2,005 research outputs found
Trade-Offs Between Size and Degree in Polynomial Calculus
Building on [Clegg et al. \u2796], [Impagliazzo et al. \u2799] established that if an unsatisfiable k-CNF formula over n variables has a refutation of size S in the polynomial calculus resolution proof system, then this formula also has a refutation of degree k + O(?(n log S)). The proof of this works by converting a small-size refutation into a small-degree one, but at the expense of increasing the proof size exponentially. This raises the question of whether it is possible to achieve both small size and small degree in the same refutation, or whether the exponential blow-up is inherent. Using and extending ideas from [Thapen \u2716], who studied the analogous question for the resolution proof system, we prove that a strong size-degree trade-off is necessary
Multicriteria VMAT optimization
Purpose: To make the planning of volumetric modulated arc therapy (VMAT)
faster and to explore the tradeoffs between planning objectives and delivery
efficiency.
Methods: A convex multicriteria dose optimization problem is solved for an
angular grid of 180 equi-spaced beams. This allows the planner to navigate the
ideal dose distribution Pareto surface and select a plan of desired target
coverage versus organ at risk sparing. The selected plan is then made VMAT
deliverable by a fluence map merging and sequencing algorithm, which combines
neighboring fluence maps based on a similarity score and then delivers the
merged maps together, simplifying delivery. Successive merges are made as long
as the dose distribution quality is maintained. The complete algorithm is
called VMERGE.
Results: VMERGE is applied to three cases: a prostate, a pancreas, and a
brain. In each case, the selected Pareto-optimal plan is matched almost exactly
with the VMAT merging routine, resulting in a high quality plan delivered with
a single arc in less than five minutes on average.
VMERGE offers significant improvements over existing VMAT algorithms. The
first is the multicriteria planning aspect, which greatly speeds up planning
time and allows the user to select the plan which represents the most desirable
compromise between target coverage and organ at risk sparing. The second is the
user-chosen epsilon-optimality guarantee of the final VMAT plan. Finally, the
user can explore the tradeoff between delivery time and plan quality, which is
a fundamental aspect of VMAT that cannot be easily investigated with current
commercial planning systems
Study of on-board compression of earth resources data
The current literature on image bandwidth compression was surveyed and those methods relevant to compression of multispectral imagery were selected. Typical satellite multispectral data was then analyzed statistically and the results used to select a smaller set of candidate bandwidth compression techniques particularly relevant to earth resources data. These were compared using both theoretical analysis and simulation, under various criteria of optimality such as mean square error (MSE), signal-to-noise ratio, classification accuracy, and computational complexity. By concatenating some of the most promising techniques, three multispectral data compression systems were synthesized which appear well suited to current and future NASA earth resources applications. The performance of these three recommended systems was then examined in detail by all of the above criteria. Finally, merits and deficiencies were summarized and a number of recommendations for future NASA activities in data compression proposed
Speed/accuracy trade-offs for modern convolutional object detectors
The goal of this paper is to serve as a guide for selecting a detection
architecture that achieves the right speed/memory/accuracy balance for a given
application and platform. To this end, we investigate various ways to trade
accuracy for speed and memory usage in modern convolutional object detection
systems. A number of successful systems have been proposed in recent years, but
apples-to-apples comparisons are difficult due to different base feature
extractors (e.g., VGG, Residual Networks), different default image resolutions,
as well as different hardware and software platforms. We present a unified
implementation of the Faster R-CNN [Ren et al., 2015], R-FCN [Dai et al., 2016]
and SSD [Liu et al., 2015] systems, which we view as "meta-architectures" and
trace out the speed/accuracy trade-off curve created by using alternative
feature extractors and varying other critical parameters such as image size
within each of these meta-architectures. On one extreme end of this spectrum
where speed and memory are critical, we present a detector that achieves real
time speeds and can be deployed on a mobile device. On the opposite end in
which accuracy is critical, we present a detector that achieves
state-of-the-art performance measured on the COCO detection task.Comment: Accepted to CVPR 201
A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT
Purpose: Current inverse planning methods for IMRT are limited because they
are not designed to explore the trade-offs between the competing objectives
between the tumor and normal tissues. Our goal was to develop an efficient
multiobjective optimization algorithm that was flexible enough to handle any
form of objective function and that resulted in a set of Pareto optimal plans.
Methods: We developed a hierarchical evolutionary multiobjective algorithm
designed to quickly generate a diverse Pareto optimal set of IMRT plans that
meet all clinical constraints and reflect the trade-offs in the plans. The top
level of the hierarchical algorithm is a multiobjective evolutionary algorithm
(MOEA). The genes of the individuals generated in the MOEA are the parameters
that define the penalty function minimized during an accelerated deterministic
IMRT optimization that represents the bottom level of the hierarchy. The MOEA
incorporates clinical criteria to restrict the search space through protocol
objectives and then uses Pareto optimality among the fitness objectives to
select individuals.
Results: Acceleration techniques implemented on both levels of the
hierarchical algorithm resulted in short, practical runtimes for optimizations.
The MOEA improvements were evaluated for example prostate cases with one target
and two OARs. The modified MOEA dominated 11.3% of plans using a standard
genetic algorithm package. By implementing domination advantage and protocol
objectives, small diverse populations of clinically acceptable plans that were
only dominated 0.2% by the Pareto front could be generated in a fraction of an
hour.
Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that
meet all dosimetric protocol criteria in a feasible amount of time. It
optimizes not only beamlet intensities but also objective function parameters
on a patient-specific basis
Environmental Design and Optimization of Modular Hydropower Plants
This research aimed to understand the pathways to cost-effective and sustainable low-head hydropower. Designing viable hydropower projects requires optimization across many economic, environmental, and social outcomes. However, existing run-of-river hydropower design models often focus on economic performance and customizing technologies for high-head diversion schemes. Standard modular hydropower is a new design approach that uses standardized rather than custom-designed technologies to achieve economies of scale. Oak Ridge National Laboratory established a conceptual outline for module classes based on functions, such as generation modules and fish passage modules, but further research was needed to identify how modules should be selected and operated for a site. Therefore, a new hydropower design model, called the waterSHED model, was created to incorporate multi-objective optimization strategies and design considerations specific to standard modular hydropower. The waterSHED model uses an object-oriented approach, heuristic optimization techniques, and a system of inter-disciplinary models to assess project feasibility and design tradeoffs. The model quantifies the non-power benefits of fish passage, sediment passage, and recreation passage by integrating existing and novel modeling approaches into an operation simulation. Two case studies were conducted to validate the model and help answer research questions related to 1) the cost-benefit tradeoffs of non-power modules, 2) the economic drivers of modular designs, and 3) the value of fish-safe designs. These case studies highlighted the potential of several technologies, such as fish-safe turbines and sediment sluice gates, to improve the environmental performance of projects with minimal impacts on generation. However, cost reductions are needed to overcome the economic and regulatory challenges of low-head projects, particularly for foundation and generation technologies. The object-oriented approach facilitates rapid integration of the innovations that will emerge to meet these challenges. This research helped modernize hydropower design thinking and provided valuable tools to the industry that will enable communities to meet clean electricity goals and protect riverine ecosystems
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