156 research outputs found
Scalable stellar evolution forecasting: Deep learning emulation vs. hierarchical nearest neighbor interpolation
Many astrophysical applications require efficient yet reliable forecasts of
stellar evolution tracks. One example is population synthesis, which generates
forward predictions of models for comparison with observations. The majority of
state-of-the-art population synthesis methods are based on analytic fitting
formulae to stellar evolution tracks that are computationally cheap to sample
statistically over a continuous parameter range. Running detailed stellar
evolution codes, such as MESA, over wide and densely sampled parameter grids is
prohibitively expensive computationally, while stellar-age based linear
interpolation in-between sparsely sampled grid points leads to intolerably
large systematic prediction errors. In this work, we provide two solutions of
automated interpolation methods that find satisfactory trade-off points between
cost-efficiency and accuracy. We construct a timescale-adapted evolutionary
coordinate and use it in a two-step interpolation scheme that traces the
evolution of stars from zero age main sequence all the way to the end of core
helium burning while covering a mass range from to . The feedforward neural network regression model (first
solution) that we train to predict stellar surface variables can make millions
of predictions, sufficiently accurate over the entire parameter space, within
tens of seconds on a 4-core CPU. The hierarchical nearest neighbor
interpolation algorithm (second solution) that we hard-code to the same end
achieves even higher predictive accuracy, the same algorithm remains applicable
to all stellar variables evolved over time, but it is two orders of magnitude
slower. Our methodological framework is demonstrated to work on the MIST data
set. Finally, we discuss prospective applications and provide guidelines how to
generalize our methods to higher dimensional parameter spaces.Comment: Submitted to A&
Learning object relationships which determine the outcome of actions
Peer reviewedPublisher PD
Weighing Counts: Sequential Crowd Counting by Reinforcement Learning
We formulate counting as a sequential decision problem and present a novel
crowd counting model solvable by deep reinforcement learning. In contrast to
existing counting models that directly output count values, we divide one-step
estimation into a sequence of much easier and more tractable sub-decision
problems. Such sequential decision nature corresponds exactly to a physical
process in reality scale weighing. Inspired by scale weighing, we propose a
novel 'counting scale' termed LibraNet where the count value is analogized by
weight. By virtually placing a crowd image on one side of a scale, LibraNet
(agent) sequentially learns to place appropriate weights on the other side to
match the crowd count. At each step, LibraNet chooses one weight (action) from
the weight box (the pre-defined action pool) according to the current crowd
image features and weights placed on the scale pan (state). LibraNet is
required to learn to balance the scale according to the feedback of the needle
(Q values). We show that LibraNet exactly implements scale weighing by
visualizing the decision process how LibraNet chooses actions. Extensive
experiments demonstrate the effectiveness of our design choices and report
state-of-the-art results on a few crowd counting benchmarks. We also
demonstrate good cross-dataset generalization of LibraNet. Code and models are
made available at: https://git.io/libranetComment: Accepted to Proc. Eur. Conf. Computer Vision (ECCV) 202
Distance in audio for VR: Constraints and opportunities
Spatial audio is enjoying a surge in attention in both scene and object based paradigms, due to the trend for, and accessibility of, immersive experience. This has been enabled through convergence in computing enhancements, component size reduction, and associated price reductions. For the first time, applications such as virtual reality (VR) are technologies for the consumer. Audio for VR is captured to provide a counterpart to the video or animated image, and can be rendered to combine elements of physical and psychoacoustic modelling, as well as artistic design. Given that distance is an inherent property of spatial audio, that it can augment sound's efficacy in cueing user attention (a problem which practitioners are seeking to solve), and that conventional film sound practices have intentionally exploited its use, the absence of research on its implementation and effects in immersive environments is notable. This paper sets out the case for its importance, from a perspective of research and practice. It focuses on cinematic VR, whose challenges for spatialized audio are clear, and at times stretches beyond the restrictions specific to distance in audio for VR, into more general audio constraints
Pattern Recognition and Event Reconstruction in Particle Physics Experiments
This report reviews methods of pattern recognition and event reconstruction
used in modern high energy physics experiments. After a brief introduction into
general concepts of particle detectors and statistical evaluation, different
approaches in global and local methods of track pattern recognition are
reviewed with their typical strengths and shortcomings. The emphasis is then
moved to methods which estimate the particle properties from the signals which
pattern recognition has associated. Finally, the global reconstruction of the
event is briefly addressed.Comment: 101 pages, 58 figure
Neural networks for modeling gene-gene interactions in association studies
<p>Abstract</p> <p>Background</p> <p>Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied.</p> <p>Results</p> <p>The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction.</p> <p>Conclusions</p> <p>Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.</p
Predicting Bevirimat resistance of HIV-1 from genotype
<p>Abstract</p> <p>Background</p> <p>Maturation inhibitors are a new class of antiretroviral drugs. Bevirimat (BVM) was the first substance in this class of inhibitors entering clinical trials. While the inhibitory function of BVM is well established, the molecular mechanisms of action and resistance are not well understood. It is known that mutations in the regions CS p24/p2 and p2 can cause phenotypic resistance to BVM. We have investigated a set of p24/p2 sequences of HIV-1 of known phenotypic resistance to BVM to test whether BVM resistance can be predicted from sequence, and to identify possible molecular mechanisms of BVM resistance in HIV-1.</p> <p>Results</p> <p>We used artificial neural networks and random forests with different descriptors for the prediction of BVM resistance. Random forests with hydrophobicity as descriptor performed best and classified the sequences with an area under the Receiver Operating Characteristics (ROC) curve of 0.93 ± 0.001. For the collected data we find that p2 sequence positions 369 to 376 have the highest impact on resistance, with positions 370 and 372 being particularly important. These findings are in partial agreement with other recent studies. Apart from the complex machine learning models we derived a number of simple rules that predict BVM resistance from sequence with surprising accuracy. According to computational predictions based on the data set used, cleavage sites are usually not shifted by resistance mutations. However, we found that resistance mutations could shorten and weaken the <it>α</it>-helix in p2, which hints at a possible resistance mechanism.</p> <p>Conclusions</p> <p>We found that BVM resistance of HIV-1 can be predicted well from the sequence of the p2 peptide, which may prove useful for personalized therapy if maturation inhibitors reach clinical practice. Results of secondary structure analysis are compatible with a possible route to BVM resistance in which mutations weaken a six-helix bundle discovered in recent experiments, and thus ease Gag cleavage by the retroviral protease.</p
Evolutionary Multi-objective Optimization for Simultaneous Generation of Signal-Type and Symbol-Type Representations
It has been a controversial issue in the research of cognitive science and artificial intelligence whether signal-type representations (typically connectionist networks) or symbol-type representations (e.g., semantic networks, production systems) should be used. Meanwhile, it has also been recognized that both types of information representations might exist in the human brain. In addition, symbol-type representations are often very helpful in gaining insights into unknown systems. For these reasons, comprehensible symbolic rules need to be extracted from trained neural networks. In this paper, an evolutionary multi-objective algorithm is employed to generate multiple models that facilitate the generation of signal-type and symbol-type representations simultaneously. It is argued that one main difference between signal-type and symbol-type representations lies in the fact that the signal-type representations are models of a higher complexity (fine representation), whereas symbol-type representations are models of a lower complexity (coarse representation). Thus, by generating models with a spectrum of model complexity, we are able to obtain a population of models of both signal-type and symbol-type quality, although certain post-processing is needed to get a fully symbol-type representation. An illustrative example is given on generating neural networks for the breast cancer diagnosis benchmark problem. © Springer-Verlag Berlin Heidelberg 2005
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