November 2019 arXiv papers — page 38

Qile Chen, Felix Janda, Rachel Webb

We generalize the results of Chang-Li, Kim-Oh and Chang-Li on the moduli of $p$-fields to the setting of (quasi-)maps to complete intersections in arbitrary smooth Deligne-Mumford stacks with projective coarse moduli. In particular, we show that the virtual cycle of stable (quasi-)maps to a complete intersection can be recovered by the cosection localized vi

Erhan Bayraktar, Ibrahim Ekren, Xin Zhang

We explicitly solve the nonlinear PDE that is the continuous limit of dynamic programming of \emph{expert prediction problem} in finite horizon setting with $N=4$ experts. The \emph{expert prediction problem} is formulated as a zero sum game between a player and an adversary. By showing that the solution is $\mathcal{C}^2$, we are able to show that the strat

M. Bzowski, J. Heerikhuisen

Models play an important role in our understanding of the global structure of the solar wind and its interaction with the interstellar medium. A critical ingredient in many types of models are the charge-exchange collisions between ions and neutrals. Some ambiguity exists in the charge-exchange cross-section for protons and hydrogen atoms, depending on which

Xiaomei Li, Congbing Tan, Peng Gao, Yuanwei Sun

The ability to controllably manipulate the complex topological polar configurations, such as polar flux-closure via external stimuli, enables many applications in electromechanical devices and nanoelectronics including high-density information storage. Here, by using the atomically resolved in situ scanning transmission electron microscopy, we find that a po

Doyong Um, Ralph Willox, Basil Grammaticos, Alfred Ramani

The discrete KdV (dKdV) equation, the pinnacle of discrete integrability, is often thought to possess the singularity confinement property because it confines on an elementary quadrilateral. Here we investigate the singularity structure of the dKdV equation through reductions of the equation, obtained for initial conditions on a staircase with height 1, and

Kohtaro Kato, Fernando G. S. L. Brandão

Topological entanglement entropy has been extensively used as an indicator of topologically ordered phases. We study the conditions needed for two-dimensional topologically trivial states to exhibit spurious contributions that contaminates topological entanglement entropy. We show that if the state at the boundary of a subregion is a stabilizer state, then i

Mingyang Zhang, Xinyi Yu, Jingtao Rong, Linlin Ou

Previous AutoML pruning works utilized individual layer features to automatically prune filters. We analyze the correlation for two layers from the different blocks which have a short-cut structure. It shows that, in one block, the deeper layer has many redundant filters which can be represented by filters in the former layer. So, it is necessary to take inf

Szu-Chi Chung, Shao-Hsuan Wang, Po-Yao Niu, Su-Yun Huang

Principal component analysis (PCA) is arguably the most widely used dimension-reduction method for vector-type data. When applied to a sample of images, PCA requires vectorization of the image data, which in turn entails solving an eigenvalue problem for the sample covariance matrix. We propose herein a two-stage dimension reduction (2SDR) method for image r

Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, Avradip Mandal, Sarvagya Upadhyay

As we approach the physical limits predicted by Moore's law, a variety of specialized hardware is emerging to tackle specialized tasks in different domains. Within combinatorial optimization, adiabatic quantum computers, CMOS annealers, and optical parametric oscillators are few of the emerging specialized hardware technology aimed at solving optimization pr

T. C. Adorno, S. P. Gavrilov, D. M. Gitman

Basic quantum processes (such as particle creation, reflection, and transmission on the corresponding Klein steps) caused by inverse-square electric fields are calculated. These results represent a new example of exact nonperturbative calculations in the framework of QED. The inverse-square electric field is time-independent, inhomogeneous in the $x$-directi

Barnaby R. M. Norris, Nick Cvetojevic, Tiphaine Lagadec, Nemanja Jovanovic

The characterisation of exoplanets is critical to understanding planet diversity and formation, their atmospheric composition and the potential for life. This endeavour is greatly enhanced when light from the planet can be spatially separated from that of the host star. One potential method is nulling interferometry, where the contaminating starlight is remo

Hoa Van Nguyen, Hamid Rezatofighi, Ba-Ngu Vo, Damith C. Ranasinghe

We consider the challenging problem of online planning for a team of agents to autonomously search and track a time-varying number of mobile objects under the practical constraint of detection range limited onboard sensors. A standard POMDP with a value function that either encourages discovery or accurate tracking of mobile objects is inadequate to simultan

Dario Paccagnan, Rahul Chandan, Bryce L Ferguson, Jason R Marden

How can we design mechanisms to promote efficient use of shared resources? Here, we answer this question in relation to the well-studied class of atomic congestion games, used to model a variety of problems, including traffic routing. Within this context, a methodology for designing tolling mechanisms that minimize the system inefficiency (price of anarchy)

Xiang Huang, Qingbin Zhang, Shengliang Xu, Xianglong Fu

The full three-dimensional photoelectron momentum distributions of argon are measured in intense near-circularly polarized laser fields. We observed that the transverse momentum distribution of ejected electrons by 410-nm near-circularly polarized field is unexpectedly narrowed with increasing laser intensity, which is contrary to the conventional rules pred

Zhijie Deng, Yucen Luo, Jun Zhu, Bo Zhang

Bayesian neural networks (BNNs) augment deep networks with uncertainty quantification by Bayesian treatment of the network weights. However, such models face the challenge of Bayesian inference in a high-dimensional and usually over-parameterized space. This paper investigates a new line of Bayesian deep learning by performing Bayesian inference on network s

Alejandro Ceballos, Akshay Pattabi, Amal El-Ghazaly, Sergiu Ruta

Ultrafast control of the magnetization in ps timescales by fs laser pulses offers an attractive avenue for applications such as fast magnetic devices for logic and memory. However, ultrafast helicity-independent all-optical switching (HI-AOS) of the magnetization has thus far only been observed in Gd-based, ferrimagnetic amorphous (\textit{a}-) rare earth-tr

Ting Ye, Jun Shao, Hyunseung Kang

Mendelian randomization (MR) has become a popular approach to study the effect of a modifiable exposure on an outcome by using genetic variants as instrumental variables. A challenge in MR is that each genetic variant explains a relatively small proportion of variance in the exposure and there are many such variants, a setting known as many weak instruments.

Sebastian Bruch

Listwise learning-to-rank methods form a powerful class of ranking algorithms that are widely adopted in applications such as information retrieval. These algorithms learn to rank a set of items by optimizing a loss that is a function of the entire set -- as a surrogate to a typically non-differentiable ranking metric. Despite their empirical success, existi

Li-Gang Cao, Shi-Sheng Zhang, H. Sagawa

Gamow-Teller (GT) and spin-dipole (SD) strength distributions of four doubly magic nuclei $^{48}$Ca, $^{90}$Zr, $^{132}$Sn and $^{208}$Pb are studied by the self-consistent Hartree-Fock plus random phase approximation (RPA) method. The Skyrme forces SAMi and SAMi-T without/with tensor interactions are adopted in our calculations. The calculated strengths are

Manoel F. Sousa, Jaziel G. Coelho, José C. N. de Araujo

Gravitational waves (GWs) emission due to magnetic deformation mechanism is applied for Soft Gamma Repeaters (SGRs) and Anomalous X-Ray Pulsars(AXPs), described as fast-spinning and magnetized white dwarfs (WDs). The emission is caused by the asymmetry around the rotation axis of the star generated by its own intense magnetic field. Thus, for the first time

Robert C. Dalang, Cheuk Yin Lee, Carl Mueller, Yimin Xiao

This paper is concerned with the existence of multiple points of Gaussian random fields. Under the framework of Dalang et al. (2017), we prove that, for a wide class of Gaussian random fields, multiple points do not exist in critical dimensions. The result is applicable to fractional Brownian sheets and the solutions of systems of stochastic heat and wave eq

Pablo Arnault, Adrian Macquet, Andreu Anglés-Castillo, Iván Márquez-Martín

Two models are first presented, of one-dimensional discrete-time quantum walk (DTQW) with temporal noise on the internal degree of freedom (i.e., the coin): (i) a model with both a coin-flip and a phase-flip channel, and (ii) a model with random coin unitaries. It is then shown that both these models admit a common limit in the spacetime continuum, namely, a

James M. Stevenson, Leif D. Jacobson, Yutong Zhao, Chuanjie Wu

We have developed a neural network potential energy function for use in drug discovery, with chemical element support extended from 41% to 94% of druglike molecules based on ChEMBL. We expand on the work of Smith et al., with their highly accurate network for the elements H, C, N, O, creating a network for H, C, N, O, S, F, Cl, P. We focus particularly on th

Alexander Ziller, Julius Hansjakob, Vitalii Rusinov, Daniel Zügner

We release a realistic, diverse, and challenging dataset for object detection on images. The data was recorded at a beer tent in Germany and consists of 15 different categories of food and drink items. We created more than 2,500 object annotations by hand for 1,110 images captured by a video camera above the checkout. We further make available the remaining

Bo Chen, Decai Li, Yuqing He, Chunsheng Hua

Technologies to predict human actions are extremely important for applications such as human robot cooperation and autonomous driving. However, a majority of the existing algorithms focus on exploiting visual features of the videos and do not consider the mining of relationships, which include spatial relationships between human and scene elements as well as

Niloofar Bayat, Kunal Mahajan, Sam Denton, Vishal Misra

Despite society's strong dependence on electricity, power outages remain prevalent. Standard methods for directly measuring power availability are complex, often inaccurate, and are prone to attack. This paper explores an alternative approach to identifying power outages through intelligent monitoring of IP address availability. In finding these outages,

Xiaotian Li, Guang Zhang, Chao Wang, Lichen He

Fresh water scarcity is one of the critical challenges for global sustainable development. Several novel water resources such as passive seawater solar desalination and atmospheric water harvesting have made some progress in recent years. However, no investigation has referred to harvesting water from shallow subsurface soils, which are potential huge water

Marek Karliner, Jonathan L. Rosner

We comment on the results of the recent search by the LHCb collaboration for the doubly charmed baryon $Ξ_{cc}^+$.

Xin Fang, Stratis Ioannidis, Miriam Leeser

Secure Function Evaluation (SFE) has received recent attention due to the massive collection and mining of personal data, but remains impractical due to its large computational cost. Garbled Circuits (GC) is a protocol for implementing SFE which can evaluate any function that can be expressed as a Boolean circuit and obtain the result while keeping each part

Dipankar Chakrabarti, Neelam Patodia, Udayan Bhattacharya, Indranil Mitra

Assessing risk for voluminous legal documents such as request for proposal; contracts is tedious and error prone. We have developed "risk-o-meter", a framework, based on machine learning and natural language processing to review and assess risks of any legal document. Our framework uses Paragraph Vector, an unsupervised model to generate vector repre

Mee Seong Im, Shifra Reif, Vera Serganova

We show that the Grothendieck ring of finite-dimensional representations of the periplectic Lie supergroup $P(n)$ is isomorphic to the ring of symmetric polynomials in $x_1^{\pm 1}, \ldots, x_n^{\pm 1}$ whose evaluation $x_1=x_2^{-1}=t$ is independent of $t$.

Yantao Lu, Yunhan Jia, Jianyu Wang, Bai Li

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they remain adversarial even against other models. Although great efforts have been delved into the transferability across models, surprisingly, less attention has been paid to the cross-task transferability, which represent

Zhiliang Chen

High performance machine learning models have become highly dependent on the availability of large quantity and quality of training data. To achieve this, various central agencies such as the government have suggested for different data providers to pool their data together to learn a unified predictive model, which performs better. However, these providers

Zhenlin Guo, Christopher Price, Vivek B. Shenoy, John Lowengrub

Vertically-stacked monolayers of graphene and other atomically-thin 2D materials have attracted considerable research interest because of their potential in fabricating materials with specifically-designed properties. Chemical vapor deposition has proved to be an efficient and scalable fabrication method. However, a lack of mechanistic understanding has hamp

Jiankai Sun, Jie Zhao, Huan Sun, Srinivasan Parthasarathy

Routing newly posted questions (a.k.a cold questions) to potential answerers with the suitable expertise in Community Question Answering sites (CQAs) is an important and challenging task. The existing methods either focus only on embedding the graph structural information and are less effective for newly posted questions, or adopt manually engineered feature

Paola Ferrario, Vicente Herrero-Bosch, José María Benlloch-Rodríguez, Carmen Romo-Luque

The fast scintillation decay time and the high scintillation yield of liquid xenon makes it an appropriate material for nuclear medicine. Moreover, being a continuous medium with a uniform response, liquid xenon allows one to avoid most of the geometrical distortions of conventional detectors based on scintillating crystals. In this paper, we describe how th

Hiroyasu Hamada

Let $K$ be a compact metric space and let $φ: K \to K$ be continuous. We study C*-algebra $\mathcal{MC}_φ$ generated by all multiplication operators by continuous functions on $K$ and a composition operator $C_φ$ induced by $φ$ on a certain $L^2$ space. Let $γ= (γ_1, \dots, γ_n)$ be a system of proper contractions on $K$. Suppose that $γ_1, \dots, γ_n$ are i

Shuangjian Guo, Xiaohui Zhang, Shengxiang Wang

We introduce the notion of 3-Hom-Lie-Rinehart algebra and systematically describe a cohomology complex by considering coefficient modules. Furthermore, we consider extensions of a 3-Hom-Lie-Rinehart algebra and characterize the first cohomology space in terms of the group of automorphisms of an $A$-split abelian extension and the equivalence classes of $A$-s

Ahmed Ayad

Supersymmetry plays a main role in all current thinking about superstring theory. Indeed, many remarkable properties of string theory have been explained using supersymmetry as a tool. In this dissertation, we review the basic formulation of supersymmetric quantum mechanics starting with introducing the concepts of supercharges and superalgebra. We show that

Kun Song

$K$-NN classifier is one of the most famous classification algorithms, whose performance is crucially dependent on the distance metric. When we consider the distance metric as a parameter of $K$-NN, learning an appropriate distance metric for $K$-NN can be seen as minimizing the empirical risk of $K$-NN. In this paper, we design a new type of continuous deci

Saad Alqithami, Musaad Alzahrani, Fahad Alghamdi, Rahmat Budiarto

The paper provides an understanding of social capital in organizations that are open membership multi-agent systems with an emphasis in our formulation on the dynamic network of social interaction that, in part, elucidate evolving structures and impromptu topologies of networks. This paper, therefore, models an open source project as an organizational networ

Serguei Naboko, Sergey Simonov

We consider a class of Jacobi matrices with unbounded entries in the so called critical (double root, Jordan box) case. We prove a formula for the spectral density of the matrix which relates its spectral density to the asymptotics of orthogonal polynomials associated with the matrix.

Rhys J. J. Poulton, Chris Power, Aaron S. G. Robotham, Pascal J. Elahi

Hierarchical models of structure formation predict that dark matter halo assembly histories are characterised by episodic mergers and interactions with other haloes. An accurate description of this process will provide insights into the dynamical evolution of haloes and the galaxies that reside in them. Using large cosmological N-body simulations, we charact

Jacob Austin, Rafael Corrales-Fatou, Sofia Wyetzner, Hod Lipson

While most robotics simulation libraries are built for low-dimensional and intrinsically serial tasks, soft-body and multi-agent robotics have created a demand for simulation environments that can model many interacting bodies in parallel. Despite the increasing interest in these fields, no existing simulation library addresses the challenge of providing a u

Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Charu Aggarwal

Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. Though many

Mauricio Martinez, Matthew D. Sievert, Douglas E. Wertepny, Jacquelyn Noronha-Hostler

We initialize the Quantum Chromodynamic conserved charges of baryon number, strangeness, and electric charge arising from gluon splitting into quark-antiquark pairs for the initial conditions of relativistic heavy-ion collisions. A new Monte Carlo procedure that can sample from a generic energy density profile is presented, called Initial Conserved Charges i

Miguel Ángel Castro Tirado, Alberto J. Castro-Tirado

This work addresses the development of the astronomical observatory all through history, from an architectural point of view, as a building in relation to the observing instruments and their functioning as a heterogeneous work center. We focused on 32 observatories (in the period 1259-2007) and carefully analyzed the architectures. Considering the impact of

Ernesto A. Matute

The Standard Model (SM) with one right-handed neutrino per generation is revisited with presymmetry being the global $U(1)_{B-L}$ symmetry of an electroweak theory of leptons and quarks with initially postulated symmetric fractional charges. The cancellation of gauge anomalies and the non-perturbative normalization of lepton charges proceed through the mixin

Vladimir Turaev, Alexis Virelizier

Let G be a discrete group and C be an additive spherical G-fusion category. We prove that the state sum 3-dimensional HQFT derived from C is isomorphic to the surgery 3-dimensional HQFT derived from the G-center of C.

Anna Kamińska, Mariusz Żyluk

Several local geometric properties of Orlicz space $L_ϕ$ are presented for an increasing Orlicz function $ϕ$ which is not necessarily convex, and thus $L_ϕ$ does not need to be a Banach space. In addition to monotonicity of $ϕ$ it is supposed that $ϕ(u^{1/p})$ is convex for some $p>0$ which is equivalent to that its lower Matuszewska-Orlicz index $α_ϕ>0$. Su

Lohit Vandanapu, Michael D. Shields

The second in a two-part series, this paper extends the 3rd-order Spectral Representation Method for simulation of ergodic multi-variate stochastic processes according to a prescribed cross power spectral density and cross bispectral density. The 2nd and 3rd order ensemble properties of the simulated stochastic vector processes are shown to satisfy the targe

Josh Javor, Alexander Stange, Corey Pollock, Nicholas Fuhr

Magnetic sensing is present in our everyday interactions with consumer electronics, and also demonstrates potential for measurement of extremely weak biomagnetic fields, such as those of the heart and brain. In this work, we leverage the many benefits of the micro-electromechanical systems (MEMS) devices to fabricate a small, low power, inexpensive sensor wh

Wadim Kehl, Federico Tombari, Slobodan Ilic, Nassir Navab

We present a novel method to track 3D models in color and depth data. To this end, we introduce approximations that accelerate the state-of-the-art in region-based tracking by an order of magnitude while retaining similar accuracy. Furthermore, we show how the method can be made more robust in the presence of depth data and consequently formulate a new joint

Peter Coogan, Robert C. Kirby

The Morse-Ingard equations of thermoacoustics are a system of coupled time-harmonic equations for the temperature and pressure of an excited gas. They form a critical aspect of modeling trace gas sensors. In this paper, we analyze a reformulation of the system that has a weaker coupling between the equations than the original form. We give a Gårding-type ine

Iván Díaz, Oleksander Savenkov, Hooman Kamel

We introduce a novel Bayesian estimator for the class proportion in an unlabeled dataset, based on the targeted learning framework. Our procedure requires the specification of a prior (and outputs a posterior) only for the target of inference, instead of the prior (and posterior) on the full-data distribution employed by classical non-parametric Bayesian met

Orion Afisiadis, Andreas Burg, Alexios Balatsoukas-Stimming

In this work, we study the coded frame error rate (FER) of LoRa under additive white Gaussian noise (AWGN) and under carrier frequency offset (CFO). To this end, we use existing approximations for the bit error rate (BER) of the LoRa modulation under AWGN and we present a FER analysis that includes the channel coding, interleaving, and Gray mapping of the Lo

Colin A. Z. Towery, Alexei Y. Poludnenko, Peter E. Hamlington

Theory and computations have established that thermodynamic gradients created by hot spots in reactive gas mixtures can lead to spontaneous detonation initiation. However, the current laminar theory of the temperature-gradient mechanism for detonation initiation is restricted to idealized physical configurations. Thus, it only predicts conditions for the ons

Yiren Wang, Hongzhao Huang, Zhe Liu, Yutong Pang

Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in inference. In this paper, we demonstrate that n-gram LM can be improved by neural LMs through a text generation based data augmentation method. In contrast to previous approaches, we e

Fred Brackx, Hennie De Schepper, Roman Lavicka, Vladimir Soucek

As is the case for the theory of holomorphic functions in the complex plane, the Cauchy Integral Formula has proven to be a corner stone of Clifford analysis, the monogenic function theory in higher dimensional euclidean space. In recent years, several new branches of Clifford analysis have emerged. Similarly as hermitian Clifford analysis in euclidean space

Kevin Mallery, Dario Canelon, Jiarong Hong, Nikolaos Papanikolopoulos

Microscopic analysis of micro particles in situ in diverse water environments is necessary for monitoring water quality and localizing contamination sources. Conventional sensors such as optical microscopes and fluorometers often require complex sample preparation, are restricted to small sample volumes, and are unable to simultaneously capture all pertinent

Vyom Raval, Kevin P. Nguyen, Ashley Gerald, Richard B. Dewey

Parkinson's disease (PD) is a common neurological disorder characterized by gait impairment. PD has no cure, and an impediment to developing a treatment is the lack of any accepted method to predict disease progression rate. The primary aim of this study was to develop a model using clinical measures and biomechanical measures of gait and postural stabil

Alexander Iomin

We study the propagation of coherent waves in a nonlinearly-induced random potential, and find regimes of self-organized criticality and other regimes where the nonlinear equivalent of Anderson localization prevails. The regime of self-organized criticality leads to power-law decay of transport [Phys. Rev. Lett. 121, 233901 (2018)], whereas the second regime

A. Liam Fitzpatrick, Emanuel Katz, Matthew T. Walters, Yuan Xin

We use Lightcone Conformal Truncation to analyze the RG flow of the two-dimensional supersymmetric Gross-Neveu-Yukawa theory, i.e. the theory of a real scalar superfield with a $\mathbb{Z}_2$-symmetric cubic superpotential. The theory depends on a single dimensionless coupling $\bar{g}$, and is expected to have a critical point at a tuned value $\bar{g}_*$ w

R. Smith, J. Bishop

We present an open source kinematic fitting routine designed for low-energy nuclear physics applications. Although kinematic fitting is commonly used in high-energy particle physics, it is rarely used in low-energy nuclear physics, despite its effectiveness. A FORTRAN and ROOT C++ version of the FUNKI_FIT kinematic fitting code have been developed and publis

Meenusree Rajapandian, Enrico Amico, Kausar Abbas, Mario Ventresca

The Identifiability Framework (If) has been shown to improve differential identifiability (reliability across-sessions and -sites, and differentiability across-subjects) of functional connectomes for a variety of fMRI tasks. But having a robust single session/subject functional connectome is just the starting point to subsequently assess network properties f

Brian C. Thomas, Jacob M. Oberle

The end-Permian mass extinction is the most severe known from the fossil record. The most likely cause is massive volcanic activity associated with the formation of the Permo-Triassic Siberian flood basalts. A proposed mechanism for extinction due to this volcanic activity is depletion of stratospheric ozone, leading to increased penetration of biologically

Erica Blum, Aggelos Kiayias, Cristopher Moore, Saad Quader

The blockchain data structure maintained via the longest-chain rule---popularized by Bitcoin---is a powerful algorithmic tool for consensus algorithms. Such algorithms achieve consistency for blocks in the chain as a function of their depth from the end of the chain. While the analysis of Bitcoin guarantees consistency with error $2^{-k}$ for blocks of depth

Philip N. Brown, Jason R. Marden

We ask if it is possible to positively influence social behavior with no risk of unintentionally incentivizing pathological behavior. In network routing problems, if network traffic is composed of many individual agents, it is known that self-interested behavior among the agents can lead to suboptimal network congestion. We study situations in which a system

Gabriele Pergola, Yulan He, David Lowe

Making sense of words often requires to simultaneously examine the surrounding context of a term as well as the global themes characterizing the overall corpus. Several topic models have already exploited word embeddings to recognize local context, however, it has been weakly combined with the global context during the topic inference. This paper proposes to

Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck

This paper introduces a differentially private mechanism to protect the information exchanged during the coordination of the sequential market-clearing of electricity and natural gas systems. The coordination between these sequential and interdependent markets represents a classic Stackelberg game and relies on the exchange of sensitive information between t

Johannes M. Henn, Gregory P. Korchemsky, Bernhard Mistlberger

We present the complete formula for the cusp anomalous dimension at four loops in QCD and in maximally supersymmetric Yang-Mills. In the latter theory it is given by \begin{equation} Γ^{\rm}_{\rm cusp}\Big|_{α_s^4} = -\left( \frac{α_s N}π\right)^4 \left[ \frac{73 π^6}{20160} + \frac{ ζ_{3}^2}{8} + \frac{1}{N^2} \left( \frac{31π^6}{5040} + \frac{9 ζ_3^2}{4} \

Natalia Bondarenko, Vjacheslav Yurko

Inverse spectral problems are studied for first-order integro-differential operators on a finite interval. These problems consist in recovering some components of the kernel from one or multiple spectra. Uniqueness theorems are proved for this class of inverse problems.

Pierre-Etienne Druet, Ansgar Jüngel

The convective transport in a multicomponent isothermal compressible fluid subject to the mass continuity equations is considered. The velocity is proportional to the negative pressure gradient, according to Darcy's law, and the pressure is defined by a state equation imposed by the volume extension of the mixture. These model assumptions lead to a parab

Joseph Pollard, Gareth P. Alexander

The description of point defects in chiral liquid crystals via topological methods requires the introduction of singular contact structures, a generalisation of regular contact structures where the plane field may have singularities at isolated points. We characterise the class of singularities that may arise in such structures, as well as the subclass of si

Peter Vasil'ev, Richard Penty, Ian White

For the visualization of quantum states, the approach based on Wigner functions can be very effective. Homodyne detection has been extensively used to obtain the density matrix, Wigner functions and tomographic reconstructions of optical fields for many thermal, coherent or squeezed states. Here, we use time-domain optical homodyne tomography for the quantum

Hermann G. Matthies, Roger Ohayon

Parametric entities appear in many contexts, be it in optimisation, control, modelling of random quantities, or uncertainty quantification. These are all fields where reduced order models (ROMs) have a place to alleviate the computational burden. Assuming that the parametric entity takes values in a linear space, we show how is is associated to a linear map

Camilo M. Signorelli, Xerxes D. Arsiwalla

Traditionally, the way one evaluates the performance of an Artificial Intelligence (AI) system is via a comparison to human performance in specific tasks, treating humans as a reference for high-level cognition. However, these comparisons leave out important features of human intelligence: the capability to transfer knowledge and make complex decisions based

Ikjyot Singh Kohli, Katherine Goff Inglis

The scheduling of films is a major problem for the movie theatre exhibition business. The problem is two-fold: movie exhibitors ideally would like to schedule films to screens in their various locations to maximize attendance and revenue, but would also like to schedule these films such that neighbouring theatre locations play the same films at different tim

Ge Cui, Jiwoong Jeong, Bob Press, Yang Lei

Objectives: Glioblastomas are the most aggressive brain and central nervous system (CNS) tumors with poor prognosis in adults. The purpose of this study is to develop a machine-learning based classification method using radio-mic features of multi-parametric MRI to classify high-grade gliomas (HGG) and low-grade gliomas (LGG). Methods: Multi-parametric MRI o

Taihong Xiao, Yi-Hsuan Tsai, Kihyuk Sohn, Manmohan Chandraker

Data privacy has emerged as an important issue as data-driven deep learning has been an essential component of modern machine learning systems. For instance, there could be a potential privacy risk of machine learning systems via the model inversion attack, whose goal is to reconstruct the input data from the latent representation of deep networks. Our work

Bingxin Zhao, Hongtu Zhu

Marginal association summary statistics have attracted great attention in statistical genetics, mainly because the primary results of most genome-wide association studies (GWAS) are produced by marginal screening. In this paper, we study the prediction accuracy of marginal estimator in dense (or sparsity free) high-dimensional settings with $(n,p,m) \to \inf

Mikhail Danilov

We present new results of the DANSS experiment on the searches for sterile neutrinos. They are based on 2.1 million of inverse beta decay events collected at 10.7, 11.7 and 12.7 meters from the reactor core of the 3.1 GW Kalinin Nuclear Power Plant in Russia. This data sample is 2.5 times larger than the data sample in the previous DANSS publication. The sea

Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor

We study the sample complexity of learning threshold functions under the constraint of differential privacy. It is assumed that each labeled example in the training data is the information of one individual and we would like to come up with a generalizing hypothesis $h$ while guaranteeing differential privacy for the individuals. Intuitively, this means that

Thibault Duhamel, Mariane Maynard, Froduald Kabanza

The ability to infer the intentions of others, predict their goals, and deduce their plans are critical features for intelligent agents. For a long time, several approaches investigated the use of symbolic representations and inferences with limited success, principally because it is difficult to capture the cognitive knowledge behind human decisions explici

Wayne B. Hayes

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological {\em networks} holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology -- the "structure&

Chiranjib Sur

In this work we have analyzed a novel concept of sequential binding based learning capable network based on the coupling of recurrent units with Bayesian prior definition. The coupling structure encodes to generate efficient tensor representations that can be decoded to generate efficient sentences and can describe certain events. These descriptions are deri

Toshiaki Koike-Akino, Ye Wang, David S. Millar, Keisuke Kojima

Recently, data-driven approaches motivated by modern deep learning have been applied to optical communications in place of traditional model-based counterparts. The application of deep neural networks (DNN) allows flexible statistical analysis of complicated fiber-optic systems without relying on any specific physical models. Due to the inherent nonlinearity

Amey Parundekar, Susan Elias, Ashwin Ashok

In this modern era, communication has become faster and easier. This means fallacious information can spread as fast as reality. Considering the damage that fake news kindles on the psychology of people and the fact that such news proliferates faster than truth, we need to study the phenomenon that helps spread fake news. An unbiased data set that depends on

Karthik Gopinath, Christian Desrosiers, Herve Lombaert

Brain surface analysis is essential to neuroscience, however, the complex geometry of the brain cortex hinders computational methods for this task. The difficulty arises from a discrepancy between 3D imaging data, which is represented in Euclidean space, and the non-Euclidean geometry of the highly-convoluted brain surface. Recent advances in machine learnin

Romain Zimmer, Thomas Pellegrini, Srisht Fateh Singh, Timothée Masquelier

Recently, it has been shown that spiking neural networks (SNNs) can be trained efficiently, in a supervised manner, using backpropagation through time. Indeed, the most commonly used spiking neuron model, the leaky integrate-and-fire neuron, obeys a differential equation which can be approximated using discrete time steps, leading to a recurrent relation for

James Wilkinson, Theodore Emms, Tim S. Evans

We develop a novel dynamical method to examine spatial interaction models (SIMs). For each SIM, we use our dynamical framework to model emigration patterns. We look at the resulting population distributions to see if they are realistic or not. We use the US census data from 2010 and various spatial statistics to access the success or failure of each model. W

Timothy Verstraeten, Pieter JK Libin, Ann Nowé

In many settings, as for example wind farms, multiple machines are instantiated to perform the same task, which is called a fleet. The recent advances with respect to the Internet of Things allow control devices and/or machines to connect through cloud-based architectures in order to share information about their status and environment. Such an infrastructur

Omar Peracha, Shawn Head

A common approach to generating symbolic music using neural networks involves repeated sampling of an autoregressive model until the full output sequence is obtained. While such approaches have shown some promise in generating short sequences of music, this typically has not extended to cases where the final target sequence is significantly longer, for examp

Ran He, Karthik Gopinath, Christian Desrosiers, Herve Lombaert

The analysis of the brain surface modeled as a graph mesh is a challenging task. Conventional deep learning approaches often rely on data lying in the Euclidean space. As an extension to irregular graphs, convolution operations are defined in the Fourier or spectral domain. This spectral domain is obtained by decomposing the graph Laplacian, which captures r

James Sharpe, Miguel A Juarez

We study two Bayesian (Reference Intrinsic and Jeffreys prior) and two frequentist (MLE and PWM) approaches to calibrating the Pareto and related distributions. Three of these approaches are compared in a simulation study and all four to investigate how much equity risk capital banks subject to Basel II banking regulations must hold. The Reference Intrinsic

Chiranjib Sur

Image captioning can be improved if the structure of the graphical representations can be formulated with conceptual positional binding. In this work, we have introduced a novel technique for caption generation using the neural-symbolic encoding of the scene-graphs, derived from regional visual information of the images and we call it Tensor Product Scene-Gr

Mohammed K. Alzaylaee, Suleiman Y. Yerima, Sakir Sezer

The Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we

Piotr Fronczak, Agata Fronczak, Anna Chmiel, Julian Sienkiewicz

The Hamiltonian Mean-Field (HMF) model belongs to a broad class of statistical physics models with non-additive Hamiltonians that reveal many non-trivial properties, such as non-equivalence of statistical ensembles, ergodicity breaking, and negative specific heat. With this paper, we add to this set another intriguing feature, which is that of super-diffusiv

Zihao Zhang, Stefan Zohren, Stephen Roberts

We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts. Both discrete and continuous action spaces are considered and volatility scaling is incorporated to create reward functions which scale trade positions based on market volatility. We test our algorithms on the 50 most liquid futures contracts from 2

Sheikh Rabiul Islam, William Eberle, Sheikh K. Ghafoor

Artificial Intelligence (AI) has become an integral part of domains such as security, finance, healthcare, medicine, and criminal justice. Explaining the decisions of AI systems in human terms is a key challenge--due to the high complexity of the model, as well as the potential implications on human interests, rights, and lives . While Explainable AI is an e