A Recommender System for NFT Collectibles with Item Feature

Recommender systems have been actively studied and applied in various domains to deal with information overload. Although there are numerous studies on recommender systems for movies, music, and e-commerce, comparatively less attention has been paid to the recommender system for NFTs despite the continuous growth of the NFT market. This paper presents a recommender system for NFTs that utilizes a variety of data sources, from NFT transaction records to external item features, to generate precise recommendations that cater to individual preferences. We develop a data-efficient graph-based recommender system to efficiently capture the complex relationship between each item and users and generate node(item) embeddings which incorporate both node feature information and graph structure. Furthermore, we exploit inputs beyond user-item interactions, such as image feature, text feature, and price feature. Numerical experiments verify the performance of the graph-based recommender system improves significantly after utilizing all types of item features as side information, thereby outperforming all other baselines.Comment: Presented at the AAAI 2023 Bridge on AI for Financial Services (https://sites.google.com/view/aaai-ai-fin/home

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

The authors of this work propose a deep learning-based fault detection model that can be implemented in the field of plastic injection molding. Compared to conventional approaches to fault detection in this domain, recent deep learning approaches prove useful for on-site problems involving complex underlying dynamics with a large number of variables. In addition, the advent of advanced sensors that generate data types in multiple modalities prompts the need for multimodal learning with deep neural networks to detect faults. This process is able to facilitate information from various modalities in an end-to-end learning fashion. The proposed deep learning-based approach opts for an early fusion scheme, in which the low-level feature representations of modalities are combined. A case study involving real-world data, obtained from a car parts company and related to a car window side molding process, validates that the proposed model outperforms late fusion methods and conventional models in solving the problem

Chromosome-level genome assembly of Patagonian moray cod (Muraenolepis orangiensis) and immune deficiency of major histocompatibility complex (MHC) class II

The Patagonian moray cod, Muraenolepis orangiensis, belongs to the family Muraenolepididae and is the sole order of Gadiformes that inhabits the temperate and cold waters of the southern hemisphere. One of the features of the Gadiformes order is that they have a remarkably unique immune gene repertoire that influences innate and adaptive immunity, and they lack major histocompatibility complex (MHC) class II, invariant chains (CD74), and CD4 genes. In this study, a high-quality chromosome-level genome assembly was constructed, resulting in a final assembled genome of 893.75 Mb, with an N50 scaffold length of 30.07 Mb and the longest scaffold being 39.77 Mb. Twenty-five high-quality pseudochromosomes were assembled, and the complete BUSCO rate was 93.4%. A total of 34,553 genes were structurally annotated, and 27,691 genes were functionally annotated. Among the 10 primary genes involved in MHC class II, only two ERAP1 genes and one AIRE gene were identified through the genome study. Although no specific reason for the MHC class II deficiency has been identified, it has been shown that the toll-like receptors (TLRs), which are significant to the innate immune response, are significantly expanded in M. orangiensis. A total of 44 TLRs have been identified, with 32 TLR13 genes distributed evenly on six different pseudochromosomes. This study is the first to reveal the whole genome of a Muraenolepididae family and provides valuable insights into the potential rationale for the MHC class II deficiency in a Gadiformes fish species

Laboratory information management system for COVID-19 non-clinical efficacy trial data

Background : As the number of large-scale studies involving multiple organizations producing data has steadily increased, an integrated system for a common interoperable format is needed. In response to the coronavirus disease 2019 (COVID-19) pandemic, a number of global efforts are underway to develop vaccines and therapeutics. We are therefore observing an explosion in the proliferation of COVID-19 data, and interoperability is highly requested in multiple institutions participating simultaneously in COVID-19 pandemic research. Results : In this study, a laboratory information management system (LIMS) approach has been adopted to systemically manage various COVID-19 non-clinical trial data, including mortality, clinical signs, body weight, body temperature, organ weights, viral titer (viral replication and viral RNA), and multiorgan histopathology, from multiple institutions based on a web interface. The main aim of the implemented system is to integrate, standardize, and organize data collected from laboratories in multiple institutes for COVID-19 non-clinical efficacy testings. Six animal biosafety level 3 institutions proved the feasibility of our system. Substantial benefits were shown by maximizing collaborative high-quality non-clinical research. Conclusions : This LIMS platform can be used for future outbreaks, leading to accelerated medical product development through the systematic management of extensive data from non-clinical animal studies.This research was supported by the National research foundation of Korea(NRF) grant funded by the Korea government(MSIT) (2020M3A9I2109027 and 2021M3H9A1030260)

Structural plasticity of BCL-2 family proteins and its functional implication in apoptosis

Dysregulated apoptosis has been implicated in the development of several diseases including cancer and neurodegenerative diseases. Bcl-2 family proteins regulate mitochondrial apoptosis through homo- and hetero-dimerization between pro- and anti-apoptotic Bcl-2 proteins. The first part of this study has been devoted to address the structural transition of Bcl-xL in forming domain-swapped dimer in the presence of n-octyl-β-D-Maltoside detergent. We postulated that this structural plasticity of Bcl-xL might play a regulatory role in the modulation of mitochondrial calcium homeostasis. In the second part of this study, we focused on the molecular interaction between Bcl-xL and Voltage-Dependent Anion Channel (VDAC). While VDAC has been reported to interact with Bcl-xL to regulate mitochondrial calcium homeostasis, detailed molecular basis of the interaction between the two molecules remains elusive. Hence, we aim to provide structural insights into the interaction of Bcl-xL with VDAC N-terminal peptides using NMR spectroscopy.​Doctor of Philosophy (SBS

Cluster analysis of cryptocurrencies via deep clustering

Department of Industrial EngineeringThe cryptocurrency market has evolved into an attractive investment destination as the number of cryptocurrency assets has increased rapidly and the amount of money invested has become significant. On the other hand, no clear method has been established for how to classify the individual cryptocurrencies. To this end, there is an attempt to divide cryptocurrencies into several sectors using clustering methodology such as k-means with dynamic time warping as a distance measure, which is commonly used in financial time series. However, the non-linear characteristics of the cryptocurrency time series cannot be effectively captured by conventional statistical methods. Deep learning models are known to capture non-linear relationships of data well. This study groups the cryptocurrency time series into four clusters by deep clustering combining an autoencoder, manifold learning technique, and traditional clustering method. We discover that deep clustering can distinguish a stablecoin cluster which other existing models did not capture well. Cluster analysis is attempted to show that deep clustering generates mutually exclusive clusters. Financial properties such as log return distribution, volatility and maximum drawdown of cryptocurrencies in each cluster are investigated. Lastly, the study shows that these clusters are helpful in risk management as a building block for asset allocation in cryptocurrency investment, achieving diversification.clos

Operation platform design for modular adaptable ships: Towards the configure-to-order strategy

Modular adaptable ships have received growing attention in recent decades as a promising approach to handling uncertainty in future operating contexts. A modular adaptable ship can be used for multiple purposes by changing its module configuration. This configuration change is based on the ship's operation platform, which is used as a common basis for multiple module configurations. The design of an operation platform is a multi-objective problem in which designers have to deal with the conflicting requirements of multiple missions and carefully determine the interfaces that affect the configurability and flexibility of the modules. In this paper, we present an optimization model for the design of an operation platform. This determines the optimal platform design that best meets the desired capabilities of multiple missions while considering its expected lifecycle cost. A platform's capabilities are evaluated based on its multiple module configurations for individual missions. The evaluation of lifecycle cost uses operation scenarios due to its sensitivity. We implemented the model in a case study involving an offshore support vessel, for which an operation platform was designed to compete with inflexible multi-purpose ships. The results give insights into the platform design problem with opportunities for further improvement of the design.acceptedVersio

A K-Means Clustering Algorithm to Determine Representative Operational Profiles of a Ship Using AIS Data

Defining the appropriate functional requirements in the early ship design stage is important in order that costs that are caused by the over- or under-specified functional capabilities do not increase. This paper presents a K-means clustering algorithm for the determination of functional requirements. It uses automatic identification system (AIS) data from a reference ship to determine the representative operational profiles, which can support decision-makers in defining the functional requirements of ships that will be performing similar missions as those of the reference ship. In a case study, we used this method as part of a ship design project, in which the functional requirements of a battery-only electric ship are defined using AIS data from a reference ship. Results indicate that the cost can be reduced by determining the functional requirements using the proposed method