3,006 research outputs found

    Ignorance is Almost Bliss: Near-Optimal Stochastic Matching With Few Queries

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    The stochastic matching problem deals with finding a maximum matching in a graph whose edges are unknown but can be accessed via queries. This is a special case of stochastic kk-set packing, where the problem is to find a maximum packing of sets, each of which exists with some probability. In this paper, we provide edge and set query algorithms for these two problems, respectively, that provably achieve some fraction of the omniscient optimal solution. Our main theoretical result for the stochastic matching (i.e., 22-set packing) problem is the design of an \emph{adaptive} algorithm that queries only a constant number of edges per vertex and achieves a (1ϵ)(1-\epsilon) fraction of the omniscient optimal solution, for an arbitrarily small ϵ>0\epsilon>0. Moreover, this adaptive algorithm performs the queries in only a constant number of rounds. We complement this result with a \emph{non-adaptive} (i.e., one round of queries) algorithm that achieves a (0.5ϵ)(0.5 - \epsilon) fraction of the omniscient optimum. We also extend both our results to stochastic kk-set packing by designing an adaptive algorithm that achieves a (2kϵ)(\frac{2}{k} - \epsilon) fraction of the omniscient optimal solution, again with only O(1)O(1) queries per element. This guarantee is close to the best known polynomial-time approximation ratio of 3k+1ϵ\frac{3}{k+1} -\epsilon for the \emph{deterministic} kk-set packing problem [Furer and Yu, 2013] We empirically explore the application of (adaptations of) these algorithms to the kidney exchange problem, where patients with end-stage renal failure swap willing but incompatible donors. We show on both generated data and on real data from the first 169 match runs of the UNOS nationwide kidney exchange that even a very small number of non-adaptive edge queries per vertex results in large gains in expected successful matches

    Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours

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    Rideshare platforms, when assigning requests to drivers, tend to maximize profit for the system and/or minimize waiting time for riders. Such platforms can exacerbate biases that drivers may have over certain types of requests. We consider the case of peak hours when the demand for rides is more than the supply of drivers. Drivers are well aware of their advantage during the peak hours and can choose to be selective about which rides to accept. Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e.g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver. Such a system can be highly unfair to riders. However, increasing fairness might come at a cost of the overall profit made by the rideshare platform. To balance these conflicting goals, we present a flexible, non-adaptive algorithm, \lpalg, that allows the platform designer to control the profit and fairness of the system via parameters α\alpha and β\beta respectively. We model the matching problem as an online bipartite matching where the set of drivers is offline and requests arrive online. Upon the arrival of a request, we use \lpalg to assign it to a driver (the driver might then choose to accept or reject it) or reject the request. We formalize the measures of profit and fairness in our setting and show that by using \lpalg, the competitive ratios for profit and fairness measures would be no worse than α/e\alpha/e and β/e\beta/e respectively. Extensive experimental results on both real-world and synthetic datasets confirm the validity of our theoretical lower bounds. Additionally, they show that \lpalg under some choice of (α,β)(\alpha, \beta) can beat two natural heuristics, Greedy and Uniform, on \emph{both} fairness and profit

    Laxatives Do Not Improve Symptoms of Opioid-Induced Constipation: Results of a Patient Survey

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    Introduction.:  Laxatives are commonly used to treat opioid-induced constipation, the commonest and most bothersome complication of opioids. However, laxatives have a nonspecific action and do not target underlying mechanisms of opioid-induced constipation; their use is associated with abdominal symptoms that negatively impact quality of life. Objective.:  To assess the effects of laxatives in patients taking opioids for chronic pain. Methods.:  One hundred ninety-eight UK patients who had taken opioid analgesics for at least one month completed a cross-sectional online or telephone survey. Questions addressed their pain condition, medication, and laxative use (including efficacy and side effects). The survey also assessed bowel function using the Bowel Function Index. Results.:  Since starting their current opioid, 134 of 184 patients (73%) had used laxatives at some point and 122 (91%) of these were currently taking them. The most common laxatives were osmotics and stimulants. Laxative side effects were reported in 75%, most commonly gas, bloating/fullness, and a sudden urge to defecate. Side effects were more common in patients less than 40 years of age. Approximately half of patients said laxatives interfered with work and social activities, and one-fifth needed an overnight hospital stay because of their pain condition and/or constipation. Laxatives did not improve the symptoms of constipation, as assessed by the Bowel Function Index. Constipation was not related to opioid strength, dose of opioid, or number of laxatives taken. Conclusions.:  Use of laxatives to treat opioid-induced constipation is often ineffective and associated with side effects. Instead of relieving the burden of opioid-induced constipation, laxative use is associated with a negative impact

    Probabilistic Fair Clustering

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    In clustering problems, a central decision-maker is given a complete metric graph over vertices and must provide a clustering of vertices that minimizes some objective function. In fair clustering problems, vertices are endowed with a color (e.g., membership in a group), and the features of a valid clustering might also include the representation of colors in that clustering. Prior work in fair clustering assumes complete knowledge of group membership. In this paper, we generalize prior work by assuming imperfect knowledge of group membership through probabilistic assignments. We present clustering algorithms in this more general setting with approximation ratio guarantees. We also address the problem of "metric membership", where different groups have a notion of order and distance. Experiments are conducted using our proposed algorithms as well as baselines to validate our approach and also surface nuanced concerns when group membership is not known deterministically

    Peripheral Calcitonin Gene-Related Peptide Receptor Activation and Mechanical Sensitization of the Joint in Rat Models of Osteoarthritis Pain

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    OBJECTIVE: To investigate the role of the sensory neuropeptide calcitonin gene-related peptide (CGRP) in peripheral sensitization in experimental models of osteoarthritis (OA) pain. METHODS: Experimental knee OA was induced in rats by intraarticular injection of monosodium iodoacetate (MIA) or by transection of the medial meniscus (MMT). Single-unit recordings of joint-innervating nociceptors were obtained in MIA- and saline-treated rats following administration of CGRP or the CGRP receptor antagonist CGRP 8-37. Effects of CGRP 8-37 were also examined in rats that underwent MMT and sham operations. Protein and messenger RNA (mRNA) levels of CGRP receptor components in the L3-L4 dorsal root ganglion (DRG) were investigated following MIA treatment. RESULTS: In both the MIA and MMT groups, the mechanical sensitivity of joint nociceptors was enhanced compared to that in the control groups. Exogenous CGRP increased mechanical sensitivity in a greater proportion of joint nociceptors in the MIA-treated rats than in the saline-treated rats. Local blockade of endogenous CGRP by CGRP 8-37 reversed both the MIA- and MMT-induced enhancement of joint nociceptor responses. Joint afferent cell bodies coexpressed the receptor for CGRP, called the calcitonin-like receptor (CLR), and the intracellular accessory CGRP receptor component protein. MIA treatment increased the levels of mRNA for CLR in the L3-L4 DRG and the levels of CLR protein in medium and large joint afferent neurons. CONCLUSION: Our findings provide new and compelling evidence implicating a role of CGRP in peripheral sensitization in experimental OA. Our novel finding of CGRP-mediated control of joint nociceptor mechanosensitivity suggests that the CGRP receptor system may be an important target for the modulation of pain during OA. CGRP receptor antagonists recently developed for migraine pain should be investigated for their efficacy against pain in OA

    Heat conductivity of DNA double helix

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    Thermal conductivity of isolated single molecule DNA fragments is of importance for nanotechnology, but has not yet been measured experimentally. Theoretical estimates based on simplified (1D) models predict anomalously high thermal conductivity. To investigate thermal properties of single molecule DNA we have developed a 3D coarse-grained (CG) model that retains the realism of the full all-atom description, but is significantly more efficient. Within the proposed model each nucleotide is represented by 6 particles or grains; the grains interact via effective potentials inferred from classical molecular dynamics (MD) trajectories based on a well-established all-atom potential function. Comparisons of 10 ns long MD trajectories between the CG and the corresponding all-atom model show similar root-mean-square deviations from the canonical B-form DNA, and similar structural fluctuations. At the same time, the CG model is 10 to 100 times faster depending on the length of the DNA fragment in the simulation. Analysis of dispersion curves derived from the CG model yields longitudinal sound velocity and torsional stiffness in close agreement with existing experiments. The computational efficiency of the CG model makes it possible to calculate thermal conductivity of a single DNA molecule not yet available experimentally. For a uniform (polyG-polyC) DNA, the estimated conductivity coefficient is 0.3 W/mK which is half the value of thermal conductivity for water. This result is in stark contrast with estimates of thermal conductivity for simplified, effectively 1D chains ("beads on a spring") that predict anomalous (infinite) thermal conductivity. Thus, full 3D character of DNA double-helix retained in the proposed model appears to be essential for describing its thermal properties at a single molecule level.Comment: 16 pages, 12 figure

    L-Drawings of Directed Graphs

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    We introduce L-drawings, a novel paradigm for representing directed graphs aiming at combining the readability features of orthogonal drawings with the expressive power of matrix representations. In an L-drawing, vertices have exclusive xx- and yy-coordinates and edges consist of two segments, one exiting the source vertically and one entering the destination horizontally. We study the problem of computing L-drawings using minimum ink. We prove its NP-completeness and provide a heuristics based on a polynomial-time algorithm that adds a vertex to a drawing using the minimum additional ink. We performed an experimental analysis of the heuristics which confirms its effectiveness.Comment: 11 pages, 7 figure


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    Habitat disturbance events are critical to ecological systems in which some bird species have become specialized. The vegetation community, reduced competition, ability to avoid predators, nest-site characteristics, and forage opportunities within a disturbed ecosystem are all aspects that make it desirable for selection by particular species (Svärdson 1949, Cody 1981, Martin 1998). Specifically, avian species rely on the forest conditions created by fire, insects, and disease (Brawn et al. 2001, Hunter et al. 2001, Devictor et al. 2008). In the Black Hills National Forest (BHNF) of South Dakota,two major types of natural disturbances include wildfires and mountain pine beetle (Dendroctonus ponderosae; MPB) infestations. Dead trees (snags) created by these disturbances attract a suite of insects and wildlife species. Bark beetles (Family: Curculionidae, Scolytinae) and wood borer beetles (Families: Buprestidae and Cerambycidae) are of particular importance to black-backed woodpeckers (Picoides arcticus; BBWO) because they feed almost exclusively on the larvae of these insects (Beal 1911, Murphy and Lehnhausen 1998, Hutto 2006, Bonnot et al. 2008, Bonnot et al. 2009). Black-backed woodpeckers are of key interest to resource management agencies due to their habitat specialization needs and the management activities like wildfire salvage logging and pre-thinning that occur in these disturbance areas (Hutto 1995, 2006). These management activities potentially reduce nest site and food availability for BBWOs and, as a result, they were recently petitioned for protection under the Endangered Species Act (Hanson et al. 2012). Following a fire event or insect infestation, the relative probability of using trees affected by the disturbance increases over surrounding healthy trees (Rota 2013). As a result, we were interested in understanding the food that is available to the woodpeckers following these forest disturbances

    Vulnerability analysis of satellite-based synchronized smart grids monitoring systems

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    The large-scale deployment of wide-area monitoring systems could play a strategic role in supporting the evolution of traditional power systems toward smarter and self-healing grids. The correct operation of these synchronized monitoring systems requires a common and accurate timing reference usually provided by a satellite-based global positioning system. Although these satellites signals provide timing accuracy that easily exceeds the needs of the power industry, they are extremely vulnerable to radio frequency interference. Consequently, a comprehensive analysis aimed at identifying their potential vulnerabilities is of paramount importance for correct and safe wide-area monitoring system operation. Armed with such a vision, this article presents and discusses the results of an experimental analysis aimed at characterizing the vulnerability of global positioning system based wide-area monitoring systems to external interferences. The article outlines the potential strategies that could be adopted to protect global positioning system receivers from external cyber-attacks and proposes decentralized defense strategies based on self-organizing sensor networks aimed at assuring correct time synchronization in the presence of external attacks