200 research outputs found
Learning to Associate Words and Images Using a Large-scale Graph
We develop an approach for unsupervised learning of associations between
co-occurring perceptual events using a large graph. We applied this approach to
successfully solve the image captcha of China's railroad system. The approach
is based on the principle of suspicious coincidence. In this particular
problem, a user is presented with a deformed picture of a Chinese phrase and
eight low-resolution images. They must quickly select the relevant images in
order to purchase their train tickets. This problem presents several
challenges: (1) the teaching labels for both the Chinese phrases and the images
were not available for supervised learning, (2) no pre-trained deep
convolutional neural networks are available for recognizing these Chinese
phrases or the presented images, and (3) each captcha must be solved within a
few seconds. We collected 2.6 million captchas, with 2.6 million deformed
Chinese phrases and over 21 million images. From these data, we constructed an
association graph, composed of over 6 million vertices, and linked these
vertices based on co-occurrence information and feature similarity between
pairs of images. We then trained a deep convolutional neural network to learn a
projection of the Chinese phrases onto a 230-dimensional latent space. Using
label propagation, we computed the likelihood of each of the eight images
conditioned on the latent space projection of the deformed phrase for each
captcha. The resulting system solved captchas with 77% accuracy in 2 seconds on
average. Our work, in answering this practical challenge, illustrates the power
of this class of unsupervised association learning techniques, which may be
related to the brain's general strategy for associating language stimuli with
visual objects on the principle of suspicious coincidence.Comment: 8 pages, 7 figures, 14th Conference on Computer and Robot Vision 201
Progressive Transfer Learning for Dexterous In-Hand Manipulation with Multi-Fingered Anthropomorphic Hand
Dexterous in-hand manipulation for a multi-fingered anthropomorphic hand is
extremely difficult because of the high-dimensional state and action spaces,
rich contact patterns between the fingers and objects. Even though deep
reinforcement learning has made moderate progress and demonstrated its strong
potential for manipulation, it is still faced with certain challenges, such as
large-scale data collection and high sample complexity. Especially, for some
slight change scenes, it always needs to re-collect vast amounts of data and
carry out numerous iterations of fine-tuning. Remarkably, humans can quickly
transfer learned manipulation skills to different scenarios with little
supervision. Inspired by human flexible transfer learning capability, we
propose a novel dexterous in-hand manipulation progressive transfer learning
framework (PTL) based on efficiently utilizing the collected trajectories and
the source-trained dynamics model. This framework adopts progressive neural
networks for dynamics model transfer learning on samples selected by a new
samples selection method based on dynamics properties, rewards and scores of
the trajectories. Experimental results on contact-rich anthropomorphic hand
manipulation tasks show that our method can efficiently and effectively learn
in-hand manipulation skills with a few online attempts and adjustment learning
under the new scene. Compared to learning from scratch, our method can reduce
training time costs by 95%.Comment: 12 pages, 7 figures, submitted to TNNL
Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters
We present several methods towards construction of precursors, which show
great promise towards early predictions, of solar flare events in this paper. A
data pre-processing pipeline is built to extract useful data from multiple
sources, Geostationary Operational Environmental Satellites (GOES) and Solar
Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare
inputs for machine learning algorithms. Two classification models are
presented: classification of flares from quiet times for active regions and
classification of strong versus weak flare events. We adopt deep learning
algorithms to capture both the spatial and temporal information from HMI
magnetogram data. Effective feature extraction and feature selection with raw
magnetogram data using deep learning and statistical algorithms enable us to
train classification models to achieve almost as good performance as using
active region parameters provided in HMI/Space-Weather HMI-Active Region Patch
(SHARP) data files. Case studies show a significant increase in the prediction
score around 20 hours before strong solar flare events
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification
Learning unbiased node representations for imbalanced samples in the graph
has become a more remarkable and important topic. For the graph, a significant
challenge is that the topological properties of the nodes (e.g., locations,
roles) are unbalanced (topology-imbalance), other than the number of training
labeled nodes (quantity-imbalance). Existing studies on topology-imbalance
focus on the location or the local neighborhood structure of nodes, ignoring
the global underlying hierarchical properties of the graph, i.e., hierarchy. In
the real-world scenario, the hierarchical structure of graph data reveals
important topological properties of graphs and is relevant to a wide range of
applications. We find that training labeled nodes with different hierarchical
properties have a significant impact on the node classification tasks and
confirm it in our experiments. It is well known that hyperbolic geometry has a
unique advantage in representing the hierarchical structure of graphs.
Therefore, we attempt to explore the hierarchy-imbalance issue for node
classification of graph neural networks with a novelty perspective of
hyperbolic geometry, including its characteristics and causes. Then, we propose
a novel hyperbolic geometric hierarchy-imbalance learning framework, named
HyperIMBA, to alleviate the hierarchy-imbalance issue caused by uneven
hierarchy-levels and cross-hierarchy connectivity patterns of labeled
nodes.Extensive experimental results demonstrate the superior effectiveness of
HyperIMBA for hierarchy-imbalance node classification tasks.Comment: Accepted by Web Conference (WWW) 202
MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features
To distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum of free energy (MFE) of the secondary structure and P-value of randomization test is used. Besides, a novel machine-learning algorithm, random forest (RF), is introduced. The results suggest that our method predicts at 98.21% specificity and 95.09% sensitivity. When compared with the previous study, Triplet-SVM-classifier, our RF method was nearly 10% greater in total accuracy. Further analysis indicated that the improvement was due to both the combined features and the RF algorithm. The MiPred web server is available at http://www.bioinf.seu.edu.cn/miRNA/. Given a sequence, MiPred decides whether it is a pre-miRNA-like hairpin sequence or not. If the sequence is a pre-miRNA-like hairpin, the RF classifier will predict whether it is a real pre-miRNA or a pseudo one
Complexity Matters: Rethinking the Latent Space for Generative Modeling
In generative modeling, numerous successful approaches leverage a
low-dimensional latent space, e.g., Stable Diffusion models the latent space
induced by an encoder and generates images through a paired decoder. Although
the selection of the latent space is empirically pivotal, determining the
optimal choice and the process of identifying it remain unclear. In this study,
we aim to shed light on this under-explored topic by rethinking the latent
space from the perspective of model complexity. Our investigation starts with
the classic generative adversarial networks (GANs). Inspired by the GAN
training objective, we propose a novel "distance" between the latent and data
distributions, whose minimization coincides with that of the generator
complexity. The minimizer of this distance is characterized as the optimal
data-dependent latent that most effectively capitalizes on the generator's
capacity. Then, we consider parameterizing such a latent distribution by an
encoder network and propose a two-stage training strategy called Decoupled
Autoencoder (DAE), where the encoder is only updated in the first stage with an
auxiliary decoder and then frozen in the second stage while the actual decoder
is being trained. DAE can improve the latent distribution and as a result,
improve the generative performance. Our theoretical analyses are corroborated
by comprehensive experiments on various models such as VQGAN and Diffusion
Transformer, where our modifications yield significant improvements in sample
quality with decreased model complexity.Comment: Accepted to NeurIPS 2023 (Spotlight
A review on N-doped biochar for oxidative degradation of organic contaminants in wastewater by persulfate activation
The Persulfate-based advanced oxidation process is the most efficient and commonly used technology to remove organic contaminants in wastewater. Due to the large surface area, unique electronic properties, abundant N functional groups, cost-effectiveness, and environmental friendliness, N-doped biochars (NBCs) are widely used as catalysts for persulfate activation. This review focuses on the NBC for oxidative degradation of organics-contaminated wastewater. Firstly, the preparation and modification methods of NBCs were reviewed. Then the catalytic performance of NBCs and modified NBCs on the oxidation degradation of organic contaminants were discussed with an emphasis on the degradation mechanism. We further summarized the detection technologies of activation mechanisms and the structures of NBCs affecting the PS activation, followed by the specific role of the N configuration of the NBC on its catalytic capacity. Finally, several challenges in the treatment of organics-contaminated wastewater by a persulfate-based advanced oxidation process were put forward and the recommendations for future research were proposed for further understanding of the advanced oxidation process activated by the NBC
- ā¦