We derive a principled framework for encoding prior knowledge of information coupling between views or camera poses (translation and orientation) of a single scene. While deep neural networks have become the prominent solution to many tasks in computer vision, some important problems not so well suited for deep models have received less attention. These include uncertainty quantification, auxiliary data fusion, and real-time processing, which are instrumental for delivering practical methods with robust inference. While these are central goals in probabilistic machine learning, there is a tangible gap between the theory and practice of applying probabilistic methods to many modern vision problems. For this, we derive a novel parametric kernel (covariance function) in the pose space, $\mathrm{SE}(3)$, that encodes information about input pose relationships into larger models. We show how this soft-prior knowledge can be applied to improve performance on several real vision tasks, such as feature tracking, human face encoding, and view synthesis.

We develop a heuristic solution method for the Consistent Vehicle Routing Problem with Time Windows (ConVRPTW), motivated by a real-world application at a distribution center of a food company. Additional to standard VRPTW restrictions, ConVRP assigns to each customer just one fixed driver to fulfill their orders during the complete multi-period planning horizon. For each driver and day of the planning horizon, a route has to be determined to serve all their assigned customers with positive demand. The customers do not buy every day and the frequency with which they do so is irregular. Moreover, the quantities ordered change from one order to another. This causes difficulties in the daily routing, negatively impacting the service level of the company. Unlike the previous works on ConVRP, where the number of drivers is fixed a priori and only the total travel time is minimized, we give priority to minimizing the number of drivers. To evaluate the performance of the heuristic, we compare the solution of the heuristic with the routing plan in use by the food company. The results show significant improvements, with a lower number of trucks and a higher rate of orders delivered within the prescribed time window.

Despite the availability of qualified research personnel, up-to-date research facilities and experience in developing applied research and innovation, many worldwide research institutions face difficulties when managing contracted Research and Development (R&D) projects due to expectations from Industry (private sector). Such difficulties have motivated funding agents to create evaluation processes to check whether the operational procedures of funded research institutions are sufficient to provide timely answers to demand for innovation from industry and also to identify aspects that require quality improvement in research development. For this purpose, several multiple criteria decision-making approaches can be applied. Among the available multiple criteria approaches, sorting methods are one prominent tool to evaluate the operational capacity. However, the first difficulty in applying multiple criteria sorting methods is the need to hierarchically structure multiple criteria in order to represent the intended decision process. Additional challenges include the elicitation of the preference information and the definition of criteria evaluation, since these are frequently affected by some imprecision. In this paper, a new sorting method is proposed to deal with all of those critical points simultaneously. To consider multiple levels for the decision criteria, the FlowSort method is extended to account for hierarchical criteria. To deal with imprecise data, the FlowSort is integrated with fuzzy approaches. To yield solutions that consider fluctuations from imprecise weights, the Stochastic Multicriteria Acceptability Analysis is used. Finally, the proposed method is applied to the evaluation of research institutions, classifying them according to their operational maturity for development of applied research.

In late September, Beijing unveiled to the world Daxing, a glimmering $11 billion airport showcasing technologies such as robots and facial recognition scanners that many other airports worldwide are either adopting or are now considering. Daxing fits the description of what experts hail as a "smart airport." Just as a smart home is where internet-connected devices control functions like security and thermostats, smart airports use cloud-based technologies to simplify and improve services. Of course, many of the nearly 4,000 scheduled service airports across the world are still embarrassingly antiquated. The good news for aviation is that more facilities are investing, finally, to better serve airlines, suppliers, and travelers. This year, airports worldwide will spend $11.8 billion -- 68 percent more than the level three years ago -- on information technology, according to an estimate published this month by SITA (Société Internationale de Telecommunications Aeronautiques, an airline-owned tech provider). A few trends are driving the rise of smart airports. Flight volumes are increasing, so airports need better ways to process flyers. Airports need better ways to make money, too, by encouraging passengers to spend more in their shops and restaurants. Data is growing in importance. Everything happening at an airport, from where passengers are flowing to which items are selling in stores, generates data. Airports can analyze this data to spot opportunities for eking out fatter profits. They can sell the data to third-parties as well.

As Dao explains, the algorithms read sequences in order to recognise which areas are forested and whether these areas are shrinking. These sequences are individual images strung together in chronological succession – much like old film reels or comic strips. So when a new road is built through the rainforest, for instance, numerous smaller roads form off it over time. It is along these roads that the forest coverage is destroyed. From a bird's-eye view, the resulting pattern resembles the skeleton of a fish, with its spine and small bones – thus the moniker "fish bones".

We study reinforcement learning (RL) in a setting with a network of agents whose states and actions interact in a local manner where the objective is to find localized policies such that the (discounted) global reward is maximized. A fundamental challenge in this setting is that the state-action space size scales exponentially in the number of agents, rendering the problem intractable for large networks. In this paper, we propose a Scalable Actor-Critic (SAC) framework that exploits the network structure and finds a localized policy that is a $O(\rho^\kappa)$-approximation of a stationary point of the objective for some $\rho\in(0,1)$, with complexity that scales with the local state-action space size of the largest $\kappa$-hop neighborhood of the network.

Traditional Reinforcement Learning (RL) algorithms either predict rewards with value functions or maximize them using policy search. We study an alternative: Upside-Down Reinforcement Learning (Upside-Down RL or UDRL), that solves RL problems primarily using supervised learning techniques. Many of its main principles are outlined in a companion report [34]. Here we present the first concrete implementation of UDRL and demonstrate its feasibility on certain episodic learning problems. Experimental results show that its performance can be surprisingly competitive with, and even exceed that of traditional baseline algorithms developed over decades of research.

The incredibly large, Nazca Lines of Peru has always been a mysterious wonder, a sight best viewed from the air. Located near Lima, these impressive geometric figures are etched on a coastal desert. Recently, a team of Japenese researchers have found 143 new figures using satellite photography, 3D imaging and artificial intelligence. They portray animal and human figures like camels, llamas, cats and snakes, while some are more abstract, appearing as geometric shapes. Already, images of a monkey, hummingbird, and spider are quite famous.

As a birthplace for global tech disrupters, Europe -- home to the likes of Spotify and Skype -- still lags behind the US and China and their juggernauts such as Apple, Alibaba, Google and Amazon. The continent is also falling behind North America and east Asia in artificial intelligence, as measured by investment and patent activity. A fragmented digital market, limited risk capital and onerous bureaucracy are several reasons cited for Europe playing catch up to Silicon Valley. However, Europe's more regulated, activist political culture has proved to be an asset, as highlighted by many of the region's start-ups tackling social-services issues in the "tech for good" sector and working directly with central and local governments in "govtech". Europe's start-ups reflect its public service traditions, says Paul Duan, founder of Bayes Impact, a non-profit group that built an AI -powered job counsellor.

One of my favorite panels was the one discussing about AI for social good, something that you can hear everywhere where people are talking about AI. Also, some of the researchers from academy gathered together in a discussion about "how to write a great paper" this was really important for the students and community in general in Latin America, since that is one of our weaknesses… research papers and publications. In order to encourage researchers in Latin America, KHIPU 2019 hold a session poster. You can see more details about the different posters sessions here. Students, professionals, practitioners from different countries, universities, institutions and/or own organization presented their findings/research/interests related to medicine, education, improvement of algorithms.