Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploitation of Geostationary Earth Radiation Budget data using simulations from a numerical weather prediction model: Methodology and data validation

[1] We describe a new methodology for comparing satellite radiation budget data with a numerical weather prediction (NWP) model. This is applied to data from the Geostationary Earth Radiation Budget (GERB) instrument on Meteosat-8. The methodology brings together, in near-real time, GERB broadband shortwave and longwave fluxes with simulations based on analyses produced by the Met Office global NWP model. Results for the period May 2003 to February 2005 illustrate the progressive improvements in the data products as various initial problems were resolved. In most areas the comparisons reveal systematic errors in the model's representation of surface properties and clouds, which are discussed elsewhere. However, for clear-sky regions over the oceans the model simulations are believed to be sufficiently accurate to allow the quality of the GERB fluxes themselves to be assessed and any changes in time of the performance of the instrument to be identified. Using model and radiosonde profiles of temperature and humidity as input to a single-column version of the model's radiation code, we conduct sensitivity experiments which provide estimates of the expected model errors over the ocean of about ±5–10 W m−2 in clear-sky outgoing longwave radiation (OLR) and ±0.01 in clear-sky albedo. For the more recent data the differences between the observed and modeled OLR and albedo are well within these error estimates. The close agreement between the observed and modeled values, particularly for the most recent period, illustrates the value of the methodology. It also contributes to the validation of the GERB products and increases confidence in the quality of the data, prior to their release.
Top‐Down Estimates of NOx and CO Emissions From Washington, D.C.‐Baltimore During the WINTER Campaign

Airborne mass balance experiments were conducted around the Washington, D.C.‐Baltimore area using research aircraft from Purdue University and the University of Maryland to quantify emissions of nitrogen oxides (NOx = NO + NO2) and carbon monoxide (CO). The airborne mass balance experiments supported the Wintertime INvestigation of Transport, Emissions, and Reactivity (WINTER) campaign, an intensive airborne study of anthropogenic emissions along the Northeastern United States in February–March 2015, and the Fluxes of Atmospheric Greenhouse Gases in Maryland project which seeks to provide best estimates of anthropogenic emissions from the Washington, D.C.‐Baltimore area. Top‐down emission rates of NOx and CO estimated from the mass balance flights are compared with the Environmental Protection Agency's 2011 and 2014 National Emissions Inventory (NEI‐11 and NEI‐14). Inventory and observation‐derived NOx emission rates are consistent within the measurement uncertainty. Observed CO emission rates are a factor of 2 lower than reported by the NEI. The NEI's accuracy has been evaluated for decades by studies of anthropogenic emissions, yet despite continuous inventory updates, observation‐inventory discrepancies persist. WINTER NOx/CO2 enhancement ratios are consistent with inventories, but WINTER CO/NOx and CO/CO2 enhancement ratios are lower than those reported by other urban summertime studies, suggesting a strong influence of CO seasonal trends and/or nationwide CO reductions. There is a need for reliable observation‐based criterion pollutant emission rate measurements independent of the NEI. Such determinations could be supplied by the community's reporting of sector‐specific criteria pollutant/CO2 enhancement ratios and subsequent multiplication with currently available and forthcoming high‐resolution CO2 inventories.
Examination of Self-Determination within the Sport Education Model

The purpose of this study was to examine the influence of the Sport Education Model (SEM) on students' self-determined motivation and underlying psychological need(s) in physical education. A total of 182 Year-9 students were engaged in 20 lesson units of volleyball, using either the SEM or a traditional approach. Data was collected using a pretest/posttest design measuring self-determined motivation and perceptions of the psycho-social climate. Analysis of data utilized four separate repeated measures ANOVAs for self-determination and basic psychological needs. Results indicated significant change in self-determination and relatedness for students engaged in the SEM. As such, implementation of the SEM may be utilized as a means for supporting students' social connectedness and motivation to engage in sport-based activities.
Weakly Supervised Facial Action Unit Recognition Through Adversarial Training

Current works on facial action unit (AU) recognition typically require fully AU-annotated facial images for supervised AU classifier training. AU annotation is a time-consuming, expensive, and error-prone process. While AUs are hard to annotate, facial expression is relatively easy to label. Furthermore, there exist strong probabilistic dependencies between expressions and AUs as well as dependencies among AUs. Such dependencies are referred to as domain knowledge. In this paper, we propose a novel AU recognition method that learns AU classifiers from domain knowledge and expression-annotated facial images through adversarial training. Specifically, we first generate pseudo AU labels according to the probabilistic dependencies between expressions and AUs as well as correlations among AUs summarized from domain knowledge. Then we propose a weakly supervised AU recognition method via an adversarial process, in which we simultaneously train two models: a recognition model R, which learns AU classifiers, and a discrimination model D, which estimates the probability that AU labels generated from domain knowledge rather than the recognized AU labels from R. The training procedure for R maximizes the probability of D making a mistake. By leveraging the adversarial mechanism, the distribution of recognized AUs is closed to AU prior distribution from domain knowledge. Furthermore, the proposed weakly supervised AU recognition can be extended to semi-supervised learning scenarios with partially AU-annotated images. Experimental results on three benchmark databases demonstrate that the proposed method successfully leverages the summarized domain knowledge to weakly supervised AU classifier learning through an adversarial process, and thus achieves state-of-the-art performance.
Learning Globally Optimized Object Detector via Policy Gradient

In this paper, we propose a simple yet effective method to learn globally optimized detector for object detection, which is a simple modification to the standard cross-entropy gradient inspired by the REINFORCE algorithm. In our approach, the cross-entropy gradient is adaptively adjusted according to overall mean Average Precision (mAP) of the current state for each detection candidate, which leads to more effective gradient and global optimization of detection results, and brings no computational overhead. Benefiting from more precise gradients produced by the global optimization method, our framework significantly improves state-of-the-art object detectors. Furthermore, since our method is based on scores and bounding boxes without modification on the architecture of object detector, it can be easily applied to off-the-shelf modern object detection frameworks.
Bioactivity and potential therapeutic benefits of some medicinal plants from the Caatinga (semi-arid) vegetation of Northeast Brazil: a review of the literature

Medicinal plants have been used in traditional medicine for several thousand years all over the world. In this sense, information from Brazilian ethnic groups on folk medicine have contributed to the discovery of pharmacological activities from various plant-derived agents potentially leading to the innovative drugs. The Caatinga (semi-arid) vegetation is a highly threatened biome, covering a vast area in northeastern Brazil and has suffered from strong human influence for many decades. Many plants species found in the Caatinga have been widely used in folk medicine and for commercial manufacturing of phytotherapeutic products. Thus, the present review aims to disseminate to the scientific community some known species of medicinal plants found in the Caatinga that have been studied and analyzed in pharmacological scientific assays. Among the species that stood out for their local importance and multiplicity of uses were: Amburana cearensis (umburana-de-cheiro), Anadenanthera colubrina (Vell.) Brenan (angico-branco), Anacardium occidentalis L. (cajueiro), Bauhinia forficata Link (mororo), Cissus sicyoides L. (insulina-vegetal), Myracrodruon urundeuva Allemao (aroeira-do-sertao) and Zingiber officinalis L. (gengibre). The present study shows that several herbal constituents from Caatinga plants, whose pharmacological actions have been well characterized, may be relevant candidates for future and innovative therapeutic development.
Politics and collaboration during NPD projects

Effective cross-functional relationships (CFRs) are a key success factor in developing successful new products for many companies. This paper adds to our existing knowledge on the Marketing/R&D CFR by examining the effects of communication behaviours, micropolitics and interpersonal trust on the dependent variable, interpersonal collaborative behaviour during NPD activities. Interpersonal collaboration is seen as a higher level of working relationship which incorporates a "volitional" and "trusting" aspect to working relationships. Our findings reveal that the perception of the Marketing Manager as a political ally has a positive effects on interpersonal collaborative behaviour. Further, affect-based trust has a strong effect on collaborative behaviours indicating that a benevolence aspect of working relationships is critical for collaborative behaviours to emerge during NPD.
Advertising Wearout of Shock-Value Anti-Speeding Ads

An advertising experiment was conducted to test the advertising wearout of four anti-speeding ads, each with varying underlying "patterns" of fear arousal. The patterns of fear were established beforehand by using a dial designed to track viewers’ reactions in terms of tenseness felt. The advertising experiment involved 284 participants from a first-year University marketing class. Four experimental groups were exposed to the same antispeeding ad each week, for three sequential weeks. Measures were obtained, via a questionnaire, of the participants’ attention paid to the ad, expected effect on speeding behaviour, emotions felt, perceptions of the relevance, believability, realism of the ad, and demographic and driving history. The groups comprised participants with homogenous demographic characteristics (with the exception of gender) and driving histories, thus allowing the different mean scores of various effectiveness measures to be attributed to the ads themselves rather than the differences between the participants’ characteristics. Repeatedmeasures analysis of covariance (ANCOVA) was used to test the results of the advertising experiment. Wearout, in terms of emotions felt (shock, fear and tension), attention paid, and expected speeding behaviour, did occur, with some fear patterns rapidly declining in effect in comparison with the other patterns of fear.
Neural Style Transfer via Meta Networks

In this paper we propose a noval method to generate the specified network parameters through one feed-forward propagation in the meta networks for neural style transfer. Recent works on style transfer typically need to train image transformation networks for every new style, and the style is encoded in the network parameters by enormous iterations of stochastic gradient descent, which lacks the generalization ability to new style in the inference stage. To tackle these issues, we build a meta network which takes in the style image and generates a corresponding image transformation network directly. Compared with optimization-based methods for every style, our meta networks can handle an arbitrary new style within 19 milliseconds on one modern GPU card. The fast image transformation network generated by our meta network is only 449 KB, which is capable of real-time running on a mobile device. We also investigate the manifold of the style transfer networks by operating the hidden features from meta networks. Experiments have well validated the effectiveness of our method. Code and trained models will be released.
Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors

We propose methods of addressing two critical issues of pedestrian detection: (i) occlusion of target objects as false negative failure, and (ii) confusion with hard negative examples like vertical structures as false positive failure. Our solutions to these two problems are general and flexible enough to be applicable to any single-stage detection models. We implement our methods into four state-of-the-art single-stage models, including SqueezeDet+ [22], YOLOv2 [17], SSD [12], and DSSD [8]. We empirically validate that our approach indeed improves the performance of those four models on Caltech pedestrian [4] and CityPersons dataset [25]. Moreover, in some heavy occlusion settings, our approach achieves the best reported performance. Specifically, our two solutions are as follows. For better occlusion handling, we update the output tensors of single-stage models so that they include the prediction of part confidence scores, from which we compute a final occlusion-aware detection score. For reducing confusion with hard negative examples, we introduce average grid classifiers as post-refinement classifiers, trainable in an end-to-end fashion with little memory and time overhead (e.g. increase of 1-5 MB in memory and 1-2 ms in inference time).
Supplementary : SGPN : Similarity Group Proposal Network for 3 D Point Clouds Instance Segmentation

In our experiments, we use both PointNet and PointNet++ as our baseline architectures. For the S3DIS dataset, we use PointNet as our baseline for fair comparison with the 3D object detection system described in the PointNet paper [2]. The network architecture is the same as the semantic segmentation network as stated in PointNet except for the last two layers. Our F is the last 1 × 1 conv layer with BatchNorm and ReLU in PointNet with 256 output channels. FSIM , FCF , FSEM are 1 × 1 conv layers with output channels (128, 128, 128), respectively. For the NYUV2 dataset, we use PointNet++ as our baseline. We use the same notations as PointNet++ to describe our architecture: SA(K, r, [l1, ., ld]) is a set abstraction (SA) level with K local regions of ball radius r using a PointNet architecture of d 1 × 1 conv layers with output channels li(i = 1, ., d). FP (l1, ., ld) is a feature propagation (FP ) level with d 1× 1 conv layers. Our network architecture is:
Prescribing pattern of antidiabetic drugs in type 2 diabetic patients of Noakhali city in Bangladesh

The aim of the study was to describe the recent trends in the prescription pattern of antidiabetic drugs and sociodemographic characteristics of type 2 diabetic patients in Noakhali city of Bangladesh. This hospital based prospective, cross-sectional study was carried out in three diabetic hospitals of Noakhali during the period of December, 2016 to February, 2017. A total of 200 outpatients were included in this study by following simple random procedure. Among 200 participants, 73.5% were treated with monotherapy and 26.5% were treated with polytherapy with the highest percentage (37.5%) of commonly prescribed group drugs of sulfonylureas, but lonely metformin (biguanide) was the most commonest (28%) drug among oral hypoglycemic agents. The most accounted fixed dose combination was sulfonylurea with biguanide, and biguanide with glucodiase inhibitors. The highly prescribed insulin preparations was insulin mixtures (11%) among the patients. Drugs were prescribed by traditional brand names (100%) of different local or foreign companies and 15% of prescription were contained at least one multivitamin supplement. It was focused from this study that oral hypoglycemic agents (OHA) yet dominate the prescribing pattern, but there was a shifting trend towards insulin preparation in the management of Type 2 diabetes mellitus.
Occlusion-Aware Rolling Shutter Rectification of 3D Scenes

A vast majority of contemporary cameras employ rolling shutter (RS) mechanism to capture images. Due to the sequential mechanism, images acquired with a moving camera are subjected to rolling shutter effect which manifests as geometric distortions. In this work, we consider the specific scenario of a fast moving camera wherein the rolling shutter distortions not only are predominant but also become depth-dependent which in turn results in intra-frame occlusions. To this end, we develop a first-of-its-kind pipeline to recover the latent image of a 3D scene from a set of such RS distorted images. The proposed approach sequentially recovers both the camera motion and scene structure while accounting for RS and occlusion effects. Subsequently, we perform depth and occlusion-aware rectification of RS images to yield the desired latent image. Our experiments on synthetic and real image sequences reveal that the proposed approach achieves state-of-the-art results.
The Intonation of Polar Questions in North American (“Heritage”) Icelandic

Using map task data, this paper investigates the intonation of polar questions in North American (heritage) Icelandic, and compares it to the intonation of polar questions in Icelandic as spoken in Iceland and in North American English as spoken in Manitoba, Canada. The results show that intonational features typical of Icelandic polar questions are present to a considerable extent in heritage Icelandic. Furthermore, intonational features typical of North American English polar questions can frequently be observed in heritage Icelandic, too. In addition, there is a tendency for intonational features typical of Icelandic polar questions to show up in North American English polar questions produced by speakers of heritage Icelandic more often than in North American English polar questions produced by speakers without Icelandic heritage. Focusing on intonation, the present study adds to the evidence for (bidirectional) prosodic interference between a heritage language (here moribund Icelandic) and the dominant language (here North American English). *
Classifier Learning with Prior Probabilities for Facial Action Unit Recognition

Facial action units (AUs) play an important role in human emotion understanding. One big challenge for data-driven AU recognition approaches is the lack of enough AU annotations, since AU annotation requires strong domain expertise. To alleviate this issue, we propose a knowledge-driven method for jointly learning multiple AU classifiers without any AU annotation by leveraging prior probabilities on AUs, including expression-independent and expression-dependent AU probabilities. These prior probabilities are drawn from facial anatomy and emotion studies, and are independent of datasets. We incorporate the prior probabilities on AUs as the constraints into the objective function of multiple AU classifiers, and develop an efficient learning algorithm to solve the formulated problem. Experimental results on five benchmark expression databases demonstrate the effectiveness of the proposed method, especially its generalization ability, and the power of the prior probabilities.
Evaluation of the Fatigue Resistance of Offshore Jacket Joints by Numerical Approaches

Jacket support structures are a preferred solution for offshore wind turbines in deeper waters. Extensive knowledge exists in relation to its construction technique as well as its crucial components, but due to considerable cost pressure, continued optimization is essential for the future competitiveness of the concept in the offshore wind business. Their joints along with their complex welds are of special interest in terms of cost reduction. The design of tubular joints is generally driven by fatigue resistance. Due to the size, complexity and cost of these joints, this is assessed by using detailed FE models. Several aspects that have an impact on the results of the models are found to require further research and are investigated within this project: (1) influence of using solid versus shell elements in the modelling of the joint members; (2) degree of accuracy of the Efthymiou equations; (3) influence of the carry-over effect in multiplanar KK-joints; (4) differences in the fatigue life predictions between the hot-spot and the effective notch stress methods. Guidelines recommend the use of both solid and shell theories for the definition of the FE models used in the hot-spot fatigue assessment. Both options are compared in terms of accuracy of the results and computational time. Generally speaking, significant differences are found between both models. The background of the differences is studied. The employment of the Efthymiou formulae is common in practice. These equations provide the SCF at the locations around the weld where they are found to be maximum. The output of this approach is compared with the results of numerical models. The validity of its use is quantified. Offshore wind jacket joints are mainly multiplanar KK-joints. Loading in the braces of one face of the jacket may yield significant carry-over effects on the out-of-plane braces connected to the same joint. A parametric comparison is carried out to study the accuracy of modelling the joint as a planar K-joint. In general, it is found that the carry-over effect cannot be neglected and the assumption is not accurate. The fatigue assessment of tubular joints by means of the hot-spot method is subjected to several assumptions that limit the optimization of the members. The notch concept is a more realistic method that is presented as an alternative. However, this method is not widely used in engineering practice due to the difficulties in building the numerical model and the high computational requisites. An algorithm to carry out the effective notch stress assessment, based on the sub-modelling technique, is proposed. A comparison of the fatigue life prediction, between the hot-spot and the effective notch methods, is presented. The latter method is found to predict a higher fatigue life for many of the situations tested. Furthermore, since this method allows for a more detailed modelling, the weld profile can also be designed in order to optimize the fatigue resistance. The weld slope is found to have a significant impact on the results.
Defects Prediction and Prevention Approaches for Quality Software Development

The demand for distributed and complex business applications in the enterprise requires error-free and high-quality application systems. Unfortunately, most of the developed software contains certain defects which cause failure of a system. Such failures are unacceptable for the development in the critical or sensitive applications. This makes the development of high quality and defect free software extremely important in software development. It is important to better understand and compute the association among software defects and its failures for the effective prediction and elimination of these defects to decline the failure and improve software quality. This paper presents a review of software defects prediction and its prevention approaches for the quality software development. It also focuses a review on the potential and constraints of those mechanisms in quality product development and maintenance.