Effective Big Data Integration In The Development Of Smart Cities In China: A Digital Continuity Approach

Development of smart cities has become a national strategy for effectively tackling the challenges in current dynamic digital environment. The rapid development of smart cities leads to the production and accumulation of a huge amount of data usually referred as big data. The challenge is the accumulated data from different sources are often isolated in different information systems. It is usually very difficult or impossible to effectively and efficiently access such data from an integrated manner. Therefore, there is an increasing demand for effectively integrating and managing big data, especially in the rapid development of smart cities in China. Nevertheless, and despite a number of data integration approaches found in the literature, there is a clear lack of appropriate approach from both social construction and technological construction for the effective integration of big data sources. Moreover, there is a theoretical gap in existing integration of big data sources for explaining the multiverse of data integration in world of practice. This issue of applicability of approach is now recognised as one of the fundamental key issues in developing smart cities. This study aims at providing insights in this area, and presents a comprehensive analysis of existing studies on the integration of big data resources for the development of smart cities in China. The study employed an inductive qualitative approach based on a mix-methods strategy consisting of literature review, document analysis and case studies. The findings of this research state that the identification of four challenges in data integration and five data governance problems in the development of smart cities. To effectively tackle such challenges and adequately address these problems, a digital continuity approach, a holistic approach, is proposed to managing big data resources that data can be tracked, traced, linked and exploited for the development of smart cities in China. The proposed approach also can be used to guide the development of strategies, policies, and action plans for integrating big data resources to improve data assurance, data integrity, data trust, data security and data reusability in the delivery of smart city services in China

Pressure induced superconductivity bordering a charge-density-wave state in NbTe4 with strong spinorbit coupling

Transition-metal chalcogenides host various phases of matter, such as charge-density wave (CDW), superconductors, and topological insulators or semimetals. Superconductivity and its competition with CDW in low-dimensional compounds have attracted much interest and stimulated considerable research. Here we report pressure induced superconductivity in a strong spin-orbit (SO) coupled quasi-one-dimensional (1D) transition-metal chalcogenide NbTe$_4$, which is a CDW material under ambient pressure. With increasing pressure, the CDW transition temperature is gradually suppressed, and superconducting transition, which is fingerprinted by a steep resistivity drop, emerges at pressures above 12.4 GPa. Under pressure $p$ = 69 GPa, zero resistance is detected with a transition temperature $T_c$ = 2.2 K and an upper critical field $H_{c2}$= 2 T. We also find large magnetoresistance (MR) up to 102\% at low temperatures, which is a distinct feature differentiating NbTe$_4$ from other conventional CDW materials.Comment: https://rdcu.be/LX8

Quantum Control in Open and Periodically Driven Systems

Quantum technology resorts to efficient utilization of quantum resources to realize technique innovation. The systems are controlled such that their states follow the desired manners to realize different quantum protocols. However, the decoherence caused by the system-environment interactions causes the states deviating from the desired manners. How to protect quantum resources under the coexistence of active control and passive decoherence is of significance. Recent studies have revealed that the decoherence is determined by the feature of the system-environment energy spectrum: Accompanying the formation of bound states in the energy spectrum, the decoherence can be suppressed. It supplies a guideline to control decoherence. Such idea can be generalized to systems under periodic driving. By virtue of manipulating Floquet bound states in the quasienergy spectrum, coherent control via periodic driving dubbed as Floquet engineering has become a versatile tool not only in controlling decoherence, but also in artificially synthesizing exotic topological phases. We will review the progress on quantum control in open and periodically driven systems. Special attention will be paid to the distinguished role played by the bound states and their controllability via periodic driving in suppressing decoherence and generating novel topological phases.Comment: A review articl

3DG-STFM: 3D Geometric Guided Student-Teacher Feature Matching

We tackle the essential task of finding dense visual correspondences between a pair of images. This is a challenging problem due to various factors such as poor texture, repetitive patterns, illumination variation, and motion blur in practical scenarios. In contrast to methods that use dense correspondence ground-truths as direct supervision for local feature matching training, we train 3DG-STFM: a multi-modal matching model (Teacher) to enforce the depth consistency under 3D dense correspondence supervision and transfer the knowledge to 2D unimodal matching model (Student). Both teacher and student models consist of two transformer-based matching modules that obtain dense correspondences in a coarse-to-fine manner. The teacher model guides the student model to learn RGB-induced depth information for the matching purpose on both coarse and fine branches. We also evaluate 3DG-STFM on a model compression task. To the best of our knowledge, 3DG-STFM is the first student-teacher learning method for the local feature matching task. The experiments show that our method outperforms state-of-the-art methods on indoor and outdoor camera pose estimations, and homography estimation problems. Code is available at: https://github.com/Ryan-prime/3DG-STFM

LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion

Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. However, due to GI anatomical constraints and hardware manufacturing limitations, WCE vision signals may suffer from insufficient illumination, leading to a complicated screening and examination procedure. Deep learning-based low-light image enhancement (LLIE) in the medical field gradually attracts researchers. Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process. The multi-scale design allows models to preserve high-resolution representation and context information from low-resolution, while the curved wavelet attention (CWA) block is proposed for high-frequency and local feature learning. Furthermore, we combine the reverse diffusion procedure to further optimize the shallow output and generate the most realistic image. The proposed method is compared with ten state-of-the-art (SOTA) LLIE methods and significantly outperforms quantitatively and qualitatively. The superior performance on GI disease segmentation further demonstrates the clinical potential of our proposed model. Our code is publicly accessible.Comment: To appear in MICCAI 2023. Code availability: https://github.com/longbai1006/LLCap

Thermodynamical Properties and Quasi-localized Energy of the Stringy Dyonic Black Hole Solution

In this article, we calculate the heat flux passing through the horizon $. {\bf TS}|_{r_h}$ and the difference of energy between the Einstein and M{\o}ller prescription within the region ${\cal M}$, in which is the region between outer horizon ${\cal H}_+$ and inner horizon ${\cal H}_-$, for the modified GHS solution, KLOPP solution and CLH solution. The formula . E_{\rm Einstein}|_{\cal M} = . E_{\rm M{\o}ller}|_{\cal M} - \sum_{\partial {\cal M}} {\bf TS}$ is obeyed for the mGHS solution and the KLOPP solution, but not for the CLH solution. Also, we suggest a RN-like stringy dyonic black hole solution, which comes from the KLOPP solution under a dual transformation, and its thermodynamical properties are the same as the KLOPP solution