Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning

In deep reinforcement learning (RL) tasks, an efficient exploration mechanism should be able to encourage an agent to take actions that lead to less frequent states which may yield higher accumulative future return. However, both knowing about the future and evaluating the frequentness of states are non-trivial tasks, especially for deep RL domains, where a state is represented by high-dimensional image frames. In this paper, we propose a novel informed exploration framework for deep RL, where we build the capability for an RL agent to predict over the future transitions and evaluate the frequentness for the predicted future frames in a meaningful manner. To this end, we train a deep prediction model to predict future frames given a state-action pair, and a convolutional autoencoder model to hash over the seen frames. In addition, to utilize the counts derived from the seen frames to evaluate the frequentness for the predicted frames, we tackle the challenge of matching the predicted future frames and their corresponding seen frames at the latent feature level. In this way, we derive a reliable metric for evaluating the novelty of the future direction pointed by each action, and hence inform the agent to explore the least frequent one

Genetic Algorithms Applied to Multi-Class Clustering for Gene Expression Data

A hybrid GA (genetic algorithm)-based clustering (HGACLUS) schema, combining merits of the Simulated Annealing, was described for finding an optimal or near-optimal set of medoids. This schema maximized the clustering success by achieving internal cluster cohesion and external cluster isolation. The performance of HGACLUS and other methods was compared by using simulated data and open microarray gene-expression datasets. HGACLUS was generally found to be more accurate and robust than other methods discussed in this paper by the exact validation strategy and the explicit cluster number

Another Look at Resampling: Replenishing Small Samples with Virtual Data through S-SMART

A new resampling method is introduced to generate virtual data through a smoothing technique for replenishing small samples. The replenished analyzable sample retains the statistical properties of the original small sample, has small standard errors and possesses adequate statistical power

High Diversity of Cytospora Associated With Canker and Dieback of Rosaceae in China, With 10 New Species Described

Cytospora canker is a destructive disease of numerous hosts and causes serious economic losses with a worldwide distribution. Identification of Cytospora species is difficult due to insufficient phylogenetic understanding and overlapped morphological characteristics. In this study, we provide an assessment of 23 Cytospora spp., which covered nine genera of Rosaceae, and focus on 13 species associated with symptomatic branch or twig canker and dieback disease in China. Through morphological observation and multilocus phylogeny of internal transcribed spacer (ITS), large nuclear ribosomal RNA subunit (LSU), actin (act), RNA polymerase II subunit (rpb2), translation elongation factor 1-α (tef1-α), and beta-tubulin (tub2) gene regions, the results indicate 13 distinct lineages with high branch support. These include 10 new Cytospora species, i.e., C. cinnamomea, C. cotoneastricola, C. mali-spectabilis, C. ochracea, C. olivacea, C. pruni-mume, C. rosicola, C. sorbina, C. tibetensis, and C. xinjiangensis and three known taxa including Cytospora erumpens, C. leucostoma, and C. parasitica. This study provides an initial understanding of the taxonomy of Cytospora associated with canker and dieback disease of Rosaceae in China

1,3-Dibenz­yloxy-5-(bromo­meth­yl)benzene

In the title compound, C21H19BrO2, the dihedral angles between the central benzene ring and the two peripheral rings are 50.28 (5) and 69.75 (2)°. The O—CH2 bonds lie in the plane of the central ring and adopt a syn–anti conformation

Discovery and Timing analysis of new pulsars in globular cluster NGC 5024: new observations from FAST

NGC 5024 (M53) is the most distant globular cluster (GC) with known pulsars. In this study, we report the discovery of a new binary millisecond pulsar PSR J1312+1810E (M53E) and present the new timing solutions for M53B to M53E, based on 22 observations from the Five-hundred-meter Aperture Spherical radio Telescope (FAST).These discoveries and timing work benefit from FAST's high sensitivity. We find that M53C is the only isolated millisecond pulsar known in this distant globular cluster, with a spin period of 12.53 ms and spin period derivative of $5.26 \times 10^{-20} \, \rm s \; s^{-1}$. Our results reveal the orbital periods of 47.7, 5.8, and 2.4 days for M53B, D, and E, respectively. The companions, with a mass of 0.25, 0.27, and 0.18 ${\rm M}_\odot$, respectively, are likely to be white dwarf stars; if they are extended objects, they don't eclipse the pulsars. We find no X-ray counterparts for these millisecond pulsars in archival $Chandra$ images in the band of 0.3-8 keV. The characteristics of this pulsar population are similar to the population of millisecond pulsars in the Galactic disk, as expected from the low stellar density of M53.Comment: 10 pages, 4 figures, accepted for publication in ApJ