Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Synthesis In : Static Scan + Expression Out : Best Fitting Sequence + Angry Out : Animated Sequence Statistical Analysis Expression Recognition

This paper presents a representation of 3D facial motion sequences that allows performing statistical analysis of 3D face shapes in motion. The resulting statistical analysis is applied to automatically generate realistic facial animations and to recognize dynamic facial expressions. To perform statistical analysis of 3D facial shapes in motion over different subjects and different motion sequences, a large database of motion sequences needs to be brought in full correspondence. Existing algorithms that compute correspondences between 3D facial motion sequences either require manual input or suffer from instabilities caused by drift. For large databases, algorithms that require manual interaction are not practical. We propose an approach to robustly compute correspondences between a large set of facial motion sequences in a fully automatic way using a multilinear model as statistical prior. In order to register the motion sequences, a good initialization is needed. We obtain this initialization by introducing a landmark prediction method for 3D motion sequences based on Markov Random Fields. Using this motion sequence registration, we find a compact representation of each motion sequence consisting of one vector of coefficients for identity and a high dimensional curve for expression. Based on this representation, we synthesize new motion sequences and perform expression recognition. We show experimentally that the obtained registration is of high quality, where 56% of all vertices are at distance at most 1mm from the input data, and that our synthesized motion sequences look realistic.
Active transfer learning for multi-view head-pose classification

This paper describes an active transfer learning technique for multi-view head-pose classification. We combine transfer learning with active learning, where an active learner asks the domain expert to label the few most informative target samples for transfer learning. Employing adaptive multiple-kernel learning for head-pose classification from four low-resolution views, we show how active sampling enables more efficient learning with few examples. Experimental results confirm that active transfer learning produces 10% higher pose-classification accuracy over several competing transfer learning approaches.
Resting Physiological Correlates of Reduced Exercise Capacity in Smokers with Mild Airway Obstruction

ABSTRACT Smokers with minor spirometric abnormalities can experience persistent activity-related dyspnea and exercise intolerance. Additional resting tests can expose heterogeneous physiological abnormalities, but their relevance and association with clinical outcomes remain uncertain. Subjects included sixty-two smokers (≥20 pack-years), with cough and/or dyspnea and minor airway obstruction [forced expiratory volume in one-second (FEV1) ≥80% predicted and >5th percentile lower limit of normal (LLN) (i.e., z-score >−1.64) using the 2012-Global Lung Function Initiative equations]. They underwent spirometry, plethysmography, oscillometry, single-breath nitrogen washout, and symptom-limited incremental cycle exercise tests. Thirty-two age-matched nonsmoking controls were also studied. Thirty-three (53%) of smokers had chronic obstructive pulmonary disease by LLN criteria. In smokers [n = 62; age 65 ± 11 years; smoking history 43 ± 19 pack-years; post-bronchodilator FEV1 z-score −0.60 ± 0.72 and FEV1/FVC z-score −1.56 ± 0.87 (mean ± SD)] versus controls, peak oxygen uptake (̇VO2) was 21 ± 7 vs. 32 ± 9 ml/kg/min, and dyspnea/̇VO2 slopes were elevated (both p < 0.0001). Smokers had evidence of peripheral airway dysfunction and maldistribution of ventilation when compared to controls. In smokers versus controls: lung diffusing capacity for carbon monoxide (DLCO) was 85 ± 22 vs. 105 ± 17% predicted, and residual volume (RV)/total lung capacity (TLC) was 36 ± 8 vs. 31 ± 6% (both p < 0.01). The strongest correlates of peak ̇VO2 were DLCO% predicted (r = 0.487, p < 0.0005) and RV/TLC% (r = −0.389, p = 0.002). DLCO% predicted was also the strongest correlate of dyspnea/̇VO2 slope (r = −0.352, p = 0.005). In smokers with mild airway obstruction, associations between resting tests of mechanics and pulmonary gas exchange and exercise performance parameters were weak, albeit consistent. Among these, DLCO showed the strongest association with important outcomes such as dyspnea and exercise intolerance measured during standardized incremental exercise tests.
Residual Networks for Computer Go

Deep learning for the game of Go recently had a tremendous success with the victory of AlphaGo against Lee Sedol in March 2016. We propose to use residual networks so as to improve the training of a policy network for computer Go. Training is faster than with usual convolutional networks and residual networks achieve high accuracy on our test set and a four dan level.
"Maybe They Don't Even Know That I Exist": Challenges Faced by Family Members and Friends of Patients with Advanced Kidney Disease.

BACKGROUND AND OBJECTIVES Family members and friends of patients with advanced chronic illness are increasingly called on to assist with ever more complex medical care and treatment decisions arising late in the course of illness. Our goal was to learn about the experiences of family members and friends of patients with advanced kidney disease. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS As part of a study intended to identify opportunities to enhance advance care planning, we conducted semistructured interviews at the Veterans Affairs Puget Sound Health Care System with 17 family members and friends of patients with advanced kidney disease. Interviews were conducted between April of 2014 and May of 2016 and were audiotaped, transcribed, and analyzed inductively using grounded theory to identify emergent themes. RESULTS The following three themes emerged from interviews with patients' family members and friends: (1) their roles in care and planning were fluid over the course of the patient's illness, shaped by the patients' changing needs and their readiness to involve those close to them; (2) their involvement in patients' care was strongly shaped by health care system needs. Family and friends described filling gaps left by the health care system and how their involvement in care and decision-making was at times constrained and at other times expected by providers, depending on system needs; and (3) they described multiple sources of tension and conflict in their interactions with patients and the health care system, including instances of being pitted against the patient. CONCLUSIONS Interviews with family members and friends of patients with advanced kidney disease provide a window on the complex dynamics shaping their engagement in patients' care, and highlight the potential value of offering opportunities for engagement throughout the course of illness.
Random GUI Testing of Android Application Using Behavioral Model

Automated GUI testing based on behavioral model is one of the most efficient testing approaches. By mining user usage, test scenarios can be generated based on statistical models such as Markov chain. However, these works require static analysis before starting the exploration which requires too much prerequisites and time. To address these challenges, we propose a behavioral-based GUI testing approach for mobile applications that achieves faster and higher coverage. The proposed approach does not conduct static analysis. It creates a behavioral model from usage logs by applying a statistical model. The events within the behavioral model are mapped to GUI components in a GUI tree. Finally, it updates the model dynamically to increase the probability of an event that rarely or never occurs when users use the application. The proposed approach was evaluated on four open-source Android applications, and compared with the state-of-the-art tools and manual testing. The main evaluation criteria are code coverag.
Bioinformatics Analysis and Characterization of Highly Efficient Polyvinyl Alcohol (PVA)-Degrading Enzymes from the Novel PVA Degrader Stenotrophomonas rhizophila QL-P4

ABSTRACT Polyvinyl alcohol (PVA) is used widely in industry, and associated environmental pollution is a serious problem. Herein, we report a novel, efficient PVA degrader, Stenotrophomonas rhizophila QL-P4, isolated from fallen leaves from a virgin forest in the Qinling Mountains. The complete genome was obtained using single-molecule real-time (SMRT) technology and corrected using Illumina sequencing. Bioinformatics analysis revealed eight PVA/vinyl alcohol oligomer (OVA)-degrading genes. Of these, seven genes were predicted to be involved in the classic intracellular PVA/OVA degradation pathway, and one (BAY15_3292) was identified as a novel PVA oxidase. Five PVA/OVA-degrading enzymes were purified and characterized. One of these, BAY15_1712, a PVA dehydrogenase (PVADH), displayed high catalytic efficiency toward PVA and OVA substrate. All reported PVADHs only have PVA-degrading ability. Most importantly, we discovered a novel PVA oxidase (BAY15_3292) that exhibited higher PVA-degrading efficiency than the reported PVADHs. Further investigation indicated that BAY15_3292 plays a crucial role in PVA degradation in S. rhizophila QL-P4. Knocking out BAY15_3292 resulted in a significant decline in PVA-degrading activity in S. rhizophila QL-P4. Interestingly, we found that BAY15_3292 possesses exocrine activity, which distinguishes it from classic PVADHs. Transparent circle experiments further proved that BAY15_3292 greatly affects extracellular PVA degradation in S. rhizophila QL-P4. The exocrine characteristics of BAY15_3292 facilitate its potential application to PVA bioremediation. In addition, we report three new efficient secondary alcohol dehydrogenases (SADHs) with OVA-degrading ability in S. rhizophila QL-P4; in contrast, only one OVA-degrading SADH was reported previously. IMPORTANCE With the widespread application of PVA in industry, PVA-related environmental pollution is an increasingly serious issue. Because PVA is difficult to degrade, it accumulates in aquatic environments and causes chronic toxicity to aquatic organisms. Biodegradation of PVA, as an economical and environment-friendly method, has attracted much interest. To date, effective and applicable PVA-degrading bacteria/enzymes have not been reported. Herein, we report a new efficient PVA degrader (S. rhizophila QL-P4) that has five PVA/OVA-degrading enzymes with high catalytic efficiency, among which BAY15_1712 is the only reported PVADH with both PVA- and OVA-degrading abilities. Importantly, we discovered a novel PVA oxidase (BAY15_3292) that is not only more efficient than other reported PVA-degrading PVADHs but also has exocrine activity. Overall, our findings provide new insight into PVA-degrading pathways in microorganisms and suggest S. rhizophila QL-P4 and its enzymes have the potential for application to PVA bioremediation to reduce or eliminate PVA-related environmental pollution.
Mesenchymal stem cell derived secretome and extracellular vesicles for acute lung injury and other inflammatory lung diseases

ABSTRACT Introduction: Acute respiratory distress syndrome is a major cause of respiratory failure in critically ill patients. Despite extensive research into its pathophysiology, mortality remains high. No effective pharmacotherapy exists. Based largely on numerous preclinical studies, administration of mesenchymal stem or stromal cell (MSC) as a therapeutic for acute lung injury holds great promise, and clinical trials are currently underway. However, concern for the use of stem cells, specifically the risk of iatrogenic tumor formation, remains unresolved. Accumulating evidence now suggest that novel cell-free therapies including MSC-derived conditioned medium and extracellular vesicles released from MSCs might constitute compelling alternatives. Areas covered: The current review summarizes the preclinical studies testing MSC conditioned medium and/or MSC extracellular vesicles as treatment for acute lung injury and other inflammatory lung diseases. Expert opinion: While certain logistical obstacles limit the clinical applications of MSC conditioned medium such as the volume required for treatment, the therapeutic application of MSC extracellular vesicles remains promising, primarily due to ability of extracellular vesicles to maintain the functional phenotype of the parent cell. However, utilization of MSC extracellular vesicles will require large-scale production and standardization concerning identification, characterization and quantification.
Learning Document Semantic Representation with Hybrid Deep Belief Network

High-level abstraction, for example, semantic representation, is vital for document classification and retrieval. However, how to learn document semantic representation is still a topic open for discussion in information retrieval and natural language processing. In this paper, we propose a new Hybrid Deep Belief Network (HDBN) which uses Deep Boltzmann Machine (DBM) on the lower layers together with Deep Belief Network (DBN) on the upper layers. The advantage of DBM is that it employs undirected connection when training weight parameters which can be used to sample the states of nodes on each layer more successfully and it is also an effective way to remove noise from the different document representation type; the DBN can enhance extract abstract of the document in depth, making the model learn sufficient semantic representation. At the same time, we explore different input strategies for semantic distributed representation. Experimental results show that our model using the word embedding instead of single word has better performance.
Observation results of actual phase defects using micro coherent EUV scatterometry microscope

One of the critical issue of EUV lithography is fabrication of defect-free mask. The origin of the defect is a particle inside the multilayer and bump or pit on glass substrate. This type of defect is called a phase defect. If there is a phase defect, the reflection phase is disordered. As a result, the phase structure is printed as a defect on a wafer. Thus, we have developed micro coherent EUV scatterometry microscope (we called micro-CSM) for phase defect characterization. Micro-CSM records scattering signal from a defect directly exposed by focused coherent EUV having a spot size of φ140-nm in diameter. An off-axis-type Fresnel zone plate was employed as a focusing optics. Phase distribution of the defect is reconstructed with the scattering image by the coherent-diffraction-imaging method. We observed actual phase defects in this work. Actual phase defects were on a mask blanks which was the same grade of the pre-production mask of the semiconductor devices. The positions of actual phase defects have been already inspected by the actinic blank inspection tool. And, the actual phase defects have been already observed using an atomic force microscope. A purpose of this work is observation of these actual defects using micro-CSM and comparison of the results.
Enhanced optical transmission of the triple-layer resonant waveguide structure

In this paper, we propose and demonstrate a novel guided-mode resonant filter based on a metallic grating sandwiched between two dielectric layers for TE polarization. A theoretical model based on rigorous coupled wave analysis has been developed. The transmission spectra of the grating-based structure show a high transmission band with the corresponding amplitude up to 70% inside the infra-red region. Moreover, the observed optical properties can be exactly tuned by the structural parameters. These properties allow using such structures as compact optical filters, and their spectral characteristics can be easily tuned and scaled.
Anaerobic microbial dehalogenation and its key players in the contaminated Bitterfeld‐Wolfen megasite

Abstract The megasite Bitterfeld‐Wolfen is highly contaminated as a result of accidents and because of dumping of wastes from local chemical industries in the last century. A variety of contaminants including chlorinated ethenes and benzenes, hexachlorohexanes and chlorinated dioxins can still be found in the groundwater and (river) sediments. Investigations of the in situ microbial transformation of organohalides have been performed only over the last two decades at this megasite. In this review, we summarise the research on the activity of anaerobic dehalogenating bacteria at the field site in Bitterfeld‐Wolfen, focusing on chlorinated ethenes, monochlorobenzene and chlorinated dioxins. Various methods and concepts were applied including ex situ cultivation and isolation, and in situ analysis of hydrochemical parameters, compound‐specific stable isotope analysis of contaminants, 13C‐tracer studies and molecular markers. Overall, biotransformation of organohalides is ongoing at the field site and Dehalococcoides mccartyi species play an important role in the detoxification process in the Bitterfeld‐Wolfen region. Figure. No Caption available.
A STUDY ON THE ALUMINIZING OF ELECTRODEPOSITED NICKELAT 500OC

In this research, aluminizing behavior of ultra fine-grained nickel was investigated. For this purpose, nanocrystalline nickel samples with the grain size of ~25 nm were prepared via direct current electrodeposition and aluminized for different durations by pack cementation method at 500 oC. The aluminide layers were examined by means of SEM, EDS and XRD techniques. According to results, short time aluminizing resulted in the formation of a single aluminide layer whereas at long duration two distinct aluminide layers were formed. The growth kinetics of the coating was non-parabolic at short times while it obeyed the parabolic law at long duration. The parabolic growth rate constant of single phase coating formed on electrodeposited samples was about 30 ìm / h1/2 approximately 3 times greater than the data reported for coarse grained nickel (8.4 ìm / h1/2). Meanwhile, the overall growth rate constant was decreased to 11.7 ìm / h1/2, when double aluminide layers formed on nanocrystalline nickel.
RSS-based respiratory rate monitoring using periodic Gaussian processes and Kalman filtering

In this paper, we propose a method for respiratory rate estimation based on the received signal strength of narrowband radio frequency transceivers. We employ a state-space formulation of periodic Gaussian processes to model the observed variations in the signal strength. This is then used in a Rao-Blackwellized unscented Kalman filter which exploits the linear substructure of the proposed model and thereby greatly improves computational efficiency. The proposed method is evaluated on measurement data from commercially available off the shelf transceivers. It is found that the proposed method accurately estimates the respiratory rate and provides a systematic way of fusing the measurements of asynchronous frequency channels.
Stock price prediction using data analytics

Accurate financial prediction is of great interest for investors. This paper proposes use of Data analytics to be used in assist with investors for making right financial prediction so that right decision on investment can be taken by Investors. Two platforms are used for operation: Python and R. various techniques like Arima, Holt winters, Neural networks (Feed forward and Multi-layer perceptron), linear regression and time series are implemented to forecast the opening index price performance in R. While in python Multi-layer perceptron and support vector regression are implemented for forecasting Nifty 50 stock price and also sentiment analysis of the stock was done using recent tweets on Twitter. Nifty 50 (ANSEI) stock indices is considered as a data input for methods which are implemented. 9 years of data is used. The accuracy was calculated using 2–3 years of forecast results of R and 2 months of forecast results of Python after comparing with the actual price of the stocks. Mean squared error and other error parameters for every prediction system were calculated and it is found that feed forward network only produces 1.81598342% error when opening price of stock is forecasted using it.
Rathayibacter agropyri (non O'Gara 1916) comb. nov., nom. rev., isolated from western wheatgrass (Pascopyrum smithii).

Aplanobacter agropyri was first described in 1915 by O'Gara and later transferred to the genus Corynebacterium by Burkholder in 1948 but it was not included in the Approved Lists of Bacterial Names in 1980 and, consequently, is not recognized as a validly published species. In the 1980s, bacteria resembling Corynebacterium agropyri were isolated from plant samples stored at the Washington State Mycological Herbarium and from a diseased wheatgrass plant collected in Cardwell, Montana, USA. In the framework of this study, eight additional isolates were recovered from the same herbarium plant samples in 2011. The isolates are slow-growing, yellow-pigmented, Gram-stain-positive and coryneform. The peptidoglycan is type B2γ containing diaminobutyric acid, alanine, glycine and glutamic acid, the cell-wall sugars are rhamnose and mannose, the major respiratory quinone is MK-10, and the major fatty acids are anteiso-15 : 0, anteiso 17 : 0 and iso-16 : 0, all of which are typical of the genus Rathayibacter. Phylogenetic analysis of 16S rRNA gene sequences placed the strains in the genus Rathayibacter and distinguished them from the six other described species of Rathayibacter. DNA-DNA hybridization confirmed that the strains were members of a novel species. Based on phenotypic, chemotaxonomic and phylogenetic characterization, it appears that strains CA-1 to CA-12 represent a novel species, and the name Rathayibacter agropyri (non O'Gara 1916) comb. nov., nom. rev. is proposed; the type strain is CA-4T (=DSM 104101T;=ATCC TSD-78T).
A Notation for Modeling Jason-Like BDI Agents

The design and development of a large Multi Agent System (MAS) is a complex and difficult activity where a proper modeling notation may offer a significant contribution to the formulation of the best solution. The support provided by a specific CASE tool can significantly contribute to make the chosen approach technically valid and it is also a fundamental element of a feasible development strategy. The present work reports a UML profile and the related graphical notation for describing a MAS based on the Jason meta model. Moreover a specific CASE tool has been developed for supporting MASs design and automatic code generation. The proposed notation is shown in details using a classical example from the Jason tutorial (domestic robot).
Revival of filtering based SLAM? Exactly sparse delayed state filter on Lie groups

Simultaneous localization and mapping (SLAM) is a core element of every autonomous mobile robot. The underlying engine of a SLAM system is its back-end, which aims at optimally estimating the trajectory and map of the environment based on sensor data abstractions. Over the past decade, SLAM solutions based on graph optimization approaches prevailed over the filtering based solutions, since they dominated in performance over a wider range of applications. In this paper we propose a novel filtering based SLAM back-end based on the exactly sparse delayed state filter (ESDSF) derived on Lie groups (LG-ESDSF). The proposed filter retains all the good characteristics of the classic ESDSF, but also respects the state space geometry by employing filtering equations directly on Lie groups. We have compared our SLAM system with two current state-of-the-art SLAM solutions, namely ORB-SLAM and LSD-SLAM, on the KITTI vision benchmark suite. Test results show that the proposed SLAM based on the LG-ESDSF back-end can achieve same level of accuracy as the methods based on the graph optimization techniques, while maintaining lower computation times.