63 research outputs found
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 governmentâs changing approaches to Myanmar, where Chinaâs strategic and economic interests face unprecedented pressure? This article examines those changes in the context of the Chinese governmentâs response to three militarized ethnic conflicts along its border with Myanmar before and after Myanmarâs 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 Chinaâs foreign policy approaches to Myanmar. The article thus contributes to ongoing interest in Chinaâs foreign policy approaches to Southeast Asia in the wake of geostrategic competition between China and the United States
Recommended from our members
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 archiveâthe decades-old but largely ignored civil war in Myanmarâwe 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/
Recommended from our members
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 formerâs grievance construction. We argue that, if the ethnic groupâs 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 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 512512 benchmark by fine-tuning only 25 epochs from a
public pre-trained ImageNet 256256 checkpoint while being 30
more training efficient than the closest competitor.Comment: Tech Repor
Recommended from our members
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 âpoisonedâ 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
- âŠ