Integrated process planning and scheduling for common prismatic parts in a 5-axis CNC environment

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation

Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without the access to source data. In medical imaging scenarios, the practical significance of SFDA methods has been emphasized due to privacy concerns. Recent State-of-the-art SFDA methods primarily rely on self-training based on pseudo-labels (PLs). Unfortunately, PLs suffer from accuracy deterioration caused by domain shift, and thus limit the effectiveness of the adaptation process. To address this issue, we propose a Chebyshev confidence guided SFDA framework to accurately assess the reliability of PLs and generate self-improving PLs for self-training. The Chebyshev confidence is estimated by calculating probability lower bound of the PL confidence, given the prediction and the corresponding uncertainty. Leveraging the Chebyshev confidence, we introduce two confidence-guided denoising methods: direct denoising and prototypical denoising. Additionally, we propose a novel teacher-student joint training scheme (TJTS) that incorporates a confidence weighting module to improve PLs iteratively. The TJTS, in collaboration with the denoising methods, effectively prevents the propagation of noise and enhances the accuracy of PLs. Extensive experiments in diverse domain scenarios validate the effectiveness of our proposed framework and establish its superiority over state-of-the-art SFDA methods. Our paper contributes to the field of SFDA by providing a novel approach for precisely estimating the reliability of pseudo-labels and a framework for obtaining high-quality PLs, resulting in improved adaptation performance

Estimating Brain Age with Global and Local Dependencies

The brain age has been proven to be a phenotype of relevance to cognitive performance and brain disease. Achieving accurate brain age prediction is an essential prerequisite for optimizing the predicted brain-age difference as a biomarker. As a comprehensive biological characteristic, the brain age is hard to be exploited accurately with models using feature engineering and local processing such as local convolution and recurrent operations that process one local neighborhood at a time. Instead, Vision Transformers learn global attentive interaction of patch tokens, introducing less inductive bias and modeling long-range dependencies. In terms of this, we proposed a novel network for learning brain age interpreting with global and local dependencies, where the corresponding representations are captured by Successive Permuted Transformer (SPT) and convolution blocks. The SPT brings computation efficiency and locates the 3D spatial information indirectly via continuously encoding 2D slices from different views. Finally, we collect a large cohort of 22645 subjects with ages ranging from 14 to 97 and our network performed the best among a series of deep learning methods, yielding a mean absolute error (MAE) of 2.855 in validation set, and 2.911 in an independent test set

Fair Multiple-bank E-cash in the Standard Model

Multiple-bank e-cash (electronic cash) model allows users and merchants to open their accounts at different banks which are monitored by the Center Bank. Some multiple-bank e-cash systems were proposed in recent years. However, prior implementations of multiple-bank e-cash all require the random oracle model idealization in their security analysis. We know some schemes are secure in the random oracle model, but are trivially insecure under any instantiation of the oracle. In this paper, based on the automorphic blind signature, the Groth-Sahai proof system and a new group blind signature, we construct a fair multiple-bank e-cash scheme. The new scheme is proved secure in the standard model and provides the following functionalities, such as owner tracing, coin tracing, identification of the double spender and signer tracing. In order to sign two messages at once, we extend Ghadafi\u27s group blind signature to a new group blind signature. The new signature scheme may be of independent interest

Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images

Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. However, existing studies have used general deep computer vision methods, which present challenges in providing interpretable results and leveraging clinical prior knowledge. To address these challenges, we propose a novel deep-learning framework called Polar-Net. Our approach involves mapping OCTA images from Cartesian coordinates to polar coordinates, which allows for the use of approximate sector convolution and enables the implementation of the ETDRS grid-based regional analysis method commonly used in clinical practice. Furthermore, Polar-Net incorporates clinical prior information of each sector region into the training process, which further enhances its performance. Additionally, our framework adapts to acquire the importance of the corresponding retinal region, which helps researchers and clinicians understand the model's decision-making process in detecting AD and assess its conformity to clinical observations. Through evaluations on private and public datasets, we have demonstrated that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD. In addition, we also show that the two innovative modules introduced in our framework have a significant impact on improving overall performance.Comment: Accepted by MICCAI202

Ultrasound Stimulation Modulates Voltage-Gated Potassium Currents Associated With Action Potential Shape in Hippocampal CA1 Pyramidal Neurons

Potassium channels (K+) play an important role in the regulation of cellular signaling. Dysfunction of potassium channels is associated with several severe ion channels diseases, such as long QT syndrome, episodic ataxia and epilepsy. Ultrasound stimulation has proven to be an effective non-invasive tool for the modulation of ion channels and neural activity. In this study, we demonstrate that ultrasound stimulation enables to modulate the potassium currents and has an impact on the shape modulation of action potentials (AP) in the hippocampal pyramidal neurons using whole-cell patch-clamp recordings in vitro. The results show that outward potassium currents in neurons increase significantly, approximately 13%, in response to 30 s ultrasound stimulation. Simultaneously, the increasing outward potassium currents directly decrease the resting membrane potential (RMP) from −64.67 ± 1.10 mV to −67.51 ± 1.35 mV. Moreover, the threshold current and AP fall rate increase while the reduction of AP half-width and after-hyperpolarization peak time is detected. During ultrasound stimulation, reduction of the membrane input resistance of pyramidal neurons can be found and shorter membrane time constant is achieved. Additionally, we verify that the regulation of potassium currents and shape of action potential is mainly due to the mechanical effects induced by ultrasound. Therefore, ultrasound stimulation may offer an alternative tool to treat some ion channels diseases related to potassium channels