Some Controversial and Important Issues about Shadow Banking Research

After the outbreak of the international financial crisis in 2008, the concept of shadow banking was first put forward by the financial circles in the United States. In the past ten years, the development of the shadow banking has been a great deal of researches and great achievements made in the academia and the industry. However, there are still some problems that have not been effectively solved or disputed. This paper extracts the periodicity of the CIS and converse of shadow bank, the influence of the shadow banking on the effectiveness of monetary policy, the portrayed “channel identification” of the shadow banking to the monetary policy response, and the discrimination of the influence of the shadow banking on the house price, and through the combing of the related contents. Reflection and re-study, in order to provide a valuable reference for the relevant researchers

Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation

Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc. Despite the demonstrated success in intra-domain face forgery detection, existing detection methods lack generalization capability and tend to suffer from dramatic performance drops when deployed to unforeseen domains. To mitigate this issue, this paper designs a more general fake face detection model based on the vision transformer(ViT) architecture. In the training phase, the pretrained ViT weights are freezed, and only the Low-Rank Adaptation(LoRA) modules are updated. Additionally, the Single Center Loss(SCL) is applied to supervise the training process, further improving the generalization capability of the model. The proposed method achieves state-of-the-arts detection performances in both cross-manipulation and cross-dataset evaluations

Simulation of memristive crossbar arrays for seizure detection and prediction using parallel Convolutional Neural Networks [Formula presented]

For epileptic seizure detection and prediction, to address the computational bottleneck of the von Neumann architecture, we develop an in-memory memristive crossbar-based accelerator simulator. The simulator software is composed of a Python-based neural network training component and a MATLAB-based memristive crossbar array component. The software provides a baseline network for developing deep learning-based signal processing tasks, as well as a platform to investigate the impact of weight mapping schemes and device and peripheral circuitry non-idealities

S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens

Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition system by presenting spoofed faces. State-of-the-art FAS techniques predominantly rely on deep learning models but their cross-domain generalization capabilities are often hindered by the domain shift problem, which arises due to different distributions between training and testing data. In this study, we develop a generalized FAS method under the Efficient Parameter Transfer Learning (EPTL) paradigm, where we adapt the pre-trained Vision Transformer models for the FAS task. During training, the adapter modules are inserted into the pre-trained ViT model, and the adapters are updated while other pre-trained parameters remain fixed. We find the limitations of previous vanilla adapters in that they are based on linear layers, which lack a spoofing-aware inductive bias and thus restrict the cross-domain generalization. To address this limitation and achieve cross-domain generalized FAS, we propose a novel Statistical Adapter (S-Adapter) that gathers local discriminative and statistical information from localized token histograms. To further improve the generalization of the statistical tokens, we propose a novel Token Style Regularization (TSR), which aims to reduce domain style variance by regularizing Gram matrices extracted from tokens across different domains. Our experimental results demonstrate that our proposed S-Adapter and TSR provide significant benefits in both zero-shot and few-shot cross-domain testing, outperforming state-of-the-art methods on several benchmark tests. We will release the source code upon acceptance

Blocking interaction between SHP2 and PD‐1 denotes a novel opportunity for developing PD‐1 inhibitors

Small molecular PD‐1 inhibitors are lacking in current immuno‐oncology clinic. PD‐1/PD‐L1 antibody inhibitors currently approved for clinical usage block interaction between PD‐L1 and PD‐1 to enhance cytotoxicity of CD8+ cytotoxic T lymphocyte (CTL). Whether other steps along the PD‐1 signaling pathway can be targeted remains to be determined. Here, we report that methylene blue (MB), an FDA‐approved chemical for treating methemoglobinemia, potently inhibits PD‐1 signaling. MB enhances the cytotoxicity, activation, cell proliferation, and cytokine‐secreting activity of CTL inhibited by PD‐1. Mechanistically, MB blocks interaction between Y248‐phosphorylated immunoreceptor tyrosine‐based switch motif (ITSM) of human PD‐1 and SHP2. MB enables activated CTL to shrink PD‐L1 expressing tumor allografts and autochthonous lung cancers in a transgenic mouse model. MB also effectively counteracts the PD‐1 signaling on human T cells isolated from peripheral blood of healthy donors. Thus, we identify an FDA‐approved chemical capable of potently inhibiting the function of PD‐1. Equally important, our work sheds light on a novel strategy to develop inhibitors targeting PD‐1 signaling axis

Vancomycin associated acute kidney injury in patients with infectious endocarditis: a large retrospective cohort study

Background: Vancomycin remains the cornerstone antibiotic for the treatment of infective endocarditis (IE). Vancomycin has been associated with significant nephrotoxicity. However, vancomycin associated acute kidney injury (AKI) has not been evaluated in patients with IE. We conducted this large retrospective cohort study to reveal the incidence, risk factors, and prognosis of vancomycin-associated acute kidney injury (VA-AKI) in patients with IE.Methods: Adult patients diagnosed with IE and receiving vancomycin were included. The primary outcome was VA-AKI.Results: In total, 435 of the 600 patients were enrolled. Of these, 73.6% were male, and the median age was 52 years. The incidence of VA-AKI was 17.01% (74). Only 37.2% (162) of the patients received therapeutic monitoring of vancomycin, and 30 (18.5%) patients had reached the target vancomycin trough concentration. Multiple logistic regression analysis revealed that body mass index [odds ratio (OR) 1.088, 95% CI 1.004, 1.179], duration of vancomycin therapy (OR 1.030, 95% CI 1.003, 1.058), preexisting chronic kidney disease (OR 2.291, 95% CI 1.018, 5.516), admission to the intensive care unit (OR 2.291, 95% CI 1.289, 3.963) and concomitant radiocontrast agents (OR 2.085, 95% CI 1.093, 3.978) were independent risk factors for VA-AKI. Vancomycin variety (Lai Kexin vs. Wen Kexin, OR 0.498, 95% CI 0.281, 0.885) were determined to be an independent protective factor for VI-AKI. Receiver operator characteristic curve analysis revealed that duration of therapy longer than 10.75 days was associated with a significantly increased risk of VA-AKI (HR 1.927). Kidney function was fully or partially recovered in 73.0% (54) of patients with VA-AKI.Conclusion: The incidence of VA-AKI in patients with IE was slightly higher than in general adult patients. Concomitant contrast agents were the most alarmingly nephrotoxic in patients with IE, adding a 2-fold risk of VA-AKI. In patients with IE, a course of vancomycin therapy longer than 10.75 days was associated with a significantly increased risk of AKI. Thus, closer monitoring of kidney function and vancomycin trough concentrations was recommended in patients with concurrent contrast or courses of vancomycin longer than 10.75 days