OpenRaiser/Intern-Atlas huggingface dataset:
Learning to detect an oddball target
Venue: arXiv cs.IT 2015
Year: 2015

We consider the problem of detecting an odd process among a group of Poisson point processes, all having the same rate except the odd process. The actual rates of the odd and non-odd processes are unknown to the decision maker. We consider a time-slotted sequential detection scenario where, at the beginning of each slot, the decision maker can choose which process to observe during that time slot. We are interested in policies that satisfy a given constraint on the probability of false detection. We propose a generalised likelihood ratio based sequential policy which, via suitable thresholding, can be made to satisfy the given constraint on the probability of false detection. Further, we show that the proposed policy is asymptotically optimal in terms of the conditional expected stopping time among all policies that satisfy the constraint on the probability of false detection. The asymptotic is as the probability of false detection is driven to zero. We apply our results to a particular visual search experiment studied recently by neuroscientists. Our model suggests a neuronal dissimilarity index for the visual search task. The neuronal dissimilarity index, when applied to visual search data from the particular experiment, correlates strongly with the behavioural data. However, the new dissimilarity index performs worse than some previously proposed neuronal dissimilarity indices. We explain why this may be attributed to the experiment conditons.
A Sard theorem for graph theory
Venue: arXiv cs.DM 2015
Year: 2015

The zero locus of a function f on a graph G is defined as the graph with vertex set consisting of all complete subgraphs of G, on which f changes sign and where x,y are connected if one is contained in the other. For d-graphs, finite simple graphs for which every unit sphere is a d-sphere, the zero locus of (f-c) is a (d-1)-graph for all c different from the range of f. If this Sard lemma is inductively applied to an ordered list functions f_1,...,f_k in which the functions are extended on the level surfaces, the set of critical values (c_1,...,c_k) for which F-c=0 is not a (d-k)-graph is a finite set. This discrete Sard result allows to construct explicit graphs triangulating a given algebraic set. We also look at a second setup: for a function F from the vertex set to R^k, we give conditions for which the simultaneous discrete algebraic set { F=c } defined as the set of simplices of dimension in {k, k+1,...,n} on which all f_i change sign, is a (d-k)-graph in the barycentric refinement of G. This maximal rank condition is adapted from the continuum and the graph { F=c } is a (n-k)-graph. While now, the critical values can have positive measure, we are closer to calculus: for k=2 for example, extrema of functions f under a constraint {g=c} happen at points, where the gradients of f and g are parallel D f = L D g, the Lagrange equations on the discrete network. As for an application, we illustrate eigenfunctions of geometric graphs and especially the second eigenvector of 3-spheres, which by Courant-Fiedler has exactly two nodal regions. The separating nodal surface of the second eigenfunction f_2 belonging to the smallest nonzero eigenvalue always appears to be a 2-sphere in experiments if G is a 3-sphere.
Hybrid 3D Localization for Visible Light Communication Systems
Venue: arXiv cs.IT 2015
Year: 2015

In this study, we investigate hybrid utilization of angle-of-arrival (AOA) and received signal strength (RSS) information in visible light communication (VLC) systems for 3D localization. We show that AOA-based localization method allows the receiver to locate itself via a least squares estimator by exploiting the directionality of light-emitting diodes (LEDs). We then prove that when the RSS information is taken into account, the positioning accuracy of AOA-based localization can be improved further using a weighted least squares solution. On the other hand, when the radiation patterns of LEDs are explicitly considered in the estimation, RSS-based localization yields highly accurate results. In order to deal with the system of nonlinear equations for RSS-based localization, we develop an analytical learning rule based on the Newton-Raphson method. The non-convex structure is addressed by initializing the learning rule based on 1) location estimates, and 2) a newly developed method, which we refer as random report and cluster algorithm. As a benchmark, we also derive analytical expression of the Cramer-Rao lower bound (CRLB) for RSS-based localization, which captures any deployment scenario positioning in 3D geometry. Finally, we demonstrate the effectiveness of the proposed solutions for a wide range of LED characteristics and orientations through extensive computer simulations.
The development of a mapping tool for the evaluation of building systems for future climate scenarios on European scale
Venue: arXiv cs.CY 2015
Year: 2015

The paper presents a tool for the mapping of the performance of building systems on European scale for different (future) time periods. The tool is to use for users and be applicable for different building systems. Users should also be able to use a broad range of climate parameters to assess the influence of climate change on these climatic parameters. Also should the calculation time be reasonable short. The mapping tool is developed in MATLAB, which can be used by other users for their own studies.
Optical images-based edge detection in Synthetic Aperture Radar images
Venue: arXiv cs.CV 2015
Year: 2015

We address the issue of adapting optical images-based edge detection techniques for use in Polarimetric Synthetic Aperture Radar (PolSAR) imagery. We modify the gravitational edge detection technique (inspired by the Law of Universal Gravity) proposed by Lopez-Molina et al, using the non-standard neighbourhood configuration proposed by Fu et al, to reduce the speckle noise in polarimetric SAR imagery. We compare the modified and unmodified versions of the gravitational edge detection technique with the well-established one proposed by Canny, as well as with a recent multiscale fuzzy-based technique proposed by Lopez-Molina et Alejandro We also address the issues of aggregation of gray level images before and after edge detection and of filtering. All techniques addressed here are applied to a mosaic built using class distributions obtained from a real scene, as well as to the true PolSAR image; the mosaic results are assessed using Baddeley's Delta Metric. Our experiments show that modifying the gravitational edge detection technique with a non-standard neighbourhood configuration produces better results than the original technique, as well as the other techniques used for comparison. The experiments show that adapting edge detection methods from Computational Intelligence for use in PolSAR imagery is a new field worthy of exploration.
Analyzing Linear Communication Networks using the Ribosome Flow Model
Venue: arXiv cs.NI 2015
Year: 2015

The Ribosome Flow Model (RFM) describes the unidirectional movement of interacting particles along a one-dimensional chain of sites. As a site becomes fuller, the effective entry rate into this site decreases. The RFM has been used to model and analyze mRNA translation, a biological process in which ribosomes (the particles) move along the mRNA molecule (the chain), and decode the genetic information into proteins. Here we propose the RFM as an analytical framework for modeling and analyzing linear communication networks. In this context, the moving particles are data-packets, the chain of sites is a one dimensional set of ordered buffers, and the decreasing entry rate to a fuller buffer represents a kind of decentralized backpressure flow control. For an RFM with homogeneous link capacities, we provide closed-form expressions for important network metrics including the throughput and end-to-end delay. We use these results to analyze the hop length and the transmission probability (in a contention access mode) that minimize the end-to-end delay in a multihop linear network, and provide closed-form expressions for the optimal parameter values.
An analysis of numerical issues in neural training by pseudoinversion
Venue: arXiv cs.LG 2015
Year: 2015

Some novel strategies have recently been proposed for single hidden layer neural network training that set randomly the weights from input to hidden layer, while weights from hidden to output layer are analytically determined by pseudoinversion. These techniques are gaining popularity in spite of their known numerical issues when singular and/or almost singular matrices are involved. In this paper we discuss a critical use of Singular Value Analysis for identification of these drawbacks and we propose an original use of regularisation to determine the output weights, based on the concept of critical hidden layer size. This approach also allows to limit the training computational effort. Besides, we introduce a novel technique which relies an effective determination of input weights to the hidden layer dimension. This approach is tested for both regression and classification tasks, resulting in a significant performance improvement with respect to alternative methods.
Energy Group-Buying with Loading Sharing for Green Cellular Networks
Venue: arXiv cs.IT 2015
Year: 2015

In the emerging hybrid electricity market, mobile network operators (MNOs) of cellular networks can make day-ahead energy purchase commitments at low prices and real-time flexible energy purchase at high prices. To minimize electricity bills, it is essential for MNOs to jointly optimize the day-ahead and real-time energy purchase based on their time-varying wireless traffic load. In this paper, we consider two different MNOs coexisting in the same area, and exploit their collaboration in both energy purchase and wireless load sharing for energy cost saving. Specifically, we propose a new approach named energy group buying with load sharing, in which the two MNOs are aggregated as a single group to make the day-ahead and real-time energy purchase, and their base stations (BSs) share the wireless traffic to maximally turn lightly-loaded BSs into sleep mode. When the two MNOs belong to the same entity and aim to minimize their total energy cost, we use the two-stage stochastic programming to obtain the optimal day-ahead and real-time energy group buying jointly with wireless load sharing. When the two MNOs belong to different entities and are self-interested in minimizing their individual energy costs, we propose a novel repeated Nash bargaining scheme for them to negotiate and share their energy costs under energy group buying and load sharing. Our proposed repeated Nash bargaining scheme is shown to achieve Pareto-optimal and fair energy cost reductions for both MNOs.
Robot Language Learning, Generation, and Comprehension
Venue: arXiv cs.RO 2015
Year: 2015

We present a unified framework which supports grounding natural-language semantics in robotic driving. This framework supports acquisition (learning grounded meanings of nouns and prepositions from human annotation of robotic driving paths), generation (using such acquired meanings to generate sentential description of new robotic driving paths), and comprehension (using such acquired meanings to support automated driving to accomplish navigational goals specified in natural language). We evaluate the performance of these three tasks by having independent human judges rate the semantic fidelity of the sentences associated with paths, achieving overall average correctness of 94.6% and overall average completeness of 85.6%.
Usability as a Dominant Quality Attribute
Venue: arXiv cs.SE 2015
Year: 2015

Whenever an architect or a team of architects begins an architectural design, there are certain goals set to achieve. There are many factors involved in setting up goals for the architecture design such as type of the project, end user perspective, functional and non-functional requirements and so on. This paper reviews and further elaborates strategy for the usability characteristics of software architecture. Although user centered designs are tremendously gaining popularity, still in many design scenarios, usability is barely even considered as one of the primary goals. This work provides an opportunity to compare different strategies and evaluate their pros and cons.
Semantic Publishing Challenge - Assessing the Quality of Scientific Output by Information Extraction and Interlinking
Venue: arXiv cs.DL 2015
Year: 2015

The Semantic Publishing Challenge series aims at investigating novel approaches for improving scholarly publishing using Linked Data technology. In 2014 we had bootstrapped this effort with a focus on extracting information from non-semantic publications - computer science workshop proceedings volumes and their papers - to assess their quality. The objective of this second edition was to improve information extraction but also to interlink the 2014 dataset with related ones in the LOD Cloud, thus paving the way for sophisticated end-user services.
Recovery of Sparse Positive Signals on the Sphere from Low Resolution Measurements
Venue: arXiv cs.IT 2015
Year: 2015

This letter considers the problem of recovering a positive stream of Diracs on a sphere from its projection onto the space of low-degree spherical harmonics, namely, from its low-resolution version. We suggest recovering the Diracs via a tractable convex optimization problem. The resulting recovery error is proportional to the noise level and depends on the density of the Diracs. We validate the theory by numerical experiments.
On the Role of Shared Randomness in Simultaneous Communication
Venue: arXiv cs.CC 2015
Year: 2015

Two parties wish to carry out certain distributed computational tasks, and they are given access to a source of correlated random bits. It allows the parties to act in a correlated manner, which can be quite useful. But what happens if the shared randomness is not perfect? In this work, we initiate the study of the power of different sources of shared randomness in communication complexity. This is done in the setting of simultaneous message passing (SMP) model of communication complexity, which is one of the most suitable models for studying the resource of shared randomness. Toward characterising the power of various sources of shared randomness, we introduce a measure for the quality of a source - we call it collision complexity. Our results show that the collision complexity tightly characterises the power of a (shared) randomness resource in the SMP model. Of independent interest is our demonstration that even the weakest sources of shared randomness can in some cases increase the power of SMP substantially: the equality function can be solved very efficiently with virtually any nontrivial shared randomness.
A solution for applying IEC 61499 function blocks in the development of substation automation systems
Venue: arXiv cs.SE 2015
Year: 2015

This paper presents a solution for applying IEC 61499 function blocks along with IEC 61850 specifications in modeling and implementing control applications for substations automation. The IEC 61499 artifacts are used for structuring the control logic, while the IEC 61850 concepts for communication and information exchange between the automation devices. The proposed control architecture was implemented and validated in a simple fault protection scenario with simulated power equipment.
On the expressive power of read-once determinants
Venue: arXiv cs.CC 2015
Year: 2015

We introduce and study the notion of read-$k$ projections of the determinant: a polynomial $f \in \mathbb{F}[x_1, \ldots, x_n]$ is called a {\it read-$k$ projection of determinant} if $f=det(M)$, where entries of matrix $M$ are either field elements or variables such that each variable appears at most $k$ times in $M$. A monomial set $S$ is said to be expressible as read-$k$ projection of determinant if there is a read-$k$ projection of determinant $f$ such that the monomial set of $f$ is equal to $S$. We obtain basic results relating read-$k$ determinantal projections to the well-studied notion of determinantal complexity. We show that for sufficiently large $n$, the $n \times n$ permanent polynomial $Perm_n$ and the elementary symmetric polynomials of degree $d$ on $n$ variables $S_n^d$ for $2 \leq d \leq n-2$ are not expressible as read-once projection of determinant, whereas $mon(Perm_n)$ and $mon(S_n^d)$ are expressible as read-once projections of determinant. We also give examples of monomial sets which are not expressible as read-once projections of determinant.
Deep Convolutional Neural Networks for Smile Recognition
Venue: arXiv cs.CV 2015
Year: 2015

This thesis describes the design and implementation of a smile detector based on deep convolutional neural networks. It starts with a summary of neural networks, the difficulties of training them and new training methods, such as Restricted Boltzmann Machines or autoencoders. It then provides a literature review of convolutional neural networks and recurrent neural networks. In order to select databases for smile recognition, comprehensive statistics of databases popular in the field of facial expression recognition were generated and are summarized in this thesis. It then proposes a model for smile detection, of which the main part is implemented. The experimental results are discussed in this thesis and justified based on a comprehensive model selection performed. All experiments were run on a Tesla K40c GPU benefiting from a speedup of up to factor 10 over the computations on a CPU. A smile detection test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches with accuracies ranging from 65.55% to 79.67%. This experiment is re-run under various variations, such as retaining less neutral images or only the low or high intensities, of which the results are extensively compared.
A Space-Efficient Approach towards Distantly Homologous Protein Similarity Searches
Venue: arXiv cs.CE 2015
Year: 2015

Protein similarity searches are a routine job for molecular biologists where a query sequence of amino acids needs to be compared and ranked against an ever-growing database of proteins. All available algorithms in this field can be grouped into two categories, either solving the problem using sequence alignment through dynamic programming, or, employing certain heuristic measures to perform an initial screening followed by applying an optimal sequence alignment algorithm to the closest matching candidates. While the first approach suffers from huge time and space demands, the latter approach might miss some protein sequences which are distantly related to the query sequence. In this paper, we propose a heuristic pair-wise sequence alignment algorithm that can be efficiently employed for protein database searches for moderately sized databases. The proposed algorithm is sufficiently fast to be applicable to database searches for short query sequences, has constant auxiliary space requirements, produces good alignments, and is sensitive enough to return even distantly related protein chains that might be of interest.
A Neural Algorithm of Artistic Style
Venue: arXiv cs.CV 2015
Year: 2015

In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.
Global Synchronization and Consensus Using Beeps in a Fault-Prone MAC
Venue: arXiv cs.DC 2015
Year: 2015

Consensus is one of the fundamental tasks studied in distributed computing. Processors have input values from some set $V$ and they have to decide the same value from this set. If all processors have the same input value, then they must all decide this value. We study the task of consensus in a Multiple Access Channel (MAC) prone to faults, under a very weak communication model called the $\mathit{beeping\ model}$. Communication proceeds in synchronous rounds. Some processors wake up spontaneously, in possibly different rounds decided by an adversary. In each round, an awake processor can either listen, i.e., stay silent, or beep, i.e., emit a signal. In each round, a fault can occur in the channel independently with constant probability $0<p<1$. In a fault-free round, an awake processor hears a beep if it listens in this round and if one or more other processors beep in this round. A processor still dormant in a fault-free round in which some other processor beeps is woken up by this beep and hears it. In a faulty round nothing is heard, regardless of the behaviour of the processors. An algorithm working with error probability at most $ε$, for a given $ε>0$, is called $ε$-$\mathit{safe}$. Our main result is the design and analysis, for any constant $ε>0$, of a deterministic $ε$-safe consensus algorithm that works in time $O(\log w)$ in a fault-prone MAC, where $w$ is the smallest input value of all participating processors. We show that this time cannot be improved, even when the MAC is fault-free. The main algorithmic tool that we develop to achieve our goal, and that might be of independent interest, is a deterministic algorithm that, with arbitrarily small constant error probability, establishes a global clock in a fault-prone MAC in constant time.
Full-text and Keyword Indexes for String Searching
Venue: arXiv cs.DS 2015
Year: 2015

In this work, we present a literature review for full-text and keyword indexes as well as our contributions (which are mostly practice-oriented). The first contribution is the FM-bloated index, which is a modification of the well-known FM-index (a compressed, full-text index) that trades space for speed. In our approach, the count table and the occurrence lists store information about selected $q$-grams in addition to the individual characters. Two variants are described, namely one using $O(n \log^2 n)$ bits of space with $O(m + \log m \log \log n)$ average query time, and one with linear space and $O(m \log \log n)$ average query time, where $n$ is the input text length and $m$ is the pattern length. We experimentally show that a significant speedup can be achieved by operating on $q$-grams (albeit at the cost of very high space requirements, hence the name "bloated"). In the category of keyword indexes we present the so-called split index, which can efficiently solve the $k$-mismatches problem, especially for 1 error. Our implementation in the C++ language is focused mostly on data compaction, which is beneficial for the search speed (by being cache friendly). We compare our solution with other algorithms and we show that it is faster when the Hamming distance is used. Query times in the order of 1 microsecond were reported for one mismatch for a few-megabyte natural language dictionary on a medium-end PC. A minor contribution includes string sketches which aim to speed up approximate string comparison at the cost of additional space ($O(1)$ per string). They can be used in the context of keyword indexes in order to deduce that two strings differ by at least $k$ mismatches with the use of fast bitwise operations rather than an explicit verification.