Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications. However, the enormous size of large-scale graphs hinders their applications under real-time inference scenarios. Although existing scalable GNNs leverage linear propagation to preprocess the features and accelerate the training and inference procedure, these methods still suffer from scalability issues when making inferences on unseen nodes, as the feature preprocessing requires the graph is known and fixed. To speed up the inference in the inductive setting, we propose a novel adaptive propagation order approach that generates the personalized propagation order for each node based on its topological information. This could successfully avoid the redundant computation of feature propagation. Moreover, the trade-off between accuracy and inference latency can be flexibly controlled by simple hyper-parameters to match different latency constraints of application scenarios. To compensate for the potential inference accuracy loss, we further propose Inception Distillation to exploit the multi scale reception information and improve the inference performance. Extensive experiments are conducted on four public datasets with different scales and characteristics, and the experimental results show that our proposed inference acceleration framework outperforms the SOTA graph inference acceleration baselines in terms of both accuracy and efficiency. In particular, the advantage of our proposed method is more significant on larger-scale datasets, and our framework achieves $75\times$ inference speedup on the largest Ogbn-products dataset.

In 2020, breast cancer amounted for 13,3 percent of newly diagnosed cancer cases in the European Union making it the most frequently occurring cancer type in the EU. On average, one in eleven European women develops breast cancer before the age of 74. An Indian Start-Up has now developed a new device that could help early detection of breast cancer with the help of AI. Breast cancer counts as the most common cancer worldwide. While it mostly affects women, a percentage of up to one percent of all cases has been diagnosed in men.

Transformer-based pretrained language models (LMs) are ubiquitous across natural language understanding, but cannot be applied to long sequences such as stories, scientific articles and long documents, due to their quadratic complexity. While a myriad of efficient transformer variants have been proposed, they are typically based on custom implementations that require expensive pretraining from scratch. In this work, we propose SLED: SLiding-Encoder and Decoder, a simple approach for processing long sequences that re-uses and leverages battle-tested short-text pretrained LMs. Specifically, we partition the input into overlapping chunks, encode each with a short-text LM encoder and use the pretrained decoder to fuse information across chunks (fusion-in-decoder). We illustrate through controlled experiments that SLED offers a viable strategy for long text understanding and evaluate our approach on SCROLLS, a benchmark with seven datasets across a wide range of language understanding tasks. We find that SLED is competitive with specialized models that are up to 50x larger and require a dedicated and expensive pretraining step.

Figure 1: Within this work, we differentiate between various levels of image understanding: The original image (first row, left) can be interpreted as a panoramic semantic map (second row, left) by assigning a label to each pixel without differentiating between different instances of countable objects. Instances of countable objects are distinguished in the panoramic instance understanding (second row, right). The panoramic panoptic understanding (first row, right), which is the proposed method in this paper, builds on top of the previous understandings by eliminating their shortcomings: If possible different instances are distinguished and we guarantee that a label is assigned to each pixel. Abstract--In this work, we introduce panoramic panoptic combining supervised and contrastive training. A complete surrounding understanding provides a maximum of information to a mobile agent. ANOPTIC segmentation is the so far most complete segmentation task to describe the context of an image [1]. The domain shift from pinhole-to panoramic images is no exception. These properties have not been observed by the model during the training and make their correct segmentation Field of View challenging. Feature (PRF) framework which allows us to generate robust backbones via a contrastive pretext task. This poses severe problems due to the lack of does not only encourage similar features to be represented information containing the entire surrounding and the inability in a similar manner but more important, it pushes dissimilar of the agent to make proper decisions which may even lead features away from each other [16], [17]. This leads to well to accidents [5]. Thus, both pieces of information are equally separated clusters in the latent space of the backbone which important: the image should cover the entire surrounding and proves to mitigate distribution shift performance drops.

A higher GDP per capita generally means that citizens have more disposable income, which can be used (Corresponding visualization is the first plot in Figure 1) to purchase goods and services that improve their health [Prompt] Generate an engaging caption for a scatter plot and wellbeing. The outlier in this data is Swaziland, titled GDP per capita VS Healthy life expectancy with which has a lower healthy life expectancy than would the x-axis labeled as GDP per capita and the y-axis labeled be expected of its GDP per capita. This is likely due as Healthy life expectancy. Other columns from to the high prevalence of HIV/AIDS in the country, as well as other factors such as poor access to healthcare, the data set include Social support, Perceptions of corruption, sanitation, and nutrition. Generosity, Overall rank, Score, Country or region, and Freedom to make life choices. The range [Added prompt] What is the reason for Swaziland's poor of GDP per capita is 0.0 to 1.684.

Tsetlin Machine (TM) has been gaining popularity as an inherently interpretable machine leaning method that is able to achieve promising performance with low computational complexity on a variety of applications. The interpretability and the low computational complexity of the TM are inherited from the Boolean expressions for representing various sub-patterns. Although possessing favorable properties, TM has not been the go-to method for AI applications, mainly due to its conceptual and theoretical differences compared with perceptrons and neural networks, which are more widely known and well understood. In this paper, we provide detailed insights for the operational concept of the TM, and try to bridge the gap in the theoretical understanding between the perceptron and the TM. More specifically, we study the operational concept of the TM following the analytical structure of perceptrons, showing the resemblance between the perceptrons and the TM. Through the analysis, we indicated that the TM's weight update can be considered as a special case of the gradient weight update. We also perform an empirical analysis of TM by showing the flexibility in determining the clause length, visualization of decision boundaries and obtaining interpretable boolean expressions from TM. In addition, we also discuss the advantages of TM in terms of its structure and its ability to solve more complex problems.

Legal Judgment Prediction is one of the most acclaimed fields for the combined area of NLP, AI, and Law. By legal prediction we mean an intelligent systems capable to predict specific judicial characteristics, such as judicial outcome, a judicial class, predict an specific case. In this research, we have used AI classifiers to predict judicial outcomes in the Brazilian legal system. For this purpose, we developed a text crawler to extract data from the official Brazilian electronic legal systems. These texts formed a dataset of second-degree murder and active corruption cases. We applied different classifiers, such as Support Vector Machines and Neural Networks, to predict judicial outcomes by analyzing textual features from the dataset. Our research showed that Regression Trees, Gated Recurring Units and Hierarchical Attention Networks presented higher metrics for different subsets. As a final goal, we explored the weights of one of the algorithms, the Hierarchical Attention Networks, to find a sample of the most important words used to absolve or convict defendants.

Remote sensing of the Earth's surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by 1) the spatial resolution of public satellite imagery, 2) classification schemes that operate at the pixel level, and 3) the need for multiple spectral bands. We advance the state-of-the-art by 1) using commercial imagery with panchromatic and multispectral resolutions of 30 cm and 1.2 m, respectively, 2) developing multiple fully convolutional neural networks (FCN) that can learn the morphological features of water bodies in addition to their spectral properties, and 3) FCN that can classify water even from panchromatic imagery. This study focuses on rivers in the Arctic, using images from the Quickbird, WorldView, and GeoEye satellites. Because no training data are available at such high resolutions, we construct those manually. First, we use the RGB, and NIR bands of the 8-band multispectral sensors. Those trained models all achieve excellent precision and recall over 90% on validation data, aided by on-the-fly preprocessing of the training data specific to satellite imagery. In a novel approach, we then use results from the multispectral model to generate training data for FCN that only require panchromatic imagery, of which considerably more is available. Despite the smaller feature space, these models still achieve a precision and recall of over 85%. We provide our open-source codes and trained model parameters to the remote sensing community, which paves the way to a wide range of environmental hydrology applications at vastly superior accuracies and 2 orders of magnitude higher spatial resolution than previously possible.

Telcos are hoping that 5G will transform enterprises the way 4G helped consumers. For instance, watching Fifa World Cup 2022 would have been a delight for many with 5G networks. The first rollouts took place in 2019 in South Korea and the US, but since then 5G has been rolled out in over 70 countries. In India, 5G services were launched by Bharti Airtel and Reliance Jio in October. According to a GSMA Intelligence report, published in October, 5G can contribute $455 billion to the Indian economy between 2023 and 2040.

More than once this year, AI experts have repeated a familiar refrain: "Please slow down." AI news in 2022 has been rapid-fire and relentless; the moment you knew where things currently stood in AI, a new paper or discovery would make that understanding obsolete. In 2022, we arguably hit the knee of the curve when it came to generative AI that can produce creative works made up of text, images, audio, and video. This year, deep-learning AI emerged from a decade of research and began making its way into commercial applications, allowing millions of people to try out the tech for the first time. AI creations inspired wonder, created controversies, prompted existential crises, and turned heads.