Opportunistic Self Organizing Migrating Algorithm for Real-Time Dynamic Traveling Salesman Problem

Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of potential solutions in the given search space to obtain an optimal solution. In this paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been proposed that introduces a novel strategy to generate perturbations effectively. This strategy allows the individual to span across more possible solutions and thus, is able to produce better solutions. A comprehensive analysis of OSOMA on multi-dimensional unconstrained benchmark test functions is performed. OSOMA is then applied to solve real-time Dynamic Traveling Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and simulated using real-time data from Google Maps with a varying cost-metric between any two cities. Although DTSP is a very common and intuitive model in the real world, its presence in literature is still very limited. OSOMA performs exceptionally well on the problems mentioned above. To substantiate this claim, the performance of OSOMA is compared with SOMA, Differential Evolution and Particle Swarm Optimization.Comment: 6 pages, published in CISS 201

IDD-3D: Indian driving dataset for 3d unstructured road scenes

Autonomous driving and assistance systems rely on annotated data from traffic and road scenarios to model and learn the various object relations in complex real-world scenarios. Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios and adapt to different situations. Currently, existing datasets, while large-scale, lack such diversities and are geographically biased towards mainly developed cities. An unstructured and complex driving layout found in several developing countries such as India poses a challenge to these models due to the sheer degree of variations in the object types, densities, and locations. To facilitate better research toward accommodating such scenarios, we build a new dataset, IDD-3D, which consists of multimodal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDAR frames across various traffic scenarios. We discuss the need for this dataset through statistical comparisons with existing datasets and highlight benchmarks on standard 3D object detection and tracking tasks in complex layouts. Code and data available (1)

A hierarchical anatomical classification schema for prediction of phenotypic side effects

<div><p>Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a ‘hierarchical anatomical classification schema’ which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects.</p></div

A hierarchical anatomical classification schema for prediction of phenotypic side effects - Fig 3

<p>(A) Distribution of side-effects across drugs showing presence of drugs associated with exceptionally large number of side effects. (B) Distribution of drugs across side effetcs depicting presence of adverse reactions that are linked to large number of drugs.</p

Comparison of prediction performance of classifiers in terms of AUC score, at different levels hierarchy.

<p>Comparison of prediction performance of classifiers in terms of AUC score, at different levels hierarchy.</p

Evaluation of classifier performance in response to AUC-ROC and F score (or any other point metric) in the presence of class imbalance.

<p>A metric requiring high F score as well as AUC-ROC provides a better measure of classification performance.</p

Comparison of prediction performance of classifiers in terms of F2 score, at different levels hierarchy.

<p>Comparison of prediction performance of classifiers in terms of F2 score, at different levels hierarchy.</p

The schema for hierarchical anatomical classification of side effects by aggregating them into their associated organs, sub-systems and systems.

<p>The schema for hierarchical anatomical classification of side effects by aggregating them into their associated organs, sub-systems and systems.</p

Enumeration of performance enhancement at different levels of coarse-graining as compared to random aggregation.

<p>Enumeration of performance enhancement at different levels of coarse-graining as compared to random aggregation.</p