Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation

Existing image segmentation networks mainly leverage large-scale labeled datasets to attain high accuracy. However, labeling medical images is very expensive since it requires sophisticated expert knowledge. Thus, it is more desirable to employ only a few labeled data in pursuing high segmentation performance. In this paper, we develop a data augmentation method for one-shot brain magnetic resonance imaging (MRI) image segmentation which exploits only one labeled MRI image (named atlas) and a few unlabeled images. In particular, we propose to learn the probability distributions of deformations (including shapes and intensities) of different unlabeled MRI images with respect to the atlas via 3D variational autoencoders (VAEs). In this manner, our method is able to exploit the learned distributions of image deformations to generate new authentic brain MRI images, and the number of generated samples will be sufficient to train a deep segmentation network. Furthermore, we introduce a new standard segmentation benchmark to evaluate the generalization performance of a segmentation network through a cross-dataset setting (collected from different sources). Extensive experiments demonstrate that our method outperforms the state-of-the-art one-shot medical segmentation methods. Our code has been released at https://github.com/dyh127/Modeling-the-Probabilistic-Distribution-of-Unlabeled-Data.Comment: AAAI 202

Suicidal Pedestrian: Generation of safety-critical scenarios for autonomous vehicles

Autonomous driving is appealing due to its significant financial potential and positive social impact. However, developing capable autonomous driving algorithms faces the difficulty of reliability testing because some safety-critical traffic scenarios are particularly challenging to acquire. To this end, this thesis proposes a method to design a suicidal pedestrian agent based on the CARLA simulation engine that can automatically generate pedestrian-related traffic scenarios for autonomous vehicle testing. In this method, the pedestrian is formulated as a reinforcement learning agent that spontaneously seeks collisions with the target vehicle and is trained using a continuous model-free learning algorithm with two custom reward functions. Besides, by allowing the pedestrian freely explore the environment with a constrained initial distance to the vehicle, the pedestrian and autonomous car can be placed anywhere, rendering generated scenarios more diverse. Furthermore, four collision-oriented evaluation metrics are also proposed to verify the performance of the designed suicidal pedestrian and the target vehicle under testing. Experiments on two state-of-the-art autonomous driving algorithms demonstrate that this suicidal pedestrian is effective in finding autonomous vehicle decision errors when cars are exposed to such pedestrian-related traffic scenarios

HYBRID OF P3HT AND ZnO@GO NANOSTRCTURED PARTICLES FOR INCREASED NO2 SENSITIVITY

The NO2 sensing properties and operational mechanism of the hybrid formed from poly(3-hexylthiophene) (P3HT) and zinc oxide-graphene oxide (ZnO@GO) nanoparticles were investigated. We prepared graphene oxide (GO) and zinc oxide (ZnO) core-shell nanostructured particles with ionic aggregation. Gas sensors were fabricated by spin coating a mixture of P3HT with ZnO@GO on oxide-coated silicon wafers leading to formation of organic field-effect transistors (OFETs). The NO2 sensing properties of the obtained devices were investigated at room temperature. By means of observing conductance changes before and after exposure to NO2, it was demonstrated that the hybrid of P3HT with 60 wt% ZnO@GO composites exhibits 210% sensing response to 5ppm NO2 gas exposure for 5min at room temperature. The sensing mechanism included a contribution from the hybrid that was not observed from pure P3HT or by adding either ZnO or GO alone

Dynamically observing the spectra of quantum droplets in optical lattice

Optical lattice plays an important role on stability and dynamics of quantum droplets. In this letter, we investigate the Bogoliubov excitation spectrum of quantum droplets in optical lattice in the thermodynamic limit. We classify the collective excitations as synchronous modes, Bloch phononic modes, and site-density imbalanced modes. For synchronous modes, we measure the dipole oscillation frequencies by quench dynamics with a sudden shift of the optical lattice, and the breathing frequencies by Floquet dynamics with a periodic change of the lattice depth. Bloch phononic modes are observable from the Landau critical velocity of the droplets. We further discuss the instability induced by the site-dependent density fluctuations, and calculate the critical filling of atoms where the growth of lattice vacancy breaks down the translational symmetry of the system. This work makes essential steps towards measuring the excitation spectrum and understanding the superfluid nature of quantum droplets in optical lattice.Comment: 5 figure

Compressive strength of concrete-filled stainless steel tube stub columns

Concrete-filled stainless steel tube (CFSST) members combine the advantages of the outstanding corrosion resistance of stainless steel and the composite action in concrete-filled steel tube (CFST) system. However, accurate calculation methods for this type of structures are currently limited and research into CFSST members with hot-rolled stainless steel tubes are not available. In this paper, the compressive behavior of CFSST stub columns has been investigated through a comprehensive experimental and numerical program. A total of 18 specimens, including 9 concrete-filled austenitic stainless steel tube (austenitic CFSST) and 9 concrete-filled duplex stainless steel tube (duplex CFSST) stub columns, were tested under compression. The varying parameters in the experimental study included the thickness of the stainless steel tube and the strength of the concrete. Finite element (FE) models duplicating the tests were developed, which were subsequently used in parametric study to generate a wider range of data and to investigate the influence of the tube thickness and concrete strength on the ultimate capacities of CFSST stub columns. Based on the generated data, it was found that the current European and Chinese standards for concrete-filled carbon steel tubes underestimate the resistances of CFSST members significantly. To this end, new calculation methods developed based on these European and Chinese design rules have been proposed, which were shown to provide improved strength predictions for both the austenitic and duplex CFSST members.</p