Patching for \'etale algebras and the period-index problem for higher degree Galois cohomology groups over Hensel semi-global fields

In this manuscript, we present a partial generalization of the field patching technique initially proposed by Harbater-Hartmann to Hensel semi-global fields, i.e., function fields of curves over excellent henselian discretely valued fields. More specifically, we show that patching holds for \'etale algebras over such fields and a suitable set of overfields. Within this new framework, we further establish a local-global principle for higher degree Galois cohomology groups over Hensel semi-global fields. As an application, we extend a recent result regarding a uniform period-index bound for higher degree Galois cohomology classes by Harbater-Hartmann-Krashen to Hensel semi-global fields. Additionally, we provide a proof of such a bound for coefficient groups of non-prime orders.Comment: 25 page

Arithmetic Invariant Theory of Reductive Groups

In this manuscript, we define the notion of linearly reductive groups over commutative unital rings and study the finiteness and the Cohen-Macaulay property of the ring of invariants under rational actions of a linearly reductive group. Moreover, we study the equivalence of different notions of reductivity over regular rings of dimension two by studying these properties locally.Comment: 17 page

Fine-grained Graph Learning for Multi-view Subspace Clustering

Multi-view subspace clustering (MSC) is a popular unsupervised method by integrating heterogeneous information to reveal the intrinsic clustering structure hidden across views. Usually, MSC methods use graphs (or affinity matrices) fusion to learn a common structure, and further apply graph-based approaches to clustering. Despite progress, most of the methods do not establish the connection between graph learning and clustering. Meanwhile, conventional graph fusion strategies assign coarse-grained weights to combine multi-graph, ignoring the importance of local structure. In this paper, we propose a fine-grained graph learning framework for multi-view subspace clustering (FGL-MSC) to address these issues. To utilize the multi-view information sufficiently, we design a specific graph learning method by introducing graph regularization and local structure fusion pattern. The main challenge is how to optimize the fine-grained fusion weights while generating the learned graph that fits the clustering task, thus making the clustering representation meaningful and competitive. Accordingly, an iterative algorithm is proposed to solve the above joint optimization problem, which obtains the learned graph, the clustering representation, and the fusion weights simultaneously. Extensive experiments on eight real-world datasets show that the proposed framework has comparable performance to the state-of-the-art methods

High-Speed Digital Detector for the Internet of Things Assisted by Signal’s Intensity Quantification

This paper proposes a high-speed digital detector for the Internet of Things (IoT) assisted by signal’s intensity quantification. The detector quantifies the amplitude of each pixel of the detected image and converts it into a digital signal, which can be directly applied to the IoT with wireless communication system. Two types of amplitude quantization algorithms, uniform quantization and non-uniform quantization, are applied to the detector, which further improves the quality of the detected image and the robustness of the image signal in a noisy environment. Related simulations have been established to verify the accuracy of the models and algorithms

CARTOS: A Charging-Aware Real-Time Operating System for Intermittent Batteryless Devices

This paper presents CARTOS, a charging-aware real-time operating system designed to enhance the functionality of intermittently-powered batteryless devices (IPDs) for various Internet of Things (IoT) applications. While IPDs offer significant advantages such as extended lifespan and operability in extreme environments, they pose unique challenges, including the need to ensure forward progress of program execution amidst variable energy availability and maintaining reliable real-time time behavior during power disruptions. To address these challenges, CARTOS introduces a mixed-preemption scheduling model that classifies tasks into computational and peripheral tasks, and ensures their efficient and timely execution by adopting just-in-time checkpointing for divisible computation tasks and uninterrupted execution for indivisible peripheral tasks. CARTOS also supports processing chains of tasks with precedence constraints and adapts its scheduling in response to environmental changes to offer continuous execution under diverse conditions. CARTOS is implemented with new APIs and components added to FreeRTOS but is designed for portability to other embedded RTOSs. Through real hardware experiments and simulations, CARTOS exhibits superior performance over state-of-the-art methods, demonstrating that it can serve as a practical platform for developing resilient, real-time sensing applications on IPDs

N/P Co-doped Micro-/Mesoporous Carbons Derived from Polyvinyl Pyrrolidone–Zn0.2@ZIF-67 with Tunable Metal Valence States towards Efficient Water Splitting

202407 bcchVersion of RecordOthersPolyU RCRE (1-BBCB) and RI-IWEAR (1-CD8E).PublishedC

Fast On-orbit Pulse Phase Estimation of X-ray Crab Pulsar for XNAV Flight Experiments

The recent flight experiments with Neutron Star Interior Composition Explorer (\textit{NICER}) and \textit{Insight}-Hard X-ray Modulation Telescope (\textit{Insight}-HXMT) have demonstrated the feasibility of X-ray pulsar-based navigation (XNAV) in the space. However, the current pulse phase estimation and navigation methods employed in the above flight experiments are computationally too expensive for handling the Crab pulsar data. To solve this problem, this paper proposes a fast algorithm of on-orbit estimating the pulse phase of Crab pulsar called X-ray pulsar navigaTion usIng on-orbiT pulsAr timiNg (XTITAN). The pulse phase propagation model for Crab pulsar data from \textit{Insight}-HXMT and \textit{NICER} are derived. When an exposure on the Crab pulsar is divided into several sub-exposures, we derive an on-orbit timing method to estimate the hyperparameters of the pulse phase propagation model. Moreover, XTITAN is improved by iteratively estimating the pulse phase and the position and velocity of satellite. When applied to the Crab pulsar data from \textit{NICER}, XTITAN is 58 times faster than the grid search method employed by \textit{NICER} experiment. When applied to the Crab pulsar data from \textit{Insight}-HXMT, XTITAN is 180 times faster than the Significance Enhancement of Pulse-profile with Orbit-dynamics (SEPO) which was employed in the flight experiments with \textit{Insight}-HXMT. Thus, XTITAN is computationally much efficient and has the potential to be employed for onboard computation

Associations between challenging parenting behavior and creative tendencies of children: the chain mediating roles of positive emotion and creative self-efficacy

BackgroundParenting behavior has been reported to be closely associated with children’s creativity, yet the association between challenging parenting behavior and children’s creative tendencies, as well as the potential mechanisms connecting the two, remains ambiguous. Based on the Social Cognitive Theory and the Self-efficacy Theory, this study aims to examine the correlation between Chinese parents’ challenging parenting behaviors and their children’s creative tendencies, as well as the chain mediating role of children’s positive emotions and creative self-efficacy.MethodsIn total, 2,647 families were surveyed with questionnaires completed by parents on the Challenging Parenting Behaviors Scale and by children on the Positive/Negative Emotions Scale, the Creative Self-efficacy Scale, and the Williams Creative Tendency Test Scale, and analyzed using structural equation modeling (SEM) in SPSS 22.0 and Mplus 8.3.ResultsThe findings indicate that challenging parenting behavior has a positive correlation with children’s positive emotions, creative self-efficacy, and creative tendencies. Through positive emotions, creative self-efficacy, and a chain mediated pathway between these two variables, challenging parenting behaviors increase children’s creative tendencies.ConclusionThe favorable impacts of challenging parenting behaviors on children’s creative tendencies, with the mediating effects of children’s positive emotions and creative self-efficacy, may help Chinese parents better grasp the mechanisms underlying this association

Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation

Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual learning features and shared model parameters, while neglecting the reliability of features and performances. In this paper, we propose a novel Uncertainty-informed Mutual Learning (UML) framework for reliable and interpretable medical image analysis. Our UML introduces reliability to joint classification and segmentation tasks, leveraging mutual learning with uncertainty to improve performance. To achieve this, we first use evidential deep learning to provide image-level and pixel-wise confidences. Then, an Uncertainty Navigator Decoder is constructed for better using mutual features and generating segmentation results. Besides, an Uncertainty Instructor is proposed to screen reliable masks for classification. Overall, UML could produce confidence estimation in features and performance for each link (classification and segmentation). The experiments on the public datasets demonstrate that our UML outperforms existing methods in terms of both accuracy and robustness. Our UML has the potential to explore the development of more reliable and explainable medical image analysis models. We will release the codes for reproduction after acceptance.Comment: 13 page

FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniformed. These fine 3D facial models can be represented as a 3D morphable model for rough shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different than the previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. We also use FaceScape data to generate the in-the-wild and in-the-lab benchmark to evaluate recent methods of single-view face reconstruction. The accuracy is reported and analyzed on the dimensions of camera pose and focal length, which provides a faithful and comprehensive evaluation and reveals new challenges. The unprecedented dataset, benchmark, and code have been released to the public for research purpose.Comment: 14 pages, 13 figures, journal extension of FaceScape(CVPR 2020). arXiv admin note: substantial text overlap with arXiv:2003.1398