RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks

Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently. It can significantly reduce energy consumption since they quantize the real-valued membrane potentials to 0/1 spikes to transmit information thus the multiplications of activations and weights can be replaced by additions when implemented on hardware. However, this quantization mechanism will inevitably introduce quantization error, thus causing catastrophic information loss. To address the quantization error problem, we propose a regularizing membrane potential loss (RMP-Loss) to adjust the distribution which is directly related to quantization error to a range close to the spikes. Our method is extremely simple to implement and straightforward to train an SNN. Furthermore, it is shown to consistently outperform previous state-of-the-art methods over different network architectures and datasets.Comment: Accepted by ICCV202

A Research on Community-Based Livestock of Qinghai-Tibet Plateau

Qinghai-Tibet Plateau locates in Southwestern China, covering the whole area of Tibet Autonomous Region, Qinghai Province, Southern part of Gansu Province, Northwestern part of Sichuan Province and Northwestern part of Yunnan Province, with an area of around 139.08 million hectares of natural grassland, accounting for 39% of the total area of natural grassland in China. It is also the largest natural ecozones in China and one of the least disturbed regions by human activities, with its air, water sources, soil, grassland, wildlife in their pristine state. Qinghai-Tibet Plateau is the native home for Tibetan people. Grassland animal husbandry is the foundation of the economy of QTP and the main source of livelihood for local nomadic people. During the long term of concerted evolution with the nature, Tibetan people living on Qinghai-Tibet Plateau have formed a uniquely holistic grassland ecological culture that is compatible with their production system and the ecosystem. The majority of Tibetan people observe Tibetan Buddhism. Their respect for nature and their belief in that all sentient beings are equal take deep root in their traditional culture. Their harmonious co-existence with nature exemplifies the eco-civilization ideas and provides a solid cultural foundation for both ecology conservation and featured animal husbandry development. On Qinghai-Tibet Plateau, national policies and initiatives such as dual contract of livestock and forage, natural grassland vegetation recovery, returning grazing land to grassland, grassland ecosystem subsidy and rewarding mechanism have been implemented, playing an important role in promoting grassland ecosystem conservation and grassland animal husbandry development. However, since grassland animal husbandry is a complex system involving grassland, farm animal, environment, society, economy, culture, etc, there are still many outstanding problems to be solved

Spiking PointNet: Spiking Neural Networks for Point Clouds

Recently, Spiking Neural Networks (SNNs), enjoying extreme energy efficiency, have drawn much research attention on 2D visual recognition and shown gradually increasing application potential. However, it still remains underexplored whether SNNs can be generalized to 3D recognition. To this end, we present Spiking PointNet in the paper, the first spiking neural model for efficient deep learning on point clouds. We discover that the two huge obstacles limiting the application of SNNs in point clouds are: the intrinsic optimization obstacle of SNNs that impedes the training of a big spiking model with large time steps, and the expensive memory and computation cost of PointNet that makes training a big spiking point model unrealistic. To solve the problems simultaneously, we present a trained-less but learning-more paradigm for Spiking PointNet with theoretical justifications and in-depth experimental analysis. In specific, our Spiking PointNet is trained with only a single time step but can obtain better performance with multiple time steps inference, compared to the one trained directly with multiple time steps. We conduct various experiments on ModelNet10, ModelNet40 to demonstrate the effectiveness of Spiking PointNet. Notably, our Spiking PointNet even can outperform its ANN counterpart, which is rare in the SNN field thus providing a potential research direction for the following work. Moreover, Spiking PointNet shows impressive speedup and storage saving in the training phase.Comment: Accepted by NeurIP

Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks

The Spiking Neural Network (SNN), as one of the biologically inspired neural network infrastructures, has drawn increasing attention recently. It adopts binary spike activations to transmit information, thus the multiplications of activations and weights can be substituted by additions, which brings high energy efficiency. However, in the paper, we theoretically and experimentally prove that the binary spike activation map cannot carry enough information, thus causing information loss and resulting in accuracy decreasing. To handle the problem, we propose a ternary spike neuron to transmit information. The ternary spike neuron can also enjoy the event-driven and multiplication-free operation advantages of the binary spike neuron but will boost the information capacity. Furthermore, we also embed a trainable factor in the ternary spike neuron to learn the suitable spike amplitude, thus our SNN will adopt different spike amplitudes along layers, which can better suit the phenomenon that the membrane potential distributions are different along layers. To retain the efficiency of the vanilla ternary spike, the trainable ternary spike SNN will be converted to a standard one again via a re-parameterization technique in the inference. Extensive experiments with several popular network structures over static and dynamic datasets show that the ternary spike can consistently outperform state-of-the-art methods. Our code is open-sourced at https://github.com/yfguo91/Ternary-Spike.Comment: Accepted by AAAI202

Membrane Potential Batch Normalization for Spiking Neural Networks

As one of the energy-efficient alternatives of conventional neural networks (CNNs), spiking neural networks (SNNs) have gained more and more interest recently. To train the deep models, some effective batch normalization (BN) techniques are proposed in SNNs. All these BNs are suggested to be used after the convolution layer as usually doing in CNNs. However, the spiking neuron is much more complex with the spatio-temporal dynamics. The regulated data flow after the BN layer will be disturbed again by the membrane potential updating operation before the firing function, i.e., the nonlinear activation. Therefore, we advocate adding another BN layer before the firing function to normalize the membrane potential again, called MPBN. To eliminate the induced time cost of MPBN, we also propose a training-inference-decoupled re-parameterization technique to fold the trained MPBN into the firing threshold. With the re-parameterization technique, the MPBN will not introduce any extra time burden in the inference. Furthermore, the MPBN can also adopt the element-wised form, while these BNs after the convolution layer can only use the channel-wised form. Experimental results show that the proposed MPBN performs well on both popular non-spiking static and neuromorphic datasets. Our code is open-sourced at \href{https://github.com/yfguo91/MPBN}{MPBN}.Comment: Accepted by ICCV202

Antibiotic resistance and molecular typing of Campylobacter spp. from diarrheal patients in Baoshan District of Shanghai， 2019‒2022

ObjectiveTo analyze the drug resistance and the molecular typing characteristics through pulsed field gel electrophoresis (PFGE) of Campylobacter spp. isolated from patients with infectious diarrhea in Baoshan District of Shanghai, and to provide a basis for Campylobacter spp. prevention and control and clinical medication.MethodsCampylobacter spp. was isolated, cultured and identified from stool samples of diarrheal patients collected from medical institutions at two monitoring sites in Baoshan District from 2019 to 2022. Antimicrobial susceptibility testing for 12 antibiotics was conducted on the isolated Campylobacter jejuni (C. jejuni) and Campylobacter. Coli (C. coli), and molecular typing was performed using PFGE.ResultsA total of 179 strains of Campylobacter spp. were isolated from 1 786 samples of diarrheal patients, with a positive rate of 10.02%. The highest resistance rate of C. jejuni was to ciprofloxacin (98.63%), followed by tetracycline (97.26%) and nalidixic acid (89.73%). C. coli was completely resistant to ciprofloxacin and nalidixic acid (100.00%), followed by tetracycline (90.91%). The multidrug resistance rates of C. jejuni and C. coli were 89.73% and 100.00%, respectively. 142 strains of C. jejuni produced 122 PFGE bands, while 33 strains of C. coli produced 33 PFGE bands, and the distribution of the bands was relatively dispersed.ConclusionFrom 2019 to 2022, the detection rate of Campylobacter in diarrheal patients was relatively high in Baoshan District of Shanghai, the multidrug resistance rate of Campylobacter isolates from diarrheal patients was relatively serious, in addition, the drug resistance pattern was complex, and the PFGE band pattern displayed a polymorphic distribution

Urbanization and environmental challenges

행사명 : KDIS-WBI-CAG Policy Foru