Tuning of the flat band and its impact on superconductivity in Mo5Si3-xPx.

The superconductivity in systems containing dispersionless (flat) bands is seemingly paradoxical, as traditional Bardeen-Cooper-Schrieffer theory requires an infinite enhancement of the carrier masses. However, the combination of flat and steep (dispersive) bands within the multiple band scenario might boost superconducting responses, potentially explaining high-temperature superconductivity in cuprates and metal hydrides. Here, we report on the magnetic penetration depths, the upper critical field, and the specific heat measurements, together with the first-principles calculations for the Mo5Si3-xPx superconducting family. The band structure features a flat band that gradually approaches the Fermi level as a function of phosphorus doping x, reaching the Fermi level at x ≃ 1.3. This leads to an abrupt change in nearly all superconducting quantities. The superfluid density data placed on the 'Uemura plot' results in two separated branches, thus indicating that the emergence of a flat band enhances correlations between conducting electrons.

Superconductivity in Mo4Ga20As with endohedral gallium clusters.

We report the discovery and detailed investigation of superconductivity in Mo4Ga20As. Mo4Ga20As crystallizes in a space group ofI4/m(No. 87), with the lattice parametersa= 12.86352 Å andc= 5.30031 Å. The resistivity, magnetization, and specific heat data reveal Mo4Ga20As to be a type-II superconductor withTc= 5.6 K. The upper and lower critical fields are estimated to be 2.78 T and 22.0 mT, respectively. In addition, electron-phonon coupling in Mo4Ga20As is possibly stronger than the BCS weak-coupling limit. First-principles calculations suggest the Fermi level being dominated by the Mo-4dand Ga-4porbitals.

Robust Estimation of Upscaling Factor on Double JPEG Compressed Images.

As one of the most important topics in image forensics, resampling detection has developed rapidly in recent years. However, the robustness to JPEG compression is still challenging for most classical spectrum-based methods, since JPEG compression severely degrades the image contents and introduces block artifacts in the boundary of the compression grid. In this article, we propose a method to estimate the upscaling factors on double JPEG compressed images in the presence of image upscaling between the two compressions. We first analyze the spectrum of scaled images and give an overall formulation of how the scaling factors along with the parameters of JPEG compression and image contents influence the appearance of tampering artifacts. The expected positions of five kinds of characteristic peaks are analytically derived. Then, we analyze the features of double JPEG compressed images in the block discrete cosine transform (BDCT) domain and present an inverse scaling strategy for the upscaling factor estimation with a detailed proof. Finally, a fusion method is proposed that through frequency-domain analysis, a candidate set of upscaling factors is given, and through analysis in the BDCT domain, the optimal estimation from all candidates is determined. The experimental results demonstrate that the proposed method outperforms other state-of-the-art methods.

Color image splicing localization algorithm by quaternion fully convolutional networks and superpixel-enhanced pairwise conditional random field.

Recently, fully convolutional network (FCN) has been successfully used to locate spliced regions in synthesized images. However, all the existing FCN-based algorithms use real-valued FCN to process each channel separately. As a consequence, they fail to capture the inherent correlation between color channels and the integrity of three channels. So, in this paper, quaternion fully convolutional network (QFCN) is proposed to generalize FCN to quaternion domain by replacing real-valued conventional blocks in FCN with quaternion conventional blocks. In addition, a new color image splicing localization algorithm is proposed by combining QFCNs and superpixel (SP)-enhanced pairwise conditional random field (CRF). QFCNs consider three different versions (QFCN32, QFCN16, and QFCN8) with different up-sampling layers. The SP-enhanced pairwise CRF is used to refine the results of QFCNs. Experimental results on three publicly available datasets demonstrate that the proposed algorithm outperforms the existing algorithms including some conventional algorithms and some deep learning-based algorithms.

Transportation Spherical Watermarking.

During the past twenty years, there has been a great interest in the study of spread spectrum (SS) watermarking. However, it is still a challenging task to design a secure and robust SS watermarking method. In this paper, we first define a family of secure SS watermarking methods, named as spherical watermarking (SW). The watermarked correlation of SW is defined to be uniformly distributed on a spherical surface, and this makes SW be key-secure against the watermarked-only attack. Then, we propose an implementation of SW, called transportation SW (TSW), which is designed to decrease embedding distortion in a recursive manner using the transportation theory, meanwhile keeping the security of SW. Moreover, we present a theoretical analysis of the embedding distortion and robustness of the proposed method. Finally, extensive experiments are conducted on simulated signals and real images. The experimental results show that TSW is more robust than existing secure SS watermarking methods.

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning.

Computer-generated graphics (CGs) are images generated by computer software. The rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images—CGs and NIs—are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.

A Study on the Security Levels of Spread-Spectrum Embedding Schemes in the WOA Framework.

Security analysis is a very important issue for digital watermarking. Several years ago, according to Kerckhoffs' principle, the famous four security levels, namely insecurity, key security, subspace security, and stego-security, were defined for spread-spectrum (SS) embedding schemes in the framework of watermarked-only attack. However, up to now there has been little application of the definition of these security levels to the theoretical analysis of the security of SS embedding schemes, due to the difficulty of the theoretical analysis. In this paper, based on the security definition, we present a theoretical analysis to evaluate the security levels of five typical SS embedding schemes, which are the classical SS, the improved SS (ISS), the circular extension of ISS, the nonrobust and robust natural watermarking, respectively. The theoretical analysis of these typical SS schemes are successfully performed by taking advantage of the convolution of probability distributions to derive the probabilistic models of watermarked signals. Moreover, simulations are conducted to illustrate and validate our theoretical analysis. We believe that the theoretical and practical analysis presented in this paper can bridge the gap between the definition of the four security levels and its application to the theoretical analysis of SS embedding schemes.

Impact of severe left ventricular dysfunction on in-hospital and mid-term outcomes of Chinese patients undergoing first isolated off-pump coronary artery bypass grafting.

BACKGROUND: Few studies focused on evaluating the impacts of preoperative severe left ventricular dysfunction on clinical outcomes of patients undergoing off-pump coronary artery bypass grafting surgery (OPCAB). This single center retrospective study aimed to evaluate the impacts of severe left ventricular dysfunction on in-hospital and mid-term clinical outcomes of Chinese patients undergoing first, scheduled, and isolated OPCAB surgery. METHODS: From January 2010 to December 2014, 2032 eligible patients were included in this study and were divided into 3 groups: a severe group (patients with preoperative left ventricular ejection fraction (LVEF) of ≤35%, n = 128), an impaired group (patients with preoperative LVEF of 36-50%, n = 680), and a normal group (patients with preoperative LVEF of >50%, n = 1224). In-hospital and follow-up clinical outcomes were investigated and compared. RESULTS: Patients in the severe group compared to the other 2 groups had higher in-hospital mortality and higher incidences of low cardiac output and prolonged ventilation. Kaplan-Meier curves showed a similar cumulative follow-up survival between the severe group and the impaired group (χ2 = 1.980, Log-rank p = 0.159) and between the severe group and the normal group (χ2 = 2.701, Log-rank p = 0.102). Multivariate Cox regression indicated that grouping was not a significant variable related to mid-term all-cause mortality. No significant difference was found in the rate of repeat revascularization between the severe group (2.4%) and the other 2 groups. CONCLUSIONS: Patients with preoperative LVEF of ≤35% compared to preoperative LVEF of >35% increased the risk of in-hospital death and incidences of postoperative low cardiac output and prolonged ventilation, but shared similar mid-term all-cause mortality and repeat revascularization after OPCAB surgery.

Blind Forensics of Successive Geometric Transformations in Digital Images Using Spectral Method: Theory and Applications.

Geometric transformations, such as resizing and rotation, are almost always needed when two or more images are spliced together to create convincing image forgeries. In recent years, researchers have developed many digital forensic techniques to identify these operations. Most previous works in this area focus on the analysis of images that have undergone single geometric transformations, e.g., resizing or rotation. In several recent works, researchers have addressed yet another practical and realistic situation: successive geometric transformations, e.g., repeated resizing, resizing-rotation, rotation-resizing, and repeated rotation. We will also concentrate on this topic in this paper. Specifically, we present an in-depth analysis in the frequency domain of the second-order statistics of the geometrically transformed images. We give an exact formulation of how the parameters of the first and second geometric transformations influence the appearance of periodic artifacts. The expected positions of characteristic resampling peaks are analytically derived. The theory developed here helps to address the gap left by previous works on this topic and is useful for image security and authentication, in particular, the forensics of geometric transformations in digital images. As an application of the developed theory, we present an effective method that allows one to distinguish between the aforementioned four different processing chains. The proposed method can further estimate all the geometric transformation parameters. This may provide useful clues for image forgery detection.

Rate and Distortion Optimization for Reversible Data Hiding Using Multiple Histogram Shifting.

Histogram shifting (HS) embedding as a typical reversible data hiding scheme is widely investigated due to its high quality of stego-image. For HS-based embedding, the selected side information, i.e., peak and zero bins, usually greatly affects the rate and distortion performance of the stego-image. Due to the massive solution space and burden in distortion computation, conventional HS-based schemes utilize some empirical criterion to determine those side information, which generally could not lead to a globally optimal solution for reversible embedding. In this paper, based on the developed rate and distortion model, the problem of HS-based multiple embedding is formulated as the one of rate and distortion optimization. Two key propositions are then derived to facilitate the fast computation of distortion due to multiple shifting and narrow down the solution space, respectively. Finally, an evolutionary optimization algorithm, i.e., genetic algorithm is employed to search the nearly optimal zero and peak bins. For a given data payload, the proposed scheme could not only adaptively determine the proper number of peak and zero bin pairs but also their corresponding values for HS-based multiple reversible embedding. Compared with previous approaches, experimental results demonstrate the superiority of the proposed scheme in the terms of embedding capacity and stego-image quality.

Analyzing the Effect of JPEG Compression on Local Variance of Image Intensity.

The local variance of image intensity is a typical measure of image smoothness. It has been extensively used, for example, to measure the visual saliency or to adjust the filtering strength in image processing and analysis. However, to the best of our knowledge, no analytical work has been reported about the effect of JPEG compression on image local variance. In this paper, a theoretical analysis on the variation of local variance caused by JPEG compression is presented. First, the expectation of intensity variance of 8×8 non-overlapping blocks in a JPEG image is derived. The expectation is determined by the Laplacian parameters of the discrete cosine transform coefficient distributions of the original image and the quantization step sizes used in the JPEG compression. Second, some interesting properties that describe the behavior of the local variance under different degrees of JPEG compression are discussed. Finally, both the simulation and the experiments are performed to verify our derivation and discussion. The theoretical analysis presented in this paper provides some new insights into the behavior of local variance under JPEG compression. Moreover, it has the potential to be used in some areas of image processing and analysis, such as image enhancement, image quality assessment, and image filtering.

Incidence of Deep Sternal Wound Infection in Diabetic Patients Undergoing Off-Pump Skeletonized Internal Thoracic Artery Grafting.

OBJECTIVES: To evaluate the impact of skeletonized bilateral or single internal thoracic artery (ITA) grafting on the risk of deep sternal wound infection (DSWI) in diabetic patients undergoing off-pump coronary artery bypass grafting (OPCAB). MATERIALS AND METHODS: A total of 803 diabetic patients undergoing OPCAB surgery from January 2010 to December 2014 were enrolled into this study and assigned to the pSITA group (patients undergoing pedicled single ITA grafting, n = 362), the sSITA group (skeletonized single ITA grafting, n = 295), or the sBITA group (skeletonized bilateral ITA grafting, n = 146). The primary end point was the diagnosis of a DSWI. RESULTS: Eighteen patients developed postoperative DSWI, with an incidence of 2.2%. Patients in the sSITA group had a significantly lower incidence of DSWI than those in the pSITA group (1.0 vs. 3.6%, p = 0.0408). In multivariate logistic regression analysis, the risk of DSWI in the sSITA group was 0.41 times that in the pSITA group. CONCLUSIONS: sSITA grafting lowered the risk of DSWI in diabetic patients undergoing OPCAB surgery compared to pSITA grafting. Multicenter clinical trials involving larger sample sizes are needed to determine the merit of pSITA grafting in reducing the risk of DSWI following OPCAB surgery.

A robust H.264/AVC video watermarking scheme with drift compensation.

A robust H.264/AVC video watermarking scheme for copyright protection with self-adaptive drift compensation is proposed. In our scheme, motion vector residuals of macroblocks with the smallest partition size are selected to hide copyright information in order to hold visual impact and distortion drift to a minimum. Drift compensation is also implemented to reduce the influence of watermark to the most extent. Besides, discrete cosine transform (DCT) with energy compact property is applied to the motion vector residual group, which can ensure robustness against intentional attacks. According to the experimental results, this scheme gains excellent imperceptibility and low bit-rate increase. Malicious attacks with different quantization parameters (QPs) or motion estimation algorithms can be resisted efficiently, with 80% accuracy on average after lossy compression.

Pairwise prediction-error expansion for efficient reversible data hiding.

In prediction-error expansion (PEE) based reversible data hiding, better exploiting image redundancy usually leads to a superior performance. However, the correlations among prediction-errors are not considered and utilized in current PEE based methods. Specifically, in PEE, the prediction-errors are modified individually in data embedding. In this paper, to better exploit these correlations, instead of utilizing prediction-errors individually, we propose to consider every two adjacent prediction-errors jointly to generate a sequence consisting of prediction-error pairs. Then, based on the sequence and the resulting 2D prediction-error histogram, a more efficient embedding strategy, namely, pairwise PEE, can be designed to achieve an improved performance. The superiority of our method is verified through extensive experiments.