Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of autosomal dwarf gene on growth and shank length of chicken

A partial diallel crossing of Rhode Island Red (RIR), White Leghorn (WLH), Fayoumi (FO), Desi normal (DN) and Desi dwarf (DD) produced RIR, WLH, FO, DN, DD, RIR x DD, WLH x DD and FO x DD offspring. A total 709 chicks, 75 RIR, 130 WLH, 100 FO, 70 DN, 66 DD, 80 RIR x DD, 80 WLH x DD and 108 FO x DD were reared for studying growth performance up to 18 weeks of age. At 19 weeks, the crossbreds RIR x DD, WLH x DD and FO x DD were separated into normal and dwarf genetic groups on the basis of shank length and thus 11 genetic groups, RIR, WLH, FO, DN, DD, RIR x DD normal, RIR x DD dwarf, WLH x DD normal, WLH x DD dwarf, FO x DD normal and FO x DD dwarf were obtained. Day-old weight differed significantly (P 0.05). The shank length differed significantly between genotypes at all ages regardless of sex (P<0.01) and differences between genotypes increased at older ages. Shank length of pure breeds and normal crossbreds were similar and much longer than in dwarf crossbreds DOI: http://dx.doi. org/10.3329/bvet.v30i1. 16282 Bangl. vet . 2013. Vol. 30, No. 1, 25-32
On Fractile Transformation of Covariates in Regression

The need for comparing two regression functions arises frequently in statistical applications. Comparison of the usual regression functions is not very meaningful in situations where the distributions and the ranges of the covariates are different for the populations. For instance, in econometric studies, the prices of commodities and people's incomes observed at different time points may not be on comparable scales due to inflation and other economic factors. In this article, we describe a method of standardizing the covariates and estimating the transformed regression function, which then become comparable. We develop smooth estimates of the fractile regression function and study its statistical properties analytically as well as numerically. We also provide a few real examples that illustrate the difficulty in comparing the usual regression functions and motivate the need for the fractile transformation. Our analysis of the real examples leads to new and useful statistical conclusions that are missed by comparison of the usual regression functions.
A Kernel-based Matrixzed One-Class Support Vector Machine

One-class support vector machine (OCSVM) is an important and efficient classifier used when only one class of data is available while others are too expensive or difficult to collect. It uses vector as input data, and trains a linear or nonlinear decision function in vector space. However, the traditional vector-based classifiers may fail when input is matrix. Therefore, it makes sense to study matrixzed classifiers which can make use of the structural information presented in the data. In this paper we propose a matrix-based one-class classification algorithm named Kernel-based Matrixzed One-class Support Vector Machine (KMatOCSVM). It aims to convert the OCSVM to suit for matrix representation data and to deal with nonlinear one-class classification problems. The efficiency and validity of the proposed method is illustrated by four real-world matrixbased human face datasets.
XML-based Web Service for Collaborative Product Data Management

This article considers the strategy of using a new generation of web technology to support product data management during the traditional development process. An approach to the data exchange and sharing by defining the infrastructure of XML-based web service for collaborative product development is proposed. The outline of the theoretical background of products information modelling and relationships with the entities of the STEP PDM Scheme is presented. Benefits of STEP/XML integration as a base concept of the proposed solution is discussed. The overview of the web services technology is given. The architecture of the web service for collaborative product data management built by using Java2 TM Enterprise Edition (J2EE) platform is described.
Anxiety and its Regulation: Neural Mechanisms and Regulation Techniques According to the Experiential-Dynamic Approach

Although anxiety is not necessarily a pathological phenomenon, it can become dysregu‐ lated, causing suffering. Indeed, emotion dysregulation lies at the core of many psychopathologies. Thus, anxiety regulation is central to all effective psychological treatment. The predominant perspective on emotion regulation and dysregulation is appraisal theory, which proposes that the cognitive appraisal of an event generates an emotional response. According to Gross’s process model, any emotion can become dysregulated when the patient lacks or fails to use an appropriate regulatory strategy. Therefore, the clinician must teach the patient better regulatory strategies. The perspec‐ tive we put forward departs from Gross’s model based on appraisal theory. The experiential‐dynamic emotion‐regulation model, EDER, grounded in affective neuro‐ science and modern psychodynamic psychotherapy proposes that (1) emotions precede cognition (temporal and neuroanatomical primacy), (2) emotions are not inherently dysregulated (they have specific properties of time and strength proportional to the quality of the stimulus), and (3) dysregulation derives from the combination of emotions plus conditioned anxiety, or from secondary‐defensive affects, both leading to dysregu‐ lated-affective states (DASs). To regulate DAS, the clinician must regulate the dysregu‐ lating anxiety or restructure the defenses, which create defensive affects, and then help the client to fully express the underlying emotions that elicit anxiety and defenses. In this chapter, we specifically focus on dysregulated anxiety, its neural bases, and how to regulate it according to the EDER model. First, we present hypotheses and data to show the neural bases of anxiety. Then, specific strategies and techniques to regulate anxiety are explained and clinical excerpts illustrate their application. 4, 48]. If anxiety is too high, it impairs reality test‐ ing, and cognition is impaired due to a shutdown of the prefrontal cortex. Once anxiety is regulated and returns to the striated muscles and reality testing is restored, cognitive re‐ working (in a psychodynamic or CBT fashion) can be done. We use two basic overarching strategies: (1) emotion expression for true feelings triggered by real stimuli and (2) regula‐ tion of anxiety (and restructuring of defenses) for DAS due to emotions paired with ex‐ cessive anxiety (see [43], for more details). We then explore true feelings as deeply as the patient can bear. The therapist upregulates true emotions while deactivating DAS until the patient fully experiences the previously avoided emotions without the DAS caused by anxiety (or by defenses). Now, the patient feels relief and can channel emotions into effective, adaptive action.
Ecotoxicology of Glyphosate and Glyphosate-Based Herbicides — Toxicity to Wildlife and Humans

The use of agrochemicals, especially herbicides, is necessary to control pests in order to produce adequate food for the global population (estimated at 7 billion). Glyphosate and glyphosate-based herbicides have been used extensively for this purpose but recent studies have reported these chemical substances to be found in aquatic ecosystems, wildlife and humans in various quantities. In this chapter, we reviewed the impacts of glyphosate and glyphosate-based herbicides on wildlife and humans using measured endpoint effects caused by genotoxicity, cytotoxicity and reproduc‐ tive toxicity. We used findings from different current investigations to demonstrate adverse effects, or otherwise, of glyphosate exposure to wildlife and humans. Our review reveals that glyphosate and its formulations may not only be considered as having genotoxic, cytotoxic or endocrine disrupting properties but they may also be causative agents of reproduction abnormalities in both wildlife and humans. Furthermore, the extensive use of glyphosate-based herbicides in genetically modified glyphosate-resistant plants grown for food and feed should be of grave concern since they can be sources of genotoxicity, cytotoxicity, and reproductive toxicity in wildlife
Lifting the Veil of Secrecy from Monetary Policy: Evidence from the Fed's Early Discount Rate Policy

Traditionally, monetary policy has been conducted under a veil of secrecy. In its landmark Freedom of Information Act case, the Federal Reserve argued that it needed to delay the disclosure of its policy decisions, claiming that immediate disclosure would cause the market to react in a way that was inconsistent with the Fed's intentions. Based on this argument and others, the Fed was permitted to delay the release of FOMC policy decisions. Most economists, however, believe that market forces would work to keep equilibrium outcomes more in line with policy maker's intentions if policy makers would announce their intentions and establish a reputation for behaving in a manner consistent with them. This paper tests the hypothesis that the market responds more closely to the Fed's intentions when the Fed makes its intentions known by investigating the market's reaction to a change in discount rate policy in the early 1960s. We find that the market responded in a manner inconsistent with the Fed's intentions when they were unknown, and responds in a manner consistent with them when the Fed made its intentions known.
Bacteriological assessment of tap water collected from different markets of Mymensingh, Gazipur and Sherpur districts of Bangladesh with special focus on the molecular detection and antimicrobial resistance of the isolated Escherichia coli

The objectives of this study were to assess the bacteriological quality of tap water samples obtained from different markets of different upazillas of Mymensingh, Sherpur & Gazipur district. For achieving the above mentioned objectives, methods of heterotrophic plate count (HPC) and total coliform count (TCC) were applied. Moreover, isolated E. coli from tap water samples were characterized by using biochemical test, molecular method and antimicrobial susceptibility tests. HPC was highest in market tap water collected from Kaligonj and TCC was highest in market tap water of collected from Mymensingh sadar. The geometric mean of HPC of Mymensingh, Gazipur and Sherpur districts water was 8.4x105, 2.5 x106 and 6.8 x105 C.F. U/100 ml. All isolates of E. coli (n=20) were amplified by using 16S rRNA gene based PCR. In respect to antimicrobial susceptibility testing, most of the E. coli isolates were susceptible to norfloxacin, ampicilin, tetracycline, streptomycin and ciprofloxacin. Furthermore, a few E. coli isolates were intermediate resistant to gentamycin and ciprofloxacin. However, a few of the E. coli isolates were resistant to erythromycin and amoxycilin. Moreover, out of 20 E. coli isolates 3 (15%) isolates were detected as multidrug resistant. This study indicated the presence of multidrug resistant E. coli isolates in tap water in Mymensingh, Sherpur and Gazipur districts that warrants particular attention. Asian Australas. J. Food Saf. Secur. 2018, 2(1), 21-28
Survey on Query Facets Mining Approaches

: Important features of a query are generally presented and repeated in the top retrieved documents in the style of lists. Query facets can be extracted by collecting these significant lists. Query facets may provide direct information or instant answers that users are seeking i.e. user can select a particular facet item which he found relevant to his search need. So the list format style is much more user friendly than displaying searches sentence wise. The scope of this survey is limited to get search results of a query in list format i.e. facets. Previously there has been lot of work done for retrieving more relevant data to users in order to meet their information needs thus improving performance of search engines. Search engine provide the platform for users to describe their information need more clearly by using query facets mining. Different approaches for extraction of query facets from web search results to assist information finding for queries are discussed along with similar techniques used earlier for information retrieval of queries. Query facets represents interesting facets of a query using groups of semantically related terms extracted from search results. This paper reviews techniques that represents interesting facets of a query using groups of semantically related terms extracted from search results along with approaches that are similar to query facets mining .
Identification of chromosomal regions with DNA copy number aberrations associated with node metastasis of colorectal adenocarcinomas based on the array CGH profiles

It is important to estimate biological characteristics of tumor, including the nodal status at the time of diagnosis for optimal treatment for individual cancer patients. Array-based comparative genomic hybridization (aCGH) was performed for 77 sporadic colorectal adenocarcinomas using a chip spotted with 4,030 BAC clones. The nodal status was compared with an array CGH profiles depicted using a combination of decision-tree classifier and a Self-Organizing Map (SOM) analysis. Node metastasis was not detected in any of the six poorly differentiated adenocarcinomas with a 3q loss. A SOM analysis following the decision-tree classification of the aCGH data allowed for the differentiation in chromosomal regions between highand low-level decreases in the DNA copy number. Node metastasis was detected in all five tumors with the high-level decrease in DNA copy number at Xp, irrespective of the histological type. Node metastasis was also found exclusively in six tumors with an increase in DNA copy number at the chromosomal region between 11q13.3 and 11q22.3. Chromosomal regions with copy number aberrations linked to nodal metastasis were identified more collectively by the combination of the decision-tree classifier and a SOM analysis than by the conventional analysis method in aCGH analysis.
Evaporation of microscopic black holes in string theory and the bound on species

We address the question how string compactifications with D‐branes are consistent with the black hole bound, which arises in any theory with number of particle species to which the black holes can evaporate. For the Kaluza‐Klein particles, both longitudinal and transversal to the D‐branes, it is relatively easy to see that the black hole bound is saturated, and the geometric relations can be understood in the language of species‐counting. We next address the question of the black hole evaporation into the higher string states and discover, that contrary to the naive intuition, the exponentially growing number of Regge states does not preclude the existence of semi‐classical black holes of sub‐stringy size. Our analysis indicates that the effective number of string resonances to which such micro black holes evaporate is not exponentially large but is bounded by N = 1/gs2, which suggests the interpretation of the well‐known relation between the Planck and string scales as the saturation of the black hole bound on the species number. In addition, we also discuss some other issues in D‐brane compactifications with a low string scale of order TeV, such as the masses of light moduli fields.
Context-Aware Asset Search for Graphic Design ( Supplemental Material ) May 30 , 2018 1 Overview

Category Number of Images Nature/Landscapes 110,059 Animals 47,114 Architecture/Buildings 39,731 Travel/Vacation 36,452 Food/Drink 24,294 People 22,021 Transportation/Traffic 14,771 Religion 13,022 Backgrounds/Textures 10,990 Emotions 6,267 Sports 6,149 Beauty/Fashion 5,810 Science/Technology 3,875 Places/Monuments 3,549 Music 3,262 Industry/Craft 3,200 Computer/Communication 2,757 Business/Finance 2,617 Education 1,534 Health/Medical 1,237
Optimizing the Capacity of a Convolutional Neural Network for Image Segmentation and Pattern Recognition

 Abstract — In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.
Primary Users ON / OFF Behaviour Models in Cognitive Radio Networks

Spectrum underutilization is attributed to Primary users (PUs) ON/OFF behaviour, giving rise to spectrum holes or TV white space (TVWS). However, Cognitive Radio Technology (CRT) has become a new paradigm to identification and exploitation of TVWS across frequency and time. Behaviours of PUs have being modelled as; ON/OFF Markov, ON/OFF Exponential and ON/OFF Geometric. This paper focuses on investigating the PUs activity in relation to these three assumed behaviours to see which of them is relatively stable so as to give the secondary users (SUs) the leverage to utilize the OFF slots/channels, since this is utmost concern to Cognitive users. The simulation result shows that Markov is relatively reliable in the sense that, its standard deviation of 4.6139 is less than Geometric and Exponential which are 5.6617 and 6.1967 respectively. At equal number of runs. Key-Words: Cognitive radio network (CRNs), PU, ON/OFF, Time-slots, Markov, Exponential, Geometric. )
Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy

This is a review of bootstrap methods, concentrating on basic ideas and applications rather than theoretical considerations. It begins with an exposition of the bootstrap estimate of standard error for one-sample situations. Several examples, some involving quite complicated statistical procedures, are given. The bootstrap is then extended to other measures of statistical accuracy such as bias and prediction error, and to complicated data structures such as time series, censored data, and regression models. Several more examples are presented illustrating these ideas. The last third of the paper deals mainly with bootstrap confidence intervals.
Identification and Utilization of Actinobacteria for Biocontrol of Rice Sheath Blight Pathogen, Rhizoctonia solani

Sheath blight of rice is an important soil-borne fungal disease caused by Rhizoctonia solani . Isolation and exploitation of bioagents associated with rice offers a great opportunity for sustainable management of rice diseases. Actinomycetes were isolated from rice rhizosphere and isolates were putatively selected based on characteristic colony morphology. 16S rDNA and ARDRA confirmed their identification as actinobacteria. Isolates belonged either to Streptomyces spp. or Actinopolymorpha spp. Under in vitro condition, isolates IABT-A1, IABTA2, IABT-A3, IABT-A6, IABT-A7, IABT-A8 and IABT-A9 showed 98-100 per cent inhibition of Rhizoctonia solani , rice sheath blight pathogen. Further, five isolates were evaluated under glass house condition and the potent actinobacteria against rice sheath blight was identified. Among these isolates, IABT-A7 ( Actinopoymorpha spp.) was most effective and application as seed treatment, soil and foliar spray was found most promising in terms of disease reduction and plant growth promotion. Actinopoymorpha spp. (IABT-A7) enhanced plant height, root length and root biomass in addition to reduced sheath blight infection in rice. This result indicates the role of actinobacteria isolate IABT-A7, as the probable stimulator of ISR (Induced Systemic Resistance) signaling pathway involved in plant disease resistance.
An argument for phrasal spell-out: Indefinites and interrogatives in Spanish

In this article we will provide evidence in favour of Phrasal Spell Out (PSO), a procedure of lexical insertion where non-terminal nodes in a tree configuration can be targeted by spell-out. We will propose that the formal differences between two Spanish indefinite pronouns, alguien and alguno , can be captured if the morpheme -ien is analyzed as a lexical item which corresponds to a syntactic phrase; this phrase, crucially, is broken in the presence of a plural number projection. Independent properties of the internal syntactic structure of the interrogative make the lexical item -ien compatible with plural in that configuration.
ERROR-DEPENDENT SMOOTHING RULES IN LOCAL LINEAR REGRESSION

We suggest an adaptive, error-dependent smoothing method for reducing the variance of local-linear curve estimators. It involves weighting the bandwidth used at the ith datum in proportion to a power of the absolute value of the ith residual. We show that the optimal power is 2/3. Arguing in this way, we prove that asymptotic variance can be reduced by 24% in the case of Normal errors, and by 35% for double-exponential errors. These results might appear to violate Jianqing Fan's bounds on performance of local-linear methods, but note that our approach to smoothing produces nonlinear estimators. In the case of Normal errors, our esti- mator has slightly better mean squared error performance than that suggested by Fan's minimax bound, calculated by him over all estimators, not just linear ones. However, these improvements are available only for single functions, not uniformly over Fan's function class. Even greater improvements in performance are achiev- able for error distributions with heavier tails. For symmetric error distributions the method has no first-order effect on bias, and existing bias-reduction techniques may be used in conjunction with error-dependent smoothing. In the case of asymmetric error distributions an overall reduction in mean squared error is achievable, involv- ing a trade-off between bias and variance contributions. However, in this setting, the technique is relatively complex and probably not practically feasible.
Quantitative Network Organization of Interactions Emerging from the Evolution of Sequence and Structure Space of Antibodies: the RADARS Model

Based on the key molecule of humoral adaptive immunity, the antibody, evolution of the system comprises molecular genetic, cell biologic and immunologic mechanisms, and as a network the system is likely governed and can be characterized by physical rules as well. While deep sequencing can provide vast amounts of data related primarily to clonal relationships, functional interpretation of such data is hampered by the inherent limitations of converting sequence to structure to function. In this paper a novel model of structural interaction space, termed radial adjustment of system resolution, or RADARS, is proposed. The model is based on the radial growth of resolution of structural recognition, corresponding to increasing affinity of immune reactivity, and the virtual infinity of directions of growth, corresponding to the ability to respond to almost any molecular structure. Levels of interaction strength appear as shells of the sphere representing the system. B-cell development and immune responses can be readily interpreted in the model and quantitative properties of the antibody network can be inferred from the physical properties of a quasi-spherical system growing multi-radially. The system is described by double-Pareto distribution, sampling the lognormally distributed equilibrium constants at a rate of phi square. Finally, general strategies for merging antibody sequence space into structural space are outlined.