OpenRaiser/Intern-Atlas huggingface dataset:
Towards Turkish ASR: Anatomy of a rule-based Turkish g2p
Venue: arXiv cs.CL 2016
Year: 2016

This paper describes the architecture and implementation of a rule-based grapheme to phoneme converter for Turkish. The system accepts surface form as input, outputs SAMPA mapping of the all parallel pronounciations according to the morphological analysis together with stress positions. The system has been implemented in Python
A Method for Image Reduction Based on a Generalization of Ordered Weighted Averaging Functions
Venue: arXiv cs.AI 2016
Year: 2016

In this paper we propose a special type of aggregation function which generalizes the notion of Ordered Weighted Averaging Function - OWA. The resulting functions are called Dynamic Ordered Weighted Averaging Functions --- DYOWAs. This generalization will be developed in such way that the weight vectors are variables depending on the input vector. Particularly, this operators generalize the aggregation functions: Minimum, Maximum, Arithmetic Mean, Median, etc, which are extensively used in image processing. In this field of research two problems are considered: The determination of methods to reduce images and the construction of techniques which provide noise reduction. The operators described here are able to be used in both cases. In terms of image reduction we apply the methodology provided by Patermain et al. We use the noise reduction operators obtained here to treat the images obtained in the first part of the paper, thus obtaining images with better quality.
Stochastic Geometry Analysis of Multi-Antenna Two-Tier Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we study the key properties of multi-antenna two-tier networks under different system configurations. Based on stochastic geometry, we derive the expressions and approximations for the users' average data rate. Through the more tractable approximations, the theoretical analysis can be greatly simplified. We find that the differences in density and transmit power between two tiers, together with range expansion bias significantly affect the users' data rate. Besides, for the purpose of area spectral efficiency (ASE) maximization, we find that the optimal number of active users for each tier is approximately fixed portion of the sum of the number of antennas plus one. Interestingly, the optimal settings are insensitive to different configurations between two tiers. Last but not the least, if the number of antennas of macro base stations (MBSs) is sufficiently larger than that of small cell base stations (SBSs), we find that range expansion will improve ASE.
Entity-oriented spatial coding and discrete topological spatial relations
Venue: arXiv cs.CG 2016
Year: 2016

Based on a newly proposed spatial data model - spatial chromatic model (SCM), we developed a spatial coding scheme, called full-coded ordinary arranged chromatic diagram (full-OACD). Full-OACD is a type of spatial tessellation, where space is partitioned into a number of subspaces such as cells, edges, and vertexes. These subspaces are called spatial particles and assigned with unique codes - chromatic codes. The generation, structures, computations, and properties of full-OACD are introduced and relations between chromatic codes and particle spatial topology are investigated, indicating that chromatic codes provide a potential useful and meaningful tool not only for spatial analysis in geographical information science, but also for other relevant disciplines such as discrete mathematics, topology, and computer science.
Cellular Communications on License-Exempt Spectrum: A Tutorial
Venue: arXiv cs.NI 2016
Year: 2016

A traditional cellular system (e.g., LTE) operates only on the licensed spectrum. This tutorial explains the concept of cellular communications on both licensed and license-exempt spectrum under a unified architecture. The purpose to extend a cellular system into the bandwidth-rich license-exempt spectrum is to form a larger cellular network for all spectrum types. This would result in an ultimate mobile converged cellular network. This tutorial examines the benefits of this concept, the technical challenges, and provides a conceptual LTE-based design example that helps to show how a traditional cellular system like the LTE can adapt itself to a different spectrum type, conform to the regulatory requirements, and harmoniously co-exist with the incumbent systems such as Wi-Fi. In order to cope with the interference and regulation rules on license-exempt spectrum, a special medium access mechanism is introduced into the existing LTE transmission frame structure to exploit the full benefits of coordinated and managed cellular architecture.
Follow, listen, feel and go: alternative guidance systems for a walking assistance device
Venue: arXiv cs.RO 2016
Year: 2016

In this paper, we propose several solutions to guide an older adult along a safe path using a robotic walking assistant (the c-Walker). We consider four different possibilities to execute the task. One of them is mechanical, with the c-Walker playing an active role in setting the course. The other ones are based on tactile or acoustic stimuli, and suggest a direction of motion that the user is supposed to take on her own will. We describe the technological basis for the hardware components implementing the different solutions, and show specialized path following algorithms for each of them. The paper reports an extensive user validation activity with a quantitative and qualitative analysis of the different solutions. In this work, we test our system just with young participants to establish a safer methodology that will be used in future studies with older adults.
Detecting and Extracting Events from Text Documents
Venue: arXiv cs.CL 2016
Year: 2016

Events of various kinds are mentioned and discussed in text documents, whether they are books, news articles, blogs or microblog feeds. The paper starts by giving an overview of how events are treated in linguistics and philosophy. We follow this discussion by surveying how events and associated information are handled in computationally. In particular, we look at how textual documents can be mined to extract events and ancillary information. These days, it is mostly through the application of various machine learning techniques. We also discuss applications of event detection and extraction systems, particularly in summarization, in the medical domain and in the context of Twitter posts. We end the paper with a discussion of challenges and future directions.
Decentralized Stochastic Optimal Power Flow in Radial Networks with Distributed Generation
Venue: arXiv cs.SY 2016
Year: 2016

This paper develops a power management scheme that jointly optimizes the real power consumption of programmable loads and reactive power outputs of photovoltaic (PV) inverters in distribution networks. The premise is to determine the optimal demand response schedule that accounts for the stochastic availability of solar power, as well as to control the reactive power generation or consumption of PV inverters adaptively to the real power injections of all PV units. These uncertain real power injections by PV units are modeled as random variables taking values from a finite number of possible scenarios. Through the use of second order cone relaxation of the power flow equations, a convex stochastic program is formulated. The objectives are to minimize the negative user utility, cost of power provision, and thermal losses, while constraining voltages to remain within specified levels. To find the global optimum point, a decentralized algorithm is developed via the alternating direction method of multipliers that results in closed-form updates per node and per scenario, rendering it suitable to implement in distribution networks with large number of scenarios. Numerical tests and comparisons with an alternative deterministic approach are provided for typical residential distribution networks that confirm the efficiency of the algorithm.
Study of Distributed Conjugate Gradient Strategies for Distributed Estimation Over Sensor Networks
Venue: arXiv cs.DC 2016
Year: 2016

This paper presents distributed conjugate gradient algorithms for distributed parameter estimation and spectrum estimation over wireless sensor networks. In particular, distributed conventional conjugate gradient (CCG) and modified conjugate gradient (MCG) are considered, together with incremental and diffusion adaptive solutions. The distributed CCG and MCG algorithms have an improved performance in terms of mean square error as compared with least--mean square (LMS)--based algorithms and a performance that is close to recursive least--squares (RLS) algorithms. In comparison with existing centralized or distributed estimation strategies, key features of the proposed algorithms are: 1) more accurate estimates and faster convergence speed can be obtained; 2) the design of preconditioners for CG algorithms, which have the ability to improve the performance of the proposed CG algorithms is presented and 3) the proposed algorithms are implemented in the area of distributed parameter estimation and spectrum estimation. The performance of the proposed algorithms for distributed estimation is illustrated via simulations and the resulting algorithms are distributed, cooperative and able to respond in real time to change in the environment.
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware
Venue: arXiv cs.NE 2016
Year: 2016

In recent years the field of neuromorphic low-power systems that consume orders of magnitude less power gained significant momentum. However, their wider use is still hindered by the lack of algorithms that can harness the strengths of such architectures. While neuromorphic adaptations of representation learning algorithms are now emerging, efficient processing of temporal sequences or variable length-inputs remain difficult. Recurrent neural networks (RNN) are widely used in machine learning to solve a variety of sequence learning tasks. In this work we present a train-and-constrain methodology that enables the mapping of machine learned (Elman) RNNs on a substrate of spiking neurons, while being compatible with the capabilities of current and near-future neuromorphic systems. This "train-and-constrain" method consists of first training RNNs using backpropagation through time, then discretizing the weights and finally converting them to spiking RNNs by matching the responses of artificial neurons with those of the spiking neurons. We demonstrate our approach by mapping a natural language processing task (question classification), where we demonstrate the entire mapping process of the recurrent layer of the network on IBM's Neurosynaptic System "TrueNorth", a spike-based digital neuromorphic hardware architecture. TrueNorth imposes specific constraints on connectivity, neural and synaptic parameters. To satisfy these constraints, it was necessary to discretize the synaptic weights and neural activities to 16 levels, and to limit fan-in to 64 inputs. We find that short synaptic delays are sufficient to implement the dynamical (temporal) aspect of the RNN in the question classification task. The hardware-constrained model achieved 74% accuracy in question classification while using less than 0.025% of the cores on one TrueNorth chip, resulting in an estimated power consumption of ~17 uW.
The discrete analogue of the method of quickest descent for an inverse acoustic problem in case of a smooth source
Venue: arXiv cs.CE 2016
Year: 2016

The article considers the discrete analogue of the method of quickest descent for an inverse Acoustics problem in case of a smooth source. The authors derived the gradient of functional in differential and discrete cases, described the algorithm of solving a problem, and compared gradients of functional in continuous and discrete cases. In the article the improved estimates of the rates of convergence of gradient-based methods are obtained, which are very important for practice because they provide with the possibility to make input data errors consistent with the iteration number. There is a practical application of the proposed new method of deriving the gradient of functional for an Acoustics discrete problem, for it provides with calculations that are more accurate. The theoretical importance of the method is the developed technique of deriving estimates and gradients of functional at a discrete level.
Some results of linear codes over the ring $\mathbb{Z}_4+u\mathbb{Z}_4+v\mathbb{Z}_4+uv\mathbb{Z}_4$
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we mainly study the theory of linear codes over the ring $R =\mathbb{Z}_4+u\mathbb{Z}_4+v\mathbb{Z}_4+uv\mathbb{Z}_4$. By the Chinese Remainder Theorem, we have $R$ is isomorphic to the direct sum of four rings $\mathbb{Z}_4$. We define a Gray map $Φ$ from $R^{n}$ to $\mathbb{Z}_4^{4n}$, which is a distance preserving map. The Gray image of a cyclic code over $R^{n}$ is a linear code over $\mathbb{Z}_4$. Furthermore, we study the MacWilliams identities of linear codes over $R$ and give the the generator polynomials of cyclic codes over $R$. Finally, we discuss some properties of MDS codes over $R$.
Coding for Locality in Reconstructing Permutations
Venue: arXiv cs.IT 2016
Year: 2016

The problem of storing permutations in a distributed manner arises in several common scenarios, such as efficient updates of a large, encrypted, or compressed data set. This problem may be addressed in either a combinatorial or a coding approach. The former approach boils down to presenting large sets of permutations with \textit{locality}, that is, any symbol of the permutation can be computed from a small set of other symbols. In the latter approach, a permutation may be coded in order to achieve locality. This paper focuses on the combinatorial approach. We provide upper and lower bounds for the maximal size of a set of permutations with locality, and provide several simple constructions which attain the upper bound. In cases where the upper bound is not attained, we provide alternative constructions using Reed-Solomon codes, permutation polynomials, and multi-permutations.
Gauss-Bonnet for multi-linear valuations
Venue: arXiv cs.DM 2016
Year: 2016

We prove Gauss-Bonnet and Poincare-Hopf formulas for multi-linear valuations on finite simple graphs G=(V,E) and answer affirmatively a conjecture of Gruenbaum from 1970 by constructing higher order Dehn-Sommerville valuations which vanish for all d-graphs without boundary. An example of a quadratic valuation is the Wu characteristic w(G) which sums (-1)^(dim(x)+dim(y)) over all intersecting pairs of complete subgraphs x,y of a G. More generally, an intersection number w(A,B) sums (-1)^(dim(x)+dim(y)) over pairs x,y, where x is in A and y is in B and x,y intersect. w(G) is a quadratic Euler characteristic X(G), where X sums (-1)^dim(x) over all complete subgraphs x of G. We prove that w is multiplicative, like Euler characteristic: w(G x H) = w(G) w(H) for any two graphs and that w is invariant under Barycentric refinements. We construct a curvature K satisfying Gauss-Bonnet w(G) = sum K(a). We also prove w(G) = X(G)-X(dG) for Euler characteristic X which holds for any d-graph G with boundary dG. We also show higher order Poincare-Hopf formulas: there is for every multi-linear valuation X and function f an index i(a) such that sum i(a)=X(G). For d-graphs G and X=w it agrees with the Euler curvature. For the vanishing multi-valuations which were conjectured to exist, like for the quadratic valuation X(G) = (V X) Y with X=(1,-1,1,-1,1),Y=(0,-2,3,-4,5) on 4-graphs, discrete 4 manifolds, where V_{ij}(G) is the f-matrix counting the number of i and j-simplices in G intersecting, the curvature is constant zero. For all graphs and multi-linear Dehn-Sommerville relations, the Dehn-Sommerville curvature K(v) at a vertex is a Dehn-Sommerville valuation on the unit sphere S(v). We show X V(G) Y = v(G) Y for any linear valuation Y of a d-graph G with f-vector v(G). This provides examples for the Gruenbaum conjecture.
Reed-Muller Codes Achieve Capacity on Erasure Channels
Venue: arXiv cs.IT 2016
Year: 2016

We introduce a new approach to proving that a sequence of deterministic linear codes achieves capacity on an erasure channel under maximum a posteriori decoding. Rather than relying on the precise structure of the codes our method exploits code symmetry. In particular, the technique applies to any sequence of linear codes where the blocklengths are strictly increasing, the code rates converge, and the permutation group of each code is doubly transitive. In other words, we show that symmetry alone implies near-optimal performance. An important consequence of this result is that a sequence of Reed-Muller codes with increasing blocklength and converging rate achieves capacity. This possibility has been suggested previously in the literature but it has only been proven for cases where the limiting code rate is 0 or 1. Moreover, these results extend naturally to all affine-invariant codes and, thus, to extended primitive narrow-sense BCH codes. This also resolves, in the affirmative, the existence question for capacity-achieving sequences of binary cyclic codes. The primary tools used in the proof are the sharp threshold property for symmetric monotone boolean functions and the area theorem for extrinsic information transfer functions.
Spectral Theory of Unsigned and Signed Graphs. Applications to Graph Clustering: a Survey
Venue: arXiv cs.LG 2016
Year: 2016

This is a survey of the method of graph cuts and its applications to graph clustering of weighted unsigned and signed graphs. I provide a fairly thorough treatment of the method of normalized graph cuts, a deeply original method due to Shi and Malik, including complete proofs. The main thrust of this paper is the method of normalized cuts. I give a detailed account for K = 2 clusters, and also for K > 2 clusters, based on the work of Yu and Shi. I also show how both graph drawing and normalized cut K-clustering can be easily generalized to handle signed graphs, which are weighted graphs in which the weight matrix W may have negative coefficients. Intuitively, negative coefficients indicate distance or dissimilarity. The solution is to replace the degree matrix by the matrix in which absolute values of the weights are used, and to replace the Laplacian by the Laplacian with the new degree matrix of absolute values. As far as I know, the generalization of K-way normalized clustering to signed graphs is new. Finally, I show how the method of ratio cuts, in which a cut is normalized by the size of the cluster rather than its volume, is just a special case of normalized cuts.
Wi-Fi/WiGig Coordination for Optimal WiGig Concurrent Transmissions in Random Access Scenario
Venue: arXiv cs.NI 2016
Year: 2016

Wireless Gigabit (WiGig) access points (APs) using 60 GHz unlicensed frequency band are considered as key enablers for future Gbps WLANs. Due to its short range transmission with high susceptibility to path blocking, a multiple number of WiGig APs should be installed to fully cover a typical target environment. However, using autonomously operated WiGig APs with IEEE 802.11ad DCF, the exhaustive search analog beamforming and the maximum received power based autonomous users association prevent the establishment of optimal WiGig concurrent links that maximize the total system throughput in random access scenarios. In this paper, we formulate the problem of WiGig concurrent transmissions in random access scenarios as an optimization problem, then we propose a Wi-Fi/WiGig coordination architecture to solve it. The proposed coordinated Wi-Fi/WiGig WLAN is based on a tight coordination between the 5 GHz (Wi-Fi) and the 60 GHz (WiGig) unlicensed frequency bands. By which, the wide coverage Wi-Fi band controls the establishment of the WiGig concurrent links. Statistical learning using Wi-Fi fingerprinting is used for estimating the best candidate AP and its best beam identification (ID) for establishing the WiGig concurrent link without making any interference to the existing WiGig data links.
Scale-aware Pixel-wise Object Proposal Networks
Venue: arXiv cs.CV 2016
Year: 2016

Object proposal is essential for current state-of-the-art object detection pipelines. However, the existing proposal methods generally fail in producing results with satisfying localization accuracy. The case is even worse for small objects which however are quite common in practice. In this paper we propose a novel Scale-aware Pixel-wise Object Proposal (SPOP) network to tackle the challenges. The SPOP network can generate proposals with high recall rate and average best overlap (ABO), even for small objects. In particular, in order to improve the localization accuracy, a fully convolutional network is employed which predicts locations of object proposals for each pixel. The produced ensemble of pixel-wise object proposals enhances the chance of hitting the object significantly without incurring heavy extra computational cost. To solve the challenge of localizing objects at small scale, two localization networks which are specialized for localizing objects with different scales are introduced, following the divide-and-conquer philosophy. Location outputs of these two networks are then adaptively combined to generate the final proposals by a large-/small-size weighting network. Extensive evaluations on PASCAL VOC 2007 show the SPOP network is superior over the state-of-the-art models. The high-quality proposals from SPOP network also significantly improve the mean average precision (mAP) of object detection with Fast-RCNN framework. Finally, the SPOP network (trained on PASCAL VOC) shows great generalization performance when testing it on ILSVRC 2013 validation set.
Separating hash families: A Johnson-type bound and new constructions
Venue: arXiv cs.DM 2016
Year: 2016

Separating hash families are useful combinatorial structures which are generalizations of many well-studied objects in combinatorics, cryptography and coding theory. In this paper, using tools from graph theory and additive number theory, we solve several open problems and conjectures concerning bounds and constructions for separating hash families. Firstly, we discover that the cardinality of a separating hash family satisfies a Johnson-type inequality. As a result, we obtain a new upper bound, which is superior to all previous ones. Secondly, we present a construction for an infinite class of perfect hash families. It is based on the Hamming graphs in coding theory and generalizes many constructions that appeared before. It provides an affirmative answer to both Bazrafshan-Trung's open problem on separating hash families and Alon-Stav's conjecture on parent-identifying codes. Thirdly, let $p_t(N,q)$ denote the maximal cardinality of a $t$-perfect hash family of length $N$ over an alphabet of size $q$. Walker II and Colbourn conjectured that $p_3(3,q)=o(q^2)$. We verify this conjecture by proving $q^{2-o(1)}<p_3(3,q)=o(q^2)$. Our proof can be viewed as an application of Ruzsa-Szemer{é}di's (6,3)-theorem. We also prove $q^{2-o(1)}<p_4(4,q)=o(q^2)$. Two new notions in graph theory and additive number theory, namely rainbow cycles and $R$-sum-free sets, are introduced to prove this result. These two bounds support a question of Blackburn, Etzion, Stinson and Zaverucha. Finally, we establish a bridge between perfect hash families and hypergraph Tur{á}n problems. This connection has not been noticed before. As a consequence, many new results and problems arise.