In fair division of indivisible goods, using sequences of sincere choices (or picking sequences) is a natural way to allocate the objects. The idea is as follows: at each stage, a designated agent picks one object among those that remain. Another intuitive way to obtain an allocation is to give objects to agents in the first place, and to let agents exchange them as long as such "deals" are beneficial. This paper investigates these notions, when agents have additive preferences over objects, and unveils surprising connections between them, and with other efficiency and fairness notions. In particular, we show that an allocation is sequenceable iff it is optimal for a certain type of deals, namely cycle deals involving a single object. Furthermore, any Paretooptimal allocation is sequenceable, but not the converse. Regarding fairness, we show that an allocation can be envy-free and non-sequenceable, but that every competitive equilibrium with equal incomes is sequenceable. To complete the picture, we show how some domain restrictions may affect the relations between these notions. Finally, we experimentally explore the links between the scales of efficiency and fairness. Keywords: Multiagent Resource Allocation, Fair Division, Efficiency, Distributed Resource Allocation 1. Introduction In this paper, we investigate fair division of indivisible goods.

We present Web-STAR, an online platform for story understanding built on top of the STAR reasoning engine for STory comprehension through ARgumentation. The platform includes a web-based IDE, integration with the STAR system, and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks. The platform also delivers a number of "social" features, including a community repository for public story sharing with a built-in commenting system, and tools for collaborative story editing that can be used for team development projects and for educational purposes.

Semiconductor Engineering sat down with Rob Aitken, an Arm fellow; Raik Brinkmann, CEO of OneSpin Solutions; Patrick Soheili, vice president of business and corporate development at eSilicon; and Chris Rowen, CEO of Babblelabs. What follows are excerpts of that conversation. To view part one, click here. SE: Is the industry's knowledge of machine learning keeping up with the pace of development? Rowen: It's clear that more theories will help us understand what is really possible and some things about what kinds of network designs will be better than others. At the same time, many of our biggest technological advancements have been when deployments got well ahead of theories.

Executives are demanding that the business take advantage of AI and machine learning with the information within their systems. The promise of the intelligence gleaned from implementing these emerging technologies is high – along with the potential to excel. But the reality is that many organizations face messy data, excessive information ROT (redundant, obsolete, and trivial), and still rely on paper-based principles to manage it all. Have a question you'd like to see answered in this webinar? We'll do our best to include it during the live event.

Predictions of the Robocalypse are everywhere. "Robots Are Winning the Race for Jobs," headlines The New York Times, which linked workplace automation to the rise of despotic rulers around the world. Elon Musk warns "Robots will do everything better than us." On one hand, 72 percent of Americans are worried about an automated future (Pew). On the other hand, 94 percent of American workers don't think a robot will take their job (NPR).

The relation between mostly concept-based lexical-semantic networks (wordnets) and lemma-based lexical resources (dictionaries) has been explored so far mainly for wordnet-building purposes, and such projects and related issues are well documented. In spite of not being meant to serve lexicographical purposes (in the case of most wordnets, with some notable exceptions), wordnets have become a de facto standard for the drafting of dictionary content. Experience resulting from using wordnets as a data source for lexicography and issues related to them have just started to be systematically discussed. In the WNLEX Workshop, we define the state of the art in the discussed topics, provide a survey of solved and unsolved issues, and an outlook on future work regarding wordnet as a resource in lexicographical workflows. Target group for this workshop is lexicographers.

Boston Dynamics is preparing to build its terrifying army of robot dogs, according to a Saturday report in Inverse that the company has set a target date of July 2019 as the time it will be ready to manufacture 1,000 of its compact SpotMini models annually. SpotMini is the smallest variant of Boston Dynamics' many different models of robo-dogs yet at approximately two feet, nine inches tall. It weighs "around 66 pounds" and has an hour and a half battery life, per TechCrunch, and the company has recently demonstrated all kinds of functionalities like opening doors for other robots and increasingly complicated navigational skills. While the company already announced plans to launch commercially in 2019 with a limited run of robots already in pre-production, Inverse's report has some new details, such as that the SpotMini is intended to eventually become a multi-use platform of sorts: The overarching goal for the 26-year-old company is to become the what Android operating system is for phones: a versatile foundation for limitless applications. The attachment point where the SpotMini's robotic arm stems from its body could in the future hold a variety of attachments "to be designed and produced by third parties," per Fortune, making it more versatile.

This technology could be put in smartphones that understand and predict our behaviour, potentially offering personalised support. They could also be used by robot companions for older people, or in self-driving cars and interactive video games. Dr Loetscher says the findings also provide an important bridge between tightly controlled laboratory studies and the study of natural eye movements in real-world environments. 'This research has tracked and measured the visual behaviour of people going about their everyday tasks, providing more natural responses than if they were in a lab. 'And thanks to our machine-learning approach, we not only validate the role of personality in explaining eye movement in everyday life, but also reveal new eye movement characteristics as predictors of personality traits.' 'Personality traits characterise an individual's patterns of behaviour, thinking, and feeling', researchers wrote previously in their paper published in Frontiers in Human Neuroscience. 'Studies reporting relationships between personality traits and eye movements suggest that people with similar traits tend to move their eyes in similar ways.' Researchers found that people who were neurotic usually blinked faster while people who were open to new experiences moved their eyes more from side-to-side. People who had high levels of conscientiousness had greater fluctuations in their pupil size. Optimists spent less time looking at negative emotional stimuli (such as image of skin cancer) than people who were pessimistic.

Investors trying to pick winners are parsing what little data is available, but that could lead them down the wrong path. Even investor darling Tesla is struggling with production and financing problems. These have pushed the stock down more than 10% in the past year. Sales data tell one story. China's BYD and BAIC are in the lead, followed by Tesla and BMW .

Like a zombie, it keeps coming back. Like zombie movies, it enjoys growing popularity by defying logic and common sense. Chicago and Stockton (CA) have launched the most recent proposals for Universal Basic Income (UBI). That the idea appeals to cities that have gone bankrupt or have unsustainable financial prospects should give us pause. Universal Basic Income is "…a periodic cash payment unconditionally delivered to all on an individual basis, without means-test or work requirement."