1,848 research outputs found
Optimal Opinion Control: The Campaign Problem
Opinion dynamics is nowadays a very common field of research. In this article
we formulate and then study a novel, namely strategic perspective on such
dynamics: There are the usual normal agents that update their opinions, for
instance according the well-known bounded confidence mechanism. But,
additionally, there is at least one strategic agent. That agent uses opinions
as freely selectable strategies to get control on the dynamics: The strategic
agent of our benchmark problem tries, during a campaign of a certain length, to
influence the ongoing dynamics among normal agents with strategically placed
opinions (one per period) in such a way, that, by the end of the campaign, as
much as possible normals end up with opinions in a certain interval of the
opinion space. Structurally, such a problem is an optimal control problem. That
type of problem is ubiquitous. Resorting to advanced and partly non-standard
methods for computing optimal controls, we solve some instances of the campaign
problem. But even for a very small number of normal agents, just one strategic
agent, and a ten-period campaign length, the problem turns out to be extremely
difficult. Explicitly we discuss moral and political concerns that immediately
arise, if someone starts to analyze the possibilities of an optimal opinion
control.Comment: 47 pages, 12 figures, and 11 table
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Improved Physical Design for Manufacturing Awareness and Advanced VLSI
Increasing challenges arise with each new semiconductor technology node, especially in advanced nodes, where the industry tries to extract every ounce of benefit as it approaches the limits of physics, through manufacturing-aware design technology co-optimization and design-based equivalent scaling. The increasing complexity of design and process technologies, and ever-more complex design rules, also become hurdles for academic researchers, separating academic researchers from the most up-to-date technical issues.This thesis presents innovative methodologies and optimizations to address the above challenges. There are three directions in this thesis: (i) manufacturing-aware design technology co-optimization; (ii) advanced node design-based equivalent scaling; and (iii) an open source academic detailed routing flow.To realize manufacturing-aware design technology co-optimization, this thesis presents two works: (i) a multi-row detailed placement optimization for neighbor diffusion effect mitigation between neighboring standard cells; and (ii) a post-routing optimization to generate 2D block mask layout for dummy segment removal in self-aligned multiple patterning.To achieve advanced node design-based equivalent scaling, this thesis presents two improved physical design methodologies: (i) a post-placement flop tray generation approach for clock power reduction; and (ii) a detailed placement approach to exploit inter-row M1 routing for congestion and wirelength reduction.To address the increasing gap between academia and industry, this thesis presents two works toward an open source academic detailed routing flow: (i) a complete, robust, scalable and design ruleaware dynamic programming-based pin access analysis framework; and (ii) TritonRoute – the open source detailed router that is capable of delivering DRC-clean detailed routing solutions in advanced nodes.This thesis concludes with a summary of its contributions and open directions for future research
Model Predictive Energy Management for Building Microgrids with IoT-based Controllable Loads
This thesis develops an economic scheduling framework for a building microgrid with internet of things (IoT) based flexible loads to synchronize the buildings’ controllable components, with occupant behavior and environmental conditions. We employ model predictive control (MPC) methods to minimize building operating costs, while maximizing the utilization of the on-site resources. The main research thrusts are: 1) Developing the building microgrid model; 2) Defining different building operation strategies; 3) Minimizing the building’s daily operating costs. Simulation results show that the proposed approach provides superior energy cost savings and peak load reduction in comparison with other operation controls, such as All from Utility (AFU), AFU with installed IoT-based Building Energy Management System (BEMS), and MPC-Mix Integer Linear Programming (MILP) without IoT-based BEMS. An economic analysis is also conducted to provide a road map for the implementation of installing advanced energy efficiency technologies across loads in building microgrid and integrating them with the building microgrid’s control strategy
Explain3D: Explaining Disagreements in Disjoint Datasets
Data plays an important role in applications, analytic processes, and many
aspects of human activity. As data grows in size and complexity, we are met
with an imperative need for tools that promote understanding and explanations
over data-related operations. Data management research on explanations has
focused on the assumption that data resides in a single dataset, under one
common schema. But the reality of today's data is that it is frequently
un-integrated, coming from different sources with different schemas. When
different datasets provide different answers to semantically similar questions,
understanding the reasons for the discrepancies is challenging and cannot be
handled by the existing single-dataset solutions.
In this paper, we propose Explain3D, a framework for explaining the
disagreements across disjoint datasets (3D). Explain3D focuses on identifying
the reasons for the differences in the results of two semantically similar
queries operating on two datasets with potentially different schemas. Our
framework leverages the queries to perform a semantic mapping across the
relevant parts of their provenance; discrepancies in this mapping point to
causes of the queries' differences. Exploiting the queries gives Explain3D an
edge over traditional schema matching and record linkage techniques, which are
query-agnostic. Our work makes the following contributions: (1) We formalize
the problem of deriving optimal explanations for the differences of the results
of semantically similar queries over disjoint datasets. (2) We design a 3-stage
framework for solving the optimal explanation problem. (3) We develop a
smart-partitioning optimizer that improves the efficiency of the framework by
orders of magnitude. (4)~We experiment with real-world and synthetic data to
demonstrate that Explain3D can derive precise explanations efficiently
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