Substrate Resistance Extraction Using a Multi-Domain Surface Integral Formulation

In order to assess and optimize layout strategies for minimizing substrate noise, it is necessary to have fast and accurate techniques for computing contact coupling resistances associated with the substrate. In this talk, we describe an extraction method capable of full-chip analysis which combines modest geometric approximations, a novel integral formulation, and an FFT-accelerated preconditioned iterative method.Singapore-MIT Alliance (SMA

Social Media Usage and Shopping Preferences: an Empirical Investigation

We empirically explore the associations between social media usage at home and shopping preferences using survey data. We focus on popular retail firms including brick-and-mortar firms such as Walmart, Target, Nordstrom, and Best Buy, and online retailers, such as Amazon, Walmart, Target, and Best Buy. Social media usage of popular platforms such as Facebook, Twitter, LinkedIn, and Skype are analyzed. We draw on Media Richness Theory (MRT) and Strength of Weak Ties from Social Network Analysis (SNA) and related theories to explain our results. Our results have important implications for social marketing campaigns and social media policies for consumer retail firms

Social Media Usage and Cultural Dimensions: an Empirical Investigation

Cultural attributes of employees affect organizations in several different ways through their impact on organizational goals and decision-making processes. Social media create ample opportunities for organizations to improve competitiveness and efficiency of marketing and communications. We empirically investigate the impact of employee cultural dimensions on social media usage at work and at home. Such a study has not been undertaken before to the best of our knowledge and this would be the first study to connect cultural dimension characteristics of individuals with social media usage. Specifically, we investigate the effect of Power Distance (PD), Uncertainty Avoidance (UA), and Individualism-Collectivism (IC) on the use of popular social media platforms such as Facebook, Twitter, Skype, and LinkedIn. Our results show that certain cultural dimensions predict higher or lower levels of use of specific social media platforms. We provide implications of our results on research and practice

Chronic impairment of ERK signaling in glutamatergic neurons of the forebrain does not affect spatial memory retention and LTP in the same manner as acute blockade of the ERK pathway

The ERK/MAPK signaling pathway has been extensively studied in the context of learning and memory. Defects in this pathway underlie genetic diseases associated with intellectual disability, including impaired learning and memory. Numerous studies have investigated the impact of acute ERK/MAPK inhibition on long-term potentiation and spatial memory. However, genetic knockouts of the ERKs have not been utilized to determine whether developmental perturbations of ERK/MAPK signaling affect LTP and memory formation in postnatal life. In this study, two different ERK2 conditional knockout mice were generated that restrict loss of ERK2 to excitatory neurons in the forebrain, but at different time-points (embryonically and post-natally). We found that embryonic loss of ERK2 had minimal effect on spatial memory retention and novel object recognition, while loss of ERK2 post-natally had more pronounced effects in these behaviors. Loss of ERK2 in both models showed intact LTP compared to control animals, while loss of both ERK1 and ERK2 impaired late phase LTP. These findings indicate that ERK2 is not necessary for LTP and spatial memory retention and provide new insights into the functional deficits associated with the chronic impairment of ERK signaling

Design and evaluation of a hybrid multi-task learning model for optimizing deep reinforcement learning agents

Driven by recent technological advancements within the artificial intelligence domain, deep learning has emerged as a promising representation learning technique. This in turn has given rise to the evolution of deep reinforcement learning that combines deep learning with reinforcement learning methods. Subsequently, performance optimization achieved by reinforcement learning intelligent agents designed with model-free based approaches were predominantly limited to systems with reinforcement learning algorithms learning single task. Such a model was found to be quite data inefficient, whenever agents needed to interact with more complex, rich data environments. This thesis introduces a hybrid multi-task learning-oriented approach for optimization of deep reinforcement learning agents operating within different but semantically similar environments with related tasks. Empirical results obtained with OpenAI Gym library-based Atari 2600 video gaming environment demonstrate that the proposed hybrid multi-task learning model is successful in addressing key challenges associated with the performance optimization of deep reinforcement learning agents

Effect of a maternal cafeteria diet on the fatty acid composition of milk and offspring red blood cells

Abstract not availableM.A. Vithayathil, J.R. Gugusheff, R.A. Gibson, Z.Y. Ong, B.S. Muhlhausle