OpenRaiser/Intern-Atlas huggingface dataset:
A Survey of Stochastic Simulation and Optimization Methods in Signal Processing
Venue: arXiv cs.IT 2015
Year: 2015

Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. Stochastic simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are analytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to stochastic simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve stochastic problems, as well as stochastic methods for deterministic optimization. Subsequently, areas of overlap between simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed.
Automatic Observer Script for StarCraft: Brood War Bot Games (technical report)
Venue: arXiv cs.AI 2015
Year: 2015

This short report describes an automated BWAPI-based script developed for live streams of a StarCraft Brood War bot tournament, SSCAIT. The script controls the in-game camera in order to follow the relevant events and improve the viewer experience. We enumerate its novel features and provide a few implementation notes.
Bayesian Policy Reuse
Venue: arXiv cs.AI 2015
Year: 2015

A long-lived autonomous agent should be able to respond online to novel instances of tasks from a familiar domain. Acting online requires 'fast' responses, in terms of rapid convergence, especially when the task instance has a short duration, such as in applications involving interactions with humans. These requirements can be problematic for many established methods for learning to act. In domains where the agent knows that the task instance is drawn from a family of related tasks, albeit without access to the label of any given instance, it can choose to act through a process of policy reuse from a library, rather than policy learning from scratch. In policy reuse, the agent has prior knowledge of the class of tasks in the form of a library of policies that were learnt from sample task instances during an offline training phase. We formalise the problem of policy reuse, and present an algorithm for efficiently responding to a novel task instance by reusing a policy from the library of existing policies, where the choice is based on observed 'signals' which correlate to policy performance. We achieve this by posing the problem as a Bayesian choice problem with a corresponding notion of an optimal response, but the computation of that response is in many cases intractable. Therefore, to reduce the computation cost of the posterior, we follow a Bayesian optimisation approach and define a set of policy selection functions, which balance exploration in the policy library against exploitation of previously tried policies, together with a model of expected performance of the policy library on their corresponding task instances. We validate our method in several simulated domains of interactive, short-duration episodic tasks, showing rapid convergence in unknown task variations.
Monotonous (Semi-)Nonnegative Matrix Factorization
Venue: arXiv cs.LG 2015
Year: 2015

Nonnegative matrix factorization (NMF) factorizes a non-negative matrix into product of two non-negative matrices, namely a signal matrix and a mixing matrix. NMF suffers from the scale and ordering ambiguities. Often, the source signals can be monotonous in nature. For example, in source separation problem, the source signals can be monotonously increasing or decreasing while the mixing matrix can have nonnegative entries. NMF methods may not be effective for such cases as it suffers from the ordering ambiguity. This paper proposes an approach to incorporate notion of monotonicity in NMF, labeled as monotonous NMF. An algorithm based on alternating least-squares is proposed for recovering monotonous signals from a data matrix. Further, the assumption on mixing matrix is relaxed to extend monotonous NMF for data matrix with real numbers as entries. The approach is illustrated using synthetic noisy data. The results obtained by monotonous NMF are compared with standard NMF algorithms in the literature, and it is shown that monotonous NMF estimates source signals well in comparison to standard NMF algorithms when the underlying sources signals are monotonous.
Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models
Venue: arXiv cs.CV 2015
Year: 2015

Deep learning has shown state-of-art classification performance on datasets such as ImageNet, which contain a single object in each image. However, multi-object classification is far more challenging. We present a unified framework which leverages the strengths of multiple machine learning methods, viz deep learning, probabilistic models and kernel methods to obtain state-of-art performance on Microsoft COCO, consisting of non-iconic images. We incorporate contextual information in natural images through a conditional latent tree probabilistic model (CLTM), where the object co-occurrences are conditioned on the extracted fc7 features from pre-trained Imagenet CNN as input. We learn the CLTM tree structure using conditional pairwise probabilities for object co-occurrences, estimated through kernel methods, and we learn its node and edge potentials by training a new 3-layer neural network, which takes fc7 features as input. Object classification is carried out via inference on the learnt conditional tree model, and we obtain significant gain in precision-recall and F-measures on MS-COCO, especially for difficult object categories. Moreover, the latent variables in the CLTM capture scene information: the images with top activations for a latent node have common themes such as being a grasslands or a food scene, and on on. In addition, we show that a simple k-means clustering of the inferred latent nodes alone significantly improves scene classification performance on the MIT-Indoor dataset, without the need for any retraining, and without using scene labels during training. Thus, we present a unified framework for multi-object classification and unsupervised scene understanding.
Can deep learning help you find the perfect match?
Venue: arXiv cs.LG 2015
Year: 2015

Is he/she my type or not? The answer to this question depends on the personal preferences of the one asking it. The individual process of obtaining a full answer may generally be difficult and time consuming, but often an approximate answer can be obtained simply by looking at a photo of the potential match. Such approximate answers based on visual cues can be produced in a fraction of a second, a phenomenon that has led to a series of recently successful dating apps in which users rate others positively or negatively using primarily a single photo. In this paper we explore using convolutional networks to create a model of an individual's personal preferences based on rated photos. This introduced task is difficult due to the large number of variations in profile pictures and the noise in attractiveness labels. Toward this task we collect a dataset comprised of $9364$ pictures and binary labels for each. We compare performance of convolutional models trained in three ways: first directly on the collected dataset, second with features transferred from a network trained to predict gender, and third with features transferred from a network trained on ImageNet. Our findings show that ImageNet features transfer best, producing a model that attains $68.1\%$ accuracy on the test set and is moderately successful at predicting matches.
Highway Networks
Venue: arXiv cs.LG 2015
Year: 2015

There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success. However, network training becomes more difficult with increasing depth and training of very deep networks remains an open problem. In this extended abstract, we introduce a new architecture designed to ease gradient-based training of very deep networks. We refer to networks with this architecture as highway networks, since they allow unimpeded information flow across several layers on "information highways". The architecture is characterized by the use of gating units which learn to regulate the flow of information through a network. Highway networks with hundreds of layers can be trained directly using stochastic gradient descent and with a variety of activation functions, opening up the possibility of studying extremely deep and efficient architectures.
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction
Venue: arXiv cs.CV 2015
Year: 2015

Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view images is a fundamental yet active research area in computer vision. Despite the steady progress in multi-view stereo reconstruction, most existing methods are still limited in recovering fine-scale details and sharp features while suppressing noises, and may fail in reconstructing regions with few textures. To address these limitations, this paper presents a Detail-preserving and Content-aware Variational (DCV) multi-view stereo method, which reconstructs the 3D surface by alternating between reprojection error minimization and mesh denoising. In reprojection error minimization, we propose a novel inter-image similarity measure, which is effective to preserve fine-scale details of the reconstructed surface and builds a connection between guided image filtering and image registration. In mesh denoising, we propose a content-aware $\ell_{p}$-minimization algorithm by adaptively estimating the $p$ value and regularization parameters based on the current input. It is much more promising in suppressing noise while preserving sharp features than conventional isotropic mesh smoothing. Experimental results on benchmark datasets demonstrate that our DCV method is capable of recovering more surface details, and obtains cleaner and more accurate reconstructions than state-of-the-art methods. In particular, our method achieves the best results among all published methods on the Middlebury dino ring and dino sparse ring datasets in terms of both completeness and accuracy.
On a fast bilateral filtering formulation using functional rearrangements
Venue: arXiv cs.CV 2015
Year: 2015

We introduce an exact reformulation of a broad class of neighborhood filters, among which the bilateral filters, in terms of two functional rearrangements: the decreasing and the relative rearrangements. Independently of the image spatial dimension (one-dimensional signal, image, volume of images, etc.), we reformulate these filters as integral operators defined in a one-dimensional space corresponding to the level sets measures. We prove the equivalence between the usual pixel-based version and the rearranged version of the filter. When restricted to the discrete setting, our reformulation of bilateral filters extends previous results for the so-called fast bilateral filtering. We, in addition, prove that the solution of the discrete setting, understood as constant-wise interpolators, converges to the solution of the continuous setting. Finally, we numerically illustrate computational aspects concerning quality approximation and execution time provided by the rearranged formulation.
Modeling Memetics using Edge Diversity
Venue: arXiv cs.SI 2015
Year: 2015

The study of meme propagation and the prediction of meme trajectory are emerging areas of interest in the field of complex networks research. In addition to the properties of the meme itself, the structural properties of the underlying network decides the speed and the trajectory of the propagating meme. In this paper, we provide an artificial framework for studying the meme propagation patterns. Firstly, the framework includes a synthetic network which simulates a real world network and acts as a testbed for meme simulation. Secondly, we propose a meme spreading model based on the diversity of edges in the network. Through the experiments conducted, we show that the generated synthetic network combined with the proposed spreading model is able to simulate a real world meme spread. Our proposed model is validated by the propagation of the Higgs boson meme on Twitter as well as many real world social networks.
Modeling Toll Lanes and Dynamic Pricing Control
Venue: arXiv cs.SY 2015
Year: 2015

In this paper we address the problem of dynamic pricing for toll lanes on freeways. The proposed toll mechanism is broken up into two parts: (1) the supply side feedback control that computes the desired split ratios for the incoming traffic flows between the general purpose and the toll lanes; and (2) the demand side price setting algorithm that aims to enforce the computed split ratios. The split ratio controller is designed and tested in the context of the link-node Cell Transmission Model with the modified node model of in/out flow distribution. The equilibrium structure of this traffic model is presented; and the case, in which the existence of a toll lane is meaningful, is discussed. For the price setting, two alternative approaches are presented. The first one is commonly used, and it relies on the known Value of Time (VoT) distribution. Its shortcoming, however, is in the difficulty of the VoT distribution estimation. The second approach employs the auction mechanism, where travelers make bids on places in the toll lane. The advantage of this approach is that it enables direct control over how many vehicles will be allowed into the toll lane.
Analyzing Alloy Formulas using an SMT Solver: A Case Study
Venue: arXiv cs.LO 2015
Year: 2015

This paper describes how Yices, a modern SAT Modulo theories solver, can be used to analyze the address-book problem expressed in Alloy, a first-order relational logic with transitive closure. Current analysis of Alloy models - as performed by the Alloy Analyzer - is based on SAT solving and thus, is done only with respect to finitized types. Our analysis generalizes this approach by taking advantage of the background theories available in Yices, and avoiding type finitization when possible. Consequently, it is potentially capable of proving that an assertion is a tautology - a capability completely missing from the Alloy Analyzer. This paper also reports on our experimental results that compare the performance of our analysis to that of the Alloy Analyzer for various versions of the address book problem.
Impact of Timing and Frequency Offsets on Multicarrier Waveform Candidates for 5G
Venue: arXiv cs.IT 2015
Year: 2015

This paper presents a study of the candidate waveforms for 5G when they are subject to timing and carrier frequency offset. These waveforms are: orthogonal frequency division multiplexing (OFDM), generalized frequency division multiplexing (GFDM), universal filtered multicarrier (UFMC), circular filter bank multicarrier (C-FBMC), and linear filter bank multicarrier (FBMC). We are particularly interested in multiple access interference (MAI) when a number of users transmit their signals to a base station in an asynchronous or a quasi-synchronous manner. We identify the source of MAI in these waveforms and present some numerical analysis that confirm our findings. The goal of this study is to answer the following question, "Which one of the 5G candidate waveforms has more relaxed synchronization requirements?".
Full-Duplex Relaying in MIMO-OFDM Frequency-Selective Channels with Optimal Adaptive Filtering
Venue: arXiv cs.IT 2015
Year: 2015

In-band full-duplex transmission allows a relay station to theoretically double its spectral efficiency by simultaneously receiving and transmitting in the same frequency band, when compared to the traditional half-duplex or out-of-band full-duplex counterpart. Consequently, the induced self-interference suffered by the relay may reach considerable power levels, which decreases the signal-to-interference-plus-noise ratio (SINR) in a decode-and-forward (DF) relay, leading to a degradation of the relay performance. This paper presents a technique to cope with the problem of self-interference in broadband multiple-input multiple-output (MIMO) relays. The proposed method uses a time-domain cancellation in a DF relay, where a replica of the interfering signal is created with the help of a recursive least squares (RLS) algorithm that estimates the interference frequency-selective channel. Its convergence mean time is shown to be negligible by simulation results, when compared to the length of a typical orthogonal-frequency division multiplexing (OFDM) sequences. Moreover, the bit-error-rate (BER) and the SINR in a OFDM transmission are evaluated, confirming that the proposed method extends significantly the range of self-interference power to which the relay is resilient to, when compared with other mitigation schemes.
Code Generator Composition for Model-Driven Engineering of Robotics Component & Connector Systems
Venue: arXiv cs.SE 2015
Year: 2015

Engineering software for robotics applications requires multidomain and application-specific solutions. Model-driven engineering and modeling language integration provide means for developing specialized, yet reusable models of robotics software architectures. Code generators transform these platform independent models into executable code specific to robotic platforms. Generative software engineering for multidomain applications requires not only the integration of modeling languages but also the integration of validation mechanisms and code generators. In this paper we sketch a conceptual model for code generator composition and show an instantiation of this model in the MontiArc- Automaton framework. MontiArcAutomaton allows modeling software architectures as component and connector models with different component behavior modeling languages. Effective means for code generator integration are a necessity for the post hoc integration of applicationspecific languages in model-based robotics software engineering.
Resource Management of energy-aware Cognitive Radio Networks and cloud-based Infrastructures
Venue: arXiv cs.NI 2015
Year: 2015

The field of wireless networks has been rapidly developed during the past decade due to the increasing popularity of the mobile devices. The great demand for mobility and connectivity makes wireless networking a field whose continuous technological development is very important as new challenges and issues are arising. Many scientists and researchers are currently engaged in developing new approaches and optimization methods in several topics of wireless networking. This survey paper study works from the following topics: Cognitive Radio Networks, Interactive Broadcasting, Energy Efficient Networks, Cloud Computing and Resource Management, Interactive Marketing and Optimization.
Reinforced Decision Trees
Venue: arXiv cs.LG 2015
Year: 2015

In order to speed-up classification models when facing a large number of categories, one usual approach consists in organizing the categories in a particular structure, this structure being then used as a way to speed-up the prediction computation. This is for example the case when using error-correcting codes or even hierarchies of categories. But in the majority of approaches, this structure is chosen \textit{by hand}, or during a preliminary step, and not integrated in the learning process. We propose a new model called Reinforced Decision Tree which simultaneously learns how to organize categories in a tree structure and how to classify any input based on this structure. This approach keeps the advantages of existing techniques (low inference complexity) but allows one to build efficient classifiers in one learning step. The learning algorithm is inspired by reinforcement learning and policy-gradient techniques which allows us to integrate the two steps (building the tree, and learning the classifier) in one single algorithm.
Green open access in computer science - an exploratory study on author-based self-archiving awareness, practice, and inhibitors
Venue: arXiv cs.DL 2015
Year: 2015

Access to the work of others is something that is too often taken for granted, yet problematic and difficult to be obtained unless someone pays for it. Green and gold open access are claimed to be a solution to this problem. While open access is gaining momentum in some fields, there is a limited and seasoned knowledge about self-archiving in computer science. In particular, there is an inadequate understanding of author-based self-archiving awareness, practice, and inhibitors. This article reports an exploratory study of the awareness of self-archiving, the practice of self-archiving, and the inhibitors of self-archiving among authors in an Italian computer science faculty. Forty-nine individuals among interns, PhD students, researchers, and professors were recruited in a questionnaire (response rate of 72.8%). The quantitative and qualitative responses suggested that there is still work needed in terms of advocating green open access to computer science authors who seldom self-archive and when they do, they often infringe the copyright transfer agreements (CTAs) of the publishers. In addition, tools from the open-source community are needed to facilitate author-based self-archiving, which should comprise of an automatic check of the CTAs. The study identified nine factors inhibiting the act of self-archiving among computer scientists. As a first step, this study proposes several propositions regarding author-based self-archiving in computer science that can be further investigated. Recommendations to foster self-archiving in computer science, based on the results, are provided.
On the Ergodic Capacity of MIMO Free-Space Optical Systems over Turbulence Channels
Venue: arXiv cs.IT 2015
Year: 2015

The free-space optical (FSO) communications can achieve high capacity with huge unlicensed optical spectrum and low operational costs. The corresponding performance analysis of FSO systems over turbulence channels is very limited, especially when using multiple apertures at both transmitter and receiver sides. This paper aim to provide the ergodic capacity characterization of multiple-input multiple-output (MIMO) FSO systems over atmospheric turbulence-induced fading channels. The fluctuations of the irradiance of optical channels distorted by atmospheric conditions is usually described by a gamma-gamma ($ΓΓ$) distribution, and the distribution of the sum of $ΓΓ$ random variables (RVs) is required to model the MIMO optical links. We use an $α$-$μ$ distribution to efficiently approximate the probability density function (PDF) of the sum of independent and identical distributed $ΓΓ$ RVs through moment-based estimators. Furthermore, the PDF of the sum of independent, but not necessarily identically distributed $ΓΓ$ RVs can be efficiently approximated by a finite weighted sum of PDFs of $ΓΓ$ distributions. Based on these reliable approximations, novel and precise analytical expressions for the ergodic capacity of MIMO FSO systems are derived. Additionally, we deduce the asymptotic simple expressions in high signal-to-noise ratio regimes, which provide useful insights into the impact of the system parameters on the ergodic capacity. Finally, our proposed results are validated via Monte-Carlo simulations.