OpenRaiser/Intern-Atlas huggingface dataset:
Distribution Modeling and Stabilization Control for Discrete-Time Linear Random Dynamical Systems Using Ensemble Kalman Filter
Venue: arXiv cs.SY 2019
Year: 2019

This paper studies an output feedback stabilization control framework for discrete-time linear systems with stochastic dynamics determined by an independent and identically distributed (i.i.d.) process. The controller is constructed with an ensemble Kalman filter (EnKF) and a feedback gain designed with our earlier result about state feedback control. The EnKF is also used for modeling the distribution behind the system, which is required in the feedback gain synthesis. The effectiveness of our control framework is demonstrated with numerical experiments. This study will become the first step toward the realization of learning type control using our stochastic systems control theory.
Minimum Spanning Trees in Weakly Dynamic Graphs
Venue: arXiv cs.DS 2019
Year: 2019

In this paper, we study weakly dynamic undirected graphs, that can be used to represent some logistic networks. The goal is to deliver all the delivery points in the network. The network exists in a mostly stable environment, except for a few edges known to be non-stable. The weight of each of these non-stable edges may change at any time (bascule or lift bridge, elevator, traffic congestion...). All other edges have stable weights that never change. This problem can be now considered as a Minimum Spanning Tree (MST) problem on a dynamic graph. We propose an efficient polynomial algorithm that computes in advance alternative MSTs for all possible configurations. No additional computation is then needed after any change in the problem because the MSTs are already known in all cases. We use these results to compute critical values for the non-stable weights and to pre-compute best paths. When the non-stable weights change, the appropriate MST may then directly and immediately be used without any recomputation.
On the $k$-error linear complexity of subsequences of $d$-ary Sidel'nikov sequences over prime field $\mathbb{F}_{d}$
Venue: arXiv cs.IT 2019
Year: 2019

We study the $k$-error linear complexity of subsequences of the $d$-ary Sidel'nikov sequences over the prime field $\mathbb{F}_{d}$. A general lower bound for the $k$-error linear complexity is given. For several special periods, we show that these sequences have large $k$-error linear complexity.
Sum-Rate Maximization of Multicell MISO Networks with Limited Information Exchange
Venue: arXiv cs.NI 2019
Year: 2019

Although there have been extensive studies on transmit beamforming in multi-input single-output (MISO) multicell networks, achieving optimal sum-rate with limited channel state information (CSI) is still a challenge even with a single user per cell. A novel cooperative downlink multicell MISO beamforming scheme is proposed with highly limited information exchange among the base stations (BSs) to maximize the sum-rate. In the proposed scheme, each BS can design its beamforming vector with only local CSI based on limited information exchange on CSI. Unlike previous studies, the proposed beamforming design is non-iterative and does not require any vector or matrix feedback but requires only quantized scalar information. The proposed scheme closely achieves the optimal sum-rate bound in almost all signal-to-noise ratio regime based on non-iterative optimization with lower amount of information exchange than existing schemes, which is justified by numerical simulations.
VL-ROUTE: A Cross-Layer Routing Protocol for Visible Light Ad Hoc Network
Venue: arXiv cs.NI 2019
Year: 2019

Visible Light Ad Hoc Networks (LANETs) is being perceived as an emerging technology to complement Radio Frequency (RF) based ad hoc networks to reduce congestion in the overloaded RF spectrum. LANET is intended to support scenarios requiring dense deployment and high data rates. In Visible Light Communication (VLC), most of the attention has been centered around physical layer with emphasis on point-to-point communication. In this work, we focus on designing a routing protocol specifically to overcome the unique challenges like blockage and deafness that render routes in LANETs highly unstable. Therefore, we propose a cross-layer optimized routing protocol (VL-ROUTE) that interacts closely with the Medium Access Control (MAC) layer to maximize the throughput of the network by taking into account the reliability of routes. To accomplish this in a distributed manner, we carefully formulate a Route Reliability Score (RRS) that can be computed by each node in the network using just the information gathered from its immediate neighbors. Each node computes an RRS for every known sink in the network. RRS of a given node can be considered as an estimate of the probability of reaching a given sink via that node. The RSS value is then integrated to the utility based three-way handshake process used by the MAC protocol (VL-MAC) to mitigate the effects of deafness, blockage, hidden node, and maximize the probability of establishing full-duplex links. All these factors contribute towards maximizing the network throughput. Extensive simulation of VL-ROUTE shows 124% improvement in network throughput over a network that uses Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) along with shortest path routing. Additionally, VL-ROUTE also showed up to 21% improvement in throughput over the network that uses VL-MAC along with a geographic routing.
Weakly-Supervised White and Grey Matter Segmentation in 3D Brain Ultrasound
Venue: arXiv cs.CV 2019
Year: 2019

Although the segmentation of brain structures in ultrasound helps initialize image based registration, assist brain shift compensation, and provides interventional decision support, the task of segmenting grey and white matter in cranial ultrasound is very challenging and has not been addressed yet. We train a multi-scale fully convolutional neural network simultaneously for two classes in order to segment real clinical 3D ultrasound data. Parallel pathways working at different levels of resolution account for high frequency speckle noise and global 3D image features. To ensure reproducibility, the publicly available RESECT dataset is utilized for training and cross-validation. Due to the absence of a ground truth, we train with weakly annotated label. We implement label transfer from MRI to US, which is prone to a residual but inevitable registration error. To further improve results, we perform transfer learning using synthetic US data. The resulting method leads to excellent Dice scores of 0.7080, 0.8402 and 0.9315 for grey matter, white matter and background. Our proposed methodology sets an unparalleled standard for white and grey matter segmentation in 3D intracranial ultrasound.
Applicability study of the PRIMAD model to LIGO gravitational wave search workflows
Venue: arXiv cs.DC 2019
Year: 2019

The PRIMAD model with its six components (i.e., Platform, Research Objective, Implementation, Methods, Actors, and Data), provides an abstract taxonomy to represent computational experiments and enforce reproducibility by design. In this paper, we assess the model applicability to a set of Laser Interferometer Gravitational-Wave Observatory (LIGO) workflows from literature sources (i.e., published papers). Our work outlines potentials and limits of the model in terms of its abstraction levels and application process.
Curriculum semi-supervised segmentation
Venue: arXiv cs.CV 2019
Year: 2019

This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of a target region. These regressions are used to effectively regularize the segmentation network, constraining softmax predictions of the unlabeled images to match the inferred label distributions. Our framework is based on inequality constraints that tolerate uncertainties with inferred knowledge, e.g., regressed region size, and can be employed for a large variety of region attributes. We evaluated our proposed strategy for left ventricle segmentation in magnetic resonance images (MRI), and compared it to standard proposal-based semi-supervision strategies. Our strategy leverages unlabeled data in more efficiently, and achieves very competitive results, approaching the performance of full-supervision.
A Model for Using Physiological Conditions for Proactive Tourist Recommendations
Venue: arXiv cs.HC 2019
Year: 2019

Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to herself and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending tourist activities. We discuss a range of wearable devices, sensors to infer physiological conditions of the users, and exemplify the feasibility using a popular self-quantification mobile app. Our main contribution then comprises a data model to derive relations between the parameters measured by the wearable sensors, such as heart rate, body temperature, blood pressure, and use them to infer the physiological condition of a user. This model can then be used to derive classes of tourist activities that determine which items should be recommended.
Simple BERT Models for Relation Extraction and Semantic Role Labeling
Venue: arXiv cs.CL 2019
Year: 2019

We present simple BERT-based models for relation extraction and semantic role labeling. In recent years, state-of-the-art performance has been achieved using neural models by incorporating lexical and syntactic features such as part-of-speech tags and dependency trees. In this paper, extensive experiments on datasets for these two tasks show that without using any external features, a simple BERT-based model can achieve state-of-the-art performance. To our knowledge, we are the first to successfully apply BERT in this manner. Our models provide strong baselines for future research.
Egocentric Visitors Localization in Cultural Sites
Venue: arXiv cs.CV 2019
Year: 2019

We consider the problem of localizing visitors in a cultural site from egocentric (first person) images. Localization information can be useful both to assist the user during his visit (e.g., by suggesting where to go and what to see next) and to provide behavioral information to the manager of the cultural site (e.g., how much time has been spent by visitors at a given location? What has been liked most?). To tackle the problem, we collected a large dataset of egocentric videos using two cameras: a head-mounted HoloLens device and a chest-mounted GoPro. Each frame has been labeled according to the location of the visitor and to what he was looking at. The dataset is freely available in order to encourage research in this domain. The dataset is complemented with baseline experiments performed considering a state-of-the-art method for location-based temporal segmentation of egocentric videos. Experiments show that compelling results can be achieved to extract useful information for both the visitor and the site-manager.
Selection of overlapping interaction through approximate decentralized fixed mode measure
Venue: arXiv cs.SY 2019
Year: 2019

This work considers the problem of selecting overlapping control structures to remove decentralized fixed modes. The selection of such controller is conventionally carried out based on minimal overlapping communications. In this paper, this selection is proposed to be through approximate decentralized fixed mode measure. Also, a framework for improving the control cost of approximate decentralized fixed modes through overlapping control is given. Application of the proposed selection is demonstrated through several numerical examples.
On the Optimality of Gauss's Algorithm over Euclidean Imaginary Quadratic Fields
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we continue our previous work on the reduction of algebraic lattices over imaginary quadratic fields for the special case when the lattice is spanned over a two dimensional basis. In particular, we show that the algebraicvariant of Gauss algorithm returns a basis that corresponds to the successive minima of the lattice in polynomial time if the chosen ring is Euclidean.
An In-Depth Comparison of s-t Reliability Algorithms over Uncertain Graphs
Venue: arXiv cs.SI 2019
Year: 2019

Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging applications, and have recently attracted the attention of the database research community. A fundamental problem on uncertain graphs is the s-t reliability, which measures the probability that a target node t is reachable from a source node s in a probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned a probability of existence. Due to the inherent complexity of the s-t reliability estimation problem (#P-hard), various sampling and indexing based efficient algorithms were proposed in the literature. However, since they have not been thoroughly compared with each other, it is not clear whether the later algorithm outperforms the earlier ones. More importantly, the comparison framework, datasets, and metrics were often not consistent (e.g., different convergence criteria were employed to find the optimal number of samples) across these works. We address this serious concern by re-implementing six state-of-the-art s-t reliability estimation methods in a common system and code base, using several medium and large-scale, real-world graph datasets, identical evaluation metrics, and query workloads. Through our systematic and in-depth analysis of experimental results, we report surprising findings, such as many follow-up algorithms can actually be several orders of magnitude inefficient, less accurate, and more memory intensive compared to the ones that were proposed earlier. We conclude by discussing our recommendations on the road ahead.
Calculation of routing value in MPLS network according to traffic fractal properties
Venue: arXiv cs.NI 2019
Year: 2019

Method for calculating routing cost of MPLS network is presented in the work. This method allows to minimize routing cost, taking into account traffic fractal properties, choice of traffic transmission path and quality of service requirements. The method uses values of the Hurst parameter and value of normalized spread of traffic values, which makes it possible to apply it to self-similar and multifractal data streams.
Testing Unateness Nearly Optimally
Venue: arXiv cs.DS 2019
Year: 2019

We present an $\tilde{O}(n^{2/3}/ε^2)$-query algorithm that tests whether an unknown Boolean function $f\colon\{0,1\}^n\rightarrow \{0,1\}$ is unate (i.e., every variable is either non-decreasing or non-increasing) or $ε$-far from unate. The upper bound is nearly optimal given the $\tildeΩ(n^{2/3})$ lower~bound of [CWX17a]. The algorithm builds on a novel use of the binary search procedure and its analysis over long random paths.
Pear2Pear (On Wifi): A Data Sharing Protocol Over Wifi through a Peer to Peer Network
Venue: arXiv cs.DC 2019
Year: 2019

A peer-to-peer system is a distributed system in which equal nodes (in terms of role and usage) exchange information and services directly. This paper describes a distributed peer-to-peer protocol that allows wifi-enabled smart devices (especially Android smartphones) to exchange data using only wifi. The protocol is designed to allow the automatic establishment of a distributed peer-to-peer network of any size without geography constraint. It is applied to file sharing between devices, but can easily be adapted to support any data sharing. The protocol defines two layers: (1) a kernel layer responsible for creating, routing, establishing and maintaining links between nodes (peer), addressing a node and adding and removing nodes; and an (2) application layer to support data sharing (file sharing in this case). The structure of the peer-to-peer network is hybrid. Regarding the file sharing use case, the application allows a node to (i) search for a file in the catalog of its subnet that is held by the root node, and (ii) download a file: if the file is not in the subnet, the root node delegates a node to make vouchers in neighboring subnetworks to get the file and make it available. A proof of concept was made on Android.
PropTech for Proactive Pricing of Houses in Classified Advertisements in the Indian Real Estate Market
Venue: arXiv cs.SI 2019
Year: 2019

Property Technology (PropTech) is the next big thing that is going to disrupt the real estate market. Nowadays, we see applications of Machine Learning (ML) and Artificial Intelligence (AI) in almost all the domains but for a long time the real estate industry was quite slow in adopting data science and machine learning for problem solving and improving their processes. However, things are changing quite fast as we see a lot of adoption of AI and ML in the US and European real estate markets. But the Indian real estate market has to catch-up a lot. This paper proposes a machine learning approach for solving the house price prediction problem in the classified advertisements. This study focuses on the Indian real estate market. We apply advanced machine learning algorithms such as Random forest, Gradient boosting and Artificial neural networks on a real world dataset and compare the performance of these methods. We find that the Random forest method is the best performer in terms of prediction accuracy.
Community detection over a heterogeneous population of non-aligned networks
Venue: arXiv cs.SI 2019
Year: 2019

Clustering and community detection with multiple graphs have typically focused on aligned graphs, where there is a mapping between nodes across the graphs (e.g., multi-view, multi-layer, temporal graphs). However, there are numerous application areas with multiple graphs that are only partially aligned, or even unaligned. These graphs are often drawn from the same population, with communities of potentially different sizes that exhibit similar structure. In this paper, we develop a joint stochastic blockmodel (Joint SBM) to estimate shared communities across sets of heterogeneous non-aligned graphs. We derive an efficient spectral clustering approach to learn the parameters of the joint SBM. We evaluate the model on both synthetic and real-world datasets and show that the joint model is able to exploit cross-graph information to better estimate the communities compared to learning separate SBMs on each individual graph.
Nine Quick Tips for Analyzing Network Data
Venue: arXiv cs.SI 2019
Year: 2019

These tips provide a quick and concentrated guide for beginners in the analysis of network data.
Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels
Venue: arXiv cs.PF 2019
Year: 2019

Over recent years heterogeneous systems have become more prevalent across HPC systems, with over 100 supercomputers in the TOP500 incorporating GPUs or other accelerators. These hardware platforms have different performance characteristics and optimization requirements. In order to make the most of multiple accelerators a developer has to provide implementations of their algorithms tuned for each device. Hardware vendors provide libraries targeting their devices specifically, which provide good performance but frequently have different API designs, hampering portability. The SYCL programming model allows users to write heterogeneous programs using completely standard C++, and so developers have access to the power of C++ templates when developing compute kernels. In this paper we show that by writing highly parameterized kernels for matrix multiplies and convolutions we achieve performance competitive with vendor implementations across different architectures. Furthermore, tuning for new devices amounts to choosing the combinations of kernel parameters that perform best on the hardware.
Tea: A High-level Language and Runtime System for Automating Statistical Analysis
Venue: arXiv cs.PL 2019
Year: 2019

Though statistical analyses are centered on research questions and hypotheses, current statistical analysis tools are not. Users must first translate their hypotheses into specific statistical tests and then perform API calls with functions and parameters. To do so accurately requires that users have statistical expertise. To lower this barrier to valid, replicable statistical analysis, we introduce Tea, a high-level declarative language and runtime system. In Tea, users express their study design, any parametric assumptions, and their hypotheses. Tea compiles these high-level specifications into a constraint satisfaction problem that determines the set of valid statistical tests, and then executes them to test the hypothesis. We evaluate Tea using a suite of statistical analyses drawn from popular tutorials. We show that Tea generally matches the choices of experts while automatically switching to non-parametric tests when parametric assumptions are not met. We simulate the effect of mistakes made by non-expert users and show that Tea automatically avoids both false negatives and false positives that could be produced by the application of incorrect statistical tests.
On Fractional Decoding of Reed-Solomon Codes
Venue: arXiv cs.IT 2019
Year: 2019

We define a virtual projection of a Reed-Solomon code $RS(q^{l},n,k)$ to an $RS(q,n,k)$ Reed-Solomon code. A new probabilistic decoding algorithm that can be used to perform fractional decoding beyond the $α$- decoding radius is considered. An upper bound for the failure probability of the new algorithm is given, and the performance is illustrated by examples.
Deep Learning for Cross-Technology Communication Design
Venue: arXiv cs.NI 2019
Year: 2019

Recently, it was shown that a communication system could be represented as a deep learning (DL) autoencoder. Inspired by this idea, we target the problem of OFDM-based wireless cross-technology communication (CTC) where both in-technology and CTC transmissions take place simultaneously. We propose DeepCTC, a DL-based autoencoder approach allowing us to exploit DL for joint optimization of transmitter and receivers for both in-technology as well as CTC communication in an end-to-end manner. Different from classical CTC designs, we can easily weight in-technology against CTC communication. Moreover, CTC broadcasts can be efficiently realized even in the presence of heterogeneous CTC receivers with diverse OFDM technologies. Our numerical analysis confirms the feasibility of DeepCTC as both in-technology and CTC messages can be decoded with sufficient low block error rate.
Optimality in Quantum Data Compression using Dynamical Entropy
Venue: arXiv cs.IT 2019
Year: 2019

In this article we study lossless compression of strings of pure quantum states of indeterminate-length quantum codes which were introduced by Schumacher and Westmoreland. Past work has assumed that the strings of quantum data are prepared to be encoded in an independent and identically distributed way. We introduce the notion of quantum stochastic ensembles, allowing us to consider strings of quantum states prepared in a more general way. For any identically distributed quantum stochastic ensemble we define an associated quantum Markov chain and prove that the optimal average codeword length via lossless coding is equal to the quantum dynamical entropy of the associated quantum Markov chain.