Modeling of Dust Emission in Northwest China

Chinese Academy of SciencesPromoting Environmental Pesearch in Pan-Japan Sea Area : Young Researchers\u27 Network, Schedule: March 8-10,2006,Kanazawa Excel Hotel Tokyu, Japan, Organized by: Kanazawa University 21st-Century COE Program, Environmental Monitoring and Prediction of Long- & Short- Term Dynamics of Pan-Japan Sea Area ; IICRC(Ishikawa International Cooperation Research Centre), Sponsors : Japan Sea Research ; UNU-IAS(United Nations University Institute of Advanced Studies)+Ishikawa Prefecture Government ; City of Kanazaw

Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimization

Backdoor defense, which aims to detect or mitigate the effect of malicious triggers introduced by attackers, is becoming increasingly critical for machine learning security and integrity. Fine-tuning based on benign data is a natural defense to erase the backdoor effect in a backdoored model. However, recent studies show that, given limited benign data, vanilla fine-tuning has poor defense performance. In this work, we provide a deep study of fine-tuning the backdoored model from the neuron perspective and find that backdoorrelated neurons fail to escape the local minimum in the fine-tuning process. Inspired by observing that the backdoorrelated neurons often have larger norms, we propose FTSAM, a novel backdoor defense paradigm that aims to shrink the norms of backdoor-related neurons by incorporating sharpness-aware minimization with fine-tuning. We demonstrate the effectiveness of our method on several benchmark datasets and network architectures, where it achieves state-of-the-art defense performance. Overall, our work provides a promising avenue for improving the robustness of machine learning models against backdoor attacks

Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy

Data-poisoning based backdoor attacks aim to insert backdoor into models by manipulating training datasets without controlling the training process of the target model. Existing attack methods mainly focus on designing triggers or fusion strategies between triggers and benign samples. However, they often randomly select samples to be poisoned, disregarding the varying importance of each poisoning sample in terms of backdoor injection. A recent selection strategy filters a fixed-size poisoning sample pool by recording forgetting events, but it fails to consider the remaining samples outside the pool from a global perspective. Moreover, computing forgetting events requires significant additional computing resources. Therefore, how to efficiently and effectively select poisoning samples from the entire dataset is an urgent problem in backdoor attacks.To address it, firstly, we introduce a poisoning mask into the regular backdoor training loss. We suppose that a backdoored model training with hard poisoning samples has a more backdoor effect on easy ones, which can be implemented by hindering the normal training process (\ie, maximizing loss \wrt mask). To further integrate it with normal training process, we then propose a learnable poisoning sample selection strategy to learn the mask together with the model parameters through a min-max optimization.Specifically, the outer loop aims to achieve the backdoor attack goal by minimizing the loss based on the selected samples, while the inner loop selects hard poisoning samples that impede this goal by maximizing the loss. After several rounds of adversarial training, we finally select effective poisoning samples with high contribution. Extensive experiments on benchmark datasets demonstrate the effectiveness and efficiency of our approach in boosting backdoor attack performance

Memristive Non-Volatile Memory Based on Graphene Materials

Resistive random access memory (RRAM), which is considered as one of the most promising next-generation non-volatile memory (NVM) devices and a representative of memristor technologies, demonstrated great potential in acting as an artificial synapse in the industry of neuromorphic systems and artificial intelligence (AI), due its advantages such as fast operation speed, low power consumption, and high device density. Graphene and related materials (GRMs), especially graphene oxide (GO), acting as active materials for RRAM devices, are considered as a promising alternative to other materials including metal oxides and perovskite materials. Herein, an overview of GRM-based RRAM devices is provided, with discussion about the properties of GRMs, main operation mechanisms for resistive switching (RS) behavior, figure of merit (FoM) summary, and prospect extension of GRM-based RRAM devices. With excellent physical and chemical advantages like intrinsic Young’s modulus (1.0 TPa), good tensile strength (130 GPa), excellent carrier mobility (2.0 × 105 cm2∙V−1∙s−1), and high thermal (5000 Wm−1∙K−1) and superior electrical conductivity (1.0 × 106 S∙m−1), GRMs can act as electrodes and resistive switching media in RRAM devices. In addition, the GRM-based interface between electrode and dielectric can have an effect on atomic diffusion limitation in dielectric and surface effect suppression. Immense amounts of concrete research indicate that GRMs might play a significant role in promoting the large-scale commercialization possibility of RRAM devices

Process optimization of the first set of fluidized bed methanol to propylene plant

In order to successfully complete the commissioning of the first industrial demonstration unit of methanol to propylene FMTP in fluidized bed, through the investigation and technical exchange of similar enterprises using methanol to olefin (DMTO) technology of Dalian Chemical Institute, methanol to olefin (SMTO) technology of Sinopec, methanol to olefin (SHMTO) technology of Shenhua Group and methanol to propylene technology (MTP) in China, through the comparison and analysis with FMTP process technology, it was found that there were deficiencies in the design of the catalyst recovery system of the original FMTP unit, the catalyst circulation pipeline between the three units, the heat transfer system of washing water, the waste heat recovery system and the reactor measurement instrument system. According to the construction and operation experience of the same industry, the waste catalyst recovery system was designed and transformed into a bucket recovery system. The catalyst circulating pipeline was optimized to be a lifting pipe and a flange tube cap, the xylene cleaning system was optimized to be in the washing water heat exchanger, the steam automatic ash blowing system was optimized to be in the waste heat recovery equipment, and reactor instrument back blower system was optimized to be in the reactor measuring instrument. After the above process optimization, the content of combustible gas in the analytical gas of the catalyst recovery system was significantly reduced, and the catalyst was prevented from sticking together; the catalyst circulation pipelines of the three reactors were designed the lifting tube, the buffer tube and tube cap were designed at the top of the lifting tube; the catalyst circulation between the three reactors was smooth during the operation of the plant, the parameters of the catalyst circulation, the temperature of the tube, the density of the tube and the pressure of the tube in the reactor were close to and reached the design indicators; start the xylene cleaning system to wash the blocked heat exchanger, and the heat exchanger cleaning effect is obvious; after the automatic steam ash blowing system is put into operation, the efficiency of waste heat recovery system is significantly improved; the inert gas of reactor instrument back blower system was optimized to process gas, avoid the influence of inert gas on the downstream separation unit. In the four commissioning runs of FMTP device, the above system runs smoothly, it lays the foundation for the long cycle safe and stable operation of the plant