We present a fluid flow model to analyze backlog-based CSMA policies. The model is obtained using a CSMA fixed point approximation that has been recently proposed and analyzed. We provide numerical case studies to ill...
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The 21st ACM Symposium on Operating Systems Principles (SOSP 2007) was held at the Skamania Lodge in Stevenson, Washington, USA from October 14th to October 17th 2007. The conference site is located in the Columbia Ri...
The 21st ACM Symposium on Operating Systems Principles (SOSP 2007) was held at the Skamania Lodge in Stevenson, Washington, USA from October 14th to October 17th 2007. The conference site is located in the Columbia River Gorge National Scenic Area, a spectacular canyon along the border between Oregon and Washington States in the Pacific Northwest, where the Columbia River cuts through the Cascades mountain range. Delegates were treated to breathtaking views of the gorge and mountains from the lodge's rustic and warm common areas and the weather cooperated for the arrival and early parts of the conference, though the rains came in force in the latter part of the conference.
Change in cell stiffness is a characteristic of blood cell diseases, such as sickle cell anemia, malaria, and leukemia. Often, increases in blood cell stiffness lead to loss of the cells' ability to squeeze throug...
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We present a novel, fully integrated microfluidic bubble trap and debubbler. The 2-layer structure, based on a PDMS valve design, utilizes a featured membrane to stop bubble progression through the device. A pneumatic...
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We present a novel, fully integrated microfluidic bubble trap and debubbler. The 2-layer structure, based on a PDMS valve design, utilizes a featured membrane to stop bubble progression through the device. A pneumatic chamber directly above the trap is evacuated, and the bubble is pulled out through the gas-permeable PDMS membrane. Normal device operation, including continuous flow at atmospheric pressure, is maintained during the entire trapping and debubbling process. We present a range of trap sizes, from 2- to 10-mm diameter, and can trap and remove bubbles up to 25 μL in under 3 hours.
In this paper, we consider the problem of designing a scheduling algorithm for input queued switches, that is both fair as well as throughput optimal. Most of the existing literature on input-queued switch fairness cr...
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In this paper, we consider the problem of designing a scheduling algorithm for input queued switches, that is both fair as well as throughput optimal. Most of the existing literature on input-queued switch fairness criteria concentrates on flow-based fairness. Since a large fraction of network traffic is about "short- flows" there is a need for packet-based fairness criterion. The significant body of literature developed over the past two decades for packet-based scheduling algorithms is primarily concerned with throughput and delay, but not fairness. One of the reasons for such a state of affairs is the lack of a proper definition for packet-based fairness. The difficulty in defining fair stems from the fact that any reasonable notion of fairness must combine the well-known notion of fairness for a single-queue with the scheduling constraint of an input queued switch in an appropriate manner. As one of the main results of this paper, we define a notion of packet-based fair scheduling by identifying it as the selection of a winner in the following ranked election: packets are voters; schedules are candidates and each packet ranks different schedules based on their priorities. Drawing upon the seminal work of Goodman and Markowitz (1952) on ranked elections, we obtain a unique characterization of the fair schedule. Another important contribution of this paper is proving that the thus obtained fair scheduling algorithm is throughput optimal. There is no a priori reason why this should be true, and we introduce some non-standard proof techniques to prove the result. Our results suggest a framework for defining fair scheduling algorithm for a constrained packet network; a nonstandard method to prove throughput stability for algorithms, such as ours, that are not based on queue-sizes.
We consider the approximate sparse recovery problem, where the goal is to (approximately) recover a high-dimensional vector x ∈R~n from its lower-dimensional sketch Ax ∈ R~m. Specifically, we focus on the sparse rec...
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ISBN:
(纸本)9781424429257
We consider the approximate sparse recovery problem, where the goal is to (approximately) recover a high-dimensional vector x ∈R~n from its lower-dimensional sketch Ax ∈ R~m. Specifically, we focus on the sparse recovery problem in the l_1 norm: for a parameter k, given the sketch Ax, compute an approximation x{top}^ of x such that the l_1 approximation error ||x - x{top}^|| is close to min_x ||x - x'||_1, where x' ranges over all vectors with at most k terms. The sparse recovery problem has been subject to extensive research over the last few years. Many solutions to this problem have been discovered, achieving different trade-offs between various attributes, such as the sketch length, encoding and recovery times. A recent paper [IR08] provided a sparse recovery scheme which achieved close to optimal performance on virtually all attributes (see Figure 1). In particular, this was the first recovery scheme that guaranteed O(k log (n/k)) sketch length, and near-linear O(n log (n/k)) recovery time simultaneously. This was achieved by using sketching matrices A which were themselves very sparse. The matrix sparsity enabled decreasing the amount of computation spent on encoding and recovery. In this paper we present a new practical variant of that algorithm, that we call Sparse Matching Pursuit, or SMP. The running time of the new algorithm is slightly higher (by a logarithmic factor) than of its predecessor, and its sketch length bound remains unchanged. However, its empirical sketch length is substantially lower. This makes our scheme an attractive option for sparse recovery problems, both in theory and in practice.
We consider the well-studied decentralized Bayesian detection problem with the twist that a small subset of nodes (all arranged in a given directed network topology) may also communicate to "long-distance" n...
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We consider the well-studied decentralized Bayesian detection problem with the twist that a small subset of nodes (all arranged in a given directed network topology) may also communicate to "long-distance" neighbors via an overlay undirected network topology. Providing a certain condition on the interface between the two networks is upheld, the natural combination of the efficient team solutions already known for each type of network alone remains team-optimal in the combined network.
We describe a methodology for solving convex constraint problems using analog circuits. We demonstrate how this methodology can be used to design circuits that solve function-fitting problems through iterated gradient...
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We describe a methodology for solving convex constraint problems using analog circuits. We demonstrate how this methodology can be used to design circuits that solve function-fitting problems through iterated gradient descent. In particular, we show how to build a small circuit that can model a nonlinearity by observation, and predistort to compensate for this nonlinearity. The system fits into a broader effort to investigate non-traditional approaches to circuit design. First, it breaks the traditional input-output abstraction barrier;all ports are bidirectional. Second, it uses primarily local properties of the circuit to show stability. Such stability arguments can be scaled to much more complex systems than traditional stability criteria.
Nanostructured thermoelectric materials have attracted lots of interest in recent years, due to their enhanced performance determined by their thermoelectric dimensionless figure of merit. However, because of equipmen...
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