771 research outputs found
K-nearest Neighbor Search by Random Projection Forests
K-nearest neighbor (kNN) search has wide applications in many areas,
including data mining, machine learning, statistics and many applied domains.
Inspired by the success of ensemble methods and the flexibility of tree-based
methodology, we propose random projection forests (rpForests), for kNN search.
rpForests finds kNNs by aggregating results from an ensemble of random
projection trees with each constructed recursively through a series of
carefully chosen random projections. rpForests achieves a remarkable accuracy
in terms of fast decay in the missing rate of kNNs and that of discrepancy in
the kNN distances. rpForests has a very low computational complexity. The
ensemble nature of rpForests makes it easily run in parallel on multicore or
clustered computers; the running time is expected to be nearly inversely
proportional to the number of cores or machines. We give theoretical insights
by showing the exponential decay of the probability that neighboring points
would be separated by ensemble random projection trees when the ensemble size
increases. Our theory can be used to refine the choice of random projections in
the growth of trees, and experiments show that the effect is remarkable.Comment: 15 pages, 4 figures, 2018 IEEE Big Data Conferenc
Automatic Objects Removal for Scene Completion
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as 3D scene reconstruction and
other big data applications. However, this is not an easy task due to the fact
the retrieved photos are neither aligned nor calibrated. Furthermore, with the
occlusion of unexpected foreground objects like people, vehicles, it is even
more challenging to find feature correspondences and reconstruct realistic
scenes. In this paper, we propose a structure based image completion algorithm
for object removal that produces visually plausible content with consistent
structure and scene texture. We use an edge matching technique to infer the
potential structure of the unknown region. Driven by the estimated structure,
texture synthesis is performed automatically along the estimated curves. We
evaluate the proposed method on different types of images: from highly
structured indoor environment to the natural scenes. Our experimental results
demonstrate satisfactory performance that can be potentially used for
subsequent big data processing: 3D scene reconstruction and location
recognition.Comment: 6 pages, IEEE International Conference on Computer Communications
(INFOCOM 14), Workshop on Security and Privacy in Big Data, Toronto, Canada,
201
Residual Attention Network for Image Classification
In this work, we propose "Residual Attention Network", a convolutional neural
network using attention mechanism which can incorporate with state-of-art feed
forward network architecture in an end-to-end training fashion. Our Residual
Attention Network is built by stacking Attention Modules which generate
attention-aware features. The attention-aware features from different modules
change adaptively as layers going deeper. Inside each Attention Module,
bottom-up top-down feedforward structure is used to unfold the feedforward and
feedback attention process into a single feedforward process. Importantly, we
propose attention residual learning to train very deep Residual Attention
Networks which can be easily scaled up to hundreds of layers. Extensive
analyses are conducted on CIFAR-10 and CIFAR-100 datasets to verify the
effectiveness of every module mentioned above. Our Residual Attention Network
achieves state-of-the-art object recognition performance on three benchmark
datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and
ImageNet (4.8% single model and single crop, top-5 error). Note that, our
method achieves 0.6% top-1 accuracy improvement with 46% trunk depth and 69%
forward FLOPs comparing to ResNet-200. The experiment also demonstrates that
our network is robust against noisy labels.Comment: accepted to CVPR201
Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand
Predicting the demand for electricity with uncertainty helps in planning and
operation of the grid to provide reliable supply of power to the consumers.
Machine learning (ML)-based demand forecasting approaches can be categorized
into (1) sample-based approaches, where each forecast is made independently,
and (2) time series regression approaches, where some historical load and other
feature information is used. When making a short-to-mid-term electricity demand
forecast, some future information is available, such as the weather forecast
and calendar variables. However, in existing forecasting models this future
information is not fully incorporated. To overcome this limitation of existing
approaches, we propose Masked Multi-Step Multivariate Probabilistic Forecasting
(MMMPF), a novel and general framework to train any neural network model
capable of generating a sequence of outputs, that combines both the temporal
information from the past and the known information about the future to make
probabilistic predictions. Experiments are performed on a real-world dataset
for short-to-mid-term electricity demand forecasting for multiple regions and
compared with various ML methods. They show that the proposed MMMPF framework
outperforms not only sample-based methods but also existing time-series
forecasting models with the exact same base models. Models trainded with MMMPF
can also generate desired quantiles to capture uncertainty and enable
probabilistic planning for grid of the future.Comment: Accepted by the 2023 IEEE Power & Energy Society General Meeting
(PESGM). arXiv admin note: substantial text overlap with arXiv:2209.1441
Shelter: Smartphone Bridged Socialized Body Networks for Epidemic Control
We propose using information, computing and networking innovations to tackle epidemic control challenges
Resident Attitudes toward Dark Tourism, a Perspective of Place-based Identity Motives
Place-based identity theories prove to be valid in better understanding resident attitudes towards support for tourism. Yet, its effectiveness is not verified in the context of dark tourism and resident attitudes towards dark tourism remains unknown. Based on a survey of 526 local residents in China’s Yingxiu, the epicentre of the Great Wenchuan Earthquake, the authors examined the relationships between the local residents’ place-based identity motives and their attitudes towards support for dark tourism development. Results show that the motive of ‘belonging/meaning’ is one of the most important determinants; residents’ involvement in dark tourism and bereavement affect their identity motives and attitudes towards support for dark tourism. The theoretical contributions and managerial implications are discussed
Daoist Harmony as a Chinese Philosophy and Psychology
Based on Lee’s prior research on Daoism (Lee, 2003; Lee, 2004; Lee, Han, Byron and Fan, 2008; Lee and Hu, 1993; Lee, Norasakkunkit, Liu, Zhang and Zhou, 2008), this article first introduces Laozi, Dao, De and Daoism in relation to harmony. Then, Daoist harmony is elaborated in the following areas: (1) the yin-yang oneness, (2) the way it is (natural), (3) wei-wu-wei (or nonintervention), (4) water-like characteristics, (5) love for peace, and (6) tolerance and appreciation of differences. The article concludes with a suggestion for harmony with the external world as well as with fellow human beings
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