Random sketch learning for deep neural networks in edge computing

Despite the great potential of deep neural networks (DNNs), they require massive weights and huge computational resources, creating a vast gap when deploying artificial intelligence at low-cost edge devices. Current lightweight DNNs, achieved by high-dimensional space pre-training and post-compression, present challenges when covering the resources deficit, making tiny artificial intelligence hard to be implemented. Here we report an architecture named random sketch learning, or Rosler, for computationally efficient tiny artificial intelligence. We build a universal compressing-while-training framework that directly learns a compact model and, most importantly, enables computationally efficient on-device learning. As validated on different models and datasets, it attains substantial memory reduction of ~50–90× (16-bits quantization), compared with fully connected DNNs. We demonstrate it on low-cost hardware, whereby the computation is accelerated by >180× and the energy consumption is reduced by ~10×. Our method paves the way for deploying tiny artificial intelligence in many scientific and industrial applications

Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction

Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute–subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission.Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3–21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression).Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321–0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397–0.7945), 0.7695 (0.6102–0.9074), and 0.8686 (0.6923–1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor.Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients

Case Report: ALK rearranged locally advanced lung adenocarcinoma showing inconsistent radiographic findings and pathological responses during neoadjuvant alectinib therapy

Alectinib has been approved as first-line treatment for anaplastic lymphoma kinase (ALK)-positive non-small cell lung carcinoma. Oncologists are also exploring the possibility of applying alectinib in the perioperative period. Here, we present a patient with locally advanced lung adenocarcinoma associated with EML4-ALK fusion mutation, who received neoadjuvant chemotherapy and alectinib treatment, and then underwent thoracoscopic left lower lung lobectomy. The patient initially received eight chemotherapy cycles and achieved partial remission. After eight cycles of chemotherapy, the lymph nodes in the hilar region again enlarged. The patient was then switched to 4 months of alectinib therapy, but no significant lesion changes were detected on imaging during this period. This raised the question of whether the patient developed alectinib resistance. The pathological findings of the postoperative lung lobe specimens indicated extensive necrosis in the tumor area with no residual tumor cells and massive chronic inflammatory cell infiltration around the tumor area, confirming inconsistency between the imaging findings and pathological results. Multi-point tumor specimen sampling was postoperatively performed. Tumor immune-related gene expression was detected in the sample with the help of the PanCancer IO360™ panel based on the nCounter platform. This is a rare case of a patient who was treated with neoadjuvant alectinib and had paradoxical radiographic findings and pathological responses. The possibility that intratumoral immune heterogeneity was responsible for this phenomenon has been discussed. Based on the findings, it is argued that the pathological response should be an important basis for assessing the effectiveness of neoadjuvant alectinib therapy

Present Situation and Problems of the Application of New Media in Rural E-commerce:A Case Study of Anhui Province

The application of new media technology in rural areas of China provides more convenient conditions for agricultural products publicity, transaction information dissemination and user feedback. In particular, e-commerce platform based on Internet technology provides more convenient sales channels for characteristic agricultural products. Through the research on the application of new media technology in rural areas of Anhui Province, this paper explores the actual effect of new media technology on the development of rural e-commerce. The questionnaire survey is used to understand the development status of new media in rural areas of China. They are: the development of "Internet government affairs" has begun to take shape; The official account of WeChat has shortened the time and space between the production and consumption of agricultural products. Live interaction gives full play to the powerful role of fan economy; Digital media has not been widely used. Through the form of on-the-spot visits, this paper expounds the application effect of new media in the agricultural field of Anhui Province from three perspectives of the government, agricultural enterprises and farmers. It analyzes the problems existing in the application of new media in rural e-commerce in Anhui Province. They are: the new media market is still full of problems; The infrastructure construction of rural e-commerce is relatively poor; Farmers lack the guidance of professional talents; Farmers' acceptance and learning ability is poor. The paper puts forward suggestions for the development status and problems of new media in rural areas of Anhui Province. They are: the provincial government should continue to promote the new media market order; all e-commerce enterprises should actively undertake social responsibility; rural e-commerce entrepreneurs should actively promote professional operation; farmers should actively change their views on new media

Insights into Different Products of Nitrosobenzene and Nitrobenzene Hydrogenation on Pd(111) under Realistic Reaction Conditions

Selective hydrogenation of nitroarene compounds is applied in many fields such as agrochemicals, pharmaceuticals, and dyes. Pd-catalyzed hydrogenation of nitrobenzene (PhNO2) and nitrosobenzene (PhNO) could exhibit different selectivities. This was regarded as the evidence to challenge the Haber mechanism for PhNO2 hydrogenation in which PhNO is an important intermediate. In this study, we systematically investigate their hydrogenation mechanisms under realistic reaction conditions based on first-principles calculations. It is found that the weak bonding between the nitro group and the Pd(111) surface leads to the flat-lying chemisorption configuration of PhNO2 and the other intermediates during PhNO2 hydrogenation. In contrast, the strong bonding between the nitroso group and the surface makes PhNO switch its chemisorption mode from flat-lying adsorption under the ultrahigh vacuum condition to vertical adsorption under reaction conditions. For the flat-lying PhNO2, the chemisorbed phenyl group makes hydrogenation easier but hinders N–O bond breaking, resulting in the production of PhNH2 via a direct pathway. Conversely, without the hinderance of the chemisorbed phenyl group, N–O bond breaking and N–N coupling become more favorable during the reduction of vertical PhNO* toward the formation of azoxy compound on Pd(111). These results unveil the fact that the difference between the selectivities of PhNO2 and PhNO hydrogenation is independent of the formation of PhNO* but dependent on the phenyl group adsorption mode