We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge. * Contributed equally. † Work done as an undergraduate at Cornell University. 1 In parts of this paper, we use the term compositional differently than it is commonly used in linguistics to refer to reasoning that requires composition. This type of reasoning often manifests itself in highly compositional language.The left image contains twice the number of dogs as the right image, and at least two dogs in total are standing.One image shows exactly two brown acorns in back-to-back caps on green foliage.

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The outbreak of COVID-19, caused by 2019 novel coronavirus (2019-nCoV), has been a global public health threat and caught the worldwide concern. Scientists throughout the world are sparing all efforts to explore strategies for the determination of the 2019-nCoV virus and diagnosis of COVID-19 rapidly. Several assays are developed for COVID-19 test , including RT-PCR, coronavirus antigens-based immunoassays, and CRISPR-based strategies (Cas13a or Cas12a), etc. Different assays have their advantages and drawbacks, and people should choose the most suitable assay according to their demands. Here, we make a brief introduction about these assays and give a simple overview of them, hoping to help doctors and researchers to select the most suitable assay for the Coronavirus Disease 2019 test (COVID-19 test) .

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Diesel engines have a wide range of functions in the industrial and military fields. An urgent problem to be solved is how to diagnose and identify their faults effectively and timely. In this paper, a diesel engine acoustic fault diagnosis method based on variational modal decomposition mapping Mel frequency cepstral coefficients (MFCC) and long-short-term memory network is proposed. Variational mode decomposition (VMD) is used to remove noise from the original signal and differentiate the signal into multiple modes. The sound pressure signals of different modes are mapped to the Mel filter bank in the frequency domain, and then the Mel frequency cepstral coefficients of the respective mode signals are calculated in the mapping range of frequency domain, and the optimized Mel frequency cepstral coefficients are used as the input of long and short time memory network (LSTM) which is trained and verified, and the fault diagnosis model of the diesel engine is obtained. The experimental part compares the fault diagnosis effects of different feature extraction methods, different modal decomposition methods and different classifiers, finally verifying the feasibility and effectiveness of the method proposed in this paper, and providing solutions to the problem of how to realise fault diagnosis using acoustic signals.

Extracting features manually and employing preeminent knowledge is overly utilized in methods to conduct fault diagnosis. A diagnosis approach utilizing intelligent methods of the optimized variational mode decomposition and deep transfer learning is proposed in this manuscript to deal with fault diagnosis. Firstly, the variational mode decomposition is optimized by K values of the dispersion entropy to realize an adaptive decomposition and reduce the noise of the signal. Secondly, an image with two dimensions is generated by a vibration signal with one dimension utilizing a short-time Fourier transform, after conducting noise reduction. Then, the ResNet18 network model is used to pre-train the model. Finally, the model transfer method is used to detect faults of a diesel engine. The results show that the proposed method outperforms the deep learning methods available in the literature. Besides, better noise reduction ability and higher diagnostic accuracy are attained.

The vaccine helps to provoke the immune system and is an efficacious means for disease prevention and treatment. At this particular time of the COVID-19 outbreak, the vaccine for COVID-19 is urgently needed to save tens of thousands of people’s lives. Here we give some basic information on vaccine classification, generation, and application, and make a brief review on the current status of COVID-19 vaccine and tumor vaccine development both in the clinical trial stage and pre-clinical stage. vaccine

At present, rotating machinery is widely used in all walks of life and has become the key equipment in many production processes. It is of great significance to strengthen the condition monitoring of rotating machinery, timely diagnose and eliminate faults to ensure the safe and efficient operation of rotating machinery and improve the economic benefits of enterprises. When the state of a rotating machine deteriorates, the thermal energy that is much more than its normal operation will be generated due to the increase in the friction between the components or other factors. Therefore, using the infrared thermal camera to collect the infrared thermal images of rotating machinery and judge the health status of rotating machinery by observing the temperature distribution in the thermal images is often more rapid and effective than other technologies. Nevertheless, after decades of development, the research achievements of infrared thermography (IRT) and its application in various industrial fields are numerous and complex, and there is a lack of systematic sorting and summary of the achievements in this field. Accordingly, this paper summarizes the development and application of IRT as a non-contact and non-invasive tool for equipment condition monitoring and fault diagnosis, and introduces the basic theory of IRT, image processing technology and fault diagnosis methods of rotating machinery in detail. Finally, the review is summarized and some future potential topics are proposed, which will make the subject easier for beginners and non-experts to understand.