Theoretical Model and Experimental Research on Mortar Corrosion by Sulfuric Acid in Laminar Flow

Surfaces of concrete structure could suffer the corrosion of flowing sulfuric acid coming from acid rain or sewage water. This paper establishes an analytical corrosion model and carries out experimental studies to investigate the corrosion rate of mortar in laminar flow of sulfuric acid with different flow velocities. The analytical model was deduced from the relationship between Flow Boundary Layer and Concentration Boundary Layer, connected by Schmidt number, and the dissolution diffusion process of mortar. The analytical model indicates that the corrosion rate will increase with flow velocity, but the increment effect will decrease with flow velocity in an exponential relationship. Nine groups of cement mortar specimens were tested in flow-corrosion devices for 1440 hours. The corrosion rate was obtained for the cases of the water cement ratio of mortar 0.5, the pH value of sulfuric acid 3.4 and the flow velocities ranging from 0.13 m/s to 1.57 m/s

ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction

Most advanced unsupervised anomaly detection (UAD) methods rely on modeling feature representations of frozen encoder networks pre-trained on large-scale datasets, e.g. ImageNet. However, the features extracted from the encoders that are borrowed from natural image domains coincide little with the features required in the target UAD domain, such as industrial inspection and medical imaging. In this paper, we propose a novel epistemic UAD method, namely ReContrast, which optimizes the entire network to reduce biases towards the pre-trained image domain and orients the network in the target domain. We start with a feature reconstruction approach that detects anomalies from errors. Essentially, the elements of contrastive learning are elegantly embedded in feature reconstruction to prevent the network from training instability, pattern collapse, and identical shortcut, while simultaneously optimizing both the encoder and decoder on the target domain. To demonstrate our transfer ability on various image domains, we conduct extensive experiments across two popular industrial defect detection benchmarks and three medical image UAD tasks, which shows our superiority over current state-of-the-art methods.Comment: NeurIPS 2023 Poste

Magnetic Field Control of the Quantum Chaotic Dynamics of Hydrogen Analogues in an Anisotropic Crystal Field

We report magnetic field control of the quantum chaotic dynamics of hydrogen analogues in an anisotropic solid state environment. The chaoticity of the system dynamics was quantified by means of energy level statistics. We analyzed the magnetic field dependence of the statistical distribution of the impurity energy levels and found a smooth transition between the Poisson limit and the Wigner limit, i.e. transition between regular Poisson and fully chaotic Wigner dynamics. Effect of the crystal field anisotropy on the quantum chaotic dynamics, which manifests itself in characteristic transitions between regularity and chaos for different field orientations, was demonstrated.Comment: 4 pages, 4 figure

Increasing but Variable Trend of Surface Ozone in the Yangtze River Delta Region of China

Surface ozone (O-3) increased by similar to 20% in the Yangtze River Delta (YRD) region of China during 2014-2020, but the aggravating trend is highly variable on interannual time and city-level space scales. Here, we employed multiple air quality observations and numerical simulation to describe the increasing but variable trend of O-3 and to reveal the main driving factors behind it. In 2014-2017, the governmental air pollution control action plan was mostly against PM2.5 (mainly to control the emissions of SO2, NOx, and primary PM2.5) and effectively reduced the PM2.5 concentration by 18%-45%. However, O-3 pollution worsened in the same period with an increasing rate of 4.9 mu g m(-3) yr(-1), especially in the Anhui province, where the growth rate even reached 14.7 mu g m(-3) yr(-1). After 2018, owing to the coordinated prevention and control of both PM2.5 and O-3, volatile organic compound (VOC) emissions in the YRD region has also been controlled with a great concern, and the O-3 aggravating trend in the same period has been obviously alleviated (1.1 mu g m(-3) yr(-1)). We further combined the precursor concentration and the corresponding O-3 formation regime to explain the observed trend of O-3 in 2014-2020. The leading O-3 formation regime in 2014-2017 is diagnosed as VOC-limited (21%) or mix-limited (58%), with the help of a simulated indicator HCHO/NOy. Under such condition, the decreasing NO2 (2.8% yr(-1)) and increasing VOCs (3.6% yr(-1)) in 2014-2017 led to a rapid increment of O-3. With the continuous reduction in NOx emission and further in ambient NOx/VOCs, the O-3 production regime along the Yangtze River has been shifting from VOC-limited to mix-limited, and after 2018, the mix-limited regime has become the dominant O-3 formation regime for 55% of the YRD cities. Consequently, the decreases of both NOx (3.3% yr(-1)) and VOCs (7.7% yr(-1)) in 2018-2020 obviously slowed down the aggravating trend of O-3. Our study argues that with the implementation of coordinated regional reduction of NOx and VOCs, an effective O-3 control is emerging in the YRD region.Peer reviewe

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

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

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