1,191 research outputs found
Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation
Existing image segmentation networks mainly leverage large-scale labeled
datasets to attain high accuracy. However, labeling medical images is very
expensive since it requires sophisticated expert knowledge. Thus, it is more
desirable to employ only a few labeled data in pursuing high segmentation
performance. In this paper, we develop a data augmentation method for one-shot
brain magnetic resonance imaging (MRI) image segmentation which exploits only
one labeled MRI image (named atlas) and a few unlabeled images. In particular,
we propose to learn the probability distributions of deformations (including
shapes and intensities) of different unlabeled MRI images with respect to the
atlas via 3D variational autoencoders (VAEs). In this manner, our method is
able to exploit the learned distributions of image deformations to generate new
authentic brain MRI images, and the number of generated samples will be
sufficient to train a deep segmentation network. Furthermore, we introduce a
new standard segmentation benchmark to evaluate the generalization performance
of a segmentation network through a cross-dataset setting (collected from
different sources). Extensive experiments demonstrate that our method
outperforms the state-of-the-art one-shot medical segmentation methods. Our
code has been released at
https://github.com/dyh127/Modeling-the-Probabilistic-Distribution-of-Unlabeled-Data.Comment: AAAI 202
BIOMECHANICAL MEASUREMENT AND EVALUATION FOR WUSU COMPULSORY PROGRAM
INTRODUCTION: The Wusu compulsory program is a new form of competition. It differs from the Wusu optional program in that all the athletes must execute the same compulsory program. Following this, judges asses the level of expertise of athletes, by scores gained, based on their performance.
The comparability and objectivity in grading a compulsory program is better than in an optional program. So it is feasible to analyze technique of the Wusu compulsory program using a biomechanical measuring method. The compulsory exercise described as, “the turn about for flying kick on the right“, was measured and analyzed quantitatively in this paper.
With the calculations from this study, a new evaluation factor and advanced development of competitive Wusu wasdeveloped
Highly-Accurate Electricity Load Estimation via Knowledge Aggregation
Mid-term and long-term electric energy demand prediction is essential for the
planning and operations of the smart grid system. Mainly in countries where the
power system operates in a deregulated environment. Traditional forecasting
models fail to incorporate external knowledge while modern data-driven ignore
the interpretation of the model, and the load series can be influenced by many
complex factors making it difficult to cope with the highly unstable and
nonlinear power load series. To address the forecasting problem, we propose a
more accurate district level load prediction model Based on domain knowledge
and the idea of decomposition and ensemble. Its main idea is three-fold: a)
According to the non-stationary characteristics of load time series with
obvious cyclicality and periodicity, decompose into series with actual economic
meaning and then carry out load analysis and forecast. 2) Kernel Principal
Component Analysis(KPCA) is applied to extract the principal components of the
weather and calendar rule feature sets to realize data dimensionality
reduction. 3) Give full play to the advantages of various models based on the
domain knowledge and propose a hybrid model(XASXG) based on Autoregressive
Integrated Moving Average model(ARIMA), support vector regression(SVR) and
Extreme gradient boosting model(XGBoost). With such designs, it accurately
forecasts the electricity demand in spite of their highly unstable
characteristic. We compared our method with nine benchmark methods, including
classical statistical models as well as state-of-the-art models based on
machine learning, on the real time series of monthly electricity demand in four
Chinese cities. The empirical study shows that the proposed hybrid model is
superior to all competitors in terms of accuracy and prediction bias
Quantum imaginary time evolution and quantum annealing meet topological sector optimization
Optimization problems are the core challenge in many fields of science and
engineering, yet general and effective methods are scarce for searching optimal
solutions. Quantum computing has been envisioned to help solve such problems,
for example, the quantum annealing (QA) method based on adiabatic evolution has
been extensively explored and successfully implemented on quantum simulators
such as D-wave's annealers and some Rydberg arrays. In this work, we
investigate topological sector optimization (TSO) problem, which attracts
particular interests in the quantum many-body physics community. We reveal that
the topology induced by frustration in the spin model is an intrinsic
obstruction for QA and other traditional methods to approach the ground state.
We demonstrate that the optimization difficulties of TSO problem are not
restricted to the gaplessness, but are also due to the topological nature which
are often ignored for the analysis of optimization problems before. To solve
TSO problems, we utilize quantum imaginary time evolution (QITE) with a
possible realization on quantum computers, which exploits the property of
quantum superposition to explore the full Hilbert space and can thus address
optimization problems of topological nature. We report the performance of
different quantum optimization algorithms on TSO problems and demonstrate that
their capability to address optimization problems are distinct even when
considering the quantum computational resources required for practical QITE
implementations
Txilm: Lossy Block Compression with Salted Short Hashing
Current blockchains are restricted by the low throughput. Aimed at this
problem, we propose Txilm, a protocol that compresses the size of transaction
presentation in each block to save the bandwidth of the network. In this
protocol, a block carries short hashes of TXIDs instead of complete
transactions. Combined with the sorted transactions based on TXIDs, Txilm
realizes 80 times of data size reduction compared with the original
blockchains. We also evaluate the probability of hash collisions, and provide
methods of resolving such collisions. Finally, we design strategies to protect
against potential attacks on Txilm.Comment: 5 pages and 2 figure
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