Miracle-dz/UniFAR-FTUs huggingface dataset:
Epidemiological Study of Mastitis in Three Different Strains of Beetal Goat in Selected Districts of Punjab, Pakistan

Received: Revised: Accepted: Published online: October 21, 2018 December 21, 2018 December 24, 2018 December 27, 2018 Mastitis in goats, analogous to dairy cattle, is among the momentous diseases of economic value in all parts of the world. Local information of epidemiology of goat mastitis is scanty. In the present study, considering geographical distribution of Beetal goat, 172 flocks were randomly pulled out by “Survey tool box” to reduce biased sampling from perie-urban and urban areas of six districts of Punjab. By following the proportionate sampling strategy and assuming the highest population (70%) of Beetal all over the Punjab, Beetal strains were screened for the prevalence of mastitis from Faisalabad, Jhang, Chiniot, Bahawalpur, Muzzafargarh and Rajanpur districts. Screening of the flocks with surf field mastitis test indicated that overall prevalence of caprine mastitis in Beetal breed was 17.39% while in Beetal Faisalabadi strain 16.53% from 116 flocks, in Beetal Makhicheeni 18.06% from 32 flocks and in Beetal Nuqri 19.33% from 24 flocks. Microbiological examination of milk samples showed various types of mastitic microorganisms. The predominant isolates included Staphylococcus (S). hyicus (45%), S. xylosus (19%), S. simulans (16%), S. aureus (9%) and unidentifiable Staphyloccocal species (11%). Antibiotic sensitivity test was conducted on the most prevalent isolates. S. aureus and S. hyicus showed sensitivity to eight antibiotics: amoxicillin, ampicilin, lincomycin, sulfamethoxazole+trimethoprim, novobiocin, enrofloxacin, amoxicillin+ clavulanic acid and oxytetracycline while S. xylosus was sensitive to all antibiotics except novobiocin and S. simulans was sensitive to lincomycin, sulfamethoxazole+ trimethoprim, enrofloxacin and resistant to all other antibiotics. The study found over all 16.74% mastitis in different strains of Beetal. The most important parameters as; type of housing, farm hygiene, age, stage of lactation, parity, color, body condition score, teat ends, SFMT score and decreased milk yield were found pertinent risk factors so that prompt attention required for mastitis control in goats. ©2018 PVJ. All rights reserved
Prediction of Cross Project Defects using Ensemble based Multinomial Classifier

BACKGROUND: The availability of defect related data of different projects leads to cross project defect prediction an open issue. Many studies have focused on analyzing and improving the performance of Cross project defect prediction. OBJECTIVE: The multinomial classification has not been much explored. This paper instanced on multiclass/multinomial classification of defect prediction of cross projects. METHOD: The ensemble based statistical models – Gradient Boosting and Random Forest are used for classification. An empirical study is carried out to determine the performance of multinomial classification for cross project defect prediction. Depending on the number of defects, class level information is classified into one of three defined multiclass class 0, class 1, and class 2. RESULTS & CONCLUSION: Major outcome of the paper concludes that multinomial/multiclass classification is applicable on cross project data and has comparable results to within project defect data.
Comparison of Speech Tasks and Recording Devices for Voice Based Automatic Classification of Healthy Subjects and Patients with Amyotrophic Lateral Sclerosis

We consider the task of speech based automatic classification of patients with amyotrophic lateral sclerosis (ALS) and healthy subjects. The role of different speech tasks and recording devices on classification accuracy is examined. Sustained phoneme production (PHON), diadochokinetic task (DDK) and spontaneous speech (SPON) have been used as speech tasks. The chosen five recording devices include a high quality microphone and built-in smartphone microphones at various price ranges. Experiments are performed using speech data from 25 ALS patients and 25 healthy subjects using support vector machines and deep neural networks as classifiers and suprasegmental features based on mel frequency cepstral coefficients. Results reveal that DDK consistently performs better than SPON and PHON across all devices for discriminating ALS patients and healthy subjects. Considering DDK, the best classification accuracy of 92.2% is obtained using a high quality microphone but the accuracy drops if there is a mismatch between the microphones for training and test. However, a classifier trained with recordings from all devices together performs more uniformly across all devices. The findings from this study could aid in determining the choice of the task and device in developing an assistive tool for detection and monitoring of ALS.
Localising aid: can using local actors strengthen them?

Since the modern era of aid giving began after the Second World War donors have sought to improve the effectiveness of their aid. In the 1980s and 1990s a shift towards policy lending (as opposed to project lending) dominated aid spending, along with a rise in the role of private agencies and contractors (both for profit and not for profit). In response to a perceived mishandling or downgrading of the potential role of the state during this period, the Paris Agenda strongly emphasised greater use of recipient government systems in the 2000s, primarily with a view to strengthening their capacity and accountability. Now, in a context of limited progress towards increased ‘use of country systems’, that agenda is also being reassessed, both for failing to accommodate non-state actors into its purview, and for applying an over-rigid set of solutions that fail to take sufficiently into account the contexts of both recipient and donor partners.
International trade and shared environmental responsibility by activity sector . Application to the Spanish economy

The aim of this work is to define a shared responsi bility criterion to analyse the impact of international trade on different sectors’ CO 2 emissions. With the proposed approach, the defined shared responsibility criterion, it is possible for the sectors in a country to be considered responsible only for a part of the emiss ions associated to exported goods and to imported goods. Agents that shall be considered as pollution responsible are activity sectors by rows or direct emissions linked to produ ction and sectors by columns or direct and indirect emissions linked to inputs cons umption, and also the countries that trade with these sectors. The criterion application is done for the Spanish economy for the period 2000 to 2005. It has been useful to eval uate the adequate economic policies to mitigate the anthropological impact on environme t.
FORMATION EVALUATION OF SIRP FIELD USING WIRELINE LOGS IN WESTERN DEPOBELT OF NIGER DELTA 1

This interpretation is based on basic open hole logs. The essence is to evaluate porosity and lithofacie of SIRP in the Western Niger Delta Depobelt. Parameters of importance include Gamma Ray Log, Resistivity Log, Density Log and Neutron log The Formation Evaluation was performed to identify hydrocarbon bearing reservoir sand and study reservoir properties based on data from five wells imported to a software after proper scaling. Three out of the five wells contained sufficient data to allow detailed analysis such as porosity and lithofacie evaluation. In some of the sands that contains less feldspathic minerals, the porosity is good and probably viable for development.
Root ® with O 3 ® Regional Oximetry Available for Infant and Neonatal Patients

A case of neonatal depression (ND) and severe acidosis (SA) is presented. In contrast to bradycardia associated ND and SA, the neonate had tachycardia at birth. The ND and SA observed were related to maternal fever and severe histological chorioamnionitis. The case presentation is followed by postulated mechanism and literature review.
Break ing up for the Kids ’ Sake : Evidence from a British Birth Cohort

This paper considers the relationship between family break-up and young adults’ cognitive and non-cognitive outcomes. We use British cohort data from the Avon Longitudinal Study of Parents and Children (ALSPAC), and consider separately parental separation and the quality of the relationship when the parents were together. We find that conflict within the household prior to parental separation entirely explains the negative correlation between divorce and child outcomes in terms of emotional health and behaviour, but not their test scores. Interactions between parental separation and prior conflict reveal that separations that end conflictual relationships can be beneficial for young adult non-cognitive outcomes.
Cognitive Inertia and the Implicit Association Test

Switzerland (e-mail: claude.messner@unibas.ch). Joachim Vosgerau is Assistant Professor of Marketing, Tepper School of Business, Carnegie Mellon University (e-mail: vosgerau@andrew.cmu.edu). The authors thank Klaus Fiedler, Jeffrey Inman, Kinshuk Jerath, Carey Morewedge, Uri Simonsohn, Peter Stuettgen, and the two anonymous JMR reviewers for their helpful comments; they thank Julia Binder, Anika Borchardt, Janine Dietler, Matthias Gasser, Noemi Merz, Martina Nussbickel, Sibylle Osusky, Anna Schnettler, and Barbara Wildermuth for help with the data collection. The authors contributed equally to this article. Michel Wedel served as associate editor for this article. CLAUDE MESSNER and JOACHIM VOSGERAU*
Image Forgery Detection Based on SURF and Machine Learning Classifier

Today manipulation of digital images has become easy due to availability powerful image editing tools like Adobe Photoshop etc. Detection of a forged image is driven by the need of authenticity and to maintain integrity of the image. The most common type of digital image forgery is known as copy-move forgery wherein a part of image is cut/copied and pasted in another area of the same image. This paper proposed a new image tampering detection method based on Speed-up robust features and Support vector machine (SVM) to detect copy-move forgery in image. -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 25-02-2019 Date of acceptance:11-03-2019 -------------------------------------------------------------------------------------------------------------------------------------
The cadherin-catenin complex in nasopharyngeal carcinoma H. Galera-RuizM. J. Rõ´osR. Gonzalez-Campora •

Abnormal Wnt signaling and impaired cell-cell adhesion due to abnormal E-cadherin and b-catenin func- tion have been implicated in many cancers, but have not been fully explored in nasopharyngeal carcinoma. The aim of this study was to analyze b-Catenin cellular location and E-cadherin expression levels in nasopharyngeal carcinoma. E-cadherin expression levels were also correlated with clinical data and underlying pathology. b-Catenin and E-cadherin expression were examined in 18 nasopharyn- geal carcinoma and 7 non-tumoral inflammatory pharynx tissues using immunohistochemical methods. Patient clin- ical data were collected, and histological evaluation was performed by hematoxylin/eosin staining. b-catenin was detected in membrane and cytoplasm in all cases of naso- pharyngeal carcinoma, regardless of histological type; in non-tumoral tissues, however, b-catenin was observed only in the membrane. As for E-cadherin expression levels, strong staining was observed in most non-tumoral tissues, but staining was only moderate in nasopharyngeal carci- noma tissues. E-cadherin expression was associated with b-catenin localization, study group, metastatic disease, and patient outcomes. Reduced levels of E-cadherin protein observed in nasopharyngeal carinoma may play an important role in invasion and metastasis. Cytoplasmic b-catenin in nasopharyngeal carcinoma may impair cell- cell adhesion, promoting invasive behavior and a metastatic tumor phenotype.
Deep Hierarchical Fusion with Application in Sentiment Analysis

Recognizing the emotional tone in spoken language is a challenging research problem that requires modeling not only the acoustic and textual modalities separately but also their crossinteractions. In this work, we introduce a hierarchical fusion scheme for sentiment analysis of spoken sentences. Two bidirectional Long-Short-Term-Memory networks (BiLSTM), followed by multiple fully connected layers, are trained in order to extract feature representations for each of the textual and audio modalities. The representations of the unimodal encoders are both fused at each layer and propagated forward, thus achieving fusion at the word, sentence and high/sentiment levels. The proposed approach of deep hierarchical fusion achieves stateof-the-art results for sentiment analysis tasks. Through an ablation study, we show that the proposed fusion method achieves greater performance gains over the unimodal baseline compared to other fusion approaches in the literature.
The Application of Qubit Neural Networks for Time Series Forecasting with Automatic Phase Adjustment Mechanism

Quantum computation, quantum information and artificial intelligence have all contributed for the new non-standard learning scheme named Qubit Neural Network (QNN). In this paper, a QNN based on the qubit neuron model is used for real world time series forecasting problem, where one chaotic series and one stock market series were predicted. Experimental results show evidences that the simulated system is able to preserve the relative phase information of neurons quantum states and thus, automatically adjust the forecast’s time shift.
DOCUMENT SAC-09-09 SPATIOTEMPORAL DYNAMICS OF THE DOLPHIN-ASSOCIATED PURSE-SEINE FISHERY FOR YELLOWFIN TUNA IN THE EASTERN PACIFIC OCEAN

CONTENTS Summary . 1 1. Background . 1 2. Data and methods . 2 3. Results . 4 4. Discussion . 4 Acknowledgements . 6 References . 6 Figures . 7
Policy Bias and Trade Distorting State Enterprises

State trading enterprises are an instrument through which the government manipulates market structure as a means of re-distributing income between producers, consumers and taxpayers. The specific way in which this occurs depends on the nature of exclusive rights bestowed on them. In an open economy, such enterprises have an impact on trade and they have come under increasing scrutiny in the World Trade Organization. We derive the trade distorting effect for state trading enterprises in an importing country and relate this effect to the bias of government policy. We estimate the distortion by calibrating the theoretical model to a state enterprise in Japan.
MODEL RELEVANCE Frameworks for Exploring the Complexity-Sensitivity-Uncertainty Trilemma

Ever more complex models play an important role in environmental assessment and adaptation to climate change. Model complexity is fundamental to the ability of environmental models to address questions, as well being a crucial determinant of uncertainty in model results. However, while increasing model complexity is introduced to answer new questions or reduce the uncertainty of the model outputs by considering refined process, often increased model complexity can have unexpected (and often unexplored) consequences on the overall model sensitivity and uncertainty. Thus modelers face a difficult trilemma relating model complexity, sensitivity, and uncertainty that can ultimately compromise the relevance of the model for a particular problem. We propose a methodological framework based on global sensitivity and uncertainty analysis to objectively and systematically explore this trilemma. An application is presented where a spatially distributed biogeochemical model to describe phosphorous dynamics in the Everglades (USA) is built and evaluated at different complexity levels. By increasing complexity, key model outputs were found to lose direct sensitivity to specific input factors and gain sensitivity to interaction effects between inputs. The relationship between complexity and uncertainty was found to be less predictable. Output uncertainty was generally found to reduce with increased complexity for summative outputs affected by the overall model (i.e., phosphorus surface water concentration), but reverse relationships were found for other outputs. The conceptual and methodological framework proved insightful and useful for characterizing the interplay between complexity, sensitivity, and uncertainty, and is proposed as an indispensable component in the model development and evaluation process. 1. Complexity, Uncertainty, and Sensitivity: A Modeling Trilemma That is what we meant by science. That both question and answer are tied up with uncertainty, and that they are painful. But that there is no way around
Improving Transformer-Based End-to-End Speech Recognition with Connectionist Temporal Classification and Language Model Integration

The state-of-the-art neural network architecture named Trans-former has been used successfully for many sequence-to-sequence transformation tasks. The advantage of this architecture is that it has a fast iteration speed in the training stage because there is no sequential operation as with recurrent neural networks (RNN). However, an RNN is still the best option for end-to-end automatic speech recognition (ASR) tasks in terms of overall training speed (i.e., convergence) and word error rate (WER) because of effective joint training and decoding meth-ods. To realize a faster and more accurate ASR system, we combine Transformer and the advances in RNN-based ASR. In our experiments, we found that the training of Transformer is slower than that of RNN as regards the learning curve and integration with the naive language model (LM) is difﬁcult. To address these problems, we integrate connectionist temporal classiﬁcation (CTC) with Transformer for joint training and decoding. This approach makes training faster than with RNNs and assists LM integration. Our proposed ASR system realizes signiﬁcant improvements in various ASR tasks. For example, it reduced the WERs from 11.1% to 4.5% on the Wall Street Journal and from 16.1% to 11.6% on the TED-LIUM by introducing CTC and LM integration into the Transformer baseline.
DIFFERENTIATING NORMAL VS MISARTICULATED TONGUE TRAJECTORIES FROM ULTRASOUND FOR FAST AUTOMATIC ARTICULATORY BIOFEEDBACK

Ultrasound images of the tongue surface have been increasingly used for immediate visual feedback while a speaker is working to change pronunciation. However, the tongue surface contour is complex and changes rapidly, meaning that speakers find it difficult to compare undesired vs. desired tongue shapes under realistic speaking conditions. For biofeedback in speech therapy, it is important to identify deviation from the desired tongue trajectory in real time. We present results from a preliminary study characterizing differences between misarticulated and normally articulated (“accurate”) American English /ɑɹ/ using an efficient, automatic tongue surface tracking method that separately characterizes motion of the blade, dorsum and root. Results from principal component analysis of trajectory data from child speakers show that misarticulated trajectories are distinctly different from accurate trajectories. A statistical model for classifying accurate vs. misarticulated productions is discussed.
Which Ones Are Speaking? Speaker-Inferred Model for Multi-Talker Speech Separation

Recent deep learning methods have gained noteworthy success in the multi-talker mixed speech separation task, which is also famous known as the Cocktail Party Problem. However, most existing models are well-designed towards some predefined conditions, which make them unable to handle the complex auditory scene automatically, such as a variable and unknown number of speakers in the mixture. In this paper, we propose a speaker-inferred model, based on the flexible and efficient Seq2Seq generation model, to accurately infer the possible speakers and the speech channel of each. Our model is totally end-to-end with several different modules to emphasize and better utilize the information from speakers. Without a priori knowledge about the number of speakers or any additional curriculum training strategy or man-made rules, our method gets comparable performance with those strong baselines.
Brewers ’ spent yeast and grain as second-generation feedstuff for aquaculture feed

Aqua feeds are formulated to contain all the essential nutrients that fishes need to keep healthy. They are highly dependent on fish meal (FM) and fish oils (FO). According to IFFO, the global production of FM was 5.13 million tonnes in 2018 (65% goes to aquaculture), while FO was 1.04 million tonnes (83% goes to aquaculture). Thus, alternative ingredients which successfully replace these marine components with non-traditional sources are required to result in sustainable and economical feeds. European Union is the 2 largest beer producer in the world (about 400 million Hl in 2017). Breweries employs different batch type operations in processing raw materials to the final beer product that produce large quantities wastes. The largest volume are brewers’ spent grains (BSG), with more than 6 million tons of BSG per year, followed by brewers’ yeast (BY) with about 1 million tons of BY. Reducing aquaculture’s dependence on marine resources in the future will depend on developing alternative raw materials that substitute away fishmeal and fish oil and improving their feed efficiencies. Life Brewery project (LIFE16ENV/ES/000160) proposes a sustainable solution for producing new feed ingredients from brewers wastes for aquaculture. The process consists of an enzymatical hydrolysis to improve the digestibility of these by-products and increase the assimilation of nutrients by fishes and an innovative drying process to achieve the desired moisture content of less than 10%. Experimental meals are tested in sea bream, senegalese sole and trout, as models of a Mediterranean, Atlantic and freshwater aquaculture, respectively.
Optimal Income Taxation with Costly Income Falsification ∗-Preliminary

Models of optimal taxation typically assume that governments can perfectly observe taxpayers’ income. There is plenty of evidence to suggest otherwise. Taxpayers can use income falsification to reduce how much taxes they pay by manipulating the information the government receives about their income through legal (tax avoidance) and illegal means (tax evasion). Additionally, participating in these activities is not costless and it involves more than just lying or sending an incorrect signal. This paper relaxes the aforementioned standard assumptions and answers the question of how the optimal tax schedule changes when a normative model of taxation allows for costly income falsification. The outcome is a tax function that encourages agents to display all of their income (non-falsification), allowing a social planner to increase tax revenue and aggregate welfare while eliminating agents’ wasteful income falsification activities. These results suggest that there are gains to reforming the tax schedule to encourage taxpayers to report their income in full. Moreover, I explore policy implications when the model is calibrated to realistic parameters of taxpayer behavior in the U.S. and show that marginal tax rates under costly income falsification are lower than those prescribed by a similarly defined static Mirrleesian model. Ultimately, this paper shows that assuming that income is perfectly observable is not innocuous. J.E. L Codes: H21, H24, H31, J22.