Leveraging Semantic Annotations to Link Wikipedia and News Archives

The incomprehensible amount of information available online has made it difficult to retrospect on past events. We propose a novel linking problem to connect excerpts from Wikipedia summarizing events to online news articles elaborating on them. To address the linking problem, we cast it into an information retrieval task by treating a given excerpt as a user query with the goal to retrieve a ranked list of relevant news articles. We find that Wikipedia excerpts often come with additional semantics, in their textual descriptions, representing the time, geolocations, and named entities involved in the event. Our retrieval model leverages text and semantic annotations as different dimensions of an event by estimating independent query models to rank documents. In our experiments on two datasets, we compare methods that consider different combinations of dimensions and find that the approach that leverages all dimensions suits our problem best

Ccl2 and Ccl3 Mediate Neutrophil Recruitment via Induction of Protein Synthesis and Generation of Lipid Mediators

Objective: Although the chemokines monocyte chemoattractant protein-1 (Ccl2/JE/MCP-1) and macrophage inflammatory protein-1α (Ccl3/MIP-1α) have recently been implicated in neutrophil migration, the underlying mechanisms remain largely unclear. Methods and Results: Stimulation of the mouse cremaster muscle with Ccl2/JE/MCP-1 or Ccl3/MIP-1α induced a significant increase in numbers of firmly adherent and transmigrated leukocytes (>70% neutrophils) as observed by in vivo microscopy. This increase was significantly attenuated in mice receiving an inhibitor of RNA transcription (actinomycin D) or antagonists of platelet activating factor (PAF; BN 52021) and leukotrienes (MK-886; AA-861). In contrast, leukocyte responses elicited by PAF and leukotriene-B4 (LTB4) themselves were not affected by actinomycin D, BN 52021, MK-886, or AA-861. Conversely, PAF and LTB4, but not Ccl2/JE/MCP-1 and Ccl3/MIP-1α, directly activated neutrophils as indicated by shedding of CD62L and marked upregulation of CD11b. Moreover, Ccl2/JE/ MCP-1- and Ccl3/MIP-1α-elicited leakage of fluorescein isothiocyanate dextran as well as collagen IV remodeling within the venular basement membrane were completely absent in neutrophil-depleted mice. Conclusions: Ccl2/JE/MCP-1 and Ccl3/MIP-1α mediate firm adherence and (subsequent) transmigration of neutrophils via protein synthesis and secondary generation of leukotrienes and PAF, which in turn directly activate neutrophils. Thereby, neutrophils facilitate basement membrane remodeling and promote microvascular leakage

A novel constraint-tightening approach for robust data-driven predictive control

In this paper, we present a data-driven model predictive control (MPC) scheme that is capable of stabilizing unknown linear time-invariant systems under the influence of process disturbances. To this end, Willems' lemma is used to predict the future behavior of the system. This allows the entire scheme to be set up using only a priori measured data and knowledge of an upper bound on the system order. First, we develop a state-feedback MPC scheme, based on input-state data, which guarantees closed-loop practical exponential stability and recursive feasibility as well as closed-loop constraint satisfaction. The scheme is extended by a suitable constraint tightening, which can also be constructed using only data. In order to control a priori unstable systems, the presented scheme contains a prestabilizing controller and an associated input constraint tightening. We first present the proposed data-driven MPC scheme for the case of full state measurements, and also provide extensions for obtaining similar closed-loop guarantees in case of output feedback. The presented scheme is applied to a numerical example

Linear tracking MPC for nonlinear systems Part II: The data-driven case

We present a novel data-driven model predictive control (MPC) approach to control unknown nonlinear systems using only measured input-output data with closed-loop stability guarantees. Our scheme relies on the data-driven system parametrization provided by the Fundamental Lemma of Willems et al. We use new input-output measurements online to update the data, exploiting local linear approximations of the underlying system. We prove that our MPC scheme, which only requires solving strictly convex quadratic programs online, ensures that the closed loop (practically) converges to the (unknown) optimal reachable equilibrium that tracks a desired output reference while satisfying polytopic input constraints. As intermediate results of independent interest, we extend the Fundamental Lemma to affine systems and we derive novel robustness bounds w.r.t. noisy data for the open-loop optimal control problem, which are directly transferable to other data-driven MPC schemes in the literature. The applicability of our approach is illustrated with a numerical application to a continuous stirred tank reactor

Planet (316) Goberta

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On the design of terminal ingredients for data-driven MPC

We present a model predictive control (MPC) scheme to control linear time-invariant systems using only measured input-output data and no model knowledge. The scheme includes a terminal cost and a terminal set constraint on an extended state containing past input-output values. We provide an explicit design procedure for the corresponding terminal ingredients that only uses measured input-output data. Further, we prove that the MPC scheme based on these terminal ingredients exponentially stabilizes the desired setpoint in closed loop. Finally, we illustrate the advantages over existing data-driven MPC approaches with a numerical example