In real world person re-identification (re-id), images of people captured at very different resolutions from different locations need be matched. Existing re-id models typically normalise all person images to the same size. However, a low-resolution (LR) image contains much less information about a person, and direct image scaling and simple size normalisation as done in conventional re-id methods cannot compensate for the loss of information. To solve this LR person re-id problem, we propose a novel joint multi-scale learning framework, termed joint multi-scale discriminant component analysis (JUDEA). The key component of this framework is a heterogeneous class mean discrepancy (HCMD) criterion for cross-scale image domain alignment, which is optimised simultaneously with discriminant modelling across multiple scales in the joint learning framework. Our experiments show that the proposed JUDEA framework outperforms existing representative reid methods as well as other related LR visual matching models applied for the LR person re-id problem.
Abstract-Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE -a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3% -43.6% less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2% with 0.17% SLA violation rate as the performance penalty.
Identification of glomerular lesions and structures is a key point for pathological diagnosis, treatment instructions, and prognosis evaluation in kidney diseases. These time‐consuming tasks require a more accurate and reproducible quantitative analysis method. We established derivation and validation cohorts composed of 400 Chinese patients with immunoglobulin A nephropathy (IgAN) retrospectively. Deep convolutional neural networks and biomedical image processing algorithms were implemented to locate glomeruli, identify glomerular lesions (global and segmental glomerular sclerosis, crescent, and none of the above), identify and quantify different intrinsic glomerular cells, and assess a network‐based mesangial hypercellularity score in periodic acid–Schiff (PAS)‐stained slides. Our framework achieved 93.1% average precision and 94.9% average recall for location of glomeruli, and a total Cohen's kappa of 0.912 [95% confidence interval (CI), 0.892–0.932] for glomerular lesion classification. The evaluation of global, segmental glomerular sclerosis, and crescents achieved Cohen's kappa values of 1.0, 0.776, 0.861, and 95% CI of (1.0, 1.0), (0.727, 0.825), (0.824, 0.898), respectively. The well‐designed neural network can identify three kinds of intrinsic glomerular cells with 92.2% accuracy, surpassing the about 5–11% average accuracy of junior pathologists. Statistical interpretation shows that there was a significant difference (P value < 0.0001) between this analytic renal pathology system (ARPS) and four junior pathologists for identifying mesangial and endothelial cells, while that for podocytes was similar, with P value = 0.0602. In addition, this study indicated that the ratio of mesangial cells, endothelial cells, and podocytes within glomeruli from IgAN was 0.41:0.36:0.23, and the performance of mesangial score assessment reached a Cohen's kappa of 0.42 and 95% CI (0.18, 0.69). The proposed computer‐aided diagnosis system has feasibility for quantitative analysis and auxiliary recognition of glomerular pathological features. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
Person re-identification is to match images of the same person captured in disjoint camera views and at different time. In order to obtain a reliable similarity measurement between images, manually annotating a large amount of pairwise cross-camera-view person images is deemed necessary. However, such a kind of annotation is both costly and impractical for efficiently deploying a re-identification system to a completely new scenario, a new setting of non-overlapping camera views between which person images are to be matched. To solve this problem, we consider utilizing other existing person images captured in other scenarios to help the re-identification system in a target (new) scenario, provided that a few samples are captured under the new scenario. More specifically, we tackle this problem by jointly learning the similarity measurements for re-identification in different scenarios in an asymmetric way. To model the joint learning, we consider that the re-identification models share certain component across tasks. A distinct consideration in our multi-task modeling is to extract the discriminant shared component that reduces the cross-task data overlap in the shared latent space during the joint learning, so as to enhance the target inter-class separation in the shared latent space. For this purpose, we propose to maximize the cross-task data discrepancy (CTDD) on the shared component during asymmetric multi-task learning, along with maximizing the local inter-class variation and minimizing local intra-class variation on all tasks. We call our proposed method the constrained asymmetric multi-task discriminant component analysis (cAMT-DCA). We show that cAMT-DCA can be solved by a simple eigen-decomposition with a closed form, getting rid of any iterative learning used in most conventional multi-task learning. The experimental results show that the proposed transfer model gains a clear improvement against the related non-transfer and general multi-task person re-identification models.
Visible watermark plays an important role in image copyright protection and the robustness of a visible watermark to an attack is shown to be essential. To evaluate and improve the effectiveness of watermark, watermark removal attracts increasing attention and becomes a hot research top. Current methods cast the watermark removal as an image-to-image translation problem where the encode-decode architectures with pixel-wise loss are adopted to transfer the transparent watermarked pixels into unmarked pixels. However, when a number of realistic images are presented, the watermarks are more likely to be unknown and diverse (i.e., the watermarks might be opaque or semi-transparent; the category and pattern of watermarks are unknown). When applying existing methods to the real-world scenarios, they mostly can not satisfactorily reconstruct the hidden information obscured under the complex and various watermarks (i.e., the residual watermark traces remain and the reconstructed images lack reality). To address this difficulty, in this paper, we present a new watermark processing framework using the conditional generative adversarial networks (cGANs) for visible watermark removal in the real-world application. The proposed method enables the watermark removal solution to be more closed to the photo-realistic reconstruction using a patch-based discriminator conditioned on the watermarked images, which is adversarially trained to differentiate the difference between the recovered images and original watermark-free images. Extensive experimental results on a large-scale visible watermark dataset demonstrate the effectiveness of the proposed method and clearly indicate that our proposed approach can produce more photo-realistic and convincing results compared with the state-of-the-art methods.
ABSTRACT:Statistical results reveal the close relationship between the hazy days in eastern China and El Nino Southern Oscillation (ENSO) events. A significant negative correlation coefficient (CC) centre is located in the western Pacific, while a positive CC centre is located in the eastern equatorial Pacific. Case analyses also confirm that an El Nino (La Nina) event is more likely to bring more (less) hazy days during winter. The key circulation pattern for less haze includes stronger Siberian high and Aleutian low pressure systems, stronger northerly wind over eastern Asia, and also a stronger high-level subtropical westerly jet (i.e. stronger East Asian winter monsoon, EAWM). While more haze often occurs under weaker EAWM pattern. The influencing mechanism of the ENSO event could be explained first, by how it alters the pressure and thermal differences between the Asian continent and the western Pacific Ocean, second, by the change of the circulation pattern of EAWM, and finally by the effect of the northerlies at higher latitudes. Compared to the relationship between ENSO and EAWM, the ENSO-haze relationship is more stable. Results also indicate an ENSO event has a leading influence on the haze frequency and can be used as a predictor in the seasonal forecasts of hazy days.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.