Compositional coding capsule network with k-means routing for text classification

Text classification is a challenging problem which aims to identify the category of texts. Recently, Capsule Networks (CapsNets) are proposed for image classification. It has been shown that CapsNets have several advantages over Convolutional Neural Networks (CNNs), while, their validity in the domain of text has less been explored. An effective method named deep compositional code learning has been proposed lately. This method can save many parameters about word embeddings without any significant sacrifices in performance. In this paper, we introduce the Compositional Coding (CC) mechanism between capsules, and we propose a new routing algorithm, which is based on k-means clustering theory. Experiments conducted on eight challenging text classification datasets show the proposed method achieves competitive accuracy compared to the state-of-the-art approach with significantly fewer parameters

A Novel Alcohol-Sensitive Site in the M3 Domain of the NMDA Receptor GluN2A Subunit

Accumulating studies have demonstrated that the N-methyl-D-aspartate receptor is one of the most important targets of ethanol in the central nervous system. Previous studies from this laboratory have found that one position in the third (F637) and two positions in the fourth (M823 and A825) membrane-associated (M) domains of the N-methyl-D-aspartate receptor GluN2A subunit modulate alcohol action and ion channel gating. Using site-directed mutagenesis and whole-cell patch-clamp recording, we have found an additional position in M3 of the GluN2A subunit, F636, which significantly influences ethanol sensitivity and functionally interacts with F637. Tryptophan substitution at F636 significantly decreased the ethanol IC50, decreased both peak and steady-state glutamate EC50, and altered agonist deactivation and apparent desensitization. There was a significant correlation between steadystate: peak current ratio, a measure of desensitization, and ethanol IC50 values for a series of mutants at this site, raising the possibility that changes in ethanol sensitivity may be secondary to changes in desensitization. Mutant cycle analysis revealed a significant interaction between F636 and F637 in regulating ethanol sensitivity. Our results suggest that F636 in the M3 domain of the GluN2A subunit not only influences channel gating and agonist potency, but also plays an important role in mediating the action of ethanol. These studies were supported by grants R01 AA015203-01A1 and AA015203-06A1 from the NIAAA to R.W.P

Probing Gravitational Dark Matter

So far all evidences of dark matter (DM) come from astrophysical and cosmological observations, due to gravitational interactions of the DM. It is possible that the true DM particle in the universe joins gravitational interactions only, but nothing else. Such a Gravitational DM (GDM) acts as a weakly interacting massive particle (WIMP), which is conceptually simple and attractive. In this work, we explore this direction by constructing the simplest scalar GDM particle $\chi_s$. It is a $Z_2$ odd singlet under the standard model (SM) gauge group, and naturally joins the unique dimension-4 interaction with Ricci curvature, $\xi_s \chi_s^2 R$, where $\xi_s$ is the dimensionless nonminimal coupling. We demonstrate that this gravitational interaction $\xi_s \chi_s^2 R$, together with Higgs-curvature nonminimal coupling term $\xi_h H^\dag H R$, induces effective couplings between $\chi_s^2$ and SM fields which can account for the observed DM thermal relic abundance. We analyze the annihilation cross sections of GDM particles and derive the viable parameter space for realizing the DM thermal relic density. We further study the direct/indirect detections and the collider signatures of such a scalar GDM. These turn out to be highly predictive and testable.Comment: 33pp, JCAP Final Version. Only minor rewordings, references adde

Analytical Potential Energy Function for the Ground State X^{1} Sigma^+ of LaCl

The equilibrium geometry, harmonic frequency and dissociation energy of lanthanum monochloride have been calculated at B3LYP, MP2, QCISD(T) levels with energy-consistent relativistic effective core potentials. The possible electronic state and reasonable dissociation limit for the ground state are determined based on atomic and molecular reaction statics. Potential energy curve scans for the ground state X^{1} Sigma^+ have been carried out with B3LYP and QCISD(T) methods due to their better performance in bond energy calculations. We find the potential energy calculated with QCISD(T) method is about 0.5 eV larger than dissociation energy when the diatomic distance is as large as 0.8 nm. The problem that single-reference ab initio methods don't meet dissociation limit during calculations of lanthanide heavy-metal elements is analyzed. We propose the calculation scheme to derive analytical Murrell-Sorbie potential energy function and Dunham expansion at equilibrium position. Spectroscopic constants got by standard Dunham treatment are in good agreement with results of rotational analyses on spectroscopic experiments. The analytical function is of much realistic importance since it is possible to be applied to predict fine transitional structure and study reaction dynamic process.Comment: 10 pages, 1 figure, 3 table

A Systemic Receptor Network Triggered by Human cytomegalovirus Entry

Virus entry is a multistep process that triggers a variety of cellular pathways interconnecting into a complex network, yet the molecular complexity of this network remains largely unsolved. Here, by employing systems biology approach, we reveal a systemic virus-entry network initiated by human cytomegalovirus (HCMV), a widespread opportunistic pathogen. This network contains all known interactions and functional modules (i.e. groups of proteins) coordinately responding to HCMV entry. The number of both genes and functional modules activated in this network dramatically declines shortly, within 25 min post-infection. While modules annotated as receptor system, ion transport, and immune response are continuously activated during the entire process of HCMV entry, those for cell adhesion and skeletal movement are specifically activated during viral early attachment, and those for immune response during virus entry. HCMV entry requires a complex receptor network involving different cellular components, comprising not only cell surface receptors, but also pathway components in signal transduction, skeletal development, immune response, endocytosis, ion transport, macromolecule metabolism and chromatin remodeling. Interestingly, genes that function in chromatin remodeling are the most abundant in this receptor system, suggesting that global modulation of transcriptions is one of the most important events in HCMV entry. Results of in silico knock out further reveal that this entire receptor network is primarily controlled by multiple elements, such as EGFR (Epidermal Growth Factor) and SLC10A1 (sodium/bile acid cotransporter family, member 1). Thus, our results demonstrate that a complex systemic network, in which components coordinating efficiently in time and space contributes to virus entry.Comment: 26 page

Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification

Image classification is a challenging problem which aims to identify the category of object in the image. In recent years, deep Convolutional Neural Networks (CNNs) have been applied to handle this task, and impressive improvement has been achieved. However, some research showed the output of CNNs can be easily altered by adding relatively small perturbations to the input image, such as modifying few pixels. Recently, Capsule Networks (CapsNets) are proposed, which can help eliminating this limitation. Experiments on MNIST dataset revealed that capsules can better characterize the features of object than CNNs. But it's hard to find a suitable quantitative method to compare the generalization ability of CNNs and CapsNets. In this paper, we propose a new image classification task called Top-2 classification to evaluate the generalization ability of CNNs and CapsNets. The models are trained on single label image samples same as the traditional image classification task. But in the test stage, we randomly concatenate two test image samples which contain different labels, and then use the trained models to predict the top-2 labels on the unseen newly-created two label image samples. This task can provide us precise quantitative results to compare the generalization ability of CNNs and CapsNets. Back to the CapsNet, because it uses Full Connectivity (FC) mechanism among all capsules, it requires many parameters. To reduce the number of parameters, we introduce the Parameter-Sharing (PS) mechanism between capsules. Experiments on five widely used benchmark image datasets demonstrate the method significantly reduces the number of parameters, without losing the effectiveness of extracting features. Further, on the Top-2 classification task, the proposed PS CapsNets obtain impressive higher accuracy compared to the traditional CNNs and FC CapsNets by a large margin.Comment: This paper is under consideration at Computer Vision and Image Understandin