Miracle-dz/UniFAR-FTUs huggingface dataset:
Grammatical Ziv-Lempel Compression: Achieving PPM-Class Text Compression Ratios with LZ-Class Decompression Speed

Summary form only given: GLZA is a free, open-source, enhanced grammar-based compressor that constructs a low entropy grammar amenable to entropy coding, using a greedy hill-climbing search guided by estimates of encoded string lengths; the estimates are efficiently computed incrementally during (parallelized) suffix tree construction in a batched iterative repeat replacement cycle. The grammar-coded symbol stream is further compressed by order-1 Markov modeling of trailing/leading subsymbols and selective recency modeling, MTF-coding only symbols that tend to recur soon. This combination results in excellent compression ratios-similar to PPMC's for small files, averaging within about five percent of PPMd's for large text files (1 MB - 10 MB)-with fast decompression on one core or two. Compression time and memory use are not dramatically higher than for similarly high-performance asymmetrical compressors of other kinds. GLZA is on the Pareto frontier for text compression ratio and decompression speed on a variety of benchmarks (LTCB, Calgary, Canterbury, Large Canterbury, Silesia, Maximum Compression, World Compression Challenge), compressing better and/or decompressing faster than its competitors (PPM, LZ77-Markov, BWT, etc.), with better compression ratios than previous grammar-based compressors such as RePair, Sequitur, Offline 3 (Greedy), Sequential/grzip, and IRR-S.
Semantic Distance in WordNet: A Simplified and Improved Measure of Semantic Relatedness

Measures of semantic distance have received a great deal of attention recently in the field of computational lexical semantics. Although techniques for approximating the semantic distance of two concepts have existed for several decades, the introduction of the WordNet lexical database and improvements in corpus analysis have enabled significant improvements in semantic distance measures. In this study we investigate a special kind of semantic distance, called semantic relatedness. Lexical semantic relatedness measures have proved to be useful for a number of applications, such as word sense disambiguation and real-word spelling error correction. Most relatedness measures rely on the observation that the shortest path between nodes in a semantic network provides a representation of the relationship between two concepts. The strength of relatedness is computed in terms of this path. This dissertation makes several significant contributions to the study of semantic relatedness. We describe a new measure that calculates semantic relatedness as a function of the shortest path in a semantic network. The proposed measure achieves better results than other standard measures and yet is much simpler than previous models. The proposed measure is shown to achieve a correlation of r = 0.897 with the judgments of human test subjects using a standard benchmark data set, representing the best performance reported in the literature. We also provide a general formal description for a class of semantic distance measures — namely, those measures that compute semantic distance from the shortest path in a semantic network. Lastly, we suggest a new methodology for developing path-based semantic distance measures that would limit the possibility of unnecessary complexity in future measures.
Applying transdisciplinarity principles in the information services co-creation process

Increasing popularity of the notion of service in the enterprise applications development leads to the new types of inter-organizational collaborations. We can observe a shift in this collaboration from simple exchange of services to the co-creation of transdisciplinary services offering a new and unique value to the involved partners. In this paper we define the notion of transdisciplinary information service and we discuss the main principles, namely communication, collaboration, co-innovation, and agility, to be considered in the transdisciplinary information services co-creation and we identify the existing approaches and techniques that support the implementation of these principles. Finally, we introduce our proposal for the elaboration of a method family supporting the co-creation of transdisciplinary information services. This method family aims to provide a flexible and agile process model based on transdisciplinarity principles and allowing to combine method chunks from different disciplines in order to support collaborative creativity, modeling and development of transdisciplinary services.
Monte Carlo studies of vacancy migration in binary ordered alloys: I

Using a Monte Carlo technique, a simulation study is made of vacancy migration in the binary ordered alloys AB (simple cubic) and B3A (face-centred cubic). The resulting self-diffusion is calculated and in the first case compares very favourably with the existing experimental results of Kuper et al. for a body-centred binary alloy. Quite different results are predicted for an alloy of form B3A and it is hoped that comparison with experiment will establish the importance of the isolated-vacancy mechanism as the means for producing self-diffusion.
Airway epithelial tight junctions and binding and cytotoxicity of Pseudomonas aeruginosa.

The role of tight junctions in the binding and cytoxicity of Pseudomonas aeruginosa to apical or basolateral membranes of lung airway epithelial cells was tested with fluorescence microscopy on living cells. Binding of noncytotoxic P. aeruginosa strain O1 was assessed with P. aeruginosa that expressed green fluorescent protein. Binding of cytotoxic P. aeruginosa strain 6206 was assessed with FITC-labeled P. aeruginosa; cytotoxicity was determined from nuclear uptake of the impermeant dye propidium iodide. The role of direct contact of P. aeruginosa to epithelial cells was tested with filters with small (0.45-micrometer) or large (2.0-micrometer) pores. High transepithelial resistance (R(t)) Calu-3 and cultured bovine tracheal monolayers (R(t) > 1,000 Omega. cm(2)) bound P. aeruginosa very infrequently (<1 P. aeruginosa/100 cells) at the apical membrane, but P. aeruginosa bound frequently to cells near "free edges" at holes, wounds, islands, and perimeters; cytotoxicity required direct interaction with basolateral membranes. Wounded high R(t) epithelia showed increased P. aeruginosa binding and cytotoxicity at the free edges because basolateral membranes were accessible to P. aeruginosa, and dead and living cells near the wound bound P. aeruginosa similarly. Compared with high R(t) epithelia, low R(t) CFT1 (R(t) = 100-200 Omega. cm(2)) and EGTA-treated Calu-3 monolayers were 25 times more susceptible to P. aeruginosa binding throughout the monolayer. Cytotoxicity to CFT1 cells (throughout the confluent monolayer, not only at the free edge) occurred after a shorter delay (0.25 vs. 2.0 h) and then five times faster than to Calu-3 cells, indicating that the time course of P. aeruginosa cytotoxicity may be limited by the rate of gaining access through tight junctions and that this occurred faster in low R(t) than in high R(t) airway epithelia. Cytotoxicity appeared to occur in a sequential process that led first to a loss of fura 2 and a later uptake of propidium iodide. P. aeruginosa bound three times more frequently to regions between cells (tight junctions? ) than to cell membranes of low R(t) CFT1 cells.
Improved approximation algorithms for MAX SAT

MAX SAT (the maximum satisfiability problem) is stated as follows: given a set of clauses with weights, find a truth assignment that maximizes the sum of the weights of the satisfied clauses. In this paper, we consider approximation algorithms for MAX SAT proposed by Goemans and Williamson and present a sharpened analysis of their performance guarantees. We also show that these algorithms, combined with recent approximation algorithms for MAX 2SAT, MAX 3SAT, and MAX SAT due to Feige and Goemans, Karloff and Zwick, and Zwick, respectively, lead to an improved approximation algorithm for MAX SAT. By using the MAX 2SAT and 3SAT algorithms, we obtain a performance guarantee of 0.7846, and by using Zwick's algorithm, we obtain a performance guarantee of 0.8331, which improves upon the performance guarantee of 0.7977 based on Zwick's conjecture. The best previous result for MAX SAT without assuming Zwick's conjecture is a 0.770-approximation algorithm of Asano. Our best algorithm requires a new family of 34-approximation algorithms that generalize a previous algorithm of Goemans and Williamson.
An Architecture for Data-to-Text Systems

I present an architecture for data-to-text systems, that is NLG systems which produce texts from non-linguistic input data; this essentially extends the architecture of Reiter and Dale (2000) to systems whose input is raw data instead of AI knowledge bases. This architecture is being used in the BabyTalk project, and is based on experiences in several projects at Aberdeen; it also seems to be compatible with many data-to-text systems developed elsewhere. It consists of four stages which are organised in a pipeline: Signal Analysis, Data Interpretation, Document Planning, and Microplanning and Realisation.
Genome-Wide Association Study of Coronary Artery Disease

Coronary artery disease (CAD) is a multifactorial disease with environmental and genetic determinants. The genetic determinants of CAD have previously been explored by the candidate gene approach. Recently, the data from the International HapMap Project and the development of dense genotyping chips have enabled us to perform genome-wide association studies (GWAS) on a large number of subjects without bias towards any particular candidate genes. In 2007, three chip-based GWAS simultaneously revealed the significant association between common variants on chromosome 9p21 and CAD. This association was replicated among other ethnic groups and also in a meta-analysis. Further investigations have detected several other candidate loci associated with CAD. The chip-based GWAS approach has identified novel and unbiased genetic determinants of CAD and these insights provide the important direction to better understand the pathogenesis of CAD and to develop new and improved preventive measures and treatments for CAD.
Application of DNA typing methods to epidemiology and taxonomy of Candida species

Methods are described for extraction of DNA from the yeast form of Candida spp. , followed by digestion and electrophoresis of DNA fragments. The resulting gel patterns (greater than 100 bands) were used to type Candida isolates. Four intense bands identified, three of which are present in each isolate (6 to 7, 3.7 or 4.2, and 2.5 to 3 kilobases), appear to be DNA encoding the rRNA. The methods proved to be both simple and reproducible. The patterns were shown to be stable through several hundred doublings from multiple single colonies. A survey of isolates showed that, on the basis of similarity of gel patterns, several Candida species could be sorted into mutually exclusive groups, and subgroups could be created. Analyses of this survey suggested the possible epidemiologic and taxonomic applications of these methods. DNA typing methods appear to offer important potential advantages over phenotyping methods. The methods provide a base for further epidemiologic studies and for further development of techniques, such as the use of cloned probes for studies of DNA homology.
Segmentation and modeling in segment-based recognition

Recently, we have developed a probabilistic framework for segmentbased speech recognition that represents the speech signal as a network of segments and associated feature vectors [2]. Although in general, each path through the network does not traverse all segments, we argued that each path must account for all feature vectors in the network. We then demonstrated an efficient search algorithm that uses a single additional model to account for segments that are not traversed. In this paper, we present two new extensions to our framework. First, we replace our acoustic segmentation algorithm with “segmentation by recognition,” a probabilistic algorithm that can combine multiple contextual constraints towards hypothesizing only the most likely segments. Second, we generalize our framework to “near-miss modeling” and describe a search algorithm that can efficiently use multiple models to enforce contextual constraints across all segments in a network. We report experiments in phonetic recognition on the TIMIT corpus in which we achieve a diphone context-dependent error rate of 26.6% on the NIST core test set over 39 classes. This is a 12.8% reduction in error rate from our best previously reported result.
Distance-weighted manifold learning in facial expression recognition

Facial expression recognition is a challenge problem in biometric identification. Many traditional methods, i.e. Local Preserving Projection (LPP) algorithm, have attempted to solve the problem. In this paper, a distance weighted supervised manifold learning algorithm based on LPP is also proposed. In the proposed method, a distance-weighted matrix, which fully takes advantage of the class information and degree of classes' diversity, is introduced. And then, the weighted LPP based on the distance-weighted matrix is applied to facial expression data. Compared with LPP, Unsupervised Discriminant Projection (UDP), Kernel Linear Discriminant Analysis (KLDA), experiment results on CK and JAFFE face database show that our method is effective and efficiency.
Application of BP Neural Network to Short-Term-Ahead Generating Power Forecasting for PV System

A short-term generating capacity forecast model of a PV power station was proposed. The forecasting days were classified by the season and weather, and various neural network models were adopted to analyze the system. The radiation of PV power station was added to the model as an input parameter to improve the forecasting precision. The forecasting precisions based on the historical data of the actual PV power station in different seasons and weather were compared and analyzed. The results show that the proposed method can improve the forecasting precision of generating capacity.
Discrimination between Similar Languages, Varieties and Dialects using CNN- and LSTM-based Deep Neural Networks

In this paper, we describe a system (CGLI) for discriminating similar languages, varieties and dialects using convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks. We have participated in the Arabic dialect identification sub-task of DSL 2016 shared task for distinguishing different Arabic language texts under closed submission track. Our proposed approach is language independent and works for discriminating any given set of languages, varieties, and dialects. We have obtained 43.29% weighted-F1 accuracy in this sub-task using CNN approach using default network parameters.
Estimation of mechanical properties of steel strip in hot dip galvanising lines

Abstract In this paper, the application of data mining and artificial intelligence techniques stemming from other problem areas to the particular case of a galvanised steel manufacturing process, is presented. The main goal is to optimise the quality control of galvanised steel by developing a predictive model of the mechanical properties according to the chemical composition and manufacturing conditions in the annealing furnace.
Improving Embeddings by Flexible Exploitation of Side Information

Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional manifold. The resulting embeddings, however, may fail to capture features of interest. One solution is to learn a distance metric which prefers embeddings that capture the salient features. We propose a novel approach to learning a metric from side information to guide the embedding process. Our approach admits the use of two kinds of side information. The first kind is class-equivalence information, where some limited number of pairwise "same/different class" statements are known. The second form of side information is a limited set of distances between pairs of points in the target metric space. We demonstrate the effectiveness of the method by producing embeddings that capture features of interest.
Particle filtering based recovery of noisy GARCH processes

In this paper, we address the problem of enhancement of a noisy GARCH process using a particle filter. We compare our approach experimentally to a previously developed recursive estimation scheme. Simulations indicate that a significant gain in performance is obtained, at the cost of higher sensitivity to errors in the GARCH parameters. The proposed method allows tackling arbitrary driving noise distributions as well as arbitrary fidelity criteria.
Elasto-damage Model for High Strength Concrete Subjected to Multiaxial Loading

An effective compliance matrix Č is proposed to model the behavior of concrete based on phenomenological evidence and physical insight. Three parameters α, β, and γ are introduced in the effective compliance matrix Č. α and β are introduced to model the different behavior of concrete in tension and compression, while the third parameter γ is introduced to account for volumetric change. The predictive capability of the proposed elasto-damage model for uniaxial and multiaxial stress paths is investigated for uniaxial compression, biaxial compression, triaxial compression, uniaxial tension, and tension—compression— compression. The simulative capability of the model to capture the phenomenological behavior of concrete such as strain softening, stiffness degradation, biaxial strength envelope, volumetric dilatation, different behavior in tension and compression, and gain in strength under increasing confinement is reflected. The predicted results correlate well with the available experimental data.
Design considerations of a contact slider and bouncing vibration in near-contact regime

This article introduces the results of our recent study on the design conditions for a contact and near-contact slider and friction-induced bouncing vibrations in a near-contact regime, based on computer simulations using a simple dynamical model of slider, air-bearing, and disk systems. Basic design conditions for contact sliding are discussed. The contact force and attractive force in asperity contact with a lubricant layer and bulk deformation are calculated numerically. Flying height hysteresis and bouncing vibration of a flying slider that are experimentally observed in nanometer spacing can be obtained by the simulation.
Static benchmarking of membrane helix predictions

Prediction of trans-membrane helices continues to be a difficult task with a few prediction methods clearly taking the lead; none of these is clearly best on all accounts. Recently, we have carefully set up protocols for benchmarking the most relevant aspects of prediction accuracy and have applied it to >30 prediction methods. Here, we present the extension of that analysis to the level of an automatic web server evaluating new methods (http://cubic.bioc.columbia.edu/services/tmh_benchmark/). The most important achievements of the tool are: (i) any new method is compared to the battery of well-established tools; (ii) the battery of measures explored allows spotting strengths in methods that may not be 'best' overall. In particular, we report per-residue and per-segment scores for accuracy and the error-rates for confusing membrane helices with globular proteins or signal peptides. An additional feature is that developers can directly investigate any hydrophobicity scale for its potential in predicting membrane helices.
Effect of functionality levels and compatibility of polycarbonate blends with maleic anhydride grafted ABS

ABS was melt grafted with maleic anhydride at three different levels of 1, 2, and 3 wt %. These three different modified ABSs were melt blended with polycarbonate using a single-screw extruder to choose a suitable maleic anhydride grafting level on ABS for better performance. For the compatibility study, binary blends of polycarbonate with ABS and maleic anhydride-grafted ABS were prepared over the entire range of compositions. Compatibility of these blends was studied using a Dynamic Mechanical Analyzer and by Differential Scanning Calorimetry. Both techniques suggest more partial compatibility for modified blend systems. In addition to this, DSC thermograms show multiple peaks between the transition points of ABS fractions and polycarbonate fractions for the polycarbonate/maleic anhydride-grafted ABS blends. These multiple peaks are characteristic of better partial compatibility with fine microstructure.
Tessa, a system to aid communication with deaf people

TESSA is an experimental system that aims to aid transactions between a deaf person and a clerk in a Post Office by translating the clerk's speech to sign language. A speech recogniser recognises speech from the clerk and the system then synthesizes the appropriate sequence of signs in British Sign language (BSL) using a specially-developed avatar. By using a phrase lookup approach to language translation, which is appropriate for the highly constrained discourse in a Post Office, we were able to build a working system that we could evaluate. We summarise the results of this evaluation (undertaken by deaf users and Post office clerks), and discuss how the findings from the evaluation are being used in the development of an improved system.
Use of sugarcane bagasse as an alternative low-cost support material during the rooting stage of apple micropropagation

SummaryStudies were carried out to evaluate sugarcane bagasse as an alternative to agar for micropropagation of apple clones to reduce the cost of micropropagation and improve the quality of the propagules. Significant improvement in the in vitro rooting process, coupled with cost reduction, were obtained by the use of sugarcane bagasse as a substitute for the traditionally used agar-gelled medium. The tests were undertaken with micro-cuttings of the apple rootstock Marubakaido (Malus prunifolia Borkh.) using a rooting medium composed of half-strength Murashige and Skoog salts and vitamins, 3% (w/v) sucrose, and 0.49 μM indole-3-butyric acid. The plants grown on sugarcane bagasse yielded a 22% increase in root length, 20% increase in plant length, and 63% increase in the number of roots, compared with agar-grown micro-cuttings. Particle size of the sugarcane bagasse had a significant impact on all those parameters, and the best results were obtained with bagasse comprising particles smaller than 0.18 mm. The results demonstrated that the sugarcane bagasse could be used effectively as a substitute for agar during rooting of apple shoots.
Estimating parameters in physical models using MathModelica (cid:1)

This paper describes a program which extends Math-Modelica so that model parameters can be estimated using measured data. Given initial values of the parameters, the parameter estimates are iteratively changed so that the sum of squared errors of the difference between the model output and the data is minimized. In each iteration an extended differential equation has to be simulated. The developed program imports the Modelica model into Mathematica and derives a symbolic expression for this extended differential equation. The extended model i converted to Mod-elica format and MathModelica is used to simulate it ef ﬁ ciently.