November 2019 arXiv papers — page 45

Thomas Demeester

Established recurrent neural networks are well-suited to solve a wide variety of prediction tasks involving discrete sequences. However, they do not perform as well in the task of dynamical system identification, when dealing with observations from continuous variables that are unevenly sampled in time, for example due to missing observations. We show how su

Tao Zhang, Yang Cong, Gan Sun, Qianqian Wang

Object clustering, aiming at grouping similar objects into one cluster with an unsupervised strategy, has been extensivelystudied among various data-driven applications. However, most existing state-of-the-art object clustering methods (e.g., single-view or multi-view clustering methods) only explore visual information, while ignoring one of most important s

Zhengyang Lu, Ying Chen

Single image super-resolution (SISR) is the task of inferring a high-resolution image from a single low-resolution image. Recent research on super-resolution has achieved great progress due to the development of deep convolutional neural networks in the field of computer vision. Existing super-resolution reconstruction methods have high performances in the c

Guy Shalev, Ran El-Yaniv, Daniel Klotz, Frederik Kratzert

Joint models are a common and important tool in the intersection of machine learning and the physical sciences, particularly in contexts where real-world measurements are scarce. Recent developments in rainfall-runoff modeling, one of the prime challenges in hydrology, show the value of a joint model with shared representation in this important context. Howe

Reza Mahini, Peng Xu, Guoliang Chen, Yansong Li

Averaging amplitudes over consecutive time samples within a time-window is widely used to calculate the amplitude of an event-related potential (ERP) for cognitive neuroscience. Objective determination of the time-window is critical for determining the ERP component. Clustering on the spatio-temporal ERP data can obtain the time-window in which the consecuti

Anderson Ara, Mateus Maia, Samuel Macêdo, Francisco Louzada

Improvement of statistical learning models in order to increase efficiency in solving classification or regression problems is still a goal pursued by the scientific community. In this way, the support vector machine model is one of the most successful and powerful algorithms for those tasks. However, its performance depends directly from the choice of the k

Suad Krilašević, Sergio Grammatico

We propose an integral Nash equilibrium seeking control (I-NESC) law which steers the multi-agent system composed of a special class of linear agents to the neighborhood of the Nash equilibrium in noncooperative strongly monotone games. First, we prove that there exist parameters of the integral controller such that the system converges to the Nash equilibri

Yan Gao, Hongming Nie

We study the convergence of graphs consisting of finitely many internal rays for degenerating Newton maps. We state a sufficient condition to guarantee the convergence. As an application, we investigate the boundedness of hyperbolic components in the moduli space of quartic Newton maps. We prove that such a hyperbolic component is bounded if and only if ever

Martín Barrère, Chris Hankin, Demetrios G. Eliades, Nicolas Nicolau

Over the last years, Industrial Control Systems (ICS) have become increasingly exposed to a wide range of cyber-physical threats. Efficient models and techniques able to capture their complex structure and identify critical cyber-physical components are therefore essential. AND/OR graphs have proven very useful in this context as they are able to semanticall

R. Smit, P. Kopietz, O. Tsyplyatyev

We study the anisotropic quantum Heisenberg antiferromagnet for spin-1/2 that interpolates smoothly between the one-dimensional (1D) and the two-dimensional (2D) limits. Using the spin Hartree-Fock approach we construct a quantitative theory of heat capacity in the quasi-1D regime with a finite coupling between spin chains. This theory reproduces closely the

Ying Wen, Kai Xie, Lianghua He

The encoder-decoder networks are commonly used in medical image segmentation due to their remarkable performance in hierarchical feature fusion. However, the expanding path for feature decoding and spatial recovery does not consider the long-term dependency when fusing feature maps from different layers, and the universal encoder-decoder network does not mak

P. K. Vishwakarma, P. Dutta

Physical properties of the tiny scale structures in the cold neutral medium (CNM) of galaxies is a long-standing puzzle. Only a few lines of sights in our Galaxy have been studies with mixed results on the scale-invariant properties of such structures. Moreover, since these studies measure the variation of neutral hydrogen optical depth, they do not directly

R. Jansana, T. Moraschini

A notion of interpretation between arbitrary logics is introduced, and the poset Log of all logics ordered under interpretability is studied. It is shown that in Log infima of arbitrarily large sets exist, but binary suprema in general do not. On the other hand, the existence of suprema of sets of equivalential logics is established. The relations between Lo

Naqash Sarfraz, Ferit Gurbuz

In this paper, boundedness of Hausdorff operator on weak central Morrey space is obtained. Furthermore, we investigate the weak bounds of p- adic fractional Hausdorff Operator on weighted p-adic weak Lebesgue Space. We also obtain the sufficient condition of commutators of p-adic fractional Hausdorff Operator by taking symbol function from Lipschitz space. M

Eivind Bøhn, Signe Moe, Tor Arne Johansen

Reinforcement Learning in domains with sparse rewards is a difficult problem, and a large part of the training process is often spent searching the state space in a more or less random fashion for any learning signals. For control problems, we often have some controller readily available which might be suboptimal but nevertheless solves the problem to some d

Yanting Pei, Yaping Huang, Xingyuan Zhang

Most existing dehazing algorithms often use hand-crafted features or Convolutional Neural Networks (CNN)-based methods to generate clear images using pixel-level Mean Square Error (MSE) loss. The generated images generally have better visual appeal, but not always have better performance for high-level vision tasks, e.g. image classification. In this paper,

Marco Mobilio, Oliviero Riganelli, Daniela Micucci, Leonardo Mariani

Dealing with the evolution of operating systems is challenging for developers of mobile apps, who have to deal with frequent upgrades that often include backward incompatible changes of the underlying API framework. As a consequence of framework upgrades, apps may show misbehaviours and unexpected crashes once executed within an evolved environment. Identify

V. G. Klochkova

We summarize the results of long-term spectral monitoring of yellow hypergiants (YHGs) of northern hemisphere with a R$\ge$60000 resolution. The spectra of these F-G stars of extremely high luminosity, compactly located at the top of the Hertzsprung-Russell diagram revealed a variety of spectral features: various types of H$α$ profile, the presence (or absen

Niayesh Afshordi

One of the most ubiquitous features of quantum theories is the existence of zero-point fluctuations in their ground states. For massive quantum fields, these fluctuations decouple from infrared observables in ordinary field theories. However, there is no "decoupling theorem" in Quantum Gravity, and we recently showed that the vacuum stress fluctuatio

Natalia Kruszewska, Piotr Bełdowski, Krzysztof Domino, Kanika D Lambert

Many different processes take place to facilitate lubrication of the joints functioning in human locomotion system. The main purpose of this is to avoid destroying the articular cartilage. Viscoelastic properties of the joints system are very sensitive on both temperature and concentration changes because of the change in conformation presented in the system

William D. Kalies, Konstantin Mischaikow, Robert C. A. M. Vandervorst

The theory of bounded, distributive lattices provides the appropriate language for describing directionality and asymptotics in dynamical systems. For bounded, distributive lattices the general notion of `set-difference' taking values in a semilattice is introduced, and is called the Conley form. The Conley form is used to build concrete, set-theoretical

Dale Frymark

The abstract theory of boundary triples is applied to the classical Jacobi differential operator and its powers in order to obtain the Weyl $m$-function for several self-adjoint extensions with interesting boundary conditions: separated, periodic and those that yield the Friedrichs extension. These matrix-valued Nevanlinna--Herglotz $m$-functions are, to the

Monika Sharma, Shikha Gupta, Arindam Chowdhury, Lovekesh Vig

Despite the improvements in perception accuracies brought about via deep learning, developing systems combining accurate visual perception with the ability to reason over the visual percepts remains extremely challenging. A particular application area of interest from an accessibility perspective is that of reasoning over statistical charts such as bar and p

Zeyi Wen, Zeyu Huang, Rui Zhang

Entity extraction is an important task in text mining and natural language processing. A popular method for entity extraction is by comparing substrings from free text against a dictionary of entities. In this paper, we present several techniques as a post-processing step for improving the effectiveness of the existing entity extraction technique. These tech

Giorgio Galanti

Axion-like particles (ALPs) are light, neutral, pseudo-scalar bosons predicted by several extensions of the Standard Model of particle physics -- such as the String Theory -- and are supposed to interact primarily only with two photons. In the presence of an external magnetic field, photon-ALP oscillations occur and can produce sizable astrophysical effects

Adithya Krishna, Sunil Rudresh, Vishal Shaw, Hemanth Reddy Sabbella

Analog-to-digital converters (ADCs) provide the link between continuous-time signals and their discrete-time counterparts, and the Shannon-Nyquist sampling theorem provides the mathematical foundation. Real-world signals have a variable amplitude range, whereas ADCs, by design, have a limited input dynamic range, which results in out-of-range signals getting

José Fuentes-Sepúlveda, Susana Ladra

In the field of algorithms and data structures analysis and design, most of the researchers focus only on the space/time trade-off, and little attention has been paid to energy consumption. Moreover, most of the efforts in the field of Green Computing have been devoted to hardware-related issues, being green software in its infancy. Optimizing the usage of c

Mariko Kubo, Jun Toshikawa, Nobunari Kashikawa, Yi-Kuan Chiang

We perform a stacking analysis of {\it Planck}, {\it AKARI}, Infrared Astronomical Satellite ($IRAS$), Wide-field Infrared Survey Eplorer ($WISE$), and {\it Herschel} images of the largest number of (candidate) protoclusters at $z\sim3.8$ selected from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Stacking the images of the $179$ candidate protoc

Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson

Generalized planning aims at computing an algorithm-like structure (generalized plan) that solves a set of multiple planning instances. In this paper we define negative examples for generalized planning as planning instances that must not be solved by a generalized plan. With this regard the paper extends the notion of validation of a generalized plan as the

Liu Guang, Wang Xiaojie, Li Ruifan

Financial time-series classification (FTC) is extremely valuable for investment management. In past decades, it draws a lot of attention from a wide extent of research areas, especially Artificial Intelligence (AI). Existing researches majorly focused on exploring the effects of the Multi-Scale (MS) property or the Temporal Dependency (TD) within financial t

Oliviero Riganelli, Daniela Micucci, Leonardo Mariani

Android applications are executed on smartphones equipped with a variety of resources that must be properly accessed and controlled, otherwise the correctness of the executions and the stability of the entire environment might be negatively affected. For example, apps must properly acquire, use, and release microphones, cameras, and other multimedia devices

Mustafa Canim, Cristina Cornelio, Arun Iyengar, Ryan Musa

Unstructured enterprise data such as reports, manuals and guidelines often contain tables. The traditional way of integrating data from these tables is through a two-step process of table detection/extraction and mapping the table layouts to an appropriate schema. This can be an expensive process. In this paper we show that by using semantic technologies (RD

Anthony Bordg, Yijun He

We point out a flaw in the unfair case of the quantum Prisoner's Dilemma as introduced in the pioneering Letter "Quantum Games and Quantum Strategies" of Eisert, Wilkens and Lewenstein. It is not true that the so-called miracle move therein always gives quantum Alice a large reward against classical Bob and outperforms tit-for-tat in an iterated

Thomas R. Weikl, Jinglei Hu, Batuhan Kav, Bartosz Rozycki

The adhesion of biomembranes is mediated by the binding of membrane-anchored receptor and ligand proteins. The proteins can only bind if the separation between apposing membranes is sufficiently close to the length of the protein complexes, which leads to an interplay between protein binding and membrane shape. In this article, we review current models of bi

Yun Fan, Hualu Liu

We construct a class of $\mathbb{Z}_2\mathbb{Z}_4$-additive cyclic codes generated by pairs of polynomials, study their algebraic structures, and obtain the generator matrix of any code in the class. Using a probabilistic method, we prove that, for any positive real number $δ<1/3$ such that the entropy at $3δ/2$ is less than $1/2$, the probability that the r

Jiaxu Chen, Jing Hao, Kai Chen, Di Xie

Audio classification can distinguish different kinds of sounds, which is helpful for intelligent applications in daily life. However, it remains a challenging task since the sound events in an audio clip is probably multiple, even overlapping. This paper introduces an end-to-end audio classification system based on raw waveforms and mix-training strategy. Co

Daniel Barlet

Let s 1 ,. .. , s k be the elementary symmetric functions of the complex variables x 1 ,. .. , x k. We say that F $\in$ C[s 1 ,. .. , s k ] is a trace function if their exists f $\in$ C[z] such that F (s 1 ,. .. , s k ] = k j=1 f (x j) for all s $\in$ C k. We give an explicit finite family of second order differential operators in the Weyl algebra W 2 := C[s

Nayyer Aafaq, Naveed Akhtar, Wei Liu, Ajmal Mian

Contemporary deep learning based video captioning follows encoder-decoder framework. In encoder, visual features are extracted with 2D/3D Convolutional Neural Networks (CNNs) and a transformed version of those features is passed to the decoder. The decoder uses word embeddings and a language model to map visual features to natural language captions. Due to i

Weizhu Qian, Fabrice Lauri, Franck Gechter

Predicting smartphone users activity using WiFi fingerprints has been a popular approach for indoor positioning in recent years. However, such a high dimensional time-series prediction problem can be very tricky to solve. To address this issue, we propose a novel deep learning model, the convolutional mixture density recurrent neural network (CMDRNN), which

Guochang Wang, Ke Zhu, Guodong Li, Wai Keung Li

Asymmetric power GARCH models have been widely used to study the higher order moments of financial returns, while their quantile estimation has been rarely investigated. This paper introduces a simple monotonic transformation on its conditional quantile function to make the quantile regression tractable. The asymptotic normality of the resulting quantile est

Tianyi Li, Sujian Li

Previous work on visual storytelling mainly focused on exploring image sequence as evidence for storytelling and neglected textual evidence for guiding story generation. Motivated by human storytelling process which recalls stories for familiar images, we exploit textual evidence from similar images to help generate coherent and meaningful stories. To pick t

Zewei Sun, Shujian Huang, Hao-Ran Wei, Xin-yu Dai

Transformer model has been widely used on machine translation tasks and obtained state-of-the-art results. In this paper, we report an interesting phenomenon in its encoder-decoder multi-head attention: different attention heads of the final decoder layer align to different word translation candidates. We empirically verify this discovery and propose a metho

Shakeel Muhammad Ibrahim, Muhammad Sohail Ibrahim, Muhammad Usman, Imran Naseem

Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the recent past, various computer assisted medical imaging systems have been proposed for the segmentation of the or

Chris Fields, Antonino Marcianò

We show that sharing a quantum reference frame requires sharing measurement operators that identify the reference frame in addition to operators that measure its state. Observers restricted to finite resources cannot, in general, operationally determine that they share such operators. Uncertainty about whether system-identification operators are shared induc

Lavish Saluja, Ashutosh Bhatia

We live in an era of information and it is very important to handle the exchange of information. While sending data to an authorized source, we need to protect it from unauthorized sources, changes, and authentication. ZKP technique can be used in designing secure authentication systems that dont involve any direct exchange of information between the claiman

Sarah Latus, Florian Griese, Matthias Schlüter, Christoph Otte

Intravascular optical coherence tomography (IVOCT) is a catheter based image modality allowing for high resolution imaging of vessels. It is based on a fast sequential acquisition of A-scans with an axial spatial resolution in the range of 5 to 10 μm, i.e., one order of magnitude higher than in conventional methods like intravascular ultrasound or computed t

Yi-Hsun Chen, Chih-Yi Cheng, Shao-Yu Chen, Jan Sebastian Dominic Rodriguez

In 2D-semiconductor-based field-effect transistors and optoelectronic devices, metal-semiconductor junctions are one of the crucial factors determining device performance. The Fermi-level (FL) pinning effect, which commonly caused by interfacial gap states, severely limits the tunability of junction characteristics, including barrier height and contact resis

Quang-Hieu Pham, Mikaela Angelina Uy, Binh-Son Hua, Duc Thanh Nguyen

In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching. Our proposed method is a dual auto-encoder neural network that maps 2D and 3D input into a shared latent space representation. We show that such local cross-domain descriptors in the shared embedding are more discriminative than those ob

Liu Yafeng, Chen Tian, Liu Zhongyu, Zhang Lei

Human actions recognition has attracted more and more people&#39;s attention. Many technology have been developed to express human action&#39;s features, such as image, skeleton-based, and channel state information(CSI). Among them, on account of CSI&#39;s easy to be equipped and undemanding for light, and it has gained more and more attention in some specia

Nejib Ghanmi

For a positive integer $N$ and $\mathbb{A}$ a subset of $\mathbb{Q}$, let $\mathbb{A}$-$\mathcal{KS}(N)$ denote the set of $α=\dfrac{α_{1}}{α_{2}}\in \mathbb{A}\setminus \{0,N\}$ verifying $α_{2}p-α_{1}$ divides $α_{2}N-α_{1}$ for every prime divisor $p$ of $N$. The set $\mathbb{A}$-$\mathcal{KS}(N)$ is called the set of Korselt bases of $N$ in $\mathbb{A}$

Minje Park

Due to the recent advances on Neural Architecture Search (NAS), it gains popularity in designing best networks for specific tasks. Although it shows promising results on many benchmarks and competitions, NAS still suffers from its demanding computation cost for searching high dimensional architectural design space, and this problem becomes even worse when we

Irina Busjatskaja, Yury Kochetkov

We consider quadrangles of perimeter $2$ in the plane with marked directed edge. To such quadrangle $Q$ a two-dimensional plane $Π\in\mathbb{R}^4$ with orthonormal base is corresponded. Orthogonal plane $Π^\bot$ defines a plane quadrangle $Q^\circ$ of perimeter $2$ and with marked directed edge. This quadrangle is defined uniquely (up to rotation and symmetr

Chenze Shao, Jinchao Zhang, Yang Feng, Fandong Meng

Non-Autoregressive Neural Machine Translation (NAT) achieves significant decoding speedup through generating target words independently and simultaneously. However, in the context of non-autoregressive translation, the word-level cross-entropy loss cannot model the target-side sequential dependency properly, leading to its weak correlation with the translati

Son Doan, Mike Conway, Nigel Collier

Identifying articles that relate to infectious diseases is a necessary step for any automatic bio-surveillance system that monitors news articles from the Internet. Unlike scientific articles which are available in a strongly structured form, news articles are usually loosely structured. In this chapter, we investigate the importance of each section and the

Yongle Qi, Suzhen Wu

Characteristic electrical curves of GaN HEMT devices from Infineon and Transphorm are compared at different X-ray radiation dose. It is shown that the device with pGaN gate is more robust having a stable threshold voltage (Vth). The Vth of device with MIS gate shifts towards negative direction firstly and shifts to forward direction then. A qualitative analy

Zhijie Chen, Bo Yang, Cailian Chen, Xinping Guan

To accommodate exponentially increasing traffic demands of vehicle-based applications, operators are utilizing offloading as a promising technique to improve quality of service (QoS), which gives rise to the application of Mobile Edge Computing (MEC). While the conventional offloading paradigms focus on delay and energy tradeoff, they either fail to find eff

Zhenyu Wu, Mingxing Wen, Guohao Peng, Xiaoyu Tang

Most of the existing mobile robot localization solutions are either heavily dependent on pre-installed infrastructures or having difficulty working in highly repetitive environments which do not have sufficient unique features. To address this problem, we propose a magnetic-assisted initialization approach that enhances the performance of infrastructure-free

Tenavi Nakamura-Zimmerer, Daniele Venturi, Qi Gong, Wei Kang

Uncertainty propagation in nonlinear dynamic systems remains an outstanding problem in scientific computing and control. Numerous approaches have been developed, but are limited in their capability to tackle problems with more than a few uncertain variables or require large amounts of simulation data. In this paper, we propose a data-driven method for approx

Yuxuan Song, Lantao Yu, Zhangjie Cao, Zhiming Zhou

Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available. To leverage and adapt the label information from source domain, most existing methods employ a feature extracting function and match the marginal distributions of source and target domains in a shared f

Zhijie Chen, Junchi Yan, Longyuan Li, Xiaokang Yang

A main concern in cognitive neuroscience is to decode the overt neural spike train observations and infer latent representations under neural circuits. However, traditional methods entail strong prior on network structure and hardly meet the demand for real spike data. Here we propose a novel neural network approach called Neuron Activation Network that extr

Noboru Ito, Jun Yoshida

Khovanov homology is a categorification of the Jones polynomial, so it may be seen as a kind of quantum invariant of knots and links. Although polynomial quantum invariants are deeply involved with Vassiliev (aka. finite type) invariants, the relation remains unclear in case of Khovanov homology. Aiming at it, in this paper, we discuss a categorified version

Hung Dang, Ee-Chien Chang

Data privacy is unarguably of extreme importance. Nonetheless, there exist various daunting challenges to safe-guarding data privacy. These challenges stem from the fact that data owners have little control over their data once it has transgressed their local storage and been managed by third parties whose trustworthiness is questionable at times. Our work s

Hang Jiang, Xianzhe Zhang, Jinho D. Choi

Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features. However, attentive neural networks with contextual embeddings, which have achieved huge success in text classification, are rarely explored for this task. In this project, we have two major contributions. First, we create the

Nishi Doshi, Gitam Shikhenawis, Suman K Mitra

Image Aesthetics Assessment is one of the emerging domains in research. The domain deals with classification of images into categories depending on the basis of how pleasant they are for the users to watch. In this article, the focus is on categorizing the images in high quality and low quality image. Deep convolutional neural networks are used to classify t

Ritwik Gupta, Richard Hosfelt, Sandra Sajeev, Nirav Patel

We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natural disaster response requires an accurate understanding of damaged buildings in an affected region. Current response strategies require in-person damage assessments within 24-48 hou

Vanlin Sathya, Adam Dziedzic, Monisha Ghosh, Sanjay Krishnan

According to the LTE-U Forum specification, a LTE-U base-station (BS) reduces its duty cycle from 50% to 33% when it senses an increase in the number of co-channel Wi-Fi basic service sets (BSSs) from one to two. The detection of the number of Wi-Fi BSSs that are operating on the channel in real-time, without decoding the Wi-Fi packets, still remains a chall

Pablo Samuel Castro

We present new algorithms for computing and approximating bisimulation metrics in Markov Decision Processes (MDPs). Bisimulation metrics are an elegant formalism that capture behavioral equivalence between states and provide strong theoretical guarantees on differences in optimal behaviour. Unfortunately, their computation is expensive and requires a tabular

Zhao Kang, Wangtao Zhou, Zhitong Zhao, Junming Shao

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the er

Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron Elmore

The convolutional layers are core building blocks of neural network architectures. In general, a convolutional filter applies to the entire frequency spectrum of the input data. We explore artificially constraining the frequency spectra of these filters and data, called band-limiting, during training. The frequency domain constraints apply to both the feed-f

Nafees Ahmed, Klaus Mueller

The increasing popularity of smart meters provides energy consumers in households with unprecedented opportunities for understanding and modifying their energy use. However, while a variety of solutions, both commercial and academic,have been proposed, research on effective visual analysis tools is still needed to achieve widespread adoption of smart meters.

Zeji Yi, Zhefeng Cao, Evangelos Theodorou, Yongxin Chen

We consider covariance control problems for nonlinear stochastic systems. Our objective is to find an optimal control strategy to steer the state from an initial distribution to a terminal one with specified mean and covariance. This problem is considerably more complicated than previous studies on covariance control for linear systems. We leverage a widely

Abhijit Suprem, Calton Pu

Global physical event detection has traditionally relied on dense coverage of physical sensors around the world; while this is an expensive undertaking, there have not been alternatives until recently. The ubiquity of social networks and human sensors in the field provides a tremendous amount of real-time, live data about true physical events from around the

Boseong Jeon, H. Jin Kim

This letter suggests an integrated approach for a drone (or multirotor) to perform an autonomous videography task in a 3-D obstacle environment by following a moving object. The proposed system includes 1) a target motion prediction module which can be applied to dense environments and 2) a hierarchical chasing planner based on a proposed metric for visibili

Venkatesh Sridhar, Xiao Wang, Gregery T. Buzzard, Charles A. Bouman

Model-Based Image Reconstruction (MBIR) methods significantly enhance the quality of computed tomographic (CT) reconstructions relative to analytical techniques, but are limited by high computational cost. In this paper, we propose a multi-agent consensus equilibrium (MACE) algorithm for distributing both the computation and memory of MBIR reconstruction acr

Zihan Liu, Genta Indra Winata, Zhaojiang Lin, Peng Xu

Recently, data-driven task-oriented dialogue systems have achieved promising performance in English. However, developing dialogue systems that support low-resource languages remains a long-standing challenge due to the absence of high-quality data. In order to circumvent the expensive and time-consuming data collection, we introduce Attention-Informed Mixed-

Alexander Levine, Soheil Feizi

Recently, techniques have been developed to provably guarantee the robustness of a classifier to adversarial perturbations of bounded L_1 and L_2 magnitudes by using randomized smoothing: the robust classification is a consensus of base classifications on randomly noised samples where the noise is additive. In this paper, we extend this technique to the L_0

Bryan Li, Xinyue Wang, Homayoon Beigi

We propose a system to develop a basic automatic speech recognizer(ASR) for Cantonese, a low-resource language, through transfer learning of Mandarin, a high-resource language. We take a time-delayed neural network trained on Mandarin, and perform weight transfer of several layers to a newly initialized model for Cantonese. We experiment with the number of l

Akshata Krishnamurthy, Joel Villasenor, Steve Kissel, George Ricker

The Transiting Exoplanet Survey Satellite (TESS) will search for planets transiting bright stars with Ic<13. TESS has been selected by NASA for launch in 2018 as an Astrophysics Explorer mission, and is expected to discover a thousand or more planets that are smaller in size than Neptune. TESS will employ four wide-field optical charge-coupled device (CCD) c

Hiroyasu Ejiri

Neutrino nuclear responses associated with medium momentum transfer of q=20-80 MeV for astro neutrinos and double beta decays were studied by using charge exchange reactions on Te128 and Te130. Gamow-Teller and spin dipole nuclear matrix elements are found to be reduced with respect to the pnQRPA matrix elements by the coefficient of around 0.35. The reducti

Yao Sun, Aayush Rajasekaran

We introduce Unity Interleave, a new consensus algorithm for public blockchain settings. It is an eventual consistency protocol merging the Proof-of-Work (PoW) and Proof-of-Stake (PoS) into a coherent stochastic process. It builds upon research previously done for the Unity protocol, improving security while maintaining fairness and scalability.

Akihiro Suzuki, Takashi J. Moriya, Tomoya Takiwaki

We perform a series of two-dimensional radiation-hydrodynamic simulations of the collision between supernova ejecta and circumstellar media (CSM). The hydrodynamic interaction of a fast flow and the surrounding media efficiently dissipates the kinetic energy of the fast flow and considered as a dominant energy source for a specific class of core-collapse sup

Xiaomin Guo, Chen Cheng, Tong Liu, Xin Fang

The second order photon correlation g^(2)(tau) of a chaotic optical-feedback semiconductor laser is precisely measured using a Hanbury Brown-Twiss interferometer. The accurate g^(2)(tau) with non-zero delay time is obtained experimentally from the photon pair time interval distribution through a ninth-order self-convolution correction. The experimental resul

Andrew Hryniowski, Alexander Wong

From fully connected neural networks to convolutional neural networks, the learned parameters within a neural network have been primarily relegated to the linear parameters (e.g., convolutional filters). The non-linear functions (e.g., activation functions) have largely remained, with few exceptions in recent years, parameter-less, static throughout training

Martin Mosny

This study utilises the Higgs Characterization model to investigate the $CP$ properties of the Higgs coupling to the top quark using the $tH$ and $t\bar{t}H$ generation processes. This is done via simulations of proton-proton collisions with ATLAS detector conditions, which are calculated for seven different $CP$ eigenstates using MadGraph5_aMC@NLO. Three or

Yujie Wu, Mitchell H. Gail, Stephanie A. Smith-Warner, Regina G. Ziegler

Pooling biomarker data across multiple studies enables researchers to get more precise estimates of the association between biomarker exposure measurements and disease risks due to increased sample sizes. However, biomarker measurements vary significantly across different assays and laboratories, and therefore calibration of the local laboratory measurements

Fei Ma, Ping Wang, Bing Yao

The bloom of complex network study, in particular, with respect to scale-free ones, is considerably triggering the research of scale-free graph itself. Therefore, a great number of interesting results have been reported in the past, including bounds of diameter. In this paper, we focus mainly on a problem of how to analytically estimate the lower bound of di

Lu Meng, Bo Wang, Guang-Juan Wang, Shi-Lin Zhu

We adopt the chiral perturbation theory to calculate the $Σ_{c}^{(*)}\bar{D}^{(*)}$ interaction to the next-to-leading order (NLO) and include the couple-channel effect in the loop diagrams. We reproduce the three $P_c$ states in the molecular picture after including the $Λ_{c}\bar{D}^{(*)}$ intermediate states. We also discuss some novel observations arisin

Hasnine Haque, Masahiro Hashimoto, Nozomu Uetake, Masahiro Jinzaki

Purpose: We propose a 2.5D deep learning neural network (DLNN) to automatically classify thigh muscle into 11 classes and evaluate its classification accuracy over 2D and 3D DLNN when trained with limited datasets. Enables operator invariant quantitative assessment of the thigh muscle volume change with respect to the disease progression. Materials and metho

Sida Peng, Yang Ning

In Regression Discontinuity (RD) design, self-selection leads to different distributions of covariates on two sides of the policy intervention, which essentially violates the continuity of potential outcome assumption. The standard RD estimand becomes difficult to interpret due to the existence of some indirect effect, i.e. the effect due to self selection.

Zewei Chu, Mingda Chen, Jing Chen, Miaosen Wang

We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. Our multi-domain question rewriting MQR dataset is constructed from human contributed Stack Exchange question edit histories. The dataset contains 427,719 question pairs which come from 303 domains. We provide human annotations for a subse

William J. Trenberth

We study the stochastic complex Ginzburg-Landau equation (SCGL) with an additive space-time white noise forcing on the two-dimensional torus. This equation is singular and thus we need to renormalize the nonlinearity in order to give proper meaning to the equation. Unlike the real-valued stochastic quantization equation, SCGL is complex valued and hence we a

R. Cong, Ravindra Nanguneri, Brenda Rubenstein, V. F. Mitrović

We present first principles calculations of the magnetic and orbital properties of Ba$_2$NaOsO$_6$ (BNOO), a 5$d^1$ Mott insulator with strong spin orbit coupling (SOC) in its low temperature emergent quantum phases. Our computational method takes into direct consideration recent NMR results that established that BNOO develops a local octahedral distortion p

Ya Wang, Dongliang He, Fu Li, Xiang Long

Images or videos always contain multiple objects or actions. Multi-label recognition has been witnessed to achieve pretty performance attribute to the rapid development of deep learning technologies. Recently, graph convolution network (GCN) is leveraged to boost the performance of multi-label recognition. However, what is the best way for label correlation

Saku Sugawara, Pontus Stenetorp, Kentaro Inui, Akiko Aizawa

Existing analysis work in machine reading comprehension (MRC) is largely concerned with evaluating the capabilities of systems. However, the capabilities of datasets are not assessed for benchmarking language understanding precisely. We propose a semi-automated, ablation-based methodology for this challenge; By checking whether questions can be solved even a

Madeleine Farris, Noah Luntzlara, Steven J. Miller, Lily Shao

There are now many theoretical explanations for why Benford&#39;s law of digit bias surfaces in so many diverse fields and data sets. After briefly reviewing some of these, we discuss in depth recurrence relations. As these are discrete analogues of differential equations and model a variety of real world phenomena, they provide an important source of system

Sarvenaz Memarzadeh, Jongbum Kim, Yigit Aytac, Thomas E. Murphy

Surface plasmon mediated hot carrier generation is widely utilized for the manipulation of the electron-photon interactions in many types of optoelectronic devices including solar cells, photodiodes, and optical modulators. A diversity of plasmonic systems such as nanoparticles, resonators, and waveguides have been introduced to enhance hot carrier generatio

J. E. Carvajal-Rubio, J. D. Sánchez-Torres, M. Defoort, A. G. Loukianov

This paper deals with the design of discrete-time algorithms for the robust filtering differentiator. Two discrete-time realizations of the filtering differentiator are introduced. The first one, which is based on an exact discretization of the continuous differentiator, is an explicit one, while the second one is an implicit algorithm which enables to remov

Atsushi Masumori, Lana Sinapayen, Takashi Ikegami

Predictive coding can be regarded as a function which reduces the error between an input signal and a top-down prediction. If reducing the error is equivalent to reducing the influence of stimuli from the environment, predictive coding can be regarded as stimulation avoidance by prediction. Our previous studies showed that action and selection for stimulatio

Bo-Jian Shen, Guo-Fu Yu

Matrix integrals used in random matrix theory for the study of eigenvalues of matrix ensembles have been shown to provide $ τ$-functions for several hierarchies of integrable equations. In this paper, we construct the matrix integral solutions to the Leznov lattice equation, semi-discrete and full-discrete version and the Pfaffianized Leznov lattice systems,

Weitang Liu, Lifeng Wei, James Sharpnack, John D. Owens

In this paper, we propose a novel architecture that iteratively discovers and segments out the objects of a scene based on the image reconstruction quality. Different from other approaches, our model uses an explicit localization module that localizes objects of the scene based on the pixel-level reconstruction qualities at each iteration, where simpler obje