November 2020 arXiv papers — page 7

J. Casal, L. Fortunato, E. G. Lanza, A. Vitturi

The molecular algebraic model based on three and four alpha clusters is used to describe the inelastic scattering of alpha particles populating low-lying states in $^{12}$C and $^{16}$O. Optical potentials and inelastic formfactors are obtained by folding densities and transition densities obtained within the molecular model. One-step and multi-step processe

Huijun Fan, Tian Lan, Zongrui Yang

The concept of $tt^*$ geometric structure was introduced by physicists (see \cite{CV1, BCOV} and references therein) , and then studied firstly in mathematics by C. Hertling \cite{Het1}. It is believed that the $tt^*$ geometric structure contains the whole genus $0$ information of a two dimensional topological field theory. In this paper, we propose the LG/C

Filomena Catapano, Alessandro Retino, Gaetano Zimbardo, Alexandra Alexandrova

Processes driven by unsteady reconnection can efficiently accelerate particles in many astrophysical plasmas. An example are the reconnection jet fronts in an outflow region. We present evidence of suprathermal ion acceleration between two consecutive reconnection jet fronts observed by the Magnetospheric Multiscale mission in the terrestrial magnetotail. An

N. I. Chashchin

By the example of the Hubbard model we analytically and numerically examine the formating and coexisting of localized electron--electron pairs (doublons) and localized electron--hole pairs (Frenkel--type excitons) . Here we demonstrate that at a variation of the on-site Coulomb repulsion U there occurs a quantum transition from the doublon to the exciton reg

Yitong Wu, Liangliang Ji, Ruxin Li

The upper limit of the laser field strength in perfect vacuum is usually considered as the Schwinger field, corresponding to ~10^29W/cm^2. We investigate such limitations under realistic non-ideal vacuum conditions and find out that intensity suppression appears starting from 10^25W/cm^2, showing an upper threshold at 1026W/cm^2 level if the residual electro

Peter Caradonna

The theoretical cross section for Compton scattering of maximally entangled Bell photons has yet to be rigorously confirmed by experiments. Test cases of Bell states for use in Compton scattering experiments can now be expanded given reports of creating all 4 Bell states in the hard X-ray regime by the process of spontaneous parametric down-conversion. We ou

Haiwen Huang, Zhihan Li, Lulu Wang, Sishuo Chen

Out-of-Distribution (OoD) detection is important for building safe artificial intelligence systems. However, current OoD detection methods still cannot meet the performance requirements for practical deployment. In this paper, we propose a simple yet effective algorithm based on a novel observation: in a trained neural network, OoD samples with bounded norms

Bernard Montaron

Infinite exponential sequences of distinct prime numbers of the form $\lfloor a c^{n^d}+b\rfloor$, $n\geq 0$, are proved to exist for well chosen real constants $a>0$, $b$, $c>1$, $d>1$, assuming Cramer's conjecture on prime gaps. There is an infinity of such prime sequences. Sequences having the least possible growth rate are of particular interest. This wo

Malte Heuer, Madeleine Jotz Lean

This paper studies linear generalised complex structures over vector bundles, as a generalised geometry version of holomorphic vector bundles. In an adapted linear splitting, a linear generalised complex structure on a vector bundle $E\to M$ is equivalent to a $\mathbb C$-multiplication $j$ in the fibers of $TM\oplus E^*$ and $\mathbb C$-Lie algebroid struct

Samuel Yen-Chi Chen, Chih-Min Huang, Chia-Wei Hsing, Ying-Jer Kao

One key step in performing quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices is the dimension reduction of the input data prior to their encoding. Traditional principle component analysis (PCA) and neural networks have been used to perform this task; however, the classical and quantum layers are usually trained separately. A f

Maximilian Aigner, Valérie Chavez-Demoulin

We give a construction of the Hawkes process as a piecewise competing risks model. We argue that the most natural interpretation of the self-excitation kernel is the hazard function of a defective random variable. This establishes a link between desired qualitative features of the process and a parametric form for the kernel, which we illustrate using exampl

J. Baumeister, A. Leitao

We study a model for the exploitation of renewable stocks developed in Clark et al. (Econometrica 47 (1979), 25-47). In this particular control problem, the control law contains a measurable and an impulsive control component. We formulate Pontryagin's maximum principle for this kind of control problems, proving first order necessary conditions of optimality

Davide Frapporti

The aim of this paper is to determine minimal models of the semi-isogenous mixed surfaces with $\chi=1$ and $K^2>0$ constructed by Cancian and Frapporti. In order to do this, we further develop the idea of orbit divisors introduced by Frapporti and Lee, to construct effective divisors on surfaces isogenous to a product of mixed type, extending it to the semi

Janamejaya Channegowda, Najath Abdul Azeez, Sheldon S. Williamson

Electric Vehicle (EV) chargers are the need of the hour as EVs have invaded the commercial automobile market. The Three-Phase Three-Switch (TPTS) converter has features which make it ideal to act as the charging station because of its high efficiency at peak power rating. Traditionally, control of the TPTS converter has been achieved by the Space Vector (SV)

Shai M. Chester, Walter Landry, Junyu Liu, David Poland

We study the critical $O(3)$ model using the numerical conformal bootstrap. In particular, we use a recently developed cutting-surface algorithm to efficiently map out the allowed space of CFT data from correlators involving the leading $O(3)$ singlet $s$, vector $\phi$, and rank-2 symmetric tensor $t$. We determine their scaling dimensions to be $(\Delta_{s

Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé

Modern machine learning and computer science conferences are experiencing a surge in the number of submissions that challenges the quality of peer review as the number of competent reviewers is growing at a much slower rate. To curb this trend and reduce the burden on reviewers, several conferences have started encouraging or even requiring authors to declar

Deepak Maurya, Arun K. Tangirala, Shankar Narasimhan

Identification of autoregressive models with exogenous input (ARX) is a classical problem in system identification. This article considers the errors-in-variables (EIV) ARX model identification problem, where input measurements are also corrupted with noise. The recently proposed DIPCA technique solves the EIV identification problem but is only applicable to

S. Herath, H. K. Weerasooriya, D. Y. L. Ranasinghe, W. G. C. Bandara

Coconut oil known for its wide range of uses is often adulterated with other edible oils. Repeated use of coconut oil in food preparation could lead to many health issues. Existing methods available for evaluating quality of oil are laborious and time consuming. Therefore, we propose an imaging system hardware and image processing-based algorithm to estimate

Michael C. Mackey, Marta Tyran-Kamińska

Although the theory of density evolution in maps and ordinary differential equations is well developed, the situation is far from satisfactory in continuous time systems with delay. This paper reviews some of the work that has been done numerically and the interesting dynamics that have emerged, and the largely unsuccessful attempts that have been made to an

Ti-Wei Xue, Zeng-Yuan Guo

Equation of state (EOS) describes the thermodynamic properties of substances. It has important applications in many fields such as power mechanics, geophysics, astrophysics, and detonation physics. Currently, most EOSs have been constructed using the ideal gas EOS as the base framework. However, this is inappropriate for the substances with high pressures or

Xiaochen Zhao, Zerong Zheng, Chaonan Ji, Zhenyi Liu

We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments. To fully utilize the template prior of vehicles, we propose a novel geometry and texture joint representation, based on implicit semantic template mapping. Compared to existing representations w

Caspar Hopfmann, Nand Lal Sharma, Weijie Nie, Robert Keil

We present a first comprehensive study on deterministic spin preparation employing excited state resonances of droplet etched GaAs quantum dots. This achievement facilitates future investigations of spin qubit based quantum memories using the GaAs quantum dot material platform. By observation of excitation spectra for a range of fundamental excitonic transit

Yoshihisa Kitazawa

The scale invariance of the universe is slightly broken by slow roll parameters. It is likely the slow roll is dual to the random walk. We investigate the distribution function of the conformal zeromode. We identify de Sitter entropy $S_{dS}$ with the distribution entropy of the conformal zeromode $\rho(\omega)$. We have collected convincing support on our p

Patrick Fuchs, Rob Remis

In this paper we present full-wave signal models for magnetic and electric field measurements in magnetic resonance imaging (MRI). Our analysis is based on a scattering formalism in which the presence of an object or body is taken into account via an electric scattering source. We show that these signal models can be evaluated, provided the Green's tensors o

Xu Chen, Yuanxing Zhang, Lun Du, Zheng Fang

Traffic flow forecasting is of great significance for improving the efficiency of transportation systems and preventing emergencies. Due to the highly non-linearity and intricate evolutionary patterns of short-term and long-term traffic flow, existing methods often fail to take full advantage of spatial-temporal information, especially the various temporal p

Ryuichiro Kitano, Ryutaro Matsudo

A domain-wall configuration of the $\eta'$ meson bounded by a string (called a pancake or a Hall droplet) is recently proposed to describe the baryons with spin $N_c/2$. In order to understand its baryon number as well as the flavor quantum number, we argue that the vector mesons (the $\rho$ and $\omega$ mesons) should play an essential role for the consiste

Run-Qiu Yang, Yu-Sen An, Chao Niu, Cheng-Yong Zhang

It is assumed that the holographic complexities such as the complexity-action (CA) and the complexity-volume (CV) conjecture are dual to complexity in field theory. However, because the definition of the complexity in field theory is still not complete, the confirmation of the holographic duality of the complexity is ambiguous. To improve this situation, we

J. Mornhinweg, M. Halbhuber, C. Ciuti, D. Bougeard

We explore the nonlinear response of tailor-cut light-matter hybrid states in a novel regime, where both the Rabi frequency induced by a coherent driving field and the vacuum Rabi frequency set by a cavity field are comparable to the carrier frequency of light. In this previously unexplored strong-field limit of ultrastrong coupling, subcycle pump-probe and

Ting Hsu, Kuan-Ting Lin, Guin-Dar Lin

Abstract We investigate the spectral shift in collective forward scattering for a cold dense atomic cloud. The shift, sometimes called collective Lamb shift, results from resonant dipole-dipole interaction mediated by real and virtual photon exchange, forming many-body states displaying various super- and subradiant spectral behavior. The scattering spectrum

Olivier Minazzoli

It is argued that de Sitter space-times might be solutions of entangled relativity once the quantum trace anomaly from matter fields in curved space-times is taken into account. This hypothesis would be an elegant solution to the acceleration of the expansion of the universe within the rigid framework of entangled relativity.

N. Mazyavkina, S. Moustafa, I. Trofimov, E. Burnaev

Reinforcement learning (RL) enjoyed significant progress over the last years. One of the most important steps forward was the wide application of neural networks. However, architectures of these neural networks are typically constructed manually. In this work, we study recently proposed neural architecture search (NAS) methods for optimizing the architecture

Yuemei Zhou, Gaochang Wu, Ying Fu, Kun Li

Various combinations of cameras enrich computational photography, among which reference-based superresolution (RefSR) plays a critical role in multiscale imaging systems. However, existing RefSR approaches fail to accomplish high-fidelity super-resolution under a large resolution gap, e.g., 8x upscaling, due to the lower consideration of the underlying scene

Shouhei Honda, Luciano Mari, Michele Rimoldi, Giona Veronelli

We investigate the density of compactly supported smooth functions in the Sobolev space $W^{k,p}$ on complete Riemannian manifolds. In the first part of the paper, we extend to the full range $p\in [1,2]$ the most general results known in the Hilbertian case. In particular, we obtain the density under a quadratic Ricci lower bound (when $k=2$) or a suitably

Denis Arruga, Olivier Rousselle, Olivier Minazzoli

We describe the first numerical Tolman-Oppenheimer-Volkoff solutions of compact objects in entangled relativity, which is an alternative to the framework of general relativity that does not have any additional free parameter. Assuming a simple polytropic equation of state and the conservation of the rest-mass density, we notably show that, for any given dens

Francesca Aicardi

The Catalan triangle, as well as a Fuss-Catalan triangle, enter a problem of counting particular tied arc diagrams. This setting allows us to prove some combinatorial properties of these triangles.

Qianqian Zhang, Ruizhi Sun

In Synthetic Aperture Radar (SAR) imaging, despeckling is very important for image analysis,whereas speckle is known as a kind of multiplicative noise caused by the coherent imaging system. During the past three decades, various algorithms have been proposed to denoise the SAR image. Generally, the BM3D is considered as the state of art technique to despeckl

Markus Hegland, Frank deHoog

Positive semi-definite matrices commonly occur as normal matrices of least squares problems in statistics or as kernel matrices in machine learning and approximation theory. They are typically large and dense. Thus algorithms to solve systems with such a matrix can be very costly. A core idea to reduce computational complexity is to approximate the matrix by

Asher Spector, Lucas Janson

Model-X knockoffs allows analysts to perform feature selection using almost any machine learning algorithm while still provably controlling the expected proportion of false discoveries. To apply model-X knockoffs, one must construct synthetic variables, called knockoffs, which effectively act as controls during feature selection. The gold standard for constr

Khashayar Teimoori, Ferri Hassani

Microwaves have been used for a variety of applications in the past two decades. However, there has been a significant and growing interest in the applications of microwaves in hard rock breakage and mineral processing industries. The purpose of this review paper is to focus on these applications and to present a careful review of the state-of-the-art experi

Andrzej Jaszkiewicz

The outbreak of the global COVID-19 pandemic results in unprecedented demand for fast and efficient testing of large numbers of patients for the presence of SARS-CoV-2 coronavirus. Beside technical improvements of the cost and speed of individual tests, pool testing may be used to improve efficiency and throughput of a population test. Dorfman pool testing p

Xiao-Min Zhang, Ang Fu, Kai Li, Qian Li

The framework of a kind of noncanonical warm inflation is introduced, and the dynamical equations of this scenario are presented. We propose the slow roll approximations and give some redefining slow roll parameters in this scenario which remain dimensionless. Performing systemic stability analysis, we calculate the slow roll conditions to guarantee that slo

Heide Narnhofer

It is shown that under the assumption of the nuclearity condition for local regions the resulting Doplicher-Longo-algebra between two double cones which due to nuclearity is type I allows to estimate its entropy by nuclearity bounds.

J. Ostrowski, A. Baran, S. Sanjayan, S. K. Sahoo

Results of evolutionary modelling of subdwarf B stars are presented. For the first time, we explore the core and near-core mixing in the subdwarf B stars using new algorithms available in the MESA code: the predictive mixing scheme and the convective premixing scheme. We show how both methods handle the problems with determination of convective boundary, dis

Maciej Zięba, Marcin Przewięźlikowski, Marek Śmieja, Jacek Tabor

Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenarios, such as modeling behavior of humans. Existing approaches provide results under strong assumptions concerning unimodality of future states, or, at best, assuming specific proba

Zhaoqi Su, Tao Yu, Yangang Wang, Yebin Liu

Garment representation, editing and animation are challenging topics in the area of computer vision and graphics. It remains difficult for existing garment representations to achieve smooth and plausible transitions between different shapes and topologies. In this work, we introduce, DeepCloth, a unified framework for garment representation, reconstruction,

Heer Ambavi, Kavita Vaishnaw, Udit Vyas, Abhisht Tiwari

The entire world is engulfed in the fight against the COVID-19 pandemic, leading to a significant surge in research experiments, government policies, and social media discussions. A multi-modal information access and data visualization platform can play a critical role in supporting research aimed at understanding and developing preventive measures for the p

Jinyi Wang, Yican Sun, Hongfei Fu, Krishnendu Chatterjee

In this work, we consider the fundamental problem of deriving quantitative bounds on the probability that a given assertion is violated in a probabilistic program. We provide automated algorithms that obtain both lower and upper bounds on the assertion violation probability in exponential forms. The main novelty of our approach is that we prove new and dedic

André Greiner-Petter

In mathematics, LaTeX is the de facto standard to prepare documents, e.g., scientific publications. While some formulae are still developed using pen and paper, more complicated mathematical expressions used more and more often with computer algebra systems. Mathematical expressions are often manually transcribed to computer algebra systems. The goal of my d

Raghav Awasthi, Prachi Patel, Vineet Joshi, Shama Karkal

Female sex workers(FSWs) are one of the most vulnerable and stigmatized groups in society. As a result, they often suffer from a lack of quality access to care. Grassroot organizations engaged in improving health services are often faced with the challenge of improving the effectiveness of interventions due to complex influences. This work combines structure

Aleksandr Farseev, Qi Yang, Andrey Filchenkov, Kirill Lepikhin

In this technical demonstration, we showcase the World's first personality-driven marketing content generation platform, called SoMin.ai. The platform combines deep multi-view personality profiling framework and style generative adversarial networks facilitating the automatic creation of content that appeals to different human personality types. The platform

Guillaume Marrelec, Alain Giron, Laura Messio

We consider Gaussian graphical models associated with an equicorrelational and one-dimensional conditional independence graph. We show that pairwise correlation decays exponentially as a function of distance. We also provide a limit when the number of variables tend to infinity and quantify the difference between the finite and infinite cases.

Alexey Ananyevskiy

We show that for a reductive group $G$ over a field $k$ the $\mathbb{A}^1$-Euler characteristic of the variety of maximal tori in $G$ is an invertible element of the Grothendieck-Witt ring $\mathrm{GW}(k)$, settling the weak form of a conjecture by Fabien Morel. As an application we obtain a generalized splitting principle which allows one to reduce the stru

Mao Wang, Eric García-Hemme, Yonder Berencén, René Hübner

Si-based photodetectors satisfy the criteria of low-cost and environmental-friendly, and can enable the development of on-chip complementary metal-oxide-semiconductor (CMOS)-compatible photonic systems. However, extending their room-temperature photoresponse into the mid-wavelength infrared (MWIR) regime remains challenging due to the intrinsic bandgap of Si

Jinlong Fan, Jing Zhang, Dacheng Tao

Deep learning has demonstrated its power in image rectification by leveraging the representation capacity of deep neural networks via supervised training based on a large-scale synthetic dataset. However, the model may overfit the synthetic images and generalize not well on real-world fisheye images due to the limited universality of a specific distortion mo

Kanghong Shi, Ian R. Petersen, Igor G. Vladimirov

This paper provides a protocol to address the robust output feedback consensus problem for networked heterogeneous nonlinear negative-imaginary (NI) systems with free body dynamics. We extend the definition of nonlinear NI systems to allow for systems with free body motion. A new stability result is developed for the interconnection of a nonlinear NI system

Primoz Sparl

{\em Honeycomb toroidal graphs} are a family of cubic graphs determined by a set of three parameters, that have been studied over the last three decades both by mathematicians and computer scientists. They can all be embedded on a torus and coincide with the cubic Cayley graphs of generalized dihedral groups with respect to a set of three reflections. In a r

Chen Xu, Bojie Hu, Yufan Jiang, Kai Feng

Large amounts of data has made neural machine translation (NMT) a big success in recent years. But it is still a challenge if we train these models on small-scale corpora. In this case, the way of using data appears to be more important. Here, we investigate the effective use of training data for low-resource NMT. In particular, we propose a dynamic curricul

Domenico Colella

ALICE is the CERN LHC experiment optimised for the study of the strongly interacting matter produced in heavy-ion collisions and devoted to the characterisation of the quark-gluon plasma. To achieve the physics program for LHC Run 3, a major upgrade of the experimental apparatus is ongoing. A key element of the upgrade is the substitution of the Inner Tracki

Jae-Yeong Lee

In this paper, we present a novel zero-shot camera calibration method that estimates camera parameters with no calibration image. It is common sense that we need at least one or more pattern images for camera calibration. However, the proposed method estimates camera parameters from the horizontal and vertical field of view information of the camera without

Xing-Tang Dong, Yong-Xin Gao, Ze-Hua Zhou

The spectrum of a weighted composition operator $C_{\psi, \varphi}$ who is induced by an automorphism has been investigated for over fifty years. However, many results are got only under the condition that the weight function $\psi$ is continuous up to the boundary. In this paper we study the spectra and essential spectra of $C_{\psi,\varphi}$ on weighted Be

Evgeniy Lokharu

We prove the Benjamin and Lighthill conjecture for all two-dimensional steady water waves with an arbitrary vorticity distribution. We show that the flow force constant of an arbitrary smooth wave is bounded by the corresponding flow force constants for conjugate laminar flows. We prove these inequalities without any assumptions on the geometry of the surfac

Riley Thornton

We leverage a correspondence between group actions and edge-labelled graphs in two ways. First, we give a unified presentation of several folklore results connecting weak containment, local-global convergence, and continuous model theory. Second, we investigate the difference between $\operatorname{Aut}(\operatorname{Cay}(\Gamma))$-fiid combinatorics and $\G

Sandesh Ramesh, Manoj Kumar M, K Aditya Shastry

Detecting and recognizing faces accurately has always been a challenge. Differentiating facial features, training images, and producing quick results require a lot of computation. The REaL system we have proposed in this paper discusses its functioning and ways in which computations can be carried out in a short period. REaL experiments are carried out on li

Shuanping Du, Zhaofang Bai

Strictly incoherent operations (SIO) proposed in [Phys. Rev. Lett. 116, 120404 (2016)] are promising to be a good candidate of free operations in the resource theory of quantum coherence, setting against the central role of local operations and classical communication in the resource theory of quantum entanglement. An important open problem is an efficient d

Carlos Castaño-Bernard, Florian Luca

For each prime $p\equiv 1\pmod{4}$ consider the Legendre character $\chi=(\frac{\cdot}{p})$. Let $p_\pm(n)$ be the number of partitions of $n$ into parts $\lambda>0$ such that $\chi(\lambda)=\pm 1$. Petersson proved a beautiful limit formula for the ratio of $p_+(n)$ to $p_-(n)$ as $n\to\infty$ expressed in terms of important invariants of the real quadratic

Byoung-moo Ann, Wouter Kessels, Gary. A. Steele

Driving quantum systems periodically in time plays an essential role in the coherent control of quantum states. The rotating wave approximation (RWA) is a good approximation technique for weak and nearly-resonance driven fields. However, these experiments sometimes require large detuning and strong driving fields, for which the RWA may not hold. In this work

Jianyu Jiang, Claudio Soriente, Ghassan Karame

Existing tools to detect side-channel attacks on Intel SGX are grounded on the observation that attacks affect the performance of the victim application. As such, all detection tools monitor the potential victim and raise an alarm if the witnessed performance (in terms of runtime, enclave interruptions, cache misses, etc.) is out of the ordinary. In this pap

Nenad Savic, Nicolas Bovio, Fabian Gilbert, Irina Guseva Canu

Objective. Epidemiological studies require data that are in alignment with the classifications established for occupations or economic activities. The classifications usually include hundreds of codes and titles. Manual coding of raw data may result in misclassification and be time consuming. The goal was to develop and test a web-tool, named Procode, for co

Chen Zhao, Ali Thabet, Bernard Ghanem

Temporal action localization (TAL) in videos is a challenging task, especially due to the large variation in action temporal scales. Short actions usually occupy a major proportion in the datasets, but tend to have the lowest performance. In this paper, we confront the challenge of short actions and propose a multi-level cross-scale solution dubbed as video

Yanming Yang, Xin Xia, David Lo, John Grundy

In 2006, Geoffrey Hinton proposed the concept of training ''Deep Neural Networks (DNNs)'' and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly us

A. S. Khvorostukhin

Calculation of hadronization, decay or scattering processes at non-zero temperatures and densities within the Nambu-Jona-Lasinio-like models requires some techniques for computation of Feynmann diagrams. Decomposition of Feynman diagrams at the one loop level leads to the appearance of elementary integrals with one, two, three, and four fermion lines. For ex

Hitoshi Kitagawa, Kanji Nanjyo, Kyoko Kitamura

Photonic crystals are periodic structure of dielectric materials that can control light propagations in the media because of their photonic-dispersion led by the well-ordered lattice-points arrangements. We here study the behavior of light propagation in distorted-photonic crystals (D-PCs), which possess the gradual spatial distortion of lattice-points posit

Tianze Gao, Huihui Pan, Zidong Wang, Huijun Gao

Online multi-object tracking (MOT) is an active research topic in the domain of computer vision. Although many previously proposed algorithms have exhibited decent results, the issue of tracklet inactivation has not been sufficiently studied. Simple strategies such as using a fixed threshold on classification scores are adopted, yielding undesirable tracking

Ziyang Wu, Christina Baek, Chong You, Yi Ma

Current deep learning architectures suffer from catastrophic forgetting, a failure to retain knowledge of previously learned classes when incrementally trained on new classes. The fundamental roadblock faced by deep learning methods is that deep learning models are optimized as "black boxes," making it difficult to properly adjust the model parameters to pre

Lucan Yan, Tatsuya Fukuyama, Megumi Yamaoka, Yusuke T. Maeda

The bipolar organization of the microtubule-based mitotic spindle is essential for the faithful segregation of chromosomes in cell division. Despite our extensive knowledge of genes and proteins, the physical mechanism of how the ensemble of microtubules can assemble into a proper bipolar shape remains elusive. Here, we study the pathways of spindle self-org

Sudeshna Basu

In this work, we introduce the notion of Ball dentable property in Banach spaces. We study certain stability results for the $w^*$-Ball dentable property leading to a discussion on Ball dentability in the context of ideals of Banach spaces. We prove that the $w^*$-Ball-dentable property can be lifted from an $M$-ideal to the whole Banach Space. We also prove

Sudeshna Basu, Susmita Seal

The geometric notion of huskability initiated and developed in [B3], [BR] ,[EW], [GM] was subsequently extensively studied in the context of dentability and Radon Nikodym Property in [GGMS]. In this work, we introduce a new geometric property of Banach space, the Ball Huskable Property ($BHP$), namely, the unit ball has relatively weakly open subsets of arbi

Qi Huang, Chao-Wei Shen, Jia-Jun Wu

In this work, the triangle singularity mechanism is investigated in the $\psi(2S) \to p \bar{p} \eta / p \bar{p} \pi^0$ process. The triangle loop composed by $J/\psi$, $\eta$ and $p$ has a singularity in the physical kinematic range for the $\psi(2S) \to p \bar{p} \eta / p \bar{p} \pi^0$ process, and it would generate a very narrow peak in the invariant mas

Tianze Gao, Huihui Pan, Huijun Gao

Monocular 3D object detection, with the aim of predicting the geometric properties of on-road objects, is a promising research topic for the intelligent perception systems of autonomous driving. Most state-of-the-art methods follow a keypoint-based paradigm, where the keypoints of objects are predicted and employed as the basis for regressing the other geome

Klaus Zollner, Aron W. Cummings, Stephan Roche, Jaroslav Fabian

We investigate the electronic structure of graphene on a series of 2D hexagonal nitride insulators hXN, X = B, Al, and Ga, with DFT calculations. A symmetry-based model Hamiltonian is employed to extract orbital parameters and spin-orbit coupling (SOC) from the low-energy Dirac bands of proximitized graphene. While commensurate hBN induces a staggered potent

Binjie Li

A discretization of an optimal control problem of a stochastic parabolic equation driven by multiplicative noise is analyzed. The state equation is discretized by the continuous piecewise linear element method in space and by the backward Euler scheme in time. The convergence rate $ O(\tau^{1/2} + h^2) $ is rigorously derived.

Stone Yun, Alexander Wong

Depth factorization and quantization have emerged as two of the principal strategies for designing efficient deep convolutional neural network (CNN) architectures tailored for low-power inference on the edge. However, there is still little detailed understanding of how different depth factorization choices affect the final, trained distributions of each laye

Jaehui Hwang, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee

The video-based action recognition task has been extensively studied in recent years. In this paper, we study the structural vulnerability of deep learning-based action recognition models against the adversarial attack using the one frame attack that adds an inconspicuous perturbation to only a single frame of a given video clip. Our analysis shows that the

Hsin-Pai Cheng, Feng Liang, Meng Li, Bowen Cheng

Scale variance among different sizes of body parts and objects is a challenging problem for visual recognition tasks. Existing works usually design dedicated backbone or apply Neural architecture Search(NAS) for each task to tackle this challenge. However, existing works impose significant limitations on the design or search space. To solve these problems, w

Wen Wei Ho, Wojciech De Roeck

Prethermalization refers to the physical phenomenon where a system evolves toward some long-lived non-equilibrium steady state before eventual thermalization sets in. One general scenario where this occurs is in driven systems with dynamics governed by an effective Hamiltonian (in some rotating frame), such that ergodicity of the latter is responsible for th

Sung Hyuck Hong, Sucheol Kim, Junil Choi, Wan Choi

Most of the prior works on designing codebooks for limited feedback systems have not considered the presence of strong line-of-sight (LOS) channel component. This paper proposes the design of polar-cap codebook (PCC) for multipleinput single-output (MISO) limited feedback systems subject to Rician fading channels. The codewords of the designed PCC are adapti

Anthony Suen

We provide a Serrin type blow-up criterion for the 3-D viscous compressible flows with large external potential force. For the Cauchy problem of the 3-D compressible Navier-Stokes system with potential force term, it can be proved that the strong solution exists globally if the velocity satisfies the Serrin's condition and the sup-norm of the density is boun

Denis Michel

Different rate theories yielded similar forms of rates constants consistent with the phenomenological Arrhenius law, although they were derived from various branches of physics including classical thermodynamics, statistical and quantum mechanics. This convergence supports the validity of the Arrhenius law but also suggests the existence of an even simpler u

Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen

In this work, we study the trade-off between differential privacy and adversarial robustness under L2-perturbations in the context of learning halfspaces. We prove nearly tight bounds on the sample complexity of robust private learning of halfspaces for a large regime of parameters. A highlight of our results is that robust and private learning is harder tha

Zhijian Qiao, Huanshu Wei, Zhe Liu, Chuanzhe Suo

3D Point cloud registration is still a very challenging topic due to the difficulty in finding the rigid transformation between two point clouds with partial correspondences, and it's even harder in the absence of any initial estimation information. In this paper, we present an end-to-end deep-learning based approach to resolve the point cloud registration p

Stone Yun, Alexander Wong

With the proliferation of deep convolutional neural network (CNN) algorithms for mobile processing, limited precision quantization has become an essential tool for CNN efficiency. Consequently, various works have sought to design fixed precision quantization algorithms and quantization-focused optimization techniques that minimize quantization induced perfor

Jaeho Shin

We give a criterion for modular extension of rank-4 hypermodular matroids, and prove a weakening of Kantor's conjecture for rank-4 realizable matroids. This proves the sticky matroid conjecture and Kantor's conjecture for realizable matroids due to an argument of Bachem, Kern, and Bonin, and due to an equivalence argument of Hochstattler and Wilhelmi, respec

Shunya Konno, Atsushi Sakaguchi, Warit Asavanant, Hisashi Ogawa

Quantum non-Gaussian gate is a missing piece to the realization of continuous-variable universal quantum operations in the optical system. In a measurement-based implementation of the cubic phase gate, a lowest-order non-Gaussian gate, non-Gaussian ancillary states that has a property we call nonlinear squeezing are required. This property, however, has neve

Zelin Wan, Yash Mahajan, Beom Woo Kang, Terrence J. Moore

Centrality metrics have been used in various networks, such as communication, social, biological, geographic, or contact networks. In particular, they have been used in order to study and analyze targeted attack behaviors and investigated their effect on network resilience. Although a rich volume of centrality metrics has been developed for decades, a limite

Yufei Xu, Jing Zhang, Stephen J. Maybank, Dacheng Tao

Previous deep learning-based video stabilizers require a large scale of paired unstable and stable videos for training, which are difficult to collect. Traditional trajectory-based stabilizers, on the other hand, divide the task into several sub-tasks and tackle them subsequently, which are fragile in textureless and occluded regions regarding the usage of h

Sudarshan Mukherjee, Ribhu Chopra

In cellular communication systems, it is conventional to assume the absence of a line of sight (LoS) path between the users and their associated access points (APs). This assumption however becomes questionable in the context of recent developments in the direction of cell free (CF) massive MIMO systems. In the CF massive MIMO, the AP density is assumed to b

Farhad Farokhi

We use gradient sparsification to reduce the adverse effect of differential privacy noise on performance of private machine learning models. To this aim, we employ compressed sensing and additive Laplace noise to evaluate differentially-private gradients. Noisy privacy-preserving gradients are used to perform stochastic gradient descent for training machine

Chao Chen, Di Zhao, Wei Chen, Sei Zhen Khong

In this paper, we propose a definition of phase for a class of stable nonlinear systems called semi-sectorial systems, from an input-output perspective. The definition involves the Hilbert transform as a critical instrument to complexify real-valued signals since the notion of phase arises most naturally in the complex domain. The proposed nonlinear system p

Kookyoung Han, Jin Hyuk Choi

To analyze strategic interactions arising in the cyber-security context, we develop a new reputation game model in which an attacker can pretend to be a normal user and a defender may have to announce attack detection at a certain point of time without knowing whether he has been attacked. We show the existence and uniqueness of sequential equilibrium in Mar

Yang Cai, Grigoris Velegkas

We investigate the algorithmic problem of selling information to agents who face a decision-making problem under uncertainty. We adopt the model recently proposed by Bergemann et al. [BBS18], in which information is revealed through signaling schemes called experiments. In the single-agent setting, any mechanism can be represented as a menu of experiments. O