Magneto-Optical Stern-Gerlach Effect in Atomic Ensemble

We study the birefringence of the quantized polarized light in a magneto-optically manipulated atomic ensemble as a generalized Stern-Gerlach Effect of light. To explain this engineered birefringence microscopically, we derive an effective Shr\"odinger equation for the spatial motion of two orthogonally polarized components, which behave as a spin with an effective magnetic moment leading to a Stern-Gerlach split in an nonuniform magnetic field. We show that electromagnetic induced transparency (EIT) mechanism can enhance the magneto-optical Stern-Gerlach effect of light in the presence of a control field with a transverse spatial profile and a inhomogeneous magnetic field.Comment: 7 pages, 5 figure

Ultra-intensified intermittent-perfusion fed-batch (UIIPFB) process quadrupled productivity of a bispecific antibody

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High hepatitis B virus load is associated with hepatocellular carcinomas development in Chinese chronic hepatitis B patients: a case control study

<p>Abstract</p> <p>Background</p> <p>Persistent hepatitis B virus (HBV) infection is a risk factor for hepatocellular carcinoma (HCC) development. This study aimed to clarify whether the high HBV DNA level is associated with HCC development by comparing HBV DNA levels between HBV infected patients with and without HCC.</p> <p>Results</p> <p>There were 78 male and 12 female patients in each group and there was no statistical difference between these two group patients' average ages. The HBV DNA level in the HCC patients was 4.73 ± 1.71 Log₁₀IU/ml while 3.90 ± 2.01 Log₁₀IU/ml in non-HCC patients (<it>P </it>< 0.01). The HBeAg positive rate was 42.2% (38/90) in the HCC group while 13.3% (12/90) in the non-HCC group (<it>P </it>< 0.001). Compared with patients with HBV DNA level of < 3 Log₁₀IU/ml, the patients with level of 3 to < 4, 4 to < 5, 5 to < 6, or ≥ 6 Log₁₀IU/ml had the odds ratio for HCC of 1.380 (95% CI, 0.544-3.499), 3.671 (95% CI, 1.363-9.886), 5.303 (95% CI, 1.847-15.277) or 3.030 (95% CI, 1.143-8.036), respectively.</p> <p>Conclusions</p> <p>HBV-related HCC patients had higher HBV DNA level than non-HCC counterparts. Our findings imply that active HBV replication is associated with the HCC development.</p

Demystifying Privacy Policy of Third-Party Libraries in Mobile Apps

The privacy of personal information has received significant attention in mobile software. Although previous researchers have designed some methods to identify the conflict between app behavior and privacy policies, little is known about investigating regulation requirements for third-party libraries (TPLs). The regulators enacted multiple regulations to regulate the usage of personal information for TPLs (e.g., the "California Consumer Privacy Act" requires businesses clearly notify consumers if they share consumers' data with third parties or not). However, it remains challenging to analyze the legality of TPLs due to three reasons: 1) TPLs are mainly published on public repositoriesinstead of app market (e.g., Google play). The public repositories do not perform privacy compliance analysis for each TPL. 2) TPLs only provide independent functions or function sequences. They cannot run independently, which limits the application of performing dynamic analysis. 3) Since not all the functions of TPLs are related to user privacy, we must locate the functions of TPLs that access/process personal information before performing privacy compliance analysis. To overcome the above challenges, in this paper, we propose an automated system named ATPChecker to analyze whether the Android TPLs meet privacy-related regulations or not. Our findings remind developers to be mindful of TPL usage when developing apps or writing privacy policies to avoid violating regulation

Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network

Accurate traffic forecasting at intersections governed by intelligent traffic signals is critical for the advancement of an effective intelligent traffic signal control system. However, due to the irregular traffic time series produced by intelligent intersections, the traffic forecasting task becomes much more intractable and imposes three major new challenges: 1) asynchronous spatial dependency, 2) irregular temporal dependency among traffic data, and 3) variable-length sequence to be predicted, which severely impede the performance of current traffic forecasting methods. To this end, we propose an Asynchronous Spatio-tEmporal graph convolutional nEtwoRk (ASeer) to predict the traffic states of the lanes entering intelligent intersections in a future time window. Specifically, by linking lanes via a traffic diffusion graph, we first propose an Asynchronous Graph Diffusion Network to model the asynchronous spatial dependency between the time-misaligned traffic state measurements of lanes. After that, to capture the temporal dependency within irregular traffic state sequence, a learnable personalized time encoding is devised to embed the continuous time for each lane. Then we propose a Transformable Time-aware Convolution Network that learns meta-filters to derive time-aware convolution filters with transformable filter sizes for efficient temporal convolution on the irregular sequence. Furthermore, a Semi-Autoregressive Prediction Network consisting of a state evolution unit and a semiautoregressive predictor is designed to effectively and efficiently predict variable-length traffic state sequences. Extensive experiments on two real-world datasets demonstrate the effectiveness of ASeer in six metrics