Indonesia's relations with China in the age of COVID-19

Seven decades after the establishment of diplomatic relations between Indonesia and China, the COVID-19 pandemic presents new prospects and challenges for bilateral cooperation. We seek to analyse various traits in China-Indonesia relations since 2020 by examining how Indonesia attempts balancing between such cooperation and maintaining peaceful ethnic relations domestically. By tracking the domestic discourse surrounding COVID-19 and China through Indonesia's domestic news media, the paper analyses the development of the pandemic in Indonesia, its procurement of vaccines, and, most significantly, domestic sentiments concerning Indonesia's ethnic Chinese Tionghoa citizens, as well as Indonesia's bilateral relations with China in general. The article argues that while the COVID-19 pandemic has created new avenues of cooperation between Indonesia and China, it has also adversely affected the domestic relations between ethnic Chinese citizens and the rest of the population. However, this has not translated into a widespread backlash toward China that might hinder bilateral cooperation

Geopolitics, Ethnic Politics along the Border, and Chinese Foreign Policy Changes toward Myanmar

Ever since Myanmar reoriented its foreign policy as a result of its transition to democratic rule in 2010, it has significantly improved its relations with the West, particularly the United States. Amid heightened geostrategic competition between the U.S. and China, how can we understand the Chinese government’s changing approaches to Myanmar, where China’s strategic and economic interests face unprecedented pressure? This article examines those changes in the context of the Chinese government’s response to three militarized ethnic conflicts along its border with Myanmar before and after Myanmar’s foreign policy reorientation. Drawing evidence from Chinese Ministry of Foreign Affairs statements and Chinese media coverage of the 2009 and 2015 Kokang conflicts and the 2011-2013 Kachin conflict, the article argues that combined geopolitical changes and domestic nationalist signaling explain the variations of China’s foreign policy approaches to Myanmar. The article thus contributes to ongoing interest in China’s foreign policy approaches to Southeast Asia in the wake of geostrategic competition between China and the United States

Forgotten conflicts: producing knowledge and ignorance in security studies

Security studies privileges the study of civil wars in some contexts over others. The field's leading journals mostly publish studies of armed conflicts in Africa, Eastern Europe, and the Middle East. Armed conflicts in Asia receive comparatively little attention, despite their prevalence and protracted nature. Against the background of our own empirical archive—the decades-old but largely ignored civil war in Myanmar—we ask why some conflicts draw more scholarly interest than others and why this uneven attention matters. In doing so, we argue that the empirical selectivity bias in the study of civil war and armed conflict reflects (1) institutional entanglements between the field of security studies and Western foreign policy; and (2) sociological factors that shape the formation of scholarly subjectivities and pertain to methodological challenges. This uneven empirical landscape shapes our conceptual understanding of civil wars. In fact, prominent debates within leading security studies journals surrounding the nature of civil war and armed conflict are inseparable from the empirical contexts in which they emerged. Leveling such an uneven empirical landscape thus generates opportunities for discussing conflict, insecurity, and violence in a different light. In shedding light on this issue, we urge closer attention to questions of place, time, and power in the scholarly production of knowledge and ignorance

Drag-A-Video: Non-rigid Video Editing with Point-based Interaction

Video editing is a challenging task that requires manipulating videos on both the spatial and temporal dimensions. Existing methods for video editing mainly focus on changing the appearance or style of the objects in the video, while keeping their structures unchanged. However, there is no existing method that allows users to interactively ``drag'' any points of instances on the first frame to precisely reach the target points with other frames consistently deformed. In this paper, we propose a new diffusion-based method for interactive point-based video manipulation, called Drag-A-Video. Our method allows users to click pairs of handle points and target points as well as masks on the first frame of an input video. Then, our method transforms the inputs into point sets and propagates these sets across frames. To precisely modify the contents of the video, we employ a new video-level motion supervision to update the features of the video and introduce the latent offsets to achieve this update at multiple denoising timesteps. We propose a temporal-consistent point tracking module to coordinate the movement of the points in the handle point sets. We demonstrate the effectiveness and flexibility of our method on various videos. The website of our work is available here: https://drag-a-video.github.io/

External Kin, Economic Disparity, and Minority Ethnic Group Mobilization

What is the relationship between economic grievance and ethnopolitical conflict? Many theories on ethnic conflict posit a relationship between economic inequality and conflict, and many tend to agree that economic inequality between groups is one of the main causes of grievance and thereby political mobilization. This article engages existing literature on horizontal inequalities, but probes the violent consequences of a different type of economic inequality. In particular, we are interested in the type of ethnic group that has extensive external kin relations, and how in such conditions the economic disparity between the ethnic group and its external kin group condition the former’s grievance construction. We argue that, if the ethnic group’s external kin enjoys positive economic advantage over the ethnic group, then the latter is more likely to feel deprived and engage in violent political mobilization toward the current host state

DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning

Diffusion models have proven to be highly effective in generating high-quality images. However, adapting large pre-trained diffusion models to new domains remains an open challenge, which is critical for real-world applications. This paper proposes DiffFit, a parameter-efficient strategy to fine-tune large pre-trained diffusion models that enable fast adaptation to new domains. DiffFit is embarrassingly simple that only fine-tunes the bias term and newly-added scaling factors in specific layers, yet resulting in significant training speed-up and reduced model storage costs. Compared with full fine-tuning, DiffFit achieves 2$\times$ training speed-up and only needs to store approximately 0.12\% of the total model parameters. Intuitive theoretical analysis has been provided to justify the efficacy of scaling factors on fast adaptation. On 8 downstream datasets, DiffFit achieves superior or competitive performances compared to the full fine-tuning while being more efficient. Remarkably, we show that DiffFit can adapt a pre-trained low-resolution generative model to a high-resolution one by adding minimal cost. Among diffusion-based methods, DiffFit sets a new state-of-the-art FID of 3.02 on ImageNet 512$\times$512 benchmark by fine-tuning only 25 epochs from a public pre-trained ImageNet 256$\times$256 checkpoint while being 30$\times$ more training efficient than the closest competitor.Comment: Tech Repor

British Colonialism and the Criminalization of Homosexuality

What explains the global variation in laws criminalizing homosexual conduct? Recent research has claimed that British colonialism is largely responsible for the criminalization of homosexuality around the world. This article utilizes a newly constructed dataset that includes up-to-date data on 185 countries to assess this claim. We find that British colonies are much more likely to have criminalization of homosexual conduct laws than other colonies or other states in general. This result holds after controlling for other variables that might be expected to influence the likelihood of repressive lesbian, gay, bisexual and transgender (LGBT) rights legislation. However, we also find that the evidence in favour of the claim that British imperialism ‘poisoned’ societies against homosexuality is weak. British colonies do not systematically take longer to decriminalize homosexual conduct than other European colonies

DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning

Recent advances in natural language processing, primarily propelled by Large Language Models (LLMs), have showcased their remarkable capabilities grounded in in-context learning. A promising avenue for guiding LLMs in intricate reasoning tasks involves the utilization of intermediate reasoning steps within the Chain-of-Thought (CoT) paradigm. Nevertheless, the central challenge lies in the effective selection of exemplars for facilitating in-context learning. In this study, we introduce a framework that leverages Dual Queries and Low-rank approximation Re-ranking (DQ-LoRe) to automatically select exemplars for in-context learning. Dual Queries first query LLM to obtain LLM-generated knowledge such as CoT, then query the retriever to obtain the final exemplars via both question and the knowledge. Moreover, for the second query, LoRe employs dimensionality reduction techniques to refine exemplar selection, ensuring close alignment with the input question's knowledge. Through extensive experiments, we demonstrate that DQ-LoRe significantly outperforms prior state-of-the-art methods in the automatic selection of exemplars for GPT-4, enhancing performance from 92.5% to 94.2%. Our comprehensive analysis further reveals that DQ-LoRe consistently outperforms retrieval-based approaches in terms of both performance and adaptability, especially in scenarios characterized by distribution shifts. DQ-LoRe pushes the boundary of in-context learning and opens up new avenues for addressing complex reasoning challenges. Our code is released at https://github.com/AI4fun/DQ-LoRe}{https://github.com/AI4fun/DQ-LoRe.Comment: Accepted in ICLR 202

LEGO-Prover: Neural Theorem Proving with Growing Libraries

Despite the success of large language models (LLMs), the task of theorem proving still remains one of the hardest reasoning tasks that is far from being fully solved. Prior methods using language models have demonstrated promising results, but they still struggle to prove even middle school level theorems. One common limitation of these methods is that they assume a fixed theorem library during the whole theorem proving process. However, as we all know, creating new useful theorems or even new theories is not only helpful but crucial and necessary for advancing mathematics and proving harder and deeper results. In this work, we present LEGO-Prover, which employs a growing skill library containing verified lemmas as skills to augment the capability of LLMs used in theorem proving. By constructing the proof modularly, LEGO-Prover enables LLMs to utilize existing skills retrieved from the library and to create new skills during the proving process. These skills are further evolved (by prompting an LLM) to enrich the library on another scale. Modular and reusable skills are constantly added to the library to enable tackling increasingly intricate mathematical problems. Moreover, the learned library further bridges the gap between human proofs and formal proofs by making it easier to impute missing steps. LEGO-Prover advances the state-of-the-art pass rate on miniF2F-valid (48.0% to 57.0%) and miniF2F-test (45.5% to 47.1%). During the proving process, LEGO-Prover also manages to generate over 20,000 skills (theorems/lemmas) and adds them to the growing library. Our ablation study indicates that these newly added skills are indeed helpful for proving theorems, resulting in an improvement from a success rate of 47.1% to 50.4%. We also release our code and all the generated skills