Background Subtraction for Night Videos

Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and reflected light is usually strong. To resolve these issues, we propose a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit, to extract the features of foreground objects. We propose a local pattern enhancement method. For the light detection unit, our method utilizes the finding that lighted areas in the evening usually have a low saturation in hue-saturation-value and hue-saturation-lightness color spaces. Finally, we update the background model and the foreground objects in the framework. This approach is able to improve foreground object detection in night videos, which do not need a large data set for pre-training

4-(4-Chloro­phen­yl)-5-(4-nitro­phen­yl)-3-phenyl-4H-1,2,4-triazole

The title compound, C20H13ClN4O2, was synthesized by the condensation of 4-nitro­benzohydrazide and N-(4-chlorophen­yl)­benzimidoyl chloride in N,N-dimethyl­acetamide. The asymmetric unit contains two independent mol­ecules. In one molecule, the triazole ring is oriented at dihedral angles of 23.1 (5), 85.4 (1) and 10.5 (1)° with respect to the phenyl, chlorophenyl and nitrophenyl rings, respectively. In the other molecule, the corresponding dihedral angles are 29.8 (9), 73.4 (7) and 16.4 (3)°

Data De-Duplication with Adaptive Chunking and Accelerated Modification Identifying

The data de-duplication system not only pursues the high de-duplication rate, which refers to the aggregate reduction in storage requirements gained from de-duplication, but also the de-duplication speed. To solve the problem of random parameter-setting brought by Content Defined Chunking (CDC), a self-adaptive data chunking algorithm is proposed. The algorithm improves the de-duplication rate by conducting pre-processing de-duplication to the samples of the classified files and then selecting the appropriate algorithm parameters. Meanwhile, FastCDC, a kind of content-based fast data chunking algorithm, is adopted to solve the problem of low de-duplication speed of CDC. By introducing de-duplication factor and acceleration factor, FastCDC can significantly boost de-duplication speed while not sacrificing the de-duplication rate through adjusting these two parameters. The experimental results demonstrate that our proposed method can improve the de-duplication rate by about 5 %, while FastCDC can obtain the increase of de-duplication speed by 50 % to 200 % only at the expense of less than 3 % de-duplication rate loss

Comparisons between simulated and in-situ measured speech intelligibility based on (binaural) room impulse responses

This study systematically compares acoustic simulation and in-situ measurement in terms of speech transmission index (STI), speech intelligibility scores and relationship curves when considering (binaural) room impulse response and four general room conditions, namely, an office, a laboratory, a multimedia lecture hall and a semi-anechoic chamber. The results reveal that STI can be predicted accurately by acoustic simulation (using room acoustics software ODEON) when there is a good agreement between the virtual models and the real rooms and that different reverberation time (RT) and signal-to-noise ratio (SNR) may exert less significant influence on the simulated STI. However, subjective intelligibility may be overestimated when using acoustic simulation due to the head-related transfer function (HRTF) filter used, and the score bias may be minimal and difficult to detect in everyday situations. There is no obvious score tendency caused by different RT, though with the decrease in the SNR, score bias may increase. Overall, considering that the accurate acoustic modelling of rooms is often problematic, it is difficult to obtain accurate speech intelligibility prediction results using a simulation technique, especially when the room has not yet been built

Elucidating the Host Interactome of EV-A71 2C Reveals Viral Dependency Factors

Viral protein 2C plays a critical role in EV-A71 replication. The discovery of 2C binding proteins will likely provide potential targets to treat EV-A71 infection. Here, we provide a global proteomic analysis of the human proteins that interact with the EV-A71 2C protein. TRIM4, exportin2, and ARFGAP1 were validated as 2C binding partners. Further functional studies revealed that TRIM4, exportin2, and ARFGAP1 were novel host dependency factors for EV-A71. Moreover, enteroviruses’ 2C family proteins interacted with exportin2 and ARFGAP1. In conclusion, our study provides a cellular interactome of the EV-A71 2C and identifies the proviral roles of TRIM4, exportin2, and ARFGAP1 in EV-A71 infection