150 research outputs found
State Space Closure: Revisiting Endless Online Level Generation via Reinforcement Learning
In this paper, we revisit endless online level generation with the recently
proposed experience-driven procedural content generation via reinforcement
learning (EDRL) framework. Inspired by an observation that EDRL tends to
generate recurrent patterns, we formulate a notion of state space closure which
makes any stochastic state appeared possibly in an infinite-horizon online
generation process can be found within a finite-horizon. Through theoretical
analysis, we find that even though state space closure arises a concern about
diversity, it generalises EDRL trained with a finite-horizon to the
infinite-horizon scenario without deterioration of content quality. Moreover,
we verify the quality and the diversity of contents generated by EDRL via
empirical studies, on the widely used Super Mario Bros. benchmark. Experimental
results reveal that the diversity of levels generated by EDRL is limited due to
the state space closure, whereas their quality does not deteriorate in a
horizon which is longer than the one specified in the training. Concluding our
outcomes and analysis, future work on endless online level generation via
reinforcement learning should address the issue of diversity while assuring the
occurrence of state space closure and quality.Comment: Accepted by the IEEE Transactions on Game
A robust and efficient statistical method for genetic association study using case and control samples from multiple cohorts
BACKGROUND: The theoretical basis of genome-wide association studies (GWAS) is statistical inference of linkage disequilibrium (LD) between any polymorphic marker and a putative disease locus. Most methods widely implemented for such analyses are vulnerable to several key demographic factors and deliver a poor statistical power for detecting genuine associations and also a high false positive rate. Here, we present a likelihood-based statistical approach that accounts properly for non-random nature of case–control samples in regard of genotypic distribution at the loci in populations under study and confers flexibility to test for genetic association in presence of different confounding factors such as population structure, non-randomness of samples etc. RESULTS: We implemented this novel method together with several popular methods in the literature of GWAS, to re-analyze recently published Parkinson’s disease (PD) case–control samples. The real data analysis and computer simulation show that the new method confers not only significantly improved statistical power for detecting the associations but also robustness to the difficulties stemmed from non-randomly sampling and genetic structures when compared to its rivals. In particular, the new method detected 44 significant SNPs within 25 chromosomal regions of size < 1 Mb but only 6 SNPs in two of these regions were previously detected by the trend test based methods. It discovered two SNPs located 1.18 Mb and 0.18 Mb from the PD candidates, FGF20 and PARK8, without invoking false positive risk. CONCLUSIONS: We developed a novel likelihood-based method which provides adequate estimation of LD and other population model parameters by using case and control samples, the ease in integration of these samples from multiple genetically divergent populations and thus confers statistically robust and powerful analyses of GWAS. On basis of simulation studies and analysis of real datasets, we demonstrated significant improvement of the new method over the non-parametric trend test, which is the most popularly implemented in the literature of GWAS
Optimized Path Planning for USVs under Ocean Currents
The proposed work focuses on the path planning for Unmanned Surface Vehicles
(USVs) in the ocean enviroment, taking into account various spatiotemporal
factors such as ocean currents and other energy consumption factors. The paper
proposes the use of Gaussian Process Motion Planning (GPMP2), a Bayesian
optimization method that has shown promising results in continuous and
nonlinear path planning algorithms. The proposed work improves GPMP2 by
incorporating a new spatiotemporal factor for tracking and predicting ocean
currents using a spatiotemporal Bayesian inference. The algorithm is applied to
the USV path planning and is shown to optimize for smoothness, obstacle
avoidance, and ocean currents in a challenging environment. The work is
relevant for practical applications in ocean scenarios where an optimal path
planning for USVs is essential for minimizing costs and optimizing performance.Comment: 9 pages and 7 figures, submitted for IEEE Transactions on Man,
systems ,and Cybernetic
Experimental and theoretical study of microwave enhanced catalytic hydrodesulfurization of thiophene in a continuous-flow reactor
Hydrodesulfurization (HDS) of thiophene, as a gasoline model oil, over an industrial Ni-Mo/Al 2O 3 catalyst was investigated in a continuous system under microwave irradiation. The HDS efficiency was much higher (5%–14%) under microwave irradiation than conventional heating. It was proved that the reaction was enhanced by both microwave thermal and non-thermal effects. Microwave selective heating caused hot spots inside the catalyst, thus improved the reaction rate. From the analysis of the non-thermal effect, the molecular collisions were significantly increased under microwave irradiation. However, instead of being reduced, the apparent activation energy increased. This may be due to the microwave treatment hindering the adsorption though upright S-bind (η 1) and enhancing the parallel adsorption (η 5), both adsorptions were considered to favor to the direct desulfurization route and the hydrogenation route respectively. Therefore, the HDS process was considered to proceed along the hydrogenation route under microwave irradiation.[Figure not available: see fulltext.]
A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes
The visual system evolved to process natural scenes, yet most of our
understanding of the topology and function of visual cortex derives from
studies using artificial stimuli. To gain deeper insights into visual
processing of natural scenes, we utilized widefield calcium-imaging of primate
V4 in response to many natural images, generating a large dataset of
columnar-scale responses. We used this dataset to build a digital twin of V4
via deep learning, generating a detailed topographical map of natural image
preferences at each cortical position. The map revealed clustered functional
domains for specific classes of natural image features. These ranged from
surface-related attributes like color and texture to shape-related features
such as edges, curvature, and facial features. We validated the model-predicted
domains with additional widefield calcium-imaging and single-cell resolution
two-photon imaging. Our study illuminates the detailed topological organization
and neural codes in V4 that represent natural scenes.Comment: 39 pages, 14 figure
- …