The Role of Loan Loss Provisions in Earnings Management and Capital Management: The Chinese Experience

Banks’ loan loss provisions play an important role in bank stability and soundness. It can help banks prevent credit risk, smooth income, manage regulatory capital and also improve their performance. This paper examines the loan loss provisions behaviours in Chinese banking system and how they are correlated with the earnings management and capital management. Based on 260 banks over the period of 2011-2017, both the Fixed Effects model and the System Generalized Method of Moments estimator are used to assist our analysis. The findings indicate that loan loss provisions are not used as a tool for income smoothing since the results show that banks may decrease their provisions when their earnings increase. However, there is a significant proof for capital management in Chinese banks, which indicates that Chinese banks may use loan loss provisions to manage their capital ratios in order to meet regulatory requirements. The lagged dependent variable is also significant, showing that Chinese banks may adjust provisions according to the amount in previous years. In terms of other control variables, the non-performing loans, the gross loans, the GDP growth rate and the unemployment rate are all significant, indicating that they may have great impacts on loan loss provisions behaviours in China

Cardiac contractility modulation to enhance optimized medical therapy and improve cardiac remodeling in advanced heart failure: a case report

BackgroundGuideline-directed medical therapy (GDMT) for heart failure (HF) with reduced ejection fraction (HFrEF) has been demonstrated to significantly reduce morbidity and mortality. However, many patients, especially those with advanced HFrEF, are unable to tolerate optimal GDMT due to hypotension. Cardiac contractility modulation (CCM) is a novel therapeutic approach that enhances myocardial contractility and reverses cardiac remodeling, thereby improving cardiac function and quality of life in patients with HFrEF. However, whether CCM can bridge the hemodynamic vulnerability phase to facilitate GDMT optimization and improve patient prognosis remains unclear.Case presentationA 56-year-old man with dilated cardiomyopathy and HFrEF (NYHA functional class III) had recurrent hospitalizations for HF over the past 4 years. Due to hypotension (systolic blood pressure ≤90 mmHg), the patient was unable to tolerate full-dose GDMT, with sacubitril-valsartan limited to 25 mg twice daily, metoprolol succinate to 23.75 mg once daily, and spironolactone to 20 mg once daily. After a comprehensive evaluation, a CCM device was implanted as the most effective and evidence-based option. Postoperatively, the patient's blood pressure gradually improved, allowing initiation of the four major therapeutic drug classes, which were uptitrated to the maximum tolerated doses. With regular follow-up for 12 months, the patient showed dramatic improvements in exercise capacity and quality of life. More surprisingly, there was significant improvement in cardiac structural and functional remodeling. Echocardiography revealed that left atrioventricular dimensions returned to normal, left ventricular ejection fraction (LVEF) increased from 15% to 48%, and left ventricular global longitudinal strain (GLS) improved from −3.3% to −16.2%. NT-proBNP levels also decreased from 6,553 pg/ml to within the normal range.ConclusionThis case suggests that CCM may serve as a promising strategy to address the issue of poor GDMT tolerance due to hypotension, thereby facilitating GDMT optimization and improving cardiac remodeling patients with HFrEF

An Integrated Contraflow Strategy for Multimodal Evacuation

To improve the efficiency of multimodal evacuation, a network aggregation method and an integrated contraflow strategy are proposed in this paper. The network aggregation method indicates the uncertain evacuation demand on the arterial subnetwork and balances accuracy and efficiency by refining the local road subnetworks. The integrated contraflow strategy contains three arterial configurations: noncontraflow to shorten the strategy setup time, full-lane contraflow to maximize the evacuation network capacity, and bus contraflow to realize the transit cycle operation. The application of this strategy takes two steps to provide transit priority during evacuation: solve the transit-based evacuation problem with a minimum-cost flow model, firstly, and then address the auto-based evacuation problem with a bilevel network flow model. The numerical results from optimizing an evacuation network for a super typhoon justify the validness and usefulness of the network aggregation method and the integrated contraflow strategy

Improving Detection in Aerial Images by Capturing Inter-Object Relationships

In many image domains, the spatial distribution of objects in a scene exhibits meaningful patterns governed by their semantic relationships. In most modern detection pipelines, however, the detection proposals are processed independently, overlooking the underlying relationships between objects. In this work, we introduce a transformer-based approach to capture these inter-object relationships to refine classification and regression outcomes for detected objects. Building on two-stage detectors, we tokenize the region of interest (RoI) proposals to be processed by a transformer encoder. Specific spatial and geometric relations are incorporated into the attention weights and adaptively modulated and regularized. Experimental results demonstrate that the proposed method achieves consistent performance improvement on three benchmarks including DOTA-v1.0, DOTA-v1.5, and HRSC 2016, especially ranking first on both DOTA-v1.5 and HRSC 2016. Specifically, our new method has an increase of 1.59 mAP on DOTA-v1.0, 4.88 mAP on DOTA-v1.5, and 2.1 mAP on HRSC 2016, respectively, compared to the baselines

Towards Real World Debiasing: A Fine-grained Analysis On Spurious Correlation

Spurious correlations in training data significantly hinder the generalization capability of machine learning models when faced with distribution shifts in real-world scenarios. To tackle the problem, numerous debias approaches have been proposed and benchmarked on datasets intentionally designed with severe biases. However, it remains to be asked: \textit{1. Do existing benchmarks really capture biases in the real world? 2. Can existing debias methods handle biases in the real world?} To answer the questions, we revisit biased distributions in existing benchmarks and real-world datasets, and propose a fine-grained framework for analyzing dataset bias by disentangling it into the magnitude and prevalence of bias. We observe and theoretically demonstrate that existing benchmarks poorly represent real-world biases. We further introduce two novel biased distributions to bridge this gap, forming a nuanced evaluation framework for real-world debiasing. Building upon these results, we evaluate existing debias methods with our evaluation framework. Results show that existing methods are incapable of handling real-world biases. Through in-depth analysis, we propose a simple yet effective approach that can be easily applied to existing debias methods, named Debias in Destruction (DiD). Empirical results demonstrate the superiority of DiD, improving the performance of existing methods on all types of biases within the proposed evaluation framework.Comment: 9 pages of main paper, 10 pages of appendi

Improving Scene Graph Generation with Superpixel-Based Interaction Learning

Recent advances in Scene Graph Generation (SGG) typically model the relationships among entities utilizing box-level features from pre-defined detectors. We argue that an overlooked problem in SGG is the coarse-grained interactions between boxes, which inadequately capture contextual semantics for relationship modeling, practically limiting the development of the field. In this paper, we take the initiative to explore and propose a generic paradigm termed Superpixel-based Interaction Learning (SIL) to remedy coarse-grained interactions at the box level. It allows us to model fine-grained interactions at the superpixel level in SGG. Specifically, (i) we treat a scene as a set of points and cluster them into superpixels representing sub-regions of the scene. (ii) We explore intra-entity and cross-entity interactions among the superpixels to enrich fine-grained interactions between entities at an earlier stage. Extensive experiments on two challenging benchmarks (Visual Genome and Open Image V6) prove that our SIL enables fine-grained interaction at the superpixel level above previous box-level methods, and significantly outperforms previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing box-level approaches in a plug-and-play fashion. In particular, SIL brings an average improvement of 2.0% mR (even up to 3.4%) of baselines for the PredCls task on Visual Genome, which facilitates its integration into any existing box-level method

Feedback RoI Features Improve Aerial Object Detection

Neuroscience studies have shown that the human visual system utilizes high-level feedback information to guide lower-level perception, enabling adaptation to signals of different characteristics. In light of this, we propose Feedback multi-Level feature Extractor (Flex) to incorporate a similar mechanism for object detection. Flex refines feature selection based on image-wise and instance-level feedback information in response to image quality variation and classification uncertainty. Experimental results show that Flex offers consistent improvement to a range of existing SOTA methods on the challenging aerial object detection datasets including DOTA-v1.0, DOTA-v1.5, and HRSC2016. Although the design originates in aerial image detection, further experiments on MS COCO also reveal our module's efficacy in general detection models. Quantitative and qualitative analyses indicate that the improvements are closely related to image qualities, which match our motivation

Activated Circulating T Follicular Helper Cells Are Associated with Disease Severity in Patients with Psoriasis

Circulating T follicular helper (cTfh) cells are known to be involved in numerous immune-mediated diseases, but their pathological role in psoriasis is less fully investigated. Herein, we aimed to identify whether cTfh cells contributed to the pathogenesis of psoriasis. The frequency and function of cTfh cells were compared between patients with psoriasis vulgaris and healthy controls, and the infiltration of Tfh cells was detected between lesional and nonlesional skin tissues of psoriasis patients. Moreover, the dynamic change of cTfh cells before and after acitretin treatment was evaluated. Our results showed both increased frequency and activation (indicated by higher expression of ICOS, PD-1, HLA-DR, and Ki-67 and increased production of IL-21, IL-17, and IFN-γ) of cTfh cells in psoriasis patients. Compared with nonlesional skin tissues of psoriasis patients, the number of infiltrated Tfh cells was significantly increased in psoriasis lesions. In addition, positive correlations between the percentage of cTfh, functional markers on cTfh cells in peripheral blood and disease severity were noted. Furthermore, the frequency of cTfh cells and the levels of cytokines secreted by cTfh cells were all significantly decreased after 1-month treatment

Epidemiology, clinical outcomes, and treatment patterns of cytomegalovirus infection after allogeneic hematopoietic stem cell transplantation in China: a scoping review and meta-analysis

IntroductionCytomegalovirus (CMV) infection poses a significant threat to individuals undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT), potentially resulting in substantial morbidity and mortality. This review summarized the epidemiology, clinical outcomes, and treatment patterns of CMV infection among allo-HSCT recipients in China.MethodsPubMed, EMBASE, the Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang and Chinese Biomedical Literature Database (CBM) were systematically searched from 2013 to March 2023. All analyses were performed using R 4.1.1 software with a random effects model.ResultsFifty-six studies, which included 13,882 patients, were reviewed. The pooled overall incidence of CMV infection was 49.99% [95% confidence interval (CI) 43.72–56.26%]. Among post allo-HSCT recipients with CMV infection, 32.03% (95% CI 22.93–41.12%) developed refractory CMV infection. The overall incidence of CMV disease was 13.30% (95% CI 8.99–19.66%). The pooled all-cause mortality rate was 29.25% (95% CI 17.96–40.55%) and the CMV-related mortality rate was 3.46% (95% CI 1.19–5.73%). Results demonstrate that management of CMV has mainly focused on pre-emptive therapy due to the treatment-limiting toxicity of anti-CMV agents. Additionally, CMV infection is continuing to occur after the discontinuation of prophylaxis, highlighting the unmet need for a more effective treatment without treatment-limiting toxicities.ConclusionThis review underscores the urgent need for improved therapeutic strategies to effectively manage cytomegalovirus infection in allo-HSCT recipients, particularly in light of the high incidence and associated morbidity, as well as the limitations of current treatment options.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024513908, identifier: CRD42024513908

LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment

For human-centric large-scale scenes, fine-grained modeling for 3D human global pose and shape is significant for scene understanding and can benefit many real-world applications. In this paper, we present LiveHPS, a novel single-LiDAR-based approach for scene-level human pose and shape estimation without any limitation of light conditions and wearable devices. In particular, we design a distillation mechanism to mitigate the distribution-varying effect of LiDAR point clouds and exploit the temporal-spatial geometric and dynamic information existing in consecutive frames to solve the occlusion and noise disturbance. LiveHPS, with its efficient configuration and high-quality output, is well-suited for real-world applications. Moreover, we propose a huge human motion dataset, named FreeMotion, which is collected in various scenarios with diverse human poses, shapes and translations. It consists of multi-modal and multi-view acquisition data from calibrated and synchronized LiDARs, cameras, and IMUs. Extensive experiments on our new dataset and other public datasets demonstrate the SOTA performance and robustness of our approach. We will release our code and dataset soon.Comment: Accepted by CVPR 202