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关键词： evolution   fine-tuning   fitness function   fitness landscape   teleology  

摘要： The effectiveness of evolutionary algorithms is one of the issues discussed in The Compatibility of Evolution and Design, where it is argued that such algorithms are only effective when stringent preconditions are met. This article considers this issue from the perspective of computer science. It explores the properties of problems that can be effectively solved by evolutionary algorithms, and the extent to which such algorithms need to be carefully adjusted. Although there are important differences between the study of evolutionary algorithms in computer science and the study of biological evolution, it is argued that the experience of computer science can help to strengthen the claim that evolutionary mechanisms generally require a considerable degree of fine-tuning.

关键词： Astronomical instrumentation   Astronomy and astrophysics   Computer science   Machine learning  

摘要： The latest science news, in brief.

关键词： biology   design argument   Stephen Freeland   metaphors   pareidolia  

摘要： The first half of this article offers two possibilities of how the argument Kojonen makes might be vulnerable to other new developments in evolutionary science and psychology-potential broadsides that might threaten to sink the salvaged ship of design once again. Work on the development of life suggests that life is a simplification of surrounding environmental information, and therefore life does not generate new information. Second, the psychology of pareidolia suggests we find design as a bias of our information processing, rather than observing something that exists. The second half of the article offers a critique of how the metaphors we use to describe God and the world shape our approaches to solving theological and philosophical questions (particularly theodicy). I offer some organic metaphors in place of the usual mechanistic metaphors to think about how the design argument could be reformulated.

关键词： COVID-19   Informatics   Adaptation models   Semantics   Predictive models   Media   Computer science   Coronavirus disease 2019 (COVID-19)   deep collaborative filtering   knowledge graph (KG)   precautionary alert recommendation   similarity  

摘要： Filtration to optimal exactness is mandatory since the options inundate the online world. Knowledge graph embedding is extraordinarily contributing to the recommendations, but the existing knowledge graph (KG)-based recommendation methods only exploit the correlations among the preferences and stand-alone entities, without bonding the cocurricular features and tendencies of the context. Additionally, the integration of the location-based current data of coronavirus disease 2019 (COVID-19) into the KG is necessary for the recommendation of region-aware precautionary alerts to the concerned people-an essential application of the current and future Internet of Medical Things. Therefore, in this article, we propose a novel deep collaborative alert recommendation (DCA) approach to cope with the situation. Particularly, DCA collects current online data about COVID-19, purifies, and transforms them to the KG. Furthermore, it independently encapsulates the cocurricular features and tendencies of the context in the embedding space and encodes them to the independent hidden factors via a graph neural network. The bi-end hidden factors are computed via matrix factorization to infer the potential connections. Moreover, a relevance estimator and a cross transistor are configured to enhance the generalization capability of the model. Experiments on two real-world datasets are performed to evaluate the effectiveness of DCA. Results and analysis show that the proposed approach has outperformed the baseline methods with fine improvements in providing the required recommendations.

关键词： Capital punishment   Death sentence   Mental damage   State compensation   Wrongful conviction   Wrongful execution  

摘要： In 1994, China passed its first State Compensation Law (SCL) for wrongfully convicted individuals who can file claims for loss of life or freedom, physical injury, and property loss due to illegal state actions in administrative and criminal cases. The 2010 amended SCL added mental damage compensation and mandated and prioritized a negotiation procedure. The current study used 144 capital exonerees to examine whether and how state compensation was claimed by and/or awarded to these exonerees in China. Our analyses revealed that almost all of the state compensation claims received compensation, and the amount claimed and the amount awarded had increased exponentially over time. In addition, after mental damage compensation was permitted in 2010, capital exonerees had successfully obtained such awards. However, despite of the increased compensation amounts over the years, the winning rates across almost all categories (e.g., mental damage, life/physical injury claims) decreased over time, and the standard of national daily average salary played a more important role on the increased amounts claimed and awarded than the SCL based on results of multivariate analyses. Policy recommendations were discussed to help improve the state compensation system in China.

关键词： Diagram understanding   topology   Computer Science  

摘要： Diagram is a special form of visual expression for representing complex concepts, logic, and knowledge, which widely appears in educational scenes such as textbooks, blogs, and encyclopedias. Current research on diagrams preliminarily focuses on natural disciplines such as Biology and Geography, whose expressions are still similar to natural images. In this article, we construct the first novel geometric type of diagrams dataset in Computer Science field, which has more abstract expressions and complex logical relations. The dataset has exhaustive annotations of objects and relations for about 1,300 diagrams and 3,500 question-answer pairs. We introduce the tasks of diagram classification (DC) and diagram question answering (DQA) based on the new dataset, and propose the Diagram Paring Net (DPN) that focuses on analyzing the topological structure and text information of diagrams. We use DPN-based models to solve DC and DQA tasks, and compare the performances to well-known natural images classification models and visual question answering models. Our experiments show the effectiveness of the proposed DPN-based models on diagram understanding tasks, also indicate that our dataset is more complex compared to previous natural image understanding datasets. The presented dataset opens new challenges for research in diagram understanding, and the DPN method provides a novel perspective for studying such data. Our dataset can be available from https://***/ WayneWong97/CSDia.

关键词： Computational methods   Atomistic models   Method development   Computer science   Scientific data  

摘要： Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs. Graph neural networks are machine learning models that directly access the structural representation of molecules and materials. This Review discusses state-of-the-art architectures and applications of graph neural networks in materials science and chemistry, indicating a possible road-map for their further development.

关键词： Computational neuroscience   Computer science   Neural circuits  

摘要： TractoInferno is the world's largest open-source multi-site tractography database, including both research- and clinical-like human acquisitions, aimed specifically at machine learning tractography approaches and related ML algorithms. It provides 284 samples acquired from 3 T scanners across 6 different sites. Available data includes T1-weighted images, single-shell diffusion MRI (dMRI) acquisitions, spherical harmonics fitted to the dMRI signal, fiber ODFs, and reference streamlines for 30 delineated bundles generated using 4 tractography algorithms, as well as masks needed to run tractography algorithms. Manual quality control was additionally performed at multiple steps of the pipeline. We showcase TractoInferno by benchmarking the learn2track algorithm and 5 variations of the same recurrent neural network architecture. Creating the TractoInferno database required approximately 20,000 CPU-hours of processing power, 200 man-hours of manual QC, 3,000 GPU-hours of training baseline models, and 4 Tb of storage, to produce a final database of 350 Gb. By providing a standardized training dataset and evaluation protocol, TractoInferno is an excellent tool to address common issues in machine learning tractography.

关键词： Civil engineering   Computer science  

摘要： This study combined crash reports, land use, real-time traffic, and weather data to form an integrated database to analyze the severity of crashes taking place on rural highways. As the traffic cameras are placed at fixed locations, there is a wide range of measured distances between crashes and the selected nearest camera for extracting traffic variables. This may change the significance of traffic variables. For the first time, spacing was introduced as the distance around the detectors in which traffic characteristics are inferred to crashes. Classification and Regression Tree (CART) was employed as an interpretable tool to explore how spacing affects model performance and the significance of traffic variables. Twelve spacing scenarios from 250 to 3000 m were evaluated. Except for short spacings suffering from the low sample size issue, each model has a good predictive performance based on overall accuracy and F-2 score in the 1000-3000 m spacings. In this range, three dominant rules emerged: (1) high deviations of speed on the roads surrounded by wastelands are associated with severe crashes;(2) faded markings in residential zones increase the likelihood of severe outcomes;(3) installation of barriers decrease the probability of severe crashes. Comparing the Variable Importance Measure (VIM) reveals that the total importance of traffic variables reduces as the spacing increases. Also, results indicate that average speed is significant until 1750 m;but speed deviation, traffic flow, and percent of heavy vehicles are more stable variables for further spacings. In conclusion, for the first time, spacing scenarios were evaluated systematically and proved that they have a remarkable impact on the significance of variables. This novel research provides guidance not only on the spacing but also on which real-time traffic variables have a greater impact on crash severity, along with design, land use, and environmental variables.