Robot introspection through learned hidden Markov models

In this paper we describe a machine learning approach for acquiring a model of a robot behaviour from raw sensor data. We are interested in automating the acquisition of behavioural models to provide a robot with an introspective capability. We assume that the behaviour of a robot in achieving a task can be modelled as a finite stochastic state transition system. Beginning with data recorded by a robot in the execution of a task, we use unsupervised learning techniques to estimate a hidden Markov model (HMM) that can be used both for predicting and explaining the behaviour of the robot in subsequent executions of the task. We demonstrate that it is feasible to automate the entire process of learning a high quality HMM from the data recorded by the robot during execution of its task.The learned HMM can be used both for monitoring and controlling the behaviour of the robot. The ultimate purpose of our work is to learn models for the full set of tasks associated with a given problem domain, and to integrate these models with a generative task planner. We want to show that these models can be used successfully in controlling the execution of a plan. However, this paper does not develop the planning and control aspects of our work, focussing instead on the learning methodology and the evaluation of a learned model. The essential property of the models we seek to construct is that the most probable trajectory through a model, given the observations made by the robot, accurately diagnoses, or explains, the behaviour that the robot actually performed when making these observations. In the work reported here we consider a navigation task. We explain the learning process, the experimental setup and the structure of the resulting learned behavioural models. We then evaluate the extent to which explanations proposed by the learned models accord with a human observer's interpretation of the behaviour exhibited by the robot in its execution of the task

Audio-based event detection for sports video

In this paper, we present an audio-based event detection approach shown to be effective when applied to the Sports broadcast data. The main benefit of this approach is the ability to recognise patterns that indicate high levels of crowd response which can be correlated to key events. By applying Hidden Markov Model-based classifiers, where the predefined content classes are parameterised using Mel-Frequency Cepstral Coefficients, we were able to eliminate the need for defining a heuristic set of rules to determine event detection, thus avoiding a two-class approach shown not to be suitable for this problem. Experimentation indicated that this is an effective method for classifying crowd response in Soccer matches, thus providing a basis for automatic indexing and summarisation

End-to-End Neural Optical Music Recognition of Monophonic Scores

[EN] Optical Music Recognition is a field of research that investigates how to computationally decode music notation from images. Despite the efforts made so far, there are hardly any complete solutions to the problem. In this work, we study the use of neural networks that work in an end-to-end manner. This is achieved by using a neural model that combines the capabilities of convolutional neural networks, which work on the input image, and recurrent neural networks, which deal with the sequential nature of the problem. Thanks to the use of the the so-called Connectionist Temporal Classification loss function, these models can be directly trained from input images accompanied by their corresponding transcripts into music symbol sequences. We also present the Printed Images of Music Staves (PrIMuS) dataset, containing more than 80,000 monodic single-staff real scores in common western notation, that is used to train and evaluate the neural approach. In our experiments, it is demonstrated that this formulation can be carried out successfully. Additionally, we study several considerations about the codification of the output musical sequences, the convergence and scalability of the neural models, as well as the ability of this approach to locate symbols in the input score.This work was supported by the Social Sciences and Humanities Research Council of Canada, and the Spanish Ministerio de Economia y Competitividad through Project HISPAMUS Ref. No. TIN2017-86576-R (supported by UE FEDER funds).Calvo-Zaragoza, J.; Rizo, D. (2018). End-to-End Neural Optical Music Recognition of Monophonic Scores. Applied Sciences. 8(4). https://doi.org/10.3390/app8040606S8

Is a multiple excitation of a single atom equivalent to a single excitation of an ensemble of atoms?

Recent technological advances have enabled to isolate, control and measure the properties of a single atom, leading to the possibility to perform statistics on the behavior of single quantum systems. These experiments have enabled to check a question which was out of reach previously: Is the statistics of a repeatedly excitation of an atom N times equivalent to a single excitation of an ensemble of N atoms? We present a new method to analyze quantum measurements which leads to the postulation that the answer is most probably no. We discuss the merits of the analysis and its conclusion.Comment: 3 pages, 3 figure

Sequential labeling with structural SVM under an average precision loss

© Springer International Publishing AG 2016. The average precision (AP) is an important and widelyadopted performance measure for information retrieval and classification systems. However, owing to its relatively complex formulation, very few approaches have been proposed to learn a classifier by maximising its average precision over a given training set. Moreover, most of the existing work is restricted to i.i.d. data and does not extend to sequential data. For this reason, we herewith propose a structural SVM learning algorithm for sequential labeling that maximises an average precision measure. A further contribution of this paper is an algorithm that computes the average precision of a sequential classifier at test time, making it possible to assess sequential labeling under this measure. Experimental results over challenging datasets which depict human actions in kitchen scenarios (i.e., TUM Kitchen and CMU Multimodal Activity) show that the proposed approach leads to an average precision improvement of up to 4.2 and 5.7% points against the runner-up, respectively

Bernoulli HMMs at subword level for handwritten word recognition

This paper presents a handwritten word recogniser based on HMMs at subword level (characters) in which state-emission probabilities are governed by multivariate Bernoulli probability functions. This recogniser works directly with raw binary pixels of the image, instead of conventional, real-valued local features. A detailed experimentation has been carried out by varying the number of states, and comparing the results with those from a conventional system based on continuous (Gaussian) densities. From this experimentation, it becomes clear that the proposed recogniser is much better than the conventional systemWork supported by the EC (FEDER) and the Spanish MEC under the MIPRCV “Consolider Ingenio 2010” research programme (CSD2007-00018), the iTransDoc research project (TIN2006-15694-CO2-01), and the FPU grant AP2005-1840.Giménez Pastor, A.; Juan, A. (2009). Bernoulli HMMs at subword level for handwritten word recognition. En Pattern Recognition and Image Analysis. Springer Verlag (Germany). 497-504. https://doi.org/10.1007/978-3-642-02172-5_64S497504Giménez-Pastor, A., Juan-Císcar, A.: Bernoulli HMMs for Off-line Handwriting Recognition. In: Proc. of the 8th Int. Workshop on Pattern Recognition in Information Systems (PRIS 2008), Barcelona, Spain, pp. 86–91 (June 2008)Günter, S., Bunke, H.: HMM-based handwritten word recognition: on the optimization of the number of states, training iterations and Gaussian components. Pattern Recognition 37, 2069–2079 (2004)Gadea, M.P.: Aportaciones al reconocimiento automático de texto manuscrito. PhD thesis, Dep. de Sistemes Informàtics i Computació, València, Spain. Advisors: Vidal, E., Tosselli, A.H. (October 2007)Juan, A., Vidal, E.: Bernoulli mixture models for binary images. In: Proc. of the 17th Int. Conf. on Pattern Recognition (ICPR 2004), Cambridge, UK, vol. 3 (August 2004)Marti, U.V., Bunke, H.: The IAM-database: an English sentence database for offline handwriting recognition.  5(1), 39–46 (2002)Rabiner, L., Juang, B.-H.: Fundamentals of speech recognition. Prentice-Hall, Englewood Cliffs (1993)Romero, V., Giménez, A., Juan, A.: Explicit Modelling of Invariances in Bernoulli Mixtures for Binary Images. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS (LNAI), vol. 4477, pp. 539–546. Springer, Heidelberg (2007)Young, S., et al.: The HTK Book. Cambridge University Engineering Department (1995

Towards low-latency real-time detection of gravitational waves from compact binary coalescences in the era of advanced detectors

Electromagnetic (EM) follow-up observations of gravitational wave (GW) events will help shed light on the nature of the sources, and more can be learned if the EM follow-ups can start as soon as the GW event becomes observable. In this paper, we propose a computationally efficient time-domain algorithm capable of detecting gravitational waves (GWs) from coalescing binaries of compact objects with nearly zero time delay. In case when the signal is strong enough, our algorithm also has the flexibility to trigger EM observation before the merger. The key to the efficiency of our algorithm arises from the use of chains of so-called Infinite Impulse Response (IIR) filters, which filter time-series data recursively. Computational cost is further reduced by a template interpolation technique that requires filtering to be done only for a much coarser template bank than otherwise required to sufficiently recover optimal signal-to-noise ratio. Towards future detectors with sensitivity extending to lower frequencies, our algorithm's computational cost is shown to increase rather insignificantly compared to the conventional time-domain correlation method. Moreover, at latencies of less than hundreds to thousands of seconds, this method is expected to be computationally more efficient than the straightforward frequency-domain method.Comment: 19 pages, 6 figures, for PR

Definition and composition of motor primitives using latent force models and hidden Markov models

In this work a different probabilistic motor primitive parameterization is proposed using latent force models (LFMs). The sequential composition of different motor primitives is also addressed using hidden Markov models (HMMs) which allows to capture the redundancy over dynamics by using a limited set of hidden primitives. The capability of the proposed model to learn and identify motor primitive occurrences over unseen movement realizations is validated using synthetic and motion capture data

Summed Parallel Infinite Impulse Response (SPIIR) Filters For Low-Latency Gravitational Wave Detection

With the upgrade of current gravitational wave detectors, the first detection of gravitational wave signals is expected to occur in the next decade. Low-latency gravitational wave triggers will be necessary to make fast follow-up electromagnetic observations of events related to their source, e.g., prompt optical emission associated with short gamma-ray bursts. In this paper we present a new time-domain low-latency algorithm for identifying the presence of gravitational waves produced by compact binary coalescence events in noisy detector data. Our method calculates the signal to noise ratio from the summation of a bank of parallel infinite impulse response (IIR) filters. We show that our summed parallel infinite impulse response (SPIIR) method can retrieve the signal to noise ratio to greater than 99% of that produced from the optimal matched filter. We emphasise the benefits of the SPIIR method for advanced detectors, which will require larger template banks.Comment: 9 pages, 6 figures, for PR