The Effects of Performance-Contingent Financial Incentives in Online Labor Markets

Online labor markets such as Amazon Mechanical Turk (MTurk) have emerged as platforms that facilitate the allocation of productive effort across global economies. Many of these markets compensate workers with monetary payments. We study the effects of performance-contingent financial rewards on work quality and worker effort in MTurk via two experiments. We find that the magnitude of performance contingent financial rewards alone affects neither quality nor effort. However, when workers working on two tasks of the same type in a sequence, the change in the magnitude of the reward over the two tasks affects both. In particular, both work quality and worker effort increase (alternatively decrease) as the reward increases (alternatively decreases) for the second task. This suggests the existence of the anchoring effect on workers’ perception of incentives in MTurk and that this effect can be leveraged in workflow design to increasethe effectiveness of financial incentives.Engineering and Applied Science

Rational catalyst design for N2 reduction under ambient conditions: Strategies towards enhanced conversion efficiency

Ammonia (NH3), one of the basic chemicals in most fertilizers and a promising carbon-free energy storage carrier, is typically synthesized via the Haber–Bosch process with high energy consumption and massive emission of greenhouse gases. The photo/electrocatalytic nitrogen reduction reaction (NRR) under ambient conditions has attracted increasing interests recently, providing alternative routes to realize green NH3 synthesis. Despite rapid advances achieved in this most attractive research field, the unsatisfactory conversion efficiency including a low NH3 yield rate, and limited Faradaic efficiency or apparent quantum efficiency still remains as a great challenge. The NRR performance is intrinsically related to the electronic and surface structure of catalysts. Rational design and preparation of advanced catalysts are indispensable to improve the performance (e.g., activity and selectivity) of NRR. In this Review, various strategies for the development of desirable catalysts are comprehensively summarized, mainly containing the defect engineering, structural manipulation, crystallographic tailoring, and interface regulation. State-of-the-art heterogeneous NRR catalysts, prevailing theories and underlying catalytic mechanisms, together with current issues, critical challenges, and perspectives are discussed. It is highly expected that this Review will promote the understanding of recent advances in this area and stimulate greater interests for designing promising NRR catalysts in future

Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations

We present a new back propagation based training algorithm for discrete-time spiking neural networks (SNN). Inspired by recent deep learning algorithms on binarized neural networks, binary activation with a straight-through gradient estimator is used to model the leaky integrate-fire spiking neuron, overcoming the difficulty in training SNNs using back propagation. Two SNN training algorithms are proposed: (1) SNN with discontinuous integration, which is suitable for rate-coded input spikes, and (2) SNN with continuous integration, which is more general and can handle input spikes with temporal information. Neuromorphic hardware designed in 40nm CMOS exploits the spike sparsity and demonstrates high classification accuracy (>98% on MNIST) and low energy (48.4-773 nJ/image).Comment: 2017 IEEE Biomedical Circuits and Systems (BioCAS

The Cognitive Load of Observation Tasks in 3D Video is Lower Than That in 2D Video

We are exposed to more and more 3D videos, some for entertainment and some for scientific research. Some experiments using 3D video as a stimulus focus only on its visual effect. We studied the cognitive difference between 3D and 2D videos by analyzing EEG. This research adopts a 2 x 4 experimental design, including 2D and 3D versions of 4 video scenes. These four video scenes can be classified into two simple task scenes and two complex task scenes. The simple task scenario and the complex task scenario each contain a video with violent content changes and a calm video. Subjects need to watch eight videos. We recorded the EEG information of the subjects and analyzed the power of alpha and theta oscillations. On this basis, we calculated the cognitive load index (CLI), which can be used as an indicator of cognitive load. The results showed that 3D videos that required subjects to perform simple tasks brought higher cognitive load to most subjects. When the video contains complex tasks, the cognitive load of subjects does not show similar regularity. Specifically, only half of the people had higher cognitive load when watching the 3D version of the video than when watching the 2D version. In addition, the cognitive load level of subjects showed significant individual differencesComment: 7 pages, 18 figure