A multi-task learning CNN for image steganalysis

Convolutional neural network (CNN) based image steganalysis are increasingly popular because of their superiority in accuracy. The most straightforward way to employ CNN for image steganalysis is to learn a CNN-based classifier to distinguish whether secret messages have been embedded into an image. However, it is difficult to learn such a classifier because of the weak stego signals and the limited useful information. To address this issue, in this paper, a multi-task learning CNN is proposed. In addition to the typical use of CNN, learning a CNN-based classifier for the whole image, our multi-task CNN is learned with an auxiliary task of the pixel binary classification, estimating whether each pixel in an image has been modified due to steganography. To the best of our knowledge, we are the first to employ CNN to perform the pixel-level classification of such type. Experimental results have justified the effectiveness and efficiency of the proposed multi-task learning CNN

Laparoscopic surgery for intestinal obstruction in children due to water absorbing gel beads

Introductions: Super absorbent polymer gel bead (SAPGB) is increasingly available as toys for children. When ingested it swells by absorbs water and leads to acute intestinal obstruction. Diagnosis and surgery is challenging as its radiolucent, fragile and slippery. We present outcome of our innovative technique of stabilization and removal SAPGBs by laparoscopy minimal invasive surgery (lap-MIS). Methods: This retrospective analyse of outcome of lap-MIS in intestinal obstruction caused by ingestion of foreign body, the SAPGBs, in children who were managed at Children's Hospital Affiliated to Zhengzhou University, China. The outcome variables included removal of SAPGBs, length of hospital stay and postoperative occurrence of anastomotic leak, wound infection, wound dehiscence, re-surgery, or mortality. Results: There were 15 children, male 9 (60%), average age 2 years, and duration of ingestion of SAPGBSs 1.5 days (range 2-4 days), parents gave history of accidental ingestion in 6 (40%). All children had uneventful postoperative recovery after lap-MIS removal of foreign body with no wound infection, anastomotic leak, re-surgery or mortality. Average hospital stay was 4 days (range 3 to 5 days). Conclusions: We had successful outcome lap-MIS with our innovative technique to stabilize and extract foreign bodies, the super water absorbent gel beads, ingested by children. Keywords: children, foreign body, gastrointestinal obstruction, laparoscopy minimal invasive surgery, super absorbent polymer gel bead

Fault Tolerant Free Gait and Footstep Planning for Hexapod Robot Based on Monte-Carlo Tree

Legged robots can pass through complex field environments by selecting gaits and discrete footholds carefully. Traditional methods plan gait and foothold separately and treat them as the single-step optimal process. However, such processing causes its poor passability in a sparse foothold environment. This paper novelly proposes a coordinative planning method for hexapod robots that regards the planning of gait and foothold as a sequence optimization problem with the consideration of dealing with the harshness of the environment as leg fault. The Monte Carlo tree search algorithm(MCTS) is used to optimize the entire sequence. Two methods, FastMCTS, and SlidingMCTS are proposed to solve some defeats of the standard MCTS applicating in the field of legged robot planning. The proposed planning algorithm combines the fault-tolerant gait method to improve the passability of the algorithm. Finally, compared with other planning methods, experiments on terrains with different densities of footholds and artificially-designed challenging terrain are carried out to verify our methods. All results show that the proposed method dramatically improves the hexapod robot's ability to pass through sparse footholds environment

UV/Ozone treatment to reduce metal-graphene contact resistance

We report reduced contact resistance of single-layer graphene devices by using ultraviolet ozone (UVO) treatment to modify the metal/graphene contact interface. The devices were fabricated from mechanically transferred, chemical vapor deposition (CVD) grown, single layer graphene. UVO treatment of graphene in the contact regions as defined by photolithography and prior to metal deposition was found to reduce interface contamination originating from incomplete removal of poly(methyl methacrylate) (PMMA) and photoresist. Our control experiment shows that exposure times up to 10 minutes did not introduce significant disorder in the graphene as characterized by Raman spectroscopy. By using the described approach, contact resistance of less than 200 {\Omega} {\mu}m was achieved, while not significantly altering the electrical properties of the graphene channel region of devices.Comment: 17 pages, 5 figure

Key Lab on Wideband Wireless Communications and Sensor Network Technology of Ministry of Education

A fairness-aware resource allocation scheme in a cooperative orthogonal frequency division multiple (OFDM) network is proposed based on jointly optimizing the subcarrier pairing, power allocation, and channel-user assignment. Compared with traditional OFDM relaying networks, the source is permitted to retransfer the same data transmitted by it in the first time slot, further improving the system capacity performance. The problem which maximizes the energy efficiency (EE) of the system with total power constraint and minimal spectral efficiency constraint is formulated into a mixed-integer nonlinear programming (MINLP) problem which has an intractable complexity in general. The optimization model is simplified into a typical fractional programming problem which is testified to be quasiconcave. Thus we can adopt Dinkelbach method to deal with MINLP problem proposed to achieve the optimal solution. The simulation results show that the joint resource allocation method proposed can achieve an optimal EE performance under the minimum system service rate requirement with a good global convergence

Relativistic quantum transport theory of hadronic matter: the coupled nucleon, delta and pion system

We derive the relativistic quantum transport equation for the pion distribution function based on an effective Lagrangian of the QHD-II model. The closed time-path Green's function technique, the semi-classical, quasi-particle and Born approximation are employed in the derivation. Both the mean field and collision term are derived from the same Lagrangian and presented analytically. The dynamical equation for the pions is consistent with that for the nucleons and deltas which we developed before. Thus, we obtain a relativistic transport model which describes the hadronic matter with $N$, $\Delta$ and $\pi$ degrees of freedom simultaneously. Within this approach, we investigate the medium effects on the pion dispersion relation as well as the pion absorption and pion production channels in cold nuclear matter. In contrast to the results of the non-relativistic model, the pion dispersion relation becomes harder at low momenta and softer at high momenta as compared to the free one, which is mainly caused by the relativistic kinetics. The theoretically predicted free $\pi N \to \Delta$ cross section is in agreement with the experimental data. Medium effects on the $\pi N \to \Delta$ cross section and momentum-dependent $\Delta$-decay width are shown to be substantial.Comment: 66 pages, Latex, 12 PostScript figures included; replaced by the revised version, to appear in Phys. Rev.

Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy

PurposeImage segmentation can be time-consuming and lacks consistency between different oncologists, which is essential in conformal radiotherapy techniques. We aimed to evaluate automatic delineation results generated by convolutional neural networks (CNNs) from geometry and dosimetry perspectives and explore the reliability of these segmentation tools in rectal cancer.MethodsForty-seven rectal cancer cases treated from February 2018 to April 2019 were randomly collected retrospectively in our cancer center. The oncologists delineated regions of interest (ROIs) on planning CT images as the ground truth, including clinical target volume (CTV), bladder, small intestine, and femoral heads. The corresponding automatic segmentation results were generated by DeepLabv3+ and ResUNet, and we also used Atlas-Based Autosegmentation (ABAS) software for comparison. The geometry evaluation was carried out using the volumetric Dice similarity coefficient (DSC) and surface DSC, and critical dose parameters were assessed based on replanning optimized by clinically approved or automatically generated CTVs and organs at risk (OARs), i.e., the Planref and Plantest. Pearson test was used to explore the correlation between geometric metrics and dose parameters.ResultsIn geometric evaluation, DeepLabv3+ performed better in DCS metrics for the CTV (volumetric DSC, mean = 0.96, P< 0.01; surface DSC, mean = 0.78, P< 0.01) and small intestine (volumetric DSC, mean = 0.91, P< 0.01; surface DSC, mean = 0.62, P< 0.01), ResUNet had advantages in volumetric DSC of the bladder (mean = 0.97, P< 0.05). For critical dose parameters analysis between Planref and Plantest, there was a significant difference for target volumes (P< 0.01), and no significant difference was found for the ResUNet-generated small intestine (P > 0.05). For the correlation test, a negative correlation was found between DSC metrics (volumetric, surface DSC) and dosimetric parameters (δD95, δD95, HI, CI) for target volumes (P< 0.05), and no significant correlation was found for most tests of OARs (P > 0.05).ConclusionsCNNs show remarkable repeatability and time-saving in automatic segmentation, and their accuracy also has a certain potential in clinical practice. Meanwhile, clinical aspects, such as dose distribution, may need to be considered when comparing the performance of auto-segmentation methods

A suitable method for alpine wetland delineation: An example for the headwater area of the yellow river, Tibetan Plateau

Alpine wetlands are one of the most important ecosystems in the Three Rivers Source Area, China, which plays an important role in regulating the regional hydrological cycle and carbon cycle. Accordingly, Wetland area and its distribution are of great significance for wetland management and scientific research. In our study, a new wetland classification model which based on geomorphological types and combine object-oriented and decision tree classification model (ODTC), and used a new wetland classification system to accurately extract the wetland distributed in the Headwater Area of the Yellow River (HAYR) of the Qinghai-Tibet Plateau (QTP), China. The object-oriented method was first used to segment the image into several areas according to similarity in Pixels and Textures, and then the wetland was extracted through a decision tree constructed based on geomorphological types. The wetland extracted by the model was compared with that by other seven commonly methods, such as support vector machine (SVM) and random forest (RF), and it proved the accuracy was improved by 10%–20%. The overall classification accuracy rate was 98.9%. According to our results, the HAYR’s wetland area is 3142.3 km2, accounting for 16.1% of the study area. Marsh wetlands and flood wetlands accounted for 37.7% and 16.7% respectively. A three-dimensional map of the area showed that alpine wetlands in the research region are distributed around lakes, piedmont groundwater overflow belts, and inter-mountain catchment basin. This phenomenon demonstrates that hydrogeological circumstances influence alpine wetlands’ genesis and evolution. This work provides a new approach to investigating alpine wetlands