1,522 research outputs found
Variance adjusted weighted UniFrac: a powerful beta diversity measure for comparing communities based on phylogeny
<p>Abstract</p> <p>Background</p> <p>Beta diversity, which involves the assessment of differences between communities, is an important problem in ecological studies. Many statistical methods have been developed to quantify beta diversity, and among them, UniFrac and weighted-UniFrac (W-UniFrac) are widely used. The W-UniFrac is a weighted sum of branch lengths in a phylogenetic tree of the sequences from the communities. However, W-UniFrac does not consider the variation of the weights under random sampling resulting in less power detecting the differences between communities.</p> <p>Results</p> <p>We develop a new statistic termed variance adjusted weighted UniFrac (VAW-UniFrac) to compare two communities based on the phylogenetic relationships of the individuals. The VAW-UniFrac is used to test if the two communities are different. To test the power of VAW-UniFrac, we first ran a series of simulations which revealed that it always outperforms W-UniFrac, as well as UniFrac when the individuals are not uniformly distributed. Next, all three methods were applied to analyze three large 16S rRNA sequence collections, including human skin bacteria, mouse gut microbial communities, microbial communities from hypersaline soil and sediments, and a tropical forest census data. Both simulations and applications to real data show that VAW-UniFrac can satisfactorily measure differences between communities, considering not only the species composition but also abundance information.</p> <p>Conclusions</p> <p>VAW-UniFrac can recover biological insights that cannot be revealed by other beta diversity measures, and it provides a novel alternative for comparing communities.</p
A Decision Support System for Building ARIMA Models: Using A Relational Database As A Knowledge Base
Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance
Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks
The performance limit of Graph Convolutional Networks (GCNs) and the fact
that we cannot stack more of them to increase the performance, which we usually
do for other deep learning paradigms, are pervasively thought to be caused by
the limitations of the GCN layers, including insufficient expressive power,
etc. However, if so, for a fixed architecture, it would be unlikely to lower
the training difficulty and to improve performance by changing only the
training procedure, which we show in this paper not only possible but possible
in several ways. This paper first identify the training difficulty of GCNs from
the perspective of graph signal energy loss. More specifically, we find that
the loss of energy in the backward pass during training nullifies the learning
of the layers closer to the input. Then, we propose several methodologies to
mitigate the training problem by slightly modifying the GCN operator, from the
energy perspective. After empirical validation, we confirm that these changes
of operator lead to significant decrease in the training difficulties and
notable performance boost, without changing the composition of parameters. With
these, we conclude that the root cause of the problem is more likely the
training difficulty than the others
FrameFire: Enabling Efficient Spiking Neural Network Inference for Video Segmentation
Fast video recognition is essential for real-time scenarios, e.g., autonomous driving. However, applying existing Deep Neural Networks (DNNs) to individual high-resolution images is expensive due to large model sizes. Spiking Neural Networks (SNNs) are developed as a promising alternative to DNNs due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity; thus they are useful to enable energy-efficient computation. However, exploiting the spatio-temporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading energy efficiency. In this work, we, therefore, propose an SNN accelerator called FrameFire for efficient video processing. We introduce a Keyframe-dominated Workload Balance Schedule (KWBS) method. It accelerates the image recognition network with sparse keyframes, then records and analyzes the current workload distribution on hardware to facilitate scheduling workloads in subsequent frames. FrameFire is implemented on a Xilinx XC7Z035 FPGA and verified by video segmentation tasks. The results show that the throughput is improved by 1.7× with the KWBS method. FrameFire achieved 1.04 KFPS throughput and 1.15 mJ/frame recognition energy
A self-evaluation system of quality planning for tourist attractions in Taiwan: an integrated AHP-Delphi approach from career professionals
This study seeks to identify a set of key indicators along with weightings for tourist attractions in Taiwan, and develop a quality management self-evaluation mechanism for tourism businesses, using an advanced integrated Analytic Hierarchy Process and Delphi (AHP-Delphi) approach derived from the supply side perspective. This research study comprises two phases: (1) Delphi method analysis that involves 17 experts, providing confirmation about the evaluation criteria; and (2) Analytical Hierarchical Process (AHP) method which aims to allocate weightings to the evaluation criteria from the experts. Findings from the Delphi method analysis revealed the acceptance of two dimensions, six sub-dimensions and 17 indicators as key evaluation criteria. The AHP method analysis indicated that the most significant dimension was managing quality, with tourism services and public sector facilities being the most important sub-dimension and indicator respectively. The self-evaluation mechanism proposed in this planning perspectives can assist tourism businesses and national/regional Destination Management Organization to identify quality management problems and possible ways of enhancing quality tourism, so that tourism experience, and tourist’s satisfaction can be further improved effectively between the conflicting views by career professionals
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
The core operation of current Graph Neural Networks (GNNs) is the aggregation
enabled by the graph Laplacian or message passing, which filters the
neighborhood node information. Though effective for various tasks, in this
paper, we show that they are potentially a problematic factor underlying all
GNN methods for learning on certain datasets, as they force the node
representations similar, making the nodes gradually lose their identity and
become indistinguishable. Hence, we augment the aggregation operations with
their dual, i.e. diversification operators that make the node more distinct and
preserve the identity. Such augmentation replaces the aggregation with a
two-channel filtering process that, in theory, is beneficial for enriching the
node representations. In practice, the proposed two-channel filters can be
easily patched on existing GNN methods with diverse training strategies,
including spectral and spatial (message passing) methods. In the experiments,
we observe desired characteristics of the models and significant performance
boost upon the baselines on 9 node classification tasks
- …