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Engineer, Economics and Mechanism Design (REMOTE) in Philadelphia, PA for BlockScience

Exciting opportunity in Philadelphia, PA for BlockScience as a Engineer, Economics and Mechanism Design (REMOTE) ....

United States , Operations Research , Stochastic Optimization ,

Data-Driven Marketing: The Revolution of Insightful Engagement and Business Transformation

Data-Driven Marketing: The Revolution of Insightful Engagement and Business Transformation
gizbot.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from gizbot.com Daily Mail and Mail on Sunday newspapers.

Dishant Banga , Mayukh Maitra , Kabir Jain , Marketing Campaign Optimization , Technology Analyst , Zs Associates , Togetherthrive Foundation , Data Driven Marketing , Insightful Engagement , Driven Solution , Content Creation , Campaign Optimization , Machine Learning , Data Science , Deal Forecasting , Stochastic Optimization , Time Series Forecasting , Data Science Manager ,

"Optimizing Sample Delivery in RF-Charging Multi-Hop IoT Networks" by Muchen Jiang and Kwan Wu Chin

This paper studies sample delivery in a multi-hop network where a power beacon charges devices via radio frequency (RF) signals. Devices forward samples with a deadline from a source to a sink. The goal is to minimize the power beacon’s transmit power and guarantee that samples arrive at the sink with probability (1-) by their deadline, where is a given probability of failure. A key challenge is that the power beacon does not have instantaneous channel gains information to devices and also between devices; i.e., it does not know the energy level of devices. To this end, we formulate a chance-constrained stochastic program for the problem at hand, and employ the sample-average approximation (SAA) method to compute a solution. We also outline two novel approximation methods: Sampling based Probabilistic Optimal Power Allocation (S-POPA) and Bayesian Optimization based Probabilistic Optimal Power Allocation (BO-POPA). Briefly, S-POPA generates a set of candidate solutions and iterativel ....

Probabilistic Optimal Power Allocation , Bayesian Optimization , Array Signal Processing , Energy Transfer , Imperfect Knowledge , Nternet Of Things , Monte Carlo , Robabilistic Logic , Radio Frequency , Resource Management , Stochastic Optimization ,

The Decade of Deep Learning

As the 2010’s draw to a close, it’s worth taking a look back at the monumental progress that has been made in Deep Learning in this decade.[1] Driven by the development of ever-more powerful comput ....

Christopher Watkin , Lee Sedol , Group Normalization , Neural Networks , Convolutional Neural Networks , Generative Adversarial Networks , Value Network Architectures Source , Rectifier Nonlinearities Improve Neural Network Acoustic Models , Convolutional Networks For Large , Accurate Deep Network , Highway Networks , Convolutional Neural Network , Network Training , Sparse Rectifier Neural Networks , Deep Learning , Computer Vision , Natural Language , Delving Deep , Surpassing Human Level Performance , Imagenet Classification , Best Multi Stage Architecture , Accurate Deep Network Learning , Exponential Linear Units , Normalizing Neural Networks , Error Linear Units , Net Classification ,

"Charging RF-Energy Harvesting Devices in IoT Networks with Imperfect C" by Hang Yu, Kwan Wu Chin et al.

This paper considers energy delivery by a Hybrid Access Point (HAP) to one or more Radio Frequency (RF)-energy harvesting devices. Unlike prior works, it considers imperfect and causal Channel State Information (CSI), and probabilistic constraints that ensure devices receive their required amount of energy over a given planning horizon. To this end, it outlines two novel contributions. The first is a chance-constrained program, which is then solved using a Mixed Integer Linear Program (MILP) coupled with a Sample Average Approximation (SAA) method. The second is a Model Predictive Control (MPC) solution that utilizes Gaussian Mixture Model (GMM) and a so called backoff that is used to tighten probabilistic constraints. The results show that the performance of the MPC based solution is within 8% of the optimal solution with a probability of 90.8%. ....

Integer Linear Program , Hybrid Access Point , Radio Frequency , Channel State Information , Mixed Integer Linear Program , Sample Average Approximation , Model Predictive Control , Gaussian Mixture Model , Internet Of Things , Power System Reliability , Robabilistic Logic , Eceding Horizon , Resource Management , Stochastic Optimization , Task Analysis , Wireless Charging ,