New lower order mixed finite element methods for linear elasticity

New lower order $H(\textrm{div})$-conforming finite elements for symmetric tensors are constructed in arbitrary dimension. The space of shape functions is defined by enriching the symmetric quadratic polynomial space with the $(d+1)$-order normal-normal face bubble space. The reduced counterpart has only $d(d+1)^2$ degrees of freedom. In two dimensions, basis functions are explicitly given in terms of barycentric coordinates. Lower order conforming finite element elasticity complexes starting from the Bell element, are developed in two dimensions. These finite elements for symmetric tensors are applied to devise robust mixed finite element methods for the linear elasticity problem, which possess the uniform error estimates with respect to the Lam\'{e} coefficient $\lambda$, and superconvergence for the displacement. Numerical results are provided to verify the theoretical convergence rates.Comment: 23 pages, 2 figure

Distributed gene clinical decision support system based on cloud computing

Background: The clinical decision support system can effectively break the limitations of doctors’ knowledge and reduce the possibility of misdiagnosis to enhance health care. The traditional genetic data storage and analysis methods based on stand-alone environment are hard to meet the computational requirements with the rapid genetic data growth for the limited scalability. Methods: In this paper, we propose a distributed gene clinical decision support system, which is named GCDSS. And a prototype is implemented based on cloud computing technology. At the same time, we present CloudBWA which is a novel distributed read mapping algorithm leveraging batch processing strategy to map reads on Apache Spark. Results: Experiments show that the distributed gene clinical decision support system GCDSS and the distributed read mapping algorithm CloudBWA have outstanding performance and excellent scalability. Compared with state-of-the-art distributed algorithms, CloudBWA achieves up to 2.63 times speedup over SparkBWA. Compared with stand-alone algorithms, CloudBWA with 16 cores achieves up to 11.59 times speedup over BWA-MEM with 1 core. Conclusions: GCDSS is a distributed gene clinical decision support system based on cloud computing techniques. In particular, we incorporated a distributed genetic data analysis pipeline framework in the proposed GCDSS system. To boost the data processing of GCDSS, we propose CloudBWA, which is a novel distributed read mapping algorithm to leverage batch processing technique in mapping stage using Apache Spark platform. Keywords: Clinical decision support system, Cloud computing, Spark, Alluxio, Genetic data analysis, Read mappin

Distributed and Deep Vertical Federated Learning with Big Data

In recent years, data are typically distributed in multiple organizations while the data security is becoming increasingly important. Federated Learning (FL), which enables multiple parties to collaboratively train a model without exchanging the raw data, has attracted more and more attention. Based on the distribution of data, FL can be realized in three scenarios, i.e., horizontal, vertical, and hybrid. In this paper, we propose to combine distributed machine learning techniques with Vertical FL and propose a Distributed Vertical Federated Learning (DVFL) approach. The DVFL approach exploits a fully distributed architecture within each party in order to accelerate the training process. In addition, we exploit Homomorphic Encryption (HE) to protect the data against honest-but-curious participants. We conduct extensive experimentation in a large-scale cluster environment and a cloud environment in order to show the efficiency and scalability of our proposed approach. The experiments demonstrate the good scalability of our approach and the significant efficiency advantage (up to 6.8 times with a single server and 15.1 times with multiple servers in terms of the training time) compared with baseline frameworks.Comment: To appear in CCPE (Concurrency and Computation: Practice and Experience

Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis

Document-level Sentiment Analysis (DSA) is more challenging due to vague semantic links and complicate sentiment information. Recent works have been devoted to leveraging text summarization and have achieved promising results. However, these summarization-based methods did not take full advantage of the summary including ignoring the inherent interactions between the summary and document. As a result, they limited the representation to express major points in the document, which is highly indicative of the key sentiment. In this paper, we study how to effectively generate a discriminative representation with explicit subject patterns and sentiment contexts for DSA. A Hierarchical Interaction Networks (HIN) is proposed to explore bidirectional interactions between the summary and document at multiple granularities and learn subject-oriented document representations for sentiment classification. Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refining the HIN with sentiment label information to learn a more sentiment-aware document representation. We extensively evaluate our proposed models on three public datasets. The experimental results consistently demonstrate the effectiveness of our proposed models and show that HIN-SR outperforms various state-of-the-art methods.Comment: 17 pages, accepted by ECML-PKDD 202

Failure patterns and survival in patients with nasopharyngeal carcinoma treated with intensity modulated radiation in Northwest China: A pilot study

<p>Abstract</p> <p>Purpose</p> <p>To evaluate the clinical outcomes and patterns of failure in patients with nasopharyngeal carcinoma (NPC) treated with intensity modulated radiotherapy (IMRT) in Northwest China.</p> <p>Methods and materials</p> <p>From January 2006 to December 2009, 138 NPC patients were treated at Xijing Hospital. Of them, 25 cases with stage I-II received IMRT only, 113 cases with stage III-IVb received IMRT plus accomplished platinum-based chemotherapy. The IMRT prescribed dose was PTV 68-74 Gy to gross disease in nasopharynx and 66-72 Gy to positive lymph nodes in 30-33 fractions, and high risk and low risk region PTV was 60-63 Gy and 50.4~56 Gy in 30~33 and 28 fractions respectively. Plasma Epstein Barr virus (EBV) DNA load was measured before treatment. The clinical toxicities, outcomes and patterns of failure were observed.</p> <p>Results</p> <p>The median follow up time was 23 months (range 2 to 53 months). EBV infection positive was only 15.9%. Overall disease failure developed in 36 patients, 99% belonged to stage III/IV disease. Among these, there were 26 distant metastases, 6 local recurrence, and 4 regional recurrence. The 3-year local control rate(LCR), distant metastasis-free survival (MFS), disease-free survival (DFS) and the overall survival (OS) was 93.9%, 79.5%, 70% and 83.1% respectively. Multivariate analyses revealed that age and anemia pre-radiotherapy were independent predictors for OS.</p> <p>Conclusion</p> <p>IMRT with or without chemotherapy can improve the long term survival of NPC patients in Northwest China. Distant metastasis becomes the main cause of treatment failure. Age and anemia before radiotherapy were the main prognosis factors of NPC patients.</p

SmartMal: A Service-Oriented Behavioral Malware Detection Framework for Mobile Devices

This paper presents SmartMal—a novel service-oriented behavioral malware detection framework for vehicular and mobile devices. The highlight of SmartMal is to introduce service-oriented architecture (SOA) concepts and behavior analysis into the malware detection paradigms. The proposed framework relies on client-server architecture, the client continuously extracts various features and transfers them to the server, and the server’s main task is to detect anomalies using state-of-art detection algorithms. Multiple distributed servers simultaneously analyze the feature vector using various detectors and information fusion is used to concatenate the results of detectors. We also propose a cycle-based statistical approach for mobile device anomaly detection. We accomplish this by analyzing the users’ regular usage patterns. Empirical results suggest that the proposed framework and novel anomaly detection algorithm are highly effective in detecting malware on Android devices