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关键词： Proteins   Annotations   Prediction algorithms   Biological processes   Labeling   Computer science   Task analysis   PPI networks   protein function prediction   graph diffusion   semi-supervised learning  

摘要： A method to improve protein function prediction for sparsely annotated PPI networks is introduced. The method extends the DSD majority vote algorithm introduced by Cao et al. to give confidence scores on predicted labels and to use predictions of high confidence to predict the labels of other nodes in subsequent rounds. We call this a majority vote cascade. Several cascade variants are tested in a stringent cross-validation experiment on PPI networks from S. cerevisiae and D. melanogaster, and we show that for many different settings with several alternative confidence functions, cascading improves the accuracy of the predictions. A list of the most confident new label predictions in the two networks is also reported. Code and networks for the cross-validation experiments appear at http://***/cascade.

摘要： No abstract available.

关键词： capital punishment   death row   final statements   last meals   United States  

摘要： Over the past two decades, a growing body of work has emerged in the social sciences that explores various aspects of capital punishment in the contemporary United States. This article reviews one strand of this literature that focuses on the execution ritual itself - specifically on condemned inmates' last meals and the final statements that they make in the execution chamber just prior to their execution. It also discusses some of the limitations of the research and offers some ideas for future scholarship in this area.

关键词： Trajectory   Clustering algorithms   Partitioning algorithms   Gene expression   Diseases   Computer science   Manifolds   Trajectory inference   consensus clustering   gene partition   single-cell transcriptome  

摘要： Cellular programs often exhibit strong heterogeneity and asynchrony in the timing of program execution. Single-cell RNA-seq technology has provided an unprecedented opportunity for characterizing these cellular processes by simultaneously quantifying many parameters at single-cell resolution. Robust trajectory inference is a critical step in the analysis of dynamic temporal gene expression, which can shed light on the mechanisms of normal development and diseases. Here, we present TiC2D, a novel algorithm for cell trajectory inference from single-cell RNA-seq data, which adopts a consensus clustering strategy to precisely cluster cells. To evaluate the power of TiC2D, we compare it with three state-of-the-art methods on four independent single-cell RNA-seq datasets. The results show that TiC2D can accurately infer developmental trajectories from single-cell transcriptome. Furthermore, the reconstructed trajectories enable us to identify key genes involved in cell fate determination and to obtain new insights about their roles at different developmental stages.

关键词： Smoothing methods   Laplace equations   Network topology   Topology   Task analysis   Multimedia Web sites   Computer science   Attributed networks   autoencoder   network representation learning   smoothing  

摘要： Attributed networks are ubiquitous in the real world, such as social networks. Therefore, many researchers take the node attributes into consideration in the network representation learning to improve the downstream task performance. In this article, we mainly focus on an untouched ``oversmoothing'' problem in the research of the attributed network representation learning. Although the Laplacian smoothing has been applied by the state-of-the-art works to learn a more robust node representation, these works cannot adapt to the topological characteristics of different networks, thereby causing the new oversmoothing problem and reducing the performance on some networks. In contrast, we adopt a smoothing parameter that is evaluated from the topological characteristics of a specified network, such as small worldness or node convergency and, thus, can smooth the nodes' attribute and structure information adaptively and derive both robust and distinguishable node features for different networks. Moreover, we develop an integrated autoencoder to learn the node representation by reconstructing the combination of the smoothed structure and attribute information. By observation of extensive experiments, our approach can preserve the intrinsical information of networks more effectively than the state-of-the-art works on a number of benchmark datasets with very different topological characteristics.

关键词： Gold   Feature extraction   Estimation   Face   Deep learning   Computer science   Learning systems   Channel-wise and spatial attention learning   pixel-level relation learning   facial AU detection  

摘要： Attention mechanism has recently attracted increasing attentions in the field of facial action unit (AU) detection. By finding the region of interest of each AU with the attention mechanism, AU-related local features can be captured. Most of the existing attention based AU detection works use prior knowledge to predefine fixed attentions or refine the predefined attentions within a small range, which limits their capacity to model various AUs. In this paper, we propose an end-to-end deep learning based attention and relation learning framework for AU detection with only AU labels, which has not been explored before. In particular, multi-scale features shared by each AU are learned first, and then both channel-wise and spatial attentions are adaptively learned to select and extract AU-related local features. Moreover, pixel-level relations for AUs are further captured to refine spatial attentions so as to extract more relevant local features. Without changing the network architecture, our framework can be easily extended for AU intensity estimation. Extensive experiments show that our framework (i) soundly outperforms the state-of-the-art methods for both AU detection and AU intensity estimation on the challenging BP4D, DISFA, FERA 2015, and BP4D+ benchmarks, (ii) can adaptively capture the correlated regions of each AU, and (iii) also works well under severe occlusions and large poses.

关键词： Computer science   Ethics  

摘要： Whereas most natural-language models are built by small in-house teams, BLOOM was the work of many hundreds of researchers - mostly academics - including ethicists, legal scholars and philosophers, but also some employees of Facebook and Google, working in a personal capacity. The collaboration also attempted to reduce the usual over-representation of porn sites (which can lead to sexist associations in the model) but without excluding keywords that would remove content associated with frank discussion of sexuality in communities that are often under-represented. [...]downloading and running it require significant hardware capacity. Because that's available to so few research teams, BigScience will also publish smaller, less-hardware-intensive versions and create a distributed system that allows laboratories to share the model across their servers.

关键词： Linguistics  

摘要： Context: The need of replicating empirical studies in Computer Science is widely recognized among the research community as a means to consolidate the acquired knowledge from previous studies and generalizing results. It is essential to report the changes in the settings of each replication to promote not only the comprehensibility of the evolution of the experimental validity of an original study across a family of studies, but also replicability itself. Unfortunately, the lack of proposals for the systematic reporting of changes in replication undermines these desirable objectives. Objective. The main goal of the work presented in this article is to provide researchers in Computer Science, and in other areas of research when appropriate, with a systematic, tool-supported approach for the specification and reporting of changes in the replications of their empirical studies. Method: Applying Design Science Research, once the problem was identified, we have developed and validated a composite artifact consisting of (i) a metamodel that formalizes all the relevant concepts related to replications and their changes;(ii) templates and linguistic patterns that facilitates the reporting of those relevant concepts;and (iii) a proof-of-concept model-based software tool that supports the proposed approach. For its validation, we have carried out a multiple case study including 9 families of empirical studies not only from Computer Science, but also from an area as different as Agrobiology, in order to check the external validity of our approach when applied to research areas for which it was not initially designed. The 9 families encompass 23 replication studies and a total of 92 replication changes, for which we have analyzed the suitability of our proposal. Results: The multiple case study revealed some initial limitations of our approach, such as shortcomings related to threats to experimental validity or context variables. After several improvement iterations on the arti

摘要： We consider the paradoxical concept of free will in a deterministic world - 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. Although a deterministic entity living in a deterministic world cannot have free will as it is typically understood, it is entirely possible for such an entity to rightly and firmly believe in its own free *** Codes 68-02 © 2022, CC BY.

关键词： Computer science   Network   Network topology  

摘要： Quantifying structural dissimilarities between networks is a fundamental and challenging problem in network science. Previous network comparison methods are based on the structural features, such as the length of shortest path and degree, which only contain part of the topological information. Therefore, we propose an efficient network comparison method based on network embedding, which considers the global structural information. In detail, we first construct a distance matrix for each network based on the distances between node embedding vectors derived from DeepWalk. Then, we define the dissimilarity between two networks based on Jensen-Shannon divergence of the distance distributions. Experiments on both synthetic and empirical networks show that our method outperforms the baseline methods and can distinguish networks well. In addition, we show that our method can capture network properties, e.g., average shortest path length and link density. Moreover, the experiment of modularity further implies the functionality of our method.