Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering

Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to the challenging topic of learning from unlabeled or weaker forms of annotations. In this paper, we take the first attempt for fully unsupervised semantic segmentation of point clouds, which aims to delineate semantically meaningful objects without any form of annotations. Previous works of unsupervised pipeline on 2D images fails in this task of point clouds, due to: 1) Clustering Ambiguity caused by limited magnitude of data and imbalanced class distribution; 2) Irregularity Ambiguity caused by the irregular sparsity of point cloud. Therefore, we propose a novel framework, PointDC, which is comprised of two steps that handle the aforementioned problems respectively: Cross-Modal Distillation (CMD) and Super-Voxel Clustering (SVC). In the first stage of CMD, multi-view visual features are back-projected to the 3D space and aggregated to a unified point feature to distill the training of the point representation. In the second stage of SVC, the point features are aggregated to super-voxels and then fed to the iterative clustering process for excavating semantic classes. PointDC yields a significant improvement over the prior state-of-the-art unsupervised methods, on both the ScanNet-v2 (+18.4 mIoU) and S3DIS (+11.5 mIoU) semantic segmentation benchmarks

Apigenin Combined With Gefitinib Blocks Autophagy Flux and Induces Apoptotic Cell Death Through Inhibition of HIF-1α, c-Myc, p-EGFR, and Glucose Metabolism in EGFR L858R+T790M-Mutated H1975 Cells

Cancer cells are characterized by abnormally increased glucose uptake and active bio-energy and biosynthesis to support the proliferation, metastasis, and drug resistant survival. We examined the therapeutic value of the combination of apigenin (a natural small-molecule inhibitor of Glut1 belonging to the flavonoid family) and gefitinib on epidermal growth factor receptor (EGFR)-resistant mutant non-small cell lung cancer, to notably damage glucose utilization and thus suppress cell growth and malignant behavior. Here, we demonstrate that apigenin combined with gefitinib inhibits multiple oncogenic drivers such as c-Myc, HIF-1α, and EGFR, reduces Gluts and MCT1 protein expression, and inactivates the 5′ adenosine monophosphate-activated protein kinase (AMPK) signaling, which regulates glucose uptake and maintains energy metabolism, leading to impaired energy utilization in EGFR L858R-T790M-mutated H1975 lung cancer cells. H1975 cells exhibit dysregulated metabolism and apoptotic cell death following treatment with apigenin + gefitinib. Therefore, the combined apigenin + gefitinib treatment presents an attractive strategy as alternative treatment for the acquired resistance to EGFR-TKIs in NSCLC

Topics on option valuation and model calibration

This dissertation is devoted to high performance numerical methods for option valuation and model calibration in L´evy process and stochastic volatility models. In the first part, a numerical scheme for simulating from an analytic characteristic function is developed. Theoretically, error bounds for bias are explicitly given. Practically, different types of options in commonly used L´evy process models could be priced through this method fast and accurately. Also, sensitivity analysis could be conducted through this approach effectively. Numerical results show that the schemes are effective for both options valuation and sensitivity analysis in L´evy process models. In the second part, a numerical scheme for Asian option pricing in jump-diffusion models is analyzed. Approximation errors are shown to decay exponentially. Numerical results show the speed and accuracy of the scheme. In the third part, for calibration purpose, certain numerical schemes are studied to price European and American options. For European options, error bounds are explicitly given. For American contracts, multiple options with different strikes and maturities could be priced simultaneously. Numerical results show that the combination of the above schemes with state-of-the-art optimization schemes makes efficient calibration of option pricing models possible

New Utilization Model of Karst Rocky Desertified Land in Southwest China: Ecological Planting of Dendrobium officinale Kimura et Migo on Bare Rocky Mountains Mengyuan CHEN, Zisheng YANG* Institute of Land & Resources and Sustainable Development, Yunnan University of Finance and Economics, Kunming 650221, China Abstract The Yunnan-Guangxi-Guizhou rocky desertified area in southwestern China is one of the contiguous extremely poor areas identified in the Outline of Poverty Alleviation and Development in the Rural Areas of China (2011-2020). In rocky desertified areas, due to long-term severe soil erosion, large areas of bedrock are exposed or gravels are accumulated. This bare rock and gravel-mulched land has become the main land type in the rocky mountains. Under normal circumstances, it cannot be directly used for agriculture, forestry, animal husbandry, and fishery production, and is classified as land that is difficult to be utilized. In recent years, in Debao County, Baise City, Guangxi Zhuang Autonomous Region, as a deeply impoverished county in the Yunnan-Guangxi-Guizhou rocky desertified area, a new land use model of karst rocky desertified land, that is, planting Dendrobium officinale Kimura et Migo on bare rocky ground, has emerged, and certain poverty alleviation benefits have been achieved initially. In this article, on the basis of analyzing the suitability of planting D. officinale Kimura et Migo on karst rocky desertified land, the practice of planning D. officinale Kimura et Migo on the rocky desertified land in Debao County was elucidated, and then suggestions for reasonable promotion of ecological planting of D. officinale Kimura et Migo in karst rocky desertified areas were put forward. Key words Karst rocky desertified land, New utilization model, Dendrobium officinale Kimura et Migo, Poverty alleviation benefit

The Yunnan-Guangxi-Guizhou rocky desertified area in southwestern China is one of the contiguous extremely poor areas identified in the Outline of Poverty Alleviation and Development in the Rural Areas of China (2011-2020). In rocky desertified areas, due to long-term severe soil erosion, large areas of bedrock are exposed or gravels are accumulated. This bare rock and gravel-mulched land has become the main land type in the rocky mountains. Under normal circumstances, it cannot be directly used for agriculture, forestry, animal husbandry, and fishery production, and is classified as land that is difficult to be utilized. In recent years, in Debao County, Baise City, Guangxi Zhuang Autonomous Region, as a deeply impoverished county in the Yunnan-Guangxi-Guizhou rocky desertified area, a new land use model of karst rocky desertified land, that is, planting Dendrobium officinale Kimura et Migo on bare rocky ground, has emerged, and certain poverty alleviation benefits have been achieved initially. In this article, on the basis of analyzing the suitability of planting D. officinale Kimura et Migo on karst rocky desertified land, the practice of planning D. officinale Kimura et Migo on the rocky desertified land in Debao County was elucidated, and then suggestions for reasonable promotion of ecological planting of D. officinale Kimura et Migo in karst rocky desertified areas were put forward

Inverse transform method for simulating levy processes and discrete Asian options pricing

The simulation of a Lévy process on a discrete time grid reduces to simulating from the distribution of a Lévy increment. For a general Lévy process with no explicit transition density, it is often desirable to simulate from the characteristic function of the Lévy increment. We show that the inverse transform method, when combined with a Hilbert transform approach for computing the cdf of the Lévy increment, is reliable and efficient. The Hilbert transform representation for the cdf is easy to implement and highly accurate, with approximation errors decaying exponentially. The inverse transform method can be combined with quasi-Monte Carlo methods and variance reduction techniques to greatly increase the efficiency of the scheme. As an illustration, discrete Asian options pricing in the CGMY model is considered, where the combination of the Hilbert transform inversion of characteristic functions, quasi-Monte Carlo methods and the control variate technique proves to be very efficient.

Fabrication of 20.19% Efficient Single-Crystalline Silicon Solar Cell with Inverted Pyramid Microstructure

Abstract This paper reports inverted pyramid microstructure-based single-crystalline silicon (sc-Si) solar cell with a conversion efficiency up to 20.19% in standard size of 156.75 × 156.75 mm2. The inverted pyramid microstructures were fabricated jointly by metal-assisted chemical etching process (MACE) with ultra-low concentration of silver ions and optimized alkaline anisotropic texturing process. And the inverted pyramid sizes were controlled by changing the parameters in both MACE and alkaline anisotropic texturing. Regarding passivation efficiency, the textured sc-Si with normal reflectivity of 9.2% and inverted pyramid size of 1 μm was used to fabricate solar cells. The best batch of solar cells showed a 0.19% higher of conversion efficiency and a 0.22 mA cm−2 improvement in short-circuit current density, and the excellent photoelectric property surpasses that of the same structure solar cell reported before. This technology shows great potential to be an alternative for large-scale production of high efficient sc-Si solar cells in the future

Unraveling the Activity and Selectivity of CO2 Electrochemical Reduction on Single Atom Catalyst by Potential Based Scaling Relationship

Achieving the fundamental understanding of electrochemical processes occurring at the complex electrode-liquid interface is a grand challenge in catalysis. Herein, to gain theoretical insights into the experimentally observed potential-dependent activity and selectivity for CO2 reduction reaction (CO2RR) on the popular single-iron-atom catalyst, we performed ab initio molecular dynamics (AIMD) simulation, constrained MD sampling and the thermodynamic integration to acquire the free energy profiles for the proton and electron transfer processes of CO2 at different potentials. We have demonstrated that the adsorption of CO2 is significantly coupled with the electron transfer from the substrate while the further protonation does not show distinct charge variation. This strongly suggest that CO2 adsorption is potential-dependent and optimizing the electrode potential is vital to achieve the efficient activated adsorption of CO2. We further identified a linear scaling relationship between the reaction free energy (ΔG) and the potential for key elementary steps of CO2RR and HER, of which the slope is adsorbate-specific and not as simple as 1 eV per Volt as suggested by the traditional Computational Hydrogen Electrode (CHE) model. The derived scaling relationship can reproduce the experimental onset potential (Uonset) of CO2RR, potential of the maximal CO2-to-CO Faraday Efficiency (FECO), and the potential where FECO = FEH2. This suggests that our state-of-the-art model could precisely interpret the activity and selectivity of CO2RR/HER on Fe-N4-C catalyst under different electrode potentials. In general, our study not only provides an innovative insight into the theoretical explanation of the origin of solvation effect from the perspective of charge transfer but also emphasizes the critical role of electrode potential on theoretical consideration of catalytic activity, which offers a profound understanding of the electrochemical environment and bridges the gap between theoretical predictions and experiment results

A Novel Adaptive Joint Time Frequency Algorithm by the Neural Network for the ISAR Rotational Compensation

We propose a novel adaptive joint time frequency algorithm combined with the neural network (AJTF-NN) to focus the distorted inverse synthetic aperture radar (ISAR) image. In this paper, a coefficient estimator based on the artificial neural network (ANN) is firstly developed to solve the time-consuming rotational motion compensation (RMC) polynomial phase coefficient estimation problem. The training method, the cost function and the structure of ANN are comprehensively discussed. In addition, we originally propose a method to generate training dataset sourcing from the ISAR signal models with randomly chosen motion characteristics. Then, prediction results of the ANN estimator is used to directly compensate the ISAR image, or to provide a more accurate initial searching range to the AJTF for possible low-performance scenarios. Finally, some simulation models including the ideal point scatterers and a realistic Airbus A380 are employed to comprehensively investigate properties of the AJTF-NN, such as the stability and the efficiency under different signal-to-noise ratios (SNRs). Results show that the proposed method is much faster than other prevalent improved searching methods, the acceleration ratio are even up to 424 times without the deterioration of compensated image quality. Therefore, the proposed method is potential to the real-time application in the RMC problem of the ISAR imaging

Evaluation of the Absorption Behavior of Main Component Compounds of Salt-Fried Herb Ingredients in Qing’e Pills by Using Caco-2 Cell Model

Qing’e Pills is a Chinese traditional herbal product, which is often used to strengthen muscles and bones in TCM (traditional Chinese Medicine) practice. Its two main component herbs, namely, Cortex Eucommiae and Fructus Psoraleae are both required to be salt-fried according to TCM theory. We have evaluated the effects of salt-frying treated herbs on Caco-2 cell uptake behavior for those active ingredients of Qing’e Pills. By investigating of various variables, including MTT, temperature, inhibitors, pH, salt concentration and herb processing methods, we tried to clarify whether the salt-processing on herbs was necessary or not. Results showed that, compared to other processing methods, the salt-frying process significantly (p < 0.01) enhanced the absorption of effective components of Qing’e Pills. The way that psoralen, isopsoralen, psoralenoside and geniposide acid entered Caco-2 cells at low concentrations was via passive diffusion. These components were not substrates of P-glycoprotein. It demonstrated that the salt-frying process not only enhanced the concentration of active components in herb extract, but also changed their absorption behaviors. Nevertheless, the mechanism of absorption behavior changing needs to be further investigated

Secure Blockchain Middleware for Decentralized IIoT towards Industry 5.0: A Review of Architecture, Enablers, Challenges, and Directions

Resilient manufacturing is a vision in the Industry 5.0 blueprint for satisfying sustainable development goals under pandemics or the rising individualized product needs. A resilient manufacturing strategy based on the Industrial Internet of Things (IIoT) networks plays an essential role in facilitating production and supply chain recovery. IIoT contains confidential data and private information, and many security issues arise through vulnerabilities in the infrastructure. The traditional centralized IIoT framework is not only of high cost for system configuration but also vulnerable to cyber-attacks and single-point failure, which is not suitable for achieving the resilient manufacturing vision in Industry 5.0. Recently, researchers are seeking a secure solution of middleware based on blockchain technology integration for decentralized IIoT, which can effectively protect the consistency, integrity, and availability of IIoT data by utilizing the auditing and tamper-proof features of the blockchain. This paper presented a review of secure blockchain middleware for decentralized IIoT towards Industry 5.0. Firstly, the security issues of conventional IIoT solutions and the advantages of blockchain middleware are analyzed. Secondly, an architecture of secure blockchain middleware for decentralized IIoT is proposed. Finally, enabling technologies, challenges, and future directions are reviewed. The innovation of this paper is to study and discuss the distributed blockchain middleware, investigating its ability to eliminate the risk of a single point of failure via a distributed feature in the context of resilient manufacturing in Industry 5.0 and to solve the security issues from traditional centralized IIoT. Also, the four-layer architecture of blockchain middleware presented based on the IIoT application framework is a novel aspect of this review. It is expected that the paper lays a solid foundation for making IIoT blockchain middleware a new venue for Industry 5.0 research