September 2019 arXiv papers — page 11

Manouchehr Zaker

Questions concerning origin of mathematical knowledge and roles of language and intuition (imagery) in mathematical thoughts are long standing and widely debated. By introspection, mathematicians usually have some beliefs regarding these questions. But these beliefs are usually in a big contrast with the recent cognitive theoretic findings concerning mathema

Alberto Bemporad, Dario Piga

This paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as "this is better than that" between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision

Mojtaba Aliakbarzadeh, Kirsty Kitto

Recent years have seen new general notions of contextuality emerge. Most of these employ context-independent symbols to represent random variables in different contexts. As an example, the operational theory of Spekkens [1] treats an observable being measured in two different contexts identically. Non-contextuality in this approach is the impossibility of dr

Wei Zhou, Yiying Li

The value of remote sensing images is of vital importance in many areas and needs to be refined by some cognitive approaches. The remote sensing detection is an appropriate way to achieve the semantic cognition. However, such detection is a challenging issue for scale diversity, diversity of views, small objects, sophisticated light and shadow backgrounds. I

Yu Liu, Lingqiao Liu, Haokui Zhang, Hamid Rezatofighi

This paper tackles the problem of video object segmentation. We are specifically concerned with the task of segmenting all pixels of a target object in all frames, given the annotation mask in the first frame. Even when such annotation is available this remains a challenging problem because of the changing appearance and shape of the object over time. In thi

Acer Y. C. Chang, Martin Biehl, Yen Yu, Ryota Kanai

Information processing in neural systems can be described and analysed at multiple spatiotemporal scales. Generally, information at lower levels is more fine-grained and can be coarse-grained in higher levels. However, information processed only at specific levels seems to be available for conscious awareness. We do not have direct experience of information

Awais Khan Jumani, Mashooque Ahmed Memon, Fida Hussain Khoso, Anwar Ali Sanjrani

Named Entity Recognition (NER) System aims to extract the existing information into the following categories such as: Persons Name, Organization, Location, Date and Time, Term, Designation and Short forms. Now, it is considered to be important aspect for many natural languages processing (NLP) tasks such as: information retrieval system, machine translation

Silvio Franz, Jacopo Rocchi

The theory of glassy fluctuations can be formulated in terms of disordered effective potentials. While the properties of the average potentials are well understood, the study of the fluctuations has been so far quite limited. Close to the MCT transition, fluctuations induced by the dynamical heterogeneities in supercooled liquids can be described by a cubic

Anastasia Halfpap, Cory Palmer

Fix graphs $F$ and $H$. Let $\mathrm{ex}(n,H,F)$ denote the maximum number of copies of a graph $H$ in an $n$-vertex $F$-free graph. In this note we will give a new general supersaturation result for $\mathrm{ex}(n,H,F)$ in the case when $\chi(H) < \chi(F)$ as well as a new proof of a stability theorem for $\mathrm{ex}(n,K_r,F)$.

Dhruvi S. Bhatt, Shambhu N. Sharma

The van de Vusse reactor is an appealing benchmark problem in industrial control, since it has a non-minimum phase response. The van de Vusse stochasticity is attributed to the fluctuating input flow rate. The novelties of the paper are two. First, we utilize the surprising power of Ito stochastic calculus for applications to account for the van de Vusse sto

A. M. Romero, J. Dobaczewski, A. Pastore

Within a simple SO(8) algebraic model, the coexistence between isoscalar and isovector pairing modes can be successfully described using a mean-field method plus restoration of broken symmetries. In order to port this methodology to real nuclei, we need to employ realistic density functionals in the pairing channel. In this article, we present an analytical

Yiming Huo, Franklin Lu, Felix Wu, Xiaodai Dong

Millimeter-wave (mmWave), massive multiple-input multiple-output (MIMO), are expected to play a crucial role for 5G and beyond cellular and next-generation wireless local area network (WLAN) communications. Moreover, unmanned aerial vehicles (UAVs) are also considered as an important component of next-generation networks. In this paper, we propose and presen

Xingyuan Chen, Ping Cai, Peng Jin, Haokun Du

The text generated by neural language models is not as good as the real text. This means that their distributions are different. Generative Adversarial Nets (GAN) are used to alleviate it. However, some researchers argue that GAN variants do not work at all. When both sample quality (such as Bleu) and sample diversity (such as self-Bleu) are taken into accou

Anjian Li, Mo Chen

Hamilton Jacobi (HJ) Reachability is a formal verification tool widely used in robotic safety analysis. Given a target set as unsafe states, a dynamical system is guaranteed not to enter the target under the worst-case disturbance if it avoids the Backward Reachable Tube (BRT). However, computing BRTs suffers from exponential computational time and space com

Markus Kühbach, Franz Roters

Deformation microstructure heterogeneities play a pivotal role during dislocation patterning and interface network restructuring. Thus, they affect indirectly how an alloy recrystallizes if at all. Given this relevance, it has become common practice to study the evolution of deformation microstructure heterogeneities with 3D experiments and full-field crysta

Zhengkun Tian, Jiangyan Yi, Jianhua Tao, Ye Bai

Recurrent neural network transducers (RNN-T) have been successfully applied in end-to-end speech recognition. However, the recurrent structure makes it difficult for parallelization . In this paper, we propose a self-attention transducer (SA-T) for speech recognition. RNNs are replaced with self-attention blocks, which are powerful to model long-term depende

William Béthune

Planet formation scenarios can be constrained by the ratio of the gaseous envelope mass relative to the solid core mass in the observed exoplanet populations. One-dimensional calculations find a critical (maximal) core mass for quasi-static envelopes to exist, suggesting that envelopes around more massive cores should collapse due to a `core-nucleated' insta

Qitian Wu, Rui Gao, Hongyuan Zha

There are two types of deep generative models: explicit and implicit. The former defines an explicit density form that allows likelihood inference; while the latter targets a flexible transformation from random noise to generated samples. While the two classes of generative models have shown great power in many applications, both of them, when used alone, su

Konstantinos Tsouvalas

We prove that a word hyperbolic group which admits a $P_{2q+1}$-Anosov representation into $\mathsf{PGL}(4q+2, \mathbb{R})$ contains a finite-index subgroup which is either free or a surface group. As a consequence, we give an affirmative answer to Sambarino's question for Borel Anosov representations into $\mathsf{SL}(4q+2,\mathbb{R})$.

Ruigang Wang, Ian R. Manchester

Nonlinear stabilization using control contraction metric (CCM) method usually involves an online optimization problem to compute a minimal geodesic (a shortest path) between pair of states, which is not desirable for real-time applications. This paper introduces a continuous-time dynamic realization which distributes the computational cost of the optimizatio

Xiaopeng Yan, Ziliang Chen, Anni Xu, Xiaoxi Wang

Resembling the rapid learning capability of human, few-shot learning empowers vision systems to understand new concepts by training with few samples. Leading approaches derived from meta-learning on images with a single visual object. Obfuscated by a complex background and multiple objects in one image, they are hard to promote the research of few-shot objec

Sarath Yasodharan, Patrick Loiseau

We study nonzero-sum hypothesis testing games that arise in the context of adversarial classification, in both the Bayesian as well as the Neyman-Pearson frameworks. We first show that these games admit mixed strategy Nash equilibria, and then we examine some interesting concentration phenomena of these equilibria. Our main results are on the exponential rat

Benjamin Patrick Evans, Harith Al-Sahaf, Bing Xue, Mengjie Zhang

Image classification is an essential task in computer vision, which aims to categorise a set of images into different groups based on some visual criteria. Existing methods, such as convolutional neural networks, have been successfully utilised to perform image classification. However, such methods often require human intervention to design a model. Furtherm

Guozhen Rong, Yixin Cao, Jianxin Wang

In a paired threshold graph, each vertex has a weight, and two vertices are adjacent if their weight sum is large enough and their weight difference is small enough. It generalizes threshold graphs and unit interval graphs, both very well studied. We present a vertex ordering characterization of this graph class, which enables us to prove that it is a subcla

Jialu Huang, Jing Liao, Tak Wu Sam Kwong

Many image-to-image (I2I) translation problems are in nature of high diversity that a single input may have various counterparts. Prior works proposed the multi-modal network that can build a many-to-many mapping between two visual domains. However, most of them are guided by sampled noises. Some others encode the reference images into a latent vector, by wh

Pei Wang

The lack of superposition of different position states or the emergence of classicality in macroscopic systems have been a puzzle for decades. Classicality exists in every measuring apparatus, and is the key for understanding what can be measured in quantum theory. Different theories have been proposed, including decoherence, einselection and the spontaneous

Aida Maraj, Uwe Nagel

Toric ideals to hierarchical models are invariant under the action of a product of symmetric groups. Taking the number of factors, say m, into account, we introduce and study invariant filtrations and their equivariant Hilbert series. We present a condition that guarantees that the equivariant Hilbert series is a rational function in m+1 variables with ratio

Mieczysław A. Kłopotek

It is proven, by example, that the version of $k$-means with random initialization does not have the property probabilistic k-richness.

Negin Heravi, Wenzhen Yuan, Allison M. Okamura, Jeannette Bohg

Rich haptic sensory feedback in response to user interactions is desirable for an effective, immersive virtual reality or teleoperation system. However, this feedback depends on material properties and user interactions in a complex, non-linear manner. Therefore, it is challenging to model the mapping from material and user interactions to haptic feedback in

Juan B. Pérez-Sánchez, Joel Yuen-Zhou

Quantum dynamics of the photoisomerization of a single 3,3'-diethyl-2,2'-thiacynine iodide molecule embedded in an optical microcavity was theoretically studied. The molecule was coupled to a single cavity mode via the quantum Rabi Hamiltonian, and the corresponding time-dependent Schr\"odinger equation starting with a purely molecular excitation was solved

Samala Rathan, G. Naga Raju, Ashlesha A. Bhise

The advantage of WENO-JS5 scheme [ J. Comput. Phys. 1996] over the WENO-LOC scheme [J. Comput. Phys.1994] is that the WENO-LOC nonlinear weights do not achieve the desired order of convergence in smooth monotone regions and at critical points. In this article, this drawback is achieved with the WENO-LOC smoothness indicators by constructing a WENO-Z type non

Martin Bauer, Oleg Brandt, Lawrence Lee, Christian Ohm

Long-lived particles are predicted by many extensions of the Standard Model and have been gaining interest in recent years. In this Letter we present a competitive proposal that substantially extends the sensitivity in lifetime by instrumenting the existing service shafts above the ATLAS or CMS experiments with tracking stations. For scenarios with electrica

Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Wong

Runtime and scalability of large neural networks can be significantly affected by the placement of operations in their dataflow graphs on suitable devices. With increasingly complex neural network architectures and heterogeneous device characteristics, finding a reasonable placement is extremely challenging even for domain experts. Most existing automated de

Benedek Rozemberczki, Carl Allen, Rik Sarkar

We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE). Capturing attribu

Guyue Zhong, Q. Xie, Gang Xu

Based on first-principles calculations, we predict that the monolayer AuTe2Cl is a quantum spin Hall (QSH) insulator with a topological band gap about 10 meV. The three-dimensional (3D) AuTe2Cl is a topological semimetal that can be viewed as the monolayer stacking along b axis. By studying the energy level distribution of p orbitals of Te atoms for the bulk

Marc Lata, Yanzeng Li, Serang Park, Micheal McLamb

Infrared optical photonic crystals fabricated using direct laser writing, which is based on the two-photon polymerization of suitable monomers, have received substantial interest since the emergence of this process. Two-photon polymerization could be a disruptive technology for the fabrication of all-dielectric photonic crystals in the infrared spectral rang

Abootaleb Shirvani, Stoyan V. Stoyanov, Frank J. Fabozzi, Svetlozar T. Rachev

In this paper, we revisit the equity premium puzzle reported in 1985 by Mehra and Prescott. We show that the large equity premium that they report can be explained by choosing a more appropriate distribution for the return data. We demonstrate that the high-risk aversion value observed by Mehra and Prescott may be attributable to the problem of fitting a pro

Ayumu Sasagawa, Kazuki Fujimoto, Sho Sakaino, Toshiaki Tsuji

Robots are required to autonomously respond to changing situations. Imitation learning is a promising candidate for achieving generalization performance, and extensive results have been demonstrated in object manipulation. However, cooperative work between humans and robots is still a challenging issue because robots must control dynamic interactions among t

Kabir Opeyemi Olorede, Waheed Babatunde Yahya

The application of standard sufficient dimension reduction methods for reducing the dimension space of predictors without losing regression information requires inverting the covariance matrix of the predictors. This has posed a number of challenges especially when analyzing high-dimensional data sets in which the number of predictors $\mathit{p}$ is much la

Giovanni Molina, Fahad AlGhamdi, Mahmoud Ghoneim, Abdelati Hawwari

We present an overview of the second shared task on language identification in code-switched data. For the shared task, we had code-switched data from two different language pairs: Modern Standard Arabic-Dialectal Arabic (MSA-DA) and Spanish-English (SPA-ENG). We had a total of nine participating teams, with all teams submitting a system for SPA-ENG and four

Neil A. Hawbaker, Donna G. Blackmond

Autocatalytic Models for the Origin of Biological Homochirality

Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie

Federated learning is a distributed framework according to which a model is trained over a set of devices, while keeping data localized. This framework faces several systems-oriented challenges which include (i) communication bottleneck since a large number of devices upload their local updates to a parameter server, and (ii) scalability as the federated net

S. V. Gusev

We completely classify all standard elements in the lattice of all monoid varieties. In particular, we prove that an element of this lattice is standard if and only if it is neutral.

Hao Xu

Recently, how to expand data transmission to reduce cell data and repeated cell transmission has received more and more research attention. In mobile social networks, content popularity prediction has always been an important part of traffic offloading and expanding data dissemination. However, current mainstream content popularity prediction methods only us

Dhwanit Agarwal, George Biros

We use numerical simulations to study the dynamics of three dimensional vesicles in unconfined and confined Poiseuille flow. Previous numerical studies have shown that when the fluid viscosity inside and outside the vesicle is same (no viscosity contrast), a transition from asymmetric slippers to symmetric parachutes takes place as viscous forcing or capilla

Rossana Ruggeri, Chris Blake

Baryon acoustic oscillations (BAO) represent one of the cleanest probes of dark energy, allowing for tests of the cosmological model through the measurement of distance and expansion rate from a 3D galaxy distribution. The signal appears at large scales in the correlation function where linear theory applies, allowing for the construction of accurate models.

Yang Zhang, Bo Wang, Xuan Ye

We study adiabatic regularization of a coupling massless scalar field in general spatially flat Robertson-Walker (RW) spacetimes. For the conformally-coupling, the 0th-order regularized power spectrum and 0th-order regularized stress tensor are zero, and no trace anomaly exists in general RW spacetimes. This is a new result which extents those found in de Si

Mona Diab, Mahmoud Ghoneim, Abdelati Hawwari, Fahad AlGhamdi

We present our effort to create a large Multi-Layered representational repository of Linguistic Code-Switched Arabic data. The process involves developing clear annotation standards and Guidelines, streamlining the annotation process, and implementing quality control measures. We used two main protocols for annotation: in-lab gold annotations and crowd sourc

Fahad AlGhamdi, Mona Diab

Data annotation is an important and necessary task for all NLP applications. Designing and implementing a web-based application that enables many annotators to annotate and enter their input into one central database is not a trivial task. These kinds of web-based applications require a consistent and robust backup for the underlying database and support to

Wen Zhao, Tan Liu, Linqing Wen, Tao Zhu

Gravitational wave (GW) data can be used to test the parity symmetry of gravity by investigating the difference between left-hand and right-hand circular polarization modes. In this article, we develop a method to decompose the circular polarizations of GWs produced during the inspiralling stage of compact binaries, with the help of stationary phase approxim

Lu Mi, Hao Wang, Yonglong Tian, Hao He

Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas. However, most state-of-the-art deep learning models either fail to obtain uncertainty estimation or need significant modification (e.g., formulating a proper Bayesian treatment) to obtai

Fahad AlGhamdi, Giovanni Molina, Mona Diab, Thamar Solorio

We address the problem of Part of Speech tagging (POS) in the context of linguistic code switching (CS). CS is the phenomenon where a speaker switches between two languages or variants of the same language within or across utterances, known as intra-sentential or inter-sentential CS, respectively. Processing CS data is especially challenging in intra-sentent

Qing Li, Xiaojiang Peng, Yu Qiao, Qiang Peng

Multi-label image recognition is a task that predicts a set of object labels in an image. As the objects co-occur in the physical world, it is desirable to model label dependencies. Previous existing methods resort to either recurrent networks or pre-defined label correlation graphs for this purpose. In this paper, instead of using a pre-defined graph which

Tianyi Luo, Yang Liu

Wisdom of the crowd revealed a striking fact that the majority answer from a crowd is often more accurate than any individual expert. We observed the same story in machine learning--ensemble methods leverage this idea to combine multiple learning algorithms to obtain better classification performance. Among many popular examples is the celebrated Random Fore

Yunbo Wang, Bo Liu, Jiajun Wu, Yuke Zhu

A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty. We cast POMDP filtering and planning problems as two closely related Sequential Monte Carlo (SMC) processes, one over the real states and the other over the future op

Y. Li, S. Park, M. McLamb, M. Lata

A powerful fabrication strategy based on three-dimensional direct laser writing for the rapid prototyping of opto-mechanical components with critical features ranging from several hundred nm to a few mm is demonstrated here. As an example, a simple optical fiber connector with optical and mechanical guides as well as integrated micro-optical elements with na

Sunghyuk Park

We study $q$-series-valued invariants of 3-manifolds that depend on the choice of a root system $G$. This is a natural generalization of the earlier works by Gukov-Pei-Putrov-Vafa [arXiv:1701.06567] and Gukov-Manolescu [arXiv:1904.06057] where they focused on $G={\rm SU}(2)$ case. Although a full mathematical definition for these ''invariants'' is lacking ye

Y. Nomura, R. Okamoto, T. Mizuno, S. Adachi

We find systematic signatures suggesting a different superconducting nature for a triple-layered cuprate Bi$_2$Sr$_2$Ca$_2$Cu$_3$O$_{10+\delta}$ with respect to a double-layer through the properties of intrinsic Josephson junctions (IJJs). Our measurements on the current-voltage characteristics reveal that the $c$-axis maximum Josephson current density is se

B. Fang, M. Menotti, M. Liscidini, J. E. Sipe

We experimentally demonstrate the generation of a three-photon discrete-energy-entangled W state using multi-photon-pair generation by spontaneous four-wave mixing in an optical fiber. We show that by making use of prior information on the photon source we can verify the state produced by this source without resorting to frequency conversion.

Xiaozhi Cao, Congyu Liao, Zijing Zhang, Siddharth Srinivasan Iyer

Purpose: To rapidly obtain high isotropic-resolution T2 maps with whole-brain coverage and high geometric fidelity. Methods: A T2 blip-up/down echo planar imaging (EPI) acquisition with generalized Slice-dithered enhanced resolution (T2-BUDA-gSlider) is proposed. A radiofrequency (RF)-encoded multi-slab spin-echo EPI acquisition with multiple echo times (TEs

Yuchen Fan

After the correction of an inaccurate result in the reference, the author uses five different methods, and gets five different inequalities on the Hausdorff measure of the Cartesian product of the middle third Cantor set with itself: $$H^s (C\times C)\leq 1.548563$$ $$H^s (C\times C)\leq 1.504975$$ $$H^s (C\times C)\leq 1.502878$$ $$H^s (C\times C)\leq 1.503

Sakurako Okamoto, Nobuo Arimoto, Annette M. N. Ferguson, Mike J. Irwin

We use Hyper Suprime-Cam on the Subaru Telescope to investigate the structural and photometric properties of early-type dwarf galaxies and young stellar systems at the center of the M81 Group. We have mapped resolved stars to $\sim2$ magnitudes below the tip of the red giant branch over almost 6.5 square degrees, corresponding to a projected area of $160\tim

Rezaul Karim, Md Amirul Islam, Neil D. B. Bruce

In this paper, we present a canonical structure for controlling information flow in neural networks with an efficient feedback routing mechanism based on a strategy of Distributed Iterative Gating (DIGNet). The structure of this mechanism derives from a strong conceptual foundation and presents a light-weight mechanism for adaptive control of computation sim

Wenhao Yu, Jie Tan, Yunfei Bai, Erwin Coumans

The ability to walk in new scenarios is a key milestone on the path toward real-world applications of legged robots. In this work, we introduce Meta Strategy Optimization, a meta-learning algorithm for training policies with latent variable inputs that can quickly adapt to new scenarios with a handful of trials in the target environment. The key idea behind

Wen-Biao Gao, Bing-Zhao Li

The short-time linear canonical transform (STLCT) can be identified as a generalization of the short-time Fourier transform (STFT). It is a novel time-frequency analysis tool. In this paper, we generalize some different uncertainty principles for the STLCT of complex signals. Firstly, one uncertainty principle for the STLCT of complex signals in time and fre

Joseph L. Shomberg

The well-posedness of a generalized Coleman--Gurtin equation equipped with dynamic boundary conditions with memory was recently established by C.G. Gal and the author. Additionally, it was established by the author that the problem admits a finite dimensional global attractor and a robust family of exponential attractors in the case where singularly perturbe

Tyler Lawson, Robert Lipshitz, Sucharit Sarkar

We note that our stable homotopy refinements of Khovanov's arc algebras and tangle invariants induce refinements of Chen-Khovanov and Stroppel's platform algebras and tangle invariants, and discuss the topological Hochschild homology of these refinements.

Seogi Kang, Douglas W. Oldenburg, Lindsey J. Heagy

The potential for extracting and interpreting induced polarization (IP) data from airborne surveys is now broadly recognized. There is, however, still considerable discussion about the conditions under which the technique can provide knowledge about the subsurface and thus, its practical applications. Foremost among these is whether, or under what conditions

Swapnil Mhaske, Predrag Spasojevic, Ahsan Aziz

A significant portion of the 5th generation of wireless networks will operate in the mm-wave bands. One of the several challenges associated with mm-wave propagation is to overcome shadowing due to signal blockage caused by environmental objects. Particularly susceptible are nodes in a device-to-device network that typically operate at low power and in a blo

Eric M Schwen, Meera Ramaswamy, Chieh-Min Cheng, Linda Jan

It has recently been shown that in a broad class of disordered systems oscillatory shear training can embed memories of specific shear protocols in relevant physical parameters such as the yield strain. These shear protocols can be used to change the physical properties of the system and memories of the protocol can later be "read" out. Here we investigate s

John J. Vastola, William R. Holmes

Stochastic mechanics---the study of classical stochastic systems governed by things like master equations and Fokker-Planck equations---exhibits striking mathematical parallels to quantum mechanics. In this article, we make those parallels more transparent by presenting a quantum mechanics-like formalism for deriving a path integral description of systems de

Linxi Fan, Yuke Zhu, Jiren Zhu, Zihua Liu

We present an overview of SURREAL-System, a reproducible, flexible, and scalable framework for distributed reinforcement learning (RL). The framework consists of a stack of four layers: Provisioner, Orchestrator, Protocol, and Algorithms. The Provisioner abstracts away the machine hardware and node pools across different cloud providers. The Orchestrator pro

Sandip Ghosal, Raed M. Shubair

A novel and compact dual band dual sense circularly polarized microstrip patch antenna with single coaxial feed has been reported in the present work. The key idea of generating dual band circular polarisation (CP) is the integration of a square patch with corner truncation and a smaller concentric circular patch with double slits. The first resonance is pro

Gustavo Garcia, Marcelo Salgado

We argue about the (non) existence of {\it superregular} scalar clouds (i.e., bound states of a massive and complex-valued scalar field $\Psi$) around exact {\it extremal} ($a = M$) Kerr black holes (BH's) possessing {\it bounded radial derivatives at the horizon} (in Boyer-Lindquist coordinates) as opposed to similar cloud solutions that exist but with unbo

Dragan Stankov

We introduce a sequence $P_{2n}$ of monic reciprocal polynomials with integer coefficients having the central coefficients fixed. We prove that the ratio between number of nonunimodular roots of $P_{2n}$ and its degree $d$ has a limit when $d$ tends to infinity. We present an algorithm for calculation the limit and a numerical method for its approximation. I

Yusuf Eroglu, Fatih Erden, Ismail Guvenc

Visible light communication (VLC) utilizes light-emitting diodes (LEDs) to transmit wireless data. A VLC network can also be used to localize mobile users in indoor environments, where the global positioning system (GPS) signals are weak. However, the line-of-sight (LOS) links of mobile VLC devices can be blocked easily, which decreases the accuracy of local

Christopher Jenkins, Ioannis Papadopoulos, S. M. Shermer

Purpose: Recent work highlights the breadth of reported spin-lattice relaxation rates ($R_1$) for individual tissues. One potential source of variation is the protocol used to determine $R_1$. The methodological dependence of R1 and relaxivity $r_1$ are investigated. Methods: $R_1$ is quantified in gel phantoms with varying concentration of MnCl2, and a smal

Pablo Navarrete Michelini, Wenbin Chen, Hanwen Liu, Dan Zhu

Here, we describe our solution for the AIM-2019 Extreme Super-Resolution Challenge, where we won the 1st place in terms of perceptual quality (MOS) similar to the ground truth and achieved the 5th place in terms of high-fidelity (PSNR). To tackle this challenge, we introduce the second generation of MultiGrid BackProjection networks (MGBPv2) whose major modi

Congzheng Song, Reza Shokri

Machine learning as a service (MLaaS), and algorithm marketplaces are on a rise. Data holders can easily train complex models on their data using third party provided learning codes. Training accurate ML models requires massive labeled data and advanced learning algorithms. The resulting models are considered as intellectual property of the model owners and

Nicholas P Bauman, Bo Peng, Karol Kowalski

We demonstrate that the effective Hamiltonians obtained with the downfolding procedure based on double unitary coupled cluster (DUCC) ansatz can be used in the context of Greens function coupled cluster (GFCC) formalism to calculate spectral functions of molecular systems. This combined approach (DUCC-GFCC) provides a significant reduction of numerical effor

Alexander Madurowicz, Bruce Macintosh, Vanessa P. Bailey, Jeffrey Chilcote

An explanation for the origin of asymmetry along the preferential axis of the PSF of an AO system is developed. When phase errors from high altitude turbulence scintillate due to Fresnel propagation, wavefront amplitude errors may be spatially offset from residual phase errors. These correlated errors appear as asymmetry in the image plane under the Fraunhof

Peter Kairouz, Jiachun Liao, Chong Huang, Maunil Vyas

We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our framework leverages recent advances in adversarial learning to allow a data holder to learn representations in which a set of sensitive attributes are decoupled from the rest of the

Maryam Bagheri, Mohammadreza Azmoodeh

Understanding the rolling and adhesion behavior of a cell on the vascular surface under viscous shear flow is important to better understand many biological processes. One of the important examples is the adhesion of the leukocytes onto sites of an inflammation. Recently, researchers have started to investigate the effects of surface rigidity on the cell rol

Yu Zhu, Fernando J. Iglesias, Antonio G. Marques, Santiago Segarra

This paper studies the problem of jointly estimating multiple network processes driven by a common unknown input, thus effectively generalizing the classical blind multi-channel identification problem to graphs. More precisely, we model network processes as graph filters and consider the observation of multiple graph signals corresponding to outputs of diffe

Francisco Huhn, Luca Magri

In an acoustic cavity with a heat source, such as a flame in a gas turbine, the thermal energy of the heat source can be converted into acoustic energy, which may generate a loud oscillation. If uncontrolled, these nonlinear acoustic oscillations, also known as thermoacoustic instabilities, can cause large vibrations up to structural failure. Numerical and e

Kiwoo Shin, Masayoshi Tomizuka

We present an endpoint box regression module(epBRM), which is designed for predicting precise 3D bounding boxes using raw LiDAR 3D point clouds. The proposed epBRM is built with sequence of small networks and is computationally lightweight. Our approach can improve a 3D object detection performance by predicting more precise 3D bounding box coordinates. The

Taojiannan Yang, Sijie Zhu, Chen Chen, Shen Yan

We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency trade-offs at runtime. Our method trains a cohort of sub-networks with different widths using different input resolutions to mutually learn multi-scale representations for each sub-ne

Sijie Zhu, Taojiannan Yang, Chen Chen

This work explores the visual explanation for deep metric learning and its applications. As an important problem for learning representation, metric learning has attracted much attention recently, while the interpretation of such model is not as well studied as classification. To this end, we propose an intuitive idea to show where contributes the most to th

Alexander Dranishnikov, Rustam Sadykov

We use the Berstein-Hilton invariant to prove the formula $\cat(M_1\sharp M_2)=\max\{\cat M_1, \cat M_2\}$ for the Lustrnik-Schnirelmann category of the connected sum of closed manifolds $M_1$ and $M_2$.

Oscar P. Bruno, Tao Yin

This paper presents novel methodologies for the numerical simulation of scattering of elastic waves by both closed and open surfaces in three-dimensional space. The proposed approach utilizes new integral formulations as well as an extension to the elastic context of the efficient high-order singular-integration methods~\cite{BG18} introduced recently for th

Jun Ma, Vadim Marmer, Artyom Shneyerov, Pai Xu

We propose a new nonparametric estimator for first-price auctions with independent private values that imposes the monotonicity constraint on the estimated inverse bidding strategy. We show that our estimator has a smaller asymptotic variance than that of Guerre, Perrigne and Vuong's (2000) estimator. In addition to establishing pointwise asymptotic normalit

Gleb Dubosarskii, Serguei Primak, Xianbin Wang

One of the challenges related to the investigation of vehicular networks is associated with predicting a network state regarding both short-term and long-term network evolutionary changes. This paper analyzes a case in which vehicles are located on a straight road, and the connectivity state between two consecutive cars is determined by the Markov chain mode

Gleb Dubosarskii, Serguei Primak, Xianbin Wang

Vehicular ad-hoc networks (VANETs) have become an extensively studied topic in contemporary research. One of the fundamental problems that has arisen in such research is understanding the network statistical properties, such as the cluster number distribution and the cluster size distribution. In this paper, we analyze these characteristics in the case in wh

Xinlei Pan, Tingnan Zhang, Brian Ichter, Aleksandra Faust

Imitation learning is a popular approach for training visual navigation policies. However, collecting expert demonstrations for legged robots is challenging as these robots can be hard to control, move slowly, and cannot operate continuously for a long time. Here, we propose a zero-shot imitation learning approach for training a visual navigation policy on l

Kwangsu Ahn, Wenda Cao

The Near-IR Imaging Spectropolarimeter (NIRIS) is a polarimeter that is installed at the New Solar Telescope at Big Bear Solar Observatory. This instrument takes advantages of the highest spatial resolution and flux. The primary mirror is an on-axis type, so it was our interest to evaluate its contribution to the crosstalk among the Stokes parameters since w

Matthew L. Olson, Lawrence Neal, Fuxin Li, Weng-Keen Wong

Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past work has mainly focused on explaining why an action was chosen in a given state. A different type of explanation that is useful is a counterfactual, which deals with "what if?" sc

B. H. Zhang, Y. S. Hou, Z. Wang, R. Q. Wu

We perform systematic first-principles calculations to investigate the spin-phonon coupling (SPC) of Cr2Ge2Te6 (CGT) monolayer (ML). It is found that the Eg phonon mode at 211.8 cm^{-1} may have a SPC as large as 3.19 cm^{-1}, as it directly alters the superexchange interaction along the Cr-Te-Cr pathway. Furthermore, the strength of SPC of the CGT ML can be

Yuan Lin, John McPhee, Nasser L. Azad

Deep Reinforcement Learning (DRL) is used here for decentralized decision-making and longitudinal control for high-speed on-ramp merging. The DRL environment state includes the states of five vehicles: the merging vehicle, along with two preceding and two following vehicles when the merging vehicle is or is projected on the main road. The control action is t

Daniel R. Reynolds, David J. Gardner, Cody J. Balos, Carol S. Woodward

In this report we document performance test results on a SUNDIALS-based multiphysics demonstration application. We aim to assess the large-scale parallel performance of new capabilities that have been added to the SUNDIALS suite of time integrators and nonlinear solvers in recent years under funding from both the Exascale Computing Project (ECP) and the Scie

Félix Therrien, Peter Graf, Vladan Stevanović

Finding an optimal match between two different crystal structures underpins many important materials science problems, including describing solid-solid phase transitions, developing models for interface and grain boundary structures. In this work, we formulate the matching of crystals as an optimization problem where the goal is to find the alignment and the