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Tragedy readiness ought to represent a good development with the every day shock technique, but are we all well prepared?
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. check details The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, typical meta-learning models use shallow neural networks, thus limiting its effectiveness. We propose a novel approach called meta-transfer learning (MTL), which learns to transfer the weights of a deep NN for few-shot learning tasks. Specifically, "meta" refers to training multiple tasks, and "transfer" is achieved by learning scaling and shifting functions of DNN weights (and biases) for each task. To further boost the learning efficiency of MTL, we introduce the hard task (HT) meta-batch scheme as an effective learning curriculum of few-shot classification tasks. We conduct experiments for five-class few-shot classification tasks on three challenging benchmarks, miniImageNet, tieredImageNet, and Fewshot-CIFAR100 (FC100).Text instance as one category of self-described objects provides valuable information for understanding and describing cluttered scenes. While most recent visual phrase grounding approaches focus on general objects, this paper explores extracting designated texts and predicting unambiguous scene text information, i.e., to accurately localize and recognize a specific targeted text instance in a cluttered image from natural language descriptions (referring expressions). First a novel recurrent Dense Text Localization Network (DTLN) is proposed to sequentially decode the intermediate convolutional representations of a cluttered scene image into a set of distinct text instances. Our approach avoids repeated text detection at multiple scales by recurrently memorizing previous detection, and effectively tackles crowded text instances in close proximity. Second, we propose a Context Reasoning Text Retrieval (CRTR) model, which jointly encodes text instances and their context information through a recurrent network, and ranks localized text bounding boxes by a scoring function of context compatibility. Third, a recurrent text recognition module is introduced to extend the applicability of aforementioned DTLN and CRTR models, via text verification or transcription. Quantitative evaluations on standard scene text extraction benchmarks and a newly collected scene text retrieval dataset demonstrate the effectiveness and advantages of our models.
Diabetic Macular Edema (DME) and macular edema secondary to retinal occlusion (RVO) are the 2 most common retinal vascular causes of visual impairment and leading cause of worldwide vision loss. The blood-retinal barrier is the key barrier for maintaining fluid balance within the retinal tissue. Vascular Endothelial Growth Factor (VEGF) has a significant role in the permeability of the blood-retinal barrier, which also leads to appearance of leakage foci. Intravitreal anti-VEGF therapy is the current gold standard treatment and has been demonstrated to improve macular thickening, improve vision acuity and reduce vascular leakage. However, treatment response and required dosing interval can vary widely across patients. Given the role of the blood-retinal barrier and vascular leakage in the pathogenesis of these disorders, the goal of this study was to present and evaluate new computer extracted features relating to morphology, spatial architecture and tortuosity of vessels and leakages from baseline ultra-witreatment response were proximity of leakage nodes to major and minor eye vessels as well as local vasculature tortuosity in the vicinity of the leakages. The imaging features were then used in conjunction with a Linear Discriminant Analysis (LDA) classifier to distinguish rebounders from non-rebounders. The 3-fold cross-validated Area Under Curve (AUC) was found to be 0.82 for the morphological based features and 0.85 for the tortuosity based features. Our findings suggest higher variation in leakage node proximity to retinal vessels in eyes tolerating extended interval dosing. In contrast, eyes with increased local vascular tortuosity demonstrated less tolerance of increased dosing interval. Moreover, a class activation map generated by a deep learning model identified regions that corresponded to regions of leakages proximal to the vessels, providing confirmation of the validity of predictive image features extracted from these regions in this study.To improve understanding of coronavirus disease (COVID-19), we assessed the epidemiology of an outbreak on a cruise ship, February 5-24, 2020. The study population included persons on board on February 3 (2,666 passengers, 1,045 crew). Passengers had a mean age of 66.1 years and were 55% female; crew had a mean age of 36.6 years and were 81% male. Of passengers, 544 (20.4%) were infected, 314 (57.7%) asymptomatic. Attack rates were highest in 4-person cabins (30.0%; n = 18). Of crew, 143 (13.7%) were infected, 64 (44.8%) asymptomatic. Passenger cases peaked February 7, and 35 had onset before quarantine. Crew cases peaked on February 11 and 13. The median serial interval between cases in the same cabin was 2 days. This study shows that severe acute respiratory syndrome coronavirus 2 is infectious in closed settings, that subclinical infection is common, and that close contact is key for transmission.We conducted a cohort study in a controlled environment to measure asymptomatic transmission of severe acute respiratory syndrome coronavirus 2 on a flight from Italy to South Korea. Our results suggest that stringent global regulations are necessary for the prevention of transmission of this virus on aircraft.We aimed to compare the efficiency of the first dose of Hepatitis B (HB) vaccine at Birth versus at 3 months and to evaluate the efficacy of HB vaccine. We conducted a cohort study in the governorate of Monastir. Vaccinated Cohort (VC) included populations receiving the first dose at 3 months (Protocol 1), and at birth (HepB-BD) (Protocol 2). First dose was followed by at least two doses. We collected, from January 2000 to December 2017, cases diagnosed by serological markers (hepatitis B surface antigen (HBsAg) and anti-HBc). We calculated Absolute Risk (AR) per 100,000 PY and the Relative risk reduction (RRR). Twenty-five cases were notified among VC and 1501 cases among not vaccinated cohort (NVC). Twenty-three cases were notified among the cohort receiving the first dose at 3 months and two cases in Protocol 2. The AR per 100,000 PY was 5.67 (CI95% 3.36-7.99) in Protocol 1 and 0.11 (CI95% 0.001-0.26) in Protocol 2. The RRR was 77% (95% CI 66; 85) in Protocol 1 and 99.4% (95% CI 97.8; 99.9) in Protocol 2. We identified 4 HB cases for children aged between 5 and 11 who benefited from protocol 1 (born between 2000 and 2006) and zero cases for children of the same age group benefiting from protocol 2 (born between 2011 and 2017).
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