December 2020 arXiv papers — page 87

Chao Zeng, Sam Kwong

The image captioning task is about to generate suitable descriptions from images. For this task there can be several challenges such as accuracy, fluency and diversity. However there are few metrics that can cover all these properties while evaluating results of captioning models.In this paper we first conduct a comprehensive investigation on contemporary me

Martin Charachon, Céline Hudelot, Paul-Henry Cournède, Camille Ruppli

Explaining decisions of black-box classifiers is paramount in sensitive domains such as medical imaging since clinicians confidence is necessary for adoption. Various explanation approaches have been proposed, among which perturbation based approaches are very promising. Within this class of methods, we leverage a learning framework to produce our visual exp

Yuma Koizumi, Yasunori Ohishi, Daisuke Niizumi, Daiki Takeuchi

The goal of audio captioning is to translate input audio into its description using natural language. One of the problems in audio captioning is the lack of training data due to the difficulty in collecting audio-caption pairs by crawling the web. In this study, to overcome this problem, we propose to use a pre-trained large-scale language model. Since an au

Yaru Niu, Yijun Gu

Humans can leverage hierarchical structures to split a task into sub-tasks and solve problems efficiently. Both imitation and reinforcement learning or a combination of them with hierarchical structures have been proven to be an efficient way for robots to learn complex tasks with sparse rewards. However, in the previous work of hierarchical imitation and re

Yakefu Reyimuaji, Xinyi Zhang

We consider natural inflation in a warm inflation framework with a temperature-dependent dissipative coefficient $\Gamma \propto T^3$. Natural inflation can be compatible with the Planck 2018 results with such warm assistance. With no a priori assumptions on the dissipative effect's magnitude, we find that the Planck results prefer a weak dissipative regime

Uwe Semmelmann, Gregor Weingart

In this article we study the stability problem for the Einstein-Hilbert functional on compact symmetric spaces following and completing the seminal work of Koiso on the subject. We classify in detail the irreducible representations of simple Lie algebras with Casimir eigenvalue less than the Casimir eigenvalue of the adjoint representation, and use this info

Jan Dudák, Benjamin Vejnar

We are dealing with the complexity of the homeomorphism equivalence relation on some classes of metrizable compacta from the viewpoint of invariant descriptive set theory. We prove that the homeomorphism equivalence relation of absolute retracts in the plane is Borel bireducible with the isomorphism equivalence relation of countable graphs. In order to stres

Carter Turnbaugh, Jeremy J. Axelrod, Sara L. Campbell, Jeske Y. Dioquino

Transmission electron microscopy (TEM) of vitrified biological macromolecules (cryo-EM) is limited by the weak phase contrast signal that is available from such samples. Using a phase plate would thus substantially improve the signal-to-noise ratio. We have previously demonstrated the use of a high-power Fabry-Perot cavity as a phase plate for TEM. We now re

Ning Jiang, Yi-Long Luo, Yangjun Ma, Shaojun Tang

For the inertial Qian-Sheng model of nematic liquid crystals in the $Q$-tensor framework, we illustrate the roles played by the entropy inequality and energy dissipation in the well-posedness of smooth solutions when we employ energy method. We first derive the coefficients requirements from the entropy inequality, and point out the entropy inequality is ins

András Frank, Kazuo Murota

Integer-valued elements of an integral submodular flow polyhedron $Q$ are investigated which are decreasingly minimal (dec-min) in the sense that their largest component is as small as possible, within this, the second largest component is as small as possible, and so on. As a main result, we prove that the set of dec-min integral elements of $Q$ is the set

Teppei Takamatsu, Shou Yoshikawa

In this paper, we study the minimal model theory for threefolds in mixed characteristic. As a generalization of a result of Kawamata, we show that the MMP holds for strictly semi-stable schemes over an excellent Dedekind scheme $V$ of relative dimension two without any assumption on the residue characteristics of $V$. We also prove that we can run a $(K_{X/V

Shin Hattori

Let $k\geq 2$ and $n\geq 1$ be any integers. In this paper, we prove that all Hecke operators act trivially on the space of ordinary Drinfeld cuspforms of level $\Gamma_1(t^n)$ and weight $k$.

Patrick Chi-Kit Cheong, Alan Tsz-Lok Lam, Harry Ho-Yin Ng, Tjonnie Guang Feng Li

We present an update of the General-relativistic multigrid numerical (Gmunu) code, a parallelized, multi-dimensional curvilinear, general relativistic magnetohydrodynamics code with an efficient non-linear cell-centred multigrid (CCMG) elliptic solver, which is fully coupled with an efficient block-based adaptive mesh refinement modules. Currently, Gmunu is

Shun Shimomura

For the fifth Painlev\'e transcendents an asymptotic representation by the Jacobi $\mathrm{sn}$-function is presented in cheese-like strips along generic directions near the point at infinity. Its elliptic main part may be understood to depend on the phase shift as a single integration constant, which is parametrised by monodromy data for the associated isom

Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa, Alan Fern

We consider the problem of optimizing expensive black-box functions over discrete spaces (e.g., sets, sequences, graphs). The key challenge is to select a sequence of combinatorial structures to evaluate, in order to identify high-performing structures as quickly as possible. Our main contribution is to introduce and evaluate a new learning-to-search framewo

Hisao Ishibuchi, Lie Meng Pang, Ke Shang

It is assumed in the evolutionary multi-objective optimization (EMO) community that a final solution is selected by a decision maker from a non-dominated solution set obtained by an EMO algorithm. The number of solutions to be presented to the decision maker can be totally different. In some cases, the decision maker may want to examine only a few representa

Ranjan Kumar, Ananta C. Pradhan, Mudumba Parthasarathy, Devendra K. Ojha

We present far ultraviolet (FUV) observations of globular cluster NGC 4147 using three FUV filters, BaF2 (F154W), Sapphire (F169M), and Silica (F172M) of Ultra-Violet Imaging Telescope (UVIT) on-board the \mbox{{\em AstroSat}} satellite. We confirmed the cluster membership of the UVIT observed sources using proper motions from Gaia data release 2 (GAIA DR2).

Terry Farrelly, Robert J. Harris, Nathan A. McMahon, Thomas M. Stace

We consider tensor-network stabilizer codes and show that their tensor-network decoder has the property that independent logical qubits can be decoded in parallel. As long as the error rate is below threshold, we show that this parallel decoder is essentially optimal. As an application, we verify this for the max-rate holographic Steane (heptagon) code. For

Ali Süleyman Üstünel

Let $(W,H,\mu)$ be the classical Wiener space on $\R^d$. Assume that $X=(X_t(x))$ is a diffusion process satisfying the stochastic differential equation with diffusion and drift coefficients $\sigma: \R^n\to \R^n\otimes \R^d$, $b: \R^n\to \R^n$, $B$ is an $\R^d$-valued Brownian motion. We suppose that $b$ and $\sigma$ are Lipschitz. Let $P(x)$ be the orthogo

Silas Nyboe Ørting, Hans Jacob Teglbjærg Stephensen, Jon Sporring

The categorical distribution is a natural representation of uncertainty in multi-class segmentations. In the two-class case the categorical distribution reduces to the Bernoulli distribution, for which grayscale morphology provides a range of useful operations. In the general case, applying morphological operations on uncertain multi-class segmentations is n

Vladislav Kozhevnikov, Andrey Raigorodskii, Maksim Zhukovskii

This paper studies thresholds in random generalized Johnson graphs for containing large cycles, i.e. cycles of variable length growing with the size of the graph. Thresholds are obtained for different growth rates.

Joao Marcos Vensi Basso, Loring W. Tu

A tensor in applied mathematics is usually defined as a multidimensional array of numbers. This presumes a choice of basis in $\mathbb{R}^n$ or in some other vector space, and tensorial concepts are defined accordingly. In this article we define eigenvalues, eigenvectors, singular values, and singular vectors of a tensor intrinsically, without reference to a

Guillaume Thiran, Ivan Stupia, Luc Vandendorpe

This paper considers the competitive resource allocation problem in Multiple-Input Multiple-Output (MIMO) interfering channels, when users maximize their energy efficiency. Considering each transmitter-receiver pair as a selfish player, conditions on the existence and uniqueness of the Nash equilibrium of the underlying noncooperative game are obtained. A de

Yicheng Zou, Lujun Zhao, Yangyang Kang, Jun Lin

In a customer service system, dialogue summarization can boost service efficiency by automatically creating summaries for long spoken dialogues in which customers and agents try to address issues about specific topics. In this work, we focus on topic-oriented dialogue summarization, which generates highly abstractive summaries that preserve the main ideas fr

Makoto Ozawa

We show that any two bridge positions of a handlebody-knot are stably equivalent.

Ruihong Huang, Jianmin Wang

Event data are prevalent in diverse domains such as financial trading, business workflows and industrial IoT nowadays. An event is often characterized by several attributes denoting the meaning associated with the corresponding occurrence time/duration. From traditional operational systems in enterprises to online systems for Web services, event data is gene

Xin Tong

In this paper we study the corresponding categories and the corresponding cohomologies of the Hodge-Iwasawa modules we developed in our series papers on Hodge-Iwasawa theory. The corresponding cohomologies will be essential in the corresponding development of the contact with the corresponding Iwasawa theoretic consideration, while they are as well very cruc

Quanjiang Yu, Ola Carlson, Serik Sagitov

Wind power is one of the most important sources of renewable energy. A large part of the wind energy cost is due to the cost of maintaining the wind power equipment. To further reduce the maintenance cost, one can improve the design of the wind turbine components. One can also reduce the maintenance costs by optimal scheduling of the component replacements.

Hilla De-Leon, Francesco Pederiva

By the end of 2020, a year since the first cases of infection by the Covid-19 virus have been reported, there is a light at the end of the tunnel. Several pharmaceutical companies made significant progress in developing effective vaccines against the Covid-19 virus that has claimed the lives of more than a million people over the world. On the other hand, th

A. Turchi, G. Agapito, E. Masciadri, O. Beltramo-Martin

The optimization and scheduling of scientific observations done with instrumentation supported by adaptive optics could greatly benefit from the forecast of PSF figures of merit (FWHM, Strehl Ratio, Encircle Energy and contrast), that depend on the AO instrument, the scientific target and turbulence conditions during the observing night. In this contribution

Neeraj Kumar, Srishti Goel, Ankur Narang, Brejesh Lall

Speech-driven facial video generation has been a complex problem due to its multi-modal aspects namely audio and video domain. The audio comprises lots of underlying features such as expression, pitch, loudness, prosody(speaking style) and facial video has lots of variability in terms of head movement, eye blinks, lip synchronization and movements of various

Fen Lyu, Yan-Zhi Meng, Zhen-Fan Tang, Ye Li

There are some similarities between bursts of repeating fast radio bursts (FRBs) and giant pulses (GPs) of pulsars. To explore possible relations between them, we study the cumulative energy distributions of these two phenomena using the observations of repeating FRB 121102 and the GPs of Crab pulsar. We find that the power-law slope of GPs (with fluence $\g

Alexander Grigor'yan, Yong Lin, Shing-Tung Yau

We define the notions of Reidemeister torsion and analytic torsion for directed graphs by means of the path homology theory introduced by the authors in \cite{Grigoryan-Lin-Muranov-Yau2013, Grigoryan-Lin-Muranov-Yau2014, Grigoryan-Lin-Muranov-Yau2015, Grigoryan-Lin-Muranov-Yau2020}. We prove the identity of the two notions of torsions as well as obtain formu

Yuanqi Liu, Ran Wang, Emmanuel Momjian, Eduardo Banados

We carry out a series of deep Karl G. Jansky Very Large Array (VLA) S-band observations of a sample of 21 quasars at $z\sim6$. The new observations expand the searches of radio continuum emission to the optically faint quasar population at the highest redshift with rest-frame $4400 \rm \AA$ luminosities down to $3 \times10^{11} \ L_{\odot}$. We report the de

Yicheng Zou, Jun Lin, Lujun Zhao, Yangyang Kang

Automatic chat summarization can help people quickly grasp important information from numerous chat messages. Unlike conventional documents, chat logs usually have fragmented and evolving topics. In addition, these logs contain a quantity of elliptical and interrogative sentences, which make the chat summarization highly context dependent. In this work, we p

S. Sofer, O. Sefi, A. G. A. Nisbet, S. Shwartz

We present measurements of the polarization dependencies of the x-ray signal photons generated by the effect of parametric down-conversion of x rays into ultraviolet radiation. The results exhibit pronounced discrepancies with the classical model for the nonlinearity but qualitatively agree with a newly developed quantum mechanical theory for the nonlinear i

Chi-Keung Ng

We introduce the notion of coarse metric. Every coarse metric induces a coarse structure on the underlying set. Conversely, we observe that all coarse spaces come from a particular type of coarse metric in a unique way. In the case when the coarse structure $\mathcal{E}$ on a set $X$ is defined by a coarse metric that takes values in a meet-complete totally

Jian Liang, Dapeng Hu, Yunbo Wang, Ran He

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but different well-labeled source domain to a new unlabeled target domain. Most existing UDA methods require access to the source data, and thus are not applicable when the data are confidential and not shareable due to privacy concerns. This paper aims to tackle a realistic setti

Ameneh Nejati, Sadegh Soudjani, Majid Zamani

In this work, we propose a compositional framework for the construction of control barrier functions for networks of continuous-time stochastic hybrid systems enforcing complex logic specifications expressed by finite-state automata. The proposed scheme is based on a notion of so-called pseudo-barrier functions computed for subsystems, by employing which one

A. Chavarría-Sibaja, S. Marín-Sosa, E. Bolaños-Jiménez, M. Hernández-Calderon

Relevant information on the origins of the solar system and the early evolution of life itself can be derive from systematic and controlled exploration of amorphous water ice here on Earth. Therefore, over the last decades, a huge effort on experimental methodologies has been made to study the multiple crystal ice phases, which are observed outside our home-

Harukuni Ikeda

We perform numerical simulations of the jamming transition of non-spherical particles in two dimensions. In particular, we systematically investigate how the physical quantities at the jamming transition point behave when the shapes of the particle deviate slightly from the perfect disks. For efficient numerical simulation, we first derive an analytical expr

Xuguang Wang, Daiyu Geng, Dayu Yan, Wenqi Hu

Two-dimensional topological insulators (2DTIs), which host the quantum spin Hall (QSH) effect, are one of the key materials in next-generation spintronic devices. To date, experimental evidence of the QSH effect has only been observed in a few materials, and thus, the search for new 2DTIs is at the forefront of physical and materials science. Here, we report

Boris Bolliet, Jens Chluba, Richard Battye

Spectral distortions (SDs) of the cosmic microwave background (CMB) provide a powerful tool for studying particle physics. Here we compute the distortion signals from decaying particles that convert directly into photons at different epochs during cosmic history, focusing on injection energies $E_\mathrm{inj}\lesssim 20\,\mathrm{keV}$. We deliver a comprehen

Yi Luo, Cong Han, Nima Mesgarani

Despite the recent progress on neural network architectures for speech separation, the balance between the model size, model complexity and model performance is still an important and challenging problem for the deployment of such models to low-resource platforms. In this paper, we propose two simple modules, group communication and context codec, that can b

Chaozheng Wu, Lin Sun, Xun Xu, Kui Jia

This paper is motivated from a fundamental curiosity on what defines a category of object shapes. For example, we may have the common knowledge that a plane has wings, and a chair has legs. Given the large shape variations among different instances of a same category, we are formally interested in developing a quantity defined for individual points on a cont

Xianming Li, Yongwei Huang, Wing-Kin Ma

Consider a robust multiple-input single-output downlink beamforming optimization problem in a frequency division duplexing system. The base station (BS) sends training signals to the users, and every user estimates the channel coefficients, quantizes the gain and the direction of the estimated channel and sends them back to the BS. Suppose that the channel s

Amit Sahu, Noelia Vállez, Rosana Rodríguez-Bobada, Mohamad Alhaddad

In this paper, we provide a guideline for using the Neural Network Dependability Kit (NNDK) during the development process of NN models, and show how the algorithm is applied in two image classification use cases. The case studies demonstrate the usage of the dependability kit to obtain insights about the NN model and how they informed the development proces

Haitian Zheng, Zhe Lin, Jingwan Lu, Scott Cohen

We tackle the problem of semantic image layout manipulation, which aims to manipulate an input image by editing its semantic label map. A core problem of this task is how to transfer visual details from the input images to the new semantic layout while making the resulting image visually realistic. Recent work on learning cross-domain correspondence has show

Pedro Savarese, Sunnie S. Y. Kim, Michael Maire, Greg Shakhnarovich

We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets. More specifically, we group image pixels into foreground and background, with the goal of minimizing predictability of one set from the other. An easily com

Jingjing Guo, Xianyong Bai, Yuanyong Deng, Hui Liu

The method of solar magnetic field calibration for the filter-based magnetograph is normally the linear calibration method under weak-field approximation that cannot generate the strong magnetic field region well due to the magnetic saturation effect. We try to provide a new method to carry out the nonlinear magnetic calibration with the help of neural netwo

Pawel Blasiak, Ewa Borsuk, Marcin Markiewicz

Reasoning about Bell nonlocality from the correlations observed in post-selected data is always a matter of concern. This is because conditioning on the outcomes is a source of non-causal correlations, known as a selection bias, rising doubts whether the conclusion concerns the actual causal process or maybe it is just an effect of processing the data. Yet,

Xiaoyu Nie, Xingchen Zhao, Tao Peng, Marlan O. Scully

We develop and experimentally demonstrate an imaging method based on the pink noise pattern in the computational ghost imaging (CGI) system, which has a strong ability to photograph moving objects. To examine its unique ability and scope of application, the object oscillates with variable amplitude in horizontal axis, and the result via commonly used white n

Sirisha Rambhatla, Sepanta Zeighami, Kameron Shahabi, Cyrus Shahabi

As countries look towards re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging. While contact tracing only aims to track past activities of infected users, one path to safe reopening is to develop reliable spatiotemporal risk scores to indicate the propensity of the disease. Existing works which

Yoshikazu Yamagishi

A general Archimedean spiral lattice is a Delone set in the relative distance if and only if its rotation angle is badly approximable.

Mariya Lizunova, Jasper Kager, Stan de Lange, Jasper van Wezel

The (1+1)-dimensional classical $\varphi^4$ theory contains stable, topological excitations in the form of solitary waves or kinks, as well as stable but non-topological solutions, such as the oscillon. Both are used in effective descriptions of excitations throughout myriad fields of physics. The oscillon is well-known to be a coherent, particle-like soluti

Seanie Lee, Dong Bok Lee, Sung Ju Hwang

Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine translation. However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being exposed to incorrectly generated tokens duri

Sohee Bae, Seungyul Han, Youngchul Sung

In this paper, a deep reinforcement learning (DRL)-based approach to the Lyapunov optimization is considered to minimize the time-average penalty while maintaining queue stability. A proper construction of state and action spaces is provided to form a proper Markov decision process (MDP) for the Lyapunov optimization. A condition for the reward function of r

Jean Feng

Machine learning algorithms in healthcare have the potential to continually learn from real-world data generated during healthcare delivery and adapt to dataset shifts. As such, the FDA is looking to design policies that can autonomously approve modifications to machine learning algorithms while maintaining or improving the safety and effectiveness of the de

Yifeng Zhu, Jonathan Tremblay, Stan Birchfield, Yuke Zhu

We present a visually grounded hierarchical planning algorithm for long-horizon manipulation tasks. Our algorithm offers a joint framework of neuro-symbolic task planning and low-level motion generation conditioned on the specified goal. At the core of our approach is a two-level scene graph representation, namely geometric scene graph and symbolic scene gra

Matthew Kennedy, Sven Raum, Guy Salomon

We show that the universal minimimal proximal flow and the universal minimal strongly proximal flow of a discrete group can be realized as the Stone spaces of translation invariant Boolean algebras of subsets of the group satisfying a higher order notion of syndeticity. We establish algebraic, combinatorial and topological dynamical characterizations of thes

Youngjin Kim

Price-based demand response (DR) of heating, ventilating, and air-conditioning (HVAC) systems is a challenging task, requiring comprehensive models to represent the building thermal dynamics and game theoretic interactions among participants. This paper proposes an online learning-based strategy for a distribution system operator (DSO) to determine optimal e

Jil Klünder, Dzejlana Karajic, Paolo Tell, Oliver Karras

Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. Every project is unique and, thus, many context factors have to be considered. Recent research took some initial steps towards statistically constructing hybrid development methods, yet, paid little attention to the peculiaritie

Young-Jin Kim

Recent advances in data sensing and processing technologies enable data-driven control of high-voltage direct-current (HVDC) systems for improving the operational stability of interfacing power grids. This paper proposes an optimal data-driven control strategy for an HVDC system with line-commutated converters (LCCs), wherein the dc-link voltage and current

Zachiri McKenzie

Hamkins and Kikuchi (2016 and 2017) show that in both set theory and class theory the definable subset ordering of the universe interprets a complete and decidable theory. If $\mathcal{M}$ is a model of set theory, then $\langle M, \subseteq^\mathcal{M} \rangle$ is an atomic unbounded relatively complemented distributive lattice. If $\mathcal{M}$ is model of

J. Shejeelammal, Aruna Goswami

The origin and evolution of neutron-capture elements in our Galaxy is poorly understood. In this work, we have attempted to understand the Galactic s-process nucleosynthesis using barium stars as probes. We have performed high resolution spectroscopic analysis for three barium stars HD 36650, HD 207585 and HD 219116. The analysis of HD 219116 is based on HCT

Seongyeon Kim, Youngwoo Koh, Ihyeok Seo

In this paper we obtain some new Strichartz estimates for the wave propagator $e^{it\sqrt{-\Delta}}$ in the context of Wiener amalgam spaces. While it is well understood for the Schr\"odinger case, nothing is known about the wave propagator. This is because there is no such thing as an explicit formula for the integral kernel of the propagator unlike the Sch

Jarrad Courts, Johannes Hendriks, Adrian Wills, Thomas Schön

This paper considers the problem of computing Bayesian estimates of both states and model parameters for nonlinear state-space models. Generally, this problem does not have a tractable solution and approximations must be utilised. In this work, a variational approach is used to provide an assumed density which approximates the desired, intractable, distribut

Jae-Young Park, Young-Jin Kim

This paper develops a new analytical model to estimate real-time variations in grid frequency and voltages resulting from dynamic network reconfiguration (DNR). In the proposed model, switching operations are considered as discrete variations in an admittance matrix, leading to step variations in node injection currents. The network model with discrete admit

Sang-Hoon Lee, Hyun-Wook Yoon, Hyeong-Rae Noh, Ji-Hoon Kim

While generative adversarial networks (GANs) based neural text-to-speech (TTS) systems have shown significant improvement in neural speech synthesis, there is no TTS system to learn to synthesize speech from text sequences with only adversarial feedback. Because adversarial feedback alone is not sufficient to train the generator, current models still require

Vanessa Böhm, Yu Feng, Max E. Lee, Biwei Dai

We present MADLens a python package for producing non-Gaussian lensing convergence maps at arbitrary source redshifts with unprecedented precision. MADLens is designed to achieve high accuracy while keeping computational costs as low as possible. A MADLens simulation with only $256^3$ particles produces convergence maps whose power agree with theoretical len

Do-Hoon Kwon, Youngjin Kim

High-voltage direct-current (HVDC) systems for constant or intermittent power delivery have recently been developed further to support grid frequency regulation (GFR). This paper proposes a new control strategy for a line-commutated converter-based (LCC) HVDC system, wherein the DC-link voltage and current are optimally regulated to improve real-time GFR in

Koun Shirai

The temperature dependence of the viscosity of glass is a major concern in the field of glass research. Strong deviations from the Arrhenius law make the interpretation of the activation energy difficult. In the present study, a reasonable interpretation of the apparent activation energy is demonstrated along similar lines as those adopted in solid-state phy

Nandagopal Manoj, Kevin Slagle, Wilbur Shirley, Xie Chen

The X-cube model, a prototypical gapped fracton model, has been shown to have a foliation structure. That is, inside the 3+1D model, there are hidden layers of 2+1D gapped topological states. A screw dislocation in a 3+1D lattice can often reveal nontrivial features associated with a layered structure. In this paper, we study the X-cube model on lattices wit

Jiafa He, Chengwei Pan, Can Yang, Ming Zhang

Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses. Existing methods based on convolutional neural networks (CNNs) may suffer from discontinuities of extracted vessels when segmenting such thin tubular structures from 3D images. We argue that preserving the continuity of extracted vessels requires to take into

Mingchao Li, Kun Huang, Qiuzhuo Xu, Jiadong Yang

Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which co

Lei Kang, Pifu Gong, Zheshuai Lin, Bing Huang

Following our ab initio nonlinear optical (NLO) materials design guidelines, in this Letter, we discovered a novel type of structure to realize potential deep-ultraviolet (DUV) NLO performance in the classical beryllium borate system. By densely stacking the NLO-active layered frameworks, the key design scheme for the structural evolution from the (Be2BO3F2)

Stefanus Agus Haryono, Ferdian Thung, David Lo, Lingxiao Jiang

Android operating system (OS) is often updated, where each new version may involve API deprecation. Usages of deprecated APIs in Android apps need to be updated to ensure the apps' compatibility with the old and new versions of Android OS. In this work, we propose AndroEvolve, an automated tool to update usages of deprecated Android APIs, that addresses the

Hayoung Choi, Seonguk Yoo

R. P. Boas showed that any single-index sequence $\left\{ \beta_i \right\}_{i=0}^\infty$ of real numbers can be represented as $\beta_i =\int_0^\infty x^i \, d\mu$ ($i=0,1,2,\ldots$), where $\mu$ is a signed measure. As Boas said his observation seemed to be quite unexpected; however, it is even possible to extend the result to any multi-index sequence of re

Sonia Castelo, Moacir Ponti, Rosane Minghim

Multiple-instance learning (MIL) is a paradigm of machine learning that aims to classify a set (bag) of objects (instances), assigning labels only to the bags. This problem is often addressed by selecting an instance to represent each bag, transforming a MIL problem into standard supervised learning. Visualization can be a useful tool to assess learning scen

Nicolas Privault

We propose a recursive method for the computation of the cumulants of self-exciting point processes of Hawkes type, based on standard combinatorial tools such as Bell polynomials. This closed-form approach is easier to implement on higher-order cumulants in comparison with existing methods based on differential equations, tree enumeration or martingale argum

Ke Xiao, Chen-Xu Wu

Based on energy conservation, we derive a critical condition theoretically for electrowettinginduced droplet detachment from a hydrophobic curved surface. Phase diagrams are constructed in terms of droplet volume, viscosity, Ohnesorge number, friction coefficient at contact line, surface curvature, surface wettability and electrowetting number. The deduced c

Qinyuan Zhao, Zhicheng He, Guilin Liu, Tinggui Wang

We present an analysis of the variability of broad absorption lines (BALs) in a quasar SDSS J141955.26+522741.1 at $z=2.145$ with 72 observations from the Sloan Digital Sky Survey Data Release 16 (SDSS DR16). The strong correlation between the equivalent widths of BAL and the continuum luminosity, reveals that the variation of BAL trough is dominated by the

Hanbing Liu, Gengsheng Wang, Yashan Xu, Huaiqiang Yu

We present several characterizations, via some weak observability inequalities, on the complete stabilizability for a control system $[A,B]$, i.e., $y'(t)=Ay(t)+Bu(t)$, $t\geq 0$, where $A$ generates a $C_0$-semigroup on a Hilbert space $X$ and $B$ is a linear and bounded operator from another Hilbert space $U$ to $X$. We then extend the aforementioned chara

Neeraj Kumar, Srishti Goel, Ankur Narang, Brejesh Lall

The style of the speech varies from person to person and every person exhibits his or her own style of speaking that is determined by the language, geography, culture and other factors. Style is best captured by prosody of a signal. High quality multi-speaker speech synthesis while considering prosody and in a few shot manner is an area of active research wi

Akira Endo, Alejandro Pascual Laguna, Sebastian Hähnle, Kenichi Karatsu

Superconducting resonators and transmission lines are fundamental building blocks of integrated circuits for millimeter-submillimeter astronomy. Accurate simulation of radiation loss from the circuit is crucial for the design of these circuits because radiation loss increases with frequency, and can thereby deteriorate the system performance. Here we show a

Xiaoyu Nie, Xingchen Zhao, Tao Peng, Marlan O. Scully

Computational ghost imaging generally requires a large number of pattern illumination to obtain a high-quality image. The colored noise speckle pattern was recently proposed to substitute the white noise pattern in a variety of noisy environments and gave a significant signal-to-noise ratio enhancement even with a limited number of patterns. We propose and e

Roberto B. Corcino, Jay M. Ontolan, Maria Rowena S. Lobrigas

In this paper, a q-analogue of r-Whitney-Lah numbers, also known as (q,r)-Whitney-Lah number, denoted by $L_{m,r}[n,k]_q$ is defined using the triangular recurrence relation. Several fundamental properties for the q-analogue are established such as vertical and horizontal recurrence relations, horizontal and exponential generating functions. Moreover, an exp

Bo Pang, Yizhuo Li, Jiefeng Li, Muchen Li

Human attention mechanisms often work in a top-down manner, yet it is not well explored in vision research. Here, we propose the Top-Down Attention Framework (TDAF) to capture top-down attentions, which can be easily adopted in most existing models. The designed Recursive Dual-Directional Nested Structure in it forms two sets of orthogonal paths, recursive a

Oliver Knill

A finite abstract simplicial complex G defines the Barycentric refinement graph phi(G) = (G,{ (a,b), a subset b or b subset a }) and the connection graph psi(G) = (G,{ (a,b), a intersected with b not empty }). We note here that both functors phi and psi from complexes to graphs are invertible on the image (Theorem 1) and that G,phi(G),psi(G) all have the sam

Stephanie Redfern, Julie K. Lundquist, Owen B. Toon, Domingo Muñoz-Esparza

Large fires can inject smoke into the upper troposphere and lower stratosphere. Detailed fire simulations allow for assessment of how local weather interacts with these fires and affects smoke lofting. In this study, we employ the fire simulation package in the Weather Research and Forecasting model (WRF-Fire), Version 4.0.1, to explore how smoke lofting fro

Kentaro Imajo, Kentaro Minami, Katsuya Ito, Kei Nakagawa

Recent developments in deep learning techniques have motivated intensive research in machine learning-aided stock trading strategies. However, since the financial market has a highly non-stationary nature hindering the application of typical data-hungry machine learning methods, leveraging financial inductive biases is important to ensure better sample effic

Raj Dandekar, Karen Chung, Vaibhav Dixit, Mohamed Tarek

Recently, Neural Ordinary Differential Equations has emerged as a powerful framework for modeling physical simulations without explicitly defining the ODEs governing the system, but instead learning them via machine learning. However, the question: "Can Bayesian learning frameworks be integrated with Neural ODE's to robustly quantify the uncertainty in the w

Ashley Milsted, Junyu Liu, John Preskill, Guifre Vidal

We simulate, using nonperturbative methods, the real-time dynamics of small bubbles of "false vacuum" in a quantum spin chain near criticality, where the low-energy physics is described by a relativistic (1+1)-dimensional quantum field theory. We consider bubbles whose walls are kink and antikink quasiparticle excitations, so that wall collisions are kink-an

Andrew Boutros, Mathew Hall, Nicolas Papernot, Vaughn Betz

Field-programmable gate arrays (FPGAs) are becoming widely used accelerators for a myriad of datacenter applications due to their flexibility and energy efficiency. Among these applications, FPGAs have shown promising results in accelerating low-latency real-time deep learning (DL) inference, which is becoming an indispensable component of many end-user appl

Mingyue Yang, Yuxin Wen, Weikai Chen, Yongwei Chen

Many learning-based approaches have difficulty scaling to unseen data, as the generality of its learned prior is limited to the scale and variations of the training samples. This holds particularly true with 3D learning tasks, given the sparsity of 3D datasets available. We introduce a new learning framework for 3D modeling and reconstruction that greatly im

Chao Zhang

In this paper we analyze the convergence of the following type of series $$ T_N^\alpha f(x,t)=\sum_{j=N_1}^{N_2} v_j(P_{a_{j+1}}^\alpha f(x,t)-P_{a_j}^\alpha f(x,t)),\quad (x,t)\in \mathbb R^{n+1}, \ N=(N_1, N_2)\in \mathbb Z^2,\ \alpha>0, $$ where $\{P_{\tau}^\alpha \}_{\tau>0}$ is the fractional Poisson-type operators generated by the parabolic operator $L

G. Chanakya, Pradeep Kumar

The effects of the semitransparent winodw's aspect ratio on the interaction of the collimated beam with natural convection have been investigated numerically in the present work. The combination of geometrical parameters of the semitransparent window, i.e., height ratio ($h_r$) and window width ratio ($w_r$) and Planck numbers of the medium have been conside

Yingkai Li, Harry Pei

We examine the long-term behavior of a Bayesian agent who has a misspecified belief about the time lag between actions and feedback, and learns about the payoff consequences of his actions over time. Misspecified beliefs about time lags result in attribution errors, which have no long-term effect when the agent's action converges, but can lead to arbitrarily

Muyi Sun, Zeyi Yao, Guanhong Zhang

Automatic cell segmentation is an essential step in the pipeline of computer-aided diagnosis (CAD), such as the detection and grading of breast cancer. Accurate segmentation of cells can not only assist the pathologists to make a more precise diagnosis, but also save much time and labor. However, this task suffers from stain variation, cell inhomogeneous int

Fan Lyu, Shuai Wang, Wei Feng, Zihan Ye

Rehearsal, seeking to remind the model by storing old knowledge in lifelong learning, is one of the most effective ways to mitigate catastrophic forgetting, i.e., biased forgetting of previous knowledge when moving to new tasks. However, the old tasks of the most previous rehearsal-based methods suffer from the unpredictable domain shift when training the ne