Giant room-temperature spin caloritronics in spin-semiconducting graphene nanoribbons

pre-printSpin caloritronics refers to generating spin current by thermal gradient. Here we report a theoretical study demonstrating giant spin caloritronic effects in a new class of materials, called spin semiconductors, which are characterized with a "spin gap," the energy gap between spin-up and -down channels. Generally, spin Seebeck coefficient (Ss ) is shown to increase linearly with the spin gap. Specifically, unprecedented large Ss ∼ 3.4 mV/K and spin figure of merit ZsT ∼ 119 were found in spin-semiconducting graphene nanoribbons (GNRs) with sawtooth (ST) zigzag edges, based on first-principles calculations. Such giant spin caloritronic effects are shown to originate from a large spin gap of ST GNRs, in addition to two other spin-independent features of large band gap and narrow bandwidth which are commonly known for good thermoelectric materials. Our studies suggest that spin-semiconducting nanostructures, such as ST GNRs, are promising candidates for room-temperature spin caloritronics with high efficiency

Age-Associated Loss of Lamin-B Leads to Systemic Inflammation and Gut Hyperplasia

SummaryAging of immune organs, termed as immunosenescence, is suspected to promote systemic inflammation and age-associated disease. The cause of immunosenescence and how it promotes disease, however, has remained unclear. We report that the Drosophila fat body, a major immune organ, undergoes immunosenescence and mounts strong systemic inflammation that leads to deregulation of immune deficiency (IMD) signaling in the midgut of old animals. Inflamed old fat bodies secrete circulating peptidoglycan recognition proteins that repress IMD activity in the midgut, thereby promoting gut hyperplasia. Further, fat body immunosenecence is caused by age-associated lamin-B reduction specifically in fat body cells, which then contributes to heterochromatin loss and derepression of genes involved in immune responses. As lamin-associated heterochromatin domains are enriched for genes involved in immune response in both Drosophila and mammalian cells, our findings may provide insights into the cause and consequence of immunosenescence during mammalian aging.PaperFlic

Toward a Brain-Inspired System: Deep Recurrent Reinforcement Learning for a Simulated Self-Driving Agent

An effective way to achieve intelligence is to simulate various intelligent behaviors in the human brain. In recent years, bio-inspired learning methods have emerged, and they are different from the classical mathematical programming principle. From the perspective of brain inspiration, reinforcement learning has gained additional interest in solving decision-making tasks as increasing neuroscientific research demonstrates that significant links exist between reinforcement learning and specific neural substrates. Because of the tremendous research that focuses on human brains and reinforcement learning, scientists have investigated how robots can autonomously tackle complex tasks in the form of making a self-driving agent control in a human-like way. In this study, we propose an end-to-end architecture using novel deep-Q-network architecture in conjunction with a recurrence to resolve the problem in the field of simulated self-driving. The main contribution of this study is that we trained the driving agent using a brain-inspired trial-and-error technique, which was in line with the real world situation. Besides, there are three innovations in the proposed learning network: raw screen outputs are the only information which the driving agent can rely on, a weighted layer that enhances the differences of the lengthy episode, and a modified replay mechanism that overcomes the problem of sparsity and accelerates learning. The proposed network was trained and tested under a third-party OpenAI Gym environment. After training for several episodes, the resulting driving agent performed advanced behaviors in the given scene. We hope that in the future, the proposed brain-inspired learning system would inspire practicable self-driving control solutions

ICALL offering individually adaptive input: Effects of complex input on L2 development

The Artificial Intelligence methods employed in Intelligent Computer Assisted Language Learning (ICALL) in principle makes it possible to individually support language learners. Second Language Acquisition (SLA) research and language teaching practitioners agree on the relevance of target language input adapted to the learner level. However, little systematic research has explored individually adapting input and how it impacts learners. Building on previous findings on apparent alignment between the complexity of learner input and their output (Chen & Meurers, 2019), the purpose of this study is to investigate how different challenge levels of adaptive input impact learners’ written output . We developed an ICALL system implementing a Complex Input Primed Writing task that selects texts for individual learners and ran an experiment grouping learners into four classes: no, low, medium, or high challenge in relation to the individual learners’ writing complexity. The results show that learners generally were able to align to low- and medium-level challenges, producing more complex writings after receiving the adaptively challenging input, but less so for the high challenge group. The study demonstrates how an ICALL system used in a regular language learning context can support SLA research into adaptive input and complexity alignment