Miracle-dz/UniFAR-FTUs huggingface dataset:
Chronic obstructive pulmonary disease exacerbation: A single-center perspective on hospital readmissions

Abstract Background. Chronic obstructive pulmonary disease (COPD) is the third most common cause in the United States of hospital readmission within 30 days of discharge. Readmissions, which are attributed to poor quality of care, are costly. We examined the factors associated with 30-day readmission in patients hospitalized with acute exacerbation of COPD. Our hypothesis was that early readmissions among patients with COPD are related to patient factors rather than system or provider factors. Methods. We performed a retrospective chart review of all patients discharged from our facility from June 2010 to May 2011 with a primary discharge diagnosis of COPD. Detailed patient characteristics were obtained from the electronic medical record. Patients were followed for 30 days post–discharge date. We examined the differences in baseline characteristics of patients readmitted within 30 days and those not readmitted. Results. A total of 160 patients were admitted for 192 hospitalizations during the study period; 31 patients (19.4%) were readmitted within 30 days. Patients who were readmitted did not differ from those who were not readmitted of the following factors: baseline medication use, length of stay, and outpatient follow-up postdischarge. Readmitted patients were more likely to be black, to have coronary artery disease, to have a history of alcohol abuse, and to be on supplemental oxygen. Multivariate analysis showed a 2.17 odds of 30-day readmission (95% CI, 1.16–4.09) in patients with alcohol abuse, and 2.52 (95% CI, 1.18–5.38) in those on supplemental oxygen. Conclusion. In our study population, 19.4% of acute exacerbation COPD patients were readmitted within 30 days. Patient factors (such as alcohol abuse and advanced disease) were associated with 30-day readmission.
Tracking multiple three-dimensional motions by using modified condensation algorithm and multiple images

In this paper we present a system for tracking objects' 3D motions by using sampling algorithm. The system consists of three cameras and 1 PC. We modified the condensation algorithm [M.A. Isard et al., 1998] to track objects' 3D positions. The condensation algorithm is a kind of "factored sampling" and it can easily cope with multi-modal probability density of objects' positions. We track objects not in images but in 3D space. We use a condensation particle-set as a tracker and represent a position of a moving object by a center of tracker. Since we don't track directly in images, a great advantage of this method is not to need to deal with occlusions or objects' mutual interferences which happen in images.
Vibrio effector protein VopQ inhibits fusion of V-ATPase–containing membranes

Significance Fusion of intracellular membranes is involved in many critical cellular processes, such as neurotransmission, protein trafficking, and in the lysosomal degradation of invading bacterial pathogens. Accordingly, some intracellular bacterial pathogens use protein effectors to alter host membrane fusion directly as a survival mechanism. In this study, we show that the Vibrio secreted effector, VopQ, is a potent inhibitor of yeast homotypic vacuole fusion in vitro. Although VopQ was shown to deacidify yeast vacuoles via its known V-type H+-ATPase (V-ATPase)-binding and channel-forming activities, its ability to inhibit vacuole fusion does not depend on channel-forming activity. Our studies suggest that yeast vacuole fusion is not regulated by lumenal acidification and identify a reagent to study the V-ATPase role in some membrane fusion events. Vesicle fusion governs many important biological processes, and imbalances in the regulation of membrane fusion can lead to a variety of diseases such as diabetes and neurological disorders. Here we show that the Vibrio parahaemolyticus effector protein VopQ is a potent inhibitor of membrane fusion based on an in vitro yeast vacuole fusion model. Previously, we demonstrated that VopQ binds to the Vo domain of the conserved V-type H+-ATPase (V-ATPase) found on acidic compartments such as the yeast vacuole. VopQ forms a nonspecific, voltage-gated membrane channel of 18 Å resulting in neutralization of these compartments. We now present data showing that VopQ inhibits yeast vacuole fusion. Furthermore, we identified a unique mutation in VopQ that delineates its two functions, deacidification and inhibition of membrane fusion. The use of VopQ as a membrane fusion inhibitor in this manner now provides convincing evidence that vacuole fusion occurs independently of luminal acidification in vitro.
Overview of research on facial ageing using the FG-NET ageing database

The face and gesture recognition network (FG-NET) ageing database was released in 2004 in an attempt to support research activities aimed at understanding the changes in facial appearance caused by ageing. Since then the database was used for carrying out research in various disciplines including age estimation, age-invariant face recognition and age progression. On the basis of the analysis of published work where the FG-NET ageing database was used, conclusions related to the type of research carried out in relation to the impact of the dataset in shaping up the research topic of facial ageing are presented. This study also includes a review of key articles from different thematic areas, where the FG-NET ageing database was used and the presentation of benchmark results. The ultimate aims of this study are to present concrete facts related to research activities in facial ageing during the past decade, provide an indication of the main methodologies adopted, present a comprehensive list of benchmark results and most importantly provide roadmaps for future trends, requirements and research directions in facial ageing.
Fast Residual Forests: Rapid Ensemble Learning for Semantic Segmentation

In recent times, Convolutional Neural Network (CNN) based approaches have performed exceptionally well in many computer vision related tasks, including classification and segmentation. These approaches have shown that given enough training data and time, they can often perform at a level significantly higher than the alternative methods. However, in the context of robotic learning, it is commonly the case that both time and training data are limited. In this work, we propose a learning approach that is more suitable for robotic learning; it substantially reduces the time required to learn and provides much higher performance when training data is limited. Our method combines random forests with deep convolution networks, leveraging the strengths of both frameworks. We develop a method for generating derivatives from our highly non-linear forest classifier which in turn enables training of the CNN. Furthermore, our method allows leaf distributions in the ensemble classifier to be trained jointly with one another using Stochastic Gradient Descent (SGD), allowing for parallel training of a large number of tree classifiers at once. This results in a drastic increase in training speed. Our model demonstrates significant performance improvements over pure deep learning methods, notably on datasets with limited training data. We apply our method to the outdoor and indoor segmentation datasets of KITTI and NYUv2-40, outperforming multiple pure deep learning methods whilst using a fraction of training time normally required.
N-type doped germanium contact resistance extraction and evaluation for advanced devices

The authors extract contact resistivity of NiGe layers on phosphorus-doped and arsenic-doped germanium, using the Transfer Length Method. It is shown experimentally that higher implant dose yields lower contact resistivity. Furthermore phosphorus is a better choice of dopant in terms of contact resistance and sheet resistance at low activation anneal temperatures, such as 500 °C. The impact of high contact resistance is evaluated for 22 nm technology NMOS germanium devices and beyond.
Determinants of transparency and disclosure – evidence from post-transition economies

Abstract In this paper, we examine the patterns of behaviour of companies from former socialist countries related to the application of good corporate governance practices. We try to assess the level of transparency and to determine if there are any factors that systematically influence corporate behaviour in this regard. Using a sample of 145 companies from Croatia, Macedonia, Slovenia and Serbia, we apply Standard & Poor’s (S&P) methodology for assessment of transparency and disclosure levels and find that the companies in these countries generally lag in terms of transparency behind their peers worldwide as measured one decade ago. Additionally, using the same sample, we apply a regression analysis and conclude that the level of transparency is positively related to the size of the company and the need for external financing, but negatively to the concentration of ownership and we also observe important country effects. We do not find a statistically significant relationship between transparency and profitability and relate this finding with the prevailing attitude of the companies towards the stock market. Having in mind the different scores by country and by area of disclosure, we believe that there is still scope for improvement using proper advising and public policy measures.
The SpeeD Grammar-based ASR System for the Romanian Language

This paper describes the grammar-based automatic speech recognition system for the Romanian language developed by the Speech and Dialogue Research Group. The paper links to previous work for the issues related to large vocabulary speech recognition and focuses on the speciﬁc optimization work done for several closed-vocabulary, grammar-based speech recognition tasks. Among the speciﬁc problems approached, of particular interest is the informal pronunciation modelling of Romanian two-digit numbers. The paper proposes solutions for within-word and cross-word pronunciation modelling of numbers and reports signiﬁcant relative improvements of the speech recognition word error rates.
Design aspects of multicore optical fibers for high-capacity long-haul transmission

Recent progress in a single-mode multicore fiber for high-capacity long-haul space-division multiplexing (SDM) transmission is reviewed, focusing on the design issues such as crosstalk suppression, core-density improvement, and core-arrangement optimization. A few-mode multicore fiber for dense SDM transmission is also briefly described.
Influence of Current Direction on AC Losses of a Multi-Wire YBCO-NiW Conductor With Horizontal Arrangement

Multi-wire conductors consisting of YBCO wires with magnetic substrate are usually applied in the high temperature superconductor (HTS) applications. In these applications, ac loss is a critical factor to determine the capacity of a refrigerator. And the ac losses of YBCO-NiW wires will be affected by the magnetic substrate. In this work, the self-field loss of the YBCO wire with the magnetic substrate is firstly measured, and compared with the numerical data. Moreover, the ac losses of a multi-wire YBCO-NiW conductor consisting of three wires are investigated for three transport modes. The main results show that the measured self-field loss of the wire is identical with the numerical ones. Accordingly, the reliability of the experimental and numerical methods is verified. Also, the ac losses of the conductor are in good agreement with the numerically calculated losses. As for the multi-wire conductor's total losses, the change of the direction of current in wires will not have an apparent influence on the total ac losses.
The infection of mosquitoes by Plasmodium vivax (Chesson strain) during the early primary parasitemias.

Investigations reported by Eyles, Young and Burgess demonstrated the infection of mosquitoes by Plasmodium vivax during asymptomatic parasitemias. Although they found infections significantly lower in asymptomatic cases than in symptomatic, their results indicated that vivax malaria patients are potentially infective whenever parasites are present. These investigations were limited to certain asymptomatic parasitemias other than those present in an early primary infection. The purpose of the present report is to describe findings during these early primary parasitemias. were it was necessary to interrupt the clinical attack mosquitoes. In our experience the effect of the better suppressives on gametocyte viability and density is variable.
Cornell Scale for Depression in Dementia: Clinical utility in a memory clinic

Abstract Objective. Both dementia and depression may produce complaints of memory impairment. Differential diagnosis may be difficult, but has practical implications regarding the choice of appropriate treatment. The clinical utility of the Cornell Scale for Depression in Dementia (CSDD), a validated instrument for the diagnosis of depression in patients both with and without dementia, was assessed for the differentiation of patients with and without dementia who had been referred with complaints of memory impairment to a dedicated memory clinic. Methods. Pragmatic prospective study of consecutive referrals with a suitable informant (n = 242) to a memory clinic, diagnosed using standard clinical diagnostic criteria for dementia (DSM-IV) and also administered the CSDD. Results. Using traditional parameters of diagnostic utility (sensitivity, specificity, positive predictive value, likelihood ratios) CSDD performance was good for the diagnosis of depression but only modest for the diagnosis of dementia. Conclusions. CSDD proved useful as a brief screen for depression in patients attending a memory clinic with a complaint of memory impairment.
Self-calibration for IGBT junction temperature measurement in power converter

Junction temperature is an important condition variable for IGBT and can be indirectly measured by the Thermo-Sensitive Electrical Parameter (TSEP). However, it is difficult to find the relationship between TSEP and device junction temperature in a real power converter system, which is essential for accurate measurement. In this paper, a self-calibration method is proposed to solve this problem. During calibration, the TSEP (e.g. dv/dt during IGBT turn-off) is measured under different operation conditions (e.g. temperature and current) by a specially programmed self-heating and on-line double-pulse testing process. By taking use of the existing hardware and sensors in a converter system, the calibration test can be conducted on-site with no need of hardware modification. Experimental work was carried out in the 3-phase voltage source inverter (VSI) drive system test rigs and the results validate the feasibility and effectiveness of the method.
An Optical Method for Pretilt and Profile Determination in LCOS VAN Displays

Pretilt angle and cell thickness are two extremely important parameters in predicting the behavior of vertically aligned negative nematic liquid crystal (LC) displays. The accurate estimation of pretilt angle and cell thickness is not a trivial task when these devices work in reflective mode, as in liquid crystal on silicon (LCOS) vertically aligned nematic (VAN) displays. Usual experimental setups are based on the proportionality between the retardation of the polarization components of the incident light and the product effective birefringence times thickness. However, any attempt to separate the two product variables is cancelled out by symmetry from reflection. This work shows a relatively simple method capable of separating both variables, allowing accurate, independent measurements of pretilt and thickness, as well as other configurations details, such as residual twist. A simulation model based on the properties of actual reflective displays has been developed. An experimental setup specifically designed for measuring LCOS VAN cells has been prepared. Initial comparisons between experimental measurements of intensity and theoretical results showed some discrepancies that could be explained assuming that the LC profile contains a residual twist from the manufacturing process. Including that twist in the model, an excellent agreement between theory and experiment has been achieved. Matching simulations and experimental results yield separate determinations of pretilt angle and thickness, and give good estimates for the residual twist angle.
Camera stability analysis and geo-referencing

In photogrammetry, the fundamental objective is to generate reliable and accurate three-dimensional information from two-dimensional imagery. In order to generate such information, the internal metric characteristics of cameras that are implemented in photogrammetric applications have to be modeled and carefully estimated. These internal characteristics are customarily known as the Interior Orientation Parameters (IOP). Once the IOP are estimated, they should not significantly change over time and must be reliable. Hence, it would be beneficial to establish a meaningful procedure to analyze the stability of the IOP from a photogrammetric point of view. The paper will introduce three quantitative methods for testing camera stability, where the degree of similarity between reconstructed bundles from two sets of IOP is evaluated. Each of these three methods of stability analysis limits the position and orientation of the bundles in a different way. Hence, this paper will prove that each method will be applicable for a specific georeferencing technique depending on similar constraints imposed by the stability methods and different geo-referencing techniques. Keywords-photogrammetry, digital cameras, interior orientation parameters, camera stability, geo-referencing
An Inertial Pen With Dynamic Time Warping Recognizer for Handwriting and Gesture Recognition

This paper presents an inertial-sensor-based digital pen (inertial pen) and its associated dynamic time warping (DTW)-based recognition algorithm for handwriting and gesture recognition. Users hold the inertial pen to write numerals or English lowercase letters and make hand gestures with their preferred handheld style and speed. The inertial signals generated by hand motions are wirelessly transmitted to a computer for online recognition. The proposed DTW-based recognition algorithm includes the procedures of inertial signal acquisition, signal preprocessing, motion detection, template selection, and recognition. We integrate signals collected from an accelerometer, a gyroscope, and a magnetometer into a quaternion-based complementary filter for reducing the integral errors caused by the signal drift or intrinsic noise of the gyroscope, which might reduce the accuracy of the orientation estimation. Furthermore, we have developed a minimal intra-class to maximal inter-class based template selection method (min-max template selection method) for a DTW recognizer to obtain a superior class separation for improved recognition. Experimental results have successfully validated the effectiveness of the DTW-based recognition algorithm for online handwriting and gesture recognition using the inertial pen.
The Rate of Entropy for Gaussian Processes

In this paper, we show that in order to obtain the Tsallis entropy rate for stochastic processes, we can use the limit of conditional entropy, as it was done for the case of Shannon and Renyi entropy rates. Using that we can obtain Tsallis entropy rate for stationary Gaussian processes. Finally, we derive the relation between Renyi, Shannon and Tsallis entropy rates for stationary Gaussian processes.
Balancing control method of a microgrid with intermittent renewable energy generators and small battery storage

A cooperative control system for gas engine generators and a lead-acid battery in a microgrid was developed. The control system enables a microgrid system to balance the electric power demand and supply and to simultaneously control the state of charge (SOC) of the battery. To evaluate this control system, we simulated its performance in balancing the control of an experimental microgrid facility with two reciprocating engine generators for a day in which large fluctuation in PV and wind turbine output was observed. Based on the simulation, the rated power ratio of renewable energy installed in the microgrid can be increased beyond 50%, and simultaneously the minimum feasible capacity of the battery can be reduced to the capacity which is enough to charge the rated output of the intermittent renewable energy for 15 minutes.
Latent Keyphrase Extraction Using Deep Belief Networks

Nowadays, automatic keyphrase extraction is considered to be an important task. Most of the previous studies focused only on selecting keyphrases within the body of input documents. These studies overlooked latent keyphrases that did not appear in documents. In addition, a small number of studies on latent keyphrase extraction methods had some structural limitations. Although latent keyphrases do not appear in documents, they can still undertake an important role in text mining because they link meaningful concepts or contents of documents and can be utilized in short articles such as social network service, which rarely have explicit keyphrases. In this paper, we propose a new approach that selects qualified latent keyphrases from input documents and overcomes some structural limitations by using deep belief networks in a supervised manner. The main idea of this approach is to capture the intrinsic representations of documents and extract eligible latent keyphrases by using them. Our experimental results showed that latent keyphrases were successfully extracted using our proposed method.
Effectiveness of Magnesium Citrate as Preparation for Capsule Endoscopy: A Randomized, Prospective, Open-Label, Inter-Group Trial

Background and Aim: Capsule endoscopy (CE) is effective in the detection of small bowel lesions. Many studies have been conducted on the effectiveness of preparations in improving diagnostic yield, but an effective method has yet to be confirmed. We used magnesium citrate as preparation for CE and evaluated its effectiveness. Methods: 50 patients who underwent CE were randomly allocated to two groups – group A (preparation provided) and group B (no preparation). Group A were administered 34 g of magnesium citrate at 8 p.m. the night before the examination. Group B were not administered a laxative. Results: The cleansing efficacy score was 24.4 ± 4.4 points for group A and 22.9 ± 4.4 points for group B, with no significant difference between groups. No significant difference was seen in the large bowel arrival rate between groups: 14 cases (63.6%) for group A and 16 cases (72.7%) for group B. Conclusion: In this study, superiority was not seen for cleansing efficacy, large bowel arrival rate for the preparation method involving administration of hypertonic MC solution 12 h prior to the procedure.
Multi-Target Tracking Using Hierarchical Convolutional Features and Motion Cues

In this paper, the problem of multi-target tracking with single camera in complex scenes is addressed. A new approach is proposed for multi-target tracking problem that learns from hierarchy of convolution features. First fast Region-based Convolutional Neutral Networks is trained to detect pedestrian in each frame. Then cooperate it with correlation filter tracker which learns target’s appearance from pretrained convolutional neural networks. Correlation filter learns from middle and last convolutional layers to enhances targets localization. However correlation filters fail in case of targets full occlusion. This lead to separated tracklets (mini-trajectories) problem. So a post processing step is added to link separated tracklets with minimum-cost network flow. A cost function is used, that depends on motion cues in associating short tracklets. Experimental results on MOT2015 benchmark show that the proposed approach produce comparable result against state-of-the-art approaches. It shows an increase 4.5 % in multiple object tracking accuracy. Also mostly tracked targets is 12.9% vs 7.5% against state-ofthe- art minimum-cost network flow tracker.