Somwrita Sarkar

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This paper presents a knowledge-lean learning and inference mechanism based on Singular Value Decomposition (SVD) for design optimization problem (re)-formulation at the problem modeling stage. The distinguishing feature of the mechanism is that it requires very few training cases to extract and generalize knowledge for large classes of problems sharing(More)
Many real world networks are reported to have hierarchically modular organization. However, there exists no algorithm-independent metric to characterize hierarchical modularity in a complex system. The main results of the paper are a set of methods to address this problem. First, classical results from random matrix theory are used to derive the spectrum of(More)
a r t i c l e i n f o a b s t r a c t Investing in R&D for a product employing new technologies is a challenging issue for companies and governments alike, especially at the critical juncture of deciding the degree of resource allocation, if any. Decision-makers generally rely either on historical data or intuitive prediction to gauge the rate of(More)
An AI algorithm to automate symbolic design reformulation is an enduring challenge in design automation. Existing research shows that design tools either require high levels of knowledge engineering or large databases of training cases. To address these limitations, we present a singular value decomposition (SVD) and unsupervised clustering based method(More)
This paper presents a learning and inference mechanism for unsupervised learning of semantic concepts from purely syntactical examples of design optimization formulation data. Symbolic design formulation is a tough problem from computational and cognitive perspectives, requiring domain and mathematical expertise. By conceptualizing the learning problem as a(More)
Rotation dynamics of eigenvectors of modular network adjacency matrices under random perturbations are presented. In the presence of q communities, the number of eigenvectors corresponding to the q largest eigenvalues form a "community" eigenspace and rotate together, but separately from that of the "bulk" eigenspace spanned by all the other eigenvectors.(More)
The anatomical structure of the brain can be observed via non-invasive techniques such as diffusion imaging. However , these are imperfect because they miss connections that are actually known to exist, especially long range inter-hemispheric ones. In this paper we formulate the inverse problem of inferring the structural connectivity of brain networks from(More)
The new generation of Head Mounted Displays (HMDs) is beginning to make the much-anticipated arrival of augmented reality (AR) and virtual reality (VR) in consumer products reality. A large body of research from the last three decades has laid the foundation for the concepts now emerging in the market. Yet, there has been little focus on the analysis of(More)
The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Abstract This paper addresses the problem of describing the decision-making process of a committee of engineers based upon their verbalized linguistic appraisals of alternatives. First, we show a way to model an in-dividual's evaluation of(More)
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