Real-Time Illegal Parking Detection System Based on Deep Learning

The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this method is weakly robust and sensitive to environment. Benefitting from deep learning, this paper proposes a novel illegal vehicle parking detection system. Illegal vehicles captured by camera are firstly located and classified by the famous Single Shot MultiBox Detector (SSD) algorithm. To improve the performance, we propose to optimize SSD by adjusting the aspect ratio of default box to accommodate with our dataset better. After that, a tracking and analysis of movement is adopted to judge the illegal vehicles in the region of interest (ROI). Experiments show that the system can achieve a 99% accuracy and real-time (25FPS) detection with strong robustness in complex environments.Comment: 5pages,6figure

HSP90 inhibitors stimulate DNAJB4 protein expression through a mechanism involving N6-methyladenosine.

Small-molecule inhibitors for the 90-kDa heat shock protein (HSP90) have been extensively exploited in preclinical studies for the therapeutic interventions of human diseases accompanied with proteotoxic stress. By using an unbiased quantitative proteomic method, we uncover that treatment with three HSP90 inhibitors results in elevated expression of a large number of heat shock proteins. We also demonstrate that the HSP90 inhibitor-mediated increase in expression of DNAJB4 protein occurs partly through an epitranscriptomic mechanism, and is substantially modulated by the writer, eraser, and reader proteins of N6-methyladenosine (m6A). Furthermore, exposure to ganetespib leads to elevated modification levels at m6A motif sites in the 5'-UTR of DNAJB4 mRNA, and the methylation at adenosine 114 site in the 5'-UTR promotes the translation of the reporter gene mRNA. This m6A-mediated mechanism is also at play upon heat shock treatment. Cumulatively, we unveil that HSP90 inhibitors stimulate the translation of DNAJB4 through an epitranscriptomic mechanism

Ultrasound-modulated optical tomography for thick tissue imaging

Continuous-wave ultrasonic modulation of scattered laser light has been used to image objects in tissue-simulating turbid media for the first time. We hypothesize that the ultrasound wave focused into the turbid media modulates the laser light passing through the ultrasonic focal spot. The modulated laser light collected by a photomultiplier tube reflects the local mechanical and optical properties in the focal zone. Buried objects in 5-cm thick tissue phantoms are located with millimeter resolution by scanning and detecting alterations of the ultrasound-modulated optical signal. Ultrasound-modulated optical tomography separates the conflict between signal and resolution in purely optical imaging of tissue and does not rely on ballistic or quasi-ballistic photons but on the abundant diffuse photons. The imaging resolution is determined by the focused ultrasonic wave. This technique has the potential to provide a noninvasive, nonionizing, inexpensive diagnostic tool for diseases such as breast cancer

Computation of the optical properties of tissues from light reflectance using a neural network

We have established a neural network to quickly deduce optical properties of tissue slabs from the diffuse reflectance distribution. Diffusion theory based on multiple image sources mirrored about the two extrapolated boundaries is used to prepare the training and testing sets for the neural network. The neural network is trained using backpropagation with the conjugate gradient method. Once the neural network is trained, it is able to deduce optical properties of tissues within on the order of a millisecond. The range of the tissue optical properties that is covered by our neural network is 0.01 - 2 cm^(-1) for absorption coefficient, 5 - 25 cm^(-1) for reduced scattering coefficient, and 0.001 - 1 cm for tissue thickness. A separate network is also trained for thick tissue slabs. A simple experimental setup applying the trained neural network is designed to measure tissue optical properties quickly

Ultrasound-modulated optical tomography for dense turbid media

Continuous-wave ultrasonic modulation of scattered laser light has been used to image objects in tissue-simulating turbid media for the first time. We hypothesized that the ultrasound wave focused into the turbid media modulates the laser light passing through the ultrasonic focal zone. The modulated laser light collected by a photomultiplier tube reflects the local mechanical and optical properties in the focal zone. Buried objects in 5-cm thick tissue phantoms (absorption coefficient µ_a = 0.1 cm^(-1), reduced scattering coefficient µ_s' = 10 cm^(-1)) were located with millimeter resolution by scanning and detecting alterations of the ultrasound-modulated optical signal

Continuous-wave ultrasonic modulation of scattered laser light to image objects in turbid media

Continuous-wave ultrasonic modulation of scattered laser light has been used to image objects in tissue-simulating turbid media for what is to our knowledge the first time. The ultrasound wave focused into the turbid media modulates the laser light passing through the ultrasonic focal zone. The modulated laser light collected by a photomultiplier tube reflects the local mechanical and optical properties in the focal zone. Buried objects are located with millimeter resolution by scanning and detecting alterations of the modulated optical signal. This technique has the potential to provide a noninvasive, nonionizing, inexpensive diagnostic tool for diseases such as breast cancer

SUVH1, a Su(var)3-9 family member, promotes the expression of genes targeted by DNA methylation.

Transposable elements are found throughout the genomes of all organisms. Repressive marks such as DNA methylation and histone H3 lysine 9 (H3K9) methylation silence these elements and maintain genome integrity. However, how silencing mechanisms are themselves regulated to avoid the silencing of genes remains unclear. Here, an anti-silencing factor was identified using a forward genetic screen on a reporter line that harbors a LUCIFERASE (LUC) gene driven by a promoter that undergoes DNA methylation. SUVH1, a Su(var)3-9 homolog, was identified as a factor promoting the expression of the LUC gene. Treatment with a cytosine methylation inhibitor completely suppressed the LUC expression defects of suvh1, indicating that SUVH1 is dispensable for LUC expression in the absence of DNA methylation. SUVH1 also promotes the expression of several endogenous genes with promoter DNA methylation. However, the suvh1 mutation did not alter DNA methylation levels at the LUC transgene or on a genome-wide scale; thus, SUVH1 functions downstream of DNA methylation. Histone H3 lysine 4 (H3K4) trimethylation was reduced in suvh1; in contrast, H3K9 methylation levels remained unchanged. This work has uncovered a novel, anti-silencing function for a member of the Su(var)3-9 family that has previously been associated with silencing through H3K9 methylation

Split private and shared L2 cache architecture for snooping-based CMP

California, US

MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting

Time series forecasting has received wide interest from existing research due to its broad applications and inherent challenging. The research challenge lies in identifying effective patterns in historical series and applying them to future forecasting. Advanced models based on point-wise connected MLP and Transformer architectures have strong fitting power, but their secondary computational complexity limits practicality. Additionally, those structures inherently disrupt the temporal order, reducing the information utilization and making the forecasting process uninterpretable. To solve these problems, this paper proposes a forecasting model, MPR-Net. It first adaptively decomposes multi-scale historical series patterns using convolution operation, then constructs a pattern extension forecasting method based on the prior knowledge of pattern reproduction, and finally reconstructs future patterns into future series using deconvolution operation. By leveraging the temporal dependencies present in the time series, MPR-Net not only achieves linear time complexity, but also makes the forecasting process interpretable. By carrying out sufficient experiments on more than ten real data sets of both short and long term forecasting tasks, MPR-Net achieves the state of the art forecasting performance, as well as good generalization and robustness performance