Miracle-dz/UniFAR-FTUs huggingface dataset:
Capture and generalisation of close interaction with objects

Robust manipulation capture and retargeting has been a longstanding goal in both the fields of animation and robotics. In this thesis I describe a new approach to capture both the geometry and motion of interactions with objects, dealing with the problems of occlusion by the use of magnetic systems, and performing the reconstruction of the geometry by an RGB-D sensor alongside visual markers. This ‘interaction capture’ allows the scene to be described in terms of the spatial relationships between the character and the object using novel topological representations such as the Electric Parameters, which parametrise the outer space of an object using properties of the surface of the object. I describe the properties of these representations for motion generalisation and discuss how they can be applied to the problems of human-like motion generation and programming by demonstration. These generalised interactions are shown to be valid by demonstration of retargeting grasping and manipulation to robots with dissimilar kinematics and morphology using only local, gradient-based planning.
Clozapine Blood Concentration Predicts Corrected QT-Interval Prolongation in Patients With Psychoses

Abstract Background Corrected QT-interval (QTc) prolongation (QTP) is a rare but fatal adverse effect of antipsychotics. Clozapine is the only antipsychotic recommended for treatment of resistant schizophrenia; however, clozapine has been reported to cause QTP. We sought factors predictive of QTP in patients who had antipsychotic polypharmacy involving clozapine. We explored whether the clozapine blood concentration might predict QTP. Methods We included 133 patients with schizophrenia spectrum disorder who had antipsychotic polypharmacy involving clozapine. We used the χ2 and nonparametric tests to compare clozapine therapeutic drug monitoring (TDM) values and QTc-prolonged person (QTPP) status. Multivariate regression and mediator models were used to identify risk factors for QTPP status and QTP. Results In total, 111 patients were prescribed clozapine. The QTPP rates were 31.3% (20) for men and 23.2% (16) for women. Compared with the non-QTPP group, the QTPP group exhibited significantly higher daily dose of all antipsychotics including clozapine, a higher clozapine dose, and elevated clozapine and norclozapine TDM values. Furthermore, such patients were prescribed a greater number of antipsychotics. Multivariate logistic regression revealed that only the clozapine TDM value could be predictive factor for QTPP status (P = 0.018). A clozapine TDM value above the therapeutic range (>600 mg/dL) was associated with a high risk of QTPP status (adjusted odds ratio, 6.5; 95% confidence interval, 1.7–25.2; P = 0.006). The mediator model revealed that the clozapine TDM values completely mediated the association between the clozapine dose and the QTc interval. Conclusions The clozapine blood concentration reliably predicts QTP in patients with clozapine use.
Content-based Medical Image Tetrieval for Liver CT Annotation

The increase number of medical image stored and saved every day presents a unique opportunity for content-based medical image retrieval (CBMIR) systems. In this paper, we propose content-based medical image retrieval for annotating liver CT scans images in order to generate a structured report. For that, we have used the Bidimentional Empirical Mode Decomposition (BEMD), and then we have applied Gabor wavelet transform to extract the mean and the standard deviation as features descriptors. Finally, a proposed similarity distance was employed to retrieve the most similar training images to the image query, and a majority voting scheme was used to select the answers for an unannotated image. We have used the IMAGECLEF 2015 annotation dataset and the obtained score was 88.9%.
Methods of dna extraction

In recent years cytogenetics and immunophenotyping has become very much advanced.The role of DNA in determining dexterity of the individual and in the field of pathology has gained momentum. A definite and concise knowledge in this field will gradually lead to various prospects in the field of genetics.The methods of DNA extraction is aiding in this area.The quality and control aspects is to be looked for.This book helps in determining a small part of it to the budding researchers
Comparison of the Automatically Calibrated Google Evapotranspiration Application—EEFlux and the Manually Calibrated METRIC Application

Reliable evapotranspiration (ET) estimation is a key factor for water resources planning, attaining sustainable water resources use, irrigation water management, and water regulation. During the past few decades, researchers have developed a variety of remote sensing techniques to estimate ET. The Earth Engine Evapotranspiration Flux (EEFlux) application uses Landsat imagery archives on the Google Earth Engine platform to calculate the daily evapotranspiration at the local field scale (30 m). Automatically calibrated for each Landsat image, the EEFlux application design is based on the widely vetted Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model and produces ET estimation maps for any Landsat 5, 7 or 8 scene in a matter of seconds. In this research we evaluate the consistency and accuracy of EEFlux products that are produced when standard US and global assets are used. Processed METRIC products for 58 scenes distributed around the western and central United States were used as the baseline for comparison. The goal of this paper is to compare the results from EEFlux with the standard METRIC applications to illustrate the utility of the EEFlux products as they currently stand. Given that EEFlux is derived from METRIC, differences are expected to occur due to differing calibration methods (automatic versus manual) and differing input datasets. The products compared include the fraction of reference ET (ETrF), actual ET (ETa), and surface energy balance components net radiation (Rn), ground heat flux (G), and sensible heat flux (H), as well as Ts, albedo and NDVI. The product comparisons show that the intermediate products of Ts, Albedo, and NDVI, and also Rn have similar values and behavior for both EEFlux and METRIC. Larger differences were found for H and G. Despite the more significant differences in H and G, results show that EEFlux is able to calculate ETrF and ETa values comparable to the values from trained expert METRIC users for agricultural areas. For non-agricultural areas such as semi-arid rangeland and forests, the automated EEFlux calibration algorithm needs to be improved in order to be able to reproduce ETrF and ETa that is similar to the manually calibrated METRIC products.
Biogasoline options for conventional spark-ignition cars

The purpose of this study is to explore feasible gasoline biocomponents supplementary to ethanol, and to assess their exhaust emissions performance. Although ethanol is the dominant liquid biofuel globally, technical restrictions limit its use in conventional gasoline cars to 10–15 v/v% (bio-energy 7–10%). Since current conventional cars will continue to take the major share of gasoline car fleets for at least the next 10–20 years, it is necessary to establish and assess biocomponent options for them. Today, higher ethanol blending ratios are possible only with the use of flexible fuel vehicle (FFV) technology. The European Union requires renewable energy to have at least a 10% share of transport energy by 2020, and even higher shares are being attempted regionally. Biocomponents alternative or supplementary to ethanol are therefore desired in the gasoline pool. Interesting gasoline biocomponents are bioethers, biobutanols and biohydrocarbons. Oxygenated and non-oxygenated biocomponents can be combined to increase the bio-share of gasoline. This study consists of a literature review of the production processes, and an evaluation of the end-use performance of gasoline biocomponents. The results show that there are many options for increasing the bio-energy content of gasoline to 20% or more without increasing the gasoline oxygen content to a higher level than can be tolerated by E10-compatible gasoline cars. ISBN 978-951-38-7529-9 (URL: http://www.vtt.fi/publications/index.jsp) Series title and ISSN Project number VTT Working Papers 1459-7683 (URL: http://www.vtt.fi/publications/index.jsp) Date Language Pages December 2011 English 200 p. + app. 9 p. Name of project Commissioned by TransEco – Korkeaseosteiset biokomponentit henkilöautojen polttoaineisiin Ministry for Employment and the Economy
Evaluations of soil resistivity in relation to basal stem rot incidences using soil moisture sensor

Basal stem rot (BSR) caused by Ganoderma boninense is a major disease attacking the oil palm plantation in Malaysia, and incur big losses in palm oil industries. The disease is spread mostly by root either through spore availability in soil or roots contacts. Soil properties were reported to have significant influence on the growth of fungi. Meanwhile, the value of soil resistivity is influenced by soil properties. This paper presents a new approach of BSR detection by using soil moisture sensor which measures resistivity of soil in unit ohm (Ω) at 15 cm surrounding the basal stem of oil palm trees. The study was conducted on 39 oil palm trees at different healthiness levels. The sensor was embedded approximately 4.7 cm deep in the soil at eight different points for each palm. The results showed that healthy oil palm trees significantly have higher mean (ERMEAN ≥ 400) of electrical resistance (ER) readings compared to infected trees (ERMEAN< 400). More specifically, ER readings at points without symptoms (i.e. fruiting bodies and/or hollow) were significantly higher compared with ER readings at points where symptoms appeared even though the points of measurements were on the same palm. This finding has brought to the introduction of a new index to detect Ganoderma infection, named as K-index. Combination of ERMEAN taken from eight points of measurement and its K-index gave better results of detection and a new model was developed based on these two parameters (i.e. ERMEAN and K-index). The developed model has accuracy rates of 82% and gained 100% successful rate during validation. This research showed that soil resistivity can contribute to Ganoderma-infected detection in oil palms with a high degree of accuracy.
Varied response of underground and aboveground plant matter: functional diversity of three different vegetational types after translocation to reclaimed postindustrial land

The indicators of functional diversity are increasingly used to assess the conservation effectiveness of the most valuable habitats. However, little is known about the response of functional traits, their diversity, and divergence in plant communities after translocation. To assess how functional diversity changes on dry heath, meadow, and fen after translocation of entire turfs of vegetation from an airport area to the Botanical Garden in Radzionków, we used leaf–height–seed (LHS) traits (specific leaf area, height, and seed mass) and vegetative traits (bud bank size, bud bank depth, and lateral spread). We also measured community weighted means and multifunctional diversity metrics (functional richness, evenness, and divergence) for all the traits and for LHS and vegetative traits separately. On the basis of our results, we found that the most sensitive type of vegetation to changes in functional diversity is meadow, where an increase in functional evenness and divergence were observed. The functional richness increased also for the LHS traits set in meadow plots. The response to the translocation on dry heath was a decline in functional richness and an upswell of functional evenness and divergence of vegetative reproduction traits. Additionally, we observed changes in community weighted means on meadow and dry heath. Fen was the most resistant to alterations in functional diversity after translocation. Despite the changes in functional diversity and community weighted means being significant, the shift in values of particular indices was not considerable. Our results may provide a better insight into function changes in translocated vegetation plots.
Multi-Level Cross-Lingual Attentive Neural Architecture for Low Resource Name Tagging

: Neural networks have been widely used for English name tagging and have delivered state-of-the-art results. However, for low resource languages, due to the limited resources and lack of training data, taggers tend to have lower performance, in comparison to the English language. In this paper, we tackle this challenging issue by incorporating multi-level cross-lingual knowledge as attention into a neural architecture, which guides low resource name tagging to achieve a better performance. Speciﬁcally, we regard entity type distribution as language independent and use bilingual lexicons to bridge cross-lingual semantic mapping. Then, we jointly apply word-level cross-lingual mutual inﬂuence and entity-type level monolingual word distributions to enhance low resource name tagging. Experiments on three languages demonstrate the effectiveness of this neural architecture: for Chinese, Uzbek, and Turkish, we are able to yield signiﬁcant improvements in name tagging over all previous baselines.
Fluvial sedimentary history of Arlington Canyon, Channel Islands National Park, California

Arlington Canyon, in the northwest part of Santa Rosa Island, Channel Islands National Park, California, has been the setting for important scientific discoveries over the past half century, including the oldest human remains in North America, several vertebrate fossil sites, and purported evidence of a catastrophic extinction event at the end of the Pleistocene. The canyon is filled with alluvial sediments that date to between 16.4 and 1.1 ka (thousands of calibrated years before present), representing accumulation that occurred primarily in response to rising sea levels during the late Pleistocene and Holocene. The deposits are laterally discontinuous, exhibit a high degree of sedimentary complexity, and contain evidence of past climates and environments, including fossil bones, burned plant macrofossils, and invertebrate microfossils. Here, we show that it is critical to view the observations, data, and conclusions of scientific studies conducted in the canyon within this larger context so that localized facets of the spatially and temporally extensive alluvial deposits are not misinterpreted or misrepresented. By improving the baseline understanding of processes and drivers of sediment accumulation in Arlington Canyon, we hope to offer a solid foundation and better underpinning for future archeological, paleontological, and geochemical studies here and throughout the northern Channel Islands. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.
Discovery of an Outbursting 12.8 Minute Ultracompact X-Ray Binary

We report the discovery of OGLE-UCXB-01, a 12.8 minute variable object located in the central field of Galactic bulge globular cluster Djorg 2. The presence of frequent, short-duration brightenings at such an ultrashort period in long-term OGLE photometry together with the blue color of the object in Hubble Space Telescope images and the detection of moderately hard X-rays by Chandra observatory point to an ultracompact X-ray binary system. The observed fast period decrease makes the system a particularly interesting target for gravitational-wave detectors such as the planned Laser Interferometer Space Antenna.
Facies Associations of Early Cretaceous Arumit Formation and Early to Late Cretaceous Ungar Formation in Vulmali and Ungar Islands, Tanimbar (Indonesia)

DOI:10.17014/ijog.6. 2.185-208 Cretaceous sediments are among the important petroleum system elements for hydrocarbon exploration in Tanimbar area. However, little is known about their facies associations and depositional environments. Facies association analyses have been carried out in fourteen surface sections. Early Cretaceous Arumit Formation comprises three facies associations: subtidal, intertidal, and supratidal deposits. A progradational open-coast tidal flat depositional environment suggests the deposition of sediments of the Arumit Formation. The presence of tidal rhythmites, mud drapes, and fluid mud in those sediments are diagnostic features of a tide domination process in deposition of this unit. Early to Late Cretaceous Ungar Formation in the studied area consists of four facies associations: intertidal, marine offshore to lower shoreface, upper shoreface, and foreshore deposits. An open-coast wave dominated depositional environment is proposed for deposition of sediments of the Ungar Formation. Wave dominated environments in the coarsening upward interval is represented by hummocky cross stratification, cross-bedded sand and gravel, planar parallel stratification, and low-angle stratified beds.
Assisted Agent-Based Simulations: Fusing non-player character movement with Space Syntax

Agent-based simulation is one of the core tools of spatial analysis utilised to provide an understanding of space when complex parameters come into play, such as how the visible space changes while traversing a building, or what happens when there is a destination to be reached. This type of simulation has a lot in common with techniques used in video games to create movement trajectories for non-player characters. Although these techniques have been developed over the years to provide more realistic and more “human-like” behaviour, they are rarely woven back into analytical and simulation tools. As a first step to remedy that, we developed a new methodology that fuses non-player character movement from computer games with simulation techniques traditionally used for agent-based analysis in Space Syntax. This first attempt utilises a different type of underlying representation of space, known as a navigation mesh. We first examine in detail two traditional techniques utilised in depthmapX agent-based analysis and highlight their strengths and limitations. We then describe how this technique differs from the classic space syntax methods, as well as how it can be combined to create hybrid analytical models of movement. The hybrid model developed in this case is that of a classic space syntax agent assisted by the aforementioned technique. We then tested and evaluated the traditional and new models for their capacity to explore two gallery spaces. The results extracted from the new hybrid simulation model depict agents with more capacity to explore, a significant addition to the traditional space syntax agent based methods.
Engagement of ESL Students with a Science Course Delivered in English with an Emphasis on Assessment Using a Learning Management System

The use of an LMS provides the potential for a particularly rich engagement of students with material in ways which can best suit, or at least supplement, their preferred learning styles. A course in environmental science was conducted with freshman ESL students where there was a heavy emphasis on on-line assessment using an LMS. The main pedagogical strategy was to allow 24/7 access to the material and allow students to practice both their reading of complex multiple choice questions along with reading and writing the explanations to these questions. Student success was high in terms of on-line invigilated assessment. Interestingly, the frequencies of student engagement with the on-line multiple choice aspects of the course were highly skewed, coinciding with deadlines for submission. Student success in an oral defence was high and strongly correlated with multiple choice results. In contrast, despite students having access to written model answers and marking schemes, performance on written components of the exams was not strongly correlated with performance in multiple choice components. In-depth interviews with students gave insight into students’ strategies in engaging with the LMS in general and the on-line tests in particular. 
Détection et correction automatique des défauts de conceptionau moyen de l’apprentissage automatique pour l’améliorationde la qualité des systèmes

RESUME: La maintenance logicielle apparait comme l’activite la plus couteuse dans le cycle du developpement : plus de 80% des ressources lui sont consacrees. Au cours des activites de maintenance, l’architecture et la conception du logiciel sont tres peu prises en compte. Il s’en suit une degradation progressive de ces artefacts dus a des defauts de conception. Ces defauts peuvent avoir ete introduits des la premiere conception mais egalement par les maintenances du logiciel. La degradation de la conception du logiciel rend encore plus difficile la comprehension du logiciel et les maintenances a venir, creant ainsi un cycle vicieux. Nous nous proposons dans ce projet de recherche de contribuer a reduire la degradation des conceptions logicielles en mettant en place un systeme integre de detection et de correction automatiques des defauts de conception et egalement un suivi de la qualite de la conception. Ce systeme, nomme SUDERCO, est base sur l’apprentissage automatique et vise a fournir un cadre souple et evolutif pour aider a reduire les couts de maintenance par la preservation de la conception. ---------- ABSTRACT: Software maintenance is emerging as the most expensive activity in the development cycle: more than 80% of resources are devoted to it. During maintenance activities, architecture and design of the software are rarely taken into account. It follows a progressive deterioration of these artifacts due to design defects. These defects may have been introduced not only in the first design, but also during the maintenance of the software. The degradation of software design makes it even harder to understand the software and perform future maintenance, creating a vicious cycle. We propose a research plan to contribute in minimizing the degradation of software designs by providing an integrated system for the automatic detection and correction of design defects, along with monitoring the design quality. This system, called SUDERCO, is based on machine learning techniques and aims at providing a flexible and scalable tool to help reduce maintenance costs by preserving the design.
Global soil respiration: interaction with macroscale environmental variables and response to climate change

Soil respiration (Rs) rates fluctuate daily and seasonally; therefore, timing of measurements is critical when estimating the daily mean and scaling up to annual Rs rates. Temporal fluctuations also vary with climate and biome, yet the current recommendation for when to measure Rs (e.g., 09:00 to 12:00) has not been evaluated for different climates, biomes, and seasons. To provide more refined recommendations for measuring Rs, we: 1) analyzed diurnal and seasonal fluctuations of Rs and tested the accuracy of typical measurement practices under different climates and biomes, and 2) identified the measurement frequency necessary for different climates and biomes to achieve certain levels of accuracy in estimating annual Rs. Across biomes, diurnal variation in Rs is considerable in spring and summer, moderate in autumn, and minimal in winter, and closely related with soil temperature and gross primary production. Based on these diurnal patterns, the best measurement time for estimating the daily mean Rs was 10:00 in almost all climates and biomes, within the recommend range (09:00 and 12:00) previously identified for temperate forests. Measurements made between 20:00 and 23:00 also accurately estimated the daily mean Rs. Regions with average high plant coverage over the year have lower seasonal variation and require less frequent measurements. For global scale estimates, Rs needs to be measured once per day to attain an accuracy of ±10% of the Rs population mean with 95% confidence, and once per month to achieve ±30% with a confidence of 80%. Results from this study provide guidelines that reduce measurement frequency while retaining reasonable accuracy for better Rs estimates using manual chamber systems.