COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.
With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life. Artificial neural network or neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language processing. However, malicious NNs could bring huge threats in the so-called coming AI era. In this paper, for the first time in the literature, we propose a novel approach to design and insert powerful neuron-level trojans or PoTrojan in pre-trained NN models. Most of the time, PoTrojans remain inactive, not affecting the normal functions of their host NN models. PoTrojans could only be triggered in very rare conditions. Once triggered, however, the PoTrojans could cause the host NN models to malfunction, either falsely predicting or falsely classifying, which is a significant threat to human society of the AI era. We would explain the principles of PoTrojans and the easiness of designing and inserting them in pre-trained deep learning models. PoTrojans doesn't modify the existing architecture or parameters of the pre-trained models, without re-training. Hence, the proposed method is very efficient. We verify the tacitness and harmfulness of the PoTrojans on two real-life deep learning models: AlexNet and VGG16.
In general, we wish to interpret the most broadband data possible. However, broadband data do not always provide the best insight for seismic attribute analysis. Obviously, spectral bands contaminated by noise should be eliminated. However, tuning gives rise to spectral bands with higher signal-to-noise ratios. To quantify geologic discontinuities in different scales, we combined spectral decomposition and coherence. Using spectral decomposition, the spectral amplitudes corresponding to a given scale geologic discontinuity, as well as some subtle features, which would otherwise be buried within the broadband seismic response, can be extracted. We applied this workflow to a 3D land data volume acquired over the Tarim Basin, Northwest China, where karst forms the principle reservoirs. We found that channels are better illuminated around 18 Hz, while subtle discontinuities were better delineated around 25 Hz.
Recent developments in seismic attributes and seismic facies classification techniques have greatly enhanced the capability of interpreters to delineate and characterize features that are not prominent in conventional 3D seismic amplitude volumes. The use of appropriate seismic attributes that quantify the characteristics of different geologic facies can accelerate and partially automate the interpretation process. Self-organizing maps (SOMs) are a popular seismic facies classification tool that extract similar patterns embedded with multiple seismic attribute volumes. By preserving the distance in the input data space into the SOM latent space, the internal relation among data vectors on an SOM facies map is better presented, resulting in a more reliable classification. We have determined the effectiveness of the modified algorithm by applying it to a turbidite system in Canterbury Basin, offshore New Zealand. By incorporating seismic attributes and distance-preserving SOM classification, we were able to observe architectural elements that are overlooked when using a conventional seismic amplitude volume for interpretation.
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