Deep neural networks are often not robust to semantically-irrelevant changes in the input. In this work we address the issue of robustness of state-of-the-art deep convolutional neural networks (CNNs) against commonly occurring distortions in the input such as photometric changes, or the addition of blur and noise. These changes in the input are often accounted for during training in the form of data augmentation. We have two major contributions: First, we propose a new regularization loss called feature-map augmentation (FMA) loss which can be used during finetuning to make a model robust to several distortions in the input. Second, we propose a new combined augmentations (CA) finetuning strategy, that results in a single model that is robust to several augmentation types at the same time in a data-efficient manner. We use the CA strategy to improve an existing state-of-the-art method called stability training (ST). Using CA, on an image classification task with distorted images, we achieve an accuracy improvement of on average 8.94% with FMA and 8.86% with ST absolute on CIFAR-10 and 8.04% with FMA and 8.27% with ST absolute on ImageNet, compared to 1.98% and 2.12%, respectively, with the well known data augmentation method, while keeping the clean baseline performance.

Many computational problems in modern society account to probabilistic reasoning, statistics, and combinatorics. A variety of these real-world questions can be solved by representing the question in (Boolean) formulas and associating the number of models of the formula directly with the answer to the question. Since there has been an increasing interest in practical problem solving for model counting over the last years, the Model Counting (MC) Competition was conceived in fall 2019. The competition aims to foster applications, identify new challenging benchmarks, and to promote new solvers and improve established solvers for the model counting problem and versions thereof. We hope that the results can be a good indicator of the current feasibility of model counting and spark many new applications. In this paper, we report on details of the Model Counting Competition 2020, about carrying out the competition, and the results. The competition encompassed three versions of the model counting problem, which we evaluated in separate tracks. The first track featured the model counting problem (MC), which asks for the number of models of a given Boolean formula. On the second track, we challenged developers to submit programs that solve the weighted model counting problem (WMC). The last track was dedicated to projected model counting (PMC). In total, we received a surprising number of 9 solvers in 34 versions from 8 groups.

In this work we introduce attention as a state of the art mechanism for classification of radio galaxies using convolutional neural networks. We present an attention-based model that performs on par with previous classifiers while using more than 50\% fewer parameters than the next smallest classic CNN application in this field. We demonstrate quantitatively how the selection of normalisation and aggregation methods used in attention-gating can affect the output of individual models, and show that the resulting attention maps can be used to interpret the classification choices made by the model. We observe that the salient regions identified by the our model align well with the regions an expert human classifier would attend to make equivalent classifications. We show that while the selection of normalisation and aggregation may only minimally affect the performance of individual models, it can significantly affect the interpretability of the respective attention maps and by selecting a model which aligns well with how astronomers classify radio sources by eye, a user can employ the model in a more effective manner.

Currently, there is a rapidly increasing need for high-quality biomedical knowledge graphs (BioKG) that provide direct and precise biomedical knowledge. In the context of COVID-19, this issue is even more necessary to be highlighted. However, most BioKG construction inevitably includes numerous conflicts and noises deriving from incorrect knowledge descriptions in literature and defective information extraction techniques. Many studies have demonstrated that reasoning upon the knowledge graph is effective in eliminating such conflicts and noises. This paper proposes a method BioGRER to improve the BioKG's quality, which comprehensively combines the knowledge graph embedding and logic rules that support and negate triplets in the BioKG. In the proposed model, the BioKG refinement problem is formulated as the probability estimation for triplets in the BioKG. We employ the variational EM algorithm to optimize knowledge graph embedding and logic rule inference alternately. In this way, our model could combine efforts from both the knowledge graph embedding and logic rules, leading to better results than using them alone. We evaluate our model over a COVID-19 knowledge graph and obtain competitive results.

A four-legged robot dog created by Chinese technology company Tencent has the balance of a King Fu master, new video footage shows. Jamoca, which has been created by Tencent's Robotics X Lab, can walk across a set of uneven poles spaced randomly apart, like'plum blossom piles' used in Kung Fu to teach better balance. It uses a front-facing camera and visual modelling to accurately perceive its environment and achieve'robust eye and foot calibration'. The robot, which is more than three feet in length and weighs 70kg, can walk, run, trot diagonally and jump just like a real dog. Compared with other four-legged robots, including those developed by US firm Boston Dynamics, Jamoca can navigate a course where there are hazardous gaps that can lead to a fall and operate at a higher altitude. According to Beijing-based technology media platform Jiqizhixin, the dog is still in its experimental stages.

Male bowerbirds in Australia use a technique called forced perspective to make themselves look bigger to potential mates who visit their carefully constructed bowers. Visual illusions remind us that we are not passive decoders of reality but active interpreters. Our eyes capture information from the environment, but our brain can play tricks on us. Perception doesn't always match reality. Scientists have used illusions for decades to explore the psychological and cognitive processes that underlie human visual perception.

As the world is anticipating the end of the COVID-19 pandemic, energy consumption in industry and services is likely to grow. In the longer term, the developing world will increase its energy utilization, leading to growth of global primary energy demand by of 0.4% - 0.6% per year, or a 25% increase by 2050. According to scenarios calculated by energy giant Total SE, massive electrification of transportation will lead to decarbonization, and will require a rapid growth in renewables as a source of electricity. This energy transformation will see an explosion of growth in Artificial Intelligence (AI) utilization in the sector – up 50% between 2020 and 2024 – to allow smart, 21st century grids to become the gold standard, gradually replacing the "dumb" grids laid down in the late 19th – early 20th century in Europe, North America, Japan, China and beyond. The grid is a meta-system of generation facilities, be it nuclear, gas, coal, solar, wind, and hydro, connected by high voltage wire networks to transformers, and then to sub-stations and individual buildings, households, and apartments.

Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing researches focus on vehicle control within a small area around an unsignalized intersection. In this paper, we expand the control domain to a large area with multiple intersections. In particular, we propose a Multi-intersection Vehicular Cooperative Control (MiVeCC) to enable cooperation among vehicles in a large area with multiple unsignalized intersections. Firstly, a vehicular end-edge-cloud computing framework is proposed to facilitate end-edge-cloud vertical cooperation and horizontal cooperation among vehicles. Then, the vehicular cooperative control problems in the cloud and edge layers are formulated as Markov Decision Process (MDP) and solved by two-stage reinforcement learning. Furthermore, to deal with high-density traffic, vehicle selection methods are proposed to reduce the state space and accelerate algorithm convergence without performance degradation. A multi-intersection simulation platform is developed to evaluate the proposed scheme. Simulation results show that the proposed MiVeCC can improve travel efficiency at multiple intersections by up to 4.59 times without collision compared with existing methods.

Change Point Detection techniques aim to capture changes in trends and sequences in time-series data to describe the underlying behaviour of the system. Detecting changes and anomalies in the web services, the trend of applications usage can provide valuable insight towards the system, however, many existing approaches are done in a supervised manner, requiring well-labelled data. As the amount of data produced and captured by sensors are growing rapidly, it is getting harder and even impossible to annotate the data. Therefore, coming up with a self-supervised solution is a necessity these days. In this work, we propose TSCP2 a novel self-supervised technique for temporal change point detection, based on representation learning with Temporal Convolutional Network (TCN). To the best of our knowledge, our proposed method is the first method which employs Contrastive Learning for prediction with the aim change point detection. Through extensive evaluations, we demonstrate that our method outperforms multiple state-of-the-art change point detection and anomaly detection baselines, including those adopting either unsupervised or semi-supervised approach. TSCP2 is shown to improve both non-Deep learning- and Deep learning-based methods by 0.28 and 0.12 in terms of average F1-score across three datasets.

In this paper, we study the task of multimodal sequence analysis which aims to draw inferences from visual, language and acoustic sequences. A majority of existing works generally focus on aligned fusion, mostly at word level, of the three modalities to accomplish this task, which is impractical in real-world scenarios. To overcome this issue, we seek to address the task of multimodal sequence analysis on unaligned modality sequences which is still relatively underexplored and also more challenging. Recurrent neural network (RNN) and its variants are widely used in multimodal sequence analysis, but they are susceptible to the issues of gradient vanishing/explosion and high time complexity due to its recurrent nature. Therefore, we propose a novel model, termed Multimodal Graph, to investigate the effectiveness of graph neural networks (GNN) on modeling multimodal sequential data. The graph-based structure enables parallel computation in time dimension and can learn longer temporal dependency in long unaligned sequences. Specifically, our Multimodal Graph is hierarchically structured to cater to two stages, i.e., intra- and inter-modal dynamics learning. For the first stage, a graph convolutional network is employed for each modality to learn intra-modal dynamics. In the second stage, given that the multimodal sequences are unaligned, the commonly considered word-level fusion does not pertain. To this end, we devise a graph pooling fusion network to automatically learn the associations between various nodes from different modalities. Additionally, we define multiple ways to construct the adjacency matrix for sequential data. Experimental results suggest that our graph-based model reaches state-of-the-art performance on two benchmark datasets.