November 2019 arXiv papers — page 12

Min Ye

We consider the problem of multiple-node repair in distributed storage systems under the cooperative model, where the repair bandwidth includes the amount of data exchanged between any two different storage nodes. Recently, explicit constructions of MDS codes with optimal cooperative repair bandwidth for all possible parameters were given by Ye and Barg (IEE

Yakefu Reyimuaji, Chun Liu

The neutrino oscillation probabilities in vacuum and matter are discussed, considering the framework of three active and one light sterile neutrinos. We study in detail the rephasing invariants and CP asymmetry observables, and investigate the four-neutrino oscillations in long-baseline neutrino experiments, such as DUNE, NO$\nu$A and T2HK. Our results show

Ryota Nakai, Kentaro Nomura

Topologically nontrivial superconducting phases have been engineered in topological materials by the proximity effect in contact with conventional superconductors. In this paper, by using the method of the Kronig-Penney model, we study the superconducting proximity effect in the bulk electronic states of Weyl semimetals by considering a multilayer structure

Jorge Aguilar-Guzmán, Jesús González, Teresa Hoekstra-Mendoza

Using the ordered analogue of Farley-Sabalka's discrete gradient field on the configuration space of a graph, we unravel a levelwise behavior of the generators of the pure braid group on a tree. This allows us to generalize Farber's equivariant description of the homotopy type of the configuration space on a tree on two particles. The results are applied to

Ragil Tsafack Ndongmo, Saleh Mahamat, Thomas Bouetou Bouetou, Timoleon Crepin Kofane

We purpose an approach for the thermodynamic analysis of rotating and non-linear magnetic-charged black hole with quintessence. Accordingly, we compute various thermodynamics quantities of the black hole, such as mass, temperature, potential provided from the magnetic charge, and the heat capacity. Moreover, we study phase transitions of this black hole, ana

Skylar W. Wurster, Arkadiusz Sitek, Jian Chen, Karla Evans

Radiologists can classify a mammogram as normal or abnormal at better than chance levels after less than a second's exposure to the images. In this work, we combine these radiologists' gist inputs into pre-trained machine learning models to validate that integrating gist with a CNN model can achieve an AUC (area under the curve) statistically significantly h

Prasad Chaugule, Mrinal Kumar, Nutan Limaye, Chandra Kanta Mohapatra

Schur Polynomials are families of symmetric polynomials that have been classically studied in Combinatorics and Algebra alike. They play a central role in the study of Symmetric functions, in Representation theory [Sta99], in Schubert calculus [LM10] as well as in Enumerative combinatorics [Gas96, Sta84, Sta99]. In recent years, they have also shown up in va

Masayuki Wakayama, Yuko Murakami, Atsushi Nakamura, Motoo Sekiguchi

We study the ground state and next radial excitation of the $a_1$ mesons from a quenched lattice QCD simulation with the truncated overlap fermions formalism based on domain wall fermions. Our results are consistent with the experimental values for $a_1$(1260) and $a_1$(1640).

Cameron R. Wolfe, Keld T. Lundgaard

In this work, we propose data augmentation methods for embeddings from pre-trained deep learning models that take a weighted combination of a pair of input embeddings, as inspired by Mixup, and combine such augmentation with extra label softening. These methods are shown to significantly increase classification accuracy, reduce training time, and improve con

Thomas Y. Hou, Zhenzhen Li, Ziyun Zhang

In this paper, we provide some analysis on the asymptotic escape of strict saddles in manifold optimization using the projected gradient descent (PGD) algorithm. One of our main contributions is that we extend the current analysis to include non-isolated and possibly continuous saddle sets with complicated geometry. We prove that the PGD is able to escape st

Wen Wang, Lijun Du, Yinxing Gao, Yanzhou Su

In this work, a discriminatively learned CNN embedding is proposed for remote sensing image scene classification. Our proposed siamese network simultaneously computes the classification loss function and the metric learning loss function of the two input images. Specifically, for the classification loss, we use the standard cross-entropy loss function to pre

Rong Ma, T. Tony Cai, Hongzhe Li

Motivated by the problem of estimating the bacterial growth rates for genome assemblies from shotgun metagenomic data, we consider the permuted monotone matrix model $Y=\Theta\Pi+Z$, where $Y\in \mathbb{R}^{n\times p}$ is observed, $\Theta\in \mathbb{R}^{n\times p}$ is an unknown approximately rank-one signal matrix with monotone rows, $\Pi \in \mathbb{R}^{p

Desmond Hill, Snezhana Abarzhi

Richtmyer-Meshkov instability (RMI) plays an important role in many areas of science and engineering, from supernovae and fusion to scramjets and nano-fabrication. Classical Richtmyer-Meshkov instability is induced by a steady shock and impulsive acceleration, whereas in realistic environments the acceleration is usually variable. We focus on RMI induced by

Wojciech Michal Matkowski, Tingting Chai, Adams Wai Kin Kong

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for f

Shaojie Tang, Jing Yuan

In this paper, we study the joint product sequencing and pricing problem faced by many online retailers such as Amazon. We assume that consumers' purchase behavior can be explained by a ``consider-then-choose'' model: they first form a consideration set by screening a subset of products sequentially, and then decide which product to purchase from their consi

Xinyang Jiang, Yifei Gong, Xiaowei Guo, Qize Yang

Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features of an entire video. However, existing video-based ReID methods do not consider the semantic difference brought by the outputs of different network stages, which potentially comprom

Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup

Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider th

Gaoxiang Wang, Avik Ranjan Adhikary, Zhengchun Zhou, Yang Yang

The construction of complementary sets (CSs) of sequences with different set size and sequence length become important due to its practical application for OFDM systems. Most of the constructions of CSs, based on generalized Boolean functions (GBFs), are of length $2^\alpha$ ($\alpha$ is a natural number). Recently some works have been reported on constructi

Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu

Pose-based action recognition has drawn considerable attention recently. Existing methods exploit the joint positions to extract the body-part features from the activation map of the convolutional networks to assist human action recognition. However, these features are simply concatenated or max-pooled in previous works. The structured correlations among the

Liguo He, Guirong Song

By using the methods of Cauchy-Binet type formula and adjugate matrix respectively, a wonderful equality relating to the elements of eigenvectors, the eigenvalues and the submatrix eigenvalues is proved in arXiv:1908.03795. In the note, we use matrix block to provide a new and shorter proof for the equality.

Thuong, Nguyen Canh, Chien, Trinh Van

Compressive sensing (CS) is an emerging sampling technology that enables reconstructing signals from a subset of measurements and even corrupted measurements. Deep learning-based compressive sensing (DCS) has improved CS performance while maintaining a fast reconstruction but requires a training network for each measurement rate. Also, concerning the transmi

Yi Huang, Xiaoshan Yang, Changsheng Xu

Due to potential applications in chronic disease management and personalized healthcare, the EHRs data analysis has attracted much attention of both researchers and practitioners. There are three main challenges in modeling longitudinal and heterogeneous EHRs data: heterogeneity, irregular temporality and interpretability. A series of deep learning methods h

Ning Zhang, Wenjian Liu, Mark R. Hoffmann

Even when starting with a very poor initial guess, the iterative configuration interaction (iCI) approach can converge from above to full CI very quickly by constructing and diagonalizing a small Hamiltonian matrix at each macro/micro-iteration. However, iCI scales exponentially with respect to the numbers of electrons and orbitals. The problem can be mitiga

Agelos Kratimenos, Kleanthis Avramidis, Christos Garoufis, Athanasia Zlatintsi

Instrument classification is one of the fields in Music Information Retrieval (MIR) that has attracted a lot of research interest. However, the majority of that is dealing with monophonic music, while efforts on polyphonic material mainly focus on predominant instrument recognition. In this paper, we propose an approach for instrument classification in polyp

Xing Xu, Rongpeng Li, Zhifeng Zhao, Honggang Zhang

With the rapid evolution of wireless mobile devices, there emerges an increased need to design effective collaboration mechanisms between intelligent agents, so as to gradually approach the final collective objective through continuously learning from the environment based on their individual observations. In this regard, independent reinforcement learning (

Zhaopei Gong, Liang Ding, Honghao Yue, Haibo Gao

Micro-vibration has been a dominant factor impairing the performance of scientific experiments which are expected to be deployed in a micro-gravity environment such as Spacelab. The micro-vibration has a serious impact on scientific experiments requiring a quasi-static environment. Therefore, we proposed a maglev vibration isolation platform (MVIP) operating

Zhen Zheng, Zhi Lin, Dan-Wei Zhang, Shi-Liang Zhu

Although Weyl semimetals have been extensively studied for exploring rich topological physics, the direct observation of the celebrated chiral magnetic effect (CME) associated with the so-called dipolar chiral anomaly has long intrigued and challenged physicists, still remaining elusive in nature. Here we propose a feasible scheme for experimental implementa

Yining Wang, Mingzhe Chen, Zhaohui Yang, Tao Luo

In this paper, the problem of dynamical deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities for optimizing the energy efficiency of UAV-enabled networks is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Since ambient illuminatio

Sanjeev Sharma, Shaoli Huang, Dacheng Tao

This work addresses the challenging problem of unconstrained 3D hand pose estimation using monocular RGB images. Most of the existing approaches assume some prior knowledge of hand (such as hand locations and side information) is available for 3D hand pose estimation. This restricts their use in unconstrained environments. We, therefore, present an end-to-en

A. Koda, H. T. Hirose, M. Miyazaki, H. Okabe

We report on a novel spin-charge fluctuation in the all-in-all-out pyrochlore magnet Cd$_2$Os$_2$O$_7$, where the spin fluctuation is driven by the conduction of thermally excited electrons/holes and associated fluctuation of Os valence. The fluctuation exhibits an activation energy significantly greater than the spin-charge excitation gap and a peculiar fre

Lei Chen, Jian Liu, Kadi Zhu

Axion remains the most convincing solution to the strong-CP problem and a well-motivated dark matter candidate, causing the search for axions and axion-like particles(ALPs) to attract attention continually. The exchange of such particles may cause anomalous spin-dependent forces, inspiring many laboratory ALP searching experiments based on the detection of m

Xiu-Bin Wang, Bo Han

Under investigation in this work is the inverse scattering transform of the general fifth-order nonlinear Schr\"{o}dinger equation with nonzero boundary conditions (NZBCs), which can be reduced to several integrable equations. Firstly, a matrix Riemann-Hilbert problem for the equation with NZBCs at infinity is systematically investigated.Then the inverse pro

Bei Ding, Jie Cui, Guizhou Xu, Zhipeng Hou

Understanding the dynamics of the magnetic skyrmion, a particle-like topologically stable spin texture, and its response dynamics to external fields are indis-pensable for the applications in spintronic devices. In this letter, the Lorentz transmis-sion electron microscopy (LTEM) was used to investigate the spin chirality of the mag-netic skyrmion bubbles (S

Moritz Hübner, Colm Talbot, Paul D. Lasky, Eric Thrane

Gravitational-wave memory, a strong-field effect of general relativity, manifests itself as a permanent displacement in spacetime. We develop a Bayesian framework to detect gravitational-wave memory with the Advanced LIGO/Virgo detector network. We apply this algorithm on the ten binary black hole mergers in LIGO/Virgo's first transient gravitational-wave ca

Sayantan Bhattacharya, Pavlos P. Vlachos

We introduce the first comprehensive approach to determine the uncertainty in volumetric Particle Tracking Velocimetry (PTV) measurements. Volumetric PTV is a state-of-the-art non-invasive flow measurement technique, which measures the velocity field by recording successive snapshots of the tracer particle motion using a multi-camera set-up. The measurement

Claus Vogl, Lynette C. Mikula, Conrad J. Burden

The stationary sampling distribution of a neutral decoupled Moran or Wright-Fisher diffusion with neutral mutations is known to first order for a general rate matrix with small but otherwise unconstrained mutation rates. Using this distribution as a starting point we derive results for maximum likelihood estimates of scaled mutation rates from site frequency

Toni Annala

We construct Chern classes of vector bundles in the universal precobordism theory of Annala--Yokura over an arbitrary Noetherian base ring of finite Krull dimension. As an immediate corollary of this, we show that the Grothendieck ring of vector bundles can be recovered from the universal precobordism ring, and that we can construct candidates for Chow rings

Seungjae Lee, Young Sul Cho

We consider chimera states of coupled identical phase oscillators where some oscillators are phase synchronized while others are desynchronized. It is known that chimera states of non-locally coupled Kuramoto--Sakaguchi oscillators in arrays of finite size are chaotic transients when the phase lag parameter $\alpha \in (0, \pi/2)$; after a transient time, al

Jangho Kim, Yash Bhalgat, Jinwon Lee, Chirag Patel

Quantization and Knowledge distillation (KD) methods are widely used to reduce memory and power consumption of deep neural networks (DNNs), especially for resource-constrained edge devices. Although their combination is quite promising to meet these requirements, it may not work as desired. It is mainly because the regularization effect of KD further diminis

Xiaofeng Liu

Under very general conditions, we construct a micro-macro model for closed economy with a large number of heterogeneous agents. By introducing both financial capital (i.e. valued capital---- equities of firms) and physical capital (i.e. capital goods), our framework gives a logically consistent, complete factor income distribution theory with micro-foundatio

Zhipeng Li, Tianmeng Wang, Chenhao Jin, Zhengguang Lu

Inversion symmetry breaking and three-fold rotation symmetry grant the valley degree of freedom to the robust exciton in monolayer transition metal dichalcogenides (TMDCs), which can be exploited for valleytronics applications. However, the short lifetime of the exciton significantly constrains the possible applications. In contrast, dark exciton could be lo

Jack Kwok, King Lau, Edward Young, Kenneth Young

A linear polarization field on a surface is expressed in terms of scalar functions, providing an invariant separation into two components; one of these is the B mode, important as a signature of primordial gravitational waves, which would lend support to the inflation hypothesis. The case of a plane already exhibits the key ideas, including the formal analog

Chao Weng, Chengzhu Yu, Jia Cui, Chunlei Zhang

In this work, we propose minimum Bayes risk (MBR) training of RNN-Transducer (RNN-T) for end-to-end speech recognition. Specifically, initialized with a RNN-T trained model, MBR training is conducted via minimizing the expected edit distance between the reference label sequence and on-the-fly generated N-best hypothesis. We also introduce a heuristic to inco

Xueya Zhang, Tong Zhang, Wenting Zhao, Zhen Cui

Graph convolutional networks (GCNs) have shown the powerful ability in text structure representation and effectively facilitate the task of text classification. However, challenges still exist in adapting GCN on learning discriminative features from texts due to the main issue of graph variants incurred by the textual complexity and diversity. In this paper,

D. A. Glazov, A. V. Malyshev, V. M. Shabaev, I. I. Tupitsyn

The nuclear recoil effect on the ground-state $g$ factor of highly charged boronlike ions is considered within the relativistic framework. The interelectronic-interaction contribution is evaluated within the Breit approximation employing two independent approaches: the second-order perturbation theory and the configuration-interaction Dirac-Fock-Sturm method

Toni Annala, Shoji Yokura

The purpose of this paper is to study an extended version of bivariant derived algebraic cobordism where the cycles carry a vector bundle on the source as additional data. We show that, over a field of characteristic 0, this extends the analogous homological theory of Lee and Pandharipande constructed earlier. We then proceed to study in detail the restricte

Bertrand Jayles, Ramón Escobedo, Roberto Pasqua, Christophe Zanon

Social media filters combined with recommender systems can lead to the emergence of filter bubbles and polarized groups. In addition, segregation processes of human groups in certain social contexts have been shown to share some similarities with phase separation phenomena in physics. Here, we study the impact of information filtering on collective segregati

Basit Ayantunde, Jane Odum, Fadlullah Olawumi, Joshua Olalekan

The physically impaired commonly have difficulties performing simple routine tasks without relying on other individuals who are not always readily available and thus make them strive for independence. While their impaired abilities can in many cases be augmented (to certain degrees) with the use of assistive technologies, there has been little attention to t

Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertisement, search ranking and recommendation. W

Mohammad Reza Zamani Kouhpanji, Bethanie J H Stadler

First-order reversal curves (FORC) measurements are broadly used for characterization of complex magnetic nanostructures, but a robust framework for quantitative analysis of the FORC data is still obscure despite numerous studies over decades. In this paper, we first establish a framework for extracting quantitative parameters from the FORC measurements cond

Yue Zhou

By combining the Gessel--Xin method with plethystic substitutions, we obtain a recursion for a symmetric function generalization of the $q$-Dyson constant term identity also known as the Zeilberger--Bressoud $q$-Dyson theorem. This yields a constant term identity which generalizes the non-zero part of Kadell's orthogonality ex-conjecture and a result of K\'{

Benjamin Nagy

This paper demonstrates that metre is a privileged indicator of authorial style in classical Latin hexameter poetry. Using only metrical features, pairwise classification experiments are performed between 5 first-century authors (10 comparisons) using four different machine-learning models. The results showed a two-label classification accuracy of at least 9

Jen-Hsu Chang, Chun-Yan Lin, Ray-Kuang Lee

We study the eigen-energy and eigen-function of a quantum particle acquiring the probability density-dependent effective mass (DDEM) in harmonic oscillators. Instead of discrete eigen-energies, continuous energy spectra are revealed due to the introduction of a nonlinear effective mass. Analytically, we map this problem into an infinite discrete dynamical sy

Mingjiang Liang, Shaoli Huang, Shirui Pan, Mingming Gong

Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning. Contemporary approaches based on weight-generation scheme delivers a straightforward and flexible solution to the problem. However, they did not fully consider both the representation power for unseen categories and weight generation c

Tarun Chitra

Proof of Stake (PoS) is a burgeoning Sybil resistance mechanism that aims to have a digital asset ("token") serve as security collateral in crypto networks. However, PoS has so far eluded a comprehensive threat model that encompasses both Byzantine attacks from distributed systems and financial attacks that arise from the dual usage of the token as a means o

Yu-Xia Liang, Ze-Hua Zhou

Given a locally compact group $G$ and $1\leq p<\infty$, a sufficient condition ensuring the \emph{disjoint hypercyclicity} of finitely many weighted translations on $L^p(G)$ was investigated in this paper.

Mouloud Belbahri, Alejandro Murua, Olivier Gandouet, Vahid Partovi Nia

Uplift models provide a solution to the problem of isolating the marketing effect of a campaign. For customer churn reduction, uplift models are used to identify the customers who are likely to respond positively to a retention activity only if targeted, and to avoid wasting resources on customers that are very likely to switch to another company. We introdu

Dang Nguyen, Sunil Gupta, Santu Rana, Alistair Shilton

Many real-world functions are defined over both categorical and category-specific continuous variables and thus cannot be optimized by traditional Bayesian optimization (BO) methods. To optimize such functions, we propose a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward dist

Jasper Lu, David Kai Zhang, Zinovi Rabinovich, Svetlana Obraztsova

Constructive election control considers the problem of an adversary who seeks to sway the outcome of an electoral process in order to ensure that their favored candidate wins. We consider the computational problem of constructive election control via issue selection. In this problem, a party decides which political issues to focus on to ensure victory for th

Wouter Cames van Batenburg, Jan Goedgebeur, Gwenaël Joret

Every $n$-vertex planar triangle-free graph with maximum degree at most $3$ has an independent set of size at least $\frac{3}{8}n$. This was first conjectured by Albertson, Bollob\'as and Tucker, and was later proved by Heckman and Thomas. Fraughnaugh and Locke conjectured that the planarity requirement could be relaxed into just forbidding a few specific no

Hao Kang, Jianming Zhang, Haoxiang Li, Zhe Lin

We introduce a novel framework for automatic capturing of human portraits. The framework allows the robot to follow a person to the desired location using a Person Re-identification model. When composing is activated, the robot attempts to adjust its position to form the view that can best match the given template image, and finally takes a photograph. A tem

Koichi Miyamoto, Kenji Shiohara

It is known that quantum computers can speed up Monte Carlo simulation compared to classical counterparts. There are already some proposals of application of the quantum algorithm to practical problems, including quantitative finance. In many problems in finance to which Monte Carlo simulation is applied, many random numbers are required to obtain one sample

Guoyong Zhang, Xiao Fu, Jun Wang, Xi-Le Zhao

Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i.e., a radio map)---from limited samples taken sparsely over the region. Classic cartography methods are mostly concerned with recovering the aggregate radio frequency (RF) information while ignoring the constituents of the radio ma

Leonardo Citraro, Mateusz Koziński, Pascal Fua

Existing performance measures rank delineation algorithms inconsistently, which makes it difficult to decide which one is best in any given situation. We show that these inconsistencies stem from design flaws that make the metrics insensitive to whole classes of errors. To provide more reliable evaluation, we design three new metrics that are far more consis

Oleksii Konashevych

There is an ongoing competition among blockchain technologies and the existence of one ultimate blockchain is impossible for many reasons. On the other hand, such variety can create difficulties in adoption, especially for the governments and corporations. The proposed technology ensures a blockchain agnostic approach and aimed to create a unified ecosystem

Carles Corbella, Sabine Portal, M. A. S. R. Saadi, Santiago D. Solares

Here, the synthesis of Molybdenum Disulphide (MoS2) flakes by means of anodic atmospheric arc discharge is reported for the first time. The vertical electrode configuration consisted of a compound anode (hollow graphite anode filled with MoS2 powder) and a solid graphite cathode placed just above of the compound anode. Arc processes were operated in pulsed m

Ali Javed, Scott D. Hamshaw, Donna M. Rizzo, Byung Suk Lee

Hydrological storm events are a primary driver for transporting water quality constituents such as turbidity, suspended sediments and nutrients. Analyzing the concentration (C) of these water quality constituents in response to increased streamflow discharge (Q), particularly when monitored at high temporal resolution during a hydrological event, helps to ch

Tao Hu, Zhizhong Han, Matthias Zwicker

3D shape completion is important to enable machines to perceive the complete geometry of objects from partial observations. To address this problem, view-based methods have been presented. These methods represent shapes as multiple depth images, which can be back-projected to yield corresponding 3D point clouds, and they perform shape completion by learning

Yue Guan, Anuradha M. Annaswamy, H. Eric Tseng

Cumulative Prospect Theory (CPT) is a modeling tool widely used in behavioral economics and cognitive psychology that captures subjective decision making of individuals under risk or uncertainty. In this paper, we propose a dynamic pricing strategy for Shared Mobility on Demand Services (SMoDSs) using a passenger behavioral model based on CPT. This dynamic p

Lukas Fleischer, Jeffrey Shallit

In 2013, Fici and Zamboni proved a number of theorems about finite and infinite words having only a small number of factors that are palindromes. In this paper we rederive some of their results, and obtain some new ones, by a different method based on finite automata.

Ke Sun, Frank Nielsen

This letter introduces an abstract learning problem called the "set embedding": The objective is to map sets into probability distributions so as to lose less information. We relate set union and intersection operations with corresponding interpolations of probability distributions. We also demonstrate a preliminary solution with experimental results on toy

Denis Rosset, David Schmid, Francesco Buscemi

Quantum theory describes multipartite objects of various types: quantum states, nonlocal boxes, steering assemblages, teleportages, distributed measurements, channels, and so on. Such objects describe, for example, the resources shared in quantum networks. Not all such objects are useful, however. In the context of space-like separated parties, devices which

Eren Balevi, Jeffrey G. Andrews

We develop a two-stage deep learning pipeline architecture to estimate the uplink massive MIMO channel with one-bit ADCs. This deep learning pipeline is composed of two separate generative deep learning models. The first one is a supervised learning model and designed to compensate for the quantization loss. The second one is an unsupervised learning model a

Ilaria Gianani, Marco Sbroscia, Marco Barbieri

Encoding information in the time-frequency domain is demonstrating its potential for quantum information processing. It offers a novel scheme for communications with large alphabets, computing with large quantum systems, and new approaches to metrology. It is then crucial to secure full control on the generation of time-frequency quantum states and their pro

Petter Brändén, Liam Solus

In this note, we investigate some of the fundamental algebraic and geometric properties of $s$-lecture hall simplices and their generalizations. We show that all $s$-lecture hall order polytopes, which simultaneously generalize $s$-lecture hall simplices and order polytopes, satisfy a property which implies the integer decomposition property. This answers on

Eric Bax

This note describes how to collect charges for ad impact on user experience. The charge may be per-view, to account for impact on user experience from viewing an ad, or per-click, to account for impact from clicking on the ad. The results for per-click charges also apply to per-conversion charges or per-action charges. Conceivably, a marketplace could assess

Sina Hojjatinia, Sajad Hamzenejadi, Hadis Mohseni

Today, Android devices are able to provide various services. They support applications for different purposes such as entertainment, business, health, education, and banking services. Because of the functionality and popularity of Android devices as well as the open-source policy of Android OS, they have become a suitable target for attackers. Android Botnet

Wojciech Słomczyński, Anna Szymusiak

We study the class of quantum measurements with the property that the image of the set of quantum states under the measurement map transforming states into probability distributions is similar to this set and call such measurements morphophoric. This leads to the generalisation of the notion of a qplex, where SIC-POVMs are replaced by the elements of the muc

Sandra N. dos Reis, Fernando Buitrago, Polychronis Papaderos, Israel Matute

Taking advantage of HST CANDELS data, we analyze the lowest redshift (z<0.5) massive galaxies in order to disentangle their structural constituents and study possible faint non-axis-symmetric features. Due to the excellent HST spatial resolution for intermediate-z objects, they are hard to model by purely automatic parametric fitting algorithms. We performed

Patrick Carzon, Mauricio Martinez, Matthew D. Sievert, Douglas E. Wertepny

At top collider energies where baryon stopping is negligible, the initial state of heavy ion collisions is overall charge neutral and predominantly composed of gluons. Nevertheless, there can also be significant local fluctuations of the baryon number, strangeness, and electric charge densities about zero, perturbatively corresponding to the production of qu

Samuel A. Hambleton, Randy Yee

Classes of pairs of ternary quadratic forms parametrize quartic rings by a result of Bhargava. We give an algorithm for finding a pair of ternary quadratic forms that parametrize a given order of a quartic field. We examine a new technique, essential pairs, for obtaining parameters of orders of quartic fields from a number field database. Essential pairs for

Yan V. Fyodorov, Rashel Tublin

We use Kac-Rice method to analyze statistical features of an "optimization landscape" of the loss function in a random version of the Oblique Procrustes Problem, one of the simplest optimization problems of the least-square type on a sphere.

Ali Borji, Seyed Mehdi Iranmanesh

Object detection remains as one of the most notorious open problems in computer vision. Despite large strides in accuracy in recent years, modern object detectors have started to saturate on popular benchmarks raising the question of how far we can reach with deep learning tools and tricks. Here, by employing 2 state-of-the-art object detection benchmarks, a

J. M. Fink, M. Kalaee, R. Norte, A. Pitanti

Microelectromechanical systems and integrated photonics provide the basis for many reliable and compact circuit elements in modern communication systems. Electro-opto-mechanical devices are currently one of the leading approaches to realize ultra-sensitive, low-loss transducers for an emerging quantum information technology. Here we present an on-chip microw

Daniel S. Graham, Xuelan Wen, Dhabih V. Chulhai, Jason D. Goodpaster

Wave function (WF) in density functional theory (DFT) embedding methods provide a framework for performing localized, high accuracy WF calculations on a system, while not incurring the full computational cost of the WF calculation on the full system. In order to effectively partition a system into localized WF and DFT subsystems, we utilize the Huzinaga leve

Chenchen Zhu, Fangyi Chen, Zhiqiang Shen, Marios Savvides

Recently, anchor-free detection methods have been through great progress. The major two families, anchor-point detection and key-point detection, are at opposite edges of the speed-accuracy trade-off, with anchor-point detectors having the speed advantage. In this work, we boost the performance of the anchor-point detector over the key-point counterparts whi

Philipp A. Witte, Mathias Louboutin, Charles Jones, Felix J. Herrmann

This abstract presents a serverless approach to seismic imaging in the cloud based on high-throughput containerized batch processing, event-driven computations and a domain-specific language compiler for solving the underlying wave equations. A 3D case study on Azure demonstrates that this approach allows reducing the operating cost of up to a factor of 6, m

Samuel Bosch, Alexander Sanchez de la Cerda, Mohsen Imani, Tajana Simunic Rosing

Machine Learning algorithms based on Brain-inspired Hyperdimensional(HD) computing imitate cognition by exploiting statistical properties of high-dimensional vector spaces. It is a promising solution for achieving high energy efficiency in different machine learning tasks, such as classification, semi-supervised learning, and clustering. A weakness of existi

Jonas Moss, Riccardo De Bin

Publication bias and p-hacking are two well-known phenomena that strongly affect the scientific literature and cause severe problems in meta-analyses. Due to these phenomena, the assumptions of meta-analyses are seriously violated and the results of the studies cannot be trusted. While publication bias is almost perfectly captured by the weighting function s

Rabia Aktaş, Iván Area, Esra Güldoğan

In this paper we introduce a six-parameter generalization of the four-parameter three-variable polynomials on the simplex and we investigate the properties of these polynomials. Sparse recurrence relations are derived by using ladder relations for shifted univariate Jacobi polynomials and bivariate polynomials on the triangle. Via these sparse recurrence rel

Matieyendou Lamboni

Weighted Poincar\'e-type and related inequalities provide upper bounds of the variance of functions. Their application in sensitivity analysis allows for quickly identifying the active inputs. Although the efficiency in prioritizing inputs depends on the upper bounds, the latter can be big, and therefore useless in practice. In this paper, an optimal weighte

Aniket Pramanik, Hemant Aggarwal, Mathews Jacob

We introduce a fast model based deep learning approach for calibrationless parallel MRI reconstruction. The proposed scheme is a non-linear generalization of structured low rank (SLR) methods that self learn linear annihilation filters from the same subject. It pre-learns non-linear annihilation relations in the Fourier domain from exemplar data. The pre-lea

Mohammad Najjartabar Bisheh, Hamid Davoudpour, G. Reza nasiri

Supply chain management is an integrated approach for planning and controlling materials, information, and finances as they move in a process which begins from suppliers and ends with customers in forward approach. As distribution network planning is strategically done, the related decisions should be optimized. This supply chain planning involves transporta

Trent Kyono, Mihaela van der Schaar

For decades, researchers in fields, such as the natural and social sciences, have been verifying causal relationships and investigating hypotheses that are now well-established or understood as truth. These causal mechanisms are properties of the natural world, and thus are invariant conditions regardless of the collection domain or environment. We show in t

Alejandra Pena-Ordieres, Daniel Molzahn, Line Roald, Andreas Waechter

Managing uncertainty and variability in power injections has become a major concern for power system operators due to the increasing levels of fluctuating renewable energy connected to the grid. This work addresses this uncertainty via a joint chance-constrained formulation of the DC optimal power flow (OPF) problem, which satisfies \emph{all} the constraint

Franziska Wall, Oliver Mey, Lorenz Maximilian Schneider, Arash Rahimi-Iman

A theoretical variation between the two distinct light-matter coupling regimes, namely weak and strong coupling, becomes uniquely feasible in open optical Fabry-P\'erot microcavities with low mode volume, as discussed here. In combination with monolayers of transition-metal dichalcogenides (TMDCs) such as WS2, which exhibits a large exciton oscillator streng

Nicola Pancotti, Matteo Scandi, Mark T. Mitchison, Martí Perarnau-Llobet

Isothermal transformations are minimally dissipative but slow processes, as the system needs to remain close to thermal equilibrium along the protocol. Here, we show that smoothly modifying the system-bath interaction can significantly speed up such transformations. In particular, we construct protocols where the overall dissipation $W_{\rm diss}$ decays wit

Oskar Triebe, Nikolay Laptev, Ram Rajagopal

In this paper we present a new framework for time-series modeling that combines the best of traditional statistical models and neural networks. We focus on time-series with long-range dependencies, needed for monitoring fine granularity data (e.g. minutes, seconds, milliseconds), prevalent in operational use-cases. Traditional models, such as auto-regression

Lior Alon, Ram Band

The Neumann points of an eigenfunction $f$ on a quantum (metric) graph are the interior zeros of $f'$. The Neumann domains of $f$ are the sub-graphs bounded by the Neumann points. Neumann points and Neumann domains are the counterparts of the well-studied nodal points and nodal domains. We prove some foundational results in this field: bounds on the number o

Cornelis Dullemond, Andrea Isella, Sean Andrews, Iuliia Skobleva

Knowledge of the midplane temperature of protoplanetary disks is one of the key ingredients in theories of dust growth and planet formation. However, direct measurement of this quantity is complicated, and often depends on the fitting of complex models to the data. In this paper we demonstrate a method to directly measure the midplane gas temperature from an