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Neuromorphic computing is expected to bridge cognitive behaviors with computing systems in an efficient, expandable, and biologically inspired way. A pivotal cognitive behavior is the attention mechanism, which is highly important in filtering and regulating spatio‐temporal information. Emerging neuromorphic devices hold prospect in utilizing their internal physical mechanisms for dynamic computing resources. Here, a basic top–down attention computing component consisting of a synaptic transistor and a neuron is proposed, where efficient information processing is realized by combining the inherent device dynamics and the feedback loop. A theoretical model is established in simulation to demonstrate the capabilities of such a computing system in information filtering and control. Notably, new dynamic circuit behaviors, such as conductance oscillation and activate function switching, are discovered from appropriate time parameters. The attention computing component contains rich dynamic behaviors, providing a power and area‐saving method to construct high‐complexity neuromorphic systems for spatio‐temporal signal preprocessing and control. [ABSTRACT FROM AUTHOR]

Neuromorphic systems provide a potential solution for overcoming von Neumann bottleneck and realizing computing with low energy consumption and latency. However, the neuromorphic devices utilized to construct the neuromorphic systems always focus on artificial synapses and neurons, and neglected the important role of astrocyte cells. Here, an astrocyte memristor is demonstrated with encapsulated yttria‐stabilized zirconia (YSZ) to emulate the function of astrocyte cells in biology. Due to the high oxygen vacancy concentration and resultant high ionic conductivity of YSZ, significantly lower forming and set voltages are achieved in the artificial astrocyte, along with high endurance (>1011). More importantly, the nonlinearity in current‐voltage characteristics that usually emerge as the testing cycle increases can be depressed in the astrocyte memristor, and the nonlinearity can also be reversed by applying a refresh operation, which implements the role of biological astrocyte in maintaining the normal activity of neurons. The recovery of linearity can dramatically improve the accuracy of Modified National Institute of Standards and Technology dataset classification from 62.98% to 94.75% when the inputs are encoded in voltage amplitudes. The astrocyte memristor in this work with improved performance and linearity recovery characteristics can well emulate the function of astrocyte cells in biology and have great potential for neuromorphic computing. [ABSTRACT FROM AUTHOR]

Teff is an ancient grain primarily produced in Ethiopia and providing more than 10% of the total calories consumed in the country. The grain is considered as "super grain" due to its nutritional qualities, and it has seen an increase in its demand and price in recent years. These trends raise public concerns about the affordability of the grain and the prevalence of food insecurity in Ethiopia. Therefore, we investigate the impacts of increasing teff prices on consumers' welfare by regions. Using data from two waves of the Ethiopia Socioeconomic Survey 2013–2014 and 2015–2016, we examine the consumption patterns of cereals in Ethiopia and estimate a two‐stage structural demand system. We find that teff is the most own‐price inelastic grain in Ethiopia and an increase of 10% in teff prices will reduce consumer welfare by 0.81, 1.29, and 1.73 Birrs per week for the average rural, town, and urban consumers, respectively. These estimates correspond to 1.64, 2.31, and 2.46% of their weekly food budgets. We also find the negative effects of an increase in teff prices are smaller for the lower‐income groups as they have relatively lower expenditures on teff. Additionally, we analyze the effects of simultaneous changes in wheat and teff prices to measure the extent to which Ethiopia's food policy of distributing subsidized wheat could offset the consumer welfare loss due to an increase in teff prices. [ABSTRACT FROM AUTHOR]

Agricultural research and development programs on new demand‐enhancing products have become increasingly important over the past decade. Large numbers of new agricultural products have been developed and commercialized in the United States to meet consumers' increasingly diverse expectations for food quality. However, little is known about their economic benefits. Focusing on the apple market, we investigate the welfare impacts of the introduction of Honeycrisp apples in a market equilibrium framework. We use market data on apple sales from 61 cities across the United States between 2009 and 2015. We find the introduction of Honeycrisp increased consumer welfare, which is mainly explained by the increased number of total apple varieties. We also find that the introduction of Honeycrisp apples has increased overall market size and total apple sales. To extrapolate our results to the entire U.S. apple market, we perform a back‐of‐the‐envelope analysis and find that the introduction of Honeycrisp apples has increased total consumer welfare by about 859 million dollars during the study period. This corresponds to approximately 19% of the annual average domestic expenditures on public food and agricultural R&D. [ABSTRACT FROM AUTHOR]

To approach an advanced neuromorphic system, a significant unsettled problem is how to realize biologically plausible memory structures that are dramatically different from classical computers. Herein, a physical system based on memristors is simulated to realize associative memory based on discrete attractor networks, which is essentially content‐based storage, and the influence of device characteristics on network performance is systematically studied. An in situ unsupervised learning method is applied to make greater use of array structure and competitions between neurons, demonstrating significant performance improvement in memory capacity and noise tolerance compared with existing supervised approaches. By extending to continuous attractor neural networks (CANNs), working memory is realized based on memristors for the first time via simulation, and the write and read noises in memristor arrays are found to have different impacts on the ability of CANN in maintaining dynamic information. This work lays a foundation for the construction of future advanced neuromorphic computing systems. [ABSTRACT FROM AUTHOR]

Increasing the consumption of fresh fruits and vegetables among children and low-income households is a public health policy priority in the United States. We investigate temporal and spatial price patterns for fresh fruits and vegetables to evaluate the extent to which the value of the fruit and vegetable Cash-Value-Voucher (CVV) of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) may be affected by unequal price levels and changes in price levels across the United States. Our findings show that price differences over space and time have real and consequential impacts on the purchasing power of the CVV. A WIC Program participant in the highest-cost Metropolitan Statistical Area (MSA) can buy significantly fewer fruits and vegetables than a participant who receives the same benefit in the lowest-cost MSA. Further, we find that the value of the CVV has substantially declined across all MSAs since 2009. We discuss the nutritional implications of the variation in the value of the CVV and evaluate potential mechanisms that could be implemented to maintain equal CVV benefits across time and space. [ABSTRACT FROM AUTHOR]