Miracle-dz/UniFAR-FTUs huggingface dataset:
Diarrheal Etiology and Impact of Coinfections on Rotavirus Vaccine Efficacy Estimates in a Clinical Trial of a Monovalent Human–Bovine (116E) Oral Rotavirus Vaccine, Rotavac, India

Abstract Background Rotavirus vaccine efficacy (VE) estimates in low-resource settings are lower than in developed countries. We detected coinfections in cases of severe rotavirus diarrhea in a rotavirus VE trial to determine whether these negatively impacted rotavirus VE estimates. Methods We performed TaqMan Array Card assays for enteropathogens on stools from rotavirus enzyme immunoassay–positive diarrhea episodes and all severe episodes (Vesikari score ≥11), from a phase 3 VE trial of Rotavac, a monovalent human–bovine (116E) rotavirus vaccine, carried out across 3 sites in India. We estimated pathogen-specific etiologies of diarrhea, described associated clinical characteristics, and estimated the impact of coinfections on rotavirus VE using a test-negative design. Results A total of 1507 specimens from 1169 infants were tested for the presence of coinfections. Rotavirus was the leading cause of severe diarrhea even among vaccinated children, followed by adenovirus 40/41, Shigella/enteroinvasive Escherichia coli, norovirus GII, sapovirus, and Cryptosporidium species. Bacterial coinfections in rotavirus-positive diarrhea were associated with a longer duration of diarrhea and protozoal coinfections with increased odds of hospitalization. Using the test-negative design, rotavirus VE against severe rotavirus gastroenteritis increased from 49.3% to 60.6% in the absence of coinfections (difference, 11.3%; 95% confidence interval, –10.3% to 30.2%). Conclusions While rotavirus was the dominant etiology of severe diarrhea even in vaccinated children, a broad range of other etiologies was identified. Accounting for coinfections led to an 11.3% increase in the VE estimate. Although not statistically significant, an 11.3% decrease in VE due to presence of coinfections would explain an important fraction of the low rotavirus VE in this setting.
Functional Test Environment for Time-Triggered Control Systems in Complex MPSoCs Using GALI

Many safety-critical and especially mixed-criticality computer systems are realized as a time-triggered (TT) system. Such systems execute one or more tasks according to a pre-determined scheduling. The main advantage of TT systems is their deterministic timing behavior. With the ability of today's programmable MPSoC architectures, complex TT systems based on hypervisors (to support spatial isolation) and timing predictable Networks-on-Chip can be built on a single chip. For the integration of functionality on such a TT MPSoC not only the timing, but also the interplay of functional behavior and timing, needs to be validated. In the past, functional integration testing has been performed on a prototyping board, sometimes in a Hardware-in-the-Loop (HiL) configuration to support testing against a complex environment model. In this work, we propose a Globally Accurate Locally Inaccurate (GALI) simulation model that combines an instruction accurate simulation engine with a global time-triggered scheduler. GALI is demonstrated and evaluated on a full functional flight-control system of a mixed-criticality case-study. For this case-study, we will show that our GALI simulation achieves the same control behavior as a fast cycle accurate simulation, but runs 160 times faster. In comparison with the fastest state-of-the-art approximately timed simulation models, GALI runs between 2.3 and 47 times faster for the considered case-study.
Image Clustering Based on Supervised Graph Regularized Discriminative Concept Factorization

Concept Factorization (CF) divides a matrix into the product of three matrices. It is considered as one variant of Non-negative Matrix Factorization (NMF). The biggest difference between the two methods is that CF can be executed in a kernel space. Because of this characteristic, many schemes based on CF have been proposed in computer vision and pattern recognition fields. Recent studies have shown that high dimensional data is often located in a low dimensional manifold space, in order to improve the performance and reduce the storage space, how to find the mapping function is particularly important. In addition, the development of supervised learning methods show that label information is critical to enhance the model’s ability. In this paper, a supervised graph regularized discriminative concept factorization (SGDCF) method is presented for image clustering. In the SGDCF, we make use of local manifold geometry structure and label information. The corresponding multiplicative update solutions and convergence verification are given. Clustering results on four image data sets reveal that the SGDCF outperforms the state-of-the-art algorithms in terms of accuracy and normalized mutual information.
Raritas: a program for counting high diversity categorical data with highly unequal abundances

Acquiring data on the occurrences of many types of difficult to identify objects are often still made by human observation, for example, in biodiversity and paleontologic research. Existing computer counting programs used to record such data have various limitations, including inflexibility and cost. We describe a new open-source program for this purpose—Raritas. Raritas is written in Python and can be run as a standalone app for recent versions of either MacOS or Windows, or from the command line as easily customized source code. The program explicitly supports a rare category count mode which makes it easier to collect quantitative data on rare categories, for example, rare species which are important in biodiversity surveys. Lastly, we describe the file format used by Raritas and propose it as a standard for storing geologic biodiversity data. ‘Stratigraphic occurrence data’ file format combines extensive sample metadata and a flexible structure for recording occurrence data of species or other categories in a series of samples.
Marine Energy Exploitation in the Mediterranean Region: Steps Forward and Challenges

Technologies for the conversion of Marine Energy (ME) into electricity are now ready for full-scale deployment in farms of devices, making the final step from demonstration to operability and commercial exploitation. Although marine energy is more abundant along the Atlantic and Nordic European coasts, significant resources are also available in the Mediterranean Sea, opening up new perspectives for sustainable energy production in sensitive coastal areas and for the economic development of Southern Europe. The implementation of ME converters in the Mediterranean is also liable to induce significant technological advancements, as the low energy levels impose more restrictive constraints on device efficiency and environmental compatibility, while the milder climate allows the testing of concepts and prototypes in the natural environment at more affordable costs. Research institutions and industrial players in Mediterranean countries have in fact already taken up the challenge. The energy sector now adds up to the many different traditional maritime activities and to the new ocean-related industries that are developing, potentially exacerbating the competition for the use of marine space in the Mediterranean region. As the prospective sea use patterns are rapidly changing, an adequate international legal and policy framework needs to be designed for the coherent management of sea space, and Marine Spatial Planning needs to be finally implemented. To this end, the creation of transnational clusters of stakeholders is expected to be an effective catalyzer.
High-Throughput One-Channel RS(255,239) Decoder

This work presents the design of a very high throughput RS(255,239) decoder for a single data stream. Implementation results show that it is possible to reach 140 Gbps when implemented in a 90nm CMOS process. The proposed architectures are more area-time efficient than previously published high-throughput RS(255,239) decoders.
DUSTER: DUal Source Write TERmination Method for STT-RAM Memories

To overcome CMOS challenges including leakage power, volatility, scalability, and soft error vulnerability, Spin Transfer Torque Random Access Memory (STT-RAM) as a non-volatile memory has been utilized. Write error occurring because of variation in the fabrication process is one of the disadvantages of STT-RAM just like other devices. Given the fact that different current densities are required to write '1' and '0', in this paper, two different methods based on modification in write circuit are proposed: 1) dual source write circuit is proposed to decrease parallel write energy that leads to a noticeable decrement in power consumption, 2) the threshold voltage of active transistors in anti-parallel writing process is decreased to raise the temperature of the memory cell. This increment in Magnetic Tunneling Junction (MTJ) temperature leads to Write Error Rate (WER) reduction. In order to validate our results, functional simulations are performed and compared with the related works. Results approved power gain, performance improvement, and WER reduction by optimizing VDD and Vth values. We achieve 11.38% decrement in overall write time without any area or total power overhead.
Sandensolide Induces Oxidative Stress-Mediated Apoptosis in Oral Cancer Cells and in Zebrafish Xenograft Model

Presently, natural sources and herbs are being sought for the treatment of human oral squamous cell carcinoma (OSCC) in order to alleviate the side effects of chemotherapy. This study investigates the effect of sandensolide, a cembrane isolated from Sinularia flexibilis, to inhibit human OSCC cell growth with the aim of developing a new drug for the treatment of oral cancer. In vitro cultured human OSCC models (Ca9.22, SCC9 and HSC-3 cell lines) and oral normal cells (HGF-1), as well as a zebrafish xenograft model, were used to test the cytotoxicity of sandensolide (MTT assay), as well as to perform cell cycle analysis and Western blotting. Both the in vitro bioassay and the zebrafish xenograft model demonstrated the anti-oral cancer effect of sandensolide. Moreover, sandensolide was able to significantly suppress colony formation and induce apoptosis, as well as cell cycle arrest, in OSCC by regulating multiple key proteins. Induction of reactive oxygen species (ROS) was observed in sandensolide-treated oral cancer cells. However, these apoptotic changes were rescued by NAC pretreatment. These findings contribute to the knowledge of the model of action of sandensolide, which may induce oxidative stress-mediated cell death pathways as a potential agent in oral cancer therapeutics.
Piecewise linear secant approximation via algorithmic piecewise differentiation

It is shown how piecewise differentiable functions F:ℝn↦ℝm that are defined by evaluation programmes can be approximated locally by a piecewise linear model based on a pair of sample points . We show that the discrepancy between function and model at any point x is of the bilinear order . As an application of the piecewise linearization procedure we devise a generalized Newton's method based on successive piecewise linearization and prove for it sufficient conditions for convergence and convergence rates equalling those of semismooth Newton. We conclude with the derivation of formulas for the numerically stable implementation of the aforedeveloped piecewise linearization methods.
User Alignment via Structural Interaction and Propagation

User alignment between different social networks is a fundamental issue for many applications, such as information diffusion and recommendation. In actuality, the observed anchor users are normally sparse due to the expensiveness of labeling data. Hence how to make the best use of these sparse anchor information is an important open issue. To this end, we proposed a User Alignment via Structural Interaction and Propagation (UASIP) model to capture the structural information interaction across two social networks, which exploits deep structural infor- mation to enhance the representations of users. UASIP learns vector representation by automatically keeping the consistency between this additional structural information and intrinsic structural information of the two social networks. Experiments on real-world social network datasets demonstrate the effectiveness of UASIP compared with several state-of-the-art methods.
Another Price to Pay: An Availability Analysis for SDN Virtualization with Network Hypervisors

Communication networks are embracing the software defined networking (SDN) paradigm. Its architectural shift assumes that a remote SDN controller (SDNC) in the control plane is responsible for configuring the underlying devices of the forwarding plane. In order to support flexibility-motivated network slicing, SDN-based networks employ another entity in the control plane, a network hypervisor (NH). This paper first discusses different protection strategies for the control plane with NHs and presents the corresponding availability models, which assume possible failures of links and nodes in the forwarding plane and the control plane. An analysis of these protection alternatives is then performed so as to compare average control plane availability, average path length for the control communication that traverses NH, and infrastructure resources required to support them. Our results confirm the intuition that the NH introduction generally results in a reduction of the control plane availability, which stresses the need for appropriate protection. However, the availability achieved by each of the considered strategies is impacted differently by the node availability and the link failure probability, thus calling for a careful selection that is based on the infrastructure features.
Combined Convolutional Neural Network for High Frequency Restoration in Acoustic Impedance Images

Domain-specific methods for deblurring particular sorts of objects have gained increasing attention due to the ineffectiveness of generic methods. We present a simple and effective convolutional neural network that deblurs synthetic acoustic impedance images. The architecture of our model is divided into two basic structures: convolutional layers and regression unit. The convolutional layers highlight edges and contours related to interfaces between rock layers whereas the regression layer performs a non-linear estimation of acoustic impedance values. We consider a training dataset composed by wedge shaped images, positioned at four different angles. In our work, we adopt a strong supervised learning that exploit the dataset of artificially blurred and high resolution images. We also present an analysis comparing the frequency band-width among the latent, blurry, and deblurred images. We additionally address the requirement of deep learning for the huge amount of training examples by inserting rectified linear units (ReLU) and keeping the network architecture simple. Experimental results on synthetic test images demonstrate the efficacy of the proposed method.
Facilitating mechanisms for integrating care to promote health equity across the life course: reflections from social work trainees

ABSTRACT Integrated care is a promising practice to promote health equity and improve population health across the life course, but the mechanisms needed to integrate services remain nebulous. This study aimed to identify the components required to achieve a fully integrated health care system as articulated by social work trainees. The authors conducted five focus groups (N = 20). Transcripts were analyzed using structural and pattern coding. Three primary themes emerged: Organizational Structure and Support, Personal and Interpersonal Dynamics, and Practitioner Knowledge. Results from this study can inform the process of integration and has implications for social work education.
Neural Network Prediction Interval Based on Joint Supervision

In this paper, a new prediction interval model based on a joint supervision loss function for capturing the uncertainties associated with the modeled phenomenon is described. This model provides the upper and lower bounds of the predicted values in accordance with the desired coverage probability, as well as their expected values. A benchmark problem is used to evaluate the proposed method, and a comparison with the neural network covariance method is performed. Additionally, the proposed method was applied to forecast the residential demand from a town in UK, considering the prediction interval performance for one-day ahead. The results show that the method is able to generate an interval with narrower width than the covariance method, and maintains the coverage probability. The information provided by the prediction interval could be used in the design of microgrid energy management systems.
DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction

Urban resource scheduling is an important part of the development of a smart city, and transportation resources are the main components of urban resources. Currently, a series of problems with transportation resources such as unbalanced distribution and road congestion disrupt the scheduling discipline. Therefore, it is significant to predict travel demand for urban resource dispatching. Previously, the traditional time series models were used to forecast travel demand, such as AR, ARIMA and so on. However, the prediction efficiency of these methods is poor and the training time is too long. In order to improve the performance, deep learning is used to assist prediction. But most of the deep learning methods only utilize temporal dependence or spatial dependence of data in the forecasting process. To address these limitations, a novel deep learning traffic demand forecasting framework which based on Deep Spatio-Temporal ConvLSTM is proposed in this paper. In order to evaluate the performance of the framework, an end-to- end deep learning system is designed and a real dataset is used. Furthermore, the proposed method can capture temporal dependence and spatial dependence simultaneously. The closeness, period and trend components of spatio-temporal data are used in three predicted branches. These branches have the same network structures, but do not share weights. Then a linear fusion method is used to get the final result. Finally, the experimental results on DIDI order dataset of Chengdu demonstrate that our method outperforms traditional models with accuracy and speed.
Reconfiguration Analysis of an RRRRS Single-Loop Mechanism

The paper deals with the reconfiguration analysis of the single-loop variable degree-of-freedom (DOF) RRRRS mechanism composed of five links connected by four revolute (R) joints and one spherical (S) joint. The mechanism may show two modes of motion: one-DOF and two-DOF motion. In the paper, a classical vector procedure is used to obtain the quartic motion equation (QME) that allows one to inspect the nature of the motion. In general, the solutions of the QME provide the one-DOF motion of the mechanism except when all the coefficients of the equation vanish. In this case, the mechanism undergoes the two-DOF motion. The motion of the mechanism built according to two specific architectures was analyzed by the numerical solutions of the QME and with the help of the solid model of the mechanism. It is revealed for the first time that the perpendicular architecture has one 2-DOF motion and two 1-DOF motion modes.
NB-IoT Optimization on Paging MCS and Coverage Level

With growing popularity of Internet of Things (IoT), Low Power Wide Area (LPWA) technologies are getting more interests. Narrowband Internet of Things (NB-IoT) is a LPWA technology specified by 3rdGeneration Partnership Project (3GPP) and in the initial stage of commercialization. The performance of NB-IoT network needs to be optimized in downlink as well as uplink for reliable quality of NB-IoT services. In this paper, we provide optimization cases of modulation and coding scheme (MCS) for paging in multicell interference environments in terms of paging success rate in NB-IoT downlink. We also present optimization cases of coverage level (CL) in a commercial NB-IoT network environment with various driving speed in terms of random access channel (RACH) success rate in NB-IoT uplink.
Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering

Models for computer vision are commonly defined either w.r. t. low-level concepts such as pixels that are to be grouped, or w.r. t. high-level concepts such as semantic objects that are to be detected and tracked. Combining bottom-up grouping with top-down detection and tracking, although highly desirable, is a challenging problem. We state this joint problem as a co-clustering problem that is principled and tractable by existing algorithms. We demonstrate the effectiveness of this approach by combining bottom-up motion segmentation by grouping of point trajectories with high-level multiple object tracking by clustering of bounding boxes. We show that solving the joint problem is beneficial at the low-level, in terms of the FBMS59 motion segmentation benchmark, and at the high-level, in terms of the Multiple Object Tracking benchmarks MOT15, MOT16, and the MOT17 challenge, and is state-of-the-art in some metrics.
Inconsistency Measure Associated Discretization Methods to Network-based Intrusion Detection

Network intrusion is one of the most serious threats to network infrastructure and digital assets. Intrusion-detection systems detects un-authorized network access which violates the permissions settings of computer systems, networks, and information systems. Data mining is the one of the businesslike techniques amongst which are available for intrusion detection. Many data mining methods, including decision trees and rough set theory, would work better on discrete or binarized data. In data sets, on account of the existence in very large numbers, data discretization is deemed to be the data pretreatment method and such technology is very important. In this paper, the discretization methods of DFSFDP, DBSCAN, CAIM and K-means are implemented and applied to the KDD intrusion detection data set. Then four evaluation indexes are used to evaluate the effect of discretization. Moreover, with regard to the four discretization methods, their performance and accuracy are put forward and contrasted. The results of experiment acquired through using these methods on NSL-KDD dataset prove that used DFSFDP which is selected by the index of inconsistency measure performs well in terms of classification accuracy.
AC2: A Policy Gradient Actor with Primary and Secondary Critics

We propose AC2, a policy gradient algorithm that employs a primary and a secondary critic to manage both bias and variance in policy gradients. We present through analyses and experiments that performance becomes more stable if a secondary critic concentrates on few problematic states (upper 95-percentile) that cause extreme changes in value estimates. This scheme can keep biases tolerable while lowering variances. We relate our algorithm with critic ensembles that have more components and show that ensemble averaging may not significantly reduce gradient variances in more difficult environments. We test our algorithm in a series of high-dimensional experiments and report better performance than ensembles with more critic components especially in harder environments. In addition, performance is more stable if the secondary critic trains on a few problematic states than by random sampling. Our algorithm reports better reward performance than single critic and other RL models.