Jeeva: Enterprise Grid-enabled Web Portal for Protein Secondary Structure Prediction

This paper presents a Grid portal for protein secondary structure prediction developed by using services of Aneka, a .NET-based enterprise Grid technology. The portal is used by research scientists to discover new prediction structures in a parallel manner. An SVM (Support Vector Machine)-based prediction algorithm is used with 64 sample protein sequences as a case study to demonstrate the potential of enterprise Grids.Comment: 7 page

Auto-TransRL: Autonomous Composition of Vision Pipelines for Robotic Perception

Creating a vision pipeline for different datasets to solve a computer vision task is a complex and time consuming process. Currently, these pipelines are developed with the help of domain experts. Moreover, there is no systematic structure to construct a vision pipeline apart from relying on experience, trial and error or using template-based approaches. As the search space for choosing suitable algorithms for achieving a particular vision task is large, human exploration for finding a good solution requires time and effort. To address the following issues, we propose a dynamic and data-driven way to identify an appropriate set of algorithms that would be fit for building the vision pipeline in order to achieve the goal task. We introduce a Transformer Architecture complemented with Deep Reinforcement Learning to recommend algorithms that can be incorporated at different stages of the vision workflow. This system is both robust and adaptive to dynamic changes in the environment. Experimental results further show that our method also generalizes well to recommend algorithms that have not been used while training and hence alleviates the need of retraining the system on a new set of algorithms introduced during test time.Comment: Presented at the IEEE ICRA 2022 Workshop in Robotic Perception and Mapping: Emerging Technique

Complex Correlation Measure: a novel descriptor for Poincaré plot

<p>Abstract</p> <p>Background</p> <p>Poincaré plot is one of the important techniques used for visually representing the heart rate variability. It is valuable due to its ability to display nonlinear aspects of the data sequence. However, the problem lies in capturing temporal information of the plot quantitatively. The standard descriptors used in quantifying the Poincaré plot (<it>SD</it>1, <it>SD</it>2) measure the gross variability of the time series data. Determination of advanced methods for capturing temporal properties pose a significant challenge. In this paper, we propose a novel descriptor "Complex Correlation Measure (<it>CCM</it>)" to quantify the temporal aspect of the Poincaré plot. In contrast to <it>SD</it>1 and <it>SD</it>2, the <it>CCM </it>incorporates point-to-point variation of the signal.</p> <p>Methods</p> <p>First, we have derived expressions for <it>CCM</it>. Then the sensitivity of descriptors has been shown by measuring all descriptors before and after surrogation of the signal. For each case study, <it>lag-1 </it>Poincaré plots were constructed for three groups of subjects (Arrhythmia, Congestive Heart Failure (CHF) and those with Normal Sinus Rhythm (NSR)), and the new measure <it>CCM </it>was computed along with <it>SD</it>1 and <it>SD</it>2. ANOVA analysis distribution was used to define the level of significance of mean and variance of <it>SD</it>1, <it>SD</it>2 and <it>CCM </it>for different groups of subjects.</p> <p>Results</p> <p><it>CCM </it>is defined based on the autocorrelation at different lags of the time series, hence giving an in depth measurement of the correlation structure of the Poincaré plot. A surrogate analysis was performed, and the sensitivity of the proposed descriptor was found to be higher as compared to the standard descriptors. Two case studies were conducted for recognizing arrhythmia and congestive heart failure (CHF) subjects from those with NSR, using the Physionet database and demonstrated the usefulness of the proposed descriptors in biomedical applications. <it>CCM </it>was found to be a more significant (<it>p </it>= 6.28E-18) parameter than <it>SD</it>1 and <it>SD</it>2 in discriminating arrhythmia from NSR subjects. In case of assessing CHF subjects also against NSR, <it>CCM </it>was again found to be the most significant (<it>p </it>= 9.07E-14).</p> <p>Conclusion</p> <p>Hence, <it>CCM </it>can be used as an additional Poincaré plot descriptor to detect pathology.</p

Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots

Tracking of inventory and rearrangement of misplaced items are some of the most labor-intensive tasks in a retail environment. While there have been attempts at using vision-based techniques for these tasks, they mostly use planogram compliance for detection of any anomalies, a technique that has been found lacking in robustness and scalability. Moreover, existing systems rely on human intervention to perform corrective actions after detection. In this paper, we present Co-AD, a Concept-based Anomaly Detection approach using a Vision Transformer (ViT) that is able to flag misplaced objects without using a prior knowledge base such as a planogram. It uses an auto-encoder architecture followed by outlier detection in the latent space. Co-AD has a peak success rate of 89.90% on anomaly detection image sets of retail objects drawn from the RP2K dataset, compared to 80.81% on the best-performing baseline of a standard ViT auto-encoder. To demonstrate its utility, we describe a robotic mobile manipulation pipeline to autonomously correct the anomalies flagged by Co-AD. This work is ultimately aimed towards developing autonomous mobile robot solutions that reduce the need for human intervention in retail store management.Comment: 8 pages, 9 figures, 2 tables, IEEE Transactions on Systems, Man and Cybernetic

Motor recovery monitoring using acceleration measurements in post acute stroke patients

C1 - Journal Articles RefereedBACKGROUND: Stroke is one of the major causes of morbidity and mortality. Its recovery and treatment depends on close clinical monitoring by a clinician especially during the first few hours after the onset of stroke. Patients who do not exhibit early motor recovery post thrombolysis may benefit from more aggressive treatment. METHOD: A novel approach for monitoring stroke during the first few hours after the onset of stroke using a wireless accelerometer based motor activity monitoring system is developed. It monitors the motor activity by measuring the acceleration of the arms in three axes. In the presented proof of concept study, the measured acceleration data is transferred wirelessly using iMote2 platform to the base station that is equipped with an online algorithm capable of calculating an index equivalent to the National Institute of Health Stroke Score (NIHSS) motor index. The system is developed by collecting data from 15 patients. RESULTS: We have successfully demonstrated an end-to-end stroke monitoring system reporting an accuracy of calculating stroke index of more than 80%, highest Cohen's overall agreement of 0.91 (with excellent κ coefficient of 0.76). CONCLUSION: A wireless accelerometer based 'hot stroke' monitoring system is developed to monitor the motor recovery in acute-stroke patients. It has been shown to monitor stroke patients continuously, which has not been possible so far with high reliability

Real value solvent accessibility prediction using adaptive support vector regression

April 1-5, 200