7,489 research outputs found
Unfaithful Glitch Propagation in Existing Binary Circuit Models
We show that no existing continuous-time, binary value-domain model for
digital circuits is able to correctly capture glitch propagation. Prominent
examples of such models are based on pure delay channels (P), inertial delay
channels (I), or the elaborate PID channels proposed by Bellido-D\'iaz et al.
We accomplish our goal by considering the solvability/non-solvability border of
a simple problem called Short-Pulse Filtration (SPF), which is closely related
to arbitration and synchronization. On one hand, we prove that SPF is solvable
in bounded time in any such model that provides channels with non-constant
delay, like I and PID. This is in opposition to the impossibility of solving
bounded SPF in real (physical) circuit models. On the other hand, for binary
circuit models with constant-delay channels, we prove that SPF cannot be solved
even in unbounded time; again in opposition to physical circuit models.
Consequently, indeed none of the binary value-domain models proposed so far
(and that we are aware of) faithfully captures glitch propagation of real
circuits. We finally show that these modeling mismatches do not hold for the
weaker eventual SPF problem.Comment: 23 pages, 15 figure
Integrated land use modelling of agri-environmental measures to maintain biodiversity at landscape level
Integrated land use models (ILM) are increasingly applied tools for the joint assessment of complex economic-environmental farming system interactions. We present an ILM that consists of the crop rotation model CropRota, the bio-physical process model EPIC, and the farm optimization model FAMOS[space]. The ILM is applied to analyze agri-environmental measures to maintain biodiversity in an Austrian landscape. We jointly consider the biodiversity effects of land use intensity (i.e. nitrogen application rates and mowing frequencies) and landscape development (e.g. provision of landscape elements) using a rich indicator set and region specific species-area relationships. The cost-effectiveness of agri-environmental measures in attaining alternative biodiversity targets is assessed by scenario analysis. The model results show the negative relationships between biodiversity maintenance and gross margins per ha. The absence of agri-environmental measures likely leads to a loss of semi-natural landscape elements such as orchard meadows and hedges as well as to farmland intensifications. The results are also relevant for external cost estimates. However, further methodologies need to be developed that can jointly and endogenously consider the complexities of the socio-economic land use system at farm and regional levels as well as the surrounding natural processes at sufficient detail for biodiversity assessments.Integrated farm land use modeling, biodiversity indicators, agri-environmental policy, landscape elements, Agricultural and Food Policy, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Food Security and Poverty, Land Economics/Use, Production Economics,
Modeling electricity spot prices - Combining mean-reversion, spikes and stochastic volatility
Starting with the liberalization of electricity trading, this market grew rapidly over the last decade. However, while spot and future markets are rather liquid nowadays, option trading is still limited. One of the potential reasons for this is that the spot price process of electricity is still puzzling researchers and practitioners. In this paper, we propose an approach to model spot prices that combines mean-reversion, spikes and stochastic volatility. Thereby we use different mean-reversion rates for 'normal' and 'extreme' (spike) periods. Another feature of the model is its ability to capture correlation structures of electricity price spikes. Furthermore, all model parameters can easily be estimated with help of historical data. Consequently, we argue that this model does not only extend academic literature on electricity spot price modeling, but is also suitable for practical purposes, e.g. as underlying price model for option pricing. --Electricity,Energy markets,LĂ©vy processes,Mean-reversion,Spikes,Stochastic volatility,GARCH
A Framework for Unbiased Model Selection Based on Boosting
Variable selection and model choice are of major concern in many statistical applications, especially in high-dimensional regression models. Boosting is a convenient statistical method that combines model fitting with intrinsic model selection.
We investigate the impact of base-learner specification on the performance of boosting as a model selection procedure.
We show that variable selection may be biased if the covariates are of different nature.
Important examples are models combining continuous and categorical covariates, especially if the number of categories is large. In this case, least squares base-learners offer increased flexibility for the categorical covariate and lead to a preference even if the categorical covariate is non-informative.
Similar difficulties arise when comparing linear and nonlinear base-learners for a continuous covariate. The additional flexibility in the nonlinear base-learner again yields a preference of the more complex modeling alternative.
We investigate these problems from a theoretical perspective and suggest a framework for unbiased model selection based on a general class of penalized least squares base-learners.
Making all base-learners comparable in terms of their degrees of freedom strongly reduces the selection bias observed in naive boosting specifications. The importance of unbiased model selection is demonstrated in simulations and an application to forest health models
Areas of Attention for Image Captioning
We propose "Areas of Attention", a novel attention-based model for automatic
image captioning. Our approach models the dependencies between image regions,
caption words, and the state of an RNN language model, using three pairwise
interactions. In contrast to previous attention-based approaches that associate
image regions only to the RNN state, our method allows a direct association
between caption words and image regions. During training these associations are
inferred from image-level captions, akin to weakly-supervised object detector
training. These associations help to improve captioning by localizing the
corresponding regions during testing. We also propose and compare different
ways of generating attention areas: CNN activation grids, object proposals, and
spatial transformers nets applied in a convolutional fashion. Spatial
transformers give the best results. They allow for image specific attention
areas, and can be trained jointly with the rest of the network. Our attention
mechanism and spatial transformer attention areas together yield
state-of-the-art results on the MSCOCO dataset.o meaningful latent semantic
structure in the generated captions.Comment: Accepted in ICCV 201
Dial It In: Rotating RF Sensors to Enhance Radio Tomography
A radio tomographic imaging (RTI) system uses the received signal strength
(RSS) measured by RF sensors in a static wireless network to localize people in
the deployment area, without having them to carry or wear an electronic device.
This paper addresses the fact that small-scale changes in the position and
orientation of the antenna of each RF sensor can dramatically affect imaging
and localization performance of an RTI system. However, the best placement for
a sensor is unknown at the time of deployment. Improving performance in a
deployed RTI system requires the deployer to iteratively "guess-and-retest",
i.e., pick a sensor to move and then re-run a calibration experiment to
determine if the localization performance had improved or degraded. We present
an RTI system of servo-nodes, RF sensors equipped with servo motors which
autonomously "dial it in", i.e., change position and orientation to optimize
the RSS on links of the network. By doing so, the localization accuracy of the
RTI system is quickly improved, without requiring any calibration experiment
from the deployer. Experiments conducted in three indoor environments
demonstrate that the servo-nodes system reduces localization error on average
by 32% compared to a standard RTI system composed of static RF sensors.Comment: 9 page
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