111 research outputs found
Chinese National Identities and Understanding the Decision for War with India in 1962
The rise of China (PRC) has dominated scholarly debates in recent days. Since China defined territorial integrity as its “core interest”, it is widely viewed as a sign that China is going to assert its territorial claims with its neighbors (including maritime neighbors such as Philippine). With China’s growing military capabilities, China’s territorial disputes with its many neighbors are becoming one of the leading destabilizing concerns in Asia. However, current scholarship on China’s decision-making in its territorial disputes is too sparse for people outside of the Chinese Politburo to devise strategies to stabilize the region. This thesis aims to understand China’s decision(s) to use force and the decision-making process from a “national identity” perspective. Specifically, this thesis studies Chinese national identities and China’s decision to go to war with India in October 1962. Borrowing largely from Ted Hopf (2002)’s method of studying Soviet identities, this thesis uses discourse analysis to inductively recover Chinese national identities from newspapers, novels and movies. This thesis’ key assumption is that as part of society and public discourse, decision-makers’ understanding of world events should not deviate significantly from national discourses. Therefore, national identities should be a reliable reference point to the decisions-making of “big” national issues, such as defending state sovereignty. The findings of this thesis confirm that assumptions for two reasons: a). findings are in line with existing, authoritative theories on China’s decision for war with India and b). findings are able to provide extra empirical support to inferential statements made by authoritative scholars on this topic
Dimension Reduction Using Samples’ Inner Structure Based Graph for Face Recognition
Acknowledgments This research is supported by (1) the Ph.D. Programs Foundation of Ministry of Education of China under Grant (no. 20120061110045) and (2) the Natural Science Foundation of Jilin Province of China under Grant (no. 201115022).Peer reviewedPublisher PD
Building recognition on subregion’s multi-scale gist feature extraction and corresponding columns information based dimensionality reduction
In this paper, we proposed a new building recognition method named subregion’s multiscale gist feature (SM-gist) extraction and corresponding columns information based dimensionality reduction (CCI-DR). Our proposed building recognition method is presented as a two-stage model: in the first stage, a building image is divided into 4 × 5 subregions, and gist vectors are extracted from these regions individually. Then, we combine these gist vectors into a matrix with relatively high dimensions. In the second stage, we proposed CCI-DR to project the high dimensional manifold matrix to low dimensional subspace. Compared with the previous building recognition method the advantages of our proposed method are that (1) gist features extracted by SM-gist have the ability to adapt to nonuniform illumination and that (2) CCI-DR can address the limitation of traditional dimensionality reduction methods, which convert gist matrices into vectors and thus mix the corresponding gist vectors from different feature maps. Our building recognition method is evaluated on the Sheffield buildings database, and experiments show that our method can achieve satisfactory performance
ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models
Colonoscopy analysis, particularly automatic polyp segmentation and
detection, is essential for assisting clinical diagnosis and treatment.
However, as medical image annotation is labour- and resource-intensive, the
scarcity of annotated data limits the effectiveness and generalization of
existing methods. Although recent research has focused on data generation and
augmentation to address this issue, the quality of the generated data remains a
challenge, which limits the contribution to the performance of subsequent
tasks. Inspired by the superiority of diffusion models in fitting data
distributions and generating high-quality data, in this paper, we propose an
Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy
images that benefit the downstream tasks. Specifically, ArSDM utilizes the
ground-truth segmentation mask as a prior condition during training and adjusts
the diffusion loss for each input according to the polyp/background size ratio.
Furthermore, ArSDM incorporates a pre-trained segmentation model to refine the
training process by reducing the difference between the ground-truth mask and
the prediction mask. Extensive experiments on segmentation and detection tasks
demonstrate the generated data by ArSDM could significantly boost the
performance of baseline methods.Comment: Accepted by MICCAI-202
Topologically Variable and Volumetric Morphing of 3D Architected Materials with Shape Locking
The morphing of 3D structures is suitable for i) future tunable material
design for customizing material properties and ii) advanced manufacturing tools
for fabricating 3D structures on a 2D plane. However, there is no inverse
design method for topologically variable and volumetric morphing or morphing
with shape locking, which limits practical engineering applications. In this
study, we construct a general inverse design method for 3D architected
materials for topologically variable and volumetric morphing, whose shapes are
lockable in the morphed states, which can contribute to future tunable
materials, design, and advanced manufacturing. Volumetric mapping of bistable
unit cells onto any 3D morphing target geometry with kinematic and kinetic
modifications can produce flat-foldable and volumetric morphing structures with
shape-locking. This study presents a generalized inverse design method for 3D
metamaterial morphing that can be used for structural applications with shape
locking. Topologically variable morphing enables the manufacture of volumetric
structures on a 2D plane, saving tremendous energy and materials compared with
conventional 3D printing. Volumetric morphing can significantly expand the
design space with tunable physical properties without limiting the selection of
base materials
CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation
Traffic simulation provides interactive data for the optimization of traffic
control policies. However, existing traffic simulators are limited by their
lack of scalability and shortage in input data, which prevents them from
generating interactive data from traffic simulation in the scenarios of real
large-scale city road networks.
In this paper, we present \textbf{C}ity \textbf{B}rain \textbf{Lab}, a
toolkit for scalable traffic simulation. CBLab consists of three components:
CBEngine, CBData, and CBScenario. CBEngine is a highly efficient simulator
supporting large-scale traffic simulation. CBData includes a traffic dataset
with road network data of 100 cities all around the world. We also develop a
pipeline to conduct a one-click transformation from raw road networks to input
data of our traffic simulation. Combining CBEngine and CBData allows
researchers to run scalable traffic simulations in the road network of real
large-scale cities. Based on that, CBScenario implements an interactive
environment and a benchmark for two scenarios of traffic control policies
respectively, with which traffic control policies adaptable for large-scale
urban traffic can be trained and tuned. To the best of our knowledge, CBLab is
the first infrastructure supporting traffic control policy optimization in
large-scale urban scenarios. CBLab has supported the City Brain Challenge @ KDD
CUP 2021. The project is available on
GitHub:~\url{https://github.com/CityBrainLab/CityBrainLab.git}.Comment: Accepted by KDD2023 (Applied Data Science Track
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