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近日，东南大学脑科学与智能技术研究院在神经元自动追踪算法基准测试与性能预测方面取得重要研究进展，相关成果以“BigNeuron: A resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets”为题，在线发表于国际方...
THE IMPORTANCE OF DIGITAL METHODS IN PRESERVATION OF CULTURAL HERITAGE THE EXAMPLE OF ZIRNIKLI MANSION
Documentation of Cultural Heritage 3D Laser Scanning CAD Drawing Restitution Restoration
2017/6/20
Documentation in maintaining cultural properties is a highly important stage of work for determination of the unique properties. The researches having been carried out over years to increase the accur...
There is a large theoretical literature on methods for estimating causal effects under unconfoundedness, exogeneity, or selection-on-observables type assumptions using matching or propensity score met...
This paper provides an overview of control function (CF) methods for solving the problem of endogenous explanatory variables (EEVs) in linear and nonlinear models. CF methods often can be justified in...
Comparision of clustering methods for genetic networks
Comparision clustering methods genetic networks
2016/1/19
The goal of network clustering algorithms is to detect dense clusters in a network, which provides a first step towards the understanding of large scale biological networks. With numerous recent advan...
Recent results have shown that several H_2 and H_2-related problems can be formulated as convex programs with a finite number of variables. We present an interior point algorithm for the solution of t...
Robust Optimal Control of Linear Discrete-Time Systems Using Primal-Dual Interior-Point Methods
Robust Optimal Control Linear Discrete-Time Systems Primal-Dual Interior-Point Methods
2015/7/10
This paper describes how to efficiently solve a robust optimal control problem using recently developed primal-dual interior-point methods. Among potential applications are model predictive control. T...
Advances in Convex Optimization:Interior-point Methods,Cone Programming, and Applications
Advances in Convex Optimization Interior-point Methods Cone Programming Applications
2015/7/10
In this talk I will give an overview of some major developments in convex optimization that have emerged over the last ten years or so. The basic idea is that convex problems are fundamentally tractab...
Cutting-Set Methods for Robust Convex Optimization with Pessimizing Oracles
robust optimization cutting-set methods semi-infi nite programming minimax optimization
2015/7/9
We consider a general worst-case robust convex optimization problem, with arbitrary dependence on the uncertain parameters, which are assumed to lie in some given set of possible values. We describe a...
Monotonicity and Restart in Fast Gradient Methods
Monotonicity Restart Fast Gradient Methods
2015/7/9
Fast gradient methods are known to be non-monotone algorithms, and oscillations typically occur around the solution. To avoid this behavior, we propose in this paper a fast gradient method with restar...
First order optimization methods often perform poorly on ill-conditioned optimization problems. However, by preconditioning the problem data and solving the preconditioned problem, the performance of ...
On the Validity of Long-Run Estimation Methods for Discrete-Event Systems
Validity Long-Run Estimation Methods Discrete-Event Systems
2015/7/8
On the Validity of Long-Run Estimation Methods for Discrete-Event Systems.
On the Marginal Standard Error Rule and the Testing of Initial Transient Deletion Methods
Marginal Standard Error Rule Testing Initial Transient Deletion Methods
2015/7/6
In this paper, we introduce several theoretically useful measures for the magni- tude of the initial transient in the setting of single replication steady-state simulations. These measures help suppor...
Laplace Inversion of Low-Resolution NMR Relaxometry Data Using Sparse Representation Methods
low-resolution NMR sparse reconstruction
2015/7/3
Low-resolution nuclear magnetic resonance (LR-NMR) relaxometry is a
powerful tool that can be harnessed for characterizing constituents in complex materials.
PROXIMAL NEWTON-TYPE METHODS FOR MINIMIZING COMPOSITE FUNCTIONS
convex optimization nonsmooth optimization
2015/7/3
We generalize Newton-type methods for minimizing smooth functions to handle a
sum of two convex functions: a smooth function and a nonsmooth function with a simple proximal mapping.