One fundamental and two taxes: when does a Tobin tax reduce financial price volatility?

We aim to make two contributions to the literature on the effects of transaction costs on financial price volatility. First, by augmenting a double differencing approach with a research design with three ingredients (a common set of companies simultaneously listed on two stock exchanges, binding capital controls, and different timing of changes in transaction costs), we obtain a control group that has identical corporate fundamentals as the treatment group. We apply the research design to Chinese stocks that are cross-listed in Hong Kong and Mainland China. Second, we allow transaction costs to have different effects in markets with different maturity. We find a significantly negative relationship, on average, between stamp duty increase and price volatility. However, this average effect masks some important heterogeneity. In particular, when institutional investors have become a significant part of the traders’ pool, we find an opposite effect. Overall, our results suggest that a Tobin tax could work in an immature market, but can backfire in a more developed market

Afatinib combined with anti-PD1 enhances immunotherapy of hepatocellular carcinoma via ERBB2/STAT3/PD-L1 signaling

BackgroundAfatinib is mainly used to treat advanced non-small cell lung cancer, but its therapeutic effect on hepatocellular carcinoma is still unclear.MethodsOver 800 drugs were screened by CCK8 technology and afatinib was found to have a significant inhibitory effect on liver cancer cells. The expression of PDL1 in tumor cells treated with drugs were detected by qRT-PCR and Weston Blot experiments. The effects of afatinib on the growth, migration and invasion of HCC cells were evaluated using wound healing, Transwell, and cell cloning assays. The in vivo effects of afatinib in combination with anti-PD1 were evaluated in C57/BL6J mice with subcutaneous tumorigenesis. Bioinformatics analysis was performed to explore the specific mechanism of afatinib's inhibition of ERBB2 in improving the expression level of PD-L1, which was subsequently verified through experiments.ResultsAfatinib was found to have a significant inhibitory effect on liver cancer cells, as confirmed by in vitro experiments, which demonstrated that it could significantly suppress the growth, invasion and migration of HCC cells. qRT PCR and Weston Blot experiments also showed that Afatinib can enhance the expression of PD-L1 in tumor cells. In addition, in vitro experiments confirmed that afatinib can significantly enhance the immunotherapeutic effect of hepatocellular carcinoma. Afatinib’s ability to increase PD-L1 expression is mediated by STAT3 activation following its action on HCC cells.ConclusionAfatinib enhances PD-L1 expression in tumor cells through the STAT3/PD-L1 pathway. The combination of afatinib and anti-PD1 treatment significantly increases the immunotherapeutic effect of HCC

Wavelet Based Optical Navigation for a Lunar Lander.

This research explores the use of passive imagery on a planetary lander to provided data to a feedback control system. The aim of this innovation is to improve the landing accuracy during the final descent phase. The work formed part of a larger project, the Magnolia mission for a low cost, pinpoint landing system. The gravity turn landing scheme is first investigated. Analytical results are derived to remove the constrains of: 1) the lander having a low initial altitude; and 2) ignoring centrifugal force. A hybrid gravity turn landing scheme based on an angle dependent controller and constant thrust controller is proposed to allows the lander to have an initial altitude up to 70% of a planetary radius and still land safely. The proposed motion estimation algorithm consists of two main steps: 1) Continuous Wavelet Transform (CWT) ridge map extraction, and 2) Random Sampling Consensus (RANSAC) for motion estimation. The CWT ridge maps represent global maps of interesting features. The RANSAC algorithm uses these ridge maps to find the correspondence between frames. The instantaneous motion parameters of the lander’s 3D relative motion with respect to the landing site is then calculated by knowing the initial altitude and attitude when the Optical Navigation system (ONS) is switched on. The dynamic motion model of the lander is therefore estimated using a least square fitting over a block of video. The proposed algorithm is evaluated using a series of videos from lab based experimental setups. Errors under a few percent are achieved. These experimental results show the potential of the proposed algorithm to incorporate an optical navigation system with a feedback controller. However, the system has not been optimized for real time implementation. Therefore, it has been the first item mentioned in the future works

Sea Surface Salinity Inversion Model for Changjiang Estuary and Adjoining Sea Area with SMAP and MODIS Data Based on Machine Learning and Preliminary Application

Sea surface salinity (SSS) is one of the most important basic parameters for studying the oceanographic processes and is of great significance in identifying oceanic currents. However, for a long time, the salinity observation in the estuary and coastal waters has not been well resolved due to the technology limitation. In this study, the SSS inversion models for the Changjiang Estuary and the adjacent sea waters were established based on machine learning methods, using SMAP (Soil Moisture Active and Passive) salinity data combined with the specific bands and bands ratios of MODIS (Moderate Resolution Imaging Spectroradiometer). The performance of the three machine learning methods (Random Forest, Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Automatic Machine Learning (TPOT)) are compared with accuracy verification by the in-situ measured SSS. Random Forest is proven to be effective for the SSS inversion in flood season, whereas TPOP performs the best for the dry season. The machine learning-based models effectively solve the problem of insufficient time span of SSS observation from salinity satellites. At the same time, an empirical algorithm was established for the SSS inversion for the sea areas with low salinity (<30 psu) where the machine learning based model fails with great errors. The average deviation of the complex SSS inversion models is −0.86 psu, validated with Copernicus Global Ocean Reanalysis Data. The long term series SSS dataset of March and August from 2003 to 2020 was then constructed to observe the salinity distribution characteristics of the flood season and the dry season, respectively. It is indicated that the distribution pattern of CDW can be divided into three categories: northeast-oriented expansion pattern, multi direction isotropic expansion pattern, and a turn pattern of which CDW shows changing direction, namely the northeast-southeast expansion pattern. The pattern of CDW expansion is indicated to be the comprehensive effect of the interaction of different currents. In addition, it is noteworthy that CDW shows increasing expansion with decreasing SSS in the front plume, especially in the flood season. This study not only gives a feasible solution for effective SSS observation, but also provides a dataset of basic oceanographic parameters for studying the coastal biogeochemical processes, evolution of land-sea interaction, and changing trend of material and energy transport by the CDW in the west Pacific boundary

Sea Surface Salinity Inversion Model for Changjiang Estuary and Adjoining Sea Area with SMAP and MODIS Data Based on Machine Learning and Preliminary Application

Sea surface salinity (SSS) is one of the most important basic parameters for studying the oceanographic processes and is of great significance in identifying oceanic currents. However, for a long time, the salinity observation in the estuary and coastal waters has not been well resolved due to the technology limitation. In this study, the SSS inversion models for the Changjiang Estuary and the adjacent sea waters were established based on machine learning methods, using SMAP (Soil Moisture Active and Passive) salinity data combined with the specific bands and bands ratios of MODIS (Moderate Resolution Imaging Spectroradiometer). The performance of the three machine learning methods (Random Forest, Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Automatic Machine Learning (TPOT)) are compared with accuracy verification by the in-situ measured SSS. Random Forest is proven to be effective for the SSS inversion in flood season, whereas TPOP performs the best for the dry season. The machine learning-based models effectively solve the problem of insufficient time span of SSS observation from salinity satellites. At the same time, an empirical algorithm was established for the SSS inversion for the sea areas with low salinity (<30 psu) where the machine learning based model fails with great errors. The average deviation of the complex SSS inversion models is −0.86 psu, validated with Copernicus Global Ocean Reanalysis Data. The long term series SSS dataset of March and August from 2003 to 2020 was then constructed to observe the salinity distribution characteristics of the flood season and the dry season, respectively. It is indicated that the distribution pattern of CDW can be divided into three categories: northeast-oriented expansion pattern, multi direction isotropic expansion pattern, and a turn pattern of which CDW shows changing direction, namely the northeast-southeast expansion pattern. The pattern of CDW expansion is indicated to be the comprehensive effect of the interaction of different currents. In addition, it is noteworthy that CDW shows increasing expansion with decreasing SSS in the front plume, especially in the flood season. This study not only gives a feasible solution for effective SSS observation, but also provides a dataset of basic oceanographic parameters for studying the coastal biogeochemical processes, evolution of land-sea interaction, and changing trend of material and energy transport by the CDW in the west Pacific boundary

BiFDANet: Unsupervised Bidirectional Domain Adaptation for Semantic Segmentation of Remote Sensing Images

When segmenting massive amounts of remote sensing images collected from different satellites or geographic locations (cities), the pre-trained deep learning models cannot always output satisfactory predictions. To deal with this issue, domain adaptation has been widely utilized to enhance the generalization abilities of the segmentation models. Most of the existing domain adaptation methods, which based on image-to-image translation, firstly transfer the source images to the pseudo-target images, adapt the classifier from the source domain to the target domain. However, these unidirectional methods suffer from the following two limitations: (1) they do not consider the inverse procedure and they cannot fully take advantage of the information from the other domain, which is also beneficial, as confirmed by our experiments; (2) these methods may fail in the cases where transferring the source images to the pseudo-target images is difficult. In this paper, in order to solve these problems, we propose a novel framework BiFDANet for unsupervised bidirectional domain adaptation in the semantic segmentation of remote sensing images. It optimizes the segmentation models in two opposite directions. In the source-to-target direction, BiFDANet learns to transfer the source images to the pseudo-target images and adapts the classifier to the target domain. In the opposite direction, BiFDANet transfers the target images to the pseudo-source images and optimizes the source classifier. At test stage, we make the best of the source classifier and the target classifier, which complement each other with a simple linear combination method, further improving the performance of our BiFDANet. Furthermore, we propose a new bidirectional semantic consistency loss for our BiFDANet to maintain the semantic consistency during the bidirectional image-to-image translation process. The experiments on two datasets including satellite images and aerial images demonstrate the superiority of our method against existing unidirectional methods

CEPC Conceptual Design Report: Volume 2 - Physics & Detector

The Circular Electron Positron Collider (CEPC) is a large international scientific facility proposed by the Chinese particle physics community to explore the Higgs boson and provide critical tests of the underlying fundamental physics principles of the Standard Model that might reveal new physics. The CEPC, to be hosted in China in a circular underground tunnel of approximately 100 km in circumference, is designed to operate as a Higgs factory producing electron-positron collisions with a center-of-mass energy of 240 GeV. The collider will also operate at around 91.2 GeV, as a Z factory, and at the WW production threshold (around 160 GeV). The CEPC will produce close to one trillion Z bosons, 100 million W bosons and over one million Higgs bosons. The vast amount of bottom quarks, charm quarks and tau-leptons produced in the decays of the Z bosons also makes the CEPC an effective B-factory and tau-charm factory. The CEPC will have two interaction points where two large detectors will be located. This document is the second volume of the CEPC Conceptual Design Report (CDR). It presents the physics case for the CEPC, describes conceptual designs of possible detectors and their technological options, highlights the expected detector and physics performance, and discusses future plans for detector R&D and physics investigations. The final CEPC detectors will be proposed and built by international collaborations but they are likely to be composed of the detector technologies included in the conceptual designs described in this document. A separate volume, Volume I, recently released, describes the design of the CEPC accelerator complex, its associated civil engineering, and strategic alternative scenarios