Solid Texture Synthesis using Generative Adversarial Networks

Solid texture synthesis, as an effective way to extend 2D exemplar to a volumetric texture, exhibits advantages in numerous application domains. However, existing methods generally suffer from synthesis distortion due to the under-utilization of information. In this paper, we propose a novel approach for the solid texture synthesis based on generative adversarial networks(GANs), named STS-GAN, learning the distribution of 2D exemplars with volumetric operation in a feature-free manner. The multi-scale discriminators evaluate the similarities between patch exemplars and slices from generated volume, promoting the generator to synthesize realistic solid texture. Experimental results demonstrate that the proposed method can synthesize high-quality solid texture with similar visual characteristics to the exemplar

OPT-GAN: Black-Box Global Optimization via Generative Adversarial Nets

Black-box optimization (BBO) algorithms are concerned with finding the best solutions for problems with missing analytical details. Most classical methods for such problems are based on strong and fixed a priori assumptions, such as Gaussianity. However, the complex real-world problems, especially when the global optimum is desired, could be very far from the a priori assumptions because of their diversities, causing unexpected obstacles to these methods. In this study, we propose a generative adversarial net-based broad-spectrum global optimizer (OPT-GAN) which estimates the distribution of optimum gradually, with strategies to balance exploration-exploitation trade-off. It has potential to better adapt to the regularity and structure of diversified landscapes than other methods with fixed prior, e.g. Gaussian assumption or separability. Experiments conducted on BBO benchmarking problems and several other benchmarks with diversified landscapes exhibit that OPT-GAN outperforms other traditional and neural net-based BBO algorithms.Comment: M. Lu and S. Ning contribute equally. Submitted to IEEE transactions on Neural Networks and Learning System

Solution-processed blue/deep blue and white phosphorescent organic light emitting diodes (PhOLEDs) hosted by a polysiloxane derivative with pendant mCP (1, 3-bis(9-carbazolyl)benzene)

The synthesis and characterization is reported of an efficient polysiloxane derivative containing the 1,3-bis(9-carbazolyl)benzene (mCP) moiety as a pendant unit on the polysiloxane backbone. In comparison with mCP, the mCP-polysiloxane hybrid (PmCPSi) has significantly improved thermal and morphological stabilities with a high decomposition temperature (Td = 523 °C) and glass transition temperature (Tg = 194 °C). The silicon–oxygen linkage of PmCPSi prevents intermolecular π-stacking and ensures a high triplet energy level (ET = 3.0 eV). Using PmCPSi as a host, blue phosphorescent organic light emitting devices (PhOLEDs) effectively confine triplet excitons, with efficient energy transfer to the guest emitter and a relatively low turn-on voltage of 5.8 V. A maximum external quantum efficiency of 9.24% and maximum current efficiency of 18.93 cd/A are obtained. These values are higher than for directly analogous poly(vinylcarbazole) (PVK) based devices (6.76%, 12.29 cd/A). Good color stability over a range of operating voltages is observed. A two-component “warm-white” device with a maximum current efficiency of 10.4 cd/A is obtained using a blend of blue and orange phosphorescent emitters as dopants in PmCPSi host. These results demonstrate that well-designed polysiloxane derivatives are highly efficient hosts suitable for low-cost solution-processed PhOLEDs

Performance Evaluation of Epileptic Seizure Prediction Using Time, Frequency, and Time–Frequency Domain Measures

The prediction of epileptic seizures is crucial to aid patients in gaining early warning and taking effective intervention. Several features have been explored to predict the onset via electroencephalography signals, which are typically non-stationary, dynamic, and varying from person-to-person. In the former literature, features applied in the classification have shared similar contributions to all patients. Therefore, in this paper, we analyze the impact of the specific combination of feature and channel from time, frequency, and time–frequency domains on prediction performance of disparate patients. Based on the minimal-redundancy-maximal-relevance criterion, the proposed framework uses a sequential forward selection approach to individually find the optimal features and channels. Trained models could discriminate the pre-ictal and inter-ictal electroencephalography with a sensitivity of 90.2% and a false prediction rate of 0.096/h. We also present the comparison between the classification accuracy obtained by the optimal features, several features summarized from optimal features, and the complete set of features from three domains. The results indicate that various patient interpretations have a certain specificity in the selection of feature-channel. Furthermore, the detailed list of optimal features and summarized features are proffered for reference to those who research the corresponding database

Endoscopic excision versus radical nephroureterectomy for non-muscle invasive upper tract urothelial carcinoma: A population-based large cohort study

Background: As an important kidney-sparing treatment for upper urothelial carcinoma (UTUC), whether endoscopic excision can be performed without sacrificing oncologic outcomes remains indefinite. This study aimed to investigate the prevalence and efficacy of endoscopic excision, in patients with non-muscle invasive UTUC (NMIUTUC) and compare them to those of radical nephroureterectomy (RNU). Methods: Using the Surveillance, Epidemiology, and End Results database, we reviewed 4347 cases with NMIUTUC (cTis/Ta/T1-N0-M0,≤ 5.0 cm) between 2004 and 2020. Surgical treatment modalities included endoscopic excision and RNU. Propensity score matching analysis was used to minimize the selection bias between endoscopic excision and RNU, selecting 1:1 matched patients in the two group. Results: A total of 794 patients with NMIUTUC were included after matching (397:397). Patients who underwent endoscopic excision had worse survival outcomes compared with those of patients who underwent RNU (5-year OS: 65.3 % vs. 80.3 %, p < 0.0001; 5-year DSS: 83.2 % vs. 94.0 %, p = 0.00021). After stratification by anatomical sites, the effect of endoscopic excision for NMI renal pelvis cancer was worse than RNU (5-year OS, 62.9 % vs. 82.8 %; 5-year DSS, 78.8 % vs. 91.6 %), while in NMI ureteral cancer, there is no statistically significant difference in OS and DSS between endoscopic excision and RNU. Further stratification according to tumor grade revealed equivalent tumor control effects of endoscopic excision and RNU in low-grade NMI ureteral cancer (5-year OS: 67.7 % vs. 72.5 %, p = 0.23; 5-year DSS: 87.2 % vs. 93.1 %, p = 0.17); while for renal pelvis tumor and high-grade ureteral tumor, endoscopic excision was related with significantly inferior prognosis. Conclusions: Only for low-grade NMI ureteral cancer, endoscopic excision and RNU are oncologically equivalent, indicating that endoscopic excision might be an effective option for low-grade NMI ureteral cancer. This result needs to be further verified in randomized controlled trials