Developing an Open-Source Lightweight Game Engine with DNN Support

With the growth of artificial intelligence and deep learning technology, we have many active research works to apply the related techniques in various fields. To test and apply the latest machine learning techniques in gaming, it will be very useful to have a light-weight game engine for quick prototyping. Our game engine is implemented in a cost-effective way, in comparison to well-known commercial proprietary game engines, by utilizing open source products. Due to its simple internal architecture, our game engine is especially beneficial for modifying and reviewing the new functions through quick and repetitive tests. In addition, the game engine has a DNN (deep neural network) module, with which the proposed game engine can apply deep learning techniques to the game features, through applying deep learning algorithms in real-time. Our DNN module uses a simple C++ function interface, rather than additional programming languages and/or scripts. This simplicity enables us to apply machine learning techniques more efficiently and casually to the game applications. We also found some technical issues during our development with open sources. These issues mostly occurred while integrating various open source products into a single game engine. We present details of these technical issues and our solution

Combinatorial Discovery of Irradiation Damage Tolerant Nano-structured W-based alloys

One of the challenges in fusion reactors is the discovery of plasma facing materials capable of withstanding extreme conditions, such as radiation damage and high heat flux. Development of fusion materials can be a daunting task since vast combinations of microstructures and compositions need to be explored, each of which requires trial-and-error based irradiation experiments and materials characterizations. Here, we utilize combinatorial experiments that allow rapid and systematic characterizations of composition-microstructure dependent irradiation damage behaviors of nanostructured tungsten alloys. The combinatorial materials library of W-Re-Ta alloys was synthesized, followed by the high-throughput experiments for probing irradiation damages to the mechanical, thermal, and structural properties of the alloys. This highly efficient technique allows rapid identification of composition ranges with excellent damage tolerance. We find that the distribution of implanted He clusters can be significantly altered by the addition of Ta and Re, which play a critical role in determining property changes upon irradiation

Fully soft organic electrochemical transistor enabling direct skin-mountable electrophysiological signal amplification

Here, we propose fully soft OECTs with all soft components, including a PEDOT:PSS-based soft channel, which show substantial mechanical/electrical properties. In addition, the further demonstrated skin-mountable amplifier implies the strong potential of this work to be an innovative development in wearable electronics

How Does Plant CO2 Physiological Forcing Amplify Amazon Warming in CMIP6 Earth System Models?

Abstract The physiological response to increasing CO2 concentrations will lead to land surface warming through a redistribution of the energy balance. As the Amazon is one of the most plant‐rich regions, the increase in surface temperature, caused by plant CO2 physiological forcing, is particularly large compared to other regions. In this study, we analyze the outputs of the 11 models in the Coupled Model Intercomparison Project Phase 6 to find out how CO2 physiological forcing amplifies Amazonian warming under elevated CO2 levels. With the CO2 concentration increase from 285 to 823 ppm, the Amazon temperature increased by 0.48 ± 0.42 K as a result of plant physiological forcing. Moreover, we assess the contributions of each climate feedback to the surface warming due to physiological forcing by quantifying climate feedbacks based on radiative kernels. Lapse rate feedback and cloud feedback, analyzed as the primary contributors, accounted for 53% and 37% of Amazon warming, respectively. The warming contributions of these two feedbacks also exhibit a significant spread, which contributes to the predictive uncertainty. The surface warming due to reduced evapotranspiration is larger than the upper tropospheric warming in the Amazon, resulting in surface warming by lapse rate feedback. In addition, cloud cover in the Amazon region decreases due to the reduced evapotranspiration. Decreased cloud cover amplifies surface warming through the shortwave cloud feedback. Furthermore, differences in circulation and local convection caused by physiological effect contribute to the inter‐model spread of the cloud feedback

sEMG-Based Hand Gesture Recognition Using Binarized Neural Network

Recently, human–machine interfaces (HMI) that make life convenient have been studied in many fields. In particular, a hand gesture recognition (HGR) system, which can be implemented as a wearable system, has the advantage that users can easily and intuitively control the device. Among the various sensors used in the HGR system, the surface electromyography (sEMG) sensor is independent of the acquisition environment, easy to wear, and requires a small amount of data. Focusing on these advantages, previous sEMG-based HGR systems used several sensors or complex deep-learning algorithms to achieve high classification accuracy. However, systems that use multiple sensors are bulky, and embedded platforms with complex deep-learning algorithms are difficult to implement. To overcome these limitations, we propose an HGR system using a binarized neural network (BNN), a lightweight convolutional neural network (CNN), with one dry-type sEMG sensor, which is implemented on a field-programmable gate array (FPGA). The proposed HGR system classifies nine dynamic gestures that can be useful in real life rather than static gestures that can be classified relatively easily. Raw sEMG data collected from a dynamic gesture are converted into a spectrogram with information in the time-frequency domain and transferred to the classifier. As a result, the proposed HGR system achieved 95.4% classification accuracy, with a computation time of 14.1 ms and a power consumption of 91.81 mW

A skin-friendly soft strain sensor with direct skin adhesion enabled by using a non-toxic surfactant

Wearable electronics, particularly soft strain sensors with direct skin adhesion, play a crucial role in applications such as smart healthcare systems and human-machine interfaces. However, the existing approaches for developing dry-adhesive soft electronic materials often involve potential biotoxicity and vulnerability to humid environments. In this study, we present an eco-friendly and biocompatible surfactant-based composite for soft conductive composite, soft dry-adhesive film, and skin-adherable soft strain sensors. Utilizing polyoxyethylene sorbitan monooleate, also known as Tween 80, as a non-toxic surfactant, polydimethylsiloxane (PDMS) as an elastomeric matrix, and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) as a conductive pathway, the composite exhibits excellent stretchability and conductivity. The soft dry-adhesive film based on Tween 80-added PDMS features exceptional softness and adhesiveness. We demonstrate a soft strain sensor based on these composites that can be directly adhered to the skin and effectively detect various human motions involving large deformations without delamination. This approach offers a promising avenue for future wearable electronics that are safe for both humans and the environment

Chatbot with Touch and Graphics: An Interaction of Users for Emotional Expression and Turn-taking

Use of chatbots for emotional exchange is recently increasing in various domains. However, as existing chatbots have been considered in terms of natural language processing techniques for interaction with text-based chatting, chatbot interaction with users is lacking in terms of considering the emotions of users and managing turn-taking in conversation. This paper suggests an interaction technique having touch interactions with graphic interfaces (TwG) to solve these problems. In the system, users send their emotions and manage turn-taking through TwG technique. We conducted a Wizard of Oz study to evaluate user experience on emotional expression and turn-taking with TwG technique. Results showed that TwG interaction improved emotional expression compared to a traditional text-based chatbot interaction. Furthermore, the results showed that TwG positively affects natural turn-taking of the conversation

Contribution of RdDM to the ecotype-specific differential methylation on conserved as well as highly variable regions between Arabidopsis ecotypes

Abstract Background Several studies showed genome-wide DNA methylation during Arabidopsis embryogenesis and germination. Although it has been known that the change of DNA methylation mainly occurs at CHH context mediated by small RNA-directed DNA methylation pathway during seed ripening and germination, the causality of the methylation difference exhibited in natural Arabidopsis ecotypes has not been thoroughly studied. Results In this study we compared DNA methylation difference using comparative pairwise multi-omics dynamics in Columbia-0 (Col) and Cape Verde Island (Cvi) ecotypes. Arabidopsis genome was divided into two regions, common regions in both ecotypes and Col-specific regions, depending on the reads mapping of whole genome bisulfite sequencing libraries from both ecotypes. Ecotype comparison was conducted within common regions and the levels of DNA methylation on common regions and Col-specific regions were also compared. we confirmed transcriptome were relatively dynamic in stage-wise whereas the DNA methylome and small RNAome were more ecotype-dependent. While the global CG methylation remains steady during maturation and germination, we found genic CG methylation differs the most between the two accessions. We also found that ecotype-specific differentially methylated regions (eDMR) are positively correlated with ecotype-specifically expressed 24-nt small RNA clusters. In addition, we discovered that Col-specific regions enriched with transposable elements (TEs) and structural variants that tend to become hypermethylated, and TEs in Col-specific regions were longer in size, more pericentromeric, and more hypermethylated than those in the common regions. Through the analysis of RdDM machinery mutants, we confirmed methylation on Col-specific region as well as on eDMRs in common region are contributed by RdDM pathway. Lastly, we demonstrated that highly variable sequences between ecotypes (HOT regions) were also affected by RdDM-mediated regulation. Conclusions Through ecotype comparison, we revealed differences and similarities of their transcriptome, methylome and small RNAome both in global and local regions. We validated the contribution of RdDM causing differential methylation of common regions. Hypermethylated ecotype-specific regions contributed by RNA-directed DNA methylation pathway largely depend on the presence of TEs and copy-gain structural variations. These ecotype-specific regions are frequently associated with HOT regions, providing evolutionary insights into the epigenome dynamics within a species