OpenRaiser/Intern-Atlas huggingface dataset:
Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint
Venue: arXiv cs.SY 2015
Year: 2015

Inspired by ideas taken from the machine learning literature, new regularization techniques have been recently introduced in linear system identification. In particular, all the adopted estimators solve a regularized least squares problem, differing in the nature of the penalty term assigned to the impulse response. Popular choices include atomic and nuclear norms (applied to Hankel matrices) as well as norms induced by the so called stable spline kernels. In this paper, a comparative study of estimators based on these different types of regularizers is reported. Our findings reveal that stable spline kernels outperform approaches based on atomic and nuclear norms since they suitably embed information on impulse response stability and smoothness. This point is illustrated using the Bayesian interpretation of regularization. We also design a new class of regularizers defined by "integral" versions of stable spline/TC kernels. Under quite realistic experimental conditions, the new estimators outperform classical prediction error methods also when the latter are equipped with an oracle for model order selection.
Securing Physical-Layer Communications for Cognitive Radio Networks
Venue: arXiv cs.IT 2015
Year: 2015

This article investigates the physical-layer security of cognitive radio (CR) networks, which are vulnerable to various newly arising attacks targeting on the weaknesses of CR communications and networking. We first review a range of physical-layer attacks in CR networks, including the primary user emulation, sensing falsification, intelligence compromise, jamming and eavesdropping attacks. Then we focus on the physical-layer security of CR networks against eavesdropping and examine the secrecy performance of cognitive communications in terms of secrecy outage probability. We further consider the use of relays for improving the CR security against eavesdropping and propose an opportunistic relaying scheme, where a relay node that makes CR communications most resistant to eavesdropping is chosen to participate in assisting the transmission from a cognitive source to its destination. It is illustrated that the physical-layer secrecy of CR communications relying on the opportunistic relaying can be significantly improved by increasing the number of relays, showing the security benefit of exploiting relay nodes. Finally, we present some open challenges in the field of relays assisted physical-layer security for CR networks.
Determining rural areas vulnerable to illegal dumping using GIS techniques. Case study: Neamt county, Romania
Venue: arXiv cs.OH 2015
Year: 2015

The paper aims to mapping the potential vulnerable areas to illegal dumping of household waste from rural areas in the extra- Carpathian region of Neamt County. These areas are ordinary in the proximity of built-up areas and buffers areas of 1 km were delimited for every locality. Based on various map layers in vector formats (land use, rivers, built-up areas, roads etc) an assessment method is performed to highlight the potential areas vulnerable to illegal dumping inside these buffer areas at local scale. The results are correlated to field observations and current situation of waste management systems. The maps outline local disparities due to various geographical conditions of county. This approach is a necessary tool in EIA studies particularly for rural waste management systems at local and regional scale which are less studied in current literature than urban areas.
Reasoning about embedded dependencies using inclusion dependencies
Venue: arXiv cs.LO 2015
Year: 2015

The implication problem for the class of embedded dependencies is undecidable. However, this does not imply lackness of a proof procedure as exemplified by the chase algorithm. In this paper we present a complete axiomatization of embedded dependencies that is based on the chase and uses inclusion dependencies and implicit existential quantification in the intermediate steps of deductions.
Fast, Provable Algorithms for Isotonic Regression in all $\ell_{p}$-norms
Venue: arXiv cs.LG 2015
Year: 2015

Given a directed acyclic graph $G,$ and a set of values $y$ on the vertices, the Isotonic Regression of $y$ is a vector $x$ that respects the partial order described by $G,$ and minimizes $||x-y||,$ for a specified norm. This paper gives improved algorithms for computing the Isotonic Regression for all weighted $\ell_{p}$-norms with rigorous performance guarantees. Our algorithms are quite practical, and their variants can be implemented to run fast in practice.
Fine-grained Recognition Datasets for Biodiversity Analysis
Venue: arXiv cs.CV 2015
Year: 2015

In the following paper, we present and discuss challenging applications for fine-grained visual classification (FGVC): biodiversity and species analysis. We not only give details about two challenging new datasets suitable for computer vision research with up to 675 highly similar classes, but also present first results with localized features using convolutional neural networks (CNN). We conclude with a list of challenging new research directions in the area of visual classification for biodiversity research.
PackageBuilder: From Tuples to Packages
Venue: arXiv cs.DB 2015
Year: 2015

In this demo, we present PackageBuilder, a system that extends database systems to support package queries. A package is a collection of tuples that individually satisfy base constraints and collectively satisfy global constraints. The need for package support arises in a variety of scenarios: For example, in the creation of meal plans, users are not only interested in the nutritional content of individual meals (base constraints), but also care to specify daily consumption limits and control the balance of the entire plan (global constraints). We introduce PaQL, a declarative SQL-based package query language, and the interface abstractions which allow users to interactively specify package queries and easily navigate through their results. To efficiently evaluate queries, the system employs pruning and heuristics, as well as state-of-the-art constraint optimization solvers. We demonstrate PackageBuilder by allowing attendees to interact with the system's interface, to define PaQL queries and to observe how query evaluation is performed.
Interactive certificate for the verification of Wiedemann's Krylov sequence: application to the certification of the determinant, the minimal and the characteristic polynomials of sparse matrices
Venue: arXiv cs.SC 2015
Year: 2015

Certificates to a linear algebra computation are additional data structures for each output, which can be used by a-possibly randomized- verification algorithm that proves the correctness of each output. Wiede-mann's algorithm projects the Krylov sequence obtained by repeatedly multiplying a vector by a matrix to obtain a linearly recurrent sequence. The minimal polynomial of this sequence divides the minimal polynomial of the matrix. For instance, if the $n\times n$ input matrix is sparse with n 1+o(1) non-zero entries, the computation of the sequence is quadratic in the dimension of the matrix while the computation of the minimal polynomial is n 1+o(1), once that projected Krylov sequence is obtained. In this paper we give algorithms that compute certificates for the Krylov sequence of sparse or structured $n\times n$ matrices over an abstract field, whose Monte Carlo verification complexity can be made essentially linear. As an application this gives certificates for the determinant, the minimal and characteristic polynomials of sparse or structured matrices at the same cost.
Weight Enumerators and Higher Support Weights of Maximally Recoverable Codes
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we establish the matroid structures corresponding to data-local and local maximally recoverable codes (MRC). The matroid structures of these codes can be used to determine the associated Tutte polynomial. Greene proved that the weight enumerators of any code can be determined from its associated Tutte polynomial. We will use this result to derive explicit expressions for the weight enumerators of data-local and local MRC. Also, Britz proved that the higher support weights of any code can be determined from its associated Tutte polynomial. We will use this result to derive expressions for the higher support weights of data-local and local MRC with two local codes.
Book to the Future - a manifesto for book liberation
Venue: arXiv cs.DL 2015
Year: 2015

The Book Liberation Manifesto is an exploration of publishing outside of current corporate constraints and beyond the confines of book piracy. We believe that knowledge should be in free circulation to benefit humankind, which means an equitable and vibrant economy to support publishing, instead of the prevailing capitalist hand-me-down system of Sisyphean economic sustainability. Readers and books have been forced into pirate libraries, while sales channels have been monopolised by the big Internet giants which exact extortionate fees from publishers. We have three proposals. First, publications should be free-at-the-point-of-reading under a variety of open intellectual property regimes. Second, they should become fully digital -- in order to facilitate ready reuse, distribution, algorithmic and computational use. Finally, Open Source software for publishing should be treated as public infrastructure, with sustained research and investment. The result of such robust infrastructures will mean lower costs for manufacturing and faster publishing lifecycles, so that publishers and publics will be more readily able to afford to invent new futures. For more information on the Hybrid Publishing Consortium.
CamSwarm: Instantaneous Smartphone Camera Arrays for Collaborative Photography
Venue: arXiv cs.HC 2015
Year: 2015

Camera arrays (CamArrays) are widely used in commercial filming projects for achieving special visual effects such as bullet time effect, but are very expensive to set up. We propose CamSwarm, a low-cost and lightweight alternative to professional CamArrays for consumer applications. It allows the construction of a collaborative photography platform from multiple mobile devices anywhere and anytime, enabling new capturing and editing experiences that a single camera cannot provide. Our system allows easy team formation; uses real-time visualization and feedback to guide camera positioning; provides a mechanism for synchronized capturing; and finally allows the user to efficiently browse and edit the captured imagery. Our user study suggests that CamSwarm is easy to use; the provided real-time guidance is helpful; and the full system achieves high quality results promising for non-professional use. A demo video is provided at https://www.youtube.com/watch?v=LgkHcvcyTTM.
On Fast and Robust Information Spreading in the Vertex-Congest Model
Venue: arXiv cs.DC 2015
Year: 2015

This paper initiates the study of the impact of failures on the fundamental problem of \emph{information spreading} in the Vertex-Congest model, in which in every round, each of the $n$ nodes sends the same $O(\log{n})$-bit message to all of its neighbors. Our contribution to coping with failures is twofold. First, we prove that the randomized algorithm which chooses uniformly at random the next message to forward is slow, requiring $Ω(n/\sqrt{k})$ rounds on some graphs, which we denote by $G_{n,k}$, where $k$ is the vertex-connectivity. Second, we design a randomized algorithm that makes dynamic message choices, with probabilities that change over the execution. We prove that for $G_{n,k}$ it requires only a near-optimal number of $O(n\log^3{n}/k)$ rounds, despite a rate of $q=O(k/n\log^3{n})$ failures per round. Our technique of choosing probabilities that change according to the execution is of independent interest.
TV News Commercials Detection using Success based Locally Weighted Kernel Combination
Venue: arXiv cs.CV 2015
Year: 2015

Commercial detection in news broadcast videos involves judicious selection of meaningful audio-visual feature combinations and efficient classifiers. And, this problem becomes much simpler if these combinations can be learned from the data. To this end, we propose an Multiple Kernel Learning based method for boosting successful kernel functions while ignoring the irrelevant ones. We adopt a intermediate fusion approach where, a SVM is trained with a weighted linear combination of different kernel functions instead of single kernel function. Each kernel function is characterized by a feature set and kernel type. We identify the feature sub-space locations of the prediction success of a particular classifier trained only with particular kernel function. We propose to estimate a weighing function using support vector regression (with RBF kernel) for each kernel function which has high values (near 1.0) where the classifier learned on kernel function succeeded and lower values (nearly 0.0) otherwise. Second contribution of this work is TV News Commercials Dataset of 150 Hours of News videos. Classifier trained with our proposed scheme has outperformed the baseline methods on 6 of 8 benchmark dataset and our own TV commercials dataset.
Computing Runs on a General Alphabet
Venue: arXiv cs.DS 2015
Year: 2015

We describe a RAM algorithm computing all runs (maximal repetitions) of a given string of length $n$ over a general ordered alphabet in $O(n\log^{\frac{2}3} n)$ time and linear space. Our algorithm outperforms all known solutions working in $Θ(n\logσ)$ time provided $σ= n^{Ω(1)}$, where $σ$ is the alphabet size. We conjecture that there exists a linear time RAM algorithm finding all runs.
Model-based optimization of MPDATA on Intel Xeon Phi through load imbalancing
Venue: arXiv cs.DC 2015
Year: 2015

Load balancing is a widely accepted technique for performance optimization of scientific applications on parallel architectures. Indeed, balanced applications do not waste processor cycles on waiting at points of synchronization and data exchange, maximizing this way the utilization of processors. In this paper, we challenge the universality of the load-balancing approach to optimization of the performance of parallel applications. First, we formulate conditions that should be satisfied by the performance profile of an application in order for the application to achieve its best performance via load balancing. Then we use a real-life scientific application, MPDATA, to demonstrate that its performance profile on a modern parallel architecture, Intel Xeon Phi, significantly deviates from these conditions. Based on this observation, we propose a method of performance optimization of scientific applications through load imbalancing. We also propose an algorithm that finds the optimal, possibly imbalanced, configuration of a data parallel application on a set of homogeneous processors. This algorithm uses functional performance models of the application to find the partitioning that minimizes its computation time but not necessarily balances the load of the processors. We show how to apply this algorithm to optimization of MPDATA on Intel Xeon Phi. Experimental results demonstrate that the performance of this carefully optimized load-balanced application can be further improved by 15\% using the proposed load-imbalancing optimization.
"Narco" Emotions: Affect and Desensitization in Social Media during the Mexican Drug War
Venue: arXiv cs.CY 2015
Year: 2015

Social media platforms have emerged as prominent information sharing ecosystems in the context of a variety of recent crises, ranging from mass emergencies, to wars and political conflicts. We study affective responses in social media and how they might indicate desensitization to violence experienced in communities embroiled in an armed conflict. Specifically, we examine three established affect measures: negative affect, activation, and dominance as observed on Twitter in relation to a number of statistics on protracted violence in four major cities afflicted by the Mexican Drug War. During a two year period (Aug 2010-Dec 2012), while violence was on the rise in these regions, our findings show a decline in negative emotional expression as well as a rise in emotional arousal and dominance in Twitter posts: aspects known to be psychological markers of desensitization. We discuss the implications of our work for behavioral health, facilitating rehabilitation efforts in communities enmeshed in an acute and persistent urban warfare, and the impact on civic engagement.
The Remixing Dilemma: The Trade-off Between Generativity and Originality
Venue: arXiv cs.CY 2015
Year: 2015

In this paper we argue that there is a trade-off between generativity and originality in online communities that support open collaboration. We build on foundational theoretical work in peer production to formulate and test a series of hypotheses suggesting that the generativity of creative works is associated with moderate complexity, prominent authors, and cumulativeness. We also formulate and test three hypotheses that these qualities are associated with decreased originality in resulting derivatives. Our analysis uses a rich data set from the Scratch Online Community --a large web-site where young people openly share and remix animations and video games. We discuss the implications of this trade-off for the design of peer production systems that support amateur creativity.
Identifiability and Stability in Blind Deconvolution under Minimal Assumptions
Venue: arXiv cs.IT 2015
Year: 2015

Blind deconvolution (BD) arises in many applications. Without assumptions on the signal and the filter, BD does not admit a unique solution. In practice, subspace or sparsity assumptions have shown the ability to reduce the search space and yield the unique solution. However, existing theoretical analysis on uniqueness in BD is rather limited. In an earlier paper, we provided the first algebraic sample complexities for BD that hold for almost all bases or frames. We showed that for BD of a pair of vectors in $\mathbb{C}^n$, with subspace constraints of dimensions $m_1$ and $m_2$, respectively, a sample complexity of $n\geq m_1m_2$ is sufficient. This result is suboptimal, since the number of degrees of freedom is merely $m_1+m_2-1$. We provided analogus results, with similar suboptimality, for BD with sparsity or mixed subspace and sparsity constraints. In this paper, taking advantage of the recent progress on the information-theoretic limits of unique low-rank matrix recovery, we finally bridge this gap, and derive an optimal sample complexity result for BD with generic bases or frames. We show that for BD of an arbitrary pair (resp. all pairs) of vectors in $\mathbb{C}^n$, with sparsity constraints of sparsity levels $s_1$ and $s_2$, a sample complexity of $n > s_1+s_2$ (resp. $n > 2(s_1+s_2)$) is sufficient. We also present analogous results for BD with subspace constraints or mixed constraints, with the subspace dimension replacing the sparsity level. Last but not least, in all the above scenarios, if the bases or frames follow a probabilistic distribution specified in the paper, the recovery is not only unique, but also stable against small perturbations in the measurements, under the same sample complexities.
Delay-aware data transmission of multi-carrier communications in the presence of renewable energy
Venue: arXiv cs.IT 2015
Year: 2015

In the paper, we investigate the delay-aware data transmission in renewable energy aided multi-carrier system. Besides utilizing the local renewables, the transmitter can also purchase grid power. By scheduling the amount of transmitted data (The data are stored in a buffer before transmission), the sub-carrier allocation, and the renewable allocation in each transmission period, the transmitter aims to minimize the purchasing cost under a buffer delay constraint. By theoretical analysis of the formulated stochastic optimization problem, we find that transmit the scheduled data through the subcarrier with best condition is optimal and greedy renewable energy is approximately optimal. Furthermore, based on the theoretical derives and Lyapunov optimization, an on-line algorithm, which does NOT require future information, is proposed. Numerical results illustrate the delay and cost performance of the proposed algorithm. In addition, the comparisons with the delay-optimal policy and cost-optimal policy are carried out.
Visual Data Deblocking using Structural Layer Priors
Venue: arXiv cs.CV 2015
Year: 2015

The blocking artifact frequently appears in compressed real-world images or video sequences, especially coded at low bit rates, which is visually annoying and likely hurts the performance of many computer vision algorithms. A compressed frame can be viewed as the superimposition of an intrinsic layer and an artifact one. Recovering the two layers from such frames seems to be a severely ill-posed problem since the number of unknowns to recover is twice as many as the given measurements. In this paper, we propose a simple and robust method to separate these two layers, which exploits structural layer priors including the gradient sparsity of the intrinsic layer, and the independence of the gradient fields of the two layers. A novel Augmented Lagrangian Multiplier based algorithm is designed to efficiently and effectively solve the recovery problem. Extensive experimental results demonstrate the superior performance of our method over the state of the arts, in terms of visual quality and simplicity.
End-to-end Convolutional Network for Saliency Prediction
Venue: arXiv cs.CV 2015
Year: 2015

The prediction of saliency areas in images has been traditionally addressed with hand crafted features based on neuroscience principles. This paper however addresses the problem with a completely data-driven approach by training a convolutional network. The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train a not very deep architecture which is both fast and accurate. The convolutional network in this paper, named JuntingNet, won the LSUN 2015 challenge on saliency prediction with a superior performance in all considered metrics.
Towards a Standard Sampling Methodology on Online Social Networks: Collecting Global Trends on Twitter
Venue: arXiv cs.SI 2015
Year: 2015

One of the most significant current challenges in large-scale online social networks, is to establish a concise and coherent method able to collect and summarize data. Sampling the content of an Online Social Network (OSN) plays an important role as a knowledge discovery tool. It is becoming increasingly difficult to ignore the fact that current sampling methods must cope with a lack of a full sampling frame i.e., there is an imposed condition determined by a limited data access. In addition, another key aspect to take into account is the huge amount of data generated by users of social networking services. This type of conditions make especially difficult to develop sampling methods to collect truly reliable data. Therefore, we propose a low computational cost method for sampling emerging global trends on social networking services such as Twitter. The main purpose of this study, is to develop a methodology able to carry out an efficient collecting process via three random generators: Brownian, Illusion and Reservoir. These random generators will be combined with a Metropolis-Hastings Random Walk (MHRW) in order to improve the sampling process. We demonstrate the effectiveness of our approach by correctly providing a descriptive statistics of the collected data. In addition, we also sketch the collecting procedure on real-time carried out on Twitter. Finally, we conclude with a trend concentration graphical description and a formal convergence analysis to evaluate whether the sample of draws has attained an equilibrium state to get a rough estimate of the sample quality.