Miracle-dz/UniFAR-FTUs huggingface dataset:
Signaling by the tyrosine kinase Yes promotes liver cancer development

Most patients with hepatocellular carcinoma (HCC) are diagnosed at a late stage and have few therapeutic options and a poor prognosis. This is due to the lack of clearly defined underlying mechanisms or a dominant oncogene that can be targeted pharmacologically, unlike in other cancer types. Here, we report the identification of a previously uncharacterized oncogenic signaling pathway in HCC that is mediated by the tyrosine kinase Yes. Using genetic and pharmacological interventions in cellular and mouse models of HCC, we showed that Yes activity was necessary for HCC cell proliferation. Transgenic expression of activated Yes in mouse hepatocytes was sufficient to induce liver tumorigenesis. Yes phosphorylated the transcriptional coactivators YAP and TAZ (YAP/TAZ), promoting their nuclear accumulation and transcriptional activity in HCC cells and liver tumors. We also showed that YAP/TAZ were effectors of the Yes-dependent oncogenic transformation of hepatocytes. Src family kinase activation correlated with the tyrosine phosphorylation and nuclear localization of YAP in human HCC and was associated with increased tumor burden in mice. Specifically, high Yes activity predicted shorter overall survival in patients with HCC. Thus, our findings identify Yes as a potential therapeutic target in HCC. Description The tyrosine kinase Yes activates the transcriptional regulators YAP and TAZ to promote hepatocellular carcinoma. A Yes to new therapies? Patients with hepatocellular carcinoma (HCC) have few therapeutic options and often face a poor prognosis. Genetic profiling of tumors has enabled the classification of different HCC subclasses but has not yet identified clear driver oncogenes to target. The transcriptional coactivators YAP/TAZ are implicated in HCC development, although mutations in their genes are not associated with disease. Guégan et al. showed that the activity of the tyrosine kinase Yes upstream of YAP/TAZ promoted HCC cell proliferation in vitro and tumor development in vivo in a manner dependent on the phosphorylation and nuclear translocation of YAP/TAZ. Data from patients with HCC showed that increased Yes activity, rather than mutations in Yes or increased Yes abundance, correlated with poor survival, suggesting that Yes may be a therapeutic target to treat HCC.
Detection of ectopic pregnancy and serum beta hCG levels in women undergoing very early medical abortion: a retrospective cohort study

Abstract Objective To evaluate the outcome of the very early medical abortion (VEMA) protocol and whether it could increase early diagnosis and treatment of an asymptomatic ectopic pregnancy (EP). To investigate serum β-hCG levels correlated to ultrasound findings, and decline in β-hCG after successful VEMA. Study design A retrospective case-note review. The population consisted of all women undergoing a VEMA during 2004–14 in Austria and 2012–13 in Sweden. Two cohorts identified based on sonography findings; 106 women with an empty uterine cavity were classified as a pregnancy of unknown location (PUL) and 576 women with an intrauterine sac-like structure without a yolk sac or foetal structure were classified as probable intrauterine pregnancy (probable IUP). Results Overall, 660 women (97.6%) had a successful VEMA, 94/101 women (93.1%) in the PUL group and 566/575 women (98.4%) in the probable IUP group (p < 0.001). We identified six asymptomatic EP (0.88%). A gestational sac (< 10 mm) was detected at a median/range β-hCG level 2728 (1600–4497) IU/l. The mean decline in β-hCG was 93%, (95% CI 91.7–94.2) 5–10 days after successful abortion. Conclusions VEMA may be of particular clinical benefit for women. Apart from offering a possibility to start an abortion without delay as soon as the woman has sought abortion care, it may also offer an opportunity to detect and treat EP at an early gestational age. However, the rate of EP was very low.
Application of Multi-Label Classification Algorithm Based on Embedded Feature Extraction in Financial Management Optimization System

With the deepening of economic globalization and the integration of the world economy, domestic and foreign companies are constantly developing. The trend of company expansion, diversification of business scope, transnationalization of business areas, and grouping of organizational forms has become increasingly obvious. Under the situation that enterprise group companies have become an important part of the market economy, group company financial management has also become an issue of great concern to the management and investors of various group companics, and the corporate financial management mode is the core of group company financial management. This paper studies the design of the financial management optimization system based on the multi-label classification algorithm of embedded feature extraction.
Survey on Disease Detection in Plant Leaf Using Data Augmentation

Plant disease is a major threat that causes a decrease in agriculture productivity and also affects food security. Deep learning methods play a major role in plant disease detection. Existing and ongoing disease detection models are investigated in this paper. Recently, the research on plant disease detection using images in deep learning has been done, and also research types may require a huge number of dataset and it is arduous to extract. Thus, data augmentation to image-based plant disease detection is proposed to solve this problem. Data augmentation is more effective in so many cases in getting a huge dataset. This paper investigates and provides an overview on plant disease detection to analyze the most contemporary methods, developed over three years leading up to 2020, for training, data augmentation, feature extraction, detection, and counting crops recognizing the diseases including how these methods can be employed to feed deep classifiers and their effects on classifier accuracy.
Medium and Far Range Ground-to-Air Missile System Effectiveness Evaluation Model

A mathematical model of medium and far range Ground-to-Air Missile system effectiveness evaluation was established with the application of ADC method.Typical reliability logic configuration was analyzed,availability matrix of weapon system was worked out with the appropriate computation hypothesis;Dependability matrix of weapon system was solved by using Kolmogorov Backward and Forward Equations of the continuous time homogeneous Markov chain;The mathematical model of capability exponent system was established by selecting target characters,firing capability,anti-jamming capability,missile capability and mobility capability.An application example validates the model and the future research orientation is put forward.
Computer vision assisted, real time blind spot detection based collision warning system for two wheelers

A computer vision-based model using a CMOS camera is built to achieve the real-time blind spot detection and collision warning rider assist system for a two wheeled vehicle on impaired roads. This model is trained and modeled to increase the true detection rate with lowest possible delay during multiple vehicles detection and filtration of unwanted elements present on the road to minimize the false detection rate. Also, this system is designed to be used in economically low-cost two-wheeled vehicles to prevent and significantly reduce vehicle crashes and motorcycle mortality due to wrong maneuverability or poor visibility.
Machine Learning Based Mobile Data Traffic Prediction in 5G Cellular Networks

The rapid evolution of cellular technologies has resulted in a drastic increase in mobile data traffic. Particularly, in 5G cellular networks, the design of accurate time-series models become essential to predict and improve the mobile data traffic and quality of services (QoS). The mobile data traffic prediction models allow the operators to adapt to the traffic demands of the network with improved resource usage and user experience. In addition, the prediction of mobile data traffic is a tedious process due to the nature of high heterogeneity amongst distinct base stations with varying traffic loads. Therefore, several artificial intelligences (AI) based machine learning (ML) and deep learning (DL) models have been developed for mobile data traffic prediction. This paper provides a comprehensive review of existing ML models to predict mobile data traffic in 5G networks. Moreover, the existing techniques are reviewed based on different aspects such as major objectives, underlying methodology, advantages, inferences, and performance measures. An extensive comparati ve study of the surveyed approaches also takes place to identify the unique characteristics of every technique. Finally, a summary of challenging issues and future directions are discussed in detail.
An Organization of the Storage and Data Flow in a Hybrid Model for Structuring, Storing and Processing Distributed Data on the Internet

This paper aims to present the structure and data flows in a hybrid model for structuring, storing and processing distributed data on the Internet. The proposed organization of data storage is a combination of centralized tables (entity - relationships) and data marts (star schema) which are the basis of operational, distributed and centralized data repositories. The presented data flow diagram illustrates the interaction between individual data sources, data storage and delivery components, and user applications.
Shaker: a Tool for Detecting More Flaky Tests Faster

A test case that intermittently passes or fails when performed under the same version of source code and test code is said to be flaky. The presence of flaky tests wastes testing time and effort. The most popular approach in industry to detect flakiness is ReRun. The idea behind ReRun is very simple: failing test cases are re-executed many times looking for inconsistencies in the output. Despite its simplicity, the ReRun strategy is very expensive both in terms of time and in terms of computational resources. This is particularly true for contexts where thousands of test cases are performed on a daily basis. Reducing the rerunning overhead is, thus, of utmost importance. This paper presents SHAKER, an open-source tool for detecting flakiness in time-constrained tests by adding noise in the execution environment. The main idea behind SHAKER is to add stressing tasks that compete with the test execution for the use of resources (CPU or memory). SHAKER is available as a GitHub Actions workflow that can be seamlessly integrated with any GitHub project. Alternatively, SHAKER can also be used via its provided Command Line Interface. In our evaluation, SHAKER was able to discover more flaky tests than ReRun and in a faster way (less re-executions); besides, our approach revealed tens of new flaky tests that went undetected by ReRun even after 50 re-executions. Thanks to its flexibility and ease of use, we believe that SHAKER can be useful for both practitioners and researchers.Demo video: https://youtu.be/7-aiQwOb4rAShaker website: https://star-rg.github. io/shaker
A 2-year locomotive exploration and scientific investigation of the lunar farside by the Yutu-2 rover

The lunar nearside has been investigated by many uncrewed and crewed missions, but the farside of the Moon remains poorly known. Lunar farside exploration is challenging because maneuvering rovers with efficient locomotion in harsh extraterrestrial environment is necessary to explore geological characteristics of scientific interest. Chang’E-4 mission successfully targeted the Moon’s farside and deployed a teleoperated rover (Yutu-2) to explore inside the Von Kármán crater, conveying rich information regarding regolith, craters, and rocks. Here, we report mobile exploration on the lunar farside with Yutu-2 over the initial 2 years. During its journey, Yutu-2 has experienced varying degrees of mild slip and skid, indicating that the terrain is relatively flat at large scales but scattered with local gentle slopes. Cloddy soil sticking on its wheels implies a greater cohesion of the lunar soil than encountered at other lunar landing sites. Further identification results indicate that the regolith resembles dry sand and sandy loam on Earth in bearing properties, demonstrating greater bearing strength than that identified during the Apollo missions. In sharp contrast to the sparsity of rocks along the traverse route, small fresh craters with unilateral moldable ejecta are abundant, and some of them contain high-reflectance materials at the bottom, suggestive of secondary impact events. These findings hint at notable differences in the surface geology between the lunar farside and nearside. Experience gained with Yutu-2 improves the understanding of the farside of the Moon, which, in return, may lead to locomotion with improved efficiency and larger range. Description Yutu-2 conducted in situ exploration on the lunar farside, yielding insights into lunar regolith, craters, and rocks.
Atmospheric Environment Data Generation Method Based on Stacked LSTM-GRU

At present, most of the meteorological prediction research is mainly aimed at one weather parameter, and in this paper, a LSTM-GRU-multi network model is proposed to generate multiple weather parameters simultaneously. Training with the LSTM model alone will take a large amount of time due to the atmospheric environment data samples that have many features and huge data volume. Compared with LSTM, GRU has a simpler structure, reduced number of network parameters, and faster convergence speed. In this paper, LSTM and GRU are combined to accelerate the training speed of the network under the premise that LSTM effectively processes a large amount of atmospheric environment data. At the same time, the single-layer network structure is too simple to meet the requirements of processing multiple features of atmospheric environment data, thus a multi-layer network structure is used in this paper. In the experiment, it also includes the processing of outliers and anomalies in the meteorological data sets, the data standardization, etc. The experimental results show that the MSE of the LSTM-GRU-multi model has decreased by at least 11.65% as compared with the traditional multi-layer LSTM model on the test set, meanwhile, the training time of the proposed model is reduced by 2.3%. Thus, the LSTM-GRU-multi model can generate multi-parameter atmospheric environment data accurately and fast.
Efficient Data Trading for Stable and Privacy Preserving Histograms in Internet of Things

Internet of Thing (IoT) systems provide novel opportunities for data acquisition, where sensing devices can flexibly collect and trade data with data brokers. A data broker may conduct sophisticated analysis on the collected data and further exchange statistics like histograms with data requestors. Considering the supply-demand correlations, several pivotal factors must be jointly treated including communication bandwidths, data utilities, privacy issues, total budget, etc. Unfortunately, the current efforts mainly apply the crowdsensing strategies during data trading and overlook the subsequent processing and analysis of the collected data. Therefore, this paper proposes a novel framework for efficient data trading in IoT systems throughout the data collection and data processing phases. In the framework, data contributors can flexibly arrive and departure from the monitored area in heterogeneous time slots. The incentives for data trading are correlated with data volume, channel condition, and privacy issues of each contributor. Meanwhile, a data broker samples partial sensing data and aggregates approximate histograms for data requestors. The objective is to minimize the total budget for data trading. First, the theoretical bound on the necessary budget for histograms with a given accuracy is proved. Then two algorithms are proposed for efficient data trading among data contributors, based on whether the behaviors of data contributors are known in advance. Both algorithms are analyzed and the corresponding guarantees on performance are discussed. Finally, the extensive evaluation results validate the advancement of the proposed algorithms.
Counterexample Guided Inductive Repair of Reactive Contracts

Using third-party executable components to build control systems poses challenges for verification. This is because the informal behavior descriptions that typically accompany the components often fall short of the needed rigor. Consequently, there is a need to formalize a component contract that is strong enough to help establish system properties and also weak enough to account for all potential component behaviors in the system’s context. In this paper, we present a novel approach that allows an analyst to hypothesize a component contract, explore if the component meets the contract, and, if not, have automated support to help repair the contract. Preliminary results show that, in more than 32% of the cases, the repaired contract is logically equivalent to a developer-written one; in a further 63% of cases, it is a distinct, valid, and non-trivial property of the component.
Selective Kernel Network based on Joint Attention Mechanism for Rolling Bearing Fault Diagnosis

Deep learning has demonstrated great vitality in various fields in recent years. However, most deep learning models lack kernel size selection capability and feature importance distinction mechanism. Although there have been studies that have considered using the channel attention mechanism to help the automatic selection of kernel size, few researchers have used the spatial attention mechanism to recalibrate the choice of kernel size. Therefore, in this paper, a selective kernel network, based on joint attention mechanism (SKNJA), is proposed for the first time to further improve the ability of the kernel size selection for the convolutional neural network. The network incorporates the newly designed selective-kernel joint attention module (SKJAM), which consists of a series connection of selective-kernel channel attention module (SKCAM) and selective-kernel spatial attention module (SKSAM). Both SKCAM and SKSAM use two parallel branches with different kernel sizes. The SKNJA is applied in the field of mechanical fault diagnosis. The Case Western Reserve University bearing dataset is used to validate the proposed model. Experiment results indicate that the SKSAM can enhance fault-related kernel size automatically. And it can be further improved by integrating SKCAM. Compared with four advanced deep learning methods, the SKNJA performs best in the bearing fault diagnosis task.
Nekara: Generalized Concurrency Testing

Testing concurrent systems remains an uncomfortable problem for developers. The common industrial practice is to stress-test a system against large workloads, with the hope of triggering enough corner-case interleavings that reveal bugs. However, stress testing is often inefficient and its ability to get coverage of interleavings is unclear. In reaction, the research community has proposed the idea of systematic testing, where a tool takes over the scheduling of concurrent actions so that it can perform an algorithmic search over the space of interleavings.We present an experience paper on the application of systematic testing to several case studies. We separate the algorithmic advancements in prior work (on searching the large space of interleavings) from the engineering of their tools. The latter was unsatisfactory; often the tools were limited to a small domain, hard to maintain, and hard to extend to other domains. We designed Nekara, an open-source cross-platform library for easily building custom systematic testing solutions.We show that (1) Nekara can effectively encapsulate state-of-the-art exploration algorithms by evaluating on prior bench-marks, and (2) Nekara can be applied to a wide variety of scenarios, including existing open-source systems as well as production distributed services of Microsoft Azure. Nekara was easy to use, improved testing, and found multiple new bugs.
Distributed Monitoring for Energy Infrastructures: A Two-Tier Analysis over Wireless Networks

Wireless networks (e.g., 5G networks) enable distributed energy infrastructures to be connected even when they are geometrically isolated. Intelligent monitoring from remote sites therefore becomes possible, allowing decision makers to examine the status of distributed energy infrastructures from a central location. The major challenge is when local devices cannot perform the monitoring independently; transmitting every signal back to the central server triggers enormous amounts of wireless communication. To address this, we propose a two-tier AI system by offloading computations to multiple devices. Specifically, we build lightweight AI models for deployment on edge clients (i.e., edge sensors) and a large-scale AI model for the central server. These two types of AI models are trained with different criteria: the models on the edges act as the filtering tools to detect abnormal events and maximally avoid making false negative predictions, whereas the server model is supposed to be an expert for accurate predictions. By validating on a power theft dataset, we show that such a cascading methodology could filter out sufficient negative examples on the edge side while still being able to provide precise predictions on the second-round analysis.
Power Quality Enhancement in AC Microgrids Using Integrated Genetic Algorithm in ANN

This article presents the detailed analysis of power quality improvement in microgrids by using the multiple schemes. The microgrid contains the various renewable energy sources, and they are interconnected with the power electronic converters. These converters are faced the many issues with respect to the reactive inrush currents and harmonic voltages. These currents also resulted in voltage drop across the line impedances. The voltage harmonics and fluctuations can generate the malfunctioning, overheating and equipment tripping issues. The microgrid stability mainly depends upon the optimization method and compensation of various micro grid units. This article mainly focused on development of smart grid system by combining the two individual microgrids using electronically coupled distributed energy resources (EC-DER) inverter and smart impedance (SI) converter. Thus, the optimal power flow was achieved based on the each individual microgrid available power. The two “parallel optimization” methods are used to optimize various power quality challenges such as operation mode transfer transients, harmonic load sharing and parallel control of current and voltage quality. Thus, artificial neural network (ANN) approach is used for improving EC-DER performance and genetic algorithm (GA) is used for improving SI performance. The stability of the system is achieved by operating the ANN-GA in parallel processing manner. Thus, two microgrids develops the individual powers and optimal power flow is generated in smart grid. The simulation results shows that the proposed ANN-GA controller results the reduced total harmonic distortion (THD) and fault setting time in microgrids as compared to the conventional model predictive control (MPC) approach.