Impact of investment behaviour on financial markets during COVID-19: a case of UK

This study aims to determine the impact of investment behavior on financial markets during COVID-19 with respect to the UK. This study is quantitative, where the data has been gathered from the primary sources of information, i.e., through a survey questionnaire. The researcher adopted the non-probability convenience sampling through which 337 responses were gathered. The questionnaire was self-administered, which was based on 7 points Likert scale. Concerning the analysis, the SEM technique has been adopted in which CFA and path analysis were carried out to determine the impact of variables. The study’s analysis determined significant moderation of COVID-19 uncertainty over the relationship of risk perception and general risk to tolerance. Similarly, the moderation of COVID-19 uncertainty over the relationship of risk perception and financial risk to tolerance was also determined. Additionally, the profitability rate’s effect was determined by the financial risk tolerance and general risk tolerance. Moreover, the effect of risk perception was also determined over the financial risk to tolerance. Lastly, the effect of satisfaction was determined to be significant over the general risk to tolerance

IC-FPS: Instance-Centroid Faster Point Sampling Module for 3D Point-base Object Detection

3D object detection is one of the most important tasks in autonomous driving and robotics. Our research focuses on tackling low efficiency issue of point-based methods on large-scale point clouds. Existing point-based methods adopt farthest point sampling (FPS) strategy for downsampling, which is computationally expensive in terms of inference time and memory consumption when the number of point cloud increases. In order to improve efficiency, we propose a novel Instance-Centroid Faster Point Sampling Module (IC-FPS) , which effectively replaces the first Set Abstraction (SA) layer that is extremely tedious. IC-FPS module is comprised of two methods, local feature diffusion based background point filter (LFDBF) and Centroid-Instance Sampling Strategy (CISS). LFDBF is constructed to exclude most invalid background points, while CISS substitutes FPS strategy by fast sampling centroids and instance points. IC-FPS module can be inserted to almost every point-based models. Extensive experiments on multiple public benchmarks have demonstrated the superiority of IC-FPS. On Waymo dataset, the proposed module significantly improves performance of baseline model and accelerates inference speed by 3.8 times. For the first time, real-time detection of point-based models in large-scale point cloud scenario is realized

Physics of arctic landfast sea ice and implications on the cryosphere : An overview

Landfast sea ice (LFSI) is a critical component of the Arctic sea ice cover, and is changing as a result of Arctic amplification of climate change. Located in coastal areas, LFSI is of great significance to the physical and ecological systems of the Arctic shelf and in local indigenous communities. We present an overview of the physics of Arctic LFSI and the associated implications on the cryosphere. LFSI is kept in place by four fasten mechanisms. The evolution of LFSI is mostly determined by thermodynamic processes, and can therefore be used as an indicator of local climate change. We also present the dynamic processes that are active prior to the formation of LFSI, and those that are involved in LFSI freeze-up and breakup. Season length, thickness and extent of Arctic LFSI are decreasing and showing different trends in different seas, and therefore, causing environmental and climatic impacts. An improved coordination of Arctic LFSI observation is needed with a unified and systematic observation network supported by cooperation between scientists and indigenous communities, as well as a better application of remote sensing data to acquire detailed LFSI cryosphere physical parameters, hence revolving both its annual cycle and long-term changes. Integrated investigations combining in situ measurements, satellite remote sensing and numerical modeling are needed to improve our understanding of the physical mechanisms of LFSI seasonal changes and their impacts on the environment and climate.Peer reviewe

Lightweight high-resolution Subject Matting in the Real World

Existing saliency object detection (SOD) methods struggle to satisfy fast inference and accurate results simultaneously in high resolution scenes. They are limited by the quality of public datasets and efficient network modules for high-resolution images. To alleviate these issues, we propose to construct a saliency object matting dataset HRSOM and a lightweight network PSUNet. Considering efficient inference of mobile depolyment framework, we design a symmetric pixel shuffle module and a lightweight module TRSU. Compared to 13 SOD methods, the proposed PSUNet has the best objective performance on the high-resolution benchmark dataset. Evaluation results of objective assessment are superior compared to U$^2$Net that has 10 times of parameter amount of our network. On Snapdragon 8 Gen 2 Mobile Platform, inference a single 640$\times$640 image only takes 113ms. And on the subjective assessment, evaluation results are better than the industry benchmark IOS16 (Lift subject from background)

EA-BEV: Edge-aware Bird' s-Eye-View Projector for 3D Object Detection

In recent years, great progress has been made in the Lift-Splat-Shot-based (LSS-based) 3D object detection method, which converts features of 2D camera view and 3D lidar view to Bird's-Eye-View (BEV) for feature fusion. However, inaccurate depth estimation (e.g. the 'depth jump' problem) is an obstacle to develop LSS-based methods. To alleviate the 'depth jump' problem, we proposed Edge-Aware Bird's-Eye-View (EA-BEV) projector. By coupling proposed edge-aware depth fusion module and depth estimate module, the proposed EA-BEV projector solves the problem and enforces refined supervision on depth. Besides, we propose sparse depth supervision and gradient edge depth supervision, for constraining learning on global depth and local marginal depth information. Our EA-BEV projector is a plug-and-play module for any LSS-based 3D object detection models, and effectively improves the baseline performance. We demonstrate the effectiveness on the nuScenes benchmark. On the nuScenes 3D object detection validation dataset, our proposed EA-BEV projector can boost several state-of-the-art LLS-based baselines on nuScenes 3D object detection benchmark and nuScenes BEV map segmentation benchmark with negligible increment of inference time

A Machine Vision Method for Correction of Eccentric Error: Based on Adaptive Enhancement Algorithm

In the procedure of surface defects detection for large-aperture aspherical optical elements, it is of vital significance to adjust the optical axis of the element to be coaxial with the mechanical spin axis accurately. Therefore, a machine vision method for eccentric error correction is proposed in this paper. Focusing on the severe defocus blur of reference crosshair image caused by the imaging characteristic of the aspherical optical element, which may lead to the failure of correction, an Adaptive Enhancement Algorithm (AEA) is proposed to strengthen the crosshair image. AEA is consisted of existed Guided Filter Dark Channel Dehazing Algorithm (GFA) and proposed lightweight Multi-scale Densely Connected Network (MDC-Net). The enhancement effect of GFA is excellent but time-consuming, and the enhancement effect of MDC-Net is slightly inferior but strongly real-time. As AEA will be executed dozens of times during each correction procedure, its real-time performance is very important. Therefore, by setting the empirical threshold of definition evaluation function SMD2, GFA and MDC-Net are respectively applied to highly and slightly blurred crosshair images so as to ensure the enhancement effect while saving as much time as possible. AEA has certain robustness in time-consuming performance, which takes an average time of 0.2721s and 0.0963s to execute GFA and MDC-Net separately on ten 200pixels 200pixels Region of Interest (ROI) images with different degrees of blur. And the eccentricity error can be reduced to within 10um by our method

Meteorological and sea ice anomalies in the western Arctic Ocean during the 2018–2019 ice season: a Lagrangian study

Rapid changes in the Arctic climate and those in Arctic sea ice in recent decades are closely coupled. In this study, we used atmospheric reanalysis data and satellite remote sensing products to identify anomalies of meteorological and sea ice conditions during the ice season of 2018–2019 relative to climatological means using a Lagrangian methodology. We obtained the anomalies along the drifting trajectories of eight sea ice mass balance buoys between the marginal ice zone and the pack ice zone in the western Arctic Ocean (~160°W–170°W and 79°N–85°N) from September 2018 to August 2019. The temporary collapse of the Beaufort High and a strong positive Arctic Dipole in the winter of 2018–2019 drove the three buoys in the north to drift gradually northeastward and merge into the Transpolar Drift Stream. The most prominent positive temperature anomalies in 2018–2019 along the buoy trajectories relative to 1979–2019 climatology occurred in autumn, early winter, and April, and were concentrated in the southern part of the study area; these anomalies can be partly related to the seasonal and spatial patterns of heat release from the Arctic ice-ocean system to the atmosphere. In the southern part of the study area and in autumn, the sea ice concentration in 2018–2019 was higher than that averaged over the past 10 years. However, we found no ice concentration anomalies for other regions or seasons. The sea ice thickness in the freezing season and the snow depth by the end of the winter of 2018–2019 can also be considered as normal. Although the wind speed in 2018–2019 was slightly lower than that in 1979–2019, the speed of sea ice drift and its ratio to wind speed were significantly higher than the climatology. In 2019, the sea ice surface began to melt at the end of June, which was close to the 1988–2019 climatology. However, spatial variations in the onsets of surface melt in 2019 differed from the climatology, and can be explained by the prevalence of a high-pressure system in the south of the Beaufort Sea in June 2019. In addition to seasonal variations, the meteorological and sea ice anomalies were influenced by spatial variations. By the end of summer 2019, the buoys had drifted to the west of the Canadian Arctic Archipelago, where the ice conditions was heavier than those at the buoy locations in early September 2018. The meteorological and sea ice anomalies identified in this study lay the foundations for subsequent analyses and simulations of sea ice mass balance based on the buoy data