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关键词： life sentences   life writing   criminal justice   capital punishment   civil death   death penalty  

摘要： Despite its growing use over past decades, there has been relatively little public or scholarly discussion of life sentences that deny the possibility of parole. This essay outlines the labyrinthine legal and political developments that have rendered life imprisonment difficult to address-including the intertwined histories of the death penalty and civil death-and draws upon the life writing of those serving life to theorize a more distinct understanding of this punishment. Witnesses reveal how the possibility of life despite the impossibility of parole punishes by subverting the goals of human growth and development. The potentiality of what can be done in the present grinds up against the futility of what could have been done and what could be done were release an option. Considered alongside the laws and court opinions and claims that characterize its convoluted development, these testimonies reveal this punishment's role in the American imagination. Life without the possibility of parole reinforces and relies upon a vision that not simply some people are unable to change, but that anyone in a democracy-no matter their position-is some steps away from irretrievable exclusion. Permanent confinement denies a restorative vision of democracy: any effort to abolish or amend it must include the voices of those imprisoned.

摘要： Nunmehr 10 Jahre nach der letzten Synopse zur Situation des Informatikunterrichts in Deutschland wird mit dieser Untersuchung ein aktuelles Bild der informatischen Bildung in den 16 Bundesländern gezeichnet. Dem allgemeinbildenden Charakter der informatischen Bildung entsprechend liegt der Fokus auf Angeboten für einen verbindlichen – durch die Existenz eines curricularen Rahmens abgesicherten – Informatikunterrichts für alle Schülerinnen und Schüler in allen Schularten weiterführender Schulen. Die Grundlage für die Erhebungen bildeten sowohl umfangreiche Analysen bildungspolitischer Dokumente, Stundentafeln und curricularer Vorgaben als auch eine Verifikation der erhobenen Daten durch Expertinnen und Experten in den jeweiligen Bundesländern. Neben einer vergleichbaren Darstellung für jedes Bundesland machen die Übersichten zum Sekundarbereich I und II über alle Bundesländer deutlich, welche Defizite in der Grundlagenbildung zur Informatik noch immer vorhanden sind und das Vorankommen in der Digitalisierung in Schulen zusätzlich hemmen.

摘要： Gabriele Kotsis, ACM President and full professor in computer science at Johannes Kepler University, shares her personal experiences and suggestions to shape the future of computer science. © 2021 ACM.

摘要： Despite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner's neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals. Learning and remembering new information is a major challenge for students of all levels. Here, the authors show that "neural alignment" across brains is associated with learning success of STEM concepts in a real-life college course and predicts learning outcomes.

关键词： Education   Machine learning   Ethics   Statistics   Computer science   Curricula   Data science  

摘要： The interdisciplinary field of data science, which applies techniques from computer science and statistics to address questions across domains, has enjoyed recent considerable growth and interest. This emergence also extends to undergraduate education, whereby a growing number of institutions now offer degree programs in data science. However, there is considerable variation in what the field actually entails and, by extension, differences in how undergraduate programs prepare students for data-intensive careers. We used two seminal frameworks for data science education to evaluate undergraduate data science programs at a subset of 4-year institutions in the United States;developing and applying a rubric, we assessed how well each program met the guidelines of each of the frameworks. Most programs scored high in statistics and computer science and low in domain-specific education, ethics, and areas of communication. Moreover, the academic unit administering the degree program significantly influenced the course-load distribution of computer science and statistics/mathematics courses. We conclude that current data science undergraduate programs provide solid grounding in computational and statistical approaches, yet may not deliver sufficient context in terms of domain knowledge and ethical considerations necessary for appropriate data science applications. Additional refinement of the expectations for undergraduate data science education is warranted.

关键词： Students  

摘要： We live in exceptional times in which the entire world is witnessing the exponential spread of a pandemic, which requires to adopt new habits of mind and behaviors. In this paper, I introduce the term exponential competence, which encompasses these cognitive and social skills, and describe a course for computer science and software engineering students in which emphasis is placed on exponential competence. I argue that exponential competence is especially important for computer science and software engineering students, since many of them will, most likely, be required to deal with exponential phenomena in their future professional development. © 2021, CC0.

关键词： Computational linguistics  

摘要： Query by Example is a well-known information retrieval task in which a document is chosen by the user as the search query and the goal is to retrieve relevant documents from a large collection. However, a document often covers multiple aspects of a topic. To address this scenario we introduce the task of faceted Query by Example in which users can also specify a finer grained aspect in addition to the input query document. We focus on the application of this task in scientific literature search. We envision models which are able to retrieve scientific papers analogous to a query scientific paper along specifically chosen rhetorical structure elements as one solution to this problem. In this work, the rhetorical structure elements, which we refer to as facets, indicate objectives, methods, or results of a scientific paper. We introduce and describe an expert annotated test collection to evaluate models trained to perform this task. Our test collection consists of a diverse set of 50 query documents in English, drawn from computational linguistics and machine learning venues. We carefully follow the annotation guideline used by TREC for depth-k pooling (k = 100 or 250) and the resulting data collection consists of graded relevance scores with high annotation agreement. State of the art models evaluated on our dataset show a significant gap to be closed in further work. Our dataset may be accessed here: https://***/iesl/CSFCube. © 2021, CC BY-NC-SA.

关键词： Computer Science   Algorithms   Artificial Intelligence  

摘要： Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge. Equilibrium propagation is a promising alternative to backpropagation as it only involves local computations, but hardware-oriented studies have so far focused on rate-based networks. In this work, we develop a spiking neural network algorithm called EqSpike, compatible with neuromorphic systems, which learns by equilibrium propagation. Through simulations, we obtain a test recognition accuracy of 97.6% on the MNIST handwritten digits dataset (Mixed National Institute of Standards and Technology), similar to rate-based equilibrium propagation, and comparing favorably to alternative learning techniques for spiking neural networks. We show that EqSpike implemented in silicon neuromorphic technology could reduce the energy consumption of inference and training, respectively, by three orders and two orders of magnitude compared to graphics processing units. Finally, we also show that during learning, EqSpike weight updates exhibit a form of spike-timing-dependent plasticity, highlighting a possible connection with biology.

关键词： Physics   Magnetism   Applied Physics   Computer Science   Artificial Intelligence  

摘要： With the development of machine learning in recent years, it is possible to glean much more information from an experimental data set to study matter. In this perspective, we discuss some state-of-the-art data-driven tools to analyze latent effects in data and explain their applicability in natural science, focusing on two recently introduced, physics-motivated computationally cheap tools-latent entropy and latent dimension. We exemplify their capabilities by applying them on several examples in the natural sciences and show that they reveal so far unobserved features such as, for example, a gradient in a magnetic measurement and a latent network of glymphatic channels from the mouse brain microscopy data. What sets these techniques apart is the relaxation of restrictive assumptions typical of many machine learning models and instead incorporating aspects that best fit the dynamical systems at hand.