The increasing use of sepsis screening in the Emergency Department (ED) and the Sepsis-3 recommendation to use the quick Sepsis-related Organ Failure Assessment (qSOFA) necessitates validation. We ...compared Systemic Inflammatory Response Syndrome (SIRS), qSOFA, and the National Early Warning Score (NEWS) for the identification of severe sepsis and septic shock (SS/SS) during ED triage.
This was a retrospective analysis from an urban, tertiary-care academic center that included 130,595 adult visits to the ED, excluding dispositions lacking adequate clinical evaluation (n = 14,861, 11.4%). The SS/SS group (n = 930) was selected using discharge diagnoses and chart review. We measured sensitivity, specificity, and area under the receiver-operating characteristic (AUROC) for the detection of sepsis endpoints.
NEWS was most accurate for triage detection of SS/SS (AUROC = 0.91, 0.88, 0.81), septic shock (AUROC = 0.93, 0.88, 0.84), and sepsis-related mortality (AUROC = 0.95, 0.89, 0.87) for NEWS, SIRS, and qSOFA, respectively (p < 0.01 for NEWS versus SIRS and qSOFA). For the detection of SS/SS (95% CI), sensitivities were 84.2% (81.5–86.5%), 86.1% (83.6–88.2%), and 28.5% (25.6–31.7%) and specificities were 85.0% (84.8–85.3%), 79.1% (78.9–79.3%), and 98.9% (98.8–99.0%) for NEWS ≥ 4, SIRS ≥ 2, and qSOFA ≥ 2, respectively.
NEWS was the most accurate scoring system for the detection of all sepsis endpoints. Furthermore, NEWS was more specific with similar sensitivity relative to SIRS, improves with disease severity, and is immediately available as it does not require laboratories. However, scoring NEWS is more involved and may be better suited for automated computation. QSOFA had the lowest sensitivity and is a poor tool for ED sepsis screening.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We study distributed optimization to minimize a sum of smooth and strongly-convex functions. Recent work on this problem uses gradient tracking to achieve linear convergence to the exact global ...minimizer. However, a connection among different approaches has been unclear. In this paper, we first show that many of the existing first-order algorithms are related with a simple state transformation, at the heart of which lies a recently introduced algorithm known as <inline-formula><tex-math notation="LaTeX">\mathcal {AB}</tex-math></inline-formula>. We then present distributed heavy-ball , denoted as <inline-formula><tex-math notation="LaTeX">\mathcal {AB}m</tex-math></inline-formula>, that combines <inline-formula><tex-math notation="LaTeX">\mathcal {AB}</tex-math></inline-formula> with a momentum term and uses nonidentical local step-sizes. By simultaneously implementing both row- and column-stochastic weights, <inline-formula><tex-math notation="LaTeX">\mathcal {AB}m</tex-math></inline-formula> removes the conservatism in the related work due to doubly stochastic weights or eigenvector estimation. <inline-formula><tex-math notation="LaTeX">\mathcal {AB}m</tex-math></inline-formula> thus naturally leads to optimization and average consensus over both undirected and directed graphs. We show that <inline-formula><tex-math notation="LaTeX">\mathcal {AB}m</tex-math></inline-formula> has a global <inline-formula><tex-math notation="LaTeX">R</tex-math></inline-formula>-linear rate when the largest step-size and momentum parameter are positive and sufficiently small. We numerically show that <inline-formula><tex-math notation="LaTeX">\mathcal {AB}m</tex-math></inline-formula> achieves acceleration, particularly when the objective functions are ill-conditioned.
In this article, we provide a distributed optimization algorithm, termed as TV-<inline-formula><tex-math notation="LaTeX">\mathcal {AB}</tex-math></inline-formula>, that minimizes a sum of convex ...functions over time-varying, random directed graphs. Contrary to the existing work, the algorithm we propose does not require eigenvector estimation to estimate the (non-<inline-formula><tex-math notation="LaTeX">\mathbf {1}</tex-math></inline-formula>) Perron eigenvector of a stochastic matrix. Instead, the proposed approach relies on a novel information mixing approach that exploits both row- and column-stochastic weights to achieve agreement toward the optimal solution when the underlying graph is directed. We show that TV-<inline-formula><tex-math notation="LaTeX">\mathcal {AB}</tex-math></inline-formula> converges linearly to the optimal solution when the global objective is smooth and strongly convex, and the underlying time-varying graphs exhibit bounded connectivity, i.e., a union of every <inline-formula><tex-math notation="LaTeX">C</tex-math></inline-formula> consecutive graphs is strongly connected. We derive the convergence results based on the stability analysis of a linear system of inequalities along with a matrix perturbation argument. Simulations confirm the findings in this article.
This paper develops a fast distributed algorithm, termed DEXTRA, to solve the optimization problem when n agents reach agreement and collaboratively minimize the sum of their local objective ...functions over the network, where the communication between the agents is described by a directed graph. Existing algorithms solve the problem restricted to directed graphs with convergence √ rates of O(ln k/ √k) for general convex objective functions and O(ln k/k) when the objective functions are strongly convex, where k is the number of iterations. We show that, with the appropriate step-size, DEXTRA converges at a linear rate O(τ k ) for 0 <; τ <; 1, given that the objective functions are restricted strongly convex. The implementation of DEXTRA requires each agent to know its local out-degree. Simulation examples further illustrate our findings.
In this paper, we study decentralized online stochastic non-convex optimization over a network of nodes. Integrating a technique called gradient tracking in decentralized stochastic gradient descent, ...we show that the resulting algorithm, GT-DSGD , enjoys certain desirable characteristics towards minimizing a sum of smooth non-convex functions. In particular, for general smooth non-convex functions, we establish non-asymptotic characterizations of GT-DSGD and derive the conditions under which it achieves network-independent performances that match the centralized minibatch SGD . In contrast, the existing results suggest that GT-DSGD is always network-dependent and is therefore strictly worse than the centralized minibatch SGD . When the global non-convex function additionally satisfies the Polyak-Łojasiewics (PL) condition, we establish the linear convergence of GT-DSGD up to a steady-state error with appropriate constant step-sizes. Moreover, under stochastic approximation step-sizes, we establish, for the first time, the optimal global sublinear convergence rate on almost every sample path, in addition to the asymptotically optimal sublinear rate in expectation. Since strongly convex functions are a special case of the functions satisfying the PL condition, our results are not only immediately applicable but also improve the currently known best convergence rates and their dependence on problem parameters.
We propose Directed-Distributed Projected Subgradient (D-DPS) to solve a constrained optimization problem over a multi-agent network, where the goal of agents is to collectively minimize the sum of ...locally known convex functions. Each agent in the network owns only its local objective function, constrained to a commonly known convex set. We focus on the circumstance when communications between agents are described by a directed network. The D-DPS combines surplus consensus to overcome the asymmetry caused by the directed communication network. The analysis shows the convergence rate to be O( ln k /√k ).
This paper describes a novel algorithmic framework to minimize a finite-sum of functions available over a network of nodes. The proposed framework, that we call GT-VR , is stochastic and ...decentralized, and thus is particularly suitable for problems where large-scale, potentially private data, cannot be collected or processed at a centralized server. The GT-VR framework leads to a family of algorithms with two key ingredients: (i) local variance reduction , that enables estimating the local batch gradients from arbitrarily drawn samples of local data; and, (ii) global gradient tracking , which fuses the gradient information across the nodes. Naturally, combining different variance reduction and gradient tracking techniques leads to different algorithms of interest with valuable practical tradeoffs and design considerations. Our focus in this paper is on two instantiations of the <inline-formula><tex-math notation="LaTeX">{\bf \mathtt {GT-VR}}</tex-math></inline-formula> framework, namely GT-SAGA and GT-SVRG , that, similar to their centralized counterparts ( SAGA and SVRG ), exhibit a compromise between space and time. We show that both GT-SAGA and GT-SVRG achieve accelerated linear convergence for smooth and strongly convex problems and further describe the regimes in which they achieve non-asymptotic, network-independent linear convergence rates that are faster with respect to the existing decentralized first-order schemes. Moreover, we show that both algorithms achieve a linear speedup in such regimes compared to their centralized counterparts that process all data at a single node. Extensive simulations illustrate the convergence behavior of the corresponding algorithms.
Abstract
Modern advanced photonic integrated circuits require dense integration of high-speed electro-optic functional elements on a compact chip that consumes only moderate power. Energy efficiency, ...operation speed, and device dimension are thus crucial metrics underlying almost all current developments of photonic signal processing units. Recently, thin-film lithium niobate (LN) emerges as a promising platform for photonic integrated circuits. Here, we make an important step towards miniaturizing functional components on this platform, reporting high-speed LN electro-optic modulators, based upon photonic crystal nanobeam resonators. The devices exhibit a significant tuning efficiency up to 1.98 GHz V
−1
, a broad modulation bandwidth of 17.5 GHz, while with a tiny electro-optic modal volume of only 0.58
μ
m
3
. The modulators enable efficient electro-optic driving of high-Q photonic cavity modes in both adiabatic and non-adiabatic regimes, and allow us to achieve electro-optic switching at 11 Gb s
−1
with a bit-switching energy as low as 22 fJ. The demonstration of energy efficient and high-speed electro-optic modulation at the wavelength scale paves a crucial foundation for realizing large-scale LN photonic integrated circuits that are of immense importance for broad applications in data communication, microwave photonics, and quantum photonics.
In this paper, we consider distributed optimization problems where the goal is to minimize a sum of objective functions over a multiagent network. We focus on the case when the interagent ...communication is described by a strongly connected, directed graph. The proposed algorithm, Accelerated Distributed Directed OPTimization (ADDOPT), achieves the best known convergence rate for this class of problems, O(μ k ), 0 <; μ <; 1, given strongly convex, objective functions with globally Lipschitz-continuous gradients, where k is the number of iterations. Moreover, ADD-OPT supports a wider and more realistic range of step sizes in contrast to existing work. In particular, we show that ADD-OPT converges for arbitrarily small (positive) step sizes. Simulations further illustrate our results.
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