亚洲男人的天堂2018av,欧美草比,久久久久久免费视频精选,国色天香在线看免费,久久久久亚洲av成人片仓井空

We extend the neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting (EPF) tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20% over the original NBEATS model, and by up to 5% over other well established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes' interactions with exogenous factors. To assist related work we made the code available in //github.com/cchallu/nbeatsx.

The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To overcome these modelling challenges, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real datasets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates. Our approach provides parameter estimates with more realistic interpretation, and one-week ahead predictions with reduced uncertainty.

Monitoring and understanding affective states are important aspects of healthy functioning and treatment of mood-based disorders. Recent advancements of ubiquitous wearable technologies have increased the reliability of such tools in detecting and accurately estimating mental states (e.g., mood, stress, etc.), offering comprehensive and continuous monitoring of individuals over time. Previous attempts to model an individual's mental state were limited to subjective approaches or the inclusion of only a few modalities (i.e., phone, watch). Thus, the goal of our study was to investigate the capacity to more accurately predict affect through a fully automatic and objective approach using multiple commercial devices. Longitudinal physiological data and daily assessments of emotions were collected from a sample of college students using smart wearables and phones for over a year. Results showed that our model was able to predict next-day affect with accuracy comparable to state of the art methods.

Cluster analysis aims at partitioning data into groups or clusters. In applications, it is common to deal with problems where the number of clusters is unknown. Bayesian mixture models employed in such applications usually specify a flexible prior that takes into account the uncertainty with respect to the number of clusters. However, a major empirical challenge involving the use of these models is in the characterisation of the induced prior on the partitions. This work introduces an approach to compute descriptive statistics of the prior on the partitions for three selected Bayesian mixture models developed in the areas of Bayesian finite mixtures and Bayesian nonparametrics. The proposed methodology involves computationally efficient enumeration of the prior on the number of clusters in-sample (termed as ``data clusters'') and determining the first two prior moments of symmetric additive statistics characterising the partitions. The accompanying reference implementation is made available in the R package 'fipp'. Finally, we illustrate the proposed methodology through comparisons and also discuss the implications for prior elicitation in applications.

Climate change in India is one of the most alarming problems faced by our community. Due to adverse and sudden changes in climate in past few years, mankind is at threat. Various impacts of climate change include extreme heat, changing rainfall patterns, droughts, groundwater, glacier melt, sea-level rise, and many more. Machine Learning can be used to analyze and predict the graph of change using previous data and thus design a model which in the future can furthermore be used to catalyze impactful work of climate change and take steps in the direction to help India fight against the upcoming climate changes. In this paper, we have analyzed 17 climate change parameters about India. We have applied linear regression, exponential regression, and polynomial regression to the parameters and evaluated the results. Using the designed model, we will predict these parameters for the years 2025,2030, 2035. These predicted values will thus help our community to prevent and take actions against the adverse and hazardous effects on mankind. We have designed and created this model which provides accurate results regarding all 17 parameters. The predicted values will therefore help India to be well equipped against climate change. This data when made available to the people of India will help create awareness among them and will help us save our country from the haphazard effects of climate change.

Lung cancer is the leading cause of mortality from cancer worldwide and has various histologic types, among which Lung Adenocarcinoma (LUAC) has recently been the most prevalent one. The current approach to determine the invasiveness of LUACs is surgical resection, which is not a viable solution to fight lung cancer in a timely fashion. An alternative approach is to analyze chest Computed Tomography (CT) scans. The radiologists' analysis based on CT images, however, is subjective and might result in a low accuracy. In this paper, a transformer-based framework, referred to as the "CAE-Transformer", is developed to efficiently classify LUACs using whole CT images instead of finely annotated nodules. The proposed CAE-Transformer can achieve high accuracy over a small dataset and requires minor supervision from radiologists. The CAE Transformer utilizes an encoder to automatically extract informative features from CT slices, which are then fed to a modified transformer to capture global inter-slice relations and provide classification labels. Experimental results on our in-house dataset of 114 pathologically proven Sub-Solid Nodules (SSNs) demonstrate the superiority of the CAE-Transformer over its counterparts, achieving an accuracy of 87.73%, sensitivity of 88.67%, specificity of 86.33%, and AUC of 0.913, using a 10-fold cross-validation.

The COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was first identified in mid-December 2019 in the Hubei province of Wuhan, China and by now has spread throughout the planet with more than 75.5 million confirmed cases and more than 1.67 million deaths. With limited number of COVID-19 test kits available in medical facilities, it is important to develop and implement an automatic detection system as an alternative diagnosis option for COVID-19 detection that can used on a commercial scale. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Computer vision and deep learning techniques can help in determining COVID-19 virus with Chest X-ray Images. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural network for image analysis and classification. In this research, we have proposed a deep convolutional neural network trained on five open access datasets with binary output: Normal and Covid. The performance of the model is compared with four pre-trained convolutional neural network-based models (COVID-Net, ResNet18, ResNet and MobileNet-V2) and it has been seen that the proposed model provides better accuracy on the validation set as compared to the other four pre-trained models. This research work provides promising results which can be further improvise and implement on a commercial scale.

Nowadays innovation is one of the main determinants of economic development. Patents are a key measure of innovation output, as patent indicators reflect the inventive performance of countries, technologies and firms. This paper provides new insights on the causal effects of the enlargement of the European Union (EU) by investigating the patents performance within the new EU member states (EU-13). The empirical results based on data collected from the OECD database from 1985-2017 and causal impact using a Bayesian structural time-series model (proposed by Google) point towards a conclusion that joining the EU has had a significant impact on patents performance in Romania, Estonia, Poland, Czech Republic, Croatia and Lithuania, although in the latter two countries the impact was negative. For the rest of the EU-13 countries there is no significant effect on patent performance. Whether the EU accession effect is significant or not, the EU-13 are far behind the EU-15 (countries which entered the EU before 2004) in terms of patent performance. The majority of patents (98.66\%) are assigned to the EU-15, with just 1.34\% of assignees belonging to the EU-13.

Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics. Such models can be personalized based on clinical measurements to build the predictive models for tumor growth. In this paper, we investigate the possibility of using deep convolutional neural networks (ConvNets) to directly represent and learn the cell invasion and mass-effect, and to predict the subsequent involvement regions of a tumor. The invasion network learns the cell invasion from information related to metabolic rate, cell density and tumor boundary derived from multimodal imaging data. The expansion network models the mass-effect from the growing motion of tumor mass. We also study different architectures that fuse the invasion and expansion networks, in order to exploit the inherent correlations among them. Our network can easily be trained on population data and personalized to a target patient, unlike most previous mathematical modeling methods that fail to incorporate population data. Quantitative experiments on a pancreatic tumor data set show that the proposed method substantially outperforms a state-of-the-art mathematical model-based approach in both accuracy and efficiency, and that the information captured by each of the two subnetworks are complementary.