We present a fast 3DGS reconstruction pipeline designed to converge within one minute, developed for the SIGGRAPH Asia 3DGS Fast Reconstruction Challenge. The challenge consists of an initial round using SLAM-generated camera poses (with noisy trajectories) and a final round using COLMAP poses (highly accurate). To robustly handle these heterogeneous settings, we develop a two-stage solution. In the first round, we use reverse per-Gaussian parallel optimization and compact forward splatting based on Taming-GS and Speedy-splat, load-balanced tiling, an anchor-based Neural-Gaussian representation enabling rapid convergence with fewer learnable parameters, initialization from monocular depth and partially from feed-forward 3DGS models, and a global pose refinement module for noisy SLAM trajectories. In the final round, the accurate COLMAP poses change the optimization landscape; we disable pose refinement, revert from Neural-Gaussians back to standard 3DGS to eliminate MLP inference overhead, introduce multi-view consistency-guided Gaussian splitting inspired by Fast-GS, and introduce a depth estimator to supervise the rendered depth. Together, these techniques enable high-fidelity reconstruction under a strict one-minute budget. Our method achieved the top performance with a PSNR of 28.43 and ranked first in the competition.
Indonesia, the largest economy in Southeast Asia, is actively seeking to strengthen its position in the global energy system. With its accession to BRICS in 2025, the country has gained a unique opportunity to expand cooperation with the world’s leading developing economies, such as China, India, Brazil, South Africa, and Russia. One of the promising areas of such collaboration is hydrogen energy, which is regarded as a key component of the energy transition and the achievement of climate goals.
Hydrogen, particularly “green” hydrogen, produced through electrolysis powered by renewable energy, is becoming an increasingly important vital resource for decarbonizing industry, transportation, and power generation as well. Indonesia, endowed with abundant renewable energy resources, aims to become a significant player in the emerging global hydrogen economy, which is gradually gaining momentum. The country is already implementing ambitious projects and collaborating with some BRICS members to develop hydrogen infrastructure.
This article examines the current state of hydrogen energy in Indonesia, its strategic initiatives, and the prospects for cooperation with other BRICS nations. Additionally, it provides an analysis of challenges and opportunities that the country faces on this path.
South Asia. Southeast Asia. East Asia, Bibliography. Library science. Information resources
This study was aimed at finding out if journalists in South East Nigeria have knowledge of Google Translate Application and also utilise it. It adopted a survey design with a sample size of 320 which was determined using Krejcie & Morgan (1970). Its objectives were to ascertain the extent journalists in South East Nigeria know about Google Translate Application, assess the utilisation of Google Translate Application among journalists in South East Nigeria, and identify the challenges affecting the journalists in South East Nigeria while using Google Translate Application. The theoretical underpin was Knowledge Attitude and Practise Model (KAP). The findings showed that journalists in South East Nigeria have knowledge of Google Translate Application but apply it mostly outside the region. It concludes that journalists in South East Nigeria have the knowledge of the App. but apply it outside the zone. The study recommends increased usage of the App. within South East Nigeria.
The main goal of the JUNO experiment is to determine the neutrino mass ordering with a 20kt liquid-scintillator detector. The 20-inch PMT and its 1F3 (one for three) electronics are crucial to realize the excellent energy resolution of at least 3% at 1MeV. The knowledge on the PMT and 1F3 electronics response is critical for detector performance understanding. A study of the JUNO 20-inch PMT and 1F3 electronics system characterization is presented using large pulses of PMT dark count at the Pan-Asia testing platform in China. Thanks to its broad amplitude range and high rate, the large pulse signals are also used to investigate the PMT after pulse response.
Sailor2 is a family of cutting-edge multilingual language models for South-East Asian (SEA) languages, available in 1B, 8B, and 20B sizes to suit diverse applications. Building on Qwen2.5, Sailor2 undergoes continuous pre-training on 500B tokens (400B SEA-specific and 100B replay tokens) to support 13 SEA languages while retaining proficiency in Chinese and English. Sailor2-20B model achieves a 50-50 win rate against GPT-4o across SEA languages. We also deliver a comprehensive cookbook on how to develop the multilingual model in an efficient manner, including five key aspects: data curation, pre-training, post-training, model customization and evaluation. We hope that Sailor2 model (Apache 2.0 license) will drive language development in the SEA region, and Sailor2 cookbook will inspire researchers to build more inclusive LLMs for other under-served languages.
Anh Truong, Ahmed H. Mahmoud, Mina Konaković Luković
et al.
Processing visual data often involves small adjustments or sequences of changes, e.g., image filtering, surface smoothing, and animation. While established graphics techniques like normal mapping and video compression exploit redundancy to encode such small changes efficiently, the problem of encoding small changes to neural fields -- neural network parameterizations of visual or physical functions -- has received less attention. We propose a parameter-efficient strategy for updating neural fields using low-rank adaptations (LoRA). LoRA, a method from the parameter-efficient fine-tuning LLM community, encodes small updates to pre-trained models with minimal computational overhead. We adapt LoRA for instance-specific neural fields, avoiding the need for large pre-trained models and yielding lightweight updates. We validate our approach with experiments in image filtering, geometry editing, video compression, and energy-based editing, demonstrating its effectiveness and versatility for representing neural field updates.
The Mekong River Basin, a lifeline for millions in Southeast Asia, faces a multifaceted challenge: balancing cooperation for sustainable development with the competing geopolitical interests of regional and global powers. This paper explores the Mekong Cooperation Frameworks (MCF), a web of partnerships between the Mekong countries and external actors. This paper delves into the strengths and weaknesses of various MCF frameworks, including the Mekong River Commission (MRC), Greater Mekong Subregion (GMS), Mekong-Ganga Cooperation (MGC), Mekong-Japan Cooperation (MJC), Lower Mekong Initiative (LMI), Mekong-Lancang Cooperation (MLC), and Mekong- Republic of Korea Cooperation (Mekong-ROK). Each framework offers unique approaches to infrastructure development, water resource management, and socio-economic progress, but also faces limitations. Beyond existing frameworks, this paper also provides suggestions for the characteristics of successful Mekong-Russia Cooperation (MRUC) within the MCF.
The author further analyses the complex geopolitical landscape of the Mekong region. The Mekong countries must navigate the influence of China, a major player in infrastructure projects and water management, while also engaging with the United States, Japan, and other regional powers. Frameworks like the LMI and Friends of the Lower Mekong appear to counter China's dominance, while the MLC raises concerns about transparency in water resource management.
This paper proposes some recommendations to ensure the Mekong River remains a symbol of regional cooperation and prosperity. Strengthening the MRC through enhanced regulatory power and fostering greater regional cooperation on water management and environmental protection is crucial. The author also emphasizes leveraging existing ASEAN mechanisms for collective action and advocating for a unified approach to the Mekong.
Conclusively, this paper argues that navigating the geopolitical complexities of the Mekong region requires a delicate balancing act. By fostering cooperation within the MCF, strengthening regional institutions, and advocating for a unified approach, the Mekong countries can ensure the Mekong River serves as a bridge for a sustainable and prosperous future.
South Asia. Southeast Asia. East Asia, Bibliography. Library science. Information resources
Rachel Sze Jen Goh, Bryan Chong, Jayanth Jayabaskaran
et al.
Summary: Background: Given the rapidly growing burden of cardiovascular disease (CVD) in Asia, this study forecasts the CVD burden and associated risk factors in Asia from 2025 to 2050. Methods: Data from the Global Burden of Disease 2019 study was used to construct regression models predicting prevalence, mortality, and disability-adjusted life years (DALYs) attributed to CVD and risk factors in Asia in the coming decades. Findings: Between 2025 and 2050, crude cardiovascular mortality is expected to rise 91.2% despite a 23.0% decrease in the age-standardised cardiovascular mortality rate (ASMR). Ischaemic heart disease (115 deaths per 100,000 population) and stroke (63 deaths per 100,000 population) will remain leading drivers of ASMR in 2050. Central Asia will have the highest ASMR (676 deaths per 100,000 population), more than three-fold that of Asia overall (186 deaths per 100,000 population), while high-income Asia sub-regions will incur an ASMR of 22 deaths per 100,000 in 2050. High systolic blood pressure will contribute the highest ASMR throughout Asia (105 deaths per 100,000 population), except in Central Asia where high fasting plasma glucose will dominate (546 deaths per 100,000 population). Interpretation: This forecast forewarns an almost doubling in crude cardiovascular mortality by 2050 in Asia, with marked heterogeneity across sub-regions. Atherosclerotic diseases will continue to dominate, while high systolic blood pressure will be the leading risk factor. Funding: This was supported by the NUHS Seed Fund (NUHSRO/2022/058/RO5+6/Seed-Mar/03), National Medical Research Council Research Training Fellowship (MH 095:003/008-303), National University of Singapore Yong Loo Lin School of Medicine's Junior Academic Fellowship Scheme, NUHS Clinician Scientist Program (NCSP2.0/2024/NUHS/NCWS) and the CArdiovascular DiseasE National Collaborative Enterprise (CADENCE) National Clinical Translational Program (MOH-001277-01).
Adama Baguiya, Mercedes Bonet, Vanessa Brizuela
et al.
The highest toll of maternal mortality due to infections is reported in low and middle-income countries (LMICs). However, more evidence is needed to understand the differences in infection-related severe maternal outcomes (SMO) and fatality rates across the WHO regions. This study aimed to compare the burden of infection-related SMO and case fatality rates across the WHO regions using the Global Maternal Sepsis Study (GLOSS) data. GLOSS was a hospital-based one-week inception prospective cohort study of pregnant or recently pregnant women admitted with suspected or confirmed infection in 2017. Four hundred and eight (408) hospitals from 43 LMICs in the six WHO regions were considered in this analysis. We used a logistic regression model to compare the odds of infection-related SMOs by region. We then calculated the fatality rate as the proportion of deaths over the total number of SMOs, defined as maternal deaths and near-misses. The proportion of SMO was 19.6% (n = 141) in Africa, compared to 18%(n = 22), 15.9%(n = 50), 14.7%(n = 48), 12.1%(n = 95), and 10.8%(n = 21) in the Western Pacific, European, Eastern Meditteranean, Americas, and South-Eastern Asian regions, respectively. Women in Africa were more likely to experience SMO than those in the Americas (aOR = 2.41, 95%CI: [1.78 to 2.83]), in South-East Asia (aOR = 2.60, 95%CI: [1.57 to 4.32]), and the Eastern Mediterranean region (aOR = 1.58, 95%CI: [1.08 to 2.32]). The case fatality rate was 14.3%[3.05% to 36.34%] (n/N = 3/21) and 11.4%[6.63% to 17.77%] (n/N = 16/141) in the South-East Asia and Africa, respectively. Infection-related SMOs and case fatality rates were highest in Africa and Southeast Asia. Specific attention and actions are needed to prevent infection-related maternal deaths and severe morbidity in these two regions.
We have developed the world's first canopy height map of the distribution area of world-level giant trees. This mapping is crucial for discovering more individual and community world-level giant trees, and for analyzing and quantifying the effectiveness of biodiversity conservation measures in the Yarlung Tsangpo Grand Canyon (YTGC) National Nature Reserve. We proposed a method to map the canopy height of the primeval forest within the world-level giant tree distribution area by using a spaceborne LiDAR fusion satellite imagery (Global Ecosystem Dynamics Investigation (GEDI), ICESat-2, and Sentinel-2) driven deep learning modeling. And we customized a pyramid receptive fields depth separable CNN (PRFXception). PRFXception, a CNN architecture specifically customized for mapping primeval forest canopy height to infer the canopy height at the footprint level of GEDI and ICESat-2 from Sentinel-2 optical imagery with a 10-meter spatial resolution. We conducted a field survey of 227 permanent plots using a stratified sampling method and measured several giant trees using UAV-LS. The predicted canopy height was compared with ICESat-2 and GEDI validation data (RMSE =7.56 m, MAE=6.07 m, ME=-0.98 m, R^2=0.58 m), UAV-LS point clouds (RMSE =5.75 m, MAE =3.72 m, ME = 0.82 m, R^2= 0.65 m), and ground survey data (RMSE = 6.75 m, MAE = 5.56 m, ME= 2.14 m, R^2=0.60 m). We mapped the potential distribution map of world-level giant trees and discovered two previously undetected giant tree communities with an 89% probability of having trees 80-100 m tall, potentially taller than Asia's tallest tree. This paper provides scientific evidence confirming southeastern Tibet--northwestern Yunnan as the fourth global distribution center of world-level giant trees initiatives and promoting the inclusion of the YTGC giant tree distribution area within the scope of China's national park conservation.
Matthew Caren, Kartik Chandra, Joshua B. Tenenbaum
et al.
We present a method for automatically producing human-like vocal imitations of sounds: the equivalent of "sketching," but for auditory rather than visual representation. Starting with a simulated model of the human vocal tract, we first try generating vocal imitations by tuning the model's control parameters to make the synthesized vocalization match the target sound in terms of perceptually-salient auditory features. Then, to better match human intuitions, we apply a cognitive theory of communication to take into account how human speakers reason strategically about their listeners. Finally, we show through several experiments and user studies that when we add this type of communicative reasoning to our method, it aligns with human intuitions better than matching auditory features alone does. This observation has broad implications for the study of depiction in computer graphics.
This work introduces MICSim, an open-source, pre-circuit simulator designed for early-stage evaluation of chip-level software performance and hardware overhead of mixed-signal compute-in-memory (CIM) accelerators. MICSim features a modular design, allowing easy multi-level co-design and design space exploration. Modularized from the state-of-the-art CIM simulator NeuroSim, MICSim provides a highly configurable simulation framework supporting multiple quantization algorithms, diverse circuit/architecture designs, and different memory devices. This modular approach also allows MICSim to be effectively extended to accommodate new designs. MICSim natively supports evaluating accelerators' software and hardware performance for CNNs and Transformers in Python, leveraging the popular PyTorch and HuggingFace Transformers frameworks. These capabilities make MICSim highly adaptive when simulating different networks and user-friendly. This work demonstrates that MICSim can easily be combined with optimization strategies to perform design space exploration and used for chip-level Transformers CIM accelerators evaluation. Also, MICSim can achieve a 9x - 32x speedup of NeuroSim through a statistic-based average mode proposed by this work.
In this work, we conduct a comprehensive study on the robustness of domain generation algorithm (DGA) classifiers. We implement 32 white-box attacks, 19 of which are very effective and induce a false-negative rate (FNR) of $\approx$ 100\% on unhardened classifiers. To defend the classifiers, we evaluate different hardening approaches and propose a novel training scheme that leverages adversarial latent space vectors and discretized adversarial domains to significantly improve robustness. In our study, we highlight a pitfall to avoid when hardening classifiers and uncover training biases that can be easily exploited by attackers to bypass detection, but which can be mitigated by adversarial training (AT). In our study, we do not observe any trade-off between robustness and performance, on the contrary, hardening improves a classifier's detection performance for known and unknown DGAs. We implement all attacks and defenses discussed in this paper as a standalone library, which we make publicly available to facilitate hardening of DGA classifiers: https://gitlab.com/rwth-itsec/robust-dga-detection
The tearing mode, a large-scale MHD instability in tokamak, typically disrupts the equilibrium magnetic surfaces, leads to the formation of magnetic islands, and reduces core electron temperature and density, thus resulting in significant energy losses and may even cause discharge termination. This process is unacceptable for ITER. Therefore, the accurate identification of a magnetic island in real time is crucial for the effective control of the tearing mode in ITER in the future. In this study, based on the characteristics induced by tearing modes, an attention-aware convolutional neural network (AM-CNN) is proposed to identify the presence of magnetic islands in tearing mode discharge utilizing the data from ECE diagnostics in the EAST tokamak. A total of 11 ECE channels covering the range of core is used in the tearing mode dataset, which includes 2.5*10^9 data collected from 68 shots from 2016 to 2021 years. We split the dataset into training, validation, and test sets (66.5%, 5.7%, and 27.8%), respectively. An attention mechanism is designed to couple with the convolutional neural networks to improve the capability of feature extraction of signals. During the model training process, we utilized adaptive learning rate adjustment and early stopping mechanisms to optimize performance of AM-CNN. The model results show that a classification accuracy of 91.96% is achieved in tearing mode identification. Compared to CNN without AM, the attention-aware convolutional neural networks demonstrate great performance across accuracy, recall metrics, and F1 score. By leveraging the deep learning model, which incorporates a physical understanding of the tearing process to identify tearing mode behaviors, the combination of physical mechanisms and deep learning is emphasized, significantly laying an important foundation for the future intelligent control of tearing mode dynamics.
We report the suppression of Type-I Edge Localized Modes (ELMs) in the EAST tokamak under ITER baseline conditions using $n = 4$ Resonant Magnetic Perturbations (RMPs), while maintaining energy confinement. Achieving RMP-ELM suppression requires a normalized plasma beta ($β_N$) exceeding 1.8 in a target plasma with $q_{95}\approx 3.1$ and tungsten divertors. Quasi-linear modeling shows high plasma beta enhances RMP-driven neoclassical toroidal viscosity torque, reducing field penetration thresholds. These findings demonstrate the feasibility and efficiency of high $n$ RMPs for ELM suppression in ITER.
[Objective] As a crucial source of food for human beings, aquatic products are essential for guaranteeing global food security and stabilizing the food supply chain network, and revealing the characteristics of the aquatic product consumption footprint chain is conducive to further optimizing the sustainable utilization of fishery resources. [Methods] Based on the FAOSTAT and FAO fishery statistics, a multi-regional input-output table of 21 regions globally in 2019 was constructed at the regional, sectoral, and species levels to reveal biomass flows among sectors and to measure the consumption footprint of each region. [Results] The results indicate that: (1) Global aquatic product consumption footprint at the regional scale showed an obvious long tail distribution. China ranked first with 68.8 million tons (38.6%), followed by East Asia and Southeast Asia, South Asia, European Union, and North America, all exceeding 7.0 million tons. (2) The global consumption footprint of aquatic products manifested strong mobility between sectors and regions. A total of 89.9% of consumption footprint flowed into the human consumption sector, and 26.0% participated in international trade to achieve cross-regional transfer. (3) Aquaculture was the second largest consumption footprint inflow sector following human consumption. Fish consumption accounted for more than 62.0% in all regions, followed by molluscs and crustaceans. (4) Of China’s aquatic product consumption footprint, 83.9% was self-sufficient, and the aquaculture sector accounted for 76.8%. However, fishmeal and fish oil, indispensable for aquaculture production, was heavily reliant on input consumption footprint in China, mainly from South America, East Asia, and Southeast Asia. [Conclusion] In order to realize the restructuring and upgrading of the aquatic product production and consumption footprint chain to ensure global food security, we should improve resource utilization efficiency, optimize international trade structure, strengthen fishery resource management, and promote green and healthy aquaculture.