfei Zhuang

We calculate the Debye screening mass in thermal, dense and magnetized QCD matter in the frame of resummed perturbation theory. In the limit of zero temperature, when the Landau energy level and Fermi surface of quarks match each other $μ_q^2=2n|qB|$, where $q$, $μ_q$ and $B$ are respectively the quark electric charge, chemical potential and external magneti

Mario J. Pinheiro

This study explores strategies to improve engine efficiency through innovative materials, design concepts, and alternative energy sources. It highlights the use of nanomaterials and surface engineering to create hydrophobic or other types of surfaces for harnessing entropy-gradient forces. Additionally, it discusses the potential of information-burning engin

Farhad Rezazadeh, Lanfranco Zanzi, Francesco Devoti, Sergio Barrachina-Munoz

Artificial intelligence (AI) and Machine Learning (ML) are considered as key enablers for realizing the full potential of fifth-generation (5G) and beyond mobile networks, particularly in the context of resource management and orchestration. In this demonstration, we consider a fully-fledged 5G mobile network and develop a multi-agent deep reinforcement lear

Sushant Veer, Apoorva Sharma, Marco Pavone

Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios. Recently, learning-based trajectory predictors have experienced considerable success in providing state-of-the-art performance due to their ability to learn multimodal behaviors of other agents fro