Genetic Representations for Evolutionary Minimization of Network Coding Resources

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.Comment: 10 pages, 3 figures, accepted to the 4th European Workshop on the Application of Nature-Inspired Techniques to Telecommunication Networks and Other Connected Systems (EvoCOMNET 2007

Analog circuit optimization using evolutionary algorithms and convex optimization

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 83-88).In this thesis, we analyze state-of-art techniques for analog circuit sizing and compare them on various metrics. We ascertain that a methodology which improves the accuracy of sizing without increasing the run time or the designer effort is a contribution. We argue that the accuracy of geometric programming can be improved without adversely influencing the run time or increasing the designer's effort. This is facilitated by decomposition of geometric programming modeling into two steps, which decouples accuracy of models and run-time of geometric programming. We design a new algorithm for producing accurate posynomial models for MOS transistor parameters, which is the first step of the decomposition. The new algorithm can generate posynomial models with variable number of terms and real-valued exponents. The algorithm is a hybrid of a genetic algorithm and a convex optimization technique. We study the performance of the algorithm on artificially created benchmark problems. We show that the accuracy of posynomial models of MOS parameters is improved by a considerable amount by using the new algorithm. The new posynomial modeling algorithm can be used in any application of geometric programming and is not limited to MOS parameter modeling. In the last chapter, we discuss various ideas to improve the state-of-art in circuit sizing.by Varun Aggarwal.S.M

Evolutionary Approaches to Minimizing Network Coding Resources

We wish to minimize the resources used for network coding while achieving the desired throughput in a multicast scenario. We employ evolutionary approaches, based on a genetic algorithm, that avoid the computational complexity that makes the problem NP-hard. Our experiments show great improvements over the sub-optimal solutions of prior methods. Our new algorithms improve over our previously proposed algorithm in three ways. First, whereas the previous algorithm can be applied only to acyclic networks, our new method works also with networks with cycles. Second, we enrich the set of components used in the genetic algorithm, which improves the performance. Third, we develop a novel distributed framework. Combining distributed random network coding with our distributed optimization yields a network coding protocol where the resources used for coding are optimized in the setup phase by running our evolutionary algorithm at each node of the network. We demonstrate the effectiveness of our approach by carrying out simulations on a number of different sets of network topologies.Comment: 9 pages, 6 figures, accepted to the 26th Annual IEEE Conference on Computer Communications (INFOCOM 2007

Experience, challenges and lessons learnt from microsurgical clipping of intracranial aneurysms at an emerging neurosurgical centre

Objectives: To share our experience, challenges faced, lessons learnt and analyze the results of surgical management by microsurgical clipping of intracranial aneurysms at an emerging neurosurgical centre at Guru Gobind Singh medical college and hospital (GGSMC&H) Faridkot, Punjab. India. Material and Methods: This study includes all the patients who presented with the diagnosis of intracranial aneurysm on CT angiography and were treated with the microsurgical clipping,  between March 2017 to April 2019. Results: There was a total of 23 patients 11female and 12 male. Age range 32 to 85years. On admission 22 patients had SAH on CT scan and one was admitted after incidental detection of the aneurysm without SAH. The time interval between ictus and admission was 0-3 days in 13 patients, 3-14 days in 8 patients and more than 14 days in 1 patient. WFNS grade (gd) I-15 patients, gd II-2, gd III-2, gd IV-3 patients. Fisher gd I-nil, gd II-9, gd III-4, gd IV-9 patients. In 23 patients 27 Aneurysms were clipped. Distribution of location was Anterior Communicating-12, Distal Anterior Cerebral Artery- 4, Middle cerebral artery (MCA) Bifurcation-3, MCA trifurcation-1, Anterior Choroidal-1, Posterior Communicating (P-com) -1, Ophthalmic Internal Carotid Artery (OICA)-4 and three patients had associated multiple aneurysms. Size of aneurysms varied from < 02mm diameter in 2 patients, 2-25mm - 23 and, more than 25mm-2 aneurysms. There was intra op rupture in 2 cases. Post-operatively 2 patients developed hemiparesis, which recovered, nine patients developed vasospasm. Two patients developed chest related complications. One patient developed renal failure.  There were 8 deaths. Patients are on follow up since March 2017 till date. Conclusions: Intracranial aneurysms are challenging to manage due to their proximity to vital intracranial structures, and difficulty in securing intracranial proximal control. Thorough knowledge of intracranial anatomy of adjacent relations, arachnoid planes and skilful dissection is a key element for a successful outcome. Data collected from GGSMC & Hospital may not be representative of the entire state or country’s population. Therefore, a large-scale data collection is necessary to create our own database to ascertain the risk factors and preventive measures that are exclusive to our state and nation

Improved alignment quality by combining evolutionary information, predicted secondary structure and self-organizing maps

BACKGROUND: Protein sequence alignment is one of the basic tools in bioinformatics. Correct alignments are required for a range of tasks including the derivation of phylogenetic trees and protein structure prediction. Numerous studies have shown that the incorporation of predicted secondary structure information into alignment algorithms improves their performance. Secondary structure predictors have to be trained on a set of somewhat arbitrarily defined states (e.g. helix, strand, coil), and it has been shown that the choice of these states has some effect on alignment quality. However, it is not unlikely that prediction of other structural features also could provide an improvement. In this study we use an unsupervised clustering method, the self-organizing map, to assign sequence profile windows to "structural states" and assess their use in sequence alignment. RESULTS: The addition of self-organizing map locations as inputs to a profile-profile scoring function improves the alignment quality of distantly related proteins slightly. The improvement is slightly smaller than that gained from the inclusion of predicted secondary structure. However, the information seems to be complementary as the two prediction schemes can be combined to improve the alignment quality by a further small but significant amount. CONCLUSION: It has been observed in many studies that predicted secondary structure significantly improves the alignments. Here we have shown that the addition of self-organizing map locations can further improve the alignments as the self-organizing map locations seem to contain some information that is not captured by the predicted secondary structure

Pain abdomen in a child - An uncommon cause

Diagnosis, identification of underlying etiology and management of pain abdomen, remains difficult. Tumors presenting as abdominal pain are rare in children. We report a case of 11-year old boy presenting with pain abdomen. On examination, he had a lump in left hypochondrium. Gastrointestinal tumors constitute about 12% of abdominal masses, 2% of which are pancreatic tumors. He underwent laparotomy was diagnosed as desmoplastic small round cell tumor in the pancreas. This report presents an uncommon cause of a common pediatric problem