Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel image matching approach for word spotting

Word spotting has been adopted and used by various researchers as a complementary technique to Optical Character Recognition for document analysis and retrieval. The various applications of word spotting include document indexing, image retrieval and information filtering. The important factors in word spotting techniques are pre-processing, selection and extraction of proper features and image matching algorithms. The Correlation Similarity Measure (CORR) algorithm is considered to be a faster matching algorithm, originally defined for finding similarities between binary patterns. In the word spotting literature the CORR algorithm has been used successfully to compare the GSC binary features extracted from binary word images, i.e., Gradient, Structural and Concavity (GSC) features. However, the problem with this approach is that binarization of images leads to a loss of very useful information. Furthermore, before extracting GSC binary features the word images must be skew corrected and slant normalized, which is not only difficult but in some cases impossible in Arabic and modified Arabic scripts. We present a new approach in which the Correlation Similarity Measure (CORR) algorithm has been used innovatively to compare Gray-scale word images. In this approach, binarization of images, skew correction and slant normalization of word images are not required at all. The various features, i.e., projection profiles, word profiles and transitional features are extracted from the Gray-scale word images and converted into their binary equivalents, which are compared via CORR algorithm with greater speed and higher accuracy. The experiments have been conducted on Gray-scale versions of newly created handwritten databases of Pashto and Dari languages, written in modified Arabic scripts. For each of these languages we have used 4599 words relating to 21 different word classes collected from 219 writers. The average precision rates achieved for Pashto and Dari languages were 93.18 % and 93.75 %, respectively. The time taken for matching a pair of images was 1.43 milli-seconds. In addition, we will present the handwritten databases for two well-known Indo- Iranian languages, i.e., Pashto and Dari languages. These are large databases which contain six types of data, i.e., Dates, Isolated Digits, Numeral Strings, Isolated Characters, Different Words and Special Symbols, written by native speakers of the corresponding languages.
A Calibration Method for Evaluation of Sentiment Analysis

Sentiment analysis is the computational task of extracting sentiment from a text document – for example whether it expresses a positive, negative or neutral opinion. Various approaches have been introduced in recent years, using a range of different techniques to extract sentiment information from a document. Measuring these methods against a gold standard dataset is a useful way to evaluate such systems. However, different sentiment analysis techniques represent sentiment values in different ways, such as discrete categorical classes or continuous numerical sentiment scores. This creates a challenge for evaluating and comparing such systems; in particular assessing numerical scores against datasets that use fixed classes is difficult, because the numerical outputs have to be mapped onto the ordered classes. This paper proposes a novel calibration technique that uses precision vs. recall curves to set class thresholds to optimize a continuous sentiment analyser’s performance against a discrete gold standard dataset. In experiments mapping a continuous score onto a three-class classification of movie reviews, we show that calibration results in a substantial increase in f-score when compared to a non-calibrated mapping.
Discovery of EST-derived microsatellite primers in the legume Lens culinaris (Fabaceae)1

Premise of the study: We developed microsatellite markers in the legume Lens culinaris from publicly available databases to enrich the limited marker resource available for the crop. Methods and Results: Eighty-two primer sets were identified using expressed sequence sets of L. culinaris available in the National Center for Biotechnology Information (NCBI) database and were characterized in six species of Lens. Among them, 20 simple sequence repeat (SSR) primers produced no amplification product, 43 produced monomorphic products, and 19 were polymorphic. The primers amplified mono-, di-, tri-, tetra-, penta-, and hexanucleotide repeats with one to four alleles. These SSR loci successfully amplified in five related wild species, with a total of 61 primer pairs in L. nigricans and L. odemensis (98.39%), 59 in L. tomentosus (95.1 %), and 60 in L. ervoides and L. orientalis (96.7%), respectively. Conclusions: The microsatellite markers discovered in this study will be useful in genetic mapping, marker-assisted breeding, and characterization of germplasm.
Monocular Road Detection Using Structured Random Forest

Road detection is a key task for autonomous land vehicles. Monocular vision-based road-detection algorithms are mostly based on machine learning approaches and are usually cast as classification problems. However, the pixel-wise classifiers are faced with the ambiguity caused by changes in road appearance, illumination and weather. An effective way to reduce the ambiguity is to model the contextual information with structured learning and prediction. Currently, the widely used structured prediction model in road detection is the Markov random field or conditional random field. However, the random field-based methods require additional complex optimization after pixel-wise classification, making them unsuitable for real-time applications. In this paper, we present a structured random forest-based road-detection algorithm which is capable of modelling the contextual information efficiently. By mapping the structured label space to a discrete label space, the test function of each split node can be trained in a similar way to that of the classical random forests. Structured random forests make use of the contextual information of image patches as well as the structural information of the labels to get more consistent results. Besides this benefit, by predicting a batch of pixels in a single classification, the structured random forest-based road detection can be much more efficient than the conventional pixel-wise random forest. Experimental results tested on the KITTI-ROAD dataset and data collected in typical unstructured environments show that structured random forest-based road detection outperforms the classical pixel-wise random forest both in accuracy and efficiency.
Inversion of shallow-seismic wavefields: II. Inferring subsurface properties from wavefield transforms

SUMMARY The problem of inferring subsurface properties from shallow-seismic data is solved by a two-stage scheme that fits the full wavefield by its synthetic counterpart. In a first stage (described in a companion paper) I derive Fourier–Bessel expansion coefficients for the recorded data through a wavefield transformation. The present paper describes the joint inversion of these coefficients together with P-wave arrival times to infer subsurface properties. In this way we exploit the full signal-content including the dispersion of higher-modes, leaky-modes, their true amplitudes, and, at least partly, body waves. Owing to the multi-mode character of shallow-seismic field-data conventional techniques of dispersion analysis are not applicable. Since an initial model appropriate for inversion of full seismograms is rarely available in shallow seismics, the direct inversion of waveforms is not feasible. The wavefield transformation removes a remarkable amount of non-linearity from the data. In consequence the proposed method is robust even in the absence of a priori information. In distinction to the inversion of dispersion curves, it does not require the identification of normal-modes prior to inversion. The method performs well when applied to the multi-mode wavefields present in most shallow-seismic data sets. Compared to waveform fitting, it can be more efficient by about a factor of ten, because we need not evaluate the Bessel-expansion and therefore need less calculations of the forward problem. Subsurface properties are derived for the two sets of field-data that were already presented in the first paper. One of them includes a pronounced low-velocity channel. For both we observe a remarkably good resolution of S-velocity down to the bedrock, which is found in 6 and 16 m depth, respectively. In both cases it would not be possible to infer the depth of bedrock from P-wave data alone. Synthetic seismograms calculated from the final model match the recorded waveforms surprisingly well, although no waveform fitting was applied. A subsequent waveform inversion becomes feasible with initial models taken from the results of this method. Finally it is shown by example, that conventional techniques of dispersion-curve fitting are likely to give misleading results when applied to our field-data.
A portable relational algebra library for high performance data-intensive query processing

[1] Independent Oracle Users Group. “A New Dimension to Data Warehousing: 2011 IOUG Data Warehousing Survey. ” [2] He, Lu, Yang, Fang, Govindaraju, Luo, Sander. “Relational query co-processing on graphics processors. ” TODS, 2009. [3] Diamos, Wu, Wang, Lele, Yalamanchili. “Relational Algorithms for Multi-Bulk-Synchronous Processors. ” PPoPP 2013. [4] Wu, Diamos, Wang, Cadambi, Yalamanchili, “Optimizing Data Warehousing Applications for GPUs Using Kernel Fusion/Fission. ” PLC 2012. [5] Wu, Diamos, Cadambi, Yalamanchili, “Kernel Weaver: Automatically Fusing Database Primitives for Efficient GPU Computation. ” MICRO 2012. [6] Wu, Diamos, Sheard, Aref, Baxter, Garland, Yalamanchili, “Red Fox: An Execution Environment for Relational Query Processing on GPUs. ” CGO 2014 [7] Zhang, He, He, Lu, “Omnidb: Towards portable and efficient query processing on parallel CPU/GPU architectures. ” VLDB 2013 Datalog Queries
A NOVEL PATTERN OF EMBRYONIC NUTRITION IN A VIVIPAROUS REPTILE

Vitellogenesis and placental transfer both contribute substantially to embryonic nutrition in the viviparous scincid lizard Pseudemoia entrecasteauxii. Neonatal wet mass was 396 % greater than egg wet mass and neonatal dry mass was 168 % greater than egg dry mass. We estimate that 49 % of the organic molecules for embryonic growth and metabolism were provided by placental nourishment. This pattern of embryonic nutrition, in which ovarian and uterine contributions are approximately equivalent, has not been reported previously for reptiles. Female Bassiana duperreyi, a closely related oviparous species, produced larger clutches of larger eggs. Although there was a great disparity in egg mass between these two species, differences in newborn composition were less pronounced. Mean dry mass, adjusted for female size, of B. duperreyi eggs was 135 % greater than mean dry mass of P. entrecasteauxii eggs, yet mean dry mass of B. duperreyi offspring was only 21 % greater than that of P. entrecasteauxii offspring. Embryonic gains in water and inorganic salts during gestation in P. entrecasteauxii were substantial and resulted in the production of neonates that contained greater quantities of these variables compared to offspring of B. duperreyi. These data confirm Weekes9 hypothesis that P. entrecasteauxii is matrotrophic and support her hypothesis that this species has undergone evolutionary reduction in yolk quantity.
The Lack of a Representative Tendinopathy Model Hampers Fundamental Mesenchymal Stem Cell Research

Overuse tendon injuries are a major cause of musculoskeletal morbidity in both human and equine athletes, due to the cumulative degenerative damage. These injuries present significant challenges as the healing process often results in the formation of inferior scar tissue. The poor success with conventional therapy supports the need to search for novel treatments to restore functionality and regenerate tissue as close to native tendon as possible. Mesenchymal stem cell (MSC)-based strategies represent promising therapeutic tools for tendon repair in both human and veterinary medicine. The translation of tissue engineering strategies from basic research findings, however, into clinical use has been hampered by the limited understanding of the multifaceted MSC mechanisms of action. In vitro models serve as important biological tools to study cell behavior, bypassing the confounding factors associated with in vivo experiments. Controllable and reproducible in vitro conditions should be provided to study the MSC healing mechanisms in tendon injuries. Unfortunately, no physiologically representative tendinopathy models exist to date. A major shortcoming of most currently available in vitro tendon models is the lack of extracellular tendon matrix and vascular supply. These models often make use of synthetic biomaterials, which do not reflect the natural tendon composition. Alternatively, decellularized tendon has been applied, but it is challenging to obtain reproducible results due to its variable composition, less efficient cell seeding approaches and lack of cell encapsulation and vascularization. The current review will overview pros and cons associated with the use of different biomaterials and technologies enabling scaffold production. In addition, the characteristics of the ideal, state-of-the-art tendinopathy model will be discussed. Briefly, a representative in vitro tendinopathy model should be vascularized and mimic the hierarchical structure of the tendon matrix with elongated cells being organized in a parallel fashion and subjected to uniaxial stretching. Incorporation of mechanical stimulation, preferably uniaxial stretching may be a key element in order to obtain appropriate matrix alignment and create a pathophysiological model. Together, a thorough discussion on the current status and future directions for tendon models will enhance fundamental MSC research, accelerating translation of MSC therapies for tendon injuries from bench to bedside.
Functional characterisation of substrate-binding proteins to address nutrient uptake in marine picocyanobacteria

Marine cyanobacteria are key primary producers, contributing significantly to the microbial food web and biogeochemical cycles by releasing and importing many essential nutrients cycled through the environment. A subgroup of these, the picocyanobacteria (Synechococcus and Prochlorococcus), have colonised almost all marine ecosystems, covering a range of distinct light and temperature conditions, and nutrient profiles. The intra-clade diversities displayed by this monophyletic branch of cyanobacteria is indicative of their success across a broad range of environments. Part of this diversity is due to nutrient acquisition mechanisms, such as the use of high-affinity ATP-binding cassette (ABC) transporters to competitively acquire nutrients, particularly in oligotrophic (nutrient scarce) marine environments. The specificity of nutrient uptake in ABC transporters is primarily determined by the peripheral substrate-binding protein (SBP), a receptor protein that mediates ligand recognition and initiates translocation into the cell. The recent availability of large numbers of sequenced picocyanobacterial genomes indicates both Synechococcus and Prochlorococcus apportion >50% of their transport capacity to ABC transport systems. However, the low degree of sequence homology among the SBP family limits the reliability of functional assignments using sequence annotation and prediction tools. This review highlights the use of known SBP structural representatives for the uptake of key nutrient classes by cyanobacteria to compare with predicted SBP functionalities within sequenced marine picocyanobacteria genomes. This review shows the broad range of conserved biochemical functions of picocyanobacteria and the range of novel and hypothetical ABC transport systems that require further functional characterisation.
G-CASPR and VDRad: extreme cases of a continuum. Intermediate may be better.

551 G-CASPR and VDRad: extreme cases of a continuum. Intermediate may be better. A. Godino-Moya, J. Royuela-del-Val, R.M. Menchón-Lara, M. Martín-Fernández, C. Alberola-López Valladolid, ES, University of Valladolid, Image Processing Lab Preclinical Studies and Basic Science » Image analysis Scientific Session Purpose / Introduction Different approaches that use Cartesian trajectories with spiral profiles and golden angle step have been proposed to accelerate MRI acquisition. G-CASPR is characterized by a scheme with uniform k-space sampling and it is usually applied to static MRI, but in [2] it is used in cardiac cine. Nevertheless, compressed sensing shows better results if the center of k-space is sampled more densely. VDRad is a variable density sampling scheme that has been reported for abdominal imaging. VDRad, however, has the drawback that some positions in the k-space are repeatedly acquired, decreasing efficiency. We define a versatile sampling scheme that varies continuously from the G-CASPR to VDRad. Our goal is to determine whether intermediate configurations can provide better sampling schemes for cine cardiac MRI applications. Subjects and Methods The proposed sampling scheme is described as follows: given a spiral length L, the (Kx,Ky)-plane is divided into L elliptical rings and each ring in turn is divided in L sectors. Rings can have constant area, constant radial width or something in between, which is controlled by the parameter alpha (Figure1). α=0 produces rings with the same number of samples (equivalent to G-CASPR), whereas α=1 produces rings with different number of samples (equivalent to VDRad). Samples within each ring are ordered by radius and by angle in the k-space. Trajectories are formed by choosing each time one sample of one ring and sector forming a spiral twist. Resulting spirals are ordered according to the golden angle 137.5o. The number of the total acquired samples is adjusted by the desired acceleration factor. Images were reconstructed using kt-SPARSE-SENSE with temporal total variation. One slice of real data from a regular fully sampled, Cartesian breath hold acquisition with 32 coils was used to test our method. SER and HF-SER were calculated to measure image reconstruction quality.
A Bayesian Singular Value Decomposition Procedure for Missing Data Imputation

Abstract Missing data are common in empirical studies. Multiple imputation is a method to handle missing values by replacing them with plausible values. A common imputation method is multiple imputation with chain equations (MICE). MICE defines a series of conditional distributions to impute missing values. Although MICE is relatively easy to implement, it may not converge to a proper joint distribution. An alternative strategy is to model the variables jointly using the general location model, but this model can become complex when the number of variables increases. Both approaches require integration of prior information when there are more variables than cases. We propose a Bayesian model that is based on the singular value decomposition components of a continuous data matrix to impute missing values. The model assumes that the matrix is of low rank by applying double exponential prior distributions on the singular values. We describe an efficient sampling algorithm to estimate the model’s parameters and impute the missing data. The performance of the model is compared to current imputation methods in simulated and real datasets. Of all the methods considered and in most of the simulated and real datasets, the proposed procedure appears to be the most accurate and precise. Supplementary materials for this article are available online.
Classical and modern Fourier analysis

Prolegomena. 1. L p Spaces and Interpolation. 2. Maximal Functions, Fourier Transform, and Distributions. 3. Fourier Analysis on the Torus. 4. Singular Integrals of Convolution Type. 5. Littlewood-Paley Theory and Multipliers. 6. Smoothness and Function Spaces. 7. BMO and Carleson Measures. 8. Singular Integrals of Nonconvolution Type. 9. Weighted Inequalities. 10. Boundedness and Convergence of Fourier Integrals. Bibliography. Index of Notation. Index.
Exploring N-gram, Word Embedding and Topic Models for Content-based Fake News Detection in FakeNewsNet Evaluation

FakeNewsNet is a repository of two novel datasets, PolitiFact and GossipCop, which are employed for evaluation of fake news detection techniques. Unlike other extensively studied benchmark fake news datasets, the FakeNewsNet datasets incorporate news content, social context, and dynamic information, which could be used to study fake news propagation, detection, and mitigation. Existing works on FakeNewsNet have focused on one-hot encoding, social contexts such as user-based models, and dynamic information such as news propagation model. However, n-gram, word embeddings, and topic models of news contents, which have been impressive in other contexts have not been explored. This paper therefore explores n-gram, word embeddings, and topic models of news contents for the evaluation of FakeNewsNet datasets. Unigram-based n-gram model, skipgram word2vec-based word embeddings model and Latent Dirichlet Allocation-based topic model are extracted after preprocessing the datasets. The features are weighted by TFIDF to overcome the shortcomings of the individual models and analyzed using Logistic Regression. The evaluation of the models and their hybrids shows that n-gram model outperforms word embedding and topic models. Specifically, n-gram model records accuracy, precision, recall and F1-score of 0.80, 0.79, 0.78 and 0.79, respectively for PolitiFact and records 0.82, 0.75, 0.79 and 0.77, respectively for GossipCop. The comparison with benchmarks also shows that the performance of n-gram model is better. General Terms Machine Learning, Computational Linguistics