The experimental realization of high-fidelity `shortcut-to-adiabaticity' quantum gates in a superconducting Xmon qubit

Based on a `shortcut-to-adiabaticity' (STA) scheme, we theoretically design and experimentally realize a set of high-fidelity single-qubit quantum gates in a superconducting Xmon qubit system. Through a precise microwave control, the qubit is driven to follow a fast `adiabatic' trajectory with the assistance of a counter-diabatic field and the correction of derivative removal by adiabatic gates. The experimental measurements of quantum process tomography and interleaved randomized benchmarking show that the process fidelities of our STA quantum gates are higher than 94.9% and the gate fidelities are higher than 99.8%, very close to the state-of-art gate fidelity of 99.9%. An alternate of high-fidelity quantum gates is successfully achieved under the STA protocol.Comment: 18 pages, 6 figure

End-to-End Single Image Fog Removal using Enhanced Cycle Consistent Adversarial Networks

Single image defogging is a classical and challenging problem in computer vision. Existing methods towards this problem mainly include handcrafted priors based methods that rely on the use of the atmospheric degradation model and learning based approaches that require paired fog-fogfree training example images. In practice, however, prior-based methods are prone to failure due to their own limitations and paired training data are extremely difficult to acquire. Inspired by the principle of CycleGAN network, we have developed an end-to-end learning system that uses unpaired fog and fogfree training images, adversarial discriminators and cycle consistency losses to automatically construct a fog removal system. Similar to CycleGAN, our system has two transformation paths; one maps fog images to a fogfree image domain and the other maps fogfree images to a fog image domain. Instead of one stage mapping, our system uses a two stage mapping strategy in each transformation path to enhance the effectiveness of fog removal. Furthermore, we make explicit use of prior knowledge in the networks by embedding the atmospheric degradation principle and a sky prior for mapping fogfree images to the fog images domain. In addition, we also contribute the first real world nature fog-fogfree image dataset for defogging research. Our multiple real fog images dataset (MRFID) contains images of 200 natural outdoor scenes. For each scene, there are one clear image and corresponding four foggy images of different fog densities manually selected from a sequence of images taken by a fixed camera over the course of one year. Qualitative and quantitative comparison against several state-of-the-art methods on both synthetic and real world images demonstrate that our approach is effective and performs favorably for recovering a clear image from a foggy image.Comment: Submitted to IEEE Transactions on Image Processin

Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers

Background: Colour is the most important feature used in quantitative immunohisto- chemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to con rm malignancy. Methods: Statistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images. Model was rst trained by massive colour pixels collected semi-automatically. To speed up the training and detection processes, we removed luminance channel, Y channel of YCbCr colour space and chose 128 histogram bins which is the optimal number. A maximum likelihood classi- er is used to classify pixels in digital slides into positively or negatively stained pixels automatically. The model-based tool was developed within ImageJ to quantify targets identi ed using IHC and histochemistry. Results: The purpose of evaluation was to compare the computer model with human evaluation. Several large datasets were prepared and obtained from human oesopha- geal cancer, colon cancer and liver cirrhosis with di erent colour stains. Experimental results have demonstrated the model-based tool achieves more accurate results than colour deconvolution and CMYK model in the detection of brown colour, and is comparable to colour deconvolution in the detection of pink colour. We have also demostrated the proposed model has little inter-dataset variations. Conclusions: A robust and e ective statistical model is introduced in this paper. The model-based interactive tool in ImageJ, which can create a visual representation of the statistical model and detect a speci ed colour automatically, is easy to use and avail- able freely at http://rsb.info.nih.gov/ij/plugins/ihc-toolbox/index.html. Testing to the tool by di erent users showed only minor inter-observer variations in results

ICP-AES analysis of inorganic elements in Carthami Flos from various geographic locations

Using microwave digestion and inductively coupled plasma atomic emission spectroscopy (ICP-AES), 28 inorganic elements were analyzed in 40 batches of safflower (Carthamus tinctorius Linn.) samples, which are the dried tubular flowers, from Anhui, Gansu, Jiangsu, Xinjiang, and Yunnan. The results revealed the following: Among the heavy metals, copper (Cu) had the highest concentration across all 40 batches of safflower samples, with mercury (Hg) and lead (Pb) also being relatively high. Safflower samples from Yunnan had significantly higher Hg levels compared to those from other provinces. The levels of Pb and Cu among samples from different provinces were generally not significantly different, while arsenic (As) and cadmium (Cd) levels were either low or undetectable. Among the major elements, potassium (K) had the highest concentration, phosphorus (P) and magnesium (Mg) were lower, and sodium (Na) had the lowest concentration. Safflower samples from Gansu had significantly higher Na and Mg concentrations, and samples from Yunnan and Xinjiang had higher P levels compared to those from other provinces. Safflower samples from Yunnan had significantly lower K levels compared to samples from other provinces, and there were no significant differences in K levels among samples from the other provinces. In the case of essential trace elements, iron (Fe) and boron (B) were relatively high, while nickel (Ni) was the lowest. Safflower samples from Xinjiang and Gansu had higher chromium (Cr) levels, and samples from Gansu had the highest levels of manganese (Mn), Fe, Ni, and strontium (Sr), while samples from Anhui had the highest levels of Zn and B. Conversely, samples from Jiangsu had the lowest levels of Cr, Mn, Fe, and Ni; Gansu samples had the lowest Zn levels; and Yunnan samples had the lowest Sr and B levels. Principal component analysis and cluster analysis showed that safflower samples from the same province tended to cluster together. Specifically, samples from Anhui and Jiangsu clustered closely, samples from Gansu and Xinjiang were relatively close, and samples from Yunnan were more distanced from those of other provinces. This study established a rapid and accurate method for determining the inorganic element content in safflower, and the results indicated notable differences in inorganic element content among safflower samples from different origins