University of Wisconsin-Madison

About: University of Wisconsin-Madison is a education organization based out in Madison, Wisconsin, United States. It is known for research contribution in the topics: Population & Gene. The organization has 108707 authors who have published 237594 publications receiving 11883575 citations.

1H, 13C and 15N chemical shift referencing in biomolecular NMR

Abstract: A considerable degree of variability exists in the way that 1H, 13C and 15N chemical shifts are reported and referenced for biomolecules. In this article we explore some of the reasons for this situation and propose guidelines for future chemical shift referencing and for conversion from many common 1H, 13C and 15N chemical shift standards, now used in biomolecular NMR, to those proposed here.

The role of inflammation in depression: from evolutionary imperative to modern treatment target

Abstract: Crosstalk between inflammatory pathways and neurocircuits in the brain can lead to behavioural responses, such as avoidance and alarm, that are likely to have provided early humans with an evolutionary advantage in their interactions with pathogens and predators. However, in modern times, such interactions between inflammation and the brain appear to drive the development of depression and may contribute to non-responsiveness to current antidepressant therapies. Recent data have elucidated the mechanisms by which the innate and adaptive immune systems interact with neurotransmitters and neurocircuits to influence the risk for depression. Here, we detail our current understanding of these pathways and discuss the therapeutic potential of targeting the immune system to treat depression.

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

Abstract: Deep learning algorithms have been shown to perform extremely well on manyclassical machine learning problems. However, recent studies have shown thatdeep learning, like other machine learning techniques, is vulnerable to adversarial samples: inputs crafted to force adeep neural network (DNN) to provide adversary-selected outputs. Such attackscan seriously undermine the security of the system supported by the DNN, sometimes with devastating consequences. For example, autonomous vehicles canbe crashed, illicit or illegal content can bypass content filters, or biometricauthentication systems can be manipulated to allow improper access. In thiswork, we introduce a defensive mechanism called defensive distillationto reduce the effectiveness of adversarial samples on DNNs. We analyticallyinvestigate the generalizability and robustness properties granted by the useof defensive distillation when training DNNs. We also empirically study theeffectiveness of our defense mechanisms on two DNNs placed in adversarialsettings. The study shows that defensive distillation can reduce effectivenessof sample creation from 95% to less than 0.5% on a studied DNN. Such dramaticgains can be explained by the fact that distillation leads gradients used inadversarial sample creation to be reduced by a factor of 1030. We alsofind that distillation increases the average minimum number of features thatneed to be modified to create adversarial samples by about 800% on one of theDNNs we tested.

Network traffic characteristics of data centers in the wild

Abstract: Although there is tremendous interest in designing improved networks for data centers, very little is known about the network-level traffic characteristics of data centers today. In this paper, we conduct an empirical study of the network traffic in 10 data centers belonging to three different categories, including university, enterprise campus, and cloud data centers. Our definition of cloud data centers includes not only data centers employed by large online service providers offering Internet-facing applications but also data centers used to host data-intensive (MapReduce style) applications). We collect and analyze SNMP statistics, topology and packet-level traces. We examine the range of applications deployed in these data centers and their placement, the flow-level and packet-level transmission properties of these applications, and their impact on network and link utilizations, congestion and packet drops. We describe the implications of the observed traffic patterns for data center internal traffic engineering as well as for recently proposed architectures for data center networks.

Finite Difference Schemes and Partial Differential Equations

Abstract: Preface to the second edition Preface to the first edition 1. Hyperbolic partial differential equations 2. Analysis of finite difference Schemes 3. Order of accuracy of finite difference schemes 4. Stability for multistep schemes 5. Dissipation and dispersion 6. Parabolic partial differential equations 7. Systems of partial differential equations in higher dimensions 8. Second-order equations 9. Analysis of well-posed and stable problems 10. Convergence estimates for initial value problems 11. Well-posed and stable initial-boundary value problems 12. Elliptic partial differential equations and difference schemes 13. Linear iterative methods 14. The method of steepest descent and the conjugate gradient method Appendix A. Matrix and vectoranalysis Appendix B. A survey of real analysis Appendix C. A Survey of results from complex analysis References Index.