November 2022 arXiv papers — page 26

Luca Thiede, Chong Sun, Alán Aspuru-Guzik

An efficient and expressive wavefunction ansatz is key to scalable solutions for complex many-body electronic structures. While Slater determinants are predominantly used for constructing antisymmetric electronic wavefunction ans\"{a}tze, this construction can result in limited expressiveness when the targeted wavefunction is highly complex. In this work, we

Jinghui Lu, Rui Zhao, Brian Mac Namee, Fei Tan

Much of named entity recognition (NER) research focuses on developing dataset-specific models based on data from the domain of interest, and a limited set of related entity types. This is frustrating as each new dataset requires a new model to be trained and stored. In this work, we present a ``versatile'' model -- the Prompting-based Unified NER system (PUn

Fengshan Zhang, Yongkui Zou, Shimin Chai, Yanzhao Cao

In this paper, we consider a nonlinear filtering model with observations driven by correlated Wiener processes and point processes. We first derive a Zakai equation whose solution is a unnormalized probability density function of the filter solution. Then we apply a splitting-up technique to decompose the Zakai equation into three stochastic differential equ

Halil Mutuk

We study $D_s^+ D_s^-$ and $D \bar D$ states assuming that they are hadronic molecules with $J^{PC}=0^{++}$ quantum number. We use two-point QCD sum rule formalism and extract the mass and decay constant values of these states. We take into account contributions of various quark, gluon, and mixed vacuum condensates up to dimension eight. The extracted mass a

Elad Hirsch, Ayellet Tal

Controllable image captioning models generate human-like image descriptions, enabling some kind of control over the generated captions. This paper focuses on controlling the caption length, i.e. a short and concise description or a long and detailed one. Since existing image captioning datasets contain mostly short captions, generating long captions is chall

Lenny Jones

We say that a monic polynomial $f(x)\in {\mathbb Z}[x]$ of degree $N$ is monogenic if $f(x)$ is irreducible over ${\mathbb Q}$ and \[\{1,\theta,\theta^2,\ldots, \theta^{N-1}\}\] is a basis for the ring of integers of ${\mathbb Q}(\theta)$, where $f(\theta)=0$. Let $k$ be a positive integer, and let $U_n:=U_n(k,-1)$ be the Lucas sequence $\{U_n\}_{n\ge 0}$ of

Alfredo Iorio

I first recall the uses of Dirac materials as tabletop realizations of high energy physics scenarios. Then I point to a specific system that might reproduce a massless BTZ black hole, where the key role is played by hyperbolic carbon pseudospheres. Finally, some considerations are offered on the possibility to realize rotating black holes, along with some co

Abbas Termos, Bertrand Hochwald

One of the primary goals in spectrum occupancy mapping is to create a system that is robust to assumptions about the number of sensors, occupancy threshold (in dBm), sensor noise, number of emitters and the propagation environment. We show that such a system may be designed with neural networks using a process of aggregation to allow a variable number of sen

Martina Cerulli, Domenico Serra, Carmine Sorgente, Claudia Archetti

In social network analysis, the size of the k-core, i.e., the maximal induced subgraph of the network with minimum degree at least k, is frequently adopted as a typical metric to evaluate the cohesiveness of a community. We address the Collapsed k-Core Problem, which seeks to find a subset of $b$ users, namely the most critical users of the network, the remo

Jagang Park, Hee Chul Park, Kyungmin Lee, Jonghwa Shin

Lyubarov et al. (Research Articles, 22 July 2022, p. 425) claim that the spontaneous emission rate of an atom vanishes at the momentum gap edges of photonic Floquet media. We show that their theoretical prediction is based on assumptions that result in misleading interpretations on the spontaneous emission rate in photonic Floquet media.

Thijs Peirelinck, Chris Hermans, Fred Spiessens, Geert Deconinck

Residential demand response programs aim to activate demand flexibility at the household level. In recent years, reinforcement learning (RL) has gained significant attention for these type of applications. A major challenge of RL algorithms is data efficiency. New RL algorithms, such as proximal policy optimisation (PPO), have tried to increase data efficien

Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland, Yiwei Jia

Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model achieved tre

Yu Guo, Zhilong Xie, Xingyan Chen, Huangen Chen

Natural language understanding (NLU) has two core tasks: intent classification and slot filling. The success of pre-training language models resulted in a significant breakthrough in the two tasks. One of the promising solutions called BERT can jointly optimize the two tasks. We note that BERT-based models convert each complex token into multiple sub-tokens

Yichun Qiu, Weijun Sun, Guoxu Zhou, Qibin Zhao

Efficient and accurate low-rank approximation (LRA) methods are of great significance for large-scale data analysis. Randomized tensor decompositions have emerged as powerful tools to meet this need, but most existing methods perform poorly in the presence of noise interference. Inspired by the remarkable performance of randomized block Krylov iteration (rBK

Alan Clark, Shoaib Ahmed Siddiqui, Robert Kirk, Usman Anwar

Existing offline reinforcement learning (RL) algorithms typically assume that training data is either: 1) generated by a known policy, or 2) of entirely unknown origin. We consider multi-demonstrator offline RL, a middle ground where we know which demonstrators generated each dataset, but make no assumptions about the underlying policies of the demonstrators

Hongfeng Liu, Xiaodong Yang, Kai Tang, Liangyu Che

Non-Hermitian quantum systems have recently attracted considerable attention due to their exotic properties. Though many experimental realizations of non-Hermitian systems have been reported, the non-Hermiticity usually resorts to the hard-to-control environments and cannot last for too long times. An alternative approach is to use quantum simulation with th

Yang Cao, Yichuan Deng, Wenyu Jin, Xiaoyu Li

A geometric graph associated with a set of points $P= \{x_1, x_2, \cdots, x_n \} \subset \mathbb{R}^d$ and a fixed kernel function $\mathsf{K}:\mathbb{R}^d\times \mathbb{R}^d\to\mathbb{R}_{\geq 0}$ is a complete graph on $P$ such that the weight of edge $(x_i, x_j)$ is $\mathsf{K}(x_i, x_j)$. We present a fully-dynamic data structure that maintains a spectra

V. V. Obukhov

The classification of exact solutions of Maxwell vacuum equations for the case when the electromagnetic fields and metrics of homogeneous spaces are invariant with respect to the motion group G(VII) is completed. All non-equivalent exact solutions of Maxwell vacuum equations for electromagnetic fields and spaces with such symmetry have been obtained. The vec

Bowen Cai, Yujie Li, Yuqin Liang, Rongfei Jia

This paper studies how to flexibly integrate reconstructed 3D models into practical 3D modeling pipelines such as 3D scene creation and rendering. Due to the technical difficulty, one can only obtain rough 3D models (R3DMs) for most real objects using existing 3D reconstruction techniques. As a result, physically-based rendering (PBR) would render low-qualit

Aref Hemati, Azam Bastanfard

This research proposes a novel adjustable algorithm for reconstructing 3D body shapes from front and side silhouettes. Most recent silhouette-based approaches use a deep neural network trained by silhouettes and key points to estimate the shape parameters but cannot accurately fit the model to the body contours and consequently are struggling to cover detail

Neham Jain, Gopi Matta, Kaushik Mitra

Recovery of true color from underwater images is an ill-posed problem. This is because the wide-band attenuation coefficients for the RGB color channels depend on object range, reflectance, etc. which are difficult to model. Also, there is backscattering due to suspended particles in water. Thus, most existing deep-learning based color restoration methods, w

Zhiqiang Wang

For the power-law potential $n$-body problem, we study a special kind of central configurations where all the masses lie on a circle and the center of mass coincides with the center of the circle. It is also called the centered co-circular central configuration. We get some symmetry results for such central configurations. We show that for positive numbers $

Rinyoichi Takezoe, Xu Liu, Shunan Mao, Marco Tianyu Chen

As an important data selection schema, active learning emerges as the essential component when iterating an Artificial Intelligence (AI) model. It becomes even more critical given the dominance of deep neural network based models, which are composed of a large number of parameters and data hungry, in application. Despite its indispensable role for developing

Junwen Yang, Ang Li, Xuewen Liao, Christos Masouros

Symbol-level precoding (SLP) manipulates the transmitted signals to accurately exploit the multi-user interference (MUI) in the multi-user downlink. This enables that all the resultant interference contributes to correct detection, which is the so-called constructive interference (CI). Its performance superiority comes at the cost of solving a nonlinear opti

Anderson Luis Albuquerque de Araujo, Edir Junior Ferreira Leite, Aldo Henrique de Souza Medeiros

In this paper we develop a comprehensive study on principal eigenvalues and both the (weak and strong) maximum and comparison principles related to an important class of nonlinear systems involving fractional $m$-Laplacian operators. Explicit lower bounds for principal eigenvalues of this system in terms of the diameter of $\Omega$ are also proved. As applic

David Gaspard, Jean-Marc Sparenberg

The decoherence of a fast quantum particle in a gas is studied by applying the Kramers-Moyal expansion to the quantum master equation for the reduced density matrix of the particle. This expansion leads to a general form of the Caldeira-Leggett master equation accounting for the angular variation of the differential cross section. The equation describes the

Sidney Donato, Rafael Montezuma

In this paper, free boundary geodesic networks whose length realize the first min-max width of the length functional are investigated. This functional acts on the space of relative flat 1-dimensional cycles modulo 2 in a compact surface with boundary. The widths are special critical values of the volume functional in some class of submanifolds which naturall

Jarosław Gruszka, Janusz Szwabiński

Parametric estimation of stochastic differential equations (SDEs) has been a subject of intense studies already for several decades. The Heston model for instance is driven by two coupled SDEs and is often used in financial mathematics for the dynamics of the asset prices and their volatility. Calibrating it to real data would be very useful in many practica

Huaishao Luo, Junwei Bao, Youzheng Wu, Xiaodong He

Recently, the contrastive language-image pre-training, e.g., CLIP, has demonstrated promising results on various downstream tasks. The pre-trained model can capture enriched visual concepts for images by learning from a large scale of text-image data. However, transferring the learned visual knowledge to open-vocabulary semantic segmentation is still under-e

A. I. Breev, D. M. Gitman

In this work, using the noncommutative integration method of linear differential equations, we obtain a complete set of solutions to the Schrodinger equation for a quantum asymmetric top in Euler angles. It is shown that the noncommutative reduction of the Schrodinger equation leads to the Lame equation. The resulting set of solutions is determined by the La

Zeqiang Lai, Ying Fu

Hyperspectral image is unique and useful for its abundant spectral bands, but it subsequently requires extra elaborated treatments of the spatial-spectral correlation as well as the global correlation along the spectrum for building a robust and powerful HSI restoration algorithm. By considering such HSI characteristics, 3D Quasi-Recurrent Neural Network (QR

Daniel Barzilai, Amnon Geifman, Meirav Galun, Ronen Basri

Over-parameterized residual networks (ResNets) are amongst the most successful convolutional neural architectures for image processing. Here we study their properties through their Gaussian Process and Neural Tangent kernels. We derive explicit formulas for these kernels, analyze their spectra, and provide bounds on their implied condition numbers. Our resul

Krishna Rudrapal, Biswajit Jana, Venimadhav Adyam, Ayan Roy Chaudhuri

We report on resistive switching (RS) characteristics of W/WO3-x/Pt-based thin film memristors modulated by precisely controlled oxygen non-stoichiometry. RS properties of the devices with varied oxygen vacancy (VO) concentration have been studied by measuring their DC current voltage properties. Switchability of the resistance states in the memristors have

Yongshuai Gao, Yujin Guo, Yan Li, Yong Luo

This paper is concerned with ground states of two-component Bose gases confined in a harmonic trap $V(x)=x_1^2+\Lambda^2 x_2^2$ rotating at the velocity $\Omega >0$, where $\Lambda\ge 1$ and $(x_1, x_2)\in R^2$. We focus on the case where the intraspecies interaction $(-a_1,-a_2)$ and the interspecies interaction $-\beta$ are both attractive, i.e, $a_1, a_2$

Mook Kwon Jung, Sang Duk Yoon, Hee-Kap Ahn, Takeshi Tokuyama

We consider the smallest-area universal covering of planar objects of perimeter 2 (or equivalently closed curves of length 2) allowing translation and discrete rotations. In particular, we show that the solution is an equilateral triangle of height 1 when translation and discrete rotation of $\pi$ are allowed. Our proof is purely geometric and elementary. We

Yufan Zhang, Honglin Wen, Tao Feng, Yize Chen

Demand response (DR) leverages demand-side flexibility, offering a promising approach to enhance market conditions like mitigating wholesale price spikes. However, poorly chosen DR locations can inadvertently increase electricity prices. For that, we introduce a method to rigorously select DR locations and corresponding demand reductions. We formulate a bile

Zixian Su, Kai Yao, Xi Yang, Qiufeng Wang

Single-source domain generalization (SDG) in medical image segmentation is a challenging yet essential task as domain shifts are quite common among clinical image datasets. Previous attempts most conduct global-only/random augmentation. Their augmented samples are usually insufficient in diversity and informativeness, thus failing to cover the possible targe

G. K. Koyanagi, R. L. Lambo, A. Ragyanszki, R. Fournier

Calculations of the BaF-Ar triatomic system are performed with a relativistic Hamiltonian and coupled cluster theory at the CCSD(T) level for 1386 positions of the Ar atom relative to the BaF molecule. Calculations are repeated with increasing basis sets (double-, triple-, quadruple- and quintuple-zeta), and these are extrapolated to estimate the complete-ba

Li Ruinan, Zhang Beibei

This paper is devoted to investigating Freidlin-Wentzell's large deviation principle for one (spatial) dimensional nonlinear stochastic wave equation $\frac{\partial^2 u^{\e}(t,x)}{\partial t^2}=\frac{\partial^2 u^{\e}(t,x)}{\partial x^2}+\sqrt{\e}\sigma(t, x, u^{\e}(t,x))\dot{W}(t,x)$, where $\dot{W}$ is white in time and fractional in space with Hurst para

Daniel Anderson, Ariel Shamir, Ohad Fried

Recent advances in deep learning techniques and applications have revolutionized artistic creation and manipulation in many domains (text, images, music); however, fonts have not yet been integrated with deep learning architectures in a manner that supports their multi-scale nature. In this work we aim to bridge this gap, proposing a network architecture cap

Evan Cavallo, Christian Sattler

We establish a Quillen equivalence between the Kan-Quillen model structure and a model structure, derived from a cubical model of homotopy type theory, on the category of cartesian cubical sets with one connection. We thereby identify a second model structure which both constructively models homotopy type theory and presents infinity-groupoids, the first exa

Deisy Morselli Gysi, Albert-Laszlo Barabasi

Network Medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions, ignoring interactions mediated by non-coding RNAs (ncRNAs). Here,

Jen-I Pan, Jheng-Wei Su, Kai-Wen Hsiao, Ting-Yu Yen

Recently, differentiable volume rendering in neural radiance fields (NeRF) has gained a lot of popularity, and its variants have attained many impressive results. However, existing methods usually assume the scene is a homogeneous volume so that a ray is cast along the straight path. In this work, the scene is instead a heterogeneous volume with a piecewise-

Der-Chen Chang, Ji Li, Jingzhi Tie, Qingyan Wu

We study the Kohn-Laplacian and its fundamental solution on some model domains in $\mathbb C^{n+1}$, and further discuss the explicit kernel of the Cauchy-Szeg\"o projections on these model domains using the real analysis method. We further show that these Cauchy-Szeg\"o kernels are Calder\'on-Zygmund kernels under the suitable quasi-metric.

Ron Bitton, Alon Malach, Amiel Meiseles, Satoru Momiyama

Model agnostic feature attribution algorithms (such as SHAP and LIME) are ubiquitous techniques for explaining the decisions of complex classification models, such as deep neural networks. However, since complex classification models produce superior performance when trained on low-level (or encoded) features, in many cases, the explanations generated by the

Tulika Tripathy

Event-by-event fluctuations of the mean transverse momentum of charged particles produced in Pb-Pb and Xe-Xe collisions at $\sqrt{s_{\rm{NN}}}$ = 5.02 TeV and $\sqrt{s_{\rm{NN}}}$ = 5.44 TeV, respectively, are studied as a function of the charged-particle multiplicity using the ALICE detector at the LHC. Dynamical fluctuations are observed in both collision

M. U. Ashraf, Junaid Tariq, A. M. Khan

We report the transverse momentum $p_T$ spectra of identified hadrons ($\pi^\pm$, $K^\pm$ and $p(\bar p)$) in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7 - 39 GeV from A Multi Phase Transport Model with string melting effect (AMPT-SM). During this study, a new set of parameters are explored to study the effect of hadronic cascade by varying hadronic cascade ti

Guangrun Wang, Philip H. S. Torr

Classifiers and generators have long been separated. We break down this separation and showcase that conventional neural network classifiers can generate high-quality images of a large number of categories, being comparable to the state-of-the-art generative models (e.g., DDPMs and GANs). We achieve this by computing the partial derivative of the classificat

David van de Sanden, Maarten Schoukens, Mauro Salazar

Mobility systems often suffer from a high price of anarchy due to the uncontrolled behavior of selfish users. This may result in societal costs that are significantly higher compared to what could be achieved by a centralized system-optimal controller. Monetary tolling schemes can effectively align the behavior of selfish users with the system-optimum. Yet,

Qi Zhou, Ben-Wei Zhang

We study the energy loss of a quark moving in a strongly coupled QGP under the influence of anisotropy. The heavy quark drag force, diffusion coefficient, and jet quenching parameter are calculated using the Einstein-Maxwell-dilaton model, where the anisotropic background is characterized by an arbitrary dynamical parameter $A$.Our findings indicate that as

Radoslav Simeonov

The forward calorimeter (FoCal) of ALICE, planned to be operational for LHC Run 4, will cover the pseudorapidity range 3.4 $ \leq \eta \leq $ 5.8 allowing to probe the unexplored region of Bjorken-x down to $10^{-6}$. The hadronic section of the FoCal (FoCal-H) will be based on copper capillary tubes and scintillating fibers inside, with light read out by si

Yuhui Zhu, Zhenxiang Chen, Qiben Yan, Shanshan Wang

One of the innovations brought by Mirai and its derived malware is the adoption of self-contained loaders for infecting IoT devices and recruiting them in botnets. Functionally decoupled from other botnet components and not embedded in the payload, loaders cannot be analysed using conventional approaches that rely on honeypots for capturing samples. Differen

Lucian Beznea, Iulian Cîmpean, Michael Röckner

The aim of this note is to show, by providing an elementary way to construct counter-examples, that the strong Feller and the joint (space-time) continuity for a semigroup of Markov kernels on a Polish space are not enough to ensure the existence of an associated c\`adl\`ag Markov process on the same space. One such simple counter-example is the Brownian sem

Benjamin Horowitz, Peter Melchior

In this abstract we explore the possibility of introducing biases in physical parameter inference models from adversarial-type attacks. In particular, we inject small amplitude systematics into inputs to a mixture density networks tasked with inferring cosmological parameters from observed data. The systematics are constructed analogously to white-box advers

Markus Schwagenscheidt, Brandon Williams

We obtain infinite product expansions in the sense of Borcherds for theta functions associated with certain positive-definite binary quadratic and binary hermitian forms. Among other things, we show that every weight 1 binary theta function is a Borcherds product. In particular, binary theta functions have zeros only at quadratic irrationalities.

Shengru Chen, Qiao Jin, Shan Lin, Haitao Hong

Transition metal nitrides (TMNs) exhibit fascinating physical properties that hold great potential in future device applications. To stack two-dimensional TMNs with other functional materials that have dissimilar orientations and symmetries requires to separate epitaxial TMNs from the growth substrates. However, the lattice constants of TMNs are not compatib

Zhenyu Liu, Li Wang, Lianming Xu, Zhi Ding

Deep learning (DL)-based channel state information (CSI) feedback has shown promising potential to improve spectrum efficiency in massive MIMO systems. However, practical DL approaches require a sizeable CSI dataset for each scenario, and require large storage or updating bandwidth for multiple learned models. To overcome this costly barrier, we develop a so

Yonadav Barry Ginat, Taras Panamarev, Bence Kocsis, Hagai B. Perets

We derive an analytical model for the so-called phenomenon of `resonant dynamical friction', where a disc of stars around a super-massive black hole interacts with a massive perturber, so as to align its inclination with the disc's orientation. We show that it stems from a singular behaviour of the orbit-averaged equations of motion, which leads to a rapid a

Jack Whongius

In this paper, we consider the generalized Laplace operator equipped with the G-dynamics operator of type I, the Dirichlet and Neumann eigenvalue problems are extended to associate with the G-dynamics of type I, it is proved that the G-dynamics of type I satisfies an integral identity. The G-dynamics of type II for generalized Laplacian is studied as well. U

Xiaonan Lu, Wenhui Diao, Yongqiang Mao, Junxi Li

Few-shot object detection, expecting detectors to detect novel classes with a few instances, has made conspicuous progress. However, the prototypes extracted by existing meta-learning based methods still suffer from insufficient representative information and lack awareness of query images, which cannot be adaptively tailored to different query images. First

Peter Hammarberg, Julia Vinogradova, Gábor Fodor, Ritesh Shreevastav

The automotive and railway industries are rapidly transforming with a strong drive towards automation and digitalization, with the goal of increased convenience, safety, efficiency, and sustainability. Since assisted and fully automated automotive and train transport services increasingly rely on vehicle-to-everything communications, and high-accuracy real-t

Hardik Kothari, Alena Kopaničáková, Rolf Krause

We propose a nonlinear additive Schwarz method for solving nonlinear optimization problems with bound constraints. Our method is used as a "right-preconditioner" for solving the first-order optimality system arising within the sequential quadratic programming (SQP) framework using Newton's method. The algorithmic scalability of this preconditioner is enhance

Zhengjie Huang, Zhenguang Liu, Jianhai Chen, Qinming He

With the popularity of cryptocurrencies and the remarkable development of blockchain technology, decentralized applications emerged as a revolutionary force for the Internet. Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the h

Daniela Bubboloni, Nicolas Pinzauti

In a graph $\Gamma=(V,E)$, we consider the common closed neighbourhood of a subset of vertices and use this notion to introduce a Moore closure operator in $V.$ We also consider the closed twin equivalence relation in which two vertices are equivalent if they have the same closed neighbourhood. Those notions are deeply explored when $\Gamma$ is the power gra

Lei Wang, Jiabang He, Xing Xu, Ning Liu

Alignment between image and text has shown promising improvements on patch-level pre-trained document image models. However, investigating more effective or finer-grained alignment techniques during pre-training requires a large amount of computation cost and time. Thus, a question naturally arises: Could we fine-tune the pre-trained models adaptive to downs

N. Bezhanishvili, M. Martins, T. Moraschini

A bi-Heyting algebra validates the G\"odel-Dummett axiom $(p\to q)\vee (q\to p)$ iff the poset of its prime filters is a disjoint union of co-trees (i.e., order duals of trees). Bi-Heyting algebras of this kind are called bi-G\"odel algebras and form a variety that algebraizes the extension $\mathsf{bi}$-$\mathsf{LC}$ of bi-intuitionistic logic axiomatized b

Baichuan Zhao, Jia Guo, Chenyang Yang

Learning-based precoding has been shown able to be implemented in real-time, jointly optimized with channel acquisition, and robust to imperfect channels. Yet previous works rarely explain the design choices and learning performance, and existing methods either suffer from high training complexity or depend on problem-specific models. In this paper, we addre

Morgan H. Lynch, Evgenii Ievlev, Michael R. R. Good

We report on the observation of thermal photons from an accelerated electron via examination of radiative beta decay of free neutrons measured by the RDK II collaboration. The emitted photon spectrum is shown to corroborate a thermal distribution consistent with the dynamical Casimir effect. Supported by a robust chi-squared statistic, we find the photons re

Shuoxi Zhang, Hanpeng Liu, John E. Hopcroft, Kun He

Knowledge distillation aims to transfer knowledge to the student model by utilizing the predictions/features of the teacher model, and feature-based distillation has recently shown its superiority over logit-based distillation. However, due to the cumbersome computation and storage of extra feature transformation, the training overhead of feature-based metho

Ming Yan, Z. Y. Xie, Miao Gao

Two-dimensional (2D) magnetic materials have attracted tremendous research interest because of the promising application in the next-generation microelectronic devices. Here, by the first-principles calculations, we propose a two-dimensional ferromagnetic material with high Curie temperature, manganese tetranitride MnN$_4$ monolayer, which is a square-planar

Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang

In next-generation Internet services, such as Metaverse, the mixed reality (MR) technique plays a vital role. Yet the limited computing capacity of the user-side MR headset-mounted device (HMD) prevents its further application, especially in scenarios that require a lot of computation. One way out of this dilemma is to design an efficient information sharing

Neeraja Kirtane, Jeshuren Chelladurai, Balaraman Ravindran, Ashish Tendulkar

Node classification is an important task to solve in graph-based learning. Even though a lot of work has been done in this field, imbalance is neglected. Real-world data is not perfect, and is imbalanced in representations most of the times. Apart from text and images, data can be represented using graphs, and thus addressing the imbalance in graphs has beco

Yunchao Zhang, Zonglin Di, Kaiwen Zhou, Cihang Xie

Federated embodied agent learning protects the data privacy of individual visual environments by keeping data locally at each client (the individual environment) during training. However, since the local data is inaccessible to the server under federated learning, attackers may easily poison the training data of the local client to build a backdoor in the ag

Fathima Zarin Faizal, Jayakrishnan Nair

We consider a constrained, pure exploration, stochastic multi-armed bandit formulation under a fixed budget. Each arm is associated with an unknown, possibly multi-dimensional distribution and is described by multiple attributes that are a function of this distribution. The aim is to optimize a particular attribute subject to user-defined constraints on the

Yan-Rong Li, Jian-Min Wang

Spectroastrometry measures source astrometry as a function of wavelength/velocity. Reverberations of spectroastrometric signals naturally arise in broad-line regions (BLRs) of active galactic nuclei (AGNs) as a result of the continuum variations that drive responses of the broad emission lines with time delays. Such signals provide a new diagnostic for mappi

Zhiyong Yu, Songtai Lv, Xin Zhang, Hongtao Liang

We demonstrate a methodology for computationally investigating the mechanical response of a pure molten lead surface system to the lateral mechanical cyclic loads and try to answer the question: how dose the dynamically driven liquid surface system follow the classical physics of the elastic-driven oscillation? The steady-state oscillation of the dynamic sur

Ming Yan, Shuo Zhang, Yanyun Wang, Fengjie Ma

We propose a scheme to fabricate the cobalt nitride CoN4 monolayer, a magnetic graphene-like two-dimensional material, in which all Co and N atoms are in a plane. Under the pressure above 40 GPa, the bulk CoN4 is stabilized in a triclinic phase. With the pressure decreasing, the triclinic phase of CoN4 is transformed into an orthorhombic phase, and the latte

Leilei Cao, Yibo Guo, Ye Yuan, Qiangguo Jin

Few-shot Semantic Segmentation (FSS) was proposed to segment unseen classes in a query image, referring to only a few annotated examples named support images. One of the characteristics of FSS is spatial inconsistency between query and support targets, e.g., texture or appearance. This greatly challenges the generalization ability of methods for FSS, which r

Kaile Du, Fan Lyu, Linyan Li, Fuyuan Hu

Multi-Label Continual Learning (MLCL) builds a class-incremental framework in a sequential multi-label image recognition data stream. The critical challenges of MLCL are the construction of label relationships on past-missing and future-missing partial labels of training data and the catastrophic forgetting on old classes, resulting in poor generalization. T

Zi-Qiang Zhao, Xing-Kun Zhang, Zhang-Yu Nie

We study a simple extension of the original Hartnoll, Herzog and Horowitz (HHH) holographic superfluid model with two nonlinear scalar self-interaction terms $\lambda |\psi|^4$ and $\tau |\psi|^6$ in the probe limit. Depending on the value of $\lambda$ and $\tau$, this setup allows us to realize a large spectrum of holographic phase transitions which are 2nd

Yuka Yamaguchi, Naoya Yamaguchi

For any positive integer $n$, let ${\rm C}_{n}$ be the cyclic group of order $n$. We determine all possible values of the integer group determinant of ${\rm C}_{4} \times {\rm C}_{2}^{2}$, which is the only unsolved abelian group of order $16$.

Antonio De Felice, Kei-ichi Maeda, Shinji Mukohyama, Masroor C. Pookkillath

We study the spherically symmetric collapse of a cloud of dust in VCDM, a class of gravitational theories with two local physical degrees of freedom. We find that the collapse corresponds to a particular foliation of the Oppenheimer-Snyder solution in general relativity (GR) which is endowed with a constant trace for the extrinsic curvature relative to the t

S. Maragkaki, G. D. Tsibidis, L. Haizer, Z. Papa

Irradiation of solids with ultrashort pulses using laser sources in the mid-infrared (mid-IR) spectral region is a yet predominantly unexplored field that opens broad possibilities for efficient and precise surface texturing for a wide range of applications. In the present work, we investigate both experimentally and theoretically the impact of laser sources

Kun Cheng, Xiaodong Cun, Yong Zhang, Menghan Xia

We present VideoReTalking, a new system to edit the faces of a real-world talking head video according to input audio, producing a high-quality and lip-syncing output video even with a different emotion. Our system disentangles this objective into three sequential tasks: (1) face video generation with a canonical expression; (2) audio-driven lip-sync; and (3

Qiyong Cao, Hongjun Gao

In this paper, we obtain the existence of random attractors for a class of evolution equations driven by a geometric fractional Brownian rough path with Hurst index $H\in(\frac{1}{3},\frac{1}{2}]$ and establish the upper semi-continuity of random attractors $\mathcal{A}_{\eta}$ for the approximated systems of the evolution equations.

Hebing Rui, Mei Si, Linliang Song

We construct the Jucys-Murphy elements and the Jucys-Murphy basis for the $q$-Brauer algebra in the sense of Mathas[11]. We also give a necessary and sufficient condition for the $q$-Brauer algebra being (split) semisimple over an arbitrary field.

Zhongren Chen, Lu Tian, Richard Olshen

This paper is motivated by the need to quantify human immune responses to environmental challenges. Specifically, the genome of the selected cell population from a blood sample is amplified by the well-known PCR process of successive heating and cooling, producing a large number of reads. They number roughly 30,000 to 300,000. Each read corresponds to a part

Pablo S. Ocal, Amrei Oswald

We endow twisted tensor products with a natural notion of counit and comultiplication, and we provide sufficient and necessary conditions making the twisted tensor product a counital coassociative coalgebra. We then characterize when the twisted tensor product of bialgebras is a bialgebra, and when the twisted tensor product of Frobenius algebras is a Froben

Zhenhao Shuai, Hongbo Liu, Zhaolin Wan, Wei-Jie Yu

Neuroevolution has greatly promoted Deep Neural Network (DNN) architecture design and its applications, while there is a lack of methods available across different DNN types concerning both their scale and performance. In this study, we propose a self-adaptive neuroevolution (SANE) approach to automatically construct various lightweight DNN architectures for

Chao Li, Hao Xu, Kun He

Heterogeneous information networks (HINs) are widely employed for describing real-world data with intricate entities and relationships. To automatically utilize their semantic information, graph neural architecture search has recently been developed on various tasks of HINs. Existing works, on the other hand, show weaknesses in instability and inflexibility.

Chuncheng Zhang, Xiubao Sui, Zheyi Yao, Guohua Gu

Optical focusing through scattering media has important implications for optical applications in medicine, communications, and detection. In recent years, many wavefront shaping methods have been successfully applied to the field, among which the population optimization algorithm has achieved remarkable results. However, the current population optimization a

Yeying Jin, Ruoteng Li, Wenhan Yang, Robby T. Tan

Estimating the reflectance layer from a single image is a challenging task. It becomes more challenging when the input image contains shadows or specular highlights, which often render an inaccurate estimate of the reflectance layer. Therefore, we propose a two-stage learning method, including reflectance guidance and a Shadow/Specular-Aware (S-Aware) networ

Binoy Saha, Sukhendu Das

The primary goal of artificial intelligence is to mimic humans. Therefore, to advance toward this goal, the AI community attempts to imitate qualities/skills possessed by humans and imbibes them into machines with the help of datasets/tasks. Earlier, many tasks which require knowledge about the objects present in an image are satisfactorily solved by vision

Himangshu Hazarika, Dhiren Kumar Basnet

For a prime power $q$, $\F$ denotes the finite field of order $q$, and for $m\geq 2$, $\Fm$ denotes the extension field of degree $m$. We establish a characteristic function for the set of $(r,\, n)$-free elements of finite cyclic $R$-module for the Euclidean domain $R$. Furthermore, we explore $(f,\, g)$-freeness through polynomial values and finally give a

Chayan Kumar Paul, Bhabani Shankar Dey, Udayan Banerjee, Indra Narayan Kar

Physical Human-Robot Interaction (pHRI) task involves tight coupling between safety constraints and compliance with human intentions. In this paper, a novel switched model reference admittance controller is developed to maintain compliance with the external force while upholding safety constraints in the workspace for an n-link manipulator involved in pHRI.

Wenting Zhou, Shijing Cheng, Hongwei Yu

The seminar discovery by Gibbons and Hawking that a freely falling detector observes an isotropic background of thermal radiation reveals that de Sitter space is equivalent to a thermal bath at the Gibbons-Hawking temperature in Minkowski space, as far as the response rate of the detector is concerned. Meanwhile, for a static detector which is endowed with a

Sark Pangrui Xing, Bart van Dijk, Pengcheng An, Miguel Bruns

A rising number of HCI scholars have begun to use materiality as a starting point for exploring the design's potential and restrictions. Despite the theoretical flourishing, the practical design process and instruction for beginner practitioners are still in scarcity. We leveraged the pictorial format to illustrate our crafting process of Puffy, a bio-inspir

Tao Zhang, Fang Yang

We introduce the concept of braided noncommutative Poisson bialgebras. The theory of cocycle bicrossproducts for noncommutative Poisson bialgebras is developed. As an application, we solve the extending problem by using some non-abelian cohomology theory.

Yuhang Lu, Xinyi Wu, Zhenyao Wu, Song Wang

Few-shot segmentation (FSS) expects models trained on base classes to work on novel classes with the help of a few support images. However, when there exists a domain gap between the base and novel classes, the state-of-the-art FSS methods may even fail to segment simple objects. To improve their performance on unseen domains, we propose to transductively fi

Chi Zhang, Yuanyuan Shi, Yize Chen

Recent advancements in reinforcement learning algorithms have opened doors for researchers to operate and optimize building energy management systems autonomously. However, the lack of an easily configurable building dynamical model and energy management task simulation and evaluation platform has arguably slowed the progress in developing advanced and dedic