Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper.

We introduce \textsc{PoliteRewrite} -- a dataset for polite language rewrite which is a novel sentence rewrite task. Compared with previous text style transfer tasks that can be mostly addressed by slight token- or phrase-level edits, polite language rewrite requires deep understanding and extensive sentence-level edits over an offensive and impolite sentence to deliver the same message euphemistically and politely, which is more challenging -- not only for NLP models but also for human annotators to rewrite with effort. To alleviate the human effort for efficient annotation, we first propose a novel annotation paradigm by a collaboration of human annotators and GPT-3.5 to annotate \textsc{PoliteRewrite}. The released dataset has 10K polite sentence rewrites annotated collaboratively by GPT-3.5 and human, which can be used as gold standard for training, validation and test; and 100K high-quality polite sentence rewrites by GPT-3.5 without human review. We wish this work (The dataset (10K+100K) will be released soon) could contribute to the research on more challenging sentence rewrite, and provoke more thought in future on resource annotation paradigm with the help of the large-scaled pretrained models.

With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring, and an accompanying growth in artificial intelligence applications to automatically derive forest properties of interest from these datasets. Many studies use their own data at small spatio-temporal scales, and demonstrate an application of an existing or adapted data science method for a particular task. This approach often involves intensive and time-consuming data collection and processing, but generates results restricted to specific ecosystems and sensor types. There is a lack of widespread acknowledgement of how the types and structures of data used affects performance and accuracy of analysis algorithms. To accelerate progress in the field more efficiently, benchmarking datasets upon which methods can be tested and compared are sorely needed. Here, we discuss how lack of standardisation impacts confidence in estimation of key forest properties, and how considerations of data collection need to be accounted for in assessing method performance. We present pragmatic requirements and considerations for the creation of rigorous, useful benchmarking datasets for forest monitoring applications, and discuss how tools from modern data science can improve use of existing data. We list a set of example large-scale datasets that could contribute to benchmarking, and present a vision for how community-driven, representative benchmarking initiatives could benefit the field.

Photo-identification (photo-id) is one of the main non-invasive capture-recapture methods utilised by marine researchers for monitoring cetacean (dolphin, whale, and porpoise) populations. This method has historically been performed manually resulting in high workload and cost due to the vast number of images collected. Recently automated aids have been developed to help speed-up photo-id, although they are often disjoint in their processing and do not utilise all available identifying information. Work presented in this paper aims to create a fully automatic photo-id aid capable of providing most likely matches based on all available information without the need for data pre-processing such as cropping. This is achieved through a pipeline of computer vision models and post-processing techniques aimed at detecting cetaceans in unedited field imagery before passing them downstream for individual level catalogue matching. The system is capable of handling previously uncatalogued individuals and flagging these for investigation thanks to catalogue similarity comparison. We evaluate the system against multiple real-life photo-id catalogues, achieving mAP@IOU[0.5] = 0.91, 0.96 for the task of dorsal fin detection on catalogues from Tanzania and the UK respectively and 83.1, 97.5% top-10 accuracy for the task of individual classification on catalogues from the UK and USA.

Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. While graph neural network (GNN)-based models are effective at learning to simulate complex physical dynamics, such as fluids, cloth and articulated bodies, they have been less effective and efficient on rigid-body physics, except with very simple shapes. Existing methods that model collisions through the meshes' nodes are often inaccurate because they struggle when collisions occur on faces far from nodes. Alternative approaches that represent the geometry densely with many particles are prohibitively expensive for complex shapes. Here we introduce the Face Interaction Graph Network (FIGNet) which extends beyond GNN-based methods, and computes interactions between mesh faces, rather than nodes. Compared to learned node- and particle-based methods, FIGNet is around 4x more accurate in simulating complex shape interactions, while also 8x more computationally efficient on sparse, rigid meshes. Moreover, FIGNet can learn frictional dynamics directly from real-world data, and can be more accurate than analytical solvers given modest amounts of training data. FIGNet represents a key step forward in one of the few remaining physical domains which have seen little competition from learned simulators, and offers allied fields such as robotics, graphics and mechanical design a new tool for simulation and model-based planning.

The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing. At ADASS 2021, we introduced astroBERT, a machine learning language model tailored to the text used in astronomy papers in ADS. In this work we: - announce the first public release of the astroBERT language model; - show how astroBERT improves over existing public language models on astrophysics specific tasks; - and detail how ADS plans to harness the unique structure of scientific papers, the citation graph and citation context, to further improve astroBERT.

Zebrafish are a common model organism used to identify new disease therapeutics. High-throughput drug screens can be performed on larval zebrafish in multi-well plates by observing changes in behaviour following a treatment. Analysis of this behaviour can be difficult, however, due to the high dimensionality of the data obtained. Statistical analysis of individual statistics (such as the distance travelled) is generally not powerful enough to detect meaningful differences between treatment groups. Here, we propose a method for classifying zebrafish models of Parkinson's disease by genotype at 5 days old. Using a set of 2D behavioural features, we train a multi-layer perceptron neural network. We further show that the use of integrated gradients can give insight into the impact of each behaviour feature on genotype classifications by the model. In this way, we provide a novel pipeline for classifying zebrafish larvae, beginning with feature preparation and ending with an impact analysis of said features.

原则上，将变异自动编码器（VAE）应用于顺序数据提供了一种用于控制序列生成，操纵和结构化表示学习的方法。但是，训练序列VAE具有挑战性：自回归解码器通常可以解释数据而无需使用潜在空间，即后置倒塌。为了减轻这种情况，最新的模型通过将均匀的随机辍学量应用于解码器输入来削弱强大的解码器。从理论上讲，我们表明，这可以消除解码器输入提供的点式互信息，该信息通过利用潜在空间来补偿。然后，我们提出了一种对抗性训练策略，以实现基于信息的随机辍学。与标准文本基准数据集上的均匀辍学相比，我们的目标方法同时提高了序列建模性能和潜在空间中捕获的信息。

解释视觉场景的含义不仅需要识别其成分对象，还需要对象相互关系的丰富语义表征。在这里，我们通过将现代计算技术应用于复杂自然场景引起的人类脑反应的大规模7T fMRI数据集，研究视觉语义转换的神经机制。使用通过将语言深度学习模型应用于人类生成的场景描述获得的语义嵌入，我们确定了编码语义场景描述的大脑区域的广泛分布网络。重要的是，这些语义嵌入比传统对象类别标签更好地解释了这些区域的活动。此外，尽管参与者没有积极从事语义任务，但它们还是活动的有效预测指标，这表明Visuo-Semantic转换是默认的视觉方式。为了支持这种观点，我们表明，可以直接通过大脑活动模式直接将场景字幕的高度精确重建。最后，经过语义嵌入训练的经常性卷积神经网络进一步超过了语义嵌入在预测大脑活动时的语义嵌入，从而提供了大脑视觉语义转换的机械模型。这些实验和计算结果在一起表明，将视觉输入转换为丰富的语义场景描述可能是视觉系统的核心目标，并且将重点放在这一新目标上可能会导致改进人类大脑中视觉信息处理的模型。

实时机器学习检测算法通常在自动驾驶汽车技术中发现，并依赖优质数据集。这些算法在日常条件以及强烈的阳光下都能正常工作。报告表明，眩光是撞车事故最突出的两个最突出的原因之一。但是，现有的数据集，例如LISA和德国交通标志识别基准，根本不反映Sun Glare的存在。本文介绍了眩光交通标志数据集：在阳光下重大视觉干扰下，具有基于美国的交通标志的图像集合。眩光包含2,157张带有阳光眩光的交通标志图像，从33个美国道路录像带中拉出。它为广泛使用的Lisa流量标志数据集提供了必不可少的丰富。我们的实验研究表明，尽管几种最先进的基线方法在没有太阳眩光的情况下对交通符号数据集进行了训练和测试，但在对眩光进行测试时，它们遭受了极大的痛苦（例如，9％至21％的平均图范围为9％至21％。 ，它明显低于LISA数据集上的性能）。我们还注意到，当对Sun Glare中的交通标志图像进行培训时，当前的架构具有更好的检测准确性（例如，主流算法平均42％的平均地图增益）。