51,764 research outputs found
Reducing car-use for leisure: can organised walking groups switch from car travel to bus and train walks?
This paper deals with the significant leisure travel sector, focusing on the attitudes of organised walking groups towards public transport use. A series of interviews with walking group leaders explored the design of organised walks, and factors affecting journeys to and from start points. The themes presented suggest an overlying group culture involving mainly circular walks, reached by car. The research indicates an underlying engrained dependency on cars to reach walks and a degree of opposition to using public transport, which generally contradicts widely–held attitudes towards protecting the environment. Future research should focus more in depth on the long-term removal of psychological barriers to using public transport for leisure, and persuasive measures aimed at groups
Gaussian process hyper-parameter estimation using parallel asymptotically independent Markov sampling
Gaussian process emulators of computationally expensive computer codes
provide fast statistical approximations to model physical processes. The
training of these surrogates depends on the set of design points chosen to run
the simulator. Due to computational cost, such training set is bound to be
limited and quantifying the resulting uncertainty in the hyper-parameters of
the emulator by uni-modal distributions is likely to induce bias. In order to
quantify this uncertainty, this paper proposes a computationally efficient
sampler based on an extension of Asymptotically Independent Markov Sampling, a
recently developed algorithm for Bayesian inference. Structural uncertainty of
the emulator is obtained as a by-product of the Bayesian treatment of the
hyper-parameters. Additionally, the user can choose to perform stochastic
optimisation to sample from a neighbourhood of the Maximum a Posteriori
estimate, even in the presence of multimodality. Model uncertainty is also
acknowledged through numerical stabilisation measures by including a nugget
term in the formulation of the probability model. The efficiency of the
proposed sampler is illustrated in examples where multi-modal distributions are
encountered. For the purpose of reproducibility, further development, and use
in other applications the code used to generate the examples is freely
available for download at https://github.com/agarbuno/paims_codesComment: Computational Statistics \& Data Analysis, Volume 103, November 201
Modality and constitution in distinctively mathematical explanations
Lange argues that some natural phenomena can be explained by appeal to mathematical, rather than natural, facts. In these “distinctively mathematical” explanations, the core explanatory facts are either modally stronger than facts about ordinary causal law or understood to be constitutive of the physical task or arrangement at issue. Craver and Povich argue that Lange’s account of DME fails to exclude certain “reversals”. Lange has replied that his account can avoid these directionality charges. Specifically, Lange argues that in legitimate DMEs, but not in their “reversals,” the empirical fact appealed to in the explanation is “understood to be constitutive of the physical task or arrangement at issue” in the explanandum. I argue that Lange’s reply is unsatisfactory because it leaves the crucial notion of being “understood to be constitutive of the physical task or arrangement” obscure in ways that fail to block “reversals” except by an apparent ad hoc stipulation or by abandoning the reliance on understanding and instead accepting a strong realism about essence
Transportation mode recognition fusing wearable motion, sound and vision sensors
We present the first work that investigates the potential of improving the performance of transportation mode recognition through fusing multimodal data from wearable sensors: motion, sound and vision. We first train three independent deep neural network (DNN) classifiers, which work with the three types of sensors, respectively. We then propose two schemes that fuse the classification results from the three mono-modal classifiers. The first scheme makes an ensemble decision with fixed rules including Sum, Product, Majority Voting, and Borda Count. The second scheme is an adaptive fuser built as another classifier (including Naive Bayes, Decision Tree, Random Forest and Neural Network) that learns enhanced predictions by combining the outputs from the three mono-modal classifiers. We verify the advantage of the proposed method with the state-of-the-art Sussex-Huawei Locomotion and Transportation (SHL) dataset recognizing the eight transportation activities: Still, Walk, Run, Bike, Bus, Car, Train and Subway. We achieve F1 scores of 79.4%, 82.1% and 72.8% with the mono-modal motion, sound and vision classifiers, respectively. The F1 score is remarkably improved to 94.5% and 95.5% by the two data fusion schemes, respectively. The recognition performance can be further improved with a post-processing scheme that exploits the temporal continuity of transportation. When assessing generalization of the model to unseen data, we show that while performance is reduced - as expected - for each individual classifier, the benefits of fusion are retained with performance improved by 15 percentage points. Besides the actual performance increase, this work, most importantly, opens up the possibility for dynamically fusing modalities to achieve distinct power-performance trade-off at run time
Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems with a Kalman-Inspired Proposal Distribution
Bayesian analysis is widely used in science and engineering for real-time
forecasting, decision making, and to help unravel the processes that explain
the observed data. These data are some deterministic and/or stochastic
transformations of the underlying parameters. A key task is then to summarize
the posterior distribution of these parameters. When models become too
difficult to analyze analytically, Monte Carlo methods can be used to
approximate the target distribution. Of these, Markov chain Monte Carlo (MCMC)
methods are particularly powerful. Such methods generate a random walk through
the parameter space and, under strict conditions of reversibility and
ergodicity, will successively visit solutions with frequency proportional to
the underlying target density. This requires a proposal distribution that
generates candidate solutions starting from an arbitrary initial state. The
speed of the sampled chains converging to the target distribution deteriorates
rapidly, however, with increasing parameter dimensionality. In this paper, we
introduce a new proposal distribution that enhances significantly the
efficiency of MCMC simulation for highly parameterized models. This proposal
distribution exploits the cross-covariance of model parameters, measurements
and model outputs, and generates candidate states much alike the analysis step
in the Kalman filter. We embed the Kalman-inspired proposal distribution in the
DREAM algorithm during burn-in, and present several numerical experiments with
complex, high-dimensional or multi-modal target distributions. Results
demonstrate that this new proposal distribution can greatly improve simulation
efficiency of MCMC. Specifically, we observe a speed-up on the order of 10-30
times for groundwater models with more than one-hundred parameters
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