OpenRaiser/Intern-Atlas huggingface dataset:
Implementing OpenSHMEM for the Adapteva Epiphany RISC Array Processor
Venue: arXiv cs.DC 2016
Year: 2016

The energy-efficient Adapteva Epiphany architecture exhibits massive many-core scalability in a physically compact 2D array of RISC cores with a fast network-on-chip (NoC). With fully divergent cores capable of MIMD execution, the physical topology and memory-mapped capabilities of the core and network translate well to partitioned global address space (PGAS) parallel programming models. Following an investigation into the use of two-sided communication using threaded MPI, one-sided communication using SHMEM is being explored. Here we present work in progress on the development of an OpenSHMEM 1.2 implementation for the Epiphany architecture.
Forward and Reverse Entropy Power Inequalities in Convex Geometry
Venue: arXiv cs.IT 2016
Year: 2016

The entropy power inequality, which plays a fundamental role in information theory and probability, may be seen as an analogue of the Brunn-Minkowski inequality. Motivated by this connection to Convex Geometry, we survey various recent developments on forward and reverse entropy power inequalities not just for the Shannon-Boltzmann entropy but also more generally for Rényi entropy. In the process, we discuss connections between the so-called functional (or integral) and probabilistic (or entropic) analogues of some classical inequalities in geometric functional analysis
Axiomatizing Analog Algorithms
Venue: arXiv cs.LO 2016
Year: 2016

We propose a formalization of generic algorithms that includes analog algorithms. This is achieved by reformulating and extending the framework of abstract state machines to include continuous-time models of computation. We prove that every hybrid algorithm satisfying some reasonable postulates may be expressed precisely by a program in a simple and expressive language.
Low-Rank Matrix Recovery using Gabidulin Codes in Characteristic Zero
Venue: arXiv cs.IT 2016
Year: 2016

We present a new approach on low-rank matrix recovery (LRMR) based on Gabidulin Codes. Since most applications of LRMR deal with matrices over infinite fields, we use the recently introduced generalization of Gabidulin codes to fields of characterstic zero. We show that LRMR can be reduced to decoding of Gabidulin codes and discuss which field extensions can be used in the code construction.
Enhanced Circuit Densities in Epitaxially Defined FinFETs (EDFinFETs) over FinFETs
Venue: arXiv cs.ET 2016
Year: 2016

FinFET technology is prone to suffer from Line Edge Roughness (LER) based VT variation with scaling. To address this, we proposed an Epitaxially Defined (ED) FinFET (EDFinFET) as an alternate to FinFET architecture for 10 nm node and beyond. We showed by statistical simulations that EDFinFET reduces LER based VT variability by 90% and overall variability by 59%. However, EDFinFET consists of wider fins as the fin widths are not constrained by electrostatics and variability (cf. FinFETs have fin width ~ LG/3 where LG is gate-length). This indicates that EDFinFET based circuits may be less dense. In this study we show that wide fins enable taller fin heights. The ability to engineer multiple STI levels on tall fins enables different transistor widths (i.e. various W/Ls e.g. 1-10) in a single fin. This capability ensures that even though individual EDFinFET devices have ~2x larger footprints than FinFETs, EDFinFET may produce equal or higher circuit density for basic building blocks like inverters or NAND gates for W/Ls of 2 and higher.
Probing the Intra-Component Correlations within Fisher Vector for Material Classification
Venue: arXiv cs.CV 2016
Year: 2016

Fisher vector (FV) has become a popular image representation. One notable underlying assumption of the FV framework is that local descriptors are well decorrelated within each cluster so that the covariance matrix for each Gaussian can be simplified to be diagonal. Though the FV usually relies on the Principal Component Analysis (PCA) to decorrelate local features, the PCA is applied to the entire training data and hence it only diagonalizes the \textit{universal} covariance matrix, rather than those w.r.t. the local components. As a result, the local decorrelation assumption is usually not supported in practice. To relax this assumption, this paper proposes a completed model of the Fisher vector, which is termed as the Completed Fisher vector (CFV). The CFV is a more general framework of the FV, since it encodes not only the variances but also the correlations of the whitened local descriptors. The CFV thus leads to improved discriminative power. We take the task of material categorization as an example and experimentally show that: 1) the CFV outperforms the FV under all parameter settings; 2) the CFV is robust to the changes in the number of components in the mixture; 3) even with a relatively small visual vocabulary the CFV still works well on two challenging datasets.
Tracking Human-like Natural Motion Using Deep Recurrent Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

Kinect skeleton tracker is able to achieve considerable human body tracking performance in convenient and a low-cost manner. However, The tracker often captures unnatural human poses such as discontinuous and vibrated motions when self-occlusions occur. A majority of approaches tackle this problem by using multiple Kinect sensors in a workspace. Combination of the measurements from different sensors is then conducted in Kalman filter framework or optimization problem is formulated for sensor fusion. However, these methods usually require heuristics to measure reliability of measurements observed from each Kinect sensor. In this paper, we developed a method to improve Kinect skeleton using single Kinect sensor, in which supervised learning technique was employed to correct unnatural tracking motions. Specifically, deep recurrent neural networks were used for improving joint positions and velocities of Kinect skeleton, and three methods were proposed to integrate the refined positions and velocities for further enhancement. Moreover, we suggested a novel measure to evaluate naturalness of captured motions. We evaluated the proposed approach by comparison with the ground truth obtained using a commercial optical maker-based motion capture system.
Cooperative Wireless Backhauling
Venue: arXiv cs.IT 2016
Year: 2016

We consider wireless backhauling for a scenario where two small-cell base stations (SC-BSs) employ the same time and frequency resources for offloading their data to a common macro-cell base station (MC-BS). The two SC-BSs allocate a part of the shared resource to exchange data in order to be able to cooperate to boost the backhaul capacity. For this scenario, we develop the optimal transmission strategy which, based on the channel state information, determines whether the SC-BSs should exchange data and cooperate or transmit their data independently to the MC-BS. Our numerical results demonstrate the superiority of the proposed cooperative~wireless backhauling protocol compared to existing protocols in the literature.
Robust Reduced-Order Model Stabilization for Partial Differential Equations Based on Lyapunov Theory and Extremum Seeking with Application to the 3D Boussinesq Equations
Venue: arXiv cs.SY 2016
Year: 2016

We present some results on stabilization for reduced-order models (ROMs) of partial differential equations. The stabilization is achieved using Lyapunov theory to design a new closure model that is robust to parametric uncertainties. The free parameters in the proposed ROM stabilization method are optimized using a model-free multi-parametric extremum seeking (MES) algorithm. The 3D Boussinesq equations provide a challenging numerical test-problem that is used to demonstrate the advantages of the proposed method.
Digital Adaptive Carrier Phase Estimation in Multi-Level Phase Shift Keying Coherent Optical Communication Systems
Venue: arXiv cs.IT 2016
Year: 2016

The analysis of adaptive carrier phase estimation is investigated in long-haul high speed n-level phase shift keying (n-PSK) optical fiber communication systems based on the one-tap normalized least-mean-square (LMS) algorithm. The close-form expressions for the estimated carrier phase and the bit-error-rate floor have been derived in the n-PSK coherent optical transmission systems. The results show that the one-tap normalized LMS algorithm performs pretty well in the carrier phase estimation, but will be less effective with the increment of modulation levels, in the compensation of both intrinsic laser phase noise and equalization enhanced phase noise.
Active Versus Passive: Receiver Model Transforms for Diffusive Molecular Communication
Venue: arXiv cs.ET 2016
Year: 2016

This paper presents an analytical comparison of active and passive receiver models in diffusive molecular communication. In the active model, molecules are absorbed when they collide with the receiver surface. In the passive model, the receiver is a virtual boundary that does not affect molecule behavior. Two approaches are presented to derive transforms between the receiver signals. As an example, two models for an unbounded diffusion-only molecular communication system with a spherical receiver are unified. As time increases in the three-dimensional system, the transform functions have constant scaling factors, such that the receiver models are effectively equivalent. Methods are presented to enable the transformation of stochastic simulations, which are used to verify the transforms and demonstrate that transforming the simulation of a passive receiver can be more efficient and more accurate than the direct simulation of an absorbing receiver.
ModelWizard: Toward Interactive Model Construction
Venue: arXiv cs.PL 2016
Year: 2016

Data scientists engage in model construction to discover machine learning models that well explain a dataset, in terms of predictiveness, understandability and generalization across domains. Questions such as "what if we model common cause Z" and "what if Y's dependence on X reverses" inspire many candidate models to consider and compare, yet current tools emphasize constructing a final model all at once. To more naturally reflect exploration when debating numerous models, we propose an interactive model construction framework grounded in composable operations. Primitive operations capture core steps refining data and model that, when verified, form an inductive basis to prove model validity. Derived, composite operations enable advanced model families, both generic and specialized, abstracted away from low-level details. We prototype our envisioned framework in ModelWizard, a domain-specific language embedded in F# to construct Tabular models. We enumerate language design and demonstrate its use through several applications, emphasizing how language may facilitate creation of complex models. To future engineers designing data science languages and tools, we offer ModelWizard's design as a new model construction paradigm, speeding discovery of our universe's structure.
Coverage Gains from the Static Cooperation of Mutually Nearest Neighbours
Venue: arXiv cs.NI 2016
Year: 2016

Cooperation in cellular networks has been recently suggested as a promising scheme to improve system performance. In this work, clusters are formed based on the Mutually Nearest Neighbour relation, which defines which stations cooperate in pair and which do not. When node positions follow a Poisson Point Process (PPP) the performance of the original clustering model can be approximated by another one, formed by the superposition of two PPPs (one for the singles and one for the pairs) equipped with adequate marks. This allows to derive exact expressions for the network coverage probability under two user-cluster association rules. Numerical evaluation shows coverage gains from different signal cooperation schemes that can reach up to 15% compared to the standard non-cooperative network coverage. The analysis is general and can be applied to any type of cooperation or coordination between pairs of transmitting nodes.
Phone-based Metric as a Predictor for Basic Personality Traits
Venue: arXiv cs.SI 2016
Year: 2016

Basic personality traits are typically assessed through questionnaires. Here we consider phone-based metrics as a way to asses personality traits. We use data from smartphones with custom data-collection software distributed to 730 individuals. The data includes information about location, physical motion, face-to-face contacts, online social network friends, text messages and calls. The data is further complemented by questionnaire-based data on basic personality traits. From the phone-based metrics, we define a set of behavioral variables, which we use in a prediction of basic personality traits. We find that predominantly, the Big Five personality traits extraversion and, to some degree, neuroticism are strongly expressed in our data. As an alternative to the Big Five, we investigate whether other linear combinations of the 44 questions underlying the Big Five Inventory are more predictable. In a tertile classification problem, basic dimensionality reduction techniques, such as independent component analysis, increase the predictability relative to the baseline from $11\%$ to $23\%$. Finally, from a supervised linear classifier, we were able to further improve this predictability to $33\%$. In all cases, the most predictable projections had an overweight of the questions related to extraversion and neuroticism. In addition, our findings indicate that the score system underlying the Big Five Inventory disregards a part of the information available in the 44 questions.
Requirements Evolution and Evolution Requirements with Constrained Goal Models
Venue: arXiv cs.SE 2016
Year: 2016

We are interested in supporting software evolution caused by changing requirements and/or environmental settings. For example, users of a system may require new functionality (changing requirements), or performance enhancements to cope with growing user population. Specifically, we propose to use goal models to capture such changes, and exploit reasoning techniques that derive optimal new specifications for a system whose requirements and/or environment have changed. Moreover, we are interested in discovering new classes of evolution requirements, for example, that give preference to evolutions that minimize implementation effort for the implementation of the evolution. To address both of these problems, we exploit Constraint Goal Models (CGMs) an expressive language for modelling goals that comes with scalable solvers that can solve hybrid constraint and optimization problems using a combination of Satisfiability Modulo Theories (SMT) and Optimization Modulo Theories (OMT) solvers. We evaluate our proposal by modeling and reasoning with a goal model for meeting scheduling.
Tasks for agent-based negotiation teams: Analysis, review, and challenges
Venue: arXiv cs.MA 2016
Year: 2016

An agent-based negotiation team is a group of interdependent agents that join together as a single negotiation party due to their shared interests in the negotiation at hand. The reasons to employ an agent-based negotiation team may vary: (i) more computation and parallelization capabilities, (ii) unite agents with different expertise and skills whose joint work makes it possible to tackle complex negotiation domains, (iii) the necessity to represent different stakeholders or different preferences in the same party (e.g., organizations, countries, and married couple). The topic of agent-based negotiation teams has been recently introduced in multi-agent research. Therefore, it is necessary to identify good practices, challenges, and related research that may help in advancing the state-of-the-art in agent-based negotiation teams. For that reason, in this article we review the tasks to be carried out by agent-based negotiation teams. Each task is analyzed and related with current advances in different research areas. The analysis aims to identify special challenges that may arise due to the particularities of agent-based negotiation teams.
Reaching Unanimous Agreements Within Agent-Based Negotiation Teams With Linear and Monotonic Utility Functions
Venue: arXiv cs.MA 2016
Year: 2016

In this article, an agent-based negotiation model for negotiation teams that negotiate a deal with an opponent is presented. Agent-based negotiation teams are groups of agents that join together as a single negotiation party because they share an interest that is related to the negotiation process. The model relies on a trusted mediator that coordinates and helps team members in the decisions that they have to take during the negotiation process: which offer is sent to the opponent, and whether the offers received from the opponent are accepted. The main strength of the proposed negotiation model is the fact that it guarantees unanimity within team decisions since decisions report a utility to team members that is greater than or equal to their aspiration levels at each negotiation round. This work analyzes how unanimous decisions are taken within the team and the robustness of the model against different types of manipulations. An empirical evaluation is also performed to study the impact of the different parameters of the model.
Studying the impact of negotiation environments on negotiation teams' performance
Venue: arXiv cs.MA 2016
Year: 2016

In this article we study the impact of the negotiation environment on the performance of several intra-team strategies (team dynamics) for agent-based negotiation teams that negotiate with an opponent. An agent-based negotiation team is a group of agents that joins together as a party because they share common interests in the negotiation at hand. It is experimentally shown how negotiation environment conditions like the deadline of both parties, the concession speed of the opponent, similarity among team members, and team size affect performance metrics like the minimum utility of team members, the average utility of team members, and the number of negotiation rounds. Our goal is identifying which intra-team strategies work better in different environmental conditions in order to provide useful knowledge for team members to select appropriate intra-team strategies according to environmental conditions.
Participation Cost Estimation: Private Versus Non-Private Study
Venue: arXiv cs.CR 2016
Year: 2016

In our study, we seek to learn the real-time crowd levels at popular points of interests based on users continually sharing their location data. We evaluate the benefits of users sharing their location data privately and non-privately, and show that suitable privacy-preserving mechanisms provide incentives for user participation in a private study as compared to a non-private study.
A Middleware for the Internet of Things
Venue: arXiv cs.NI 2016
Year: 2016

The Internet of Things (IoT) connects everyday objects including a vast array of sensors, actuators, and smart devices, referred to as things to the Internet, in an intelligent and pervasive fashion. This connectivity gives rise to the possibility of using the tracking capabilities of things to impinge on the location privacy of users. Most of the existing management and location privacy protection solutions do not consider the low-cost and low-power requirements of things, or, they do not account for the heterogeneity, scalability, or autonomy of communications supported in the IoT. Moreover, these traditional solutions do not consider the case where a user wishes to control the granularity of the disclosed information based on the context of their use (e.g. based on the time or the current location of the user). To fill this gap, a middleware, referred to as the Internet of Things Management Platform (IoT-MP) is proposed in this paper.
Toward Efficient Task Assignment and Motion Planning for Large Scale Underwater Mission
Venue: arXiv cs.RO 2016
Year: 2016

An Autonomous Underwater Vehicle (AUV) needs to acquire a certain degree of autonomy for any particular underwater mission to fulfill the mission objectives successfully and ensure its safety in all stages of the mission in a large scale operating filed. In this paper, a novel combinatorial conflict-free-task assignment strategy consisting an interactive engagement of a local path planner and an adaptive global route planner, is introduced. The method is established upon the heuristic search potency of the Particle Swarm Optimisation (PSO) algorithm to address the discrete nature of routing-task assignment approach and the complexity of NP-hard path planning problem. The proposed hybrid method is highly efficient for having a reactive guidance framework that guarantees successful completion of missions specifically in cluttered environments. To examine the performance of the method in a context of mission productivity, mission time management and vehicle safety, a series of simulation studies are undertaken. The results of simulations declare that the proposed method is reliable and robust, particularly in dealing with uncertainties, and it can significantly enhance the level of vehicle's autonomy by relying on its reactive nature and capability of providing fast feasible solutions.
From Incremental Meaning to Semantic Unit (phrase by phrase)
Venue: arXiv cs.CL 2016
Year: 2016

This paper describes an experimental approach to Detection of Minimal Semantic Units and their Meaning (DiMSUM), explored within the framework of SemEval 2016 Task 10. The approach is primarily based on a combination of word embeddings and parserbased features, and employs unidirectional incremental computation of compositional embeddings for multiword expressions.
PAS-MC: Privacy-preserving Analytics Stream for the Mobile Cloud
Venue: arXiv cs.CR 2016
Year: 2016

In today's digital world, personal data is being continuously collected and analyzed without data owners' consent and choice. As data owners constantly generate data on their personal devices, the tension of storing private data on their own devices yet allowing third party analysts to perform aggregate analytics yields an interesting dilemma. This paper introduces PAS-MC, the first practical privacy-preserving and anonymity stream analytics system. PAS-MC ensures that each data owner locally privatizes their sensitive data before responding to analysts' queries. PAS-MC also protects against traffic analysis attacks with minimal trust vulnerabilities.We evaluate the scheme over the California Transportation Dataset and show that we can privately and anonymously stream vehicular location updates yet preserve high accuracy.
Channel Estimation for mmWave Massive MIMO Based Access and Backhaul in Ultra-Dense Network
Venue: arXiv cs.IT 2016
Year: 2016

Millimeter-wave (mmWave) massive MIMO used for access and backhaul in ultra-dense network (UDN) has been considered as the promising 5G technique. We consider such an heterogeneous network (HetNet) that ultra-dense small base stations (BSs) exploit mmWave massive MIMO for access and backhaul, while macrocell BS provides the control service with low frequency band. However, the channel estimation for mmWave massive MIMO can be challenging, since the pilot overhead to acquire the channels associated with a large number of antennas in mmWave massive MIMO can be prohibitively high. This paper proposes a structured compressive sensing (SCS)-based channel estimation scheme, where the angular sparsity of mmWave channels is exploited to reduce the required pilot overhead. Specifically, since the path loss for non-line-of-sight paths is much larger than that for line-of-sight paths, the mmWave massive channels in the angular domain appear the obvious sparsity. By exploiting such sparsity, the required pilot overhead only depends on the small number of dominated multipath. Moreover, the sparsity within the system bandwidth is almost unchanged, which can be exploited for the further improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterpart, and it can approach the performance bound.