In the era of deep learning, modeling for most NLP tasks has converged to several mainstream paradigms. For example, we usually adopt the sequence labeling paradigm to solve a bundle of tasks such as POS-tagging, NER, Chunking, and adopt the classification paradigm to solve tasks like sentiment analysis. With the rapid progress of pre-trained language models, recent years have observed a rising trend of Paradigm Shift, which is solving one NLP task by reformulating it as another one. Paradigm shift has achieved great success on many tasks, becoming a promising way to improve model performance. Moreover, some of these paradigms have shown great potential to unify a large number of NLP tasks, making it possible to build a single model to handle diverse tasks. In this paper, we review such phenomenon of paradigm shifts in recent years, highlighting several paradigms that have the potential to solve different NLP tasks.

In the literature of transmission scheduling in wireless networked control systems (WNCSs) over shared wireless resources, most research works have focused on partially distributed settings, i.e., where either the controller and actuator, or the sensor and controller are co-located. To overcome this limitation, the present work considers a fully distributed WNCS with distributed plants, sensors, actuators and a controller, sharing a limited number of frequency channels. To overcome communication limitations, the controller schedules the transmissions and generates sequential predictive commands for control. Using elements of stochastic systems theory, we derive a sufficient stability condition of the WNCS, which is stated in terms of both the control and communication system parameters. Once the condition is satisfied, there exists at least one stationary and deterministic scheduling policy that can stabilize all plants of the WNCS. By analyzing and representing the per-step cost function of the WNCS in terms of a finite-length countable vector state, we formulate the optimal transmission scheduling problem into a Markov decision process problem and develop a deep-reinforcement-learning-based algorithm for solving it. Numerical results show that the proposed algorithm significantly outperforms the benchmark policies.

Copy mechanisms explicitly obtain unchanged tokens from the source (input) sequence to generate the target (output) sequence under the neural seq2seq framework. However, most of the existing copy mechanisms only consider single word copying from the source sentences, which results in losing essential tokens while copying long spans. In this work, we propose a plug-and-play architecture, namely BioCopy, to alleviate the problem aforementioned. Specifically, in the training stage, we construct a BIO tag for each token and train the original model with BIO tags jointly. In the inference stage, the model will firstly predict the BIO tag at each time step, then conduct different mask strategies based on the predicted BIO label to diminish the scope of the probability distributions over the vocabulary list. Experimental results on two separate generative tasks show that they all outperform the baseline models by adding our BioCopy to the original model structure.

Abstract-- Future Moon bases will likely be constructed using resources mined from the surface of the Moon. The difficulty of maintaining a human workforce on the Moon and communications lag with Earth means that mining will need to be conducted using collaborative robots with a high degree of autonomy. In this paper, we explore the utility of robotic vision towards addressing several major challenges in autonomous mining in the lunar environment: lack of satellite positioning systems, navigation in hazardous terrain, and delicate robot interactions. The competition provided a simulated lunar environment that exhibits the complexities alluded to above. This argues for a high degree of intelligence on each agent and a robust multi-robot The need to transport resources from Earth is a serious coordination system to ensure long-term operation. In-Situ Resource some of the key challenges towards autonomous robots Utilisation (ISRU), where resources are extracted on for collaborative space mining: lack of satellite positioning other astronomical objects and exploited to support longer systems, navigation in hazardous terrain, and the need for and deeper space missions, has been proposed as a way to delicate robot interactions.

Worldwide Artificial Intelligence (Ai) In Education Market Size (Sales) Market Share by Type (Product Category) [, Machine Learning, Deep Learning & Natural Learning Process (NLP)] in 2018 Worldwide Artificial Intelligence (Ai) In Education Market by Application/End Users [Higher Education, K-12 Education & Corporate Learning] Worldwide Artificial Intelligence (Ai) In Education Sales (Volume) and Market Share Comparison by Applications Global Worldwide Artificial Intelligence (Ai) In Education Sales and Growth Rate (2014-2025) Worldwide Artificial Intelligence (Ai) In Education Competition by Players/Suppliers, Region, Type and Application Worldwide Artificial Intelligence (Ai) In Education (Volume, Value and Sales Price) table defined for each geographic region defined.

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LONDON – Long before the real-world effects of climate change became so abundantly obvious, the data painted a bleak picture – in painful detail – of the scale of the problem. For decades, carefully collected data on weather patterns and sea temperatures were fed into models that analyzed, predicted, and explained the effects of human activities on our climate. And now that we know the alarming answer, one of the biggest questions we face in the next few decades is how data-driven approaches can be used to overcome the climate crisis. Data and technologies like artificial intelligence (AI) are expected to play a very large role. But that will happen only if we make major changes in data management.

A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world -- especially in problematic regions unable to cope with these conditions. However, the movement to a low-carbon energy supply can not happen instantaneously due to the existing fossil-fuel infrastructure and the requirement to maintain a reliable energy supply. Therefore, a low-carbon transition is required, however, the decisions various stakeholders should make over the coming decades to reduce these carbon emissions are not obvious. This is due to many long-term uncertainties, such as electricity, fuel and generation costs, human behaviour and the size of electricity demand. A well choreographed low-carbon transition is, therefore, required between all of the heterogenous actors in the system, as opposed to changing the behaviour of a single, centralised actor. The objective of this thesis is to create a novel, open-source agent-based model to better understand the manner in which the whole electricity market reacts to different factors using state-of-the-art machine learning and artificial intelligence methods. In contrast to other works, this thesis looks at both the long-term and short-term impact that different behaviours have on the electricity market by using these state-of-the-art methods.

We propose a novel monolingual sentence paraphrasing method for augmenting the training data for statistical machine translation systems "for free" -- by creating it from data that is already available rather than having to create more aligned data. Starting with a syntactic tree, we recursively generate new sentence variants where noun compounds are paraphrased using suitable prepositions, and vice-versa -- preposition-containing noun phrases are turned into noun compounds. The evaluation shows an improvement equivalent to 33%-50% of that of doubling the amount of training data.