Miracle-dz/UniFAR-FTUs huggingface dataset:
3D semantic segmentation for high-resolution aerial survey derived point clouds using deep learning (demonstration)

Three-dimensional (3D) Semantic segmentation of aerial derived point cloud aims at assigning each point to a semantic class such as building, tree, road, and so on. Accurate 3D-segmentation results can be used as an essential information for constructing 3D city models, for assessing the urban expansion and economical condition. However, the fine-grained semantic segmentation is a challenge in high-resolution point cloud due to irregularly distributed points unlike regular pixels of image. In this demonstration, we present a case study to apply PointNet, a novel deep learning network, to outdoor aerial survey derived point clouds by considering intensity (depth) as well as spectral information (RGB). PointNet was basically designed for indoor point cloud data based on the permutation invariance of 3D points. We firstly fuse two surveying datasets of Light Detection and ranging (LiDAR) and aerial images for generating multi-sourced aerial point clouds (RGB-DI). Then, each point of fused data is classified into a semantic class of ordinary building, public facility, apartment, factory, transportation network, park, and water by reworking PointNet. The result of our approach by using deep learning shows about 0.88 accuracy and 0.64 F-measure of semantic segmentation with the RGB-DI data we have fused. It outperforms a Support Vector Machine(SVM) approach based on geometric features of linearity, planarity, scattering, and verticality of a set of 3D points.
Effects of a Pasty Bone Cement Containing Brain-Derived Neurotrophic Factor-Functionalized Mesoporous Bioactive Glass Particles on Metaphyseal Healing in a New Murine Osteoporotic Fracture Model

The development of new and better implant materials adapted to osteoporotic bone is still urgently required. Therefore, osteoporotic muscarinic acetylcholine receptor M3 (M3 mAChR) knockout (KO) and corresponding wild type (WT) mice underwent osteotomy in the distal femoral metaphysis. Fracture gaps were filled with a pasty α-tricalcium phosphate (α-TCP)-based hydroxyapatite (HA)-forming bone cement containing mesoporous bioactive CaP-SiO2 glass particles (cement/MBG composite) with or without Brain-Derived Neurotrophic Factor (BDNF) and healing was analyzed after 35 days. Histologically, bone formation was significantly increased in WT mice that received the BDNF-functionalized cement/MBG composite compared to control WT mice without BDNF. Cement/MBG composite without BDNF increased bone formation in M3 mAChR KO mice compared to equally treated WT mice. Mass spectrometric imaging showed that the BDNF-functionalized cement/MBG composite implanted in M3 mAChR KO mice was infiltrated by newly formed tissue. Leukocyte numbers were significantly lower in M3 mAChR KO mice treated with BDNF-functionalized cement/MBG composite compared to controls without BDNF. C-reactive protein (CRP) concentrations were significantly lower in M3 mAChR KO mice that received the cement/MBG composite without BDNF when compared to WT mice treated the same. Whereas alkaline phosphatase (ALP) concentrations in callus were significantly increased in M3 mAChR KO mice, ALP activity was significantly higher in WT mice. Due to a stronger effect of BDNF in non osteoporotic mice, higher BDNF concentrations might be needed for osteoporotic fracture healing. Nevertheless, the BDNF-functionalized cement/MBG composite promoted fracture healing in non osteoporotic bone.
Automated Area Assessment of Objects Using Deep Learning Approach and Satellite Imagery Data

We describe an actual case of applying deep neural networks for area assessment of different types of objects in selected geographical region through analysis of satellite images. The case was to detect, segment and asses area of buildings and agricultural lands on satellite images. We illustrate our framework of solving the problem and results validation methods. We compare performance of different convolutional neural networks in applying to our case and discuss the best quality segmentation model that was found – the U-net convolutional network. There was no training dataset of images and their corresponding masks available for our geographical region, but we constructed our own training set. Paper reports in detail on the processes of satellite imagery data preparation, images pre-processing, construction of training dataset and learning neural networks.
Comparing Bayesian and non-Bayesian accounts of human confidence reports

Humans can meaningfully report their confidence in a perceptual or cognitive decision. It is widely believed that these reports reflect the Bayesian probability that the decision is correct, but this hypothesis has not been rigorously tested against non-Bayesian alternatives. We use two perceptual categorization tasks in which Bayesian confidence reporting requires subjects to take sensory uncertainty into account in a specific way. We find that subjects do take sensory uncertainty into account when reporting confidence, suggesting that brain areas involved in reporting confidence can access low-level representations of sensory uncertainty, a prerequisite of Bayesian inference. However, behavior is not fully consistent with the Bayesian hypothesis and is better described by simple heuristic models that use uncertainty in a non-Bayesian way. Both conclusions are robust to changes in the uncertainty manipulation, task, response modality, model comparison metric, and additional flexibility in the Bayesian model. Our results suggest that adhering to a rational account of confidence behavior may require incorporating implementational constraints.
Prevalence of α-Thalassemia in the Egyptian Population

Abstract Hemoglobinopathies are the most common monogenic diseases in the world, causing many health problems worldwide. In Egypt, thalassemia is the most common cause of chronic hemolytic anemia and correlated with significant morbidity and mortality. One thousand Egyptian newborns were screened to detect α-thalassemia (α-thal) deletions using polymerase chain reaction (PCR)-based DNA analysis of cord blood samples. Ninety-one cases (9.1%) of the studied samples were proved to have at least one of the α genes deleted and 851 cases (85.1%) were normal by PCR analysis, while 58 samples (5.8%) failed to be amplified so further DNA analysis could not be done. In the studied group with α gene deletions, we found different types including silent carriers with only one α-globin gene deleted (3.1%), α-thal trait with two α-globin genes deleted (4.2%), Hb H disease with three α-globin genes deleted (1.8%) and no cases carrying Hb Bart’s disease with loss of four α-globin genes. We determined the deletional spectrum of α-thal, which might be used in the future for molecular investigations of the disease in susceptible patients in our population.
Estimating User's Attitude in Multimodal Conversational System for Elderly People with Dementia

Toward constructing a multimodal conversation agentsystem which can be used to interview elderly patients with dementia, we propose a turn taking mechanism based on recognition of the subjects attitude as to whether the subject has (or relinquish) the right to speak. A key strategy in the recognition task is to extract features from pausing behavior in subject’s spontaneous speech and to fuse multimodal signals (gaze, head motion, and speech). In this paper, we focus on evaluation of the recognition module used in guiding turn taking. To evaluate it, we collect multimodal data corpus from 42 dyadic conversations between subjects with dementia and the virtual agent we have developed as a prototype and annotate subject’s multimodal data manually. In experiments, we validate recognition models trained multimodal dataset by machine learning methods.Experimental results shows that pause features are effective to improve the attitude recognition accuracy and the accuracy is improved up to 88%.
Differences in Intercellular Communication During Clinical Relapse and Gadolinium-Enhanced MRI in Patients With Relapsing Remitting Multiple Sclerosis: A Study of the Composition of Extracellular Vesicles in Cerebrospinal Fluid

This study was designed based on the hypothesis that changes in both the levels and surface marker expression of extracellular vesicles (EVs) isolated from the cerebrospinal fluid (CSF) may be associated with the clinical form, disease activity, and severity of multiple sclerosis (MS). The analyzes were performed on subjects affected by MS or other neurological disorders. EVs, which were isolated by ultracentrifugation of CSF samples, were characterized by flow cytometry. A panel of fluorescent antibodies was used to identify the EV origin: CD4, CCR3, CCR5, CD19, and CD200, as well as isolectin IB4. The Mann–Whitney U-test and Kruskal–Wallis test were used for statistical analyzes. EVs isolated from the CSF were more abundant in patients with progressive MS and in those with a clinically isolated syndrome than in all the other groups examined. Furthermore, an important change in the number of EVs and in their surface marker expression occurred during active phases of MS [i.e., clinical relapses and the presence of enhancing lesions on magnetic resonance imaging (MRI)]. In particular, the number of CSF-EVs increased in patients affected by MS during clinical relapse; this finding was associated with a decrease in the number of CD19+/CD200+ (naïve B cells) EVs. These markers are expressed by immature and naïve B lymphocytes, and to the best of our knowledge, this double staining has never been associated with MS, but their reduction has been observed in patients with another type of Th1 cell-mediated autoimmune disease. In contrast, the presence of lesions in the brain and spine on gadolinium-enhanced MRI was associated with an increase in the numbers of CCR3+/CCR5+ (subset of CD8 memory T cells), CD4+/CCR3+ (Th2 cells), and CD4+/CCR5+ (Th1 cells) CSF-EVs. Two points are worth emphasizing: (i) the data obtained in this study confirm that CSF-EVs represent a potentially promising tool to identify biomarkers specific for different phases of MS; and (ii) Considering the role of EVs in intercellular communication, our results provide some insights that improve our understanding of the relationships among some of the cell types that are mainly involved in MS pathogenesis (e.g., lymphocytes, glia, and neurons).
Having an Eating Disorder and Still Being Able to Flourish? Examination of Pathological Symptoms and Well-Being as Two Continua of Mental Health in a Clinical Sample

Introduction: Eating Disorders (EDs) are serious psychiatric disorders, impacting physical and psychosocial functioning, often with a chronic course and high mortality rates. The two continua model of mental health states that mental health is a complete state, that is, not merely the absence of mental illness, but also the presence of mental health. This model was studied among ED patients by examining the presence and correlates of well-being and psychopathology. In addition, the levels of well-being were compared to the Dutch general population. Method: A total of 468 female ED patients participated in this study during application and intake at a specialized ED treatment Center in the Netherlands. They filled out questionnaires about well-being (MHC-SF), general psychopathology (OQ-45), and ED psychopathology (EDE-Q). Categorical andmean well-being levels were calculated. Also, the relationships between these variables were examined with Pearson correlation and multiple hierarchical regression analysis. Results: ED patients showed lower levels of emotional, psychological, and social well-being on average compared to the general population. About 26% of the ED patients experienced low levels of well-being (languishing). However, also 13% experienced high levels of well-being (flourishing), varying between 9% in Anorexia Nervosa to 25% in Binge Eating Disorder. ED psychopathology and general well-being showed a moderate negative correlation. For patients with Bulimia Nervosa and Binge Eating Disorder however no such correlation was found. Lower general psychopathology, not having a history of hospitalization for the ED, and adaptive personal functioning were correlated with well-being among ED patients. Conclusion: This study shows initial support for the two continua model of mental health among ED patients. Psychopathology and well-being should be considered as related, but distinct dimensions of mental health in ED patients. Further research should focus on the possible reciprocal relationships between psychopathology and well-being during recovery. It is recommended to monitor well-being during treatment and to implement interventions for well-being to realize complete recovery for those patients with inadequate levels of well-being.
Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China.
Cross-lingual Short-text Matching with Deep Learning

The problem of short text matching is formulated as follows: given a pair of sentences or questions, a matching model determines whether the input pair mean the same or not. Models that can automatically identify questions with the same meaning have a wide range of applications in question answering sites and modern chatbots. In this article, we describe the approach by team hahu to solve this problem in the context of the "CIKM AnalytiCup 2018 - Cross-lingual Short-text Matching of Question Pairs" that is sponsored by Alibaba. Our solution is an end-to-end system based on current advances in deep learning which avoids heavy feature-engineering and achieves improved performance over traditional machine-learning approaches. The log-loss scores for the first and second rounds of the contest are 0.35 and 0.39 respectively. The team was ranked 7th from 1027 teams in the overall ranking scheme by the organizers that consisted of the two contest scores as well as: innovation and system integrity, understanding data as well as practicality of the solution for business.
A Seq2Seq learning approach for modeling semantic trajectories and predicting the next location

Proactive mobile applications and services have the advantage of providing their users with timely and customized solutions improving in this way the human-machine interaction. For this reason, Location Based Services (LBS) rely increasingly on predictive models that estimate how likely it is for a user to visit a certain location. Recently, Artificial Neural Networks, and especially recurrent architectures such as the LSTMs, have shown a particularly good performance in this field. In this work, we extend a LSTM network by applying Sequence to Sequence (Seq2Seq) learning on human semantic trajectories. In particular, we explore whether and to what extent Attention-based Seq2Seq learning in combination with neural networks can contribute to improving the accuracy in a location prediction scenario. We compare the performance of our framework with the performance of a standard LSTM, a semantic trajectory tree-based approach and a probabilistic graph of first and higher order on two different real-world datasets. It can be shown that Sequence to Sequence learning may well be used to model semantic trajectories and predict future human movement patterns.
Protocol The Ready to Reduce Risk ( 3 R ) Study for a Group Educational Intervention With Telephone and Text Messaging Support to Improve Medication Adherence for the Primary Prevention of Cardiovascular Disease : Protocol for a Randomized Controlled Trial

Background: Poor adherence to cardiovascular medications is associated with worse clinical outcomes. Evidence for effective education interventions that address medication adherence for the primary prevention of cardiovascular disease is lacking. The Ready to Reduce Risk (3R) study aims to investigate whether a complex intervention, involving group education plus telephone and text messaging follow-up support, can improve medication adherence and reduce cardiovascular risk. Objective: This protocol paper details the design and rationale for the development of the 3R intervention and the study methods used. Methods: This is an open and pragmatic randomized controlled trial with 12 months of follow-up. We recruited participants from primary care and randomly assigned them at a 1:1 frequency, stratified by sex and age, to either a control group (usual care from a general practitioner) or an intervention group involving 2 facilitated group education sessions with telephone and text messaging follow-up support, with a theoretical underpinning and using recognized behavioral change techniques. The primary outcome was medication adherence to statins. The primary measure was an objective, novel, urine-based biochemical measure of medication adherence. We also used the 8-item Morisky Medication Adherence Scale to assess medication adherence. Secondary outcomes were changes in total cholesterol, blood pressure, high-density lipoprotein, total cholesterol to high-density lipoprotein ratio, body mass index, waist to hip ratio, waist circumference, smoking behavior, physical activity, fruit and vegetable intake, patient activation level, quality of life, health status, health and medication beliefs, and overall cardiovascular disease risk score. We also considered process outcomes relating to acceptability and feasibility of the 3R intervention. Results: We recruited 212 participants between May 2015 and March 2017. The 12-month follow-up data collection clinics were completed in April 2018, and data analysis will commence once all study data have been collected and verified. JMIR Res Protoc 2018 | vol. 7 | iss. 11 | e11289 | p.1 http://www.researchprotocols. org/2018/11/e11289/ (page number not for citation purposes) Byrne et al JMIR RESEARCH PROTOCOLS
Multi-resolution sketches and locality sensitive hashing for fast trajectory processing

Searching for similar GPS trajectories is a fundamental problem that faces challenges of large data volume and intrinsic complexity of trajectory comparison. In this paper, we present a suite of sketches for trajectory data that drastically reduce the computation costs associated with near neighbor search, distance estimation, clustering and classification, and subtrajectory detection. Apart from summarizing the dataset, our sketches have two uses. First, we obtain simple provable locality sensitive hash families for both the Hausdorff and Fréchet distance measures, useful in near neighbour queries. Second, we build a data structure called MRTS (Multi Resolution Trajectory Sketch), which contains sketches of varying degrees of detail. The MRTS is a user-friendly, compact representation of the dataset that allows to efficiently answer various other types of queries. Moreover, MRTS can be used in a dynamic setting with fast insertions of trajectories into the database. Experiments on real data show effective locality sensitive hashing substantially improves near neighbor search time. Distances defined on the skteches show good correlation with Fréchet and Hausdorff distances.
Nonmutational mechanism of inheritance in the Archaeon Sulfolobus solfataricus

Significance Archaea have considerable importance in ecology and evolution and have emerging roles in health. However, many of their cellular processes are under active study. Archaea are thought to acquire and inherit adaptive traits solely through mutation. Here, adaptive laboratory evolution of an extremophile trait revealed that an alternative nonmutational process was operative. When genes whose expression had been altered in a heritable manner were replaced by recombination using identical DNA, the evolved traits were changed. This implicated a regulatory role for chromatin proteins and was consistent with an epigenetic-like regulatory mechanism. This finding has evolutionary relevance for the origin of epigenetics, transcriptional regulation, and functional genome architecture. Epigenetic phenomena have not yet been reported in archaea, which are presumed to use a classical genetic process of heritability. Here, analysis of independent lineages of Sulfolobus solfataricus evolved for enhanced fitness implicated a non-Mendelian basis for trait inheritance. The evolved strains, called super acid-resistant Crenarchaeota (SARC), acquired traits of extreme acid resistance and genome stability relative to their wild-type parental lines. Acid resistance was heritable because it was retained regardless of extensive passage without selection. Despite the hereditary pattern, in one strain, it was impossible for these SARC traits to result from mutation because its resequenced genome had no mutation. All strains also had conserved, heritable transcriptomes implicated in acid resistance. In addition, they had improved genome stability with absent or greatly decreased mutation and transposition relative to a passaged control. A mechanism that would confer these traits without DNA sequence alteration could involve posttranslationally modified archaeal chromatin proteins. To test this idea, homologous recombination with isogenic DNA was used to perturb native chromatin structure. Recombination at up-regulated loci from the heritable SARC transcriptome reduced acid resistance and gene expression in the majority of recombinants. In contrast, recombination at a control locus that was not part of the heritable transcriptome changed neither acid resistance nor gene expression. Variation in the amount of phenotypic and expression changes across individuals was consistent with Rad54-dependent chromatin remodeling that dictated crossover location and branch migration. These data support an epigenetic model implicating chromatin structure as a contributor to heritable traits.