Adversarial Attack using Neural Image Modification

In order to help development into analyzing the characteristics of adversarial sample generation in artificial neural networks, this work proposes a framework for an adversarial attack that utilizes neural image modification to generate an adversarial sample. This method proves to be effective in reducing a target network’s accuracy in both untargeted and targeted attacks with good success rates. This method also shows some effectiveness against defensive distillation, but not transferrable between multiple models.

Adversarial Attack using Neural Image Modification

In order to help development into analyzing the characteristics of adversarial sample generation in artificial neural networks, this work proposes a framework for an adversarial attack that utilizes neural image modification to generate an adversarial sample. This method proves to be effective in reducing a target network’s accuracy in both untargeted and targeted attacks with good success rates. This method also shows some effectiveness against defensive distillation, but not transferrable between multiple models.

Adversarial Attack using Neural Image Modification

In order to help development into analyzing the characteristics of adversarial sample generation in artificial neural networks, this work proposes a framework for an adversarial attack that utilizes neural image modification to generate an adversarial sample. This method proves to be effective in reducing a target network’s accuracy in both untargeted and targeted attacks with good success rates. This method also shows some effectiveness against defensive distillation, but not transferrable between multiple models.

Fusion+SNA

Multiple models exist for understanding and predicting the adoption of technological innovations including SAMR, TPACK, and ADDIE. The authors have found these models are generally static, thus discounting the inherently iterative nature of adopting technological innovations. To address this gap, the authors have proposed a new model called Fusion+SNA which combines the dynamic nature of activity theory and diffusion of innovation research with an in-depth understanding of social networks. The authors employ the Fusion+SNA model in a case study that reported on a real attempt at an adoption of technological innovation in a K-12 context with a cohort of fifth-grade students and their teachers.

Synergistic Effect of PARP Inhibitor and BRD4 Inhibitor in Multiple Models of Ovarian Cancer

Background: Ovarian cancer has the highest fatality rate among patients with gynaecological tumours. Current therapies including poly-ADP ribose polymerase (PARP) inhibitors have limitations due to the frequent recurrence of ovarian cancer after treatment and resistance to therapy.Methods: In this study, we used multiple models with different genetic backgrounds to investigate the potential synergism effect and mechanism between the bromodomain-containing protein 4 (BRD4) inhibitor AZD5153 and the PARP inhibitor Olaparib. The models were two-dimensional (2D) and 3D cell lines, patient-derived organoids (PDO) and patient-derived xenografts (PDX). Results: Cotreatment with Olaparib and AZD5153 exhibited marked synergistic effects, and significantly attenuated cell viability, whereas it increased DNA replication fork instability, chromosomal breakage and apoptosis compared to treatment with either drug alone. Mechanistically, the tumor upregulates PTEN after Olaparib treatment to make its DNA and chromosome more stable and therefore induces Olaparib resistance. AZD5153 can downregulate PTEN to reverse Olaparib resistance and thus increase joint lethal effect with Olaparib.Conclusion: This study reveals that AZD5153 can downregulate PTEN to reverse Olaparib resistance and thus increase joint lethal effect on DNA replication fork instability, chromosomal breakage, and apoptosis with Olaparib.

The Prediction-Explanation Fallacy: A Pervasive Problem in Scientific Applications of Machine Learning

In this paper, I highlight a problem that has become ubiquitous in scientific applications of machine learning methods, and can lead to seriously distorted inferences about the phenomena under study. I call it the prediction-explanation fallacy. The fallacy occurs when researchers use prediction-optimized models for explanatory purposes, without considering the tradeoffs between explanation and prediction. This is a problem for at least two reasons. First, prediction-optimized models are often deliberately biased and unrealistic in order to prevent overfitting, and hence fail to accurately explain the phenomenon of interest. In other cases, they have an exceedingly complex structure that is hard or impossible to interpret, which greatly limits their explanatory value. Second, different predictive models trained on the same or similar data can be biased in different ways, so that multiple models may predict equally well but suggest conflicting explanations of the underlying phenomenon. In this note I introduce the tradeoffs between prediction and explanation in a non-technical fashion, present some illustrative examples from neuroscience, and end by discussing some mitigating factors and methods that can be used to limit or circumvent the problem.

Latin American Modernities

Modernity has been associated with a series of trends that have altered many societies worldwide, including those in Latin America. Chapter 2 analyzes Latin America and its path to global insertion and modernity. The chapter also addresses the images attributed to this multistate region. Latin American societies patterned their political institutions and public spheres after models that they conceived as the epitome of advanced global progress and modernity. They incorporated modern notions of citizenship, representative democracy, civic associations, elections, public debate and public spheres, justice, and equality before the law. Yet these multiple models were hybrid in nature, resulting from their international insertion and the format of internal colonialism and biases toward the centers of world development. Many of the promises of modernity were unfulfilled, generating new political demands, social change, and transnational spillover from one society to the others.