PU-Ray: Point Cloud Upsampling via Ray Marching on Implicit Surface

While the recent advancements in deep-learning-based point cloud upsampling methods improve the input to autonomous driving systems, they still suffer from the uncertainty of denser point generation resulting from end-to-end learning. For example, due to the vague training objectives of the models, their performance depends on the point distributions of the input and the ground truth. This causes problems of domain dependency between synthetic and real-scanned point clouds and issues with substantial model sizes and dataset requirements. Additionally, many existing methods upsample point clouds with a fixed scaling rate, making them inflexible and computationally redundant. This paper addresses the above problems by proposing a ray-based upsampling approach with an arbitrary rate, where a depth prediction is made for each query ray. The method simulates the ray marching algorithm to achieve more precise and stable ray-depth predictions through implicit surface learning. The rule-based mid-point query sampling method enables a uniform output point distribution without requiring model training using the Chamfer distance loss function, which can exhibit bias towards the training dataset. Self-supervised learning becomes possible with accurate ground truths within the input point cloud. The results demonstrate the method's versatility across different domains and training scenarios with limited computational resources and training data. This allows the upsampling task to transition from academic research to real-world applications.Comment: 13 pages (10 main + 3 supplement), 19 figures (10 main + 9 supplement), 6 table

Rapid and simple single-chamber nucleic acid detection system prepared through nature-inspired surface engineering

Background: Nucleic acid (NA)-based diagnostics enable a rapid response to various diseases, but current techniques often require multiple labor-intensive steps, which is a major obstacle to successful translation to a clinical setting. Methods: We report on a surface-engineered single-chamber device for NA extraction and in situ amplification without sample transfer. Our system has two reaction sites: A NA extraction chamber whose surface is patterned with micropillars and a reaction chamber filled with reagents for in situ polymerase-based NA amplification. These two sites are integrated in a single microfluidic device; we applied plastic injection molding for cost-effective, mass-production of the designed device. The micropillars were chemically activated via a nature-inspired silica coating to possess a specific affinity to NA. Results: As a proof-of-concept, a colorimetric pH indicator was coupled to the on-chip analysis of NA for the rapid and convenient detection of pathogens. The NA enrichment efficiency was dependent on the lysate incubation time, as diffusion controls the NA contact with the engineered surface. We could detect down to 1×103 CFU by the naked eye within one hour of the total assay time. Conclusion: We anticipate that the surface engineering technique for NA enrichment could be easily integrated as a part of various types of microfluidic chips for rapid and convenient nucleic acid-based diagnostics. © 2021 Ivyspring International Publisher. All rights reserved.1

Recent advances in earth-abundant transition metal-catalyzed dihydrosilylation of terminal alkynes

Over the past few years, earth-abundant transition metal-catalyzed hydrosilylation has emerged as an ideal strategy for the synthesis of organosilanes. The success in this area of research has expanded to the advancements of alkyne dihydrosilylation reactions, offering broadened synthetic applications through the selective installation of two silyl groups. In particular, catalysts based on Fe, Co, and Ni have engendered enabling platforms for mild transformations with a range of distinct regioselectivity. This mini-review summarizes recent advances in this research field, highlighting the unique features of each system from both synthetic and mechanistic perspectives

Research on Extensible Non-Fungible Token Framework for Hyperledger Fabric

2

Experimental study to evaluate the flexural behavior of concrete beams reinforced with CFRP grid

Fiber reinforced polymer (FRP) is evaluated as an excellent material to replace rebar in reinforced concrete due to its advantages such as high tensile strength-to-weight ratio and high corrosion resistance. Many studies have been conducted to use FRP as substitutes for steel rebar. However, most of these studies were conducted using FRP bars, and research on using FRP grids in the construction field is focused on the field of repair and strengthening. Therefore, this study reports the results of an experimental study to evaluate the flexural behavior of concrete beam with Carbon FRP (CFRP) grid tensile reinforcements as a basic study to evaluate the feasibility of using FRP grid as a as substitutes for steel reinforcement. Thirteen test specimens were prepared for the experiments. The CFRP grids were applied to the specimens as tensile reinforcement. The concrete cover thickness, layers of the CFRP grid and the spacing between the CFRP grids were considered as test variables. The experiment results showed that the specimens without spacing between the CFRP grids were destroyed by side CFRP grid fracture. Also, as the CFRP grid layer increased, the ultimate load increased by 53% to 94%, and when the cover thickness decreased by 10 mm, the ultimate load decreased about 10%. The ratios of the nominal flexural strengths calculated according to the CSA S806–12 and ACI 440.1R-15 standards were 1.004 and 1.09, respectively. Thus, the use of the CSA S806–12 standard enables a relatively accurate prediction of the nominal flexural strength of concrete beams reinforced with CFRP grids

NFT를 활용한 메신저 서비스 보안 지원 기법

2

FabAsset: Unique Digital Asset Management System for Hyperledger Fabric

—Business is innovating with the advent of blockchain that tokenizes digital assets. To expand the blockchain’s potential, Ethereum, a representative permissionless blockchain platform, supports the fungible token (FT) standard ERC-20 and the non-fungible token (NFT) standard ERC-721. Hyperledger Fabric (Fabric), a representative permissioned blockchain platform, proposed FabToken to support tokens in version 2.0.0 alpha. But FabToken contains only FTs, not NFTs. Given the market share in the enterprise blockchains, Fabric needs to support NFTs as soon as possible. This paper presents a unique digital asset management system called FabAsset so that Fabric can run decentralized applications that require NFTs. This paper describes the design of FabAsset, consisting of chaincode and SDK (Software Development Kit), and the prototype of a decentralized signature service leveraging FabAsset to validate its usefulness. ©2020 IEEE1

P2PCheck: Digital Check Platform based on Non-Fungible Token in Hyperledger Fabric Utilizing Open Banking API

2

Monitoring Rotation Dynamics of Membrane Protein in Live Cells

Dynamic behavior of membrane protein provides critical information in molecular and cellular mechanisms. To have access to the mobility of a membrane protein, single-particle tracking has been advanced for the microscopic mechanism understandings. Among various molecular motions, however, only the lateral motion of the protein has been monitored due to the lack of in situ imaging tool enabling observation for rotation and vibration. Here, we developed plasmonic nanoparticles which can monitor rotational diffusion dynamics as well as lateral motion. This nanoparticle probe allows direct evidence and quantitative analysis of rotation dynamics, and furthermore, observation of conformation changes of proteins and the protein-protein interactions in live cells. This study provides an insight into the molecular mechanism regarding the intracellular signaling process.1

Automated detection of panic disorder based on multimodal physiological signals using machine learning

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs