747,411 research outputs found
Frontal midline theta and N200 amplitude reflect complementary information about expectancy and outcome evaluation
Feedback ERN (fERN) and frontal midline theta have both been proposed to index a dopamine-like reinforcement learning signal in anterior cingulate cortex (ACC). We investigated these proposals by comparing fERN amplitude and theta power with respect to their sensitivities to outcome valence and probability in a previously collected EEG dataset. Bayesian model comparison revealed a dissociation between the two measures, with fERN amplitude mainly sensitive to valence and theta power mainly sensitive to probability. Further, fERN amplitude was highly correlated with the portion of theta power that is consistent in phase across trials (i.e., evoked theta power). These results suggest that although both measures provide valuable information about cognitive function of frontal midline cortex, fERN amplitude is specifically sensitive to dopamine reinforcement learning signals whereas theta power reflects the ACC response to unexpected events
Probability density estimation of photometric redshifts based on machine learning
Photometric redshifts (photo-z's) provide an alternative way to estimate the
distances of large samples of galaxies and are therefore crucial to a large
variety of cosmological problems. Among the various methods proposed over the
years, supervised machine learning (ML) methods capable to interpolate the
knowledge gained by means of spectroscopical data have proven to be very
effective. METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric
Redshifts) is a novel method designed to provide a reliable PDF (Probability
density Function) of the error distribution of photometric redshifts predicted
by ML methods. The method is implemented as a modular workflow, whose internal
engine for photo-z estimation makes use of the MLPQNA neural network (Multi
Layer Perceptron with Quasi Newton learning rule), with the possibility to
easily replace the specific machine learning model chosen to predict photo-z's.
After a short description of the software, we present a summary of results on
public galaxy data (Sloan Digital Sky Survey - Data Release 9) and a comparison
with a completely different method based on Spectral Energy Distribution (SED)
template fitting.Comment: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
784995
A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients
In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes
Benchmarking for Bayesian Reinforcement Learning
In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise
the collected rewards while interacting with their environment while using some
prior knowledge that is accessed beforehand. Many BRL algorithms have already
been proposed, but even though a few toy examples exist in the literature,
there are still no extensive or rigorous benchmarks to compare them. The paper
addresses this problem, and provides a new BRL comparison methodology along
with the corresponding open source library. In this methodology, a comparison
criterion that measures the performance of algorithms on large sets of Markov
Decision Processes (MDPs) drawn from some probability distributions is defined.
In order to enable the comparison of non-anytime algorithms, our methodology
also includes a detailed analysis of the computation time requirement of each
algorithm. Our library is released with all source code and documentation: it
includes three test problems, each of which has two different prior
distributions, and seven state-of-the-art RL algorithms. Finally, our library
is illustrated by comparing all the available algorithms and the results are
discussed.Comment: 37 page
Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts
We apply a machine learning algorithm, the artificial neural network, to the
search for gravitational-wave signals associated with short gamma-ray bursts.
The multi-dimensional samples consisting of data corresponding to the
statistical and physical quantities from the coherent search pipeline are fed
into the artificial neural network to distinguish simulated gravitational-wave
signals from background noise artifacts. Our result shows that the data
classification efficiency at a fixed false alarm probability is improved by the
artificial neural network in comparison to the conventional detection
statistic. Therefore, this algorithm increases the distance at which a
gravitational-wave signal could be observed in coincidence with a gamma-ray
burst. In order to demonstrate the performance, we also evaluate a few seconds
of gravitational-wave data segment using the trained networks and obtain the
false alarm probability. We suggest that the artificial neural network can be a
complementary method to the conventional detection statistic for identifying
gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure
Perbandingan Hasil Belajar Biologi Yang Diajar Menggunakan Model Cooperative Integrated Reading and Composition (Circ) Dengan Model Pembelajaran Langsung Berdasarkan Keterampilan Berpikir Kritis Siswa Kelas X SMA Negeri 4 Palu
The study aimed, to describe the difference of students\u27 learning outcomes of (1) cooperative integrated reading and composition (CIRC) and direct instruction on biology subject, (2) describe the different of critical thinking high level and the lowest by student learning outcomes. This study using quasi experiment. All students in grade 10 who enrolled 2013-2014 would become research subject. The sample of this research consisted of two classes: X MIA5 and X MIA6. The results showed that the comparison between the two learning models can be seen with the magnitude of the significant probability value 0.05. There is also a difference between the learning outcomes of students who have highest critical thinking skills with students who have the lowest, where the average of each value 55.63 and 38.42 or significant probability value = 0.05. It could be concluded, that the student who have the highest critical-thinking skills were taught by using both learning models was better learning-outcomes than the lowest
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