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摘要： Fifty-seven men of color were sentenced to death by the courts of England and Wales in the twentieth century and were less likely to receive mercy than white contemporaries. Though shocking, the data is perhaps unsurprising considering institutional racism and unequal access to justice widely highlighted by criminologists since the 1970s. We find discourses of racial difference were frequently mobilized tactically in nineteenth- and twentieth-century England and Wales: to support arguments for mercy and attempt to save prisoners from the gallows. Scholars have identified historically and culturally contingent narratives traditionally deployed to speak to notions of lesser culpability. These mercy narratives reveal contemporary ideals and attitudes to gender or class. This article is original in identifying strategic mercy narratives told in twentieth-century England and Wales that called on contemporary tropes about defendants' race. The narratives and cases we explore suggest contemporary racism in the criminal justice system of England and Wales has a longer history than previously acknowledged.

关键词： Correlation   Principal component analysis   Linear systems   Knowledge engineering   Education   Power supplies   Computer engineering   computer science   correlation   electrical engineering   knowledge integration (KI)   Kolmogorov–   Smirnov (KS) distance   National Science Foundation (NSF)   principal component analysis (PCA)   Revolutionizing Engineering Department (RED)  

摘要： Contribution: This article presents quantitative support that the changes implemented as part of Colorado State University's (CSU's) Revolutionizing Engineering Departments (REDs) grant produce statistically significant positive change through a series of nonparametric analysis techniques. Additionally, the set of nonparametric analysis techniques provides a novel approach to quantitatively analyzing student data after significant pedagogical changes are made to the undergraduate curriculum. Background: As part of the grant, a series of significant pedagogical changes were made to the electrical and computer engineering (ECE) undergraduate curriculum. A large portion of these changes relates to knowledge integration techniques, which are used to highlighting the intricate relationships between the three topics of electronics, signals and systems, and electromagnetics. This article presents an analysis of the outcomes that are in part due to these changes. Intended Outcomes: As a result of the grant and the associated curriculum changes, it was anticipated that the cumulative inmajor grade point average for third-year students would increase. It was also anticipated that the in-major intercourse grades would be more positively correlated. The analysis techniques that were used provide novel examples of applications to student data. Application Design: The implemented changes described in this article directly follow from the goals of the National Science Foundation's RED program. Findings: Three nonparametric analysis techniques are performed on a collection of data from ECE undergraduates that was collected over 20 years. It is shown that the intertopical correlations between courses increase immediately following the implementation of the intervention discussed in this article, and statistically, significant evidence is presented supporting that the distribution of grades has positively changed following the intervention.

关键词： Feature extraction   Correlation   Noise measurement   Kernel   Learning systems   Computer science   Task analysis   Feature selection   sparse learning   multi-view learning   multi-label learning   image annotation  

摘要： In image analysis, image samples are always represented by multiple view features and associated with multiple class labels for better interpretation. However, multiple view data may include noisy, irrelevant and redundant features, while multiple class labels can be noisy and incomplete. Due to the special data characteristic, it is hard to perform feature selection on multi-view multi-label data. To address these challenges, in this paper, we propose a novel multi-view multi-label sparse feature selection (MSFS) method, which exploits both view relations and label correlations to select discriminative features for further learning. Specifically, the multi-labeled information is decomposed into a reduced latent label representation to capture higher level concepts and correlations among multiple labels. Multiple local geometric structures are constructed to exploit visual similarities and relations for different views. By taking full advantage of the latent label representation and multiple local geometric structures, the sparse regression model with an l2,1-norm and an Frobenius norm (F-norm) penalty terms is utilized to perform hierarchical feature selection, where the F-norm penalty performs high-level (i.e., view-wise) feature selection to preserve the informative views and the l2,1-norm penalty conducts low-level (i.e., rowwise) feature selection to remove noisy features. To solve the proposed formulation, we also devise a simple yet efficient iterative algorithm. Experiments and comparisons on real-world image datasets demonstrate the effectiveness and potential of MSFS.

关键词： Computer Science   Ordering inference   Estimation statistics   Bootstrapping   Nonparametric method   Data Science   Income inequality  

摘要： Given a dataset of careers and incomes, how large a difference of incomes between any pair of careers would be? Given a dataset of travel time records, how long do we need to spend more when choosing a public transportation mode A instead of B to travel? In this paper, we propose a framework that is able to infer orders of categories as well as magnitudes of difference of real numbers between each pair of categories using an estimation statistics framework. Our framework not only reports whether an order of categories exists, but it also reports magnitudes of difference of each consecutive pair of categories in the order. In a large dataset, our framework is scalable well compared with existing frameworks. The proposed framework has been applied to two real-world case studies: 1) ordering careers by incomes from 350,000 households living in Khon Kaen province, Thailand, and 2) ordering sectors by closing prices from 1,060 companies in NASDAQ stock market between years 2000 and 2016. The results of careers ordering demonstrate income inequality among different careers. The stock market results illustrate dynamics of sector domination that can change over time. Our approach is able to be applied in any research area that has category-real pairs. Our proposed Dominant-Distribution Networkprovides a novel approach to gain new insight of analyzing category orders. A software of this framework is available for researchers or practitioners in an R CRAN package: EDOIF.

摘要： This article discusses the impact of the COVID-19 pandemic and the Black Lives Matter movement on the arts and cultural sector in the United States, placing the 2020 crises in the context of the United States's historically decentralized approach to supporting the arts and culture. After providing an overview of the United States's private, locally focused history of arts funding, we use this historical lens to analyze the combined effects of the pandemic and Black Lives Matter movement on a single metropolitan area - Philadelphia, Pennsylvania. We trace a timeline of key events in the national and local pandemic response and the reaction of the arts community to the Black Lives Matter movement, arguing that the nature of these intersecting responses, and their fallout for the arts and cultural sector, stem directly from weaknesses in the United States's historical approach to administering the arts. We suggest that, in the context of widespread organizational vulnerability caused by the pandemic, the United States's decentralized approach to funding culture also undermines cultural organizations' abilities to respond to issues of public relevance and demonstrate their civic value, threatening these organizations' legitimacy.

摘要： Alice and Bob fell in love while studying computer science together. They love computers and are the sort of people who think it's romantic that computers use a number system that is base two (1s and 0s).

关键词： Microscopy   Image enhancement   Adaptation models   Computer science   Nerve fibers   Biomedical engineering   Aerospace engineering  

摘要： In the above article [1] , there were two errors in the printed article that the authors want to correct.

关键词： Piscine Behavior   Zoology   Computer Science   Artificial Intelligence  

摘要： Many behaviors that are critical for survival and reproduction are expressed over extended time periods. The ability to inexpensively record and store large volumes of video data creates new opportunities to understand the biological basis of these behaviors and simultaneously creates a need for tools that can automatically quantify behaviors from large video datasets. Here, we demonstrate that 3D Residual Networks can be used to classify an array of complex behaviors in Lake Malawi cichlid fishes. We first apply pixel-based hidden Markov modeling combined with density-based spatiotemporal clustering to identify sand disturbance events. After this, a 3D ResNet, trained on 11,000 manually annotated video clips, accurately (>76%) classifies the sand disturbance events into 10 fish behavior categories, distinguishing between spitting, scooping, fin swipes, and spawning. Furthermore, animal intent can be determined from these clips, as spits and scoops performed during bower construction are classified independently from those during feeding.