Towards a Rigorous Methodology for Measuring Adoption of RPKI Route Validation and Filtering

A proposal to improve routing security---Route Origin Authorization (ROA)---has been standardized. A ROA specifies which network is allowed to announce a set of Internet destinations. While some networks now specify ROAs, little is known about whether other networks check routes they receive against these ROAs, a process known as Route Origin Validation (ROV). Which networks blindly accept invalid routes? Which reject them outright? Which de-preference them if alternatives exist? Recent analysis attempts to use uncontrolled experiments to characterize ROV adoption by comparing valid routes and invalid routes. However, we argue that gaining a solid understanding of ROV adoption is impossible using currently available data sets and techniques. Our measurements suggest that, although some ISPs are not observed using invalid routes in uncontrolled experiments, they are actually using different routes for (non-security) traffic engineering purposes, without performing ROV. We conclude with a description of a controlled, verifiable methodology for measuring ROV and present three ASes that do implement ROV, confirmed by operators

A Cognitive Robotic Imitation Learning System Based On Cause-Effect Reasoning

As autonomous systems become more intelligent and ubiquitous, it is increasingly important that their behavior can be easily controlled and understood by human end users. Robotic imitation learning has emerged as a useful paradigm for meeting this challenge. However, much of the research in this area focuses on mimicking the precise low-level motor control of a demonstrator, rather than interpreting the intentions of a demonstrator at a cognitive level, which limits the ability of these systems to generalize. In particular, cause-effect reasoning is an important component of human cognition that is under-represented in these systems. This dissertation contributes a novel framework for cognitive-level imitation learning that uses parsimonious cause-effect reasoning to generalize demonstrated skills, and to justify its own actions to end users. The contributions include new causal inference algorithms, which are shown formally to be correct and have reasonable computational complexity characteristics. Additionally, empirical validations both in simulation and on board a physical robot show that this approach can efficiently and often successfully infer a demonstrator’s intentions on the basis of a single demonstration, and can generalize learned skills to a variety of new situations. Lastly, computer experiments are used to compare several formal criteria of parsimony in the context of causal intention inference, and a new criterion proposed in this work is shown to compare favorably with more traditional ones. In addition, this dissertation takes strides towards a purely neurocomputational implementation of this causally-driven imitation learning framework. In particular, it contributes a novel method for systematically locating fixed points in recurrent neural networks. Fixed points are relevant to recent work on neural networks that can be “programmed” to exhibit cognitive-level behaviors, like those involved in the imitation learning system developed here. As such, the fixed point solver developed in this work is a tool that can be used to improve our engineering and understanding of neurocomputational cognitive control in the next generation of autonomous systems, ultimately resulting in systems that are more pliable and transparent

Antigen Presentation and Allogeneic Stimulation by Langerhans Cells

Isolated Langerhans cells were studied for 2 immunologic functions, the ability to present antigen to sensitized T lymphocytes and the ability to act as stimulator cells for mixed lymphocyte reactions. Langerhans cells can perform both of these functions. This fact, with the previous finding that Langerhans cells possess surface la antigens and Fe and C3 receptors, strongly suggests that Langerhans cells act as epidermal macrophages

Third-person self-talk facilitates emotion regulation without engaging cognitive control: Converging evidence from ERP and fMRI.

Does silently talking to yourself in the third-person constitute a relatively effortless form of self control? We hypothesized that it does under the premise that third-person self-talk leads people to think about the self similar to how they think about others, which provides them with the psychological distance needed to facilitate self control. We tested this prediction by asking participants to reflect on feelings elicited by viewing aversive images (Study 1) and recalling negative autobiographical memories (Study 2) using either I or their name while measuring neural activity via ERPs (Study 1) and fMRI (Study 2). Study 1 demonstrated that third-person self-talk reduced an ERP marker of self-referential emotional reactivity (i.e., late positive potential) within the first second of viewing aversive images without enhancing an ERP marker of cognitive control (i.e., stimulus preceding negativity). Conceptually replicating these results, Study 2 demonstrated that third-person self-talk was linked with reduced levels of activation in an a priori defined fMRI marker of self-referential processing (i.e., medial prefrontal cortex) when participants reflected on negative memories without eliciting increased levels of activity in a priori defined fMRI markers of cognitive control. Together, these results suggest that third-person self-talk may constitute a relatively effortless form of self-control

(How Much) Does a Private WAN Improve Cloud Performance?

The buildout of private Wide Area Networks (WANs) by cloud providers allows providers to extend their network to more locations and establish direct connectivity with end user Internet Service Providers (ISPs). Tenants of the cloud providers benefit from this proximity to users, which is supposed to provide improved performance by bypassing the public Internet. However, the performance impact of private WANs is not widely understood. To isolate the impact of a private WAN, we measure from globally distributed vantage points to a large cloud provider, comparing performance when using its worldwide WAN and when forcing traffic to instead use the public Internet. The benefits are not universal. While 40% of our vantage points saw improved performance when using the WAN, half of our vantage points did not see significant performance improvement, and 10% had better performance over the public Internet. We find that the benefits of the private WAN tend to improve with client-to-server distance, but that the benefits (or drawbacks) to a particular vantage point depend on specifics of its geographic and network connectivity

Report on the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3)

This report records and discusses the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3). The report includes a description of the keynote presentation of the workshop, which served as an overview of sustainable scientific software. It also summarizes a set of lightning talks in which speakers highlighted to-the-point lessons and challenges pertaining to sustaining scientific software. The final and main contribution of the report is a summary of the discussions, future steps, and future organization for a set of self-organized working groups on topics including developing pathways to funding scientific software; constructing useful common metrics for crediting software stakeholders; identifying principles for sustainable software engineering design; reaching out to research software organizations around the world; and building communities for software sustainability. For each group, we include a point of contact and a landing page that can be used by those who want to join that group's future activities. The main challenge left by the workshop is to see if the groups will execute these activities that they have scheduled, and how the WSSSPE community can encourage this to happen

Mobile network performance from user devices: A longitudinal, multidimensional analysis

Abstract. In the cellular environment, operators, researchers and end users have poor visibility into network performance for devices. Improving visibility is challenging because this performance depends factors that include carrier, access technology, signal strength, geographic location and time. Addressing this requires longitudinal, continuous and large-scale measurements from a diverse set of mobile devices and networks. This paper takes a first look at cellular network performance from this perspective, using 17 months of data collected from devices located throughout the world. We show that (i) there is significant variance in key performance metrics both within and across carriers; (ii) this variance is at best only partially explained by regional and time-of-day patterns; (iii) the stability of network performance varies substantially among carriers. Further, we use the dataset to diagnose the causes behind observed performance problems and identify additional measurements that will improve our ability to reason about mobile network behavior

Sibyl:A Practical Internet Route Oracle

Network operators measure Internet routes to troubleshoot problems, and researchers measure routes to characterize the Internet. However, they still rely on decades-old tools like traceroute, BGP route collectors, and Looking Glasses, all of which permit only a single query about Internet routes—what is the path from here to there? This limited interface complicates answering queries about routes such as "find routes traversing the Level3/AT&T peering in Atlanta," to understand the scope of a reported problem there. This paper presents Sibyl, a system that takes rich queries that researchers and operators express as regular expressions, then issues and returns traceroutes that match even if it has never measured a matching path in the past. Sibyl achieves this goal in three steps. First, to maximize its coverage of Internet routing, Sibyl integrates together diverse sets of traceroute vantage points that provide complementary views, measuring from thousands of networks in total. Second, because users may not know which measurements will traverse paths of interest, and because vantage point resource constraints keep Sibyl from tracing to all destinations from all sources, Sibyl uses historical measurements to predict which new ones are likely to match a query. Finally, based on these predictions, Sibyl optimizes across concurrent queries to decide which measurements to issue given resource constraints. We show that Sibyl provides researchers and operators with the routing information they need—in fact, it matches 76% of the queries that it could match if an oracle told it which measurements to issue

Delayed Effects of Acute Radiation Exposure in a Murine Model of the H-ARS: Multiple-Organ Injury Consequent to <10 Gy Total Body Irradiation

The threat of radiation exposure from warfare or radiation accidents raises the need for appropriate animal models to study the acute and chronic effects of high dose rate radiation exposure. The goal of this study was to assess the late development of fibrosis in multiple organs (kidney, heart, and lung) in survivors of the C57BL/6 mouse model of the hematopoietic-acute radiation syndrome (H-ARS). Separate groups of mice for histological and functional studies were exposed to a single uniform total body dose between 8.53 and 8.72 Gy of gamma radiation from a Cs radiation source and studied 1-21 mo later. Blood urea nitrogen levels were elevated significantly in the irradiated mice at 9 and 21 mo (from ∼22 to 34 ± 3.8 and 69 ± 6.0 mg dL, p < 0.01 vs. non-irradiated controls) and correlated with glomerosclerosis (29 ± 1.8% vs. 64 ± 9.7% of total glomeruli, p < 0.01 vs. non-irradiated controls). Glomerular tubularization and hypertrophy and tubular atrophy were also observed at 21 mo post-total body irradiation (TBI). An increase in interstitial, perivascular, pericardial and peribronchial fibrosis/collagen deposition was observed from ∼9-21 mo post-TBI in kidney, heart, and lung of irradiated mice relative to age-matched controls. Echocardiography suggested decreased ventricular volumes with a compensatory increase in the left ventricular ejection fraction. The results indicate that significant delayed effects of acute radiation exposure occur in kidney, heart, and lung in survivors of the murine H-ARS TBI model, which mirrors pathology detected in larger species and humans at higher radiation doses focused on specific organs