OpenRaiser/Intern-Atlas huggingface dataset:
On the Calculation of the Incomplete MGF with Applications to Wireless Communications
Venue: arXiv cs.IT 2016
Year: 2016

The incomplete moment generating function (IMGF) has paramount relevance in communication theory, since it appears in a plethora of scenarios when analyzing the performance of communication systems. We here present a general method for calculating the IMGF of any arbitrary fading distribution. Then, we provide exact closed-form expressions for the IMGF of the very general κ-μ shadowed fading model, which includes the popular κ-μ, η-μ, Rician shadowed and other classical models as particular cases. We illustrate the practical applicability of this result by analyzing several scenarios of interest in wireless communications: (1) Physical layer security in the presence of an eavesdropper, (2) Outage probability analysis with interference and background noise, (3) Channel capacity with side information at the transmitter and the receiver, and (4) Average bit-error rate with adaptive modulation, when the fading on the desired link can be modeled by any of the aforementioned distributions.
Triangles as basis to detect communities: an application to Twitter's network
Venue: arXiv cs.SI 2016
Year: 2016

Nowadays, the interest given by the scientific community to the investigation of the data generated by social networks is increasing as much as the exponential increasing of social network data. The data structure complexity is one among the snags, which slowdown their understanding. On the other hand, community detection in social networks helps the analyzers to reveal the structure and the underlying semantic within communities. In this paper we propose an interactive visualization approach relying on our application NLCOMS, which uses synchronous and related views for graph and community visualization. Additionally, we present our algorithm for community detection in networks. A computation study is conducted on instances generated with the LFR [9]-[10] benchmark. Finally, in order to assess our approach on real-world data, we consider the data of the ANR-Info-RSN project. The latter addresses community detection in Twitter.
High-speed real-time single-pixel microscopy based on Fourier sampling
Venue: arXiv cs.CV 2016
Year: 2016

Single-pixel cameras based on the concepts of compressed sensing (CS) leverage the inherent structure of images to retrieve them with far fewer measurements and operate efficiently over a significantly broader spectral range than conventional silicon-based cameras. Recently, photonic time-stretch (PTS) technique facilitates the emergence of high-speed single-pixel cameras. A significant breakthrough in imaging speed of single-pixel cameras enables observation of fast dynamic phenomena. However, according to CS theory, image reconstruction is an iterative process that consumes enormous amounts of computational time and cannot be performed in real time. To address this challenge, we propose a novel single-pixel imaging technique that can produce high-quality images through rapid acquisition of their effective spatial Fourier spectrum. We employ phase-shifting sinusoidal structured illumination instead of random illumination for spectrum acquisition and apply inverse Fourier transform to the obtained spectrum for image restoration. We evaluate the performance of our prototype system by recognizing quick response (QR) codes and flow cytometric screening of cells. A frame rate of 625 kHz and a compression ratio of 10% are experimentally demonstrated in accordance with the recognition rate of the QR code. An imaging flow cytometer enabling high-content screening with an unprecedented throughput of 100,000 cells/s is also demonstrated. For real-time imaging applications, the proposed single-pixel microscope can significantly reduce the time required for image reconstruction by two orders of magnitude, which can be widely applied in industrial quality control and label-free biomedical imaging.
Cost Effective Campaigning in Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

Campaigners are increasingly using online social networking platforms for promoting products, ideas and information. A popular method of promoting a product or even an idea is incentivizing individuals to evangelize the idea vigorously by providing them with referral rewards in the form of discounts, cash backs, or social recognition. Due to budget constraints on scarce resources such as money and manpower, it may not be possible to provide incentives for the entire population, and hence incentives need to be allocated judiciously to appropriate individuals for ensuring the highest possible outreach size. We aim to do the same by formulating and solving an optimization problem using percolation theory. In particular, we compute the set of individuals that are provided incentives for minimizing the expected cost while ensuring a given outreach size. We also solve the problem of computing the set of individuals to be incentivized for maximizing the outreach size for given cost budget. The optimization problem turns out to be non trivial; it involves quantities that need to be computed by numerically solving a fixed point equation. Our primary contribution is, that for a fairly general cost structure, we show that the optimization problems can be solved by solving a simple linear program. We believe that our approach of using percolation theory to formulate an optimization problem is the first of its kind.
3D zigzag for multislicing, multiband and video processing
Venue: arXiv cs.CV 2016
Year: 2016

We present a 3D zigzag rafter (first in literature) which allows us to obtain the exact sequence of spectral components after application of Discrete Cosine Transform 3D (DCT-2D) over a cube. Such cube represents part of a video or eventually a group of images such as multislicing (e.g., Magnetic Resonance or Computed Tomography imaging) and multi or hyperspectral imagery (optical satellites). Besides, we present a new version of the traditional 2D zigzag, including the case of rectangular blocks. Finally, all the attached code is done in MATLAB, and that code serves both blocks of pixels or blocks of blocks.
Feedback-Aided Coded Caching for the MISO BC with Small Caches
Venue: arXiv cs.IT 2016
Year: 2016

This work explores coded caching in the symmetric $K$-user cache-aided MISO BC with imperfect CSIT-type feedback, for the specific case where the cache size is much smaller than the library size. Building on the recently explored synergy between caching and delayed-CSIT, and building on the tradeoff between caching and CSIT quality, the work proposes new schemes that boost the impact of small caches, focusing on the case where the cumulative cache size is smaller than the library size. For this small-cache setting, based on the proposed near-optimal schemes, the work identifies the optimal cache-aided degrees-of-freedom (DoF) performance within a factor of 4.
Taming Weak Memory Models
Venue: arXiv cs.PL 2016
Year: 2016

Speculative techniques in microarchitectures relax various dependencies in programs, which contributes to the complexity of (weak) memory models. We show using WMM, a new weak memory model, that the model becomes simpler if it includes load-value speculation and thus, does not enforce any dependency! However, in the absence of good value-prediction techniques, a programmer may end up paying a price for the extra fences. Thus, we also present WMM-D, which enforces the dependencies captured by the current microarchitectures. WMM-D is still much simpler than other existing models. We also show that non-atomic multi-copy stores arise as a result of sharing write-through caches. We think restricting microarchitectures to write-back caches (and thus simpler weak memory models) will not incur any performance penalty. Nevertheless, we present WMM-S, another extension to WMM, which could model the effects of non-atomic multi-copy stores. WMM, WMM-D, and WMM-S are all defined using Instantaneous Instruction Execution (I^2E), a new way of describing memory models without explicit reordering or speculative execution.
Most central or least central? How much modeling decisions influence a node's centrality ranking in multiplex networks
Venue: arXiv cs.SI 2016
Year: 2016

To understand a node's centrality in a multiplex network, its centrality values in all the layers of the network can be aggregated. This requires a normalization of the values, to allow their meaningful comparison and aggregation over networks with different sizes and orders. The concrete choices of such preprocessing steps like normalization and aggregation are almost never discussed in network analytic papers. In this paper, we show that even sticking to the most simple centrality index (the degree) but using different, classic choices of normalization and aggregation strategies, can turn a node from being among the most central to being among the least central. We present our results by using an aggregation operator which scales between different, classic aggregation strategies based on three multiplex networks. We also introduce a new visualization and characterization of a node's sensitivity to the choice of a normalization and aggregation strategy in multiplex networks. The observed high sensitivity of single nodes to the specific choice of aggregation and normalization strategies is of strong importance, especially for all kinds of intelligence-analytic software as it questions the interpretations of the findings.
On computing the total displacement number via weighted Motzkin paths
Venue: arXiv cs.DS 2016
Year: 2016

Counting the number of permutations of a given total displacement is equivalent to counting weighted Motzkin paths of a given area (Guay-Paquet and Petersen, 2014). The former combinatorial problem is still open. In this work, we show that this connection allows to construct efficient algorithms for counting and for sampling such permutations. These algorithms provide a tool to better understand the original combinatorial problem. A by-product of our approach is a different way of counting based on certain building sequences for Motzkin paths, which may be of independent interest.
Self-adaptation of Mutation Rates in Non-elitist Populations
Venue: arXiv cs.NE 2016
Year: 2016

The runtime of evolutionary algorithms (EAs) depends critically on their parameter settings, which are often problem-specific. Automated schemes for parameter tuning have been developed to alleviate the high costs of manual parameter tuning. Experimental results indicate that self-adaptation, where parameter settings are encoded in the genomes of individuals, can be effective in continuous optimisation. However, results in discrete optimisation have been less conclusive. Furthermore, a rigorous runtime analysis that explains how self-adaptation can lead to asymptotic speedups has been missing. This paper provides the first such analysis for discrete, population-based EAs. We apply level-based analysis to show how a self-adaptive EA is capable of fine-tuning its mutation rate, leading to exponential speedups over EAs using fixed mutation rates.
SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder
Venue: arXiv cs.CL 2016
Year: 2016

In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a small set of la- belled data samples. Features are extracted using topic modelling based on latent Dirichlet allocation, and then a comprehensive data model is created using a Stacked Denoising Autoencoder (SDA). Topic modelling summarises the data providing ease of use and high interpretability by visualising the topics using word clouds. Given that the SMS messages can be regarded as either spam (unwanted) or ham (wanted), the SDA is able to model the messages and accurately discriminate between the two classes without the need for a pre-labelled training set. The results are compared against the state-of-the-art spam detection algorithms with our proposed approach achieving over 97% accuracy which compares favourably to the best reported algorithms presented in the literature.
A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection
Venue: arXiv cs.CL 2016
Year: 2016

We investigate the novel task of online dispute detection and propose a sentiment analysis solution to the problem: we aim to identify the sequence of sentence-level sentiments expressed during a discussion and to use them as features in a classifier that predicts the DISPUTE/NON-DISPUTE label for the discussion as a whole. We evaluate dispute detection approaches on a newly created corpus of Wikipedia Talk page disputes and find that classifiers that rely on our sentiment tagging features outperform those that do not. The best model achieves a very promising F1 score of 0.78 and an accuracy of 0.80.
Network-Coded Macrocell Offloading in Femtocaching-Assisted Cellular Networks
Venue: arXiv cs.NI 2016
Year: 2016

The femtocaching idea was proposed as a solution to compensate for the weak backhaul capacity, by deploying coverage-limited nodes with high storage capacity called femtocaches (FCs). In this paper, the macrocell offloading problem in femtocaching-assisted cellular networks is investigated. The objective is to minimize the number of transmissions by the macrocell base station (MBS) given that all requests should be served simultaneously to satisfy quality-of-experience (QoE) of the clients. We first formulate this MBS offloading problem as an optimization problem over a network coding graph, and show that it is NP-hard. Therefore, we propose an ONC-broadcast offloading scheme that exploits both broadcasting and opportunistic network coding (ONC) to minimize the number of required MBS transmissions. We utilize a random graph model to approximate the performance of the proposed ONC-broadcast scheme in terms of the resultant average number of transmissions by the MBS. Moreover, despite the complexity of finding the optimal solution for each and every case, we prove that this ONC-broadcast scheme is asymptotically optimal, i.e., for large number of requests, the ONC-broadcast scheme achieves a similar macrocell offloading performance to that of the optimal solution. To implement the ONC-broadcast scheme, we devise a heuristic that employs a dual conflict graph or broadcasting at the FCs such that the remaining requests can be served using the minimum number of transmissions at the MBS. Simulations show that the dual graph scheme improves MBS offloading as compared to the traditional separate graph scheme. Furthermore, the simple heuristic proposed to implement the ONC-broadcast scheme achieves a very close performance to the optimal ONC-broadcast scheme.
A Hybrid ICT-Solution for Smart Meter Data Analytics
Venue: arXiv cs.DB 2016
Year: 2016

Smart meters are increasingly used worldwide. Smart meters are the advanced meters capable of measuring energy consumption at a fine-grained time interval, e.g., every 15 minutes. Smart meter data are typically bundled with social economic data in analytics, such as meter geographic locations, weather conditions and user information, which makes the data sets very sizable and the analytics complex. Data mining and emerging cloud computing technologies make collecting, processing, and analyzing the so-called big data possible. This paper proposes an innovative ICT-solution to streamline smart meter data analytics. The proposed solution offers an information integration pipeline for ingesting data from smart meters, a scalable platform for processing and mining big data sets, and a web portal for visualizing analytics results. The implemented system has a hybrid architecture of using Spark or Hive for big data processing, and using the machine learning toolkit, MADlib, for doing in-database data analytics in PostgreSQL database. This paper evaluates the key technologies of the proposed ICT-solution, and the results show the effectiveness and efficiency of using the system for both batch and online analytics.
Mathematical Foundations of the GraphBLAS
Venue: arXiv cs.MS 2016
Year: 2016

The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. Mathematically the Graph- BLAS defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the mathematics of the GraphBLAS. Graphs represent connections between vertices with edges. Matrices can represent a wide range of graphs using adjacency matrices or incidence matrices. Adjacency matrices are often easier to analyze while incidence matrices are often better for representing data. Fortunately, the two are easily connected by matrix mul- tiplication. A key feature of matrix mathematics is that a very small number of matrix operations can be used to manipulate a very wide range of graphs. This composability of small number of operations is the foundation of the GraphBLAS. A standard such as the GraphBLAS can only be effective if it has low performance overhead. Performance measurements of prototype GraphBLAS implementations indicate that the overhead is low.
Associative Array Model of SQL, NoSQL, and NewSQL Databases
Venue: arXiv cs.DB 2016
Year: 2016

The success of SQL, NoSQL, and NewSQL databases is a reflection of their ability to provide significant functionality and performance benefits for specific domains, such as financial transactions, internet search, and data analysis. The BigDAWG polystore seeks to provide a mechanism to allow applications to transparently achieve the benefits of diverse databases while insulating applications from the details of these databases. Associative arrays provide a common approach to the mathematics found in different databases: sets (SQL), graphs (NoSQL), and matrices (NewSQL). This work presents the SQL relational model in terms of associative arrays and identifies the key mathematical properties that are preserved within SQL. These properties include associativity, commutativity, distributivity, identities, annihilators, and inverses. Performance measurements on distributivity and associativity show the impact these properties can have on associative array operations. These results demonstrate that associative arrays could provide a mathematical model for polystores to optimize the exchange of data and execution queries.
Guaranteed bounds on the Kullback-Leibler divergence of univariate mixtures using piecewise log-sum-exp inequalities
Venue: arXiv cs.LG 2016
Year: 2016

Information-theoretic measures such as the entropy, cross-entropy and the Kullback-Leibler divergence between two mixture models is a core primitive in many signal processing tasks. Since the Kullback-Leibler divergence of mixtures provably does not admit a closed-form formula, it is in practice either estimated using costly Monte-Carlo stochastic integration, approximated, or bounded using various techniques. We present a fast and generic method that builds algorithmically closed-form lower and upper bounds on the entropy, the cross-entropy and the Kullback-Leibler divergence of mixtures. We illustrate the versatile method by reporting on our experiments for approximating the Kullback-Leibler divergence between univariate exponential mixtures, Gaussian mixtures, Rayleigh mixtures, and Gamma mixtures.
Evaluating the predicted reliability of mechatronic systems: state of the art
Venue: arXiv cs.OH 2016
Year: 2016

Reliability analysis of mechatronic systems is a recent field and a dynamic branch of research. It is addressed whenever there is a need for reliable, available, and safe systems. The studies of reliability must be conducted earlier during the design phase, in order to reduce costs and the number of prototypes required in the validation of the system. The process of reliability is then deployed throughout the full cycle of development. This process is broken down into three major phases: the predictive reliability, the experimental reliability and operational reliability. The main objective of this article is a kind of portrayal of the various studies enabling a noteworthy mastery of the predictive reliability. The weak points are highlighted. Presenting an overview of all the quantitative and qualitative approaches concerned with modelling and evaluating the prediction of reliability is so important for future reliability studies and for academic research to come up with new methods and tools. The mechatronic system is a hybrid system, it is dynamic, reconfigurable, and interactive. The modeling carried out of reliability prediction must take into account these criteria. Several methodologies have been developed in this track of research. In this regard, the aforementioned methodologies will be analytically sketched in this paper.
The gene family-free median of three
Venue: arXiv cs.DS 2016
Year: 2016

The gene family-free framework for comparative genomics aims at developing methods for gene order analysis that do not require prior gene family assignment, but work directly on a sequence similarity multipartite graph. We present a model for constructing a median of three genomes in this family-free setting, based on maximizing an objective function that generalizes the classical breakpoint distance by integrating sequence similarity in the score of a gene adjacency. We show that the corresponding computational problem is MAX SNP-hard and we present a 0-1 linear program for its exact solution. The result of our FF-median program is a median genome with median genes associated to extant genes, in which median adjacencies are assumed to define positional orthologs. We demonstrate through simulations and comparison with the OMA orthology database that the herein presented method is able compute accurate medians and positional orthologs for genomes comparable in size of bacterial genomes.
Criticality Aware Multiprocessors
Venue: arXiv cs.AR 2016
Year: 2016

Typically, a memory request from a processor may need to go through many intermediate interconnect routers, directory node, owner node, etc before it is finally serviced. Current multiprocessors do not give preference to any particular memory request. But certain memory requests are more critical to multiprocessor's performance than other requests. Example: memory requests from critical sections, load request feeding into multiple dependent instructions, etc. This knowledge can be used to improve the performance of current multiprocessors by letting the ordering point and the interconnect routers prioritize critical requests over non-critical ones. In this paper, we evaluate using SIMICS/GEMS infrastructure. For lock-intensive microbenchmarks, criticality-aware multiprocessors showed 5-15% performance improvement over baseline multiprocessor. Criticality aware multiprocessor provides a new direction for tapping performance in a shared memory multiprocessor and can provide substantial speedup in lock intensive benchmarks.
From Events to Reactions: A Progress Report
Venue: arXiv cs.PL 2016
Year: 2016

Syndicate is a new coordinated, concurrent programming language. It occupies a novel point on the spectrum between the shared-everything paradigm of threads and the shared-nothing approach of actors. Syndicate actors exchange messages and share common knowledge via a carefully controlled database that clearly scopes conversations. This approach clearly simplifies coordination of concurrent activities. Experience in programming with Syndicate, however, suggests a need to raise the level of linguistic abstraction. In addition to writing event handlers and managing event subscriptions directly, the language will have to support a reactive style of programming. This paper presents event-oriented Syndicate programming and then describes a preliminary design for augmenting it with new reactive programming constructs.