Unsupervised Synthetic Image Refinement via Contrastive Learning and Consistent Semantic-Structural Constraints

Ensuring the realism of computer-generated synthetic images is crucial to deep neural network (DNN) training. Due to different semantic distributions between synthetic and real-world captured datasets, there exists semantic mismatch between synthetic and refined images, which in turn results in the semantic distortion. Recently, contrastive learning (CL) has been successfully used to pull correlated patches together and push uncorrelated ones apart. In this work, we exploit semantic and structural consistency between synthetic and refined images and adopt CL to reduce the semantic distortion. Besides, we incorporate hard negative mining to improve the performance furthermore. We compare the performance of our method with several other benchmarking methods using qualitative and quantitative measures and show that our method offers the state-of-the-art performance

Palladium Cobalt-nickel mixed oxides Surface modification Synergistic interaction Lean methane combustion

The effective transformation of lignin is an essential part of realizing the comprehensive utilization of biomass. In this study, a one-pot method for the depolymerization of corn stover lignin used aluminum phosphate (NiAPO-5) zeolite catalyst contained Brønsted acid, Lewis acid and hydrogenation sites was proposed. It was found that the number of Brønsted acid sites was increased after NiAPO-5 was reduced with H2. The yield of monomers and residue were 35.70% and 38.09% at 235 ◦C for 3 h, respectively. The result of 2D HSQC NMR showed that the NiAPO-5 (H2) catalyst significantly affected the cleavage of β-O-4 bonds. The distribution of products and the stability of catalyst revealed that NiAPO-5 (H2) was an efficient catalyst for the depolymerization of lignin

Towards Exascale Computation for Turbomachinery Flows

A state-of-the-art large eddy simulation code has been developed to solve compressible flows in turbomachinery. The code has been engineered with a high degree of scalability, enabling it to effectively leverage the many-core architecture of the new Sunway system. A consistent performance of 115.8 DP-PFLOPs has been achieved on a high-pressure turbine cascade consisting of over 1.69 billion mesh elements and 865 billion Degree of Freedoms (DOFs). By leveraging a high-order unstructured solver and its portability to large heterogeneous parallel systems, we have progressed towards solving the grand challenge problem outlined by NASA, which involves a time-dependent simulation of a complete engine, incorporating all the aerodynamic and heat transfer components.Comment: SC23, November, 2023, Denver, CO., US

MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results

Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects. This paper proposes a new SOD dataset consisting of 39,070 images including 137,121 bird instances, which is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The detail of the challenge with the SOD4SB dataset is introduced in this paper. In total, 223 participants joined this challenge. This paper briefly introduces the award-winning methods. The dataset, the baseline code, and the website for evaluation on the public testset are publicly available.Comment: This paper is included in the proceedings of the 18th International Conference on Machine Vision Applications (MVA2023). It will be officially published at a later date. Project page : https://www.mva-org.jp/mva2023/challeng

Spark for HPC: a comparison with MPI on compute-intensive applications using Monte Carlo method

With the emergence of various big data platforms in recent years, Apache Spark - a distributed large-scale computing platform, is perceived as a potential substitute for Message Passing Interface (MPI) in High Performance Computing (HPC). Due to the limitations in fault-tolerance, dynamic resource handling and ease of use, MPI, as a dominant method to achieve parallel computing in HPC, is often associated with higher development time and costs in enterprises such as Scania IT. This thesis project aims to examine Apache Spark as an alternative to MPI on HPC clusters and compare their performance in various aspects. The test results are obtained by running a compute- intensive application on both platforms to solve a Bayesian inference problem of a extended Lotka-Volterra model using particle Markov chain Monte Carlo methods. As is confirmed by the tests, Spark is demonstrated to be superior in fault tolerance, dynamic resource handling and ease of use, whilst having its shortcomings in performance and resource consumption compared with MPI. Overall, Spark proves to be a promising alternative of MPI on HPC clusters. As a result, Scania IT continues to explore Spark on HPC clusters for use in different departments

Spark for HPC: a comparison with MPI on compute-intensive applications using Monte Carlo method

With the emergence of various big data platforms in recent years, Apache Spark - a distributed large-scale computing platform, is perceived as a potential substitute for Message Passing Interface (MPI) in High Performance Computing (HPC). Due to the limitations in fault-tolerance, dynamic resource handling and ease of use, MPI, as a dominant method to achieve parallel computing in HPC, is often associated with higher development time and costs in enterprises such as Scania IT. This thesis project aims to examine Apache Spark as an alternative to MPI on HPC clusters and compare their performance in various aspects. The test results are obtained by running a compute- intensive application on both platforms to solve a Bayesian inference problem of a extended Lotka-Volterra model using particle Markov chain Monte Carlo methods. As is confirmed by the tests, Spark is demonstrated to be superior in fault tolerance, dynamic resource handling and ease of use, whilst having its shortcomings in performance and resource consumption compared with MPI. Overall, Spark proves to be a promising alternative of MPI on HPC clusters. As a result, Scania IT continues to explore Spark on HPC clusters for use in different departments

Enhanced thermal conductivity for traditional epoxy packaging composites by constructing hybrid conductive network

A cost-efficient and practical strategy was developed for preparing high thermal conductive epoxy packaging composites. The effective conductive network was constructed by the bridging effect between boron nitride (BN) and spherical silica (SiO _2 ). Compared to the epoxy (EP) composites with randomly dispersed BN and SiO _2 , the EP/SiO _2 @BN showed a great enhancement in thermal conduction. The thermal conductivity of EP/SiO _2 @BN reached to 0.86 W m ^−1  K ^−1 with 60 wt% content of hybrid filler, which was 91% higher than that of EP/SiO _2 samples and was around 12% higher than that of epoxy composites with unmodified BN and SiO _2 . In addition, the EP/SiO _2 @BN exhibited lower thermal interface resistance in comparison with EP/SiO _2 &BN composites according to the effective medium theory (EMT). The encapsulation of BN on the surface of SiO _2 greatly enhanced the thermal transfer efficiency of the epoxy matrix and showed great potential in the epoxy packaging practical application

Bioinspired artificial channel based polymeric membranes

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Design, synthesis, and characterization of artificial water channel based polymeric membranes

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Improved Self-Supporting and Ceramifiable Properties of Ceramifiable EPDM Composites by Adding Aramid Fiber

Ceramifiable ethylene propylene diene monomer (EPDM) composites with fiber network structures were prepared by using aramid fiber (AF), ammonium polyphosphate (APP), and silicate glass frits (SGF). The effect of AF on the curing characteristic of the ceramifiable EPDM composites was studied. The morphology of AF in the composites system was observed by optical microscopy (OM) and scanning electron microscope (SEM). The effects of the observed AF network structures on the solvent resistance, mechanical properties, ablative resistance, self-supporting property, and ceramifiable properties of the composites were investigated. Results suggested that the existence of the AF network structure improved the vulcanization properties, solvent resistance, thermal stability, and ablative resistance of the EPDM composites. An excellent self-supporting property of the EPDM composites was obtained by combining the formation of the AF network and the formation of crystalline phases at higher temperature (above 600 °C). The thermal shrinkage performance of AF and the increased thermal stability of the EPDM composites improved the ceramifiable properties of the EPDM composites