Inversion sequences avoiding pairs of patterns

The enumeration of inversion sequences avoiding a single pattern was initiated by Corteel--Martinez--Savage--Weselcouch and Mansour--Shattuck independently. Their work has sparked various investigations of generalized patterns in inversion sequences, including patterns of relation triples by Martinez and Savage, consecutive patterns by Auli and Elizalde, and vincular patterns by Lin and Yan. In this paper, we carried out the systematic study of inversion sequences avoiding two patterns of length $3$. Our enumerative results establish further connections to the OEIS sequences and some classical combinatorial objects, such as restricted permutations, weighted ordered trees and set partitions. Since patterns of relation triples are some special multiple patterns of length $3$, our results complement the work by Martinez and Savage. In particular, one of their conjectures regarding the enumeration of $(021,120)$-avoiding inversion sequences is solved

Deep Learning with S-shaped Rectified Linear Activation Units

Rectified linear activation units are important components for state-of-the-art deep convolutional networks. In this paper, we propose a novel S-shaped rectified linear activation unit (SReLU) to learn both convex and non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters. The SReLU is learned jointly with the training of the whole deep network through back propagation. During the training phase, to initialize SReLU in different layers, we propose a "freezing" method to degenerate SReLU into a predefined leaky rectified linear unit in the initial several training epochs and then adaptively learn the good initial values. SReLU can be universally used in the existing deep networks with negligible additional parameters and computation cost. Experiments with two popular CNN architectures, Network in Network and GoogLeNet on scale-various benchmarks including CIFAR10, CIFAR100, MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement compared to other activation functions.Comment: Accepted by AAAI-1

Genome-wide identification and functional analysis of lincRNAs acting as miRNA targets or decoys in maize

LincRNA information derived from three articles. (XLS 20 kb

Screening of Multimeric β-Xylosidases from the Gut Microbiome of a Higher Termite, \u3cem\u3eGlobitermes brachycerastes\u3c/em\u3e

Termite gut microbiome is a rich reservoir for glycoside hydrolases, a suite of enzymes critical for the degradation of lignocellulosic biomass. To search for hemicellulases, we screened 12,000 clones from a fosmid gut library of a higher termite, Globitermes brachycerastes. As a common Southeastern Asian genus, Globitermes distributes predominantly in tropical rain forests and relies on the lignocellulases from themselves and bacterial symbionts to digest wood. In total, 22 positive clones with β-xylosidase activity were isolated, in which 11 representing different restriction fragment length polymorphism (RFLP) patterns were pooled and subjected to 454 pyrosequencing. As a result, eight putative β-xylosidases were cloned and heterologously expressed in Escherichia coli BL21 competent cells. After purification using Ni-NTA affinity chromatography, recombinant G. brachycerastes symbiotic β-xylosidases were characterized enzymatically, including their pH and temperature optimum. In addition to β-xylosidase activity, four of them also exhibited either β-glucosidase or α-arabinosidases activities, suggesting the existence of bifunctional hemicellulases in the gut microbiome of G. brachycerastes. In comparison to multimeric protein engineering, the involvement of naturally occurring multifunctional biocatalysts streamlines the genetic modification procedures and simplifies the overall production processes. Alternatively, these multimeric enzymes could serve as the substitutes for β-glucosidase, β-xylosidase and α-arabinosidase to facilitate a wide range of industrial applications, including food processing, animal feed, environment and waste management, and biomass conversion

Resonant waves in the gap between two advancing barges

The gap resonance between two advancing rectangular barges in side-by-side arrangement is investigated using a 3-D Rankine source method. A modified Sommerfeld radiation condition accounting for Doppler shift is applied for the low forward speed problem when the scattered waves could propagate ahead of the barges. Numerical studies are conducted to investigate various factors which will influence the wave resonance in the narrow gap with particular attention paid on the forward speed effect and its coupling effects with gap width and draft. It is found that in the absence of forward speed, the trapped water surface oscillates like a flexible plate and the wave flow within the gap behaves like a standing wave. When the two barges are travelling ahead, the resonant wave patterns within the gap are reshaped. Additionally, the resonant frequencies shift to lower value and are compressed within a narrow range. Gap resonances are reduced by the augment of gap width. The effect of draft is shown to be associated with resonant modes. Draft effect becomes less pronounced at higher order resonant modes. Furthermore, both gap width and draft effects on gap resonance are found to be independent from forward speed

Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation

In this paper, we propose a novel Pattern-Affinitive Propagation (PAP) framework to jointly predict depth, surface normal and semantic segmentation. The motivation behind it comes from the statistic observation that pattern-affinitive pairs recur much frequently across different tasks as well as within a task. Thus, we can conduct two types of propagations, cross-task propagation and task-specific propagation, to adaptively diffuse those similar patterns. The former integrates cross-task affinity patterns to adapt to each task therein through the calculation on non-local relationships. Next the latter performs an iterative diffusion in the feature space so that the cross-task affinity patterns can be widely-spread within the task. Accordingly, the learning of each task can be regularized and boosted by the complementary task-level affinities. Extensive experiments demonstrate the effectiveness and the superiority of our method on the joint three tasks. Meanwhile, we achieve the state-of-the-art or competitive results on the three related datasets, NYUD-v2, SUN-RGBD and KITTI.Comment: 10 pages, 9 figures, CVPR 201