Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have relied upon massive amounts of human annotated training data. This time consuming process has begun impeding the progress of these deep learning efforts. This paper describes a method to incorporate photo-realistic computer images from a simulation engine to rapidly generate annotated data that can be used for the training of machine learning algorithms. We demonstrate that a state of the art architecture, which is trained only using these synthetic annotations, performs better than the identical architecture trained on human annotated real-world data, when tested on the KITTI data set for vehicle detection. By training machine learning algorithms on a rich virtual world, real objects in real scenes can be learned and classified using synthetic data. This approach offers the possibility of accelerating deep learning's application to sensorbased classification problems like those that appear in selfdriving cars. The source code and data to train and validate the networks described in this paper are made available for researchers.

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Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

<p>The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.</p>

The present study aims to detect the trends in annual and seasonal rainfall and its magnitude in Amaravathi basin, Tamil Nadu. In this study, the mean annual and seasonal rainfall in 10 rain gauge stations of Amaravathi basin have been analysed to determine the trend and its magnitude for the period of 1982-2014. The trend analysis is done using Mann-Kendall and Sen’s slope estimator. The trend analysis results showed wide variations during all the seasons. The increasing trend of rainfall is found to be dominant during north-east monsoon season when compared to other seasons.

The use of data mining techniques to improve the diagnostic system accuracy is investigated in this paper. The data mining algorithms aim to discover patterns and extract useful knowledge from facts recorded in databases. Generally, the expert systems are constructed for automating diagnostic procedures. The learning component uses the data mining algorithms to extract the expert system rules from the database automatically. Learning algorithms can assist the clinicians in extracting knowledge automatically. As the number and variety of data sources is dramatically increasing, another way to acquire knowledge from databases is to apply various data mining algorithms that extract knowledge from data. As data sets are inherently distributed, the distributed system uses agents to transport the trained classifiers and uses meta learning to combine the knowledge. Commonsense reasoning is also used in association with distributed data mining to obtain better results. Combining human expert knowledge and data mining knowledge improves the performance of the diagnostic system. This work suggests a framework of combining the human knowledge and knowledge gained by better data mining algorithms on a renal and gallstone data set.

Structured representations such as scene graphs serve as an efficient and compact representation that can be used for downstream rendering or retrieval tasks. However, existing efforts to generate realistic images from scene graphs perform poorly on scene composition for cluttered or complex scenes. We propose two contributions to improve the scene composition. First, we enhance the scene graph representation with heuristic-based relations, which add minimal storage overhead. Second, we use extreme points representation to supervise the learning of the scene composition network. These methods achieve significantly higher performance over existing work (69.0% vs 51.2% in relation score metric). We additionally demonstrate how scene graphs can be used to retrieve pose-constrained image patches that are semantically similar to the source query. Improving structured scene graph representations for rendering or retrieval is an important step towards realistic image generation.