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

Human Pose Estimation in Extremely Low-Light Conditions

We study human pose estimation in extremely low-light images. This task is challenging due to the difficulty of collecting real low-light images with accurate labels, and severely corrupted inputs that degrade prediction quality significantly. To address the first issue, we develop a dedicated camera system and build a new dataset of real low-light images with accurate pose labels. Thanks to our camera system, each low-light image in our dataset is coupled with an aligned well-lit image, which enables accurate pose labeling and is used as privileged information during training. We also propose a new model and a new training strategy that fully exploit the privileged information to learn representation insensitive to lighting conditions. Our method demonstrates outstanding performance on real extremely low light images, and extensive analyses validate that both of our model and dataset contribute to the success.Comment: Accepted to CVPR 202

FPGA Implementation of Keyword Spotting System Using Depthwise Separable Binarized and Ternarized Neural Networks

Keyword spotting (KWS) systems are used for human–machine communications in various applications. In many cases, KWS involves a combination of wake-up-word (WUW) recognition for device activation and voice command classification tasks. These tasks present a challenge for embedded systems due to the complexity of deep learning algorithms and the need for optimized networks for each application. In this paper, we propose a depthwise separable binarized/ternarized neural network (DS-BTNN) hardware accelerator capable of performing both WUW recognition and command classification on a single device. The design achieves significant area efficiency by redundantly utilizing bitwise operators in the computation of the binarized neural network (BNN) and ternary neural network (TNN). In a complementary metal-oxide semiconductor (CMOS) 40 nm process environment, the DS-BTNN accelerator demonstrated significant efficiency. Compared with a design approach where BNN and TNN were independently developed and subsequently integrated as two separate modules into the system, our method achieved a 49.3% area reduction while yielding an area of 0.558 mm2. The designed KWS system, which was implemented on a Xilinx UltraScale+ ZCU104 field-programmable gate array (FPGA) board, receives real-time data from the microphone, preprocesses them into a mel spectrogram, and uses this as input to the classifier. Depending on the order, the network operates as a BNN or a TNN for WUW recognition and command classification, respectively. Operating at 170 MHz, our system achieved 97.1% accuracy in BNN-based WUW recognition and 90.5% in TNN-based command classification

Experimental data management platform for data-driven investigation of combinatorial alloy thin films

Experimental materials data are heterogeneous and include a variety of metadata for processing and characterization conditions, making the implementation of data-driven approaches for developing novel materials difficult. In this paper, we introduce the Thin-Film Alloy Database (TFADB), a materials data management platform, designed for combinatorially investigated thin-film alloys through various experimental tools. Using TFADB, researchers can readily upload, edit, and retrieve multi-dimensional experimental alloy data, such as composition, thickness, x-ray diffraction, electrical resistivity, nanoindentation, and image data. Furthermore, composition-dependent properties from the database can easily be managed in a format adequate to be preprocessed for machine learning analyses. The high flexibility of the software allows the management of new types of materials data that can be potentially acquired from new combinatorial experiments

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