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  1. The success of artificial neural networks (ANNs) in machine vision techniques has driven hardware researchers to explore more efficient computing elements for energy-expensive operations such as vector-matrix multiplication (VMM). In this work, InP-based floating-gate photo-field-effective transistors (FG-PFETs) are demonstrated as computing elements that integrate both photodetection and initial signal processing at the sensor level. These devices are fabricated from semiconductor channels grown via a back-end CMOS compatible templated liquid phase (TLP) approach. Individual devices are shown to exhibit programmable responsivity, mimicking the effect of a synapse connecting the photodetector to a neuron. Using these devices, a simulated optical neural network (ONN) where the experimentally measured performance of FG-PFETs is used as an input shows excellent image recognition accuracy for color-mixed handwritten digits. 
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    With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various real-world matching markets ranging from ridesharing to crowdsourcing. There are some emerging applications such as rebalancing in bike sharing and trip-vehicle dispatching in ridesharing, which involve a two-stage resource allocation process. The process consists of an offline phase and another sequential online phase, and both phases compete for the same set of resources. In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework. Our model assumes non-uniform and known arrival distributions for online agents in the second online phase, which can be learned from historical data. We propose a parameterized linear programming (LP)-based algorithm, which is shown to be at most a constant factor of 1/4 from the optimal. Experimental results on the real dataset show that our LP-based approaches outperform the LP-agnostic heuristics in terms of robustness and effectiveness.

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