December 2022 arXiv papers — page 2

Xiaolin Wang

Measurements of coherent charmonium production cross sections together with their ratio in ultra-peripheral PbPb collisions are studied at a nucleon-nucleon centre-of-mass energy of $5.02\,\mathrm{TeV}$, the differential cross-sections are measured as a function of rapidity and transverse momentum, separately. The photo-production of \jpsi mesons at low tran

Nihar B. Shah

There is widespread debate on whether to anonymize author identities in peer review. The key argument for anonymization is to mitigate bias, whereas arguments against anonymization posit various uses of author identities in the review process. The Innovations in Theoretical Computer Science (ITCS) 2023 conference adopted a middle ground by initially anonymiz

Wei Tao, Ruilin Zhu, Zhen-Jun Xiao

We compute the matching coefficient between the quantum chromodynamics (QCD) and the non-relativistic QCD ( NRQCD) for the flavor-changing scalar current involving the heavy charm and bottom quark, up to the three-loop order within the NRQCD factorization. For the first time, we obtain the analytical expressions for the three-loop renormalization constant $\

Stefano Marni, Giovanni Nava, Raouf Barboza, Tommaso Bellini

We show that the motion of ferroelectric liquid sessile droplets deposited on a ferroelectric lithium niobate substrate can be controlled by a light beam of moderate intensity irradiating the substrate at a distance of several droplet diameters from the droplet itself. The ferroelectric liquid is a nematic liquid crystal in which almost complete polar orderi

Ivana Dimitrova, Stuart Flannigan, Yoo Kyung Lee, Hanzhen Lin

Quantum many-body phases offer unique properties and emergent phenomena, making them an active area of research. A promising approach for their experimental realization in model systems is to adiabatically follow the ground state of a quantum Hamiltonian from a product state of isolated particles to one that is strongly-correlated. Such protocols are relevan

Elena Mirela Babalic, Calin Iuliu Lazaroiu

We discuss the behavior of cosmological curves and their first order infrared approximants near critical ends of the scalar manifold $\Sigma$ and near interior critical points of the scalar potential for tame hyperbolizable two-field cosmological models by determining the universal forms of the asymptotic gradient flow of the classical effective potential wi

Youwei He, Jinliang Luo

Multi-fidelity Kriging model is a promising technique in surrogate-based design as it can balance the model accuracy and cost of sample preparation by fusing low- and high-fidelity data. However, the cost for building a multi-fidelity Kriging model increases significantly with the increase of the problem dimension. To attack this issue, an efficient Hierarch

Wenqi Wei, Qinying Liu, Le Yuan, Jian Zhang

Nuclear magnetic resonance (NMR) technique benefits from high magnetic field not only due to the field-enhanced measurement sensitivity and resolution, but also because it is a powerful tool to investigate field-induced physics in modern material science. In this study, we successfully performed NMR measurements in high flat-top pulsed magnetic field (FTPMF)

Pierluigi Mondelli, Pascal Kaienburg, Francesco Silvestri, Rebecca Scatena

The active layer crystallinity has long been associated with favourable organic solar cells (OSCs) properties such as high mobility and Fill Factor. In particular, this applies to acceptor materials such as fullerene-derivatives and the most recent Non-Fullerene Acceptors (NFAs), which are now surpassing 19% of Power Conversion Efficiency. Despite these adva

Jyun-Fong Wong, Ko-Hsuan Mandy Chen, Jui-Min Chia, Zih-Ping Huang

Topological Hall effect (THE), an electrical transport signature of systems with chiral spin textures like skyrmions, has been observed recently in topological insulator (TI)-based magnetic heterostructures. However, the intriguing interplay between the topological surface state and THE is yet to be fully understood. In this work, we report a large THE of ~1

Jun-En Ding, Chih-Ho Hsu, Kuan-Chia Ling, Ling Chen

The global COVID-19 pandemic has caused more than six million deaths worldwide. Medicalized hotels were established in Taiwan as quarantine facilities for COVID-19 patients with no or mild symptoms. Due to limited medical care available at these hotels, it is of paramount importance to identify patients at risk of clinical deterioration. This study aimed to

Yingxun Zhang, Yangyang Liu, Yongjia Wang, Qingfeng Li

By simultaneously describing the data of isospin sensitive nucleonic flow and pion observables, such as $v_2^n/v_2^{ch}$ and $\pi^-/\pi^+$, with ultra-relativistic quantum molecular dynamics (UrQMD) model, we got the symmetry energy at flow and pion characteristic densities which are $S(1.2\rho_0)=34\pm 4$ MeV and $S(1.5\rho_0)=36\pm 8$ MeV. Within the uncer

Kush Kinra, Manil T. Mohan, Renhai Wang

This article is concerned with the \emph{asymptotically autonomous robustness} (almost surely and in probability) of non-autonomous random attractors for two stochastic versions of 3D convective Brinkman-Forchheimer (CBF) equations defined on the whole space $\mathbb{R}^3$: $$\frac{\partial\boldsymbol{v}}{\partial t}-\mu \Delta\boldsymbol{v}+(\boldsymbol{v}\

Gabino Estevez-Delgado, Joaquin Estevez-Delgado, Modesto Pineda Duran, Arthur Cleary-Balderas

Starting from the solution of the Einstein field equations in a static and spherically symmetric spacetime which contains an isotropic fluid, we construct a model to represent the interior of compact objects with compactness rate $u=\frac{GM}{c^2R}<0.23577$. The solution is obtained by imposing the isotropy condition for the radial and tangential pressures,

Supritha Bhowmick, Diptimoy Ghosh, Divya Sachdeva

Elastic collisions with relativistic electrons from the blazar's jet can accelerate dark matter (DM) particles in the DM spike surrounding the supermassive black hole at its center. This can allow one to set stringent limits on the DM-electron scattering cross section ($\bar{\sigma}_{e\chi}$) for DM masses less than 100 MeV. We consider DM particles boosted

Martin Mathieu, Francois Schulz

We prove that every surjective unital linear mapping which preserves invertible elements from a Banach algebra onto a C*-algebra carrying a faithful tracial state is a Jordan homomorphism thus generalising Aupetit's 1998 result for finite von Neumann algebras.

Sayantan Choudhury

In this work, we study the signatures of quantum entanglement by computing entanglement negativity between two causally unrelated regions in $3+1$ dimensional global de Sitter space. We investigate a bipartite quantum field theoretic setup for this purpose, driven by an axionic Bell pair resulting from Type IIB string compactification on a Calabi-Yau three f

Qichen Xu, Zhuanglin Shen, Manuel Pereiro, Pawel Herman

A long-standing and difficult problem in, e.g., condensed matter physics is how to find the ground state of a complex many-body system where the potential energy surface has a large number of local minima. Spin systems containing complex and/or topological textures, for example spin spirals or magnetic skyrmions, are prime examples of such systems. We propos

Jiayin Du, Shuguan Ji, Yong Li

In this paper, we study the Melnikov's persistence for completely degenerate Hamiltonian systems with the following Hamiltonian \begin{equation*} H(x,y,u,v)=h(y)+g(u,v)+\varepsilon P(x,y,u,v),~~~(x,y,u,v)\in \mathbb{T}^n\times{G}\times \mathbb{R}^d\times \mathbb{R}^d, \end{equation*} where $n\geq2$ and $d\geq1$ are positive integers, $G\subset\mathbb{R}^n$,

Shashank Gupta

The behaviour of genuine EPR steering of three qubit states under various environmental noises is investigated. In particular, we consider the two possible steering scenarios in the tripartite setting: (1 -> 2), where Alice demonstrates genuine steering to Bob-Charlie, and (2 -> 1), where Alice-Bob together demonstrates genuine steering to Charlie. In both t

Francesco De Anna, Joshua Kortum, Stefano Scrobogna

In the present paper, we address a physically-meaningful extension of the linearised Prandtl equations around a shear flow. Without any structural assumption, it is well-known that the optimal regularity of Prandtl is given by the class Gevrey 2 along the horizontal direction. The goal of this paper is to overcome this barrier, by dealing with the linearisat

Marcin Moszyński, Grzegorz Świderski

We explore to what extent the relation between the absolute continuous spectrum and non-existence of subordinate generalized eigenvectors, known for scalar Jacobi operators, can be formulated also for block Jacobi operators with $d$-dimensional blocks. The main object here allowing to make some progress in that direction is the new notion of the barrier nons

A. M. Dmytrenko, P. N. Fedorov, V. S. Akhmetov, A. B. Velichko

We present the results of determining the coordinates of the vertices of various stellar systems, the centroids of which are located in the Galactic plane. To do this, the positions, parallaxes, proper motions, and radial velocities of red giants and subgiants contained in the $Gaia$~DR3 catalogue have been used. When determining the components of the deform

Patrick Wenzel, Nan Yang, Rui Wang, Niclas Zeller

In this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset. The proposed benchmark provides drastic appearance variations caused by seasonal changes and diverse weather and illumination conditions. While significant progress has been made in adv

Fangyuan Wang, Yingxun Zhang, Zhipan Li

In this work, we derived a transport equation based on a generalized equation of time-dependent generator coordinate method (TDGCM) under the Gaussian overlap approximation (GOA). The transport equation is obtained by using quantum-mechanics phase space distributions under a ``quasi-particle" picture and strategy of Bogoliubov-Born-Green-Kirkood-Yvon (BBGKY)

Martin Andersson, Benny Avelin

We develop theory and methods that use the graph Laplacian to analyze the geometry of the underlying manifold of datasets. Our theory provides theoretical guarantees and explicit bounds on the functional forms of the graph Laplacian when it acts on functions defined close to singularities of the underlying manifold. We use these explicit bounds to develop te

Sebastian Erhardt, Mainak Ghosh, Erik Buunk, Michael E. Rose

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate

Frits Vaandrager, Thorsten Wißmann

We provide a new perspective on the problem how high-level state machine models with abstract actions can be related to low-level models in which these actions are refined by sequences of concrete actions. We describe the connection between high-level and low-level actions using \emph{action codes}, a variation of the prefix codes known from coding theory. F

Kangcheng Liu

The vision of unmanned aerial vehicles is very significant for UAV-related applications such as search and rescue, landing on a moving platform, etc. In this work, we have developed an integrated system for the UAV landing on the moving platform, and the UAV object detection with tracking in the complicated environment. Firstly, we have proposed a robust LoG

Daria Bolbot, Dimitrios Mitsotakis, Athanasios E. Tzavaras

The aim is to assess the combined effect of diffusion and dispersion on shocks in the moderate dispersion regime. For a diffusive dispersive approximation of the equations of one-dimensional elasticity (or p-system), we study convergence of traveling waves to shocks. The problem is recast as a Hamiltonian system with small friction, and an analysis of the le

Xue-Qun Yan, Yan-Jiao Du, Wen-Tao Hou, Xiao-Ming Liu

The first law of thermodynamics restates the law of conservation of energy. It partitions the change in energy of a system into two pieces, heat and work. While there is no ambiguity to define heat and work in classical thermodynamics, their classification in the quantum regime is not that obvious. Thus, the first law of thermodynamics becomes problematic in

Naeem Akhtar, Jizhou Wu, Jia-Xin Peng, Wu-Ming Liu

The Wigner function of the compass state (a superposition of four coherent states) develops phase-space structures of dimension much less than the Planck scale, which are crucial in determining the sensitivity of these states to phase-space displacements. In the present work, we introduce compass-like states that may have connection to the contemporary exper

Jordi Castellví, Michael Drmota, Marc Noy, Clément Requilé

Given $t\geq 2$ and $0\leq k\leq t$, we prove that the number of labelled $k$-connected chordal graphs with $n$ vertices and tree-width at most $t$ is asymptotically $c n^{-5/2} \gamma^n n!$, as $n\to\infty$, for some constants $c,\gamma >0$ depending on $t$ and $k$. Additionally, we show that the number of $i$-cliques ($2\leq i\leq t$) in a uniform random $

Shuqiang Zhu

We consider the three body problem on $S^1$ under the cotangent potential. We first construct homothetic orbits ending in singularities, including total collision singularity and collision-antipodal singularity. Then certain symmetrical periodic orbits with two equal masses, called Schubart orbits, are shown to exist. The proof is based on the construction o

Cyril Gadal, Matthieu Mercier, Marie Rastello, Laurent Lacaze

Most gravitational currents occur on sloping topographies, often in the presence of particles that can settle during the current propagation. Yet, an exhaustive exploration of associated parameters in experimental studies is still lacking. Here, we present an extensive experimental investigation of the slumping regime of turbidity (particle-laden) currents i

Youngchae Cho, Insoon Yang

Intensively studied in theory as a promising data-driven tool for decision-making under ambiguity, two-stage distributionally robust optimization (DRO) problems over Wasserstein balls are not necessarily easy to solve in practice. This is partly due to large sample size. In this article, we study a generic two-stage distributionally robust linear program (2-

Abubakar Siddique, Henry Medeiros

We introduce a novel framework to track multiple objects in overhead camera videos for airport checkpoint security scenarios where targets correspond to passengers and their baggage items. We propose a Self-Supervised Learning (SSL) technique to provide the model information about instance segmentation uncertainty from overhead images. Our SSL approach impro

Lars Holmberg, Paul Davidsson, Per Linde

The success of neural networks builds to a large extent on their ability to create internal knowledge representations from real-world high-dimensional data, such as images, sound, or text. Approaches to extract and present these representations, in order to explain the neural network's decisions, is an active and multifaceted research field. To gain a deeper

Yunjiao Lei, Dayong Ye, Sheng Shen, Yulei Sui

Reinforcement learning (RL) is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be

Mohammad Yousuf Jamal

We conducted a study on the collective modes within the hot QCD medium generated in heavy-ion collision experiments. These modes, whether real or imaginary, stable or unstable, play a crucial role in shaping the medium's evolution. To gain a deeper understanding, we considered several factors affecting the medium, including anisotropy, interactions among med

Guixiang Hong, Wei Liu, Bang Xu

In this paper, we establish the quantitative mean ergodic theorems for two subclasses of power bounded operators on a fixed noncommutative $L_p$-space with $1<p<\infty$, which mainly concerns power bounded invertible operators and Lamperti contractions. Our approach to the quantitative ergodic theorems is the noncommutative square function inequalities. The

L. Ridgway Scott, Tabea Tscherpel

We examine the dimensions of various inf-sup stable mixed finite element spaces on tetrahedral meshes in 3D with exact divergence constraints. More precisely, we compare the standard Scott-Vogelius elements of higher polynomial degree and low order methods on split meshes, the Alfeld and the Worsey-Farin split. The main tool is a counting strategy to express

Wenhao Wu, Haipeng Luo, Bo Fang, Jingdong Wang

Most existing text-video retrieval methods focus on cross-modal matching between the visual content of videos and textual query sentences. However, in real-world scenarios, online videos are often accompanied by relevant text information such as titles, tags, and even subtitles, which can be utilized to match textual queries. This insight has motivated us to

Frank Schweitzer, Georges Andres, Giona Casiraghi, Christoph Gote

Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, non-equilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) \emph{delimitation}, i.e., narrowing down the target systems, (ii) \emph{co

Wenhao Wu, Xiaohan Wang, Haipeng Luo, Jingdong Wang

Vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such powerful VLMs is a promising direction for building effective video recognition models. However, current exploration in this field is still limited. We believe that the greatest value

I. K. Hong

This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth function, which only comprised of a few weight parameters, through discussing a few topics about regression. First, we reinte

Jiayu Li, Chaona Zhu

Let $(M, J^\alpha, \alpha =1,2,3)$ and $(N, {\cal J}^\alpha, \alpha =1,2,3)$ be Hyperk\"ahler manifolds. Suppose that $u_k$ is a sequence of stationary quaternionic maps and converges weakly to $u$ in $H^{1,2}(M,N)$, we derive a blow-up formula for $\lim_{k\to\infty}d(u_k^*{\cal J}^\alpha)$, for $\alpha=1,2,3$, in the weak sense. As a corollary, we show that

Georgia Papacharalampous, Hristos Tyralis, Anastasios Doulamis, Nikolaos Doulamis

Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products. Machine and statistical learning regression algorithms are regularly utilized in this endeavour. At the same time, tree-base

Arindam Debnath, Wesley F Reinhart

The design of new High Entropy Alloys that can achieve exceptional mechanical properties is presently of great interest to the materials science community. However, due to the difficulty of designing these alloys using traditional methods, machine learning has recently emerged as an essential tool. Particularly, the screening of candidate alloy compositions

P. Montero de Hijes, J. R. Espinosa, C. Vega, C. Dellago

Despite the importance of ice nucleation, this process has been barely explored at negative pressures. Here, we study homogeneous ice nucleation in stretched water by means of Molecular Dynamics Seeding simulations using the TIP4P/Ice model. We observe that the critical nucleus size, interfacial free energy, free energy barrier, and nucleation rate barely ch

Simon K. Niederländer

In a real Hilbert space setting, we reconsider the classical Arrow-Hurwicz differential system in view of solving linearly constrained convex minimization problems. We investigate the asymptotic properties of the differential system and provide conditions for which its solutions converge towards a saddle point of the Lagrangian associated with the convex min

Yun Zeng, Deren Han, Yansheng Su, Jiaxin Xie

We investigate the randomized Kaczmarz method that adaptively updates the stepsize using readily available information for solving inconsistent linear systems. A novel geometric interpretation is provided which shows that the proposed method can be viewed as an orthogonal projection method in some sense. We prove that this method converges linearly in expect

Ilse Fischer

An identity that is reminiscent of the Littlewood identity plays a fundamental role in recent proofs of the facts that alternating sign triangles are equinumerous with totally symmetric self-complementary plane partitions and that alternating sign trapezoids are equinumerous with holey cyclically symmetric lozenge tilings of a hexagon. We establish a bounded

Sho Cremers, Valentin Robu, Peter Zhang, Merlinda Andoni

With the emergence of energy communities, where a number of prosumers invest in shared generation and storage, the issue of fair allocation of benefits is increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings - however, computing it exactly is intractable beyond a few dozen prosumers. In this paper,

Pedro J. Chocano, Ana Luzón, Manuel Alonso Morón, Luis Felipe Prieto Martínez

We point out how to use the classical characteristic method, that is used to solve quasilinear PDE's, to obtain the matrix exponential of some lower triangle infinite matrices. We use the Lie Frechet structure of the Riordan group described in [4]. After that we describe some linear dynamical systems in $\mathbb{K}[[x]]$ with a concrete involution being a sy

T. Kangas, A. Ahola, C. Fransson, J. Larsson

We use adaptive-optics imaging and integral field spectroscopy from the Very Large Telescope, together with images from the \emph{Hubble Space Telescope}, to study the near-infrared (NIR) evolution of the equatorial ring (ER) of SN~1987A. We study the NIR line and continuum flux and morphology over time in order to lay the groundwork for \emph{James Webb Spa

Jialin Lei, Qiang Zhang

For an automorphism $\phi$ of a free group $F_n$ of rank $n$, Bestvina and Handel showed that the rank $rk Fix(\phi)$ of the fixed subgroup is not greater than $n$ (the so-called Scott conjecture). Soon after Bestvina and Handel's announcement, their result was generalized by many authors in various directions. In this paper, we are interested in the fixed s

Tolga Buz, Gerard de Melo

The recent hype around Reddit's WallStreetBets (WSB) community has inspired research on its impact on our economy and society. Still, one important question remains: Can WSB's community of anonymous contributors actually provide valuable investment advice and possibly even outperform top financial institutions? We present a data-driven empirical study of inv

Xingping Xian, Tao Wu, Xiaoke Ma, Shaojie Qiao

Inferring missing links or detecting spurious ones based on observed graphs, known as link prediction, is a long-standing challenge in graph data analysis. With the recent advances in deep learning, graph neural networks have been used for link prediction and have achieved state-of-the-art performance. Nevertheless, existing methods developed for this purpos

Fangyu Han, Zhong Tan

The existence of finite time blowup solutions for the two-dimensional Landau--Lifshitz equation is a long-standing problem, which exists in the literature at least since 2001 (E, Mathematics Unlimited--2001 and Beyond, Springer, Berlin, P.410, 2001). A more refined description in the equivariant class is given in (van den Berg and Williams, European J. Appl.

Yibo Li, Jianfeng Pei, Luhua Lai

Finding drug-like compounds with high bioactivity is essential for drug discovery, but the task is complicated by the high cost of chemical synthesis and validation. With their outstanding performance in de novo drug design, deep generative models represent promising tools for tackling this challenge. In recently years, 3D molecule generative models have gai

Armand Bernou, Mitia Duerinckx, Antoine Gloria

We consider a suspension of active rigid particles (swimmers) in a steady Stokes flow, where particles are distributed according to a stationary ergodic random process, and we study its homogenization in the macroscopic limit. A key point in the model is that swimmers are allowed to adapt their propulsion to the surrounding fluid deformation: swimming forces

Mitia Duerinckx, Antoine Gloria

This review is devoted to the large-scale rheology of suspensions of rigid particles in Stokes fluid. After describing recent results on the definition of the effective viscosity of such systems in the framework of homogenization theory, we turn to our new results on the asymptotic expansion of the effective viscosity in the dilute regime. This includes an o

Omid Rezaei, Maryam Masjedi, Ali Kanaani, Mohammad Mahdi Naghsh

In this paper, we optimize a Wireless Powered Communication (WPC) system including multiple pair of users, where transmitters employ single-antenna to transmit their information and power to their receivers with the help of one multiple-antennas Amplify-and-Forward (AF) relay or an active Intelligent Reflecting Surface (IRS). We propose a joint Time Switchin

Ahmed Naguib, Dina Reda Eldamak

According to the World Health Organization (WHO), 30 million people are in need of prosthetic and orthotic devices. Some people are born with this limb loss, while others lose limbs due to diseases such as Cancer, diabetes, and work accidents. Additionally, limb amputation is among the most severe and heavily reported injuries among veterans during war. The

Zhi-Wei Lu, Xin-Di Hou, Feng Wan, Yousef I. Salamin

Rapid development of ultrafast ultraintense laser technologies continues to create opportunities for studying strong-field physics under extreme conditions. However, accurate determination of the spatial and temporal characteristics of a laser pulse is still a great challenge, especially when laser powers higher than hundreds of terawatts are involved. In th

Zijian Zhang, Linglong Dai, Xibi Chen, Changhao Liu

Reconfigurable intelligent surfaces (RISs) have emerged as a candidate technology for future 6G networks. However, due to the "multiplicative fading" effect, the existing passive RISs only achieve a negligible capacity gain in environments with strong direct links. In this paper, the concept of active RISs is studied to overcome this fundamental limitation.

Fusheng Deng, Jinjin Hu, Xiangsen Qin

We consider the curvature strict positivity of the direct image bundle associated to a pseudoconvex family of bounded domains. The main result is that the curvature of the direct image bundle associated to a strictly pseudoconvex family of bounded circular domains or Reinhardut domains are strictly positive in the sense of Nakano, even if the weight function

Florian Nill

As shown recently by the author, constant population SI(R)S models map to Hethcote's classic endemic model originally proposed in 1973. This unifies a whole class of models with up to 10 parameters, all being isomorphic to a simple 2-parameter master model for endemic bifurcation. In this work this procedure is extended to a 14-parameter SSISS Model, includi

Pedro Casau, Ricardo G. Sanfelice, Carlos Silvestre

Synergistic hybrid feedback refers to a collection of feedback laws that allow for global asymptotic stabilization of a compact set through the following switching logic: given a collection of Lyapunov functions that are indexed by a logic variable, whenever the currently selected Lyapunov function exceeds the value of another function in the collection by a

Di Huang, Sida Peng, Tong He, Honghui Yang

We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural rendering. Motivated by the fact that informative point cloud features should be able to encode rich geometry and appearance cues and render realistic images, we train a point-cloud encoder within a devised point-based neural renderer by comparing

Spiros Cotsakis, John Miritzis

We study the effects of trans-planckian censorship conjecture (TCC) bounds on geodesic completeness of spacetime and the associated existence for an infinite proper time. Using Gronwall's lemma, TCC bounds can be derived directly, leading to a result about the absence of blowup solutions. We show that the TCC provides part of the required criteria for geodes

Xuan Bu, Liang-Jun Zhai, Shuai Yin

In this work, we explore the driven dynamics of the one-dimensional ($1$D) localization transitions. By linearly changing the strength of disorder potential, we calculate the evolution of the localization length $\xi$ and the inverse participation ratio (IPR) in a disordered Aubry-Andr\'{e} (AA) model, and investigate the dependence of these quantities on th

Livia Campo

We show that five of Reid's Fano 3-fold hyperurfaces containing at least one compound Du Val singularity of type $cA_n$ have pliability at least two. The two elements of the pliability set are the singular hypersurface itself, and another non-isomorphic Fano hypersurface of the same degree, embedded in the same weighted projective space, but with different c

Peter Švec, Štefan Balogh, Martin Homola, Ján Kľuka

Ontologies are a standard for semantic schemata in many knowledge-intensive domains of human interest. They are now becoming increasingly important also in areas until very recently dominated by subsymbolic representations and machine-learning-based data processing. One such area is information security, and more specifically malware detection. We propose PE

Sayar Ghosh Roy, Anshul Padhi, Risubh Jain, Manish Gupta

Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We introduce the task of proactively forecasting popularities of sentences within online news documents solely utilizing their natural language content. We model sentence-specific popu

Leyang Xue, Kai-Cheng Yang, Peng-Bi Cui, Zengru Di

Innovation diffusion in the networked population is an essential process that drives the progress of human society. Despite the recent advances in network science, a fundamental understanding of network properties that regulate such processes is still lacking. Focusing on an innovation diffusion model with pairwise transmission and macro-level social influen

Laura Budewig, Sang-Kil Son, Robin Santra

We theoretically study orbital alignment in x-ray-ionized atoms and ions, based on improved electronic-structure calculations starting from the Hartree-Fock-Slater model. We employ first-order many-body perturbation theory to improve the Hartree-Fock-Slater calculations and show that the use of first-order-corrected energies yields significantly better trans

Jianhui Yu, Chaoyi Zhang, Weidong Cai

Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned. Examinations of learning frameworks for invariance have seldom been looked into. In this work, we review rotation invariance in terms of point cloud registra

Zixiang Luo, Kaining Peng, Zhichao Liang, Shengyuan Cai

Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain. Traditional approaches, which infer EC from neural responses to stimulations, are not suited for mapping whole-brain EC in humans due to being invasive and having limited spatial coverage of stimulatio

Rajesh Deb, Saumya R. Mohapatra, Manjula G. Nair, Ujjal Das

Usually, resistive switching (RS) devices show digital RS memory (sharp SET and RESET process), which is most suitable for digital data storage applications. Some RS devices also manifest ideal memristive behavior or analog memory characteristics (gradual change in resistance states). The analog RS properties of memristive devices widen their application dom

Liguang Zhou, Junjie Hu, Yuhongze Zhou, Tin Lun Lam

Unbiased scene graph generation (USGG) is a challenging task that requires predicting diverse and heavily imbalanced predicates between objects in an image. To address this, we propose a novel framework peer learning that uses predicate sampling and consensus voting (PSCV) to encourage multiple peers to learn from each other. Predicate sampling divides the p

Liguang Zhou, Yuhongze Zhou, Xiaonan Qi, Junjie Hu

Audio-Visual scene understanding is a challenging problem due to the unstructured spatial-temporal relations that exist in the audio signals and spatial layouts of different objects and various texture patterns in the visual images. Recently, many studies have focused on abstracting features from convolutional neural networks while the learning of explicit s

Xinyi Wang, Chaowei Jiang, Xueshang Feng

Data-driven simulation is becoming an important approach for realistically characterizing the configuration and evolution of solar active regions, revealing the onset mechanism of solar eruption events and hopefully achieving the goal of accurate space weather forecast, which is beyond the scope of any existing theoretical modelling. Here we performed a full

Xiaoqiang Liu, Yihao Lin, Ji Feng

Quantifying materials' dynamical responses to external electromagnetic fields is central to understanding their physical properties. Here we present an implementation of the density functional perturbation theory for the computation of linear susceptibilities using the projector augmented-wave method. The Sternheimer equations are solved self-consistently th

Björn W. Schuller, Shahin Amiriparian, Anton Batliner, Alexander Gebhard

Charisma is considered as one's ability to attract and potentially also influence others. Clearly, there can be considerable interest from an artificial intelligence's (AI) perspective to provide it with such skill. Beyond, a plethora of use cases opens up for computational measurement of human charisma, such as for tutoring humans in the acquisition of char

Sam Powers, Eliot Xing, Abhinav Gupta

The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries, and thus depend on human supervision. Our task-agnostic method, Self-Activa

Fabio E. G. Cipriani, Jean-Luc Sauvageot

We study natural conditions on essentially discrete spectral triples by which the quantum differential $da$ belongs to the ideal generated by the unit length $ds=D^{-1}$. We also study upper and lower bounds on the singular values of the $da$'s and apply the general framework to natural spectral triples of Dirichlet spaces and, in particular, those on dual o

Fei Jiang, Yeqing Zhou, Jianxuan Liu, Yanyuan Ma

We study estimation and testing in the Poisson regression model with noisy high dimensional covariates, which has wide applications in analyzing noisy big data. Correcting for the estimation bias due to the covariate noise leads to a non-convex target function to minimize. Treating the high dimensional issue further leads us to augment an amenable penalty te

Xiaofa Liu, Jianqin Yin, Yuan Sun, Zhicheng Zhang

In this paper, we develop an efficient multi-scale network to predict action classes in partial videos in an end-to-end manner. Unlike most existing methods with offline feature generation, our method directly takes frames as input and further models motion evolution on two different temporal scales.Therefore, we solve the complexity problems of the two stag

Pengju Chen, Nan Yang, Austen Couvertier, Quanzhen Ding

We study the chaotic motion of a semi-classical optomechanical system coupled to a non-Markovian environment with a finite correlation time. We show that the non-Markovian environment can significantly enhance chaos, by studying the emergence of chaos using Lyapunov exponent with the changing non-Markovian parameter. It is observed that non-Markovian environ

Benjamin A. Frandsen, Raju Baral, Barry Winn, V. Ovidiu Garlea

We report the first example of magnetic pair distribution function (mPDF) data obtained through use of neutron polarization analysis. Using the antiferromagnetic semiconductor MnTe as a test case, we present high-quality mPDF data collected on the HYSPEC instrument at the Spallation Neutron Source using longitudinal polarization analysis to isolate the magne

Prashanth Amireddy, Sai Jayasurya, Jayalal Sarma

In this paper, we initiate study of the computational power of adaptive and non-adaptive monotone decision trees - decision trees where each query is a monotone function on the input bits. In the most general setting, the monotone decision tree height (or size) can be viewed as a measure of non-monotonicity of a given Boolean function. We also study the rest

Xu Gu, Yuchong Sun, Feiyue Ni, Shizhe Chen

A video storyboard is a roadmap for video creation which consists of shot-by-shot images to visualize key plots in a text synopsis. Creating video storyboards, however, remains challenging which not only requires cross-modal association between high-level texts and images but also demands long-term reasoning to make transitions smooth across shots. In this p

Eleonora Achiluzzi, Menglu Li, Md Fahd Al Georgy, Rasha Kashef

The Internet of Things (IoT) is a system that connects physical computing devices, sensors, software, and other technologies. Data can be collected, transferred, and exchanged with other devices over the network without requiring human interactions. One challenge the development of IoT faces is the existence of anomaly data in the network. Therefore, researc

Benjamin A. Frandsen, Parker K. Hamilton, Jacob A. Christensen, Eric Stubben

The open-source python package diffpy.mpdf, part of the DiffPy suite for diffraction and pair distribution function analysis, provides a user-friendly approach for performing magnetic pair distribution function (mPDF) analysis. The package builds on existing libraries in the DiffPy suite to allow users to create models of magnetic structures and calculate co

Jiaqing Zhang, Jie Lei, Weiying Xie, Yunsong Li

Considering the computation complexity, we propose a Guided Hybrid Quantization with One-to-one Self-Teaching (GHOST}) framework. More concretely, we first design a structure called guided quantization self-distillation (GQSD), which is an innovative idea for realizing lightweight through the synergy of quantization and distillation. The training process of

Wen Wu, Kaige Qu, Peng Yang, Ning Zhang

In this paper, we study a network slicing problem for edge-cloud orchestrated vehicular networks, in which the edge and cloud servers are orchestrated to process computation tasks for reducing network slicing cost while satisfying the quality of service requirements. We propose a two-stage network slicing framework, which consists of 1) network planning stag

Wen Wu, Peng Yang, Weiting Zhang, Conghao Zhou

Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services which require low delay and high accuracy. Sampling rate adaption which dynamically configures the sampling rates of industrial IoT devices according to network conditions, is the key in

Mancheon Han, Hyoung Joon Choi

We develop a reliable parameter-free analytic continuation method for quantum many-body calculations. Our method is based on a kernel grid, a causal spline, a regularization using the second-derivative roughness penalty, and the L-curve criterion. We also develop the L-curve averaged deviation to estimate the precision of our analytic continuation. To deal w