Miracle-dz/UniFAR-FTUs huggingface dataset:
Country : United Kingdom Final Report Summary-EQUIPT ( European-study on Quantifying Utility of Investment in Protection from Tobacco )

Final Report Summary EQUIPT (European-study on Quantifying Utility of Investment in Protection from Tobacco) Executive Summary: Context and aims: Protection from tobacco has been a global public health agenda, as tobacco smoking worldwide accounts for millions of deaths both directly and as the result of second-hand smoke. The Framework Convention on Tobacco Control (FCTC) is a comprehensive response to address the scourge of tobacco, of which most EU member states are signatories. Despite the availability of many cost-effective interventions to tackle tobacco use, decision makers in the EU member states and beyond still lack the justification on the economic and wider returns that their tobacco control agenda could generate. Therefore, the EQUIPT study aimed to fill in this important gap by providing European stakeholders with a decision-support tool to estimate the return on investment (ROI) from investing in tobacco cessation interventions. This was done by transferring the existing ROI Tool that was jointly developed by the Brunel University London and the National Institute of Health and Care Excellence in the UK in 2012 to five European countries. In transferring the ROI evidence and tools, the EQUIPT researchers carefully examined several transferability issues, using a methodology that was grounded on the cocreation of research evidence with strong stakeholder partnership.
Schedule WRF model executions in parallel computing environments using Python

The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. WRF is suitable for a broad spectrum of applications across scales ranging from meters to thousands of kilometers. Preparing an operational forecast that uses available resources efficiently for its execution requires certain programming knowledge in parallel computing environments. This work shows how to construct a tool that allows for scheduling operational executions of the WRF model. This tool will allow users without advanced programming knowledge to perform their own periodic planning operative executions of WRF model.
ProTractor: A Lightweight Ground Imaging and Analysis System for Early-Season Field Phenotyping

Acquiring high-resolution images in the field for image-based crop phenotyping is typically performed by complicated, custom built "pheno-mobiles. " In this paper, we demonstrate that large datasets of crop row images can be easily acquired with consumer cameras attached to a regular tractor. Localization and labeling of individual rows of plants are performed by a computer vision approach, rather than sophisticated real-time geo-location hardware on the tractor. We evaluate our approach for cropping rows of early-season plants from a Brassica carinata field trial where we achieve 100% recall and 99% precision. We also demonstrate a proof-of-concept plant counting method for our ProTractor system using an object detection network that achieves a mean average precision of 0.82 when detecting plants, and an R2 of 0.89 when counting plants. The ProTractor design and software are open source to advance the collection of large outdoor plant phenotyping datasets with inexpensive and easy to use acquisition systems.
Evaluation of the Fibrinolytic Potential of Recombinant Subtilisin from Bacillus subtilis

The aim of this study was to investigate the potential for the treatment of thromboembolic diseases by determining the fibrinolytic, fibrinogenolytic activities and substrate specificity of the recombinant subtilisin from Bacillus subtilis strain PTTC 1023. The subtilisin gene from Bacillus sp. was synthesized, cloned into the vector pD441-NH and expressed in E. coli BL21 (DE3). Recombinant subtilisin (~40 kDa) was purified in a single-step procedure by affinity chromatography. The fibrinolytic, fibrinogenolytic activities and substrate specificity of the pure recombinant enzyme was determined. The recombinant protease of B. subtilis displayed an optimum fibrinolytic activity at pH 7.5 and 37°C.The α, β and Υ-Υ chains of fibrinogen were totally degraded by the recombinant enzymes at 37°C for 30 min. However, the Υ chains were more resistant to enzyme digestion in all times. The clear zones produced by recombinant enzyme and plasmin on the fibrin plate did not significant difference The enzyme showed the highest proteolytic activity against fibrin, while it showed low activity against serum albumin and lactoferrin. The enzyme did not show activity against immunoglobulin. The data obtained indicates that recombinant enzyme is a plasmin-like fibrinolytic serine protease. The recombinant protease may have potential applications in the prevention and treatment of thrombosis and other cardiovascular diseases.
Touchless Interaction with a Smartphone : EMG-Based Sleeve for Cyclists

In this paper we present the development of a system that allows cyclists to control the music player on their smartphone in a touchless manner. A custom built electromyography (EMG) based sleeve is used to recognize 6 hand gestures that correspond to specific commands as input to the music player. The sleeve uses two channels to measure electrical potential generated in the Extensor Digitorum and Flexor Carpi Ulnaris muscles when performing gestures. To increase the accuracy of classifier a number of features were used, including Minimum value, Mean, Median, Modified Mean Absolute Value 2, Zero Crossing, and Successive Difference Sum. The performance of the system was evaluated using 6 subjects, who were using exercise bike while performing the various gestures. Overall, the highest accuracy of 68.00% was achieved using Neural Network classifier with a Low-Pass Filter for data smoothing.
Human Action Recognition Oriented to Family Service

In order to increase the intelligence of the family service robot,a human action recognition method was put forward.First,the scheme of Motion History Image was used to adaptively update the background model.Then,YCbCr images combined with gray images were used to segment the images to decrease the shadow.At last,space-time interest points based on high entropy changes were brought forward to recognize the human action.Experiments showed that the background model can obtain the family environment background satisfactorily.And the recognition rate of the action recognition scheme combined with the environmental information could reach 95%.
Designing a Method of Creating a Hermetic Seal for a Microfluidic Device

An elastomer based microfluidic device has been designed to facilitate artery culture experiments. This device includes an open reservoir component, with a single inlet and outlet channel, that will overflow during long-term experiments. It is necessary to create a hermetic seal around the reservoir after experimentation has begun and for the duration of. Pressure within the sealed reservoir is estimated to reach 27kPa, including a 200% safety factor. Two methods of sealing have been developed and tested. The first solution uses frictional forces and the elastic property of PDMS by using a machined self-sealing plug. The second solution uses magnetic forces between a permanent magnet and an iron filing solution. Both methods showed positive results by steadily opposing a fraction of the required upward force generated by the internal pressure of the reservoir. Further testing is required based on the results of experiments with updated geometries for the plug design and improved fabrication methods of the magnetic force design.
Caching in the Multiverse Mania

To get good performance for data stored in Object storage services like S3, data analysis clusters need to cache data locally. Recently these caches have started taking into account higher-level information from analysis framework, allowing prefetching based on predictions of future data accesses. There is, however, a broader opportunity; rather than using this information to predict one future, we can use it to select a future that is best for caching. This paper provides preliminary evidence that we can exploit the directed acyclic graph (DAG) of inter-task dependencies used by data-parallel frameworks such as Spark, PIG and Hive to improve application performance, by optimizing caching for the critical path through the DAG for the application. We present experimental results for PIG running TPC-H queries, showing completion time improvements of up to 23% vs our implementation of MRD, a state-of-the-art DAG-based prefetching system, and improvements of up to 2.5x vs LRU caching. We then discuss the broader opportunity for building a system based on this opportunity.
Crank Identification of the Rotary Kiln Based on WTD-EEMD Using Vibration Monitoring of the Supporting Rollers

It is impending to identify the kiln crank as it greatly influences the safety operation of the rotary kiln. In this paper, a novel strategy based on WTD-EEMD method to extract the effective information of the cyclic loading caused by the kiln crank in the vibration signals of the supporting rollers was proposed. Firstly, the vibration model of the supporting rollers was established on the basis of the operation principle of the rotary kiln and the characteristic frequencies of the cyclic loading were obtained. Secondly, the WTD-EEMD method is employed to denoise the vibration signals and the sensitive Intrinsic Mode Functions (IMFs) components contain the rich cyclic loading information were selected. Finally, the new signals were constructed by the selected sensitive IMF components, and the degree of the kiln crank in the supporting stations was evaluated by the energy of the constructed signal. The proposed method is verified with the designed measurement system in the industry field. The experiment results indicated that the proposed method is effective to identify the crank of the rotary kiln.
Data Stream Algorithms

How does one deal with massive data sets that is available for analyses? We will describe the classical data stream model in which we make one pass over the data and with sublinear resources perform much of the data analyses we care about, such as frequent items, summaries, compressed sensing, clustering and others. We will present the basic algorithmic techniques used to build the sophisticated analyses above. In addition, we will present extensions to other problems (graph, matrix, statistics) and to other models (probabilistic models, parallel models such as Google’s MapReduce). One of the reasons this area of research thrives is its immediate application to a number of scenarios, which we will describe.
t-wise Coverage by Uniform Sampling

Efficiently testing large configuration spaces of Software Product Lines (SPLs) needs a sampling algorithm that is both scalable and provides good t-wise coverage. The 2019 SPLC Sampling Challenge provides large real-world feature models and asks for a t-wise sampling algorithm that can work for those models. We evaluated t-wise coverage by uniform sampling (US) the configurations of one of the provided feature models. US means that every (legal) configuration is equally likely to be selected. US yields statistically representative samples of a configuration space and can be used as a baseline to compare other sampling algorithms. We used existing algorithm called Smarch to uniformly sample SPL configurations. While uniform sampling alone was not enough to produce 100% 1-wise and 2-wise coverage, we used standard probabilistic analysis to explain our experimental results and to conjecture how uniform sampling may enhance the scalability of existing t-wise sampling algorithms.
Multi-objective integrated ant colony optimization scheduling algorithm based on cloud resource

With regard to the complexity of cloud computing and uncertainty of cloud resources,an integrated multi-objective ant colony optimization scheduling algorithm was proposed.Using entropy to measure the uncertainty of cloud resources,and updating the global pheromone,the algorithm's convergence rate could be improved.In order to achieve the minimum activation time,the expected minimum completion time calculated by Min-min algorithm was used as heuristic information.When the local pheromone was updated partly,the load factor was added according to the current load regulation of pheromone,to achieve the load balance.Collaboration between ants could be enhanced and the performance of the optimal solution would be improved,considering the pheromone diffusion factors,that is to consider not only the current node pheromone but also neighbor node pheromone.The advanced ant colony algorithm reduced the complexity of the algorithm better than the original algorithm,and improved the optimal solution accuracy.The experiments of the cloud simulation system prove that the performance of time scheduling and load balancing of the proposed algorithm is better than other algorithms.
Registration Pluralism and the Cartographic Approach to Data Aggregation across Brains

Neuroscience has become increasingly reliant on multi-subject research in addition to studies of unusual single patients. This research has brought with it a challenge: how are data from different human brains to be combined? The dominant strategy for aggregating data across brains is what I call the ‘cartographic approach’, which involves mapping data from individuals to a spatial template. Here I characterize the cartographic approach and argue that one of its key steps, registration, should be carried out in a way that is sensitive to the target of investigation. Because registration aims to align homologous brain locations, but not all homologous locations can be simultaneously aligned, a multiplicity of registration methods is required to meet the needs of researchers investigating different phenomena. I call this position ‘registration pluralism’. Registration pluralism has potential implications for neuroscientific practice, three of which I discuss here. This work shows the importance of reflecting more carefully on data aggregation methods, especially in light of the substantial individual differences that exist between brains.
Trauma, Resilience and the Power of Human Connection: Reflections from the Field of Cambodia

Political and economic instability, poverty, war and terrorism, are just some of the harsh realities facing many young people globally. Various theoretical frameworks that conceptualize resilience exist in Western countries, yet there is limited research that looks specifically at resilience for children and young people in developing nations who have experienced significant trauma. Earlier research conducted with staff at Hagar International Cambodia (hereafter Hagar) indicated that for a child, recovery from trauma was contingent on their relationships and their ability to establish trust and connection to others. Hagar is a global human rights organisation, providing wraparound support services for women and children who have survived the most extreme cases of human rights abuse. There is much to be gained from the insight and lived experiences of those who have risen from complex trauma situations, such as the young people from Hagar. As such, research is currently being conducted which examines the resiliency factors that have contributed to these young people’s overall wellbeing. This article will explore the intersection between relationships, trust and power of human connection for trauma survivors in Cambodia, bringing together current research with reflections from the field of Cambodia.
A Symphony of Flavours : Using the device tree to describe embedded hardware Grant Likely

As part of the merger of 32-bit and 64-bit PowerPC support in the kernel, the decision was to also standardize the firmware interface by using an OpenFirmware-style device tree for all PowerPC platforms; server, desktop, and embedded. Up to this point, most PowerPC embedded systems were using an inflexible and fragile, board-specific data structure to pass data between the boot loader and the kernel. The move to using a device tree is expected to simplify and generalize the PowerPC boot sequence. This paper discusses the implications of using a device tree in the context of embedded systems. We’ll cover the current state of device tree support in arch/powerpc, and both the advantages and disadvantages for embedded system support.
Predicting City Poverty Using Satellite Imagery

Reliable data about socio-economic conditions of individuals, such as health indexes, consumption expenditures and wealth assets, remain scarce for most countries. Traditional methods to collect such data include on site surveys that can be expensive and labour intensive. On the other hand, remote sensing data, such as high-resolution satellite imagery, are becoming largely available. To circumvent the lack of socio-economic data at high granularity, computer vision has already been applied successfully to raw satellite imagery sampled from resource poor countries. In this work we apply a similar approach to the metropolitan areas of five different cities in North and South America, starting from pre-trained convolutional models used for poverty mapping in developing regions. Applying a transfer learning process we estimate household income from visual satellite features. The urban environment we consider is characterized by different features with respect to the resource-poor training environment, such as the high heterogeneity in population density. By leveraging both official and crowd-sourced data at city scale, we show the feasibility of estimating the socio-economic conditions of different neighborhoods from satellite data.
Attention Driven Vehicle Re-identification and Unsupervised Anomaly Detection for Traffic Understanding

Vehicle re-identification and anomaly detection are useful tools in traffic analytics applications. Vehicle reidentification is particularly challenging due to variations in viewpoint, illumination and occlusion. Moreover, the reality of multiple vehicles having the same make and model hinders the design of traditional deep network-based solutions. In this work, we leverage an attention-based model which learns to focus on different parts of a vehicle by conditioning the feature maps on visible key-points. We use triplet embedding to reduce the dimensionality of the features obtained from the ensemble of networks trained using different datasets. To address the problem of anomaly detection, we design an unsupervised algorithm to detect and localize anomalies in traffic scenes. To handle moving cameras, we use the results obtained from tracking to generate anomaly proposals which are then filtered in successive steps. We show the effectiveness of our method on the Nvidia AI City vehicle re-identification dataset, where we obtain mean Average Precision (mAP) score of 60.78% placing us at the 8th position out of 84 participating teams. In addition, we achieved the S3 score of 22.07% for vehicle anomaly detection.
RAINFALL INTERCEPTION BY AN EVERGREEN BEECH FOREST

Rowe, L.K. , 1983. Rainfall interception by an evergreen beech forest, Nelson, New Zealand. J. Hydrol. , 66: 143--158. Throughfall under a beech (Nothofagus) forest canopy at Donald Creek, Nelson, averaged 69% of the rain falling on the canopy, i.e. 1060 mm of 1530 mm in a year of normal rainfall. Using an estimate for stemflow at 2% of gross rainfall, interception loss averaged 29% of the annual rainfall, or 440 mm yr_ 1 . Seasonal differences in interception loss were significant, ranging from 22% in winter to 35% in summer, and resulted from seasonal variation in evaporation rates from a wet canopy. Seasonal variation in rainfall rate was slight. Four models, storm linear regression, monthly linear regression, sine curve and Gash's analytical model, were tested by comparison of predicted and observed interception. All gave very satisfactory estimates (~ 10% error) and tended to slightly underestimate the measured interception loss.
Powering Robust Fashion Retrieval With Information Rich Feature Embeddings

Visual content based product retrieval has become increasingly important for e-commerce. Fashion retrieval, in particular, is a challenging problem owing to a wide range of deformations of clothing items along with visual distortions in their product images. In this paper, we propose a Grid Search Network (GSN) for learning feature embeddings for fashion retrieval. The proposed approach posits the training procedure as a search problem, focused on locating matches for a reference query image in a grid containing both positive and negative images w.r. t the query. The proposed framework significantly outperforms existing state-of-art methods on benchmark fashion datasets. We also utilize a reinforcement learning based strategy to learn a specialized transformation function which further improves retrieval performance when applied over the feature embeddings. We also extend the reinforcement learning based strategy to learn custom kernel functions for SVM based classification over FashionMNIST and MNIST datasets, showing improved performance. We highlight the generalization capabilities of this search strategy by showing performance improvement in search and attribution tasks in domains beyond fashion.
ON LASER BUTT-WELDED T-JOINTS

This work concerns numerical modelling of laser butt-welding of T-joints, made of S355 steel. Numerical analysis of thermomechanical phenomena is performed in ABAQUS FEA engineering software. Additional, author’s subroutines are implemented in designed three dimensional model using Fortran programming language. Developed subroutines describe the distribution of volumetric laser beam heat source power, the kinetics of phase transformations in solid state as well as isotropic strain generated by the temperature field and structural strain during heating and cooling of welded steel. Performed computational analysis takes into account thermomechanical properties changing with temperature. Butt-welded T-joints are performed on Yb:YAG laser in order to verify the results of numerical simulations. Characteristic zones of the cross section of the joint are compared to numerically predicted geometry of fusion zone and heat affected zone. Numerically predicted displacement in chosen sections of the joint is compared to real displacement, measured using profilographometer New Form Talysurf 2D/3D 120.
A Nonlinear, Noise-aware, Quasi-clustering Approach to Learning Deep CNNs from Noisy Labels

The success of deep convolutional networks on image classification and recognition tasks depends on the availability of large, labeled datasets, but it can be difficult to obtain a large number of accurate labels. To deal with this problem, we present Nonlinear, Noise-aware, Quasiclustering (NNAQC), a method for learning deep convolutional networks from datasets corrupted by unknown label noise. We append a nonlinear noise model to a standard convolutional network, which is learned in tandem with the parameters of the network. Further, we train the network using a loss function that encourages the clustering of training images. We argue that the non-linear noise model, while not rigorous as a probabilistic model, results in a more effective denoising operator during backpropagation. We evaluate the performance of NNAQC on artificially injected label noise to MNIST, CIFAR-10, CIFAR-100 and ImageNet datasets and on a large-scale Clothing1M dataset with inherent label noise. On all these datasets, NNAQC provides significantly improved classification performance over the state of the art and is robust to the amount of label noise and the training samples.