Hasil untuk "General works"

Di Wu, A. Sedgwick, Thorfinnur Gunnlaugsson et al.

Fluorescent chemosensors for ions and neutral analytes have been widely applied in many diverse fields such as biology, physiology, pharmacology, and environmental sciences. The field of fluorescent chemosensors has been in existence for about 150 years. In this time, a large range of fluorescent chemosensors have been established for the detection of biologically and/or environmentally important species. Despite the progress made in this field, several problems and challenges still exist. This tutorial review introduces the history and provides a general overview of the development in the research of fluorescent sensors, often referred to as chemosensors. This will be achieved by highlighting some pioneering and representative works from about 40 groups in the world that have made substantial contributions to this field. The basic principles involved in the design of chemosensors for specific analytes, problems and challenges in the field as well as possible future research directions are covered. The application of chemosensors in various established and emerging biotechnologies, is very bright.

Chen Gao, Yu Zheng, Nian Li et al.

Recommender system is one of the most important information services on today’s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems. We first introduce the background and the history of the development of both recommender systems and graph neural networks. For recommender systems, in general, there are four aspects for categorizing existing works: stage, scenario, objective, and application. For graph neural networks, the existing methods consist of two categories: spectral models and spatial ones. We then discuss the motivation of applying graph neural networks into recommender systems, mainly consisting of the high-order connectivity, the structural property of data and the enhanced supervision signal. We then systematically analyze the challenges in graph construction, embedding propagation/aggregation, model optimization, and computation efficiency. Afterward and primarily, we provide a comprehensive overview of a multitude of existing works of graph neural network-based recommender systems, following the taxonomy above. Finally, we raise discussions on the open problems and promising future directions in this area. We summarize the representative papers along with their code repositories in https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems.

P. Genevet, F. Capasso, F. Aieta et al.

This article reviews recent progress leading to the realization of planar optical components made of a single layer of phase shifting nanostructures. After introducing the principles of planar optics and discussing earlier works on subwavelength diffractive optics, we introduce a classification of metasurfaces based on their different phase mechanisms and profiles and a comparison between plasmonic and dielectric metasurfaces. We place particular emphasis on the recent developments on electric and magnetic field control of light with dielectric nanostructures and highlight the physical mechanisms and designs required for efficient all-dielectric metasurfaces. Practical devices of general interest such as metalenses, beam deflectors, holograms, and polarizing interfaces are discussed, including high-performance metalenses at visible wavelengths. Successful strategies to achieve achromatic response at selected wavelengths and near unity transmission/reflection efficiency are discussed. Dielectric metasurfaces and dispersion management at interfaces open up technology opportunities for applications including wavefront control, lightweight imaging systems, displays, electronic consumer products, and conformable and wearable optics.

Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. The primary goal of this article is to provide a clear overview of current multiagent deep reinforcement learning (MDRL) literature. Additionally, we complement the overview with a broader analysis: (i) we revisit previous key components, originally presented in MAL and RL, and highlight how they have been adapted to multiagent deep reinforcement learning settings. (ii) We provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research. (iii) We take a more critical tone raising practical challenges of MDRL (e.g., implementation and computational demands). We expect this article will help unify and motivate future research to take advantage of the abundant literature that exists (e.g., RL and MAL) in a joint effort to promote fruitful research in the multiagent community.

David Freedberg, Vittorio Gallese

Jinchao Xu

N. Rose

Xiao Yang, R. Kwitt, M. Niethammer

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software.

A. Amin

Abstract This paper proposes a non‐territorial reading of a politics of place. Focusing on the politics of contemporary regionalism, it argues that globalisation and the general rise of a society of transnational flows and networks no longer allow a conceptualisation of place politics in terms of spatially bound processes and institutions. The second part of the paper outlines an alternative politics of place that works with the varied distanciated geographies that cut across a given region.

Vida J. Morkunas, Jeannette Paschen, E. Boon

Abstract Much of the attention surrounding blockchain today is focused on financial services, with very little discussion about nonfinancial services firms and how blockchain technology may affect organizations, their business models, and how they create and deliver value. In addition, some confusion remains between the blockchain (with definite article) and blockchain (no article), distributed ledger technologies, and their applications. Our article offers a primer on blockchain technology aimed at general managers and executives. The key contributions of this article lie in providing an explanation of blockchain, including how a blockchain transaction works and a clarification of terms, and outlining different types of blockchain technologies. We also discuss how different types of blockchain impact business models. Building on the well-established business model framework by Osterwalder and Pigneur, we outline the effect that blockchain technologies can have on each element of the business model, along with illustrations from firms developing blockchain technology.

Jianning Li, Amin Dada, B. Puladi et al.

The recent release of ChatGPT, a chat bot research project/product of natural language processing (NLP) by OpenAI, stirs up a sensation among both the general public and medical professionals, amassing a phenomenally large user base in a short time. This is a typical example of the 'productization' of cutting-edge technologies, which allows the general public without a technical background to gain firsthand experience in artificial intelligence (AI), similar to the AI hype created by AlphaGo (DeepMind Technologies, UK) and self-driving cars (Google, Tesla, etc.). However, it is crucial, especially for healthcare researchers, to remain prudent amidst the hype. This work provides a systematic review of existing publications on the use of ChatGPT in healthcare, elucidating the 'status quo' of ChatGPT in medical applications, for general readers, healthcare professionals as well as NLP scientists. The large biomedical literature database PubMed is used to retrieve published works on this topic using the keyword 'ChatGPT'. An inclusion criterion and a taxonomy are further proposed to filter the search results and categorize the selected publications, respectively. It is found through the review that the current release of ChatGPT has achieved only moderate or 'passing' performance in a variety of tests, and is unreliable for actual clinical deployment, since it is not intended for clinical applications by design. We conclude that specialized NLP models trained on (bio)medical datasets still represent the right direction to pursue for critical clinical applications.

Zak Murez, Soheil Kolouri, D. Kriegman et al.

We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without requiring any training annotations in the target domain. This is achieved by adding extra networks and losses that help regularize the features extracted by the backbone encoder network. To this end we propose the novel use of the recently proposed unpaired image-to-image translation framework to constrain the features extracted by the encoder network. Specifically, we require that the features extracted are able to reconstruct the images in both domains. In addition we require that the distribution of features extracted from images in the two domains are indistinguishable. Many recent works can be seen as specific cases of our general framework. We apply our method for domain adaptation between MNIST, USPS, and SVHN datasets, and Amazon, Webcam and DSLR Office datasets in classification tasks, and also between GTA5 and Cityscapes datasets for a segmentation task. We demonstrate state of the art performance on each of these datasets.

E. Cârstea, J. Bridgeman, A. Baker et al.

Wastewater quality is usually assessed using physical, chemical and microbiological tests, which are not suitable for online monitoring, provide unreliable results, or use hazardous chemicals. Hence, there is an urgent need to find a rapid and effective method for the evaluation of water quality in natural and engineered systems and for providing an early warning of pollution events. Fluorescence spectroscopy has been shown to be a valuable technique to characterize and monitor wastewater in surface waters for tracking sources of pollution, and in treatment works for process control and optimization. This paper reviews the current progress in applying fluorescence to assess wastewater quality. Studies have shown that, in general, wastewater presents higher fluorescence intensity compared to natural waters for the components associated with peak T (living and dead cellular material and their exudates) and peak C (microbially reprocessed organic matter). Furthermore, peak T fluorescence is significantly reduced after the biological treatment process and peak C is almost completely removed after the chlorination and reverse osmosis stages. Thus, simple fluorometers with appropriate wavelength selectivity, particularly for peaks T and C could be used for online monitoring in wastewater treatment works. This review also shows that care should be taken in any attempt to identify wastewater pollution sources due to potential overlapping fluorophores. Correlations between fluorescence intensity and water quality parameters such as biochemical oxygen demand (BOD) and total organic carbon (TOC) have been developed and dilution of samples, typically up to ×10, has been shown to be useful to limit inner filter effect. It has been concluded that the following research gaps need to be filled: lack of studies on the on-line application of fluorescence spectroscopy in wastewater treatment works and lack of data processing tools suitable for rapid correction and extraction of data contained in fluorescence excitation-emission matrices (EEMs) for real-time studies.

Feng Yu, Q. Liu, Shu Wu et al.

E. Learned-Miller, Gary B. Huang, Aruni RoyChowdhury et al.

John P. Martin, John P. Martin, David B Grubb

Hong Zheng, Zhouyang Ye, Xinyi Bai et al.

Abstract In the digital education era, artificial intelligence has developed rapidly in education in China, and hybrid teaching models have become popular. This study aims to explore whether college students’ enjoyment in online classes influences their learning interest and achievement of learning goals, and to examine the moderating role of teacher-student interaction in the realization of learning goals. We conducted an online questionnaire survey of 1736 college students in China to explore the relationships among enjoyment, learning interest, teacher-student interaction, and the achievement of learning goals of online classes. All statistical analyses were performed using SPSS 25.0 and Mplus 8.0. These findings show that college students’ enjoyment influences the achievement of learning goals through the mediating role of learning interest in online classes. The high level of teacher–student interaction is conducive to the transformation of students’ enjoyment into learning interest and the achievement of learning goals. Our study is expected to serve as an important reference for increasing students’ learning interest and achieving their learning goals in online classes.

Marco Iori

This paper presents the initial findings of a qualitative study investigating the relationship between ethics and education in Italian preschool settings. The research employs the methodology of Constructivist Grounded Theory (CGT) to emphasise the indispensable role of promoting ethical awareness in early childhood education. The preliminary results from the interpretation of intensive interviews with preschool teachers indicate that the structuring of ethically significant educational experiences can be a factor in the growth of ethical awareness. From a theoretical and practical perspective, the study posits that stimulating ethical awareness from early childhood can prevent the rise of an increasingly widespread phenomenon: ethical illiteracy.

Alex Glynn

The tendency of generative artificial intelligence (AI) to ‘hallucinate’ false information is well known; AI-generated citations to non-existent sources have penetrated the bibliographies of peer-reviewed publications. Drawing from the Transparency and Openness Promotion guidelines, American judicial contention with generative AI, and the submission of prior art to the US Patent and Trademark Office, the author proposes that journals require authors to submit the full text of each cited source along with their manuscripts, thereby preventing authors from citing material whose full text they cannot produce. This solution requires limited additional work by authors or editors while effectively immunizing journals against hallucinated references.

Xiaodong Ding, Hao Huang, Zhengyu Shi et al.

Abstract This article analyzes the divergence between China’s Personal Information Protection Law (PIPL) and the EU’s General Data Protection Regulation (GDPR), despite their textual similarities. It argues that China’s approach to data protection is shaped by distinct domestic understandings of “risk,” rooted in past legislation, judicial practices, and social concerns. Using focal point theory, the authors identify three key dimensions of risk in China: large-scale participation, economic loss, and threats from third parties. These focal points explain why China’s risk-based approach prioritizes different enforcement goals than the GDPR. The article also shows how these differences manifest in several areas, including the definition of personal information, the regulation of automated decision-making, and the design of enforcement authorities. Ultimately, the article challenges the assumption that legal diffusion through the “Brussels Effect” leads to uniform global standards. Instead, it highlights how domestic cultural and institutional factors reshape transplanted laws, creating seemingly performative enforcement that reflects localized regulatory logics.