Miracle-dz/UniFAR-FTUs huggingface dataset:
Radix Tetrastigma hemsleyani flavone exhibits antitumor activity in colorectal cancer via Wnt/β-catenin signaling pathway

Background Radix Tetrastigma hemsleyani flavone (RTHF) is extracted from a traditional Chinese medicinal herb T. hemsleyani, which is conventionally used as a folk medicine for its anti-inflammation activity and antiviral activity. In this study, the effects of RTHF on inhibiting malignant biological properties in colorectal cancer (CRC) were evaluated by conducting both in vitro and in vivo experiments, and the underlying mechanism was investigated. Materials and methods Cell Counting Kit-8, colony formation, and flow cytometry assays were performed to evaluate the proliferation of RTHF-treated colon tumor cells. Migration and invasion capacities were also tested by cell wound scratch assay and Transwell invasion assay. Moreover, the antitumor effects of RTHF on azoxymethane/dextran sulfate sodium-induced colitis-related CRC were investigated in C57BL/6 mice. In addition, Western blot and/or quantitative reverse transcription polymerase chain reaction analysis were used to evaluate the expressions of Lgr5, Cyclin D1, c-Myc, and E-cadherin. Results These experiments showed that RTHF could decrease the cell growth kinetics and clone-forming capacity. RTHF could also dose dependently induce cell cycle arrest at G0/G1 phase and inhibit epithelial-mesenchymal transition process. Furthermore, downregulation of β-catenin activation and downstream protein expression were detected in CRC cells after being treated with RTHF. RTHF daily gavage suppressed the number and size of CRC in mice and inhibited Lgr5 and Cyclin D1 expressions in tumor tissue. Conclusion In conclusion, RTHF treatment inhibits colorectal tumor growth, decreases Wnt/β-catenin pathway activity, and downregulates target genes’ expression.
Road Pollution Estimation Using Static Cameras And Neural Networks

This paper presents a methodology for estimating pollution on roads by analyzing traffic video sequences. The objective is to take advantage of the huge network of static cameras which is possible to lind in the road system of any state or country to estimate the pollution on each area. This proposal uses deep learning neural networks for the object detection, and a pollution estimation model based on the frequency of vehicles and their speed. The experiments show promising results which suggest that the system can be used alone or combined with existing systems for measuring pollution on roads.
Depth Assisted Adaptive Workload Balancing for Parallel View Synthesis

Depth image-based rendering has been adopted by MPEG as the recommended view synthesis technique for free viewpoint TV applications. In this paper, a workload balancing algorithm is proposed for parallel view synthesis on multicore platforms. First, view synthesis workload is defined as the function of the number of hole-pixels in the warped images. Then, a novel depth assisted prediction method is proposed to predict the number of hole-pixels in the current frame by exploiting the depth differences between the neighboring frames, which reflects the movement of objects in video content. Feeding the predicted workload to the proposed cost function, each input frame is partitioned adaptively to balance the synthesis workload among the cores. The proposed workload prediction method outperforms the existing approaches both in terms of frame average prediction error and standard deviation in prediction error. Applying the proposed workload balancing method, the parallel view synthesis system provides higher acceleration ratio and better synchronization performance among the cores compared with other parallel processing systems without sacrificing the subjective and objective quality. It is also robust to different platforms, which shows high potential in being applied to mobile oriented applications.
Detecting Communities in Networks Using Competitive Hopfield Neural Network

Community detection finds its applications in the biological networks and social networks, like predicting functional modules of proteins, recommending items to the users based on their interests, and exploring potential relationships among persons. Modularity is a widely-used criterion for evaluating the quality of the detected community structures. Due to modularity maximization is an NP-hard problem, developing the approximate algorithms with good accuracy and computational complexity is challenging and of great significance. In this paper, a novel algorithm based on competitive Hopfield neural network (CHNN for short) for maximizing modularity is proposed, where a new energy function and a two-dimensional topology is designed, and the winner-takes-all strategy for updating the outputs of neurons in each row of CHNN is adopted. Moreover, the convergence of the proposed algorithm is proved. The algorithm is capable of converging fast and achieving good modularity. Experimental results on multiple empirical and synthetic networks show the proposed algorithm can effectively and efficiently identify the community structures of the networks, and has the competitive performance compared to several other baseline algorithms for community detection.
Dynamic Query Re-Planning using QOOP

Modern data processing clusters are highly dynamic – both in terms of the number of concurrently running jobs and their resource usage. To improve job performance, recent works have focused on optimizing the cluster scheduler and the jobs’ query planner with a focus on picking the right query execution plan (QEP) – represented as a directed acyclic graph – for a job in a resource-aware manner, and scheduling jobs in a QEP-aware manner. However, because existing solutions use a fixed QEP throughout the entire execution, the inability to adapt a QEP in reaction to resource changes often leads to large performance inefficiencies. This paper argues for dynamic query re-planning, wherein we re-evaluate and re-plan a job’s QEP during its execution. We show that designing for re-planning requires fundamental changes to the interfaces between key layers of data analytics stacks today, i.e., the query planner, the execution engine, and the cluster scheduler. Instead of pushing more complexity into the scheduler or the query planner, we argue for a redistribution of responsibilities between the three components to simplify their designs. Under this redesign, we analytically show that a greedy algorithm for re-planning and execution alongside a simple max-min fair scheduler can offer provably competitive behavior even under adversarial resource changes. We prototype our algorithms atop Apache Hive and Tez. Via extensive experiments, we show that our design can offer a median performance improvement of 1.47× compared to state-of-the-art alternatives.
Power to participants: a call for person‐centred HIV prevention services and research

While biomedical HIV prevention offers promise for preventing new HIV infections, access to and uptake of these technologies remain unacceptably low in some settings. New models for delivery of HIV prevention are clearly needed. This commentary highlights the potential of person‐centred programming and research for increasing the cultural relevance, applicability and use of efficacious HIV prevention strategies. It calls for a shift in perspective within HIV prevention programmes and research, whereby people are recognized for their agency rather than assumed to be passive beneficiaries or research participants.
Distributed Neural Networks for Missing Big Data Imputation

In this paper we investigate the use of Distributed Neural Networks for the imputation of missing values in Big Data context. The presented framework for data imputation is implemented in Spark, allowing easy imputation as an additional step to the data pre-processing pipeline. The Distributed Neural Networks model is using Mini-batch Stochastic Gradient Descent, scaling well with the cluster size and minimizing the communication among the workers. The model is tested on a real-world Recommender Systems dataset, where the missing data is generally a problem for new items, as the systems ranking is usually biased towards the popular items. The model is compared with univariate (Mean and Median Imputation) and multivariate (K-Nearest Neighbours and Linear Regression) imputation techniques, and its performance is validated using prediction accuracy and speed. Furthermore, we evaluate the speedup compared to the sequential implementation of Neural Networks with Stochastic Gradient Descent.
Weighted Autocorrelation based Prediction Interval Optimization for Wind Power Generation

In this paper, an optimization methodology for the weighted autocorrelation based prediction interval is proposed and applied for the prediction of the wind power generation. The Coverage Width Based Criterion (CWC) is applied as the optimization criterion. The improved execution steps are as follows- At first, the string of 20 recent samples is autocorrelated with the similar strings of previous samples. Then, the samples next to the highest normalized correlation values and corresponding indexes are selected. After that, the amplitudes of the matched samples are adjusted by multiplying the value with the amplitude of recent string and by dividing by the amplitude of matched strings. These amplitude-adjusted samples are the prediction value for the next sample. Each prediction values are given a weight depending on the ratio of the amplitude of the string and the value of normalized correlation. The weight equation is trained with the CWC equation to find the optimum relation between amplitude and correlation values. The probability density distribution is derived from the weighted autocorrelation values. Finally, least relevant areas from corners are discarded to achieve the required coverage with smaller PI width. However, as the level of uncertainty changes over time, discarding historical percentile may result in a different coverage on later targets. Therefore, the percentage of discarding is also optimized with the CWC. Wind power generation is predicted and different weight equation and discarding percentages are achieved.
P-300 and N-400 Induced Decoding of Learning-Skill of Driving Learners Using Type-2 Fuzzy Sets

The paper aims at classifying the learning-skill of driving-learners by utilizing the acquired P300 and N400 event related potentials during the learning phase of a simulated car-driving task. A set of driving stimuli, including braking on sudden appearance of bumpers, steering control at the sharp bending points, and the like, are prepared to experimentally classify the learning skill of the subjects in three distinct classes: Low, Medium and High. The complexity in classifying the subjective learning-skill arises because of intra- and inter-session variations in brain signals due to temporal fluctuation in activation levels of the involved brain lobes. A novel z-slice based model of general type-2 fuzzy set is proposed to represent the intra- and inter-session variations by the footprint of uncertainty and their composite variations by z-slices at different values of a given feature. Further, a novel technique for z-slice based classifier is proposed to classify the learning-skill under intra-and inter-session uncertainty. The proposed classifier is found to outperform traditional and well-known type-2 classifiers for the present learning-skill classification task in Experiments undertaken confirm the frontal and parietal theta (4-7 Hz) activation in discrete learning steps. The proposed research outcome may directly be utilized to certify driving learners with required learning skills.
Floem: A Programming System for NIC-Accelerated Network Applications

Developing server applications that offload computation to a NIC accelerator is complex and laborious. Developers have to explore the design space, which includes semantic changes for different offloading strategies, as well as variations on parallelization, program-to-resource mapping, and communication strategies for program components across devices. We therefore design FLOEM -- a language, compiler, and runtime -- for programming NIC-accelerated applications. FLOEM enables offload design exploration by providing programming abstractions to assign computation to hardware resources; control mapping of logical queues to physical queues; access fields of a packet and its metadata without manually marshaling a packet; use a NIC to memoize expensive computation; and interface with an external application. The compiler infers which data must be transferred between the CPU and NIC and generates a complete cache implementation, while the runtime transparently optimizes DMA throughput. We use FLOEM to explore NIC-offloading designs of real-world applications, including a key-value store and a distributed real-time data analytics system; improve their throughput by 1.3-3.6× and by 75-96%, respectively, over a CPU-only implementation.
Feasibility of FPGA Accelerated IPsec on Cloud

Line-rate speed requirements for performance hungry network applications like IPsec are getting problematic due to the virtualization trend. A single virtual network application hardly can provide 40 Gbps operation. This research considers the IPsec packet processing without IKE to be offloaded on an FPGA in a network. We propose an IPsec accelerator in an FPGA and explain the details that need to be considered for a production ready design. Based on our evaluation, Intel Arria 10 FPGA can provide 10 Gbps line-rate operation for the IPsec accelerator and to be responsible for 1000 IPsec tunnels. The research points out that for future data centers it is beneficial to rely on HW acceleration in terms of speed and energy efficiency for applications like IPsec.
Adversarial Attacks on Cognitive Self-Organizing Networks: The Challenge and the Way Forward

Future communications and data networks are expected to be largely cognitive self-organizing networks (CSON). Such networks will have the essential property of cognitive self-organization, which can be achieved using machine learning techniques (e.g., deep learning). Despite the potential of these techniques, these techniques in their current form are vulnerable to adversarial attacks that can cause cascaded damages with detrimental consequences for the whole network. In this paper, we explore the effect of adversarial attacks on CSON. Our experiments highlight the level of threat that CSON have to deal with in order to meet the challenges of next-generation networks and point out promising directions for future work.
Alteration of the colostrum whey proteome in mothers with gestational hypothyroidism

Background Gestational hypothyroidism (G-HypoT) is one of the most common thyroid diseases in pregnant women. Human milk, which closely links the mother with infant, is an important factor to the infant health. Here, we analyzed the colostrum whey proteome of women with or without G-HypoT. Methods and results Using high–mass accuracy and high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS), 1055 proteins were identified. Tandem Mass Tags (TMT) analysis identified differentially expressed proteins between G-HypoT and non-G-HypoT mothers. Of 44 proteins identified, 15 proteins were significantly increased in G-HypoT colostrum whey, while 29 were significantly decreased. Analysis revealed that enzymes involved in carbohydrate metabolism, and that reflect the metabolic activities in breastfeeding women, including fructose-1, 6-bisphosphatase 1, phosphoglycerate mutase 1 were down-regulated. Cell structural proteins, biomarkers of mammary integrity development, including Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and actin were lower in G-HypoT colostrum whey. However, immune protein fragments like Ig gamma-3 chain C region increased in G-HypoT colostrum whey. Conclusion These results implied that G-HypoT may changed human colostrum whey protein in composition level, decreasing levels of metabolic proteins and cell-structure proteins, while increasing levels of immune-related proteins, which may compromise or reflect mothers’ and infants’ health.
LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation

The monolithic server model where a server is the unit of deployment, operation, and failure is meeting its limits in the face of several recent hardware and application trends. To improve resource utilization, elasticity, heterogeneity, and failure handling in datacenters, we believe that datacenters should break monolithic servers into disaggregated, network-attached hardware components. Despite the promising benefits of hardware resource disaggregation, no existing OSes or software systems can properly manage it. We propose a new OS model called the splitkernel to manage disaggregated systems. Splitkernel disseminates traditional OS functionalities into loosely-coupled monitors, each of which runs on and manages a hardware component. A splitkernel also performs resource allocation and failure handling of a distributed set of hardware components. Using the splitkernel model, we built LegoOS, a new OS designed for hardware resource disaggregation. LegoOS appears to users as a set of distributed servers. Internally, a user application can span multiple processor, memory, and storage hardware components. We implemented LegoOS on x86-64 and evaluated it by emulating hardware components using commodity servers. Our evaluation results show that LegoOS' performance is comparable to monolithic Linux servers, while largely improving resource packing and reducing failure rate over monolithic clusters.
Computationally relevant generalized derivatives: theory, evaluation and applications

A new method for evaluating generalized derivatives in nonsmooth problems is reviewed. Lexicographic directional (LD-)derivatives are a recently developed tool in nonsmooth analysis for evaluating generalized derivative elements in a tractable and robust way. Applicable to problems in both steady-state and dynamic settings, LD-derivatives exhibit a number of advantages over current theory and algorithms. As highlighted in this article, the LD-derivative approach now admits a suitable theory for inverse and implicit functions, nonsmooth dynamical systems and optimization problems, among others. Moreover, this technique includes an extension of the standard vector forward mode of automatic differentiation (AD) and acts as the natural extension of classical calculus results to the nonsmooth case in many ways. The theory of LD-derivatives is placed in the context of state-of-the-art methods in nonsmooth analysis, with an application in multistream heat exchanger modelling and design used to illustrate the usefulness of the approach.
FEATS: Synthetic Feature Tracks for Structure from Motion Evaluation

We present FEATS (Feature Extraction and Tracking Simulator), that synthesizes feature tracks using a camera trajectory and scene geometry (e.g. CAD, multi-view stereo). We introduce 2D feature and matching noise models that can be controlled using a few parameters. We also provide a new dataset of images and ground truth camera pose. We process this data (and a synthetic version) with several current SfM algorithms and show that the synthetic tracks are representative of the real tracks. We then show two practical uses of FEATS: (1) we generate hundreds of trajectories with varying noise and show that COLMAP is more robust to noise than OpenSfM and VisualSfM; and (2) we calculate 3D point error and show that accurate camera pose estimates do not guarantee accurate 3D maps.
A Two-stage Vehicle Type Recognition Method

Vehicle type recognition is a common question in modern intelligent transportation system. Although many methods have been proposed in literatures, how to recognize the vehicle type in the case of small samples is still troubling. To solve the problem mentioned above, this paper presents a method for vehicle type recognition based on a two-stage strategy that includes data preprocessing phrase and training phrase. The main purpose of the first stage is to remove the background of the vehicle image to reduce the interference of redundant information on subsequent stage. In second stage, a concept of multi-scale fusion feature which integrates handcrafted features with learning-based feature is to put forward to describe the characteristics of vehicles. The combined features are input to Support Vector Machine with Racial Basis Function (RBF-SVM) to train the recognition model. The proposed method is tested on MVVTR and the recognition accuracy is up to 90.96%, which is better than other methods. It is verified that strong generalization ability can be achieved in the case of small samples by using the two- stage strategy.
It’s better together! European perspective on benefits and challenges associated with cross-border health communication campaigns

Recent years have witnessed greater involvement of European Union (EU) organisations in health communication campaigns that address chronic diseases and that are designed for implementation in multiple countries. This development raises challenges inherent in adapting the design of public health communication campaigns to multi-national settings. This article provides a first exploratory investigation of these challenges and how to address them based on data gathered from four expert focus groups, each concentrated on a common risk factor for chronic disease: smoking, alcohol consumption, unhealthy diet and sedentary lifestyle. Despite the exploratory nature of the data, it was possible to identify several common key challenges: variation in behaviours, social and cultural norms, and issues related to language and communication channels, the divide between EU stakeholders and local actors, and differences in national legislation and available resources. Two risk factor-specific challenges were also identified: effective messaging for complex issues (unhealthy diet) and the involvement of industry representatives (smoking, sedentary lifestyle). We propose conceiving of cross-national communication campaigns as providing a common blueprint and structure that can inform and support the development of differentiated yet harmonised local campaigns.
Perceiving Abstract Concepts Via Evolving Computational Cognitive Modeling

Any cognizable thing (object, or idea) can be recognized as a concept, and they are usually described by modality-specific, experience-dependent, or localist-distributed representations. There has been a significant effort in studying concrete concepts, along with their underlying cognitive and psychological processes through conceptual representations produced by computational approaches (like Artificial Neural Networks). However, the notion of abstract concepts, though debated, is seldom explored. In this article, we propose an approach to learn abstract representation of concepts with Regulated Activation Networks (RANs) evolving computational modeling. RANs methodology is illustrated using a toydata problem, and evaluated for metrics Precision, Recall, Fl-score, and Accuracy, along with ROC curve analysis. RANs comparison is presented using a benchmark data from human activity recognition domain, yielding Precision= 98.20% (ca.), Recall= 97.62% (ca.), Fl-score= 97.8 (ca.), and Accuracy= 97.62% (ca.). Sleep Detection data from SOCIALITE project is used to model active and inactive students, showing a convincing performance. Further, the model is used in studying 3 students in order to deduce biomarkers for understanding their psychological conditions.