1,011 research outputs found
Warping Cache Simulation of Polyhedral Programs
Techniques to evaluate a program’s cache performance fall
into two camps: 1. Traditional trace-based cache simulators
precisely account for sophisticated real-world cache models
and support arbitrary workloads, but their runtime is proportional to the number of memory accesses performed by
the program under analysis. 2. Relying on implicit workload
characterizations such as the polyhedral model, analytical approaches often achieve problem-size-independent runtimes,
but so far have been limited to idealized cache models.
We introduce a hybrid approach, warping cache simulation, that aims to achieve applicability to real-world cache
models and problem-size-independent runtimes. As prior
analytical approaches, we focus on programs in the polyhedral model, which allows to reason about the sequence
of memory accesses analytically. Combining this analytical
reasoning with information about the cache behavior obtained from explicit cache simulation allows us to soundly
fast-forward the simulation. By this process of warping, we
accelerate the simulation so that its cost is often independent
of the number of memory accesses
Exact Approaches for Bias Detection and Avoidance with Small, Sparse, or Correlated Categorical Data
Every day, traditional statistical methodology are used world wide to study a variety of topics and provides insight regarding countless subjects. Each technique is based on a distinct set of assumptions to ensure valid results. Additionally, many statistical approaches rely on large sample behavior and may collapse or degenerate in the presence of small, spare, or correlated data. This dissertation details several advancements to detect these conditions, avoid their consequences, and analyze data in a different way to yield trustworthy results.
One of the most commonly used modeling techniques for outcomes with only two possible categorical values (eg. live/die, pass/fail, better/worse, ect.) is logistic regression. While some potential complications with this approach are widely known, many investigators are unaware that their particular data does not meet the foundational assumptions, since they are not easy to verify. We have developed a routine for determining if a researcher should be concerned about potential bias in logistic regression results, so they can take steps to mitigate the bias or use a different procedure altogether to model the data.
Correlated data may arise from common situations such as multi-site medical studies, research on family units, or investigations on student achievement within classrooms. In these circumstance the associations between cluster members must be included in any statistical analysis testing the hypothesis of a connection be-tween two variables in order for results to be valid.
Previously investigators had to choose between using a method intended for small or sparse data while assuming independence between observations or a method that allowed for correlation between observations, while requiring large samples to be reliable. We present a new method that allows for small, clustered samples to be assessed for a relationship between a two-level predictor (eg. treatment/control) and a categorical outcome (eg. low/medium/high)
Mitigating demand risk of durable goods in online retailing
Purpose
An uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when e-retailers store excessive inventory of durable goods to fulfill uncertain demand, it results in significant inventory holding and obsolescence cost. In view of such overstocking/understocking situations, this study attempts to mitigate online demand risk by exploring novel e-retailing approaches considering the trade-offs between opportunity cost/customer dissatisfaction and inventory holding/obsolescence cost.
Design/methodology/approach
Four e-retailing approaches are introduced to mitigate uncertain demand and minimize the economic losses to e-retailer. Using three months of purchased history data of online consumers for durable goods, four proposed approaches are tested by developing product attribute based algorithm to calculate the economic loss to the e-retailer.
Findings
Mixed e-retailing method of selling unavailable products from collaborative e-retail partner and alternative product's suggestion from own e-retailing method is found to be best for mitigating uncertain demand as well as limiting customer dissatisfaction.
Research limitations/implications
Limited numbers of risk factor have been considered in this study. In the future, others risk factors like fraudulent order of high demand products, long delivery time window risk, damage and return risk of popular products can be incorporated and handled to reduce the economic loss.
Practical implications
The analysis can minimize the economic losses to an e-retailer and also can maximize the profit of collaborative e-retailing partner.
Originality/value
The study proposes a retailer to retailer collaboration approach without sharing the forecasted products' demand information
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
We present a novel hybrid algorithm for Bayesian network structure learning,
called H2PC. It first reconstructs the skeleton of a Bayesian network and then
performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
The algorithm is based on divide-and-conquer constraint-based subroutines to
learn the local structure around a target variable. We conduct two series of
experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is
currently the most powerful state-of-the-art algorithm for Bayesian network
structure learning. First, we use eight well-known Bayesian network benchmarks
with various data sizes to assess the quality of the learned structure returned
by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in
terms of goodness of fit to new data and quality of the network structure with
respect to the true dependence structure of the data. Second, we investigate
H2PC's ability to solve the multi-label learning problem. We provide
theoretical results to characterize and identify graphically the so-called
minimal label powersets that appear as irreducible factors in the joint
distribution under the faithfulness condition. The multi-label learning problem
is then decomposed into a series of multi-class classification problems, where
each multi-class variable encodes a label powerset. H2PC is shown to compare
favorably to MMHC in terms of global classification accuracy over ten
multi-label data sets covering different application domains. Overall, our
experiments support the conclusions that local structural learning with H2PC in
the form of local neighborhood induction is a theoretically well-motivated and
empirically effective learning framework that is well suited to multi-label
learning. The source code (in R) of H2PC as well as all data sets used for the
empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author
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