scholarly journals Recursive sequence generation in monkeys, children, U.S. adults, and native Amazonians

2020 ◽  
Vol 6 (26) ◽  
pp. eaaz1002 ◽  
Author(s):  
Stephen Ferrigno ◽  
Samuel J. Cheyette ◽  
Steven T. Piantadosi ◽  
Jessica F. Cantlon
Keyword(s):  
Training Data ◽  
Recursive Structure ◽  
Generation Task ◽  
Sequence Generation ◽  
Human Thought ◽  
Bayesian Mixture Model ◽  
Indigenous Group ◽  
Recursive Sequence ◽  
Bayesian Mixture ◽  
Recursive Structures

The question of what computational capacities, if any, differ between humans and nonhuman animals has been at the core of foundational debates in cognitive psychology, anthropology, linguistics, and animal behavior. The capacity to form nested hierarchical representations is hypothesized to be essential to uniquely human thought, but its origins in evolution, development, and culture are controversial. We used a nonlinguistic sequence generation task to test whether subjects generalize sequential groupings of items to a center-embedded, recursive structure. Children (3 to 5 years old), U.S. adults, and adults from a Bolivian indigenous group spontaneously induced recursive structures from ambiguous training data. In contrast, monkeys did so only with additional exposure. We quantify these patterns using a Bayesian mixture model over logically possible strategies. Our results show that recursive hierarchical strategies are robust in human thought, both early in development and across cultures, but the capacity itself is not unique to humans.

An alternative computing strategy for genomic prediction using a Bayesian mixture model

2015 ◽  
Vol 95 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Liuhong Chen ◽  
Changxi Li ◽  
Flavio Schenkel
Keyword(s):  
Mixture Model ◽  
Bayesian Methods ◽  
Genomic Prediction ◽  
Large Scale ◽  
Training Data ◽  
Nucleotide Polymorphisms ◽  
Sampling Schemes ◽  
Mcmc Sampling ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture

Chen, L., Li, C. and Schenkel, F. 2015. An alternative computing strategy for genomic prediction using a Bayesian mixture model. Can. J. Anim. Sci. 95: 1–11. Bayesian methods for genomic prediction are commonly implemented via Markov chain Monte Carlo (MCMC) sampling schemes, which are computationally demanding in large-scale applications. An alternative computing algorithm, called right-hand side updating strategy (RHSU), was proposed by exploiting the sparsity feature of the marker effects in a Bayesian mixture model. The new algorithm was compared with the conventional Gauss–Seidel residual update (GSRU) algorithm by the number of floating point operations (FLOP) required in one round of MCMC sampling. The two algorithms were also compared in a Holstein data example with the training data size varying from 1000 to 10 000 and a marker density of 35 790 single nucleotide polymorphisms (SNP). Results showed that the proposed RHSU algorithm would outperform the traditional GSRU algorithm when the sample size exceeded a fraction of the number of the SNPs, which typically varied from 0.05 to 0.18 when the proportion of SNPs with no effect on the trait varied from 0.90 to 0.95. Results from the Holstein data example agreed very well with theoretical expectations. With adoption of a 50 k SNP panel and an increasing training data size, RHSU would be very useful if Bayesian methods are preferable for genomic prediction.

App2Vec: Context-Aware Application Usage Prediction

2021 ◽  
Vol 15 (6) ◽  
pp. 1-21
Author(s):  
Huandong Wang ◽  
Yong Li ◽  
Mu Du ◽  
Zhenhui Li ◽  
Depeng Jin
Keyword(s):  
Dirichlet Process ◽  
Service Providers ◽  
State Of The Art ◽  
Representation Learning ◽  
Context Aware ◽  
Challenging Problem ◽  
Performance Gap ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Spatio Temporal

Both app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when , where , and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.

scholarly journals Recurrent Stacking of Layers for Compact Neural Machine Translation Models

2019 ◽  
Vol 33 ◽  
pp. 6292-6299 ◽  
Author(s):  
Raj Dabre ◽  
Atsushi Fujita
Keyword(s):  
Machine Translation ◽  
Single Layer ◽  
Training Data ◽  
Neural Machine Translation ◽  
Parallel Corpora ◽  
Translation Quality ◽  
Sequence Generation ◽  
Sequence Modeling ◽  
Back Translation

In encoder-decoder based sequence-to-sequence modeling, the most common practice is to stack a number of recurrent, convolutional, or feed-forward layers in the encoder and decoder. While the addition of each new layer improves the sequence generation quality, this also leads to a significant increase in the number of parameters. In this paper, we propose to share parameters across all layers thereby leading to a recurrently stacked sequence-to-sequence model. We report on an extensive case study on neural machine translation (NMT) using our proposed method, experimenting with a variety of datasets. We empirically show that the translation quality of a model that recurrently stacks a single-layer 6 times, despite its significantly fewer parameters, approaches that of a model that stacks 6 different layers. We also show how our method can benefit from a prevalent way for improving NMT, i.e., extending training data with pseudo-parallel corpora generated by back-translation. We then analyze the effects of recurrently stacked layers by visualizing the attentions of models that use recurrently stacked layers and models that do not. Finally, we explore the limits of parameter sharing where we share even the parameters between the encoder and decoder in addition to recurrent stacking of layers.

scholarly journals Three years of Sun-as-a-star radial-velocity observations on the approach to solar minimum

2019 ◽  
Vol 487 (1) ◽  
pp. 1082-1100 ◽  
Author(s):  
A Collier Cameron ◽  
A Mortier ◽  
D Phillips ◽  
X Dumusque ◽  
R D Haywood ◽  
...  
Keyword(s):  
Radial Velocity ◽  
Zero Point ◽  
Spatially Resolved ◽  
Solar Observations ◽  
Bayesian Mixture Model ◽  
Mixture Model Approach ◽  
Calibration Uncertainty ◽  
Bayesian Mixture ◽  
Solar Type ◽  
Line Asymmetry

Abstract The time-variable velocity fields of solar-type stars limit the precision of radial-velocity determinations of their planets’ masses, obstructing detection of Earth twins. Since 2015 July, we have been monitoring disc-integrated sunlight in daytime using a purpose-built solar telescope and fibre feed to the HARPS-N stellar radial-velocity spectrometer. We present and analyse the solar radial-velocity measurements and cross-correlation function (CCF) parameters obtained in the first 3 yr of observation, interpreting them in the context of spatially resolved solar observations. We describe a Bayesian mixture-model approach to automated data-quality monitoring. We provide dynamical and daily differential-extinction corrections to place the radial velocities in the heliocentric reference frame, and the CCF shape parameters in the sidereal frame. We achieve a photon-noise-limited radial-velocity precision better than 0.43 m s−1 per 5-min observation. The day-to-day precision is limited by zero-point calibration uncertainty with an RMS scatter of about 0.4 m s−1. We find significant signals from granulation and solar activity. Within a day, granulation noise dominates, with an amplitude of about 0.4 m s−1 and an autocorrelation half-life of 15 min. On longer time-scales, activity dominates. Sunspot groups broaden the CCF as they cross the solar disc. Facular regions temporarily reduce the intrinsic asymmetry of the CCF. The radial-velocity increase that accompanies an active-region passage has a typical amplitude of 5 m s−1 and is correlated with the line asymmetry, but leads it by 3 d. Spectral line-shape variability thus shows promise as a proxy for recovering the true radial velocity.

scholarly journals Bayesian Mixture Model Analysis for Detecting Differentially Expressed Genes

2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
Zhenyu Jia ◽  
Shizhong Xu
Keyword(s):  
Mixture Model ◽  
Differentially Expressed Genes ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Control Treatment ◽  
Microarray Gene Expression ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Truncated Normal ◽  
Upregulated Genes

Control-treatment design is widely used in microarray gene expression experiments. The purpose of such a design is to detect genes that express differentially between the control and the treatment. Many statistical procedures have been developed to detect differentially expressed genes, but all have pros and cons and room is still open for improvement. In this study, we propose a Bayesian mixture model approach to classifying genes into one of three clusters, corresponding to clusters of downregulated, neutral, and upregulated genes, respectively. The Bayesian method is implemented via the Markov chain Monte Carlo (MCMC) algorithm. The cluster means of down- and upregulated genes are sampled from truncated normal distributions whereas the cluster mean of the neutral genes is set to zero. Using simulated data as well as data from a real microarray experiment, we demonstrate that the new method outperforms all methods commonly used in differential expression analysis.

Bayesian Mixture Model for Prediction of Bus Arrival Time

2015 ◽  
Vol 6 (6) ◽  
pp. 961
Author(s):  
Misbahuddin Misbahuddin ◽  
Riri Fitri Sari
Keyword(s):  
Mixture Model ◽  
Arrival Time ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture
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