A Real-time Edge-AI System for Reef Surveys

Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COTS populations to ecologically sustainable levels. In this paper, we present a comprehensive real-time machine learning-based underwater data collection and curation system on edge devices for COTS monitoring. In particular, we leverage the power of deep learning-based object detection techniques, and propose a resource-efficient COTS detector that performs detection inferences on the edge device to assist marine experts with COTS identification during the data collection phase. The preliminary results show that several strategies for improving computational efficiency (e.g., batch-wise processing, frame skipping, model input size) can be combined to run the proposed detection model on edge hardware with low resource consumption and low information loss.

BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation

Spiking neural networks (SNNs) have attracted extensive attentions in Brain-inspired Artificial Intelligence and computational neuroscience. They can be used to simulate biological information processing in the brain at multiple scales. More importantly, SNNs serve as an appropriate level of abstraction to bring inspirations from brain and cognition to Artificial Intelligence. In this paper, we present the Brain-inspired Cognitive Intelligence Engine (BrainCog) for creating brain-inspired AI and brain simulation models. BrainCog incorporates different types of spiking neuron models, learning rules, brain areas, etc., as essential modules provided by the platform. Based on these easy-to-use modules, BrainCog supports various brain-inspired cognitive functions, including Perception and Learning, Decision Making, Knowledge Representation and Reasoning, Motor Control, and Social Cognition. These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions. For brain simulation, BrainCog realizes the function simulation of decision-making, working memory, the structure simulation of the Neural Circuit, and whole brain structure simulation of Mouse brain, Macaque brain, and Human brain. An AI engine named BORN is developed based on BrainCog, and it demonstrates how the components of BrainCog can be integrated and used to build AI models and applications. To enable the scientific quest to decode the nature of biological intelligence and create AI, BrainCog aims to provide essential and easy-to-use building blocks, and infrastructural support to develop brain-inspired spiking neural network based AI, and to simulate the cognitive brains at multiple scales. The online repository of BrainCog can be found at https://github.com/braincog-x.

Transferability-Guided Cross-Domain Cross-Task Transfer Learning

We propose two novel transferability metrics F-OTCE (Fast Optimal Transport based Conditional Entropy) and JC-OTCE (Joint Correspondence OTCE) to evaluate how much the source model (task) can benefit the learning of the target task and to learn more transferable representations for cross-domain cross-task transfer learning. Unlike the existing metric that requires evaluating the empirical transferability on auxiliary tasks, our metrics are auxiliary-free such that they can be computed much more efficiently. Specifically, F-OTCE estimates transferability by first solving an Optimal Transport (OT) problem between source and target distributions, and then uses the optimal coupling to compute the Negative Conditional Entropy between source and target labels. It can also serve as a loss function to maximize the transferability of the source model before finetuning on the target task. Meanwhile, JC-OTCE improves the transferability robustness of F-OTCE by including label distances in the OT problem, though it may incur additional computation cost. Extensive experiments demonstrate that F-OTCE and JC-OTCE outperform state-of-the-art auxiliary-free metrics by 18.85% and 28.88%, respectively in correlation coefficient with the ground-truth transfer accuracy. By eliminating the training cost of auxiliary tasks, the two metrics reduces the total computation time of the previous method from 43 minutes to 9.32s and 10.78s, respectively, for a pair of tasks. When used as a loss function, F-OTCE shows consistent improvements on the transfer accuracy of the source model in few-shot classification experiments, with up to 4.41% accuracy gain.

Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge

Domain generalization aims at learning a universal model that performs well on unseen target domains, incorporating knowledge from multiple source domains. In this research, we consider the scenario where different domain shifts occur among conditional distributions of different classes across domains. When labeled samples in the source domains are limited, existing approaches are not sufficiently robust. To address this problem, we propose a novel domain generalization framework called Wasserstein Distributionally Robust Domain Generalization (WDRDG), inspired by the concept of distributionally robust optimization. We encourage robustness over conditional distributions within class-specific Wasserstein uncertainty sets and optimize the worst-case performance of a classifier over these uncertainty sets. We further develop a test-time adaptation module leveraging optimal transport to quantify the relationship between the unseen target domain and source domains to make adaptive inference for target data. Experiments on the Rotated MNIST, PACS and the VLCS datasets demonstrate that our method could effectively balance the robustness and discriminability in challenging generalization scenarios.

An Unsupervised Spiking Neural Network Inspired By Biologically Plausible Learning Rules and Connections

The backpropagation algorithm has promoted the rapid development of deep learning, but it relies on a large amount of labeled data, and there is still a large gap with the way the human learns. The human brain can rapidly learn various concept knowledge in a self-organized and unsupervised way, which is accomplished through the coordination of multiple learning rules and structures in the human brain. Spike-timing-dependent plasticity (STDP) is a widespread learning rule in the brain, but spiking neural network trained using STDP alone are inefficient and performs poorly. In this paper, taking inspiration from the short-term synaptic plasticity, we design an adaptive synaptic filter, and we introduce the adaptive threshold balance as the neuron plasticity to enrich the representation ability of SNNs. We also introduce an adaptive lateral inhibitory connection to dynamically adjust the spikes balance to help the network learn richer features. To accelerate and stabilize the training of the unsupervised spiking neural network, we design a sample temporal batch STDP which update the weight based on multiple samples and multiple moments. We have conducted experiments on MNIST and FashionMNIST, and have achieved state-of-the-art performance of the current unsupervised spiking neural network based on STDP. And our model also shows strong superiority in small samples learning.

Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation

Spiking neural network (SNN) has been attached to great importance due to the properties of high biological plausibility and low energy consumption on neuromorphic hardware. As an efficient method to obtain deep SNN, the conversion method has exhibited high performance on various large-scale datasets. However, it typically suffers from severe performance degradation and high time delays. In particular, most of the previous work focuses on simple classification tasks while ignoring the precise approximation to ANN output. In this paper, we first theoretically analyze the conversion errors and derive the harmful effects of time-varying extremes on synaptic currents. We propose the Spike Calibration (SpiCalib) to eliminate the damage of discrete spikes to the output distribution and modify the LIPooling to allow conversion of the arbitrary MaxPooling layer losslessly. Moreover, Bayesian optimization for optimal normalization parameters is proposed to avoid empirical settings. The experimental results demonstrate the state-of-the-art performance on classification, object detection, and segmentation tasks. To the best of our knowledge, this is the first time to obtain SNN comparable to ANN on these tasks simultaneously. Moreover, we only need 1/50 inference time of the previous work on the detection task and can achieve the same performance under 0.492$\times$ energy consumption of ANN on the segmentation task.

Auto-Encoder-Extreme Learning Machine Model for Boiler NOx Emission Concentration Prediction

An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to predict the NOx emission concentration based on the combination of mutual information algorithm (MI), AE, and ELM. First, the importance of practical variables is computed by the MI algorithm, and the mechanism is analyzed to determine the variables related to the NOx emission concentration. Then, the time delay correlations between the selected variables and NOx emission concentration are further analyzed to reconstruct the modeling data. Subsequently, the AE is applied to extract hidden features within the input variables. Finally, an ELM algorithm establishes the relationship between the NOx emission concentration and deep features. The experimental results on practical data indicate that the proposed model shows promising performance compared to state-of-art models.

Efficient End-to-End AutoML via Scalable Search Space Decomposition

End-to-end AutoML has attracted intensive interests from both academia and industry which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning. Existing AutoML systems, however, suffer from scalability issues when applying to application domains with large, high-dimensional search spaces. We present VolcanoML, a scalable and extensible framework that facilitates systematic exploration of large AutoML search spaces. VolcanoML introduces and implements basic building blocks that decompose a large search space into smaller ones, and allows users to utilize these building blocks to compose an execution plan for the AutoML problem at hand. VolcanoML further supports a Volcano-style execution model -- akin to the one supported by modern database systems -- to execute the plan constructed. Our evaluation demonstrates that, not only does VolcanoML raise the level of expressiveness for search space decomposition in AutoML, it also leads to actual findings of decomposition strategies that are significantly more efficient than the ones employed by state-of-the-art AutoML systems such as auto-sklearn. This paper is the extended version of the initial VolcanoML paper appeared in VLDB 2021.

Warped Convolution Networks for Homography Estimation

Homography transformation has an essential relationship with special linear group and the embedding Lie algebra structure. Although the Lie algebra representation is elegant, few researchers have established the connection between homography estimation and algebra expression. In this paper, we propose Warped Convolution Networks (WCN) to effectively estimate the homography transformation by SL(3) group and sl(3) algebra with group convolution. To this end, six commutative subgroups within SL(3) group are composed to form a homography transformation. For each subgroup, a warping function is proposed to bridge the Lie algebra structure to its corresponding parameters in tomography. By taking advantage of the warped convolution, homography estimation is formulated into several simple pseudo-translation regressions. By walking along the Lie topology, our proposed WCN is able to learn the features that are invariant to homography transformation. It can be easily plugged into other popular CNN-based methods. Extensive experiments on POT benchmark and MNIST-Proj dataset show that our proposed method is effective for both homography estimation and classification.