September 2019 arXiv papers — page 6

Xueyang Kang, Shunying Yuan

Simultaneous mapping and localization (SLAM) in an real indoor environment is still a challenging task. Traditional SLAM approaches rely heavily on low-level geometric constraints like corners or lines, which may lead to tracking failure in textureless surroundings or cluttered world with dynamic objects. In this paper, a compact semantic SLAM framework is p

Zhigang Yao, Bingjie Li, Wee Chin Tan

Modern sample points in many applications no longer comprise real vectors in a real vector space but sample points of much more complex structures, which may be represented as points in a space with a certain underlying geometric structure, namely a manifold. Manifold learning is an emerging field for learning the underlying structure. The study of manifold

Nozomu Sekino

We determine the condition on a given lens space having a realization as a closure of homology cobordism over a planar surface with a given number of boundary components. As a corollary, we see that every lens space is represented as a closure of homology cobordism over a planar surface with three boundary components. In the proof of this corollary, we use C

Suparna Biswas, Dibyendu Shee, Saibal Ray, F. Rahaman

In this article we propose a relativistic model of a static spherically symmetric anisotropic strange star with the help of Tolman-Kuchowicz (TK) metric potentials [Tolman, Phys. Rev. {\bf55}, 364 (1939) and Kuchowicz, Acta Phys. Pol. {\bf33}, 541 (1968)]. The form of the potentials are $\lambda(r)=\ln(1+ar^2+br^4)$ and $\nu(r)=Br^2+2\ln C$ where $a$, $b$, $

Sarabjeet Singh, Manu Awasthi

In this paper we provide a comprehensive, memory-centric characterization of the SPEC CPU2017 benchmark suite, using a number of mechanisms including dynamic binary instrumentation, measurements on native hardware using hardware performance counters and OS based tools. We present a number of results including working set sizes, memory capacity consumption an

Manabu Takahashi, Nozomu Hiraoka

We argue that the magnetic circular dichroism (MCD) of the hard x-ray Raman scattering (XRS) could be used as an element selective probe of local spin polarization. The magnitude of the XRS-MCD signal is directly proportional to the local spin polarization when the angle between the incident wavevector and the magnetization vector is $135^{\circ}$ or $-45^{\

S. M. Koksbang

Earlier studies have conjectured that redshift drift is described by spatially averaged quantities and thus becomes positive if the average expansion of the Universe accelerates. This conclusion is reevaluated here by considering exact light propagation in a simple toy-model with average accelerated expansion. The toy-model and light propagation setup is exp

Guang-He Lee, Tommi S. Jaakkola

We show how neural models can be used to realize piece-wise constant functions such as decision trees. The proposed architecture, which we call locally constant networks, builds on ReLU networks that are piece-wise linear and hence their associated gradients with respect to the inputs are locally constant. We formally establish the equivalence between the cl

Mohamed Vall Ould Moustapha

In this paper we give an integral representation for the resolvent kernels with uniform magnetic field on the hyperbolic plane, as applications of our results we solve explicitly two times dependent Schr\"odinger equations with uniform magnetic field on the hyperbolic plane

Stuart Eiffert, Salah Sukkarieh

Robotic navigation through crowds or herds requires the ability to both predict the future motion of nearby individuals and understand how these predictions might change in response to a robot's future action. State of the art trajectory prediction models using Recurrent Neural Networks (RNNs) do not currently account for a planned future action of a robot,

Joseph Y. Halpern

This is a review of "The Book of Why", by Judea Pearl.

Lujun Wang, Péter Makk, Simon Zihlmann, Andreas Baumgartner

Microscopic corrugations are ubiquitous in graphene even when placed on atomically flat substrates. These result in random local strain fluctuations limiting the carrier mobility of high quality hBN-supported graphene devices. We present transport measurements in hBN-encapsulated devices where such strain fluctuations can be in situ reduced by increasing the

Emanuel Ciprian Cismas

We present a brief spray theory necessary for the geometric method in hydrodynamics. We make use of the convenient calculus to complete a unitary approach started by P. Michor and A. Kriegl.

Frank Ong, Xucheng Zhu, Joseph Y. Cheng, Kevin M. Johnson

Purpose: To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast enhanced (DCE) imaging. Theory and Methods: The problem considered here requires recovering hundred-gigabytes of dynamic volumetric image data from a few gigabytes of k-space d

Takumi Ichimura, Shin Kamada

AffectNet contains more than 1,000,000 facial images which manually annotated for the presence of eight discrete facial expressions and the intensity of valence and arousal. Adaptive structural learning method of DBN (Adaptive DBN) is positioned as a top Deep learning model of classification capability for some large image benchmark databases. The Convolutio

Shin Kamada, Takumi Ichimura

Deep learning builds deep architectures such as multi-layered artificial neural networks to effectively represent multiple features of input patterns. The adaptive structural learning method of Deep Belief Network (DBN) can realize a high classification capability while searching the optimal network structure during the training. The method can find the opti

Luke A. Prendergast, Jodie A. Smith

Whilst influence functions for linear discriminant analysis (LDA) have been found for a single discriminant when dealing with two groups, until now these have not been derived in the setting of a general number of groups. In this paper we explore the relationship between Sliced Inverse Regression (SIR) and LDA, and exploit this relationship to develop influe

Mehrab Momennia, Seyed Hossein Hendi

The recent reported gravitational wave detection motivates one to investigate the properties of different black hole models, especially their behavior under (axial) gravitational perturbation. Here, we study the quasinormal modes of black holes in Weyl gravity. We derive the master equation describing the quasinormal radiation by using a relation between the

Takanobu Jujo

The density of states of the disordered s-wave superconductor is calculated perturbatively. The effect of Coulomb interaction on diffusively moving electrons in the normal state has been known before, but in the superconducting state both diffuson and the screened Coulomb interaction are modified. Therefore, the correction to the density of states in the sup

Dali Liu, Zheng Li, Hanchao Wang, Zengjing Chen

In this paper, a new technique is introduced to obtain non-uniform Berry-Esseen bounds of normal and nonnormal approximation for unbounded exchangeable pairs. This technique does not rely on the concentration inequalities developed by Chen and Shao \cite{cls1, cls2} and can be applied to the quadratic forms, general Curie-Weiss model and an independence test

Kartik Gupta, Lars Petersson, Richard Hartley

We present a new approach for a single view, image-based object pose estimation. Specifically, the problem of culling false positives among several pose proposal estimates is addressed in this paper. Our proposed approach targets the problem of inaccurate confidence values predicted by CNNs which is used by many current methods to choose a final object pose

Norihito Toyota

The evolution of cooperation among unrelated individuals in human and animal societies remains a challenging issue across disciplines. It is an important subject also in the evolutionary game theory to understand how cooperation arises. The subject has been extensively studied especially in Prisoners' dilemma game(PD) but the emergence of cooperation is also

Alexandros Stergiou, Ronald Poppe

Effective processing of video input is essential for the recognition of temporally varying events such as human actions. Motivated by the often distinctive temporal characteristics of actions in either horizontal or vertical direction, we introduce a novel convolution block for CNN architectures with video input. Our proposed Fractioned Adjacent Spatial and

Monimoy Bujarbaruah, Xiaojing Zhang, Marko Tanaskovic, Francesco Borrelli

This paper deals with the problem of formulating an adaptive Model Predictive Control strategy for constrained uncertain systems. We consider a linear system, in presence of bounded time varying additive uncertainty. The uncertainty is decoupled as the sum of a process noise with known bounds, and a time varying offset that we wish to identify. The time vary

Martin Royer, Frédéric Chazal, Clément Levrard, Umeda Yuhei

Robust topological information commonly comes in the form of a set of persistence diagrams, finite measures that are in nature uneasy to affix to generic machine learning frameworks. We introduce a fast, learnt, unsupervised vectorization method for measures in Euclidean spaces and use it for reflecting underlying changes in topological behaviour in machine

Yeshwant Pandit, S. L. Sravanthi, Suresh Dara, S. M. Hegde

Let ${[n] \choose k}$ and ${[n] \choose l}$ $( k > l ) $ where $[n] = \{1,2,3,...,n\}$ denote the family of all $k$-element subsets and $l$-element subsets of $[n]$ respectively. Define a bipartite graph $G_{k,l} = ({[n] \choose k},{[n] \choose l},E)$ such that two vertices $S\, \epsilon \,{[n] \choose k} $ and $T\, \epsilon \,{[n] \choose l} $ are adjacent

Damien Teney, Ehsan Abbasnejad, Anton van den Hengel

The knowledge that humans hold about a problem often extends far beyond a set of training data and output labels. While the success of deep learning mostly relies on supervised training, important properties cannot be inferred efficiently from end-to-end annotations alone, for example causal relations or domain-specific invariances. We present a general tech

Albert Mosella-Montoro, Javier Ruiz-Hidalgo

Geometric 3D scene classification is a very challenging task. Current methodologies extract the geometric information using only a depth channel provided by an RGB-D sensor. These kinds of methodologies introduce possible errors due to missing local geometric context in the depth channel. This work proposes a novel Residual Attention Graph Convolutional Netw

Shubhadeep Chakraborty, Xianyang Zhang

The paper presents new metrics to quantify and test for (i) the equality of distributions and (ii) the independence between two high-dimensional random vectors. We show that the energy distance based on the usual Euclidean distance cannot completely characterize the homogeneity of two high-dimensional distributions in the sense that it only detects the equal

Craig Gidney

In [Hastings et al 2014] it is stated that the code distance of a logical qubit stored using dislocations is 2L + O(1), where L is the separation between the dislocation twists. This code distance assumed only physical X and Z errors are permitted. This short note shows that, when Y errors are allowed, the code distance reduces to L + O(1). See Figure 1.

Michal Duška

The liquid spinodal, which is the bedrock of water thermodynamics, has long been discussed alongside the elusive liquid-liquid critical point hidden behind the limit of homogeneous nucleation. This has inspired numerous scenarios that attempt to explain water anomalies. Despite recent breakthrough experiments eliminating several of thous scenarios, we lacked

Pavel Krtouš, Andrei Zelnikov

We study a system of two charged non-rotating black holes separated by a strut. Using the exact solution of the Einstein-Maxwell equations, which describes this system, we construct a consistent form of the first law of thermodynamics. We derive thermodynamic parameters related to the strut in an explicit form. The intensive thermodynamical quantity associat

Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi

In recent years, neural machine translation (NMT) has become the dominant approach in automated translation. However, like many other deep learning approaches, NMT suffers from overfitting when the amount of training data is limited. This is a serious issue for low-resource language pairs and many specialized translation domains that are inherently limited i

Shin Kamada, Takumi Ichimura

Deep learning forms a hierarchical network structure for representation of multiple input features. The adaptive structural learning method of Deep Belief Network (DBN) can realize a high classification capability while searching the optimal network structure during the training. The method can find the optimal number of hidden neurons for given input data i

Sen Zhao, Stephen Ottinger, Suzanne Peck, Christine Mac Donald

Identifying differences in networks has become a canonical problem in many biological applications. Here, we focus on testing whether two Gaussian graphical models are the same. Existing methods try to accomplish this goal by either directly comparing their estimated structures, or testing the null hypothesis that the partial correlation matrices are equal.

Edmond Shami

Networking companies, especially the ones with the biggest market shares, tend to offer end to end solutions for their customers, with large discounts, that it would sound irrational to decline such offers, helping contractors make larger profits, leaving clients more fragile to future uncertainties, and robbing the client from the leverage of optionality. T

Yazhou Zhou, Priscila F. S. Rosa, Jing Guo, Shu Cai

In this study, we report the first results of the high-pressure Hall coefficient (RH) measurements in the putative topological Kondo insulator SmB6 up to 37 GPa. Below 10 GPa, our data reveal that RH(T) exhibits a prominent peak upon cooling below 20 K. Remarkably, the temperature at which surface conduction dominates coincides with the temperature of the pe

Rikito Ohta

We prove that the Seshadri constant of a polarized abelian variety is equal to the Seshadri constant of its abelian subvariety if the Seshadri constant is relatively small with respect to its degree, or it contains an abelian divisor which has sufficiently small degree. As an application of these results, we show that the Seshadri constant of a polarized abe

Kirill A. Grishin, Igor V. Chilingarian, Anton V. Afanasiev, Ivan Yu. Katkov

Observational studies of ultra-diffuse galaxies (UDGs) represent a significant challenge because of their very low surface brightnesses. A feasible approach is to identify "future" UDGs when their stars are still young. Using data mining, we found 12 such low-mass spatially extended quiescent galaxies in the Coma and Abell 2147 clusters in the SDSS legacy ga

Jie Liu, Xiao Yan, Xinyan Dai, Zhirong Li

The inner-product navigable small world graph (ip-NSW) represents the state-of-the-art method for approximate maximum inner product search (MIPS) and it can achieve an order of magnitude speedup over the fastest baseline. However, to date it is still unclear where its exceptional performance comes from. In this paper, we show that there is a strong norm bias

Yuandong Tian

We consider a deep ReLU / Leaky ReLU student network trained from the output of a fixed teacher network of the same depth, with Stochastic Gradient Descent (SGD). The student network is \emph{over-realized}: at each layer $l$, the number $n_l$ of student nodes is more than that ($m_l$) of teacher. Under mild conditions on dataset and teacher network, we prov

Takahiro Orito, Yoshihito Kuno, Ikuo Ichinose

In this paper, we study periodically modulated $s=1/2$ spin chain in a linear gradient potential (LP) that is generated by an external magnetic field. In the absence of the LP, the system has topological states that exhibit a magnetization plateau for a uniform external magnetic field. These topological states have a finite integer Chern number and their sta

Wenlin Wang, Chenyang Tao, Zhe Gan, Guoyin Wang

The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensional vertex-based vector representations. This paper considers a novel variational formulation of network embeddings, with special focus on textual networks. Different from most ex

Zhiyuan Liu, Guohui Ding, Lijun Chen, Enoch Yeung

We propose an alternating optimization algorithm to the nonconvex Koopman operator learning problem for nonlinear dynamic systems. We show that the proposed algorithm will converge to a critical point with rate $O(1/T)$ and $O(\frac{1}{\log T})$ for the constant and diminishing learning rates, respectively, under some mild conditions. To cope with the high d

Zhenzhen Xiang, Jingrui Yu, Jie Li, Jianbo Su

In this paper, we present a multi-camera visual odometry (VO) system for an autonomous vehicle. Our system mainly consists of a virtual LiDAR and a pose tracker. We use a perspective transformation method to synthesize a surround-view image from undistorted fisheye camera images. With a semantic segmentation model, the free space can be extracted. The scans

Sang-Eon Bak, Paul M. Alsing, Warner A. Miller, Shahabeddin M. Aslmarand

We investigate the quantum correlation for tripartite entangled states in de Sitter space. First, we adopt the noisy quantum channel model. In this model, the expansion effect is represented by an operator sum representation with its corresponding Kraus operator. This map is shown to be trace-preserving and completely positive. Second, we analyze the quantum

Sijun Du

One of the key design considerations for biomedical implants is to minimize the system volume while achieving high performance. An attractive approach to power biomedical implants is to use wireless power transfer (WPT) with ultrasound. To receive the ultrasonic energy, a piezoelectric transducer is implemented, which is designed to have the resonance freque

Ryota Katsumi, Yasutomo Ota, Alto Osada, Takeyoshi Tajiri

Silicon quantum photonics provides a promising pathway to realize large-scale quantum photonic integrated circuits (QPICs) by exploiting the power of complementary-metal-oxide-semiconductor (CMOS) technology. Toward scalable operation of such silicon-based QPICs, a straightforward approach is to integrate deterministic single-photon sources (SPSs). To this e

Liqun Qi, Shenglong Hu, Xinzhen Zhang

Biquadratic tensors play a central role in many areas of science. Examples include elasticity tensor and Eshelby tensor in solid mechanics, and Riemann curvature tensor in relativity theory. The singular values and spectral norm of a general third order tensor are the square roots of the M-eigenvalues and spectral norm of a biquadratic tensor. The tensor pro

Hamed Pejhan, Mohammad Enayati, Jean-Pierre Gazeau, Anzhong Wang

We present a covariant quantization of the "massive" spin-${\frac{3}{2}}$ Rarita-Schwinger field in de Sitter (dS) spacetime. The dS group representation theory and its Wigner interpretation combined with the Wightman-G$\mbox{\"{a}}$rding axiomatic and analyticity requirements in the complexified pseudo-Riemanian manifold constitute the basis of the quantiza

Qi-Xin Yu, J. M. Dias, Wei-Hong Liang, E. Oset

We have studied the meson-baryon interaction in coupled channels with the same quantum numbers of $\Xi_{bc}$. The interaction is attractive in some channels and of sufficient intensity to lead to bound states or resonances. We use a model describing the meson-baryon interaction based on an extrapolation of the local hidden gauge approach to the heavy sector,

Tetsutaro Shibata

We consider the nonlinear eigenvalue problem $[D(u(t))u(t)']' + \lambda g(u(t)) = 0$, $u(t) > 0$, $t \in I := (0,1)$, $u(0) = u(1) = 0$, which comes from the porous media type equation. Here, $D(u) = pu^{2n} + \sin u$ ($n \in \mathbb{N}$, $p > 0$: given constants), $g(u) = u$ or $g(u) = u + \sin u$. $\lambda > 0$ is a bifurcation parameter which is a continu

Maarten Van Segbroeck, Ahmed Zaid, Ksenia Kutsenko, Cirenia Huerta

We present a speech data corpus that simulates a "dinner party" scenario taking place in an everyday home environment. The corpus was created by recording multiple groups of four Amazon employee volunteers having a natural conversation in English around a dining table. The participants were recorded by a single-channel close-talk microphone and by five far-f

Xinlin Li, Vahid Partovi Nia

Binary neural networks improve computationally efficiency of deep models with a large margin. However, there is still a performance gap between a successful full-precision training and binary training. We bring some insights about why this accuracy drop exists and call for a better understanding of binary network geometry. We start with analyzing full-precis

Xi Sisi Shen

We prove a priori estimates for constant Chern scalar curvature metrics on a compact complex manifold conditional on an upper bound on the entropy, extending a recent result by Chen-Cheng in the K\"ahler setting.

Hiromi Tanaka

We introduce a non-associative and non-commutative version of propositional intuitionistic linear logic, called propositional non-associative non-commutative intuitionistic linear logic (NACILL for short). We prove that NACILL and any of its extensions by the rules of exchange and/or contraction are undecidable. Furthermore, we introduce two types of classic

Nalin Chhibber, Edith Law

Conversational agents are becoming increasingly popular for supporting and facilitating learning. Conventional pedagogical agents are designed to play the role of human teachers by giving instructions to the students. In this paper, we investigate the use of conversational agents to support the 'learning-by-teaching' paradigm where the agent receives instruc

He Wang, Zhoujian Cao, Xiaolin Liu, Shichao Wu

Deep learning method develops very fast as a tool for data analysis these years. Such a technique is quite promising to treat gravitational wave detection data. There are many works already in the literature which used deep learning technique to process simulated gravitational wave data. In this paper we apply deep learning to LIGO O1 data. In order to impro

Ye Wang, Xiaodan Xi, Michael Orshansky

We propose a strong physical unclonable function (PUF) provably secure against machine learning (ML) attacks with both classical and quantum computers. Its security is derived from cryptographic hardness of learning decryption functions of public-key cryptosystems. Our design compactly realizes the decryption function of the learning-with-errors (LWE) crypto

Luiz C L Botelho

We study and clarify in a reduced dynamical model for QCD(SU(infinite)) (called Bollini-Giambiagi model ) , and defined by constant gauge fields Yang- Mills path integral , several concepts on the validity of the string representation for QCD (SU(infinite)) and the confinement problem

Mostafa Peysokhan, Esmaeil Mobini, Arman Allahverdi, Behnam Abaie

Recent advances in power scaling of fiber lasers are hindered by the thermal issues, which deteriorate the beam quality. Anti-Stokes fluorescence cooling has been suggested as a viable method to balance the heat generated by the quantum defect and background absorption. Such radiation-balanced configurations rely on the availability of cooling-grade rare-ear

Takuto Nakamura, Yoshiyuki Ohtsubo, Naoki Tokumasu, Patrick Le Fèvre

We fabricated spin-polarized surface electronic states with tunable Fermi level from semiconductor to low-dimensional metal in the Bi/GaSb(110)-(2$\times$1) surface using angle-resolved photoelectron spectroscopy (ARPES) and spin-resolved ARPES. The spin-polarized surface band of Bi/GaSb(110) exhibits quasi-one-dimensional character with the Rashba parameter

M. Leconte, R. Singh

For H-mode, standard decorrelation theory predicts that it is the turbulence intensity $|\phi_k|^2$ that is mainly affected via flow-induced shearing of turbulent eddies. However, for other regimes (e.g. I-mode, characterized by high energy confinement but low particle confinement), this decrease of turbulence amplitude cannot explain the decoupling of parti

Jennifer D. Schuler, Charlette M. Grigorian, Christopher M. Barr, Brad L. Boyce

Nanocrystalline metals are promising radiation tolerant materials due to their large interfacial volume fraction, but irradiation-induced grain growth can eventually degrade any improvement in radiation tolerance. Therefore, methods to limit grain growth and simultaneously improve the radiation tolerance of nanocrystalline metals are needed. Amorphous interg

Gloria Marí Beffa, Annalisa Calini

We show that discrete $W_m$ lattices are bi-Hamiltonian, using geometric realizations of discretizations of the Adler-Gel'fand-Dikii flows as local evolutions of arc length-parametrized polygons in centro-affine space. We prove the compatibility of two known Hamiltonian structure defined on the space of geometric invariants by lifting them to a pair of pre-s

Lifu Tu, Xiaoan Ding, Dong Yu, Kevin Gimpel

We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs. We focus on the setting of generating the next sentence of a story given its context. As controllable dimensions, we consider several sentence attributes, including sentiment, length, predicates, frames, and automatically-induced clusters. Our empiric

Juho Lee, Yoonho Lee, Yee Whye Teh

We propose a deep amortized clustering (DAC), a neural architecture which learns to cluster datasets efficiently using a few forward passes. DAC implicitly learns what makes a cluster, how to group data points into clusters, and how to count the number of clusters in datasets. DAC is meta-learned using labelled datasets for training, a process distinct from

Yuan-Yuan Zhao, Chao Zhang, Shuming Cheng, Xinhui Li

Entanglement lies at the heart of quantum mechanics, and has been identified an essential resource for diverse applications in quantum information. If entanglement could be verified without any trust in the devices of observers, i.e., in a device-independent (DI) way, then unconditional security can be guaranteed for various quantum information tasks. In thi

Peng-Hui Chen, Fei Niu, Wei Zuo, Zhao-Qing Feng

The dynamical mechanism of multinucleon transfer (MNT) reactions has been investigated within the dinuclear system (DNS) model, in which the sequential nucleon transfer is described by solving a set of microscopically derived master equations. Production cross sections, total kinetic energy spectra, angular distribution of formed fragments in the reactions o

Ian Gent, Toby Walsh

In 1999, we introduced CSPLib, a benchmark library for the constraints community. Our CP-1999 poster paper about CSPLib discussed the advantages and disadvantages of building such a library. Unlike some other domains such as theorem proving, or machine learning, representation was then and remains today a major issue in the success or failure to solve proble

Andrew Polar, Michael Poluektov

The block-oriented models are usually based on linear dynamic and non-linear static blocks that are connected in various sequential/parallel ways. Some particular configurations of the involved blocks result in the well-known Hammerstein, Wiener, Hammerstein-Wiener and generalised Hammerstein models. The Urysohn model is a lesser-known model; it is represent

Yixing Xu, Yunhe Wang, Kai Han, Yehui Tang

An effective and efficient architecture performance evaluation scheme is essential for the success of Neural Architecture Search (NAS). To save computational cost, most of existing NAS algorithms often train and evaluate intermediate neural architectures on a small proxy dataset with limited training epochs. But it is difficult to expect an accurate performa

Yusen Huo, Qinghua Tao, Jianming Hu

Traffic signal control has long been considered as a critical topic in intelligent transportation systems. Most existing learning methods mainly focus on isolated intersections and suffer from inefficient training. This paper aims at the cooperative control for large scale multi-intersection traffic signal, in which a novel end-to-end learning based model is

Y. L. Wang, H. B. Sang, B. S. Xie

In this paper, we give formal results of Schwinger pair production correction in thermal systems with external background field by using the evolution operator method of thermo field dynamics, where especially tree level correction of thermal photons is considered with linear response approaches by an effective mass shift. We consider initial systems in two

Yiheng Zhou, He He, Alan W Black, Yulia Tsvetkov

Negotiation is a complex activity involving strategic reasoning, persuasion, and psychology. An average person is often far from an expert in negotiation. Our goal is to assist humans to become better negotiators through a machine-in-the-loop approach that combines machine's advantage at data-driven decision-making and human's language generation ability. We

Yiheng Zhou, Yulia Tsvetkov, Alan W Black, Zhou Yu

We study non-collaborative dialogs, where two agents have a conflict of interest but must strategically communicate to reach an agreement (e.g., negotiation). This setting poses new challenges for modeling dialog history because the dialog's outcome relies not only on the semantic intent, but also on tactics that convey the intent. We propose to model both s

Afrooz Jalilzadeh, Uday V. Shanbhag

We consider a stochastic variational inequality (SVI) problem with a continuous and monotone mapping over a closed and convex set. In strongly monotone regimes, we present a variable sample-size averaging scheme (VS-Ave) that achieves a linear rate with an optimal oracle complexity. In addition, the iteration complexity is shown to display a muted dependence

Gines Hidalgo, Yaadhav Raaj, Haroon Idrees, Donglai Xiang

We present the first single-network approach for 2D~whole-body pose estimation, which entails simultaneous localization of body, face, hands, and feet keypoints. Due to the bottom-up formulation, our method maintains constant real-time performance regardless of the number of people in the image. The network is trained in a single stage using multi-task learn

Lichao Chen, Sudhir Singh, Thomas Kailath, Vwani Roychowdhury

Despite significant recent progress, machine vision systems lag considerably behind their biological counterparts in performance, scalability, and robustness. A distinctive hallmark of the brain is its ability to automatically discover and model objects, at multiscale resolutions, from repeated exposures to unlabeled contextual data and then to be able to ro

Darshana Wickramaratne, Noam Bernstein, I. I. Mazin

Using first-principles calculations we determine the role of compressive and tensile uniaxial and equibiaxial strain on the structural, electronic and magnetic properties of V$_2$O$_3$. We find that compressive strain increases the energy cost to transition from the high-temperature paramagnetic metallic phase to the low-temperature antiferromagnetic insulat

Han Cai, Da-Wei Wang

Topological photonics is an emerging research area that focuses on the topological states of classical light. Here we reveal the topological phases that are intrinsic to the particle nature of light, i.e., solely related to the quantized Fock states and the inhomogeneous coupling between them. The Hamiltonian of two cavities coupled with a two-level atom is

Qiao Zhang, Chang Chen, Weidong Chen, Jun Ding

A new approach for designing the single-band and dual-band balanced bandpass filters (BPFs) based on the circular patch resonator is proposed in this work. By modifying the output balanced ports of the balanced BPFs to inhibit or transport TM31 mode of the circular patch resonator under the differential-mode (DM) excitation, a single-band or dual-band balanc

Leonard Kwuida, Claudia Mureşan

We study the existence of nontrivial and of representable (dual) weak complementations, along with the lattice congruences that preserve them, in different constructions of bounded lattices, then use this study to determine the finite (dual) weakly complemented lattices with the largest numbers of congruences, along with the structures of their congruence la

Hao Fang, Wei Wei

We prove a convergence theorem on the moduli space of constant $\sigma_{2}$ metrics for conic 4-spheres. We show that when a numerical condition is convergent to the boundary case, the geometry of conic 4-spheres converges to the boundary case while preserving capacity.

M. A. Caprio, P. J. Fasano, J. P. Vary, P. Maris

No-core configuration interaction (NCCI) calculations for p-shell nuclei give rise to rotational bands, identified by strong intraband E2 transitions and by rotational patterns for excitation energies, electromagnetic moments, and electromagnetic transitions. However, convergence rates differ significantly for different rotational observables and for differe

Kei Matsushima, Hisashi Kotegawa, Yoshiki Kuwata, Hideki Tou

We report $^{75}$As-NMR results for CrAs under pressure, which shows superconductivity adjoining a helimagnetically ordered state. We successfully evaluated the Knight shift from the spectrum, which is strongly affected by the quadrupole interaction. The Knight shift shows the remarkable feature that the uniform spin susceptibility increases toward low tempe

Minki Song, Seunghwan Lee, Eunsang Lee, Dong-Joon Shin

Among many submissions to the NIST post-quantum cryptography (PQC) project, NewHope is a promising key encapsulation mechanism (KEM) based on the Ring-Learning with errors (Ring-LWE) problem. Since NewHope is an indistinguishability (IND)-chosen ciphertext attack secure KEM by applying the Fujisaki-Okamoto transform to an IND-chosen plaintext attack secure p

T. M. Dunster, A. Gil, J. Segura

Recently, the present authors derived new asymptotic expansions for linear differential equations having a simple turning point. These involve Airy functions and slowly varying coefficient functions, and were simpler than previous approximations, in particular being computable to a high degree of accuracy. Here we present explicit error bounds for these expa

Mika Inda-Koide, Shinji Koide, Ryogo Morino

We performed numerical simulations of general relativistic magnetohydrodynamics with uniform resistivity to investigatethe occurrence of magnetic reconnection in a split-monopole magnetic field around a Schwarzschild black hole. We found that magnetic reconnection happens near the black hole at its equatorial plane. The magnetic reconnection has a point-like

Eric Primozic

For the split group $G_{2}$ defined over $\mathbb{Z},$ we show that the de Rham cohomology ring of $B(G_{2})_{\mathbb{F}_{2}}$ is isomorphic to the singular cohomology ring with $\mathbb{F}_{2}$-coefficients of $B(G_{2})_{\mathbb{C}}.$ For the spin groups $\textrm{Spin}(n)$ defined over $\mathbb{Z},$ we show that the de Rham cohomology ring of $B\textrm{Spin

Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Junlin Yang

Deep neural networks are vulnerable to adversarial attacks and hard to interpret because of their black-box nature. The recently proposed invertible network is able to accurately reconstruct the inputs to a layer from its outputs, thus has the potential to unravel the black-box model. An invertible network classifier can be viewed as a two-stage model: (1) i

Hiroaki Yoshida, Tomoyuki Kinjo, Hitoshi Washizu

We present a numerical scheme for simulating the dynamics of Brownian particles suspended in a fluid. The motion of the particles is tracked by the Langevin equation, whereas the host fluid flow is analyzed by using the lattice Boltzmann method. The friction force between a particle and the fluid is evaluated correctly based on the velocity difference at the

Zhenlin Fan, Guoqiang Zhong

Mesoscale eddies play a significant role in marine energy transport, marine biological environment and marine climate. Due to their huge impact on the ocean, mesoscale eddy detection has become a hot research area in recent years. Therefore, more and more people are entering the field of mesoscale eddy detection. However, the existing detection methods mainl

Fei Ma, Ping Wang, Xudong Luo

Geodesic distance, sometimes called shortest path length, has proven useful in a great variety of applications, such as information retrieval on networks including treelike networked models. Here, our goal is to analytically determine the exact solutions to geodesic distances on two different families of growth trees which are recursively created upon an arb

Keenan Lyon, María Rosa Preciado-Rivas, Duncan John Mowbray, Vito Despoja

Understanding, optimizing, and controlling the optical absorption process, exciton gemination, and electron-hole separation and conduction in low dimensional systems is a fundamental problem in materials science. However, robust and efficient methods capable of modelling the optical absorbance of low dimensional macromolecular systems and providing physical

Paweł Widera, Paco M. J. Welsing, Christoph Ladel, John Loughlin

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and re

David M. Jacobs

Long-range effective methods are ubiquitous in physics and in quantum theory, in particular. Furthermore, the reliability of such methods is higher when the nature of short-ranged interactions need not be modeled explicitly. This may be necessary for two reasons: (1) there are interactions that occur over a short range that cannot be accurately modeled with

Maciej Wiatrak, Stefano V. Albrecht, Andrew Nystrom

Generative Adversarial Networks (GANs) are a type of generative model which have received much attention due to their ability to model complex real-world data. Despite their recent successes, the process of training GANs remains challenging, suffering from instability problems such as non-convergence, vanishing or exploding gradients, and mode collapse. In r

R. Amzi Jeffs

Two tantalizing invariants of a combinatorial code $\mathcal C\subseteq 2^{[n]}$ are cdim$(\mathcal C)$ and odim$(\mathcal C)$, the smallest dimension in which $\mathcal C$ can be realized by convex closed or open sets, respectively. Cruz, Giusti, Itskov, and Kronholm showed that for intersection complete codes $\mathcal C$ with $m+1$ maximal codewords, odim

Masato Tsuboi, Yoshimi Kitamura, Kenta Uehara, Atsushi Miyazaki

We have observed the compact HII region complex nearest to the dynamical center of the Galaxy, G-0.02-0.07, using ALMA in the H42a recombination line, CS J=2-1, H13CO+ J=1-0, and SiO v=0, J=2-1 emission lines, and 86 GHz continuum emission. The HII regions HII-A to HII-C in the cluster are clearly resolved into a shell-like feature with a bright-half and a d