Market effects associated with different financial restatements announcement strategies by Canadian firms

Canadian firms generally use one of two different announcement strategies when they detect the possible need to issue financial restatements; namely: single-announcement restatements (directly uploading and disclosing the financial restatements) and multiple-announcement restatements (initially announcing the possibility of accounting problems through press releases or firm reports before the later issue of the final restatements). We find that error-related single-announcement financial restatements are associated with significant negative market impacts in a two day event window [0, +1]. The median idiosyncratic volatility associated with error-related single-announcement restatements increases significantly following the announcements. For multiple-announcement restatements we observe significant market impacts at the intention announcement day and additional market impacts prior to but not on the official restatement dates. In the in-between period after the intention to restate is announced, bid-ask spreads increase and trading volumes, trading values and the idiosyncratic volatilities decrease significantly. After the official restatement is announced, trading volumes, trading values and idiosyncratic volatilities increase significantly. We observe higher total market impacts for multiple versus single announcement financial restatements

Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps

A road is the skeleton of a city and is a fundamental and important geographical component. Currently, many countries have built geo-information databases and gathered large amounts of geographic data. However, with the extensive construction of infrastructure and rapid expansion of cities, automatic updating of road data is imperative to maintain the high quality of current basic geographic information. However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases.To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road.Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results. Further, to fully utilize the limited amount of label information and to enhance the prediction accuracy on unlabeled images, we utilized the mean teacher framework as the basic semi-supervised learning framework and introduced Regional Contrast (ReCo) in our work to improve the model capacity for distinguishing between the characteristics of roads and background elements.We applied our method to two datasets. Our model can effectively improve the performance of a model with fewer labels. Overall, the proposed SRUNet can provide stable, up-to-date, and reliable prediction results for a wide range of road renewal tasks.Comment: 22 pages, 8 figure

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

In-memory photonic dot-product engine with electrically programmable weight banks

Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic–electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating an in-memory photonic–electronic dot-product engine, one that decouples electronic programming of phase-change materials (PCMs) and photonic computation. Specifically, we develop non-volatile electronically reprogrammable PCM memory cells with a record-high 4-bit weight encoding, the lowest energy consumption per unit modulation depth (1.7 nJ/dB) for Erase operation (crystallization), and a high switching contrast (158.5%) using non-resonant silicon-on-insulator waveguide microheater devices. This enables us to perform parallel multiplications for image processing with a superior contrast-to-noise ratio (≥87.36) that leads to an enhanced computing accuracy (standard deviation σ ≤ 0.007). An in-memory hybrid computing system is developed in hardware for convolutional processing for recognizing images from the MNIST database with inferencing accuracies of 86% and 87%

Coordination of Axial Trunk Rotations During Gait in Low Back Pain. A Narrative Review

Chronic low back pain patients have been observed to show a reduced shift of thorax-pelvis relative phase towards out-of-phase movement with increasing speed compared to healthy controls. Here, we review the literature on this phase shift in patients with low back pain and we analyze the results presented in literature in view of the theoretical motivations to assess this phenomenon. Initially, based on the dynamical systems approach to movement coordination, the shift in thorax-pelvis relative phase with speed was studied as a self-organizing transition. However, the phase shift is gradual, which does not match a self-organizing transition. Subsequent emphasis in the literature therefore shifted to a motivation based on biomechanics. The change in relative phase with low back pain was specifically linked to expected changes in trunk stiffness due to ‘guarded behavior’. We found that thorax-pelvis relative phase is affected by several interacting factors, including active drive of thorax rotation through trunk muscle activity, stride frequency and the magnitude of pelvis rotations. Large pelvis rotations and high stride frequency observed in low back pain patients may contribute to the difference between patients and controls. This makes thorax-pelvis relative phase a poor proxy of trunk stiffness. In conclusion, thorax-pelvis relative phase cannot be considered as a collective variable reflecting the orderly behaviour of a complex underlying system, nor is it a marker of specific changes in trunk biomechanics. The fact that it is affected by multiple factors may explain the considerable between-subject variance of this measure in low back pain patients and healthy controls alike

Comprehensive Evaluation of Fruit Quality of Actinidia arguta Based on Principal Component Analysis and Cluster Analysis

In order to scientifically evaluate the fruit quality of different Actinidia arguta varieties and establish the quality evaluation system, 10 Actinidia arguta varieties were used as experimental materials, and the indexes of fruit appearance quality and nutritional quality were measured and compared under edible conditions. The fruit quality of Actinidia arguta was comprehensively evaluated by correlation analysis, principal component analysis and cluster analysis. The results showed that the quality indexes of different varieties of Actinidia arguta were different and correlated. The difference of the content of Vitamin C was largest, and the coefficient of variation was 53.08%. The difference of fruit color brightness (L* value) was the smallest, and the coefficient of variation was 6.04%. By principal component analysis, 18 quality indicators were simplified into 6 principal components, and the cumulative variance contribution rate was 90.571%, which could reflect most of the information of the original quality indexes. The comprehensive scores of quality indexes of 10 Actinidia arguta varieties were ranked as ‘Longcheng No.2’, ‘Kuilü’, ‘Jialü’, ‘Wanlü’, ‘Tianxinbao’, ‘Lübao’, ‘Xinlü’, ‘Cuiyu’, ‘Fenglü’ and ‘Pingllü’. According to cluster analysis, 10 Actinidia arguta varieties were divided into five categories, among which ‘Longcheng No.2’ and ‘Kuilü’ in the first category had better comprehensive quality traits. The study provided a reference for the variety breeding, planting, extension and rational processing and utilization of Actinidia arguta

Mechanism for the Bio-Oxidation and Decomposition of Pentlandite: Implication for Nickel Bioleaching at Elevated pH

This work investigated the effects of Fe3+, H+ and adsorbed leaching bacteria on the bioleaching of pentlandite. Collectively, an integrated model for the oxidation and decomposition of pentlandite was built to describe the behaviors of different components in a bioleaching system. Proton ions and ferric ions could promote the break and oxidation of Ni-S and Fe-S bonds. The iron-oxidizing microorganisms could regenerate ferric ions and maintain a high Eh value. The sulfur-oxidizing microorganisms showed significant importance in the oxidation of polysulfide and elemental sulfur. The atoms in pentlandite show different modification pathways during the bioleaching process: iron transformed through a (Ni,Fe)9S8 → Fe2+ → Fe3+ → KFe3(SO4)2(OH)6 pathway; nickel experienced a transformation of (Ni,Fe)9S8 → NiS → Ni2+; sulfur modified through the pathway of S2−/S22− → Sn2− → S0 → SO32− → SO42−. During bioleaching, a sulfur-rich layer and jarosite layer formed on the mineral surface, and the rise of pH value accelerated the process. However, no evidence for the inhibition of the layers was shown in the bioleaching of pentlandite at pH 3.00. This study provides a novel method for the extraction of nickel from pentlandite by bioleaching at elevated pH values

A Low-Carbon Decision-Making Algorithm for Water-Spot Tourists, Based on the k-NN Spatial-Accessibility Optimization Model

This study presents a low-carbon decision-making algorithm for water-spot tourists, based on the k-NN spatial-accessibility optimization model, to address the problems of water-spot tourism spatial decision-making. The attributes of scenic water spots previously visited by the tourists were knowledge-mined, to ascertain the tourists’ interest-tendencies. A scenic water-spot classification model was constructed, to classify scenic water spots in tourist cities. Then, a scenic water spot spatial-accessibility optimization model was set up, to sequence the scenic spots. Based on the tourists’ interest-tendencies, and the spatial accessibility of the scenic water spots, a spatial-decision algorithm was constructed for water-spot tourists, to make decisions for the tourists, in regard to the tour routes with optimal accessibility and lowest cost. An experiment was performed, in which the tourist city of Leshan was chosen as the research object. The scenic water spots were classified, and the spatial accessibility for each scenic spot was calculated; then, the optimal tour routes with optimal spatial accessibility and the lowest cost were output. The experiment verified that the tour routes that were output via the proposed algorithm had stronger spatial accessibility, and cost less than the sub-optimal ones, and were thus more environmentally friendly

A Multi-Scale Residential Areas Matching Method Considering Spatial Neighborhood Features

Residential areas is one of the basic geographical elements on the map and an important content of the map representation. Multi-scale residential areas matching refers to the process of identifying and associating entities with the same name in different data sources, which can be widely used in map compilation, data fusion, change detection and update. A matching method considering spatial neighborhood features is proposed to solve the complex matching problem of multi-scale residential areas. The method uses Delaunay triangulation to divide complex matching entities in different scales into closed domains through spatial neighborhood clusters, which can obtain many-to-many matching candidate feature sets. At the same time, the geometric features and topological features of the residential areas are fully considered, and the Relief-F algorithm is used to determine the weight values of different similarity features. Then the similarity and spatial neighborhood similarity of the polygon residential areas are calculated, after which the final matching results are obtained. The experimental results show that the accuracy rate, recall rate and F value of the matching method are all above 90%, which has a high matching accuracy. It can identify a variety of matching relationships and overcome the influence of certain positional deviations on matching results. The proposed method can not only take account of the spatial neighborhood characteristics of residential areas, but also identify complex matching relationships in multi-scale residential areas accurately with a good matching effect