Miracle-dz/UniFAR-FTUs huggingface dataset:
DEFINING SUITABLE HABITAT AND CONSERVATION STATUS FOR

Curtis M. Bradley, Center for Biological Diversity, Tucson, AZ; and Philip C. Rosen, University of Arizona, Tucson, AZ. Abstract. — The Tucson shovel-nosed snake (Chionactis annulata klauberi) is a small specialized colubrid associated with sandy loams of the elevated portions of the Lower Colorado River Valley Sonoran Desert in central Arizona. This taxon is a recently redefined subspecies based on genetic data, and may be extirpated in the Tucson region, including the type locality. A recent (USFWS 2014) decision against protecting it was based in part on an expansive definition of its geographic range and a habitat model. Here, we redefine the subspecies distribution by uniting published results of mitochondrial and nuclear DNA. We then present a new ecologically-based model of its original and current habitat using the machine learning algorithm Random Forests. The new model accurately matches known and estimated presence-absence data for this taxon, and is consistent with morphometrics and, largely, with color pattern variation. It estimates 60% less available habitat than the USFWS model. We estimate that 39% of the 1,255,946 ha (3,103,505 acres) of original habitat has been converted to urban developments, roads, agriculture, or otherwise altered non-habitat. Of the remaining 770,971 ha (1,905,108 acres), 60% is susceptible to habitat conversion in the region, with only 10.9% of habitat having current legal protection. Ongoing and projected urbanization and energy development in its flatland desert habitat present a bleak future for this subspecies.
EFFECT OF LEVELS, DOSES OF NITROGEN FERTILIZER AND SEEDING RATES ON YIELD AND QUALITY OF SUGARCANE

Fayoum J. Agric. Res. & Dev. , Vol. 34, No.1, January, 2020 EFFECT OF LEVELS, DOSES OF NITROGEN FERTILIZER AND SEEDING RATES ON YIELD AND QUALITY OF SUGARCANE Ahmed F.I. Gadallah Sugar Crops Res. Inst. , Agric. Res. Center, Giza, Egypt ABSTRACT Two field experiments were conducted at Shandaweel Agricultural Research Station, Sohag Governorate, Egypt (latitude of 26 61° N, longitude of 31 52° E and altitude of 72 m above sea level) in 2016/2017 and 2017/2018 seasons to assess the effect of levels and number of doses of nitrogen fertilizer as well as seed rates on yield and quality of sugarcane. This work included 27 treatments, represent the combinations among three rates of seeds [1.0, 1.5 and 2.0 rows of 3-budded cane cuttings (25200, 37800 and 50400 buds/fed)], three levels of N (180, 210 and 240 kg N/fed), and three N doses (each N level was split and applied as 2, 3 and 4 equal doses). The sugarcane cultivar namely G. 2004-27 (Giza-4) was used as a planting material. A randomized complete block design (RCBD) was used a split-split plot arrangement and replicated three times. The main-plots were devoted for seed rates, N levels were randomly applied in the sub plots, while number of N doses were distributed in the sub-sub plots, in both seasons. The results revealed that seed rates had a significant effect on all studied traits of sugarcane at harvest time. Planting sugarcane using 2.0 rows of cuttings (50400 buds/fed) improved millable cane height, number of millable canes/fed, cane and sugar yields/fed, while significant improvements in millable cane diameter, millable cane weight, brix%, sucrose% and sugar recovery% was recorded in case of growing sugarcane using 1.0 row (25200 buds/fed), in both seasons. Increasing N fertilizer level to 240 kg N/fed resulted a significant increase in millable cane height, diameter, millable cane weight, number of millable canes/fed, cane and sugar yields/fed, while the application of 210 kg N/fed led to a significant increase in brix, sucrose and sugar recovery percentages, in both seasons. The results indicated that increasing the number of N doses from 2 to 4 had significant positive effects on millable cane height, diameter, millable cane weight, number of millable canes/fed, cane and sugar yields/fed, along with brix, sucrose, and sugar recovery percentages, in both seasons. Under conditions of this work, it was found that growing Giza-4 sugarcane variety using 2.0 rows of cane setts (50400 buds/fed) with add 240 kg N/fed into 4doses can be recommended to get the maximum cane and sugar yields.
Mid-low Resolution Remote Sensing Ship Detection Using Super-Resolved Feature Representation

: Existing methods enhance mid-low resolution remote sensing ship detection by feed-1 ing super-resolved images to the detectors. Although these methods marginally improve the 2 detection accuracy, the correlation between image super-resolution (SR) and ship detection is 3 under-exploited. In this paper, we propose a simple but effective ship detection method called 4 ShipSR-Det , in which both the output image and the intermediate features of the SR module are 5 fed to the detection module. Using the super-resolved feature representation, the potential beneﬁt 6 introduced by image SR can be fully used for ship detection. We apply our method to the SSD and 7 Faster-RCNN detectors and develop ShipSR-SSD and ShipSR-Faster-RCNN , respectively. Extensive 8 ablation studies validate the effectiveness and generality of our method. Moreover, we compare 9 ShipSR-Faster-RCNN with several state-of-the-art ship detection methods. Comparative results on 10 the HRSC2016, DOTA and NWPU VHR-10 datasets demonstrate the superior performance of our 11 proposed method. 12
Tower of London and Tower of Hanoi: distinct contributions to the assessment of executive functioning

Objective: To produce theoretical study about comparisons of cognitive and methodological aspects involved in the performance of Towers of Hanoi and London in normal individuals. Method: Narrative review of the literature. Results: The performances in these instruments do not share the expected variance due to the apparent similarities in your structure and solution demand. Correlations significant, however moderate, among the two towers performance, were found reflecting the average of 75% of the nonshared variance. Conclusions: Both towers recruit different executive processes for task solution and cannot be used as exchangeable instruments. Key-words Executive functions, planning, Tower of London, Tower of Hanoi. Recebido 10/04/2007 Aprovado 20/07/2007 OS 3670 J Bras Psiq 56_2. indd 134 26/9/2007 17:43:37 J Bras Psiquiatr, 56(2): 134-139, 2007 135 Torre de Londres e Torre de Hanói: avaliação do funcionamento reVisÃo De LiterAturA
Estimation of Melting of Moving Target Based on Background Subtraction

In this paper, the image processing technology is used to remove the interference factors of the original image, and then the time-series image is further processed based on the background difference method and centroid correlation method. The binary image containing only the target crystal can be extracted more accurately, and the two-dimensional centroid coordinates of the target crystal can be calculated. We use the idea of segmentation to divide the silicon dioxide region into several quasi-circular regions, calculate the volume of crystal pixel, estimate the actual volume of crystal according to the ratio of background reference object and image pixel, and then calculate the mass. Later, we get the melting rate through the derivation of mass to time. Finally, we use MATLAB to fit the volume and time of silica, when it is exponential function the fitting degree is the best. The model not only provides guidance for the recovery of iron tailings but also provides ideas for the establishment of a crystal tracking model and melting speed model for tracking melting in the molten pool.
Graph? Yes! Which one? Help!

Amazon Neptune is a graph database service that supports two graph (meta)models: W3C's Resource Description Framework (RDF) and Labeled Property Graphs (LPG). Customers opt in for one or the other model, and this choice determines which data modeling features can be used, and - perhaps more importantly - which query languages are available to query and manipulate the graph. The choice between the two technology stacks is difficult and requires consideration of data modeling aspects, query language features, their adequacy for current and future use cases, as well as many other factors (including developer preferences). Sometimes we see customers make the wrong choice with no easy way to reverse it later. It is therefore highly desirable that the choice of the query language can be made without consideration of what graph model is chosen, and can be easily revised or complemented at a later point. In this paper, we advocate and explore the idea of a single, unified graph data model that embraces both RDF and LPGs, and naturally supports different graph query languages on top. We investigate obstacles towards unifying the two graph data models, and propose an initial unifying model, dubbed"one graph"("1G"for short), as the basis for moving forward.
TexSmart: A System for Enhanced Natural Language Understanding

This paper introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. Compared to most previous publicly available text understanding systems and tools, TexSmart holds some unique features. First, the NER function of TexSmart supports over 1,000 entity types, while most other public tools typically support several to (at most) dozens of entity types. Second, TexSmart introduces new semantic analysis functions like semantic expansion and deep semantic representation, that are absent in most previous systems. Third, a spectrum of algorithms (from very fast algorithms to those that are relatively slow but more accurate) are implemented for one function in TexSmart, to fulfill the requirements of different academic and industrial applications. The adoption of unsupervised or weakly-supervised algorithms is especially emphasized, with the goal of easily updating our models to include fresh data with less human annotation efforts.
A New Hybrid Multilevel Inverter with Extended Number of Voltage Steps

This paper presents and studies an improved multilevel inverter topology to produce higher voltage levels with reduced switching devices at the inverter output. The suggested topology is based on SCSS multilevel inverter. In this topology, DC sources are divided into two groups, inner group and outer group. The function of the outer dc source group is to create the square waveforms that are close in nature to the required output waveforms. However, the inner group is used to increase the number of the DC voltage levels to produce an output voltage close to the desired wave. This task is accomplished by arithmetic operation (addition or subtraction) of the value of the inner DC source with the blocks created by the outer group, respectively. A hybrid modulation scheme has been utilized to substantially reduce harmonics distortion. The performance of the proposed topology is then compared with other conventional and concurrent structures of multilevel inverter as well as evaluated by simulation results.
Style-Specific Beat Tracking with Deep Neural Networks

In this thesis, a computational method for extracting the beat positions from audio signals is presented. The proposed beat tracking system is based on temporal convolutional networks which capture the sequential structure of audio input. A dynamic Bayesian network is used to model beat periods of various lengths and align the predicted beat positions to the best global solution. The system is evaluated on four datasets of various musical genres and styles and achieves state-of-the-art performance. Compared against a current state-of-the-art beat tracker, the proposed approach maintains competitive performance but with two distinct computational advantages. It works causally and requires considerably less learnable parameters. In addition, due to the highly parallelizable structure of convolutional neural networks, computational efficiency dramatically increases when training on GPUs.
Coincident In-situ and Triple-Frequency Radar Airborne Observations in the Arctic

Abstract. The dataset collected during the Radar Snow Experiment (RadSnowExp) presents the first-ever triple-frequency radar reflectivities combined with almost perfectly co-located and coincident airborne in situ microphysics probes on board the National Research Council Canada (NRC) Convair-580 aircraft. Over 12 hours of flight data in mixed phased and glaciated clouds with more than 3.4 hours in non-Rayleigh regions for at least one of the radar frequencies provide a unique opportunity for studying the relationship between cloud microphysical properties and radar triple-frequency signals. The in situ particle imagery data for this study include imagery from the CPI probe, which provides high resolution particle imagery and allow accurate identification of particle types including level of riming within the DFR plane. The airborne triple-frequency radar data are analysed and grouped based on the dominant particle compositions and microphysical processes (level of aggregation and riming). The results from this study are consistent with the main findings of previous modelling studies with specific regions of the dual-frequency ratio (DFR) plane associated with unique scattering properties of different ice habits, especially in clouds where radar signal is dominated by large aggregates. Moreover, the analysis shows that the close relationships between the triple-frequency signatures and particles’ bulk density, level of riming and aggregations and characteristic size of the particle size distribution (PSD).
NDT_A_235675 771..780

Jane Pei-Chen Chang* Kuo-Hao Huang * Chieh-Hsin Lin 2,3 Hsien-Yuan Lane 1,3,4 1Department of Psychiatry & Brain Disease Research Center, China Medical University Hospital, Taichung, Taiwan; 2Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan; 3Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan; 4Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan
Music Genre Classification Using 1D Convolution Neural Network

: Music genre classification system is a system that is important to the users for effectiveness in the digital music industry. One of the effective ways of genre classification is in music recommendation and access to users. With accurate classification system built, songs can be readily accessed by the users when the genre of the song is known and recommendation of songs to the users is made easy. Also, automatic classification of genre is important to solve problems such as tracking down related songs, discovering societies that will like specific songs and also for survey purposes.
!e brain and its states

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Wizard-of-Oz Testing as an Instrument for Chatbot Development An experimental Pre-study for Setting up a Recruiting Chatbot Prototype

—Chatbots can be utilized to automate various business processes to add value for companies and users – for example, in the form of efﬁciency enhancement. Throughout the process of chatbot development, the integration of user feedback within a user-centered conversational design process is essential. In our study, we investigated chatbots in recruiting, a ﬁeld within human resource management that is characterized by a high proportion of repetitive and standardized tasks. This pre-study applies a Wizard-of-Oz approach in which a basic dialog concept is tested in a very early phase of the project, simulating the chatbot functionality by a human operator. In this way, valuable user feedback on the general suitability of the dialog design can be gathered without coding chatbot functionalities. In total, eight users participated in our 60-minute experiment to conceptionally validate our idea and test the simulated Frequently Asked Questions (FAQ) chatbot. The research brought important insights into the basic concept and allowed us to collect new user intents not considered in the design. As a result, the tested concept proved to be suitable and of value for the users. Despite relatively long response times, only one participant suspected that they were not interacting with a chatbot but a human operator. The feedback on the user satisfaction with the completeness of the predeﬁned answers and competence setup of the simulated chatbot was indifferent and rather moderate. However, most of the participants considered the tested scenario as relevant and stated a high user value for implementing the proposed chatbot in a recruiting process. Moreover, the Wizard-of-Oz approach generated appropriate input for improving the chatbot concept (e.g., intents, entities, criteria for satisfaction and acceptance enhancement) and valuable practical insights for developing a recruiting FAQ chatbot aligned to user needs.
Load Balancing in Cloud Environment Exploiting Hybridization of Chicken Swarm and Enhanced Raven Roosting Optimization Algorithm

: All over the world, cloud computing offers a shared resources pool for several users. The cloud model encompasses the virtual machines and PMs from the users in order to processes the tasks in an equivalent way. In particular circumstances, the demand for users might be maximum that causes the processing units overloading and this circumstance influences the evaluation of the cloud setup. To balance the load of the cloud environment, numerous works have been proposed for the load balancing approach; however, they need to decrease the quantity of task migration. This model develops the balancing of load approach adopting crucial the optimization technique and the multi-objective design. This work develops chicken swarm and enhanced raven roosting optimization, for balancing the load (CS-ERRO) algorithm via the hybridization of the Chicken Swarm Algorithm (CSA), Enhanced Raven Roosting Optimization Algorithm (ERROA). Additionally, this work exploits the multi-objective model on the basis of the probabilities selection, the frequency scaling due to the ability of the machine and the data length of the task. Finally, the performance of the proposed CS-ERRO algorithm is evaluated in different cloud cases, and from the consequences, it is obvious that the proposed algorithm attained reduced load while comparing with the conventional algorithms.
MDER_A_253792 163..172

Shah Newaz Ahmed 1 Ratinder Jhaj 1 Balakrishnan Sadasivam 1 Rajnish Joshi 2 1Department of Pharmacology, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, India; 2Department of General Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, India Background: Current electrocardiography (ECG) criteria indicate only the presence or absence of left ventricular hypertrophy (LVH). LVH is a continuum and a direct relationship exists between left ventricular mass (LVM) and cardiovascular event rate. We developed a mathematical model predictive of LVM index (LVMI) using ECG and non-ECG variables by correlating them with echocardiography determined LVMI. Patients and Methods: The model was developed in a cohort of patients on treatment for essential hypertension (BP>140/90 mm of Hg) who underwent concurrent ECG and echocardiography. One hundred and forty-seven subjects were included in the study (56.38±11.84 years, 66% males). LVMI was determined by echocardiography (113.76±33.06 gm/m). A set of ECG and non-ECG variables were correlated with LVMI for inclusion in the multiple linear regression model. The model was checked for multicollinearity, normality and homogeneity of variances. Results: The final regression equation formulated with the help of unstandardized coefficients and constant was LVMI=18.494+ 1.704 (aLL) + 0.969 (RaVL+SV3) + 0.295 (MBP) + 15.406 (IHD) (aLL – sum of deflections in augmented limb leads; RaVL+SV3 – sum of deflection of (R wave in aVL + S wave in V3); MBP – mean blood pressure; IHD=1 for the presence of the disease, IHD=0 for the absence of the disease). Conclusion: In the model, 50.4% of the variability in LV mass is explained by the variables used. The findings warrant further studies for the development of better and validated models that can be incorporated in microprocessor-based ECG devices. The determination of LVMI with ECG only will be a cost-effective and readily accessible tool in patient care.
Software Quality Evaluation via Static Analysis and Static Measurement: an Industrial Experience

—Business organizations that outsource software development need to evaluate the quality of the code delivered by suppliers. In this paper, we illustrate an experience in setting up and using a toolset for evaluating code quality for a company that outsources software development. The selected tools perform static code analysis and static measurement, and provide evidence of possible quality issues. To verify whether the issues reported by tools are associated to real problems, code inspections were carried out. The combination of automated analysis and inspections proved effective, in that several types of defects were identiﬁed. Based on our ﬁndings, the business company was able to learn what are the most frequent and dangerous types of defects that affect the acquired code: currently, this knowledge is being used to perform focused veriﬁcation activities.
A mutation in budding yeast BRR6 affecting nuclear envelope insertion of the spindle pole body

BRR6 and BRL1 are two paralogs that encode transmembrane proteins of the nuclear envelope (NE) involved in membrane fluidity and nuclear pore complex biogenesis in organisms that undergo a closed mitosis. We show that mutation of a conserved cysteine in the intralumenal domain of Saccharomyces cerevisiae Brr6p results in a novel temperature sensitive allele, brr6-Y100H, that arrests growth due to defects in spindle formation. Analysis of brr6-Y100H cells by electron tomography and Brr6p localization by super-resolution imaging supports the idea that Brr6p is involved in insertion of the newly duplicated spindle pole body into the NE.