Bitewing Radiography Semantic Segmentation Base on Conditional Generative Adversarial Nets

Currently, Segmentation of bitewing radiograpy images is a very challenging task. The focus of the study is to segment it into caries, enamel, dentin, pulp, crowns, restoration and root canal treatments. The main method of semantic segmentation of bitewing radiograpy images at this stage is the U-shaped deep convolution neural network, but its accuracy is low. in order to improve the accuracy of semantic segmentation of bitewing radiograpy images, this paper proposes the use of Conditional Generative Adversarial network (cGAN) combined with U-shaped network structure (U-Net) approach to semantic segmentation of bitewing radiograpy images. The experimental results show that the accuracy of cGAN combined with U-Net is 69.7%, which is 13.3% higher than the accuracy of u-shaped deep convolution neural network of 56.4%.Comment: 12pages, in Chines

Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network

Accurate segmentation of retinal vessels is a basic step in Diabetic retinopathy(DR) detection. Most methods based on deep convolutional neural network (DCNN) have small receptive fields, and hence they are unable to capture global context information of larger regions, with difficult to identify lesions. The final segmented retina vessels contain more noise with low classification accuracy. Therefore, in this paper, we propose a DCNN structure named as D-Net. In the proposed D-Net, the dilation convolution is used in the backbone network to obtain a larger receptive field without losing spatial resolution, so as to reduce the loss of feature information and to reduce the difficulty of tiny thin vessels segmentation. The large receptive field can better distinguished between the lesion area and the blood vessel area. In the proposed Multi-Scale Information Fusion module (MSIF), parallel convolution layers with different dilation rates are used, so that the model can obtain more dense feature information and better capture retinal vessel information of different sizes. In the decoding module, the skip layer connection is used to propagate context information to higher resolution layers, so as to prevent low-level information from passing the entire network structure. Finally, our method was verified on DRIVE, STARE and CHASE dataset. The experimental results show that our network structure outperforms some state-of-art method, such as N4-fields, U-Net, and DRIU in terms of accuracy, sensitivity, specificity, and AUCROC. Particularly, D-Net outperforms U-Net by 1.04%, 1.23% and 2.79% in DRIVE, STARE, and CHASE three dataset, respectively

Learning Disentangled Representations for Timber and Pitch in Music Audio

Timbre and pitch are the two main perceptual properties of musical sounds. Depending on the target applications, we sometimes prefer to focus on one of them, while reducing the effect of the other. Researchers have managed to hand-craft such timbre-invariant or pitch-invariant features using domain knowledge and signal processing techniques, but it remains difficult to disentangle them in the resulting feature representations. Drawing upon state-of-the-art techniques in representation learning, we propose in this paper two deep convolutional neural network models for learning disentangled representation of musical timbre and pitch. Both models use encoders/decoders and adversarial training to learn music representations, but the second model additionally uses skip connections to deal with the pitch information. As music is an art of time, the two models are supervised by frame-level instrument and pitch labels using a new dataset collected from MuseScore. We compare the result of the two disentangling models with a new evaluation protocol called "timbre crossover", which leads to interesting applications in audio-domain music editing. Via various objective evaluations, we show that the second model can better change the instrumentation of a multi-instrument music piece without much affecting the pitch structure. By disentangling timbre and pitch, we envision that the model can contribute to generating more realistic music audio as well

Multitask learning for frame-level instrument recognition

For many music analysis problems, we need to know the presence of instruments for each time frame in a multi-instrument musical piece. However, such a frame-level instrument recognition task remains difficult, mainly due to the lack of labeled datasets. To address this issue, we present in this paper a large-scale dataset that contains synthetic polyphonic music with frame-level pitch and instrument labels. Moreover, we propose a simple yet novel network architecture to jointly predict the pitch and instrument for each frame. With this multitask learning method, the pitch information can be leveraged to predict the instruments, and also the other way around. And, by using the so-called pianoroll representation of music as the main target output of the model, our model also predicts the instruments that play each individual note event. We validate the effectiveness of the proposed method for framelevel instrument recognition by comparing it with its singletask ablated versions and three state-of-the-art methods. We also demonstrate the result of the proposed method for multipitch streaming with real-world music. For reproducibility, we will share the code to crawl the data and to implement the proposed model at: https://github.com/biboamy/ instrument-streaming.Comment: This is a pre-print version of an ICASSP 2019 pape

Nuclear mass parabola and its applications

We propose a method to extract the properties of the isobaric mass parabola based on the total double $\beta$ decay energies of isobaric nuclei. Two important parameters of the mass parabola, the location of the most $\beta$-stable nuclei $Z_{A}$ and the curvature parameter $b_{A}$, are obtained for 251 A values based on the total double $\beta$ decay energies of nuclei compiled in AME2016 database.The advantage of this approach is that we can remove the pairing energy term $P_{A}$ caused by odd-even variation, and the mass excess $M(A,Z_{A})$ of the most stable nuclide for mass number $A$ in the performance process, which are used in the mass parabolic fitting method. The Coulomb energy coefficient $a_{c}=0.6910$ MeV is determined by the mass difference relation of mirror nuclei, and the symmetry energy coefficient is also studied by the relation $a_{\rm sym}(A)=0.25b_{A}Z_{A}$.Comment: 16 pages, 5 figures, To be published in Chinese Physics

Retinal Vessel Segmentation Based on Conditional Deep Convolutional Generative Adversarial Networks

The segmentation of retinal vessels is of significance for doctors to diagnose the fundus diseases. However, existing methods have various problems in the segmentation of the retinal vessels, such as insufficient segmentation of retinal vessels, weak anti-noise interference ability, and sensitivity to lesions, etc. Aiming to the shortcomings of existed methods, this paper proposes the use of conditional deep convolutional generative adversarial networks to segment the retinal vessels. We mainly improve the network structure of the generator. The introduction of the residual module at the convolutional layer for residual learning makes the network structure sensitive to changes in the output, as to better adjust the weight of the generator. In order to reduce the number of parameters and calculations, using a small convolution to halve the number of channels in the input signature before using a large convolution kernel. By used skip connection to connect the output of the convolutional layer with the output of the deconvolution layer to avoid low-level information sharing. By verifying the method on the DRIVE and STARE datasets, the segmentation accuracy rate is 96.08% and 97.71%, the sensitivity reaches 82.74% and 85.34% respectively, and the F-measure reaches 82.08% and 85.02% respectively. The sensitivity is 4.82% and 2.4% higher than that of R2U-Net.Comment: in Chines

High-energy gamma-ray afterglows from low-luminosity gamma-ray bursts

The observations of gamma-ray bursts (GRBs) such as 980425, 031203 and 060218, with luminosities much lower than those of other classic bursts, lead to the definition of a new class of GRBs -- low-luminosity GRBs. The nature of the outflow responsible for them is not clear yet. Two scenarios have been suggested: one is the conventional relativistic outflow with initial Lorentz factor of order of \Gamma_0\ga 10 and the other is a trans-relativistic outflow with $\Gamma_0\simeq 1-2$. Here we compare the high energy gamma-ray afterglow emission from these two different models, taking into account both synchrotron self inverse-Compton scattering (SSC) and the external inverse-Compton scattering due to photons from the cooling supernova or hypernova envelope (SNIC). We find that the conventional relativistic outflow model predicts a relatively high gamma-ray flux from SSC at early times ($<10^4 {\rm s}$ for typical parameters) with a rapidly decaying light curve, while in the trans-relativistic outflow model, one would expect a much flatter light curve of high-energy gamma-ray emission at early times, which could be dominated by both the SSC emission and SNIC emission, depending on the properties of the underlying supernova and the shock parameter $\epsilon_e$ and $\epsilon_B$. The Fermi Gamma-ray Space Telescope should be able to distinguish between the two models in the future.Comment: Published in ApJ, 29 pages (aastex style), 6 figure

Hit Song Prediction for Pop Music by Siamese CNN with Ranking Loss

A model for hit song prediction can be used in the pop music industry to identify emerging trends and potential artists or songs before they are marketed to the public. While most previous work formulates hit song prediction as a regression or classification problem, we present in this paper a convolutional neural network (CNN) model that treats it as a ranking problem. Specifically, we use a commercial dataset with daily play-counts to train a multi-objective Siamese CNN model with Euclidean loss and pairwise ranking loss to learn from audio the relative ranking relations among songs. Besides, we devise a number of pair sampling methods according to some empirical observation of the data. Our experiment shows that the proposed model with a sampling method called A/B sampling leads to much higher accuracy in hit song prediction than the baseline regression model. Moreover, we can further improve the accuracy by using a neural attention mechanism to extract the highlights of songs and by using a separate CNN model to offer high-level features of songs

Multitask learning for instrument activation aware music source separation

Music source separation is a core task in music information retrieval which has seen a dramatic improvement in the past years. Nevertheless, most of the existing systems focus exclusively on the problem of source separation itself and ignore the utilization of other~---possibly related---~MIR tasks which could lead to additional quality gains. In this work, we propose a novel multitask structure to investigate using instrument activation information to improve source separation performance. Furthermore, we investigate our system on six independent instruments, a more realistic scenario than the three instruments included in the widely-used MUSDB dataset, by leveraging a combination of the MedleyDB and Mixing Secrets datasets. The results show that our proposed multitask model outperforms the baseline Open-Unmix model on the mixture of Mixing Secrets and MedleyDB dataset while maintaining comparable performance on the MUSDB dataset

Initial Sampling in Symmetrical Quasiclassical Dynamics Based on Li-Miller Mapping Hamiltonian

A symmetrical quasiclassical (SQC) dynamics approach based on the Li-Miller (LM) mapping Hamiltonian (SQC-LM) was employed to describe nonadiabatic dynamics. In principle, the different initial sampling procedures may be applied in the SQC-LM dynamics, and the results may be dependent on the initial sampling. We provided various initial sampling approaches and checked their influence. We selected two groups of models including site-exciton models for exciton dynamics and linear vibronic coupling models for conical intersections to test the performance of SQC-LM dynamics with the different initial sampling methods. The results were examined with respect to those of the accurate multilayer multiconfigurational time-dependent Hartree (ML-MCTDH) quantum dynamics. For both two models, the SQC-LM method more-or-less gives a reasonable description of the population dynamics, while the influence of the initial sampling approaches on the final results is noticeable. It seems that the proper initial sampling methods should be determined by the system under study. This indicates that the combination of the SQC-LM method with a suitable sampling approach may be a potential method in the description of nonadiabatic dynamics