May 2020 arXiv papers — page 45

P. A. Krachkov, A. I. Milstein, A. G. Shamov

A method to derive the convenient representations for many two-photon amplitudes is suggested. It is based on the use of the gauge in which the photon propagator has only space components. The amplitudes obtained have no any strong numerical cancellations and, therefore, are very convenient for numerical evaluations. Our approach is illustrated by the consid

Matthieu Paturet, Uros Markovic, Stefanos Delikaraoglou, Evangelos Vrettos

This paper studies the procurement and pricing of inertial response using a frequency-constrained unit commitment formulation, which co-optimizes the provision of energy and inertia services while accounting for their complementary properties. The proposed approach builds on a two-step process that allows to differentiate between the units being online for e

Jesse Goodman

The saddlepoint approximation gives an approximation to the density of a random variable in terms of its moment generating function. When the underlying random variable is itself the sum of $n$ unobserved i.i.d. terms, the basic classical result is that the relative error in the density is of order $1/n$. If instead the approximation is interpreted as a like

Sofya Alexeeva, Tianxiang Chen, Tatyana Ryabchikova, Weibin Shi

We constructed a comprehensive model atom for NeI -- NeII using the most up-to-date atomic data available and evaluated the non-local thermodynamic equilibrium (NLTE) line formation for NeI and NeII in classical 1D models representing the atmospheres of B-type stars. We find that the large NLTE strengthening of the NeI lines corresponding to the 2p$^5$3p $-$

Xingwu Liu, Zhida Pan, Yuyi Wang

Motivated by scheduling in Geo-distributed data analysis, we propose a target location problem for multi-commodity flow (LoMuF for short). Given commodities to be sent from their resources, LoMuF aims at locating their targets so that the multi-commodity flow is optimized in some sense. LoMuF is a combination of two fundamental problems, namely, the facility

A. Santiago, M. Zavala-Aké, R. Borell, G. Houzeaux

In this work we present a robust interface coupling algorithm called Compact Interface quasi-Newton (CIQN). It is designed for computationally intensive applications using an MPI multi-code partitioned scheme. The algorithm allows to reuse information from previous time steps, feature that has been previously proposed to accelerate convergence. Through algeb

Kentaro Sato, Kazuhiko Ikeuchi, Ryoichi Kajimoto, Shuichi Wakimoto

To elucidate the spin dynamics of La$_{2-x}$Sr$_x$CuO$_4$, which couples with the charge degree of freedom, the spin excitations spanning the characteristic energy ($E_{\rm cross}$ $\sim$35-40 meV) are investigated for underdoped $x$ = 0.10 and optimally doped (OP) $x$ = 0.16 through inelastic neutron scattering measurements. Analysis based on a two-componen

Onno R. Diermann, Heinz-Jürgen Schmidt, Jürgen Schnack, Martin Holthaus

We study the response of ideal spin systems which are interacting with both a strong oscillating magnetic field, and a thermal environment, to a weak probing magnetic field. We demonstrate that even the sign of the resulting mean magnetization depends on the amplitude of the driving field, and that its absolute value can be significantly larger than the equi

Wenjun Shi, Qinxiang Cao, Yuxin Deng, Hanru Jiang

A quantum circuit is a computational unit that transforms an input quantum state to an output one. A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by it. However, when the number of qubits increases, the matrix dimension grows exponentially and the computation becomes intractable. In this paper, we propose a

Philippe Artzner, Karl-Theodor Eisele, Thorsten Schmidt

Most insurance contracts are inherently linked to financial markets, be it via interest rates, or -- as hybrid products like equity-linked life insurance and variable annuities -- directly to stocks or indices. However, insurance contracts are not for trade except sometimes as surrender to the selling office. This excludes the situation of arbitrage by buyin

Philipp Scharpf, Moritz Schubotz, Abdou Youssef, Felix Hamborg

In this paper, we show how selecting and combining encodings of natural and mathematical language affect classification and clustering of documents with mathematical content. We demonstrate this by using sets of documents, sections, and abstracts from the arXiv preprint server that are labeled by their subject class (mathematics, computer science, physics, e

Parvathy Panicker

When learning technologies are introduced in educational environments, it is assumed that the educational environment is culture neutral i.e, all educational environments have the same challenges, problems and cultural norms. However, it can be observed that cultural factors can influence the successful implementation and use of learning technologies. In thi

Florian Richoux

This competition paper presents microPhantom, a bot playing microRTS and participating in the 2020 microRTS AI competition. microPhantom is based on our previous bot POAdaptive which won the partially observable track of the 2018 and 2019 microRTS AI competitions. In this paper, we focus on decision-making under uncertainty, by tackling the Unit Production P

Jingge Zhu

Semi-supervised learning algorithms attempt to take advantage of relatively inexpensive unlabeled data to improve learning performance. In this work, we consider statistical models where the data distributions can be characterized by continuous parameters. We show that under certain conditions on the distribution, unlabeled data is equally useful as labeled

Mengxi Wei, Yifan He, Qiong Zhang

Many business documents processed in modern NLP and IR pipelines are visually rich: in addition to text, their semantics can also be captured by visual traits such as layout, format, and fonts. We study the problem of information extraction from visually rich documents (VRDs) and present a model that combines the power of large pre-trained language models an

Anis Najar, Mohamed Chetouani

In this paper, we provide an overview of the existing methods for integrating human advice into a Reinforcement Learning process. We first propose a taxonomy of the different forms of advice that can be provided to a learning agent. We then describe the methods that can be used for interpreting advice when its meaning is not determined beforehand. Finally, w

Thomas Führer, Dirk Praetorius

We show that adaptive least-squares finite element methods driven by the canonical least-squares functional converge under weak conditions on PDE operator, mesh-refinement, and marking strategy. Contrary to prior works, our plain convergence does neither rely on sufficiently fine initial meshes nor on severe restrictions on marking parameters. Finally, we pr

Tobias Fischer, Michael Milford

Event cameras are bio-inspired sensors capable of providing a continuous stream of events with low latency and high dynamic range. As a single event only carries limited information about the brightness change at a particular pixel, events are commonly accumulated into spatio-temporal windows for further processing. However, the optimal window length varies

Klaus Wilhelm, Bhola N. Dwivedi

The physical processes defining the dynamics of disk galaxies are still poorly understood. Hundreds of articles have appeared in the literature over the last decades without arriving at an understanding within a consistent gravitational theory. Dark matter (DM) scenarios or a modification of Newtonian dynamics (MOND) are employed to model the non-Keplerian r

Ajay Chatterjee, Shubhashis Sengupta

Conversational systems are of primary interest in the AI community. Chatbots are increasingly being deployed to provide round-the-clock support and to increase customer engagement. Many of the commercial bot building frameworks follow a standard approach that requires one to build and train an intent model to recognize a user input. Intent models are trained

J. B. Lestz

Neutral-beam-driven, sub-cyclotron compressional (CAE) and global (GAE) \Alfven eigenmodes are routinely excited in spherical tokamaks such as NSTX(-U) and MAST, have been observed on the conventional aspect ratio tokamak DIII-D, and may be unstable in ITER burning plasmas. Their presence has been experimentally linked to the anomalous flattening of electron

Wei Yang, Chong-jie Mo, Shi-Bin Fu, Yu Yang

Recent experiments have tuned the monolayer 1T'-WTe2 to be superconducting by electrostatic gating. Here, we theoretically study the phonon-mediated superconductivity in monolayer 1T'-WTe2 via charge doping. We reveal that the emergence of soft-mode phonons with specific momentum is crucial to give rise to the superconductivity in electron-doping regime, whe

Kevin J. Sung, Jiahao Yao, Matthew P. Harrigan, Nicholas C. Rubin

Variational quantum algorithms are a leading candidate for early applications on noisy intermediate-scale quantum computers. These algorithms depend on a classical optimization outer-loop that minimizes some function of a parameterized quantum circuit. In practice, finite sampling error and gate errors make this a stochastic optimization with unique challeng

Yiqi Wang, Yao Ma, Wei Jin, Chaozhuo Li

Recently, Graph Neural Networks (GNNs) have greatly advanced the task of graph classification. Typically, we first build a unified GNN model with graphs in a given training set and then use this unified model to predict labels of all the unseen graphs in the test set. However, graphs in the same dataset often have dramatically distinct structures, which indi

Qiang Yang, Ren-You Zhang, Ming-Ming Long, Shao-Ming Wang

We investigate in detail the charged Higgs production associated with a $W$ boson at electron-positron colliders within the framework of the Type-I two-Higgs-doublet model (THDM). We calculate the integrated cross section at the LO and analyze the dependence of the cross section on the THDM parameters and the colliding energy in a benchmark scenario of the i

Liang Wang, Jinlong Liu, Jingming Liu

Beam search is an effective and widely used decoding algorithm in many sequence-to-sequence (seq2seq) text generation tasks. However, in open-ended text generation, beam search is often found to produce repetitive and generic texts, sampling-based decoding algorithms like top-k sampling and nucleus sampling are more preferred. Standard seq2seq models suffer

Yasser Khan, Hossain Mohammad Fahad, Sifat Muin, Karthik Gopalan

Coronavirus disease 2019 (COVID-19) has created an unprecedented need for breathing assistance devices. Since the demand for commercial, full-featured ventilators is far higher than the supply capacity, many rapid-response ventilators are being developed for invasive mechanical ventilation of patients. Most of these emergency ventilators utilize mechanical s

Yeongjae Gil, Xiaoqian Jiang, Miran Kim, Junghye Lee

Data integration and sharing maximally enhance the potential for novel and meaningful discoveries. However, it is a non-trivial task as integrating data from multiple sources can put sensitive information of study participants at risk. To address the privacy concern, we present a distributed Bayesian learning approach via Preconditioned Stochastic Gradient L

Ryota Kojima

Recently, the accordiohedron in kinematic space was proposed as the positive geometry for planar tree-level scattering amplitudes in the $\phi^p$ theory \cite{Raman:2019utu}. The scattering amplitudes are given as a weighted sum over canonical forms of some accordiohedra with appropriate weights. These weights were determined by demanding that the weighted s

Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao

Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data size. Recently, bidirectional encoder representations from transf

Teruaki Hayashi, Gensei Ishimura, Yukio Ohsawa

In recent years, the expectation that new businesses and economic value can be created by combining/exchanging data from different fields has risen. However, value creation by data exchange involves not only data, but also technologies and a variety of stakeholders that are integrated and in competition with one another. This makes the data exchange ecosyste

Hieu-Thi Luong, Junichi Yamagishi

We introduce a novel speech synthesis system, called NAUTILUS, that can generate speech with a target voice either from a text input or a reference utterance of an arbitrary source speaker. By using a multi-speaker speech corpus to train all requisite encoders and decoders in the initial training stage, our system can clone unseen voices using untranscribed

Jieli Zhou, Baoyu Jing, Zeya Wang

Due to the shortage of COVID-19 viral testing kits and the long waiting time, radiology imaging is used to complement the screening process and triage patients into different risk levels. Deep learning based methods have taken an active role in automatically detecting COVID-19 disease in chest x-ray images, as witnessed in many recent works in early 2020. Mo

Mutaz Melhem, Christiana Chamon, Shahriar Ferdous, Laszlo B. Kish

A new attack against the Kirchhoff-Law-Johnson-Noise(KLJN) secure key exchange scheme is introduced. The attack exploits a parasitic (periodic) AC voltage-source at either Alice or Bob ends. Such situations exist due to AC ground loops and electromagnetic interference (EMI). In the low-frequency case, the procedure is the generalized form of the former DC gr

Junyi Lee, Victor Leong, Dmitry Kalashnikov, Jibo Dai

The realization of scalable systems for quantum information processing and networking is of utmost importance to the quantum information community. However, building such systems is difficult because of challenges in achieving all the necessary functionalities on a unified platform while maintaining stringent performance requirements of the individual elemen

Gregor Gantner, Rob Stevenson

In [2019, Space-time least-squares finite elements for parabolic equations, arXiv:1911.01942] by F\"uhrer& Karkulik, well-posedness of a space-time First-Order System Least-Squares formulation of the heat equation was proven. In the present work, this result is generalized to general second order parabolic PDEs with possibly inhomogenoeus boundary conditions

Chengwei Chen, Wang Yuan, Xuequan Lu, Lizhuang Ma

Face spoofing causes severe security threats in face recognition systems. Previous anti-spoofing works focused on supervised techniques, typically with either binary or auxiliary supervision. Most of them suffer from limited robustness and generalization, especially in the cross-dataset setting. In this paper, we propose a semi-supervised adversarial learnin

Hiroto Katsumata

The inverse probability weighting (IPW) is broadly utilized to address missing data problems including causal inference but may suffer from large variances and biases due to propensity score model misspecification. To solve these problems, I propose an estimation method called the navigated weighting (NAWT), which utilizes estimating equations suitable for a

Ohgan Kim, Bong Ju Lee, Yun-Woo Lee, Ho-Soon Yang

For the optical testing of a large mirror with a long radius of curvature, it is generally necessary to use a single-shot phase-shifting interferometer to take several measurements because the influence of air turbulence on air stratification prevention should be reduced for an accurate measurement. In this paper, a new technique that applies hierarchical cl

Garrett Wilson, Janardhan Rao Doppa, Diane J. Cook

Domain adaptation (DA) offers a valuable means to reuse data and models for new problem domains. However, robust techniques have not yet been considered for time series data with varying amounts of data availability. In this paper, we make three main contributions to fill this gap. First, we propose a novel Convolutional deep Domain Adaptation model for Time

Stephanie Kovalchik, Martin Ingram, Kokum Weeratunga, Cagatay Goncu

Sports tracking data are the high-resolution spatiotemporal observations of a competitive event. The growing collection of these data in professional sport allows us to address a fundamental problem of modern sport: how to attribute value to individual actions? Taking advantage of the smoothness of ball and player movement in tennis, we present a functional

Jinglian He, Kaiqiang Yu, Yong Zhou, Yuanming Shi

The cognitive radio (CR) network is a promising network architecture that meets the requirement of enhancing scarce radio spectrum utilization. Meanwhile, reconfigurable intelligent surfaces (RIS) is a promising solution to enhance the energy and spectrum efficiency of wireless networks by properly altering the signal propagation via tuning a large number of

Yulin Zhang, Dylan A. Shell

Whether a robot can perform some specific task depends on several aspects, including the robot's sensors and the plans it possesses. We are interested in search algorithms that treat plans and sensor designs jointly, yielding solutions---i.e., plan and sensor characterization pairs---if and only if they exist. Such algorithms can help roboticists explore the

Weinan Xu, Hengxu He, Minshi Tan, Yunming Li

Deep Interest Network (DIN) is a state-of-the-art model which uses attention mechanism to capture user interests from historical behaviors. User interests intuitively follow a hierarchical pattern such that users generally show interests from a higher-level then to a lower-level abstraction. Modeling such an interest hierarchy in an attention network can fun

E. Di Nardo

Hypergeometric functions and zonal polynomials are the tools usually addressed in the literature to deal with the expected value of the elementary symmetric functions in non-central Wishart latent roots. The method here proposed recovers the expected value of these symmetric functions by using the umbral operator applied to the trace of suitable polynomial m

Nhan T. Nguyen, Dat Q. Tran, Nghia T. Nguyen, Ha Q. Nguyen

We propose a novel method that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) mechanism for accurate prediction of intracranial hemorrhage on computed tomography (CT) scans. The CNN plays the role of a slice-wise feature extractor while the LSTM is responsible for linking the features across slices. The whole architecture

Angel Rincon, Ernesto Contreras, Francisco Tello-Ortiz, Pedro Bargueño

In the present paper, we analyze the well-known 2+1 dimensional black holes (assuming a non-vanishing cosmological constant) in light of the gravitational decoupling by the minimal geometric deformation approach. To illustrate our results, we consider the BTZ geometry as the seed solution to generate new anisotropic ones. To complement the study, the curvatu

Giorgio Adamo, Harish Krishnamoorthy, Daniele Cortecchia, Bhumika Chaudhary

Metal-halide perovskites are rapidly emerging as solution-processable optical materials for light emitting applications. Here we adopt a plasmonic metamaterial approach to enhance photoluminescence emission and extraction of methylammonium lead iodide (MAPbI3) thin films, based on the Purcell effect. We show that hybridization of the active metal-halide film

Timothy Kohl

For a group $G$, embedded in its group of permutations $B=Perm(G)$ via the left regular representation $\lambda:G\rightarrow B$, the normalizer of $\lambda(G)$ in $B$ is $\operatorname{Hol}(G)$, the holomorph of $G$. The set $\mathcal{H}(G)$ of those regular $N\leq \operatorname{Hol}(G)$ such that $N\cong G$ and $\operatorname{Norm}_B(N)=\operatorname{Hol}(G

Matthew Evans

The COVID-19 pandemic has brought into sharp focus the need to understand respiratory virus transmission mechanisms. In preparation for an anticipated influenza pandemic, a substantial body of literature has developed over the last few decades showing that the short-range aerosol route is an important, though often neglected transmission path. We develop a s

Youngjoon Yu, Hong Joo Lee, Byeong Cheon Kim, Jung Uk Kim

The success of multimodal data fusion in deep learning appears to be attributed to the use of complementary in-formation between multiple input data. Compared to their predictive performance, relatively less attention has been devoted to the robustness of multimodal fusion models. In this paper, we investigated whether the current multimodal fusion model uti

Jia-Wei Chen, Rongfang Wang, Fan Ding, Bo Liu

In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. In this paper, we propose a multi-scale spatial pooling (MSSP) network to exploit the changed information from the noisy difference image. Being different from the traditional convolutio

SeonWoo Lee, HyeonTak Yu, HoJun Yang, JaeHeung Yang

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hypergravity accelerator. The method proposed in this paper was to conve

Tenglong Li, Kenneth A. Frank

The internal validity of observational study is often subject to debate. In this study, we define the unobserved sample based on the counterfactuals and formalize its relationship with the null hypothesis statistical testing (NHST) for regression models. The probability of a robust inference for internal validity, i.e., the PIV, is the probability of rejecti

Donggen Dai, Wangkit Wong, Zhuojun Chen

We address the challenging problem of RGB image-based head pose estimation. We first reformulate head pose representation learning to constrain it to a bounded space. Head pose represented as vector projection or vector angles shows helpful to improving performance. Further, a ranking loss combined with MSE regression loss is proposed. The ranking loss super

F. F. Santos

In this work, we present the effect of a probe string on the complexity of a black hole according to the CA (Complexity equals action) conjecture on Horndeski's gravity. In our system, we consider a particle moving on the boundary of black hole spacetime in ($2+1$)-dimensions. To obtain a dual description, we need to insertion a fundamental string on the bul

Akihito Ishizaki

Quantum light is a key resource for promoting quantum technology. One such class of technology aims to improve the precision of optical measurements using engineered quantum states of light. In this study, we investigate transmission measurement of frequency-entangled broadband photon pairs generated via parametric down-conversion with a monochromatic laser.

Yujia Wang, Chuncheng Wang, Dejun Fan

In this paper, a single population model with memory effect and the heterogeneity of the environment, equipped with the Neumann boundary, is considered. The global existence of a spatial nonhomogeneous steady state is proved by the method of upper and lower solutions, which is asymptotically stable for relatively small memorized diffusion. However, after the

Jintae Kim, Minsoo Kim, Naoki Kawashima, Jung Hoon Han

We propose a simple variational wave function that captures the correct ground state energy of the spin-1 Heisenberg chain model to within 0.04\%. The wave function is written in the matrix product state (MPS) form with the bond dimension $D=8$, and characterized by three fugacity parameters. The proposed MPS generalizes the Affleck-Kennedy-Lieb-Tasaki (AKLT

Prateek Shroff, Tianlong Chen, Yunchao Wei, Zhangyang Wang

Deep Neural Network has shown great strides in the coarse-grained image classification task. It was in part due to its strong ability to extract discriminative feature representations from the images. However, the marginal visual difference between different classes in fine-grained images makes this very task harder. In this paper, we tried to focus on these

Yi Yang, Bo Zhen, John D. Joannopoulos, Marin Soljačić

The Hofstadter model, well-known for its fractal butterfly spectrum, describes two-dimensional electrons under a perpendicular magnetic field, which gives rise to the integer quantum hall effect. Inspired by the real-space building blocks of non-Abelian gauge fields from a recent experiment [Science, 365, 1021 (2019)], we introduce and theoretically study tw

Zhi Qiao, Yu Zhou, Dongbao Yang, Yucan Zhou

Scene text recognition is a hot research topic in computer vision. Recently, many recognition methods based on the encoder-decoder framework have been proposed, and they can handle scene texts of perspective distortion and curve shape. Nevertheless, they still face lots of challenges like image blur, uneven illumination, and incomplete characters. We argue t

Shashank Agarwal, Andras Karsai, Daniel I Goldman, Ken Kamrin

Granular intrusions, such as dynamic impact or wheel locomotion, are complex multiphase phenomena where the grains exhibit solid-like and fluid-like characteristics together with an ejected gas-like phase. Despite decades of modeling efforts, a unified description of the physics in such intrusions is as yet unknown. Here we show that a continuum model based

Hans-Christoph Grunau, Nobuhito Miyake, Shinya Okabe

This paper is concerned with the positivity of solutions to the Cauchy problem for linear and nonlinear parabolic equations with the biharmonic operator as fourth order elliptic principal part. Generally, Cauchy problems for parabolic equations of fourth order have no positivity preserving property due to the change of sign of the fundamental solution. One h

Yi-Xian Chen, Ya-Ping Li, Hui Li, Douglas N. C. Lin

The omnipresence of super-Earths suggests that they are able to be retained in natal disks around low-mass stars, whereas exoplanets' mass distribution indicates that some cores have transformed into gas giants through runaway gas accretion at 1AU from solar-type stars. In this paper, we show that transition to runaway gas accretion by cores may be self-impe

Yiling Yang, Engui Fan

In this paper, we study the Cauchy problem for the focusing nonlinear short-pluse equation by using $\overline\partial$ steepest descent method. \begin{align} &u_{xt}=u+\frac{1}{6}(u^3)_{xx}, \nonumber\\ &u(x,0)=u_0(x)\in H^{1,1}(R),\nonumber \end{align} where $H^{1,1}(R)$ is a weighted Sobolev space. Because the spectral variable z is the same order in the

Masoud M Nasari, Mohamedou Ould-Haye

We introduce an estimation method for the scaled skewness coefficient of the sample mean of short and long memory linear processes. This method can be extended to estimate higher moments such as curtosis coefficient of the sample mean. Also a general result on computing all asymptotic moments of partial sums is obtained, allowing in particular a much easier

Zhiyuan Geng, Fanghua Lin

In this paper, we study large $m$ asymptotics of the $l^1$ minimal $m$-partition problem for Dirichlet eigenvalue. For any smooth domain $\Omega\in \mathbb{R}^n$ such that $|\Omega|=1$, we prove that the limit $\lim\limits_{m\rightarrow\infty}l_m^1(\Omega)=c_0$ exists, and the constant $c_0$ is independent of the shape of $\Omega$. Here $l_m^1(\Omega)$ denot

Jie Liu, Lin Wang, Ka Shen

The existence of the magnetostatic surface spin waves in ferromagnets, known as Damon-Eshbach mode, was recently demonstrated to originate from the topology of the dipole-dipole interaction. In this work, we study the topological characteristics of magnons in easy-axis antiferromagnets with uniaxial anisotropy. The dipolar spin waves are found to be, driven

Kechen Qin, Yu Wang, Cheng Li, Kalpa Gunaratna

Multi-hop knowledge based question answering (KBQA) is a complex task for natural language understanding. Many KBQA approaches have been proposed in recent years, and most of them are trained based on labeled reasoning path. This hinders the system's performance as many correct reasoning paths are not labeled as ground truth, and thus they cannot be learned.

Samuel Schmidgall

The adaptive learning capabilities seen in biological neural networks are largely a product of the self-modifying behavior emerging from online plastic changes in synaptic connectivity. Current methods in Reinforcement Learning (RL) only adjust to new interactions after reflection over a specified time interval, preventing the emergence of online adaptivity.

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

Let $U_q'(\mathfrak{g})$ be a quantum affine algebra of untwisted affine $ADE$ type, and $\mathcal{C}_{\mathfrak{g}}^0$ the Hernandez-Leclerc category of finite-dimensional $U_q'(\mathfrak{g})$-modules. For a suitable infinite sequence $\widehat{w}_0= \cdots s_{i_{-1}}s_{i_0}s_{i_1} \cdots$ of simple reflections, we introduce subcategories $\mathcal{C}_{\mat

Laura Felicia Matusevich, Byeongsu Yu

We extend the notion of standard pairs to the context of monomial ideals in semigroup rings. Standard pairs can be used as a data structure to encode such monomial ideals, providing an alternative to generating sets that is well suited to computing intersections, decompositions, and multiplicities. We give algorithms to compute standard pairs from generating

Mohammad Rami Koujan, Nikolai Dochev, Anastasios Roussos

4D face reconstruction from a single camera is a challenging task, especially when it is required to be performed in real time. We demonstrate a system of our own implementation that solves this task accurately and runs in real time on a commodity laptop, using a webcam as the only input. Our system is interactive, allowing the user to freely move their head

Liangang Ma

Following the pioneering work of Iommi-Kiwi and Jenkinson-Pollicott-Vytnova, we continue to study the inflection points of the Lyapunov spectrum in this work. We prove that for any 3-branch piecewise linear expanding map on an interval, the number of its Lyapunov inflections is bounded above by 2. Then we continue to show that, there is a 4-branch piecewise

Narayan Ganesan, Yajie Yu, Bernhard Hientzsch

This paper presents a novel and direct approach to price boundary and final-value problems, corresponding to barrier options, using forward deep learning to solve forward-backward stochastic differential equations (FBSDEs). Barrier instruments are instruments that expire or transform into another instrument if a barrier condition is satisfied before maturity

Hongqiang Xie, Hongbin Lei, Guihua Li, Qian Zhang

We experimentally investigated the rotationally resolved polarization characteristics of N$_2^+$ lasing at 391 and 428 nm using a pump-seed scheme. By varying the relative angle between the linear polarizations of the pump and seed, it is found that the polarizations of the P and R branches of 391-nm lasing are counter-rotated. By contrast, both branches of

Ethan Pickering, Georgios Rigas, Oliver T. Schmidt, Denis Sipp

Response modes computed via linear resolvent analysis of a turbulent mean-flow field have been shown to qualitatively capture characteristics of the observed turbulent coherent structures in both wall-bounded and free shear flows. To make such resolvent models predictive, the nonlinear forcing term must be closed. Strategies to do so include imposing self-co

Yongxin Chen, Tryphon T. Georgiou, Michele Pavon

In 1931/32, Schroedinger studied a hot gas Gedankenexperiment, an instance of large deviations of the empirical distribution and an early example of the so-called maximum entropy inference method. This so-called Schroedinger bridge problem (SBP) was recently recognized as a regularization of the Monge-Kantorovich Optimal Mass Transport (OMT), leading to effe

C. Hunt, G. V. Rogachev, S. Almaraz-Calderon, A. Aprahamian

Background: Resonance scattering has been extensively used to study the structure of exotic, neutron-deficient nuclei. Extension of the resonance scattering technique to neutron-rich nuclei was suggested more than 20 years ago. This development is based on the isospin conservation law. In spite of broad field of the application, it has never gained a wide-sp

Jim Samuel, Md. Mokhlesur Rahman, G. G. Md. Nawaz Ali, Yana Samuel

The Coronavirus pandemic has created complex challenges and adverse circumstances. This research discovers public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and

Harini Suresh, Natalie Lao, Ilaria Liccardi

ML decision-aid systems are increasingly common on the web, but their successful integration relies on people trusting them appropriately: they should use the system to fill in gaps in their ability, but recognize signals that the system might be incorrect. We measured how people's trust in ML recommendations differs by expertise and with more system informa

Robert A. Wittenmyer, Jake T. Clark, Sanjib Sharma, Dennis Stello

Accurate and precise radius estimates of transiting exoplanets are critical for understanding their compositions and formation mechanisms. To know the planet, we must know the host star in as much detail as possible. We present complete results for planet-candidate hosts from the K2-HERMES survey, which uses the HERMES multi-object spectrograph on the Anglo-

Quanzhi Ye, Michael S. Kelley, Dennis Bodewits, James M. Bauer

We present a preliminary analysis of comet P/2019 LM$_4$ (Palomar) as observed by the Zwicky Transient Facility (ZTF) survey in 2019 and 2020. We find that the discovery of the comet in 2019 and the recovery in 2020 is largely attributed to two separate outbursts that are $\gtrsim2$ and $\gtrsim3.9$ mag in strength. The outbursts occurred around the end of A

Yiping Wang, David Farnell, Hossein Farahani, Mitchell Nursey

Ovarian cancer is the most lethal cancer of the female reproductive organs. There are $5$ major histological subtypes of epithelial ovarian cancer, each with distinct morphological, genetic, and clinical features. Currently, these histotypes are determined by a pathologist's microscopic examination of tumor whole-slide images (WSI). This process has been ham

Tong Yang, Long Sha, Pengyu Hong

We demonstrated the existence of a group algebraic structure hidden in relational knowledge embedding problems, which suggests that a group-based embedding framework is essential for designing embedding models. Our theoretical analysis explores merely the intrinsic property of the embedding problem itself hence is model-independent. Motivated by the theoreti

Mohammad Rami Koujan, Michail Christos Doukas, Anastasios Roussos, Stefanos Zafeiriou

Video-to-video synthesis is a challenging problem aiming at learning a translation function between a sequence of semantic maps and a photo-realistic video depicting the characteristics of a driving video. We propose a head-to-head system of our own implementation capable of fully transferring the human head 3D pose, facial expressions and eye gaze from a so

Lina Zhao, Dohyun Kim, Eun-Jae Park, Eric Chung

In this paper, we present and analyze a staggered discontinuous Galerkin method for Darcy flows in fractured porous media on fairly general meshes. A staggered discontinuous Galerkin method and a standard conforming finite element method with appropriate inclusion of interface conditions are exploited for the bulk region and the fracture, respectively. Our c

Nikhita Vedula, Rahul Gupta, Aman Alok, Mukund Sridhar

One of the primary tasks in Natural Language Understanding (NLU) is to recognize the intents as well as domains of users' spoken and written language utterances. Most existing research formulates this as a supervised classification problem with a closed-world assumption, i.e. the domains or intents to be identified are pre-defined or known beforehand. Real-w

Mohammad Rami Koujan, Michail Christos Doukas, Anastasios Roussos, Stefanos Zafeiriou

In this paper, we propose a novel machine learning architecture for facial reenactment. In particular, contrary to the model-based approaches or recent frame-based methods that use Deep Convolutional Neural Networks (DCNNs) to generate individual frames, we propose a novel method that (a) exploits the special structure of facial motion (paying particular att

Xiaoxu Li, Zhuo Sun, Jing-Hao Xue, Zhanyu Ma

Few-shot meta-learning has been recently reviving with expectations to mimic humanity's fast adaption to new concepts based on prior knowledge. In this short communication, we give a concise review on recent representative methods in few-shot meta-learning, which are categorized into four branches according to their technical characteristics. We conclude thi

A. Korobenko, K. Johnston, M. Kubullek, L. Arissian

We demonstrate a novel method to measure the temporal evolution of electric fields with optical frequencies. Our technique is based on the detection of transient currents in air plasma. These directional currents result from sub-cycle ionization of air with a short pump pulse, and the steering of the released electrons with the pulse to be sampled. We assess

Zhijian Yang, Daniel Olszewski, Chujun He, Giulia Pintea

Thanks to advancements in diagnosis and treatment, prostate cancer patients have high long-term survival rates. Currently, an important goal is to preserve quality-of-life during and after treatment. The relationship between the radiation a patient receives and the subsequent side effects he experiences is complex and difficult to model or predict. Here, we

Thomas Siegert, Steven E. Boggs, John A. Tomsick, Andreas Zoglauer

The balloon-borne Compton Spectrometer and Imager (COSI) had a successful 46-day flight in 2016. The instrument is sensitive to photons in the energy range $0.2$-$5$ MeV. Compton telescopes have the advantage of a unique imaging response and provide the possibility of strong background suppression. With its high-purity germanium detectors, COSI can precisely

Gui-Qiang G. Chen, Monica Torres

The classical Gauss-Green formula for the multidimensional case is generally stated for $C^{1}$ vector fields and domains with $C^{1}$ boundaries. However, motivated by the physical solutions with discontinuity/singularity for Partial Differential Equations (PDEs) and Calculus of Variations, such as nonlinear hyperbolic conservation laws and Euler-Lagrange e

Tong Yang, Kai Shen, Sixuan He, Enyu Li

Timely, creditable, and fine-granular case information is vital for local communities and individual citizens to make rational and data-driven responses to the COVID-19 pandemic. This paper presents CovidNet, a COVID-19 tracking project associated with a large scale epidemic dataset, which was initiated by 1Point3Acres. To the best of our knowledge, the proj

Jonathan J. Mize

Recently there have been numerous proposed solutions to the problem of logical omniscience in doxastic and epistemic logic. Though these solutions display an impressive breadth of subtlety and motivation, the crux of these approaches seems to have a common theme-minor revisions around the ubiquitous Kripke semantics-rooted approach. In addition, the psycholo

Arsalan Sharifnassab, S. Jamaloddin Golestani

We investigate sensitivity to cumulative perturbations for a few dynamical system classes of practical interest. A system is said to have bounded sensitivity to cumulative perturbations (bounded sensitivity, for short) if an additive disturbance leads to a change in the state trajectory that is bounded by a constant multiple of the size of the cumulative dis

Roel Dobbe, Ye Pu, Jingge Zhu, Kannan Ramchandran

Real-time data-driven optimization and control problems over networks may require sensitive information of participating users to calculate solutions and decision variables, such as in traffic or energy systems. Adversaries with access to coordination signals may potentially decode information on individual users and put user privacy at risk. We develop loca

Marcello D'Abbicco, Marcelo Rempel Ebert

In this paper, we derive suitable optimal $L^p-L^q$ decay estimates, $1\leq p\leq q\leq \infty$, for the solutions to the $\sigma$-evolution equation, $\sigma>1$, with structural damping and power nonlinearity $|u|^{1+\alpha}$ or $|u_t|^{1+\alpha}$, \[ u_{tt}+(-\Delta)^\sigma u +(-\Delta)^\theta u_t=\begin{cases} |u|^{1+\alpha}, \\ |u_t|^{1+\alpha}, \end{cas