"I really liked the setup.

Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists. However, implementation of ML tools often requires advanced skills in computer science that may not be immediately available in the traditional wet-lab environment. Here, we propose a simple and accessible workflow to automate detection and quantification of brain epithelial metastases on digitized histological slides. A supervised training of the Trainable Weka Segmentation (TWS) from Fiji was achieved from annotated WSIs. Upon comparison with manually drawn regions, it is apparent that the algorithm learned to identify and segment cancer cell-specific nuclei and normal brain tissue.

Joint extraction of entities and relations has received significant attention due to its potential of providing higher performance for both tasks. Among existing methods, CopyRE is effective and novel, which uses a sequence-to-sequence framework and copy mechanism to directly generate the relation triplets. However, it suffers from two fatal problems. The model is extremely weak at differing the head and tail entity, resulting in inaccurate entity extraction. It also cannot predict multi-token entities (e.g. \textit{Steven Jobs}). To address these problems, we give a detailed analysis of the reasons behind the inaccurate entity extraction problem, and then propose a simple but extremely effective model structure to solve this problem. In addition, we propose a multi-task learning framework equipped with copy mechanism, called CopyMTL, to allow the model to predict multi-token entities. Experiments reveal the problems of CopyRE and show that our model achieves significant improvement over the current state-of-the-art method by 9% in NYT and 16% in WebNLG (F1 score). Our code is available at https://github.com/WindChimeRan/CopyMTL

In this paper, we describe SemEval-2013 Task 4: the definition, the data, the evaluation and the results. The task is to capture some of the meaning of English noun compounds via paraphrasing. Given a two-word noun compound, the participating system is asked to produce an explicitly ranked list of its free-form paraphrases. The list is automatically compared and evaluated against a similarly ranked list of paraphrases proposed by human annotators, recruited and managed through Amazon's Mechanical Turk. The comparison of raw paraphrases is sensitive to syntactic and morphological variation. The "gold" ranking is based on the relative popularity of paraphrases among annotators. To make the ranking more reliable, highly similar paraphrases are grouped, so as to downplay superficial differences in syntax and morphology. Three systems participated in the task. They all beat a simple baseline on one of the two evaluation measures, but not on both measures. This shows that the task is difficult.

Imitation learning has gained immense popularity because of its high sample-efficiency. However, in real-world scenarios, where the trajectory distribution of most of the tasks dynamically shifts, model fitting on continuously aggregated data alone would be futile. In some cases, the distribution shifts, so much, that it is difficult for an agent to infer the new task. We propose a novel algorithm to generalize on any related task by leveraging prior knowledge on a set of specific tasks, which involves assigning importance weights to each past demonstration. We show experiments where the robot is trained from a diversity of environmental tasks and is also able to adapt to an unseen environment, using few-shot learning. We also developed a prototype robot system to test our approach on the task of visual navigation, and experimental results obtained were able to confirm these suppositions.

We present $\Gamma$-nets, a method for generalizing value function estimation over timescale. By using the timescale as one of the estimator's inputs we can estimate value for arbitrary timescales. As a result, the prediction target for any timescale is available and we are free to train on multiple timescales at each timestep. Here we empirically evaluate $\Gamma$-nets in the policy evaluation setting. We first demonstrate the approach on a square wave and then on a robot arm using linear function approximation. Next, we consider the deep reinforcement learning setting using several Atari video games. Our results show that $\Gamma$-nets can be effective for predicting arbitrary timescales, with only a small cost in accuracy as compared to learning estimators for fixed timescales. $\Gamma$-nets provide a method for compactly making predictions at many timescales without requiring a priori knowledge of the task, making it a valuable contribution to ongoing work on model-based planning, representation learning, and lifelong learning algorithms.

"It is now too late to stop a future collapse of our societies because of climate change." These are not the words of a tinfoil hat-donning survivalist. This is from a paper delivered by a senior sustainability academic at a leading business school to the European Commission in Brussels, earlier this year. Before that, he delivered a similar message to a UN conference: "Climate change is now a planetary emergency posing an existential threat to humanity." In the age of climate chaos, the collapse of civilization has moved from being a fringe, taboo issue to a more mainstream concern. As the world reels under each new outbreak of crisis--record heatwaves across the Western hemisphere, devastating fires across the Amazon rainforest, the slow-moving Hurricane Dorian, severe ice melting at the poles--the question of how bad things might get, and how soon, has become increasingly urgent. The fear of collapse is evident in the framing of movements such as'Extinction Rebellion' and in resounding warnings that business-as-usual means heading toward an uninhabitable planet. But a growing number of experts not only point at the looming possibility that human civilization itself is at risk; some believe that the science shows it is already too late to prevent collapse. The outcome of the debate on this is obviously critical: it throws light on whether and how societies should adjust to this uncertain landscape. Yet this is not just a scientific debate. It also raises difficult moral questions about what kind of action is warranted to prepare for, or attempt to avoid, the worst. Scientists may disagree about the timeline of collapse, but many argue that this is entirely beside the point. While scientists and politicians quibble over timelines and half measures, or how bad it'll all be, we are losing precious time.

Try Hyperloop, rocket travel, and robotic avatars. Hyperloop is currently working towards 670 mph (1080 kph) passenger pods, capable of zipping us from Los Angeles to downtown Las Vegas in under 30 minutes. Rocket Travel (think SpaceX's Starship) promises to deliver you almost anywhere on the planet in under an hour. Think New York to Shanghai in 39 minutes. As 5G connectivity, hyper-realistic virtual reality, and next-gen robotics continue their exponential progress, the emergence of "robotic avatars" will all but nullify the concept of distance, replacing human travel with immediate remote telepresence.

Accenture (NYSE:ACN) has opened a new innovation hub for mining and energy in Perth, Australia. The hub gives mining and energy companies access to "technology innovations including cloud computing, artificial intelligence, the internet of things, virtual and augmented reality, quantum computing, blockchain and drones". Ann Burns, who leads Accenture's Resources sector in Australia and New Zealand, commented: "With this new innovation hub, we are helping raise the innovation profile of Western Australia and Australia overall. We believe that the Western Australian energy and mining sectors can become world leaders in digitalization. Crucial to this is a focus on what we refer to as'triple zero': ideas, design and technologies that help achieve zero harm to workers and machines, zero loss across the value chain, and zero waste for sustainability."

The benefits of machine translation are easy to see and experience for ourselves, but those practical applications are only one part of what makes the technology valuable. Microsoft and the government of New Zealand are demonstrating the potential of translation tech to help preserve and hopefully breathe new life into the Māori language. Te reo Māori, as it is called in full, is of course the language of New Zealand's largest indigenous community. But as is common elsewhere as well, the tongue has fallen into obscurity as generations of Māori have assimilated into the dominant culture of their colonizers. Māori people make up about 15 percent of the population, and only a quarter of them speak the language, making for a grand total of 3 percent that speak te reo Māori.