OpenRaiser/Intern-Atlas huggingface dataset:
Can Uplink Transmissions Survive in Full-duplex Cellular Environments?
Venue: arXiv cs.IT 2016
Year: 2016

In-band full-duplex (FD) communication is considered a potential candidate to be adopted by the fifth generation (5G) cellular networks. FD communication renders the entire spectrum simultaneously accessible by uplink and downlink, and hence, is optimistically promoted to double the transmission rate. While this is true for a single communication link, cross-mode interference (i.e., interference between uplink and downlink) may diminish the full-duplexing gain. This paper studies FD operation in large-scale cellular networks with real base stations (BSs) locations and 3GPP propagation environment. The results show that the uplink is the bottleneck for FD operation due to the overwhelming cross-mode interference from BSs. Operating uplink and downlink on a common set of channels in an FD fashion improves the downlink rate but significantly degrades (over 1000-fold) the uplink rate. Therefore, we propose the $α$-duplex scheme to balance the tradeoff between the uplink and downlink rates via adjustable partial overlap between uplink and downlink channels. The $α$-duplex scheme can provide a simultaneous $30\%$ improvement in each of the uplink and downlink rates. To this end, we discuss the backward compatibility of the $α$-duplex scheme with half-duplex user-terminals. Finally, we point out future research directions for FD enabled cellular networks.
Information, Processes and Games
Venue: arXiv cs.LO 2016
Year: 2016

We survey the prospects for an Information Dynamics which can serve as the basis for a fundamental theory of information, incorporating qualitative and structural as well as quantitative aspects. We motivate our discussion with some basic conceptual puzzles: how can information increase in computation, and what is it that we are actually computing in general? Then we survey a number of the theories which have been developed within Computer Science, as partial exemplifications of the kind of fundamental theory which we seek: including Domain Theory, Dynamic Logic, and Process Algebra. We look at recent work showing new ways of combining quantitative and qualitative theories of information, as embodied respectively by Domain Theory and Shannon Information Theory. Then we look at Game Semantics and Geometry of Interaction, as examples of dynamic models of logic and computation in which information flow and interaction are made central and explicit. We conclude by looking briefly at some key issues for future progress.
Multi-State Perfect Phylogeny Mixture Deconvolution and Applications to Cancer Sequencing
Venue: arXiv cs.DS 2016
Year: 2016

The reconstruction of phylogenetic trees from mixed populations has become important in the study of cancer evolution, as sequencing is often performed on bulk tumor tissue containing mixed populations of cells. Recent work has shown how to reconstruct a perfect phylogeny tree from samples that contain mixtures of two-state characters, where each character/locus is either mutated or not. However, most cancers contain more complex mutations, such as copy-number aberrations, that exhibit more than two states. We formulate the Multi-State Perfect Phylogeny Mixture Deconvolution Problem of reconstructing a multi-state perfect phylogeny tree given mixtures of the leaves of the tree. We characterize the solutions of this problem as a restricted class of spanning trees in a graph constructed from the input data, and prove that the problem is NP-complete. We derive an algorithm to enumerate such trees in the important special case of cladisitic characters, where the ordering of the states of each character is given. We apply our algorithm to simulated data and to two cancer datasets. On simulated data, we find that for a small number of samples, the Multi-State Perfect Phylogeny Mixture Deconvolution Problem often has many solutions, but that this ambiguity declines quickly as the number of samples increases. On real data, we recover copy-neutral loss of heterozygosity, single-copy amplification and single-copy deletion events, as well as their interactions with single-nucleotide variants.
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Venue: arXiv cs.NI 2016
Year: 2016

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.
Direction matters: hand pose estimation from local surface normals
Venue: arXiv cs.CV 2016
Year: 2016

We present a hierarchical regression framework for estimating hand joint positions from single depth images based on local surface normals. The hierarchical regression follows the tree structured topology of hand from wrist to finger tips. We propose a conditional regression forest, i.e., the Frame Conditioned Regression Forest (FCRF) which uses a new normal difference feature. At each stage of the regression, the frame of reference is established from either the local surface normal or previously estimated hand joints. By making the regression with respect to the local frame, the pose estimation is more robust to rigid transformations. We also introduce a new efficient approximation to estimate surface normals. We verify the effectiveness of our method by conducting experiments on two challenging real-world datasets and show consistent improvements over previous discriminative pose estimation methods.
Approximating Nash Equilibria in Tree Polymatrix Games
Venue: arXiv cs.GT 2016
Year: 2016

We develop a quasi-polynomial time Las Vegas algorithm for approximating Nash equilibria in polymatrix games over trees, under a mild renormalizing assumption. Our result, in particular, leads to an expected polynomial-time algorithm for computing approximate Nash equilibria of tree polymatrix games in which the number of actions per player is a fixed constant. Further, for trees with constant degree, the running time of the algorithm matches the best known upper bound for approximating Nash equilibria in bimatrix games (Lipton, Markakis, and Mehta 2003). Notably, this work closely complements the hardness result of Rubinstein (2015), which establishes the inapproximability of Nash equilibria in polymatrix games over constant-degree bipartite graphs with two actions per player.
The Intensity Matching Approach: A Tractable Stochastic Geometry Approximation to System-Level Analysis of Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

The intensity matching approach for tractable performance evaluation and optimization of cellular networks is introduced. It assumes that the base stations are modeled as points of a Poisson point process and leverages stochastic geometry for system-level analysis. Its rationale relies on observing that system-level performance is determined by the intensity measure of transformations of the underlaying spatial Poisson point process. By approximating the original system model with a simplified one, whose performance is determined by a mathematically convenient intensity measure, tractable yet accurate integral expressions for computing area spectral efficiency and potential throughput are provided. The considered system model accounts for many practical aspects that, for tractability, are typically neglected, e.g., line-of-sight and non-line-of-sight propagation, antenna radiation patterns, traffic load, practical cell associations, general fading channels. The proposed approach, more importantly, is conveniently formulated for unveiling the impact of several system parameters, e.g., the density of base stations and blockages. The effectiveness of this novel and general methodology is validated with the aid of empirical data for the locations of base stations and for the footprints of buildings in dense urban environments.
GPIC - GPU Power Iteration Cluster
Venue: arXiv cs.DC 2016
Year: 2016

This work presents a new clustering algorithm, the GPIC, a Graphics Processing Unit (GPU) accelerated algorithm for Power Iteration Clustering (PIC). Our algorithm is based on the original PIC proposal, adapted to take advantage of the GPU architecture, maintining the algorith original properties. The proposed method was compared against the serial and parallel Spark implementation, achieving a considerable speed-up in the test problems.
Construction methods for generalized bent functions
Venue: arXiv cs.IT 2016
Year: 2016

Generalized bent (gbent) functions is a class of functions $f: \mathbb{Z}_2^n \rightarrow \mathbb{Z}_q$, where $q \geq 2$ is a positive integer, that generalizes a concept of classical bent functions through their co-domain extension. A lot of research has recently been devoted towards derivation of the necessary and sufficient conditions when $f$ is represented as a collection of Boolean functions. Nevertheless, apart from the necessary conditions that these component functions are bent when $n$ is even (respectively semi-bent when $n$ is odd), no general construction method has been proposed yet for $n$ odd case. In this article, based on the use of the well-known Maiorana-McFarland (MM) class of functions, we give an explicit construction method of gbent functions, for any even $q >2$ when $n$ is even and for any $q$ of the form $q=2^r$ (for $r>1$) when $n$ is odd. Thus, a long-term open problem of providing a general construction method of gbent functions, for odd $n$, has been solved. The method for odd $n$ employs a large class of disjoint spectra semi-bent functions with certain additional properties which may be useful in other cryptographic applications.
Reverse Engineering and Symbolic Knowledge Extraction on Łukasiewicz Fuzzy Logics using Linear Neural Networks
Venue: arXiv cs.AI 2016
Year: 2016

This work describes a methodology to combine logic-based systems and connectionist systems. Our approach uses finite truth valued Łukasiewicz logic, where we take advantage of fact what in this type of logics every connective can be define by a neuron in an artificial network having by activation function the identity truncated to zero and one. This allowed the injection of first-order formulas in a network architecture, and also simplified symbolic rule extraction. Our method trains a neural network using Levenderg-Marquardt algorithm, where we restrict the knowledge dissemination in the network structure. We show how this reduces neural networks plasticity without damage drastically the learning performance. Making the descriptive power of produced neural networks similar to the descriptive power of Łukasiewicz logic language, simplifying the translation between symbolic and connectionist structures. This method is used in the reverse engineering problem of finding the formula used on generation of a truth table for a multi-valued Łukasiewicz logic. For real data sets the method is particularly useful for attribute selection, on binary classification problems defined using nominal attribute. After attribute selection and possible data set completion in the resulting connectionist model: neurons are directly representable using a disjunctive or conjunctive formulas, in the Łukasiewicz logic, or neurons are interpretations which can be approximated by symbolic rules. This fact is exemplified, extracting symbolic knowledge from connectionist models generated for the data set Mushroom from UCI Machine Learning Repository.
Multi-diagrams of relations between fuzzy sets: weighted limits, colimits and commutativity
Venue: arXiv cs.LO 2016
Year: 2016

Limits and colimits of diagrams, defined by maps between sets, are universal constructions fundamental in different mathematical domains and key concepts in theoretical computer science. Its importance in semantic modeling is described by M. Makkai and R. Paré, where it is formally shown that every axiomatizable theory in classical infinitary logic can be specified using diagrams defined by maps between sets, and its models are structures characterized by the commutativity, limit and colimit of those diagrams. Z. Diskin taking a more practical perspective, presented an algebraic graphic-based framework for data modeling and database design. The aim of our work is to study the possibility of extending these algebraic frameworks to the specification of fuzzy structures and to the description of fuzzy patterns on data. For that purpose, in this paper we describe a conservative extension for the notions of diagram commutativity, limit and colimit, when diagrams are constructed using relations between fuzzy sets, evaluated in a multi-valued logic. These are used to formalize what we mean by "a relation $R$ is similar to a limit of diagram $D$," "a similarity relation $S$ is identical to a colimit of diagram $D$ colimit," and "a diagram $D$ is almost commutative."
Cross-Layer Design of Influence Maximization in Mobile Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

Most prior algorithms for influence maximization focused are designed for Online Social Networks (OSNs) and require centralized computation. Directly deploying the above algorithms in distributed Mobile Social Networks (MSNs) will overwhelm the networks due to an enormous number of messages required for seed selection. In this paper, therefore, we design a new cross-layer strategy to jointly examine MSN and mobile ad hoc networks (MANETs) to facilitate efficient seed selection, by extracting a subset of nodes as agents to represent nearby friends during the distributed computation. Specifically, we formulate a new optimization problem, named Agent Selection Problem (ASP), to minimize the message overhead transmitted in MANET. We prove that ASP is NP-Hard and design an effectively distributed algorithm. Simulation results in real and synthetic datasets manifest that the message overhead can be significantly reduced compared with the existing approaches.
Cooperative Localization in WSNs: a Hybrid Convex/non-Convex Solution
Venue: arXiv cs.NI 2016
Year: 2016

We propose an efficient solution to peer-to-peer localization in a wireless sensor network which works in two stages. At the first stage the optimization problem is relaxed into a convex problem, given in the form recently proposed by Soares, Xavier, and Gomes. The convex problem is efficiently solved in a distributed way by an ADMM approach, which provides a significant improvement in speed with respect to the original solution. In the second stage, a soft transition to the original, non-convex, non relaxed formulation is applied in such a way to force the solution towards a local minimum. The algorithm is built in such a way to be fully distributed, and it is tested in meaningful situations, showing its effectiveness in localization accuracy and speed of convergence, as well as its inner robustness.
Analysing health professionals' learning interactions in online social networks: A social network analysis approach
Venue: arXiv cs.SI 2016
Year: 2016

Online Social Networking may be a way to support health professionals' need for continuous learning through interaction with peers and experts. Understanding and evaluating such learning is important but difficult, and Social Network Analysis (SNA) offers a solution. This paper demonstrates how SNA can be used to study levels of participation as well as the patterns of interactions that take place among health professionals in a large online professional learning network. Our analysis has shown that their learning network is highly centralised and loosely connected. The level of participation is low in general, and most interactions are structured around a small set of users consisting of moderators and core members. The structural patterns of interaction indicates there is a chance of small group learning occurring and requires further investigation to identify those potential learning groups. This first stage of analysis, to be followed by longitudinal study of the dynamics of interaction and complemented by content analysis of their discussion, may contribute to greater sophistication in the analysis and utilisation of new environments for health professional learning.
The vulnerability of the diameter of enhanced hypercubes
Venue: arXiv cs.DM 2016
Year: 2016

For an interconnection network $G$, the {\it $ω$-wide diameter} $d_ω(G)$ is the least $\ell$ such that any two vertices are joined by $ω$ internally-disjoint paths of length at most $\ell$, and the {\it $(ω-1)$-fault diameter} $D_ω(G)$ is the maximum diameter of a subgraph obtained by deleting fewer than $ω$ vertices of $G$. The enhanced hypercube $Q_{n,k}$ is a variant of the well-known hypercube. Yang, Chang, Pai, and Chan gave an upper bound for $d_{n+1}(Q_{n,k})$ and $D_{n+1}(Q_{n,k})$ and posed the problem of finding the wide diameters and fault diameters of $Q_{n,k}$. By constructing internally disjoint paths between any two vertices in the enhanced hypercube, for $n\ge3$ and $2\le k\le n$ we prove $$ D_ω(Q_{n,k})=d_ω(Q_{n,k})=\begin{cases} d(Q_{n,k}) & \textrm{for $1 \leq ω< n-\lfloor\frac{k}{2}\rfloor$;}\\ d(Q_{n,k})+1 & \textrm{for $n-\lfloor\frac{k}{2}\rfloor \leq ω\leq n+1$.} \end{cases} $$ where $d(Q_{n,k})$ is the diameter of $Q_{n,k}$. These results mean that interconnection networks modelled by enhanced hypercubes are extremely robust.
Adding Semantic Information into Data Models by Learning Domain Expertise from User Interaction
Venue: arXiv cs.HC 2016
Year: 2016

Interactive visual analytic systems enable users to discover insights from complex data. Users can express and test hypotheses via user interaction, leveraging their domain expertise and prior knowledge to guide and steer the analytic models in the system. For example, semantic interaction techniques enable systems to learn from the user's interactions and steer the underlying analytic models based on the user's analytical reasoning. However, an open challenge is how to not only steer models based on the dimensions or features of the data, but how to add dimensions or attributes to the data based on the domain expertise of the user. In this paper, we present a technique for inferring and appending dimensions onto the dataset based on the prior expertise of the user expressed via user interactions. Our technique enables users to directly manipulate a spatial organization of data, from which both the dimensions of the data are weighted, and also dimensions created to represent the prior knowledge the user brings to the system. We describe this technique and demonstrate its utility via a use case.
PERCCOM: A Master Program in Pervasive Computing and COMmunications for Sustainable Development
Venue: arXiv cs.CY 2016
Year: 2016

This paper presents the Erasmus Mundus Joint Master Degree in Pervasive Computing and Communications for Sustainable Development (PERCCOM). This program brings together 11 academic partners and 8 industry partners to combine advanced Information and Communication Technologies (ICT) with environmental awareness to enable world-class education and unique competences for ICT professionals who can build cleaner, greener, more resource and energy efficient cyber-physical systems. First, this paper describes the rationale and motivations for ICT education for sustainability challenges. It then details the structure and contents of the programs including the courses offered at the three teaching locations (Nancy France, Lappeenranta Finland, and Lulea Sweden). The ways that the program has been running as well as students selection, their thesis works, involvement of industry, are also discussed. The program was built and managed using a solid academic standards and strategies student-centered learning.
Toward a Science of Autonomy for Physical Systems
Venue: arXiv cs.CY 2016
Year: 2016

Our lives have been immensely improved by decades of automation research -- we are more comfortable, more productive and safer than ever before. Just imagine a world where familiar automation technologies have failed. In that world, thermostats don't work -- you have to monitor your home heating system manually. Cruise control for your car doesn't exist. Every elevator has to have a human operator to hit the right floor, most manufactured products are assembled by hand, and you have to wash your own dishes. Who would willingly adopt that world -- the world of last century -- today? Physical systems -- elevators, cars, home appliances, manufacturing equipment -- were more troublesome, ore time consuming, less safe, and far less convenient. Now, suppose we put ourselves in the place someone 20 years in the future, a future of autonomous systems. A future where transportation is largely autonomous, more efficient, and far safer; a future where dangerous occupations like mining or disaster response are performed by autonomous systems supervised remotely by humans; a future where manufacturing and healthcare are twice as productive per person-hour by having smart monitoring and readily re-tasked autonomous physical agents; a future where the elderly and infirm have 24 hour in-home autonomous support for the basic activities, both physical and social, of daily life. In a future world where these capabilities are commonplace, why would someone come back to today's world where someone has to put their life at risk to do a menial job, we lose time to mindless activities that have no intrinsic value, or be consumed with worry that a loved one is at risk in their own home? In what follows, and in a series of associated essays, we expand on these ideas, and frame both the opportunities and challenges posed by autonomous physical systems.
Stronger wireless signals appear more Poisson
Venue: arXiv cs.NI 2016
Year: 2016

Keeler, Ross and Xia (2016) recently derived approximation and convergence results, which imply that the point process formed from the signal strengths received by an observer in a wireless network under a general statistical propagation model can be modelled by an inhomogeneous Poisson point process on the positive real line. The basic requirement for the results to apply is that there must be a large number of transmitters with different locations and random propagation effects.The aim of this note is to apply some of the main results of Keeler, Ross and Xia (2016) in a less general but more easily applicable form to illustrate how the results can be applied in practice. New results are derived that show that it is the strongest signals, after being weakened by random propagation effects, that behave like a Poisson process, which supports recent experimental work.