This paper investigates the problem of trajectory planning for autonomous vehicles at unsignalized intersections, specifically focusing on scenarios where the vehicle lacks the right of way and yet must cross safely. To address this issue, we have employed a method based on the Partially Observable Markov Decision Processes (POMDPs) framework designed for planning under uncertainty. The method utilizes the Adaptive Belief Tree (ABT) algorithm as an approximate solver for the POMDPs. We outline the POMDP formulation, beginning with discretizing the intersection's topology. Additionally, we present a dynamics model for the prediction of the evolving states of vehicles, such as their position and velocity. Using an observation model, we also describe the connection of those states with the imperfect (noisy) available measurements. Our results confirmed that the method is able to plan collision-free trajectories in a series of simulations utilizing real-world traffic data from aerial footage of two distinct intersections. Furthermore, we studied the impact of parameter adjustments of the ABT algorithm on the method's performance. This provides guidance in determining reasonable parameter settings, which is valuable for future method applications.

This paper presents a scalable solution with adjustable computation time for the joint problem of scheduling and assigning machines and transporters for missions that must be completed in a fixed order of operations across multiple stages. A battery-operated multi-robot system with a maximum travel range is employed as the transporter between stages and charging them is considered as an operation. Robots are assigned to a single job until its completion. Additionally, The operation completion time is assumed to be dependent on the machine and the type of operation, but independent of the job. This work aims to minimize a weighted multi-objective goal that includes both the required time and energy consumed by the transporters. This problem is a variation of the flexible flow shop with transports, that is proven to be NP-complete. To provide a solution, time is discretized, the solution space is divided temporally, and jobs are clustered into diverse groups. Finally, an integer linear programming solver is applied within a sliding time window to determine assignments and create a schedule that minimizes the objective. The computation time can be reduced depending on the number of jobs selected at each segment, with a trade-off on optimality. The proposed algorithm finds its application in a water sampling project, where water sampling jobs are assigned to robots, sample deliveries at laboratories are scheduled, and the robots are routed to charging stations.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. A recent VentureBeat article referenced Gartner analyst Whit Andrews saying that more and more companies are entering an era where artificial intelligence (AI) is an aspect of every new project. One such AI application uses facial recognition to analyze expressions based on a person's faceprint to detect their internal emotions or feelings, motivations and attitudes. Known as emotion AI or affective computing, this application is based on the theory of "basic emotions" [$], which states that people everywhere communicate six basic internal emotional states -- happiness, surprise, fear, disgust, anger and sadness -- using the same facial movements based on our biological and evolutionary origins. On the surface, this assumption seems reasonable, as facial expressions are an essential aspect of nonverbal communications.

State-of-the-art Named Entity Recognition(NER) models rely heavily on large amountsof fully annotated training data. However, ac-cessible data are often incompletely annotatedsince the annotators usually lack comprehen-sive knowledge in the target domain. Normallythe unannotated tokens are regarded as non-entities by default, while we underline thatthese tokens could either be non-entities orpart of any entity. Here, we study NER mod-eling with incomplete annotated data whereonly a fraction of the named entities are la-beled, and the unlabeled tokens are equiva-lently multi-labeled by every possible label.Taking multi-labeled tokens into account, thenumerous possible paths can distract the train-ing model from the gold path (ground truthlabel sequence), and thus hinders the learn-ing ability. In this paper, we propose AdaK-NER, named the adaptive top-Kapproach, tohelp the model focus on a smaller feasible re-gion where the gold path is more likely to belocated. We demonstrate the superiority ofour approach through extensive experimentson both English and Chinese datasets, aver-agely improving 2% in F-score on the CoNLL-2003 and over 10% on two Chinese datasetscompared with the prior state-of-the-art works.

An autonomous robotic system invented by researchers at MIT and the Woods Hole Oceanographic Institution (WHOI) efficiently sniffs out the most scientifically interesting -- but hard-to-find -- sampling spots in vast, unexplored waters. Environmental scientists are often interested in gathering samples at the most interesting locations, or "maxima," in an environment. One example could be a source of leaking chemicals, where the concentration is the highest and mostly unspoiled by external factors. But a maximum can be any quantifiable value that researchers want to measure, such as water depth or parts of coral reef most exposed to air. Efforts to deploy maximum-seeking robots suffer from efficiency and accuracy issues.

Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, where, at each layer, the variables follow a mixture of Gaussian distributions. Thus, the deep mixture model consists of a set of nested mixtures of linear models, which globally provide a nonlinear model able to describe the data in a very flexible way. In order to avoid overparameterized solutions, dimension reduction by factor models can be applied at each layer of the architecture thus resulting in deep mixtures of factor analysers.

It has long been suspected that using satnavs can make drivers abandon their senses and do silly things. The persuasive voice of the satnav has led to countless cases of big lorries stuck in narrow country lanes, or motorists who have ignored their own eyes and driven into rivers or the sea. Now research has shown that when we use the handy devices, it'switches off' the parts of our brain we normally use to navigate. But don't panic if you're a Google Maps junkie - although your brain might be in sleep mode now, using real maps will wake it up again. UCL researchers studied the brains of 24 volunteers.

In the beginning, marketing automation platforms grew other channels and tools around their core of email marketing. The basic mode involved if/then rules: if a customer takes this action, show this response. But overlapping campaigns with if/then rules become very complicated very quickly, especially when you're talking about millions of customers, each one in a different frame of mind, and each expecting his/her own personalized experience. As a result, marketing platforms are evolving from their traditional if/then campaigns to the newer approach of customer journeys that are often guided by machine learning. It's the difference between setting up all the rules for the encounter on the one hand, B2B marketing startup YesPath CEO Jason Garoutte told me, and employing something like Netflix's recommendation engine, on the other. "Netflix doesn't write rules about what [movie] you should watch next," he pointed out.

We study the problem of computing possible and necessary winners for partially specified weighted and unweighted tournaments. This problem arises naturally in elections with incompletely specified votes, partially completed sports competitions, and more generally in any scenario where the outcome of some pairwise comparisons is not yet fully known. We specifically consider a number of well-known solution concepts---including the uncovered set, Borda, ranked pairs, and maximin---and show that for most of them, possible and necessary winners can be identified in polynomial time. These positive algorithmic results stand in sharp contrast to earlier results concerning possible and necessary winners given partially specified preference profiles.

The maze can be changed _ any desired mantler by rearranging the partitions between the twen --:five squares. In the maze there is a sensing finger, which can feel the -.:titions of the maze as it comes against them. This finger is moved .- The goal is mounted on a pin which can be slipped into a jack _ any of the twenty-five squares. Thus you can change the problem ..' way you choose, within the limits of the 5 x 5 maze.