DSD-MatchingNet:Deformable sparse-to-dense feature matching for learning accurate correspondences

Background Exploring correspondences across multiview images is the basis of various computer vision tasks.However,most existing methods have limited accuracy under challenging conditions.Method To learn more robust and accurate correspondences,we propose DSD-MatchingNet for local feature matching in this study.First,we develop a deformable feature extraction module to obtain multilevel feature maps,which harvest contextual information from dynamic receptive fields.The dynamic receptive fields provided by the deformable convolution network ensure that our method obtains dense and robust correspondence.Second,we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching,which enables our method to produce more accurate correspondences.Result Experiments show that our proposed DSD-MatchingNet achieves a better performance on the image matching benchmark,as well as on the visual localization benchmark.Specifically,our method achieved 91.3%mean matching accuracy on the HPatches dataset and 99.3%visual localization recalls on the Aachen Day-Night dataset.

Optimization of the Financial Strategy of Bilibili Barrage Website from the Perspective of Social Responsibility

This paper studies the modern corporate financial strategy from the perspective of corporate social responsibility performance,using the Internet video site Bilibili as an example to analyze,starting with Bilibili’s strategic environment,based on financing,investment,distribution,and social responsibility.This paper comprehensively analyzes its advantages and disadvantages from its financial strategy.Finally,from the perspective of social responsibility,from three aspects of investment,financing,and profit distribution,the authors made optimization suggestions for Bilibili’s financial strategy.

Microfluidic QCM enables ultrahigh Q-factor:a new paradigm for in-liquid gravimetric sensing

Acoustic gravimetric biosensors attract attention due to their simplicity,robustness,and low cost.However,a prevailing challenge in these sensors is dissipation which manifests in a low quality factor(Q-factor),which limits their sensitivity and accuracy.To mitigate dissipation of acoustic sensors in liquid environments we introduce an innovative approach in which we combine microfluidic channels with gravimetric sensors.To implement this novel paradigm we chose the quartz crystal microbalance(QCM)as our model system,owing to its wide applicability in biosensing and the relevance of its operating principles to other types of acoustic sensors.We postulate that the crucial determinant for enhancing performance lies in the ratio between the width of the microfluidic channels and the wavelength of the pressure wave generated by the oscillating channel side walls driven by the QCM.Our hypothesis is supported by finite element analysis(FEA)and dimensional studies,which revealed two key factors that affect device performance:(1)the ratio of the channel width to the pressure wavelength(W=λp)and(2)the ratio of the channel height to the shear evanescent wavelength(H=λs).To validate our hypothesis,we fabricated a microfluidic QCM(μ-QCM)and demonstrated a remarkable 10-fold improvement in its dissipation when compared to conventional QCM.The novel microfluidic approach offers several additional advantages,such as direct data interpretation,reduced volume requirement for sample liquids,and simplified temperature control,augmenting the sensor’s overall performance.By fostering increased sensitivity,accuracy,and ease of operation,our novel paradigm unlocks new possibilities for advancing gravimetric technologies,potentially for biosensing applications.

Host cell cycle checkpoint as antiviral target for SARS-CoV-2 revealed by integrative transcriptome and proteome analyses

Dear Editor,Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the causative agent of the COVID-19 pandemic,has posed severe threats to global public health,highlighting an urgent need to understand its pathogenesis and to develop antiviral therapies.Both DNA and RNA viruses can modulate cell cycle progression to maximize their replication.1 However,the effects of SARS-CoV-2 on cell cycle progression remains largely unknown.

Beta-adrenergic signaling on neuroendocrine differentiation, angiogenesis, and metastasis in prostate cancer progression

Prostate cancer is a complex, heterogeneous disease that mainly affects the older male population with a high-mortality rate. The mechanisms underlying prostate cancer progression are still incompletely understood. Beta-adrenergic signaling has been shown to regulate multiple cellular processes as a mediator of chronic stress. Recently, beta-adrenergic signaling has been reported to affect the development of aggressive prostate cancer by regulating neuroendocrine differentiation, angiogenesis, and metastasis. Here, we briefly summarize and discuss recent advances in these areas and their implications in prostate cancer therapeutics. We aim to provide a better understanding of the contribution of beta-adrenergic signaling to the progression of aggressive prostate cancer.