November 2020 arXiv papers — page 35

Yukihiro Fujimoto, Kohkichi Konno, Tomoaki Nagasawa

We discuss quantum dynamics in the ring systems with double Y-junctions in which two arms have same length. The node of a Y-junction can be parametrized by U(3). Considering mathematically permitted junction conditions seriously, we formulate such systems by scattering matrices. We show that the symmetric ring systems, which consist of two nodes with the sam

J. C. Hill, W. Huie, P. Lunia, J. D. Whalen

We demonstrate photoassociation (PA) of ultracold fermionic $^{87}$Sr atoms. The binding energies of a series of molecular states on the $^1\Sigma^+_u$ $5s^2\,^1$S$_0+5s5p\,^1$P$_1$ molecular potential are fit with the semiclassical LeRoy-Bernstein model, and PA resonance strengths are compared to predictions based on the known $^1$S$_0+^1$S$_0$ ground state

Jack H. Koolen, Meng-Yue Cao, Qianqian Yang

We give a survey on graphs with fixed smallest eigenvalue, especially on graphs with large minimal valency and also on graphs with good structures. Our survey mainly consists of the following two parts: (i) Hoffman graphs, the basic theory related to Hoffman graphs and the applications of Hoffman graphs to graphs with fixed smallest eigenvalue and large mini

Xia Zhou, Ang Li, Bao-An Li

The nature of GW190814's secondary component $m_2$ of mass $(2.50-2.67)\,\text{M}_{\odot}$ in the mass gap between the currently known maximum mass of neutron stars and the minimum mass of black holes is currently under hot debate. Among the many possibilities proposed in the literature, the $m_2$ was suggested as a superfast pulsar while its r-mode stabilit

Xiupeng Shi, Yiik Diew Wong, Chen Chai, Michael Zhi-Feng Li

Early risk diagnosis and driving anomaly detection from vehicle stream are of great benefits in a range of advanced solutions towards Smart Road and crash prevention, although there are intrinsic challenges, especially lack of ground truth, definition of multiple risk exposures. This study proposes a domain-specific automatic clustering (termed Autocluster)

Qian Wang, Francisco Pérez-Bernal

Using the Husimi function, we investigate the phase space signatures of the excited state quantum phase transitions (ESQPTs) in the Lipkin and coupled top models. We show that the time evolution of the Husimi function exhibits distinct behaviors between the different phases of an ESQPT and the presence of an ESQPT is signaled by the particular dynamics of th

Alexander V. Poshakinskiy, Janet Zhong, Alexander N. Poddubny

We study theoretically quantum states of a pair of photons interacting with a finite periodic array of two-level atoms in a waveguide. Our calculation reveals two-polariton eigenstates that have a highly irregular wave-function in real space. This indicates the Bethe ansatz breakdown and the onset of quantum chaos, in stark contrast to the conventional integ

Yue-Lin Sming Tsai, Yi-Lun Chung, Qiang Yuan, Kingman Cheung

We propose a machine learning method to investigate the propagation of cosmic rays based on the precisely measured spectra of the primary and secondary cosmic ray nuclei of Li, Be, B, C, and O from AMS-02, ACE, and Voyager-1. We train two convolutional neural networks. One network learns how to infer propagation and source parameters from the energy spectra

Z. W. Huang, X. Cheng, M. D. Ding

In this Letter, we study the kinematic properties of ascending hot blobs associated with confined flares. Taking advantage of high-cadence extreme-ultraviolet images provided by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory, we find that for the 26 events selected here, the hot blobs are first impulsively accelerated outward, but t

Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi

Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus on a range of Natural Language Understanding (NLU) tasks, without considering the Natural Language Generation (NLG) models. In this paper, we present the General Language Generati

Tadashi Wadayama, Satoshi Takabe

A distributed algorithm for least mean square (LMS) can be used in distributed signal estimation and in distributed training for multivariate regression models. The convergence speed of an algorithm is a critical factor because a faster algorithm requires less communications overhead and it results in a narrower network bandwidth. The goal of this paper is t

Jinping Yao, Luojia Wang, Jinming Chen, Yuexin Wan

Quantum coherence in quantum optics is an essential part of optical information processing and light manipulation. Alkali metal vapors, despite the numerous shortcomings, are traditionally used in quantum optics as a working medium due to convenient near-infrared excitation, strong dipole transitions and long-lived coherence. Here, we proposed and experiment

Jiajun He, Young Jin Chun

Millimeter-wave (mmWave) communication is a promising solution for achieving high data rate and low latency in 5G wireless cellular networks. Since directional beamforming and antenna arrays are exploited in the mmWave networks, accurate angle-of-arrival (AOA) information can be obtained and utilized for localization purposes. The performance of a localizati

Deng Wang

We assess comprehensively the potential of galaxy cluster edges as a standard ruler in measuring cosmological distances and probing exotic physics. Confronting five alternative cosmological models with cluster edges from the near future Dark Energy Spectroscopic Instrument survey, we conclude that cluster edges can serve as a promising probe to constrain mod

Li-Wei Shih, Cheng-Wei Chen

Many techniques have been developed for the loop-shaping method in control design. While most loop-shaping methods apply a model of the open-loop controlled plant, the resulting performance depends on the accuracy of the dynamical model. This paper aims to develop a model-free loop-shaping technique. The core idea is to convert the model matching problem to

Jiachen Sun, Karl Koenig, Yulong Cao, Qi Alfred Chen

3D point clouds play pivotal roles in various safety-critical applications, such as autonomous driving, which desires the underlying deep neural networks to be robust to adversarial perturbations. Though a few defenses against adversarial point cloud classification have been proposed, it remains unknown whether they are truly robust to adaptive attacks. To t

G. G. Adamian, N. V. Antonenko, H. Lenske, V. V. Sargsyan

Based on the consideration of potential energy of the di-black-hole as a function of mass asymmetry (transfer) collective coordinate, the possibility of matter transfer between the black holes in a binary system is investigated. The sensitivity of the calculated results is studied to the value of the total mass of binary system. The conditions for the merger

Brandon Tay Kaiheng, Carvalho Andrea Roby, Jodi Wu Wenjiang, Da Yang Tan

We investigate the impact of the delay in compulsory mask wearing on the spread of COVID-19 in the community, set in the Singapore context. By using modified SEIR-based compartmental models, we focus on macroscopic population-level analysis of the relationships between the delay in compulsory mask wearing and the maximum infection, through a series of scenar

Tomer D. Yavetz, Kathryn V. Johnston, Sarah Pearson, Adrian M. Price-Whelan

Flattened axisymmetric galactic potentials are known to host minor orbit families surrounding orbits with commensurable frequencies. The behavior of orbits that belong to these orbit families is fundamentally different than that of typical orbits with non-commensurable frequencies. We investigate the evolution of stellar streams on orbits near the boundaries

Sagnik Chatterjee, Debajyoti Bera

The Quantum Alternating Operator Ansatz (QAOA+) framework has recently gained attention due to its ability to solve discrete optimization problems on noisy intermediate-scale quantum (NISQ) devices in a manner that is amenable to derivation of worst-case guarantees. We design a technique in this framework to tackle a few problems over maximal matchings in gr

Chih-Chuen Lin, Phani Motamarri, Vikram Gavini

We present a tensor-structured algorithm for efficient large-scale DFT calculations by constructing a Tucker tensor basis that is adapted to the Kohn-Sham Hamiltonian and localized in real-space. The proposed approach uses an additive separable approximation to the Kohn-Sham Hamiltonian and an $L_1$ localization technique to generate the 1-D localized functi

Yoshiaki Sofue, Mikito Kohno, Tomofumi Umemoto

A morphological} search for molecular shells and cavities was performed around 63 Galactic supernova remnants (SNR) at $10^\circ \le l \le 50^\circ$, $|b|\le 1^\circ $using the FUGIN (FOREST Unbiased Galactic Imaging survey with the Nobeyama 45-m telescope) CO line data at high angular ($20''$) and velocity (1.3 km s$^{-1}$) resolutions. The results are pres

Jin Hyoun Kang, Yong-il Shin

We investigate the topological properties of a resonantly shaken one-dimensional optical lattice system, where the lattice position is periodically driven with two harmonic frequencies to generate one- and two-photon couplings between the two lowest orbitals. In a two-band approximation, we numerically show that degenerate edge states appear under a certain

Alexander Frenkel, Petr Horava, Stephen Randall

We find the regime of our recently constructed topological nonrelativistic quantum gravity, in which Perelman's Ricci flow equations on Riemannian manifolds appear precisely as the localization equations in the path integral. In this mapping between physics and mathematics, the role of Perelman's dilaton is played by our lapse function. Perelman's local fixe

Ahmadreza Ahmadi, Tønnes Nygaard, Navinda Kottege, David Howard

Legged robots are popular candidates for missions in challenging terrains due to the wide variety of locomotion strategies they can employ. Terrain classification is a key enabling technology for autonomous legged robots, as it allows the robot to harness their innate flexibility to adapt their behaviour to the demands of their operating environment. In this

Noriaki Hirose, Shun Taguchi, Keisuke Kawano, Satoshi Koide

Self-supervised learning for monocular depth estimation is widely investigated as an alternative to supervised learning approach, that requires a lot of ground truths. Previous works have successfully improved the accuracy of depth estimation by modifying the model structure, adding objectives, and masking dynamic objects and occluded area. However, when usi

Masaki Morimoto, Kai Fukami, Kai Zhang, Koji Fukagata

We demonstrate several techniques to encourage practical uses of neural networks for fluid flow estimation. In the present paper, three perspectives which are remaining challenges for applications of machine learning to fluid dynamics are considered: 1. interpretability of machine-learned results, 2. bulking out of training data, and 3. generalizability of n

Madhusudan Manjunath

We study Poincar\'e series associated to a finite collection of divisors on i. a finite graph and ii. a certain family of metric graphs called chain of loops. Our main results are proofs of rationality of the Poincar\'e series and algorithms for computing it in both these cases. The main tools used in the proof of rationality are the following. For graphs, w

Alexander Spitzer, Nathan Michael

We analyze and experimentally compare various rotational error metrics for use in quadrotor controllers. Traditional quadrotor attitude controllers have used Euler angles or the full rotation to compute an attitude error and scale that to compute a control response. Recently, several works have shown that prioritizing quadrotor tilt, or thrust vector error,

Andras Gaspar, George H. Rieke, Pierre Guillard, Daniel Dicken

Arsenic doped back illuminated blocked impurity band (BIBIB) silicon detectors have advanced near and mid-IR astronomy for over thirty years; they have high quantum efficiency (QE), especially at wavelengths longer than 10 $\mu$m, and a large spectral range. Their radiation hardness is also an asset for space based instruments. Three examples of Si:As BIBIB

Huan Hu, Jianzhong Li

Nearest neighbor search is fundamental to a wide range of applications. Since the exact nearest neighbor search suffers from the "curse of dimensionality", approximate approaches, such as Locality-Sensitive Hashing (LSH), are widely used to trade a little query accuracy for a much higher query efficiency. In many scenarios, it is necessary to perform nearest

Chong Chen

We propose a variational model with diffeomorphic optimal transportation for joint image reconstruction and motion estimation. The proposed model is a production of assembling the Wasserstein distance with the Benamou--Brenier formula in optimal transportation and the flow of diffeomorphisms involved in large deformation diffeomorphic metric mapping, which i

Ruchi Guo, Yanping Lin, Jun Zou

In this article, we develop and analyze a finite element method with the first family N\'ed\'elec elements of the lowest degree for solving a Maxwell interface problem modeled by a $\mathbf{H}(\text{curl})$-elliptic equation on unfitted meshes. To capture the jump conditions optimally, we construct and use $\mathbf{H}(\text{curl})$ immersed finite element (I

Xiangyu Niu, Yifan Sun, Jinyuan Sun

In multi-task learning (MTL), we improve the performance of key machine learning algorithms by training various tasks jointly. When the number of tasks is large, modeling task structure can further refine the task relationship model. For example, often tasks can be grouped based on metadata, or via simple preprocessing steps like K-means. In this paper, we p

Paul Duncan, Matthew Kahle, Benjamin Schweinhart

We study higher-dimensional homological analogues of bond percolation on a square lattice and site percolation on a triangular lattice. By taking a quotient of certain infinite cell complexes by growing sublattices, we obtain finite cell complexes with a high degree of symmetry and with the topology of the torus $\mathbb{T}^d$. When random subcomplexes induc

Hari Krishnan S. V., Shubhrangshu Dasgupta

Preparation of entangled states of photons are useful for quantum computing and communication. In this paper, we present a simplistic protocol of entanglement generation using beam splitters with suitable reflectivity. The photons in an initial state with fully classical probability distribution pass through an optical network, made up of sequential beam spl

Sk Saniur Rahaman, Shaon Sahoo, Manoranjan Kumar

We study a frustrated two-leg spin ladder with alternate isotropic Heisenberg and Ising rung exchange interactions, whereas, interactions along legs and diagonals are Ising-type. All the interactions in the ladder are anti-ferromagnetic in nature and induce frustration in the system. This model shows four interesting quantum phases: (i) stripe rung ferromagn

Jeong-gi Kwak, David K. Han, Hanseok Ko

The goal of face attribute editing is altering a facial image according to given target attributes such as hair color, mustache, gender, etc. It belongs to the image-to-image domain transfer problem with a set of attributes considered as a distinctive domain. There have been some works in multi-domain transfer problem focusing on facial attribute editing emp

Roberto Vila

In this paper, we prove sharpness of the phase transition for the random-cluster model in summable positive external fields, with cluster weight q=2,3,..., on the hypercubic lattice. That is, there exists some nontrivial critical parameter that depends on the cluster weight and the external field, below which the model exhibits exponential decay and above wh

Zhenghang Xu, Zhijian He, Xiaoqun Wang

We consider the problem of estimating the probability of a large loss from a financial portfolio, where the future loss is expressed as a conditional expectation. Since the conditional expectation is intractable in most cases, one may resort to nested simulation. To reduce the complexity of nested simulation, we present a method that combines multilevel Mont

Konstantin G. Zloshchastiev

Using the logarithmic superfluid model of physical vacuum, one can formulate a quantum theory, which successfully recovers Einstein's theory of relativity in low-momenta limit, but otherwise has different foundations and predictions. We present an analytical example of the dispersion relation and argue that it should have a Landau "roton" form which ensures

Xiaomin Liu, Huazhi Lun, Ruoxuan Gao, Meng Cai

For further improving the capacity and reliability of optical networks, a closed-loop autonomous architecture is preferred. Considering a large number of optical components in an optical network and many digital signal processing modules in each optical transceiver, massive real-time data can be collected. However, for a traditional monitoring structure, col

Santanu Mondal, Rajan Gupta, Sungwoo Park, Boram Yoon

High statistics results for the isovector momentum fraction, $\langle x \rangle_{u-d}$, helicity moment, $\langle x \rangle_{\Delta u-\Delta d}$, and the transversity moment, $\langle x\rangle_{\delta u-\delta d}$, of the nucleon are presented using seven ensembles of gauge configurations generated by the JLab/W&M/LANL/MIT collaborations using $2+1$-flavors

Jingtian Shi, Jihang Zhu, A. H. MacDonald

The quantum anomalous Hall (QAH) effect is sometimes observed in twisted bilayer graphene (tBG) when it is nearly aligned with an encapsulating hexagonal boron nitride (hBN) layer. We propose that the appearance or absence of the QAH effect in individual devices could be related to commensurability between the graphene/graphene and graphene/hBN moir\'e patte

Tomohiro Fujita, Kai Murai, Hiromasa Nakatsuka, Shinji Tsujikawa

We investigate the possibility that axion-like particles (ALPs) with various potentials account for the isotropic birefringence recently reported by analyzing the Planck 2018 polarization data. For the quadratic and cosine potentials, we obtain lower bounds on the mass, coupling constant to photon $g$, abundance and equation of state of the ALP to produce th

Baifeng Shi, Qi Dai, Judy Hoffman, Kate Saenko

Training temporal action detection in videos requires large amounts of labeled data, yet such annotation is expensive to collect. Incorporating unlabeled or weakly-labeled data to train action detection model could help reduce annotation cost. In this work, we first introduce the Semi-supervised Action Detection (SSAD) task with a mixture of labeled and unla

Ziyuan Gu, Meead Saberi

Simulation-based optimization (SO or SBO) has become increasingly important to address challenging transportation network design problems. In this paper, we propose to solve two toll pricing problems with different levels of complexity using the concept of the macroscopic or network fundamental diagram (MFD or NFD), where a large-scale simulation-based dynam

Zehao Jin, Joshua Yao-Yu Lin, Siao-Fong Li

Nature provides a way to understand physics with reinforcement learning since nature favors the economical way for an object to propagate. In the case of classical mechanics, nature favors the object to move along the path according to the integral of the Lagrangian, called the action $\mathcal{S}$. We consider setting the reward/penalty as a function of $\m

Mahmoud Afifi, Jonathan T. Barron, Chloe LeGendre, Yun-Ta Tsai

We present "Cross-Camera Convolutional Color Constancy" (C5), a learning-based method, trained on images from multiple cameras, that accurately estimates a scene's illuminant color from raw images captured by a new camera previously unseen during training. C5 is a hypernetwork-like extension of the convolutional color constancy (CCC) approach: C5 learns to g

G. Wendell Misch, T. M. Sprouse, M. R. Mumpower

We study the impact of astrophysically relevant nuclear isomers (astromers) in the context of the rapid neutron capture process (r-process) nucleosynthesis. We compute thermally mediated transition rates between long-lived isomers and the corresponding ground states in neutron-rich nuclei. We calculate the temperature-dependent beta-decay feeding factors whi

Natalia Iyudu, Maxim Kontsevich

We prove $L_{\infty}$-formality for the higher cyclic Hochschild complex $\chH$ over free associative algebra or path algebra of a quiver. The $\chH$ complex is introduced as an appropriate tool for the definition of pre-Calabi-Yau structure. We show that cohomologies of this complex are pure in case of free algebras (path algebras), concentrated in degree z

Yuiki Takahashi, David Shlivko, Gabriel Woolls, Nicholas R. Hutzler

The cryogenic buffer gas beam (CBGB) is an important tool in the study of cold and ultracold molecules. While there are known techniques to enhance desired beam properties, such as high flux, low velocity, or reduced divergence, they have generally not undergone detailed numerical optimization. Numerical simulation of buffer gas beams is challenging, as the

Indrani Banerjee, Tanmoy Paul, Soumitra SenGupta

We explore the possibility of a non-singular bounce in our universe from a warped braneworld scenario with dynamical branes and a non-zero brane cosmological constant. Such models naturally incorporate a scalar sector known as the radion originating from the modulus of the theory. The presence of brane cosmological constant renders the branes to be non-flat

Zachary Stier, Julian Wellman, Zixuan Xu

The cyclic sieving phenomenon of Reiner, Stanton, and White characterizes the stabilizers of cyclic group actions on finite sets using q-analogue polynomials. Eu and Fu demonstrated a cyclic sieving phenomenon on generalized cluster complexes of every type using the q-Catalan numbers. In this paper, we exhibit the dihedral sieving phenomenon, introduced for

Trang H. Tran, Lam M. Nguyen, Quoc Tran-Dinh

We combine two advanced ideas widely used in optimization for machine learning: shuffling strategy and momentum technique to develop a novel shuffling gradient-based method with momentum, coined Shuffling Momentum Gradient (SMG), for non-convex finite-sum optimization problems. While our method is inspired by momentum techniques, its update is fundamentally

Ashok K. Verma, P. Modak, Fabian Schrodi, Alex Aperis

We investigate the role of specific phonon mode symmetries for the room temperature superconductivity in atomic hydrogen under large pressure. Using anisotropic Migdal-Eliashberg theory with ab initio input from density functional theory, we show that the $E_u$ phonon modes are the dominant driving force for obtaining such high critical temperatures. When go

Tao Li, Quan Qiu, Chunjiang Zhao

This paper investigates the communication strategy for second-order multi-agent systems with nonlinear dynamics. To save the scarce resources of communication channels, a novel event-triggered communication mechanism is designed without using continuous signals among the followers. To get rid of the centralized information depending on the spectrum of the La

Mohit Gola

This work is proposing and exploring the use of multichannel readout electronics, already used in quality assurance for gain uniformity studies, to measure the uniformity of the induction gap in GEM based detectors. The measurement will furthermore provide a qualification of the readout electrodes in terms of disconnected or shorted channels. The proposed me

Hantao Cui, Fangxing Li, Xin Fang

This letter investigates parallelism approaches for equation and Jacobian evaluations in large-scale power flow calculation. Two levels of parallelism are proposed and analyzed: inter-model parallelism, which evaluates models in parallel, and intra-model parallelism, which evaluates calculations within each model in parallel. Parallelism techniques such as m

Cheng Jiang, Jun Liao, Pei Dong, Zhaoxuan Ma

Artificial Intelligence (AI)-powered pathology is a revolutionary step in the world of digital pathology and shows great promise to increase both diagnosis accuracy and efficiency. However, defocus and motion blur can obscure tissue or cell characteristics hence compromising AI algorithms'accuracy and robustness in analyzing the images. In this paper, we dem

Hyemi Kim, Seungjae Shin, JoonHo Jang, Kyungwoo Song

The problem of fair classification can be mollified if we develop a method to remove the embedded sensitive information from the classification features. This line of separating the sensitive information is developed through the causal inference, and the causal inference enables the counterfactual generations to contrast the what-if case of the opposite sens

Baihe Huang, Zhao Song, Runzhou Tao, Junze Yin

Training neural networks usually require large numbers of sensitive training data, and how to protect the privacy of training data has thus become a critical topic in deep learning research. InstaHide is a state-of-the-art scheme to protect training data privacy with only minor effects on test accuracy, and its security has become a salient question. In this

Nway Nway Ei, Madyan Alsenwi, Yan Kyaw Tun, Zhu Han

Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) has become one promising solution for energy-constrained devices to meet the computation demand and the stringent delay requirement. In this work, we investigate a multiple UAVs-assisted two-stage MEC system in which the computation-intensive and delay-sensitive tasks of mobile devices

Shiyuan Qiang, Yan Li, Minghui Yang, Keqin Feng

In cryptography, we hope a sequence over $\mathbb{Z}_m$ with period $N$ having larger $m$-adic complexity. Compared with the binary case, the computation of 4-adic complexity of knowing quaternary sequences has not been well developed. In this paper, we determine the 4-adic complexity of the quaternary cyclotomic sequences with period 2$p$ defined in [6]. Th

Sarah A. Reifeis, Michael G. Hudgens

In the analysis of observational studies, inverse probability weighting (IPW) is commonly used to consistently estimate the average treatment effect (ATE) or the average treatment effect in the treated (ATT). The variance of the IPW ATE estimator is often estimated by assuming the weights are known and then using the so-called "robust" (Huber-White) sandwich

Xudong Sun, Mark C. M. Cheung

Active regions (ARs) often possess an observed net electric current in a single magnetic polarity. We show that such "non-neutralized" currents can arise from a geometric projection effect when a twisted flux tube obliquely intersects the photosphere. To this end, we emulate surface maps of an emerging AR by sampling horizontal slices of a semi-torus flux tu

Sungbin Choi

This paper describes our UNet based experiments on the Traffic4cast challenge 2020. Similar to the Traffic4cast challenge 2019, the task is to predict traffic flow volume, direction and speed on a high resolution map of three large cities worldwide. We mainly experimented with UNet based deep convolutional networks with various compositions of densely connec

Arman Afrasiyabi, Jean-François Lalonde, Christian Gagné

We introduce Mixture-based Feature Space Learning (MixtFSL) for obtaining a rich and robust feature representation in the context of few-shot image classification. Previous works have proposed to model each base class either with a single point or with a mixture model by relying on offline clustering algorithms. In contrast, we propose to model base classes

John Joseph Marchetta, Prachi Parashar, K. V. Shajesh

Casimir-Polder interaction energies between a point anisotropically polarizable atom and an annular dielectric are shown to exhibit localized repulsive long-range forces in specific configurations. We show that when the atom is positioned at the center of the annular dielectric, it is energetically favorable for the atom to align its polarizability with resp

John Joseph Marchetta, Prachi Parashar, K. V. Shajesh

Repulsion, induced from quantum vacuum fluctuations, for an anisotropically polarizable atom on the symmetry axis of an anisotropically polarizable annular disc is studied. There exists two torsion free points on each side of the annular disc, where the interaction energy is orientation independent. The position of second of the two torsion free points, on e

Reinis Ignatans, Maxim Ziatdinov, Rama Vasudevan, Mani Valleti

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switching and phase transition mechanisms. However, identification of these mechanisms from observational data

Kazuya Yonekura

We discuss a topological reason why global symmetries are not conserved in quantum gravity, at least when the symmetry comes from compactification of a higher form symmetry. The mechanism is purely topological and does not require any explicit breaking term in the UV Lagrangian. Local current conservation does not imply global charge conservation in a sum ov

A. Bezryadin, A. Belkin, E. Ilin, M. Pak

Electric capacitors are commonly used in electronic circuits for short-term storage of small amounts of energy. It is desirable however to use capacitors to store much larger energy amounts to replace rechargeable batteries. Unfortunately, the existing capacitors cannot store a sufficient energy to be able to replace common electrochemical energy storage sys

Gurcan Comert

Dynamic behavior of traffic adversely affect the performance of the prediction models in intelligent transportation applications. This study applies Gaussian processes (GPs) to traffic speed prediction. Such predictions can be used by various transportation applications, such as real-time route guidance, ramp metering, congestion pricing and special events t

Lagnajit Pattanaik, Octavian-Eugen Ganea, Ian Coley, Klavs F. Jensen

Molecules with identical graph connectivity can exhibit different physical and biological properties if they exhibit stereochemistry-a spatial structural characteristic. However, modern neural architectures designed for learning structure-property relationships from molecular structures treat molecules as graph-structured data and therefore are invariant to

Chuhua Xian, Kun Qian, Zitian Zhang, Charlie C. L. Wang

Limited by the cost and technology, the resolution of depth map collected by depth camera is often lower than that of its associated RGB camera. Although there have been many researches on RGB image super-resolution (SR), a major problem with depth map super-resolution is that there will be obvious jagged edges and excessive loss of details. To tackle these

Yijian Zou, Karthik Siva, Tomohiro Soejima, Roger S. K. Mong

Motivated by conjectures in holography relating the entanglement of purification and reflected entropy to the entanglement wedge cross-section, we introduce two related non-negative measures of tripartite entanglement $g$ and $h$. We prove structure theorems which show that states with nonzero $g$ or $h$ have nontrivial tripartite entanglement. We then estab

Eduard Ilin, Yaofa Li, Eugene V. Colla, Kenneth T. Christensen

Microseismicity in subsurface geologic environments, such as sandstone gas reservoirs, is expected in the presence of liquid or gas injection. Although difficult to predict, the potential for microseismic events is important to field-scale projects, such as geologic storage of CO2 whereby the gas is injected into natural sandstone formations. We conjecture t

Tie Zhang, Shangyou Zhang

We present and analyze a weak Galerkin finite element method for solving the transport-reaction equation in $d$ space dimensions. This method is highly flexible by allowing the use of discontinuous finite element on general meshes consisting of arbitrary polygon/polyhedra. We derive the \textcolor[rgb]{0.00,0.00,1.00}{$L_2$-error estimate} of $O(h^{k+\frac{1

Zhao Li, Yixin Liu, Zhen Zhang, Shirui Pan

Graph neural networks (GNNs) have emerged as effective approaches for graph analysis, especially in the scenario of semi-supervised learning. Despite its success, GNN often suffers from over-smoothing and over-fitting problems, which affects its performance on node classification tasks. We analyze that an alternative method, the label propagation algorithm (

Hexin Bai, Wensheng Cheng, Peng Chu, Juehuan Liu

Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years. However, existing studies dominantly request prior knowledge of the tracking target, and hence may not generalize well to unseen categories. In contrast, Generic Multiple Object Tracking (GMOT), which requires little prior information about the target, is largely under-explored

Jérôme Rony, Eric Granger, Marco Pedersoli, Ismail Ben Ayed

Adversarial attack algorithms are dominated by penalty methods, which are slow in practice, or more efficient distance-customized methods, which are heavily tailored to the properties of the distance considered. We propose a white-box attack algorithm to generate minimally perturbed adversarial examples based on Augmented Lagrangian principles. We bring seve

Sihyeon Jo, Donghwi Jung, Keonwoo Kim, Eun Gyo Joung

Can a robot be a personal dating coach? Even with the increasing amount of conversational data on the internet, the implementation of conversational robots remains a challenge. In particular, a detailed and professional counseling log is expensive and not publicly accessible. In this paper, we develop a robot dating coaching system leveraging corpus from onl

Ana María Cetto, Luis de la Peña, Andrea Valdés-Hernández

We offer a clear physical explanation for the emergence of the quantum operator formalism, by revisiting the role of the vacuum field in quantum mechanics. The vacuum or random zero-point radiation field has been shown previously, using the tools of stochastic electrodynamics, to be central in allowing a particle subject to a conservative binding force to re

Tianhan Zhang, Yaoyu Zhang, Weinan E, Yiguang Ju

Developing efficient and accurate algorithms for chemistry integration is a challenging task due to its strong stiffness and high dimensionality. The current work presents a deep learning-based numerical method called DeepCombustion0.0 to solve stiff ordinary differential equation systems. The homogeneous autoignition of DME/air mixture, including 54 species

Shoucheng Wang, Bingqiu Chen, Jun Ma

Context. Determining the metallicities and ages of M31 clusters is fundamental to the study of the formation and evolution of M31 itself. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has carried out a systematic spectroscopic campaign of clusters and candidates in M31. Aims. We constructed a catalogue of 346 M31 clusters observed by

Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud

Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning. In this paper, we investigate the global convergence of gradient-based policy optimization methods for quadratic optimal control of discrete-time Markovian jump linear systems (MJLS). First, we study the optimization lan

Woohwan Jung, Kyuseok Shim

Relation extraction (RE) has been extensively studied due to its importance in real-world applications such as knowledge base construction and question answering. Most of the existing works train the models on either distantly supervised data or human-annotated data. To take advantage of the high accuracy of human annotation and the cheap cost of distant sup

Soham Gadgil, Yunfeng Xin, Chengzhe Xu

Reinforcement Learning (RL) is an area of machine learning concerned with enabling an agent to navigate an environment with uncertainty in order to maximize some notion of cumulative long-term reward. In this paper, we implement and analyze two different RL techniques, Sarsa and Deep QLearning, on OpenAI Gym's LunarLander-v2 environment. We then introduce ad

Logesh Kumar, Parikshit Pareek, Sivakumar Nadarajan, Souvik Dasgupta

In this paper, we propose a novel Health-Focused Optimal Power Flow (HF-OPF) to take into account the equipment health in operational and physical constraints. The health condition index is estimated based on the possible fault characteristics for generators and batteries. The paper addresses the need for understanding the relationship between health conditi

Gregory Quiroz, Lauren Ice, Andrea Delgado, Travis S. Humble

Pattern recognition algorithms are commonly employed to simplify the challenging and necessary step of track reconstruction in sub-atomic physics experiments. Aiding in the discrimination of relevant interactions, pattern recognition seeks to accelerate track reconstruction by isolating signals of interest. In high collision rate experiments, such algorithms

Yuzhou Tian, Yulin Zhao

The real Jacobian conjecture claims that if $F=\left(f^1,\ldots,f^n\right):\mathbb{R}^n\rightarrow \mathbb{R}^n$ is a polynomial map such that $\det DF$ is nowhere zero, then $F$ is a global injective. The first part is to study the two-dimensional real Jacobian conjecture via the method of the qualitative theory of dynamical systems. By Bendixson compactifi

Yu-Ding Lu, Hsin-Ying Lee, Hung-Yu Tseng, Ming-Hsuan Yang

As recent generative models can generate photo-realistic images, people seek to understand the mechanism behind the generation process. Interpretable generation process is beneficial to various image editing applications. In this work, we propose a framework to discover interpretable directions in the latent space given arbitrary pre-trained generative adver

Farshud Sorourifar, Georgios Makrygirgos, Ali Mesbah, Joel A. Paulson

The closed-loop performance of model predictive controllers (MPCs) is sensitive to the choice of prediction models, controller formulation, and tuning parameters. However, prediction models are typically optimized for prediction accuracy instead of performance, and MPC tuning is typically done manually to satisfy (probabilistic) constraints. In this work, we

Omobayode Fagbohungbe, Lijun Qian

Analog hardware implemented deep learning models are promising for computation and energy constrained systems such as edge computing devices. However, the analog nature of the device and the associated many noise sources will cause changes to the value of the weights in the trained deep learning models deployed on such devices. In this study, systematic eval

Jonathan Higgins

Two vertices $u$ and $v$ of a graph $\Gamma$ are strucuturally equivalent if and only if the transposition $(u\,v)$ is in Aut($\Gamma$), the automorphism group of $\Gamma$. Some properties of structural equivalence and the group of vertex permutations generated by the transpositions in Aut($\Gamma$) are discussed, along with the prime graphs of these groups.

Bardia H. Fahim, Valerio Faraoni, Andrea Giusti

We report a new one-parameter family of spherically symmetric, inhomogeneous, and time-dependent solutions of the vacuum Brans-Dicke field equations which are conformal to the Roberts scalar field geometries of Einstein gravity. The new solution is spherical and time-dependent and contains a naked central singularity. We use it as a seed to generate another

Maopeng Ran, Lihua Xie

This paper develops an adaptive observation-based efficient reinforcement learning (RL) approach for systems with uncertain drift dynamics. A novel concurrent learning adaptive extended observer (CL-AEO) is first designed to jointly estimate the system state and parameter. This observer has a two-timescale structure and doesn't require any additional numeric

Ke Sun, Brent Schlotfeldt, George Pappas, Vijay Kumar

In this paper, we address the problem of stochastic motion planning under partial observability, more specifically, how to navigate a mobile robot equipped with continuous range sensors such as LIDAR. In contrast to many existing robotic motion planning methods, we explicitly consider the uncertainty of the robot state by modeling the system as a POMDP. Rece

Miao Yang, Hongbin Zhu, Hua Qian, Yevgeni Koucheryavy

Comparing to cloud computing, fog computing performs computation and services at the edge of networks, thus relieving the computation burden of the data center and reducing the task latency of end devices. Computation latency is a crucial performance metric in fog computing, especially for real-time applications. In this paper, we study a peer computation of