Miracle-dz/UniFAR-FTUs huggingface dataset:
Smoky Vehicle Detection Based on Range Filtering on Three Orthogonal Planes and Motion Orientation Histogram

Smoky vehicle detection is an important task in reducing motor vehicle pollution. This paper presents a method to automatically detect smoky vehicles from the traffic surveillance videos. More specifically, the visual background extractor background subtraction algorithm and some rules are adopted to detect moving vehicle object and locate the key region at the back of the vehicle. Based on sufficient observations of the smoke characteristics in the real scene, three groups of features, including color moments (CMs) features, improved motion orientation histogram features, and the new model range filtering on three orthogonal planes (RF-TOP)-based features, are designed and proposed to distinguish smoky vehicles and non-smoke vehicles. The color information CM features are used as a preliminary sieve to filter out the samples that are obviously non-smoke regions. The other two groups of features are combined to one feature vector to obtain motion information and spatiotemporal information of the key region. Two strategies, including histogram and projection, are designed to extract discriminative dynamic features from the proposed model RF-TOP to characterize the key region. The pruning radial basis function neural network classifier is adopted to classify the extracted features. For the traffic surveillance videos in the daylight with sunny weather, the experimental results show that the proposed methods have better performances and work effectively with lower false alarm rates than existing methods, and the proposed method with histogram strategy achieves the best performance.
Parametric Evaluation of Different ANN Architectures: Forecasting Wind Power Across Different Time Horizons

The participation of volatile wind energy resources in the generation mix of power systems is increasing. It is therefore becoming more and more crucial for system operators to accurately predict the wind power generation across different short term horizons (5 to 60 minutes ahead) in order to adequately balance the system and maintain system security. This paper presents a comprehensive assessment of the influence of different parameters in artificial neural networks, such as the amount of historic data, batch size, number of hidden layers, number of neurons per hidden layer, and the amount of training data on the short term forecast accuracy. In order to identify the parameters which are most influential with respect to forecast accuracy, a sensitivity study isolating the various factors on a one-at-a-time basis has been performed. To minimize the forecast error across the investigated forecast horizons, the developed neural networks use the feed forward back propagation algorithm. From the investigated cases it is concluded that a neural network with two hidden layers is most suitable for wind forecasting on the timeframes considered. Furthermore, with increasing forecast horizons (from 5 to 60 minutes ahead), better performance is achieved when neural networks contain increased neurons in the hidden layers and have enlarged training data sets.
Preliminary analysis of learners in developing student book oriented research based learning models using 3D pageflip professionals on science lessons junior high school

Student’s Book is a book that students use as a guide activity in learning to facilitate students in mastering certain competencies. The purpose of this study is to describe the results of the preliminary analysis and analysis of learners. This research is a descriptive research using qualitative approach. The analysis was done by: preliminary analysis, analysis of learners and interviews. Based on observation results obtained: preliminary analysis was obtained 78,47% good category and it was found that students have average ability 76,52% good category in the analysis of graduate competence standard. In the Standard analysis of the learning process obtained 83,90% good category with active students in the learning process, and assessment analysis obtained 75% good category. Through analysis of learners, 77,83% students enjoyed it doing lab work, utilizing technology in learning. Based on the interviews that researchers did, it was obtained that the learning model used depends on the subject lesson, teaching materials used by textbooks, that in science subjects’ teachers still divide the time, 2 hours of lesson for Biology material by biology teacher and 3 hours lesson for Physics material by physics teacher. Therefore, the need to develop the book of students based on research-based learning using 3D pageflip Professional in learning science for junior high school.
Alcohol misuse among women in Brazil: recent trends and associations with unprotected sex, early pregnancy, and abortion

Objective: This study compared the rates of binge drinking (BD) and alcohol use disorder (AUD) reported for 2006 with those reported for 2012, exploring their associations with unprotected sex, early pregnancy, and abortion in a representative sample of women in Brazilian households. Methods: This was a descriptive analysis of data from a cross-sectional study involving randomized multistage cluster sampling of the population ≥ 14 years of age. Weighted prevalence rates and odds ratios were estimated, and serial mediation analysis was performed. Results: A total of 4,256 women were analyzed. The BD prevalence was 35.1% and 47.1% in 2006 and 2012, respectively, a significant increase, especially among women 40-59 years of age. There was no significant difference in AUD prevalence. BD (without AUD) was found to increase the odds of unprotected sex and abortion. The path analysis showed that early pregnancy was a mediator of the relationship between alcohol consumption and abortion. Conclusion: Among women in Brazil, the harmful use of alcohol is increasing, which has an impact on female reproductive health and exposure to risks. There is a need for specific prevention initiatives focusing on alcohol-related behaviors in women.
Baseline Longitudinal Strain Predicts Recovery of Left Ventricular Ejection Fraction in Hospitalized Patients With Nonischemic Cardiomyopathy

Background Heart failure (HF) with “recovered” ejection fraction (HFrecEF) is an emerging phenotype, but no tools exist to predict ejection fraction (EF) recovery in acute HF. We hypothesized that indices of baseline cardiac structure and function predict HFrecEF in nonischemic cardiomyopathy and reduced EF. Methods and Results We identified a nonischemic cardiomyopathy cohort with EF<40% during the first HF hospitalization (n=166). We performed speckle‐tracking echocardiography to measure longitudinal, circumferential, and radial strain, and the average of these measures (myocardial systolic performance). HFrecEF was defined as follow‐up EF ≥40% and ≥10% improvement from baseline EF. Fifty‐nine patients (36%) achieved HFrecEF (baseline EF 26±7%; follow‐up EF 51±7%) within a median of 135 (interquartile range 58‐239) days after the first HF hospitalization. Baseline demographics, biomarker profiles, and comorbid conditions (except lower chronic kidney disease in HFrecEF) were similar between HFrecEF and persistent reduced‐EF groups. HFrecEF patients had smaller baseline left ventricular end‐systolic dimension (3.6 versus 4.8 cm; P<0.01), higher baseline myocardial systolic performance (9.2% versus 8.1%; P=0.02), and improved survival (adjusted hazard ratio 0.27, 95% confidence interval 0.11, 0.62). We found a significant interaction between baseline left ventricular end‐systolic dimension and absolute longitudinal strain. Among patients with left ventricular end‐systolic dimension >4.35 cm, higher absolute longitudinal strain (≥8%) was associated with HFrecEF (unadjusted odds ratio=3.9, 95% CI)confidence interval 1.2, 12.8). Incorporation of baseline indices of cardiac mechanics with clinical variables resulted in a predictive model for HFrecEF with c‐statistic=0.85. Conclusions Factors associated with achieving HFrecEF were specific to cardiac structure and indices of cardiac mechanics. Higher baseline absolute longitudinal strain is associated with HFrecEF among nonischemic cardiomyopathy patients with reduced EF and larger left ventricular dimensions.
Latina/o Children Living With an Immigrant Mother With Depression: Developmental and Cultural Nuances in Recognition and Coping.

The psychological experience of maternal depression and its impact on immigrant Latina/o families often goes unrecognized and unaddressed. Children may feel especially helpless and confused about the changes they observe in their mothers' mood and behavior, and about the deterioration of family relationships. Given the interdependence of family structures of immigrant Latina/o households, maternal depression can be detrimental to Latina/o youth attributions and coping strategies, and to their relationship with their mothers. The quantitative focus of most research on maternal depression in Latina/o samples limits our understanding of family processes in maternal depression. The current qualitative study explores the perceived impact of maternal depression on Latina/o youths' attributions and coping strategies. This inquiry involved focus groups with 12 participants aged 9-16 years to explore their perspectives on maternal depression. All youth had participated in a 12-week multifamily group intervention focused on building family and cultural strengths to address maternal depression on immigrant Latina/o families. Findings of the focus groups illuminated the essential experience of youth living with maternal depression, and indicated that there are developmental considerations for how youth recognize and make meaning of maternal depression, and cope with disrupted family life. Additionally, youth reported engaging in these culture-specific ways of coping: using close sibling relationships and family structure as support, having fathers and extended family members engage in additional and restorative parenting practices, and participating in religious practices to seek refuge from family stress. We propose considerations for intervention and further areas of research.
Citronellal prevents endothelial dysfunction and atherosclerosis in rats

Atherosclerosis is a chronical inflammatory disease in arterial walls, which is involved in oxidative stress and endothelial dysfunction. Aromatherapy is one of the complementary therapies that use essential oils as the major therapeutic agents to treat several diseases. Citronellal (CT) is a monoterpene predominantly formed by the secondary metabolism of plants, producing antithrombotic, antiplatelet, and antihypertensive activities.
Optic Disc and Cup Segmentation with Blood Vessel Removal from Fundus Images for Glaucoma Detection

Glaucoma is one of the major causes of blindness. Researchers keep looking for better ways to detect glaucoma in its early stage before it gets worse and cannot be cured. Among existing methods, the vertical cup to disc ratio (CDR) has been found to be effective for glaucoma measurement, which is calculated from the diameters of the optic cup and disc regions. Therefore, in order to achieve a more accurate CDR, a good segmentation of the optic disc and cup regions is quite important. Noting that the shape of the disc and cup regions can be assumed to be an ellipse, in this work we propose to find the minimal bounding boxes for the two regions based on the recent advances of deep learning. Also, considering blood vessels, passing through the disc area in a fundus image, can affect the detection of the bounding boxes, we further propose to remove the blood vessels beforehand in order to further boost the overall performance. Comprehensive experiments show that our proposed method achieves state-of-the-art performance on ORIGA-650 for optic disc and cup segmentation.
Optimizing Energy Harvesting for Foot Based Wearable Sensors

Wearable devices have the potential to improve healthcare, but suffer from significant barriers to adoption, including the need for constant recharging. Harvesting energy from the ambient environment to top-up batteries can overcome this, but the actual energy available is very small, and hence it is critical that the whole system is highly optimized. This paper presents an investigation into the optimization of inertial energy harvesters for placement at the human foot. Lower body locations have previously been shown to be very energy dense, however previous energy harvester modeling has focused on the lower leg rather than the foot itself for ease of device placement. We show that the typical energy density can be almost double at the foot compared with lower leg positions, with substantially more energy concentrated in a smaller bandwidth. There is thus a dual benefit of placing a harvester at the foot: there is more energy due to the larger movement of the foot, and more efficient (higher Q) harvesters can be used to increase the collected energy. We place these results in context by analyzing the power demands of a typical wearable, and identify that with appropriate harvester tuning the peak current requirements of the electronics can be fitted into the energy peaks generated from each footstep.
Development of a Smart Home-Based Package for Unobtrusive Physiological Monitoring

Home-based programs have been shown to be effective in improving health conditions, patient self-management, quality of life, and health outcomes. However, there is mixedevidence on the effectiveness of these programs due to limitations in the intervention tools that are used, primarily the burden that is placed on the user, especially among seniors. In this paper we developed a novel home-based package that measures critically important physiological information such that neither active compliance or interaction from the user is required. To this end, we embedded passive sensors (including load cells, electrodes, pulse sensor and color sensors) into common household items such as tiling, furniture and wall. The smart package measures subject’s electrocardiogram (ECG), photoplethysmogram (PPG), ballistocardiogram (BCG), electromyogram (EMG) and imaging photoplethysmogram (IPPG). In contrast to the previous studies, the proposed package measures all the physiological information unobtrusively, simultaneously and in a synchronized manner such that all the data samples corresponding to different intervals of a specific cardiovascular cycle can be identified. Such information can be analyzed by a clinician or be used for a higher level information extraction such as beat-to-beat blood pressure estimation. In addition, the proposed package is the first and only homebased technology that can simultaneously and unobtrusively capture both mechanical and electrical characteristics of user’s heart activities. This results in a more accurate home-based vital parameters monitoring.
Fluid Intelligence Predicts Change in Depressive Symptoms in Later Life: The Lothian Birth Cohort 1936

We examined reciprocal, time-ordered associations between age-related changes in fluid intelligence and depressive symptoms. Participants were 1,091 community-dwelling older adults from the Lothian Birth Cohort 1936 study who were assessed repeatedly at 3-year intervals between the ages of 70 and 79 years. On average, fluid intelligence and depressive symptoms worsened with age. There was also a dynamic-coupling effect, in which low fluid intelligence at a given age predicted increasing depressive symptoms across the following 3-year interval, whereas the converse did not hold. Model comparisons showed that this coupling parameter significantly improved overall fit and had a correspondingly moderately strong effect size, accounting on average for an accumulated 0.9 standard-deviation increase in depressive symptoms, following lower cognitive performance, across the observed age range. Adjustment for sociodemographic and health-related covariates did not significantly attenuate this association. This implies that monitoring for cognitive decrements in later life may expedite interventions to reduce related increases in depression risk.
ActionXPose: A Novel 2D Multi-view Pose-based Algorithm for Real-time Human Action Recognition

We present ActionXPose, a novel 2D pose-based algorithm for posture-level Human Action Recognition (HAR). The proposed approach exploits 2D human poses provided by OpenPose detector from RGB videos. ActionXPose aims to process poses data to be provided to a Long Short-Term Memory Neural Network and to a 1D Convolutional Neural Network, which solve the classification problem. ActionXPose is one of the first algorithms that exploits 2D human poses for HAR. The algorithm has real-time performance and it is robust to camera movings, subject proximity changes, viewpoint changes, subject appearance changes and provide high generalization degree. In fact, extensive simulations show that ActionXPose can be successfully trained using different datasets at once. State-of-the-art performance on popular datasets for posture-related HAR problems (i3DPost, KTH) are provided and results are compared with those obtained by other methods, including the selected ActionXPose baseline. Moreover, we also proposed two novel datasets called MPOSE and ISLD recorded in our Intelligent Sensing Lab, to show ActionXPose generalization performance.
Characterization and Drug Sensitivity of a New High-Grade Myxofibrosarcoma Cell Line

Myxofibrosarcoma (MFS) belongs to the group of sarcoma tumors, which represent only 1% of the totality of adult tumors worldwide. Thus, given the rare nature of this cancer, this makes the availability of MFS cell lines difficult. In an attempt to partially fill this gap, we immortalized a primary culture of MFS (IM-MFS-1) and compared the cell morphology with patient’s tumor tissue. IM-MFS-1 was genetically characterized through a Comparative Genomic Hybridization (CGH) array and the mesenchymal phenotype was evaluated using Polymerase chain reaction (PCR) and immunofluorescence staining. Drug sensitivity for MFS therapies was monitored over time in cultures. We confirmed the conservation of the patient’s tumor cell morphology and of the mesenchymal phenotype. Conversely, the synthesis and expression of CD109, a TGFβ co-receptor used to facilitate the diagnosis of high-grade MFS diagnosis, was maintained constant until high cancer cell line passages. The CGH array revealed a complex karyotype with cytogenetic alterations that include chromosome regions associated with genes involved in tumor processes. Cytotoxicity assays show drug sensitivity constantly increased during the culture passages until a plateau was reached. In conclusion, we established and characterized a new MFS cell line that can be used for future preclinical and molecular studies on soft tissue sarcomas.
Unsupervised Sentence Embeddings for Answer Summarization in Non-factoid CQA

This paper presents a method for summarizing answers in Community Question Answering.We explore deep Auto-encoder and Long-short-termmemory Auto-encoder for sentence representation. The sentence representations are used to measure similarity in Maximal Marginal Relevance algorithm for extractive summarization. Experimental results on a benchmark dataset show that our unsupervised method achieves state-of-the-art performance while requiring no annotated data.
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented LMM, to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing. The proposed approach models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity and atmospheric effects) and instrumental configurations (e.g., sensor noise), and material nonlinear mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low-coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unmixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with the previous state-of-the-art methods.
Corpus-Driven Thematic Hierarchy Induction

Thematic role hierarchy is a widely used linguistic tool to describe interactions between semantic roles and their syntactic realizations. Despite decades of dedicated research and numerous thematic hierarchy suggestions in the literature, this concept has not been used in NLP so far due to incompatibility and limited scope of existing hierarchies. We introduce an empirical framework for thematic hierarchy induction and evaluate several role ranking strategies on English and German full-text corpus data. We hypothesize that global thematic hierarchy induction is feasible, that a hierarchy can be induced from just fractions of training data and that resulting hierarchies apply cross-lingually. We evaluate these assumptions empirically.
A sample preparation workflow for adipose tissue shotgun proteomics and proteogenomics

ABSTRACT Animals with large adipose stores, such as marine mammals, may provide insights into the evolution and function of this multifunctional tissue in health and disease. In the absence of sequenced genomes, molecular information can be rapidly obtained by proteomics and transcriptomics, but their application to adipose tissue is hindered by low nucleic acid and protein yields. We sequenced and compared proteomes isolated from the blubber of four elephant seals using phenol and guanidine thiocyanate (Qiazol) or detergent (sodium deoxycholate) buffer. Qiazol recovered more subcellular proteins such as metabolic enzymes, in addition to extracting RNA, facilitating proteogenomic analyses of small lipid-rich tissue biopsies. We also compared proteomics data analysis platforms and found that de novo peptide sequencing improved protein identification sensitivity compared to database search alone. We report sample preparation and data analysis workflows for proteogenomics and a proteome of elephant seal blubber containing 2678 proteins, including many of interest for further functional studies. This article has an associated First Person interview with the first author of the paper. Summary: Proteins that are compatible with shotgun proteomics can be isolated from small amounts of adipose tissue at the same time as RNA, facilitating proteogenomics studies in non-model animals.