An Online Resource Scheduling for Maximizing Quality-of-Experience in Meta Computing

Meta Computing is a new computing paradigm, which aims to solve the problem of computing islands in current edge computing paradigms and integrate all the resources on a network by incorporating cloud, edge, and particularly terminal-end devices. It throws light on solving the problem of lacking computing power. However, at this stage, due to technical limitations, it is impossible to integrate the resources of the whole network. Thus, we create a new meta computing architecture composed of multiple meta computers, each of which integrates the resources in a small-scale network. To make meta computing widely applied in society, the service quality and user experience of meta computing cannot be ignored. Consider a meta computing system providing services for users by scheduling meta computers, how to choose from multiple meta computers to achieve maximum Quality-of-Experience (QoE) with limited budgets especially when the true expected QoE of each meta computer is not known as a priori? The existing studies, however, usually ignore the costs and budgets and barely consider the ubiquitous law of diminishing marginal utility. In this paper, we formulate a resource scheduling problem from the perspective of the multi-armed bandit (MAB). To determine a scheduling strategy that can maximize the total QoE utility under a limited budget, we propose an upper confidence bound (UCB) based algorithm and model the utility of service by using a concave function of total QoE to characterize the marginal utility in the real world. We theoretically upper bound the regret of our proposed algorithm with sublinear growth to the budget. Finally, extensive experiments are conducted, and the results indicate the correctness and effectiveness of our algorithm

A salient edges detection algorithm of multi-sensor images and its rapid calculation based on PFCM kernel clustering

AbstractMulti-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means (PFCM) kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means (FCM) clustering is sensitive to noises and possibility C-means (PCM) clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness

Optimization simulation and crushing experiment of anti-impact energy absorption component of hydraulic support column

Hydraulic support, being the primary supporting equipment in coal mining operations, is frequently subjected to rock burst pressure. Hence, the anti-impact energy absorption components play a pivotal role in safeguarding the hydraulic support system. Based on the research foundation established by our anti-impact components research group, a detailed investigation on the parameters of energy-absorbing components was conducted for achieving an enhanced initial peak force and absorption energy, as well as reducing the dispersion of reaction forces. Subsequently, the ABAQUS finite element software was employed for modeling and simulating the crushing impact behavior of these energy-absorbing components. The energy absorption performance and buckling deformation characteristics of the energy absorption component were determined, and the optimal size was experimentally validated for its energy absorption performance. By comparing the predicted average support force data of the energy-absorbing member with the finite element simulation results, it was observed that the error is below 15%. Furthermore, for the optimal size member, the prediction error of the average support force model is −3.40%, thus confirming a higher level of accuracy in predicting data for the energy-absorbing members. A test platform was constructed to evaluate the crushing behavior of energy-absorbing components. The experiment involved conducting axial loading crushing tests on the custom-designed components under quasi-static conditions, with five different loading speeds selected. The experimental results demonstrate that the fluctuation of support reaction remains consistent across axial crushing experiments conducted at different loading speeds. The maximum peak value of initial support reaction is 2 253.52 kN, with a standard deviation of 206.23 kN. The minimum peak value of the initial support reaction is recorded as 2 096.26 kN, with a standard deviation of 189.83 kN. The average value for the initial support reaction peak is determined to be 2 149.32 kN, accompanied by an average standard deviation of 196.77 kN. The relative errors of the initial support reaction peak and standard deviation, compared to the finite element simulation data, are 5.6% and 11.07%, respectively. The energy absorption performance of the optimally sized energy-absorbing component was analyzed using three methods: a prediction model, finite element simulation, and crushing experiments. The average support reaction force obtained from the prediction model method is 1 879.7 kN, while that from the finite element simulation method is 1 945.9 kN, and that from the crushing experiment method is 1 919.8 kN. The prediction model exhibits an error rate of 3.41%, while the crushing experiment demonstrates a deviation of −1.3%. The reliability and feasibility of the analysis method for the energy-absorbing components are substantiated through the data verification results from these three approaches

Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images

We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. The difficulty of reliably capturing geometric context in existing methods impedes their ability to accurately enforce the consistency between the different geometric properties, thereby leading to a bottleneck of geometric estimation quality. We therefore propose the Adaptive Surface Normal (ASN) constraint, a simple yet efficient method. Our approach extracts geometric context that encodes the geometric variations present in the input image and correlates depth estimation with geometric constraints. By dynamically determining reliable local geometry from randomly sampled candidates, we establish a surface normal constraint, where the validity of these candidates is evaluated using the geometric context. Furthermore, our normal estimation leverages the geometric context to prioritize regions that exhibit significant geometric variations, which makes the predicted normals accurately capture intricate and detailed geometric information. Through the integration of geometric context, our method unifies depth and surface normal estimations within a cohesive framework, which enables the generation of high-quality 3D geometry from images. We validate the superiority of our approach over state-of-the-art methods through extensive evaluations and comparisons on diverse indoor and outdoor datasets, showcasing its efficiency and robustness.Comment: Accepted by TPAMI. arXiv admin note: substantial text overlap with arXiv:2103.1548

Model-based closed-loop control of thalamic deep brain stimulation

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson’s disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists’ expertise and patients’ experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients’ symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity.Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim–DBS are linked to symptomatic changes in EMG signals. By using a proportional–integral–derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim–DBS so that the power of EMG reaches a desired control target.Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems

Pharmacokinetic model of unfractionated heparin during and after cardiopulmonary bypass in cardiac surgery

Background: Unfractionated heparin (UFH) is widely used as a reversible anti-coagulant in cardiopulmonary bypass (CPB). However, the pharmacokinetic characteristics of UFH in CPB surgeries remain unknown because of the lack of means to directly determine plasma UFH concentrations. The aim of this study was to establish a pharmacokinetic model to predict plasma UFH concentrations at the end of CPB for optimal neutralization with protamine sulfate. Methods: Forty-one patients undergoing CPB during cardiac surgery were enrolled in this observational clinical study of UFH pharmacokinetics. Patients received intravenous injections of UFH, and plasma anti-F-IIa activity was measured with commercial anti-F-IIa assay kits. A population pharmacokinetic model was established by using nonlinear mixed-effects modeling (NONMEM) software and validated by visual predictive check and Bootstrap analyses. Estimated parameters in the final model were used to simulate additional protamine administration after cardiac surgery in order to eliminate heparin rebound. Plans for postoperative protamine intravenous injections and infusions were quantitatively compared and evaluated during the simulation. Results: A two-compartment pharmacokinetic model with first-order elimination provided the best fit. Subsequent simulation of postoperative protamine administration suggested that a lower-dose protamine infusion over 24 h may provide better elimination and prevent heparin rebound than bolus injection and other infusion regimens that have higher infusion rates and shorter duration. Conclusion: A two-compartment model accurately reflects the pharmacokinetics of UFH in Chinese patients during CPB and can be used to explain postoperative heparin rebound after protamine neutralization. Simulations suggest a 24-h protamine infusion is more effective for heparin rebound prevention than a 6-h protamine infusion.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000350506400005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Medicine, Research & ExperimentalSCI(E)[email protected]

Prognostic significance of peripheral and tumor-infiltrating lymphocytes in newly diagnosed stage III/IV non-small-cell lung cancer

Background and aimLymphocytes are effector cells that fight cancer by killing tumor cells. Here, we aim to explore the prognostic significance of both peripheral and tumor-infiltrating lymphocytes (TILs) in newly diagnosed stage III/IV non-small-cell lung cancer (NSCLC).Materials and methodsIn total, 105 cases of newly diagnosed stage III/IV NSCLC from July 2017 to October 2022 at the Tianjin Beichen Hospital were retrospectively investigated. Peripheral blood samples at the time of diagnosis and tumor tissue slices from these patients were collected. General peripheral blood cell composition and TILs were measured and analyzed via an automatic blood analyzer and immunofluorescence staining analysis. The overall survival (OS) time of all patients was also obtained and analyzed.ResultsThe median overall survival (mOS) of all patients is 12 months. The 1-, 2-, and 3-year overall survival rates were 60.5, 28.4, and 18.6%, respectively. Peripheral lymphocyte and neutrophil percentages, serum C-reactive protein (CRP) expression, tumor size, and tumor pathology are the prognostic factors of OS for newly diagnosed stage III/IV NSCLC patients. Moreover, patients with high tumor CD4+ and CD8+ T cell infiltration survived significantly longer compared to patients with low tumor CD4+ and CD8+ T cell infiltration (p < 0.0001 and p = 0.011, respectively). Compared to low tumor CD33+ cell infiltration, high tumor CD33+ cell infiltration was associated with worse OS (p = 0.018). High tumor CD8+ T cell infiltration was associated with lower peripheral lymphocyte number, lower serum CRP expression, smaller tumor size, and better tumor pathology (p = 0.012, p = 0.040, p = 0.012, and p = 0.029, respectively).ConclusionIncreased numbers of peripheral lymphocytes, CD33+ cells, CD4+ TILs, and CD8+ TILs were significantly associated with OS in newly diagnosed stage III/IV NSCLC patients, which were positively associated with several basic clinical factors