Changes in deep neck muscle length from the neutral to forward head posture. A cadaveric study using Thiel cadavers

Forward head posture (FHP) is one of the most common postural deviations. Deep neck muscle imbalance of individuals with FHP is of primary concern in clinical rehabilitation. However, there is scarce quantitative research on changes in deep neck muscle length with the head moving forward. This study aimed to investigate changes in deep neck muscle length with different severity levels of FHP. Six Thiel‐embalmed cadavers (four males and two females) were dissected, and 16 deep neck muscles in each cadaver were modeled by a MicroScribe 3D Digitizer in the neutral head posture, slight FHP, and severe FHP. The craniovertebral angle was used to evaluate the degrees of FHP. Quantitative length change of the deep neck muscles was analyzed using Rhinoceros 3D. In slight FHP significant changes in length occurred in four muscles: two shortened (upper semispinalis capitis, rectus capitis posterior minor) and two lengthened (longus capitis, splenius cervicis). In severe FHP all occipital extensors were significantly shortened (10.6 ± 6.4%), except for obliquus capitis superior, and all cervical extensors were significantly lengthened (4.8 ± 3.4%), while longus capitis (occipital flexor) and the superior oblique part of the longus colli (cervical flexor) were lengthened by 8.8 ± 3.8% and 4.2 ± 3.1%, respectively. No significant length change was observed for the axial rotator. This study presents an alternate anatomical insight into the clinical rehabilitation of FHP. Six muscles appear to be important in restoring optimal head posture, with improvements in FHP being related to interventions associated with the occipital and cervical extensors

A Flexible Monopole Antenna with Dual-Notched Band Function for Ultrawideband Applications

We present a flexible ultrawideband (UWB) planar monopole antenna with dual-notched band characteristic printed on a polyimide substrate. The antenna is fed by a step coplanar waveguide (CPW) that provides smooth transitional impedance for improved matching. It operates from 2.76 to 10.6 GHz with return loss greater than 10 dB except for the notch band to reduce the interference with existing 3.5 GHz WiMAX band and 5.5 GHz WLAN band. With a combination of rectangular and circle patches in which the U-shaped slot is carved, the overall size of antenna is 30 mm × 20 mm. Moreover, a pair of arc-shaped stubs located at both sides of the feed line is utilized to create the notch band for WiMAX band. The results also show that the antenna has omnidirectional radiation pattern and smooth gain over the entire operational band

Wasserstein Generative Regression

In this paper, we propose a new and unified approach for nonparametric regression and conditional distribution learning. Our approach simultaneously estimates a regression function and a conditional generator using a generative learning framework, where a conditional generator is a function that can generate samples from a conditional distribution. The main idea is to estimate a conditional generator that satisfies the constraint that it produces a good regression function estimator. We use deep neural networks to model the conditional generator. Our approach can handle problems with multivariate outcomes and covariates, and can be used to construct prediction intervals. We provide theoretical guarantees by deriving non-asymptotic error bounds and the distributional consistency of our approach under suitable assumptions. We also perform numerical experiments with simulated and real data to demonstrate the effectiveness and superiority of our approach over some existing approaches in various scenarios.Comment: 50 pages, including appendix. 5 figures and 6 tables in the main text. 1 figure and 7 tables in the appendi

Efficient Multi-objective Evolutionary 3D Neural Architecture Search for COVID-19 Detection with Chest CT Scans

COVID-19 pandemic has spread globally for months. Due to its long incubation period and high testing cost, there is no clue showing its spread speed is slowing down, and hence a faster testing method is in dire need. This paper proposes an efficient Evolutionary Multi-objective neural ARchitecture Search (EMARS) framework, which can automatically search for 3D neural architectures based on a well-designed search space for COVID-19 chest CT scan classification. Within the framework, we use weight sharing strategy to significantly improve the search efficiency and finish the search process in 8 hours. We also propose a new objective, namely potential, which is of benefit to improve the search process's robustness. With the objectives of accuracy, potential, and model size, we find a lightweight model (3.39 MB), which outperforms three baseline human-designed models, i.e., ResNet3D101 (325.21 MB), DenseNet3D121 (43.06 MB), and MC3\_18 (43.84 MB). Besides, our well-designed search space enables the class activation mapping algorithm to be easily embedded into all searched models, which can provide the interpretability for medical diagnosis by visualizing the judgment based on the models to locate the lesion areas.Comment: Neural Architecture Search, Evolutionary Algorithm, COVID-19, C

Magnetic-Assisted Initialization for Infrastructure-free Mobile Robot Localization

Most of the existing mobile robot localization solutions are either heavily dependent on pre-installed infrastructures or having difficulty working in highly repetitive environments which do not have sufficient unique features. To address this problem, we propose a magnetic-assisted initialization approach that enhances the performance of infrastructure-free mobile robot localization in repetitive featureless environments. The proposed system adopts a coarse-to-fine structure, which mainly consists of two parts: magnetic field-based matching and laser scan matching. Firstly, the interpolated magnetic field map is built and the initial pose of the mobile robot is partly determined by the k-Nearest Neighbors (k-NN) algorithm. Next, with the fusion of prior initial pose information, the robot is localized by laser scan matching more accurately and efficiently. In our experiment, the mobile robot was successfully localized in a featureless rectangular corridor with a success rate of 88% and an average correct localization time of 6.6 seconds