1,141 research outputs found
Light Field Denoising via Anisotropic Parallax Analysis in a CNN Framework
Light field (LF) cameras provide perspective information of scenes by taking
directional measurements of the focusing light rays. The raw outputs are
usually dark with additive camera noise, which impedes subsequent processing
and applications. We propose a novel LF denoising framework based on
anisotropic parallax analysis (APA). Two convolutional neural networks are
jointly designed for the task: first, the structural parallax synthesis network
predicts the parallax details for the entire LF based on a set of anisotropic
parallax features. These novel features can efficiently capture the high
frequency perspective components of a LF from noisy observations. Second, the
view-dependent detail compensation network restores non-Lambertian variation to
each LF view by involving view-specific spatial energies. Extensive experiments
show that the proposed APA LF denoiser provides a much better denoising
performance than state-of-the-art methods in terms of visual quality and in
preservation of parallax details
Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions
Depth estimation is a fundamental problem for light field photography
applications. Numerous methods have been proposed in recent years, which either
focus on crafting cost terms for more robust matching, or on analyzing the
geometry of scene structures embedded in the epipolar-plane images. Significant
improvements have been made in terms of overall depth estimation error;
however, current state-of-the-art methods still show limitations in handling
intricate occluding structures and complex scenes with multiple occlusions. To
address these challenging issues, we propose a very effective depth estimation
framework which focuses on regularizing the initial label confidence map and
edge strength weights. Specifically, we first detect partially occluded
boundary regions (POBR) via superpixel based regularization. Series of
shrinkage/reinforcement operations are then applied on the label confidence map
and edge strength weights over the POBR. We show that after weight
manipulations, even a low-complexity weighted least squares model can produce
much better depth estimation than state-of-the-art methods in terms of average
disparity error rate, occlusion boundary precision-recall rate, and the
preservation of intricate visual features
An Improved Ant Colony Algorithm for New energy Industry Resource Allocation in Cloud Environment
The new energy industry development is affected by many factors. Among them, the resources utilization ratio is a major reason for the low productivity of enterprises. As the core problem of cloud computing, the resource allocation problem has been widely concerned by the people, and the resource allocation problem of the new energy industry as the key to energy innovation and transformation should be more paid attention to. In multi-resource cloud computing scenarios, requests made by users often involve multiple types of resources. Traditional resource allocation algorithms have a single optimization object, typically time efficiency. In order to achieve cluster load balancing, utilization of system resources and improvement of system work efficiency, this paper proposes a new cloud computing allocation algorithm based on improved ant colony algorithm. According to the limit conditions of cloud computing environment and computing resources, this paper finds the shortest response time of all resource nodes and gets a set of best available nodes. This method can meet the quality requirements of cloud computing, and the task completion time of the improved algorithm is shorter, the number of algorithm iterations is less, and the load balancing effect is better. Through MATLAB simulation experiments, the effectiveness of the proposed method is verified
Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing
Key challenges in running a retail business include how to select products to
present to consumers (the assortment problem), and how to price products (the
pricing problem) to maximize revenue or profit. Instead of considering these
problems in isolation, we propose a joint approach to assortment-pricing based
on contextual bandits. Our model is doubly high-dimensional, in that both
context vectors and actions are allowed to take values in high-dimensional
spaces. In order to circumvent the curse of dimensionality, we propose a simple
yet flexible model that captures the interactions between covariates and
actions via a (near) low-rank representation matrix. The resulting class of
models is reasonably expressive while remaining interpretable through latent
factors, and includes various structured linear bandit and pricing models as
particular cases. We propose a computationally tractable procedure that
combines an exploration/exploitation protocol with an efficient low-rank matrix
estimator, and we prove bounds on its regret. Simulation results show that this
method has lower regret than state-of-the-art methods applied to various
standard bandit and pricing models. Real-world case studies on the
assortment-pricing problem, from an industry-leading instant noodles company to
an emerging beauty start-up, underscore the gains achievable using our method.
In each case, we show at least three-fold gains in revenue or profit by our
bandit method, as well as the interpretability of the latent factor models that
are learned
STK11 loss and SMARCB1 deficiency mutation in a dedifferentiated lung cancer patient present response to neo-adjuvant treatment with pembrolizumab and platinum doublet: A case report
Cancers harboring serine threonine kinase (STK11) alteration or SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily B, member 1 (SMARCB1) mutation are conventionally considered as treatment-refractory to immune checkpoint inhibitors or chemotherapy, respectively. However in the present report, we demonstrated a case of dedifferentiated non-small cell lung cancer, characterized by STK11 loss (due to promoter loss) mutation co-mutated with SMARCB1 deficiency mutation, has achieved significantly partial response to neo-adjuvant treatment with pembrolizumab and platinum doublet regimen. Our case highlighted that either STK11 loss, or SMARCB1 deficiency mutation, might not be used to select patients for PD-(L)1 blockade therapy or chemotherapy, respectively. SKT11 loss accompanied with SMARCB1 deficiency mutation may benefit from immunotherapy combined with chemotherapy
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