Advantage Of Local Information Research Articles

A commentary on the EU money laundering reform in light of the subsidiarity principle

Purpose In July 2021, the European Commission has proposed a set of conjunct initiatives to reform the antimoney laundering/countering the financing of terrorism (AML/CFT) regulatory regime in Europe with the main aims to (i) harmonize the AML/CFT regulation and (ii) centralize the authority to a higher degree at European Union (EU) level. This paper aims to assess the reform in light of the EU subsidiarity principle. Design/methodology/approach The paper uses a benchmark approach to compare the proposed EU money laundering reform against Article 5(3) of the Treaty on the Functioning of the European Union. Findings The paper confirms that more centralized decision-making at EU level in this policy area is justified, mainly because (i) the policy area is not an area where the EU has exclusive competence, (ii) EU centralized action is necessary and (iii) it also adds value, for instance, for level playing field and efficiency considerations as long as local information advantage will not be lost. As such, the subsidiarity principle can be applied and is an adequate tool to legitimize EU centralized action in the field of money laundering combat. Originality/value As the EU AML regulatory reform has not yet been sufficiently discussed in light of the subsidiarity principle, the article is of innovative nature.

Multi-scale nested UNet with transformer for colorectal polyp segmentation.

Polyp detection and localization are essential tasks for colonoscopy. U-shape network based convolutional neural networks have achieved remarkable segmentation performance for biomedical images, but lack of long-range dependencies modeling limits their receptivefields. Our goal was to develop and test a novel architecture for polyp segmentation, which takes advantage of learning local information with long-range dependenciesmodeling. A novel architecture combining with multi-scale nested UNet structure integrated transformer for polyp segmentation was developed. The proposed network takes advantage of both CNN and transformer to extract distinct feature information. The transformer layer is embedded between the encoder and decoder of a U-shape net to learn explicit global context and long-range semantic information. To address the challenging of variant polyp sizes, a MSFF unit was proposed to fuse features with multipleresolution. Four public datasets and one in-house dataset were used to train and test the model performance. Ablation study was also conducted to verify each component of the model. For dataset Kvasir-SEG and CVC-ClinicDB, the proposed model achieved mean dice score of 0.942 and 0.950 respectively, which were more accurate than the other methods. To show the generalization of different methods, we processed two cross dataset validations, the proposed model achieved the highest mean dice score. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-artmethods. The proposed model produced more accurate polyp segmentation than current methods on four different public and one in-house datasets. Its capability of polyps segmentation in different sizes shows the potential clinicalapplication.

Local information advantage and stock returns: Evidence from social media

AbstractWe examine the information asymmetry between local and nonlocal investors with a large dataset of stock message board postings. We document that abnormal relative postings of a firm, that is, unusual changes in the volume of postings from local versus nonlocal investors, capture locals' information advantage. This measure positively predicts firms' short‐term stock returns as well as those of peer firms in the same city. Sentiment analysis shows that posting activities primarily reflect good news, potentially due to social transmission bias and short‐sales constraints. We identify the information driving return predictability through content‐based analysis. Abnormal relative postings also lead analysts' forecast revisions. Overall, investors' interactions on social media contain valuable geography‐based private information.

Learning Disentangled Graph Convolutional Networks Locally and Globally.

Graph convolutional networks (GCNs) emerge as the most successful learning models for graph-structured data. Despite their success, existing GCNs usually ignore the entangled latent factors typically arising in real-world graphs, which results in nonexplainable node representations. Even worse, while the emphasis has been placed on local graph information, the global knowledge of the entire graph is lost to a certain extent. In this work, to address these issues, we propose a novel framework for GCNs, termed LGD-GCN, taking advantage of both local and global information for disentangling node representations in the latent space. Specifically, we propose to represent a disentangled latent continuous space with a statistical mixture model, by leveraging neighborhood routing mechanism locally. From the latent space, various new graphs can then be disentangled and learned, to overall reflect the hidden structures with respect to different factors. On the one hand, a novel regularizer is designed to encourage interfactor diversity for model expressivity in the latent space. On the other hand, the factor-specific information is encoded globally via employing a message passing along these new graphs, in order to strengthen intrafactor consistency. Extensive evaluations on both synthetic and five benchmark datasets show that LGD-GCN brings significant performance gains over the recent competitive models in both disentangling and node classification. Particularly, LGD-GCN is able to outperform averagely the disentangled state-of-the-arts by 7.4% on social network datasets.

Distributed Coordination LFC Approach for Interconnected Power Systems Under Detection and Compensation Mechanism Targeting DoS Attacks

A distributed coordination load frequency control (LFC) approach is proposed for interconnected power systems, and a detection and compensation mechanism targeting denial-of-service (DoS) attacks is designed in this work. Firstly, different from traditional LFC, a hybrid-type distributed control strategy is proposed, which takes full advantage of local control information and coordination control information. Then, for discovering DoS attacks timely, a feasible detection algorithm targeting DoS attacks is presented. Furthermore, in order to weaken the harm of cyber-attacks, a triggered-type compensation mechanism for control data is proposed. By constructing appropriate Lyapunov-Krasovskii functionals, a stability criterion with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance index is developed. At last, the feasibility of detection and compensation mechanism targeting DoS attacks and the effectiveness of proposed control approach are demonstrated by presenting contrastive illustrative examples.

Do local investors know more? Evidence from securities class actions

This paper explores the informed trading by mutual funds of local investments. Using hand-collected securities class actions (SCAs), we examine whether local mutual funds can avoid potential economic damage from SCAs against their portfolio firms by selling them in advance – local information advantage hypothesis. Our findings are consistent with the hypothesis. The informed selling by local funds of the SCA firms is driven by the firms with low analyst coverage and high forecasting errors, which suggests that local funds benefit the most when an SCA firm's information environment is poor. Furthermore, we find that informed trading of local funds can predict a possible outcome of the lawsuit. Finally, the magnitude of informed selling is greater when firms are closer to local funds and the class period is shorter.

SqueezExpNet: Dual-stage convolutional neural network for accurate facial expression recognition with attention mechanism

Facial expression recognition (FER) using a deep convolutional neural network (DCNN) is important and challenging. Although a substantial effort is made to increase FER accuracy through DCNN, previous studies are still not sufficiently generalisable for real-world applications. Traditional FER studies are mainly limited to controlled lab-posed frontal facial images, which lack the challenges of motion blur, head poses, occlusions, face deformations and lighting under uncontrolled conditions. In this work, we proposed a SqueezExpNet architecture that can take advantage of local and global facial information for a highly accurate FER system that can handle environmental variations. Our network was divided into two stages: a geometrical attention stage that possesses a SqueezeNet-like architecture to obtain local highlight information and a spatial texture stage comprising several squeezed and expanded layers to exploit high-level global features. In particular, we created a weighted mask of 3D face landmarks and used element-wise multiplication with a spatial feature in the first stage to draw attention to important local facial regions. Next, we input the face spatial image and its augmentations into the second stage of the network. Finally, like a classifier, a recurrent neural network was designed to collaborate the highlighted information from dual stages rather than simply using the SoftMax function, thereby aiding in overcoming the uncertainties. Experiments covering basic and compound FER tasks were performed using the three leading facial expression datasets. Our strategy outperformed the existing DCNN methods and achieved state-of-the-art results. The developed architecture, adopted research methodology and reported findings may find potential applications of real-time FER in surveillance, health and feedback systems.

A hybrid of transformer and CNN for efficient single image super-resolution via multi-level distillation

In recent years, single image super-resolution (SISR) models based on convolutional neural networks (CNN) have made significant progress and have gradually become the mainstream method. However, they still suffer from high computational costs, heavy memory consumption, and a limited receptive field. Although Vision Transformer has a stronger modeling capability and larger receptive field, it also incurs high computing power consumption and memory occupation. To address these issues, we propose a hybrid network of Transformer and CNN with cascaded feature distillation blocks for efficient image super-resolution (TCFDN), which can take advantage of both local information and long-term interactions while being flexible enough. Concretely, TCFDN consists of cascaded Transformer-CNN feature distillation blocks (TCFDB) and an upsampling module. The feature distillation pipeline of TCFDB can help our model gradually learn refined features with better representation ability while remaining lightweight. Besides, we also designed an enhanced Swin Transformer layer (ESTL) by replacing the multi-layer perceptron (MLP) in the standard Transformer with a convolutional feed-forward layer (CFF), which is more suitable for SR tasks. Then, the enhanced spatial attention embedded in TCFDB can boost SR performance further. Moreover, we observe that using a more advanced loss function, i.e., the contrastive loss, can also bring a PSNR gain of 0.01 dB–0.03 dB on public benchmarks. Extensive experiments demonstrate that TCFDN outperforms the state-of-the-art methods in terms of a better trade-off between performance and model size. Under the 4X SR task on the public benchmark Urban100, our TCFDN outperforms the second-best model by 0.37 dB in terms of PSNR. Compared with other state-of-the-art methods, the total number of parameters in TCFDN can be reduced by up to 32 % while maintaining competitive performance.

Market Reaction to Local Attention around Earnings Announcements in China: Evidence from Internet Search Activity

The existing literature shows that, due to locality and familiarity, spatial investor–firm adjacency plays a key role in determining stock investor attention, as proxied by the location where investors initiate an Internet search of the ticker symbol. This paper investigates whether Chinese stock markets exhibit the same pattern observed in the U.S. market—demand for a firm’s information can exert strong effects on the stock market response during earnings announcements of the firm. Specifically, for each Chinese publicly listed firm, this paper constructs a dynamic local attention ratio that divides the search volume generated within the province where the firm is registered by the contemporaneous nationwide search volume consisting of 31 provinces in total. During 2011–2018 in China’s A-share market, it was found that the local attention measure leads the market response around annual earnings announcement dates. For firms with higher local attention, trading is more active before the announcement, and the price changes can better predict incoming earnings news. At the announcement, while trading remains active, the predictivity of price changes for earnings becomes weaker. Furthermore, we only found evidence that supports the local information advantage argument in explaining the locality of Chinese investors’ attention.

Joint global metric learning and local manifold preservation for scene recognition

Scene recognition plays an important role in many computer vision tasks. However, the recognition performance hardly meets the development of computer vision, since scene images show large variations in spatial position, illumination, and scale. To address this issue, a joint global metric learning and local manifold preservation (JGML-LMP) approach is proposed. First, we formulate a new global metric learning problem based on the cluster centers of each specific class, allowing to capture the global discriminative information with more informative samples. Second, in order to exploit the local manifold structure, we introduce an adaptive nearest neighbors constraint through which the local intrinsic relationships can be preserved in the new metric space instead of the Euclidean space. Third, through performing global metric learning and local manifold preservation jointly within a unified optimization framework, our approach can take advantage of both global and local information, and hence produces more discriminative and robust feature representations for scene recognition. Extensive experiments on four benchmark scene datasets demonstrate the superiority of the proposed method over state-of-the-art methods.

Real-time scheduling simulation optimisation of job shop in a production-logistics collaborative environment

ABSTRACT In a complex and dynamic job shop containing logistics factor, schedule needs to be generated rapidly, so the real-time scheduling method is more suitable for such scenario. Such method takes advantage of local information within a short time due to the rapid changes of information under uncertain environment. Therefore, how to make use of the future information by prediction while ensuring the robustness of schedule is a valuable problem. To solve it, firstly, a new real-time scheduling model and algorithm is proposed. There is a new kind of release moment of task information which can give AGVs the longest time to prepare for the task than existing research. Secondly, a real-time information update mechanism is designed to increase schedule’s robustness. Finally, a large-scale and dynamic job shop simulation experimental platform is developed. Dynamic factors include the random insertion of orders and failures of equipment. Results show that the method proposed outperforms existing research in terms of customer satisfaction, equipment utilisation and energy consumption. The robustness of schedule can also be acceptable. This paper also finds a rule that in job shop with the large proportion of logistics transportation time, the above method can achieve more competitive results.

Face-to-face Social Interactions and Local Informational Advantage

Face-to-face Social Interactions and Local Informational Advantage

Does the location of directors' additional positions matter? A new dimension of board structure

This paper analyzes the impact on firm value of directors with outside directorships and/or executive positions in the same geographic area (DSAs). The findings show that DSAs are associated with higher firm value because they better understand the local business environment and have better local connections. This value impact is more prominent for rural firms, innovative firms, and firms with high growth rates, indicating that DSAs' local information advantage is more important for firms in areas with less effective information transmission, firms with high R&D expenses, and firms facing high uncertainty. Also, DSAs help corporations better manage local economic shocks, improve financial reporting quality, and increase CEO incentive pay.

The Firm Next Door: Using Satellite Images to Study Local Information Advantage

ABSTRACTWe use novel satellite data that track the number of cars in the parking lots of 92,668 stores for 71 publicly listed U.S. retailers to study the local information advantage of institutional investors. We establish car counts as a timely measure of store‐level performance and find that institutional investors adjust their holdings in response to the performance of local stores, and that these trades are profitable on average. These results suggest that local investors have an advantage when processing information about nearby operations. However, some institutional investors do not adjust for the quality of their local information and continue to rely on local signals even when they are poor predictors of firm performance and returns. This overreliance on poor local information is reduced for institutional investors with greater industry expertise and those with greater incentives to maximize short‐term trading profits.

Attention, Acquisition Costs, or Executive Information? Using Satellite Data to Uncover the Sources of Local Information

We use novel satellite data that track the number of cars in the parking lots of 92,668 stores for 71 publicly listed U.S. retailers to study the local information advantage of institutional investors. We establish car counts as a timely measure of store-level performance and find that institutional investors adjust their holdings in response to the performance of local stores, and that these trades are profitable on average. These results suggest that local investors have an advantage when processing information about nearby operations. However, some institutional investors do not adjust for the quality of their local information and continue to rely on local signals even when they are poor predictors of firm performance and returns. This overreliance on poor local information is reduced for institutional investors with greater industry expertise and those with greater incentives to maximize short-term trading profits.

OAENet: Oriented attention ensemble for accurate facial expression recognition

Facial Expression Recognition (FER) is a challenging yet important research topic owing to its significance with respect to its academic and commercial potentials. In this work, we propose an oriented attention pseudo-siamese network that takes advantage of global and local facial information for high accurate FER. Our network consists of two branches, a maintenance branch that consisted of several convolutional blocks to take advantage of high-level semantic features, and an attention branch that possesses a UNet-like architecture to obtain local highlight information. Specifically, we first input the face image into the maintenance branch. For the attention branch, we calculate the correlation coefficient between a face and its sub-regions. Next, we construct a weighted mask by correlating the facial landmarks and the correlation coefficients. Then, the weighted mask is sent to the attention branch. Finally, the two branches are fused to output the classification results. As such, a direction-dependent attention mechanism is established to remedy the limitation of insufficient utilization of local information. With the help of our attention mechanism, our network not only grabs a global picture but can also concentrate on important local areas. Experiments are carried out on 4 leading facial expression datasets. Our method has achieved a very appealing performance compared to other state-of-the-art methods.

Local and foreign institutional investors, information asymmetries, and state ownership

Extant evidence is mixed on whether local investors or foreign investors are better informed. In this study, we shed light on this debate by investigating whether the information asymmetries between local and foreign institutional investors relate to state ownership of publicly listed firms. Prior research documents that state ownership is associated with worse information asymmetry stemming from poor governance structures and financial transparency, implying that investing in firms with state ownership requires more local knowledge and experience. In analyzing Chinese listed firms, we find strong, robust evidence that the information advantage of local versus foreign institutional investors varies with the extent of state ownership: in partially state-owned enterprises (SOEs), local institutional investors have an advantage over foreign institutional investors in predicting future stock returns, whereas this local advantage does not extend to firms with zero state ownership (non-SOEs). In additional analysis, we find evidence that local institutional investors' information advantage in SOEs narrows in the presence of highly independent boards and Big Four auditors.

Deep Multiphase Level Set for Scene Parsing.

Recently, Fully Convolutional Network (FCN) seems to be the go-to architecture for image segmentation, including semantic scene parsing. However, it is difficult for a generic FCN to predict semantic labels around the object boundaries, thus FCN-based methods usually produce parsing results with inaccurate boundaries. Meanwhile, many works have demonstrate that level set based active contours are superior to the boundary estimation in sub-pixel accuracy. However, they are quite sensitive to initial settings. To address these limitations, in this paper we propose a novel Deep Multiphase Level Set (DMLS) method for semantic scene parsing, which efficiently incorporates multiphase level sets into deep neural networks. The proposed method consists of three modules, i.e., recurrent FCNs, adaptive multiphase level set, and deeply supervised learning. More specifically, recurrent FCNs learn multi-level representations of input images with different contexts. Adaptive multiphase level set drives the discriminative contour for each semantic class, which makes use of the advantages of both global and local information. In each time-step of the recurrent FCNs, deeply supervised learning is incorporated for model training. Extensive experiments on three public benchmarks have shown that our proposed method achieves new state-of-the-art performances. The source codes will be released at https://github.com/Pchank/DMLS-for-SSP.

CAN NONLOCAL TRADERS CAPTURE THE LOCAL INFORMATION ADVANTAGE AND PROFIT?

AbstractMarket makers located in geographic proximity (local) to companies possess a local information advantage that comes from access to soft information. We study whether a nonlocal trader can capture the local information advantage and profit without relocating. We develop a trading strategy for the nonlocal trader that generates “buy” and “sell” signals for stocks based on quotes of local market makers. Our findings suggest it is possible, albeit difficult, for nonlocal traders to extract local information from local market makers’ quotes. Using limit orders from buy signals, we generate up to 7.6 basis points of abnormal return per day.

Identification and optimal control of an electrical storage system for microgrids with renewables

Battery systems are becoming more and more widespread for stationary applications both at power grid level and user level. In this second context, small battery-based storage systems are frequently proposed for the installation in combination with local renewable sources, to increase the self-consumption of the locally generated renewable energy and, in some cases, even to enable the user to disconnect from the main network. Increasing use of storage devices for stationary applications implies a more detailed characterization of the “behavior at the terminals” of these systems. In the same time, the development of new Energy Management Systems is required in order to take advantage of both local information and data from the service provider, such as radiation forecasts from weather forecast services. In this paper, a new EMS is proposed, characterized by a two-level architecture: the higher level, based on a receding horizon control scheme, optimally schedules the operation of the storage by using information on radiation from a forecast service provider; the lower level implements a heuristic procedure (if–then) on a low-cost local controller, in order to perform corrective actions. The proposed architecture has been tested on a real case study.