16,960 research outputs found
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 . It is a odd singlet under the
standard model (SM) gauge group, and naturally joins the unique dimension-4
interaction with Ricci curvature, , where is the
dimensionless nonminimal coupling. We demonstrate that this gravitational
interaction , together with Higgs-curvature nonminimal
coupling term , induces effective couplings between
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
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