Mixture Density Regression reveals frequent recent adaptation in the human genome

How much genome differences between species reflect neutral or adaptive evolution is a central question in evolutionary genomics. In humans and other mammals, the prevalence of adaptive versus neutral genomic evolution has proven particularly difficult to quantify. The difficulty notably stems from the highly heterogenous organization of mammalian genomes at multiple levels (functional sequence density, recombination, etc.) that complicates the interpretation and distinction of adaptive vs. neutral evolution signals. Here, we introduce Mixture Density Regressions (MDRs) for the study of the determinants of recent adaptation in the human genome. MDRs provide a flexible regression model based on multiple Gaussian distributions. We use MDRs to model the association between recent selection signals and multiple genomic factors likely to affect positive selection, if the latter was common enough in the first place to generate these associations. We find that a MDR model with two Gaussian distributions provides an excellent fit to the genome-wide distribution of a common sweep summary statistic (iHS), with one of the two distributions likely capturing the positively selected component of the genome. We further find several factors associated with recent adaptation, including the recombination rate, the density of regulatory elements in immune cells and testis, GC-content, gene expression in immune cells, the density of mammal-wide conserved elements, and the distance to the nearest virus-interacting gene. These results support that strong positive selection was relatively common in recent human evolution and highlight MDRs as a powerful tool to make sense of signals of recent genomic adaptation.

Effects of Mixture and Aggregate Type on Over-Compaction in Hot Mix Asphalt in Tennessee

Correct compaction is vital for asphalt mixture service life. An adequately compacted mixture with inferior properties can achieve better performance than a mixture with excellent properties but poorly compacted. This study investigated resistance to damage caused by over-compaction by utilizing the locking point concept. Over-compaction might cause damage to the aggregate structure and decrease service life. The locking point is defined as the moment during mixture compaction at which an aggregate skeleton is developed and becomes stable. Beyond the locking point, more compaction energy does not significantly increase mixture density and can damage aggregate particles. A total of 15 mixtures was utilized and evaluated using the gyratory compactor. Among them, five dense-graded plant mixtures contained different aggregates and binders, and 10 laboratory mixtures (three types: the surface, the base, and stone mastic asphalt [SMA]) were designed with the most popular coarse aggregates in Tennessee: hard limestone, soft limestone, gravel, and granite. The results of this study show that the highest locking point was reached by the mixtures containing gravel. The SMA mixtures have, on average, lower locking points than the dense-graded mixtures. Most of the dense-graded mixtures made with crushed stones failed in the range of +20 to +30 gyrations, whereas the samples made with gravels failed in the range of +30 to +40 gyrations, indicating that gravel seems to be the most resistant to damage.

Updates to LUCI: A New Fitting Paradigm Using Mixture Density Networks

LUCI is an general-purpose spectral line-fitting pipeline which natively integrates machine learning algorithms to initialize fit functions. LUCI currently uses point-estimates obtained from a convolutional neural network (CNN) to inform optimization algorithms; this methodology has shown great promise by reducing computation time and reducing the chance of falling into a local minimum using convex optimization methods. In this update to LUCI, we expand upon the CNN developed in Rhea et al. so that it outputs Gaussian posterior distributions of the fit parameters of interest (the velocity and broadening) rather than simple point-estimates. Moreover, these posteriors are then used to inform the priors in a Bayesian inference scheme, either emcee or dynesty. The code is publicly available at crhea93:LUCI (https://github.com/crhea93/LUCI).

Research on the Application of Intelligent Choreography for Musical Theater Based on Mixture Density Network Algorithm

Musical choreography is usually completed by professional choreographers, which is very professional and time-consuming. In order to realize the intelligent choreography of musical, based on the mixed density network (MDN), this paper generates the dance matching with the target music through three steps: motion generation, motion screening, and feature matching. The choreography results in this paper have a high degree of matching with music, which makes it possible for the development of motion capture technology and artificial intelligence and computer automatic choreography based on music. In the process of motion generation, the average value of Gaussian model output by MDN is used as the bone position and the consistency of motion is measured according to the change rate of joint velocity in adjacent frames in the process of motion selection. Compared with the existing studies, the dance generated in this paper has improved in motion coherence and realism. In this paper, a multilevel music and action feature matching algorithm combining global feature matching and local feature matching is proposed. The algorithm improves the unity and coherence of music and action. The algorithm proposed in this paper improves the consistency and novelty of movement, the compatibility with music, and the controllability of dance characteristics. Therefore, the algorithm in this paper technically changes the way of artistic creation and provides the possibility for the development of motion capture technology and artificial intelligence.

History Marginalization Improves Forecasting in Variational Recurrent Neural Networks

Deep probabilistic time series forecasting models have become an integral part of machine learning. While several powerful generative models have been proposed, we provide evidence that their associated inference models are oftentimes too limited and cause the generative model to predict mode-averaged dynamics. Mode-averaging is problematic since many real-world sequences are highly multi-modal, and their averaged dynamics are unphysical (e.g., predicted taxi trajectories might run through buildings on the street map). To better capture multi-modality, we develop variational dynamic mixtures (VDM): a new variational family to infer sequential latent variables. The VDM approximate posterior at each time step is a mixture density network, whose parameters come from propagating multiple samples through a recurrent architecture. This results in an expressive multi-modal posterior approximation. In an empirical study, we show that VDM outperforms competing approaches on highly multi-modal datasets from different domains.

Analysis of the Impact of Redispersible Polymer Powder on the Water and Frost Resistance of Cold-Recycled Mixture with Bitumen Emulsion

The subject of the research presented in the article is the assessment of the effect of redispersible polymer powder (RPP) on water and frost resistance of a cold-recycled mixture with bitumen emulsion (BE-CRM). The article presents the results of research on the influence of polymer powder EVA based on polymer (polyethylene-co-vinyl acetate) on the properties of BE-RCM. The impact analysis was determined using the assumptions of the Box-Behnken experiment plan in which three components are controlled. In this case, the variables were the content of: polymer, cement and asphalt emulsion. All ingredients were dosed with a step of 1.5% of the percentage share in the mixture composition. Polymer and Portland cement in an amount of 0.5% to 3.5%. On the other hand, the pure asphalt originating from the asphalt emulsion was 0.0%, 1.5% and 3.0%, respectively. The scope of the tests included the determination of: mixture density, void content (Vm), water absorption (nw), intermediate tensile strength (ITS), to water (TSR) as well as water and frost according to AASHTO T283.