Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks

Car-following Behavior Analysis of Left-turn Vehicles at Signalized Intersections

In order to enrich the car-following theory of urban signalized intersections, and reveal the car-following characteristics of left turn at signalized intersections, the car-following behavior of left turn at signalized intersections is studied. The car-following data acquisition test which was based on high precision GPS was designed. And the car-following characteristics of left-turning vehicles at signalized intersections with different turning radii were analyzed. Based on which, the influence of radius on the car-following behavior was explained, and the New Full Velocity Difference (NFVD) model was developed. The genetic algorithm was used to calibrate the parameters of the NFVD model. The stability and accuracy of the calibrated model was further analyzed by using field data. The results showed that the average speed of the following car increases with the turning radius of the signalized intersection; the car-following speed which the highest frequency occurs under different turning radii tends to increase with the enlargement of turning radius; the larger the average headway distance between the car-following vehicles, the more intense of the driver’s response to the deceleration of the front vehicle. These findings could be used in traffic simulation and to make engineering decisions

Solid-state ensemble of highly entangled photon sources at rubidium atomic transitions

Semiconductor InAs/GaAs quantum dots grown by the Stranski-Krastanov method are among the leading candidates for the deterministic generation of polarization entangled photon pairs. Despite remarkable progress in the last twenty years, many challenges still remain for this material, such as the extremely low yield (<1% quantum dots can emit entangled photons), the low degree of entanglement, and the large wavelength distribution. Here we show that, with an emerging family of GaAs/AlGaAs quantum dots grown by droplet etching and nanohole infilling, it is possible to obtain a large ensemble (close to 100%) of polarization-entangled photon emitters on a wafer without any post-growth tuning. Under pulsed resonant two-photon excitation, all measured quantum dots emit single pairs of entangled photons with ultra-high purity, high degree of entanglement (fidelity up to F=0.91, with a record high concurrence C=0.90), and ultra-narrow wavelength distribution at rubidium transitions. Therefore, a solid-state quantum repeater - among many other key enabling quantum photonic elements - can be practically implemented with this new material

Coresets for Relational Data and The Applications

A coreset is a small set that can approximately preserve the structure of the original input data set. Therefore we can run our algorithm on a coreset so as to reduce the total computational complexity. Conventional coreset techniques assume that the input data set is available to process explicitly. However, this assumption may not hold in real-world scenarios. In this paper, we consider the problem of coresets construction over relational data. Namely, the data is decoupled into several relational tables, and it could be very expensive to directly materialize the data matrix by joining the tables. We propose a novel approach called ``aggregation tree with pseudo-cube'' that can build a coreset from bottom to up. Moreover, our approach can neatly circumvent several troublesome issues of relational learning problems [Khamis et al., PODS 2019]. Under some mild assumptions, we show that our coreset approach can be applied for the machine learning tasks, such as clustering, logistic regression and SVM

One-domain-one-input: adaptive random testing by orthogonal recursive bisection with restriction

One goal of software testing may be the identification or generation of a series of test cases that can detect a fault with as few test executions as possible. Motivated by insights from research into failure-causing regions of input domains, the even-spreading (even distribution) of tests across the input domain has been identified as a useful heuristic to more quickly find failures. This finding has encouraged a shift in focus from traditional random testing (RT) to its enhancement, adaptive random testing (ART), which retains the randomness of test input selection, but also attempts to maintain a more evenly distributed spread of test inputs across the input domain. Given that there are different ways to achieve the even distribution, several different ART methods and approaches have been proposed. This paper presents a new ART method, called ART-ORB, which explores the advantages of repeated geometric bisection of the input domain, combined with restriction regions, to evenly spread test inputs. Experimental results show a better performance in terms of fewer test executions than RT to find failures. Compared with other ART methods, ART-ORB has comparable performance (in terms of required test executions), but incurs lower test input selection overheads, especially in higher dimensional input space. It is recommended that ART-ORB be used in testing situations involving expensive test input execution

Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement

Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction. However, their implicit volumetric representations differ significantly from the widely-adopted polygonal meshes and lack support from common 3D software and hardware, making their rendering and manipulation inefficient. To overcome this limitation, we present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency decomposed appearance with a NeRF. Subsequently, a coarse mesh is extracted, and an iterative surface refining algorithm is developed to adaptively adjust both vertex positions and face density based on re-projected rendering errors. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering. Extensive experiments demonstrate that our method achieves superior mesh quality and competitive rendering quality.Comment: ICCV 2023 camera-ready, Project Page: https://me.kiui.moe/nerf2mes

Efficient Test-Time Model Adaptation without Forgetting

Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly important for deep models when the test environment changes frequently. Although some recent attempts have been made to handle this task, we still face two practical challenges: 1) existing methods have to perform backward computation for each test sample, resulting in unbearable prediction cost to many applications; 2) while existing TTA solutions can significantly improve the test performance on out-of-distribution data, they often suffer from severe performance degradation on in-distribution data after TTA (known as catastrophic forgetting). In this paper, we point out that not all the test samples contribute equally to model adaptation, and high-entropy ones may lead to noisy gradients that could disrupt the model. Motivated by this, we propose an active sample selection criterion to identify reliable and non-redundant samples, on which the model is updated to minimize the entropy loss for test-time adaptation. Furthermore, to alleviate the forgetting issue, we introduce a Fisher regularizer to constrain important model parameters from drastic changes, where the Fisher importance is estimated from test samples with generated pseudo labels. Extensive experiments on CIFAR-10-C, ImageNet-C, and ImageNet-R verify the effectiveness of our proposed method.Comment: 15 pages, conferenc