The typical way for relation extraction is fine-tuning large pre-trained language models on task-specific datasets, then selecting the label with the highest probability of the output distribution as the final prediction. However, the usage of the Top-k prediction set for a given sample is commonly overlooked. In this paper, we first reveal that the Top-k prediction set of a given sample contains useful information for predicting the correct label. To effectively utilizes the Top-k prediction set, we propose Label Graph Network with Top-k Prediction Set, termed as KLG. Specifically, for a given sample, we build a label graph to review candidate labels in the Top-k prediction set and learn the connections between them. We also design a dynamic $k$-selection mechanism to learn more powerful and discriminative relation representation. Our experiments show that KLG achieves the best performances on three relation extraction datasets. Moreover, we observe that KLG is more effective in dealing with long-tailed classes.

Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, achieving highly precise alignment is still challenging, especially when nodes with long-range connectivity to the labeled anchors are encountered. To alleviate this limitation, we purposefully designed WL-Align which adopts a regularized representation learning framework to learn distinctive node representations. It extends the Weisfeiler-Lehman Isormorphism Test and learns the alignment in alternating phases of "across-network Weisfeiler-Lehman relabeling" and "proximity-preserving representation learning". The across-network Weisfeiler-Lehman relabeling is achieved through iterating the anchor-based label propagation and a similarity-based hashing to exploit the known anchors' connectivity to different nodes in an efficient and robust manner. The representation learning module preserves the second-order proximity within individual networks and is regularized by the across-network Weisfeiler-Lehman hash labels. Extensive experiments on real-world and synthetic datasets have demonstrated that our proposed WL-Align outperforms the state-of-the-art methods, achieving significant performance improvements in the "exact matching" scenario. Data and code of WL-Align are available at https://github.com/ChenPengGang/WLAlignCode.

From a pig heart being successfully transplanted into a human, to being able to redirect an asteroid on a collision course with Earth, there have been all manner of weird and wonderful scientific discoveries in 2022. They include the human genome finally been mapped after two decades, the unearthing of Africa's oldest known dinosaur, and the release of the first ever image of a supermassive black hole at the heart of our Milky Way galaxy. There was also the alarming discovery that microplastics are everywhere – including in us – and the hugely-anticipated first images from the world's most powerful space telescope James Webb, which will peer back to the dawn of the universe. Here, MailOnline looks at 10 of the most interesting advances this year. The year began with a bang scientifically when just a week into it a dying man became the first patient in the world to get a heart transplant from a genetically-modified pig.

Nowadays, the current neural network models of dialogue generation(chatbots) show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. Modelling a dialogue's future direction is critical for generating coherent, interesting dialogues, a need that has led traditional NLP dialogue models that rely on reinforcement learning. In this article, we explain how to combine these objectives by using deep reinforcement learning to predict future rewards in chatbot dialogue. The model simulates conversations between two virtual agents, with policy gradient methods used to reward sequences that exhibit three useful conversational characteristics: the flow of informality, coherence, and simplicity of response (related to forward-looking function). We assess our model based on its diversity, length, and complexity with regard to humans. In dialogue simulation, evaluations demonstrated that the proposed model generates more interactive responses and encourages a more sustained successful conversation. This work commemorates a preliminary step toward developing a neural conversational model based on the long-term success of dialogues.

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.