Semantic levels of domain-independent commonsense knowledgebase for visual indexing and retrieval applications

Building intelligent tools for searching, indexing and retrieval applications is needed to congregate the rapidly increasing amount of visual data. This raised the need for building and maintaining ontologies and knowledgebases to support textual semantic representation of visual contents, which is an important block in these applications. This paper proposes a commonsense knowledgebase that forms the link between the visual world and its semantic textual representation. This domain-independent knowledge is provided at different levels of semantics by a fully automated engine that analyses, fuses and integrates previous commonsense knowledgebases. This knowledgebase satisfies the levels of semantic by adding two new levels: temporal event scenarios and psycholinguistic understanding. Statistical properties and an experiment evaluation, show coherency and effectiveness of the proposed knowledgebase in providing the knowledge needed for wide-domain visual applications

An incremental dual nu-support vector regression algorithm

© 2018, Springer International Publishing AG, part of Springer Nature. Support vector regression (SVR) has been a hot research topic for several years as it is an effective regression learning algorithm. Early studies on SVR mostly focus on solving large-scale problems. Nowadays, an increasing number of researchers are focusing on incremental SVR algorithms. However, these incremental SVR algorithms cannot handle uncertain data, which are very common in real life because the data in the training example must be precise. Therefore, to handle the incremental regression problem with uncertain data, an incremental dual nu-support vector regression algorithm (dual-v-SVR) is proposed. In the algorithm, a dual-v-SVR formulation is designed to handle the uncertain data at first, then we design two special adjustments to enable the dual-v-SVR model to learn incrementally: incremental adjustment and decremental adjustment. Finally, the experiment results demonstrate that the incremental dual-v-SVR algorithm is an efficient incremental algorithm which is not only capable of solving the incremental regression problem with uncertain data, it is also faster than batch or other incremental SVR algorithms

Gene and pathway identification with Lp penalized Bayesian logistic regression

<p>Abstract</p> <p>Background</p> <p>Identifying genes and pathways associated with diseases such as cancer has been a subject of considerable research in recent years in the area of bioinformatics and computational biology. It has been demonstrated that the magnitude of differential expression does not necessarily indicate biological significance. Even a very small change in the expression of particular gene may have dramatic physiological consequences if the protein encoded by this gene plays a catalytic role in a specific cell function. Moreover, highly correlated genes may function together on the same pathway biologically. Finally, in sparse logistic regression with <it>L</it>_{<it>p </it>}(<it>p </it>< 1) penalty, the degree of the sparsity obtained is determined by the value of the regularization parameter. Usually this parameter must be carefully tuned through cross-validation, which is time consuming.</p> <p>Results</p> <p>In this paper, we proposed a simple Bayesian approach to integrate the regularization parameter out analytically using a new prior. Therefore, there is no longer a need for parameter selection, as it is eliminated entirely from the model. The proposed algorithm (BLpLog) is typically two or three orders of magnitude faster than the original algorithm and free from bias in performance estimation. We also define a novel similarity measure and develop an integrated algorithm to hunt the regulatory genes with low expression changes but having high correlation with the selected genes. Pathways of those correlated genes were identified with DAVID <url>http://david.abcc.ncifcrf.gov/</url>.</p> <p>Conclusion</p> <p>Experimental results with gene expression data demonstrate that the proposed methods can be utilized to identify important genes and pathways that are related to cancer and build a parsimonious model for future patient predictions.</p

Graphical modeling of binary data using the LASSO: a simulation study

Background: Graphical models were identified as a promising new approach to modeling high-dimensional clinical data. They provided a probabilistic tool to display, analyze and visualize the net-like dependence structures by drawing a graph describing the conditional dependencies between the variables. Until now, the main focus of research was on building Gaussian graphical models for continuous multivariate data following a multivariate normal distribution. Satisfactory solutions for binary data were missing. We adapted the method of Meinshausen and Buhlmann to binary data and used the LASSO for logistic regression. Objective of this paper was to examine the performance of the Bolasso to the development of graphical models for high dimensional binary data. We hypothesized that the performance of Bolasso is superior to competing LASSO methods to identify graphical models. Methods: We analyzed the Bolasso to derive graphical models in comparison with other LASSO based method. Model performance was assessed in a simulation study with random data generated via symmetric local logistic regression models and Gibbs sampling. Main outcome variables were the Structural Hamming Distance and the Youden Index. We applied the results of the simulation study to a real-life data with functioning data of patients having head and neck cancer. Results: Bootstrap aggregating as incorporated in the Bolasso algorithm greatly improved the performance in higher sample sizes. The number of bootstraps did have minimal impact on performance. Bolasso performed reasonable well with a cutpoint of 0.90 and a small penalty term. Optimal prediction for Bolasso leads to very conservative models in comparison with AIC, BIC or cross-validated optimal penalty terms. Conclusions: Bootstrap aggregating may improve variable selection if the underlying selection process is not too unstable due to small sample size and if one is mainly interested in reducing the false discovery rate. We propose using the Bolasso for graphical modeling in large sample sizes

Energy-Efficient Packet Processing

Packet processing systems (e.g., routers, virus scanners, intrusion detectors, SSL accelerators, etc.) provision sufficient number of processors to handle the expected maximum workload. The observed load at any instant, however, is often substantially lower; further, the load fluctuates significantly over time. These properties offer an opportunity to conserve energy (e.g., by deactivating idle processors or running them in low-power mode). In this paper, we present an on-line algorithm for adapting the number of activated processors such that (1) the total energy consumption of the system across a packet trace is minimized and (2) the additional delay suffered by packets as a result of adaptation is deterministically bounded. The resulting Power Management Algorithm (PMA) is simple, but it accounts for system reconfiguration overheads, copes with the unpredictability of packet arrival patterns, and consumes nearly the same energy as an optimal off-line strategy. A conservative version of the algorithm (CPMA), turns off processors less aggressively than is optimal but still provides good energy savings while reducing the additional packet latency introduced by power management. We demonstrate that for a set of trace workloads both algorithms can reduce the core power consumption by 60-70 % while increasing the average packet processing delay by less than 560µs (PMA) and less than 170µs (CPMA).

The quest for reliable prediction of chemotherapy-induced delayed nausea among breast cancer patients

Aim: Though female sex is considered a risk factor when predicting chemotherapy-induced nausea, not all women will experience nausea. Therefore, the aim of this pilot study was to evaluate the accuracy, and usefulness, of a blood-based assay for predicting chemotherapy-induced delayed nausea among breast cancer patients.Methods: Whole blood from consented breast cancer patients, determined to benefit from chemotherapy, were used to test each individual for their intrinsic glutathione recycling capacity. Both highly-emetogenic and moderately-emetogenic chemotherapies were included in the study. Test results obtained from chemotherapy naïve patients were used to predict delayed nausea. Predicted outcomes were later compared to reported outcomes documented in medical records. Statistical analyses were used to test the accuracy and efficacy of our blood-based test.Results: Even with current and effective anti-emetics, we report that ~31% of breast cancer patients reported delayed nausea. Using the SAS/STAT classification and regression tree method we were able to show that this assay can be used as a predictive tool with an AUC of 0.71-0.74 depending on treatment regimen.Conclusion: The new predictive assay provides an added value in identifying individual breast cancer patients at high risk of developing moderate or severe delayed nausea after treatment with taxane- based therapies such as docetaxel/cyclophosphamide and docetaxel/carboplatin/trastuzumab/pertuzumab. We believe that this assay could help guide the use of anti-emetics for improved patient-oriented care