The dynamic process of syncretism: Datuk Gong worship in Malaysia

The Datuk Gong worship in Malaysia is a fusion of Malay keramat and Chinese Tudi Shen, hence easy to be labelled ‘syncretism’. Nevertheless, the rich dynamism of syncretism as a process in Datuk Gong worship is still underexplored. Through the combination of historical documentary method and anthropological multi-sited field work, this article examines the three stages in the syncretic process of Datuk Gong worship: syncretic amity, syncretic encompassment and synthesis, as well as diverse strategies Chinese devotees adopted in each stage. Compared with other worship of non-Chinese deities in Southeast Asia, the peculiarity of Datuk Gong worship in West Malaysia is that it has reached a high level of synthesis, hence its own independence. Contribution: Through the examination of Datuk Gong worship in Malaysia, a syncretism of Chinese Religion, local animism and Islam, the study provides a rare and excellent example to mirror the rich dynamism of syncretism as a process in Southeast Asia, a meeting point of different civilisations

Everything You Always Wanted to Know About Storage Compressibility of Pre-Trained ML Models but Were Afraid to Ask

As the number of pre-trained machine learning (ML) models is growing exponentially, data reduction tools are not catching up. Existing data reduction techniques are not specifically designed for pre-trained model (PTM) dataset files. This is largely due to a lack of understanding of the patterns and characteristics of these datasets, especially those relevant to data reduction and compressibility. This paper presents the first, exhaustive analysis to date of PTM datasets on storage compressibility. Our analysis spans different types of data reduction and compression techniques, from hash-based data deduplication, data similarity detection, to dictionary-coding compression. Our analysis explores these techniques at three data granularity levels, from model layers, model chunks, to model parameters. We draw new observations that indicate that modern data reduction tools are not effective when handling PTM datasets. There is a pressing need for new compression methods that take into account PTMs' data characteristics for effective storage reduction. Motivated by our findings, we design ELF, a simple yet effective, error-bounded, lossy floating-point compression method. ELF transforms floating-point parameters in such a way that the common exponent field of the transformed parameters can be completely eliminated to save storage space. We develop Elves, a compression framework that integrates ELF along with several other data reduction methods. Elves uses the most effective method to compress PTMs that exhibit different patterns. Evaluation shows that Elves achieves an overall compression ratio of $1.52\times$, which is $1.31\times$, $1.32\times$ and $1.29\times$ higher than a general-purpose compressor (zstd), an error-bounded lossy compressor (SZ3), and the uniform model quantization, respectively, with negligible model accuracy loss.Comment: This paper presents the first, exhaustive analysis to date of PTM datasets on storage compressibility. Motivated by our findings, we design ELF, a simple yet effective, error-bounded, lossy floating-point compression metho

An improved MOEA/D algorithm for multi-objective multicast routing with network coding

Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to minimize various network resources and end-to-end delays while exploiting promising benefits of network coding. Multicast has been used in increasingly more applications, such as video conferencing and remote education. In this paper the multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously. We adapt the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for this MOP by hybridizing it with a population-based incremental learning technique which makes use of the global and historical information collected to provide additional guidance to the evolutionary search. Three new schemes are devised to facilitate the performance improvement, including a probability-based initialization scheme, a problem-specific population updating rule, and a hybridized reproduction operator. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art MOEAs regarding the solution quality and computational time

Conjugate Calculation of Gas Turbine Vanes Cooled with Leading Edge Films

AbstractConjugate calculation methodology is used to simulate the C3X gas turbine vanes cooled with leading edge films of “shower-head” type. By comparing calculated results of different turbulence models with the measured data, it is clear that calculation with the transition model can better simulate the flow and heat transfer in the boundary layers with leading edge film cooling. In the laminar boundary layers, on the upstream suction side, the film cooling flow presents 3D turbulent characteristics before transition, which quickly disappear on the downstream suction side owing to its intensified mixing with hot gas boundary layer after transition. On the pressure side, the film cooling flow retains the 3D turbulent characteristics all the time because the local boundary layers' consistent laminar flow retains a smooth mixing of the cooling flow and the hot gas. The temperature gradients formed between the cooled metallic vane and the hot gas can improve the stability of the boundary layer flow because the gradients possess a self stable convective structure

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. Most existing methods are bottom-up approaches that try to group pixels into regions based on their visual cues or certain predefined rules. As a result, it is difficult for these bottom-up approaches to generate fine-grained semantic segmentation when coming to complicated scenes with multiple objects and some objects sharing similar visual appearance. In contrast, we propose the first top-down unsupervised semantic segmentation framework for fine-grained segmentation in extremely complicated scenarios. Specifically, we first obtain rich high-level structured semantic concept information from large-scale vision data in a self-supervised learning manner, and use such information as a prior to discover potential semantic categories presented in target datasets. Secondly, the discovered high-level semantic categories are mapped to low-level pixel features by calculating the class activate map (CAM) with respect to certain discovered semantic representation. Lastly, the obtained CAMs serve as pseudo labels to train the segmentation module and produce the final semantic segmentation. Experimental results on multiple semantic segmentation benchmarks show that our top-down unsupervised segmentation is robust to both object-centric and scene-centric datasets under different semantic granularity levels, and outperforms all the current state-of-the-art bottom-up methods. Our code is available at \url{https://github.com/damo-cv/TransFGU}.Comment: Accepted by ECCV 2022, Oral, open-source

A modified ant colony optimization algorithm for network coding resource minimization

The paper presents a modified ant colony optimization approach for the network coding resource minimization problem. It is featured with several attractive mechanisms specially devised for solving the network coding resource minimization problem: 1) a multi-dimensional pheromone maintenance mechanism is put forward to address the issue of pheromone overlapping; 2) problem-specific heuristic information is employed to enhance the heuristic search (neighboring area search) capability; 3) a tabu-table based path construction method is devised to facilitate the construction of feasible (link-disjoint) paths from the source to each receiver; 4) a local pheromone updating rule is developed to guide ants to construct appropriate promising paths; 5) a solution reconstruction method is presented, with the aim of avoiding prematurity and improving the global search efficiency of proposed algorithm. Due to the way it works, the ant colony optimization can well exploit the global and local information of routing related problems during the solution construction phase. The simulation results on benchmark instances demonstrate that with the five extended mechanisms integrated, our algorithm outperforms a number of existing algorithms with respect to the best solutions obtained and the computational time

Three-dimensional Turbulent Reconnection within Solar Flare Current Sheet

Solar flares can release coronal magnetic energy explosively and may impact the safety of near-earth space environments. Their structures and properties on macroscale have been interpreted successfully by the generally-accepted two-dimension standard model invoking magnetic reconnection theory as the key energy conversion mechanism. Nevertheless, some momentous dynamical features as discovered by recent high-resolution observations remain elusive. Here, we report a self-consistent high-resolution three-dimension magnetohydrodynamical simulation of turbulent magnetic reconnection within a flare current sheet. It is found that fragmented current patches of different scales are spontaneously generated with a well-developed turbulence spectrum at the current sheet, as well as at the flare loop-top region. The close coupling of tearing-mode and Kelvin-Helmholtz instabilities plays a critical role in developing turbulent reconnection and in forming dynamical structures with synthetic observables in good agreement with realistic observations. The sophisticated modeling makes a paradigm shift from the traditional to three-dimension turbulent reconnection model unifying flare dynamical structures of different scales.Comment: 15 pages, 8 figure, accepted for publication in ApJ

Technological Innovation Research: A Structural Equation Modelling Approach

The paper explores the relationship among technological innovation, technological trajectory transition, and firms’ innovation performance. Technological innovation is studied from the perspectives of innovation novelty and innovation openness. Technological trajectory transition is categorized into creative cumulative technological trajectory transition and creative disruptive technological trajectory transition. A structural equation model is developed and tested with data collected by surveying 366 Chinese firms. The results indicate that both innovation novelty and innovation openness positively affects creative cumulative technological trajectory transition as well as creative disruptive technological trajectory transition. Innovation openness and creative disruptive technological trajectory transition both positively affect firms’ innovation performance. However, neither innovation novelty nor creative cumulative technological trajectory transition positively affects firms’ innovation performance. Implications for managers and directions for future studies are discussed