November 2020 arXiv papers — page 11

Lei Wang, Yuchun Huang, Pengjie Tao, Yaolin Hou

We study the problem of generating point clouds of 3D objects. Instead of discretizing the object into 3D voxels with huge computational cost and resolution limitations, we propose a novel geometry image based generator (GIG) to convert the 3D point cloud generation problem to a 2D geometry image generation problem. Since the geometry image is a completely r

Yutong Dai, Hao Lu, Chunhua Shen

We show that learning affinity in upsampling provides an effective and efficient approach to exploit pairwise interactions in deep networks. Second-order features are commonly used in dense prediction to build adjacent relations with a learnable module after upsampling such as non-local blocks. Since upsampling is essential, learning affinity in upsampling c

Louis Omenyi, McSylvester Omaba

We first prove Kato's inequalities for the Laplacian and a Schr$\ddot{o}$dinger-type operator on smooth functions on closed Riemannian manifolds. We then apply the result to establish some new domination inequalities for spectral zeta functions and their related spectral zeta kernels on $n$-dimensional unit spheres using Kato's inequalities and majorisation

Allen Schmaltz, Andrew Beam

We present a novel end-to-end language model for joint retrieval and classification, unifying the strengths of bi- and cross- encoders into a single language model via a coarse-to-fine memory matching search procedure for learning and inference. Evaluated on the standard blind test set of the FEVER fact verification dataset, classification accuracy is signif

Victoria Chen, Yong Cheol Shin, Evgeny Mikheev, Joel Martis

Hexagonal boron nitride (h-BN) is unique among two-dimensional materials, with a large band gap (~6 eV) and high thermal conductivity (>400 W/m/K), second only to diamond among electrical insulators. Most electronic studies to date have relied on h-BN exfoliated from bulk crystals; however, for scalable applications the material must be synthesized by method

Haoxi Ran, Li Lu

Prevalence of deeper networks driven by self-attention is in stark contrast to underexplored point-based methods. In this paper, we propose groupwise self-attention as the basic block to construct our network: SepNet. Our proposed module can effectively capture both local and global dependencies. This module computes the features of a group based on the summ

Girisha S, Ujjwal Verma, Manohara Pai M M, Radhika Pai

Semantic segmentation of aerial videos has been extensively used for decision making in monitoring environmental changes, urban planning, and disaster management. The reliability of these decision support systems is dependent on the accuracy of the video semantic segmentation algorithms. The existing CNN based video semantic segmentation methods have enhance

Sami Ouali

Software Product Lines SPL are recognized as a successful approach to reuse in software development.Its purpose is to reduce production costs. This approach allows products to be different with respect of particular characteristics and constraints in order to cover different markets. Software Product Line engineering is the production process in product line

Sergey Bereg, Mohammadreza Haghpanah

A given order type in the plane can be represented by a point set. However, it might be difficult to recognize the orientations of some point triples. Recently, Aichholzer \etal \cite{abh19} introduced exit graphs for visualizing order types in the plane. We present a new class of geometric graphs, called {\em OT-graphs}, using abstract order types and their

Changxi Zhu, Ho-fung Leung, Shuyue Hu, Yi Cai

In teacher-student framework, a more experienced agent (teacher) helps accelerate the learning of another agent (student) by suggesting actions to take in certain states. In cooperative multiagent reinforcement learning (MARL), where agents need to cooperate with one another, a student may fail to cooperate well with others even by following the teachers' su

Sudhir Kumar Suman, Hrithwik Shalu, Lakshya A Agrawal, Archit Agrawal

Text sentiment analysis for preliminary depression status estimation of users on social media is a widely exercised and feasible method, However, the immense variety of users accessing the social media websites and their ample mix of vocabularies makes it difficult for commonly applied deep learning-based classifiers to perform. To add to the situation, the

Lizhen Nie, Veronika Ročková

The impracticality of posterior sampling has prevented the widespread adoption of spike-and-slab priors in high-dimensional applications. To alleviate the computational burden, optimization strategies have been proposed that quickly find local posterior modes. Trading off uncertainty quantification for computational speed, these strategies have enabled spike

Beatrix Haddock, James Leng, Cesar E. Silva

We study two properties of nonsingular and infinite measure-preserving ergodic systems: weak double ergodicity, and ergodicity with isometric coefficients. We show that there exist infinite measure-preserving transformations that are ergodic with isometric coefficients but are not weakly doubly ergodic. We also give type $\text{III}_\lambda$ examples of such

Zhiruo Wang, Renfen Hu

Recent NLP tasks have benefited a lot from pre-trained language models (LM) since they are able to encode knowledge of various aspects. However, current LM evaluations focus on downstream performance, hence lack to comprehensively inspect in which aspect and to what extent have they encoded knowledge. This paper addresses both queries by proposing four tasks

Hector J. Hortua, Riccardo Volpi, Dimitri Marinelli, Luigi Malago

Markov Chain Monte Carlo (MCMC) algorithms are commonly used for their versatility in sampling from complicated probability distributions. However, as the dimension of the distribution gets larger, the computational costs for a satisfactory exploration of the sampling space become challenging. Adaptive MCMC methods employing a choice of proposal distribution

Kai Wang, Avik Dutt, Ki Youl Yang, Casey C. Wojcik

The non-trivial topological features in the energy band of non-Hermitian systems provide promising pathways to achieve robust physical behaviors in classical or quantum open systems. A key topological feature, unique to non-Hermitian systems, is the non-trivial winding of the energy band in the complex energy plane. Here we provide direct experimental demons

Yuxing Shi

The Suzuki algebra $A_{Nn}^{\mu \lambda}$ was introduced by Suzuki Satoshi in 1998, which is a class of cosemisimple Hopf algebras. It is not categorically Morita-equivalent to a group algebra in general. In this paper, the author gives a complete set of simple Yetter-Drinfeld modules over the Suzuki algebra $A_{N\,2n}^{\mu\lambda}$ and investigates the Nich

Kento Yasuda, Shigeyuki Komura

We propose a model that describes cyclic state transitions of a micromachine driven by a catalytic chemical reaction. We consider a mechano-chemical coupling of variables representing the degree of a chemical reaction and the internal state of a micromachine. The total free energy consists of a tilted periodic potential and a mechano-chemical coupling energy

Chongzhen Zhang, Yang Tang, Chaoqiang Zhao, Qiyu Sun

Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving. Although several works are proposed to jointly train these two tasks using some small modifications, like changing the last layer, the result of one task is not utilized to improve the performance of the ot

Fankun Bu, Kaveh Dehghanpour, Zhaoyu Wang

In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which can record high-resolution data; however, in practice, micro-PMUs are installed at limited locations in distribution networks due to budgetary constraints. In contrast, smart meters are widely deployed but can only measure relatively low-resolution energy consumption,

Danny Nam, Allan Sly, Youngtak Sohn

In a broad class of sparse random constraint satisfaction problems(CSP), deep heuristics from statistical physics predict that there is a condensation phase transition before the satisfiability threshold, governed by one-step replica symmetry breaking(1RSB). In fact, in random regular k-NAE-SAT, which is one of such random CSPs, it was verified \cite{ssz22}

Hongkang Yang, Weinan E

Models for learning probability distributions such as generative models and density estimators behave quite differently from models for learning functions. One example is found in the memorization phenomenon, namely the ultimate convergence to the empirical distribution, that occurs in generative adversarial networks (GANs). For this reason, the issue of gen

Susan M. Mniszewski, Pavel A. Dub, Sergei Tretiak, Petr M. Anisimov

Calculating the ground state energy of a molecule efficiently is of great interest in quantum chemistry. The exact numerical solution of the electronic Schrodinger equation remains unfeasible for most molecules requiring approximate methods at best. In this paper we introduce the use of Quantum Community Detection performed using the D-Wave quantum annealer

Qiwen Cui, Lin F. Yang

The empirical success of Multi-agent reinforcement learning is encouraging, while few theoretical guarantees have been revealed. In this work, we prove that the plug-in solver approach, probably the most natural reinforcement learning algorithm, achieves minimax sample complexity for turn-based stochastic game (TBSG). Specifically, we plan in an empirical TB

Hongseok Namkoong, Samuel Daulton, Eytan Bakshy

Thompson sampling (TS) has emerged as a robust technique for contextual bandit problems. However, TS requires posterior inference and optimization for action generation, prohibiting its use in many online platforms where latency and ease of deployment are of concern. We operationalize TS by proposing a novel imitation-learning-based algorithm that distills a

Hu Wang, Peng Chen, Bohan Zhuang, Chunhua Shen

With the rising popularity of intelligent mobile devices, it is of great practical significance to develop accurate, realtime and energy-efficient image Super-Resolution (SR) inference methods. A prevailing method for improving the inference efficiency is model quantization, which allows for replacing the expensive floating-point operations with efficient fi

Pankaj Bhalla

Irradiation of the strong light on the material leads to numerous non-linear effects that are essential to understand the physics of excited states of the system and for optoelectronics. Here, we study the non-linear thermoelectric effect due to the electric and thermal fields applied on a non-centrosymmetric system. The phenomenon arises on the Fermi surfac

K. C. Kosaraju, S. Sivaranjani, W. Suttle, V. Gupta

We consider the problem of designing distributed controllers to stabilize a class of networked systems, where each subsystem is dissipative and designs a reinforcement learning based local controller to maximize an individual cumulative reward function. We develop an approach that enforces dissipativity conditions on these local controllers at each subsystem

Juntao Chen, Yunhan Huang, Rui Zhang, Quanyan Zhu

Optimal curing strategy of suppressing competing epidemics spreading over complex networks is a critical issue. In this paper, we first establish a framework to capture the coupling between two epidemics, and then analyze the system's equilibrium states by categorizing them into three classes, and deriving their stability conditions. The designed curing stra

Toshinori Hayashi, Yasushi Suto

A large number of binary black holes (BBHs) with longer orbital periods are supposed to exist as progenitors of BBH mergers recently discovered with gravitational wave (GW) detectors. In our previous papers, we proposed to search for such BBHs in triple systems through the radial-velocity modulation of the tertiary orbiting star. If the tertiary is a pulsar,

Tsukasa Ishibashi, Hironori Oya

For a marked surface $\Sigma$ and a semisimple algebraic group $G$ of adjoint type, we study the Wilson line morphism $g_{[c]}:\mathcal{P}_{G,\Sigma} \to G$ associated with the homotopy class of an arc $c$ connecting boundary intervals of $\Sigma$, which is the comparison element of pinnings via parallel-transport. The matrix coefficients of the Wilson lines

Julian D. Arias-Londoño, Jorge A. Gomez-Garcia, Laureano Moro-Velazquez, Juan I. Godino-Llorente

Current standard protocols used in the clinic for diagnosing COVID-19 include molecular or antigen tests, generally complemented by a plain chest X-Ray. The combined analysis aims to reduce the significant number of false negatives of these tests, but also to provide complementary evidence about the presence and severity of the disease. However, the procedur

Alex May, Mark Van Raamsdonk

The recent paper 1809.01197 described how states of a holographic CFT can be approximated by states of a large collection of non-interacting BCFTs, such that the dual of the new system accurately approximates an arbitrarily large causal patch of the original geometry. In this paper, we first describe in more detail the geometries dual to such discrete BCFT s

Mohammad Muntasir Hassan, Farhan Sadik Sium, Fariba Islam, Sajid Muhaimin Choudhury

Our atmosphere is constantly changing and new pathogens are erupting now and then and the existing pathogens are mutating continuously. Some of these pathogens, such as SARS-CoV-2, become so deadly that they put the whole technological advancement of healthcare under challenge. Within this very decade several other deadly virus outbreaks were witnessed by hu

Pengkun Huang

We propose a criterion, referred to as order-n transversality, for transitivity of area preserving partially hyperbolic endomorphisms. Besides, we also give a further answer to the Gan's problem, as proposed in the work of Baolin He.

Chao Cao, Jian-Xin Zhu

Using the state-of-art dynamical mean-field theory combined with density functional theory method, we have performed systematic study on the temperature and pressure dependent electronic structure of ferromagnetic quantum critical material candidate CeRh$_6$Ge$_4$. At -3.9 GPa and -8.3 GPa, the Ce-4$f$ occupation variation, the local magnetic susceptibility,

Yang Ye, Qingpeng Zhang, Zhidong Cao, Daniel Dajun Zeng

There are often multiple diseases with cross immunity competing for vaccination resources. Here we investigate the optimal vaccination program in a two-layer Susceptible-Infected-Removed (SIR) model, where two diseases with cross immunity spread in the same population, and vaccines for both diseases are available. We identify three scenarios of the optimal v

Jiachen Jiang, Huaqing Cheng, Luigi C. Gallo, Luis C. Ho

KUG 1141+371 is a Seyfert 1 galaxy that shows a simultaneous flux increase in the optical and UV bands in the past decade. For instance, the latest Swift observation in 2019 shows that the UVW2 flux of the AGN in KUG 1141+371 has increased by over one order of magnitude since 2009. Meanwhile, the soft X-ray flux of KUG 1141+371 also shows a steady increase b

Masaki Kashiwara, Myungho Kim, Se-jin Oh, Euiyong Park

Let $U_q'(\mathfrak{g})$ be a quantum affine algebra of arbitrary type and let $\mathcal{C}_{\mathfrak{g}}$ be Hernandez-Leclerc's category. We can associate the quantum affine Schur-Weyl duality functor $F_D$ to a duality datum $D$ in $\mathcal{C}_{\mathfrak{g}}$. We introduce the notion of a strong (complete) duality datum $D$ and prove that, when $D$ is s

Peter Frankl

Several new applications of Katona's circle are given.

Kamyar Azizzadenesheli

We study generalization under labeled shift for categorical and general normed label spaces. We propose a series of methods to estimate the importance weights from labeled source to unlabeled target domain and provide confidence bounds for these estimators. We deploy these estimators and provide generalization bounds in the unlabeled target domain.

Benjamin Jones, Sheik Ahmed Ullah, Siwen Wang, Shan Zhao

This work further improves the pseudo-transient approach for the Poisson Boltzmann equation (PBE) in the electrostatic analysis of solvated biomolecules. The numerical solution of the nonlinear PBE is known to involve many difficulties, such as exponential nonlinear term, strong singularity by the source terms, and complex dielectric interface. Recently, a p

Yan He, Jifang Qiu, Chang Liu, Yue Liu

The latest theoretical advances in the field of unlimited sampling framework (USF) show the potential to avoid clipping problems of analog-to-digital converters (ADC). To date, most of the related works have focused on real-valued modulo samples, but little has been reported about the impact of quantization. In this paper, we study more practical USF system

Richard Rimanyi, Alexander Varchenko

We prove an $\mathbb F_p$-Selberg integral formula, in which the $\mathbb F_p$-Selberg integral is an element of the finite field $\mathbb F_p$ with odd prime number $p$ of elements. The formula is motivated by analogy between multidimensional hypergeometric solutions of the KZ equations and polynomial solutions of the same equations reduced modulo $p$.

Masato Hisakado, Shintaro Mori

In this article, we discuss the random graph, Barab\'asi-Albert (BA) model, and lattice networks from a unified view point, with the parameter $\omega$ with values $1,0,-1$ characterizing these networks, respectively. The parameter is related to the preferential attachment of nodes in the networks and has different weights for the incoming and outgoing links

Scott Hottovy, Samuel N. Stechmann

A moisture process with dynamics that switch after hitting a threshold gives rise to a rainfall process. This rainfall process is characterized by its random holding times for dry and wet periods. On average, the holding times for the wet periods are much shorter than the dry. Here convergence is shown for the rain fall process to a point process that is a s

Elana Kozak, Scott Hottovy

Monte Carlo Tree Search (MCTS) is a branch of stochastic modeling that utilizes decision trees for optimization, mostly applied to artificial intelligence (AI) game players. This project imagines a game in which an AI player searches for a stationary target within a 2-D lattice. We analyze its behavior with different target distributions and compare its effi

Lingchii Kong, Genwang Fan, Shi-Guo Peng, Xiao-Long Chen

We theoretically generalize a systematic language to describe the phase-imprinting technique to investigate the dynamical generation of solitons in a one-dimensional Raman-type spin-orbit-coupled Fermi superfluid. We check our method with the simulation of time-dependent Bogoliubov-de Gennes equations and find that our method not only can generate stable dar

Yao Fu, Chuanqi Tan, Bin Bi, Mosha Chen

Learning to control the structure of sentences is a challenging problem in text generation. Existing work either relies on simple deterministic approaches or RL-based hard structures. We explore the use of structured variational autoencoders to infer latent templates for sentence generation using a soft, continuous relaxation in order to utilize reparameteri

Henry D. Chacon, Paul Rad

The input space complexity determines the model's capabilities to extract their knowledge and translate the space of attributes into a function which is assumed in general, as a concatenation of non-linear functions between layers. In the presence of backdoor attacks, the space complexity changes, and induces similarities between classes that directly affect

Liang Luo, Peter West, Arvind Krishnamurthy, Luis Ceze

Finding the best VM configuration is key to achieve lower cost and higher throughput, two primary concerns in cloud-based distributed neural network (NN) training today. Optimal VM selection that meets user constraints requires efficiently navigating a large search space while controlling for the performance variance associated with sharing cloud instances a

Jordan Wilson-Gerow, P. C. E. Stamp

Starting from a formulation of Correlated Worldline (CWL) theory in terms of functional integrals over paths, we define propagators for particles and matter fields in this theory. We show that the most natural formulation of CWL theory involves a rescaling of the generating functional for the theory; correlation functions then simplify, and all loops contain

Sakshi Piplani, Puneet Singh, David A. Winkler, Nikolai Petrovsky

For fast development of COVID-19, it is only feasible to use drugs (off label use) or approved natural products that are already registered or been assessed for safety in previous human trials. These agents can be quickly assessed in COVID-19 patients, as their safety and pharmacokinetics should already be well understood. Computational methods offer promise

Vishal Ramadoss, Keerthi Sagar, Mohamed Sadiq Ikbal, Jesus Hiram Lugo Calles

There is a surge of research interest in the field of tensegrity robotics. Robots developed under this paradigm provide many advantages and have distinguishing features in terms of structural compliance, dexterity, safety, and weight reduction. This paper proposes a new robotic mechanism based on tensegrity ('tension-integrity') robots and reconfigurable mod

Ramon Becar, P. A. Gonzalez, Joel Saavedra, Yerko Vasquez

In this work we consider black hole solutions to Einstein theory coupled to a nonlinear power-law electromagnetic field with a fixed exponent value. We study the extended phase space thermodynamics in canonical and grand canonical ensembles where the varying cosmological constant plays the role of an effective thermodynamic pressure. We examine thermodynamic

Amy X. Zhang, Le Bao, Changcheng Li, Michael J. Daniels

We introduce a novel procedure for obtaining cross-validated predictive estimates for Bayesian hierarchical regression models (BHRMs). Bayesian hierarchical models are popular for their ability to model complex dependence structures and provide probabilistic uncertainty estimates, but can be computationally expensive to run. Cross-validation (CV) is therefor

Niels R. Walet, Francisco Guinea

We study the effect of twisting on bilayer graphene. The effect of lattice relaxation is included; we look at the electronic structure, piezo-electric charges and spontaneous polarisation. We show that the electronic structure without lattice relaxation shows a set of extremely flat in-gap states similar to Landau-levels, where the spacing scales with twist

Sandra Cerrai, Guangyu Xi

We study the validity of a Smoluchowski-Kramers approximation for a class of wave equations in a bounded domain of $\mathbb{R}^n$ subject to a state-dependent damping and perturbed by a multiplicative noise. We prove that in the small mass limit the solution converges to the solution of a stochastic quasilinear parabolic equation where a noise-induced extra

Gregory W. Wimsatt, Alexander B. Boyd, Paul M. Riechers, James P. Crutchfield

Nonequilibrium information thermodynamics determines the minimum energy dissipation to reliably erase memory under time-symmetric control protocols. We demonstrate that its bounds are tight and so show that the costs overwhelm those implied by Landauer's energy bound on information erasure. Moreover, in the limit of perfect computation, the costs diverge. Th

John C. Baez

The tenfold way became important in physics around 2010: it implies that there are ten fundamentally different kinds of matter. But it goes back to 1964, when C. T. C. Wall classified real super division algebras. These are finite-dimensional real $\mathbb{Z}/2$-graded algebras where every nonzero homogeneous element is invertible. He found that besides $\ma

Zachary A. Collier, Jeffrey M. Keisler, Benjamin D. Trump, Jeffrey C. Cegan

With the emerging COVID-19 crisis, a critical task for public health officials and policy makers is to decide how to prioritize, locate, and allocate scarce resources. To answer these questions, decision makers need to be able to determine the location of the required resources over time based on emerging hot spot locations. Hot spots are defined as concentr

Kiran S. Kedlaya, Alexander Kolpakov, Bjorn Poonen, Michael Rubinstein

We classify all sets of nonzero vectors in $\mathbb{R}^3$ such that the angle formed by each pair is a rational multiple of $\pi$. The special case of four-element subsets lets us classify all tetrahedra whose dihedral angles are multiples of $\pi$, solving a 1976 problem of Conway and Jones: there are $2$ one-parameter families and $59$ sporadic tetrahedra,

Maureen S. Golan, Benjamin D. Trump, Jeffrey C. Cegan, Igor Linkov

The COVID19 pandemic has highlighted the lack of resilience in supply chains, as global networks fail from disruptions at single nodes and connections. Through an overview of the existing vaccine and pharmaceutical supply chain publications focusing on resilience, as well as recent papers reporting modeling of resilience in supply chains across multiple fiel

Dani Kiyasseh, Tingting Zhu, David A. Clifton

The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks. Such tasks include disease diagnosis, clinical trial recruitment, and continuing medical education. This manual search-and-extract process, however, has been hampered by the growth of large-scale clinical da

Jian Wang, Miaomiao Zhang

This paper presents a predictive model for estimating regularization parameters of diffeomorphic image registration. We introduce a novel framework that automatically determines the parameters controlling the smoothness of diffeomorphic transformations. Our method significantly reduces the effort of parameter tuning, which is time and labor-consuming. To ach

Youjun Deng, Hongyu Liu, Xianchao Wang, Dong Wei

This paper is concerned with a practical inverse problem of simultaneously reconstructing the surface heat flux and the thickness of a solid structure from the associated ultrasonic measurements. In a thermoacoustic coupling model, the thermal boundary condition and the thickness of a solid structure are both unknown, while the measurements of the propagatio

Dani Kiyasseh, Tingting Zhu, David A. Clifton

Many clinical deep learning algorithms are population-based and difficult to interpret. Such properties limit their clinical utility as population-based findings may not generalize to individual patients and physicians are reluctant to incorporate opaque models into their clinical workflow. To overcome these obstacles, we propose to learn patient-specific em

Youjun Deng, Chaohua Duan, Hongyu Liu

In this paper, we consider the transmission eigenvalue problem associated with a general conductive transmission condition and study the geometric structures of the transmission eigenfunctions. We prove that under a mild regularity condition in terms of the Herglotz approximations of one of the pair of the transmission eigenfunctions, the eigenfunctions must

Faraj. A. Abdunabi, Ahmed Shletie

In this paper, we introduce the upper and lower approximations on the invers set-valued mapping and the approximations an established on a powerful set valued homomorphism from a ring R1 to power sets of a ring R2. Moreover, the properties of lower and upper approximations of a powerful set valued are studies. In addition, we will give a proof of the theorem

Dor Farbiash, Rami Puzis

Today arbitrary synthetic DNA can be ordered online and delivered within several days. In order to regulate both intentional and unintentional generation of dangerous substances, most synthetic gene providers screen DNA orders. A weakness in the Screening Framework Guidance for Providers of Synthetic Double-Stranded DNA allows screening protocols based on th

Steve Tammes, Tyler Roth, Philip Kaaret, Casey DeRoo

Recently CMOS (complementary metal-oxide semiconductor) sensors have progressed to a point where they may offer improved performance in imaging x-ray detection compared to the CCDs often used in x-ray satellites. We demonstrate x-ray detection in the soft x-ray band (250-1700 eV) by a commercially available back-illuminated Sony IMX290LLR CMOS sensor using t

Ching-Wei Ho

Let $x_0$ be an unbounded self-adjoint operator such that the Brown measure of $x_0$ exists in the sense of Haagerup and Schultz. Also let $\tilde\sigma_\alpha$ and $\sigma_\beta$ be semicircular variables with variances $\alpha\geq 0$ and $\beta>0$ respectively. Suppose $x_0$, $\sigma_\alpha$, and $\tilde\sigma_\beta$ are all freely independent. We compute

K. M. Belotsky, M. M. El Kasmi, S. G. Rubin

We consider the temperature dynamics of hypothetical primordial hot areas in the Universe. Such areas can be produced by the primordial density inhomogeneities and can survive to the modern era, in particular due to primordial black hole (PBH) cluster of size $R \gtrsim 1$ pc and more. Here we concentrate on the neutrino cooling effect which is realized due

Harsh S. Dhiman, Dipankar Deb

Globally, wind energy has lessened the burden on conventional fossil fuel based power generation. Wind resource assessment for onshore and offshore wind farms aids in accurate forecasting and analyzing nature of ramp events. From an industrial point of view, a large ramp event in a short time duration is likely to cause damage to the wind farm connected to t

Koohyun Kwon, Soonwoo Kwon

We consider the problem of adaptive inference on a regression function at a point under a multivariate nonparametric regression setting. The regression function belongs to a H\"older class and is assumed to be monotone with respect to some or all of the arguments. We derive the minimax rate of convergence for confidence intervals (CIs) that adapt to the unde

Debayan Deb, Xiaoming Liu, Anil K. Jain

Prevailing defense mechanisms against adversarial face images tend to overfit to the adversarial perturbations in the training set and fail to generalize to unseen adversarial attacks. We propose a new self-supervised adversarial defense framework, namely FaceGuard, that can automatically detect, localize, and purify a wide variety of adversarial faces witho

Bastien Politi, Stephanie Parola, Antoine Gademer, Diane Pegart

Indoor light is known to be a new energy source to power uW low consumption wireless sensor networks (WSNs). For wireless electronic devices that consume tens of mW, it is still challenging to harvest this amount of power in a low light indoor environment. The challenge comes from the fact that the irradiance is low, as well the fact that the source is a mul

Koohyun Kwon, Soonwoo Kwon

We provide an inference procedure for the sharp regression discontinuity design (RDD) under monotonicity, with possibly multiple running variables. Specifically, we consider the case where the true regression function is monotone with respect to (all or some of) the running variables and assumed to lie in a Lipschitz smoothness class. Such a monotonicity con

Binh Duc Truong, Thuy Thi-Thien Le, Berardi Sensale-Rodriguez

The dynamics and performance of a two-coil resonant coupled wireless power transfer system are investigated. At high coupling, the frequency-splitting phenomenon occurs, in which the power transferred to the load attains its maximum at two frequencies away from the resonance frequency. However, this behavior is not a universal property; there exist certain r

Anadi Chaman, Ivan Dokmanić

Thanks to the use of convolution and pooling layers, convolutional neural networks were for a long time thought to be shift-invariant. However, recent works have shown that the output of a CNN can change significantly with small shifts in input: a problem caused by the presence of downsampling (stride) layers. The existing solutions rely either on data augme

Yuxuan Yu, Xiaodong Wei, Angran Li, Jialei Ginny Liu

In this paper, we present two software packages, HexGen and Hex2Spline, that seamlessly integrate geometry design with isogeometric analysis (IGA) in LS-DYNA. Given a boundary representation of a solid model, HexGen creates a hexahedral mesh by utilizing a semi-automatic polycube-based mesh generation method. Hex2Spline takes the output hexahedral mesh from

Gwenn Boedec, Julien Deschamps

We study the deflection by gravity of a circular elastic disk deposited on a rigid support. The axisymmetric deflection induces a compressive orthoradial stresses which leads to a wrinkling instability above a critical threshold of the dimensionless gravity force. We study this instability by a combination of experiments, numerical simulations and analytical

Benjamin Gravell, Iman Shames, Tyler Summers

We present a midpoint policy iteration algorithm to solve linear quadratic optimal control problems in both model-based and model-free settings. The algorithm is a variation of Newton's method, and we show that in the model-based setting it achieves cubic convergence, which is superior to standard policy iteration and policy gradient algorithms that achieve

Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Chunxu Zhang

Recent research on graph embedding has achieved success in various applications. Most graph embedding methods preserve the proximity in a graph into a manifold in an embedding space. We argue an important but neglected problem about this proximity-preserving strategy: Graph topology patterns, while preserved well into an embedding manifold by preserving prox

Mark Tuddenham, Adam Prügel-Bennett, Jonathan Hare

Classification problems using deep learning have been shown to have a high-curvature subspace in the loss landscape equal in dimension to the number of classes. Moreover, this subspace corresponds to the subspace spanned by the logit gradients for each class. An obvious strategy to speed up optimisation would be to use Newton's method in the high-curvature s

Abhimanyu Kumar, Anuraag Saxena

The degree of insulation of a prime $p$ is defined as the largest interval around it in which no other prime exists. Based on this, the $n$-th prime $p_{n}$ is said to be insulated if and only if its degree of insulation is higher than its neighboring primes. Consequently, a new special sequence emerges given as 7, 13, 23, 37, 53, 67, 89, 103, 113, 131, 139,

Philippe Barbe, Antonio Cicone, Wing Suet Li, Haomin Zhou

Iterative filtering methods were introduced around 2010 to improve definitions and measurements of structural features in signal processing. Like many applied techniques, they present considerable challenges for mathematicians to theorize their effectiveness and limitations in commercial and scientific usages. In this paper we recast iterative filtering meth

Alex Myers, Muhammadyusuf Odinaev, David Walmsley

Let $H(\mathbb{C})$ be the set of all entire functions endowed with the topology of uniform convergence on compact sets. Let $\lambda,b\in\mathbb{C}$, let $C_{\lambda,b}:H(\mathbb{C})\to H(\mathbb{C})$ be the composition operator $C_{\lambda,b} f(z)=f(\lambda z+b)$, and let $D$ be the derivative operator. We extend results on the hypercyclicity of the non-co

Nicki Skafte Detlefsen, Søren Hauberg, Wouter Boomsma

How we choose to represent our data has a fundamental impact on our ability to subsequently extract information from them. Machine learning promises to automatically determine efficient representations from large unstructured datasets, such as those arising in biology. However, empirical evidence suggests that seemingly minor changes to these machine learnin

A. Masuoka, A. N. Zubkov

We develop a general theory of algebraic group superschemes, which are not necessarily affine. Our key result is a category equivalence between those group superschemes and Harish-Chandra pairs, which generalizes the result known for affine algebraic group superschemes. Then we present the applications, including the Barsotti-Chevalley Theorem in the super c

Zhenjia Xu, Beichun Qi, Shubham Agrawal, Shuran Song

This paper aims to improve robots' versatility and adaptability by allowing them to use a large variety of end-effector tools and quickly adapt to new tools. We propose AdaGrasp, a method to learn a single grasping policy that generalizes to novel grippers. By training on a large collection of grippers, our algorithm is able to acquire generalizable knowledg

Fudong Wang, Wen-Xiu Ma

We study the asymptotic behavior of Riemann-Hilbert problems (RHP) arising in the AKNS hierarchy of integrable equations. Our analysis is based on the $\dbar$-steepest descent method. We consider RHPs arising from the inverse scattering transform of the AKNS hierarchy with $H^{1,1}(\R)$ initial data. The analysis will be divided into three regions: fast deca

Ayush Jaiswal, Yue Wu, Pradeep Natarajan, Premkumar Natarajan

Object detection models perform well at localizing and classifying objects that they are shown during training. However, due to the difficulty and cost associated with creating and annotating detection datasets, trained models detect a limited number of object types with unknown objects treated as background content. This hinders the adoption of conventional

Thierry Tambe, Coleman Hooper, Lillian Pentecost, Tianyu Jia

Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. However, their hefty computational and memory demands make them challenging to deploy to resource-constrained edge platforms with strict latency requirements. We present EdgeBERT, an in-depth algorithm-hardware c

Biswaroop Mukherjee, Buddhapriya Chakrabarti

We extend the Cahn-Landau-de Gennes mean field theory of binary mixtures to understand the wetting thermodynamics of a three phase system, that is in contact with an external surface which prefers one of the phases. We model the system using a phenomenological free energy having three minima corresponding to low, intermediate and high density phases. By syst

Jedsada Lertthanasarn, Chen Liu, Minh-Son Pham

The mimicry of crystalline microstructure at meso-scale creates a new class of architected materials, termed meta-crystals, and offers effective ways to significantly improve the toughness and eliminate the post-yield collapse of architected materials. This study investigated the mechanical behaviour of polygrain-like meta-crystals fabricated from Ti6Al4V by

Sandesh Ramesh, Manoj Kumar M, Sanjay H A

It is human nature to give prime importance to facial appearances. Often, to look good is to feel good. Also, facial features are unique to every individual on this planet, which means it is a source of vital information. This work proposes a framework named E-Pro for the detection and recognition of faces by taking facial images as inputs. E-Pro has its pot

Soroush Haseli, Hazhir Dolatkhah, Shahriar Salimi

Quantum theory sets a limit on the minimum time required to transform from an initial state to a target state. It is known as quantum speed limit time. quantum speed limit time can be used to determine the rate of quantum evolution for closed and open quantum systems. Given that in the real world we are dealing with open quantum systems, the study of quantum

Hadis Anahideh, Lulu Kang, Nazanin Nezami

We aim to design a fairness-aware allocation approach to maximize the geographical diversity and avoid unfairness in the sense of demographic disparity. During the development of this work, the COVID-19 pandemic is still spreading in the U.S. and other parts of the world on large scale. Many poor communities and minority groups are much more vulnerable than

Helin Yang, Jun Zhao, Zehui Xiong, Kwok-Yan Lam

Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, artificial intelligence (AI) model training, and wireless communications. However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers f