Toward the Global Optimum in Lithographic Lens Design

2010 ◽  
Author(s):  
Aurelian Dodoc
Keyword(s):  
Global Optimum ◽  
Lens Design

Recent developments in automated Transmission Electron MI

1989 ◽  
Vol 47 ◽  
pp. 46-47
Author(s):  
W.J. de Ruijter ◽  
P. Rez ◽  
David J. Smith
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
Lens Design ◽  
Computer Image Analysis ◽  
Transmission Electron ◽  
Resolution Electron ◽  
Recent Developments ◽  
On Line ◽  
Routine Procedures ◽  
Near Future

There is growing interest in the on-line use of computers in high-resolution electron n which should reduce the demands on highly skilled operators and thereby extend the r of the technique. An on-line computer could obviously perform routine procedures hand, or else facilitate automation of various restoration, reconstruction and enhan These techniques are slow and cumbersome at present because of the need for cai micrographs and off-line processing. In low resolution microscopy (most biologic; primary incentive for automation and computer image analysis is to create a instrument, with standard programmed procedures. In HREM (materials researc computer image analysis should lead to better utilization of the microscope. Instru (improved lens design and higher accelerating voltages) have improved the interpretab the level of atomic dimensions (approximately 1.6 Å) and instrumental resolutior should become feasible in the near future.

The Added Value of Graphical Input and Display for Electron Lens Design

1990 ◽  
Vol 48 (1) ◽  
pp. 190-191
Author(s):  
B. Lencova ◽  
G. Wisselink
Keyword(s):  
Flux Density ◽  
Numerical Data ◽  
Added Value ◽  
Lens Design ◽  
Step Size ◽  
Magnetic Lens ◽  
Flux Lines ◽  
Fine Mesh ◽  
Electron Lens ◽  
User Friendly

Recent progress in computer technology enables the calculation of lens fields and focal properties on commonly available computers such as IBM ATs. If we add to this the use of graphics, we greatly increase the applicability of design programs for electron lenses. Most programs for field computation are based on the finite element method (FEM). They are written in Fortran 77, so that they are easily transferred from PCs to larger machines.The design process has recently been made significantly more user friendly by adding input programs written in Turbo Pascal, which allows a flexible implementation of computer graphics. The input programs have not only menu driven input and modification of numerical data, but also graphics editing of the data. The input programs create files which are subsequently read by the Fortran programs. From the main menu of our magnetic lens design program, further options are chosen by using function keys or numbers. Some options (lens initialization and setting, fine mesh, current densities, etc.) open other menus where computation parameters can be set or numerical data can be entered with the help of a simple line editor. The "draw lens" option enables graphical editing of the mesh - see fig. I. The geometry of the electron lens is specified in terms of coordinates and indices of a coarse quadrilateral mesh. In this mesh, the fine mesh with smoothly changing step size is calculated by an automeshing procedure. The options shown in fig. 1 allow modification of the number of coarse mesh lines, change of coordinates of mesh points or lines, and specification of lens parts. Interactive and graphical modification of the fine mesh can be called from the fine mesh menu. Finally, the lens computation can be called. Our FEM program allows up to 8000 mesh points on an AT computer. Another menu allows the display of computed results stored in output files and graphical display of axial flux density, flux density in magnetic parts, and the flux lines in magnetic lenses - see fig. 2. A series of several lens excitations with user specified or default magnetization curves can be calculated and displayed in one session.

Keynote

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Jeanne LIEDTKA
Keyword(s):  
Organizational Performance ◽  
Design Thinking ◽  
Social Systems ◽  
Field Research ◽  
Higher Order ◽  
Lens Design ◽  
Social Sector ◽  
Extensive Field ◽  
Innovative Solutions ◽  
The Impact

The value delivered by design thinking is almost always seen to be improvements in the creativity and usefulness of the solutions produced. This paper takes a broader view of the potential power of design thinking, highlighting its role as a social technology for enhancing the productivity of conversations for change across difference. Examined through this lens, design thinking can be observed to aid diverse sets of stakeholders’ abilities to work together to both produce higher order, more innovative solutions and to implement them more successfully. In this way, it acts as a facilitator of the processes of collectives, by enhancing their ability to learn, align and change together. This paper draws on both the author’s extensive field research on the use of design thinking in social sector organizations, as well as on the literature of complex social systems, to discuss implications for both practitioners and scholars interested in assessing the impact of design thinking on organizational performance.

Opinion Dynamics Optimization by Varying Susceptibility to Persuasion via Non-Convex Local Search

2021 ◽  
Vol 16 (2) ◽  
pp. 1-34
Author(s):  
Rediet Abebe ◽  
T.-H. HUBERT Chan ◽  
Jon Kleinberg ◽  
Zhibin Liang ◽  
David Parkes ◽  
...  
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Iterative Process ◽  
Large Scale ◽  
Opinion Dynamics ◽  
Theoretical Models ◽  
Global Optimum ◽  
Local Optimum ◽  
Opinion Formation ◽  
Long Line

A long line of work in social psychology has studied variations in people’s susceptibility to persuasion—the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people’s intrinsic opinions, it is also natural to consider interventions that modify people’s susceptibility to persuasion. In this work, motivated by this fact, we propose an influence optimization problem. Specifically, we adopt a popular model for social opinion dynamics, where each agent has some fixed innate opinion, and a resistance that measures the importance it places on its innate opinion; agents influence one another’s opinions through an iterative process. Under certain conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to modify the resistance of any number of agents (within some given range) such that the sum of the equilibrium opinions is minimized; for the budgeted variant, in addition the algorithm is given upfront a restriction on the number of agents whose resistance may be modified. We prove that the objective function is in general non-convex. Hence, formulating the problem as a convex program as in an early version of this work (Abebe et al., KDD’18) might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Finally, we propose and evaluate experimentally a family of heuristics for the budgeted variant of the problem.

scholarly journals Global optimization based on active preference learning with radial basis functions

2020 ◽  
Author(s):  
Alberto Bemporad ◽  
Dario Piga
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Decision Maker ◽  
Optimization Problems ◽  
Global Optimum ◽  
Bayesian Optimization ◽  
Preference Learning ◽  
Global Optimizer ◽  
Radial Basis ◽  
Decision Vector

AbstractThis paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as “this is better than that” between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences. A radial-basis function surrogate is fit via linear or quadratic programming, satisfying if possible the preferences expressed by the decision maker on existing samples. The surrogate is used to propose a new sample of the decision vector for comparison with the current best candidate based on two possible criteria: minimize a combination of the surrogate and an inverse weighting distance function to balance between exploitation of the surrogate and exploration of the decision space, or maximize a function related to the probability that the new candidate will be preferred. Compared to active preference learning based on Bayesian optimization, we show that our approach is competitive in that, within the same number of comparisons, it usually approaches the global optimum more closely and is computationally lighter. Applications of the proposed algorithm to solve a set of benchmark global optimization problems, for multi-objective optimization, and for optimal tuning of a cost-sensitive neural network classifier for object recognition from images are described in the paper. MATLAB and a Python implementations of the algorithms described in the paper are available at http://cse.lab.imtlucca.it/~bemporad/glis.

scholarly journals Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 955
Author(s):  
Zhiyuan Li ◽  
Ershuai Peng
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Dynamic Network ◽  
Parameter Tuning ◽  
Global Optimum ◽  
Research Field ◽  
Edge Computing ◽  
Transmission Delay ◽  
Smart Vehicles ◽  
Network Bandwidth

With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. Recently, vehicular computation-intensive task offloading has become a new research field for the vehicular edge computing networks. However, dynamic network topology and the bursty computation tasks offloading, which causes to the computation load unbalancing for the VEC networking. To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. The proposed algorithm can obtain global optimum results and achieve the load-balancing by the virtue of the global load status information. Besides, the proposed algorithm has strong adaptiveness in dynamic network environments by automatic parameter tuning. Experimental results show that the proposed algorithm can effectively improve the utilization of computation resources and meet the requirements of computation and transmission delay for various vehicular tasks.

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