August 2022 arXiv papers — page 122

Sam Sanders

The uncountability of $\mathbb{R}$ is one of its most basic properties, known far outside of mathematics. Cantor's 1874 proof of the uncountability of $\mathbb{R}$ even appears in the very first paper on set theory, i.e. a historical milestone. In this paper, we study the uncountability of $\mathbb{R}$ in Kohlenbach's higher-order Reverse Mathematics (RM for

Len Brandes, Wolfram Weise, Norbert Kaiser

Information on the phase structure of strongly interacting matter at high baryon densities can be gained from observations of neutron stars and their detailed analysis. In the present work Bayesian inference methods are used to set constraints on the speed of sound in the interior of neutron stars, based on recent multi-messenger data in combination with lim

Bohan Zhou, Matthew Parno

In this paper, we address the numerical solution to the multimarginal optimal transport (MMOT) with pairwise costs. MMOT, as a natural extension from the classical two-marginal optimal transport, has many important applications including image processing, density functional theory and machine learning, but yet lacks efficient and exact numerical methods. The

K. Anjali, I. Reena, Sudha, B. G. Divyamani

The quantum steering ellipsoid inscribed inside the Bloch sphere offers an elegant geometric visualization of two-qubit states shared between Alice and Bob. The set of Bloch vectors of Bob's qubit, steered by Alice via all possible local measurements on her qubit, constitutes the steering ellipsoid. The steering ellipsoids are shown to be effective in captur

Ailing Zhong, Zhidu Li, Tong Tang, Dapeng Wu

This paper considers the distribution of a general peak age of information (AoI) model and develops a general analysis approach for probabilistic performance guarantee from the time-domain perspective. Firstly, a general relationship between the peak AoI and the inter-arrival and service times of packets is revealed. With the help of martingale theory, a pro

Zheng-Wei Liao, Xin-Wei Yi, Jing-Yang You, Bo Gu

Superconductivity in transition metal nitrides (TMNs) has been investigated for a long time, such as zirconium nitride (ZrN) with a superconducting transition temperature Tc of 10 K. Recently, a phase diagram has been revealed in ZrNx with different nitrogen concentrations, which is very similar to that of high-temperature copper oxide superconductors. Here,

Takeaki Kadota, Hideaki Hayashi, Ryoma Bise, Kiyohito Tanaka

Automatic image-based disease severity estimation generally uses discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult due to the images with ambiguous severity. An easier alternative is to use relative annotation, which compares the severity level between image pairs. By using a learning-to-rank framework with relative an

Jens A. Griepentrog, Joachim Naumann

This paper is concerned with weak solutions (e,h) in L^2 x L^2 of the Maxwell equations with nonlinear Ohm law and under perfect conductor boundary conditions. These solutions are defined in terms of integral identities with appropriate test functions. The main result of our paper is an energy equality that holds for any weak solution (e,h).

Khang Nhut Lam, Feras Al Tarouti, Jugal Kalita

Past approaches to translate a phrase in a language L1 to a language L2 using a dictionary-based approach require grammar rules to restructure initial translations. This paper introduces a novel method without using any grammar rules to translate a given phrase in L1, which does not exist in the dictionary, to L2. We require at least one L1-L2 bilingual dict

Thomas Dougherty, Rishee Jain

The reduction of greenhouse gases from buildings forms the cornerstone of policy to mitigate the effects of climate change. However, the automation of urban scale building energy modeling systems required to meet global urban demand has proven challenging due to the bespoke characteristics of each city. One such point of uniqueness between cities is that of

Junde Wu, Huihui Fang, Hoayi Xiong, Lixin Duan

On the medical images, many of the tissues/lesions may be ambiguous. That is why the medical segmentation is typically annotated by a group of clinical experts to mitigate the personal bias. However, this clinical routine also brings new challenges to the application of machine learning algorithms. Without a definite ground-truth, it will be difficult to tra

Giada Maria Battaglia, Andrea Francesco Morabito, Roberta Palmeri, Tommaso Isernia

The Phase Retrieval problem is dealt with for the challenging case where just a single set of (phaseless) radiated field data is available. In particular, even still emulating the solution of crosswords puzzles, we provide decisive improvements over our recent approaches. In fact, by exploiting bandlimitedness and a suitable set of intersecting curves, we de

Anton Kulagin, Alexander Shapovalov

The one-parameter two-dimensional cellular automaton with the Margolus neighbourhood is analyzed based on the considering the projection of the stochastic movements of a single particle. Introducing the auxiliary random variable associated with the direction of the movement, we reduce the problem under consideration to the study of a two-dimensional Markov c

Hongfang Liu, Ming Lu, Zhi-Qiang Zhang, Hua Jiang

We study the characterization of the non-Hermitian skin effect (NHSE) in non-Hermitian systems with on-site disorder. We extend the application of generalized-Brillouin-zone (GBZ) theory to these systems. By proposing a modified GBZ theory, we give a faithfully description of the NHSE. For applications, we obtain a unified $\beta$ for system with long-range

Zhongwei Qiu, Huan Yang, Jianlong Fu, Dongmei Fu

Compressed video super-resolution (VSR) aims to restore high-resolution frames from compressed low-resolution counterparts. Most recent VSR approaches often enhance an input frame by borrowing relevant textures from neighboring video frames. Although some progress has been made, there are grand challenges to effectively extract and transfer high-quality text

Salvatore Capozziello, Vittorio De Falco, Carmen Ferrara

We discuss equivalent representations of gravity in the framework of metric-affine geometries pointing out basic concepts from where these theories stem out. In particular, we take into account tetrads and spin connection to describe the so called {\it Geometric Trinity of Gravity}. Specifically, we consider General Relativity, constructed upon the metric te

Prasanta Malik, Samiran Das

In this paper using a non-negative regular summability matrix $\mathcal{A}$ and a non-trivial admissible ideal $\mathcal{I}$ in $\mathbb{N}$ we study some basic properties of strong $\mathcal{A}^{\mathcal{I}}$-statistical convergence and strong $\mathcal{A}^{\mathcal{I}}$-statistical Cauchyness of sequences in probabilistic metric spaces not done earlier. We

Vladimir Dragovic, Borislav Gajic, Bozidar Jovanovic

We first construct nonholonomic systems of $n$ homogeneous balls $\mathbf B_1,\dots,\mathbf B_n$ with centers $O_1,...,O_n$ and with the same radius $r$ that are rolling without slipping around a fixed sphere $\mathbf S_0$ with center $O$ and radius $R$. In addition, it is assumed that a dynamically nonsymmetric sphere $\mathbf S$ of radius $R+2r$ and the ce

Yongsong Huang, Qingzhong Wang, Shinichiro Omachi

In this paper, we present a medical AttentIon Denoising Super Resolution Generative Adversarial Network (AID-SRGAN) for diographic image super-resolution. First, we present a medical practical degradation model that considers various degradation factors beyond downsampling. To the best of our knowledge, this is the first composite degradation model proposed

Huanqia Cai, Fanglei Xue, Lele Xu, Lili Guo

Image matting refers to predicting the alpha values of unknown foreground areas from natural images. Prior methods have focused on propagating alpha values from known to unknown regions. However, not all natural images have a specifically known foreground. Images of transparent objects, like glass, smoke, web, etc., have less or no known foreground. In this

Ruining Tang, Zhenyu Liu, Yangguang Li, Yiguo Song

Mainstream object detectors are commonly constituted of two sub-tasks, including classification and regression tasks, implemented by two parallel heads. This classic design paradigm inevitably leads to inconsistent spatial distributions between classification score and localization quality (IOU). Therefore, this paper alleviates this misalignment in the view

Björn Erik Haase, Joshua Hennig, Mirco Kutas, Erik Waller

Sensing with undetected photons allows access to spectral regions with simultaneous detection of photons of another region and is based on nonlinear interferometry. To obtain the full information of a sample, the corresponding interferogram has to be analyzed in terms of amplitude and phase, which has been realized so far by multiple measurements followed by

Pankaj Saini, Sajad A. Bhat, K. G. Arun

The planned Laser Interferometric Space Antenna (LISA) will be able to detect gravitational waves (GWs) from intermediate mass binary black holes (IMBBHs) in the mass range $\sim 10^{2} \mbox{-} 10^{4}\,M_{\odot}$ up to a redshift $z\sim20$. Modulation effects due to orbital motion of LISA around the Sun facilitate precise premerger localization of the sourc

Steven van Kesteren, Laura Alvarez, Silvia Arrese-Igor, Angel Alegria

Endowing materials with physical intelligence holds the key for a progress leap in robotic systems. In spite of the growing success for macroscopic devices, transferring these concepts to the microscale presents several challenges connected to the lack of suitable fabrication and design techniques, and of internal response schemes that connect the materials'

Qingxu Fu, Tenghai Qiu, Zhiqiang Pu, Jianqiang Yi

Multiagent reinforcement learning (MARL) can solve complex cooperative tasks. However, the efficiency of existing MARL methods relies heavily on well-defined reward functions. Multiagent tasks with sparse reward feedback are especially challenging not only because of the credit distribution problem, but also due to the low probability of obtaining positive r

Joseph Samuel

It is known that, within the Born-Oppenheimer approximation, the slow modes of the nuclear motion are altered by three effects that emerge from integrating out the fast modes of the electronic motion. The first is an effective scalar potential $V_{\mathrm dyn}$ coming from the eigenvalue of the electronic state, the second is an effective magnetic field comi

Tao Hu, Shihao Zhu, Zhaojin Li, Da Li

Topochemically transforming layered non-van der Waals solid into two dimensional (2D) materials involves selective etching reactions with atomic precision. The element-specific, structure-sensitive etching at nanoscale urgently requires in-depth understanding. Here, by means of density functional theory calculations and a laboratory-made operando reaction mo

Ertem Nusret Tas, Dan Boneh

Layer 2 systems have received increasing attention due to their potential to scale the throughput of L1 blockchains. To avoid the cost of putting data on chain, these systems increasingly turn to off-chain data availability solutions such as data availability committees (DACs). However, placing trust on DACs conflicts with the goal of obtaining an L2 archite

Chengliang Liu, Zhihao Wu, Jie Wen, Chao Huang

Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in recent years. Although numerous methods have been developed, most of the methods either cannot flexibly handle the incomplete multi-view data with arbitrary missing views or do not c

Sungmo Kang, Seungjin Kang, Heung-Sik Kim, Jaejun Yu

We report Chern insulating phases emerging from a single layer of layered chalcogenide CrSiTe$_{3}$, a transition metal trichacogenides (TMTC) material, in the presence of charge doping. Due to strong hybridization with Te $p$ orbitals, the spin-orbit coupling effect opens a finite band gap, leading to a nontrivial topology of the Cr $e_{\mathrm{g}}$ conduct

Pranta Saha, Sridhar Majety, Marina Radulaski

Silicon carbide is among the leading quantum information material platforms due to the long spin coherence and single-photon emitting properties of its color center defects. Applications of silicon carbide in quantum networking, computing, and sensing rely on the efficient collection of color center emission into a single optical mode. Recent hardware develo

Carlo Janna, Andrea Franceschini, Jacob B. Schroder, Luke Olson

Algebraic multigrid (AMG) is one of the most widely used solution techniques for linear systems of equations arising from discretized partial differential equations. The popularity of AMG stems from its potential to solve linear systems in almost linear time, that is with an O(n) complexity, where n is the problem size. This capability is crucial at the pres

Yuehan Zhang, Bo Ji, Jia Hao, Angela Yao

In image super-resolution, both pixel-wise accuracy and perceptual fidelity are desirable. However, most deep learning methods only achieve high performance in one aspect due to the perception-distortion trade-off, and works that successfully balance the trade-off rely on fusing results from separately trained models with ad-hoc post-processing. In this pape

Sriganapathy Raghav, Barun Halder, Pradosh Basu, Utpal Roy

We provide an analytical model to fabricate an exponential localization of a Bose-Einstein condensate under bichromatic optical lattice. Such localization is famously known as Anderson localization. The degree of localization is investigated by the Participation Ratio to recognize the laser parameter domain for Anderson localization. The exponential nature o

Jingtao Tang, Yuan Gao, Tin Lun Lam

For massive large-scale tasks, a multi-robot system (MRS) can effectively improve efficiency by utilizing each robot's different capabilities, mobility, and functionality. In this paper, we focus on the multi-robot coverage path planning (mCPP) problem in large-scale planar areas with random dynamic interferers in the environment, where the robots have limit

Ajinkya Gaikwad, Soumen Maity

A set $S\subseteq V$ of vertices is an offensive alliance in an undirected graph $G=(V,E)$ if each $v\in N(S)$ has at least as many neighbours in $S$ as it has neighbours (including itself) not in $S$. We study the classical and parameterized complexity of the Offensive Alliance problem, where the aim is to find a minimum size offensive alliance. Our focus h

Sandipan Banerjee, Walter Scheirer, Kevin Bowyer, Patrick Flynn

In this article, we analyze how changing the underlying 3D shape of the base identity in face images can distort their overall appearance, especially from the perspective of deep face recognition. As done in popular training data augmentation schemes, we graphically render real and synthetic face images with randomly chosen or best-fitting 3D face models to

Nora Bauer, Sabine M. Neumayer, Petro Maksymovych, Maxim O. Lavrentovich

Ferroelectric domain wall motion is fundamental to the switching properties of ferroelectric devices and is influenced by a wide range of factors including spatial disorder within the material and thermal noise. We build a Landau-Ginzburg-Devonshire (LGD) model of 180${}^{\circ}$ ferroelectric domain wall motion that explicitly takes into account the presenc

Huili Xing

The notion of covariant-contravariant refinement (CC-refinement, for short) is a generalization of the notions of bisimulation, simulation and refinement. This paper introduces CC-refinement modal $\mu$-calculus (CCRML$^{\mu}$) obtained from the modal $\mu$-calculus system K$^{\mu}$ by adding CC-refinement quantifiers, establishes an axiom system for CCRML$^

Zhai Mingqing, Liu Ruifang

A set of cycles is called independent if no two of them have a common vertex. Let $S_{n, 2k-1}$ be the complete split graph, which is the join of a clique of size $2k-1$ with an independent set of size $n-2k+1$. In 1962, Erd\H{o}s and P\'{o}sa established the following edge-extremal result: for every graph $G$ of order $n$ which contains no $k$ independent c

Jiashu Wu, Jingpan Xiong, Hao Dai, Yang Wang

A large volume of remote sensing (RS) data has been generated with the deployment of satellite technologies. The data facilitates research in ecological monitoring, land management and desertification, etc. The characteristics of RS data (e.g., enormous volume, large single-file size and demanding requirement of fault tolerance) make the Hadoop Distributed F

R. Priyatikanto, C. Knigge, S. Scaringi, J. Brink

Binaries with a white dwarf primary and a main sequence secondary can be used to test our understanding of both single and binary star evolution. A small fraction of such systems experienced a common-envelope phase from which they emerged with a relatively short orbital period. Here, we present the characterisation of an eclipsing post-common-envelope binary

Ramit Pahwa

Deep neural networks (DNNs) have proven successful in a wide variety of applications such as speech recognition and synthesis, computer vision, machine translation, and game playing, to name but a few. However, existing deep neural network models are computationally expensive and memory intensive, hindering their deployment in devices with low memory resourc

Iori Azuma, Tadahaya Mizuno, Hiroyuki Kusuhara

Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effects demonstrated that most such methods,

Pan Zhao, Ilya Kolmanovsky, Naira Hovakimyan

This paper presents an adaptive reference governor (RG) framework for a linear system with matched nonlinear uncertainties that can depend on both time and states, subject to both state and input constraints. The proposed framework leverages an L1 adaptive controller (L1AC) that estimates and compensates for the uncertainties, and provides guaranteed transie

Khaled Qazaqzeh

We prove that there are only finitely many values of the Jones polynomial of quasi-alternating links of a given determinant. Consequently, we prove that there are only finitely many quasi-alternating links of a given Jones polynomial iff there are only finitely many quasi-alternating links of a given determinant.

Sandipan Sengupta

We develop a Hamiltonian description of the `Carroll' (Levy Leblond-Sen Gupta) limit of gravity theory in the first-order formalism. Through a constraint analysis, the number of local degrees of freedom are shown to be two in this singular limit. The associated Hamiltonian constraint depends only on the densitized triad fields and their space derivatives. We

Rodney Downey, Lu Liu, Keng Meng Ng, Daniel Turetsky

Let $K$ denote prefix-free Kolmogorov Complexity, and $K^A$ denote it relative to an oracle $A$. We show that for any $n$, $K^{\emptyset^{(n)}}$ is definable purely in terms of the unrelativized notion $K$. It was already known that 2-randomness is definable in terms of $K$ (and plain complexity $C$) as those reals which infinitely often have maximal complex

Jiashu Wu, Yang Wang, Xiaopeng Fan, Kejiang Ye

As one of the most useful online processing techniques, the theta-join operation has been utilized by many applications to fully excavate the relationships between data streams in various scenarios. As such, constant research efforts have been put to optimize its performance in the distributed environment, which is typically characterized by reducing the num

Michio Seto

In this paper, we give a new approach to the theory of strictly positive kernels. Our method is based on the structure of Fock spaces. As its applications, various examples of strictly positive kernels are given. Moreover, we give a new proof of the universal approximation theorem for the Gauss kernel.

Haeyoon Jung, Jiyeon Kim, Sooran Kim

Considering electron correlation appropriately is important to predict the properties of layered transition metal oxides, which have drawn a lot of attention as cathode materials for sodium-ion batteries. Here, we explore the phonon and electronic properties of layered NaMnO2 using the recently developed strongly constrained and appropriately normed (SCAN) f

Seonghyeon Kim, Mikk Lippmaa, Jaehyeok Lee, Hyeongmin Cho

Based on the interface polarization model, the two-dimensional electron gas (2DEG) at $LaInO_{3}$(LIO)/$BaSnO_{3}$(BSO) interfaces is understood to originate from a polarization discontinuity at the interface and the conduction band offset between LIO and BSO. In this scenario, the direction of polarization at the interface is determined by whether the first

Susumu Hirose, Efstratia Kalfagianni, Eiko Kin

We prove that for any closed, connected, oriented 3-manifold M, there exists an infinite family of 2-fold branched covers of M that are hyperbolic 3-manifolds and surface bundles over the circle with arbitrarily large volume.

Shi-fan Qi, Jun Jing

We propose a concise and deterministic protocol to generate NOON states in a hybrid system consisting of a superconducting qubit, a circuit resonator mode, and two magnonic modes, based on Floquet engineering. In particular, we construct a time-reversal-symmetry broken Hamiltonian for chiral state propagation of the three continuous-variable modes depending

Neha Verma, S. Sivaprasad Kumar

We prove a conjecture concerning the third Hankel determinant, proposed in ``Anal. Math. Phys., https://doi.org/10.1007/s13324-021-00483-7", which states that $|H_3(1)|\leq 1/9$ is sharp for the class $\mathcal{S}_{\wp}^{*}=\{zf'(z)/f(z) \prec \varphi(z):=1+ze^z\}$. In addition, we also establish bounds for sixth and seventh coefficient, and $|H_4(1)|$ for f

Ri He, Hongyu Wu, Yi Lu, Zhicheng Zhong

Understanding various macroscopic pressure-volume-temperature properties of materials on the atomistic level has always been an ambition for physicists and material scientists. Particularly, some materials such as zirconium tungstate (ZrW2O8), exhibit multiple exotic properties including negative thermal expansion (NTE) and pressure-induced amorphization (PI

Junde Wu, Yu Zhang, Rao Fu, Yuanpei Liu

Open checkout-free grocery is the grocery store where the customers never have to wait in line to check out. Developing a system like this is not trivial since it faces challenges of recognizing the dynamic and massive flow of people. In particular, a clustering method that can efficiently assign each snapshot to the corresponding customer is essential for t

Zheng-Yang Zhuang, Zhongbo Yan

Three-dimensional Hopf insulators are a class of topological phases beyond the tenfold-way classification. The critical point separating two rotation-invariant Hopf insulator phases with distinct Hopf invariants is quite different from the usual Dirac-type or Weyl-type critical points and uniquely characterized by a quantized Berry dipole. Close to such Berr

Yongxiang Tang, Wentao Bai, Guilin Li, Xialong Liu

In large-scale recommender systems, retrieving top N relevant candidates accurately with resource constrain is crucial. To evaluate the performance of such retrieval models, Recall@N, the frequency of positive samples being retrieved in the top N ranking, is widely used. However, most of the conventional loss functions for retrieval models such as softmax cr

Chao-Te Li, Derek Kubo, Jen-Chieh Cheng, John Kuroda

This report presents a down-conversion method involving digital sideband separation for the Yuan Tseh Lee Array to double the processing bandwidth. The receiver consists of a MMIC HEMT LNA frontend operating at a wavelength of 3 mm, and sub-harmonic mixers that output signals at intermediate frequencies of 2 - 18 GHz. The sideband separation scheme involves

Reggie C. Pantig, Ali Övgün, Durmuş Demir

In this paper, we study rotating black holes in symmergent gravity, and use deviations from the Kerr black hole to constrain the parameters of the symmergent gravity. Symmergent gravity induces the gravitational constant $G$ and quadratic curvature coefficient $c_{\rm O}$ from the flat spacetime matter loops. In the limit in which all fields are degenerate i

Taylor R. Brown

For a Bayesian, real-time forecasting with the posterior predictive distribution can be challenging for a variety of time series models. First, estimating the parameters of a time series model can be difficult with sample-based approaches when the model's likelihood is intractable and/or when the data set being used is large. Second, once samples from a para

Hongtao Rong, Zhenqiao Huang, Xin Zhang, Shiv Kumar

Space groups describing the symmetry of lattice structure allow the emergence of fermionic quasiparticles with various degeneracy in the band structure. Theoretical efforts have predicted many materials hosting fermions with the highest degeneracy, i.e., eightfold fermions, yet lacking experimental realization. Here, we explore the band degeneracies in TaCo$

Sha-Sha Li, Hai-Cheng Feng, H. T. Liu, J. M. Bai

Changing-look active galactic nucleus NGC~4151, which has attracted a lot of attention, is undergoing the second dramatic outburst stage in its evolutionary history. To investigate the geometry and kinematics of the broad-line region (BLR), and measure the mass of supermassive black hole in NGC~4151, we perform a seven-month photometric and spectroscopic mon

Yitian Chen, Prayush Kumar, Neev Khera, Nils Deppe

We construct the covariantly defined multipole moments on the common horizon of an equal-mass, non-spinning, quasicircular binary-black-hole system. We see a strong correlation between these multipole moments and the gravitational waveform. We find that the multipole moments are well described by the fundamental quasinormal modes at sufficiently late times.

Daigo Imamura, Takashi Ishio, Raula Gaikovina Kula, Kenichi Matsumoto

Forking is a common practice for developers when building upon on already existing projects. These forks create variants, which have a common code base but then evolve the code in different directions, which is specific to that forked project requirements. An interesting side-effect of having multiple forks is the ability to select between different evolutio

Maxime Lombart, Mark Hutchison, Yueh-Ning Lee

Small grains play an essential role in astrophysical processes such as chemistry, radiative transfer, gas/dust dynamics. The population of small grains is mainly maintained by the fragmentation process due to colliding grains. An accurate treatment of dust fragmentation is required in numerical modelling. However, current algorithms for solving fragmentation

Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu

Activation compressed training provides a solution towards reducing the memory cost of training deep neural networks~(DNNs). However, state-of-the-art work combines a search of quantization bit-width with the training, which makes the procedure complicated and less transparent. To this end, we propose a simple and effective method to compress DNN training. O

Xiao-Qiu He, Yong-Liang Xiong, Andrew D. Bragg, Patrick Fischer

We use direct numerical simulation (DNS) to explore the effect of tilt on two-dimensional turbulent thermal convection on a half-soap bubble that is heated at its equator.In the DNS, the bubble is tilted by an angle $\delta\in[0^{\circ},90^{\circ}]$, the Rayleigh number is varied between $Ra\in[3\times10^6, 3\times10^9]$, and the Prandlt number is fixed at $

Eric Bahuaud, Sharmila Gunasekaran, Hari K. Kunduri, Eric Woolgar

Static vacuum near horizon geometries are solutions $(M,g,X)$ of a certain quasi-Einstein equation on a closed manifold $M$, where $g$ is a Riemannian metric and $X$ is a closed 1-form. It is known that when the cosmological constant vanishes, there is rigidity: $X$ vanishes and consequently $g$ is Ricci flat. We study this form of rigidity for all signs of

Naoki Asuke, Nicolas Chauvet, André Röhm, Kazutaka Kanno

Allan variance has been widely utilized in evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long. This multi-scale examination capability of the Allan variance may also be beneficial in evaluating the chaotic oscillating dynamics of semiconductor lasers, not just for convention

Fu-Ya Luo, Yi-Jun Cao, Kai-Fu Yang, Yong-Jie Li

Nighttime thermal infrared (NTIR) image colorization, also known as translation of NTIR images into daytime color images (NTIR2DC), is a promising research direction to facilitate nighttime scene perception for humans and intelligent systems under unfavorable conditions (e.g., complete darkness). However, previously developed methods have poor colorization p

Junjie Wang, Yuxiang Zhang, Ping Yang, Ruyi Gan

This report describes a pre-trained language model Erlangshen with propensity-corrected loss, the No.1 in CLUE Semantic Matching Challenge. In the pre-training stage, we construct a dynamic masking strategy based on knowledge in Masked Language Modeling (MLM) with whole word masking. Furthermore, by observing the specific structure of the dataset, the pre-tr

Qi Zhang, Zijian Yang, Yilun Huang, Ze Chen

This paper mainly describes our winning solution (team name: www) to Amazon ESCI Challenge of KDD CUP 2022, which achieves a NDCG score of 0.9043 and wins the first place on task 1: the query-product ranking track. In this competition, participants are provided with a real-world large-scale multilingual shopping queries data set and it contains query-product

Steven T. Piantadosi, Felix Hill

The widespread success of large language models (LLMs) has been met with skepticism that they possess anything like human concepts or meanings. Contrary to claims that LLMs possess no meaning whatsoever, we argue that they likely capture important aspects of meaning, and moreover work in a way that approximates a compelling account of human cognition in whic

Xingran Liao, Baoliang Chen, Hanwei Zhu, Shiqi Wang

Existing deep learning-based full-reference IQA (FR-IQA) models usually predict the image quality in a deterministic way by explicitly comparing the features, gauging how severely distorted an image is by how far the corresponding feature lies from the space of the reference images. Herein, we look at this problem from a different viewpoint and propose to mo

Hao Xu, Junsheng Zeng, Dongxiao Zhang

Data-driven discovery of PDEs has made tremendous progress recently, and many canonical PDEs have been discovered successfully for proof-of-concept. However, determining the most proper PDE without prior references remains challenging in terms of practical applications. In this work, a physics-informed information criterion (PIC) is proposed to measure the p

Hang Li, Zhaoli Dong, Stefano Longhi, Qian Liang

Aharonov-Bohm (AB) caging, a special flat-band localization mechanism, has spurred great interest in different areas of physics. AB caging can be harnessed to explore the rich and exotic physics of quantum transport in flatband systems, where geometric frustration, disorder and correlations act in a synergetic and distinct way than in ordinary dispersive ban

Jianlin Su, Mingren Zhu, Ahmed Murtadha, Shengfeng Pan

In the era of deep learning, loss functions determine the range of tasks available to models and algorithms. To support the application of deep learning in multi-label classification (MLC) tasks, we propose the ZLPR (zero-bounded log-sum-exp \& pairwise rank-based) loss in this paper. Compared to other rank-based losses for MLC, ZLPR can handel problems that

Dmitri Pavlov

We prove that categories enriched in the Thomason model structure admit a model structure that is Quillen equivalent to the Bergner model structure on simplicial categories, providing a new model for (infinity,1)-categories. Along the way, we construct model structures on modules and monoids in the Thomason model structure and prove that any model structure

Nitesh Banskota, Abeer Alsadoon, P. W. C. Prasad, Ahmed Dawoud

Facial emotional recognition is one of the essential tools used by recognition psychology to diagnose patients. Face and facial emotional recognition are areas where machine learning is excelling. Facial Emotion Recognition in an unconstrained environment is an open challenge for digital image processing due to different environments, such as lighting condit

Tommaso Ghigna, Tomotake Matsumura, Yuki Sakurai, Ryota Takaku

Most upcoming CMB experiments are planning to deploy between a few thousand and a few hundred thousand TES bolometers in order to drastically increase sensitivity and unveil the B-mode signal. Differential systematic effects and $1/f$ noise are two of the challenges that need to be overcome in order to achieve this result. In recent years, rotating Half-Wave

Dandan Guo, Zhuo Li, Meixi Zheng, He Zhao

Imbalanced data pose challenges for deep learning based classification models. One of the most widely-used approaches for tackling imbalanced data is re-weighting, where training samples are associated with different weights in the loss function. Most of existing re-weighting approaches treat the example weights as the learnable parameter and optimize the we

Yuyang Ji, Peize Lin, Xinguo Ren, Lixin He

We investigate the lead-free halide double perovskites (HDPs) Cs$ _2BB'X_6$ ($B$=Ag, Na; $B'$=In, Bi; $X$=Cl, Br) via first-principles calculations. We find that both the geometric and electric structures of the HDPs obtained by the Heyd-Scuseria-Ernzerhof (HSE) hybrid functional are much better than those of the Perdew-Burke-Ernzerhof (PBE) functional. Impo

Khalid Aram

In this paper, we describe two approaches to model the behavior of stock prices. The first approach considers the underlying probability distribution of day-to-day price differences. The second approach models the movement of the price as a stochastic birth-death process. We demonstrated the two approaches using historical opening prices of Apple inc. and co

Mehdi Rostami, Olli Saarela

The problem of selecting a handful of truly relevant variables in supervised machine learning algorithms is a challenging problem in terms of untestable assumptions that must hold and unavailability of theoretical assurances that selection errors are under control. We propose a distribution-free feature selection method, referred to as Data Splitting Selecti

Tong Xu, Lin Wang, Wu Ning, Chunyan Lyu

As a study on the efficient usage of data, Multi-source Unsupervised Domain Adaptation transfers knowledge from multiple source domains with labeled data to an unlabeled target domain. However, the distribution discrepancy between different domains and the noisy pseudo-labels in the target domain both lead to performance bottlenecks of the Multi-source Unsup

Rundi Wu, Changxi Zheng

Existing generative models for 3D shapes are typically trained on a large 3D dataset, often of a specific object category. In this paper, we investigate the deep generative model that learns from only a single reference 3D shape. Specifically, we present a multi-scale GAN-based model designed to capture the input shape's geometric features across a range of

Mohamed Naguib, Yahia Shabara, Can Emre Koksal

Millimeter-wave transceivers use large antenna arrays to form narrow high-directional beams and overcome severe attenuation. Narrow beams require large signaling overhead to be aligned if no prior information about beam directions is available. Moreover, beams drift with time due to user mobility and may need to be realigned. Beam tracking is commonly used t

Qixuan Hu, Guoyi Xu, Chengjie Yu

In this note, we obtain the rigidity of the sharp Cheng-Yau gradient estimate for positive harmonic functions on surfaces with nonegative Gaussian curvature, the rigidity of the sharp Li-Yau gradient estimate for positive solutions to heat equations and the related estimates for Dirichlet Green's functions on Riemannian manifolds with nonnegative Ricci curva

Shinji Mukohyama, Kazufumi Takahashi, Vicharit Yingcharoenrat

Recently, the Effective Field Theory (EFT) of perturbations on an arbitrary background metric with a timelike scalar profile was formulated in the context of scalar-tensor theories. Here, we generalize the dictionary between the coefficients in the EFT action and those in covariant theories to accommodate shift- and reflection-symmetric quadratic higher-orde

Xiaoxuan Liang, Aaron Cohen, Anibal Solón Heinsfeld, Franco Pestilli

The sparse group lasso is a high-dimensional regression technique that is useful for problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this paper we discuss a new R package for computing such regularized models. The intention is to provide highly optimized soluti

Takahiko Matsubara, Misao Sasaki

We perturbatively study the effect of non-Gaussianities on the mass fraction of primordial black holes (PBHs) at the time of formation by systematically taking its effect into account in the one-point probability distribution function of the primordial curvature perturbation. We focus on the bispectrum and trispectrum and derive formulas that describe their

J. Erkal, C. F. Manara, P. C. Schneider, M. Vincenzi

The He I 1 micron line is a high excitation line which allows us to probe the innermost regions of protostellar disks, and to trace both accreting and outflowing material. We use X-Shooter observations of a sample of 107 young stars in the Lupus (1-3 Myr) and Upper Scorpius (5-10 Myr) star-forming regions to search for correlations between the line propertie

Zhijie Qiao, Xiatao Sun, Helen Loeb, Rahul Mangharam

Autonomous vehicles are increasingly introduced into our lives. Yet, people's misunderstanding and mistrust have become the major obstacles to the use of these technologies. In response to this problem, proper work must be done to increase public's understanding and awareness and help drivers rationally evaluate the system. The method proposed in this paper

Tan Zhi-Xuan, Joshua B. Tenenbaum, Vikash K. Mansinghka

Domain-general model-based planners often derive their generality by constructing search heuristics through the relaxation or abstraction of symbolic world models. We illustrate how abstract interpretation can serve as a unifying framework for these abstraction-based heuristics, extending the reach of heuristic search to richer world models that make use of

Philip T. Gressman

We establish a broad notion of admissible tilings of frequency space which admit associated wave packet frames with elements which are smooth and compactly supported. The framework is designed to allow for tile geometries which are minimally constrained by the need to accommodate Schwartz tails on the Fourier side and goes beyond the usual scale of geometrie

Lili Wang, Ji Liu, A. Stephen Morse

A hybrid observer is described for estimating the state of a system of the form dot x=Ax, y_i=C_ix, i=1,...,m. The system's state x is simultaneously estimated by m agents assuming agent i senses y_i and receives appropriately defined data from its neighbors. Neighbor relations are characterized by a time-varying directed graph N(t). Agent i updates its esti

Faxi Yuan, Hamed Farahmand, Russell Blessing, Samuel Brody

Despite the criticality of dialysis facilities, limited knowledge exists regarding the extent and inequality of disrupted access caused by weather events. This study uses mobility data in the context of the 2017 Hurricane Harvey in Harris County to examine the impact of flooding on access to dialysis centers. We examined access dimensions using multiple stat

Igor Borovikov, Karine Levonyan, Jon Rein, Pawel Wrotek

Many modern online 3D applications and videogames rely on parametric models of human faces for creating believable avatars. However, manual reproduction of someone's facial likeness with a parametric model is difficult and time-consuming. Machine Learning solution for that task is highly desirable but is also challenging. The paper proposes a novel approach