Chest CT had higher sensitivity for diagnosis of COVID-19 as compared with initial reverse-transcription polymerase chain reaction from swab samples in the epidemic area of China.

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020200642

Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases.

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering --- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.

Neural Collaborative Filtering

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Importance The coronavirus disease 2019 (COVID-19) pandemic, due to the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide sudden and substantial increase in hospitalizations for pneumonia with multiorgan disease. This review discusses current evidence regarding the pathophysiology, transmission, diagnosis, and management of COVID-19. Observations SARS-CoV-2 is spread primarily via respiratory droplets during close face-to-face contact. Infection can be spread by asymptomatic, presymptomatic, and symptomatic carriers. The average time from exposure to symptom onset is 5 days, and 97.5% of people who develop symptoms do so within 11.5 days. The most common symptoms are fever, dry cough, and shortness of breath. Radiographic and laboratory abnormalities, such as lymphopenia and elevated lactate dehydrogenase, are common, but nonspecific. Diagnosis is made by detection of SARS-CoV-2 via reverse transcription polymerase chain reaction testing, although false-negative test results may occur in up to 20% to 67% of patients; however, this is dependent on the quality and timing of testing. Manifestations of COVID-19 include asymptomatic carriers and fulminant disease characterized by sepsis and acute respiratory failure. Approximately 5% of patients with COVID-19, and 20% of those hospitalized, experience severe symptoms necessitating intensive care. More than 75% of patients hospitalized with COVID-19 require supplemental oxygen. Treatment for individuals with COVID-19 includes best practices for supportive management of acute hypoxic respiratory failure. Emerging data indicate that dexamethasone therapy reduces 28-day mortality in patients requiring supplemental oxygen compared with usual care (21.6% vs 24.6%; age-adjusted rate ratio, 0.83 [95% CI, 0.74-0.92]) and that remdesivir improves time to recovery (hospital discharge or no supplemental oxygen requirement) from 15 to 11 days. In a randomized trial of 103 patients with COVID-19, convalescent plasma did not shorten time to recovery. Ongoing trials are testing antiviral therapies, immune modulators, and anticoagulants. The case-fatality rate for COVID-19 varies markedly by age, ranging from 0.3 deaths per 1000 cases among patients aged 5 to 17 years to 304.9 deaths per 1000 cases among patients aged 85 years or older in the US. Among patients hospitalized in the intensive care unit, the case fatality is up to 40%. At least 120 SARS-CoV-2 vaccines are under development. Until an effective vaccine is available, the primary methods to reduce spread are face masks, social distancing, and contact tracing. Monoclonal antibodies and hyperimmune globulin may provide additional preventive strategies. Conclusions and Relevance As of July 1, 2020, more than 10 million people worldwide had been infected with SARS-CoV-2. Many aspects of transmission, infection, and treatment remain unclear. Advances in prevention and effective management of COVID-19 will require basic and clinical investigation and public health and clinical interventions.

/pdf/pathophysiology-transmission-diagnosis-and-treatment-of-2o6dok06xg.pdf

Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review.

Tracking Changes in SARS-CoV-2 Spike: Evidence that D614G Increases Infectivity of the COVID-19 Virus.

Effects of Cu Content on the Mechanical, Degradable, and Antibacterial Properties of the As-Cast Zn-3Al-xCu Alloys

Plants show different beauty since the leaves and structures are in a variety of colors, shapes, sizes and texture in our vision. Thus, noise and edge bonding in plant modeling reconstruction are highly challenging. In order to separate the adhesive and gain ideal point cloud, we present a method of limited detail multi-density point reconstruction. The core of proposed method is a point-to-surface approach, which constantly refines the density of points so as to generate the fuzzy surface. Comparing with traditional mesh reconstruction, our method can solve the problem of adhesion between the branches and leaves. Results show that the limited detail multi-density point reconstruction is feasible, and as well as good effect and fast speed.

Plants Modeling Based on Limited Points

Mo1397: WENSHENJ-JIANPI IMPROVES BOWEL FUNCTION, VISCERAL HYPERSENSITIVITY, ANXIETY/DEPRESSION-LIKE BEHAVIORS, AND FMRI BRAIN ACTIVITY IN A RODENT MODEL OF IBS-D

Abstract. Edge-preserving image smoothing is a fundamental tool in computational photography and graphics. It aims to suppress insignificant details while maintaining salient structures. The classical L0 filter provides an elegant framework for a variety of applications. However, it inclines to sharpen the salient edges, thus suffering from gradient reversals and color deviations. We propose a solution toward the objective of optimizing summed squared error regularized by the L0-norm of the gradients. The proposed solution explores an iterative strategy, where each iteration is an optimization problem based on the truncated L1 regularization, which can be solved efficiently with the combination of alternating direction method of multipliers and Fourier domain optimization. Leveraging L1-norm limits the aggressive modifications of gradients during the iterative process, which alleviates various artifacts in classical L0 filter. Experiment results indicate that the proposed method achieves superior performance in various applications, including texture removal, detail enhancement, HDR tone mapping, and compression artifact removal. The proposed filter can be conveniently implemented on GPU. It takes our filter 0.51 s to process a 720P color image on a NVIDIA GTX 1080 GPU.

Yang Yang

Papers

Effects of Cu Content on the Mechanical, Degradable, and Antibacterial Properties of the As-Cast Zn-3Al-xCu Alloys

Plants Modeling Based on Limited Points

Mo1397: WENSHENJ-JIANPI IMPROVES BOWEL FUNCTION, VISCERAL HYPERSENSITIVITY, ANXIETY/DEPRESSION-LIKE BEHAVIORS, AND FMRI BRAIN ACTIVITY IN A RODENT MODEL OF IBS-D

L 0 image smoothing via iterating truncated L1 gradient regularization

Enhancing splitting tensile strength of biocarbonated reactive magnesia-based sand using polypropylene fiber reinforcement