288 research outputs found
Online Filter Clustering and Pruning for Efficient Convnets
Pruning filters is an effective method for accelerating deep neural networks
(DNNs), but most existing approaches prune filters on a pre-trained network
directly which limits in acceleration. Although each filter has its own effect
in DNNs, but if two filters are the same with each other, we could prune one
safely. In this paper, we add an extra cluster loss term in the loss function
which can force filters in each cluster to be similar online. After training,
we keep one filter in each cluster and prune others and fine-tune the pruned
network to compensate for the loss. Particularly, the clusters in every layer
can be defined firstly which is effective for pruning DNNs within residual
blocks. Extensive experiments on CIFAR10 and CIFAR100 benchmarks demonstrate
the competitive performance of our proposed filter pruning method.Comment: 5 pages, 4 figure
An enhanced and highly efficient semi-implicit combined Lagrange multiplier approach with preserving original energy law for dissipative systems
Recently, a new Lagrange multiplier approach was introduced by Cheng, Liu and
Shen in \cite{cheng2020new}, which has been broadly used to solve various
challenging phase field problems. To design original energy stable schemes,
they have to solve a nonlinear algebraic equation to determine the introduced
Lagrange multiplier, which can be computationally expensive, especially for
large-scale and long-time simulations involving complex nonlinear terms. This
paper presents an essential improved technique to modify this issue, which can
be seen as a semi-implicit combined Lagrange multiplier approach. In general,
the new constructed schemes keep all the advantages of the Lagrange multiplier
method and significantly reduce the computation costs. Besides, the new
proposed BDF2 scheme dissipates the original energy, as opposed to a modified
energy for the classical Lagrange multiplier approach in \cite{cheng2020new}.
We further construct high-order BDF schemes based on the new proposed
approach. In addition, we establish a general framework for extending our
constructed method to dissipative systems. Finally several examples have been
presented to demonstrate the effectiveness of the proposed approach
High-efficiency and positivity-preserving stabilized SAV methods for gradient flows
The scalar auxiliary variable (SAV)-type methods are very popular techniques
for solving various nonlinear dissipative systems. Compared to the
semi-implicit method, the baseline SAV method can keep a modified energy
dissipation law but doubles the computational cost. The general SAV approach
does not add additional computation but needs to solve a semi-implicit solution
in advance, which may potentially compromise the accuracy and stability. In
this paper, we construct a novel first- and second-order unconditional energy
stable and positivity-preserving stabilized SAV (PS-SAV) schemes for and
gradient flows. The constructed schemes can reduce nearly half
computational cost of the baseline SAV method and preserve its accuracy and
stability simultaneously. Meanwhile, the introduced auxiliary variable is
always positive while the baseline SAV cannot guarantee this
positivity-preserving property. Unconditionally energy dissipation laws are
derived for the proposed numerical schemes. We also establish a rigorous error
analysis of the first-order scheme for the Allen-Cahn type equation in
norm. In addition we propose an energy
optimization technique to optimize the modified energy close to the original
energy. Several interesting numerical examples are presented to demonstrate the
accuracy and effectiveness of the proposed methods
Energy Efficient Power Allocation for OFDM-Based Cognitive Radio Systems with Partial Intersystem CSI
This paper investigates energy efficient
power allocation for orthogonal frequency division multiplexing- (OFDM-) based cognitive radio (CR) systems
with partial intersystem channel state information (CSI)
available. The goal is to maximize energy efficiency
(EE) while ensuring the minimum rate of secondary
user (SU) and keeping the average interference power
(AIP) introduced to primary user (PU) within a target
probability level. We propose a suboptimal algorithm
to solve this optimization problem based on classic
water-filling (WF) technique. Moreover, we first address
the relation between EE and water level. In
order to reduce complexity, a simplified algorithm with
closed-form solution is also proposed. Numerical results
are provided to corroborate our theoretical analysis
and to demonstrate the effectiveness of the proposed
schemes
Combined 3D-QSAR and Docking Modelling Study on Indolocarbazole Series Compounds as Tie-2 Inhibitors
Tie-2, a kind of endothelial cell tyrosine kinase receptor, is required for embryonic blood vessel development and tumor angiogenesis. Several compounds that showed potent activity toward this attractive anticancer drug target in the assay have been reported. In order to investigate the structure-activity correlation of indolocarbazole series compounds and modify them to improve their selectivity and activity, 3D-QSAR models were built using CoMFA and CoMSIA methods and molecular docking was used to check the results. Based on the common sketch align, two good QSAR models with high predictabilities (CoMFA model: q2 = 0.823, r2 = 0.979; CoMSIA model: q2 = 0.804, r2 = 0.967) were obtained and the contour maps obtained from both models were applied to identify the influence on the biological activity. Molecular docking was then used to confirm the results. Combined with the molecular docking results, the detail binding mode between the ligands and Tie-2 was elucidated, which enabled us to interpret the structure-activity relationship. These satisf actory results not only offered help to comprehend the action mechanism of indolocarbazole series compounds, but also provide new information for the design of new potent inhibitors
Revealing the Biexciton and Trion-exciton Complexes in BN Encapsulated WSe2
Strong Coulomb interactions in single-layer transition metal dichalcogenides
(TMDs) result in the emergence of strongly bound excitons, trions and
biexcitons. These excitonic complexes possess the valley degree of freedom,
which can be exploited for quantum optoelectronics. However, in contrast to the
good understanding of the exciton and trion properties, the binding energy of
the biexciton remains elusive, with theoretical calculations and experimental
studies reporting discrepant results. In this work, we resolve the conflict by
employing low-temperature photoluminescence spectroscopy to identify the
biexciton state in BN encapsulated single-layer WSe2. The biexciton state only
exists in charge neutral WSe2, which is realized through the control of
efficient electrostatic gating. In the lightly electron-doped WSe2, one free
electron binds to a biexciton and forms the trion-exciton complex. Improved
understanding of the biexciton and trion-exciton complexes paves the way for
exploiting the many-body physics in TMDs for novel optoelectronics
applications
- β¦