The impact of government ownership on performance: Evidence from major Chinese banks

Chinese banking system plays increasingly more important role in the word financial system and has attracted a lot of attention during recent years. The purpose of this paper is to study and analyze the relationship between government ownership and major Chinese banks’ performance.Our paper studies the sample data collected during the period between 2000 and 2011, and regression analysis is conducted for the purpose of examining how the government ownership change would impact the bank performance. As indicated by previous literature about bank performance, bank performance is often affected by bank size, capital ratio and net interest margin (NIM). Our results show that decreased government ownership can improve major Chinese banks’ performance

SHERF: Generalizable Human NeRF from a Single Image

Existing Human NeRF methods for reconstructing 3D humans typically rely on multiple 2D images from multi-view cameras or monocular videos captured from fixed camera views. However, in real-world scenarios, human images are often captured from random camera angles, presenting challenges for high-quality 3D human reconstruction. In this paper, we propose SHERF, the first generalizable Human NeRF model for recovering animatable 3D humans from a single input image. SHERF extracts and encodes 3D human representations in canonical space, enabling rendering and animation from free views and poses. To achieve high-fidelity novel view and pose synthesis, the encoded 3D human representations should capture both global appearance and local fine-grained textures. To this end, we propose a bank of 3D-aware hierarchical features, including global, point-level, and pixel-aligned features, to facilitate informative encoding. Global features enhance the information extracted from the single input image and complement the information missing from the partial 2D observation. Point-level features provide strong clues of 3D human structure, while pixel-aligned features preserve more fine-grained details. To effectively integrate the 3D-aware hierarchical feature bank, we design a feature fusion transformer. Extensive experiments on THuman, RenderPeople, ZJU_MoCap, and HuMMan datasets demonstrate that SHERF achieves state-of-the-art performance, with better generalizability for novel view and pose synthesis.Comment: Accepted by ICCV2023. Project webpage: https://skhu101.github.io/SHERF

Towards a Learner-Centered Explainable AI: Lessons from the learning sciences

In this short paper, we argue for a refocusing of XAI around human learning goals. Drawing upon approaches and theories from the learning sciences, we propose a framework for the learner-centered design and evaluation of XAI systems. We illustrate our framework through an ongoing case study in the context of AI-augmented social work.Comment: 7 pages, 2 figure

Identification and validation of SERPINE1 as a prognostic and immunological biomarker in pan-cancer and in ccRCC

Background:SERPINE1, a serine protease inhibitor involved in the regulation of the plasminogen activation system, was recently identified as a cancer-related gene. However, its clinical significance and potential mechanisms in pan-cancer remain obscure.Methods: In pan-cancer multi-omics data from public datasets, including The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), and online web tools were used to analyze the expression of SERPINE1 in different cancers and its correlation with prognosis, genetic alteration, DNA promoter methylation, biological processes, immunoregulator expression levels, immune cell infiltration into tumor, tumor mutation burden (TMB), microsatellite instability (MSI), immunotherapy response and drug sensitivity. Further, two single-cell databases, Tumor Immune Single-cell Hub 2 (TISCH2) and CancerSEA, were used to explore the expression and potential roles of SERPINE1 at a single-cell level. The aberrant expression of SERPINE1 was further verified in clear cell renal cell carcinoma (ccRCC) through qRT-PCR of clinical patient samples, validation in independent cohorts using The Gene Expression Omnibus (GEO) database, and proteomic validation using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database.Results: The expression of SERPINE1 was dysregulated in cancers and enriched in endothelial cells and fibroblasts. Copy number amplification and low DNA promoter methylation could be partly responsible for high SERPINE1 expression. High SERPINE1 expression was associated with poor prognosis in 21 cancers. The results of gene set enrichment analysis (GSEA) indicated SERPINE1 involvement in the immune response and tumor malignancy. SERPINE1 expression was also associated with the expression of several immunoregulators and immune cell infiltration and could play an immunosuppression role. Besides, SERPINE1 was found to be related with TMB, MSI, immunotherapy response and sensitivity to several drugs in cancers. Finally, the high expression of SERPINE1 in ccRCC was verified using qRT-PCR performed on patient samples, six independent GEO cohorts, and proteomic data from the CPTAC database.Conclusion: The findings of the present study revealed that SERPINE1 exhibits aberrant expression in various types of cancers and is associated with cancer immunity and tumor malignancy, providing novel insights for individualized cancer treatment

The prognostic impact of severe grade immune checkpoint inhibitor related pneumonitis in non-small cell lung cancer patients

ObjectiveTo compare the prognostic differences between non-small cell lung cancer (NSCLC) patients with mild and severe checkpoint inhibitor-associated pneumonitis (CIP), and explore the causes of death and prognostic risk factors in NSCLC patients with severe CIP.MethodsA retrospective study of a cohort of 116 patients with unresectable stage III or IV NSCLC with any grade CIP from April 2016 to August 2022 were conducted. To analyze the clinical characteristics of patients with different CIP grades, patients were divided into mild CIP group (grade 1-2, n=49) and severe CIP group (grade 3-5, n=67) according to the grade of CIP. To explore the OS-related risk factors in the severe CIP group, the patients were divided into a good prognosis (GP) group (≥ median OS, n=30) and a poor prognosis (PP) group (< median OS, n=37) based on whether their overall survival (OS) were greater than median OS. Baseline clinical and laboratory data were collected for analysis.ResultsThe median OS of all NSCLC patients combined with CIP was 11.4 months (95%CI, 8.070–16.100), The median OS for mild CIP and severe CIP was 22.1 months and 4.4 months respectively (HR=3.076, 95%CI, 1.904-4.970, P<0.0001). The results showed that the most common cause of death among severe CIP patients in the PP group was CIP and the most common cause in the GP group was tumor. The univariate regression analysis showed that suspension of antitumor therapy was a risk factor for poor prognosis (OR=3.598, 95%CI, 1.307-9.905, p=0.013). The multivariate logistic regression analysis showed that suspension of anti-tumor therapy (OR=4.24, 95%CI, 1.067-16.915, p=0.040) and elevated KL-6 (OR=1.002, 95%CI, 1.001-1.002, p<0.001) were independent risk factors for poor prognosis.ConclusionIn conclusion, patients with severe CIP had a poor prognosis, especially those with elevated KL-6, and the main cause of death is immune checkpoint inhibitor-associated pneumonitis complicated with infection. In addition, anti-tumor therapy for severe CIP patients should be resumed in time and should not be delayed for too long

Asymptotic properties of maximum likelihood estimators for determinantal point processes

We obtain the almost sure consistency and the Berry-Esseen type bound of the maximum likelihood estimator for determinantal point processes (DPPs), completing and extending previous work initiated in Brunel, Moitra, Rigollet, and Urschel [BMRU17]. We also give explicit formula and a detailed discussion for the maximum likelihood estimator for blocked determinantal matrix of two by two submatrices and compare it with the frequency method