Stochastic Variance Reduction Methods for Saddle-Point Problems

We consider convex-concave saddle-point problems where the objective functions may be split in many components, and extend recent stochastic variance reduction methods (such as SVRG or SAGA) to provide the first large-scale linearly convergent algorithms for this class of problems which is common in machine learning. While the algorithmic extension is straightforward, it comes with challenges and opportunities: (a) the convex minimization analysis does not apply and we use the notion of monotone operators to prove convergence, showing in particular that the same algorithm applies to a larger class of problems, such as variational inequalities, (b) there are two notions of splits, in terms of functions, or in terms of partial derivatives, (c) the split does need to be done with convex-concave terms, (d) non-uniform sampling is key to an efficient algorithm, both in theory and practice, and (e) these incremental algorithms can be easily accelerated using a simple extension of the "catalyst" framework, leading to an algorithm which is always superior to accelerated batch algorithms.Comment: Neural Information Processing Systems (NIPS), 2016, Barcelona, Spai

An explorative study to assess the cardio pulmonary responses to upperlimb and lower limb free exercises in atheletes

The purpose of this study is to enhance the idea towards prescribing a Standard exercise programme to the cardio Pulmonary risk patients. By understanding the difference in physiologic response between upper and lower extremity free exercises, the professionals can formulate a standard exercise programme. About 15 athletes were selected randomly and conducted the study in Bama Hospital & Bama Sports Club, Vadakkampatti. To these individuals, the resting Blood pressure, Heart Rate were monitored and pressure product is calculated. Then the prescribed free exercises, given to the subject were done to upper extremity. Then the Heart rate, Blood pressure was monitored and Rate pressure product is calculated. On the consecutive day, the and post test values were calculated while doing lower extremity free exercise. Then the difference in hemodynamic changes were calculated by using “t” test. From the results we can conclude that hemodynamic changes while doing upper extremity free exercise is more than lower extremity free exercise. This study aids in prescriping a standard exercise programme for cardio-pulmonary patients

Construction of Control Charts Based On Six Sigma Initiatives for the Number of Defects and Average Number of Defects per Unit

A control chart is a statistical device used for the study and control of a repetitive process. In 1931, Shewart suggested control charts based on 3 sigma limits. Today manufacturing companies around the world apply Six Sigma initiatives, with a result offewer product defects. Companies practicing Six Sigma initiatives are expected to produce 3.4 or less number of defects per million opportunities, a concept suggested by Motorola in 1980. If companies practicing Six Sigma initiatives use control limits suggested by Shewhart, then no points will fall outside the control limits due to the improvement in the quality of the process. ASix Sigma based control chart is constructed for the number of defects and average number of defects per unit. Tables are providedto aid engineers in decision making

Selective biochemical studies in a freshwater prawn, Macrobrachium nobilli(Crustecea: Palaemodinae)

Calcium and phosphorous contents of abdomen and cheliped muscles of juvenile, male and female Macrobrchium nobilii were determined from field collected samples. In all the three groups calcium concentration was higher in chelipeds while the phosphorous content was more in abdomen muscles than in the chelipeds. However between three groups the calcium content varied significantly both in the abdomen and cheliped muscles (P<O.OOl) while the phosphorous content differed (P<O.OS) only in abdomen muscles

Implementation of Three Phase UPS system using Multi-Loop Fuzzy Logic Controller for Linear and Non-Linear Loads

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A novel power optimized hybrid renewable energy system using neural computing and bee algorithm

With rapid depletion of non-renewable energy resources or the fossil fuels like coal, petroleum etc., there has been a significant shift in innovations towards exploiting and tapping of energy from renewable energy resources like sun, bio gas, wind etc., E Off late, there has been an increased research towards combined or hybrid integrated energy generation systems based on renewable resources like sun-wind, sun-biogas etc., These hybrid systems effectively address the past demerits observed in standalone systems which could provide substantial power only during specific periods and seasons. For example, solar power would be much reduced during the night time. Hence hybrid systems effectively counteract this issue as the lack of stability in one system is well compensated by the other. This research paper proposes an optimized hybrid PV-wind power generation system with optimization towards maximization of power generated from the system with the help of neural architecture and bee colony algorithm. The proposed system has been implemented and tested for a wide range of solar irradiances and wind velocities and maximum and stable power generation has been observed when compared to existing techniques

A novel power optimized hybrid renewable energy system using neural computing and bee algorithm

With rapid depletion of non-renewable energy resources or the fossil fuels like coal, petroleum etc., there has been a significant shift in innovations towards exploiting and tapping of energy from renewable energy resources like sun, bio gas, wind etc., E Off late, there has been an increased research towards combined or hybrid integrated energy generation systems based on renewable resources like sun-wind, sun-biogas etc., These hybrid systems effectively address the past demerits observed in standalone systems which could provide substantial power only during specific periods and seasons. For example, solar power would be much reduced during the night time. Hence hybrid systems effectively counteract this issue as the lack of stability in one system is well compensated by the other. This research paper proposes an optimized hybrid PV-wind power generation system with optimization towards maximization of power generated from the system with the help of neural architecture and bee colony algorithm. The proposed system has been implemented and tested for a wide range of solar irradiances and wind velocities and maximum and stable power generation has been observed when compared to existing techniques

Breast Cancer Classification by Gene Expression Analysis using Hybrid Feature Selection and Hyper-heuristic Adaptive Universum Support Vector Machine

Comprehensive assessments of the molecular characteristics of breast cancer from gene expression patterns can aid in the early identification and treatment of tumor patients. The enormous scale of gene expression data obtained through microarray sequencing increases the difficulty of training the classifier due to large-scale features. Selecting pivotal gene features can minimize high dimensionality and the classifier complexity with improved breast cancer detection accuracy. However, traditional filter and wrapper-based selection methods have scalability and adaptability issues in handling complex gene features. This paper presents a hybrid feature selection method of Mutual Information Maximization - Improved Moth Flame Optimization (MIM-IMFO) for gene selection along with an advanced Hyper-heuristic Adaptive Universum Support classification model Vector Machine (HH-AUSVM) to improve cancer detection rates. The hybrid gene selection method is developed by performing filter-based selection using MIM in the first stage followed by the wrapper method in the second stage, to obtain the pivotal features and remove the inappropriate ones. This method improves standard MFO by a hybrid exploration/exploitation phase to accomplish a better trade-off between exploration and exploitation phases. The classifier HH-AUSVM is formulated by integrating the Adaptive Universum learning approach to the hyper- heuristics-based parameter optimized SVM to tackle the class samples imbalance problem. Evaluated on breast cancer gene expression datasets from Mendeley Data Repository, this proposed MIM-IMFO gene selection-based HH-AUSVM classification approach provided better breast cancer detection with high accuracies of 95.67%, 96.52%, 97.97% and 95.5% and less processing time of 4.28, 3.17, 9.45 and 6.31 seconds, respectively