Detecting Visual Relationships with Deep Relational Networks

Relationships among objects play a crucial role in image understanding. Despite the great success of deep learning techniques in recognizing individual objects, reasoning about the relationships among objects remains a challenging task. Previous methods often treat this as a classification problem, considering each type of relationship (e.g. "ride") or each distinct visual phrase (e.g. "person-ride-horse") as a category. Such approaches are faced with significant difficulties caused by the high diversity of visual appearance for each kind of relationships or the large number of distinct visual phrases. We propose an integrated framework to tackle this problem. At the heart of this framework is the Deep Relational Network, a novel formulation designed specifically for exploiting the statistical dependencies between objects and their relationships. On two large datasets, the proposed method achieves substantial improvement over state-of-the-art.Comment: To be appeared in CVPR 2017 as an oral pape

ADVANCED SYNCHRONOUS MACHINE MODELING

The synchronous machine is one of the critical components of electric power systems. Modeling of synchronous machines is essential for power systems analyses. Electric machines are often interfaced with power electronic components. This work presents an advanced synchronous machine modeling, which emphasis on the modeling and simulation of systems that contain a mixture of synchronous machines and power electronic components. Such systems can be found in electric drive systems, dc power systems, renewable energy, and conventional synchronous machine excitation. Numerous models and formulations have been used to study synchronous machines in different applications. Herein, a unified derivation of the various model formulations, which support direct interface to external circuitry in a variety of scenarios, is presented. Selection of the formulation with the most suitable interface for the simulation scenario has better accuracy, fewer time steps, and less run time. Brushless excitation systems are widely used for synchronous machines. As a critical part of the system, rotating rectifiers have a significant impact on the system behavior. This work presents a numerical average-value model (AVM) for rotating rectifiers in brushless excitation systems, where the essential numerical functions are extracted from the detailed simulations and vary depending on the loading conditions. The proposed AVM can provide accurate simulations in both transient and steady states with fewer time steps and less run time compared with detailed models of such systems and that the proposed AVM can be combined with AVM models of other rectifiers in the system to reduce the overall computational cost. Furthermore, this work proposes an alternative formulation of numerical AVMs of machine-rectifier systems, which makes direct use of the natural dynamic impedance of the rectifier without introducing low-frequency approximations or algebraic loops. By using this formulation, a direct interface of the AVM is achieved with inductive circuitry on both the ac and dc sides allowing traditional voltage-in, current-out formulations of the circuitry on these sides to be used with the proposed formulation directly. This numerical AVM formulation is validated against an experimentally validated detailed model and compared with previous AVM formulations. It is demonstrated that the proposed AVM formulation accurately predicts the system\u27s low-frequency behavior during both steady and transient states, including in cases where previous AVM formulations cannot predict accurate results. Both run times and numbers of time steps needed by the proposed AVM formulation are comparable to those of existing AVM formulations and significantly decreased compared with the detailed model

ROBOMIRROR: A SIMULATED MIRROR DISPLAY WITH A ROBOTIC CAMERA

Simulated mirror displays have a promising prospect in applications, due to its capability for virtual visualization. In most existing mirror displays, cameras are placed on top of the displays and unable to capture the person in front of the display at the highest possible resolution. The lack of a direct frontal capture of the subject\u27s face and the geometric error introduced by image warping techniques make realistic mirror image rendering a challenging problem. The objective of this thesis is to explore the use of a robotic camera in tracking the face of the subject in front of the display to obtain a high-quality image capture. Our system uses a Bislide system to control a camera for face capture, while using a separate color-depth camera for accurate face tracking. We construct an optical device in which a one-way mirror is used so that the robotic camera behind can capture the subject while the rendered images can be displayed by reflecting off the mirror from an overhead projector. A key challenge of the proposed system is the reduction of light due to the one-way mirror. The optimal 2D Wiener filter is selected to enhance the low contrast images captured by the camera

Studies on external transportation development and spatial structure transformation of modern Kunming from a Southeast Asian perspective, 1885- 1945

From a regional perspective of Southeast Asia, the paper focuses on Kunming, a gateway between China and Southeast Asian countries. The research elucidates the planning ideas and construction process of external routes, via both land and air, such as Yunnan-Vietnam Railway, Yunnan-Burma Railway, Burma Road, Stilwell Road and Hump Airline in early 20th century. These external routes became the arteries of cargo transportation, and Kunming became a regional economic center and military command center during wartime. The paper further reveals the transformation of Kunming’s spatial structures influenced by these external routes, which accelerated Kunming’s urban growth along the traffic lines. The city center shifted to the Station area, where industrial and commercial developments also congregated. New industrial zones were planned to the east and north of the old city, where new passages brought more convenient transportations. The internal road network plan also emphasized the connection with new railway station and bus stations. The research construes the planning ideas and implementation, traces their theoretical origins, and uncovers their indigenous considerations

Remove-Win: a Design Framework for Conflict-free Replicated Data Collections

Internet-scale distributed systems often replicate data within and across data centers to provide low latency and high availability despite node and network failures. Replicas are required to accept updates without coordination with each other, and the updates are then propagated asynchronously. This brings the issue of conflict resolution among concurrent updates, which is often challenging and error-prone. The Conflict-free Replicated Data Type (CRDT) framework provides a principled approach to address this challenge. This work focuses on a special type of CRDT, namely the Conflict-free Replicated Data Collection (CRDC), e.g. list and queue. The CRDC can have complex and compound data items, which are organized in structures of rich semantics. Complex CRDCs can greatly ease the development of upper-layer applications, but also makes the conflict resolution notoriously difficult. This explains why existing CRDC designs are tricky, and hard to be generalized to other data types. A design framework is in great need to guide the systematic design of new CRDCs. To address the challenges above, we propose the Remove-Win Design Framework. The remove-win strategy for conflict resolution is simple but powerful. The remove operation just wipes out the data item, no matter how complex the value is. The user of the CRDC only needs to specify conflict resolution for non-remove operations. This resolution is destructed to three basic cases and are left as open terms in the CRDC design skeleton. Stubs containing user-specified conflict resolution logics are plugged into the skeleton to obtain concrete CRDC designs. We demonstrate the effectiveness of our design framework via a case study of designing a conflict-free replicated priority queue. Performance measurements also show the efficiency of the design derived from our design framework.Comment: revised after submissio

A Study on the Long-term Spatio-Temporal Changes of Shrinking Cities in China

Urban shrinkage has become a global phenomenon, appearing not only in developed countries but also in China, which is undergoing rapid urbanization. Although numerous studies have investigated the distribution of shrinking cities, most of them analyzed from the population dimension. It is necessary to consider the economic dimension and long-term studies. This study takes all prefecture-level cities in mainland China as the research subject. It addresses three questions: 1) How did the spatio-temporal distribution of shrinking cities change between 2000 and 2020, from the perspectives of demography and economy? 2) What are the types of shrinking cities and their distribution, according to the shrinking dimensions and shrinking period? 3) How does the distribution of shrinking cities vary across different urban contexts, such as city size and urban resources? The results show that more than half of Chinese cities are experiencing permanent resident loss. And population shrinkage is observed before the economic decline. Among shrinking cities, the largest proportion shows shrinkage in the single dimension of the population. Additionally, the number of cities with both shrinkage in population and economy increased significantly after 2015, accounting for 19.7% of all shrinking cities. Moreover, 44.7% of shrinking cities are suffering continuous shrinkage. Furthermore, the proportion of shrinking cities is higher in small and medium-sized cities and resource-based cities. This study describes the evolution of shrinking cities in China and enriches the discussion on urban shrinkage worldwide. The findings can remind urban policymakers and planners of more attention on shrinking cities and planning strategies to cope with urban shrinkage

4D Unsupervised Object Discovery

Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. Therefore, discovering objects without annotations has great significance. However, this task seems impractical on still-image or point cloud alone due to the lack of discriminative information. Previous studies underlook the crucial temporal information and constraints naturally behind multi-modal inputs. In this paper, we propose 4D unsupervised object discovery, jointly discovering objects from 4D data -- 3D point clouds and 2D RGB images with temporal information. We present the first practical approach for this task by proposing a ClusterNet on 3D point clouds, which is jointly iteratively optimized with a 2D localization network. Extensive experiments on the large-scale Waymo Open Dataset suggest that the localization network and ClusterNet achieve competitive performance on both class-agnostic 2D object detection and 3D instance segmentation, bridging the gap between unsupervised methods and full supervised ones. Codes and models will be made available at https://github.com/Robertwyq/LSMOL.Comment: Accepted by NeurIPS 2022. 17 pages, 6 figure