Miracle-dz/UniFAR-FTUs huggingface dataset:
DYAN-BicycleGAN: A NEURAL NETWORK FOR VIDEO PREDICTION

With the rapid development of neural networks and computer vision, video prediction has become a topic of interest. Inspired by the GAN related image-to-image translation network, this work addresses the challenge by modeling the dynamic information in a video sequence into the latent space and inferring the next frames with a domain-to-domain network. In this thesis, a novel video prediction network is presented. This DYAN-BicycleGAN hybrid model is able to predict the frames with the same length as input in one shot. The results show that the model can predict the dynamic information in videos and generate realistic frames.
Efficiency of microbial fuel cells based on the sulphate-reduction by ethanol

The influence of hydraulic retention time (HRT) on the rate of sulphate-reduction and electricity generation of microbial fuel cell fed with ethanol was studied. The experiments were performed in the laboratory installation, consisting of an anaerobic fixed-bed reactor and a microbial fuel cell with air-cathode. An effective sulphate removal was achieved at hydraulic retention times 44 and 66 h. The effectiveness of COD removal raises with increasing the hydraulic retention time. The highest maximum power density of 258 mW/m2 was obtained at 22 h HRT. The use of ethanol as an electron donor had a great impact on the composition of the microbial community. The metagenomic data obtained showed that the most abundant phylum in bacterial community was Proteobacteria – 87.8 %, and particularly Gammaproteobacteria – 53,1%. The sulphate reducing bacteria that can incompletely oxidise organic compounds usually with acetate as an end product were presented in the microbial community in anodic chamber. The dominant microbial species among sulphate reducing bacteria was Desulfomicrobium mexicanus (19,79%).
Regulates Suture Stability in the Zebrafish Lens.

P URPOSE . To investigate the roles of Aquaporin 0a (Aqp0a) and Aqp0b in zebraﬁsh lens development and transparency. M ETHODS . CRISPR/Cas9 gene editing was used to generate loss-of-function deletions in zebraﬁsh aqp0a and/or aqp0b . Wild type (WT), single mutant, and double mutant lenses were analyzed from embryonic to adult stages. Lens transparency, morphology, and growth were assessed. Immunohistochemistry was used to map protein localization as well as to assess tissue organization and distribution of cell nuclei. R ESULTS . aqp0a (cid:2) / (cid:2) and/or aqp0b (cid:2) / (cid:2) cause embryonic cataracts with variable penetrance. While lenses of single mutants of either gene recover transparency in juveniles, double mutants consistently form dense cataracts that persist in adults, indicating partially redundant functions. Double mutants also reveal redundant Aqp0 functions in lens growth. The nucleus of WT lenses moves from the anterior pole to the lens center with age. In aqp0a (cid:2) / (cid:2) mutants, the nucleus fails to centralize as it does in WT or aqp0b (cid:2) / (cid:2) lenses, and in double mutant lenses there is no consistent lens nuclear position. In addition, the anterior sutures of aqp0a (cid:2) / (cid:2) , but not aqp0b (cid:2) / (cid:2) mutants, are unstable resulting in failure of suture maintenance at older stages and anterior polar opacity. C ONCLUSIONS . Zebrafish Aqp0s have partially redundant functions, but only Aqp0a promotes suture stability, which directs the lens nucleus to centralize, failure of which results in anterior polar opacity. These studies support the hypothesis that the two Aqp0s subfunctionalized during fish evolution and that Aqp0-dependent maintenance of the anterior suture is essential for lens transparency.
Determinants of the Intention to Use Internet Banking

: The rapid development of internet and electronic business has stimulated the banking sector towards offering its services on-line. This paper examines the determinants of the individual’s intention to use internet banking. While there has been considerable research on the technology acceptance model (TAM) that predicts whether individuals will accept to use information system, the research variables are developed based on an extended ( TAM ), by incorporating two additional variables “ perceived trust “ and “ perceived self-efficacy”. The data was collected from 332 persons in Algeria through questionnaire. The hypotheses were tested using multiple linear regression to determine the effect of the variables on the intention to use internet banking. The results revealed that “ perceived usefulness “, perceived ease to use “ and “ perceived trust “ have positive significant effect, while “ perceived self-efficacy “ have no effect on the Algerians’ intention to use internet banking. They revealed also that “perceived trust” has more influence on the intention to use internet banking than the other variables. A number of recommendations
A Class of Exponential Integrators Based on Spectral Deferred Correction

We introduce a new class of arbitrary-order exponential time differencing methods based on spectral deferred correction (ETDSDC) and describe a simple procedure for initializing the requisite matrix functions. We compare the stability and accuracy properties of our ETDSDC meth- ods to those of an existing implicit-explicit spectral deferred correction scheme (IMEXSDC). We find that ETDSDC methods have larger accuracy regions and comparable stability regions. We conduct numerical experiments to compare ETD and IMEX spectral deferred correction schemes against a competing fourth-order ETD Runge-Kutta scheme. We find that high-order ETDSDC schemes are the most efficient in terms of function evaluations and overall speed when solving partial differential equations to high accuracy. Our results suggest that high-order ETDSDC schemes are well-suited to work in conjunction with spectral spatial methods or other high-order spatial discritizations. Addi- tionally, ETDSDC schemes appear to be immune to severe order reduction, a problem which affects other ETD and IMEX schemes, including IMEXSDC.
2017-07 Social Interactions, Mechanisms, and Equilibrium: Evidence from a Model of Study Time and Academic Achievement

We develop and estimate a model of student study time on a social network. The model is designed to exploit unique data collected in the Berea Panel Study. Study time data allow us to quantify an intuitive mechanism for academic social interactions: own study time may depend on friend study time in a heterogeneous manner. Social network data allow us to embed study time and resulting academic achievement in an estimable equilibrium framework. We develop a specification test that exploits the equilibrium nature of social interactions and use it to show that novel study propensity measures mitigate econometric endogeneity concerns.
Financial Market Developments and Management Compensation

We examine how the properties of executive compensation contracts change following a significant shift in the financing opportunities faced by nonfinancial firms. Specifically, we assess whether the branching provisions in the Interstate Banking and Branching Efficiency Act (IBBEA) affect borrowing firms’ use of stock options in executive compensation. Using the hand-collected compensation data of firms with market capitalization less than $75 million, we find that nonfinancial firms are likely to increase the use of stock options after IBBEA and that this increase is more pronounced for firms located in states with lower regulatory branch entry barriers. We further find that the increase in stock option use is more pronounced in states where the diversification benefits to the banks are the greatest. These findings suggest that with banks’ enhanced ability to diversify credit risk geographically and their increased tolerance for borrowers’ risk-taking, their clients – nonfinancial firms – adjust their compensation contracts that are previously constrained by bank distaste for risk. These findings not only suggest macro financial market developments affect firm corporate governance – in this case compensation structures – they also help us broaden our understanding of how financial market developments affect firm investment and innovation.
Simulation of Vertical Filling Processes of Granular Foods for typical Retail Amounts

Vertical baggers play an important role in the packaging of food granulates such as bite-sized snacks, candy and nuts. Increasing demands regarding productivity and throughput pressure manufacturers to employ increasingly sophisticated engineering tools over conventional design through trial and error and experience. The Discrete Element Method (DEM) is a powerful tool for prediction of bulk behavior, however randomness in the packaging process impairs reproducibility of simulations, especially when working in the typical range of particle counts less than 1000. This study looks into statistic methods to determine under which conditions adequate reliability of simulation results can be achieved. Both the influence of the goal variables used to describe the process and the influence of the particle count are discussed. The examples given can be adapted to similar scenarios and lay the foundation for future simulation assisted design of vertical filling processes.
A survey of fully integrated oscillator

Time is undoubtedly a tool more and more and interfaces with our world, that we want to have in our digital era. Either to keep track of the transaction or schedule and synchronize. Integrated-Circuit design technology is getting rather complex with plenty of challenge. One of the key drivers is clock technology. CMOS technology has followed a beautiful evolution of with gate length scaling, combined with an incredible amount of circuit functionality. Another research direction proposed the co-integration of the sensor; Micro Electro Mechanical System so-called “More than Moore”ﬂavor. The Clock technology has a long-lasting history associated with the crystal oscillator, further integration level would be some CMOS resonator, or fully integrated on die. There are a few closed form Power and Precision requirement, in the last decade circuits innovations has been proposed fostering our knowledge on this trade-off. In this article, we propose a review of the and benchmark of fully integrated oscillator on primary metrics and ﬁgure of merits.
Classiﬁcation Rule Generation for Cancer Prediction using Locality Sensitive Hashing Similarity Measure

This paper aims to develop a decision support system for healthcare in predicting stage of cancer (whether benign or malignant) using a novel classiﬁer technique based on Locality Sensitive Hashing (LSH). We propose a new classiﬁcation rule generations scheme based on Locality Sensitive Hashing. By applying LSH based classiﬁcation instance selection algorithms, we get a minimal set of class representative patterns, on which we apply discretization and classiﬁcation rule generation manually . Thus, have high chances of coming up with best prediction. Confusion matrix is used to compare test results. The above technique is applied on two datasets –Iris and Breast Cancer Wisconsin. We get better accuracy, speciﬁcity, sensitivity and precision than traditional classiﬁers. Manual diagnosis takes time and is a trial-error procedure and needs knowledge from medical specialists. We better the accuracy and speed of this manual procedure. classiﬁcation model concept is used.
Data Correction For Enhancing Classification Accuracy By Unknown Deep Neural Network Classifiers

Deep neural networks provide excellent performance in pattern recognition, audio classification, and image recognition. It is important that they accurately recognize input data, particularly when they are used in autonomous vehicles or for medical services. In this study, we propose a data correction method for increasing the accuracy of an unknown classifier by modifying the input data without changing the classifier. This method modifies the input data slightly so that the unknown classifier will correctly recognize the input data. It is an ensemble method that has the characteristic of transferability to an unknown classifier by generating corrected data that are correctly recognized by several classifiers that are known in advance. We tested our method using MNIST and CIFAR-10 as experimental data. The experimental results exhibit that the accuracy of the unknown classifier is a 100% correct recognition rate owing to the data correction generated by the proposed method, which minimizes data distortion to maintain the data’s recognizability by humans.
CIA_A_302468 887..895

*These authors contributed equally to this work Objective: To investigate the relationship between asymptomatic target organ damage (TOD) and different somatotypes in a population of elderly from Chinese communitydwelling. Methods: A total of 2098 Chinese senior residents from northern Shanghai older than 65 years were recruited in the research. The following somatotype parameters were recorded and analyzed: body mass index, waist circumference, hip circumference, and waist-hip ratio were recorded and calculated. Asymptomatic TOD, including urine albumin/creatinine ratio, estimated glomerular filtration rate (eGFR), intima-media thickness (IMT), left ventricular mass index (LVMI), left ventricular diastolic function, and carotid-femoral pulse wave velocity (PWV) was recorded using the MyLab30 Gold CV system and SphygmoCor. Results: Of all 2098 residents, 817 (38.9%) were overweight and 289 (13.8%) were obese. All somatotype measures were significantly correlated with TOD parameters (p<0.05). After adjustment for age and male gender, in total population, LVMI (p<0.001), cardiac diastolic function (E/Ea, p<0.001), PWV (p<0.001), eGFR (p=0.03), and urine albumin/creatinine ratio (p<0.001) changed gradually and significantly correlated with increasing BMI values. Obesity and overweight were independently related to the incidence of LVH, LVDD, artery stiffness, carotid arterial plaque, and microalbuminuria. Conclusion: The incidence of asymptomatic TOD was significantly correlated with overweight and obesity, especially in women, whereas the underweight may favor in the prevention of TOD.
Pilot study of a new rehabilitation tool: Improved unilateral 1 adaptation of the human angular vestibulo-ocular reflex

24 HYPOTHESIS: Unilateral vestibulo-ocular reflex (VOR) adaptation training causes some 25 increase towards the non-adapting side (~30% of increase on adapting side). We conducted a 26 pilot study to determine if the increase could be reduced by providing a visual stimulus during 27 rotations to the non-adapting side. 28 BACKGROUND: Unilateral vestibular adaptation is a technique that could increase the 29 ipsilesional VOR response of vestibular patients with unilateral hypofunction. However, this 30 technique results in the VOR response increasing for rotations towards the non-adapting 31 (normal) side, which is undesirable because the VOR will be over-compensatory (causing non- 32 stable vision) during head rotations towards the normal side. 33 METHODS: We built a portable helmet device that sensed horizontal angular head velocity to 34 generate a visual target that required a preset VOR gain (eye velocity / head velocity) for optimal 35 image stabilisation that could be set differently for leftward and rightward head rotations. We 36 tested 10 subjects (6 controls and 4 patients with vestibular hypofunction). We measured the 37 active and passive VOR gain before and after unilateral VOR adaptation training. 38 RESULTS: In control subjects, for rotations towards the adapting side (target gain = 1.5) the 39 VOR gain increased due to training by 26.1 ± 23.4% during active head impulses and by 14.6 ± 40 13.0% during passive head impulses. In contrast, for rotations towards the non-adapting side 41 there were no statistically significant increases. 42 CONCLUSION: A visual stimulus driving the VOR gain to unity towards the non-adapting side 43 aids unilateral adaptation more so than no visual stimulus. 44
TGF (cid:2) induces GDNF responsiveness in neurons by recruitment of GFR (cid:3) 1 to the plasma membrane

e have previously shown that the neurotrophic effect of glial cell line–derived neurotrophic factor (GDNF) in vitro and in vivo requires the presence of transforming growth factor (TGF) (cid:2) . Using primary neurons (chick E8 ciliary) we show that the combination of GDNF plus TGF (cid:2) promotes survival, whereas the single factors do not. This cooperative effect is inhibited by blocking the extracellular signal-regulated kinase (ERK)/MAPK pathway, but not by interfering with the PI3 kinase signaling cascade. Although there is no functional GDNF signaling in the absence of TGF (cid:2) , pretreatment with TGF (cid:2) confers GDNF responsiveness to the cells. This is not due to upregulation of GDNF receptors mRNA and protein, but to TGF (cid:2) -induced recruitment of the glycosyl-phosphatidyl-inositol-anchored GDNF receptor (GFR) (cid:3) 1 to the plasma membrane. This is supported by the fact that GDNF in the presence of a soluble GFR (cid:3) 1 can promote survival in the absence of TGF (cid:2) . Our data suggest that TGF (cid:2) is involved in GFR (cid:3) 1 membrane translocation, thereby permitting GDNF signaling and neurotrophic effects.
Moderate Deviation Principles for Empirical Covariance in the Neighbourhood of the Unit Root

In this paper, we consider the linear autoregressive model with varying coefficients θn∈[0,1). When θn tending to the unit root, the moderate deviation principle for empirical covariance is discussed, and as statistical applications, we provide the moderate deviation estimates of the least square and the Yule–Walker estimators of the parameter θn.
2019). Store buildings as tourist attractions: Mining retail meaning of store building pictures through a machine learning approach . Journal of Retailing and Consumer Services

The aim of this paper is to understand the extent to which a store building can function as a tourism attraction, using a large luxury department store as case research. The study draws upon the idea that people complete a hermeneutic circle to create an extraordinary tourism experience to share with others. The data gathering is based on the collection of pictures posted online on Flickr and analysed using a machine learning approach. A sample of 1,557 pictures related to a specific area in London (UK) were collected and analysed by means of a cluster analysis in order to determine which objects are most photographed. Findings reveal that the store building of a luxury department store is the central object in the majority of pictures within a 1km radius of the store main entrance, which demonstrates the role of store building attractiveness in tourism experience. The theoretical contribution is that this is the first paper adding the exterior of the building as attribute of the department store, and demonstrating the role of department stores in place attractiveness.
The effect of depressive symptoms, mental distress and empathy on embodiment of the rubber hand illusion

The purpose of the present study was to examine the effect of depression, mental distress and empathy on the strength of the rubber hand illusion (RHI). The RHI is a perceptual illusion that is thought to occur as a result of visual capture during multimodal sensory stimulation. The RHI was induced in participants by synchronous stroking of their real hand and the visible fake hand for two minutes. Participants were then requested to complete a nine-item questionnaire on the strength of the illusion, the Depression Anxiety Stress Scale (DASS), which was used to screen for depressive symptoms and general mental distress, and the Interpersonal Reactivity Index (IRI) to assess each participant’s level of empathy. Results showed that depression and mental distress were positively correlated with embodiment of the RHI, and that the mean score on both the depression and mental distress scales was higher for individuals who experienced the illusion than those who did not. Contrary to the existing literature the correlations found between empathy and strength of the RHI were found for cognitive aspects rather than affective aspects of empathy. The implications of the current study are that individuals who are psychologically less healthy are more likely to experience the illusion. It is possible that diminished interoceptive awareness is responsible for entrainment of the illusion, and this represents a promising avenue for future research.
Deciphering SARS CoV-2-associated pathways from RNA sequencing data of COVID-19-infected A549 cells and potential therapeutics using in silico methods

Background: Coronavirus (CoV) disease (COVID-19) identified in Wuhan, China, in 2019, is mainly characterized by atypical pneumonia and severe acute respiratory syndrome (SARS) and is caused by SARS CoV-2, which belongs to the Coronaviridae family. Determining the underlying disease mechanisms is central to the identification and development of COVID-19-specific drugs for effective treatment and prevention of human-to-human transmission, disease complications, and deaths. Methods: Here, next-generation RNA sequencing (RNA Seq) data were obtained using Illumina Next Seq 500 from SARS CoV-infected A549 cells and mock-treated A549 cells from the Gene Expression Omnibus (GEO) (GSE147507), and quality control (QC) was assessed before RNA Seq analysis using CLC Genomics Workbench 20.0. Differentially expressed genes (DEGs) were imported into BioJupies to decipher COVID-19 induced signaling pathways and small molecules derived from chemical synthesis or natural sources to mimic or reverse COVID -19 specific gene signatures. In addition, iPathwayGuide was used to identify COVID-19-specific signaling pathways, as well as drugs and natural products with anti-COVID-19 potential. Results: Here, we identified the potential activation of upstream regulators such as signal transducer and activator of transcription 2 (STAT2), interferon regulatory factor 9 (IRF9), and interferon beta (IFNβ), interleukin-1 beta (IL-1β), and interferon regulatory factor 3 (IRF3). COVID-19 infection activated key infectious disease-specific immune-related signaling pathways such as influenza A, viral protein interaction with cytokine and cytokine receptors, measles, Epstein-Barr virus infection, and IL-17 signaling pathway. Besides, we identified drugs such as prednisolone, methylprednisolone, diclofenac, compound JQ1, and natural products such as Withaferin-A and JinFuKang as candidates for further experimental validation of COVID-19 therapy. Conclusions: In conclusion, we have used the in silico next-generation knowledge discovery (NGKD) methods to discover COVID-19-associated pathways and specific therapeutics that have the potential to ameliorate the disease pathologies associated with COVID-19.
Security research of blockchain technology in electronic medical records

Background: A blockchain-based Electronic Health Record(EHR) data-sharing scheme was proposed to solve the problems of data sharing difficulties and privacy disclosure. Methods: This paper designs a blockchain-based electronic health record model based on the characteristics of blockchain antitampering, decentralization, and distributed storage. Utilize blockchain network and distributed database to store encryption-related access control policies to prevent EHR data from being tampered with and leaked. Data security sharing protocol combines Distributed Key Generation (DKG) and reencryption. Results: The protocol used the Delegated Proof of Stake(DPOS) algorithm to select the proxy node, which reencrypted the EHR to share data between a pair of users. Simulation experiments and comparative analysis showed that DPOS efficiency was higher than Proof of Work (POW) and slightly lower than the Practical Byzantine Fault Tolerance(PBFT). Conclusions: The scheme proposed in this paper is more decentralized and less computationally intensive.