Exploring the heterogeneity of musculoskeletal pain

Musculoskeletal pain often includes pain in the back, neck, knee, and hip, and is associated with a substantial financial and personal burden. Eight chapters are included in this thesis that aims to improve the understanding of the heterogeneity in treatment effects and prognosis of musculoskeletal pain. Four issues were identified: i) people with different pain phenotypes (i.e. back pain with or without neurological deficit) or with distinct underlying health conditions (e.g. pregnancy-related back pain) may respond differently to treatment strategies; ii) people with chronic back pain and presenting different radiological phenotypes may experience different course of the disease; iii) different patterns of analgesic use over time may be associated with different long term health status; iv) different types and number of sites of musculoskeletal pain may be associated with different clinical prognoses

CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution

Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread attention, aiming to super sample medical volumes at arbitrary scales via a single model. However, existing MIASSR methods face two major limitations: (i) reliance on high-resolution (HR) volumes and (ii) limited generalization ability, which restricts their application in various scenarios. To overcome these limitations, we propose Cube-based Neural Radiance Field (CuNeRF), a zero-shot MIASSR framework that can yield medical images at arbitrary scales and viewpoints in a continuous domain. Unlike existing MIASSR methods that fit the mapping between low-resolution (LR) and HR volumes, CuNeRF focuses on building a coordinate-intensity continuous representation from LR volumes without the need for HR references. This is achieved by the proposed differentiable modules: including cube-based sampling, isotropic volume rendering, and cube-based hierarchical rendering. Through extensive experiments on magnetic resource imaging (MRI) and computed tomography (CT) modalities, we demonstrate that CuNeRF outperforms state-of-the-art MIASSR methods. CuNeRF yields better visual verisimilitude and reduces aliasing artifacts at various upsampling factors. Moreover, our CuNeRF does not need any LR-HR training pairs, which is more flexible and easier to be used than others. Our code will be publicly available soon

Hard Nominal Example-aware Template Mutual Matching for Industrial Anomaly Detection

Anomaly detectors are widely used in industrial production to detect and localize unknown defects in query images. These detectors are trained on nominal images and have shown success in distinguishing anomalies from most normal samples. However, hard-nominal examples are scattered and far apart from most normalities, they are often mistaken for anomalies by existing anomaly detectors. To address this problem, we propose a simple yet efficient method: \textbf{H}ard Nominal \textbf{E}xample-aware \textbf{T}emplate \textbf{M}utual \textbf{M}atching (HETMM). Specifically, \textit{HETMM} aims to construct a robust prototype-based decision boundary, which can precisely distinguish between hard-nominal examples and anomalies, yielding fewer false-positive and missed-detection rates. Moreover, \textit{HETMM} mutually explores the anomalies in two directions between queries and the template set, and thus it is capable to capture the logical anomalies. This is a significant advantage over most anomaly detectors that frequently fail to detect logical anomalies. Additionally, to meet the speed-accuracy demands, we further propose \textbf{P}ixel-level \textbf{T}emplate \textbf{S}election (PTS) to streamline the original template set. \textit{PTS} selects cluster centres and hard-nominal examples to form a tiny set, maintaining the original decision boundaries. Comprehensive experiments on five real-world datasets demonstrate that our methods yield outperformance than existing advances under the real-time inference speed. Furthermore, \textit{HETMM} can be hot-updated by inserting novel samples, which may promptly address some incremental learning issues

Dual-Band Dual-Mode Substrate Integrated Waveguide Filters with Independently Reconfigurable TE101 Resonant Mode

A novel perturbation approach using additional metalized via-holes for implementation of the dual-band or wide-band dual-mode substrate integrated waveguide (SIW) filters is proposed in this paper. The independent perturbation on the first resonant mode TE101 can be constructed by applying the proposed perturbation approach, whereas the second resonant mode TE102 is not affected. Thus, new kinds of dual-band or wide-band dual-mode SIW filters with a fixed or an independently reconfigurable low-frequency band have been directly achieved. In order to experimentally verify the proposed design method, four two-cavity dual-band SIW filters, which have different numbers of perturbation via-holes in each cavity, and a two-cavity dual-band SIW filter, which includes four via-holes and eight reconfigurable states in each cavity, are designed and experimentally assessed. The measured results indicate that the available frequency-ratio range from 1 to 1.3 can be realized by using four two-cavity dual-band SIW filters. The center frequency of the first band can be tuned from 4.61 GHz to 5.24 GHz, whereas the center frequency of the second one is fixed at around 6.18 GHz for the two-cavity dual-band SIW filter with four reconfigurable states via-holes. All the simulated and measured results show an acceptable agreement with the predicted data

Realistic Volume Rendering with Environment-Synced Illumination in Mixed Reality

Interactive volume visualization using a mixed reality (MR) system helps provide users with an intuitive spatial perception of volumetric data. Due to sophisticated requirements of user interaction and vision when using MR head-mounted display (HMD) devices, the conflict between the realisticness and efficiency of direct volume rendering (DVR) is yet to be resolved. In this paper, a new MR visualization framework that supports interactive realistic DVR is proposed. An efficient illumination estimation method is used to identify the high dynamic range (HDR) environment illumination captured using a panorama camera. To improve the visual quality of Monte Carlo-based DVR, a new spatio-temporal denoising algorithm is designed. Based on a reprojection strategy, it makes full use of temporal coherence between adjacent frames and spatial coherence between the two screens of an HMD to optimize MR rendering quality. Several MR development modules are also developed for related devices to efficiently and stably display the DVR results in an MR HMD. Experimental results demonstrate that our framework can better support immersive and intuitive user perception during MR viewing than existing MR solutions.Comment: 6 pages, 6 figure

Observation of the chiral anomaly induced negative magneto-resistance in 3D Weyl semi-metal TaAs

Weyl semi-metal is the three dimensional analog of graphene. According to the quantum field theory, the appearance of Weyl points near the Fermi level will cause novel transport phenomena related to chiral anomaly. In the present paper, we report the first experimental evidence for the long-anticipated negative magneto-resistance generated by the chiral anomaly in a newly predicted time-reversal invariant Weyl semi-metal material TaAs. Clear Shubnikov de Haas oscillations (SdH) have been detected starting from very weak magnetic field. Analysis of the SdH peaks gives the Berry phase accumulated along the cyclotron orbits to be {\pi}, indicating the existence of Weyl points.Comment: Submitted in February'1

Fourier-Flow model generating Feynman paths

As an alternative but unified and more fundamental description for quantum physics, Feynman path integrals generalize the classical action principle to a probabilistic perspective, under which the physical observables' estimation translates into a weighted sum over all possible paths. The underlying difficulty is to tackle the whole path manifold from finite samples that can effectively represent the Feynman propagator dictated probability distribution. Modern generative models in machine learning can handle learning and representing probability distribution with high computational efficiency. In this study, we propose a Fourier-flow generative model to simulate the Feynman propagator and generate paths for quantum systems. As demonstration, we validate the path generator on the harmonic and anharmonic oscillators. The latter is a double-well system without analytic solutions. To preserve the periodic condition for the system, the Fourier transformation is introduced into the flow model to approach a Matsubara representation. With this novel development, the ground-state wave function and low-lying energy levels are estimated accurately. Our method offers a new avenue to investigate quantum systems with machine learning assisted Feynman Path integral solving