Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Learning for BPSO-Based Pilot Pattern Design Over Sparse OFDM Channels

In this paper, to investigate sparse channel estimation in OFDM communication systems, we propose a novel binary particle swarm optimization (BPSO) based pilot pattern design scheme and develop an efficient sparse Bayesian learning (SBL) scheme for sparse channel recovery. First, through modifying the mutual incoherence property (MIP) criterion, we outline a new penalty function for optimizing pilot pattern design, which comprehensively takes into account the overall coherence of the measurement matrix. Second, we modify the conventional BPSO algorithm by proposing a new adaptive inertia weight scheme, in which the inertia weight varies with the current state of particle swarm and the number of iterations. Furthermore, we map the pilot pattern design into the framework of the modified BPSO algorithm. Third, we develop a partitioned matrix iterative mechanism to compute matrix inversion in each iteration involved in SBL techniques. Finally, our numerical and simulation results illustrate the efficacy of the BPSO based pilot pattern design scheme and our proposed SBL algorithms in terms of mean-square estimation (MSE) error performance.
WEAVE Trial: Final Results in 152 On-Label Patients

Background and Purpose— The WEAVE trial (Wingspan Stent System Post Market Surveillance) is a postmarket surveillance trial mandated by the Food and Drug Administration to assess the periprocedural safety of the Wingspan Stent system in the treatment of symptomatic intracranial atherosclerotic disease. Methods— A total of 152 consecutive patients who met the Food and Drug Administration on-label usage criteria were enrolled at 24 hospitals and underwent angioplasty and stenting with the Wingspan stent. On-label criteria included age 22 to 80 years, symptomatic intracranial atherosclerotic stenosis of 70% to 99%, baseline modified Rankin Scale score ⩽3, ≥2 strokes in the vascular territory of the stenotic lesion with at least 1 stroke while on medical therapy, and stenting of the lesion ≥8 days after the last stroke. The primary analysis assessed the periprocedural stroke, bleed, and death rate within 72 hours of the procedure with adjudication by a core study Stroke Neurologist. Results— The trial was stopped early after interim analysis of 152 consecutive patients demonstrated a lower than expected 2.6% (4/152 patients) periprocedural stroke, bleed, and death rate. This was lower than the 4% periprocedural primary event safety benchmark set for the interim analysis in the study. A total of 97.4% (148/152) patients were event-free at 72 hours, 1.3% (2/152) had nonfatal strokes, and 1.3% (2/152) of patients died. Conclusions— With experienced interventionalists, and proper patient selection following the on-label usage guidelines, the use of the Wingspan stent for intracranial atherosclerotic disease demonstrated a low periprocedural complication rate and excellent safety profile. This is the largest on-label, multicenter, prospective trial of the Wingspan stent system to date with the lowest reported complication rate. Clinical Trial Registration— URL: https://www.clinicaltrials. gov. Unique identifier: NCT02034058.
On the Gap between System Level Synthesis and Structured Controller Design: the Case of Relative Feedback

We consider the optimal distributed control design problem with subcontroller communication constraints, by designing controllers with state-space realizations composed of matrices with specified sparsity patterns. The recently developed System Level Synthesis (SLS) framework provides a computationally tractable method for optimizing over a convex subset of these structured-realizable controllers, which we refer to as SLS-structured. The performance of the optimal SLS-structured controller will thus provide an upper bound on the performance of the optimal structured-realizable controller. We take a first step toward quantifying this bound by considering the design of controllers with access to only relative system measurements. Our main result demonstrates through an example, that, when relative feedback constraints are imposed, the gap in performance between the optimal SLS-structured controller and optimal controller with structured realization may be infinite.
Performance Analysis of Downlink OSTBC-OFDM and OSTBC-GFDM signals in the context of 5th Generation C-RAN

In this work, a simulation study of baseband radio over fibre technology for 5G and Beyond technology has been realized. The proposed architecture deploys an Orthogonal Space-Time Block Code Generalized Frequency Division Multiplexing (OSTBC-GFDM) waveforms generated at the central office (CO). The signal is transmitted via a single-mode fibre (SMF) to the cell site. A Volterra non-linear equalizer (VNLE) is implemented into the cell site to improve signal quality by reducing the optical channel effect along with the transmission link. Then, the signal is up-converted to the frequency range 1 (FR1) and transmitted over the air to the user equipment (UE). The system performance is evaluating by measuring error vector magnitude (EVM) and bit error rate (BER).
A Control Scheme for Smooth Transition in Physical Human-Robot-Environment Between Two Modes: Augmentation and Autonomous

There has been an increasing demand for physical human-robot collaboration during the design prototyping phase. For example, users would like to maneuver the end-effector compliantly in free space followed by supplying a contact force to obtain a firm adhesive connection. The technical challenges is the design of the controller, especially during the switching from human-robot interaction (human guides robot) to robot-environment interaction for the robot to continuously maintain the contact force even after the human lets go. Traditional controllers often result in unstable interaction during the switches of the controllers. Therefore, this letter proposes a control scheme that unifies impedance and admittance in the outer loop, and unifies the adaptive position and velocity control in the inner loop to address this issue. The cooperation of the cobot is divided into two modes, namely, an augmentation mode where the human force is the desired input to guide the motion of the cobot, and an autonomous mode where predefined position and force commands are used (e.g., to maintain a desired holding force). With the proposed control scheme, the physical interaction between the robot, human and environment can be smoothly and stably transited from augmentation mode to autonomous mode. Experiments are then conducted to validate the proposed approach.
Confidence Regions for Simulations with Learned Probabilistic Models*

Due to the growing amount of data and processing capabilities, machine learning techniques are increasingly applied for the identification of dynamical systems. Especially probabilistic methods are promising for learning models, which in turn are frequently used for simulations. Although confidence regions around predicted trajectories are of crucial importance in many control approaches, few rigorous mathematical analysis methods are available for learned probabilistic models. Therefore, we propose a novel method to estimate confidence regions for predicted trajectories, and assign them a confidence level based on Monte Carlo random trajectory sampling. Since the confidence level has a strongly nonlinear dependence on the number of Monte Carlo samples, we derive a lower bound on the number of samples that ensures a desired minimum confidence level. The efficiency and flexibility of the proposed method is demonstrated in simulations of a Bayesian hidden Markov model and a Gaussian process state space model.
Nonlinear Model Predictive Control for the Transient Load Share Management of a Hybrid Diesel-Electric Marine Propulsion Plant

A Non-linear Model Predictive Control (NMPC) scheme is proposed for the optimal power-spit problem of a hybrid diesel-electric marine propulsion plant. The NMPC controller directly regulates the torque setpoints of the internal combustion engine and the electric motor/generator and ensures that certain constraints concerning the engine overloading are applied. In this way, fuel consumption and NOx emissions can be reduced. The modeling for the controller design was based on experimental data gathered from the hybrid plant and on first principles for the diesel engine behavior and battery charging. The controller was experimentally tested in real-time operation. Results showed that the controller rejected successfully load disturbances and maintained the rotational speed reference of the powertrain as well as the desirable state of charge in battery, in respect to the limits of the system.
A Collaborative Multi-Metric Interface Ranking Scheme for Named Data Networks

Named Data Networking (NDN) uses the content name to enable content sharing in a network using Interest and Data messages. In essence, NDN supports communication through multiple interfaces, therefore, it is imperative to think of the interface that better meets the communication requirements of the application. The current interface ranking is based on single static metric such as minimum number of hops, maximum satisfaction rate, or minimum network delay. However, this ranking may adversely affect the network performance. To fill the gap, in this paper, we propose a new multi-metric robust interface ranking scheme that combines multiple metrics with different objective functions. Furthermore, we also introduce different forwarding modes to handle the forwarding decision according to the available ranked interfaces. Extensive simulation experiments demonstrate that the proposed scheme selects the best and suitable forwarding interface to deliver content.
Learning Framework for IoT Services Chain Implementation in Edge Cloud Platform

As an emerging solution to latency requirements of Internet of Things (IoT) services, edge computing can bring powerful processing capacity closer to data sources. However, with the limited resources at edge nodes, a major challenge is finding optimal resources in distributed edges to reduce the operational costs of service deployment. Prior works focus mainly on static optimization which may not work efficiently with the time-varying workloads and resource constraints. In this paper, we, therefore, consider a dynamic allocation framework in the edge-cloud network over the long run with uncertainty workloads. In such a system, we introduce a JOint Routing and Placement problem for IoT services, called JORP, that dynamically assigns resources according to workload demand in order to reduce the operational costs in long term. Inspired from the well-known algorithm, branch-and-bound (BnB), for solving the mixed-integer non linear problems (MINLPs) like JORP, we bring the learning concept to address the high complexity of BnB when the search space is huge. Particularly, we design a deep neural network (DNN) and train it under the imitation learning to mimic branching behaviors in BnB for searching the optimal solution. Finally, simulations show our solution outperforms baselines in terms of convergence and operational cost.
Barrier Protection and Recovery Effects of Gut Commensal Bacteria on Differentiated Intestinal Epithelial Cells In Vitro

Alterations in the gut microbiota composition play a crucial role in the pathogenesis of inflammatory bowel disease (IBD) as specific commensal bacterial species are underrepresented in the microbiota of IBD patients. In this study, we examined the therapeutic potential of three commensal bacterial species, Faecalibacterium prausnitzii (F. prausnitzii), Roseburia intestinalis (R. intestinalis) and Bacteroides faecis (B. faecis) in an in vitro model of intestinal inflammation, by using differentiated Caco-2 and HT29-MTX cells, stimulated with a pro-inflammatory cocktail consisting of interleukin-1β (IL-1β), tumor necrosis factor-α (TNFα), interferon-γ (IFNγ), and lipopolysaccharide (LPS). Results obtained in this work demonstrated that all three bacterial species are able to recover the impairment of the epithelial barrier function induced by the inflammatory stimulus, as determined by an amelioration of the transepithelial electrical resistance (TEER) and the paracellular permeability of the cell monolayer. Moreover, inflammatory stimulus increased claudin-2 expression and decreased occludin expression were improved in the cells treated with commensal bacteria. Furthermore, the commensals were able to counteract the increased release of interleukin-8 (IL-8) and monocyte chemoattractant protein-1 (MCP-1) induced by the inflammatory stimulus. These findings indicated that F. prausnitzii, R. intestinalis and B. faecis improve the epithelial barrier integrity and limit inflammatory responses.
Elevated stress impairs the accuracy of eyewitness memory but not the confidence-accuracy relationship.

Although numerous studies have identified factors that affect eyewitness identification accuracy, recent studies report that many of these factors do not affect the accuracy of high-confidence identifications. This is critical because legal cases are more likely to be prosecuted if they involve high-confidence eyewitnesses. Using a confidence-accuracy characteristic (CAC) analysis, we explored whether stress affects the accuracy of high-confidence identifications. In two experiments, people viewed faces followed by an old/new recognition-memory test and provided confidence ratings. Stress was manipulated by pairing a low- or high-valence image with each studied face. Identification accuracy was higher in the low- than high-stress condition, yet the proportion correct for high-confidence positive identifications was similar in the two stress conditions. Elevated stress impairs eyewitness identification accuracy overall. However, the results of this study suggest that confidence is a better predictor of recognition-memory accuracy than is stress even though confidence alone is still an imperfect predictor. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Exhaled breath analysis by use of eNose technology: a novel diagnostic tool for interstitial lung disease

Introduction Early and accurate diagnosis of interstitial lung diseases (ILDs) remains a major challenge. Better noninvasive diagnostic tools are much needed. We aimed to assess the accuracy of exhaled breath analysis using eNose technology to discriminate between ILD patients and healthy controls, and to distinguish ILD subgroups. Methods In this cross-sectional study, exhaled breath of consecutive ILD patients and healthy controls was analysed using eNose technology (SpiroNose). Statistical analyses were done using partial least square discriminant analysis and receiver operating characteristic analysis. Independent training and validation sets (2:1) were used in larger subgroups. Results A total of 322 ILD patients and 48 healthy controls were included: sarcoidosis (n=141), idiopathic pulmonary fibrosis (IPF) (n=85), connective tissue disease-associated ILD (n=33), chronic hypersensitivity pneumonitis (n=25), idiopathic nonspecific interstitial pneumonia (n=10), interstitial pneumonia with autoimmune features (n=11) and other ILDs (n=17). eNose sensors discriminated between ILD and healthy controls, with an area under the curve (AUC) of 1.00 in the training and validation sets. Comparison of patients with IPF and patients with other ILDs yielded an AUC of 0.91 (95% CI 0.85–0.96) in the training set and an AUC of 0.87 (95% CI 0.77–0.96) in the validation set. The eNose reliably distinguished between individual diseases, with AUC values ranging from 0.85 to 0.99. Conclusions eNose technology can completely distinguish ILD patients from healthy controls and can accurately discriminate between different ILD subgroups. Hence, exhaled breath analysis using eNose technology could be a novel biomarker in ILD, enabling timely diagnosis in the future. Exhaled breath analysis using eNose technology can accurately discriminate between different ILD subgroups and individual diseases. eNose technology could be a novel diagnostic tool in ILD, enabling timely diagnosis in the future. https://bit.ly/3jJs2hf
Burnout among gastroenterologists: How to manage and prevent it

The term ‘burnout’ was coined by the psychologist Herbert Freudenberg. During therapy for drug addicts, he would notice that patients stared at their cigarettes as they burned out. This behaviour encapsulates the meaning of ‘burnout’: a state of mind that is characterised by intense depersonalisation, exhaustion and a decreased sense of personal achievement. In many ways, burnout may be similar to depression. In fact, it has been argued that if depression is modelled in a continuum, burnout is merely a more moderate form rather than a categorically different condition. Thus, it is a phenomenon that should be considered as a significant mental-health condition and consequently a major impediment towards a physician’s ability to perform their work. Professional burnout has a significant impact on the individual as well as on the health-care institution. Physicians’ burnout is associated with broken relationships, alcohol and substance abuse and suicide. It has also been shown to result in poor work performance, lack of engagement in the institution’s mission, a significant decline in patient satisfaction and a higher likelihood of reporting medical errors.
Precision and accuracy of robot‐assisted technology with simplified express femoral workflow in measuring leg length and offset in total hip arthroplasty

Semi‐active robot‐assisted total hip arthroplasty (THA) has two options to measure the leg length discrepancy (LLD) and combined offset (CO), the ‘enhanced’ femoral workflow and the so‐called ‘express’ simplified workflow. The purpose of this study was to determine the precision and accuracy of intraoperative LLD and CO measurement with express workflow robotic THA.
Fin-Spine Selection and Section Level Influence Potential Age Estimates of Striped Marlin (Kajikia audax)

Annulus counts were compared between four sectioned levels of first dorsal-fin spines (D1–6) and anal-fin spines (A1–3) of Striped Marlin (Kajikia audax) caught in the southwest Pacific Ocean. The number of annuli observed in transverse sections varied significantly between spines of individual fish and between sections cut at varying distances along spines. Sections at levels ¼ and 1 condyle width (CW) of spines D4–6 and A3 displayed the greatest number of annuli and were not significantly different from one another. Pending validation of yearly periodicity of annulus formation, these sections could be used interchangeably in future age and growth studies on this species. The percentage of bone remodelling (vascularization) present within the central portion of fin-spine sections was negatively correlated with annulus counts. Vascularization affected the visibility of early formed annuli, which resulted in reduced counts and therefore ‘age’ estimates. Annulus counts were reduced in fin-spines D1–3, A1–2, and in sections from 2 CW and 3 CW in most spines. Statistical replacement techniques for annulus loss due to vascularization effectively nullified the differences observed between sections. This study demonstrated that annulus counts and subsequent ‘age’ estimates may vary significantly within and among calcified structures of individual fish. The risk of intra-structural variation is of particular concern to aging studies focused on species that are affected by vascularization, or where calcified structures are used indiscriminately.
Analog-to-Information Conversion for Nonstationary Signals

In this paper, we consider the problem of analog-to-information conversion for nonstationary signals, which exhibit time-varying properties with respect to spectral contents. Nowadays, sampling for nonstationary signals is mainly based on Nyquist sampling theorem or signal-dependent techniques. Unfortunately, in the context of the efficient ‘blind’ sampling, these methods are infeasible. To deal with this problem, we propose a novel analog-to-information conversion architecture to achieve the sub-Nyquist sampling for nonstationary signals. With the proposed scheme, we present a multi-channel sampling system to sample the signals in time-frequency domain. We analyze the sampling process and establish the reconstruction model for recovering the original signals. To guarantee the wide application, we establish the completeness under the frame theory. Besides, we provide the feasible approach to simplify the system construction. The reconstruction error for the proposed system is analyzed. We show that, with the consideration of noises and mismatch, the total error is bounded. The effectiveness of the proposed system is verified in the numerical experiments. It is shown that our proposed scheme outperforms the other sampling methods state-of-the-art.
Geolocation of IP Hosts in Large Computer Networks with Congestion

This paper presents geolocation of Internet Protocol (IP) hosts in large computer networks with congestion, using round trip time (RTT) of ping packets. Most current approaches consider RTT to be composed only of propagation delay. However, in practice, queuing delay forms a significant component of RTT, and hence, current RTT measurement based approaches result in large errors in estimation of the geolocation.This paper proposes two geolocation algorithms: LOGTHRESH and ASHYP that consider queuing delay due to network congestion, in addition to propagation delay of the RTT. The proposed algorithms are shown to reduce the error in geo-location to as low as 0.7% in some scenarios. When some existing enhancement mechanisms like semi-definite programming available in the literature was used on top of our proposed approach, median estimation errors as low as 0.3 % can be achieved.
An IoT-oriented Cloud Platform for Intelligent Management of Emergency Equipment

The management of city security faces the severe challenges, and urgently needs such as the intelligent and efficient coordinated emergency command platform. Since emergency equipment is susceptible to the impact of natural environment, its organization, scheduling and coordination are extremely important. This paper first introduces the problems involved in the current industrial big data cloud platform process, and the relevant work done by the industry to solve these problems. Different from the previous IoT-oriented cloud platforms, we built a cloud platform with the characteristics of real-time monitoring, fault early warning, and remote map diagnosis. This platform is specially designed for industrial emergency equipment based on the Internet of Things for on-board equipment, ground control equipment and cloud control equipment, and elaborated the node architecture of the cloud platform in detail. It also introduces the case of industry emergency equipment intelligent management cloud platform from the aspects of features and advantages. And then the future concerns about intelligent management of emergency equipment are discussed. It provides beneficial exploration for building a new generation of vehicle emergency command system and promoting the ubiquitous cloud platform construction of emergency equipment.