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

A Data-Driven Simulation of the New York State Foster Care System

We introduce an analytic pipeline to model and simulate youth trajectories through the New York state foster care system. Our goal in doing so is to forecast how proposed interventions may impact the foster care system’s ability to achieve it’s stated goals before these interventions are actually implemented and impact the lives of thousands of youth. Here, we focus on two specific stated goals of the system: racial equity, and, as codified most recently by the 2018 Family First Prevention Services Act (FFPSA), a focus on keeping all youth out of foster care. We also focus on one specific potential intervention— a predictive model, proposed in prior work and implemented elsewhere in the U.S., which aims to determine whether or not a youth is in need of care. We use our method to explore how the implementation of this predictive model in New York would impact racial equity and the number of youth in care. While our findings, as in any simulation model, ultimately rely on modeling assumptions, we find evidence that the model would not necessarily achieve either goal. Primarily, then, we aim to further promote the use of data-driven simulation to help understand the ramifications of algorithmic interventions in public systems

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

A qualitative, network-centric method for modeling socio-technical systems, with applications to evaluating interventions on social media platforms to increase social equality

We propose and extend a qualitative, complex systems methodology from cognitive engineering, known as theabstraction hierarchy, to model how potential interventions that could be carried out by social media platforms might impact social equality. Social media platforms have come under considerable ire for their role in perpetuating social inequality. However, there is also significant evidence that platforms can play a role inreducingsocial inequality, e.g. through the promotion of social movements. Platforms’ role in producing or reducing social inequality is, moreover, not static; platforms can and often do take actions targeted at positive change. How can we develop tools to help us determine whether or not a potential platform change might actually work to increase social equality? Here, we present the abstraction hierarchy as a tool to help answer this question. Our primary contributions are two-fold. First, methodologically, we extend existing research on the abstraction hierarchy in cognitive engineering with principles from Network Science. Second, substantively, we illustrate the utility of this approach by using it to assess the potential effectiveness of a set of interventions, proposed in prior work, for how online dating websites can help mitigate social inequality

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