April 2023 arXiv papers — page 99

Andre Ráth, Dino Pjanić, Bo Bernhardsson, Fredrik Tufvesson

Cellular user positioning is a promising service provided by Fifth Generation New Radio (5G NR) networks. Besides, Machine Learning (ML) techniques are foreseen to become an integrated part of 5G NR systems improving radio performance and reducing complexity. In this paper, we investigate ML techniques for positioning using 5G NR fingerprints consisting of u

Jacky Cresson, Anna Szafranska

We give a self-contained presentation of fractal R-L ladder networks as well as a detailed computation of the admittance of these systems. We also discuss the conditions under which such systems display a fractional behavior. Finally, we give a full discussion of the connection existing between fractal R-L network and the diffusion equation.

Zhiying Jiang, Zengxi Zhang, Jinyuan Liu, Xin Fan

Since the differences in viewing range, resolution and relative position, the multi-modality sensing module composed of infrared and visible cameras needs to be registered so as to have more accurate scene perception. In practice, manual calibration-based registration is the most widely used process, and it is regularly calibrated to maintain accuracy, which

Zhenxiang Lin, Xidong Peng, Peishan Cong, Ge Zheng

We introduce the task of 3D visual grounding in large-scale dynamic scenes based on natural linguistic descriptions and online captured multi-modal visual data, including 2D images and 3D LiDAR point clouds. We present a novel method, dubbed WildRefer, for this task by fully utilizing the rich appearance information in images, the position and geometric clue

Mohamed Amine Ferrag, Djallel Hamouda, Merouane Debbah, Leandros Maglaras

While the benefits of 6G-enabled Internet of Things (IoT) are numerous, providing high-speed, low-latency communication that brings new opportunities for innovation and forms the foundation for continued growth in the IoT industry, it is also important to consider the security challenges and risks associated with the technology. In this paper, we propose a t

Xiaolong Deng, Florentin Jaffredo, Minoru Tanaka

We introduce a dark photon considering a U(1) gauge extension of the standard model in particle physics. Provided that the extra U(1) symmetry is unbroken, the dark photon is massless and has no coupling to the standard electromagnetic current. Higher-dimensional operators describe interactions of the massless dark photon with particles in the standard model

Chi Liu, Haochun Wang, Nuwa Xi, Sendong Zhao

As a novel approach to tuning pre-trained models, prompt tuning involves freezing the parameters in downstream tasks while inserting trainable embeddings into inputs in the first layer. However, previous methods have mainly focused on the initialization of prompt embeddings. The strategy of training and utilizing prompt embeddings in a reasonable way has bec

Jouni Järvinen, Sándor Radeleczki

We study the pseudo-Kleene algebras of the Dedekind-MacNeille completion of the ordered set of rough set determined by a reflexive relation. We characterize the cases when PBZ and PBZ*-lattices can be defined on these pseudo-Kleene algebras.

Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu

Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain labels to align the distribution of each domain for learning domain-invariant representations. However, artificial domain labels are coarse-grained and subjective, which cannot ref

Bert Wang-Chak Chan

Inspired by biological and cultural evolution, there have been many attempts to explore and elucidate the necessary conditions for open-endedness in artificial intelligence and artificial life. Using a continuous cellular automata called Lenia as the base system, we built large-scale evolutionary simulations using parallel computing framework JAX, in order t

Marlon Löppenberg, Andreas Schwung

The digital transformation of automation places new demands on data acquisition and processing in industrial processes. Logical relationships between acquired data and cyclic process sequences must be correctly interpreted and evaluated. To solve this problem, a novel approach based on evolutionary algorithms is proposed to self optimise the system logic of

Anum Talpur, Mohan Gurusamy

In this work, we investigate an online service management problem in vehicular edge computing networks. To satisfy the varying service demands of mobile vehicles, a service management framework is required to make decisions on the service lifecycle to maintain good network performance. We describe the service lifecycle consists of creating an instance of a g

Ziqian Lin, Yuan Gao, Feifei Wang, Hansheng Wang

Modern statistical analysis often encounters high dimensional models but with limited sample sizes. This makes the target data based statistical estimation very difficult. Then how to borrow information from another large sized source data for more accurate target model estimation becomes an interesting problem. This leads to the useful idea of transfer lear

Huansang Xu, Ruyi Liu, Marek Rutkowski

We propose to develop a new class of investment insurance products for holders of superannuation accounts in Australia, which we tentatively call equity protection swaps (EPSs). An EPS is a standalone financial derivative, which is reminiscent of a total return swap but also shares some features with the variable annuity known as the registered index-linked

Li Lin, Jiewei Wu, Yixiang Liu, Kenneth K. Y. Wong

Federated learning (FL) enables multiple sites to collaboratively train powerful deep models without compromising data privacy and security. The statistical heterogeneity (e.g., non-IID data and domain shifts) is a primary obstacle in FL, impairing the generalization performance of the global model. Weakly supervised segmentation, which uses sparsely-grained

Dhruv Srivastava, Aditya Kumar Singh, Makarand Tapaswi

Movie story analysis requires understanding characters' emotions and mental states. Towards this goal, we formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie scene and for each character. We propose EmoTx, a multimodal Transformer-based architecture that ingests videos, multiple characters, and dial

Debarshi Mitra, Apratim Chatterji

Helical motifs are ubiquitious in macromolecular systems. The mechanism of spontaneous emergence of helicity is unknown, especially in cases where torsional interactions are absent. Emergence of helical order needs coordinated organization over long distances in polymeric macromolecules. We establish a very generic mechanism to obtain spontaneous helicity by

Haozhi Wang, Yinchuan Li, Qing Wang, Yunfeng Shao

Modern multi-agent reinforcement learning (RL) algorithms hold great potential for solving a variety of real-world problems. However, they do not fully exploit cross-agent knowledge to reduce sample complexity and improve performance. Although transfer RL supports knowledge sharing, it is hyperparameter sensitive and complex. To solve this problem, we propos

Brian F. Farrell, Petros J. Ioannou

Turbulence in wall-bounded flows is characterized by stable statistics. Although, in many turbulent systems, this stable statistical state corresponds to a stable fixed point of an associated statistical state dynamics (SSD) closed at second order, referred to as S3T, this is not the case for wall turbulence. In wall-turbulence the trajectory of the statisti

Hunter Brooks, J. Davy Kirkpatrick, Aaron M. Meisner, Christopher R. Gelino

Using a sample of 361 nearby brown dwarfs, we have searched for 4.6$\mu$m variability indicative of large-scale rotational modulations or large-scale long-term changes on timescales of over 10 years. Our findings show no statistically significant variability in \textit{Spitzer} ch2 or \textit{WISE} W2 photometry. For \textit{Spitzer} the ch2 1$\sigma$ limits

Kiryong Chung, Jeong-Seop Kim

Let $\mathbf{R}_d$ be the space of stable sheaves $F$ which satisfy the Hilbert polynomial $\chi(F(m))=dm+1$ and are supported on rational curves in the projective plane $\mathbb{P}^2$. Then $\mathbf{R}_1$ (resp. $\mathbf{R}_2$) is isomorphic to $\mathbf{R}_1\cong\mathbb{P}^2$ (resp. $\mathbf{R}_2\cong \mathbb{P}^5$). Also it is very well-known that $\mathbf

Patricia Dietzsch

We provide an upper bound on the Lagrangian Hofer distance between equators in the cylinder in terms of the barcode of persistent Floer homology. The bound consists of a weighted sum of the lengths of the finite bars and the spectral distance.

Qi Xu, Yaxin Li, Jiangrong Shen, Jian K Liu

Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, close to the biological neural systems. Although spiking based models are energy efficient by taking advantage of discrete spike signals, their performance is limited by current netwo

Ram Sagar

Astronomy and Astrophysics is an observational science dealing with celestial objects. Aryabhatta Research Institute of Observational Sciences (ARIES) is one of the premier institutions in astronomy and astrophysics and has contributed significantly in this field. No doubt, India is a part of several mega-science projects in the domain of Astronomy and Astro

M. Farasat Shamir, Zoya Asghar, Adnan Malik

This work aims to investigate the behaviour of compact stars in the background of $f(R, T)$ theory of gravity. For current work, we consider the Krori-Barua metric potential i.e., $\nu(r)= Br^2+C$ and $\lambda(r)= Ar^2,$ where, $A, B$ and $C$ are constants. We use matching conditions of spherically symmetric space-time with Schwarzschild solution as an exter

Ildikó Pethes, László Pusztai, László Temleitner

The hydrogen-bonded structure of methanol-water mixtures is investigated over the entire alcohol concentration range (from $x_{\mathrm{Methanol}}=$ 0.1 to 1.0) at several temperatures, from 300 K down to the freezing point of the given mixture. Classical molecular dynamics simulations have been carried out, using the all-atom OPLS-AA force field for methanol

Chi-en Amy Tai, Hayden Gunraj, Alexander Wong

Recently, a new form of magnetic resonance imaging (MRI) called synthetic correlated diffusion (CDI$^s$) imaging was introduced and showed considerable promise for clinical decision support for cancers such as prostate cancer when compared to current gold-standard MRI techniques. However, the efficacy for CDI$^s$ for other forms of cancers such as breast can

Yihao Liu, Jiaming Zhang, Zhangcong She, Amir Kheradmand

The Segment Anything Model (SAM) is a new image segmentation tool trained with the largest available segmentation dataset. The model has demonstrated that, with prompts, it can create high-quality masks for general images. However, the performance of the model on medical images requires further validation. To assist with the development, assessment, and appl

Vishnu Vig, Harishankar Manikantan

Biological membranes are self-assembled complex fluid interfaces that host proteins, molecular motors and other macromolecules essential for cellular function. These membranes have a distinct in-plane fluid response with a surface viscosity that has been well characterized. The resulting quasi-2D fluid dynamical problem describes the motion of embedded prote

Chi-en Amy Tai, Jason Li, Sriram Kumar, Saeejith Nair

With the growth in capabilities of generative models, there has been growing interest in using photo-realistic renders of common 3D food items to improve downstream tasks such as food printing, nutrition prediction, or management of food wastage. Despite 3D modelling capabilities being more accessible than ever due to the success of NeRF based view-synthesis

Chi-en Amy Tai, Matthew Keller, Mattie Kerrigan, Yuhao Chen

77% of adults over 50 want to age in place today, presenting a major challenge to ensuring adequate nutritional intake. It has been reported that one in four older adults that are 65 years or older are malnourished and given the direct link between malnutrition and decreased quality of life, there have been numerous studies conducted on how to efficiently tr

Siyao Liu, Yong Wang

In this paper, we obtain a Lichnerowicz type formula for J-Witten deformation and give the proof of the Kastler-Kalau-Walze type theorems associated with J-Witten deformation on four-dimensional and six-dimensional almost product Riemannian spin manifold with (respectively without) boundary.

Deyun Lyu, Jiayang Song, Zhenya Zhang, Zhijie Wang

Cyber-Physical Systems (CPS) have been widely deployed in safety-critical domains such as transportation, power and energy. Recently, there comes an increasing demand in employing deep neural networks (DNNs) in CPS for more intelligent control and decision making in sophisticated industrial safety-critical conditions, giving birth to the class of DNN control

Dionne Ibarra, Gabriel Montoya-Vega

Gram determinants earned traction among knot theorists after E. Witten's presumption about the existence of a 3-manifold invariant connected to the Jones polynomial. Triggered by the creation of such an invariant by N. Reshetikhin and V. Turaev, several mathematicians have explored this line of research ever since. Gram determinants came into play by W. B. R

Qiang Liu, Zhaocheng Liu, Zhenxi Zhu, Shu Wu

Recently, multi-interest models, which extract interests of a user as multiple representation vectors, have shown promising performances for sequential recommendation. However, none of existing multi-interest recommendation models consider the Out-Of-Distribution (OOD) generalization problem, in which interest distribution may change. Considering multiple in

Kazuki Enomoto, Koichi Hamaguchi, Kohei Kamada, Juntaro Wada

We revisit the Affleck-Dine leptogenesis via the $L H_u$ flat direction with a light slepton field. Although the light slepton field is favored in low-energy SUSY phenomenologies, such as the muon $g-2$ anomaly and bino-slepton coannihilation, it may cause a problem in the Affleck-Dine leptogenesis: it may create an unwanted charge-breaking vacuum in the Aff

Viet Dac Lai, Nghia Trung Ngo, Amir Pouran Ben Veyseh, Hieu Man

Over the last few years, large language models (LLMs) have emerged as the most important breakthroughs in natural language processing (NLP) that fundamentally transform research and developments in the field. ChatGPT represents one of the most exciting LLM systems developed recently to showcase impressive skills for language generation and highly attract pub

Shimin Zhang, Kejun Li, Chunhao Guo, Yuan Ping

Point defects in hexagonal boron nitride (hBN) are promising candidates as single-photon emitters (SPEs) in nanophotonics and quantum information applications. The precise control of SPEs requires in-depth understanding of their optoelectronic properties. However, how the surrounding environment of host materials, including number of layers, substrates, and

Behrooz Mansouri, Ricardo Campos

This paper presents a new test collection for Legal IR, FALQU: Finding Answers to Legal Questions, where questions and answers were obtained from Law Stack Exchange (LawSE), a Q&A website for legal professionals, and others with experience in law. Much in line with Stack overflow, Law Stack Exchange has a variety of questions on different topics such as copy

Dejian Meng, Wei Xiao, Lijun Zhang, Zhuang Zhang

For driving safely and efficiently in highway scenarios, autonomous vehicles (AVs) must be able to predict future behaviors of surrounding object vehicles (OVs), and assess collision risk accurately for reasonable decision-making. Aiming at autonomous driving in highway scenarios, a predictive collision risk assessment method based on trajectory prediction o

Anamika Avinash Pathak, Konka Raviteja, Swastik Bhattacharya, Sashideep Gutti

We consider marginally trapped surfaces in a spherically symmetric spacetime evolving due to the presence of a perfect fluid in D-dimensions and look at the various definitions of the surface gravity for these marginally trapped surfaces. We show that using Einstein equations it is possible to simplify and obtain general formulae for the surface gravity in t

Mohammad Vahid Takook

Quantum de Sitter geometry is discussed using elementary field operator algebras in Krein space quantization from an observer-independent point of view, {\it i.e.} ambient space formalism. In quantum geometry, the conformal sector of the metric becomes a dynamical degree of freedom, which can be written in terms of a massless minimally coupled scalar field.

Yuting Shen, Yiyun Wang, Chengxiao Liu, Niansong Ye

Imaging of teeth is very important to doctors in the diagnosis and treatment of dental diseases. The main imaging modality currently used is Cone-Beam Computed Tomography (CBCT), which however suffers from ionizing radiation causing potential damage to human body. In this work, photoacoustic imaging is proposed for the imaging of tooth, specifically the toot

José Eduardo Rosales Quintero

We study a pure connection formulation plus algebraic constraints in four spacetime dimensions where the gauge group $G \supset SO(1, 3)$. We show that the action has, as particular cases, the MacDowell-Mansouri and the Stelle-West formulations for gravity. Also, under adequate specification of the constraint terms, we obtain Einstein manifolds, i.e., torsio

Audrey Guo

Economic models assume that payroll tax burdens fall fully on workers, but where does tax incidence fall when taxes are firm-specific and time-varying? Unemployment insurance in the United States has the key feature of varying both across employers and over time, creating the potential for labor demand responses if tax costs cannot be fully passed on to work

Chutian Huang, Shuyang Dai, Xiaohua Niu, Tianpeng Jiang

Dislocation climb plays an important role in understanding plastic deformation of metallic materials at high temperature. In this paper, we present a continuum formulation for dislocation climb velocity based on densities of dislocations. The obtained continuum formulation is an accurate approximation of the Green's function based discrete dislocation dynami

Benjamin Q. Huynh, Elizabeth T. Chin, Allison Koenecke, Derek Ouyang

Neighborhood-level screening algorithms are increasingly being deployed to inform policy decisions. We evaluate one such algorithm, CalEnviroScreen - designed to promote environmental justice and used to guide hundreds of millions of dollars in public funding annually - assessing its potential for allocative harm. We observe the model to be sensitive to subj

Yifeng Shi, Feng Lv, Xinliang Wang, Chunlong Xia

With the continuous improvement of computing power and deep learning algorithms in recent years, the foundation model has grown in popularity. Because of its powerful capabilities and excellent performance, this technology is being adopted and applied by an increasing number of industries. In the intelligent transportation industry, artificial intelligence f

Lingli Zhu, Bingbing Jin, Huili Liu, Tao Yang

The aim of this paper is the Ore extension of group-cograded Hopf coquasigroups. This paper first shows a categorical interpretation and some examples of group-cograded Hopf coquasigroups, and then gives a necessary and sufficient conditions for the Ore extensions of group-cograded Hopf coquasigroups to be group-cograded Hopf coquasigroups. Finally, a certai

Zhaoyou Wang, Amir H. Safavi-Naeini

Time-periodic systems allow engineering new effective Hamiltonians from limited physical interactions. For example, the inverted position of the Kapitza pendulum emerges as a stable equilibrium with rapid drive of its pivot point. In this work, we propose the $\textit{Kapitzonium}$: a Floquet qubit that is the superconducting circuit analog of a mechanical K

Nikhil Singh, Chih-Wei Wu, Iroro Orife, Mahdi Kalayeh

Audiovisual representation learning typically relies on the correspondence between sight and sound. However, there are often multiple audio tracks that can correspond with a visual scene. Consider, for example, different conversations on the same crowded street. The effect of such counterfactual pairs on audiovisual representation learning has not been previ

Ferdi Kara, Hakan Kaya, Halim Yanikomeroglu, Chan-Tong Lam

In this paper, we propose bit-interleaved multiple access (BIMA) to enable Internet-of-Things (IoT) networks where a massive connection is required with limited resource blocks. First, by providing a true power allocation (PA) constraint for conventional NOMA with practical constraints, we demonstrate that it cannot support massive connections. To this end,

Dor Minzer, Kai Zheng

A local tester for an error correcting code $C\subseteq \Sigma^{n}$ is a tester that makes $Q$ oracle queries to a given word $w\in \Sigma^n$ and decides to accept or reject the word $w$. An optimal local tester is a local tester that has the additional properties of completeness and optimal soundness. By completeness, we mean that the tester must accept wit

Donghwan Lee

In this paper, the primary goal is to offer additional insights into the value iteration through the lens of switching system models in the control community. These models establish a connection between value iteration and switching system theory and reveal additional geometric behaviors of value iteration in solving discounted Markov decision problems. Spec

Mauricio Valenzuela

We present a fermion model characterized by an anticommuting-parameter shift symmetry. The Hamiltonian formulation exhibits a combination of first-class and second-class constraints. We derive the well-known Dirac equation by fixing the gauge in a covariant manner, enabling the fields to propagate accordingly. Notably, the model inherently possesses invarian

Robert A. Robinson, Lujin Min, Seng Huat Lee, Peigang Li

The Wiedemann-Franz (WF) law relates the electronic component of the thermal conductivity to the electrical conductivity in metals through the Lorenz number. The WF law has proven to be remarkably robust, however violations have been observed in many topological materials. In this work, we report thermoelectric measurements conducted on Heusler, ferromagneti

Seng Huat Lee, David Graf, Robert Robinson, John Singleton

We report c-axis transport studies on magnetic topological insulators Mn(Bi1-xSbx)2Te4. We performed systematic c-axis magnetoresistivity measurements under high magnetic fields (up to 35 T) on several representative samples. We find the lightly hole- and lightly electron-doped samples, while both having the same order of magnitude of carrier density and sim

Swarnav Mukhopadhyay, Cheng Liu, Jiahao Chen, Surjava Sanyal

In this article, high composition (>35%) thick (>30 nm) barrier AlGaN/AlN/GaN HEMT structure grown on a sapphire substrate with ultra-low sheet resistivity (<250 \Omega / \Box ) is reported. Optimization of growth conditions, such as reduced growth rate, low carbon incorporation, and thickness optimization of different epitaxial layers allowed to grow a crac

Mathias Louboutin, Ziyi Yin, Rafael Orozco, Thomas J. Grady

We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating mul

Siddharth Suresh, Kushin Mukherjee, Timothy T. Rogers

This study evaluates the potential of a large language model for aiding in generation of semantic feature norms - a critical tool for evaluating conceptual structure in cognitive science. Building from an existing human-generated dataset, we show that machine-verified norms capture aspects of conceptual structure beyond what is expressed in human norms alone

Zhibo Xing, Zijian Zhang, Meng Li, Jiamou Liu

Since the concern of privacy leakage extremely discourages user participation in sharing data, federated learning has gradually become a promising technique for both academia and industry for achieving collaborative learning without leaking information about the local data. Unfortunately, most federated learning solutions cannot efficiently verify the execut

Oscar Leong, Angela F. Gao, He Sun, Katherine L. Bouman

We consider solving ill-posed imaging inverse problems without access to an image prior or ground-truth examples. An overarching challenge in these inverse problems is that an infinite number of images, including many that are implausible, are consistent with the observed measurements. Thus, image priors are required to reduce the space of possible solutions

Alexandru Maries, Chandralekha Singh

Understanding issues involved in expertise in physics problem solving is important for helping students become good problem solvers. In part 1 of this article, we summarize the research on problem-solving relevant for physics education across three broad categories: knowledge organization, information processing and cognitive load, and metacognition and prob

Felix Wang

With the increasing development of neuromorphic platforms and their related software tools as well as the increasing scale of spiking neural network (SNN) models, there is a pressure for interoperable and scalable representations of network state. In response to this, we discuss a parallel extension of a widely used format for efficiently representing sparse

Sudhakantha Girmohanta, Seung J. Lee, Yuichiro Nakai, Motoo Suzuki

5D warped extra dimension models with multiple 3-branes can naturally realize multiple hierarchical mass scales which are ubiquitous in physics beyond the Standard Model. We discuss cosmological consequences of such multi-brane models with stabilized radions. It is confirmed that for temperatures below the scale of the IR brane at the end of the extra dimens

Chandralekha Singh, Alexandru Maries, Kenneth Heller, Patricia Heller

Helping students become proficient problem solvers is a major goal of many physics courses from introductory to advanced levels. In fact, physics has often been used by cognitive scientists to investigate the differences between the problem-solving strategies of expert and novice problem solvers because it is a domain in which there is reasonably good agreem

Ervin Győri, Kitti Varga, Xiutao Zhu

The planar Tur\'an number ${\rm ex}_{\mathcal{P}}(n,C_k)$ is the largest number of edges in an $n$-vertex planar graph with no cycle of length $k$. Let $k\ge 11$ and $C,D$ be constants. Cranston, Lidick\'y, Liu and Shantanam \cite{2021Planar}, and independently Lan and Song \cite{LanSong} showed that ${\rm ex}_{\mathcal{P}}(n,C_k)\ge 3n-6-\frac{Cn}{k}$ for l

Chia-Rui Chang, Rui Wang

Analyses of cluster randomized trials (CRTs) can be complicated by informative missing outcome data. Methods such as inverse probability weighted generalized estimating equations have been proposed to account for informative missingness by weighting the observed individual outcome data in each cluster. These existing methods have focused on settings where mi

Gehua Tong, Rishi Ananth, John Thomas Vaughan,, Sairam Geethanath

Background: Magnetic Resonance Imaging (MRI) is a medical imaging technique that combines principles of physics, math, and engineering to look inside humans and animals, hence delivering a lifesaving technology. However, resource-poor regions aiming to expand access to MRI suffer from a lack of imaging expertise. Education of a new generation of MRI technici

Ioana Ciucă, Yuan-Sen Ting

We demonstrate the potential of the state-of-the-art OpenAI GPT-4 large language model to engage in meaningful interactions with Astronomy papers using in-context prompting. To optimize for efficiency, we employ a distillation technique that effectively reduces the size of the original input paper by 50\%, while maintaining the paragraph structure and overal

Mao-Ting Chien, Steve Kirkland, Chi-Kwong Li, Hiroshi Nakazato

We study the numerical range of an $n\times n$ cyclic shift matrix, which can be viewed as the adjacency matrix of a directed cycle with $n$ weighted arcs. In particular, we consider the change in the numerical range if the weights are rearranged or perturbed. In addition to obtaining some general results on the problem, a permutation of the given weights is

Jacob D. Baxley, David R. Lambert, Mauro Bologna, Bruce J. West

Noise-induced phase transitions are common in various complex systems, from physics to biology. In this article, we investigate the emergence of crucial events in noise-induced phase transition processes and their potential significance for understanding complexity in such systems. We utilize the first-passage time technique and coordinate transformations to

Leonid Chekhov, Michael Shapiro

We consider the symplectic groupoid of pairs $(B,\mathbb{A})$ with $\mathbb A$ unipotent upper-triangular matrices and $B\in GL_n$ being such that $\widetilde {\mathbb A}=B{\mathbb A} B^{\text{T}}$ are also unipotent upper-triangular matrices. We explicitly solve this groupoid condition using Fock--Goncharov--Shen cluster variables and show that for $B$ sati

Zeping Jin, Daohai Li, Zong-Hong Zhu

About 25-50% of white dwarfs (WDs) show metal lines in their spectra. Among the widely accepted explanations for this effect is that the these WDs are accreting asteroids that are perhaps flung onto the WDs by a planet via resonance, for instance. A number of theoretical works have looked into the accretion of asteroids onto WDs and obtained a fair agreement

Wei Tan, Jionghao Lin, David Lang, Guanliang Chen

Dialogue Acts (DAs) can be used to explain what expert tutors do and what students know during the tutoring process. Most empirical studies adopt the random sampling method to obtain sentence samples for manual annotation of DAs, which are then used to train DA classifiers. However, these studies have paid little attention to sample informativeness, which ca

S. Niu, F. G. Xie, Q. D. Wang, L. Ji

Despite advances in our understanding of low luminosity active galactic nuclei (LLAGNs), the fundamental details about the mechanisms of radiation and flare/outburst in hot accretion flow are still largely missing. We have systematically analyzed the archival Chandra and NuSTAR X-ray data of the nearby LLAGN M81*, whose $L_{\rm bol}\sim 10^{-5} L_{\rm Edd}$.

Fazal-E-Asim, Bruno Sokal, André L. F. de Almeida, Behrooz Makki

This paper proposes tensor-based channel estimation for reconfigurable intelligent surface (RIS)-assisted communication networks. We exploit the inherent geometrical structure of the Terahertz propagation channel, including the antenna array geometries at the base station, the RIS, and the user equipment to design a tensor-based channel estimator, referred t

Sooyeong Kim, Steve Kirkland

In spectral bisection, a Fielder vector is used for partitioning a graph into two connected subgraphs according to its sign pattern. In this article, we investigate graphs having Fiedler vectors with unbalanced sign patterns such that a partition can result in two connected subgraphs that are distinctly different in size. We present a characterization of gra

Rui-Chen Zheng, Yang Ai, Zhen-Hua Ling

This paper studies the task of speech reconstruction from ultrasound tongue images and optical lip videos recorded in a silent speaking mode, where people only activate their intra-oral and extra-oral articulators without producing sound. This task falls under the umbrella of articulatory-to-acoustic conversion, and may also be refered to as a silent speech

Mengqi Yao, Sandip Roy, Johanna L. Mathieu

With the increase of uncertain and intermittent renewable energy supply on the grid, the power system has become more vulnerable to instability. In this paper, we develop a demand response strategy to improve power system small-signal stability. We pose the problem as an optimization problem wherein the total demand-responsive load is held constant at each t

Junlei Mu, Congshan Zhuo, Qingdian Zhang, Sha Liu

The high-order gas-kinetic scheme (HGKS) features good robustness, high efficiency and satisfactory accuracy,the performaence of which can be further improved combined with WENO-AO (WENO with adaptive order) scheme for reconstruction. To reduce computational costs in the reconstruction procedure, this paper proposes to combine HGKS with a hybrid WENO-AO sche

Xudong Zhang, Shuang Gao, Xiaohu Nan, Haikuan Ning

Camera localization is a classical computer vision task that serves various Artificial Intelligence and Robotics applications. With the rapid developments of Deep Neural Networks (DNNs), end-to-end visual localization methods are prosperous in recent years. In this work, we focus on the scene coordinate prediction ones and propose a network architecture name

Arpita Chatterjee, Rupamanjari Ghosh

We construct a distinct class of nonlinear displaced Kerr state by application of the displacement operator upon a state which is prepared by sending the well-known photon-added coherent state through a normal Kerr medium. A sketch for the experimental set-up for preparing the state is suggested. We evaluate some statistical properties such as the photon num

K. Itakura

We study resonances for the Schr\"odinger operators with quadratic or sub-quadratic repulsive potential. In the present paper, we show the non-existence of resonances in some complex neighborhood of a fixed energy by employing schemes of Briet-Combes-Duclos and Hunziker and by introducing a proper distortion based on, but slightly modified, positive commutat

Jiabao Ji, Guanhua Zhang, Zhaowen Wang, Bairu Hou

Scene text editing is a challenging task that involves modifying or inserting specified texts in an image while maintaining its natural and realistic appearance. Most previous approaches to this task rely on style-transfer models that crop out text regions and feed them into image transfer models, such as GANs. However, these methods are limited in their abi

Shota Ono, Yu Kumagai

Motivated by the experimental synthesis of two-dimensional (2D) perovskite materials, we study the stability of 2D SrTiO$_3$ from first principles. We find that the TiO$_6$ octahedral rotations emerge in 2D SrTiO$_3$ with a rotation angle twice that in the 3D bulk. The rotation angle decreases significantly with the film thickness, reflecting the strong inte

L. Hernández-Sánchez, I. Ramos-Prieto, F. Soto-Eguibar, H. M. Moya-Cessa

We show that the Markovian dynamics of two coupled harmonic oscillators may be analyzed using a Schr\"odinger equation and an effective non-Hermitian Hamiltonian. This may be achieved by a non-unitary transformation that involves superoperators; such transformation enables the removal of quantum jump superoperators, that allows us to rewrite the Lindblad mas

Anabella C. Doctor

This study aims to determine a predictive model to learn students probability to pass their courses taken at the earliest stage of the semester. To successfully discover a good predictive model with high acceptability, accurate, and precision rate which delivers a useful outcome for decision making in education systems, in improving the processes of conveyin

Shuang Cui, Bingnan Wang, Quan Zheng

Optical aberrations of optical systems cause significant degradation of imaging quality. Aberration correction by sophisticated lens designs and special glass materials generally incurs high cost of manufacturing and the increase in the weight of optical systems, thus recent work has shifted to aberration correction with deep learning-based post-processing.

Tianyi Ding, Lin Chen

Entanglement distillation is a key task in quantum-information processing. In this paper, we distill non-positive-partial-transpose (NPT) bipartite states of some given Schmidt rank and matrix rank. We show that all bipartite states of Schmidt rank two are locally equivalent to classical-classical states, and all bipartite states of Schmidt rank three are 1-

G. Lusztig

We define (in two, equivalent ways) the notion of a rigid stratum of a reductive group. This generalizes the notion of rigid unipotent class.

Gioacchino Tangari, Shreesh Keskar, Hassan Jameel Asghar, Dali Kaafar

Biometric authentication service providers often claim that it is not possible to reverse-engineer a user's raw biometric sample, such as a fingerprint or a face image, from its mathematical (feature-space) representation. In this paper, we investigate this claim on the specific example of deep neural network (DNN) embeddings. Inversion of DNN embeddings has

Jie Gao, Kenny Tsu Wei Choo, Junming Cao, Roy Ka Wei Lee

While AI-assisted individual qualitative analysis has been substantially studied, AI-assisted collaborative qualitative analysis (CQA)-a process that involves multiple researchers working together to interpret data-remains relatively unexplored. After identifying CQA practices and design opportunities through formative interviews, we designed and implemented

Joseph P. Vantassel, Andrew C. Stolte, Liam M. Wotherspoon, Brady R. Cox

This work proposes the AutoHVSR algorithm that allows for fully-automated processing of horizontal-to-vertical spectral ratio (HVSR) measurements, including those with zero, one, or multiple clear resonances. The AutoHVSR algorithm integrates robust signal processing and computational methods with state-of-the-art machine-learning models trained using a dive

Tao Liu, Ho-Kei Chan, Duanduan Wan

Inspired by recent developments in self-assembled chiral nanostructures, we have explored the possibility of using spherical particles packed in cylinders as building blocks for chiral photonic crystals. In particular, we focused on an array of parallel cylinders arranged in a perfect triangular lattice, each containing an identical densest sphere packing st

Joshua Cesca, Cédric Simenel

Background: The Hartree-Fock mean-field approximation is standard in combination with energy density functionals (EDF) that account for some dynamical correlations. Breaking and restoring the symmetries of the system allow for the inclusion of additional static correlations. However, exact solutions to evaluate the effectiveness of these methods are rare. Pu

Heyu Chen, Jianfeng Li, Shigang Li

Nowadays, panoramic images can be easily obtained by panoramic cameras. However, when the panoramic camera orientation is tilted, a non-upright panoramic image will be captured. Existing upright adjustment models focus on how to estimate more accurate camera orientation, and attribute image reconstruction to offline or post-processing tasks. To this end, we

Yu Chen, Xiaoling Cui

The highly excited super-Tonks-Girardeau (sTG) gas was recently observed to be extremely stable in the presence of a weak dipolar repulsion. Here we reveal the underlying reason for this mysterious phenomenon. By exactly solving the trapped small clusters with both contact and dipolar interactions, we show that the reason lies in the distinct spectral respon

Liping Bao, Longhui Wei, Xiaoyu Qiu, Wengang Zhou

Recent researches on unsupervised person re-identification~(reID) have demonstrated that pre-training on unlabeled person images achieves superior performance on downstream reID tasks than pre-training on ImageNet. However, those pre-trained methods are specifically designed for reID and suffer flexible adaption to other pedestrian analysis tasks. In this pa

Chun Chen, Yan Chen, Xiaoqun Wang

A common wisdom posits that transports of conserved quantities across clean nonintegrable quantum systems at high temperatures are diffusive when probed from the emergent hydrodynamic regime. We show that this empirical paradigm may alter if the strong interaction limit is taken. Using Krylov-typicality and purification matrix-product-state methods, we estab