Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks

Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learning-based channel allocation scheme using graph convolutional networks (GCNs). As a deep reinforcement learning method, we use a well-known method double deep Q-network. In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs. We apply GCNs to a contention graph where APs within their carrier sensing ranges are connected to extract the features of carrier sensing relationships. Additionally, to improve the learning speed especially in an early stage of learning, we employ a game theory-based method to collect the training data independently of the neural network model. The simulation results indicate that the proposed method can appropriately control the channels when compared to extant methods

Fibulin-4 and -5, but not Fibulin-2, are Associated with Tropoelastin Deposition in Elastin-Producing Cell Culture

Elastic system fibers consist of microfibrils and tropoelastin. During development, microfibrils act as a template on which tropoelastin is deposited. Fibrillin-1 is the major component of microfibrils. It is not clear whether elastic fiber-associated molecules, such as fibulins, contribute to tropoelastin deposition. Among the fibulin family, fibulin-2, -4 and -5 are capable of binding to tropoelastin and fibrillin-1. In the present study, we used the RNA interference (RNAi) technique to establish individual gene-specific knockdown of fibulin-2, -4 and -5 in elastin-producing cells (human gingival fibroblasts; HGF). We then examined the extracellular deposition of tropoelastin using immunofluorescence. RNAi-mediated down-regulation of fibulin-4 and -5 was responsible for the diminution of tropoelastin deposition. Suppression of fibulin-5 appeared to inhibit the formation of fibrillin-1 microfibrils, while that of fibulin-4 did not. Similar results to those for HGF were obtained with human dermal fibroblasts. These results suggest that fibulin-4 and -5 may be associated in different ways with the extracellular deposition of tropoelastin during elastic fiber formation in elastin-producing cells in culture

Situating the social issues of image generation models in the model life cycle: a sociotechnical approach

The race to develop image generation models is intensifying, with a rapid increase in the number of text-to-image models available. This is coupled with growing public awareness of these technologies. Though other generative AI models--notably, large language models--have received recent critical attention for the social and other non-technical issues they raise, there has been relatively little comparable examination of image generation models. This paper reports on a novel, comprehensive categorization of the social issues associated with image generation models. At the intersection of machine learning and the social sciences, we report the results of a survey of the literature, identifying seven issue clusters arising from image generation models: data issues, intellectual property, bias, privacy, and the impacts on the informational, cultural, and natural environments. We situate these social issues in the model life cycle, to aid in considering where potential issues arise, and mitigation may be needed. We then compare these issue clusters with what has been reported for large language models. Ultimately, we argue that the risks posed by image generation models are comparable in severity to the risks posed by large language models, and that the social impact of image generation models must be urgently considered

Fibulin-5 Contributes to Microfibril Assembly in Human Periodontal Ligament Cells

The elastic system fibers comprise oxytalan, elaunin and elastic fibers, which differ in their relative microfibril and elastin content. Human periodontal ligaments (PDL) contain only oxytalan fibers (pure microfibrils) among them. Since fibulin-5 regulates the organization of elastic fibers to link the fibers to cells, we hypothesized that fibulin-5 may contribute to the formation of oxytalan fibers. We used siRNA for fibulin-5 in PDL cell culture to examine the extracellular deposition of fibrillin-1 and -2, which are the major components of microfibrils. Fibulin-5 was labeled on microfibrils positive for fibrillin-1 and -2. Fibulin-5 suppression reduced the level of fibrillin-1 and -2 deposition to 60% of the control level. These results suggest that fibulin-5 may control the formation of oxytalan fibers, and play a role in the homeostasis of oxytalan fibers

TORC1 regulates autophagy induction in response to proteotoxic stress in yeast and human cells

Misfolded and aggregated proteins are eliminated to maintain protein homeostasis. Autophagy contributes to the removal of protein aggregates. However, if and how proteotoxic stress induces autophagy is poorly understood. Here we show that proteotoxic stress after treatment with azetidine-2-carboxylic acid (AZC), a toxic proline analog, induces autophagy in budding yeast. AZC treatment attenuated target of rapamycin complex 1 (TORC1) activity, resulting in the dephosphorylation of Atg13, a key factor of autophagy. By contrast, AZC treatment did not affect target of rapamycin complex 2 (TORC2). Proteotoxic stress also induced TORC1 inactivation and autophagy in fission yeast and human cells. This study suggested that TORC1 is a conserved key factor to cope with proteotoxic stress in eukaryotic cells

Fibulin-4 and -5, but not Fibulin-2, are Associated with Tropoelastin Deposition in Elastin-Producing Cell Culture.

Elastic system fibers consist of microfibrils and tropoelastin. During development, microfibrils act as a template on which tropoelastin is deposited. Fibrillin-1 is the major component of microfibrils. It is not clear whether elastic fiber-associated molecules, such as fibulins, contribute to tropoelastin deposition. Among the fibulin family, fibulin-2, -4 and -5 are capable of binding to tropoelastin and fibrillin-1. In the present study, we used the RNA interference (RNAi) technique to establish individual gene-specific knockdown of fibulin-2, -4 and -5 in elastin-producing cells (human gingival fibroblasts; HGF). We then examined the extracellular deposition of tropoelastin using immunofluorescence. RNAi-mediated down-regulation of fibulin-4 and -5 was responsible for the diminution of tropoelastin deposition. Suppression of fibulin-5 appeared to inhibit the formation of fibrillin-1 microfibrils, while that of fibulin-4 did not. Similar results to those for HGF were obtained with human dermal fibroblasts. These results suggest that fibulin-4 and -5 may be associated in different ways with the extracellular deposition of tropoelastin during elastic fiber formation in elastin-producing cells in culture.福岡歯科大学2013年

Temperature-dependent magnetoresistance effects in FeSi/FeSi/FeSi trilayered spin valve junctions

Fe3Si/FeSi2/Fe3Si trilayered junctions were fabricated by facing targets direct-current sputtering combined with a mask method, and the spin valve signals of the junctions were studied in the temperature range from 50 to 300 K. Whereas the magnetoresistance ratio of giant magnetoresistance and tunnel magnetoresistance junctions monotonically increases with decreasing temperature, that of our samples has the maximum value around 80 K and decreases with decreasing temperature at lower than 80 K, which might be due to an increase in the electrical conductivity mismatch between the metallic Fe3Si layers and semiconducting FeSi2 interlayer in the low temperature range.Asia-Pacific Conference on Semiconducting Silicides and Related Materials — Science and Technology Towards Sustainable Electronics (APAC Silicide 2016), July 16-18, 2016, Fukuoka, Japa