Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge [26].

For policymakers wishing to make evidence-based decisions, one of the challenges is how to combine the relevant information and evidence in a coherent and defensible manner in order to formulate and evaluate candidate policies. Policymakers often need to rely on experts with disparate fields of expertise when making policy choices in complex, multi-faceted, dynamic environments such as those dealing with ecosystem services. The pressures affecting the survival and pollination capabilities of honey bees (Apis mellifera), wild bees and other pollinators is well-documented, but incomplete. In order to estimate the potential effectiveness of various candidate policies to support pollination services, there is an urgent need to quantify the effect of various combinations of variables on the pollination ecosystem service, utilising available information, models and expert judgement. In this paper, we present a new application of the integrating decision support system methodology for combining inputs from multiple panels of experts to evaluate policies to support an abundant pollinator population.

机器学习和计算机视觉是动态增长的领域，事实证明，它们能够解决非常复杂的任务。它们也可以用于监测蜜蜂菌落和检查其健康状态，在这种情况至关重要之前，可以确定潜在的危险状态，或者更好地计划定期的蜜蜂殖民地检查，从而节省大量费用。在本文中，我们介绍了用于蜜蜂监视的最先进的计算机视觉和机器学习应用程序。我们还证明了这些方法的潜力，作为自动蜜蜂计数器算法的一个例子。该论文针对的是兽医和养育专业人士和专家，他们可能不熟悉机器学习来向他们介绍其可能性，因此，每个应用程序都通过与基本方法相关的简短理论介绍和动机来打开。我们希望本文能够激发其他科学家将机器学习技术用于蜜蜂监测中的其他应用。

昆虫是我们生态系统的关键部分。可悲的是，在过去的几十年中，他们的人数令人担忧。为了更好地了解这一过程并监测昆虫的种群，深度学习可能会提供可行的解决方案。但是，鉴于其分类法的广度和典型的细粒度分析障碍，例如与低类变异性相比，较高的类内变异性，昆虫分类仍然是一项艰巨的任务。很少有基准数据集，这阻碍了更好的AI模型的快速发展。但是，稀有物种培训数据的注释需要专家知识。可解释的人工智能（XAI）可以协助生物学家执行这些注释任务，但是选择最佳XAI方法很难。我们对这些研究挑战的贡献是三重：1）从inaturist数据库中取样的野生蜜蜂的彻底注释图像的数据集，2）在野生蜜蜂数据集中训练的重新网络模型，可在野生蜜蜂数据集上获得与类似的最新出手的分类分数。经过其他细粒数据集培训的模型和3）对XAI方法的研究，以支持注释任务中的生物学家。

作为解决复杂优化问题的有效算法，人造蜜蜂菌落（ABC）算法表明竞争，但与其他基于人口的算法相同，它难以平衡整个解决方案空间中全球搜索的能力（命名作为探索）和快速搜索定义为剥削的本地解决方案空间。为了提高ABC的性能，引入了自适应组协作ABC（AGABC）算法，其中不同阶段的群体划分为特定的组，并且分配给成员的不同能力的不同搜索策略，以及成员或策略获得最佳解决方案将采用进一步搜索。基准函数的实验结果表明，具有动态机制的提议算法优于其他搜索精度和稳定性的算法。此外，数值实验表明，该方法可以为复杂调度问题产生最佳解决方案。

标准预审进的语言模型可在子字代币序列上运行，而无需直接访问组成每个令牌字符串表示的字符。我们探究了预审前的语言模型的嵌入层，并表明模型在一个令人惊讶的程度上学习了整个单词和子字代币的内部字符组成，而没有看到字符和令牌。我们的结果表明，罗伯塔（Roberta）的嵌入层具有足够的信息，可以准确地阐明词汇的三分之一，并在所有令牌类型上达到高平均角色Ngram重叠。我们进一步测试了使用其他字符信息丰富子词模型是否可以改善语言建模，并观察到该方法具有几乎相同的学习曲线，作为训练而无需基于拼写的丰富。总体而言，我们的结果表明，语言建模目标激励模型隐式学习一些拼写概念，并且明确教授模型如何拼写的方式似乎并没有增强其在此类任务上的绩效。

In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other computer vision tasks. Existing public datasets are insufficient to develop learning-based methods that handle various surveillance for outdoor and extreme situations such as harsh weather and low illuminance conditions. Therefore, we introduce a new large-scale outdoor surveillance dataset named eXtremely large-scale Multi-modAl Sensor dataset (X-MAS) containing more than 500,000 image pairs and the first-person view data annotated by well-trained annotators. Moreover, a single pair contains multi-modal data (e.g. an IR image, an RGB image, a thermal image, a depth image, and a LiDAR scan). This is the first large-scale first-person view outdoor multi-modal dataset focusing on surveillance tasks to the best of our knowledge. We present an overview of the proposed dataset with statistics and present methods of exploiting our dataset with deep learning-based algorithms. The latest information on the dataset and our study are available at https://github.com/lge-robot-navi, and the dataset will be available for download through a server.

The Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in early December 2019 and now becoming a pandemic. When COVID-19 patients undergo radiography examination, radiologists can observe the present of radiographic abnormalities from their chest X-ray (CXR) images. In this study, a deep convolutional neural network (CNN) model was proposed to aid radiologists in diagnosing COVID-19 patients. First, this work conducted a comparative study on the performance of modified VGG-16, ResNet-50 and DenseNet-121 to classify CXR images into normal, COVID-19 and viral pneumonia. Then, the impact of image augmentation on the classification results was evaluated. The publicly available COVID-19 Radiography Database was used throughout this study. After comparison, ResNet-50 achieved the highest accuracy with 95.88%. Next, after training ResNet-50 with rotation, translation, horizontal flip, intensity shift and zoom augmented dataset, the accuracy dropped to 80.95%. Furthermore, an ablation study on the effect of image augmentation on the classification results found that the combinations of rotation and intensity shift augmentation methods obtained an accuracy higher than baseline, which is 96.14%. Finally, ResNet-50 with rotation and intensity shift augmentations performed the best and was proposed as the final classification model in this work. These findings demonstrated that the proposed classification model can provide a promising result for COVID-19 diagnosis.

Feature acquisition algorithms address the problem of acquiring informative features while balancing the costs of acquisition to improve the learning performances of ML models. Previous approaches have focused on calculating the expected utility values of features to determine the acquisition sequences. Other approaches formulated the problem as a Markov Decision Process (MDP) and applied reinforcement learning based algorithms. In comparison to previous approaches, we focus on 1) formulating the feature acquisition problem as a MDP and applying Monte Carlo Tree Search, 2) calculating the intermediary rewards for each acquisition step based on model improvements and acquisition costs and 3) simultaneously optimizing model improvement and acquisition costs with multi-objective Monte Carlo Tree Search. With Proximal Policy Optimization and Deep Q-Network algorithms as benchmark, we show the effectiveness of our proposed approach with experimental study.

Uniform-precision neural network quantization has gained popularity since it simplifies densely packed arithmetic unit for high computing capability. However, it ignores heterogeneous sensitivity to the impact of quantization errors across the layers, resulting in sub-optimal inference accuracy. This work proposes a novel neural architecture search called neural channel expansion that adjusts the network structure to alleviate accuracy degradation from ultra-low uniform-precision quantization. The proposed method selectively expands channels for the quantization sensitive layers while satisfying hardware constraints (e.g., FLOPs, PARAMs). Based on in-depth analysis and experiments, we demonstrate that the proposed method can adapt several popular networks channels to achieve superior 2-bit quantization accuracy on CIFAR10 and ImageNet. In particular, we achieve the best-to-date Top-1/Top-5 accuracy for 2-bit ResNet50 with smaller FLOPs and the parameter size.