DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling

In this paper, we~present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently scheduled for continuously arriving hierarchical jobs with their tasks represented by a directed acyclic graph. Traditionally, heuristic algorithms have been widely used for many resource scheduling domains, and Heterogeneous Earliest Finish Time (HEFT) has been a dominating state-of-the-art technique across a broad range of heterogeneous resource scheduling domains over many years. Despite their long-standing popularity, HEFT-like algorithms are known to be vulnerable to a small amount of noise added to the environment. Our Deep Reinforcement Learning (DRL)-based SoC Scheduler (DeepSoCS), capable of learning the "best" task ordering under dynamic environment changes, overcomes the brittleness of rule-based schedulers such as HEFT with significantly higher performance across different types of jobs. We~describe a DeepSoCS design process using a real-time heterogeneous SoC scheduling emulator, discuss major challenges, and present two novel neural network design features that lead to outperforming HEFT: (i) hierarchical job- and task-graph embedding; and (ii) efficient use of real-time task information in the state space. Furthermore, we~introduce effective techniques to address two fundamental challenges present in our environment: delayed consequences and joint actions. Through an extensive simulation study, we~show that our DeepSoCS exhibits the significantly higher performance of job execution time than that of HEFT with a higher level of robustness under realistic noise conditions. We~conclude with a discussion of the potential improvements for our DeepSoCS neural scheduler.Comment: 18 pages, Accepted by Electronics 202

Association between Dietary Mineral Intake and Chronic Kidney Disease: The Health Examinees (HEXA) Study

Few studies have explored the association between mineral intake and chronic kidney disease (CKD). A cross-sectional analysis investigated the association between mineral intake (calcium, phosphorus, sodium, potassium, iron, and zinc) and CKD using the Health Examinee (HEXA) cohort of the Korean Genome and Epidemiologic Study (KoGES). For 159,711 participants, mineral intake was assessed by a food frequency questionnaire. CKD was defined as an estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m2. Dietary intake of each mineral was divided into quartiles and the quartile including recommended dietary allowance (RDA) or adequate intake (AI) of each mineral was used as a reference. We assessed the association between the quartile of mineral intakes and CKD using polytomous logistic regression models. The lowest quartiles of phosphorus (≤663.68 mg/day, odds ratio [OR] = 1.64, 95% confidence interval [CI]: 1.25–2.15), potassium (≤1567.53 mg/day, OR = 1.87, 95% CI: 1.27–2.75), iron (≤6.93 mg/day, OR = 1.53, 95% CI: 1.17–2.01), and zinc (≤5.86 mg/day, OR = 1.52, 95% CI: 1.02–2.26) were associated with higher odds for advanced CKD compared with the references. The present study suggests that an inadequate intake of some minerals may be associated with CKD occurrence in the general population. Due to the reverse causation issue in this cross-sectional study design, further longitudinal prospective studies are needed in order to prove the results

D-vlog: Multimodal Vlog Dataset for Depression Detection

Detecting depression based on non-verbal behaviors has received great attention. However, most prior work on detecting depression mainly focused on detecting depressed individuals in laboratory settings, which are difficult to be generalized in practice. In addition, little attention has been paid to analyzing the non-verbal behaviors of depressed individuals in the wild. Therefore, in this paper, we present a multimodal depression dataset, D-Vlog, which consists of 961 vlogs (i.e., around 160 hours) collected from YouTube, which can be utilized in developing depression detection models based on the non-verbal behavior of individuals in real-world scenario. We develop a multimodal deep learning model that uses acoustic and visual features extracted from collected data to detect depression. Our proposed model employs the cross-attention mechanism to effectively capture the relationship across acoustic and visual features, and generates useful multimodal representations for depression detection. The extensive experimental results demonstrate that the proposed model significantly outperforms other baseline models. We believe our dataset and the proposed model are useful for analyzing and detecting depressed individuals based on non-verbal behavior

Secure and reliable blockchain-based eBook transaction system for self-published eBook trading.

As eBook readers have expanded on the market, various online eBook markets have arisen as well. Currently, the online eBook market consists of at least publishers and online platform providers and authors, and these actors inevitably incur intermediate costs between them. In this paper, we introduce a blockchain-based eBook market system that enables self-published eBook trading and direct payments from readers to authors without any trusted party; because authors publish themselves and readers purchase directly from authors, neither actor incurs any intermediate costs. However, because of this trustless environment, the validity, ownership and intellectual property of digital contents cannot be verified and protected, and the safety of purchase transactions cannot be ensured. To address these shortcomings, we propose a secure and reliable eBook transaction system that satisfies the following security requirements: (1) verification of the ownership of each eBook, (2) confidentiality of eBook contents, (3) authorization of a right to read a book, (4) authentication of a legitimate purchaser, (5) verification of the validity and integrity of eBook contents, (6) safety of direct purchase transactions, and (7) preventing eBook piracy and illegal distribution. We provide practical cryptographic protocols for the proposed system and analyze the security and simulated performance of the proposed schemes

Controlled Phase Separation in Poly(p-phenyleneethynylene) Thin Films and Its Relationship to Vapor-Sensing Properties

In this paper, we report the synthesis and mesoporous film formation of hydrophobic rodlike poly(pphenyleneethynylene)s (PPEs) and present porosity-dependent quenching studies using 1,3,5-trinitrotoluene (TNT) vapors. Nonsolvent vapor-induced phase separation was used to induce pore formation during film casting, and the concentration of PPEs in the casting solution was controlled carefully to prevent excimer formation. We found that the structures of the sidechains of the PPEs strongly influence the range of relative humidity at which controlled pore generation occurs, which could be rationalized from interfacial energies calculated from contact angle measurements. Porosity of the PPE films resulted in increased efficiency of fluorescence quenching toward TNT vapors, which previously required very thin films (below 5 nm) for sensing applications. The control of the porous structure as well as film thickness constitutes a promising strategy for enhancing the efficiency of chemosensors and in more general applications requiring fine-tuned polymer-gas interactions.N

Functional Group-Dependent Induction of Astrocytogenesis and Neurogenesis by Flavone Derivatives

Neural stem cells (NSCs) differentiate into multiple cell types, including neurons, astrocytes, and oligodendrocytes, and provide an excellent platform to screen drugs against neurodegenerative diseases. Flavonoids exert a wide range of biological functions on several cell types and affect the fate of NSCs. In the present study, we investigated whether the structure-activity relationships of flavone derivatives influence NSC differentiation. As previously reported, we observed that PD98059 (2′-amino-3′-methoxy-flavone), compound 2 (3′-methoxy-flavone) induced astrocytogenesis. In the present study, we showed that compound 3 (2′-hydroxy-3′-methoxy-flavone), containing a 3′-methoxy group, and a non-bulky group at C2′ and C4′, induced astrocytogenesis through JAK-STAT3 signaling pathway. However, compound 1 and 7–12 without the methoxy group did not show such effects. Interestingly, the compounds 4 (2′,3′-dimethoxyflavone), 5 (2′-N-phenylacetamido-3′-methoxy-flavone), and 6 (3′,4′-dimethoxyflavone) containing 3′-methoxy could not promote astrocytic differentiation, suggesting that both the methoxy groups at C3′ and non-bulky group at C2′ and C4′ are required for the induction of astrocytogenesis. Notably, compound 6 promoted neuronal differentiation, whereas its 4′-demethoxylated analog, compound 2, repressed neurogenesis, suggesting an essential role of the methoxy group at C4′ in neurogenesis. These findings revealed that subtle structural changes of flavone derivatives have pronounced effects on NSC differentiation and can guide to design and develop novel flavone chemicals targeting NSCs fate regulation

Fine particulate concentrations over East Asia derived from aerosols measured by the advanced Himawari Imager using machine learning

Fine particulate matter with a diameter below 2.5 lim (PM2.5) is deleterious to the cardiovascular and respiratory systems. It is often difficult to assess the effects of PM2.5 on human health over regions with limited ground monitoring sites, especially in East Asia. As an alternative, we estimated near-surface PM2.5 concentrations by analyzing Advanced Himawari Imager (AHI) Yonsei Aerosol Retrieval (YAER) products. This study incorporates daytime data for East Asia covering the Korean Peninsula, China, Japan, Southeast Asia, and southern Mongolia. We collocated AHI YAER product pixels with meteorological, land-cover, and other ancillary data for the period from March 2018 to February 2019. To estimate PM2.5 concentrations over wide areas spanning many countries displaying various relationships between aerosol optical depth and PM2.5, monthly models were developed by considering both the spatial and temporal characteristics of ground-based PM2.5 measurements. Random forest machine learning model estimated ground-level mass concentrations of PM2.5; subsequent 10-fold cross vali-dation (CV) yielded a CV R-2 value of 0.81 and a CV root mean squared error (RMSE) of 12.3 lig m(-3). We investigated the spatial pattern of PM2.5 concentrations over multiple countries and seasonal variation in PM2.5 concentrations. Diurnal variation of a severe PM2.5 event in the Korean Peninsula was investigated as a case study. The model captured the extremely heterogeneous spatial distribution of PM2.5 concentrations peaked around local noon. To measure the capability of the developed model to estimate PM2.5 concentrations in areas with few in-situ data, its predictive performance was evaluated using a dataset independent of the training process with an R-2 of 0.60 and RMSE of 8.18 lig m(-3). This study demonstrates the potential for satellite-based PM2.5 estimation for areas with insufficient measuring stations

Association between Dietary Mineral Intake and Chronic Kidney Disease: The Health Examinees (HEXA) Study

Few studies have explored the association between mineral intake and chronic kidney disease (CKD). A cross-sectional analysis investigated the association between mineral intake (calcium, phosphorus, sodium, potassium, iron, and zinc) and CKD using the Health Examinee (HEXA) cohort of the Korean Genome and Epidemiologic Study (KoGES). For 159,711 participants, mineral intake was assessed by a food frequency questionnaire. CKD was defined as an estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m2. Dietary intake of each mineral was divided into quartiles and the quartile including recommended dietary allowance (RDA) or adequate intake (AI) of each mineral was used as a reference. We assessed the association between the quartile of mineral intakes and CKD using polytomous logistic regression models. The lowest quartiles of phosphorus (≤663.68 mg/day, odds ratio [OR] = 1.64, 95% confidence interval [CI]: 1.25–2.15), potassium (≤1567.53 mg/day, OR = 1.87, 95% CI: 1.27–2.75), iron (≤6.93 mg/day, OR = 1.53, 95% CI: 1.17–2.01), and zinc (≤5.86 mg/day, OR = 1.52, 95% CI: 1.02–2.26) were associated with higher odds for advanced CKD compared with the references. The present study suggests that an inadequate intake of some minerals may be associated with CKD occurrence in the general population. Due to the reverse causation issue in this cross-sectional study design, further longitudinal prospective studies are needed in order to prove the results

Transparent, Ultrahigh-Refractive Index Polymer Film (n ∼1.97) with Minimal Birefringence (Δ n <0.0010)

High refractive index (RI) thin films are of critical importance for advanced optical devices, and the high refractive index polymers (HRIPs) constitute an interesting class of materials for high RI thin films due to low cost, good processability, light weight, and high flexibility. However, HRIPs have yet to realize their full potential in high RI thin film applications due to their relatively low RI, strong absorption in the blue light region, and limited film formation methods such as rapid vitrification. Herein, we report a development of a new HRIP thin film generated through a one-step vapor-phase process, termed sulfur chemical vapor deposition (sCVD), using elemental sulfur and divinyl benzene. The developed poly(sulfur-co-divinyl benzene) (pSDVBs-CVD) film exhibited RI (measured at 632.8 nm) exceeding 1.97, one of the highest RIs among polymers without metallic elements reported to date. Because the sCVD utilized vaporized sulfur with a unique sulfur-cracking step, formation of long polysulfide chains was suppressed efficiently, while high sulfur content as high as 85 wt % could be achieved with no apparent phase separation. Unlike most of inorganic high RI materials, pSDVB-sCVD was highly transparent in the entire visible range and showed extremely low birefringence of 10 x 10(-4). The HRIP thin film with unprecedentedly high RI, together with outstanding transparency and low birefringence, will serve as a key component in a wide range of high-end optical device applications.N