OpenRaiser/Intern-Atlas huggingface dataset:
FDD Massive MIMO: Efficient Downlink Probing and Uplink Feedback via Active Channel Sparsification
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose a novel method for efficient implementation of a massive Multiple-Input Multiple-Output (massive MIMO) system with Frequency Division Duplexing (FDD) operation. Our main objective is to reduce the large overhead incurred by Downlink (DL) common training and Uplink (UL) feedback needed to obtain channel state information (CSI) at the base station. Our proposed scheme relies on the fact that the underlying angular distribution of a channel vector, also known as the angular scattering function, is a frequency-invariant entity yielding a UL-DL reciprocity and has a limited angular support. We estimate this support from UL CSI and interpolate it to obtain the corresponding angular support of the DL channel. Finally we exploit the estimated support of the DL channel of all the users to design an efficient channel probing and feedback scheme that maximizes the total spectral efficiency of the system. Our method is different from the existing compressed-sensing (CS) based techniques in the literature. Using support information helps reduce the feedback overhead from O(s*log M) in CS techniques to O(s) in our proposed method, with $s$ and $M$ being sparsity order of the channel vectors and the number of base station antennas, respectively. Furthermore, in order to control the channel sparsity and therefore the DL common training and UL feedback overhead, we introduce the novel concept of active channel sparsification. In brief, when the fixed pilot dimension is less than the required amount for reliable channel estimation, we introduce a pre-beamforming matrix that artificially reduces the effective channel dimension of each user to be not larger than the DL pilot dimension, while maximizing both the number of served users and the number of probed angles. We provide numerical experiments to compare our method with the state-of-the-art CS technique.
Bringing Semantic Structures to User Intent Detection in Online Medical Queries
Venue: arXiv cs.CL 2017
Year: 2017

The Internet has revolutionized healthcare by offering medical information ubiquitously to patients via web search. The healthcare status, complex medical information needs of patients are expressed diversely and implicitly in their medical text queries. Aiming to better capture a focused picture of user's medical-related information search and shed insights on their healthcare information access strategies, it is challenging yet rewarding to detect structured user intentions from their diversely expressed medical text queries. We introduce a graph-based formulation to explore structured concept transitions for effective user intent detection in medical queries, where each node represents a medical concept mention and each directed edge indicates a medical concept transition. A deep model based on multi-task learning is introduced to extract structured semantic transitions from user queries, where the model extracts word-level medical concept mentions as well as sentence-level concept transitions collectively. A customized graph-based mutual transfer loss function is designed to impose explicit constraints and further exploit the contribution of mentioning a medical concept word to the implication of a semantic transition. We observe an 8% relative improvement in AUC and 23% relative reduction in coverage error by comparing the proposed model with the best baseline model for the concept transition inference task on real-world medical text queries.
Characterizing and Enumerating Walsh-Hadamard Transform Algorithms
Venue: arXiv cs.DS 2017
Year: 2017

We propose a way of characterizing the algorithms computing a Walsh-Hadamard transform that consist of a sequence of arrays of butterflies ($I_{2^{n-1}}\otimes \text{DFT}_2$) interleaved by linear permutations. Linear permutations are those that map linearly the binary representation of its element indices. We also propose a method to enumerate these algorithms.
Sequential Matrix Completion
Venue: arXiv cs.IR 2017
Year: 2017

We propose a novel algorithm for sequential matrix completion in a recommender system setting, where the $(i,j)$th entry of the matrix corresponds to a user $i$'s rating of product $j$. The objective of the algorithm is to provide a sequential policy for user-product pair recommendation which will yield the highest possible ratings after a finite time horizon. The algorithm uses a Gamma process factor model with two posterior-focused bandit policies, Thompson Sampling and Information-Directed Sampling. While Thompson Sampling shows competitive performance in simulations, state-of-the-art performance is obtained from Information-Directed Sampling, which makes its recommendations based off a ratio between the expected reward and a measure of information gain. To our knowledge, this is the first implementation of Information Directed Sampling on large real datasets. This approach contributes to a recent line of research on bandit approaches to collaborative filtering including Kawale et al. (2015), Li et al. (2010), Bresler et al. (2014), Li et al. (2016), Deshpande & Montanari (2012), and Zhao et al. (2013). The setting of this paper, as has been noted in Kawale et al. (2015) and Zhao et al. (2013), presents significant challenges to bounding regret after finite horizons. We discuss these challenges in relation to simpler models for bandits with side information, such as linear or gaussian process bandits, and hope the experiments presented here motivate further research toward theoretical guarantees.
Self-Stabilizing Supervised Publish-Subscribe Systems
Venue: arXiv cs.DC 2017
Year: 2017

In this paper we present two major results: First, we introduce the first self-stabilizing version of a supervised overlay network by presenting a self-stabilizing supervised skip ring. Secondly, we show how to use the self-stabilizing supervised skip ring to construct an efficient self-stabilizing publish-subscribe system. That is, in addition to stabilizing the overlay network, every subscriber of a topic will eventually know all of the publications that have been issued so far for that topic. The communication work needed to processes a subscribe or unsubscribe operation is just a constant in a legitimate state, and the communication work of checking whether the system is still in a legitimate state is just a constant on expectation for the supervisor as well as any process in the system.
A 3D Human Body Blockage Model for Outdoor Millimeter-Wave Cellular Communication
Venue: arXiv cs.IT 2017
Year: 2017

Blocking is one of the most important challenges in exploiting millimeter-wave for fifth-generation (5G) cellular communication systems. Compared to blockages caused by buildings or terrains, human body blockage exhibits a higher complexity due to the mobility and dynamic statistics of humans. To support development of outdoor millimeter-wave cellular systems, in this paper we present a novel 3D physical model of human body blockage. Based on the proposed model, the impact of human body blockage on frame-based data transmission is discussed, with respect to the system specifications and environment conditions.
Satisfiability in multi-valued circuits
Venue: arXiv cs.CC 2017
Year: 2017

Satisfiability of Boolean circuits is among the most known and important problems in theoretical computer science. This problem is NP-complete in general but becomes polynomial time when restricted either to monotone gates or linear gates. We go outside Boolean realm and consider circuits built of any fixed set of gates on an arbitrary large finite domain. From the complexity point of view this is strictly connected with the problems of solving equations (or systems of equations) over finite algebras. The research reported in this work was motivated by a desire to know for which finite algebras $\mathbf A$ there is a polynomial time algorithm that decides if an equation over $\mathbf A$ has a solution. We are also looking for polynomial time algorithms that decide if two circuits over a finite algebra compute the same function. Although we have not managed to solve these problems in the most general setting we have obtained such a characterization for a very broad class of algebras from congruence modular varieties. This class includes most known and well-studied algebras such as groups, rings, modules (and their generalizations like quasigroups, loops, near-rings, nonassociative rings, Lie algebras), lattices (and their extensions like Boolean algebras, Heyting algebras or other algebras connected with multi-valued logics including MV-algebras). This paper seems to be the first systematic study of the computational complexity of satisfiability of non-Boolean circuits and solving equations over finite algebras. The characterization results provided by the paper is given in terms of nice structural properties of algebras for which the problems are solvable in polynomial time.
An Empirical Investigation On Search Engine Ad Disclosure
Venue: arXiv cs.IR 2017
Year: 2017

This representative study of German search engine users (N=1,000) focuses on the ability of users to distinguish between organic results and advertisements on Google results pages. We combine questions about Google's business with task-based studies in which users were asked to distinguish between ads and organic results in screenshots of results pages. We find that only a small percentage of users is able to reliably distinguish between ads and organic results, and that user knowledge of Google's business model is very limited. We conclude that ads are insufficiently labelled as such, and that many users may click on ads assuming that they are selecting organic results.
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
Venue: arXiv cs.LG 2017
Year: 2017

Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4) publicly available dataset, which includes all patients admitted to an ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the benchmarking tasks. Our results show that deep learning models consistently outperform all the other approaches especially when the `raw' clinical time series data is used as input features to the models.
To Send or Not to Send: An Optimal Stopping Approach to Network Coding in Multi-hop Wireless Networks
Venue: arXiv cs.NI 2017
Year: 2017

Network coding is all about combining a variety of packets and forwarding as much packets as possible in each transmission operation. The network coding technique improves the throughput efficiency of multi-hop wireless networks by taking advantage of the broadcast nature of wireless channels. However, there are some scenarios where the coding cannot be exploited due to the stochastic nature of the packet arrival process in the network. In these cases, the coding node faces two critical choices: forwarding the packet towards the destination without coding, thereby sacrificing the advantage of network coding, or, waiting for a while until a coding opportunity arises for the packets. Current research works have addressed this challenge for the case of a simple and restricted scheme called reverse carpooling where it is assumed that two flows with opposite directions arrive at the coding node. In this paper the issue is explored in a general sense based on the COPE architecture requiring no assumption about flows in multi-hop wireless networks. In particular, we address this sequential decision making problem by using the solid framework of optimal stopping theory, and derive the optimal stopping rule for the coding node to choose the optimal action to take, i.e. to wait for more coding opportunity or to stop immediately (and send packet). Our simulation results validate the effectiveness of the derived optimal stopping rule and show that the proposed scheme outperforms existing methods in terms of network throughput and energy consumption.
Iterative Receivers for Downlink MIMO-SCMA: Message Passing and Distributed Cooperative Detection
Venue: arXiv cs.IT 2017
Year: 2017

The rapid development of the mobile communications requires ever higher spectral efficiency. The non-orthogonal multiple access (NOMA) has emerged as a promising technology to further increase the access efficiency of wireless networks. Amongst several NOMA schemes, the sparse code multiple access (SCMA) has been shown to be able to achieve better performance. In this paper, we consider a downlink MIMO-SCMA system over frequency selective fading channels. For optimal detection, the complexity increases exponentially with the product of the number of users, the number of antennas and the channel length. To tackle this challenge, we propose near optimal low-complexity iterative receivers based on factor graph. By introducing auxiliary variables, a stretched factor graph is constructed and a hybrid belief propagation (BP) and expectation propagation (EP) receiver, named as `Stretch-BP-EP', is proposed. Considering the convergence problem of BP algorithm on loopy factor graph, we convexify the Bethe free energy and propose a convergence-guaranteed BP-EP receiver, named as `Conv-BP-EP'. We further consider cooperative network and propose two distributed cooperative detection schemes to exploit the diversity gain, namely, belief consensus-based algorithm and Bregman alternative direction method of multipliers (ADMM)-based method. Simulation results verify the superior performance of the proposed Conv-BP-EP receiver compared with other methods. The two proposed distributed cooperative detection schemes can improve the bit error rate performance by exploiting the diversity gain. Moreover, Bregman ADMM method outperforms the belief consensus-based algorithm in noisy inter-user links.
Clickbait Identification using Neural Networks
Venue: arXiv cs.CL 2017
Year: 2017

This paper presents the results of our participation in the Clickbait Detection Challenge 2017. The system relies on a fusion of neural networks, incorporating different types of available informations. It does not require any linguistic preprocessing, and hence generalizes more easily to new domains and languages. The final combined model achieves a mean squared error of 0.0428, an accuracy of 0.826, and a F1 score of 0.564. According to the official evaluation metric the system ranked 6th of the 13 participating teams.
Barrier Free Internet Access: Evaluating the Cyber Security Risk Posed by the Adoption of Bring Your Own Devices to e-Learning Network Infrastructure
Venue: arXiv cs.CY 2017
Year: 2017

The adoption of Bring Your Own Device (BYOD), also known as Bring Your Own Technology (BYOT), Bring Your Own Phone (BYOP), or Bring Your Own Personal Computer (BYOPC), is a policy which allows people access to privileged resources, information and services available on the private computer network of an organization using their own personal computer devices. BYOD, since its emergence in 2009, courtesy of Intel, is now a common practice in many organizations. Academic institutions that attempt to implement BYOD, can derive many benefits as well as many risks to its network infrastructure, largely security-based. This paper presents an assessment of a WLAN network which has been deployed for a campus-wide data centric e-learning platform at Kwame Nkrumah University of Science and Technology (KNUST) towards the overall objective of achieving a barrier free internet access to enhance the teaching and learning process at the university. The paper subsequently evaluates the WLAN infrastructure, its accompanying BYOD set-up, and associated likely security risks and threats, and recommends appropriate solutions.
Algorithmic infeasibility of community detection in higher-order networks
Venue: arXiv cs.SI 2017
Year: 2017

In principle, higher-order networks that have multiple edge types are more informative than their lower-order counterparts. In practice, however, excessively rich information may be algorithmically infeasible to extract. It requires an algorithm that assumes a high-dimensional model and such an algorithm may perform poorly or be extremely sensitive to the initial estimate of the model parameters. Herein, we address this problem of community detection through a detectability analysis. We focus on the expectation-maximization (EM) algorithm with belief propagation (BP), and analytically derive its algorithmic detectability threshold, i.e., the limit of the modular structure strength below which the algorithm can no longer detect any modular structures. The results indicate the existence of a phase in which the community detection of a lower-order network outperforms its higher-order counterpart.
A Quantitative Study of the Impact of Social Media Reviews on Brand Perception
Venue: arXiv cs.CY 2017
Year: 2017

This thesis can be categorized under the Influencer Marketing industry with respect to social media initiatives. Influencer marketing is a modern tactic used by brands to enhance their visibility to their target audience by using the services of influential people. The objective of this thesis is to quantify the impact of social media reviews on brand perception. Specifically, this thesis focuses on two diverse media platforms commonly used for sharing opinions about products or services by publishing audio-visual or textual reviews: YouTube and Yelp. First, we quantitatively analyze the impact of YouTube reviews of Smartphones on the audience through their response to these video reviews. Second, using our findings from 942 YouTube Smartphone reviews, we introduce a statistical model to predict audience engagement on a given video. Finally, we apply our method of quantifying the impact of reviews on the content published on Yelp.com in the restaurant industry. The results from this validation show that our method can be generically applied to other social media platforms and consumer-focused industries. Our method can be employed by brand managers to turn social media reviews into real-time feedback mechanism in order to improve brand perception in the minds of their target audience.
Relative Transfer Function Inverse Regression from Low Dimensional Manifold
Venue: arXiv cs.SD 2017
Year: 2017

In room acoustic environments, the Relative Transfer Functions (RTFs) are controlled by few underlying modes of variability. Accordingly, they are confined to a low-dimensional manifold. In this letter, we investigate a RTF inverse regression problem, the task of which is to generate the high-dimensional responses from their low-dimensional representations. The problem is addressed from a pure data-driven perspective and a supervised Deep Neural Network (DNN) model is applied to learn a mapping from the source-receiver poses (positions and orientations) to the frequency domain RTF vectors. The experiments show promising results: the model achieves lower prediction error of the RTF than the free field assumption. However, it fails to compete with the linear interpolation technique in small sampling distances.
A Simple Text Analytics Model To Assist Literary Criticism: comparative approach and example on James Joyce against Shakespeare and the Bible
Venue: arXiv cs.CL 2017
Year: 2017

Literary analysis, criticism or studies is a largely valued field with dedicated journals and researchers which remains mostly within the humanities scope. Text analytics is the computer-aided process of deriving information from texts. In this article we describe a simple and generic model for performing literary analysis using text analytics. The method relies on statistical measures of: 1) token and sentence sizes and 2) Wordnet synset features. These measures are then used in Principal Component Analysis where the texts to be analyzed are observed against Shakespeare and the Bible, regarded as reference literature. The model is validated by analyzing selected works from James Joyce (1882-1941), one of the most important writers of the 20th century. We discuss the consistency of this approach, the reasons why we did not use other techniques (e.g. part-of-speech tagging) and the ways by which the analysis model might be adapted and enhanced.
A software framework for pipelined arithmetic algorithms in field programmable gate arrays
Venue: arXiv cs.OH 2017
Year: 2017

Pipelined algorithms implemented in field programmable gate arrays are being extensively used for hardware triggers in the modern experimental high energy physics field and the complexity of such algorithms are increases rapidly. For development of such hardware triggers, algorithms are developed in $\texttt{C++}$, ported to hardware description language for synthesizing firmware, and then ported back to $\texttt{C++}$ for simulating the firmware response down to the single bit level. We present a $\texttt{C++}$ software framework which automatically simulates and generates hardware description language code for pipelined arithmetic algorithms.
Line Formation by Fat Robots under Limited Visibility
Venue: arXiv cs.DC 2017
Year: 2017

This paper proposes a distributed algorithm for a set of tiny unit disc shaped robot to form a straight line. The robots are homoge- neous, autonomous, anonymous. They observe their surrounding up to a certain distance, compute destinations to move to and move there. They do not have any explicit message sending or receiving capability. They forget their past observed or computed data. The robots do not have any global coordinate system or origin. Each robot considers its position as its origin. However, they agree on the X and Y axis. The robots are not aware of the total number of robots in the system. The algorithm presented in this paper assures collision free movements of the robots. To the best of our knowledge this paper is the first reported result on line formation by fat robots under limited visibility.
DPCA: Dimensionality Reduction for Discriminative Analytics of Multiple Large-Scale Datasets
Venue: arXiv cs.LG 2017
Year: 2017

Principal component analysis (PCA) has well-documented merits for data extraction and dimensionality reduction. PCA deals with a single dataset at a time, and it is challenged when it comes to analyzing multiple datasets. Yet in certain setups, one wishes to extract the most significant information of one dataset relative to other datasets. Specifically, the interest may be on identifying, namely extracting features that are specific to a single target dataset but not the others. This paper develops a novel approach for such so-termed discriminative data analysis, and establishes its optimality in the least-squares (LS) sense under suitable data modeling assumptions. The criterion reveals linear combinations of variables by maximizing the ratio of the variance of the target data to that of the remainders. The novel approach solves a generalized eigenvalue problem by performing SVD just once. Numerical tests using synthetic and real datasets showcase the merits of the proposed approach relative to its competing alternatives.