1,721,086 research outputs found
Learning Minimum-Energy Controls from Heterogeneous Data
No full textIn this paper we study the problem of learning minimum-energy controls for linear systems from heterogeneous data. Specifically, we consider datasets comprising input, initial and final state measurements collected using experiments with different time horizons and arbitrary initial conditions. In this setting, we first establish a general representation of input and sampled state trajectories of the system based on the available data. Then, we leverage this data-based representation to derive closed-form data-driven expressions of minimum-energy controls for a wide range of control horizons. Further, we characterize the minimum number of data required to reconstruct the minimum-energy inputs, and discuss the numerical properties of our expressions. Finally, we investigate the effect of noise on our data-driven formulas, and, in the case of noise with known second-order statistics, we provide corrected expressions that converge asymptotically to the true optimal control inputs
Finite-Field Consensus
No full textThis work studies consensus networks over finite fields, where agents process and communicate values from the set of integers f0; : : : ; p 1g, for some prime number p, and operations are performed modulo p. For consensus networks over finite fields we provide necessary and sufficient conditions on the network topology and weights to ensure convergence. For instance we show that, differently from the case of consensus networks over the field of real numbers, consensus networks over finite fields converge in finite time, and that properties of the agents interaction graph are not sufficient to ensure finitefield consensus. Finally, we discuss the application of finite-field consensus to distributed averaging in sensor network
Data-Driven Minimum-Energy Controls for Linear Systems
No full textIn this letter, we study the problem of computing minimum-energy controls for linear systems from experimental data. The design of open-loop minimum-energy control inputs to steer a linear system between two different states in finite time is a classic problem in control theory, whose solution can be computed in closed form using the system matrices and its controllability Gramian. Yet, the computation of these inputs is known to be ill-conditioned, especially when the system is large, the control horizon long, and the system model uncertain. Due to these limitations, open-loop minimum-energy controls and the associated state trajectories have remained primarily of theoretical value. Surprisingly, in this letter, we show that open-loop minimum-energy controls can be learned exactly from experimental data, with a finite number of control experiments over the same time horizon, without knowledge or estimation of the system model, and with an algorithm that is significantly more reliable than the direct model-based computation. These findings promote a new philosophy of controlling large, uncertain, linear systems where data is abundantly available
Distributed Estimation and Detection under Local Information
Full text linkThis work considers the problem of obtaining optimal estimates via distributed computation in a large scale system. The electric power system, the transportation system, and generally any computer or network system, are examples of large scale systems: a decentralized estimation of signals based on observations acquired by spatially distributed sensors is the basis for a wide range of important applications. In this work, we focus on the problem of reconstructing the initial state of a linear network in the presence of process and measurement noise. We consider a local model information setup, in which the entire dynamical and measurement model is nowhere available and cannot be reconstructed for the computation. Our estimation procedure relies upon a novel technique to solve a consistent system of linear equations, for which we prove correctness and convergence. In the second part of the paper we consider the problem of detecting anomalies in a large scale network driven by noise. Despite the theoretical advances in this field of research, the currently available procedures to enforce security in large scale systems are computationally inefficient and numerically unreliable. Using our optimal estimation scheme, we describe a distributed procedure with performance guarantees that only requires local knowledge of the system model
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
EP-1227 The impact of first MR in clinical decision making of patients with HGG treated with RTCT
Full text linkPurpose or Objective
Standard up-front therapy of high grade glioma (HGG) is focused on the so called Stupp protocol, that includes surgical resection followed by radiotherapy (RT) combined with concomitant and adjuvant chemotherapy with temozolomide (TMZ). As supported by several international guidelines, disease assessment is performed using magnetic resonance (MR) one month since the end of RT and then every 3 months: in case of tumour progression the administration of temozolomide (the most active agent against glioma) is interrupted and salvage therapy or best supportive care are recommended. The aim of this study is to investigate in a retrospective manner the real value of first MR following RT and its relevance in clinical decision making about up-front therapy.
Material and Methods
Between April 2005 and July 2017, data of 78 patients (pts) with a proven diagnosis of HGG and treated with Stupp protocol at the University Hospital of Pisa were collected. Tumor progression was defined according to Mac-Donald’s Criteria. Considering the potential presence of pseudoprogression (PSP) and the evolutionary pattern of the suspected recurrences, lesions suggestive for tumor progression inside the radiotherapy field were investigate with a new MR after 6-8 weeks. Otherwise, the presence of new lesions outside the radiotherapy field was interpreted as disease progression (PD) and patient’s therapy was changed. Presence or absence of symptoms, extent of surgery and MGMT methylation status were recorded.
Results
The first MR after RT-CT evidenced infield progression (interpreted as PSP) in 16 pts (20,5%) and outfield progression in 8 (10.2%).Three out of 8 patients with outfield progression were symptomatic for the tumor growth. The second MRI confirmed the presence of PSP in 10 pts out of 16 pts whereas in 6 patients a true progression (PD) was present since the first MR.
Conclusion
In absence of symptoms, the first MR after radiochemotherapy influenced clinical decision making (sending the patients to further salvage therapy or BSC) only in 5 out of 78 patients (6.4%). In 72 patients, even in presence of radiological signs suggestive for disease progression inside the RT field, clinical decision making did not change. Further studies involving a higher number of patients are required in order to confirm our findings
Cervical intramedullary spinal cord metastasis from colon cancer: a systematic review and report of an illustrative case
No full textBackground: Intramedullary spinal cord metastasis (ISCM) is rare and affects 0.9-2.1% of all cancer patients. Colorectal cancer accounts for about 3% of all ISCMs.
Methods: A systematic review of the literature in the most common electronic database (PubMed, Ovid MEDLINE and Ovid EMBASE) on cervical ISCM from colon cancer, according with "PRISMA statement" criteria, was done. In addition, we present a 76-year-old man with progressive paraparesis and negative anamnesis for primary tumors, who underwent surgical and complete resection of a C5-C6 intramedullary spinal cord colon metastasis.
Results: From a systematic review of the literature, only 8 previous cases of cervical ISCM from colon cancer were reported. The mean age at presentation was 68.3 years. Surgery was performed in 6 patients, including our case, whereas 1 patient was treated with radiotherapy and two patients were untreated. Survival time ranges from 2 weeks to 14 months (mean 3.8 months). The survival rates at 60 days and 120 days are 50% and 12.5%, respectively.
Conclusion: Cervical ISCM from colon cancer is rare and is usually detected at an advanced stage of primary tumor disease. The prognosis is poor and definitive recommendations cannot be made due to the lack of controlled comparative clinical studies
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