A5101363464- Machine Learning and Algorithms
- Blockchain Technology Applications and Security
- Machine Learning and Data Classification
- Artificial Intelligence in Law
- Natural Language Processing Techniques
- Topic Modeling
- Image Processing Techniques and Applications
- Auction Theory and Applications
- VLSI and Analog Circuit Testing
- Privacy, Security, and Data Protection
- Cybercrime and Law Enforcement Studies
- S100 Proteins and Annexins
- Regional Development and Environment
- Land Use and Ecosystem Services
- Imbalanced Data Classification Techniques
- Autism Spectrum Disorder Research
- Digital Transformation in Law
- Genetics and Neurodevelopmental Disorders
- Arctic and Antarctic ice dynamics
- Consumer Market Behavior and Pricing
- Law in Society and Culture
- Atmospheric Ozone and Climate
- Biometric Identification and Security
- FinTech, Crowdfunding, Digital Finance
- Meteorological Phenomena and Simulations
University of Washington
2023-2024
Seattle University
2024
Fudan University
2024
University of Iowa
2006-2023
New York University
2023
Ringling College of Art and Design
2016
This paper explores the evidentiary significance of blockchain records and procedural implications integrating this technology into U.S. judicial system, as several states have undertaken legislative measures to facilitate admissibility evidence. We employ a comprehensive methodological approach, including analysis, comparative case law technical examination mechanics, stakeholder engagement. Our study suggests that evidence may be categorized hearsay exceptions or non-hearsay, depending on...
The decline of sea ice in the Arctic region is a critical indicator rapid global warming and can also influence feedback processes Arctic, so prediction extent thickness plays an important role climate modeling prediction. This paper uses machine learning methods to predict extent, by adjusting factors, which include variables, past simple linear-regression-simulated then we found best combination give result with highest R2 score. We noticed that longer periods data shorter data, results...
Facial recognition technology (FRT) has emerged as a powerful tool for public governance and security, but its rapid adoption also raised significant concerns about privacy, civil liberties, ethical implications. This paper critically examines the current rules policies governing FRT, highlighting tensions between state corporate interests on one hand, individual rights considerations other. The study investigates international legal frameworks aimed at protecting arguing that legislative...
Abstract Active Learning has emerged as a viable solution for addressing the challenge of labeling extensive amounts data in data-intensive applications such computer vision and neural machine translation. The main objective is to automatically identify subset unlabeled samples annotation. This identification process based on an acquisition function that assesses value each sample model training. In context vision, image classification crucial task typically requires substantial training...
Modern supply chain systems face significant challenges, including lack of transparency, inefficient inventory management, and vulnerability to disruptions security threats. Traditional optimization methods often struggle adapt the complex dynamic nature these systems. This paper presents a novel blockchain-based zero-trust framework integrated with deep reinforcement learning (SAC-rainbow) address challenges. The SAC-rainbow leverages Soft Actor–Critic (SAC) algorithm prioritized experience...
Introduction Algorithmic decision-making systems are widely used in various sectors, including criminal justice, employment, and education. While these celebrated for their potential to enhance efficiency objectivity, they also pose risks of perpetuating amplifying societal biases discrimination. This paper aims provide an indepth analysis the types algorithmic discrimination, exploring both challenges solutions. Methods The methodology includes a systematic literature review, legal...
This research addresses the critical challenge of classifying surface defects in lithium electronic components, crucial for ensuring reliability and safety batteries. With a scarcity specific defect data, we introduce an innovative Cross-Domain Generalization (CDG) approach, incorporating Cross-domain Augmentation, Multi-task Learning, Iteration Learning. Leveraging steel dataset as foundational knowledge, our approach compensates limited lithium-specific data enhances model generalization....
The rapid progression of Decentralized Finance (DeFi) has established Exchanges (DEX) as critical elements in the financial landscape. Nevertheless, open and transparent nature DEX makes them susceptible to strategic manipulations, especially sandwich attack. During such maneuvers, ill-intentioned actors exploit price slippage by positioning their transactions strategically around a target’s order reap unfair profits. This paper introduces ground-breaking framework rooted mechanism design...
Sustainable Development Goals (SDGs) are important indicators to evaluate the conditions of urban and rural development, their quantitative evaluation considering administrative units is still under exploration. Ecosystem services crucial links in achieving SDGs, implementation mechanisms remain analysis. This research takes 288 cities east Hu Line as object, firstly assessing SDGs realization status, then analyzing zoning types status through cluster Based on this, we explore influencing...
In recent years, waste segregation, treatment, and recycling have become critical global issues, drawing significant attention worldwide. However, the efficiency of processes can be influenced by numerous factors. Among these, classification plays a crucial role, researchers explored application machine learning to automate this step. Nonetheless, traditional approaches often require skilled professionals invest substantial time in debugging, achieving satisfactory accuracy challenging. To...
Abstract Active Learning has emerged as a viable solution for addressing the challenge of labeling extensive amounts data in data-intensive applications such computer vision and neural machine translation. The main objective is to automatically identify subset unlabeled samples annotation. This identification process based on an acquisition function that assesses value each sample model training. In context vision, image classification crucial task typically requires substantial training...
In the digital age, rapid growth of web information has made it increasingly challenging for individuals and organizations to effectively explore extract valuable insights from vast amount available. This paper presents a novel approach automated text summarization that combines advanced natural language processing techniques with recent breakthroughs in deep learning. we propose dual-faceted technique leverages extensive pre-training on broad dataset outside domain, followed by unique...
Abstract This paper investigates the effects of reservation prices in all‐pay auctions based on Bayesian Nash equilibrium symmetric distributions with binary and correlated types. Our study finds that affect players' behavior two ways. Given price, some give up original strategy offering a bid lower than such minimum requirement. Therefore, may discourage effort supply weaker players. However, stronger innovators do not modify their strategies: They will start giving bids above price. Hence,...
Abstract Neutrophils, the most abundant and efficient defenders against pathogens, exert opposing functions across cancer types. However, given their short half-life fragile proliferation, it remains challenging to explore how neutrophils adapt specific fates in cancer. Here we generated integrated single-cell neutrophil transcriptomes from 17 types (225 samples, 143 patients). Neutrophils exhibited extraordinary complexity, with 10 distinct cell states along differentiation paths including...
Automated summarization of legal texts poses a significant challenge due to the complex and specialized nature documentation. Despite recent progress in reinforcement learning for natural language text summarization, its application domain has been less effective. This paper introduces SAC-VAE, novel framework specifically designed summarization. We leverage Variational Autoencoder (VAE) condense high-dimensional state space into more manageable lower-dimensional feature space. These...
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