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To enhance the security and reliability of automatic speaker verification (ASV) systems, ASVspoof 2017 challenge focuses on the detection problem of known and unknown audio replay attacks. We proposed an ensemble learning classifier for CNCB team's submitted system scores, which across uses a variety of acoustic features and classifiers. An effective postprocessing method is studied to improve the performance of Constant Q cepstral coefficients (CQCC) and to form a base feature set with some other classical acoustic features. We also proposed using an ensemble classifier set, which includes multiple Gaussian Mixture Model (GMM) based classifiers and two novel GMM mean supervector-Gradient Boosting Decision Tree (GSV-GBDT) and GSV-Random Forest (GSV-RF) classifiers. Experimental results have shown that the proposed ensemble learning system can provide substantially better performance than baseline. On common training condition of the challenge, Equal Error Rate (EER) of primary system on development set is 1.5%, compared to baseline 10.4%. EER of primary system (S02 in ASVspoof 2017 board) on evaluation data set are 12.3% (with only train dataset) and 10.8% (with train+dev dataset), which are also much better than baseline 30.6% and 24.8%, given by ASVSpoof 2017 organizer, with 59.7% and 56.4% relative performance improvement.

BackgroundIntracranial aneurysms (IAs) are common in the population and may cause death.ObjectiveTo develop a new fully automated detection and segmentation deep neural network based framework to assist neurologists in evaluating and contouring intracranial aneurysms from 2D+time digital subtraction angiography (DSA) sequences during diagnosis.MethodsThe network structure is based on a general U-shaped design for medical image segmentation and detection. The network includes a fully convolutional technique to detect aneurysms in high-resolution DSA frames. In addition, a bidirectional convolutional long short-term memory module is introduced at each level of the network to capture the change in contrast medium flow across the 2D DSA frames. The resulting network incorporates both spatial and temporal information from DSA sequences and can be trained end-to-end. Furthermore, deep supervision was implemented to help the network converge. The proposed network structure was trained with 2269 DSA sequences from 347 patients with IAs. After that, the system was evaluated on a blind test set with 947 DSA sequences from 146 patients.ResultsOf the 354 aneurysms, 316 (89.3%) were successfully detected, corresponding to a patient level sensitivity of 97.7% at an average false positive number of 3.77 per sequence. The system runs for less than one second per sequence with an average dice coefficient score of 0.533.ConclusionsThis deep neural network assists in successfully detecting and segmenting aneurysms from 2D DSA sequences, and can be used in clinical practice.

Layered lithium‐rich cathode materials are one of the most promising cathode materials owing to their higher mass energy density than the commercial counterparts. A series of trace Yb‐doped lithium‐rich cathode materials Li1.2Mn0.54Ni0.13Co0.13−xYbxO2 (0≤x≤0.050) were synthesized and the effects were investigated by XRD, X‐ray photoelectron spectroscopy, and high‐resolution TEM. The participation of Yb ions in electrochemical reactions and the larger binding energy of Yb−O than M−O (M=Mn, Ni, Co), which expands the lithium layer spacing and stabilizes the oxygen stacking, resulted in excellent performance of materials doped with a limited Yb content (x≤0.005). However, higher doping amounts (x>0.005) significantly increased the charge‐transfer impedance and led to a sharp deterioration in electrochemical performance. The reason lies in the large difference in ionic radius between the transition metals (Mn, Co, and Ni) and Yb. There is an upper limit to the amount of Yb ions in the lattice. If the amount of Yb is higher than the limit, excess Yb ions enter the Li layers instead of staying in the transition‐metal layers or even segregate on the surface and form electrochemically inert oxides.

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