Shakeout: A New Approach to Regularized Deep Neural Network Training

Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines $L_0$, $L_1$ and $L_2$ regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.Comment: Appears at T-PAMI 201

B(s)→SB_{(s)}\to S transitions in the light cone sum rules with the chiral current

$B_{(s)}$ semi-leptonic decays to the light scalar meson, $B_{(s)}\to S l\bar{\nu}_l, S l \bar{l}\,\,(l=e,\mu,\tau)$, are investigated in the QCD light-cone sum rules (LCSR) with chiral current correlator. Having little knowledge of ingredients of the scalar mesons, we confine ourself to the two quark picture for them and work with the two possible Scenarios. The resulting sum rules for the form factors receive no contributions from the twist-3 distribution amplitudes (DA's), in comparison with the calculation of the conventional LCSR approach where the twist-3 parts play usually an important role. We specify the range of the squared momentum transfer $q^2$, in which the operator product expansion (OPE) for the correlators remains valid approximately. It is found that the form factors satisfy a relation consistent with the prediction of soft collinear effective theory (SCET). In the effective range we investigate behaviors of the form factors and differential decay widthes and compare our calculations with the observations from other approaches. The present findings can be beneficial to experimentally identify physical properties of the scalar mesons.Comment: 22 pages,16 figure

Genetic transformation of Torenia fournieri L. mediated by Agrobacterium rhizogenes

AbstractThe transformation of Torenia fournieri L. mediated by Agrobacterium rhizogenes was studied. Almost all roots induced by four bacterial strains, R1000, R1601, A4 and R1205 were putative hairy roots. The effects of bacterial strains, bacterial concentration, acetosyringone, silver nitrate and co-cultivation pH on Torenia transformation were investigated. Strain R1000, co-cultivation for 3 days, 30 μmol L−1 acetosyringone, 4 mg L−1 silver nitrate and pH 6.5 in the cultivation medium provided the optimal conditions under which transformation frequency approached 90%

Set Representations of Linegraphs

Let $G$ be a graph with vertex set $V(G)$ and edge set $E(G)$. A family $\mathcal{S}$ of nonempty sets $\{S_1,\ldots,S_n\}$ is a set representation of $G$ if there exists a one-to-one correspondence between the vertices $v_1, \ldots, v_n$ in $V(G)$ and the sets in $\mathcal{S}$ such that $v_iv_j \in E(G)$ if and only if S_i\cap S_j\neq \es. A set representation $\mathcal{S}$ is a distinct (respectively, antichain, uniform and simple) set representation if any two sets $S_i$ and $S_j$ in $\mathcal{S}$ have the property $S_i\neq S_j$ (respectively, $S_i\nsubseteq S_j$, $|S_i|=|S_j|$ and $|S_i\cap S_j|\leqslant 1$). Let $U(\mathcal{S})=\bigcup_{i=1}^n S_i$. Two set representations $\mathcal{S}$ and $\mathcal{S}'$ are isomorphic if $\mathcal{S}'$ can be obtained from $\mathcal{S}$ by a bijection from $U(\mathcal{S})$ to $U(\mathcal{S}')$. Let $F$ denote a class of set representations of a graph $G$. The type of $F$ is the number of equivalence classes under the isomorphism relation. In this paper, we investigate types of set representations for linegraphs. We determine the types for the following categories of set representations: simple-distinct, simple-antichain, simple-uniform and simple-distinct-uniform