An ESPC algorithm based approach to solve inventory deployment problem

Global competitiveness has enforced the hefty industries to become more customized. To compete in the market they are targeting the customers who want exotic products, and faster and reliable deliveries. Industries are exploring the option of satisfying a portion of their demand by converting strategically placed products, this helps in increasing the variability of product produced by them in short lead time. In this paper, authors have proposed a new hybrid evolutionary algorithm named Endosymbiotic-Psychoclonal (ESPC) algorithm to determine the amount and type of product to stock as a semi product in inventory. In the proposed work the ability of previously proposed Psychoclonal algorithm to exploit the search space has been increased by making antibodies and antigen more cooperative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results obtained, are compared with other evolutionary algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained, and convergence time required to reach the optimal /near optimal value of the solution

From which world is your graph?

Discovering statistical structure from links is a fundamental problem in the analysis of social networks. Choosing a misspecified model, or equivalently, an incorrect inference algorithm will result in an invalid analysis or even falsely uncover patterns that are in fact artifacts of the model. This work focuses on unifying two of the most widely used link-formation models: the stochastic blockmodel (SBM) and the small world (or latent space) model (SWM). Integrating techniques from kernel learning, spectral graph theory, and nonlinear dimensionality reduction, we develop the first statistically sound polynomial-time algorithm to discover latent patterns in sparse graphs for both models. When the network comes from an SBM, the algorithm outputs a block structure. When it is from an SWM, the algorithm outputs estimates of each node's latent position.Comment: To appear in NIPS 201

The Role of the Lateral Habenula in inhibitory-driven action selection

In order to behave adaptively, animals are required to use cues that predicts the presence or the absence of the desired outcome to guide their selection of actions. Previous studies of how cue influences on choice, using the Specific Pavlovian Instrumental Transfer (S-PIT) paradigm, have yielded considerable progress in our understanding of the underlying mechanism. However, most of them focused on the influence from the excitatory association, where the cue is signalling the presence of outcome. While some pioneering studies have demonstrated the possibility of a cue predicting the absence of outcome affecting the animal's choice behaviour, the neural mechanism that specific to this effect is still largely unexplored. Therefore, the aim of the current thesis was to investigate the role of the Lateral Habenula (LHb) in this type of inhibitory-driven action selection process, as there is reasonable evidences suggesting that this region involved heavily in processing cue that signal the absence of outcome. Our result has shown that the LHb lesion i) weakened the effect of conditioned inhibition, ii) abolished the reversed S-PIT effect that caused by the negative predicting cue, but iii) not affected the normal S-PIT that elicited by the positive predicting cue nor the choice bias that based on the value of the outcomes. Overall speaking, it suggested that the LHb is essential for stimulus-based, and not value-based, choice in situations where the stimuli have been trained as negative, but not positive, predictors of their associated outcomes

Adaptive Reduced Rank Regression

We study the low rank regression problem $\mathbf{y} = M\mathbf{x} + \epsilon$, where $\mathbf{x}$ and $\mathbf{y}$ are $d_1$ and $d_2$ dimensional vectors respectively. We consider the extreme high-dimensional setting where the number of observations $n$ is less than $d_1 + d_2$. Existing algorithms are designed for settings where $n$ is typically as large as $\mathrm{rank}(M)(d_1+d_2)$. This work provides an efficient algorithm which only involves two SVD, and establishes statistical guarantees on its performance. The algorithm decouples the problem by first estimating the precision matrix of the features, and then solving the matrix denoising problem. To complement the upper bound, we introduce new techniques for establishing lower bounds on the performance of any algorithm for this problem. Our preliminary experiments confirm that our algorithm often out-performs existing baselines, and is always at least competitive.Comment: 40 page