1,720,970 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Learning Algorithm Designs for Biological Neural Networks
No full textArtificial neural networks have shown great success in solving real-world problems in recent years. Nowadays, most of the widely used neural network algorithms are running on silicon-based computers, where the resource requirement and energy consumption become a challenge when the network size grows. In contrast, brain contains trillions of neurons and synapses which naturally processe information with far less energy as compared to silicon-based computers.In vitro biological neural networks created from dissociated neurons may be used for computing and performing machine learning tasks. In this dissertation, learning algorithms are explored for a network of neurons with detailed biological properties with feedforward and recurrent structures. For feedforward networks, a two-layer hybrid bio-silicon platform is constructed and a five-step design method is proposed for the fast development of living neural network algorithms. Neural variations and dynamics are verified by fitting model parameters with biological experimental results. Random connections are generated under different connection probabilities to vary network sparsity. A multi-layer perceptron algorithm is tested with biological constraints to investigate the impact of neural variations and random connections. The results show that a reasonable inference accuracy can be achieved despite the presence of neural variations and random network connections. A new adaptive pre-processing technique is proposed to ensure good learning accuracy with different living neural network sparsity. On the training side, a supervised STDP-based learning algorithm is proposed for networks with biological constraints. For recurrent spiking neural networks (RSNN), temporal dynamics are studied with detailed biological features. An automatic fitting tool is used to match the precise spike timing of the in vitro neurons and the modeled neurons to get the fitted neuron parameters. Model fidelity and learning performance of different spiking neural models are compared when performing a temporal delay task with Neural Engineering Framework (NEF). Besides the NEF learning approach, a deep learning optimization method-backpropagation through time, is studied on recurrent spiking neural networks. This work studied the influence of the synapse and adaptation time constant on the network performance. Results suggest that, when incorporating adaptation and synapse function, the network can achieve similar accuracy as the state-of-the-art RSNN works while requiring fewer neurons and smaller time constants
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Improving Data Movement Efficiency in the Memory Systems for Irregular Applications
No full textModern processors have a large processor-memory frequency gap, which urges the computer designer to address the issue of the inefficiency of the memory system. Conventional memory hierarchy design is guided by assuming good spatial and temporal locality. Based on this assumption, both the off-chip main memory and on-chip caches have adopted coarse-grained access for amortizing high memory reference latency and metadata overhead. However, the modern applications (textit{e.g}, deep learning, graph processing, scientific computing textit{etc.},) have poor spatial locality, which leads to an inefficient data movement problem. In this dissertation, we target to improve the data movement efficiency in the memory system from both hardware mechanisms and software/hardware co-designs.\ Data over-fetching from both off-chip and on-chip causes energy wastes, bandwidth inefficiency, and cache pollution. To address this issue, prior works proposed to reduce the data access granularity for both DRAM and caches. However, existing fine-grained DRAM either needs to increase the width of the internal I/Os with unaffordable area overhead or increase the internal data transfer latency with a high performance overhead. In this dissertation, a new fine-grained DRAM with interleaved I/O is proposed to trade off both area and performance by letting fine-grained data from different local I/Os interleaved at global I/O. As a result, the proposed fine-grained DRAM can achieve near-optimal performance with low chip area overhead.\ Instead of fine-grained access to off-chip DRAM, fine-grained caches can help to improve effective capacity by reducing data access and storage granularity to reduce the amount of unused data. However, small data blocks typically require greater metadata and control overhead. Sharing the common bits among tags of fine-grained blocks can reduce the metadata overhead but the constraints on which fine-grained blocks can share tag bits can cause fragmentation. To address this issue, we proposed a fine-grained cache named Scrabble cache to merge multiple non-contiguous fine-grained blocks into a variable size merged block, which can effectively improve the remaining issues of the existing fine-grained caches with trade-off among data capacity, space utilization, and data utilization. As a result, the proposed Scrabble cache can achieve the best effective capacity as compared to other state-of-the-art fine-grained caches. \ With fine-grained DRAM and caches effectively reduce the energy overhead from irregular applications, the performance issue still exists. For applications dominated by irregular memory access, caching typically shows minimal performance contribution. Prefetching can help to hide the miss penalty by predicting the memory addresses that will be accessed in the near future and issuing memory requests ahead of time. However, software prefetching adds instruction overhead and hard to program, whereas hardware prefetching cannot accurately predict irregular memory access sequences. To address this issue, we proposed a software-assisted hardware prefetcher that focuses on the iterative algorithm that has repeating irregular memory access patterns. In this work, we provide a lightweight software interface so that the programmers can specify the data structure that accessed irregularly, the time when the repetition first occurs, and the time when the repetition should be replayed. By doing this, the proposed prefetching mechanism can improve prefetching accuracy, coverage, and timeliness significantly.\ Other than prefetching, improving the core utilization is also essential for applications such as sparse linear algebra. In  Gustavson’s column-wise SpGEMM, the computation of the output matrix is independent and can be easily parallelized. However, the sparse accumulation (SPA) process that merges partial summations based on the row indices consists of intensive data-dependent branches, which is the bottleneck of the entire computation. In this dissertation, we propose to extend the current instruction set that packing complicated collision handling and accumulation branches into a single instruction so that the core utilization can be significantly improved. Moreover, we provide dedicated hardware storage with higher throughput for speeding up the index matching process in the sparse accumulation
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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