OpenRaiser/Intern-Atlas huggingface dataset:
On the Smooth Renyi Entropy and Variable-Length Source Coding Allowing Errors
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we consider the problem of variable-length source coding allowing errors. The exponential moment of the codeword length is analyzed in the non-asymptotic regime and in the asymptotic regime. Our results show that the smooth Renyi entropy characterizes the optimal exponential moment of the codeword length.
A Practically Efficient Algorithm for Generating Answers to Keyword Search over Data Graphs
Venue: arXiv cs.DB 2015
Year: 2015

In keyword search over a data graph, an answer is a non-redundant subtree that contains all the keywords of the query. A naive approach to producing all the answers by increasing height is to generalize Dijkstra's algorithm to enumerating all acyclic paths by increasing weight. The idea of freezing is introduced so that (most) non-shortest paths are generated only if they are actually needed for producing answers. The resulting algorithm for generating subtrees, called GTF, is subtle and its proof of correctness is intricate. Extensive experiments show that GTF outperforms existing systems, even ones that for efficiency's sake are incomplete (i.e., cannot produce all the answers). In particular, GTF is scalable and performs well even on large data graphs and when many answers are needed.
Reactive Receiver Modeling for Diffusive Molecular Communication Systems with Molecule Degradation
Venue: arXiv cs.ET 2015
Year: 2015

In this paper, we consider the diffusive molecular communication channel between a transmitter nano-machine and a receiver nano-machine in a fluid environment. The information molecules released by the transmitter nano-machine into the environment can degrade in the channel via a first-order degradation reaction and those that reach the receiver nano-machine can participate in a reversible bimolecular-reaction with receiver receptor proteins. We derive a closed-form analytical expression for the expected received signal at the receiver, i.e., the expected number of activated receptors on the surface of the receiver. The accuracy of the derived analytical result is verified with a Brownian motion particle-based simulation of the environment.
Predicting the Co-Evolution of Event and Knowledge Graphs
Venue: arXiv cs.LG 2015
Year: 2015

Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities. Knowledge graphs are typically treated as static: A knowledge graph grows more links when more facts become available but the ground truth values associated with links is considered time invariant. In this paper we address the issue of knowledge graphs where triple states depend on time. We assume that changes in the knowledge graph always arrive in form of events, in the sense that the events are the gateway to the knowledge graph. We train an event prediction model which uses both knowledge graph background information and information on recent events. By predicting future events, we also predict likely changes in the knowledge graph and thus obtain a model for the evolution of the knowledge graph as well. Our experiments demonstrate that our approach performs well in a clinical application, a recommendation engine and a sensor network application.
Linear Approximations to AC Power Flow in Rectangular Coordinates
Venue: arXiv cs.SY 2015
Year: 2015

This paper explores solutions to linearized powerflow equations with bus-voltage phasors represented in rectangular coordinates. The key idea is to solve for complex-valued perturbations around a nominal voltage profile from a set of linear equations that are obtained by neglecting quadratic terms in the original nonlinear power-flow equations. We prove that for lossless networks, the voltage profile where the real part of the perturbation is suppressed satisfies active-power balance in the original nonlinear system of equations. This result motivates the development of approximate solutions that improve over conventional DC power-flow approximations, since the model includes ZIP loads. For distribution networks that only contain ZIP loads in addition to a slack bus, we recover a linear relationship between the approximate voltage profile and the constant-current component of the loads and the nodal active and reactive-power injections.
Adaptive Feed Rate Policies for Spiral Drilling Using Markov Decision Process
Venue: arXiv cs.SY 2015
Year: 2015

In this study, the feed rate optimization model based on a Markov Decision Process (MDP) was introduced for spiral drilling process. Firstly, the experimental data on spiral drilling was taken from literature for different axial force parameters and with various feed rate decisions made, having the length of a hole being drilled as a reward. Proposed optimization model was computed using value iteration method. Secondly, the results of computations were displayed for optimal decision to be made on each state. Proposed decisions for an optimal feed rate could be utilized in order to improve the efficiency of spiral drilling process in terms of cost and time.
Deep Learning with S-shaped Rectified Linear Activation Units
Venue: arXiv cs.CV 2015
Year: 2015

Rectified linear activation units are important components for state-of-the-art deep convolutional networks. In this paper, we propose a novel S-shaped rectified linear activation unit (SReLU) to learn both convex and non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters. The SReLU is learned jointly with the training of the whole deep network through back propagation. During the training phase, to initialize SReLU in different layers, we propose a "freezing" method to degenerate SReLU into a predefined leaky rectified linear unit in the initial several training epochs and then adaptively learn the good initial values. SReLU can be universally used in the existing deep networks with negligible additional parameters and computation cost. Experiments with two popular CNN architectures, Network in Network and GoogLeNet on scale-various benchmarks including CIFAR10, CIFAR100, MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement compared to other activation functions.
Using HistCite software to identify significant articles in subject searches of the Web of Science
Venue: arXiv cs.DL 2015
Year: 2015

HistCite TM is a large-scale computer tool for mapping science. Its power of visualization combines the production of historiographs on the basis of the analysis of co-citations of documents, with the use of bibliometrics specific indicators. The objective of this article is, to present the advantages of the new bibliometrics configuration of HistCite TM (2004) when identifying articles. The analysis of the histograms that produces HistCite TM , in terms of cumulative advantage and aging of the citations. And the comparative study of the results of HistCite TM , in its indicators of amplitude and recognition. Also is examined its treatment of the sampling problems, by formalizing the Kendall method of estimating the robust standard deviation.
A Novel Approach to Compress Centralized Text Data using Indexed Dictionary
Venue: arXiv cs.OH 2015
Year: 2015

Data compression is very important feature in terms of saving the memory space. In this proposal, an indexed dictionary based compression is used for text data, where the word's reference in dictionary is used for compression. This approach is not file based, a common dictionary is used for compression. Which contains the words, the position of the word in dictionary is one of the key parts of encoded frame which is compressed form of the text word. This is loss-less compression. This compression approach is also take cares of small words like one or two characters words which usually decrease the efficiency of compression algorithms. This approach is also deals with file having special characters as a word. Special character words, alpha numeric words, normal texted words and small words all deals differently which makes this approach more efficient. Since a centralized dictionary is used for data compression, therefore, this approach is not preferred for transfer compressed file, while it is suitable to store text data in compressed form in hard disk drive and centralized storage or cloud drive for memory utilization.
Risk Sensitive, Nonlinear Optimal Control: Iterative Linear Exponential-Quadratic Optimal Control with Gaussian Noise
Venue: arXiv cs.SY 2015
Year: 2015

In this contribution, we derive ILEG, an iterative algorithm to find risk sensitive solutions to nonlinear, stochastic optimal control problems. The algorithm is based on a linear quadratic approximation of an exponential risk sensitive nonlinear control problem. ILEG allows to find risk sensitive policies and thus generalizes previous algorithms to solve nonlinear optimal control based on iterative linear-quadratic methods. Depending on the setting of the parameter controlling the risk sensitivity, two different strategies on how to cope with the risk emerge. For positive-value parameters, the control policy uses high feedback gains whereas for negative-value parameters, it uses a robust feedforward control strategy (a robust plan) with low gains. These results are illustrated with a simple example. This note should be considered as a preliminary report.
Measuring Social Well Being in The Big Data Era: Asking or Listening?
Venue: arXiv cs.CY 2015
Year: 2015

The literature on well being measurement seems to suggest that "asking" for a self-evaluation is the only way to estimate a complete and reliable measure of well being. At the same time "not asking" is the only way to avoid biased evaluations due to self-reporting. Here we propose a method for estimating the welfare perception of a community simply "listening" to the conversations on Social Network Sites. The Social Well Being Index (SWBI) and its components are proposed through to an innovative technique of supervised sentiment analysis called iSA which scales to any language and big data. As main methodological advantages, this approach can estimate several aspects of social well being directly from self-declared perceptions, instead of approximating it through objective (but partial) quantitative variables like GDP; moreover self-perceptions of welfare are spontaneous and not obtained as answers to explicit questions that are proved to bias the result. As an application we evaluate the SWBI in Italy through the period 2012-2015 through the analysis of more than 143 millions of tweets.
Optimal Sample Complexity for Blind Gain and Phase Calibration
Venue: arXiv cs.IT 2015
Year: 2015

Blind gain and phase calibration (BGPC) is a structured bilinear inverse problem, which arises in many applications, including inverse rendering in computational relighting (albedo estimation with unknown lighting), blind phase and gain calibration in sensor array processing, and multichannel blind deconvolution. The fundamental question of the uniqueness of the solutions to such problems has been addressed only recently. In a previous paper, we proposed studying the identifiability in bilinear inverse problems up to transformation groups. In particular, we studied several special cases of blind gain and phase calibration, including the cases of subspace and joint sparsity models on the signals, and gave sufficient and necessary conditions for identifiability up to certain transformation groups. However, there were gaps between the sample complexities in the sufficient conditions and the necessary conditions. In this paper, under a mild assumption that the signals and models are generic, we bridge the gaps by deriving tight sufficient conditions with optimal sample complexities.
Galois Self-Dual Constacyclic Codes
Venue: arXiv cs.IT 2015
Year: 2015

Generalizing Euclidean inner product and Hermitian inner product, we introduce Galois inner products, and study the Galois self-dual constacyclic codes in a very general setting by a uniform method. The conditions for existence of Galois self-dual and isometrically Galois self-dual constacyclic codes are obtained. As consequences, the results on self-dual, iso-dual and Hermitian self-dual constacyclic codes are derived.
Energy-aware Fixed-Priority Multi-core Scheduling for Real-time Systems
Venue: arXiv cs.OS 2015
Year: 2015

Multi-core processors are becoming more and more popular in embedded and real-time systems. While fixed-priority scheduling with task-splitting in real-time systems are widely applied, current approaches have not taken into consideration energy-aware aspects such as dynamic voltage/frequency scheduling (DVS). In this paper, we propose two strategies to apply dynamic voltage scaling (DVS) to fixed-priority scheduling algorithms with task-splitting for periodic real-time tasks on multi-core processors. The first strategy determines voltage scales for each processor after scheduling (Static DVS), which ensures all tasks meet the timing requirements on synchronization. The second strategy adaptively determines the frequency of each task before scheduling (Adaptive DVS) according to the total utilization of task-set and number of cores available. The combination of frequency pre-allocation and task-splitting makes it possible to maximize energy savings with DVS. Simulation results show that it is possible to achieve significant energy savings with DVS while preserving the schedulability requirements of real-time schedulers for multi-core processors.
A short proof of the first selection lemma and weak $\frac{1}{r}$-nets for moving points
Venue: arXiv cs.DM 2015
Year: 2015

(i) We provide a short and simple proof of the first selection lemma. (ii) We also prove a selection lemma of a new type in $\Re^d$. For example, when $d=2$ assuming $n$ is large enough we prove that for any set $P$ of $n$ points in general position there are $Ω(n^4)$ pairs of segments spanned by $P$ all of which intersect in some fixed triangle spanned by $P$. (iii) Finally, we extend the weak $\frac{1}{r}$-net theorem to a kinetic setting where the underlying set of points is moving polynomially with bounded description complexity. We establish that one can find a kinetic analog $N$ of a weak $\frac{1}{r}$-net of cardinality $O(r^{\frac{d(d+1)}{2}}\log^{d}r)$ whose points are moving with coordinates that are rational functions with bounded description complexity. Moreover, each member of $N$ has one polynomial coordinate.
A New PID Neural Network Controller Design for Nonlinear Processes
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, a novel adaptive tuning method of PID neural network (PIDNN) controller for nonlinear process is proposed. The method utilizes an improved gradient descent method to adjust PIDNN parameters where the margin stability will be employed to get high tracking performance and robustness with regard to external load disturbance and parameter variation. Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.
Representation and Coding of Signal Geometry
Venue: arXiv cs.IT 2015
Year: 2015

Approaches to signal representation and coding theory have traditionally focused on how to best represent signals using parsimonious representations that incur the lowest possible distortion. Classical examples include linear and non-linear approximations, sparse representations, and rate-distortion theory. Very often, however, the goal of processing is to extract specific information from the signal, and the distortion should be measured on the extracted information. The corresponding representation should, therefore, represent that information as parsimoniously as possible, without necessarily accurately representing the signal itself. In this paper, we examine the problem of encoding signals such that sufficient information is preserved about their pairwise distances and their inner products. For that goal, we consider randomized embeddings as an encoding mechanism and provide a framework to analyze their performance. We also demonstrate that it is possible to design the embedding such that it represents different ranges of distances with different precision. These embeddings also allow the computation of kernel inner products with control on their inner product-preserving properties. Our results provide a broad framework to design and analyze embeddins, and generalize existing results in this area, such as random Fourier kernels and universal embeddings.
Desensitized Cubature Kalman Filter with Uncertain Parameter
Venue: arXiv cs.SY 2015
Year: 2015

A robust desensitized cubature Kalman filtering (DCKF) for nonlinear systems with uncertain parameter is proposed. Sensitivity matrices are defined as the integral form, and desensitized cost function is designed by penalizing the posterior covariance trace by a sensitivity-weighting sum of the posteriori sensitivities. The DCKF gain is obtained by minimizing the desensitized cost function to amend the state estimation. Then, the sensitivity propagation of the state estimate errors is described, and the sensitivity of the root square matrix is obtained by solving a special equation. The effectiveness of the proposed DCKF was demonstrated by two numerical simulations with uncertain parameters.
A Context-Oriented Extension of F#
Venue: arXiv cs.PL 2015
Year: 2015

Context-Oriented programming languages provide us with primitive constructs to adapt program behaviour depending on the evolution of their operational environment, namely the context. In previous work we proposed ML_CoDa, a context-oriented language with two-components: a declarative constituent for programming the context and a functional one for computing. This paper describes the implementation of ML_CoDa as an extension of F#.
Service Choreography, SBVR, and Time
Venue: arXiv cs.SE 2015
Year: 2015

We propose the use of structured natural language (English) in specifying service choreographies, focusing on the what rather than the how of the required coordination of participant services in realising a business application scenario. The declarative approach we propose uses the OMG standard Semantics of Business Vocabulary and Rules (SBVR) as a modelling language. The service choreography approach has been proposed for describing the global orderings of the invocations on interfaces of participant services. We therefore extend SBVR with a notion of time which can capture the coordination of the participant services, in terms of the observable message exchanges between them. The extension is done using existing modelling constructs in SBVR, and hence respects the standard specification. The idea is that users - domain specialists rather than implementation specialists - can verify the requested service composition by directly reading the structured English used by SBVR. At the same time, the SBVR model can be represented in formal logic so it can be parsed and executed by a machine.
Fast Acquisition for Quantitative MRI Maps: Sparse Recovery from Non-linear Measurements
Venue: arXiv cs.CV 2015
Year: 2015

This work addresses the problem of estimating proton density and T1 maps from two partially sampled K-space scans such that the total acquisition time remains approximately the same as a single scan. Existing multi parametric non linear curve fitting techniques require a large number (8 or more) of echoes to estimate the maps resulting in prolonged (clinically infeasible) acquisition times. Our simulation results show that our method yields very accurate and robust results from only two partially sampled scans (total scan time being the same as a single echo MRI). We model PD and T1 maps to be sparse in some transform domain. The PD map is recovered via standard Compressed Sensing based recovery technique. Estimating the T1 map requires solving an analysis prior sparse recovery problem from non linear measurements, since the relationship between T1 values and intensity values or K space samples is not linear. For the first time in this work, we propose an algorithm for analysis prior sparse recovery for non linear measurements. We have compared our approach with the only existing technique based on matrix factorization from non linear measurements; our method yields considerably superior results.
RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles
Venue: arXiv cs.AI 2015
Year: 2015

Recently, several large-scale RDF knowledge bases have been built and applied in many knowledge-based applications. To further increase the number of facts in RDF knowledge bases, logic rules can be used to predict new facts based on the existing ones. Therefore, how to automatically learn reliable rules from large-scale knowledge bases becomes increasingly important. In this paper, we propose a novel rule learning approach named RDF2Rules for RDF knowledge bases. RDF2Rules first mines frequent predicate cycles (FPCs), a kind of interesting frequent patterns in knowledge bases, and then generates rules from the mined FPCs. Because each FPC can produce multiple rules, and effective pruning strategy is used in the process of mining FPCs, RDF2Rules works very efficiently. Another advantage of RDF2Rules is that it uses the entity type information when generates and evaluates rules, which makes the learned rules more accurate. Experiments show that our approach outperforms the compared approach in terms of both efficiency and accuracy.
Hangable Graphs
Venue: arXiv cs.DM 2015
Year: 2015

Let $G=(V_G,E_G)$ be a connected graph. The distance $d_G(u,v)$ between vertices $u$ and $v$ in $G$ is the length of a shortest $u-v$ path in $G$. The eccentricity of a vertex $v$ in $G$ is the integer $e_G(v)= \max\{ d_G(v,u) \colon u\in V_G\}$. The diameter of $G$ is the integer $d(G)= \max\{e_G(v)\colon v\in V_G\}$. The periphery of a~vertex $v$ of $G$ is the set $P_G(v)= \{u\in V_G\colon d_G(v,u)= e_G(v)\}$, while the periphery of $G$ is the set $P(G)= \{v\in V_G\colon e_G(v)=d(G)\}$. We say that graph $G$ is hangable if $P_G(v)\subequal P(G)$ for every vertex $v$ of $G$. In this paper we prove that every block graph is hangable and discuss the hangability of products of graphs.
Mining Top-K Co-Occurrence Items
Venue: arXiv cs.DB 2015
Year: 2015

Frequent itemset mining has emerged as a fundamental problem in data mining and plays an important role in many data mining tasks, such as association analysis, classification, etc. In the framework of frequent itemset mining, the results are itemsets that are frequent in the whole database. However, in some applications, such recommendation systems and social networks, people are more interested in finding out the items that occur with some user-specified itemsets (query itemsets) most frequently in a database. In this paper, we address the problem by proposing a new mining task named top-k co-occurrence item mining, where k is the desired number of items to be found. Four baseline algorithms are presented first. Then, we introduce a special data structure named Pi-Tree (Prefix itemset Tree) to maintain the information of itemsets. Based on Pi-Tree, we propose two algorithms, namely PT (Pi-Tree-based algorithm) and PT-TA (Pi-Tree-based algorithm with TA pruning), for mining top-k co-occurrence items by incorporating several novel strategies for pruning the search space to achieve high efficiency. The performance of PT and PT-TA was evaluated against the four proposed baseline algorithms on both synthetic and real databases. Extensive experiments show that PT not only outperforms other algorithms substantially in terms execution time but also has excellent scalability.