On cost effectiveness analysis and fairness: Normalizing control of and resistance to NICE technology appraisals

This study examines NICE’s application of cost effectiveness analysis (CEA) for normalizing patients’ access to newly licensed health technologies. Drawing upon evidence from the appraisal of four drugs developed for a rare form of cancer, this study demonstrates that the discourse of CEA provided a medium whereby contradicting ideologies of fairness were contested and resistance was provoked. Far from being docile the patients whom the NICE technology appraisal sought to administer were actively challenging the legitimacy of the calculation of CEA. The patients’ recalcitrance not only undermined the normalizing force but also compelled NICE to revise its application of CEA to suit their own interests. This study concludes that the discursive characteristic of calculating technologies not only constituted but was also constituted by conflicting interests and power struggles

Anomalous Electron Trajectory in Topological Insulators

We present a general theory about electron orbital motions in topological insulators. An in-plane electric field drives spin-up and spin-down electrons bending to opposite directions, and skipping orbital motions, a counterpart of the integer quantum Hall effect, are formed near the boundary of the sample. The accompanying Zitterbewegung can be found and controlled by tuning external electric fields. Ultrafast flipping electron spin leads to a quantum side jump in the topological insulator, and a snake-orbit motion in two-dimensional electron gas with spin-orbit interactions. This feature provides a way to control electron orbital motion by manipulating electron spin.Comment: 5 pages and 4 figures for the letter, 6 pagers for the online supplemental materia

MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks

Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs. In this work, we revisit the DNN training process that includes adversarial examples into the training dataset so as to improve DNN's resilience to adversarial attacks, namely, adversarial training. Our experiments show that different adversarial strengths, i.e., perturbation levels of adversarial examples, have different working zones to resist the attack. Based on the observation, we propose a multi-strength adversarial training method (MAT) that combines the adversarial training examples with different adversarial strengths to defend adversarial attacks. Two training structures - mixed MAT and parallel MAT - are developed to facilitate the tradeoffs between training time and memory occupation. Our results show that MAT can substantially minimize the accuracy degradation of deep learning systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.Comment: 6 pages, 4 figures, 2 table