OpenRaiser/Intern-Atlas huggingface dataset:
Joint Design of Digital and Analog Processing for Downlink C-RAN with Large-Scale Antenna Arrays
Venue: arXiv cs.IT 2017
Year: 2017

In millimeter-wave communication systems with large-scale antenna arrays, conventional digital beamforming may not be cost-effective. A promising solution is the implementation of hybrid beamforming techniques, which consist of low-dimensional digital beamforming followed by analog radio frequency (RF) beamforming. This work studies the optimization of hybrid beamforming in the context of a cloud radio access network (C-RAN) architecture. In a C-RAN system, digital baseband signal processing functionalities are migrated from remote radio heads (RRHs) to a baseband processing unit (BBU) in the "cloud" by means of finite-capacity fronthaul links. Specifically, this work tackles the problem of jointly optimizing digital beamforming and fronthaul quantization strategies at the BBU, as well as RF beamforming at the RRHs, with the goal of maximizing the weighted downlink sum-rate. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraints on the RF beamforming matrices. An iterative algorithm is proposed that is based on successive convex approximation and on the relaxation of the constant modulus constraint. The effectiveness of the proposed scheme is validated by numerical simulation results.
Secured Outsourced Content Based Image Retrieval Based on Encrypted Signatures Extracted From Homomorphically Encrypted Images
Venue: arXiv cs.CR 2017
Year: 2017

In this paper, we present a novel Secured Outsourced Content Based Image Retrieval solution, which allows looking for similar images stored into the cloud in a homomorphically encrypted form. Its originality is fourfold. In a first time, it extracts from a Paillier encrypted image a wavelet based global image signature. In second, this signature is extracted in an encrypted form and gives no clues about the image content. In a third time, its calculation does not require the cloud to communicate with a trusted third party as usually proposed by other existing schemes. More clearly, all computations required in order to look for similar images are conducted by the cloud only with no extra-communications. To make possible such a computation, we propose a new fast way to compare encrypted data, these ones being encrypted by the same public key or not, and using a recursive relationship in-between Paillier random values when computing the different resolution levels of the image wavelet transform. Experiments conducted in two distinct frameworks: medical image retrieval with as purpose diagnosis aid support, and face recognition for user authentication; indicate that the proposed SOCBIR does not change image retrieval performance.
Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points
Venue: arXiv cs.CV 2017
Year: 2017

Hand motion capture has been an active research topic in recent years, following the success of full-body pose tracking. Despite similarities, hand tracking proves to be more challenging, characterized by a higher dimensionality, severe occlusions and self-similarity between fingers. For this reason, most approaches rely on strong assumptions, like hands in isolation or expensive multi-camera systems, that limit the practical use. In this work, we propose a framework for hand tracking that can capture the motion of two interacting hands using only a single, inexpensive RGB-D camera. Our approach combines a generative model with collision detection and discriminatively learned salient points. We quantitatively evaluate our approach on 14 new sequences with challenging interactions.
Distributed FD-MIMO: Cellular Evolution for 5G and Beyond
Venue: arXiv cs.IT 2017
Year: 2017

This paper presents the next evolution of FD-MIMO technology for beyond 5G, where antennas of the FD-MIMO system are placed in a distributed manner throughout the cell in a multi-cell deployment scenario. This system, referred to as Distributed FD-MIMO (D-FD-MIMO) system, is capable of providing higher cell average throughput as well as more uniform user experience compared to the conventional FD-MIMO system. System level simulations are performed to evaluate performance. Our results show that the proposed D-FD-MIMO system achieves 1.4-2 times cell average throughput gain compared to the FD-MIMO system. The insights of performance gain are provided. Hardware implementation challenges and potential standards impact are also presented.
Multi-Advisor Reinforcement Learning
Venue: arXiv cs.LG 2017
Year: 2017

We consider tackling a single-agent RL problem by distributing it to $n$ learners. These learners, called advisors, endeavour to solve the problem from a different focus. Their advice, taking the form of action values, is then communicated to an aggregator, which is in control of the system. We show that the local planning method for the advisors is critical and that none of the ones found in the literature is flawless: the egocentric planning overestimates values of states where the other advisors disagree, and the agnostic planning is inefficient around danger zones. We introduce a novel approach called empathic and discuss its theoretical aspects. We empirically examine and validate our theoretical findings on a fruit collection task.
Does Confidence Reporting from the Crowd Benefit Crowdsourcing Performance?
Venue: arXiv cs.SI 2017
Year: 2017

We explore the design of an effective crowdsourcing system for an $M$-ary classification task. Crowd workers complete simple binary microtasks whose results are aggregated to give the final classification decision. We consider the scenario where the workers have a reject option so that they are allowed to skip microtasks when they are unable to or choose not to respond to binary microtasks. Additionally, the workers report quantized confidence levels when they are able to submit definitive answers. We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize crowd's classification performance. We obtain a couterintuitive result that the classification performance does not benefit from workers reporting quantized confidence. Therefore, the crowdsourcing system designer should employ the reject option without requiring confidence reporting.
Resolution-Based Content Discovery in Network of Caches: Is the Control Traffic an Issue?
Venue: arXiv cs.NI 2017
Year: 2017

As networking attempts to cleanly separate the control plane and forwarding plane abstractions, it also defines a clear interface between these two layers. An underlying network state is represented as a view to act upon in the control plane. We are interested in studying some fundamental properties of this interface, both in a general framework, and in the specific case of content routing. We try to evaluate the traffic between the two planes based on allowing a minimum level of acceptable distortion in the network state representation in the control plane. We apply our framework to content distribution, and see how we can compute the overhead of maintaining the location of content in the control plane. This is of importance to evaluate resolution-based content discovery in content-oriented network architectures: we identify scenarios where the cost of updating the control plane for content routing overwhelms the benefit of fetching the nearest copy. We also show how to minimize the cost of this overhead when associating costs to peering traffic and to internal traffic for network of caches.
A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems
Venue: arXiv cs.NE 2017
Year: 2017

Traditional Linear Genetic Programming (LGP) algorithms are based only on the selection mechanism to guide the search. Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP) methods were proposed to overcome this issue through a probability model that captures the structure of the fit individuals and use it to sample new individuals. This work proposes the use of LGP with a Stochastic Context-Free Grammar (SCFG), that has a probability distribution that is updated according to selected individuals. We proposed a method for adapting the grammar into the linear representation of LGP. Tests performed with the proposed probabilistic method, and with two hybrid approaches, on several symbolic regression benchmark problems show that the results are statistically better than the obtained by the traditional LGP.
Locally Self-Adjusting Skip Graphs
Venue: arXiv cs.DC 2017
Year: 2017

We present a distributed self-adjusting algorithm for skip graphs that minimizes the average routing costs between arbitrary communication pairs by performing topological adaptation to the communication pattern. Our algorithm is fully decentralized, conforms to the $\mathcal{CONGEST}$ model (i.e. uses $O(\log n)$ bit messages), and requires $O(\log n)$ bits of memory for each node, where $n$ is the total number of nodes. Upon each communication request, our algorithm first establishes communication by using the standard skip graph routing, and then locally and partially reconstructs the skip graph topology to perform topological adaptation. We propose a computational model for such algorithms, as well as a yardstick (working set property) to evaluate them. Our working set property can also be used to evaluate self-adjusting algorithms for other graph classes where multiple tree-like subgraphs overlap (e.g. hypercube networks). We derive a lower bound of the amortized routing cost for any algorithm that follows our model and serves an unknown sequence of communication requests. We show that the routing cost of our algorithm is at most a constant factor more than the amortized routing cost of any algorithm conforming to our computational model. We also show that the expected transformation cost for our algorithm is at most a logarithmic factor more than the amortized routing cost of any algorithm conforming to our computational model.
Modeling Uncertainty and Evolving Self-Adaptive Software: A Fuzzy Theory Based Requirements Engineering Approach
Venue: arXiv cs.SE 2017
Year: 2017

Self-adaptive software (SAS) is capable of adjusting its behavior in response to meaningful changes in the operational context and itself. Due to the inherent volatility of the open and changeable environment in which SAS is embedded, the ability of adaptation is highly demanded by many software-intensive systems. Two concerns, i.e., the requirements uncertainty and the context uncertainty are most important among others at Requirements Engineering (RE) stage. However, requirements analyzers can hardly figure out the mathematical relation between requirements, system behavior and context, especially for complex and nonlinear systems, due to the existence of above uncertainties, misunderstanding and ambiguity of prior knowledge. An essential issue to be addressed is how to model and specify these uncertainties at RE stage and how to utilize the prior knowledge to achieve adaptation. In this paper, we propose a fuzzy-based approach to modeling uncertainty and achieving evolution. The approach introduces specifications to describe fuzziness. Based on the specifications, we derive a series of reasoning rules as knowledge base for achieving adaptation and evolution. These two targets are implemented through four reasoning schemas from a control theory perspective. Specifically, forward reasoning schema is used for direct adaptation; backward reasoning schema is used for optimal adaptation. Parameter-identified schema implements learning evolution by considering SAS as the gray-box system, while system-identified reasoning schema implements learning evolution by considering SAS as the gray-box system. The former two schemas function as the control group, while the latter two are de-signed as the experimental groups to illustrate the learning ability. Our approach is implemented under three types of context through the demonstration of a mobile computing application.
Interpretation of Semantic Tweet Representations
Venue: arXiv cs.CL 2017
Year: 2017

Research in analysis of microblogging platforms is experiencing a renewed surge with a large number of works applying representation learning models for applications like sentiment analysis, semantic textual similarity computation, hashtag prediction, etc. Although the performance of the representation learning models has been better than the traditional baselines for such tasks, little is known about the elementary properties of a tweet encoded within these representations, or why particular representations work better for certain tasks. Our work presented here constitutes the first step in opening the black-box of vector embeddings for tweets. Traditional feature engineering methods for high-level applications have exploited various elementary properties of tweets. We believe that a tweet representation is effective for an application because it meticulously encodes the application-specific elementary properties of tweets. To understand the elementary properties encoded in a tweet representation, we evaluate the representations on the accuracy to which they can model each of those properties such as tweet length, presence of particular words, hashtags, mentions, capitalization, etc. Our systematic extensive study of nine supervised and four unsupervised tweet representations against most popular eight textual and five social elementary properties reveal that Bi-directional LSTMs (BLSTMs) and Skip-Thought Vectors (STV) best encode the textual and social properties of tweets respectively. FastText is the best model for low resource settings, providing very little degradation with reduction in embedding size. Finally, we draw interesting insights by correlating the model performance obtained for elementary property prediction tasks with the highlevel downstream applications.
Interacting With a Mobile Robot with a Natural Infrastructure-Less Interface
Venue: arXiv cs.RO 2017
Year: 2017

In this paper we introduce a novel approach that enables users to interact with a mobile robot in a natural manner. The proposed interaction system does not require any specific infrastructure or device, but relies on commonly utilized objects while leaving the user's hands free. Specifically, we propose to utilize a smartwatch (or a sensorized wristband) for recognizing the motion of the user's forearm. Measurements of accelerations and angular velocities are exploited to recognize user's gestures and define velocity commands for the robot. The proposed interaction system is evaluated experimentally with different users controlling a mobile robot and compared to the use of a remote control device for the teleoperation of robots. Results show that the usability and effectiveness of the proposed natural interaction system based on the use of a smartwatch provide significant improvement in the human-robot interaction experience.
A New Transmitted Reference Pulse Cluster Based Ultra-Wideband Transmitter Design
Venue: arXiv cs.IT 2017
Year: 2017

An energy efficient ultra-wideband (UWB) transmitter based on the novel transmitted reference pulse cluster (TRPC) modulation scheme is presented. The TRPC-UWB transmitter integrates, namely, wideband active baluns, wideband I-Q modulator based up-conversion mixer, and differential to single-ended converter. The integrated circuits of TRPC-UWB front end is designed and implemented in the 130-nm CMOS process technology. the measured worst-case carrier leakage suppression is 22.4 dBc, while the single sideband suppression is higher than 31.6 dBc, operating at the frequency from 3.1 GHz to 8.2 GHz. With adjustable data rate of 10 to 300 Mbps, the transmitter achieves a high energy efficiency of 38.4 pJ/pulse.
Natural Scales in Geographical Patterns
Venue: arXiv cs.SI 2017
Year: 2017

Human mobility is known to be distributed across several orders of magnitude of physical distances , which makes it generally difficult to endogenously find or define typical and meaningful scales. Relevant analyses, from movements to geographical partitions, seem to be relative to some ad-hoc scale, or no scale at all. Relying on geotagged data collected from photo-sharing social media, we apply community detection to movement networks constrained by increasing percentiles of the distance distribution. Using a simple parameter-free discontinuity detection algorithm, we discover clear phase transitions in the community partition space. The detection of these phases constitutes the first objective method of characterising endogenous, natural scales of human movement. Our study covers nine regions, ranging from cities to countries of various sizes and a transnational area. For all regions, the number of natural scales is remarkably low (2 or 3). Further, our results hint at scale-related behaviours rather than scale-related users. The partitions of the natural scales allow us to draw discrete multi-scale geographical boundaries, potentially capable of providing key insights in fields such as epidemiology or cultural contagion where the introduction of spatial boundaries is pivotal.
Communication Complexity of Correlated Equilibrium in Two-Player Games
Venue: arXiv cs.GT 2017
Year: 2017

We show a communication complexity lower bound for finding a correlated equilibrium of a two-player game. More precisely, we define a two-player $N \times N$ game called the 2-cycle game and show that the randomized communication complexity of finding a 1/poly($N$)-approximate correlated equilibrium of the 2-cycle game is $Ω(N)$. For small approximation values, this answers an open question of Babichenko and Rubinstein (STOC 2017). Our lower bound is obtained via a direct reduction from the unique set disjointness problem.
Design and Analysis of a Task-based Parallelization over a Runtime System of an Explicit Finite-Volume CFD Code with Adaptive Time Stepping
Venue: arXiv cs.DC 2017
Year: 2017

FLUSEPA (Registered trademark in France No. 134009261) is an advanced simulation tool which performs a large panel of aerodynamic studies. It is the unstructured finite-volume solver developed by Airbus Safran Launchers company to calculate compressible, multidimensional, unsteady, viscous and reactive flows around bodies in relative motion. The time integration in FLUSEPA is done using an explicit temporal adaptive method. The current production version of the code is based on MPI and OpenMP. This implementation leads to important synchronizations that must be reduced. To tackle this problem, we present the study of a task-based parallelization of the aerodynamic solver of FLUSEPA using the runtime system StarPU and combining up to three levels of parallelism. We validate our solution by the simulation (using a finite-volume mesh with 80 million cells) of a take-off blast wave propagation for Ariane 5 launcher.
Conical: an extended module for computing a numerically satisfactory pair of solutions of the differential equation for conical functions
Venue: arXiv cs.MS 2017
Year: 2017

Conical functions appear in a large number of applications in physics and engineering. In this paper we describe an extension of our module CONICAL for the computation of conical functions. Specifically, the module includes now a routine for computing the function ${\rm R}^{m}_{-\frac{1}{2}+iτ}(x)$, a real-valued numerically satisfactory companion of the function ${\rm P}^m_{-\tfrac12+iτ}(x)$ for $x>1$. In this way, a natural basis for solving Dirichlet problems bounded by conical domains is provided.
Tackling Diversity and Heterogeneity by Vertical Memory Management
Venue: arXiv cs.OS 2017
Year: 2017

Existing memory management mechanisms used in commodity computing machines typically adopt hardware based address interleaving and OS directed random memory allocation to service generic application requests. These conventional memory management mechanisms are challenged by contention at multiple memory levels, a daunting variety of workload behaviors, and an increasingly complicated memory hierarchy. Our ISCA-41 paper proposes vertical partitioning to eliminate shared resource contention at multiple levels in the memory hierarchy. Combined with horizontal memory management policies, our framework supports a flexible policy space for tackling diverse application needs in production environment and is suitable for future heterogeneous memory systems.
All binary linear codes that are invariant under $\PSL_2(n)$
Venue: arXiv cs.IT 2017
Year: 2017

The projective special linear group $\PSL_2(n)$ is $2$-transitive for all primes $n$ and $3$-homogeneous for $n \equiv 3 \pmod{4}$ on the set $\{0,1, \cdots, n-1, \infty\}$. It is known that the extended odd-like quadratic residue codes are invariant under $\PSL_2(n)$. Hence, the extended quadratic residue codes hold an infinite family of $2$-designs for primes $n \equiv 1 \pmod{4}$, an infinite family of $3$-designs for primes $n \equiv 3 \pmod{4}$. To construct more $t$-designs with $t \in \{2, 3\}$, one would search for other extended cyclic codes over finite fields that are invariant under the action of $\PSL_2(n)$. The objective of this paper is to prove that the extended quadratic residue binary codes are the only nontrivial extended binary cyclic codes that are invariant under $\PSL_2(n)$.
Ranking with social cues: Integrating online review scores and popularity information
Venue: arXiv cs.IR 2017
Year: 2017

Online marketplaces, search engines, and databases employ aggregated social information to rank their content for users. Two ranking heuristics commonly implemented to order the available options are the average review score and item popularity-that is, the number of users who have experienced an item. These rules, although easy to implement, only partly reflect actual user preferences, as people may assign values to both average scores and popularity and trade off between the two. How do people integrate these two pieces of social information when making choices? We present two experiments in which we asked participants to choose 200 times among options drawn directly from two widely used online venues: Amazon and IMDb. The only information presented to participants was the average score and the number of reviews, which served as a proxy for popularity. We found that most people are willing to settle for items with somewhat lower average scores if they are more popular. Yet, our study uncovered substantial diversity of preferences among participants, which indicates a sizable potential for personalizing ranking schemes that rely on social information.
Geometry of Factored Nuclear Norm Regularization
Venue: arXiv cs.NA 2017
Year: 2017

This work investigates the geometry of a nonconvex reformulation of minimizing a general convex loss function $f(X)$ regularized by the matrix nuclear norm $\|X\|_*$. Nuclear-norm regularized matrix inverse problems are at the heart of many applications in machine learning, signal processing, and control. The statistical performance of nuclear norm regularization has been studied extensively in literature using convex analysis techniques. Despite its optimal performance, the resulting optimization has high computational complexity when solved using standard or even tailored fast convex solvers. To develop faster and more scalable algorithms, we follow the proposal of Burer-Monteiro to factor the matrix variable $X$ into the product of two smaller rectangular matrices $X=UV^T$ and also replace the nuclear norm $\|X\|_*$ with $(\|U\|_F^2+\|V\|_F^2)/2$. In spite of the nonconvexity of the factored formulation, we prove that when the convex loss function $f(X)$ is $(2r,4r)$-restricted well-conditioned, each critical point of the factored problem either corresponds to the optimal solution $X^\star$ of the original convex optimization or is a strict saddle point where the Hessian matrix has a strictly negative eigenvalue. Such a geometric structure of the factored formulation allows many local search algorithms to converge to the global optimum with random initializations.