Graph-based Semi-Supervised & Active Learning for Edge Flows

We present a graph-based semi-supervised learning (SSL) method for learning edge flows defined on a graph. Specifically, given flow measurements on a subset of edges, we want to predict the flows on the remaining edges. To this end, we develop a computational framework that imposes certain constraints on the overall flows, such as (approximate) flow conservation. These constraints render our approach different from classical graph-based SSL for vertex labels, which posits that tightly connected nodes share similar labels and leverages the graph structure accordingly to extrapolate from a few vertex labels to the unlabeled vertices. We derive bounds for our method's reconstruction error and demonstrate its strong performance on synthetic and real-world flow networks from transportation, physical infrastructure, and the Web. Furthermore, we provide two active learning algorithms for selecting informative edges on which to measure flow, which has applications for optimal sensor deployment. The first strategy selects edges to minimize the reconstruction error bound and works well on flows that are approximately divergence-free. The second approach clusters the graph and selects bottleneck edges that cross cluster-boundaries, which works well on flows with global trends

Myosin isoenzymes in human hypertrophic hearts. Shift in atrial myosin heavy chains and in ventricular myosin light chains

The myosin light chain complement and proteolytic peptide patterns of myosin heavy chains were studied by two-dimensional and one-dimensional electrophoretic techniques respectively, in a total of 57 samples from ventricular and atrial tissues of normal and hypertrophied human hearts. Hypertrophies were classified haemodynamically as due to pressure-overload and volume-overload. In addition to the occurrence of ventricular light chains in hypertrophied atria we also observed the atrial light chain-1 (ALC-1) in hypertrophied ventricular tissues. On average over 6% of total light-chain-1 comprised ALC-1 in pressure-overloaded ventricles and around 3% in volume-overloaded ventricles. In single cases of pressure-overload ALC-1 amounted up to over 20% of total light chain-1. With regard to the myosin heavy chains limited digestion by two different proteinases produced over 200 clearly resoluble peptides. The absence of any detectable differences in the peptide patterns between myosin heavy chains from normal and hypertrophic tissues of left or right ventricle is in line with the findings of J. J. Schier and R. S. Adelstein (J Clin Invest 1982; 69: 816-825). In atrial tissues however, reproducible qualitative differences in the peptide patterns indicated that during hypertrophy a different type of myosin heavy chains becomes expressed. No differences were seen between the myosin heavy chains from normal left and right atri

Encoding dynamics for multiscale community detection: Markov time sweeping for the Map equation

The detection of community structure in networks is intimately related to finding a concise description of the network in terms of its modules. This notion has been recently exploited by the Map equation formalism (M. Rosvall and C.T. Bergstrom, PNAS, 105(4), pp.1118--1123, 2008) through an information-theoretic description of the process of coding inter- and intra-community transitions of a random walker in the network at stationarity. However, a thorough study of the relationship between the full Markov dynamics and the coding mechanism is still lacking. We show here that the original Map coding scheme, which is both block-averaged and one-step, neglects the internal structure of the communities and introduces an upper scale, the `field-of-view' limit, in the communities it can detect. As a consequence, Map is well tuned to detect clique-like communities but can lead to undesirable overpartitioning when communities are far from clique-like. We show that a signature of this behavior is a large compression gap: the Map description length is far from its ideal limit. To address this issue, we propose a simple dynamic approach that introduces time explicitly into the Map coding through the analysis of the weighted adjacency matrix of the time-dependent multistep transition matrix of the Markov process. The resulting Markov time sweeping induces a dynamical zooming across scales that can reveal (potentially multiscale) community structure above the field-of-view limit, with the relevant partitions indicated by a small compression gap.Comment: 10 pages, 6 figure

Anaesthetics and cardiac preconditioning. Part II. Clinical implications

There is compelling evidence that preconditioning occurs in humans. Experimental studies with potential clinical implications as well as clinical studies evaluating ischaemic, pharmacological and anaesthetic cardiac preconditioning in the perioperative setting are reviewed. These studies reveal promising results. However, there are conflicting reports on the efficacy of preconditioning in the diseased and aged myocardium. In addition, many anaesthetics and a significant number of perioperatively administered drugs affect the activity of cardiac sarcolemmal and mitochondrial KATP channels, the end‐effectors of cardiac preconditioning, and thereby markedly modulate preconditioning effects in myocardial tissue. Although these modulatory effects on KATP channels have been investigated almost exclusively in laboratory investigations, they may have potential implications in clinical medicine. Important questions regarding the clinical utility and applicability of perioperative cardiac preconditioning remain unresolved and need more experimental work and randomized controlled clinical trials. Br J Anaesth 2003; 91: 566-7

Electrochromic orbit control for smart-dust devices

Recent advances in MEMS (micro electromechanical systems) technology are leading to spacecraft which are the shape and size of computer chips, so-called SpaceChips, or ‘smart dust devices’. These devices can offer highly distributed sensing when used in future swarm applications. However, they currently lack a feasible strategy for active orbit control. This paper proposes an orbit control methodology for future SpaceChip devices which is based on exploiting the effects of solar radiation pressure using electrochromic coatings. The concept presented makes use of the high area-to-mass ratio of these devices, and consequently the large force exerted upon them by solar radiation pressure, to control their orbit evolution by altering their surface optical properties. The orbital evolution of Space Chips due to solar radiation pressure can be represented by a Hamiltonian system, allowing an analytic development of the control methodology. The motion in the orbital element phase space resembles that of a linear oscillator, which is used to formulate a switching control law. Additional perturbations and the effect of eclipses are accounted for by modifying the linearized equations of the secular change in orbital elements around an equilibrium point in the phase space of the problem. Finally, the effectiveness of the method is demonstrated in a test case scenario

Disagreeable Privacy Policies: Mismatches between Meaning and Users’ Understanding

Privacy policies are verbose, difficult to understand, take too long to read, and may be the least-read items on most websites even as users express growing concerns about information collection practices. For all their faults, though, privacy policies remain the single most important source of information for users to attempt to learn how companies collect, use, and share data. Likewise, these policies form the basis for the self-regulatory notice and choice framework that is designed and promoted as a replacement for regulation. The underlying value and legitimacy of notice and choice depends, however, on the ability of users to understand privacy policies. This paper investigates the differences in interpretation among expert, knowledgeable, and typical users and explores whether those groups can understand the practices described in privacy policies at a level sufficient to support rational decision-making. The paper seeks to fill an important gap in the understanding of privacy policies through primary research on user interpretation and to inform the development of technologies combining natural language processing, machine learning and crowdsourcing for policy interpretation and summarization. For this research, we recruited a group of law and public policy graduate students at Fordham University, Carnegie Mellon University, and the University of Pittsburgh (“knowledgeable users”) and presented these law and policy researchers with a set of privacy policies from companies in the e-commerce and news & entertainment industries. We asked them nine basic questions about the policies’ statements regarding data collection, data use, and retention. We then presented the same set of policies to a group of privacy experts and to a group of non-expert users. The findings show areas of common understanding across all groups for certain data collection and deletion practices, but also demonstrate very important discrepancies in the interpretation of privacy policy language, particularly with respect to data sharing. The discordant interpretations arose both within groups and between the experts and the two other groups. The presence of these significant discrepancies has critical implications. First, the common understandings of some attributes of described data practices mean that semi-automated extraction of meaning from website privacy policies may be able to assist typical users and improve the effectiveness of notice by conveying the true meaning to users. However, the disagreements among experts and disagreement between experts and the other groups reflect that ambiguous wording in typical privacy policies undermines the ability of privacy policies to effectively convey notice of data practices to the general public. The results of this research will, consequently, have significant policy implications for the construction of the notice and choice framework and for the US reliance on this approach. The gap in interpretation indicates that privacy policies may be misleading the general public and that those policies could be considered legally unfair and deceptive. And, where websites are not effectively conveying privacy policies to consumers in a way that a “reasonable person” could, in fact, understand the policies, “notice and choice” fails as a framework. Such a failure has broad international implications since websites extend their reach beyond the United States

Swarm Keeping Strategies for Spacecraft under J_2 and Atmospheric Drag Perturbations

This paper presents several new open-loop guidance methods for spacecraft swarms composed of hundreds to thousands of agents with each spacecraft having modest capabilities. These methods have three main goals: preventing relative drift of the swarm, preventing collisions within the swarm, and minimizing the propellant used throughout the mission. The development of these methods progresses by eliminating drift using the Hill-Clohessy-Wiltshire equations, removing drift due to nonlinearity, and minimizing the J_2 drift. In order to verify these guidance methods, a new dynamic model for the relative motion of spacecraft is developed. These dynamics include the two main disturbances for spacecraft in Low Earth Orbit (LEO), J_2 and atmospheric drag. Using this dynamic model, numerical simulations are provided at each step to show the effectiveness of each method and to see where improvements can be made. The main result is a set of initial conditions for each spacecraft in the swarm which provides the trajectories for hundreds of collision-free orbits in the presence of J_2. Finally, a multi-burn strategy is developed in order to provide hundreds of collision-free orbits under the influence of atmospheric drag. This last method works by enforcing the initial conditions multiple times throughout the mission thereby providing collision-free trajectories for the duration of the mission