2,194 research outputs found
Research Directions and Projects In an Institute of Developmental Psychology in China
We are a team who maintained to focus on 3 fields in people’s mental health recent years: marriage and family research and therapy, mental health of middle and primary school students, and internet addiction in youth. In every field, we focus on both fundamental research and clinical practice. We aim to explore mechanisms using survey, observation and cognitive neuroscience methods (fMRI), and develop prediction and intervention projects based on research, to improve people’s life and policies
Learning to Purification for Unsupervised Person Re-identification
Unsupervised person re-identification is a challenging and promising task in
computer vision. Nowadays unsupervised person re-identification methods have
achieved great progress by training with pseudo labels. However, how to purify
feature and label noise is less explicitly studied in the unsupervised manner.
To purify the feature, we take into account two types of additional features
from different local views to enrich the feature representation. The proposed
multi-view features are carefully integrated into our cluster contrast learning
to leverage more discriminative cues that the global feature easily ignored and
biased. To purify the label noise, we propose to take advantage of the
knowledge of teacher model in an offline scheme. Specifically, we first train a
teacher model from noisy pseudo labels, and then use the teacher model to guide
the learning of our student model. In our setting, the student model could
converge fast with the supervision of the teacher model thus reduce the
interference of noisy labels as the teacher model greatly suffered. After
carefully handling the noise and bias in the feature learning, our purification
modules are proven to be very effective for unsupervised person
re-identification. Extensive experiments on three popular person
re-identification datasets demonstrate the superiority of our method.
Especially, our approach achieves a state-of-the-art accuracy 85.8\% @mAP and
94.5\% @Rank-1 on the challenging Market-1501 benchmark with ResNet-50 under
the fully unsupervised setting. The code will be released
Big Data Analytics in Immunology: A Knowledge-Based Approach
With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow
Nonlinear quantum input-output analysis using Volterra series
Quantum input-output theory plays a very important role for analyzing the
dynamics of quantum systems, especially large-scale quantum networks. As an
extension of the input-output formalism of Gardiner and Collet, we develop a
new approach based on the quantum version of the Volterra series which can be
used to analyze nonlinear quantum input-output dynamics. By this approach, we
can ignore the internal dynamics of the quantum input-output system and
represent the system dynamics by a series of kernel functions. This approach
has the great advantage of modelling weak-nonlinear quantum networks. In our
approach, the number of parameters, represented by the kernel functions, used
to describe the input-output response of a weak-nonlinear quantum network,
increases linearly with the scale of the quantum network, not exponentially as
usual. Additionally, our approach can be used to formulate the quantum network
with both nonlinear and nonconservative components, e.g., quantum amplifiers,
which cannot be modelled by the existing methods, such as the
Hudson-Parthasarathy model and the quantum transfer function model. We apply
our general method to several examples, including Kerr cavities, optomechanical
transducers, and a particular coherent feedback system with a nonlinear
component and a quantum amplifier in the feedback loop. This approach provides
a powerful way to the modelling and control of nonlinear quantum networks.Comment: 12 pages, 7 figure
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