Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering (CF) algorithms. The effect of this subsampling on the computing time and accuracy of CF is not fully understood, and clear guidelines for selecting optimal or even appropriate subsampling levels are not available. In this paper, we present a Density-based Random Stratified Subsampling using Clustering (DRSC) algorithm in which the desired Fraction of Users Dropped (FUD) and Fraction of Items Dropped (FID) are specified, and the overall density during subsampling is maintained. Subsequently, we develop simple models of the Training Time Improvement (TTI) and the Accuracy Loss (AL) as functions of FUD and FID, based on extensive simulations of seven standard CF algorithms as applied to various primary matrices from MovieLens, Yahoo Music Rating, and Amazon Automotive data. Simulations show that both TTI and a scaled AL are bi-linear in FID and FUD for all seven methods. The TTI linear regression of a CF method appears to be same for all datasets. Extensive simulations illustrate that TTI can be estimated reliably with FUD and FID only, but AL requires considering additional dataset characteristics. The derived models are then used to optimize the levels of subsampling addressing the tradeoff between TTI and AL. A simple sub-optimal approximation was found, in which the optimal AL is proportional to the optimal Training Time Reduction Factor (TTRF) for higher values of TTRF, and the optimal subsampling levels, like optimal FID/(1FID), are proportional to the square root of TTRF.

The growth of mobile devices has provided significant opportunities for developing healthcare apps based on the mobile device ability to collect data. Unfortunately, the data collection is often intermittent. Missing data present significant challenges to trend analysis of time series. Straightforward approaches consisting of supplementing missing data with constant or zero values or with linear trends can severely degrade the quality of the trend analysis. In this paper, we present a robust adaptive approach to discover the trends from fragmented time series. The approach proposed in this paper is based on the HASF (Hypothesis-testing-based Adaptive Spline Filtering) trend analysis algorithm, which can accommodate non-uniform sampling and is therefore inherently robust to missing data. HASF adapts the nodes of the spline based on hypothesis testing and variance minimization, which adds to its robustness. Further improvement is obtained by filling gaps by data estimated in an earlier trend analysis, provided by HASF itself. Three variants for filling the gaps of missing data are considered, the best of which seems to consist of filling significantly large gaps with linear splines matched for continuity and smoothness with cubic splines covering data-dense regions. Small gaps are ignored and addressed by the underlying cubic spline fitting. Finally, the existing measurements are weighted according to their importance by simply transferring the importance of the missing data to their existing neighbors. The methods are illustrated and evaluated using heart rate datasets, blood pressure datasets, and noisy sine datasets.

The 24-hour news cycle and barrage of online media is a constant drum beat. The flow of positive and negative news is always in flux, influencing our current perspective and reassessing our future outlook. Nowhere is this more true than in the capital markets where assets are priced and risk assessed based on future expectations. While many factors influence a trader's decision to buy or sell an asset it can be argued that the sentiment from the 24-hour news cycle greatly impacts their outlook on the future value of an asset. In this paper we propose new methods to predict the positive or negative sentiment of financial news. Our analysis has found that contemporary document level sentiment analysis methods break down at fine-grained levels. Fine-grained analysis methods are vitally important as the velocity and impact of small texts, such as tweets and news flashes, increase their influence over the decision process. Using Natural Language Processing methods we extract syntactic sentence patterns from financial news headlines. From these patterns we conduct experiments using both lexicon and machine learning sentiment analysis approaches to predict sentiment. We find that our sentiment prediction methods are able to consistently out perform lexicon methods. Our robust techniques give the financial practitioner a method to fold a fine-grained news sentiment factor into their pricing or risk prediction models.

This paper presents an algorithm for the recognition of similar electrical poles from an aerial image by detecting the pole shadow. One pole is used as a template (already identified by a human operator) for the algorithm. The algorithm includes feature extraction, candidate position determination, and elimination of redundant candidates. First, features of a pole shadow are extracted using standard filters and image processing techniques. Then the extracted features are used to design convolution filters tailored to emphasize possible locations for the shadows. Subsequently, an image candidate is submitted to radon transformation to verify adherence to expected shadow characteristics. Simulations show that most poles are made much more noticeable by the algorithm.

Loads in a power distribution system network are mostly inductive and lead to poor power factor. In order to utilize the generated power optimally it is necessary to maintain close-to-unity power factor. Power factor correction is possible by introducing the capacitive loads in the circuit, as to nullify the effect of inductive loading. Due to simplicity of analysis of radial distribution systems, most previous work [1] studied the effect of nonlinear and capacitive loads on the optimal solution of the Capacitor Placement Problem (CPP) for radial distribution systems only. In this paper, we study optimal capacitor placement on interconnected distribution systems in the presence of nonlinear loads. The placement problem is solved using Genetic Algorithms (GA) as implemented in the ETAP Power station software. Results (power losses, operating voltages and annual benefits) are analyzed. Computational results show that harmonic components affect optimal capacitor placement in all system configurations. If all loads were linear, interconnected and loop system configurations offer lower power losses and better operating conditions than the radial system configuration.