The Dynamics of UN Peacekeeping Operations

A burgeoning literature examines UN peacekeeping operations, including the rationales of UN peacekeeping contributions, UN peacekeeping as a foreign policy, and the effectiveness of UN peacekeeping operations. This thesis builds on the existing literature and seeks a further understanding on the dynamics of UN peacekeeping operations. In three chapters, speaking to each of the topics above, it pursues relevant new topics by bridging new variables from different research fields with peacekeeping studies. Chapter One uses the variable, trade potential, from the economic integration literature, and argues that trade potential rather than actual trade should help explain countries' decision to commit troops to UN peace operations. It uses a gravity model to estimate expected trade and then calculates and measures trade potential. Chapter Two employs a network inferential model to examine whether UN peacekeeping participation can actually enhance a country’s status, given that the literature argues that status motivates many countries to participate in peacekeeping operations. Chapter Three examines how peacekeeping and humanitarian aid affect the risks of local conflict. The existing literature has found that peacekeeping reduces violence, while humanitarian aid could exacerbate the violence. It uses subnational datasets regarding peacekeeping, aid, and conflict to test the integrated effect of peacekeeping and humanitarian aid. The thesis therefore presents new theoretical and empirical findings on the dynamics of UN peacekeeping operations. Three chapters aim to bridge peacekeeping studies with different research fields including economic integration, foreign policy analysis, network analysis, and aid

Mx2M: Masked Cross-Modality Modeling in Domain Adaptation for 3D Semantic Segmentation

Existing methods of cross-modal domain adaptation for 3D semantic segmentation predict results only via 2D-3D complementarity that is obtained by cross-modal feature matching. However, as lacking supervision in the target domain, the complementarity is not always reliable. The results are not ideal when the domain gap is large. To solve the problem of lacking supervision, we introduce masked modeling into this task and propose a method Mx2M, which utilizes masked cross-modality modeling to reduce the large domain gap. Our Mx2M contains two components. One is the core solution, cross-modal removal and prediction (xMRP), which makes the Mx2M adapt to various scenarios and provides cross-modal self-supervision. The other is a new way of cross-modal feature matching, the dynamic cross-modal filter (DxMF) that ensures the whole method dynamically uses more suitable 2D-3D complementarity. Evaluation of the Mx2M on three DA scenarios, including Day/Night, USA/Singapore, and A2D2/SemanticKITTI, brings large improvements over previous methods on many metrics