1,301 research outputs found
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
We investigate conditions under which test statistics exist that can reliably
detect examples, which have been adversarially manipulated in a white-box
attack. These statistics can be easily computed and calibrated by randomly
corrupting inputs. They exploit certain anomalies that adversarial attacks
introduce, in particular if they follow the paradigm of choosing perturbations
optimally under p-norm constraints. Access to the log-odds is the only
requirement to defend models. We justify our approach empirically, but also
provide conditions under which detectability via the suggested test statistics
is guaranteed to be effective. In our experiments, we show that it is even
possible to correct test time predictions for adversarial attacks with high
accuracy
Statuae Deorum Hominumque: The Distinction in Epigraphic Statuary Terminology between Divine and Human Representation in Africa Proconsularis and Beyond
The sheer number of Latin words for ‘statue’ suggests that there might be some semantic difference among them. Some scholars have claimed that statua and imago refer only to statues of persons, while signum and simulacrum are reserved for statues of gods. Analysis of epigraphic evidence from Africa Proconsularis reveals that this assessment is only partially valid: statua is used indiscriminately for human and divine statues. Evidence from the rest of the Roman Empire confirms the flexibility of the term statua
Stabilizing Training of Generative Adversarial Networks through Regularization
Deep generative models based on Generative Adversarial Networks (GANs) have
demonstrated impressive sample quality but in order to work they require a
careful choice of architecture, parameter initialization, and selection of
hyper-parameters. This fragility is in part due to a dimensional mismatch or
non-overlapping support between the model distribution and the data
distribution, causing their density ratio and the associated f-divergence to be
undefined. We overcome this fundamental limitation and propose a new
regularization approach with low computational cost that yields a stable GAN
training procedure. We demonstrate the effectiveness of this regularizer across
several architectures trained on common benchmark image generation tasks. Our
regularization turns GAN models into reliable building blocks for deep
learning
Is There an Energy Paradox in Fuel Economy? A Note on the Role of Consumer Heterogeneity and Sorting Bias
Previous literature finds that consumers tend to undervalue discounted future energy costs in their purchase decisions for energy-using durables. We argue that this finding could result from ignoring consumer heterogeneity in empirical analyses as opposed to true undervaluation. In the context of automobile demand, we show that, if not accounted for, consumer heterogeneity could lead to sorting, which in turn biases toward zero the estimate of marginal willingness to pay for discounted future fuel costs.energy paradox, fuel economy, consumer heterogeneity
In-cylinder pressure measurements with optical fiber and piezoelectric pressure transducers.
Highly accurate cylinder pressure data can be acquired using a wall-mounted and water-cooled quartz piezoelectric transducer. However, this type of transducer does not satisfy the cost and packaging constraints for use in a production engine application. A potential solution to these issues is the much smaller and less expensive optical fiber based pressure transducer. This research compares Kistler piezoelectric transducers to Optrand optical fiber transducers. The influence of the transducer type and mounting arrangement on the quality of cylinder pressure data was examined. The transducers were evaluated on a DaimlerChrysler 4.7L V-8 Compressed Natural Gas fuelled test engine. The analysis method is comprised of examining measured individual cycle and ensemble-averaged cylinder pressure records to assess the quality of the data and its potential usefulness for engine management. The variation in performance in terms of thermal shock error among the four Optrand transducers was much larger than those among the four Kistler transducers. The best performing Optrand transducer both over and underestimated cylinder pressure, leading to more accurate results of Indicated Mean Effective Pressure and overestimations in peak cylinder pressure compared to the Kistler transducer which always underestimated cylinder pressure. (Abstract shortened by UMI.)Dept. of Mechanical, Automotive, and Materials Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .R68. Source: Masters Abstracts International, Volume: 44-01, page: 0427. Thesis (M.A.Sc.)--University of Windsor (Canada), 2002
From Paraphrase Database to Compositional Paraphrase Model and Back
The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive
semantic resource, consisting of a list of phrase pairs with (heuristic)
confidence estimates. However, it is still unclear how it can best be used, due
to the heuristic nature of the confidences and its necessarily incomplete
coverage. We propose models to leverage the phrase pairs from the PPDB to build
parametric paraphrase models that score paraphrase pairs more accurately than
the PPDB's internal scores while simultaneously improving its coverage. They
allow for learning phrase embeddings as well as improved word embeddings.
Moreover, we introduce two new, manually annotated datasets to evaluate
short-phrase paraphrasing models. Using our paraphrase model trained using
PPDB, we achieve state-of-the-art results on standard word and bigram
similarity tasks and beat strong baselines on our new short phrase paraphrase
tasks.Comment: 2015 TACL paper updated with an appendix describing new 300
dimensional embeddings. Submitted 1/2015. Accepted 2/2015. Published 6/201
Model of Brain Activation Predicts the Neural Collective Influence Map of the Brain
Efficient complex systems have a modular structure, but modularity does not
guarantee robustness, because efficiency also requires an ingenious interplay
of the interacting modular components. The human brain is the elemental
paradigm of an efficient robust modular system interconnected as a network of
networks (NoN). Understanding the emergence of robustness in such modular
architectures from the interconnections of its parts is a long-standing
challenge that has concerned many scientists. Current models of dependencies in
NoN inspired by the power grid express interactions among modules with fragile
couplings that amplify even small shocks, thus preventing functionality.
Therefore, we introduce a model of NoN to shape the pattern of brain
activations to form a modular environment that is robust. The model predicts
the map of neural collective influencers (NCIs) in the brain, through the
optimization of the influence of the minimal set of essential nodes responsible
for broadcasting information to the whole-brain NoN. Our results suggest new
intervention protocols to control brain activity by targeting influential
neural nodes predicted by network theory.Comment: 18 pages, 5 figure
Live Case Analysis: Pedagogical Problems And Prospects In Management Education
The selection of an appropriate and effective pedagogy has been a central theme in management education for decades. There currently exists a wide range of pedagogical options designed to match course content with the most appropriate technique(s) for effective learning outcomes. Most recently, a variety of experiential learning methods have been employed to provide students with real-life experiences and applications in the overall class design. Live case analysis is typically identified as one of a series of options within the domain of experiential learning methods. This paper examines the live case approach as a tool for achieving desired outcomes in management education. Perspectives are offered from multiple stakeholder groups that highlight both the challenges and prospects in the use of this method of teaching. Results demonstrate the usefulness of the live case approach for achieving assessment objectives and measuring important program outcomes
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