OpenRaiser/Intern-Atlas huggingface dataset:
Supervised Dictionary Learning and Sparse Representation-A Review
Venue: arXiv cs.CV 2015
Year: 2015

Dictionary learning and sparse representation (DLSR) is a recent and successful mathematical model for data representation that achieves state-of-the-art performance in various fields such as pattern recognition, machine learning, computer vision, and medical imaging. The original formulation for DLSR is based on the minimization of the reconstruction error between the original signal and its sparse representation in the space of the learned dictionary. Although this formulation is optimal for solving problems such as denoising, inpainting, and coding, it may not lead to optimal solution in classification tasks, where the ultimate goal is to make the learned dictionary and corresponding sparse representation as discriminative as possible. This motivated the emergence of a new category of techniques, which is appropriately called supervised dictionary learning and sparse representation (S-DLSR), leading to more optimal dictionary and sparse representation in classification tasks. Despite many research efforts for S-DLSR, the literature lacks a comprehensive view of these techniques, their connections, advantages and shortcomings. In this paper, we address this gap and provide a review of the recently proposed algorithms for S-DLSR. We first present a taxonomy of these algorithms into six categories based on the approach taken to include label information into the learning of the dictionary and/or sparse representation. For each category, we draw connections between the algorithms in this category and present a unified framework for them. We then provide guidelines for applied researchers on how to represent and learn the building blocks of an S-DLSR solution based on the problem at hand. This review provides a broad, yet deep, view of the state-of-the-art methods for S-DLSR and allows for the advancement of research and development in this emerging area of research.
Composite repetition-aware data structures
Venue: arXiv cs.DS 2015
Year: 2015

In highly repetitive strings, like collections of genomes from the same species, distinct measures of repetition all grow sublinearly in the length of the text, and indexes targeted to such strings typically depend only on one of these measures. We describe two data structures whose size depends on multiple measures of repetition at once, and that provide competitive tradeoffs between the time for counting and reporting all the exact occurrences of a pattern, and the space taken by the structure. The key component of our constructions is the run-length encoded BWT (RLBWT), which takes space proportional to the number of BWT runs: rather than augmenting RLBWT with suffix array samples, we combine it with data structures from LZ77 indexes, which take space proportional to the number of LZ77 factors, and with the compact directed acyclic word graph (CDAWG), which takes space proportional to the number of extensions of maximal repeats. The combination of CDAWG and RLBWT enables also a new representation of the suffix tree, whose size depends again on the number of extensions of maximal repeats, and that is powerful enough to support matching statistics and constant-space traversal.
Incorporating Spontaneous Reporting System Data to Aid Causal Inference in Longitudinal Healthcare Data
Venue: arXiv cs.CE 2015
Year: 2015

Inferring causality using longitudinal observational databases is challenging due to the passive way the data are collected. The majority of associations found within longitudinal observational data are often non-causal and occur due to confounding. The focus of this paper is to investigate incorporating information from additional databases to complement the longitudinal observational database analysis. We investigate the detection of prescription drug side effects as this is an example of a causal relationship. In previous work a framework was proposed for detecting side effects only using longitudinal data. In this paper we combine a measure of association derived from mining a spontaneous reporting system database to previously proposed analysis that extracts domain expertise features for causal analysis of a UK general practice longitudinal database. The results show that there is a significant improvement to the performance of detecting prescription drug side effects when the longitudinal observation data analysis is complemented by incorporating additional drug safety sources into the framework. The area under the receiver operating characteristic curve (AUC) for correctly classifying a side effect when other data were considered was 0.967, whereas without it the AUC was 0.923 However, the results of this paper may be biased by the evaluation and future work should overcome this by developing an unbiased reference set.
An Approach to 2D Signals Recovering in Compressive Sensing Context
Venue: arXiv cs.IT 2015
Year: 2015

In this paper we study the compressive sensing effects on 2D signals exhibiting sparsity in 2D DFT domain. A simple algorithm for reconstruction of randomly under-sampled data is proposed. It is based on the analytically determined threshold that precisely separates signal and non-signal components in the 2D DFT domain. The algorithm operates fast in a single iteration providing the accurate signal reconstruction. In the situations that are not comprised by the analytic derivation and constrains, the algorithm is still efficient and need just a couple of iterations. The proposed solution shows promising results in ISAR imaging (simulated data are used), where the reconstruction is achieved even in the case when less than 10% of data is available.
Multi-level Loop-less Algorithm for Multi-set Permutations
Venue: arXiv cs.DS 2015
Year: 2015

We present an algorithm that generates multiset permutations in O(1) time for each permutation, that is, by a loop-less algorithm with O(n) extra memory requirement. There already exist several such algorithms that generate multiset permutations in various orders. For multiset permutations, we combine two loop-less algorithms that are designed in the same principle of tree traversal. Our order of generation is different from any existing order, and the algorithm is simpler and faster than the previous ones. We also apply the new algorithm to parking functions.
Finding Pairwise Intersections Inside a Query Range
Venue: arXiv cs.DS 2015
Year: 2015

We study the following problem: preprocess a set O of objects into a data structure that allows us to efficiently report all pairs of objects from O that intersect inside an axis-aligned query range Q. We present data structures of size $O(n({\rm polylog} n))$ and with query time $O((k+1)({\rm polylog} n))$ time, where k is the number of reported pairs, for two classes of objects in the plane: axis-aligned rectangles and objects with small union complexity. For the 3-dimensional case where the objects and the query range are axis-aligned boxes in R^3, we present a data structures of size $O(n\sqrt{n}({\rm polylog} n))$ and query time $O((\sqrt{n}+k)({\rm polylog} n))$. When the objects and query are fat, we obtain $O((k+1)({\rm polylog} n))$ query time using $O(n({\rm polylog} n))$ storage.
Study of a Robust Algorithm Applied in the Optimal Position Tuning for the Camera Lens in Automated Visual Inspection Systems
Venue: arXiv cs.CV 2015
Year: 2015

This paper present the mathematical fundaments and experimental study of an algorithm used to find the optimal position for the camera lens to obtain a maximum of details. This information can be further applied to a appropriate system to automatically correct this position. The algorithm is based on the evaluation of a so called resolution function who calculates the maximum of gradient in a certain zone of the image. The paper also presents alternative forms of the function, results of measurements and set up a set of practical rules for the right application of the algorithm.
Opportunistic Cooperative Channel Access in Distributed Wireless Networks with Decode-and-Forward Relays
Venue: arXiv cs.IT 2015
Year: 2015

This letter studies distributed opportunistic channel access in a wireless network with decode-and-forward relays. All the sources use channel contention to get transmission opportunity. If a source wins the contention, the channel state information in the first-hop channel (from the source to its relay) is estimated, and a decision is made for the winner source to either give up the transmission opportunity and let all sources start a new contention, or transmit to the relay. Once the relay gets the traffic, it may have a sequence of probings of the second-hop channel (from the relay to the destination). After each probing, if the second-hop channel is good enough, the relay transmits to the destination and completes the transmission process of the source; otherwise, the relay decides either to give up and let all sources start a new contention, or to continue to probe the second-hop channel. The optimal decision strategies for the two hops are derived in this letter. The first-hop strategy is a pure-threshold strategy, i.e., when the first-hop channel signal-to-noise ratio (SNR) is more than a threshold, the winner source should transmit to the relay, and subsequently the second-hop strategy should let the relay keep probing the second-hop channel until a good enough second-hop channel is observed. Simulation results show that our scheme is beneficial when the second-hop channels have larger average SNR.
Unified vector space mapping for knowledge representation systems
Venue: arXiv cs.AI 2015
Year: 2015

One of the most significant problems which inhibits further developments in the areas of Knowledge Representation and Artificial Intelligence is a problem of semantic alignment or knowledge mapping. The progress in its solution will be greatly beneficial for further advances of information retrieval, ontology alignment, relevance calculation, text mining, natural language processing etc. In the paper the concept of multidimensional global knowledge map, elaborated through unsupervised extraction of dependencies from large documents corpus, is proposed. In addition, the problem of direct Human - Knowledge Representation System interface is addressed and a concept of adaptive decoder proposed for the purpose of interaction with previously described unified mapping model. In combination these two approaches are suggested as basis for a development of a new generation of knowledge representation systems.
On Sequences, Rational Functions and Decomposition
Venue: arXiv cs.SC 2015
Year: 2015

Our overall goal is to unify and extend some results in the literature related to the approximation of generating functions of finite and infinite sequences over a field by rational functions. In our approach, numerators play a significant role. We revisit a theorem of Niederreiter on (i) linear complexities and (ii) '$n^{th}$ minimal polynomials' of an infinite sequence, proved using partial quotients. We prove (i) and its converse from first principles and generalise (ii) to rational functions where the denominator need not have minimal degree. We prove (ii) in two parts: firstly for geometric sequences and then for sequences with a jump in linear complexity. The basic idea is to decompose the denominator as a sum of polynomial multiples of two polynomials of minimal degree; there is a similar decomposition for the numerators. The decomposition is unique when the denominator has degree at most the length of the sequence. The proof also applies to rational functions related to finite sequences, generalising a result of Massey. We give a number of applications to rational functions associated to sequences.
Using NLP to measure democracy
Venue: arXiv cs.CL 2015
Year: 2015

This paper uses natural language processing to create the first machine-coded democracy index, which I call Automated Democracy Scores (ADS). The ADS are based on 42 million news articles from 6,043 different sources and cover all independent countries in the 1993-2012 period. Unlike the democracy indices we have today the ADS are replicable and have standard errors small enough to actually distinguish between cases. The ADS are produced with supervised learning. Three approaches are tried: a) a combination of Latent Semantic Analysis and tree-based regression methods; b) a combination of Latent Dirichlet Allocation and tree-based regression methods; and c) the Wordscores algorithm. The Wordscores algorithm outperforms the alternatives, so it is the one on which the ADS are based. There is a web application where anyone can change the training set and see how the results change: democracy-scores.org
SDCA without Duality
Venue: arXiv cs.LG 2015
Year: 2015

Stochastic Dual Coordinate Ascent is a popular method for solving regularized loss minimization for the case of convex losses. In this paper we show how a variant of SDCA can be applied for non-convex losses. We prove linear convergence rate even if individual loss functions are non-convex as long as the expected loss is convex.
Nearly optimal classification for semimetrics
Venue: arXiv cs.LG 2015
Year: 2015

We initiate the rigorous study of classification in semimetric spaces, which are point sets with a distance function that is non-negative and symmetric, but need not satisfy the triangle inequality. For metric spaces, the doubling dimension essentially characterizes both the runtime and sample complexity of classification algorithms --- yet we show that this is not the case for semimetrics. Instead, we define the {\em density dimension} and discover that it plays a central role in the statistical and algorithmic feasibility of learning in semimetric spaces. We present nearly optimal sample compression algorithms and use these to obtain generalization guarantees, including fast rates. The latter hold for general sample compression schemes and may be of independent interest.
Video Text Localization with an emphasis on Edge Features
Venue: arXiv cs.CV 2015
Year: 2015

The text detection and localization plays a major role in video analysis and understanding. The scene text embedded in video consist of high-level semantics and hence contributes significantly to visual content analysis and retrieval. This paper proposes a novel method to robustly localize the texts in natural scene images and videos based on sobel edge emphasizing approach. The input image is preprocessed and edge emphasis is done to detect the text clusters. Further, a set of rules have been devised using morphological operators for false positive elimination and connected component analysis is performed to detect the text regions and hence text localization is performed. The experimental results obtained on publicly available standard datasets illustrate that the proposed method can detect and localize the texts of various sizes, fonts and colors.
Sales pipeline win propensity prediction: a regression approach
Venue: arXiv cs.CY 2015
Year: 2015

Sales pipeline analysis is fundamental to proactive management of an enterprize's sales pipeline and critical for business success. In particular, win propensity prediction, which involves quantitatively estimating the likelihood that on-going sales opportunities will be won within a specified time window, is a fundamental building block for sales management and lays the foundation for many applications such as resource optimization and sales gap analysis. With the proliferation of big data, the use of data-driven predictive models as a means to drive better sales performance is increasingly widespread, both in business-to-client (B2C) and business-to-business (B2B) markets. However, the relatively small number of B2B transactions (compared with the volume of B2C transactions), noisy data, and the fast-changing market environment pose challenges to effective predictive modeling. This paper proposes a machine learning-based unified framework for sales opportunity win propensity prediction, aimed at addressing these challenges. We demonstrate the efficacy of our proposed system using data from a top-500 enterprize in the business-to-business market.
Compressive Hyperspectral Imaging with Side Information
Venue: arXiv cs.CV 2015
Year: 2015

A blind compressive sensing algorithm is proposed to reconstruct hyperspectral images from spectrally-compressed measurements.The wavelength-dependent data are coded and then superposed, mapping the three-dimensional hyperspectral datacube to a two-dimensional image. The inversion algorithm learns a dictionary {\em in situ} from the measurements via global-local shrinkage priors. By using RGB images as side information of the compressive sensing system, the proposed approach is extended to learn a coupled dictionary from the joint dataset of the compressed measurements and the corresponding RGB images, to improve reconstruction quality. A prototype camera is built using a liquid-crystal-on-silicon modulator. Experimental reconstructions of hyperspectral datacubes from both simulated and real compressed measurements demonstrate the efficacy of the proposed inversion algorithm, the feasibility of the camera and the benefit of side information.
Analysis of Design Principles and Requirements for Procedural Rigging of Bipeds and Quadrupeds Characters with Custom Manipulators for Animation
Venue: arXiv cs.GR 2015
Year: 2015

Character rigging is a process of endowing a character with a set of custom manipulators and controls making it easy to animate by the animators. These controls consist of simple joints, handles, or even separate character selection windows.This research paper present an automated rigging system for quadruped characters with custom controls and manipulators for animation.The full character rigging mechanism is procedurally driven based on various principles and requirements used by the riggers and animators. The automation is achieved initially by creating widgets according to the character type. These widgets then can be customized by the rigger according to the character shape, height and proportion. Then joint locations for each body parts are calculated and widgets are replaced programmatically.Finally a complete and fully operational procedurally generated character control rig is created and attached with the underlying skeletal joints. The functionality and feasibility of the rig was analyzed from various source of actual character motion and a requirements criterion was met. The final rigged character provides an efficient and easy to manipulate control rig with no lagging and at high frame rate.
Recommendations on Future Operational Environments Command Control and Cyber Security
Venue: arXiv cs.CR 2015
Year: 2015

It is a well-known fact that today a nation's telecommunication networks, critical infrastructure, and information systems are vulnerable to growing number of attacks in cyberspace. Cyber space contains very different problems involving various sets of threats, targets and costs. Cyber security is not only problem of banking, communication or transportation. It also threatens core systems of army as command control. Some significant recommendations on command control (C2) and cyber security have been suggested for army computing environment in this paper. This study addresses priorities of "what should be done for a better army cyber future" to cyber security researchers.
How to Make Chord Correct
Venue: arXiv cs.DC 2015
Year: 2015

The Chord distributed hash table (DHT) is well-known and frequently used to implement peer-to-peer systems. Chord peers find other peers, and access their data, through a ring-shaped pointer structure in a large identifier space. Despite claims of proven correctness, i.e., eventual reachability, previous work has shown that the Chord ring-maintenance protocol is not correct under its original operating assumptions. It has not, however, discovered whether Chord could be made correct with reasonable operating assumptions. The contribution of this paper is to provide the first specification of correct operations and initialization for Chord, an inductive invariant that is necessary and sufficient to support a proof of correctness, and the proof itself. Most of the proof is carried out by automated analysis of an Alloy model. The inductive invariant reflects the fact that a Chord network must have a minimum ring size (the minimum being the length of successor lists plus one) to be correct. The invariant relies on an assumption that there is a stable base, of the minimum size, of permanent ring members. Because a stable base has only a few members and a Chord network can have millions, we learn that the obstacles to provable correctness are anomalies in small networks, and that a stable base need not be maintained once a Chord network grows large.
Enhancing Programming Interface to Effectively Meet Multiple Information Needs of Developers
Venue: arXiv cs.SE 2015
Year: 2015

In the past decades, integrated development environments (IDEs) have been largely advanced to facilitate common software engineering tasks. Yet, with growing information needs driven by increasing complexity in developing modern high-quality software, developers often need to switch among multiple user interfaces, even across different applications, in their development process, which breaks their mental workflow thus tends to adversely affect their working efficiency and productivity. This position paper discusses challenges faced by current IDE designs mainly from working context transitions of developers during the process of seeking multiple information needs for their development tasks. It remarks the primary blockades behind and initially explores some high-level design considerations for overcoming such challenges in the next-generation IDEs. Specifically, a few design enhancements on top of modern IDEs are envisioned, attempting to reduce the overheads of frequent context switching commonly seen in the multitasking of developers.
Database-assisted Spectrum Access in Dynamic Networks: A Distributed Learning Solution
Venue: arXiv cs.IT 2015
Year: 2015

This paper investigates the problem of database-assisted spectrum access in dynamic TV white spectrum networks, in which the active user set is varying. Since there is no central controller and information exchange, it encounters dynamic and incomplete information constraints. To solve this challenge, we formulate a state-based spectrum access game and a robust spectrum access game. It is proved that the two games are ordinal potential games with the (expected) aggregate weighted interference serving as the potential functions. A distributed learning algorithm is proposed to achieve the pure strategy Nash equilibrium (NE) of the games. It is shown that the best NE is almost the same with the optimal solution and the achievable throughput of the proposed learning algorithm is very close to the optimal one, which validates the effectiveness of the proposed game-theoretic solution.