August 2022 arXiv papers — page 112

Zheng Qu, Xindong Tang

In this paper, we consider polynomial optimization with correlative sparsity. We construct correlatively sparse Lagrange multiplier expressions (CS-LMEs) and propose CS-LME reformulations for polynomial optimization problems using the Karush-Kuhn-Tucker optimality conditions. Correlatively sparse sum-of-squares (CS-SOS) relaxations are applied to solve the C

Milan Pokorný, Maja Szlenk

We investigate the existence of weak solutions to a certain system of partial differential equations, modelling the behaviour of a compressible non-Newtonian fluid for small Reynolds number. We construct the weak solutions despite the lack of the $L^\infty$ estimate on the divergence of the velocity field. The result was obtained by combining the regularity

J. L. Chen, J. S. Zhang, C. Henkel, Y. T. Yan

Our aim is to measure the interstellar 14N/15N ratio across the Galaxy, to establish a standard data set on interstellar ammonia isotope ratios, and to provide new constraints on the Galactic chemical evolution. The (J, K ) = (1, 1), (2, 2), and (3, 3) lines of 14NH3 and 15NH3 were observed with the Shanghai Tianma 65 m radio telescope (TMRT) and the Effelsb

Benedetto Silvestri

We provide sufficient conditions for the existence of a strong derivable map and calculate its derivative by employing a result in our previous work on strong derivability of maps arising by functional calculus of an unbounded scalar type spectral operator $R$ in a Banach space and the generalization to complete locally convex spaces of a classical result va

Yuanyuan Wang, Mamiya Kawaguchi, Shinya Matsuzaki, Akio Tomiya

The first-order nature of the chiral phase transition in QCD-like theories can play crucial roles to address a dark side of the Universe, where the created out-of equilibrium is essential to serve as cosmological and astrophysical probes such as gravitational wave productions, which have extensively been explored. This interdisciplinary physics is built base

Yue Hu, Shaoheng Fang, Weidi Xie, Siheng Chen

Drones equipped with cameras can significantly enhance human ability to perceive the world because of their remarkable maneuverability in 3D space. Ironically, object detection for drones has always been conducted in the 2D image space, which fundamentally limits their ability to understand 3D scenes. Furthermore, existing 3D object detection methods develop

Nicholas Sudar, Yuantao Ding

Many applications of high brightness, highly relativistic electron beams carry strict requirements on longitudinal phase space quality. To meet these requirements, accelerator systems typically utilize dispersive elements to manipulate the correlated energy spread acquired during acceleration or via collective effects. The many free variables in these system

Anton Glushchenko, Konstantin Lastochkin

In this paper an attempt is made to extend the concept of the exponentially stable adaptive control to one class of multi-input-multi-output (MIMO) plants with matched nonlinearity and unknown piecewise constant parameters. Within the intervals between two consecutive parameter switches, the proposed adaptive control system ensures: 1) exponential convergenc

Giacomo Beccari, Henri M. J. Boffin, Paola Andreani, Selma de Mink

Being one of the most fascinating and ancient sciences, astronomy has always played a special role in society. In 2022 ESO organised an online conference to offer the community a platform to discuss astronomical topics of sociological and philosophical relevance in a professional atmosphere. The talks touched on several crucial aspects, moving from the metho

Chikun Liao, Feng Wang, Vincent K. N. Lau

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system design in a clutter environment. Assisted by an IRS equipped with a uniform linear array (ULA), a multi-antenna base station (BS) is targeted for communicating with multiple communication users (CUs) and sensing multiple targets s

Tong Yang, Rong Xiong, Yue Wang

The main contribution of this paper is the proof of the convexity of the omni-directional tethered robot workspace (namely, the set of all tether-length-admissible robot configurations), as well as a set of distance-optimal tethered path planning algorithms that leverage the workspace convexity. The workspace is proven to be topologically a simply-connected

Yi Luo, Bijie Bai, Yuhang Li, Ege Cetintas

Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields. Recent deep learning-based approaches demonstrated the classification of objects using diffuser-distorted patterns collected by an image sensor. These methods demand relatively large-scale computing using deep

Daniele Corradetti, Alessio Marrani, Francesco Zucconi

We present a deformation of the Okubic Albert algebra introduced by Elduque whose rank-1 idempotent elements are in biunivocal correspondence with points of the Okubic projective plane, thus extending to Okubic algebras the correspondence found by Jordan, Von Neumann, Wigner between the octonionic projective plane and the rank-3 Exceptional Jordan Algebra.

Samah Khawaled, Moti Freiman

Uncertainty quantification in deep-learning (DL) based image reconstruction models is critical for reliable clinical decision making based on the reconstructed images. We introduce "NPB-REC", a non-parametric fully Bayesian framework for uncertainty assessment in MRI reconstruction from undersampled "k-space" data. We use Stochastic gradient Langevin dynamic

Li Feng, Zhongyi Huang

In this paper, we propose a numerical method for turning point problems in one dimension based on Petrov-Galerkin finite element method (PGFEM). We first give a priori estimate for the turning point problem with a single boundary turning point. Then we use PGFEM to solve it, where test functions are the solutions to piecewise approximate dual problems. We pr

Krisztina Regős, Rémy Pawlak, Xing Wang, Ernst Meyer

Molecular self-assembly plays a very important role in various aspects of technology as well as in biological systems. Governed by the covalent, hydrogen or van der Waals interactions - self-assembly of alike molecules results in a large variety of complex patterns even in two dimensions (2D). Prediction of pattern formation for 2D molecular networks is extr

Maximilian Gilles, Yuhao Chen, Tim Robin Winter, E. Zhixuan Zeng

Autonomous bin picking poses significant challenges to vision-driven robotic systems given the complexity of the problem, ranging from various sensor modalities, to highly entangled object layouts, to diverse item properties and gripper types. Existing methods often address the problem from one perspective. Diverse items and complex bin scenes require divers

G. A. Bobkov, I. V. Bobkova, A. M. Bobkov, Akashdeep Kamra

Spin-splitting induced in a conventional superconductor weakens superconductivity by destroying spin-singlet and creating spin-triplet Cooper pairs. We demonstrate theoretically that such an effect is also caused by an adjacent compensated antiferromagnet, which yields no net spin-splitting. We find that the antiferromagnet produces N\'eel triplet Cooper pai

Ricardo Kleinlein, Alexander Hepburn, Raúl Santos-Rodríguez, Fernando Fernández-Martínez

Many problems in computer vision have recently been tackled using models whose predictions cannot be easily interpreted, most commonly deep neural networks. Surrogate explainers are a popular post-hoc interpretability method to further understand how a model arrives at a particular prediction. By training a simple, more interpretable model to locally approxi

Jian-Chen Zhang, Kang Jiao, Tingting Zhang, Tong-Jie Zhang

The $L-\sigma$ relation of HII galaxies (HIIGx) calibrated by a distance indicator is a reliable standard candle for measuring the Hubble constant $H_0$. The most straightforward calibration technique anchors them with the first tier of distance ladders from the same galaxies. Recently another promising method that uses the cosmological model-independent Cos

Hakan Yildiz, Axel Küpper, Dirk Thatmann, Sebastian Göndör

Self-sovereign identity is the latest digital identity paradigm that allows users, organizations, and things to manage identity in a decentralized fashion without any central authority controlling the process of issuing identities and verifying assertions. Following this paradigm, implementations have emerged in recent years, with some having different under

Petra Laketa, Stanislav Nagy

The halfspace depth of a $d$-dimensional point $x$ with respect to a finite (or probability) Borel measure $\mu$ in $\mathbb{R}^d$ is defined as the infimum of the $\mu$-masses of all closed halfspaces containing $x$. A natural question is whether the halfspace depth, as a function of $x \in \mathbb{R}^d$, determines the measure $\mu$ completely. In general,

Jinyu Fan, Yi Zeng

Even the state-of-the-art deep learning models lack fundamental abilities compared to humans. Multiple comparison paradigms have been proposed to explore the distinctions between humans and deep learning. While most comparisons rely on corruptions inspired by mathematical transformations, very few have bases on human cognitive phenomena. In this study, we pr

Harry Yserentant

We represent in this note the solutions of the electronic Schr\"odinger equation as traces of higher-dimensional functions. This allows to decouple the electron-electron interaction potential but comes at the price of a degenerate elliptic operator replacing the Laplace operator on the higher-dimensional space. The surprising observation is that this operato

Ana F. Cunha, André F. V. Matias, Cristóvão S. Dias, Mariana B. Oliveira

The study of the interactions of living adherent cells with mechanically stable (visco)elastic materials enables understanding and exploiting physiological phenomena mediated by cell-extracellular communication. However, insight on the interaction of cells and surrounding objects with different stability patterns upon cell contact might unveil cell responses

Daniel Spitz, Julian M. Urban, Jan M. Pawlowski

We investigate the structure of confining and deconfining phases in SU(2) lattice gauge theory via persistent homology, which gives us access to the topology of a hierarchy of combinatorial objects constructed from given data. Specifically, we use filtrations by traced Polyakov loops, topological densities, holonomy Lie algebra fields, as well as electric an

Chih-Teng Ling, Seong Jin Kim, Cossas K. -W. Wu, Tomotsugu Goto

We present mid-infrared galaxy number counts based on the Early Release Observations obtained by the James Webb Space Telescope (JWST) at 7.7-, 10- and 15-$\mu$m (F770W, F1000W and F1500W, respectively) bands of the Mid-Infrared Instrument (MIRI). Due to the superior sensitivity of JWST, the 80 percent completeness limits reach 0.32, 0.79 and 2.0 $\mu$Jy in

Haomiao Huo, Jindan Xu, Gege Su, Wei Xu

In a K-best detector for multiple-input-multiple-output(MIMO) systems, the value of K needs to be sufficiently large to achieve near-maximum-likelihood (ML) performance. By treating K as a variable that can be adjusted according to a fitting function of some learnable coefficients, an intelligent MIMO detection network based on deep neural networks (DNN) is

Zhihong Huang, Ye Guo, Qiuwei Wu, Li Xiao

A multiple market trading mechanism for the VPP to participate in electricity, renewable energy certificate (REC) and carbon emission right (CER) markets is proposed. With the introduction of the inventory mechanism of REC and CER, the profit of the VPP increases and better trading decisions with multiple markets are made under the requirements of renewable

Lucas Mayr, Gustavo Zambonin, Frederico Schardong, Ricardo Custódio

Electronic documents are signed using private keys and verified using the corresponding digital certificates through the well-known public key infrastructure model. Private keys must be kept in a safe container so they can be reused. This makes private key management a critical component of public key infrastructures with no failproof answer. Therefore, exis

Haiteng Wang, Weixuan Zhang, Houjun Sun, Xiangdong Zhang

It is well known that Anderson transition is a disorder-induced metal-insulator transition.Contrary to this conventional wisdom, some investigations have shown that disorders could destroy the phase coherence of localized modes in flatbands, making the localized states melt into extended states. This phenomenon is called the inverse Anderson transition. Whil

Alexander Tsaregorodtsev, Johannes Müller, Jan Strohbeck, Martin Herrmann

Monocular camera sensors are vital to intelligent vehicle operation and automated driving assistance and are also heavily employed in traffic control infrastructure. Calibrating the monocular camera, though, is time-consuming and often requires significant manual intervention. In this work, we present an extrinsic camera calibration approach that automatizes

Tianxing Zhang, Hanzhou Wu, Xiaofeng Lu, Guangling Sun

As a self-supervised learning paradigm, contrastive learning has been widely used to pre-train a powerful encoder as an effective feature extractor for various downstream tasks. This process requires numerous unlabeled training data and computational resources, which makes the pre-trained encoder become valuable intellectual property of the owner. However, t

Miguel Alvarez, Neil K. Chada, Ajay Jasra

In this article we consider the development of an unbiased estimator for the ensemble Kalman--Bucy filter (EnKBF). The EnKBF is a continuous-time filtering methodology which can be viewed as a continuous-time analogue of the famous discrete-time ensemble Kalman filter. Our unbiased estimators will be motivated from recent work [Rhee \& Glynn 2010, [31]] whic

Foteini Lykou, Quentin A. Parker, Andreas Ritter, Albert A. Zijlstra

We report observations and modeling of the stellar remnant and presumed double-degenerate merger of Type~Iax supernova Pa30, which is the probable remnant of SN~1181~AD. It is the only known bound stellar SN remnant and the only star with Wolf-Rayet features that is neither a planetary nebula central star nor a massive Pop I progenitor. We model the unique e

Shuai Zhang, Liang Zhao, Shoudong Huang, Hua Wang

Total knee arthroplasty (TKA) is a common orthopaedic surgery to replace a damaged knee joint with artificial implants. The inaccuracy of achieving the planned implant position can result in the risk of implant component aseptic loosening, wear out, and even a joint revision, and those failures most of the time occur on the tibial side in the conventional ji

Yong Liu, Hanzhou Wu, Xinpeng Zhang

Recently, more and more attention has been focused on the intellectual property protection of deep neural networks (DNNs), promoting DNN watermarking to become a hot research topic. Compared with embedding watermarks directly into DNN parameters, inserting trigger-set watermarks enables us to verify the ownership without knowing the internal details of the D

Mao-Jun Yan, Ying-Hui Ge, Xiao-Hai Liu

We investigate the $B^0\to \pi^- K^+ X(3872)$ decay via the $D_{s1}(2536)\bar{D} D^*$ rescattering diagram. The line shape of the $K^+X(3872)$ distribution curve around $D_{s1}(2536)\bar{D}$ threshold is very sensitive to the $X(3872)$ mass because the triangle singularity (TS) can be generated from the loop. By means of this characteristic, we can determine

Thomas Meier, Dominique Laniel, Florian Trybel

High-pressure metal-hydride (MH) research evolved into a thriving field within condensed matter physics following the realisation of metallic compounds showing phonon mediated near room-temperature superconductivity. However, severe limitations in determining the chemical formula of the reaction products, especially with regards to their hydrogen content, im

Xin Liu, Wei Tao, Wei Li, Dazhi Zhan

Due to its simplicity and efficiency, the first-order gradient method has been extensively employed in training neural networks. Although the optimization problem of the neural network is non-convex, recent research has proved that the first-order method is capable of attaining a global minimum during training over-parameterized neural networks, where the nu

Ge Chen, Hongcai Zhang, Yonghua Song

Scheduling flexible sources to promote the integration of renewable generation is one fundamental problem for operating active distribution networks (ADNs). However, existing works are usually based on power flow models, which require network parameters (e.g., topology and line impedance) that may be unavailable in practice. To address this issue, we propose

Johannes Assefa, Philipp Harms

We develop a novel - cylindrical - solution concept for stochastic evolution equations. Our motivation is to establish a Heath-Jarrow-Morton framework capable of analysing financial term structures with discontinuities, overcoming deep stochastic-analytic limitations posed by mild or weak solution concepts. Our cylindrical approach, which we investigate in f

Zeyan Li, Nengwen Zhao, Shenglin Zhang, Yongqian Sun

Recently, AIOps (Artificial Intelligence for IT Operations) has been well studied in academia and industry to enable automated and effective software service management. Plenty of efforts have been dedicated to AIOps, including anomaly detection, root cause localization, incident management, etc. However, most existing works are evaluated on private datasets

Tobias Hatt, Stefan Feuerriegel

In order to steer e-commerce users towards making a purchase, marketers rely upon predictions of when users exit without purchasing. Previously, such predictions were based upon hidden Markov models (HMMs) due to their ability of modeling latent shopping phases with different user intents. In this work, we develop a duration-dependent hidden Markov model. In

Taisuke Kobayashi, Ryoma Watanuki

Extraction of low-dimensional latent space from high-dimensional observation data is essential to construct a real-time robot controller with a world model on the extracted latent space. However, there is no established method for tuning the dimension size of the latent space automatically, suffering from finding the necessary and sufficient dimension size,

Pravin Kumar Dahal

We develop geometric optics expansion up to the subleading order for circularly polarized electromagnetic waves on curved spacetime. This subleading order geometric optics expansion, in which the conventional eikonal function is modified by inserting a carefully chosen helicity-dependent correction, is called spin optics. We derive the propagation and polari

Hidemaro Suwa

The choice of transition kernel critically influences the performance of the Markov chain Monte Carlo method. Despite the importance of kernel choice, guiding principles for optimal kernels have not been established. Here, we propose a one-parameter rejection control transition kernel that can be applied to various Monte Carlo samplings and demonstrate that

Yanbin Liu, Girish Dwivedi, Farid Boussaid, Frank Sanfilippo

The generation of three-dimensional (3D) medical images has great application potential since it takes into account the 3D anatomical structure. Two problems prevent effective training of a 3D medical generative model: (1) 3D medical images are expensive to acquire and annotate, resulting in an insufficient number of training images, and (2) a large number o

Michał Marczenko, Krzysztof Redlich, Chihiro Sasaki

We consider the parity doublet model for nucleonic and delta matter to investigate the structure of neutron stars. We show that it is possible to reconcile the multi-messenger astronomy constraints within a purely hadronic equation of state (EOS), which accounts for the self-consistent treatment of the chiral symmetry restoration in the baryonic sector. We d

Andrés F. Vargas, Andrew Melatos

Results are presented for a semi-coherent search for continuous gravitational waves from the millisecond pulsar PSR J0437$-$4715, using a hidden Markov model to track spin wandering, in LIGO data from the third LIGO-Virgo observing run. This is the first search for PSR J0437$-$4715 to cover a wide frequency range from $60$ Hz to $500$ Hz and simultanously ac

Kisun Lee, Xindong Tang

The generalized Nash equilibrium problem (GNEP) is a kind of game to find strategies for a group of players such that each player's objective function is optimized. Solutions for GNEPs are called generalized Nash equilibria (GNEs). In this paper, we propose a numerical method for finding GNEs of GNEPs of polynomials based on the polyhedral homotopy continuat

Hao Zhang, Yuan Li, Chen Zhang, Tao Huang

With the rapid development of quantum information and technology in recent years, the construction of quantum internet for interconnecting all kinds of quantum devices, such as quantum processors and sensors, will be the next trend for practical quantum applications. In this paper, we propose the protocols for construction of connection-oriented and connecti

Andrei Piryatinski, Maxim Sukharev

We study the effect of degenerate parametric down-conversion (DPDC) in an ensemble of two-level quantum emitters (QEs) coupled via near-field interactions to a single surface plasmon (SP) mode of a nonlinear plasmonic cavity. For this purpose, we develop a quantum driven-dissipative model capturing non-equilibrium dynamics of the system in which incoherently

Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak

Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits accuracy due to high computational cost and strict time-to-solution limits. We report that a data-driven deep learning Earth system emulator, FourCastNet, can predict global weath

Kangchun Zhao, Yijie Mao, Zhaohui Yang, Lixiang Lian

Cooperative rate splitting (CRS), built upon rate splitting multiple access (RSMA) and opportunistic user relaying, has been recognized as a promising transmission strategy to enhance the user fairness and spectral efficiency in multiantenna broadcast channels. To further boost its performance, the interplay of CRS and reconfigurable intelligent surface (RIS

Weixu Su, Shenxing Zhang

We give a comparison between the Avila-Gou\"ezel-Yoccoz norm and the Teichm\"uller norm on the principal stratum of holomorphic quadratic differentials.

Lin Bai, Zhuangfei Wu, Lin Zhou

We study the mismatched successive refinement problem where one uses Gaussian codebooks to compress an arbitrary memoryless source with successive minimum Euclidean distance encoding under the quadratic distortion measure. Specifically, we derive achievable refined asymptotics under both the joint excess-distortion probability (JEP) and the separate excess-d

Zhipeng Cheng, Xuwei Fan, Minghui Liwang, Ning Chen

We investigate a data quality-aware dynamic client selection problem for multiple federated learning (FL) services in a wireless network, where each client offers dynamic datasets for the simultaneous training of multiple FL services, and each FL service demander has to pay for the clients under constrained monetary budgets. The problem is formalized as a no

Roderick Batchelor

We present some new methods for logical deduction, based on ideas from ground theory. Roughly speaking, in our calculi a typical deduction will proceed as follows: we first analyse the premiss down to its ultimate grounds; then we discard information irrelevant to the conclusion; and then we synthesize the conclusion up from its ultimate grounds. We give a s

Fujia Chen, Haoran Xue, Yuang Pan, Maoren Wang

Slow-light devices are able to significantly enhance light-matter interaction due to the reduced group velocity of light, but a very low group velocity is usually achieved in a narrow bandwidth, accompanied by extreme sensitivity to imperfections that causes increased disorder-induced attenuation. Recent theories have suggested an ideal solution to this prob

Daeyeol Jeon, Soon-Yi Kang, Chang Heon Kim

Recently, a weak converse theorem for Borcherds' lifting operator of type $O(2,1)$ for $\G_0(N)$ is proved and the logarithmic derivative of a modular form for $\G_0(N)$ is explicitly described in terms of the values of Niebur-Poincar\'e series at its divisors in the complex upper half-plane. In this paper, we prove that the generalized Borcherds' lifting op

Asif Khan, Amos Storkey

In an unsupervised attack on variational autoencoders (VAEs), an adversary finds a small perturbation in an input sample that significantly changes its latent space encoding, thereby compromising the reconstruction for a fixed decoder. A known reason for such vulnerability is the distortions in the latent space resulting from a mismatch between approximated

Yi Hu, Bo Cai, Yaoxiang Yu

Despite the continuous efforts in improving both the effectiveness and efficiency of code search, two issues remained unsolved. First, programming languages have inherent strong structural linkages, and feature mining of code as text form would omit the structural information contained inside it. Second, there is a potential semantic relationship between cod

Hong Duc Nguyen

We prove in this paper the original version of Kontsevich and Soibelman's motivic integral identity conjecture for formal functions by developing a novel framework for equivariant motivic integration on special rigid varieties. This theory is built upon our recent research on equivariant motivic integration within the realm of special formal schemes. The cen

Nodoka Yamanaka, Makoto Oka

The contribution of the CP violating three-gluon interaction, proposed by Weinberg, to the short-range CP-odd nuclear force is evaluated in the nonrelativistic quark model. We first show that the naive leading contribution generated by the quark exchange process vanishes at sufficiently short distance within the resonating group method, by considering the on

Man Kit Cheng, Cheuk Yin Ng, Sui Lun Ho, Omargeldi Atanov

Bi2Te3/Fe1+yTe heterostructures are known to exhibit interfacial superconductivity between two non-superconducting materials: Fe1+yTe as the parent compound of Fe-based superconducting materials and the topological insulator Bi2Te3. Here, we present a top-down approach starting from two-dimensional (2D) heterostructures to fabricate one-dimensional (1D) Bi2T

Wenbo Zhang, Keren Fu, Zhuo Wang, Ge-Peng Ji

Recently CNN-based RGB-D salient object detection (SOD) has obtained significant improvement on detection accuracy. However, existing models often fail to perform well in terms of efficiency and accuracy simultaneously. This hinders their potential applications on mobile devices as well as many real-world problems. To bridge the accuracy gap between lightwei

Runa Koizumi, Dmitry Golovaty, Ali Alqarni, Shawn W. Walker

Nuclei of ordered materials emerging from the isotropic state usually show a shape topologically equivalent to a sphere; the well-known examples are crystals and nematic liquid crystal droplets. In this work, we explore experimentally and theoretically the nuclei of columnar lyotropic chromonic liquid crystal coexisting with the isotropic phase that are toro

Dongdong Yan, Kwang-il Seon, Jianheng Guo, Guo Chen

Escaping atmosphere has been detected by the excess absorption of Ly$\alpha$, H$\alpha$ and He triplet (10830$\rm\AA$) lines. Simultaneously modeling the absorption of the H$\alpha$ and He 10830 lines can provide useful constraints about the exoplanetary atmosphere. In this paper, we use a hydrodynamic model combined with a non-local thermodynamic model and

Zachary Teed, Lahav Lipson, Jia Deng

We propose Deep Patch Visual Odometry (DPVO), a new deep learning system for monocular Visual Odometry (VO). DPVO uses a novel recurrent network architecture designed for tracking image patches across time. Recent approaches to VO have significantly improved the state-of-the-art accuracy by using deep networks to predict dense flow between video frames. Howe

Jiehao Liang, Zhao Song, Zhaozhuo Xu, Junze Yin

Kernel density estimation (KDE) stands out as a challenging task in machine learning. The problem is defined in the following way: given a kernel function $f(x,y)$ and a set of points $\{x_1, x_2, \cdots, x_n \} \subset \mathbb{R}^d$, we would like to compute $\frac{1}{n}\sum_{i=1}^{n} f(x_i,y)$ for any query point $y \in \mathbb{R}^d$. Recently, there has b

Alexis Benamira, Sachin Shah, Sumanta Pattanaik

Finding a low dimensional parametric representation of measured BRDF remains challenging. Currently available solutions are either not interpretable, or rely on limited analytical solutions, or require expensive test subject based investigations. In this work, we strive to establish a parametrization space that affords the data-driven representation variance

Niem Tu Nguyen, Giang Huong Bach, Thao Huong Pham, Huy Duy Nguyen

Temperature and field dependence of collective spin excitations or magnon in monolayer honeycomb spin lattices is investigated using an anisotropic exchange XZ-Heisenberg model in an external field. Magnetic phase transition in the presence of the transverse field is the spin reorientation (SR) transition with magnon intensity existing above the SR temperatu

Jia-Li Du, Yan-Quan Feng, Sejeong Bang

A finite group $G$ admits an {\em oriented regular representation} if there exists a Cayley digraph of $G$ such that it has no digons and its automorphism group is isomorphic to $G$. Let $m$ be a positive integer. In this paper, we extend the notion of oriented regular representations to oriented $m$-semiregular representations using $m$-Cayley digraphs. Giv

Shaoyin Huang

Traditional radar imaging methods suffer from the problems of low resolution and poor noise suppression. We propose a new radar imaging method based on Self-supervised deep-learning-assisted compressed sensing (SS-DL-CS-Net). The original radar image as the input of net. The net is trained to learn the mapping function between the original radar image and th

Fei Liu

Using the age-structure formalism, we definitely establish connections between semi-Markov processes and the dynamics of open quantum systems that satisfy the Markov quantum master equations. A generalized Feynman-Kac formula of the semi-Markov processes is also proposed. In addition to inheriting all statistical properties possessed by the piecewise determi

Guangsheng Yu, Xu Wang, Ping Yu, Caijun Sun

Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties for edge applications. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights - in terms of the model accuracy, Frobenius-norm

Padma Sharma, Trambak Banerjee

When banks fail amidst financial crises, the public criticizes regulators for bailing out or liquidating specific banks, especially the ones that gain attention due to their size or dominance. A comprehensive assessment of regulators, however, requires examining all their decisions, and not just specific ones, against the regulator's dual objective of preser

Nayoung Kim, Ahmadreza Mosallanezhad, Lu Cheng, Baoxin Li

With the onset of the COVID-19 pandemic, news outlets and social media have become central tools for disseminating and consuming information. Because of their ease of access, users seek COVID-19-related information from online social media (i.e., online news) and news outlets (i.e., offline news). Online and offline news are often connected, sharing common t

David Mikhail, Benoit Voisin, Dominique Didier St Medar, Gilles Buchs

We study the effects of electron-electron interactions on the charge excitation spectrum of the spinful Su-Schrieffer-Heeger (SSH) model, a prototype of a 1D bulk obstructed topological insulator. In view of recent progress in the fabrication of dopant-based quantum simulators we focus on experimentally detectable signatures of interacting topology in finite

Tiebin Mi, Jianan Zhang, Rujing Xiong, Zhengyu Wang

Physically accurate and mathematically tractable models are presented to characterize scattering and reflection properties of reconfigurable intelligent surfaces (RISs). We take continuous and discrete strategies to model a single patch and patch array and their interactions with multiple incident electromagnetic (EM) waves. The proposed models consider the

Juan Zou, Cheng Li, Sen Jia, Ruoyou Wu

Lately, deep learning has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampled reference data for training, current approaches may have limited abilities in recovering fine details or structures. To address this challenge, this paper proposes a self-superv

Aiwei Liu, Xuming Hu, Li Lin, Lijie Wen

The generalizability to new databases is of vital importance to Text-to-SQL systems which aim to parse human utterances into SQL statements. Existing works achieve this goal by leveraging the exact matching method to identify the lexical matching between the question words and the schema items. However, these methods fail in other challenging scenarios, such

Chao-Te Li, C. -Y. E. Tong, Ming-Jye Wang, Tse-Jun Chen

Since the start of full science operations from 2004, the Submillimeter Array has been implementing plans to expand IF bandwidths and upgrade receivers and cryostats. Metal mesh low-pass filters were designed to block infrared (IR) radiation to reduce the thermal load on the cryostats. Filters were fabricated on a quartz wafer through photolithography and co

Ziqi Zhou, Lei Qi, Yinghuan Shi

For medical image analysis, segmentation models trained on one or several domains lack generalization ability to unseen domains due to discrepancies between different data acquisition policies. We argue that the degeneration in segmentation performance is mainly attributed to overfitting to source domains and domain shift. To this end, we present a novel gen

Kris Coward, D. R. Toliver

An important use of computational systems is updating the state of an object while preserving some set of invariants. That object might be a file, a row in a database, or perhaps an entry in a distributed system. Its invariants may place limits on its relationships with other objects, and generally include maintaining a unique identity across updates. The sy

Jeremy E. Block, Shaghayegh Esmaeili, Eric D. Ragan, John R. Goodall

Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing th

Hong-Tao An, Si-Qiang Luo, Zhan-Wei Liu, Xiang Liu

Stimulated by the observation of the $X(6900)$ from LHCb in 2020 and the recent results from CMS and ATLAS in the di-$J/\psi$ invariant mass spectrum, in this work we systemically study all possible configurations for the ground fully heavy tetraquark states in constituent quark model. By our calculation, we present their spectroscopy behaviors like binding

Yulong Chen, Naihao Deng, Yang Liu, Yue Zhang

We report the results of DialogSum Challenge, the shared task on summarizing real-life scenario dialogues at INLG 2022. Four teams participate in this shared task and three submit their system reports, exploring different methods to improve the performance of dialogue summarization. Although there is a great improvement over the baseline models regarding aut

Jie Chen, Yongming Liu

A novel neural network (NN) approach is proposed for constrained optimization. The proposed method uses a specially designed NN architecture and training/optimization procedure called Neural Optimization Machine (NOM). The objective functions for the NOM are approximated with NN models. The optimization process is conducted by the neural network's built-in b

James Pascaleff, Nicolò Sibilla

Given a smooth 3-fold $Y$, a line bundle $L \to Y$, and a section $s$ of $L$ such that the vanishing locus of $s$ is a normal crossings surface $X$ with graph-like singular locus, we present a way to reconstruct the singularity category of $X$ as a homotopy limit of several copies of the category of matrix factorizations of $xyz : \mathbb{A}^{3} \to \mathbb{

Hanwen Du, Hui Shi, Pengpeng Zhao, Deqing Wang

Contrastive learning with Transformer-based sequence encoder has gained predominance for sequential recommendation. It maximizes the agreements between paired sequence augmentations that share similar semantics. However, existing contrastive learning approaches in sequential recommendation mainly center upon left-to-right unidirectional Transformers as base

Mengjun Wu, Wangbin Ding, Mingjin Yang, Liqin Huang

Automatic segmentation of left atrial (LA) scars from late gadolinium enhanced CMR images is a crucial step for atrial fibrillation (AF) recurrence analysis. However, delineating LA scars is tedious and error-prone due to the variation of scar shapes. In this work, we propose a boundary-aware LA scar segmentation network, which is composed of two branches to

Ye Chen, Chunfeng Huang, Zihao Chen, Wenjie He

The quantum key distribution (QKD), guaranteed by the principle of quantum physics, is a promising solution for future secure information and communication technology. However, device imperfections compromise the security of real-life QKD systems, restricting the wide deployment of QKD. This study reports a decoy-state BB84 QKD experiment that considers both

Genqiang Liu, Mingjie Li

Let $\mathfrak{W}$ be the Lie algebra of vector fields on the line. Via computing extensions between all simple modules in the category $\mathcal{O}$, we give the block decomposition of $\mathcal{O}$, and show that the representation type of each block of $\mathcal{O}$ is wild using the Ext-quiver. Each block of $\mathcal{O}$ has infinite simple objects. Thi

Robert F. Allen, Katherine Heller, Matthew A. Pons

We investigate composition-differentiation operators acting on the space $S^2$, the space of analytic functions on the open unit disk whose first derivative is in $H^2$. Specifically, we determine characterizations for bounded and compact composition-differentiation operators acting on $S^p$. In addition, for particular classes of inducing maps, we compute t

Mohammad K. Mardini, Anna Frebel, Rana Ezzeddine, Anirudh Chiti

We present a high-resolution ($R\sim35,000$), high signal-to-noise ($S/N=350$) Magellan/MIKE spectrum of the bright extremely metal-poor star 2MASS~J1808$-$5104. We find [Fe/H] = $-$4.01 (spectroscopic LTE stellar parameters), [Fe/H] = $-$3.8 (photometric stellar parameters), [Fe/H] = $-$3.7 (spectroscopic NLTE stellar parameters). We measured a carbon-to-ir

Nanyi Zheng, Xiaofeng Cai, Jing-Mei Qiu, Jianxian Qiu

We present fourth-order conservative non-splitting semi-Lagrangian (SL) Hermite essentially non-oscillatory (HWENO) schemes for linear transport equations with applications for nonlinear problems including the Vlasov-Poisson system, the guiding center Vlasov model, and the incompressible Euler equations in the vorticity-stream function formulation. The propo

Saber Jafarpour, Alexander Davydov, Matthew Abate, Francesco Bullo

This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks based upon non-Euclidean contraction theory. The basic idea is to cast the robustness analysis of a neural network as a reachability problem and use (i) the $\ell_{\infty}$-norm input-output Lipschitz constant and (ii) the tight

Maxim Dzero, Alex Levchenko

We consider a two-dimensional electron system subjected to a short-ranged nonmagnetic disorder potential, Coulomb interactions, and Rashba spin-orbit coupling. The path-integral approach incorporated within the Keldysh formalism is used to derive the kinetic equation for the semiclassical Green's function and applied to compute the spin current within the li