1,606 research outputs found
Collaborative Feature Learning from Social Media
Image feature representation plays an essential role in image recognition and
related tasks. The current state-of-the-art feature learning paradigm is
supervised learning from labeled data. However, this paradigm requires
large-scale category labels, which limits its applicability to domains where
labels are hard to obtain. In this paper, we propose a new data-driven feature
learning paradigm which does not rely on category labels. Instead, we learn
from user behavior data collected on social media. Concretely, we use the image
relationship discovered in the latent space from the user behavior data to
guide the image feature learning. We collect a large-scale image and user
behavior dataset from Behance.net. The dataset consists of 1.9 million images
and over 300 million view records from 1.9 million users. We validate our
feature learning paradigm on this dataset and find that the learned feature
significantly outperforms the state-of-the-art image features in learning
better image similarities. We also show that the learned feature performs
competitively on various recognition benchmarks
Building a St\"uckelberg Portal
We construct explicit string theory models realizing the recently proposed
"St\"uckelberg Portal" scenario, a framework for building Z' mediation models
without the need to introduce unwanted exotic matter charged under the Standard
Model. This scenario can be viewed purely field-theoretically, although it is
particularly well motivated from string theory. By analyzing carefully the
St\"uckelberg couplings between the Abelian gauge bosons and the RR axions, we
construct the first global intersecting brane models which extend the Standard
Model with a genuine hidden sector, to which it is nonetheless connected via
U(1) mass mixings. Utilizing the explicit models we construct, we discuss some
broad phenomenological properties and experimental implications of this
scenario such as Z-Z' mixings, dark matter stability and relic density, and
supersymmetry mediation. With an appropriate confining hidden sector, our setup
also provides a minimal realization of the hidden valley scenario. We further
explore the possibility of obtaining small Z' masses from a large ensemble of
U(1) bosons. Related to the St\"uckelberg portal are two mechanisms that
connect the visible and the hidden sectors, namely mediation by
non-perturbative operators and the hidden photon scenario, on which we briefly
comment.Comment: 39 pages, 11 figure
Changer: Feature Interaction is What You Need for Change Detection
Change detection is an important tool for long-term earth observation
missions. It takes bi-temporal images as input and predicts "where" the change
has occurred. Different from other dense prediction tasks, a meaningful
consideration for change detection is the interaction between bi-temporal
features. With this motivation, in this paper we propose a novel general change
detection architecture, MetaChanger, which includes a series of alternative
interaction layers in the feature extractor. To verify the effectiveness of
MetaChanger, we propose two derived models, ChangerAD and ChangerEx with simple
interaction strategies: Aggregation-Distribution (AD) and "exchange". AD is
abstracted from some complex interaction methods, and "exchange" is a
completely parameter\&computation-free operation by exchanging bi-temporal
features. In addition, for better alignment of bi-temporal features, we propose
a flow dual-alignment fusion (FDAF) module which allows interactive alignment
and feature fusion. Crucially, we observe Changer series models achieve
competitive performance on different scale change detection datasets. Further,
our proposed ChangerAD and ChangerEx could serve as a starting baseline for
future MetaChanger design.Comment: 11 pages, 5 figure
Measurement and Analysis of Bilateral Costs between China and Trading Partners Based on the Revised Gravity Model
Abstract A revised gravity model has been adopted in the thesis to measure the changes of bilateral trade costs of China and other 28 countries during 1992~ 2007.The results are as follows: China’s trade costs take on a declining trendand the bilateral trade costs between China and developed countries is lower than that of developing countries. As the trade costs between China and major trading partners take on a declining trend, which even has room for further decline, the major policy significances in this thesis are that China shall continue to excavate the way to reduce the trade costs in order tofurther enhance the export competitiveness
Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes
Tensor decomposition is an important tool for multiway data analysis. In
practice, the data is often sparse yet associated with rich temporal
information. Existing methods, however, often under-use the time information
and ignore the structural knowledge within the sparsely observed tensor
entries. To overcome these limitations and to better capture the underlying
temporal structure, we propose Dynamic EMbedIngs fOr dynamic Tensor
dEcomposition (DEMOTE). We develop a neural diffusion-reaction process to
estimate dynamic embeddings for the entities in each tensor mode. Specifically,
based on the observed tensor entries, we build a multi-partite graph to encode
the correlation between the entities. We construct a graph diffusion process to
co-evolve the embedding trajectories of the correlated entities and use a
neural network to construct a reaction process for each individual entity. In
this way, our model can capture both the commonalities and personalities during
the evolution of the embeddings for different entities. We then use a neural
network to model the entry value as a nonlinear function of the embedding
trajectories. For model estimation, we combine ODE solvers to develop a
stochastic mini-batch learning algorithm. We propose a stratified sampling
method to balance the cost of processing each mini-batch so as to improve the
overall efficiency. We show the advantage of our approach in both simulation
study and real-world applications. The code is available at
https://github.com/wzhut/Dynamic-Tensor-Decomposition-via-Neural-Diffusion-Reaction-Processes
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