Miracle-dz/UniFAR-FTUs huggingface dataset:
1/8 STATUS OF THE AUTOMATIC OBSERVATION ON AERODROME AND ONGOING IMPROVEMENTS IN FRANCE

Météo-France began automatic observations on aerodrome, both for METAR (AUTO) and local reports, in 2001. Contacts with aeronautic users were made before and after the first installation. The users were satisfied. Then, three levels of service were defined with the French Civil Aviation Authorities and applied for the French main airports. 52 airports are currently issuing AUTO METAR continuously or when a human observer is not on duty. It was decided to code AUTO METAR as complete as possible, therefore a present weather sensor was added to the existing visibility sensors (forward scatter meter and/or transmissomètres) and ceilometers. Up to 2006, it was not possible to detect and code automatically convective clouds (CB, TCU). A study was conducted to test the capacity of detection of convective clouds from the radar network and the lightning network. Following this study, operational procedures were developed to add convective information in AUTO METAR and AUTO local reports. The main problems to solve were the timing of the calculus and the transmission of information to each airport. Currently, 26 airports are including convective information in the cloud layers. Occurrence of thunderstorm is also added from the lightning network. Another improvement will be introduced next year with the addition of a state of ground sensor, which will improve the detection and identification of freezing precipitation.
ASYNC: Asynchronous Machine Learning on Distributed Systems

ASYNC is a framework that supports the implementation of asynchronous machine learning methods on cloud and distributed computing platforms. The popularity of asynchronous optimization methods has increased in distributed machine learning. However, their applicability and practical experimentation on distributed systems are limited because current engines do not support many of the algorithmic features of asynchronous optimization methods. ASYNC implements the functionality and the API to provide practitioners with a framework to develop and study asynchronous machine learning methods and execute them on cloud and distributed platforms. The synchronous and asynchronous variants of two well-known optimization methods, stochastic gradient descent and SAGA, are implemented in ASYNC and examples of implementing other algorithms are also provided.
Influence of cutting frequency in Alpine permanent grassland on nutritive value , DM yield and agronomic parameters

The objective of the study was to investigate the impact of cutting frequency and fertilisation on yield and nutrient content in Alpine permanent grassland as well as on agronomic parameters of milk production with dairy cows depending on these grassland management factors. The experiment was based on a 3 × 3 two-factorial design, consisting of 3 levels of cutting frequency (2, 3, 4 cuts per year) and 3 levels of fertilisation (80, 160, 240 kg N per hectare). The nutrients were supplied both by liquid manure (15, 30 and 45 mł per ha) and mineral fertilizers. Cutting frequency showed a highly significant impact on an all nutrient parameters investigated (10.5, 13.0, 16.4 % cP; 60.0, 52.4, 45.1 % NDF; 54.4, 65.7, 74.2 % doM in vitro; 69.1, 75.9, 82.1 % in situ DM degradability). DM yield was highest at medium cutting frequency (10717, 11198, 10756 kg DM), but differences were not great, although significant. Based on the results of the grassland experiment, model calculations were carried out for both a feeding regime without concentrate and a concentrate supply according to animal requirements. The impact of forage quality on feed intake is of major consequence. By higher forage intake – accompanied with lower DM yield of grassland – the possible stocking rate is significantly reduced with cutting frequency (1.62, 1.60, 1.47 and 2.21, 1.87, 1.55 cows per ha with the two concentrate levels). From this it follows that the improved feed intake (and therefore milk yield) on animal level does not necessarily result in higher milk productivity on area level (i.e. milk yield per hectare). The same is true for N excretion. Furthermore, there is a significant interaction between cutting frequency of grassland and concentrate level in dairy cow feeding. At low concentrate levels, the increase of individual milk yield and N excretion exceeds the effect of the reduced stocking rate, resulting in higher milk yield (4654, 7049, 8310 kg) and N excretion (127, 149, 181 kg) per hectare forage area. The opposite is true for concentrate levels necessary to fulfill nutrient requirements of the cows. The highest milk yield (14207, 12003, 10118 kg) and N excretion (207, 183, 189 kg) per hectare forage area can be expected at low cutting frequency. However, when the results are related to the total area necessary for milk production (i.e. forage plus concentrates), the highest milk yield and N excretion is achieved with high cutting frequency of grassland. It is concluded that optimal cutting frequency of permanent grassland has to consider a sustainable grassland management aiming at a stable botanical composition, nutrient content, DM yield and dense swards. From the viewpoint of dairy cow nutrition, the forage quality required to feed dairy cows has to be enhanced in proportion with the intended milk yield.
METHODOLOGY OF A GRID-BASED HYDROLOGICAL

On the basis of raster digital elevation model data, raster flow vectors, watershed delineation and spatial topological structure were generated by the Martz and Garbrecht method for the upper area of Hanzhong station within the Hanjiang River in China. Then, the Xin’anjiang Model was applied to runoff generation in each grid element with raster precipitation data interpolated by the step-by-step correction method as the model input. Finally the Muskingum-Cunge method considering lateral flow into the stream was applied for flood routing on the raster drainage network. Thus a grid-based hydrological model was established. The model performs well in terms of the model efficiency coefficient and of the relative error of flood discharge peak value. The model is not only able to route the streamflow to the outlet, but also capable of computing conveniently the hydrograph at any grid within the catchment. As a result, the raster-based digital basin coupling with the grid-based hydrological model is of theoretical value and of practical significance to deep understanding of hydrological physical mechanism, water resources management, and flood prevention within the catchment.
Application of SWAT Model for Estimation of Runoff in Pindwara Watershed and Assessment of its Feasibility

Land and water are the two most basic yet essential components in agriculture. However, these two natural resources are also the ones that are most subjected to deficiency, deterioration and degradation. Surface runoff is a key factor for driving many watershed processes especially erosion. In absence of proper soil and water conservation measures, erosion would slowly, yet surely, lead to formation of gullies and ravines upon the land, thus decreasing the area available for agriculture. The design criteria of all soil and water conservation structures that are constructed to tackle the problem of soil erosion due to surface runoff, accounts for the amount of surface runoff that is generated from the rainfall in the form of either discharge or depth of runoff.
Prevalence of Diseases caused by White Spot Virus and Enterocytozoon Hepatopenaei in Penaeus vannamei Shrimp Farms in Nagapattinam District, Tamil Nadu, India

Total shrimp production in the world is dominated by P. vannamei and P. monodon which contributes around 80% of total shrimp production (FAO, 2009). In the worldwide the dominance of P. vannamei over P. monodon is due to its availability of SPF, SPR brood stock, fast growth rate and low protein requirement. The global production of crustaceans was about 4128 million tonnes in quantity. China remains to be the top producer of crustaceans globally with 32.5% of production and India stands at sixth place with around 7.0% of production (FAO statistics, 2016). In the shrimp culture system, most of the disease incidence has been attributed to viral pathogens (Kiran and Salim, 2012). But in recent years, there has been incidence of new parasitic pathogens International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 8 Number 06 (2019) Journal homepage: http://www.ijcmas. com
Collective Reputation in Trade: Evidence from the Chinese Dairy Industry

Abstract The existence of collective reputation implies an important externality. Among firms trading internationally, quality shocks about one firm's products could affect the demand of other firms from the same origin country. We study such a reputation spillover in the context of a large-scale scandal that affected the Chinese dairy industry in 2008. Leveraging detailed firm-product level administrative data and official quality inspection reports, we document sizable reputation spillovers on uncontaminated firms. We further investigate potential mechanisms that could mediate the strength of collective reputation, including information accuracy, observability of the supply chain, and prior export experience.
Distribution and Abundance of the Endemic Scaleless Toothcarp, Aphanius furcatus Teimori, Esmaeili, Erpenbeck & Reichenbacher 2014 (Teleostei: Aphaniidae) in Iran

Aphanius furcatus-known as scaleless tooth-carpis a unique species of the genus Aphanius, endemic to the drainage of the Hormuzgan, Persian Gulf Basin in southern Iran. This species has not yet assessed in the list of IUCN’s Red Data Book. Investigation of the current distribution and estimation of the individual abundance revealed that the abundance of A. furcatus is low, and it has low population sizes in their current distribution ranges. In addition, its current habitats are under several threats due to the recent severe drought in southern Iran, contamination and habitat destruction. Therefore, immediate necessary plans should be taking into consideration to conserve this unique Aphanius species.
Molecular and morphological characterization of leaf heading in Chinese cabbage ( Brassica rapa )

2 Acknowledgements . 3 Introduction . 6 Aim thesis . 9 Material and methods . 10 Plant material and growth conditions . 10 In situ hybridization . 10 Fixation and embedding . 11 Probe design . 12 Hybridization . 13 RNA quality . 13 Dot blot . 13 Meristem morphology observation . 13 Leaf number counting . 13 EMS genotyping and phenotyping . 14 qRT-PCR and preparations . 16 Results . 18 Fixation, embedding and cutting of meristems . 18 General observation of shoot apical meristems . 18 Leaf counting . 21 qRT-PCR . 23 In situ hybridization: Probe design . 25 First in situ hybridization . 26 Second in situ hybridization . 28 RNA presence validation . 29 Third in situ hybridization . 30 EMS phenotyping . 33 EMS genotyping . 40 Discussion. 41 Fixation, embedding and cutting of meristems . 41 Meristem morphology observation . 41 Leaf number and function . 42 qRT-PCR . 42 Probes for in situ hybridization . 43
POI Data Taxi Data Spatial View Temporal View MoBike Data Origin View Destination View Time View MVM Destination

Bike-Sharing Systems (BSSs) are being introduced to more and more cities recently, and therefore they have generated huge amounts of data. Mobike is a stationless BSS which is suffering from the chaotic parking problem. To solve this problem, it is necessary to predict where the bikes are going. Traditional works dealing with destination prediction mainly focus on stationbased BSSs, and they merely leverages context-aware information technically. Thus it is naturally promising to investigate how to improve the destination prediction of station-less bikes by context information. To that end, in this paper, we develop a multi-view machine (MVM) method, by incorporating the context information from Point of Interest (POI) data and human mobility data into destination prediction. Specifically, we first describe three different views, namely start position, start time and destination by features extracted from POI data and human mobility data. Then, we capture the relationship between these three views’ interactions and the trip’s possibility by a multi-view machine. Finally, since multi-view machine contains too many parameters to be optimized, we leverage tensor factorization (TF) to reduce the computation costs. The experimental results show that the model can effectively capture the potential relationship of three views with trip’s possibility and the approach is thus much more effective than traditional prediction methods for destination.
Extraction From Natural Scene Images Using Adaptive Methods

Text extraction refers to the process of separating text region from a given image. Scene text extraction is more challenging than document test extraction because of degradationssuch as shadows, reflections from background surface, and uneven lightening conditions. In this paper, we propose an adaptive method for detecting and extracting text from natural scene images. This method is robust against the shadows and uneven lighting conditions. The proposed methoduses adaptive thresholding technique to binarize image and smoothen degradation factors mentioned. Canny edge detection is used to obtain edge image and Block operation of localization is used to remove non-text area from image. Connected component analysis is used forextracting text from image. The work is applied onimages without and with shadows and uneven lighting conditions. Experimental results showthat the performance of the proposed approach is robust for both.It was observed 1 International Journal of Pure and Applied Mathematics Volume 120 No. 6 2018, 11669-11682 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl. eu/hub/ Special Issue http://www.acadpubl. eu/hub/
Sample : Differential Item Functioning Between African American and Caucasian Youth A Measurement Invariance Examination of the Revised Child Anxiety and Depression Scale in a Southern

This study examined the psychometric properties of the Revised Child Anxiety and Depression Scale in a large sample of youth from the Southern United States. The authors aimed to determine (a) if the established six-factor Revised Child Anxiety and Depression Scale structure could be replicated in this Southern sample and (b) if scores were associated with measurement invariance across African American and Caucasian youth representative of youth from this region of the United States. The established six-factor model evidenced the best fit in comparison to one-, two-, and five-factor models in the total sample (N = 12,695), as well as in the African American (n = 4,906) and Caucasian (n = 6,667) subsamples. Multigroup confirmatory factor analysis also supported measurement invariance across African American and Caucasian youth at the levels of equal factor structure and equal factor loadings. Noninvariant item intercepts were identified, however, indicating differential functioning for a subset of items. Clinical and measurement implications of these findings are discussed and new norms are presented.
D REKONSTRUKCIÓ OMNIDIREKCIONÁLIS KAMERÁKBÓL SZFÉRIKUS MODELL HASZNÁLATÁVAL 3 D RECONSTRUCTION FROM OMNIDIRECTIONAL CAMERAS USING SPHERICAL MODEL

Cikktörténet: Beérkezett 2015. október 10. Átdolgozva 2015. október 21. Elfogadva 2015. november 5. Összefoglalás A 3D rekonstrukció jól ismert kutatási terület a számítógépes látás világában. Mivel ez a feladat függ az alkalmazástól, a színtértől és sok esetben az alkalmazott kamera rendszertől, az elmúlt 20 évben számos különböző rekonstrukciós algoritmus született. Ezek az algoritmusok és megoldások azonban javarészt keskeny bázistávolságú perspektív sztereó kamera rendszerekhez készültek. A mi célunk meghatározni azt a kamera geometriai modellt, amely omnidirekcionális kamerák esetén legjobban támogatja a hagyományos rekonstrukciós eljárásokat. Bemutatjuk a szférikus kameramodellt, és hozzá kapcsolódóan a szférikus rektifikációt, mint egy lehetséges változatot. A rekonstrukciós eljárás menetének tárgyalása mellett eredményeket is mutatunk többkamerás rendszerből származó valós képek esetén.
Computational creativity specification for music : comparative analysis of machine improvisers Favoleggiatori 2 and MASOM

I introduce my machine music improviser entitled Favoleggiatori 2 which is an implementation based on swarm algorithms that operate in dynamic topological spaces, where these topological spaces represent memory of musical events. I align Favoleggiatori 2 with a formal specification for computational creativity which I have developed previously. The formal specification is used as an analytical tool to understand the limitations of the Favoleggiatori 2 architecture. This analysis based on the specification also facilitates comparative study of the MASOM machine improviser architecture in order to constructively formulate some of the differences and similarities of these architectures. The limitations indicated by the analytical use of the formal specification suggest a hybrid implementation architecture which is potentially more flexible and more musically adaptable than the two implementations analysed. This analytical approach can be useful in theoretical and technical development towards the next generation computational creativities with greater ‘creative’ potentials than current implementations.
Identify Stable Campaigns Detect Anomalies Aggregate Campaign Clusters Generate Time Series Stable Campaigns Aggregated Campaign Metrics Aggregated Time Series Control Server TSDB Detector

Online media offers opportunities to marketers to deliver brand messages to a large audience. Advertising technology platforms enables the advertisers to find the proper group of audiences and deliver ad impressions to them in real time. The recent growth of the real time bidding has posed a significant challenge on monitoring such a complicated system. With so many components we need a reliable system that detects the possible changes in the system and alerts the engineering team. In this paper we describe the mechanism that we invented for recovering the representative metrics and detecting the change in their behavior. We show that this mechanism is able to detect the possible problems in time by describing some incident cases.
Performance and Saliency Analysis of Data from the Anomaly Detection Task Study

Studies have shown that finding the changes and differences within a scene or image to detect important objects and areas of interest is a key skill for object detection, particularly for soldiers. These changes and differences can be considered anomalies within the content and context of the scene. The detection of anomalies can be seen as a data point related to different attributes to an individual’s ability to detect key objects and areas. An Anomaly Detection Task (ADT) study conducting at the Army Research Laboratory (ARL) explored using this technique to investigate the relationship between individuals who were proficient in detecting targets. Initially, the multidimensional scaling (MDS) methodology was used to begin investigating similarities between the characteristics of these individuals. The findings of the MDS study showed some correlations between performance in the ADT and individuals that were considered proficient in object detection. During the analysis of the results from the study an additional research question was posed. The new question: Could using the ADT data aid the development of computational algorithms that would detect objects in images in the same manner that these individuals detected object related anomalies. To start this investigation the Ideal Observer Model (ioM) was selected as the algorithm. The ioM is a saliency based algorithm that incorporates how human visual processing focuses attention on important objects and features. We compared the results from the ioM to the results of the ADT study. If the findings show correlation this information can be used to fine tune the algorithm. This paper will present a summary of analysis of the results from (ADT) study and present sample results of the analysis of the ioM for comparison.
Using evolutionary computing to improve black box monkey testing on a Graphical User Interface Smar ter Mon keys

states the number of unique abstract states visited, in which an abstract state is a group of states that are the same when a certain set of properties is disregarded (for example the color of controls) Longest path the length of the longest path in the state transition model State coverage the minimum and maximum state coverage, defined as the lowest and highest rate of executed actions over the total number of available actions of a state Even with an incomplete state-transition-model, these metrics can be used to compare test coverage of action selection strategies. When more graph states are visited, a larger part of the SUT is covered and there is a larger chance of faults being detected. The above mentioned works have used Graph states, Abstract states, Longest path and State coverage. Chapter 5 explains the metrics used in the presented work. Apart from the state transition model, TESTAR also observes and reports other events that can be used as metrics or for diagnostics. Memory usage the peak amount of memory used by the SUT CPU usage the peak amount of CPU used by the SUT Faults the number of faults detected by TESTAR, according to the configured oracles The first two metrics do not indicate how well the SUT is tested, but they can be used to detect faults, for example memory leaks. The number of faults may seem to be the most important metric, as the goal of testing is to find faults. However, the number of faults in a SUT is unknown and the chances of finding many of them in a short time are small. While a typical short test may have covered 70 states, it may only have found one fault. The number of faults encountered therefore depends too much on chance to make it a reliable metric.
A Novel Approach for Web Personalization through Web Mining Techniques

Data on World Wide Web has been growing in an exponential manner. This raises a severe concern over information over load challenges for the users. Retrieving the most relevant information from the web as per the user requirement has become hard because of the large collection of heterogeneous documents. It is time consuming for the users to go through the long list of odds and ends to choose their relevant one. One approach to overcome this is to personalize the information available on the Web according to user requirements. The information or services provided by a Web to the requirements of individual or cluster of users, by considering their navigational patterns is termed as Web Personalization. The objective of Web Personalization is to provide users with what they really want or need, without having to ask or search for it explicitly. This approach effectively improves the performance of Information Retrieval (IR) systems. This paper presents an extensive survey on the various approaches proposed by the researchers in Web Personalization and challenges with a focus on future work.
A Tabu search for the multi-compartment vehicle routing problem

The multi-compartment vehicle routing problem (MC-VRP) consists in finding routes with minimum total length that satisfy the demands of a set of customers for several products that must be loaded in specific vehicle compartments. Despite its wide practical applicability this problems has been rarely studied in the literature. In this paper we propose a constructive heuristic and a Tabu search algorithm for this problem. We evaluate the algorithm on twenty different VRP instances and four different sets of MC-VRP instances with two compartments and compare with previous results from the literature.