Hasil untuk "General works"

P. Lockhart, M. Steel, M. Hendy et al.

D. Levin

J. Norcross, M. Lambert

Baoqi Wang, Yu Han, Xiao Wang et al.

Summary Non-lithium energy storage devices, especially sodium ion batteries, are drawing attention due to insufficient and uneven distribution of lithium resources. Prussian blue and its analogs (Prussian blue analogs [PBAs]), or hexacyanoferrates, are well-known since the 18th century and have been used for hydrogen storage, cancer therapy, biosensing, seawater desalination, and sewage treatment. Owing to their unique features, PBAs are receiving increasing interest in the field of energy storage, such as their high theoretical specific capacity, ease of synthesis, as well as low cost. In this review, a general summary and evaluation of the applications of PBAs for rechargeable batteries are given. After a brief review of the history of PBAs, their crystal structure, nomenclature, synthesis, and working principle in rechargeable batteries are discussed. Then, previous works classified based on the combination of insertion cations and transition metals are analyzed comprehensively. The review includes an outlook toward the further development of PBAs in electrochemical energy storage.

N. Krasnogor, James Smith

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement (Dawkins, 1976). In the case of MA's, "memes" refer to the strategies (e.g., local refinement, perturbation, or constructive methods, etc.) that are employed to improve individuals. In this paper, we review some works on the application of MAs to well-known combinatorial optimization problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of metaheuristics, it is possible to explore their design space and better understand their behavior from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient MAs.

Hao Wang, Yan Yang, Bing Liu et al.

Abstract This paper studies clustering of multi-view data, known as multi-view clustering. Among existing multi-view clustering methods, one representative category of methods is the graph-based approach. Despite its elegant and simple formulation, the graph-based approach has not been studied in terms of (a) the generalization of the approach or (b) the impact of different graph metrics on the clustering results. This paper extends this important approach by first proposing a general Graph-Based System (GBS) for multi-view clustering, and then discussing and evaluating the impact of different graph metrics on the multi-view clustering performance within the proposed framework. GBS works by extracting data feature matrix of each view, constructing graph matrices of all views, and fusing the constructed graph matrices to generate a unified graph matrix, which gives the final clusters. A novel multi-view clustering method that works in the GBS framework is also proposed, which can (1) construct data graph matrices effectively, (2) weight each graph matrix automatically, and (3) produce clustering results directly. Experimental results on benchmark datasets show that the proposed method outperforms state-of-the-art baselines significantly.

S. Shahnazar, S. Bagheri, S. A. Hamid

Azhar Hussain, S. Ali, Madiha Ahmed et al.

There have been recent trends of parents in Western countries refusing to vaccinate their children due to numerous reasons and perceived fears. While opposition to vaccines is as old as the vaccines themselves, there has been a recent surge in the opposition to vaccines in general, specifically against the MMR (measles, mumps, and rubella) vaccine, most notably since the rise in prominence of the notorious British ex-physician, Andrew Wakefield, and his works. This has caused multiple measles outbreaks in Western countries where the measles virus was previously considered eliminated. This paper evaluates and reviews the origins of the anti-vaccination movement, the reasons behind the recent strengthening of the movement, role of the internet in the spread of anti-vaccination ideas, and the repercussions in terms of public health and safety.

A. Stahl, K. Connor, P. Sapieha et al.

Margaux Zaffran, Aymeric Dieuleveut, Olivier F'eron et al.

Uncertainty quantification of predictive models is crucial in decision-making problems. Conformal prediction is a general and theoretically sound answer. However, it requires exchangeable data, excluding time series. While recent works tackled this issue, we argue that Adaptive Conformal Inference (ACI, Gibbs and Cand{\`e}s, 2021), developed for distribution-shift time series, is a good procedure for time series with general dependency. We theoretically analyse the impact of the learning rate on its efficiency in the exchangeable and auto-regressive case. We propose a parameter-free method, AgACI, that adaptively builds upon ACI based on online expert aggregation. We lead extensive fair simulations against competing methods that advocate for ACI's use in time series. We conduct a real case study: electricity price forecasting. The proposed aggregation algorithm provides efficient prediction intervals for day-ahead forecasting. All the code and data to reproduce the experiments is made available.

J. Jumper, D. Hassabis

J. Rodrigues, Dante Borges De Rezende Segundo, Heres Arantes Junqueira et al.

The Internet of Things (IoT) is one of the most promising technologies for the near future. Healthcare and well-being will receive great benefits with the evolution of this technology. This paper presents a review of techniques based on IoT for healthcare and ambient-assisted living, defined as the Internet of Health Things (IoHT), based on the most recent publications and products available in the market from industry for this segment. Also, this paper identifies the technological advances made so far, analyzing the challenges to be overcome and provides an approach of future trends. Through selected works, it is possible to notice that further studies are important to improve current techniques and that novel concept and technologies of IoHT are needed to overcome the identified challenges. The presented results aim to serve as a source of information for healthcare providers, researchers, technology specialists, and the general population to improve the IoHT.

Marc Finzi, M. Welling, A. Wilson

Symmetries and equivariance are fundamental to the generalization of neural networks on domains such as images, graphs, and point clouds. Existing work has primarily focused on a small number of groups, such as the translation, rotation, and permutation groups. In this work we provide a completely general algorithm for solving for the equivariant layers of matrix groups. In addition to recovering solutions from other works as special cases, we construct multilayer perceptrons equivariant to multiple groups that have never been tackled before, including $\mathrm{O}(1,3)$, $\mathrm{O}(5)$, $\mathrm{Sp}(n)$, and the Rubik's cube group. Our approach outperforms non-equivariant baselines, with applications to particle physics and dynamical systems. We release our software library to enable researchers to construct equivariant layers for arbitrary matrix groups.

Deven Santosh Shah, H. A. Schwartz, Dirk Hovy

An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques at different parts of the standard NLP pipeline (data and models). However, these works have been conducted individually, without a unifying framework to organize efforts within the field. This situation leads to repetitive approaches, and focuses overly on bias symptoms/effects, rather than on their origins, which could limit the development of effective countermeasures. In this paper, we propose a unifying predictive bias framework for NLP. We summarize the NLP literature and suggest general mathematical definitions of predictive bias. We differentiate two consequences of bias: outcome disparities and error disparities, as well as four potential origins of biases: label bias, selection bias, model overamplification, and semantic bias. Our framework serves as an overview of predictive bias in NLP, integrating existing work into a single structure, and providing a conceptual baseline for improved frameworks.

P. A. A. Resende, A. Drummond

Over the past decades, researchers have been proposing different Intrusion Detection approaches to deal with the increasing number and complexity of threats for computer systems. In this context, Random Forest models have been providing a notable performance on their applications in the realm of the behaviour-based Intrusion Detection Systems. Specificities of the Random Forest model are used to provide classification, feature selection, and proximity metrics. This work provides a comprehensive review of the general basic concepts related to Intrusion Detection Systems, including taxonomies, attacks, data collection, modelling, evaluation metrics, and commonly used methods. It also provides a survey of Random Forest based methods applied in this context, considering the particularities involved in these models. Finally, some open questions and challenges are posed combined with possible directions to deal with them, which may guide future works on the area.

Dylan J. Foster, A. Rakhlin

A fundamental challenge in contextual bandits is to develop flexible, general-purpose algorithms with computational requirements no worse than classical supervised learning tasks such as classification and regression. Algorithms based on regression have shown promising empirical success, but theoretical guarantees have remained elusive except in special cases. We provide the first universal and optimal reduction from contextual bandits to online regression. We show how to transform any oracle for online regression with a given value function class into an algorithm for contextual bandits with the induced policy class, with no overhead in runtime or memory requirements. We characterize the minimax rates for contextual bandits with general, potentially nonparametric function classes, and show that our algorithm is minimax optimal whenever the oracle obtains the optimal rate for regression. Compared to previous results, our algorithm requires no distributional assumptions beyond realizability, and works even when contexts are chosen adversarially.

Anh Nu Nguyet Nguyen, Ninh Van Nguyen

Purpose: The objective of this study is to explore and comprehend the factors from the perceived environment that impact travellers' attitudes and trust in agritourism at farms integrated with aquaculture, which have been creatively adapted for tourism purposes. These findings contribute to understanding how agritourism fosters rural innovation and sustainable development by transforming traditional agricultural practices into diversified tourism experiences. The findings of the study could demonstrate that certain outcomes play a crucial role in the successful innovation of rural areas. Methodology/design/approach: The study extensively utilized the Theory of Planned Behavior framework to develop its measurement constructs. Data collection occurred in regions where tourists frequented farms that combine aquaculture with traditional farming practices, yielding a total sample size of 332 respondents. The data were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, employing the SmartPLS software version 4.0.9.2. Results: The results identified factors perceived environmentally positive influence on personal perception. Attitude and trust were found to mediate the relationships between perceived environment and revisit intention, with the mediating effect of attitude being stronger than that of trust. Originality of the research: Visitor attitudes significantly determine the innovation from making farms, orchards, aquaculture areas to the experiential tourism business. Successful innovations, such as enhancing rural incomes and sustaining agricultural livelihoods through agritourism transformation, are significantly driven by positive visitor perceptions and trust.

Chuhang Zou, Alex Colburn, Qi Shan et al.

We propose an algorithm to predict room layout from a single image that generalizes across panoramas and perspective images, cuboid layouts and more general layouts (e.g. "L"-shape room). Our method operates directly on the panoramic image, rather than decomposing into perspective images as do recent works. Our network architecture is similar to that of RoomNet [15], but we show improvements due to aligning the image based on vanishing points, predicting multiple layout elements (corners, boundaries, size and translation), and fitting a constrained Manhattan layout to the resulting predictions. Our method compares well in speed and accuracy to other existing work on panoramas, achieves among the best accuracy for perspective images, and can handle both cuboid-shaped and more general Manhattan layouts.

Rita Matulionyte, Jyh-An Lee

In Thaler v The Comptroller-General of Patents, Designs and Trade Marks (DABUS), Smith J. held that an AI owner can possibly claim patent ownership over an AI-generated invention based on their ownership and control of the AI system. This AI-owner approach reveals a new option to allocate property rights over AI-generated output. While this judgment was primarily about inventorship and ownership of AI-generated invention in patent law, it has important implications for copyright law. After analysing the weaknesses of applying existing judicial approaches to copyright ownership of AI-generated works, this paper examines whether the AI-owner approach is a better option for determining copyright ownership of AI-generated works. The paper argues that while contracts can be used to work around the AI-owner approach in scenarios where users want to commercially exploit the outputs, this approach still provides more certainty and less transaction costs for relevant parties than other approaches proposed so far.

Qilin Tian

Labeling is required by the interpretive system. When a head merges with a phrase, the head provides the label. However, lexical heads and T with poor inflectional features are too weak to be labels. Although insightful, this theory leaves at least one problem that needs prompt solutions: are there other kinds of weak heads? In this paper, we address this issue by proposing that phonological features play a crucial role in the labeling algorithm and by putting forward an additional version of weak heads. That is, a head that loses phonological features in the syntax is also weak. This approach to weak heads, together with the constraint that a structure must be labeled for interpretation, can capture the distribution of empty categories in topicalization, relativization, ellipsis, and other phenomena, some of which have not received enough scholarly attention. Therefore, our syntactic-phonological approach to labeling can open up new possibilities to account for the distribution of empty categories in a principled manner.