363 research outputs found
Logical gaps in the approximate solutions of the social learning game and an exact solution
After the social learning models were proposed, finding the solutions of the
games becomes a well-defined mathematical question. However, almost all papers
on the games and their applications are based on solutions built upon either an
add-hoc argument or a twisted Bayesian analysis of the games. Here, we present
logical gaps in those solutions and an exact solution of our own. We also
introduced a minor extension to the original game such that not only logical
difference but also difference in action outcomes among those solutions become
visible.Comment: A major revisio
Linear Convergence of Adaptively Iterative Thresholding Algorithms for Compressed Sensing
This paper studies the convergence of the adaptively iterative thresholding
(AIT) algorithm for compressed sensing. We first introduce a generalized
restricted isometry property (gRIP). Then we prove that the AIT algorithm
converges to the original sparse solution at a linear rate under a certain gRIP
condition in the noise free case. While in the noisy case, its convergence rate
is also linear until attaining a certain error bound. Moreover, as by-products,
we also provide some sufficient conditions for the convergence of the AIT
algorithm based on the two well-known properties, i.e., the coherence property
and the restricted isometry property (RIP), respectively. It should be pointed
out that such two properties are special cases of gRIP. The solid improvements
on the theoretical results are demonstrated and compared with the known
results. Finally, we provide a series of simulations to verify the correctness
of the theoretical assertions as well as the effectiveness of the AIT
algorithm.Comment: 15 pages, 5 figure
Integration of <18/sup>O Labeling and Solution Isoelectric Focusing in a Shotgun Analysis of Mitochondrial Proteins
The coupling of efficient separations and mass spectrometry instrumentation is highly desirable to provide global proteomic analysis. When quantitative comparisons are part of the strategy, separation and analytical methods should be selected, which optimize the isotope labeling procedure. Enzyme-catalyzed O labeling is considered to be the labeling method most compatible with analysis of proteins from tissue and other limited samples. The introduction of label at the peptide stage mandates that protein manipulation be minimized in favor of peptide fractionation post-labeling. In the present study, forward and reverse O labeling are integrated with solution isoelectric focusing and capillary LC-tandem mass spectrometry to study changes in mitochondrial proteins associated with drug resistance in human cancer cells.
A total of 637 peptides corresponding to 278 proteins were identified in this analysis. Of these, twelve proteins have been demonstrated from the forward and reverse labeling experiments to have abundances altered by greater than a factor of two between the drug susceptible MCF-7 cell line and the MCF-7 cell line selected for resistance to mitoxantrone. Galectin-3 binding protein precursor was detected in the resistant cell line, but was not detected in the drug susceptible line. Such proteins are challenging to O and other isotope strategies and a solution is offered, based on reverse labeling. These twelve proteins play a role in several pathways including apoptosis, oxidative phosphorylation, fatty acid metabolism and amino acid metabolism. For some of these proteins, their possible functions in drug resistance have been proposed
Structural Prior Guided Generative Adversarial Transformers for Low-Light Image Enhancement
We propose an effective Structural Prior guided Generative Adversarial
Transformer (SPGAT) to solve low-light image enhancement. Our SPGAT mainly
contains a generator with two discriminators and a structural prior estimator
(SPE). The generator is based on a U-shaped Transformer which is used to
explore non-local information for better clear image restoration. The SPE is
used to explore useful structures from images to guide the generator for better
structural detail estimation. To generate more realistic images, we develop a
new structural prior guided adversarial learning method by building the skip
connections between the generator and discriminators so that the discriminators
can better discriminate between real and fake features. Finally, we propose a
parallel windows-based Swin Transformer block to aggregate different level
hierarchical features for high-quality image restoration. Experimental results
demonstrate that the proposed SPGAT performs favorably against recent
state-of-the-art methods on both synthetic and real-world datasets
LSTM Pose Machines
We observed that recent state-of-the-art results on single image human pose
estimation were achieved by multi-stage Convolution Neural Networks (CNN).
Notwithstanding the superior performance on static images, the application of
these models on videos is not only computationally intensive, it also suffers
from performance degeneration and flicking. Such suboptimal results are mainly
attributed to the inability of imposing sequential geometric consistency,
handling severe image quality degradation (e.g. motion blur and occlusion) as
well as the inability of capturing the temporal correlation among video frames.
In this paper, we proposed a novel recurrent network to tackle these problems.
We showed that if we were to impose the weight sharing scheme to the
multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN).
This property decouples the relationship among multiple network stages and
results in significantly faster speed in invoking the network for videos. It
also enables the adoption of Long Short-Term Memory (LSTM) units between video
frames. We found such memory augmented RNN is very effective in imposing
geometric consistency among frames. It also well handles input quality
degradation in videos while successfully stabilizes the sequential outputs. The
experiments showed that our approach significantly outperformed current
state-of-the-art methods on two large-scale video pose estimation benchmarks.
We also explored the memory cells inside the LSTM and provided insights on why
such mechanism would benefit the prediction for video-based pose estimations.Comment: Poster in IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 201
Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration
Recent years have witnessed the remarkable performance of diffusion models in
various vision tasks. However, for image restoration that aims to recover clear
images with sharper details from given degraded observations, diffusion-based
methods may fail to recover promising results due to inaccurate noise
estimation. Moreover, simple constraining noises cannot effectively learn
complex degradation information, which subsequently hinders the model capacity.
To solve the above problems, we propose a coarse-to-fine diffusion Transformer
(C2F-DFT) for image restoration. Specifically, our C2F-DFT contains diffusion
self-attention (DFSA) and diffusion feed-forward network (DFN) within a new
coarse-to-fine training scheme. The DFSA and DFN respectively capture the
long-range diffusion dependencies and learn hierarchy diffusion representation
to facilitate better restoration. In the coarse training stage, our C2F-DFT
estimates noises and then generates the final clean image by a sampling
algorithm. To further improve the restoration quality, we propose a simple yet
effective fine training scheme. It first exploits the coarse-trained diffusion
model with fixed steps to generate restoration results, which then would be
constrained with corresponding ground-truth ones to optimize the models to
remedy the unsatisfactory results affected by inaccurate noise estimation.
Extensive experiments show that C2F-DFT significantly outperforms
diffusion-based restoration method IR-SDE and achieves competitive performance
compared with Transformer-based state-of-the-art methods on tasks,
including deraining, deblurring, and real denoising.Comment: 9 pages, 8 figure
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