2,386 research outputs found
The Neglected Arboviral Infections in Mainland China
The major arboviral diseases in mainland China include Japanese encephalitis, dengue fever, Crimean-Congo hemorrhagic fever (also known as Xinjiang hemorrhagic fever), and tick-borne encephalitis. These and other newly found arbovirus infections due to Banna virus and Tahyna virus contribute to a large and relatively neglected disease burden in China. Here we briefly review the literature regarding these arboviral infections in mainland China with emphasis on their epidemiology, primary vectors, phylogenetic associations, and the prevention programs associated with these agents in China
DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition
While transformers have shown great potential on video recognition with their
strong capability of capturing long-range dependencies, they often suffer high
computational costs induced by the self-attention to the huge number of 3D
tokens. In this paper, we present a new transformer architecture termed
DualFormer, which can efficiently perform space-time attention for video
recognition. Concretely, DualFormer stratifies the full space-time attention
into dual cascaded levels, i.e., to first learn fine-grained local interactions
among nearby 3D tokens, and then to capture coarse-grained global dependencies
between the query token and global pyramid contexts. Different from existing
methods that apply space-time factorization or restrict attention computations
within local windows for improving efficiency, our local-global stratification
strategy can well capture both short- and long-range spatiotemporal
dependencies, and meanwhile greatly reduces the number of keys and values in
attention computation to boost efficiency. Experimental results verify the
superiority of DualFormer on five video benchmarks against existing methods. In
particular, DualFormer achieves 82.9%/85.2% top-1 accuracy on Kinetics-400/600
with ~1000G inference FLOPs which is at least 3.2x fewer than existing methods
with similar performance. We have released the source code at
https://github.com/sail-sg/dualformer.Comment: Accepted by ECCV 202
GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
One of the biggest issues facing the use of machine learning in medical
imaging is the lack of availability of large, labelled datasets. The annotation
of medical images is not only expensive and time consuming but also highly
dependent on the availability of expert observers. The limited amount of
training data can inhibit the performance of supervised machine learning
algorithms which often need very large quantities of data on which to train to
avoid overfitting. So far, much effort has been directed at extracting as much
information as possible from what data is available. Generative Adversarial
Networks (GANs) offer a novel way to unlock additional information from a
dataset by generating synthetic samples with the appearance of real images.
This paper demonstrates the feasibility of introducing GAN derived synthetic
data to the training datasets in two brain segmentation tasks, leading to
improvements in Dice Similarity Coefficient (DSC) of between 1 and 5 percentage
points under different conditions, with the strongest effects seen fewer than
ten training image stacks are available
Electronic and Information Technology Accessibility And IT Asset Management (ITAM)
No abstract availabl
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
Spatio-temporal graph learning is a fundamental problem in the Web of Things
era, which enables a plethora of Web applications such as smart cities, human
mobility and climate analysis. Existing approaches tackle different learning
tasks independently, tailoring their models to unique task characteristics.
These methods, however, fall short of modeling intrinsic uncertainties in the
spatio-temporal data. Meanwhile, their specialized designs limit their
universality as general spatio-temporal learning solutions. In this paper, we
propose to model the learning tasks in a unified perspective, viewing them as
predictions based on conditional information with shared spatio-temporal
patterns. Based on this proposal, we introduce Unified Spatio-Temporal
Diffusion Models (USTD) to address the tasks uniformly within the
uncertainty-aware diffusion framework. USTD is holistically designed,
comprising a shared spatio-temporal encoder and attention-based denoising
networks that are task-specific. The shared encoder, optimized by a
pre-training strategy, effectively captures conditional spatio-temporal
patterns. The denoising networks, utilizing both cross- and self-attention,
integrate conditional dependencies and generate predictions. Opting for
forecasting and kriging as downstream tasks, we design Gated Attention (SGA)
and Temporal Gated Attention (TGA) for each task, with different emphases on
the spatial and temporal dimensions, respectively. By combining the advantages
of deterministic encoders and probabilistic diffusion models, USTD achieves
state-of-the-art performances compared to deterministic and probabilistic
baselines in both tasks, while also providing valuable uncertainty estimates
Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC
The 75,848 lead tungstate crystals in CMS experiment at the CERN Large Hadron
Collider are used to measure the energy of electrons and photons produced in
the proton-proton collisions. The optical transparency of the crystals degrades
slowly with radiation dose due to the beam-beam collisions. The transparency of
each crystal is monitored with a laser monitoring system that tracks changes in
the optical properties of the crystals due to radiation from the collision
products. Predicting the optical transparency of the crystals, both in the
short-term and in the long-term, is a critical task for the CMS experiment. We
describe here the public data release, following FAIR principles, of the
crystal monitoring data collected by the CMS Collaboration between 2016 and
2018. Besides describing the dataset and its access, the problems that can be
addressed with it are described, as well as an example solution based on a Long
Short-Term Memory neural network developed to predict future behavior of the
crystals
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The Internet of Things and its implications for international business theory
While the Internet of Things (IoT) has been seen to provide a global network infrastructure facilitating cross-border flow of goods and information, much of the current discussion focuses rather on consumer applications, leaving its implications for global businesses underexplored. We believe that the IoT has the potential to reconfigure industry structure and particularly global value chains. In this article, we map out the benefits of the Internet of Things to firms and to industries, whilst drawing attention to numerous constraints, including a Sino-US technology race, that may prohibit the realization of these benefits for developing country firms. Furthermore, we reflect on how the Internet of Things may pose new questions to international business theory, with a particular focus on internalization decisions, as well as drawing on the thriving ecosystem perspective. Our discussion culminates with the dark side of the Internet of Things which could amplify the asymmetric power relationships already observed in global value chains
Production and characterization of biochar from biomasses
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Graph Neural Processes for Spatio-Temporal Extrapolation
We study the task of spatio-temporal extrapolation that generates data at
target locations from surrounding contexts in a graph. This task is crucial as
sensors that collect data are sparsely deployed, resulting in a lack of
fine-grained information due to high deployment and maintenance costs. Existing
methods either use learning-based models like Neural Networks or statistical
approaches like Gaussian Processes for this task. However, the former lacks
uncertainty estimates and the latter fails to capture complex spatial and
temporal correlations effectively. To address these issues, we propose
Spatio-Temporal Graph Neural Processes (STGNP), a neural latent variable model
which commands these capabilities simultaneously. Specifically, we first learn
deterministic spatio-temporal representations by stacking layers of causal
convolutions and cross-set graph neural networks. Then, we learn latent
variables for target locations through vertical latent state transitions along
layers and obtain extrapolations. Importantly during the transitions, we
propose Graph Bayesian Aggregation (GBA), a Bayesian graph aggregator that
aggregates contexts considering uncertainties in context data and graph
structure. Extensive experiments show that STGNP has desirable properties such
as uncertainty estimates and strong learning capabilities, and achieves
state-of-the-art results by a clear margin.Comment: SIGKDD 202
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