November 2019 arXiv papers — page 30

Bo Wang, Jun Zhou, Weng-Fai Wong, Li-Shiuan Peh

The next wave of on-device AI will likely require energy-efficient deep neural networks. Brain-inspired spiking neural networks (SNN) has been identified to be a promising candidate. Doing away with the need for multipliers significantly reduces energy. For on-device applications, besides computation, communication also incurs a significant amount of energy

Donal O'Sullivan, Christopher Martin, Mateusz Matuszewski, Keri Hoadley

We present the pilot study component of the Fluorescent Lyman-Alpha Structures in High-z Environments (FLASHES) Survey; the largest integral-field spectroscopy survey to date of the circumgalactic medium at $z=2.3-3.1$. We observed 48 quasar fields between 2015 and 2018 with the Palomar Cosmic Web Imager (Matuszewski et al. 2010). Extended HI Lyman-$\mathrm{

Ikki Kishida, Hideki Nakayama

Deep Neural Networks (DNNs) generalize well despite their massive size and capability of memorizing all examples. There is a hypothesis that DNNs start learning from simple patterns and the hypothesis is based on the existence of examples that are consistently well-classified at the early training stage (i.e., easy examples) and examples misclassified (i.e.,

Tingting Liu, Xiaoyun Jiang, Huaixing Wang, Yong Liu

We present a monolayer black phosphorus (BP)-based metamaterial structure for tunable anisotropic absorption in the mid-infrared. Based on the critical coupling mechanism of guided resonance, the structure realizes the high absorption efficiency of 99.65$\%$ for TM polarization, while only 2.61$\%$ at the same wavelength for TE polarization due to the intrin

Liangchen Liu, Louis Ly, Colin Macdonald, Yen-Hsi Richard Tsai

We propose a new framework for the sampling, compression, and analysis of distributions of point sets and other geometric objects embedded in Euclidean spaces. Our approach involves constructing a tensor called the RaySense sketch, which captures nearest neighbors from the underlying geometry of points along a set of rays. We explore various operations that

Zhentao Wang, Ying Su, Shi-Zeng Lin, Cristian D. Batista

We consider a C$_6$ invariant lattice of magnetic moments coupled via a Kondo exchange $J$ with a 2D electron gas (2DEG). The effective Ruderman-Kittel-Kasuya-Yosida interaction between the moments stabilizes a magnetic skyrmion crystal in the presence of magnetic field and easy-axis anisotropy. An attractive aspect of this mechanism is that the magnitude of

Julien Girard-Satabin, Guillaume Charpiat, Zakaria Chihani, Marc Schoenauer

The topic of provable deep neural network robustness has raised considerable interest in recent years. Most research has focused on adversarial robustness, which studies the robustness of perceptive models in the neighbourhood of particular samples. However, other works have proved global properties of smaller neural networks. Yet, formally verifying percept

Gerard Meijer, Boris G. Sartakov

Thallium monofluoride (TlF) is a prime candidate molecule for precision measurements aimed at discovering new physics. Optical cycling on the B $\leftarrow$ X transition around 271 nm enhances this potential. Hyperfine resolved ultraviolet spectra have been reported to determine the degree of rotational level mixing in the B-state and the efficiency of laser

Mohsen Kian, M. S. Moslehian, Yuki Seo

For an $n$-tuple of positive invertible operators on a Hilbert space, we present some variants of Ando--Hiai type inequalities for deformed means from an $n$-variable operator mean by an operator mean, which is related to the information monotonicity of a certain unital positive linear map. As an application, we investigate the monotonicity of the power mean

Qian Cao, Shaohui Kuang, Deyi Xiong

In this paper, we study the problem of enabling neural machine translation (NMT) to reuse previous translations from similar examples in target prediction. Distinguishing reusable translations from noisy segments and learning to reuse them in NMT are non-trivial. To solve these challenges, we propose an Example-Guided NMT (EGNMT) framework with two models: (

Ilkka Törmä

The generic limit set of a topological dynamical system of the smallest closed subset of the phase space that has a comeager realm of attraction. It intuitively captures the asymptotic dynamics of almost all initial conditions. It was defined by Milnor and studied in the context of cellular automata, whose generic limit sets are subshifts, by Djenaoui and Gu

Rajesh kumar R, C. Dhanasekhar, N. Vijay Prakash Chaudhary, A. Das

We report the enhancement of N\'eel temperature of Cr2O3 by replacing 50% of Cr by Fe prepared by sol-gel method. The structural analysis by neutron diffraction has revealed that FeCrO3 belongs to a corundum structure (R-3c space group) with an antiferromagnetic spin structure having collinear spins along a-axis with propagation vector k=0; the high-temperat

Makoto Nakatsuji, Sohei Okui

This paper tackles the goal of conclusion-supplement answer generation for non-factoid questions, which is a critical issue in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI), as users often require supplementary information before accepting a conclusion. The current encoder-decoder framework, however, has difficulty generatin

Pengxiang Wu, Chao Chen, Jingru Yi, Dimitris Metaxas

We present a new permutation-invariant network for 3D point cloud processing. Our network is composed of a recurrent set encoder and a convolutional feature aggregator. Given an unordered point set, the encoder firstly partitions its ambient space into parallel beams. Points within each beam are then modeled as a sequence and encoded into subregional geometr

Xiaojin Zhang

We consider the problem of selecting a seed set to maximize the expected number of influenced nodes in the social network, referred to as the \textit{influence maximization} (IM) problem. We assume that the topology of the social network is prescribed while the influence probabilities among edges are unknown. In order to learn the influence probabilities and

Takumi Saikawa, Quan Huu Cap, Satoshi Kagiwada, Hiroyuki Uga

In image-based plant diagnosis, clues related to diagnosis are often unclear, and the other factors such as image backgrounds often have a significant impact on the final decision. As a result, overfitting due to latent similarities in the dataset often occurs, and the diagnostic performance on real unseen data (e,g. images from other farms) is usually dropp

Alok Shukla

Many mathematicians find mathematics aesthetically beautiful and even comparable to art forms such as music or painting. On the other hand, every year a great number of school students leave mathematics with total disillusionment and bitterness, without ever witnessing any beauty in it. In this work, we give some strategies to teach mathematics, especially t

Fan Yang, Takatomi Kubo, Kazushi Ikeda

In the prospect of ecology and biology, studying insect-plant predation will considerably contribute to pest control, benefit agriculture and afforestation, and also help people to better understand insect-plant co-evolution. Therefore, we are motivated to do two work in this study. The first part is to cluster the insect-plant predation, in such manner, uno

Chunlei Liu, Wenrui Ding, Yuan Hu, Baochang Zhang

Training 1-bit deep convolutional neural networks (DCNNs) is one of the most challenging problems in computer vision, because it is much easier to get trapped into local minima than conventional DCNNs. The reason lies in that the binarized kernels and activations of 1-bit DCNNs cause a significant accuracy loss and training inefficiency. To address this prob

Qingyang Wu, Lei Li, Hao Zhou, Ying Zeng

Many social media news writers are not professionally trained. Therefore, social media platforms have to hire professional editors to adjust amateur headlines to attract more readers. We propose to automate this headline editing process through neural network models to provide more immediate writing support for these social media news writers. To train such

Waqas Mahmood, Bing Dong

The structural and electronic properties of zinc-blende (ZB) GaAs were calculated within the framework of plane wave density-functional theory (DFT) code JDFTx by using Becke 86 in 2D and PBE exchange correlation functionals from libXC. The standard optimized norm-conserving Vanderbilt pseudopotentials were used to calculate optimized lattice constant, band

Xinyu Huang, Lijun He, Xin Chen, Fan Li

In this paper, we propose a more refined video segment based Mobile Edge Computing (MEC) enhanced cache update strategy, which takes into account the client's playback status and transmission state, MEC cache capacity and the popularity of each segment, to improve the quality of experience (QoE) of clients and the use ratio of MEC cache. In each cache update

Xie-Gang Zhu, Yu Liu, Ya-Wen Zhao, Yue-Chao Wang

The physical mechanism driving the $\gamma$-$\alpha$ phase transition of face-centre-cubic (fcc) cerium (Ce) remains controversial until now. In this work, high quality single crystalline fcc-Ce thin films were grown on Graphene/6$H$-SiC(0001) substrate, and explored by XRD and ARPES measurement. XRD spectra showed a clear $\gamma$-$\alpha$ phase transition

C. Y. Kuo, J. Y. Hsiang, H. H. Chung, A. Constantin

We present a new investigation of the dependence of H2O maser detection rates and properties on the mid-IR AGN luminosity, L_AGN, and the obscuring column density, N_H, based on mid-IR and hard X-ray photometry. Based on spectral energy distribution fitting that allows for decomposition of the black hole accretion and star-formation components in the mid-inf

Soufiane Belharbi, Ismail Ben Ayed, Luke McCaffrey, Eric Granger

We propose a new constrained-optimization formulation for deep ordinal classification, in which uni-modality of the label distribution is enforced implicitly via a set of inequality constraints over all the pairs of adjacent labels. Based on (c-1) constraints for c labels, our model is non-parametric and, therefore, more flexible than the existing deep ordin

Yohei Yoshimoto, Masaharu Kataoka, Yoshimasa Takabatake, Tomohiro I

We consider an efficient two-party protocol for securely computing the similarity of strings w.r.t. an extended edit distance measure. Here, two parties possessing strings $x$ and $y$, respectively, want to jointly compute an approximate value for $\mathrm{EDM}(x,y)$, the minimum number of edit operations including substring moves needed to transform $x$ int

Dongmin Gang, Seonhwa Kim, Seokbeom Yoon

We introduce a vanishing property of adjoint Reidemeister torsions of a cusped hyperbolic 3-manifold derived from the physics of wrapped M5-branes on the manifold. To support our physical observation, we present a rigorous proof for the figure-eight knot complement with respect to all slopes. We also present numerical verification for several knots.

Gareth Jones, Andrey Mudrov

Quaternionic projective plane $\mathbb{H} P^2$ is the next simplest conjugacy class of the symplectic group $SP(6)$ with pseudo-Levi stabilizer subgroup after the sphere $\mathbb{S}^4\simeq \mathbb{H} P^1$. Its quantization gives rise to a module category $\mathcal{O}_t\bigl(\mathbb{H} P^2\bigr)$ over finite-dimensional representations of $U_q\bigl(\mathfrak

Gewinner Senderanto Sinaga, Keishu Utimula, Kousuke Nakano, Kenta Hongo

High critical temperature (T$_c$) superconductor has a great potential in many industrial applications. However, discovering a compound having high T$_c$ is still remaining a big challenge for experimental approach due to time-consuming and high cost. In this paper, we investigated the critical temperature (T$_c$) of several compounds of ThCr$_2$Si$_2$-type

Yong Liu, Weixun Wang, Yujing Hu, Jianye Hao

In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all othe

Zhichao Fu, Yu Kong, Yingbin Zheng, Hao Ye

The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results. Among these scenarios, the low-resolution image is a common and challenging case. In this paper, we propose the cascaded networks for document image super-resolution. Our model is composed by the Detail-Pres

Jinlu Liu, Liang Song, Yongqiang Qin

Few-shot learning requires to recognize novel classes with scarce labeled data. Prototypical network is useful in existing researches, however, training on narrow-size distribution of scarce data usually tends to get biased prototypes. In this paper, we figure out two key influencing factors of the process: the intra-class bias and the cross-class bias. We t

Emily Barnard, Gordana Todorov, Shijie Zhu

For finite semidistributive lattices the map $\kappa$ gives a bijection between the sets of completely join-irreducible elements and completely meet-irreducible elements. Here we study the $\kappa$-map in the context of torsion classes. It is well-known that the lattice of torsion classes for an artin algebra is semidistributive, but in general it is far fro

Kunie Ishioka, Terumasa Tadano, Masatoshi Yanagida, Yasuhiro Shirai

Ultrafast vibrational dynamics of inorganic-organic hybrid lead halide perovskite CH3NH3PbI$ in the room-temperature tetragonal phase is investigated under moderate photoexcitation slightly below the band gap. Time-resolved optical Kerr effect signal exhibits periodic modulations due to the libration and twisting of the methylammonium molecule at 4 and 8 THz

Cesare G. Ardito, Benjamin Sambale

We prove Brauer's k(B)-Conjecture for the 3-blocks with abelian defect groups of rank at most 5 and for all 3-blocks of defect at most 4. For this purpose we develop a computer algorithm to construct isotypies based on a method of Usami and Puig. This leads further to some previously unknown perfect isometries for the 5-blocks of defect 2. We also investigat

J. Darulová, S. J. Pauka, N. Wiebe, K. W. Chan

Defining quantum dots in semiconductor based heterostructures is an essential step in initializing solid-state qubits. With growing device complexity and increasing number of functional devices required for measurements, a manual approach to finding suitable gate voltages to confine electrons electrostatically is impractical. Here, we implement a two-stage d

Tomasz M. Rutkowski, Masato S. Abe, Marcin Koculak, Mihoko Otake-Matsuura

We present a practical health-theme machine learning (ML) application concerning `AI for social good' domain for `Producing Good Outcomes' track. In particular, the solution is concerning the problem of a potential elderly adult dementia onset prediction in aging societies. The paper discusses our attempt and encouraging preliminary study results of behavior

Idris Abdulmumin, Bashir Shehu Galadanci

Words embedding (distributed word vector representations) have become an essential component of many natural language processing (NLP) tasks such as machine translation, sentiment analysis, word analogy, named entity recognition and word similarity. Despite this, the only work that provides word vectors for Hausa language is that of Bojanowski et al. [1] tra

Kazuya Fujimoto, Ryusuke Hamazaki, Yuki Kawaguchi

Family-Vicsek scaling is one of the most essential scale-invariant laws emerging in surface-roughness growth of classical systems. In this Letter, we theoretically elucidate the emergence of the Family-Vicsek scaling even in a strongly interacting quantum bosonic system by introducing a surface-height operator. This operator is comprised of a summation of lo

Tonima Tasnim Ananna, Ezequiel Treister, Claudia M. Urry, Claudio Ricci

We constrain X-ray spectral shapes for the ensemble of AGN based on the shape of the Cosmic X-ray Background (CXB). Specifically, we rule out regions of X-ray spectral parameter space that do not reproduce the CXB in the energy range 1-100 keV. The key X-ray spectral parameters are the photon index, {\Gamma}; the cutoff energy, Ecutoff; and the reflection sc

Wilson Yan, Jonathan Ho, Pieter Abbeel

Deep autoregressive models are one of the most powerful models that exist today which achieve state-of-the-art bits per dim. However, they lie at a strict disadvantage when it comes to controlled sample generation compared to latent variable models. Latent variable models such as VAEs and normalizing flows allow meaningful semantic manipulations in latent sp

Paul Samuel Ignacio, David Uminsky, Christopher Dunstan, Esteban Escobar

Atrial Fibrillation is a heart condition characterized by erratic heart rhythms caused by chaotic propagation of electrical impulses in the atria, leading to numerous health complications. State-of-the-art models employ complex algorithms that extract expert-informed features to improve diagnosis. In this note, we demonstrate how topological features can be

Lee R. Spitler, Fergus D. Longbottom, Jaime A. Alvarado-Montes, Amir E. Bazkiaei

The Huntsman Telescope, located at Siding Spring Observatory in Australia, is a system of ten telephoto Canon lenses designed for low surface brightness imaging in the Southern sky. Based upon the Dragonfly Telephoto Array, the refractive lens-based system provides an obstruction free optical path, which reduces the number of scattering surfaces and allows e

Michel Dekking

In the base phi expansion any natural number is written uniquely as a sum of powers of the golden mean with digits 0 and 1, where one requires that the product of two consecutive digits is always 0. In this paper we show that the sum of digits function modulo 2 of these expansions is a morphic sequence. In particular we prove that --- like for the Thue-Morse

Elizabeth Jasmi George, Radhika Mamidi

Human dialogue often contains utterances having meanings entirely different from the sentences used and are clearly understood by the interlocutors. But in human-computer interactions, the machine fails to understand the implicated meaning unless it is trained with a dataset containing the implicated meaning of an utterance along with the utterance and the c

Jihyeug Jang, Jang Soo Kim

Recently, Benedetti et al. introduced an Ehrhart-like polynomial associated to a graph. This polynomial is defined as the volume of a certain flow polytope related to a graph and has the property that the leading coefficient is the volume of the flow polytope of the original graph with net flow vector $(1,1,\dots,1)$. Benedetti et al. conjectured a formula f

Zhipeng Hou, Qiang Zhang, Guizhou Xu, Chen Gong

Nanoscale topologically non-trivial spin textures, such as magnetic skyrmions, have been identified as promising candidates for the transport and storage of information for spintronic applications, notably magnetic racetrack memory devices. The design and realization of single skyrmion chain at room temperature (RT) and above in the low-dimensional nanostruc

Jin-Liang Wang

The relation between El Ni\~{n}o-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) perplexes the researchers. Probably, this is due to the default choice on empirical orthogonal function (EOF) method whose outputs are merely static maps without evolution characteristics. To change this situation, the new extreme-point symmetric mode decomposi

I. A. Koop, A. I. Milstein, N. N. Nikolaev, A. S. Popov

A possible experimental setup for measuring the effect of parity violation in the interaction of the polarized proton or deuteron beams with an unpolarized target is discussed. One possibility is investigation of scattering of the proton or deuteron polarized beams on a thick internal target in one of the rings of the NICA collider. In this case, the spin of

Zhipeng Hou, Qiang Zhang, Guizhou Xu, Senfu Zhang

The discovery of magnetic skyrmion bubbles in centrosymmetric magnets has been receiving increasing interest from the research community, due to the fascinating physics of topological spin textures and its possible applications to spintronics. However, key challenges remain, such as how to manipulate the nucleation of skyrmion bubbles to exclude the trivial

Xiao Wang, Ruijia Wang, Chuan Shi, Guojie Song

The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph. In recent years, we have witnessed an emerging research effort in exploring user-item graph for collaborative filtering methods. Nevertheless, the formation of user-item interactions typically arises from highly complex latent purchasing motiv

Jin-Liang Wang

Different to classical theories which emphasize east--west feedback along the equator, data analyses via the extreme-point symmetric mode decomposition (ESMD) method support a new viewpoint that the El Ni\~{n}o--Southern Oscillation (ENSO) cycle is horizontally dependent, rather than vertically dependent. The consistency between the ocean current changes and

Arnab Bhattacharyya, L. Sunil Chandran, Suprovat Ghoshal

A code is called a $q$-query locally decodable code (LDC) if there is a randomized decoding algorithm that, given an index $i$ and a received word $w$ close to an encoding of a message $x$, outputs $x_i$ by querying only at most $q$ coordinates of $w$. Understanding the tradeoffs between the dimension, length and query complexity of LDCs is a fascinating and

Zhipeng Hou, Qiang Zhang, Xichao Zhang, Guizhou Xu

Helicity indicates the in-plane magnetic-moment swirling direction of a skyrmionic configuration. The ability to reverse the helicity of a skyrmionic bubble via purely electrical means has been predicted in frustrated magnetic systems, however its experimental observation has remained challenging. Here, we experimentally demonstrate the current-driven helici

Daniel S. Seara, Benjamin B. Machta, Michael P. Murrell

Living and non-living active matter consumes energy at the microscopic scale to drive emergent, macroscopic behavior including traveling waves and coherent oscillations. Recent work has characterized non-equilibrium systems by their total energy dissipation, but little has been said about how dissipation manifests in distinct spatiotemporal patterns. We intr

Minghao Guo, Yuzhe Yang, Rui Xu, Ziwei Liu

Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks. Since then, extensive efforts have been devoted to enhancing the robustness of deep networks via specialized learning algorithms and loss functions. In this work, we take an architectural perspective and investigate the patterns of network architectures

A. Yu. Okulov

The propagation of stable coherent entities of an electromagnetic field in nonlinear media with parameters varying in space can be described in the framework of iterations of nonlinear integral transformations. It is shown that for a set of geometries relevant to typical problems of nonlinear optics, numerical modeling by reducing to dynamical systems with d

Gunnar Carlsson, Anjan Dwaraknath, Bradley J. Nelson

Over the past two decades, topological data analysis has emerged as a field of applied mathematics with new applications and algorithmic developments appearing rapidly. Two fundamental computations in this field are persistent homology and zigzag homology. In this paper, we show how these computations in the most general case reduce to finding a canonical fo

Mei Wang, Weihong Deng

Racial equality is an important theme of international human rights law, but it has been largely obscured when the overall face recognition accuracy is pursued blindly. More facts indicate racial bias indeed degrades the fairness of recognition system and the error rates on non-Caucasians are usually much higher than Caucasians. To encourage fairness, we int

Sardar Hamidian, Mona T Diab

With the pervasiveness of online media data as a source of information verifying the validity of this information is becoming even more important yet quite challenging. Rumors spread a large quantity of misinformation on microblogs. In this study we address two common issues within the context of microblog social media. First we detect rumors as a type of mi

David Harel, Rami Marelly, Assaf Marron, Smadar Szekely

In all software development projects, engineers face the challenge of translating the requirements layer into a design layer, then into an implementation-code layer, and then validating the correctness of the result. Many methodologies, languages and tools exist for facilitating the process, including multiple back-and-forth `refinement trips' across the req

Jonas Chaves-Montero, Carlos Hernandez-Monteagudo, Raul E. Angulo, J. D. Emberson

A complete census of baryons in the late universe is a long-standing challenge due to the intermediate temperate and rarefied character of the majority of cosmic gas. To gain insight into this problem, we extract measurements of the kinematic Sunyaev-Zel'dovich (kSZ) effect from the cross-correlation of angular redshift fluctuations maps, which contain preci

Seiichiro Onari, Hiroshi Kontani

In several Fe-based superconductors, slight $C_4$ symmetry breaking occurs at $T^*$, which is tens of Kelvin higher than the structural transition temperature $T_S$. In this "hidden" nematic state at $T_S<T<T^*$, the orthorhombicity is tiny [$\phi=(a-b)/(a+b) \ll 0.1$%], but clear evidences of bulk phase transition have been accumulated. To explain this long

Zhenyue Qin, Dongwoo Kim, Tom Gedeon

Mutual information is widely applied to learn latent representations of observations, whilst its implication in classification neural networks remain to be better explained. We show that optimising the parameters of classification neural networks with softmax cross-entropy is equivalent to maximising the mutual information between inputs and labels under the

Muneki Yasuda, Seishirou Ueno

In this study, we consider classification problems based on neural networks in data-imbalanced environment. Learning from an imbalanced data set is one of the most important and practical problems in the field of machine learning. A weighted loss function based on cost-sensitive approach is a well-known effective method for imbalanced data sets. We consider

Hejia Zhang, Jie Zhong, Stefanos Nikolaidis

People often watch videos on the web to learn how to cook new recipes, assemble furniture or repair a computer. We wish to enable robots with the very same capability. This is challenging; there is a large variation in manipulation actions and some videos even involve multiple persons, who collaborate by sharing and exchanging objects and tools. Furthermore,

Pouya Rezazadeh Kalehbasti, Yufei Miao, Gregory Andrew Forbes

Ultra-Fast Charging (UFC) is a rising technology that can shorten the time of charging an Electric Vehicle (EV) from hours to minutes. However, the power consumption characteristics of UFC bring new challenges to the existing power system, and its pros and cons are yet to be studied. This project aims to set up a framework for studying the different aspects

Yuguang Yang

Deep reinforcement learning for high dimensional, hierarchical control tasks usually requires the use of complex neural networks as functional approximators, which can lead to inefficiency, instability and even divergence in the training process. Here, we introduce stacked deep Q learning (SDQL), a flexible modularized deep reinforcement learning architectur

Xu Zhong, Elaheh ShafieiBavani, Antonio Jimeno Yepes

Important information that relates to a specific topic in a document is often organized in tabular format to assist readers with information retrieval and comparison, which may be difficult to provide in natural language. However, tabular data in unstructured digital documents, e.g., Portable Document Format (PDF) and images, are difficult to parse into stru

Zhiqiang Tan

Analysis of 2 by 2 tables and two-sample survival data has been widely used. Exact calculation is computational intractable for conditional likelihood inference in odds ratio models with large marginals in 2 by 2 tables, or partial likelihood inference in Cox's proportional hazards models with considerable tied event times. Approximate methods are often empl

Haisu Li, Mei Xian Low, Rajour Ako, Madhu Bhaskaran

A novel terahertz hybrid waveguide chip consisting of silicon photonic crystals sandwiched in parallel gold plates is developed. Both simulation and experimental results demonstrate that the hybrid waveguide offers a wide single-mode transmission window with low group velocity dispersion and low loss. This compact, substrate-free terahertz chip would play a

Yu Bao, Hao Zhou, Jiangtao Feng, Mingxuan Wang

Non-autoregressive models are promising on various text generation tasks. Previous work hardly considers to explicitly model the positions of generated words. However, position modeling is an essential problem in non-autoregressive text generation. In this study, we propose PNAT, which incorporates positions as a latent variable into the text generative proc

Chaoqin Huang, Fei Ye, Jinkun Cao, Maosen Li

With the recent advances in deep neural networks, anomaly detection in multimedia has received much attention in the computer vision community. While reconstruction-based methods have recently shown great promise for anomaly detection, the information equivalence among input and supervision for reconstruction tasks can not effectively force the network to le

Robert Page, Leon Zhang, Ruriko Yoshida

In 2019, Yoshida et al. introduced a notion of tropical principal component analysis (PCA). The output is a tropical polytope with a fixed number of vertices that best fits the data. We here apply tropical PCA to dimension reduction and visualization of data sampled from the space of phylogenetic trees. Our main results are twofold: the existence of a tropic

Zhenyu Weng, Yuesheng Zhu

Online hashing methods are efficient in learning the hash functions from the streaming data. However, when the hash functions change, the binary codes for the database have to be recomputed to guarantee the retrieval accuracy. Recomputing the binary codes by accumulating the whole database brings a timeliness challenge to the online retrieval process. In thi

Behnaz Pahlavsay, Elisa Palezzato, Michele Torielli

In combinatorics, a latin square is a $n\times n$ matrix filled with n different symbols, each occurring exactly once in each row and exactly once in each column. Associated to each latin square, we can define a simple graph called a latin square graph. In this article, we compute lower and upper bounds for the domination number and the k-tuple total dominat

Dongxu Wei, Xiaowei Xu, Haibin Shen, Kejie Huang

Human video motion transfer has a wide range of applications in multimedia, computer vision and graphics. Recently, due to the rapid development of Generative Adversarial Networks (GANs), there has been significant progress in the field. However, almost all existing GAN-based works are prone to address the mapping from human motions to video scenes, with sce

Bogdan Groza, Lucian Popa, Pal-Stefan Murvay

Previous research works have endorsed the use of delays and clock skews for detecting intrusions or fingerprinting ECUs on the CAN bus. Similar techniques have been also proposed for establishing a time-covert cryptographic authentication channel, in this way cleverly removing the need for cryptographic material inside the limited payload of CAN frames. The

Tomoya Naito, Ryosuke Akashi, Haozhao Liang, Shinji Tsuneyuki

The Breit correction, the finite-light-speed correction for the Coulomb interaction of the electron-electron interaction in $ O \left( 1/ c^2 \right) $, is introduced to density functional theory (DFT) based on the non-relativistic reduction with the local density approximation. Using this newly developed relativistic DFT, it is found that the possible outer

Toshiro Hiranouchi

We study the Galois symbol map associated to the multiplicative group and an abelian variety which has good ordinary reduction over a $p$-adic field. As a byproduct, one can calculate the "class group" in the view of the class field theory for curves over a $p$-adic field.

Makoto Morishita, Jun Suzuki, Masaaki Nagata

Recent machine translation algorithms mainly rely on parallel corpora. However, since the availability of parallel corpora remains limited, only some resource-rich language pairs can benefit from them. We constructed a parallel corpus for English-Japanese, for which the amount of publicly available parallel corpora is still limited. We constructed the parall

Patrick van Meurs

In this paper we derive the continuum limit of a multiple-species, interacting particle system by proving a $\Gamma$-convergence result on the interaction energy as the number of particles tends to infinity. As the leading application, we consider $n$ edge dislocations in multiple slip systems. Since the interaction potential of dislocations has a logarithmi

Henghui Zhu, Feng Nan, Zhiguo Wang, Ramesh Nallapati

Conversation structure is useful for both understanding the nature of conversation dynamics and for providing features for many downstream applications such as summarization of conversations. In this work, we define the problem of conversation structure modeling as identifying the parent utterance(s) to which each utterance in the conversation responds to. P

Seung Gyo Jeong, Taewon Min, Sungmin Woo, Jiwoong Kim

Artificial crystals synthesized by atomic-scale epitaxy provides the ability to control the dimensions of the quantum phases and associated phase transitions via precise thickness modulation. In particular, reduction in dimensionality via quantized control of atomic layers is a powerful approach to revealing hidden electronic and magnetic phases. Here, we de

Rene Carmona, Daniel Cooney, Christy Graves, Mathieu Lauriere

We consider static finite-player network games and their continuum analogs, graphon games. Existence and uniqueness results are provided, as well as convergence of the finite-player network game optimal strategy profiles to their analogs for the graphon games. We also show that equilibrium strategy profiles of a graphon game provide approximate Nash equilibr

Daniel Louis Jafferis, David K. Kolchmeyer

We compute the entanglement entropy and Renyi entropies of arbitrary pure states in pure Jackiw-Teitelboim gravity in Lorentz signature. We apply the quantum Hubeny-Rangamani-Ryu-Takayanagi formula by computing the quantum corrected area term and the bulk entropy term. The sum of these two terms for the Hartle-Hawking state agrees with the black hole entropy

Shang Gao, Fan Xiao, Kazuya Kamazawa, Kazuhiko Ikeuchi

The delafossite family of compounds with a triangular lattice of rare earth ions has been recently proposed as a candidate host for quantum spin liquid (QSL) states. To realize QSLs, the crystal-electric-field (CEF) ground state of the rare earth ions should be composed of a doublet that allows sizable quantum tunneling, but till now the knowledge on CEF sta

Daniel Lear, Roman Shvydkoy

In this note we reveal new classes of solutions to hydrodynamic Euler alignment systems governing collective behavior of flocks. The solutions describe unidirectional parallel motion of agents, and are globally well-posed in multi-dimensional settings subject to a threshold condition similar to the one dimensional case. We develop the flocking and stability

Nanshun Huang, Kai Li, Haixiao Deng

High-intensity, fully coherent X-ray radiation with a tunable polarization over a wide spectral range is of great importance to many experiments. In this paper, we propose a tapered crossed-polarized undulator configuration for X-ray free electron laser oscillator (XFELO) to produce arbitrarily polarized X-ray pulses in hard X-ray region. A numerical example

Tomosato Hioki, Rei Tsuboi, Tom. H. Johansen, Yusuke Hashimoto

We report experimental observation of the refraction and re ection of propagating magnetostatic spin waves crossing a 90-degree domain wall (DW) in terms of time-resolved magneto-optical imaging. Due to the magnetization rotation across the 90-degree DW, the dispersion relation of magnetostatic spin waves rotates by 90 degrees, which results in the change in

Wenye Ma

This paper considers online convex optimization (OCO) problems - the paramount framework for online learning algorithm design. The loss function of learning task in OCO setting is based on streaming data so that OCO is a powerful tool to model large scale applications such as online recommender systems. Meanwhile, real-world data are usually of extreme high-

Teaghan O'Briain, Kyong Hwan Jin, Hongyoon Choi, Erika Chin

We aim to reduce the tedious nature of developing and evaluating methods for aligning PET-CT scans from multiple patient visits. Current methods for registration rely on correspondences that are created manually by medical experts with 3D manipulation, or assisted alignments done by utilizing mutual information across CT scans that may not be consistent when

Zidu Lin, Cecilia Lunardini, Michele Zanolin, Kei Kotake

We introduce a novel methodology for establishing the presence of Standing Accretion Shock Instabilities (SASI) in the dynamics of a core collapse supernova from the observed neutrino event rate at water- or ice-based neutrino detectors. The methodology uses a likelihood ratio in the frequency domain as a test-statistics; it is also employed to assess the po

Xiu-Bin Wang, Bo Han

Under investigation in this work is an extended nonlinear Schr\"{o}dinger equation with nonzero boundary conditions, which can model the propagation of waves in dispersive media. Firstly, a matrix Riemann-Hilbert problem for the equation with nonzero boundary conditions at infinity is systematically discussed. Then the inverse problems are solved through the

Md Rashidul Hasan, Muntasir Al Kabir

Lung cancer is one of the death threatening diseases among human beings. Early and accurate detection of lung cancer can increase the survival rate from lung cancer. Computed Tomography (CT) images are commonly used for detecting the lung cancer.Using a data set of thousands of high-resolution lung scans collected from Kaggle competition [1], we will develop

James Wingate, Ilianna Kollia, Luc Bidaut, Stefanos Kollias

The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging. The approach includes analysis and use of the knowledge extracted by Deep Convolutional and Recurrent Neural Networks (DNNs) when trained with medical images, such as Magnetic Resonance Images and DaTscans. Internal representations of th

Alexandru D. Ionescu, Benoit Pausader

We prove definitive results on the global stability of the flat space among solutions of the Einstein-Klein-Gordon system. Our main theorems in this monograph include: (1) A proof of global regularity (in wave coordinates) of solutions of the Einstein-Klein-Gordon coupled system, in the case of small, smooth, and localized perturbations of the stationary Min

Ilan Price, Jared Tanner

This paper considers the growth in the length of one-dimensional trajectories as they are passed through deep ReLU neural networks, which, among other things, is one measure of the expressivity of deep networks. We generalise existing results, providing an alternative, simpler method for lower bounding expected trajectory growth through random networks, for

Andrei K. Svinin

Sequences of Genocchi numbers of the first and second kind are considered. For these numbers, an approach based on their representation using sequences of polynomials is developed. Based on this approach, for these numbers some identities generalizing the known identities are constructed.

Filippo A. E. Nuccio, Ramdorai Sujatha

For an odd prime $p$ and a supersingular elliptic curve over a number field, this article introduces a fine signed residual Selmer group, under certain hypotheses on the base field. This group depends purely on the residual representation at $p$, yet captures information about the Iwasawa theoretic invariants of the signed $p^\infty$-Selmer group that arise