Piecewise Latent Variables for Neural Variational Text Processing

Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.Comment: 19 pages, 2 figures, 8 tables; EMNLP 201

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.Comment: 8 pages with references; Published in AAAI 2016 (Special Track on Cognitive Systems

Shocks in dense clouds. IV. Effects of grain-grain processing on molecular line emission

Grain-grain processing has been shown to be an indispensable ingredient of shock modelling in high density environments. For densities higher than \sim10^5 cm-3, shattering becomes a self-enhanced process that imposes severe chemical and dynamical consequences on the shock characteristics. Shattering is accompanied by the vaporization of grains, which can directly release SiO to the gas phase. Given that SiO rotational line radiation is used as a major tracer of shocks in dense clouds, it is crucial to understand the influence of vaporization on SiO line emission. We have developed a recipe for implementing the effects of shattering and vaporization into a 2-fluid shock model, resulting in a reduction of computation time by a factor \sim100 compared to a multi-fluid modelling approach. This implementation was combined with an LVG-based modelling of molecular line radiation transport. Using this model we calculated grids of shock models to explore the consequences of different dust-processing scenarios. Grain-grain processing is shown to have a strong influence on C-type shocks for a broad range of magnetic fields: they become hotter and thinner. The reduction in column density of shocked gas lowers the intensity of molecular lines, at the same time as higher peak temperatures increase the intensity of highly excited transitions compared to shocks without grain-grain processing. For OH the net effect is an increase in line intensities, while for CO and H2O it is the contrary. The intensity of H2 emission is decreased in low transitions and increased for highly excited lines. For all molecules, the highly excited lines become sensitive to the value of the magnetic field. Although vaporization increases the intensity of SiO rotational lines, this effect is weakened by the reduced shock width. The release of SiO early in the hot shock changes the excitation characteristics of SiO radiation.Comment: Published in Astronomy and Astrophysics (2013). 26 pages, 16 figures, 14 table

Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses

Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.Comment: ACL 201

Dual mobility hip arthroplasty wear measurement: Experimental accuracy assessment using radiostereometric analysis (RSA)

SummaryIntroductionThe use of dual mobility cups is an effective method to prevent dislocations. However, the specific design of these implants can raise the suspicion of increased wear and subsequent periprosthetic osteolysis.HypothesisUsing radiostereometric analysis (RSA), migration of the femoral head inside the cup of a dual mobility implant can be defined to apprehend polyethylene wear rate.Study objectivesThe study aimed to establish the precision of RSA measurement of femoral head migration in the cup of a dual mobility implant, and its intra- and interobserver variability.Material and methodsA total hip prosthesis phantom was implanted and placed under weight loading conditions in a simulator. Model-based RSA measurement of implant penetration involved specially machined polyethylene liners with increasing concentric wear (no wear, then 0.25, 0.5 and 0.75mm). Three examiners, blinded to the level of wear, analyzed (10 times) the radiostereometric films of the four liners. There was one experienced, one trained, and one inexperienced examiner. Statistical analysis measured the accuracy, precision, and intra- and interobserver variability by calculating Root Mean Square Error (RMSE), Concordance Correlation Coefficient (CCC), Intra Class correlation Coefficient (ICC), and Bland-Altman plots.ResultsOur protocol, that used a simple geometric model rather than the manufacturer's CAD files, showed precision of 0.072mm and accuracy of 0.034mm, comparable with machining tolerances with low variability. Correlation between wear measurement and true value was excellent with a CCC of 0.9772. Intraobserver reproducibility was very good with an ICC of 0.9856, 0.9883 and 0.9842, respectively for examiners 1, 2 and 3. Interobserver reproducibility was excellent with a CCC of 0.9818 between examiners 2 and 1, and 0.9713 between examiners 3 and 1.DiscussionQuantification of wear is indispensable for the surveillance of dual mobility implants. This in vitro study validates our measurement method. Our results, and comparison with other studies using different measurement technologies (RSA, standard radiographs, Martell method) make model-based RSA the reference method for measuring the wear of total hip prostheses in vivo.Level of evidenceLevel 3. Prospective diagnostic study

Efficient Model Learning for Human-Robot Collaborative Tasks

We present a framework for learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human. The learning takes place completely automatically, without any human intervention. First, we describe the clustering of demonstrated action sequences into different human types using an unsupervised learning algorithm. These demonstrated sequences are also used by the robot to learn a reward function that is representative for each type, through the employment of an inverse reinforcement learning algorithm. The learned model is then used as part of a Mixed Observability Markov Decision Process formulation, wherein the human type is a partially observable variable. With this framework, we can infer, either offline or online, the human type of a new user that was not included in the training set, and can compute a policy for the robot that will be aligned to the preference of this new user and will be robust to deviations of the human actions from prior demonstrations. Finally we validate the approach using data collected in human subject experiments, and conduct proof-of-concept demonstrations in which a person performs a collaborative task with a small industrial robot

Chemical sensitivity to the ratio of the cosmic-ray ionization rates of He and H2 in dense clouds

Aim: To determine whether or not gas-phase chemical models with homogeneous and time-independent physical conditions explain the many observed molecular abundances in astrophysical sources, it is crucial to estimate the uncertainties in the calculated abundances and compare them with the observed abundances and their uncertainties. Non linear amplification of the error and bifurcation may limit the applicability of chemical models. Here we study such effects on dense cloud chemistry. Method: Using a previously studied approach to uncertainties based on the representation of rate coefficient errors as log normal distributions, we attempted to apply our approach using as input a variety of different elemental abundances from those studied previously. In this approach, all rate coefficients are varied randomly within their log normal (Gaussian) distribution, and the time-dependent chemistry calculated anew many times so as to obtain good statistics for the uncertainties in the calculated abundances. Results: Starting with so-called ``high-metal'' elemental abundances, we found bimodal rather than Gaussian like distributions for the abundances of many species and traced these strange distributions to an extreme sensitivity of the system to changes in the ratio of the cosmic ray ionization rate zeta\_He for He and that for molecular hydrogen zeta\_H2. The sensitivity can be so extreme as to cause a region of bistability, which was subsequently found to be more extensive for another choice of elemental abundances. To the best of our knowledge, the bistable solutions found in this way are the same as found previously by other authors, but it is best to think of the ratio zeta\_He/zeta\_H2 as a control parameter perpendicular to the ''standard'' control parameter zeta/n\_H.Comment: Accepted for publicatio