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