November 2019 arXiv papers — page 11

Jaime Ramírez, M. Julia Flores

Music genre classification is one of the sub-disciplines of music information retrieval (MIR) with growing popularity among researchers, mainly due to the already open challenges. Although research has been prolific in terms of number of published works, the topic still suffers from a problem in its foundations: there is no clear and formal definition of wha

Rohit Kumar, Anirudh Sreeram, Anurenjan Purushothaman, Sriram Ganapathy

The state-of-art methods for acoustic beamforming in multi-channel ASR are based on a neural mask estimator that predicts the presence of speech and noise. These models are trained using a paired corpus of clean and noisy recordings (teacher model). In this paper, we attempt to move away from the requirements of having supervised clean recordings for trainin

Zhe Zhang, Ming Wu, Xinyu Han, Jun Yang

Many sound source localization (SSL) algorithms based on circular microphone array (CMA), including uniform circular array (UCA) and uniform concentric circular array (UCCA), have been well developed and verified via computer simulations and offline processing. On the other hand, beamforming in the harmonic domain has been shown to be a very efficient tool f

David Zimmerer, Jens Petersen, Simon A. A. Kohl, Klaus H. Maier-Hein

Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions. Previous work on deep-learning-based anomaly detection has primarily focused on the reconstruction error. We argue instead, that pixel-wise anomaly ratings derived from a Variational Autoencoder based score approximation

Jan-Willem Goossens, Hartmut Hafermann, Yves Jaouën

The nonlinear Fourier transform has the potential to overcome limits on performance and achievable data rates which arise in modern optical fiber communication systems when nonlinear interference is treated as noise. The periodic nonlinear Fourier transform (PNFT) has been much less investigated compared to its counterpart based on vanishing boundary conditi

Jan-Willem Goossens, Hartmut Hafermann, Yves Jaouën

The nonlinear Fourier transform (NFT) decomposes waveforms propagating through optical fiber into nonlinear degrees of freedom, which are preserved during transmission. By encoding information on the nonlinear spectrum, a transmission scheme inherently compatible with the nonlinear fiber is obtained. Despite potential advantages, the periodic NFT (PNFT) has

S. P. Glasby, Cheryl E. Praeger, W. R. Unger

We prove that most permutations of degree $n$ have some power which is a cycle of prime length approximately $\log n$. Explicitly, we show that for $n$ sufficiently large, the proportion of such elements is at least $1-5/\log\log n$ with the prime between $\log n$ and $(\log n)^{\log\log n}$. The proportion of even permutations with this property is at least

Swarup Chattopadhyay, Tanujit Chakraborty, Kuntal Ghosh, Asit K. das

Real-world networks are generally claimed to be scale-free, meaning that the degree distributions follow the classical power-law, at least asymptotically. Yet, closer observation shows that the classical power-law distribution is often inadequate to meet the data characteristics due to the existence of a clearly identifiable non-linearity in the entire degre

Sam F. Greenbury, Mauricio Barahona, Iain G. Johnston

The explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. Here, we describe a highly generalisable statistical platform to infer the dynamic pathways by which many, potentially interacting,

Zhiqiang Huang

The Nakajima-Zwanzig(N-Z) equation is a powerful tool to analyze the open quantum system and non-Markovian behavior. In this paper, we modify the N-Z equation with assignment maps. This approach extends the application to those situations in which the bipartite state of the system and environment contains non-negligible discord initial and/or during the dyna

Huifang Ma, Yue Wang, Rong Xiong, Sarath Kodagoda

Recent research on automotive driving developed an efficient end-to-end learning mode that directly maps visual input to control commands. However, it models distinct driving variations in a single network, which increases learning complexity and is less adaptive for modular integration. In this paper, we re-investigate human's driving style and propose to l

Mitsuo Higaki, Christophe Prange

We investigate regularity estimates for the stationary Navier-Stokes equations above a highly oscillating Lipschitz boundary with the no-slip boundary condition. Our main result is an improved Lipschitz regularity estimate at scales larger than the boundary layer thickness. We also obtain an improved $C^{1,\mu}$ estimate and identify the building blocks of t

V. I. Alekseev, V. A. Baskov, V. A. Dronov, A. I. L'vov

Presented are results of calibration, with a quasi-monochromatic electron beam, of a total absorption Cherenkov spectrometer based on the 14.8 X0 lead glass TF-1. It was found that the energy resolution of the spectrometer was 89% to 10% in the energy range of the electron beam E = 6 to 285 MeV, respectively.

Adrian Phoulady, Ole-Christoffer Granmo, Saeed Rahimi Gorji, Hady Ahmady Phoulady

The Tsetlin Machine (TM) is an interpretable mechanism for pattern recognition that constructs conjunctive clauses from data. The clauses capture frequent patterns with high discriminating power, providing increasing expression power with each additional clause. However, the resulting accuracy gain comes at the cost of linear growth in computation time and m

ATLAS Collaboration

This paper presents results of searches for electroweak production of supersymmetric particles in models with compressed mass spectra. The searches use 139 fb$^{-1}$ of $\sqrt{s}=13$ TeV proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider. Events with missing transverse momentum and two same-flavor, oppositely charged,

H. Kurokawa, Y. Kinoshita, F. Nabeshima, M. Tokunaga

The accurate understanding of flux dynamics is essential for the design and operation of superconducting circuits. The time evolution of flux-density distribution in an NbN strip by the transport current was observed using high-speed magneto-optical microscopy. It was determined that even for the dynamic penetration and exclusion of vortices under the transp

Patrick Peltzer, Johannes Lotz, Uwe Naumann

In this work we present useful techniques and possible enhancements when applying an Algorithmic Differentiation (AD) tool to the linear algebra library Eigen using our in-house AD by overloading (AD-O) tool dco/c++ as a case study. After outlining performance and feasibility issues when calculating derivatives for the official Eigen release, we propose Eige

Guanhua Zheng, Jitao Sang, Houqiang Li, Jian Yu

Existing generalization theories analyze the generalization performance mainly based on the model complexity and training process. The ignorance of the task properties, which results from the widely used IID assumption, makes these theories fail to interpret many generalization phenomena or guide practical learning tasks. In this paper, we propose a new Inde

Mingchuan Shao, Fei Liang, Haohai Yu, Huaijin Zhang

Nonlinear frequency conversion in optics can originate the coherent light at the wavelength where it is hard or unlikely to achieve by directly lasing and is a fundamental topic in science and engineering covering both classical and quantum regions. The critical requirement for efficient nonlinear frequency conversion is phase-matching, a momentum conservati

Gaurav Tomar, Sunghyun Kang, Stefano Scopel, Jong-Hyun Yoon

We show that the weakly interacting massive particle (WIMP) scenario of proton-philic spin-dependent inelastic dark matter can still provide a viable explanation of the observed DAMA effect in compliance with the constraints from other experiments. We also show that, although the COSINE-100 collaboration has recently tested the DAMA effect using the same tar

Johann Gehringer, Xue-Mei Li

We consider a system of differential equations in a fast long range dependent random environment and prove a homogenization theorem involving multiple scaling constants. The effective dynamics solves a rough differential equation, which is `equivalent' to a stochastic equation driven by mixed It\^o integrals and Young integrals with respect to Wiener process

Zi-xuan Ding, Zi-nan Huang, Ye Chen, Cheng-bo Mou

Ultrafast lasers generating high repetition rate ultrashort pulses through various mode-locking methods can benefit many important applications including communication, materials processing, astronomical observation, etc. For decades, mode-locking based on dissipative four-wave-mixing (DFWM) has been fundamental in producing pulses with repetition rates on t

Chaoyue Niu, Zhenzhe Zheng, Fan Wu, Shaojie Tang

The society's insatiable appetites for personal data are driving the emergency of data markets, allowing data consumers to launch customized queries over the datasets collected by a data broker from data owners. In this paper, we study how the data broker can maximize her cumulative revenue by posting reasonable prices for sequential queries. We thus propose

Luca Caltagirone, Lennart Svensson, Mattias Wahde, Martin Sanfridson

Recent advances in the field of machine learning and computer vision have enabled the development of fast and accurate road detectors. Commonly such systems are trained within a supervised learning paradigm where both an input sensor's data and the corresponding ground truth label must be provided. The task of generating labels is commonly carried out by hum

Peiwan Wang, Lu Zong, Ye Ma

This study constructs an integrated early warning system (EWS) that identifies and predicts stock market turbulence. Based on switching ARCH (SWARCH) filtering probabilities of the high volatility regime, the proposed EWS first classifies stock market crises according to an indicator function with thresholds dynamically selected by the two-peak method. A hyb

Yawei Zhao, Qian Zhao, Xingxing Zhang, En Zhu

As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in many previous researches, we still have little knowledge about the relation between the dynamic regret and the switching cost. In the paper, we investig

Mohammad Farrokhi Derakhshandeh Ghouchan, Ali Azimi

We introduce the factorization graph of a finite group and study its connectedness and forbidden structures. We characterize all finite groups with connected factorization graphs and classify those with connected bipartite factorization graphs. Also, we obtain a classification of all groups with claw-free, $K_{1,4}$-free, and square-free factorization graphs

David Mohaisen, Songqing Chen

Machine learning techniques are finding many applications in computer systems, including many tasks that require decision making: network optimization, quality of service assurance, and security. We believe machine learning systems are here to stay, and to materialize on their potential we advocate a fresh look at various key issues that need further attenti

B. Mohammed-Azizi

The main purpose of this paper is to rigorously establish the Strutinsky method from the least squares principle. Thus, it is the mathematical basis of this method (aspect often neglected) which is revisited in an extensive way. Some formulas previously given without demonstration or in a simplified way are set out here with all the details. In this respect,

Wolfgang Lucha, Dmitri Melikhov, Hagop Sazdjian

Considering quantum chromodynamics in the consistent limit of simultaneous correlated increase of the number of colours, $N_{\rm c}$, beyond bounds and decrease of the strong coupling to zero allows for solid statements about qualitative features of the class of "truly exotic" tetraquark mesons carrying four mutually distinct quark flavours. Consistency crit

Jan-Philipp Steghöfer, Eric Knauss, Jennifer Horkoff, Rebekka Wohlrab

Automotive companies increasingly adopt scaled agile methods to allow them to deal with their organisational and product complexity. Suitable methods are needed to ensure safety when developing automotive systems. On a small scale, R-Scrum and SafeScrum are two concrete suggestions for how to develop safety-critical systems using agile methods. However, for

M. E. Changa, M. A. Korolev

In the paper, we establish a new estimate for Kloosterman sum over primes with respect to an arbitrary modulus $q$. This estimate together with some recent results of the second author are applied to the problem of solvability of the congruence \[ g(p_{1})\, + \,\ldots\, + \,g(p_{k}) \,\equiv\, m\pmod{q} \] in prime variables $p_{1},\ldots, p_{k}\le N$, $N\l

Rong Zhang, Wei Li, Peng Wang, Chenye Guan

Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm \emph{AutoRemover}, designed specifically for generating street-view videos without any moving objects. In our setup we have two challenges: the first is the shadow, shadows are usually unlabeled but tightly coupled with the movin

Sebastian Schelter, Yuxuan He, Jatin Khilnani, Julia Stoyanovich

The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact computational processes are missed if we do no

Stavros Christopoulos, Peter Moroshkin, Lars Weller, Benedikt Gerwers

Optical spectroscopy of atoms and molecules is a field where one usually operates very far from thermal equilibrium conditions. A prominent example is spectroscopy of thin vapors, where the pump irradiation leads to a non equilibrium distribution within the electronic structure that is well shielded from the environment. Here we describe experimental work in

Kyungduck Yoon, Shijie Qin, Siyao Shao, Jiarong Hong

Our study provides the first experimental investigation of the internal flows of ventilated partial cavitation (VPC) formed by air injection behind a backward-facing step. The experiments are conducted using flow visualization and planar particle image velocimetry (PIV) with fog particles for two different cavity regimes of VPC, i.e., open cavity (OC) and tw

C. Moritz Carmesin, Peter Kling, Enno Giese, Roland Sauerbrey

In a quantum mechanical description of the free-electron laser (FEL) the electrons jump on discrete momentum ladders, while they follow continuous trajectories according to the classical description. In order to observe the transition from quantum to classical dynamics, it is not sufficient that many momentum levels are involved. Only if additionally the ini

Changrim Ahn, Janos Balog, Francesco Ravanini

We complete the derivation of the sausage model NLIE by giving a proof of the crucial relation (3.24) of the original paper based on the analytic properties of $Q$ and $\bar Q$.

Wanling Rudkin, Charlie X Cai

Listing on the Dow Jones Sustainability Index is seen as a gold-standard, verifying to the market that a firm is fully engaged with a corporate social responsibility agenda. Robustly quantifying the impact of listing, and de-listing, against any industry level shocks, as well as evolution in the competitive relationship between firms within the industry, pro

R. -F. Shen, C. D. Matzner, A. W. Howard, W. Zhang

At about 70 solar masses, the recently-discovered dark object orbited by a B-type star in the system LB-1 is difficult to understand as the end point of standard stellar evolution, except as a binary black hole (BBH). LB-1 shows a strong, broad H-alpha emission line that is best attributed to a gaseous disk surrounding the dark mass. We use the observed H-al

Zheyan Shen, Peng Cui, Tong Zhang, Kun Kuang

We consider the problem of learning linear prediction models with model misspecification bias. In such case, the collinearity among input variables may inflate the error of parameter estimation, resulting in instability of prediction results when training and test distributions do not match. In this paper we theoretically analyze this fundamental problem and

Wazir Ali, Jay Kumar, Junyu Lu, Zenglin Xu

Representing words and phrases into dense vectors of real numbers which encode semantic and syntactic properties is a vital constituent in natural language processing (NLP). The success of neural network (NN) models in NLP largely rely on such dense word representations learned on the large unlabeled corpus. Sindhi is one of the rich morphological language,

Y. Seino, T. Inada, T. Yamazaki, T. Namba

Quantum electrodynamics predicts x-ray diffractions under a high-intensity laser field via virtual charged particles, and this phenomenon is called as vacuum diffraction (VD). In this paper, we derive a new formula to describe VD in a head-on collision geometry of an XFEL pulse and a laser pulse. A wavefront curvature of the XFEL pulse is newly considered in

Pepijn Demol, Thomas Duguet, Andreas Ekström, Mikael Frosini

Quantum many-body theory has witnessed tremendous progress in various fields, ranging from atomic and solid-state physics to quantum chemistry and nuclear structure. Due to the inherent computational burden linked to the ab initio treatment of microscopic fermionic systems, it is desirable to obtain accurate results through low-order perturbation theory. In

Oliver Müller, Ana Vudragović, Michal Bílek

Stellar streams are regarded as crucial objects to test galaxy formation models, with their morphology tracing the underlying potentials and their occurrence tracking the assembly history of the galaxies. The existence of one of the most iconic stellar streams, the double loop around NGC5907, has recently been questioned by new observations with the Dragonfl

Kenji Homma, Yu Liu, Masato Sumita, Ryo Tamura

Mixing heterogeneous Li-ion conductive materials is one of potential ways to enhance the Li-ion conductivity more than that of the parent materials. However, the development of the mixtures had not exhibited significant progress because it is a formidable task to cover the vast possible composition of the parent materials using traditional ways. Here, we int

ATLAS Collaboration

A search is presented for pair-production of long-lived neutral particles using 33 fb$^{-1}$ of $\sqrt{s} = 13$ TeV proton-proton collision data, collected during 2016 by the ATLAS detector at the LHC. This search focuses on a topology in which one long-lived particle decays in the ATLAS inner detector and the other decays in the muon spectrometer. Special t

Qi Zhou, Houqiang Li, Jie Wang

Model-based reinforcement learning algorithms tend to achieve higher sample efficiency than model-free methods. However, due to the inevitable errors of learned models, model-based methods struggle to achieve the same asymptotic performance as model-free methods. In this paper, We propose a Policy Optimization method with Model-Based Uncertainty (POMBU)---a

F. Zaidi, H. Haider, M. Sajjad Athar, S. K. Singh

We study the effect of various perturbative and nonperturbative QCD corrections on the free nucleon structure functions ($F_{iN}^{WI}(x,Q^2); ~i=1-3$) and their implications in the determination of nuclear structure functions. The evaluation of the nucleon structure functions has been performed by using the MMHT 2014 PDFs parameterization, and the TMC and HT

Dimitry Leites

The definition of Kaehler manifold is superized. In the super setting, it admits a continuous parameter, unlike their analogs on manifolds. This parameter runs the same singular supervariety of parameters that parameterize deformations of the Schouten bracket (a.k.a. Buttin bracket, a.k.a. anti-bracket) considered as deformations of the Lie superalgebra stru

Jagjit Singh, W. Horiuchi

Recently, the energy spectrum of $^{10}$Li was measured upto 4.6 MeV, via one-neutron transfer reaction d($^{9}$Li, p)$^{10}$Li. Considering the ambiguities on the $^{10}$Li continuum spectrum with reference to new data, we report the configuration mixing in the ground state of the two-neutron halo nucleus $^{11}$Li for two different choices of the $^{9}$Li$

Hezi Halawi, Avner Segal

In this paper, we study the degenerate principal series of a split, simply-connected, simple p-adic group of type $E_7$. We determine the points of reducibility and the maximal semi-simple subrepresentation at each point.

Abhishek Kumar, Asif Ekbal, Daisuke Kawahra, Sadao Kurohashi

In this paper, we propose a two-layered multi-task attention based neural network that performs sentiment analysis through emotion analysis. The proposed approach is based on Bidirectional Long Short-Term Memory and uses Distributional Thesaurus as a source of external knowledge to improve the sentiment and emotion prediction. The proposed system has two lev

Ramya Korlakai Vinayak, Weihao Kong, Sham M. Kakade

Paired estimation of change in parameters of interest over a population plays a central role in several application domains including those in the social sciences, epidemiology, medicine and biology. In these domains, the size of the population under study is often very large, however, the number of observations available per individual in the population is

Thomas Chatain, Mathilde Boltenhagen, Josep Carmona

Processes are a crucial artefact in organizations, since they coordinate the execution of activities so that products and services are provided. The use of models to analyse the underlying processes is a well-known practice. However, due to the complexity and continuous evolution of their processes, organizations need an effective way of analysing the relati

Min-Kyu Shin, Daniel Lee, Han-Lim Choi

In this paper, we propose an approach to formulate the Weapon Target Assignment (WTA) problem with physical and seeker interference constraints which is solvable in Mixed Integer Linear Programming (MILP). To handle the interference constraint which is intractable in continuous time domain, we discretize the time window and generate predicted intercept point

Anh D. Phan, Cuong V. Nguyen, Pham T. Linh, Tran V. Huynh

We theoretically investigate the plasmonic properties of mid-infrared graphene-based metamaterials and apply deep learning of a neural network for the inverse design. These artificial structures have square periodic arrays of graphene plasmonic resonators deposited on dielectric thin films. Optical spectra vary significantly with changes in structural parame

Tao Fan, Artem R. Oganov

mDCThermalC is a program written in Python for computing lattice thermal conductivity of crystalline bulk materials using the modified Debye-Callaway model. Building upon the traditional Debye-Callaway theory, the modified model obtains the lattice thermal conductivity by averaging the contributions from acoustic and optical branches based on their specific

Yixiang Wang, Feng Wu

We propose a novel approach to address one aspect of the non-stationarity problem in multi-agent reinforcement learning (RL), where the other agents may alter their policies due to environment changes during execution. This violates the Markov assumption that governs most single-agent RL methods and is one of the key challenges in multi-agent RL. To tackle t

Simone Floreani, Frank Redig, Federico Sau

In this paper, we introduce a random environment for the exclusion process in $\mathbb{Z}^d$ obtained by assigning a maximal occupancy to each site. This maximal occupancy is allowed to randomly vary among sites, and partial exclusion occurs. Under the assumption of ergodicity under translation and uniform ellipticity of the environment, we derive a quenched

Onno R. Diermann, Martin Holthaus

We demonstrate that a periodically driven quantum system can adopt a quasistationary state which is effectively much colder than a thermal reservoir it is coupled to, in the sense that certain Floquet states of the driven-dissipative system can carry much higher population than the ground state of the corresponding undriven system in thermal equilibrium. Thi

Yingzhe He, Guozhu Meng, Kai Chen, Xingbo Hu

Deep learning has gained tremendous success and great popularity in the past few years. However, deep learning systems are suffering several inherent weaknesses, which can threaten the security of learning models. Deep learning's wide use further magnifies the impact and consequences. To this end, lots of research has been conducted with the purpose of exhau

Run-Qiu Yang

This paper studies cross-sections inside black holes and conjectures a universal inequality: in a static $(d+1)$-dimensional asymptotically planar/spherical Schwarzschild-AdS spacetime of given energy $E$ and AdS radius $\ell_{\text{AdS}}$, the ``size of cross-section'' inside black holes is bounded by $8\pi E\ell_{\text{AdS}}/(d-1)$. To support this conject

Jierui Lin, Min Du, Jian Liu

Federated learning is a recently proposed paradigm that enables multiple clients to collaboratively train a joint model. It allows clients to train models locally, and leverages the parameter server to generate a global model by aggregating the locally submitted gradient updates at each round. Although the incentive model for federated learning has not been

Elizaveta Rebrova, Deanna Needell

Projection-based iterative methods for solving large over-determined linear systems are well-known for their simplicity and computational efficiency. It is also known that the correct choice of a sketching procedure (i.e., preprocessing steps that reduce the dimension of each iteration) can improve the performance of iterative methods in multiple ways, such

Ygor Gallina, Florian Boudin, Béatrice Daille

Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily available for the scholarly domain and include non-expert annotations. In this paper we present KPTimes, a large-scale dataset of news texts paired with editor-curated keyphrases. Ex

Hao Liao, Jiao Wu, Mingyang Zhou, Alexandre Vidmer

The goal of the ranking problem in networks is to rank nodes from best to worst, according to a chosen criterion. In this work, we focus on ranking the nodes according to their quality. The problem of ranking the nodes in bipartite networks is valuable for many real-world applications. For instance, high-quality products can be promoted on an online shop or

Junyi Liu, Li Zhou, Gilles Barthe, Mingsheng Ying

We study expected runtimes for quantum programs. Inspired by recent work on probabilistic programs, we first define expected runtime as a generalisation of quantum weakest precondition. Then, we show that the expected runtime of a quantum program can be represented as the expectation of an observable (in physics). A method for computing the expected runtimes

Zhengqiu He, Wenliang Chen, Yuyi Wang, Wei zhang

We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning. This approach first applies reinforcement learning to decide whether a sentence is positive to a given relation, and then positive and unlabeled bags are constructed. In contrast to most previous studies, which mainly u

Ao Li, Jemin Andrew Choi, Fan Long

We present Solythesis, a source to source Solidity compiler which takes a smart contract code and a user specified invariant as the input and produces an instrumented contract that rejects all transactions that violate the invariant. The design of Solythesis is driven by our observation that the consensus protocol and the storage layer are the primary and th

Jian Xu, Engui Fan

In this paper, we investigate the long-time asymptotic behavior of the solution to the initial value problem for the modified Camassa-Holm (mCH) equation with cubic nonlinearity. The equation is known to be integrable, which we mean it admits an Lax pair. We formulate the initial value problem as an associate vector Riemann-Hilbert problem, which allows us t

Ashutosh Kumar Tiwari, Sandeep Varma Nadimpalli

Model-based reinforcement learning strategies are believed to exhibit more significant sample complexity than model-free strategies to control dynamical systems,such as quadcopters.This belief that Model-based strategies that involve the use of well-trained neural networks for making such high-level decisions always give better performance can be dispelled b

Ye Lin, Keren Fu, Shenggui Ling, Cheng Peng

Unsupervised multi-domain image-to-image translation aims to synthesis images among multiple domains without labeled data, which is more general and complicated than one-to-one image mapping. However, existing methods mainly focus on reducing the large costs of modeling and do not pay enough attention to the quality of generated images. In some target domain

Brecken Beers, Yih Sung

For a given elliptic curve, its associated $L$-function evaluated at $1$ is closely related to its real period. In this article, we generalize this principle to a rational curve. We count the rational points over all finite fields and use all the counting information to define two $L$-type series. Then we consider special values of these series at $1$. One o

Ayreena Bakhtawar

In this article we aim to investigate the Hausdorff dimension of the set of points $x \in [0,1)$ such that for any $r\in\mathbb{N},$ \begin{align*} a_{n+1}(x)a_{n+2}(x)\cdots a_{n+r}(x)\geq e^{\tau(x)(h(x)+\cdots+h(T^{n-1}(x)))} {align*} holds for infinitely many $n\in\mathbb{N},$ where $h$ and $\tau$ are positive continuous functions, $T$ is the Gauss map a

Victoria S. Scotney, Jasmine Schwartz, Nicole Carbert, Adam Saab

Creative thought is conventionally believed to involve searching memory and generating multiple independent candidate ideas followed by selection and refinement of the most promising. Honing theory, which grew out of the quantum approach to describing how concepts interact, posits that what appears to be discrete, separate ideas are actually different projec

Jordan Burns, David Maughan, Yih Sung

We present a new machine learning technique which calculates a real-valued, time independent, finite dimensional Hamiltonian matrix from only experimental data. A novel cost function is given along with a proof that the cost function has the theoretically correct Hamiltonian as a global minimum. We present results based on data simulated on a classical compu

Shafiq Joty, Francisco Guzman, Lluis Marquez, Preslav Nakov

We present novel automatic metrics for machine translation evaluation that use discourse structure and convolution kernels to compare the discourse tree of an automatic translation with that of the human reference. We experiment with five transformations and augmentations of a base discourse tree representation based on the rhetorical structure theory, and w

Christopher X. Ren, Amanda Ziemann, Alice M. S. Durieux, James Theiler

This paper introduces a new method of generating realistic pervasive changes in the context of evaluating the effectiveness of change detection algorithms in controlled settings. The method, a cycle-consistent adversarial network (CycleGAN), requires low quantities of training data to generate realistic changes. Here we show an application of CycleGAN in cre

Rujun Jiang, Man-Chung Yue, Zhishuo Zhou

We propose a first-order method to solve the cubic regularization subproblem (CRS) based on a novel reformulation. The reformulation is a constrained convex optimization problem whose feasible region admits an easily computable projection. Our reformulation requires computing the minimum eigenvalue of the Hessian. To avoid the expensive computation of the ex

Veselin Raychev, Preslav Nakov

We describe a novel language-independent approach to the task of determining the polarity, positive or negative, of the author's opinion on a specific topic in natural language text. In particular, weights are assigned to attributes, individual words or word bi-grams, based on their position and on their likelihood of being subjective. The subjectivity of ea

Zhengbao Jiang, Frank F. Xu, Jun Araki, Graham Neubig

Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as "Obama is a _ by profession". These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as "Obama worked as a _" may result in more accurately predicting the correc

Guanglong Yu, by Shuguang Guo, Lin Sun, Hailiang Zhang

Among all simple 2-connected graphs, and among all $\theta$-graphs, the graphs with the minimum algebraic connectivity are completely determined, respectively.

Guanglong Yu, by Lin Sun, Chao Yan, Yarong Wu

Among all simple nonbipartite 2-connected graphs and among all nonbipartite $\theta$-graphs, the minimum least $Q$-eigenvalues are completely determined, respectively.

Ehsan Hoseinzade, Saman Haratizadeh, Arash Khoeini

The performance of financial market prediction systems depends heavily on the quality of features it is using. While researchers have used various techniques for enhancing the stock specific features, less attention has been paid to extracting features that represent general mechanism of financial markets. In this paper, we investigate the importance of extr

Yutaro Akahoshi, Sinya Aoki, Tatsumi Aoyama, Takaya Miyamoto

In this paper, we report recent developments of the HAL QCD method for two hadron systems which contain quark annihilation processes using all-to-all quark propagators. We employ the hybrid method for all-to-all propagators, which combines a low-mode spectral decomposition of the quark propagator and stochastic estimators for remaining high modes, to evaluat

Weijie Yuan, Zhiqiang Wei, Jinhong Yuan, Derrick Wing Kwan Ng

The emerging orthogonal time frequency space (OTFS) modulation technique has shown its superiority to the current orthogonal frequency division multiplexing (OFDM) scheme, in terms of its capabilities of exploiting full time-frequency diversity and coping with channel dynamics. The optimal maximum a posteriori (MAP) detection is capable of eliminating the ne

Xintong Ling, Yuwei Le, Jiaheng Wang, Zhi Ding

The continually rising demand for wireless services and applications in the era of Internet of things (IoT) and artificial intelligence (AI) presents a significant number of unprecedented challenges to existing network structures. To meet the rapid growth need of mobile data services, blockchain radio access network (B-RAN) has emerged as a decentralized, tr

Ming Gong, Feihu Xu, Zheng-Da Li, Zizhu Wang

Quantum resetting protocols allow a quantum system to be sent to a state in the past by making it interact with quantum probes when neither the free evolution of the system nor the interaction is controlled. We experimentally verify the simplest non-trivial case of a quantum resetting protocol, known as the $\mathcal{W}_4$ protocol, with five superconducting

Xiaobo Liu, Chuu-Lian Terng

Mean curvature flow for isoparametric submanifolds in Euclidean spaces and spheres was studied by the authors in [LT]. In this paper, we will show that all these solutions are ancient solutions. We also discuss rigidity of ancient mean curvature flows for hypersurfaces in spheres and its relation to the Chern's conjecture on the norm of the second fundamenta

Yun Feng, Han-Xiong Li

Identification of abnormal source hidden in distributed parameter systems (DPSs) belongs to the category of inverse source problems. It is important in industrial applications but seldom studied. In this paper, we make the first attempt to investigate the abnormal spatio-temporal (S-T) source identification for a class of DPSs. An inverse S-T model for abnor

Koichi Nagasaki

We consider a D5-brane solution in AdS black hole spacetime. This is a defect solution moving in subspace of AdS5 x S5. This non-local object is realized by the probe D5-brane moving in black hole spacetime. We found this probe brane does not penetrate the black hole horizon. We also found the solution does not depend on the motion on S5 subspace.

R. Thomas, L. Pentericci, O. Le Fèvre, G. Zamorani

Aims. Our aim is to estimate the intergalactic medium transmission towards UV-selected star-forming galaxies at redshift 4 and above and study the effect of the dust attenuation on these measurements. Methods. The ultra-violet spectrum of high redshift galaxies is a combination of their intrinsic emission and the effect of the Inter-Galactic medium (IGM) abs

Ryan Vaughn, Tyrus Berry, Harbir Antil

Given only a collection of points sampled from a Riemannian manifold embedded in a Euclidean space, in this paper we propose a new method to solve elliptic partial differential equations (PDEs) supplemented with boundary conditions. Notice that the construction of triangulations on unknown manifolds can be both difficult and expensive, both in terms of compu

Xu-Chang Zheng, Chao-Hsi Chang, Xing-Gang Wu, Jun Zeng

In this paper, we calculate the total decay widths for the $W^+$-boson decays, $W^+ \to B_c+b+\bar{s}+X$ and $W^+ \to B^*_c+b+\bar{s}+X$, up to next-to-leading order (NLO) accuracy within the framework of the nonrelativistic QCD theory. Both the fixed-order and the fragmentation approaches are adopted to do the calculation. Differential decay widths $d\Gamma

Belle Collaboration, Y. Li, Y. B. Li, C. P. Shen

Using the data sample of 711 fb$^{-1}$ of $\Upsilon(4S)$ on-resonance data taken by the Belle detector at the KEKB asymmetric-energy electron-positron collider, we present the first measurements of branching fractions of the decays $B^{-} \to \bar{\Lambda}_{c}^{-} \Xi_{c}^{'0}$, $B^{-} \to \bar{\Lambda}_{c}^{-} \Xi_{c}(2645)^{0}$, and $B^{-} \to \bar{\Lambda

Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen, Tyng-Luh Liu

This paper aims to tackle the challenging problem of one-shot object detection. Given a query image patch whose class label is not included in the training data, the goal of the task is to detect all instances of the same class in a target image. To this end, we develop a novel {\em co-attention and co-excitation} (CoAE) framework that makes contributions in

Istvan Fehervari, Avinash Ravichandran, Srikar Appalaraju

Deep metric learning (DML) is a popular approach for images retrieval, solving verification (same or not) problems and addressing open set classification. Arguably, the most common DML approach is with triplet loss, despite significant advances in the area of DML. Triplet loss suffers from several issues such as collapse of the embeddings, high sensitivity t

Kang Zhou, Shenghua Gao, Jun Cheng, Zaiwang Gu

With the development of convolutional neural network, deep learning has shown its success for retinal disease detection from optical coherence tomography (OCT) images. However, deep learning often relies on large scale labelled data for training, which is oftentimes challenging especially for disease with low occurrence. Moreover, a deep learning system trai

Logan Stokols

In this paper we study small shocks of 1D scalar viscous conservation laws with uniformly convex flux and nonlinear dissipation. We show that such shocks are L2 stable independent of the strength of the dissipation, even with large perturbations. The proof uses the relative entropy method with a spatially-inhomogeneous psuedo-norm.