Visitors at Cebit in Hanover last week had a chance to do what some people in Switzerland have been doing for almost a year now: take a ride on an autonomous shuttle. Two such shuttles have been operating in Sion since the summer of 2016, despite the program being shorty halted in September due to a small accident. Although the shuttles do not require a driver, a safety attendant is on board at all times to make sure they run smoothly and to stop the vehicles in an emergency. The shuttles are build by french company, Navya, and the fleet management software is developed by Swiss start-up Bestmile. Testing in Switzerland is expected to continue until October.

This article presents a logical formalization of the emotional appraisal theory, i.e., it formalizes the cognitive process of evaluation that elicits an emotion. This formalization is psychologically grounded on the OCC cognitive model of emotions. More specifically, we are interested in event-based emotions, i.e., emotions that are elicited by the evaluation of the consequences of an event that either happened or will happen. The formal modelling presented here is based on the AfPL Probabilistic Logic, a BDI-like probabilistic modal logic, which allows our model to verify whether the variables that determine the elicitation of emotions achieved the necessary threshold or not. The proposed logical formalization aims at addressing how the emotions are elicited by the agent cognitive mental states (desires, beliefs and intentions), and how to represent the intensity of the emotions. These are important initial points in the investigation of the dynamic interaction among emotions and other mental states.

Automatic Music Transcription (AMT) is one of the oldest and most well-studied problems in the field of music information retrieval. Within this challenging research field, onset detection and instrument recognition take important places in transcription systems, as they respectively help to determine exact onset times of notes and to recognize the corresponding instrument sources. The aim of this study is to explore the usefulness of multiscale scattering operators for these two tasks on plucked string instrument and piano music. After resuming the theoretical background and illustrating the key features of this sound representation method, we evaluate its performances comparatively to other classical sound representations. Using both MIDI-driven datasets with real instrument samples and real musical pieces, scattering is proved to outperform other sound representations for these AMT subtasks, putting forward its richer sound representation and invariance properties.

Automatic Music Transcription (AMT) consists in automatically estimating the notes in an audio recording, through three attributes: onset time, duration and pitch. Probabilistic Latent Component Analysis (PLCA) has become very popular for this task. PLCA is a spectrogram factorization method, able to model a magnitude spectrogram as a linear combination of spectral vectors from a dictionary. Such methods use the Expectation-Maximization (EM) algorithm to estimate the parameters of the acoustic model. This algorithm presents well-known inherent defaults (local convergence, initialization dependency), making EM-based systems limited in their applications to AMT, particularly in regards to the mathematical form and number of priors. To overcome such limits, we propose in this paper to employ a different estimation framework based on Particle Filtering (PF), which consists in sampling the posterior distribution over larger parameter ranges. This framework proves to be more robust in parameter estimation, more flexible and unifying in the integration of prior knowledge in the system. Note-level transcription accuracies of 61.8 $\%$ and 59.5 $\%$ were achieved on evaluation sound datasets of two different instrument repertoires, including the classical piano (from MAPS dataset) and the marovany zither, and direct comparisons to previous PLCA-based approaches are provided. Steps for further development are also outlined.

We consider a nonlinear state-space model with the state transition and observation functions expressed as basis function expansions. The coefficients in the basis function expansions are learned from data. Using a connection to Gaussian processes we also develop priors on the coefficients, for tuning the model flexibility and to prevent overfitting to data, akin to a Gaussian process state-space model. The priors can alternatively be seen as a regularization, and helps the model in generalizing the data without sacrificing the richness offered by the basis function expansion. To learn the coefficients and other unknown parameters efficiently, we tailor an algorithm using state-of-the-art sequential Monte Carlo methods, which comes with theoretical guarantees on the learning. Our approach indicates promising results when evaluated on a classical benchmark as well as real data.

We consider the learning algorithms under general source condition with the polynomial decay of the eigenvalues of the integral operator in vector-valued function setting. We discuss the upper convergence rates of Tikhonov regularizer under general source condition corresponding to increasing monotone index function. The convergence issues are studied for general regularization schemes by using the concept of operator monotone index functions in minimax setting. Further we also address the minimum possible error for any learning algorithm.

There are now a broad range of time series classification (TSC) algorithms designed to exploit different representations of the data. These have been evaluated on a range of problems hosted at the UCR-UEA TSC Archive (www.timeseriesclassification.com), and there have been extensive comparative studies. However, our understanding of why one algorithm outperforms another is still anecdotal at best. This series of experiments is meant to help provide insights into what sort of discriminatory features in the data lead one set of algorithms that exploit a particular representation to be better than other algorithms. We categorise five different feature spaces exploited by TSC algorithms then design data simulators to generate randomised data from each representation. We describe what results we expected from each class of algorithm and data representation, then observe whether these prior beliefs are supported by the experimental evidence. We provide an open source implementation of all the simulators to allow for the controlled testing of hypotheses relating to classifier performance on different data representations. We identify many surprising results that confounded our expectations, and use these results to highlight how an over simplified view of classifier structure can often lead to erroneous prior beliefs. We believe ensembling can often overcome prior bias, and our results support the belief by showing that the ensemble approach adopted by the Hierarchical Collective of Transform based Ensembles (HIVE-COTE) is significantly better than the alternatives when the data representation is unknown, and is significantly better than, or not significantly significantly better than, or not significantly worse than, the best other approach on three out of five of the individual simulators.

There are lots of reasons why doctors encourage new mothers to breastfeed their babies. Compared with babies who get formula, babies who are breastfed are less likely to die as a result of infections, sudden infant death syndrome or any other reason. The longer a mother nurses -- and the longer she does so exclusively -- the bigger the benefits, studies show. Another perceived benefit of breastfeeding is the possibility that it boosts a baby's brain. A clinical trial involving more than 16,000 infants in Belarus who were randomly assigned to get either special support for breastfeeding (based on a program from the World Health Organization and UNICEF) or a hospital's usual care found that babies in the first group scored an average of 7.5 points higher on a verbal IQ test and 5.9 points higher on overall IQ.

Stephen Hawking made an appearance in Hong Kong to discuss President Donald Trump and Brexit and he did it without having to travel anywhere. The Brief History of Time author, cosmologist and Cambridge professor appeared to the audience of hundreds at an "Evening with Stephen Hawking" event on Friday as a "HumaGram," or human hologram, to deliver his remarks, according to the Independent. The HumaGram is 3D interactive holographic technology developed by the company ARHT Media. The technology can be used life or can be prerecorded and is then only visible to audience members wearing a special pair of glasses. Hawking, who has ALS, used his speech aiding computer to discuss Donald Trump and Brexit saying that between the two, "we are witnessing a global revolt against experts," according to the Independent.

A startup based in Petah Tikva, Isreal, Airobotics,has scored the right to fly drones autonomously for business purposes in Israel. The Civil Aviation Authority of Israel (CAAI) was the first in the world to authorize commercial, fully unmanned drone flights in their nation's airspace. Airobotics' drones are marketed for use in site surveying, security and other industrial applications. Allowing these drones to fly sans operator means that companies can run inspections for miles along power lines, train tracks, or acres of farmland, for example, without humans positioned along the route or token interruptions for point-checks. The startup's self-flying, quadcopter drones launch and land from a base station where they can swap out spent batteries for newly charged ones.