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This paper presents a set of continuous-time distributed algorithms that solve unconstrained, separable, convex optimization problems over undirected networks with fixed topologies. The algorithms are developed using a Lyapunov function candidate that exploits convexity, and are called Zero-Gradient-Sum (ZGS) algorithms as they yield nonlinear networked dynamical systems that evolve invariantly on a zero-gradient-sum manifold and converge asymptotically to the unknown optimizer. We also describe a systematic way to construct ZGS algorithms, show that a subset of them actually converge exponentially, and obtain lower and upper bounds on their convergence rates in terms of the network topologies, problem characteristics, and algorithm parameters, including the algebraic connectivity, Laplacian spectral radius, and function curvatures. The findings of this paper may be regarded as a natural generalization of several well-known algorithms and results for distributed consensus, to distributed convex optimization.

This paper studies a distributed continuous-time bi-virus model in which two competing viruses spread over a network consisting of multiple groups of individuals. Limiting behaviors of the network are characterized by analyzing the equilibria of the system and their stability. Specifically, when the two viruses spread over possibly different directed infection graphs, the system may have (1) a unique equilibrium, the healthy state, which is globally stable, implying that both viruses will eventually be eradicated, (2) two equilibria including the healthy state and a dominant virus state, which is almost globally stable, implying that one virus will pervade the entire network causing a single-virus epidemic while the other virus will be eradicated, or (3) at least three equilibria including the healthy state and two dominant virus states, depending on certain conditions on the healing and infection rates. When the two viruses spread over the same directed infection graph, the system may have zero or infinitely many coexisting epidemic equilibria, which represents the pervasion of the two viruses. Sensitivity properties of some nontrivial equilibria are investigated in the context of a decentralized control technique, and an impossibility result is given for a certain type of distributed feedback controller.

Mobile computing devices, such as smart phones, offer benefits that may be especially valuable to older adults (age 65+). Yet, older adults have been shown to have difficulty learning to use these devices. In the research presented in this article, we sought to better understand how older adults learn to use mobile devices, their preferences and barriers, in order to find new ways to support them in their learning process. We conducted two complementary studies: a survey study with 131 respondents from three age groups (20--49, 50--64, 65+) and an in-depth field study with 6 older adults aged 50+. The results showed, among other things, that the preference for trial-and-error decreases with age, and while over half of older respondents and participants preferred using the instruction manual, many reported difficulties using it. We discuss implications for design and illustrate these implications with an example help system, Help Kiosk, designed to support older adults’ learning to use mobile devices.

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Abstract. We use a multicategory sea ice model coupled to the Princeton ocean model, which is driven by monthly climatological atmospheric forcing, to study the seasonal variation of ice cover in the Labrador Sea. Initial ocean conditions are derived from a gridded, objectively analyzed temperature-salinity data set that provides improved resolution of gradients in the vicinity of the shelf break. The model produces a realistic seasonal variation of sea ice. There is ice growth over the inner shelf and ice melt over the outer shelf and slope. Over the inner shelf, advection and diffusion decrease the ice mass; over the outer shelf, advection and diffusion increase the ice mass, which maintains the location of the ice edge. Near the offshore ice edge the melt rate exceeds 1 m per month, and the heat to melt ice together with the heat lost to the atmosphere exceeds 500 W m -2. The heat lost at the ocean surface is compensated for by advection of heat from an offshore convective region. The dominant heat source for the spring retreat of ice in the south is shortwave radiation over the open water fraction. In this study we investigate the seasonal evolution of sea ice over the Labrador shelf with a coupled ice-ocean model. Our objective is to assess the capability of the model in simulating the evolution of the ice cover and to identify model deficiencies. We examine the processes that limit the ice extent.Labrador sea ice has been the subject of a number of studies with numerical models.

Since health care teams are often distributed across time and location, information sharing is crucial for effective patient care. Studying the use of a mobile information technology in a local hospital ward at two months and eleven months after its deployment identifies both shortand long-term phenomena and reveals a mismatch between the intentions behind the deployed mobile technology and the nurses' current work practices. We contrast the new mobile technology with the paper artifacts that were previously relied upon in nursing work. Finally, in light of these findings, we suggest design directions for future technology to support the nursing shift work.

In many applications, nodes in a network desire not only a consensus, but an optimal one. To date, a family of subgradient algorithms have been proposed to solve this problem under general convexity assumptions. This paper shows that, for the scalar case and by assuming a bit more, novel non-gradient-based algorithms with appealing features can be constructed. Specifically, we develop Pairwise Equalizing (PE) and Pairwise Bisectioning (PB), two gossip algorithms that solve unconstrained, separable, convex consensus optimization problems over undirected networks with time-varying topologies, where each local function is strictly convex, continuously differentiable, and has a minimizer. We show that PE and PB are easy to implement, bypass limitations of the subgradient algorithms, and produce switched, nonlinear, networked dynamical systems that admit a common Lyapunov function and asymptotically converge. Moreover, PE generalizes the well-known Pairwise Averaging and Randomized Gossip Algorithm, while PB relaxes a requirement of PE, allowing nodes to never share their local functions.