Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing Clustered Latent Dirichlet Allocation

Dynamic Topic Models (DTM) are a way to extract time-variant information from a collection of documents. The only available implementation of this is slow, taking days to process a corpus of 533,588 documents. In order to see how topics both their key words and their proportional size in all documents change over time, we analyze Clustered Latent Dirichlet Allocation (CLDA) as an alternative to DTM. This algorithm is based on existing parallel components, using Latent Dirichlet Allocation (LDA) to extract topics at local times, and k-means clustering to combine topics from di erent time periods. This method is two orders of magnitude faster than DTM, and allows for more freedom of experiment design. Results show that most topics generated by this algorithm are similar to those generated by DTM at both the local and global level using the Jaccard index and Sørensen-Dice coe cient, and that this method's perplexity compares favorably to DTM. We also explore tradeo s in CLDA method parameters.
Differential Changes in Autonomic Functioning, HypothalamicÃ¢ÂÂPituitaryÃ¢ÂÂAdrenal Axis Activity and Inflammation during Melatonergic Antidepressant Treatment between Depressed Patients with and without Suicidal Ideation

Abstract Objective: The existing literature has not yet simultaneously examined the modification of autonomic functioning, hypothalamic–pituitary–adrenal (HPA) axis activity and inflammation following agomelatine treatment of depression. We investigated the changes in the three biological systems that are closely linked to cardiovascular risks among depressed patients with/without suicidal ideation (SI) following agomelatine treatment. Methods Thirty-two unmedicated depressed patients with current SI (SI+ group) and 28 without current/lifetime SI (SI− group) completed 6-week agomelatine treatment. None of them had any current or lifetime suicidal behavior before entering the study. The peripheral levels of cortisol, erythrocyte sedimentation rate (ESR) and high sensitivity C-reactive protein (hs- CRP), and heart rate variability (HRV) were measured at baseline and week 6. The Hamilton Depression Rating Scale (HAM-D) and the Columbia Suicide Severity Rating Scale (C-SSRS) were assessed at baseline, week 2, 4 and 6. Results Between-group analyses showed a significant treatment-by-group interaction for cortisol, ESR, hs-CRP, the ratio of cortisol to hs-CRP (COR/CRP ratio), variance (total HRV), low frequency (LF)-HRV, and high frequency (HF)-HRV. The results were driven by decreases in cortisol, hs- CRP and ESR levels and increases in total HRV, LF-HRV and HF-HRV in the SI+ group, coupled with an increase in hs-CRP levels and a decrease in COR/CRP ratio in the SI− group. Moreover, in the SI+ group the lessening of the intensity of SI independently predicted the reduction in hs-CRP levels. Conclusions Agomelatine treatment differentially modified biomarkers of cardiac risk between depressed patients with current SI and those without SI. The specificity and underlying mechanism of melatonergic antidepressant beneficially affecting markers of cardiac risk in only depressed patients with current SI merits further investigation. Peripheral hs-CRP levels may serve as a potential tool to monitor the fluctuation of SI in depressed patients under agomelatine treatment.
Restoring Inflammatory Mediator Balance after Sofosbuvir-Induced Viral Clearance in Patients with Chronic Hepatitis C

This study aimed at analyzing circulating levels of inflammatory and profibrogenic cytokines in patients with hepatitis C virus (HCV) chronic infection undergoing therapy with direct-acting antiviral agents (DAA) and correlating these immune biomarkers with liver disease status. We studied 88 Brazilian monoinfected chronic hepatitis C patients receiving interferon- (IFN-) free sofosbuvir-based regimens for 12 or 24 weeks, followed-up before therapy initiation and three months after the end of treatment. Liver disease was determined by transient elastography, in addition to APRI and FIB-4 indexes. Analysis of 30 immune mediators was carried out by multiplex or enzymatic immunoassays. Sustained virological response rate was 98.9%. Serum levels of cytokines were increased in HCV-infected patients when compared to control group. CCL-2, CCL-3, CCL-4, CXCL-8, CXCL-10, IL-1β, IL-15, IFN-γ, IL-4, IL-10, TGF-β, FGFb, and PAI-1 decreased significantly after antiviral therapy, reaching values similar to noninfected controls. TGF-β and suPAR levels were associated with fibrosis/cirrhosis. Also, we observed amelioration in hepatic parameters after DAA treatment. Together, our results suggest that viral control induced by IFN-free DAA therapy restores inflammatory mediators in association with improvement in liver function.
Formation of positron-atom bound states in collisions between Rydberg Ps and neutral atoms

Predicted 20 years ago, positron binding to neutral atoms has not yet been observed experimentally. A scheme is proposed to detect positron-atom bound states by colliding Rydberg positronium (Ps) with neutral atoms. Estimates of the charge-transfer reaction cross section are obtained using the first Born approximation for a selection of neutral atom targets and a wide range of incident Ps energies and principal quantum numbers. We also estimate the corresponding Ps ionization cross section. The accuracy of the calculations is tested by comparison with earlier predictions for charge transfer in Ps collisions with hydrogen and antihydrogen. We describe an existing Rydberg Ps beam suitable for producing positron-atom bound states and estimate signal rates based on the calculated cross sections and realistic experimental parameters. We conclude that the proposed methodology is capable of producing such states and of testing theoretical predictions of their binding energies.
Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification

Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. Such a setting severely limits their scalability in real-world applications where no labelled samples are available during the training phase. To overcome this limitation, we develop a novel unsupervised Multi-task Mid-level Feature Alignment (MMFA) network for the unsupervised cross-dataset person re-identification task. Under the assumption that the source and target datasets share the same set of mid-level semantic attributes, our proposed model can be jointly optimised under the person's identity classification and the attribute learning task with a cross-dataset mid-level feature alignment regularisation term. In this way, the learned feature representation can be better generalised from one dataset to another which further improve the person re-identification accuracy. Experimental results on four benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art baselines.
Does SMS-Support Make a Difference? Effectiveness of a Two-Week Online-Training to Overcome Procrastination. A Randomized Controlled Trial

The primary purpose of this randomized controlled trial (RCT) was to evaluate the efficacy of an unguided, 2-week internet-based training program to overcome procrastination, called ON.TOP. Because adherence is a typical problem among individuals who tend to procrastinate, especially with internet-based interventions, the secondary purpose of the present study was to investigate whether adding SMS support increases subjects’ frequency of engagement in training. In a three-armed RCT (N = 161), the effects of the intervention alone and intervention with daily SMS-support were compared to a waiting list control condition in a sample of students. The primary outcome of interest was procrastination. The secondary outcome of interest was the extent of training behavior. Baseline (T0), immediate post-treatment (T1) and 8-week post-treatment (T2) assessments were conducted. Results indicated that procrastination decreased significantly only with intervention group with daily SMS support, relative to control. Moreover, incorporating SMS support also may enhance extent of training behavior.
Secure Beamformer Designs in MU-MIMO Systems: An Interference Exploitation Approach

Physical layer security has drawn significant attention in recent years because of its advances in preventing confidential information leakage to eavesdroppers. In this paper, we focus on secure beamformer designs in multi-user multi-input multi-output (MU-MIMO) systems with a multi-antenna eavesdropper attempting to overhear the confidential information of a particular user. Unlike existing works, we aim to exploit inherent multi-user interference to disrupt the reception of the eavesdropper and enhance the security of wireless communications. Particularly, utilizing practical signal-to-interference-noise (SINR) metrics, the objective of secure beamformer design is to minimize the eavesdropper's SINR, while providing legitimate users with required pre-specified SINRs to guarantee quality of service (QoS). We propose secure beamformer designs for the downlink transmission situation, under the conditions of known and unknown eavesdropper's channels, respectively. With the proposed secure beamformers, the multi-user signal behaves like artificial noise or distributed friendly jamming, which can further degrade the reception of the eavesdropper. Extensive simulation studies confirm our analytical performance predictions and illustrate the advantages of the proposed beamformer designs for secure MU-MIMO transmissions.
SHARP: A novel hybrid architecture for industrial wireless sensor and actuator networks

Industrial communications have very challenging requirements, especially in Factory Automation (FA) scenarios. Some of these requirements are: packet deadline bounds, high reliability, low transmission jitter, and communication determinism. Wireless communications solutions offer significant advantages over wired solutions: lower costs, faster and seamless deployment, higher flexibility and scalability, and free movement of the systems communicated wirelessly. However, standard wireless technologies do not provide enough performance to satisfy all of the industrial communications requirements in most cases and, therefore, wired solutions cannot be directly replaced by wireless solutions. In this work, we present SHARP (Synchronous and Hybrid Architecture for Real-time Performance in IWSAN), a novel hybrid architecture specially designed for industrial automation, where Ultra-Reliable Low-Latency Communications (URLLC) are required. This paper is mainly focused on the wireless segment of SHARP, a wireless architecture that includes a physical layer based on 802.11g along with a Time Division Multiple Access (TDMA) Medium Access Control (MAC) layer to ensure communication determinism, while maintaining backward compatibility with 802.11. Wireless SHARP segment behavior and its performance are evaluated through OMNeT++ simulations.
The directional migration and differentiation of mesenchymal stem cells toward vascular endothelial cells stimulated by biphasic calcium phosphate ceramic

Abstract Osteoinductivity of porous calcium phosphate (CaP) ceramics has been widely investigated and confirmed, and it might be attributed to the rapid formation of the vascular networks after in vivo implantation of the ceramics. In this study, to explore the vascularization mechanism within the CaP ceramics, the migration and differentiation of bone marrow-derived mesenchymal stem cells (BMSCs) under the stimulation of porous biphasic calcium phosphate (BCP) ceramic with excellent osteoinductivity were systematically investigated. The results indicated that the directional migration of BMSCs toward BCP ceramic occurred when evaluated by using a transwell model, and the BMSCs migration was enhanced by the seeded macrophages on the ceramic in advance. Besides, by directly culturing BMSCs on BCP ceramic discs under both in vitro and in vivo physiological environment, it was found that the differentiation of BMSCs toward vascular endothelial cells (VECs) happened under the stimulation of BCP ceramic, as was confirmed by the up-regulated gene expressions and protein secretions of VECs-related characteristic factors, including kinase insert domain receptor, von willebrand factor, vascular cell adhesion molecule-1 and cadherin 5 in the BMSCs. This study offered a possibility for explaining the origin of VECs during the rapid vascularization process after in vivo implantation of porous CaP ceramics and could give some useful guidance to reveal the vascularization mechanism of the ceramics.
Progressive Multi‐Jittered Sample Sequences

We introduce three new families of stochastic algorithms to generate progressive 2D sample point sequences. This opens a general framework that researchers and practitioners may find useful when developing future sample sequences. Our best sequences have the same low sampling error as the best known sequence (a particular randomization of the Sobol’ (0,2) sequence). The sample points are generated using a simple, diagonally alternating strategy that progressively fills in holes in increasingly fine stratifications. The sequences are progressive (hierarchical): any prefix is well distributed, making them suitable for incremental rendering and adaptive sampling. The first sample family is only jittered in 2D; we call it progressive jittered. It is nearly identical to existing sample sequences. The second family is multi‐jittered: the samples are stratified in both 1D and 2D; we call it progressive multi‐jittered. The third family is stratified in all elementary intervals in base 2, hence we call it progressive multi‐jittered (0,2). We compare sampling error and convergence of our sequences with uniform random, best candidates, randomized quasi‐random sequences (Halton and Sobol'), Ahmed's ART sequences, and Perrier's LDBN sequences. We test the sequences on function integration and in two settings that are typical for computer graphics: pixel sampling and area light sampling. Within this new framework we present variations that generate visually pleasing samples with blue noise spectra, and well‐stratified interleaved multi‐class samples; we also suggest possible future variations.
The rise of Newtonian drops in a nematic liquid crystal

We simulate the rise of Newtonian drops in a nematic liquid crystal parallel to the far-field molecular orientation. The moving interface is computed in a diffuse-interface framework, and the anisotropic rheology of the liquid crystal is represented by the Leslie–Ericksen theory, regularized to permit topological defects. Results reveal interesting coupling between the flow field and the orientational field surrounding the drop, especially the defect configuration. The flow generally sweeps the point and ring defects downstream, and may transform a ring defect into a point defect. The stability of these defects and their transformation are depicted in a phase diagram in terms of the Ericksen number and the ratio between surface anchoring and bulk elastic energies. The nematic orientation affects the flow field in return. Drops with planar anchoring on the surface rise faster than those with homeotropic anchoring, and the former features a vortex ring in the wake. These are attributed to the viscous anisotropy of the nematic. With homeotropic anchoring, the drop rising velocity experiences an overshoot, owing to the transformation of the initial surface ring defect to a satellite point defect. With both types of anchoring, the drag coefficient of the drop decreases with increasing Ericksen number as the flow-alignment of the nematic orientation reduces the effective viscosity of the liquid crystal.
A Range-Based Approach for Long-Term Forecast of Weather Using Probabilistic Markov Model

Weather forecasts serve to incline individual behaviors and interactions, commercial intentions and organizational efforts. A normal user is usually indifferent to weather statistics and corresponding value predictions but obtains an approximate idea from the average weather conditions. Forecasts justifying overall conditions for a duration which is usually rely on previous observations. Correspondingly, they extend the probability of inducing incorrect predictions as relatively insignificant variations consequently compound to substantial errors. As such, long term predictions are usually limited and unreliable. This paper aims to bridge this gap, by adopting a range specific approach to a probabilistic markov model (PMM). To develop a certainty in availability, we employ a cloud server to house for the analytics. We have achieved a considerable rise in accuracy in the results, along with a simplistic convenience for the user as compared to other available state-of- the-art methods.
Iodine speciation in rain, snow and aerosols

Iodine oxides, such as iodate, should be the only thermodynamically stable sink species for iodine in the tro- posphere. However, field observations have increasingly found very little iodate and significant amounts of iodide and soluble organically bound iodine (SOI) in precipitation and aerosols. The aim of this study was to investigate io- dine speciation, including the organic fraction, in rain, snow, and aerosols in an attempt to further clarify aqueous phase iodine chemistry. Diurnal aerosol samples were taken with a 5 stage cascade impactor and a virtual impactor (PM2.5) from the Mace Head research station, Ireland, during sum- mer 2006. Rain was collected from Australia, New Zealand, Patagonia, Germany, Ireland, and Switzerland and snow was obtained from Greenland, Germany, Switzerland, and New Zealand. Aerosols were extracted from the filters with water and all samples were analysed for total soluble iodine (TSI) by inductively coupled plasma mass spectrometry (ICP-MS) and iodine speciation was determined by coupling an ion chromatography unit to the ICP-MS. The median concentra- tion of TSI in aerosols from Mace Head was 222 pmol m 3 (summed over all impactor stages) of which the majority was associated with the SOI fraction (median day: 90±4%, night: 94±2% of total iodine). Iodide exhibited higher concen- trations than iodate (median 6% vs. 1.2% of total iodine), and displayed significant enrichment during the day com- pared to the night. Interestingly, up to 5 additional, presum- ably anionic iodo-organic peaks were observed in all IC-ICP- MS chromatograms, composing up to 15% of the TSI. Sol- uble organically bound iodine was also the dominant frac- tion in all rain and snow samples, with lesser amounts of iodide and iodate (iodate was particularly low in snow). Two of the same unidentified peaks found in aerosols were also observed in precipitation from both Southern and Northern Hemispheres. This suggests that these species are transferred from the aerosols into precipitation and that they have ei- ther a relatively long lifetime or are rapidly recycled. It is thought that SOI is formed by reactions between HOI or I2 and organic matter derived from the ocean surface layer. SOI may then photolytically decompose to yield iodide and the unidentified species. The data in this study show that io- dine oxides are the least abundant species in rain, snow, and aerosols and therefore considerably more effort is required on aqueous phase iodine chemistry for a holistic understand- ing of the iodine cycle.
Utilization of Data Mining Classification Approach for Disease Prediction: A Survey

Early diagnosis of a disease is a vital task in medical informatics. Data mining is one of the principal contributors in this discipline. Utilization of Data Mining Technology in Disease Forecasting System is a recognized trend and is successfully emerging in this domain. In today`s world, Heart Disease is the one of the most prevalent disease among people with a high mortality rate. It is essential to classify the reports of heart patients into correct subclasses to lower fatality rate. Over the years, Data mining classification and prediction approaches has been used extensively for disease prediction. This paper comes out with the compilation, analysis as well as comparative study of numerous classification approaches used for predictive analysis of several diseases. The goal of the survey is to provide a comprehensive review of the work done on disease prediction using different classification approaches in data mining.
MODEL FOR SOFTWARE TESTING AND QUALITY ASSESSMENT USING CLASSIFICATION APPROACH 1

Defect in software systems continue to be a major problem. High quality of software is ensured by Software reliability and Software quality assurance. A software defect causes software failure in an executable product. A variety of software fault predictions techniques have been proposed, but none has proven to be consistently accurate. The objective in the construction of models of software error prediction is to use measures that may be obtained relatively early in the software development life cycle to provide reasonable initial estimates of quality of an evolving software system. Here various data mining classification and prediction techniques viz. Neural Network (NN), Decision tree (DT), Support Vector Machine (SVM),K-Nearest Neighbour(k-NN) and Naïve Bayes (NB) have beenanalysed and compared for software defect prediction model development. Further, stacking, bagging and boosting, a meta modelling techniques in order to enhance the accuracy of the classification techniques have also been used. For this DATATRIEVETM project carried out at Digital Engineering, Italy has been used to validate the algorithm. The results showed that model using NN classification technique was a better prediction model.
Improved Layered Architecture for Internet of Things

The internet of things (IoT) is a concept that has a role in facilitating people lives through the use of tablets, smartphones, PCs and other devices connected to the internet. Knowing that the number of people using connected devices on the internet increases every year dramatically. Also, internet of things (IoT) provides a vision of the future internet where connecting physical things; from backnotes to bicycles through a network letting them take an active part in the Internet and exchanging information about themselves and their surroundings, which gives immediate access to information about the physical world, leading to innovative services and increasing efficiency and productivity. This paper proposes an improved architecture to the internet of things (IoT) that takes into consideration the environment surrounding the devices, including the objects to be detected or the places to observed, and other factors affecting the sensing operation. Finally, conclusions are made and future work is discussed briefly.