Several Issues on Hieroglyph of Naxi Ethnic Minority

Hieroglyph of Naxi ethnic minority is the picture text, which has been so far the only “living hieroglyph”. Naxi Hieroglyph is the general name of Dongba Script, Geba Script Malimasha Script as well as Ruanke Script. Moreover, the creation of Naxi Hieroglyph is closely related to the migration routes of Naxi Geba Script, based on Do ancestors, which corresponds with the dialect areas of Naxi ethnic language, and its creation can date back to 11th century. Geba Script, is created when contacting with foreign culture, which carries the characteristics of Chinese and Tibetan writings

CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise

In this paper, we study the problem of learning image classification models with label noise. Existing approaches depending on human supervision are generally not scalable as manually identifying correct or incorrect labels is time-consuming, whereas approaches not relying on human supervision are scalable but less effective. To reduce the amount of human supervision for label noise cleaning, we introduce CleanNet, a joint neural embedding network, which only requires a fraction of the classes being manually verified to provide the knowledge of label noise that can be transferred to other classes. We further integrate CleanNet and conventional convolutional neural network classifier into one framework for image classification learning. We demonstrate the effectiveness of the proposed algorithm on both of the label noise detection task and the image classification on noisy data task on several large-scale datasets. Experimental results show that CleanNet can reduce label noise detection error rate on held-out classes where no human supervision available by 41.5% compared to current weakly supervised methods. It also achieves 47% of the performance gain of verifying all images with only 3.2% images verified on an image classification task. Source code and dataset will be available at kuanghuei.github.io/CleanNetProject.Comment: Accepted to CVPR 201

Energy consumption analysis and new process of CO2 compression liquefaction based on exergy analysis

Among various carbon dioxide capture technologies, the organic amine absorption is the most widely used and reliable choice today. The combination of liquefied natural gas cold energy with carbon dioxide compression and liquefaction process can not only solve the problem of liquefied natural gas cold energy utilization but also obtain the low temperature required for liquefied carbon dioxide directly, which can reduce energy consumption. A new process of carbon dioxide compression and liquefaction by applying liquefied natural gas cold energy to chemical absorption capture is proposed, using organic amine to absorb the high concentration carbon dioxide feed gas captured, and using Aspen Hysys software to simulate the process flow with Peng-Robison equation of state. Firstly, the system performance of the conventional compression process and the pumping compression process were investigated. It was found that the system unit energy consumption could be reduced from 931.65 kJ/kg gas source to 892.61 kJ/kg gas source under the pumping process, the system exergy efficiency increased from 63.28% to 63.67%, and the water consumption decreased from 3.84 kg gas source to 3.01 kg gas source. On this basis, the pumping process was optimized and five optimized processes were proposed according to the different ways of heat exchange series connection. Under the optimal optimized process, the system unit energy consumption was 892.61 kJ/kg gas source, the system exergy efficiency increased from 63.67% to 64.10%, and the water consumption decreased from 3.01 kg gas source to 2.44 kg gas source. Finally, the system sensitivity analysis of interstage cooling temperature and heat transfer medium flow rate was conducted for the optimal optimized process. The results showed that the lower the interstage cooling temperature, the lower the system energy consumption and the greater the exergy efficiency. The lowest unit energy consumption was 879.5 kJ/kg gas source at 10 ℃, and the maximum system exergy efficiency was 65.5%. The effect of heat transfer mass flow rate on the system energy consumption was not obvious, but the system exergy efficiency increased with the increase of heat transfer mass flow rate. The effect of liquefied natural gas mass flow rate on the system exergy efficiency was greater, and the system exergy efficiency was 68.91% when the water mass flow rate was 8000 kg/h and the liquefied natural gas mass flow rate was 1000 kg/h

Inference with Reference: Lossless Acceleration of Large Language Models

We propose LLMA, an LLM accelerator to losslessly speed up Large Language Model (LLM) inference with references. LLMA is motivated by the observation that there are abundant identical text spans between the decoding result by an LLM and the reference that is available in many real world scenarios (e.g., retrieved documents). LLMA first selects a text span from the reference and copies its tokens to the decoder and then efficiently checks the tokens' appropriateness as the decoding result in parallel within one decoding step. The improved computational parallelism allows LLMA to achieve over 2x speed-up for LLMs with identical generation results as greedy decoding in many practical generation scenarios where significant overlap between in-context reference and outputs exists (e.g., search engines and multi-turn conversations).Comment: 9 page