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Prediction methods are important for many applications. In particular, an accurate prediction for the total number of cases for pandemics such as the Covid-19 pandemic could help medical preparedness by providing in time a sucient supply of testing kits, hospital beds and medical personnel. This paper experimentally compares the accuracy of ten prediction methods for the cumulative number of Covid- 19 pandemic cases. These ten methods include three types of neural networks and extrapola- tion methods based on best fit quadratic, best fit cubic and Lagrange interpolation, as well as an extrapolation method proposed by the second author. We also consider the Kriging and inverse distance weighting spatial interpolation methods. We also develop a novel spatiotemporal prediction method by combining temporal and spatial prediction methods. The experiments show that among these ten prediction methods, the spatiotemporal method has the smallest root mean square error and mean absolute error on Covid-19 cumulative data for counties in New York State between May and July, 2020. © This article is published under the terms of the Creative Commons Attribution License 4.0 https://creativecommons.org/licenses/by/4.0/deed.en_US

Dummy filling is widely applied to significantly improve the planarity of topographic patterns for the chemical mechanical polishing process in VLSI manufactures. The main challenge of dummy filling is balancing multiple objectives, such as fill amounts, planarity, parasitic capacitance, etc. An obvious drawback of traditional rule-based dummy filling methods is pattern densities, instead of post-chemical mechanical polishing (CMP) topographies, being included in optimization objectives. Although the quality of post-CMP topography strongly depends on pattern features of layouts, especially the density uniformity, however, experimental results show that chip surface variations are not exactly the same as density variations. In this article, a unified dummy fill insertion optimization framework is proposed, integrated with the multiple starting points-sequential quadratic programming (MSP-SQP) optimization solver, where all objectives are considered without approximation. Inside this framework, a full-chip CMP simulator is first integrated to evaluate the planarity of the chip surface. By selecting the initial points smartly with heuristic prior knowledge, the proposed method can be effectively accelerated. The effectiveness of the proposed algorithm is verified with the average 25.8% improvement of quality compared with rule-based methods.

Dummy filling is widely applied to significantly improve the planarity of topographic patterns for the chemical mechanical polishing (CMP) process in VLSI manufacturing. This paper proposes a novel model-based dummy filling synthesis framework NeurFill, integrated with multiple starting points-sequential quadratic programming (MSP-SQP) optimization solver. Inside this framework, a full-chip CMP simulator is first migrated to the neural network, achieving $8134 \times$ speedup on gradient calculation by backward propagation. Multi-modal starting points search is further applied in the framework to obtain satisfying filling quality optimums. The experimental results show that the proposed NeurFill outperforms existing rule- and model-based methods.

Long non-coding RNA (lncRNA) exists in different locations of sub-cells and participates in various physiological and pathological processes to ensure the normal operation of life activities. However, with the explosion of information resources, the use of traditional bioinformatics methods to locate the sub-cells of lncRNA is time-consuming and labor-intensive. Therefore, it is particularly important to study the sub-cell localization of lncRNA based on machine learning. In order to master the current research status and explore the direction of follow-up research, this article reviews the research of lncRNA subcellular location prediction from four aspects: the establishment of lncRNA data set, the feature extraction of lncRNA, the design of prediction algorithm and the algorithm evaluation criteria. Among them, the current problems and some solutions are analyzed, and the future research directions are prospected.

Although cyanogen ion (CN-) plays important role in industry which also bring acute environmental pollution. More serious, trace CN- enters the human body can cause serious consequences and even death. Therefore, it is of great significance to detect trace CN- with high sensitivity. Herein, a novel aggregation-induced emission (AIE) probe C-BH was synthesized based on coumarin matrix. Probe C-BH showed high selectivity and sensitivity toward CN- by dual channel response due to the excited state intramolecular proton transfer (ESIPT). The low detection limit was calculated to be 0.05 µM. Moreover, probe C-BH was successfully used for imaging CN- in living cells and zebrafish due to its low toxicity and excellent optical properties.

Although cyanogen ion (CN