On the dynamics of two photons interacting with a two-qubit coherent feedback network}

The purpose of this paper is to study the dynamics of a quantum coherent feedback network composed of two two-level systems (qubits) driven by two counter-propagating photons, one in each input channel. The coherent feedback network enhances the nonlinear photon-photon interaction inside the feedback loop. By means of quantum stochastic calculus and the input-output framework, the analytic form of the steady-state output two-photon state is derived. Based on the analytic form, the applications on the Hong-Ou-Mandel (HOM) interferometer and marginally stable single-photon devices using this coherent feedback structure have been demonstrated. The difference between continuous-mode and single-mode few-photon states is demonstrated.Comment: 15 pages, 4 figures; accepted by Automatica; comments are welcome

A novel multipath-transmission supported software defined wireless network architecture

The inflexible management and operation of today\u27s wireless access networks cannot meet the increasingly growing specific requirements, such as high mobility and throughput, service differentiation, and high-level programmability. In this paper, we put forward a novel multipath-transmission supported software-defined wireless network architecture (MP-SDWN), with the aim of achieving seamless handover, throughput enhancement, and flow-level wireless transmission control as well as programmable interfaces. In particular, this research addresses the following issues: 1) for high mobility and throughput, multi-connection virtual access point is proposed to enable multiple transmission paths simultaneously over a set of access points for users and 2) wireless flow transmission rules and programmable interfaces are implemented into mac80211 subsystem to enable service differentiation and flow-level wireless transmission control. Moreover, the efficiency and flexibility of MP-SDWN are demonstrated in the performance evaluations conducted on a 802.11 based-testbed, and the experimental results show that compared to regular WiFi, our proposed MP-SDWN architecture achieves seamless handover and multifold throughput improvement, and supports flow-level wireless transmission control for different applications

Fracturing and thermal extraction optimization methods in enhanced geothermal systems

Fracture networks, fluid flow and heat extraction within fractures constitute pivotal aspects of enhanced geothermal system advancement. Conventional hydraulic fracturing in dry hot rock reservoirs typically requires high breakdown pressure and only produces a single major fracture morphology. Thus, it is imperative to explore better fracturing methods and consider more reasonable coupling mechanisms to improve the prediction efficiency. Cyclic fracturing using liquid nitrogen instead of water can generate more complex fracture networks and improve the fracturing performance. The simulation of fluid flow and heat transfer processes in the fracture network is crucial for an enhanced geothermal system, which requires a more comprehensive coupled thermo-hydro-mechanical-chemical model for matching, especially the characterization of coupling mechanism between the chemical and mechanical field. Based on the results of field engineering, laboratory experiments and numerical simulation, the optimum engineering scheme can be obtained by a multi-objective optimization and decision-making method. Furthermore, combining it with the deep-learning-based proxy model to achieve dynamic optimization with time is a meaningful future research direction.Document Type: PerspectiveCited as: Yang, R., Wang, Y., Song, G., Shi, Y. Fracturing and thermal extraction optimization methods in enhanced geothermal systems. Advances in Geo-Energy Research, 2023, 9(2): 136-140. https://doi.org/10.46690/ager.2023.08.0

DiffRoom: Diffusion-based High-Quality 3D Room Reconstruction and Generation

We present DiffRoom, a novel framework for tackling the problem of high-quality 3D indoor room reconstruction and generation, both of which are challenging due to the complexity and diversity of the room geometry. Although diffusion-based generative models have previously demonstrated impressive performance in image generation and object-level 3D generation, they have not yet been applied to room-level 3D generation due to their computationally intensive costs. In DiffRoom, we propose a sparse 3D diffusion network that is efficient and possesses strong generative performance for Truncated Signed Distance Field (TSDF), based on a rough occupancy prior. Inspired by KinectFusion's incremental alignment and fusion of local SDFs, we propose a diffusion-based TSDF fusion approach that iteratively diffuses and fuses TSDFs, facilitating the reconstruction and generation of an entire room environment. Additionally, to ease training, we introduce a curriculum diffusion learning paradigm that speeds up the training convergence process and enables high-quality reconstruction. According to the user study, the mesh quality generated by our DiffRoom can even outperform the ground truth mesh provided by ScanNet

Loss of the Transcriptional Repressor PAG-3/Gfi-1 Results in Enhanced Neurosecretion that is Dependent on the Dense-Core Vesicle Membrane Protein IDA-1/IA-2

It is generally accepted that neuroendocrine cells regulate dense core vesicle (DCV) biogenesis and cargo packaging in response to secretory demands, although the molecular mechanisms of this process are poorly understood. One factor that has previously been implicated in DCV regulation is IA-2, a catalytically inactive protein phosphatase present in DCV membranes. Our ability to directly visualize a functional, GFP-tagged version of an IA-2 homolog in live Caenorhabditis elegans animals has allowed us to capitalize on the genetics of the system to screen for mutations that disrupt DCV regulation. We found that loss of activity in the transcription factor PAG-3/Gfi-1, which functions as a repressor in many systems, results in a dramatic up-regulation of IDA-1/IA-2 and other DCV proteins. The up-regulation of DCV components was accompanied by an increase in presynaptic DCV numbers and resulted in phenotypes consistent with increased neuroendocrine secretion. Double mutant combinations revealed that these PAG-3 mutant phenotypes were dependent on wild type IDA-1 function. Our results support a model in which IDA-1/IA-2 is a critical element in DCV regulation and reveal a novel genetic link to PAG-3-mediated transcriptional regulation. To our knowledge, this is the first mutation identified that results in increased neurosecretion, a phenotype that has clinical implications for DCV-mediated secretory disorders

PVO: Panoptic Visual Odometry

We present PVO, a novel panoptic visual odometry framework to achieve more comprehensive modeling of the scene motion, geometry, and panoptic segmentation information. Our PVO models visual odometry (VO) and video panoptic segmentation (VPS) in a unified view, which makes the two tasks mutually beneficial. Specifically, we introduce a panoptic update module into the VO Module with the guidance of image panoptic segmentation. This Panoptic-Enhanced VO Module can alleviate the impact of dynamic objects in the camera pose estimation with a panoptic-aware dynamic mask. On the other hand, the VO-Enhanced VPS Module also improves the segmentation accuracy by fusing the panoptic segmentation result of the current frame on the fly to the adjacent frames, using geometric information such as camera pose, depth, and optical flow obtained from the VO Module. These two modules contribute to each other through recurrent iterative optimization. Extensive experiments demonstrate that PVO outperforms state-of-the-art methods in both visual odometry and video panoptic segmentation tasks.Comment: CVPR2023 Project page: https://zju3dv.github.io/pvo/ code: https://github.com/zju3dv/PV

Sliding Mode Control Design for a Class of SISO Systems with Uncertain Sliding Surface

The problem of designing a sliding mode controller with uncertain sliding surface for a class of uncertain single-input-single-output systems is studied. The design case is handled by using the invariant transformation first in order to separate the sliding mode and the reaching mode of the sliding mode control system. It is shown that the sliding mode design needs not to consider the uncertainties of the sliding surface, which can be handled in the reaching phase design. The results generalize the robust design of the reaching phase such that one specific reaching phase design may agree with several sliding surfaces

Deep Clustering: A Comprehensive Survey

Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this paper we provide a comprehensive survey for deep clustering in views of data sources. With different data sources and initial conditions, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, deep clustering methods are introduced according to four categories, i.e., traditional single-view deep clustering, semi-supervised deep clustering, deep multi-view clustering, and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering