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关键词： Computer science   gender equity   gender gap   science   STEM   women in computing  

摘要： ACM-W is the community within the Association for Computing Machinery (ACM) that is dedicated to issues of gender equality in Computer Science. ACM-W works globally to support, celebrate and advocate for the full participation of women in all aspects of the computing field. This article presents a brief history of ACM-W with an emphasis on the global growth of the organization. A summary of the primary programs of ACM-W is provided to further highlight the global impact of our work. Also included are examples of how ACM-W is partnering with other computing and scientific organizations to realize greater impact in the arena of gender equality.

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关键词： Combinatorics   Convex and Discrete Geometry   Order   Lattices   Ordered Algebraic Structures   Computer Science   general   Group Theory and Generalizations  

摘要： For the positive integer m, let Z(m) denote the cyclic group of order m. Vertex-minimal planar graphs with prescribed automorphism group Z(m) were first considered by Marusi.c. In particular, when m is odd he produced a vertex-minimal planar graph with Z(m)-symmetry. Marusic then conjectured the order of a vertex-minimal planar graph with Z(m)-symmetry when m is a power of 2;in this article, we prove his conjecture. We conclude by discussing vertex-minimal planar graphs with noncyclic automorphism groups and pose some open questions.

关键词： Mineral Resources   Fossil Fuels (incl. Carbon Capture)   Geography   general   Sustainable Development   Statistics for Engineering   Physics   Computer Science   Chemistry and Earth Sciences   Mathematical Modeling and Industrial Mathematics  

关键词： Turing machines  

摘要： The quest to understand consciousness, once the purview of philosophers and theologians, is now actively pursued by scientists of many stripes. We examine consciousness from the perspective of Theoretical Computer Science (TCS), a branch of mathematics concerned with understanding the underlying principles of computation and complexity, including the implications and surprising consequences of resource limitations. We are inspired by Alan Turing's simple yet powerful definition of a computer, the Turing Machine (TM), and by the Global Workspace Theory (GWT) of consciousness originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, Jean-Pierre Changeux, George Mashour and others. We are not looking for a complex model of the brain nor of cognition but for a simple substrate independent computational model of (the admittedly complex concept of) consciousness. We do this by defining the Conscious Turing Machine (CTM), also called a Conscious AI, and then we define consciousness and related notions in the CTM. While these are only mathematical TCS definitions, we suggest why the CTM has feelings of consciousness. The TCS perspective provides a simple framework to employ tools from computational complexity theory and machine learning to help us understand consciousness and related concepts. Previously we explored explanations for the feelings of pain and pleasure in the CTM. Here we consider additional phenomena generally associated with consciousness, again from the perspective of the CTM. We start with three examples related to vision (blindsight, inattentional blindness, and change blindness), then follow with a discussion of dreams, altered states, and free will. We give explanations derived from the model and draw confirmation from consistencies at a high level - well above the level of neurons - with the psychology and neuroscience literature. This paper is intended to be an introduction to a much-expanded monograph, in p

关键词： Systolic arrays  

摘要： This paper proposes a novel hardware accelerator for inference of sparse convolutional neural networks (CNNs) by building a hardware unit to perform Image to Column (IM2COL) transformation of the input feature map coupled with a systolic array-based general matrix-matrix multiplication (GEMM) unit. Our design carefully overlaps the IM2COL transformation with the GEMM computation to maximize *** propose a novel design for the IM2COL unit that uses a set of distributed local memories connected by a ring network, which improves energy efficiency and latency by streaming the input feature map only once. The systolic array based GEMM unit in the accelerator can be dynamically configured as multiple GEMM units with square systolic arrays or as a single GEMM unit with a tall systolic array. This dynamic reconfigurability enables effective pipelining of IM2COL and GEMM operations and attains high processing element utilization for a wide range of CNNs. Further, our accelerator is sparsity-aware, improving performance and energy efficiency by effectively mapping the sparse feature maps and weights to the processing elements, skipping ineffectual operations and unnecessary data movements involving zeros. Our prototype, SPOTS, is on average 1.74× and 2.16× faster than two systolic array-based hardware accelerators, Eyeriss and Gemmini, respectively. SPOTS is also 78×, and 12× more energy-efficient when compared to CPU and GPU implementations, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Coal   Spinning   Rails   Gold   Electrical engineering   Voting   Production  

摘要： In electing me your president for the first year of the existence of the South African Institute of Electrical Engineers, an honour has been conferred on me of which I am very proud, and for which I have to thank you. Feeling strongly as I do that this Institute is even now, at such a young stage, fairly representative of the Electrical Engineers of South Africa, I feel doubly honoured by such election and I will endeavour to further the interests of the Institute as far as my ability, enthusiasm for the profession, and the time which can be taken from a somewhat busy life, will permit.

关键词： Bioinformatics   Computer science   Genomics  

摘要： The need to include the genetic variation within a population into a reference genome led to the concept of a genome sequence graph. Nodes of such a graph are labeled with DNA sequences occurring in represented genomes. Due to double-stranded nature of DNA, each node may be oriented in one of two possible ways, resulting in marking one end of the labeling sequence as in-side and the other as out-side. Edges join pairs of sides and reflect adjacency between node sequences in genomes constituting the graph. Linearization of a sequence graph aims at orienting and ordering graph nodes in a way that makes it more efficient for visualization and further analysis, e.g. access and traversal. We propose a new linearization algorithm, called ALIBI - Algorithm for Linearization by Incremental graph BuIlding. The evaluation shows that ALIBI is computationally very efficient and generates high-quality results.

关键词： Algorithms   Bioinformatics   Computer science   High-performance computing in bioinformatics  

摘要： Search schemes constitute a flexible and generic framework to describe how all approximate occurrences of a search pattern in a text can be found efficiently. We propose an algorithm for the dynamic partitioning of search patterns which can be universally applied to any kind of search scheme and demonstrate that this technique significantly reduces the search space. We present Columba, a software tool written in C++, in which a multitude of search schemes are implemented. We discuss implementation aspects such as memory interleaving of Burrows-Wheeler transform representations and the reduction of redundancy that is inherently associated with the edit distance metric. Ultimately, we demonstrate that Columba has superior performance to the state of the art. Using a single CPU core, Columba is able to retrieve all occurrences of 100,000 Illumina reads and their reverse complements within a maximum edit distance of four in the human genome in less than 3 min.

关键词： Computer science   Developing world   Environmental impact  

摘要： Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of satellite imagery can generalize across diverse prediction tasks (e.g., forest cover, house price, road length). Our method achieves accuracy competitive with deep neural networks at orders of magnitude lower computational cost, scales globally, delivers label super-resolution predictions, and facilitates characterizations of uncertainty. Since image encodings are shared across tasks, they can be centrally computed and distributed to unlimited researchers, who need only fit a linear regression to their own ground truth data in order to achieve state-of-the-art SIML performance. This paper presents MOSAIKS, a system for planet-scale prediction of multiple outcomes using satellite imagery and machine learning (SIML). MOSAIKS generalizes across prediction domains and has the potential to enhance accessibility of SIML across research disciplines.