Miracle-dz/UniFAR-FTUs huggingface dataset:
An LS Localisation Method for Massive MIMO Transmission Systems

We present a novel localization method based on directional beams, as available in novel massive MIMO transmission techniques instead of radius information, and derive a least squares (LS) estimation method. The new method is a direct LS method that can be solved by a linear set of equations rather than an iterative method required for radius information. In a further step, we also show how to transform radius information into virtual beams to apply the proposed method. Finally, we evaluate the accuracy of the new methods by simulations.
Current Perspectives on Molecular Imaging for Tracking Stem Cell Therapy

The main objective of stem cell therapy is to repair the tissue with functional cells differentiated from stem cells, and contribute to the lost organ function together with the remaining functional native cells. Nevertheless, there also remain important questions unanswered, regarding the en‐ graftment, viability, biology and safety of transplanted stem cells, as well as interaction with the environment. Consequently, novel molecular imaging techniques are necessary for investigat‐ ing the behaviors and the ultimate feasibility of cell transplantation therapy of stem cell.
Regional Anesthesia and Elderly Patients: Continuous Subarachnoid Anesthesia versus Single Dose in Peripheral Vascular Surgery

Background and Objectives. Elderly patients undergoing surgery present special risks because of coexisting disease. The choice of regional techniques offers several advantages: the blocking of nociceptive transmission that diminishes the stress response, postoperative analgesia, the risk of deep venous thrombosis, and the need for airway manipulation. Subarachnoid anesthesia offers a clear end point for needle placement, rapid onset, and low dose of local anesthetic. The purpose of the present study is to compare the efficacy and safety of the continuous subarachnoid anesthesia (CSA) and the single dose subarachnoid anesthesia (SA) in patients presenting for peripheral arterial vascular surgery of the lower limbs. Methods. A prospective study involving 112 consecutive patients was performed. Seventy patients undergoing surgery expected to last over 3 hours received CSA and the remainder received a single dose. The local anesthetic used was plain bupivacaine 0.5%. Results. Both groups were identical in terms of coexisting disease. The total dose in the CSA group was 7.7 ± 0.8 mg versus that of the SA group that was 16.5 ± 3.7 mg (P < .001). The mean level of sensory block was similar, corresponding to the spinal segments T9 (CSA) and T10 (SA). The degree of motor block was higher in the SA group (P < .001). We did not find any differences in technical difficulties, incidence of postdural puncture headache, or hemodynamic changes. No prolonged neurologic problems were found in either group. Postoperative mortality was also comparable. Conclusions. Both CSA and SA are appropriate techniques for elderly patients undergoing peripheral vascular surgery of the lower limb; CSA has the advantage of providing a longer duration of anesthesia.
Approximations for Binary Gaussian Process Classification

We provide a comprehensive overview of many recent algorithms for approximate inference in Gaussian process models for probabilistic binary classification. The relationships between several approaches are elucidated theoretically, and the properties of the different algorithms are corroborated by experimental results. We examine both 1) the quality of the predictive distributions and 2) the suitability of the different marginal likelihood approximations for model selection (selecting hyperparameters) and compare to a gold standard based on MCMC. Interestingly, some methods produce good predictive distributions although their marginal likelihood approximations are poor. Strong conclusions are drawn about the methods: The Expectation Propagation algorithm is almost always the method of choice unless the computational budget is very tight. We also extend existing methods in various ways, and provide unifying code implementing all approaches.
Distributed Supervised Fault Detection and Diagnosis for a Non-Gaussian Process

In this paper, a novel method named distributed independent component–principal component regression (distributed ICPCR) is proposed to monitor the large-scale non-Gaussian process. First, multiple sub-blocks are obtained, and the key process variables are selected for the following distribution monitoring. Second, the monitoring model of each sub-block is constructed on the basis of the proposed ICPCR method. In this algorithm, the total latent space which contains both Gaussian and non-Gaussian characteristics is constructed, and the regression model between the total latent variables and quality variables is established for the quality-related monitoring. Afterward, the global monitoring result can be obtained by the Bayesian fusion strategy. Third, a probability-based method is proposed to determine the fault sub-blocks, and the ICPCR-based relative contribution plot is presented to locate the fault variables. Finally, a numerical example and the Tennessee Eastman process are used to demonstrate the e.
High-quality volume rendering using texture mapping hardware

We present a method for volume rendering of regular grids which takes advantage of 3D texture mapping hardware currently available on graphics workstations. Our method produces accurate shading for arbitrary and dynamically changing directional lights, viewing parameters, and transfer functions. This is achieved by hardware interpolating the data values and gradients before software classiﬁcation and shading. The method works equally well for parallel and perspective projections. We present two approaches for our method: one which takes advantage of software ray casting optimizations and another which takes advantage of hardware blending acceleration.
Initial Broth Microdilution Quality Control Guidelines for Debio 1452, a FabI Inhibitor Antimicrobial Agent

Debio 1452, formerly called AFN-1252 ([Fig. 1][1]), is an investigational FabI inhibitor that has demonstrated potent activity against Staphylococcus aureus ([1][2][–][3][5][4]). Currently, Debio 1450 (prodrug of Debio 1452) is in clinical development by the Debiopharm Group ([6][5], [7][6]).
Capturing Value From Innovation—Diverging Views of R&D and Marketing Managers

Profiting from technological innovation requires both the development of new products and the capture or appropriation of profits from them. For new product development, the interplay of marketing and R&D has been intensively researched. In contrast, for capturing value, this interplay has been largely neglected in the literature. To fill this gap, we study choices by marketing and R&D managers regarding activities aimed at appropriating profits from new products. We study, in detail, how managers perceive the effectiveness of product-related patents, overall patent portfolio size, marketing, sales and services quality, lead time, and contributions to open standards. We conducted discrete choice experiments with 143 managers working in R&D or marketing functions in upper and middle management in a leading communications equipment firm, and analyzed the resulting data by comparing marginal effects of rank-ordered mixed logit models between the two groups. We find that choices of R&D and marketing functions on how to capture the most value differ strongest on the mechanism that is perceived as most important by R&D managers, “lead time advantages. ” Top management needs to consider and deal with these diverging perceptions when formulating business strategies on value capture.
Alkaloids production and antioxidant properties in Catharanthus roseus (L.) G. Don. shoots and study of alkaloid biosynthesis-related gene expression levels in response to methyl jasmonate and putrescine treatments as eco-friendly elicitors

Introduction This study aimed to determine the effects of methyl jasmonate (MJ) combined with putrescine as eco-friendly elicitors on secondary metabolism and gene expression of alkaloid biosynthetic pathway in Catharanthus roseus in vifro-propagated shoots. Methods The expression of mitogen-activated protein kinase 3 and the transcription factor, octadecanoid-responsive Catharanthus AP2-domain3, upstream of plant alkaloids’ biosynthetic pathway, and of key genes in the pathway (CrPRXl, STR, DAT, and GS) are investigated as well using qRt-PCR. Antioxidant enzyme (superoxide dismutase, peroxidase, and catalase) activities and non-enzymatic antioxidants (phenolics, flavonoids, and carotenoids) contents have studied to determine the stress levels of the plant by spectrophotometer. Results Results showed increased contents of non-enzymatic antioxidants after 4–8 hr and enzymatic antioxidants activities after 24 hr. Alkaloids contents increased mostly after 1 week. The investigated signaling genes upregulated after 8 hr and biosynthetic genes after 24 hr of treatments. Combined treatments had more positive effects on gene expression levels, antioxidant responses, and secondary metabolite production than MJ individually. Discussion Increased effects of combined elicitor on genes expression may be due to cross talks between their signaling pathways. Combination of MJ and putrescine can be used as an eco-friendly elicitor for enhancing the production of economically important alkaloids in C. roseus.
Proposals on the source-destination traffic matrix estimation for IP-based VPNs

We study the problem of estimating a source-destination (SD) traffic matrix of an IP-based VPN from measurements of aggregated traffic along all access links. We derive two different estimators by formulating the SD-traffic-matrix estimation as a deterministic or stochastic optimization problem. One estimator does not rely on any stochastic information about the SD traffic and the other estimator assumes the linear relationship between the average and variance of the SD traffic. The proposed estimators have expressions much simpler than those of standard SD-traffic-matrix estimators; one estimator has an explicit representation that does not involve matrix inversion. The other is given by a simple matrix formula and does not need any prior SD-traffic matrices. Through simulation experiments, we show that these proposals are superior or comparable to the standard SD-traffic-matrix estimator in term of the estimation accuracy.
Trajectory generation for the N-trailer problem using Goursat normal form

Shows how the machinery of exterior differential systems can be used to help solve nonholonomic motion planning problems. Since the Goursat normal form, for exterior differential systems is dual to chained form for vector fields, the authors solve the problem of steering a mobile robot with N trailers by converting the system into chained form, doing the path-planning in the chained form coordinates, and converting the path back into the original coordinates. Simulations of the N-trailer system parallel parking and backing into a loading dock are included. <<ETX>>
Associations of Trunk Muscle Thickness and Muscle Strength in Healthy Male Adults

Background: Sincethetestof musclestrengthneedsaperson’smaximumexertion,theobjectiveassessmentof musclesizeisoften used. Among the imaging techniques for assessing muscle size, muscle thickness is easy to measure by ultrasonography. However, little is known about the associations of trunk muscle thickness and muscle strength. Objectives: This study aimed to investigate the correlation between trunk muscle thickness and muscle strength. Methods: Seventeen healthy male university students participated in this study (20.8 ± 0.6 years). Abdominal (rectus abdominis, external oblique, internal oblique, transverse abdominis) and low back (lumbar erector spinae, lumbar multiﬁdus) muscle thickness were measured at rest using B-mode ultrasonography. Maximum voluntary isometric trunk ﬂexion, extension, and rotation torque were measured using dynamometers. The relationships between all tested variables were investigated using the Pearson’s correlation coeﬃcient. Results: A signiﬁcant positive correlation was found between ﬂexion and extension torque (r = 0.664, P = 0.004), ﬂexion and right rotation torque (r = 0.565, P = 0.018), ﬂexion and left rotation torque (r = 0.641, P = 0.006), extension and right rotation torque (r = However, there was no signiﬁcant correlation between trunk muscle thickness and torque. Conclusions: Thisstudyprovidesbasicinformationabouttrunkmusclethicknessandmusclestrengthinhealthymaleadults. The associations of trunk muscle thickness and muscle strength could not be identiﬁed through our measurements.
A Complexity Analysis of Space-Bounded Learning Algorithms for the Constraint Satisfaction Problem

Learning during backtrack search is a space-intensive process that records information (such as additional constraints) in order to avoid redundant work. In this paper, we analyze the effects of polynomial-space-bounded learning on runtime complexity of backtrack search. One space-bounded learning scheme records only those constramts With limited size, and another records arbitrarily large constraints but deletes those that become irrelevant to the portion of the search space being explored. We find that relevance-bounded learning allows better runtime bounds than size-bounded learning on structurally restricted constraint satisfaction problems. Even when restricted to linear space, our relevance-bounded learning algorithm has runtime complexity near that of unrestricted (exponential space-consuming) learning schemes.
Smooth trajectory control of robotic manipulators based on minimum acceleration criterion

A method for optimal trajectory control of robotic manipulators is considered in this paper. In particular, a control scheme is proposed such that, while the manipulator passes two or more prescribed points, the resulting trajectory of motion is smooth. Such a problem is formulated by employing a criterion of minimum-mean-square acceleration during the entire period of motion. Although the motion of robot manipulators is governed by a system of second-order differential equations which are highly non-linear, it is shown that the control problem can be solved rigorously under such a criterion. The optimal control consists of non-linear compensation terms and other linear feedback terms. The results of computer simulation studies for a two-link manipulator are also included.
A Gene Name Normalization Framework

In Biology Natural Language Processing (BioNLP), the identiﬁcation of genes or protein names in medical articles is particularly signiﬁcant and challenging. The recognition and normalization of textual gene mentions make it more convenient for researchers to organize and manage their domain knowledge. However, this task is usually conducted by human experts, which is relatively expensive for economic and time reason. With more and more biological and medical articles being published, human efforts on this task seem to be impractical. To automate the process of recognizing and normalizing of textual gene mentions, we propose a generic framework integrating various methodologies and resources, focusing on 2 main problems: 1) automatic recognition of gene names in medical articles; 2) the identiﬁcation of detected gene names, assigning an EntrezGene identiﬁer to each gene name. The contributions of this paper include: A practical and complete gene name normalization framework using public data resources and open-source software libraries. Compared with previous related work in BioCreAtIvEII, our framework consists of several functional modules and methodologies and theories that are fully explained, explored and evaluated independently. The paper provides direct insight into designing and building a practical Gene Name Normalization Framework both from an engineering and theoretical point of view. Our framework currently achieves a very promising performance: 0.8 Precision, 0.69 Recall and a F-measure score of 0.74 on the BioCreAtIvE II benchmark, which proves its validity and competency.
Influenza newspaper reports and the influenza epidemic: an observational study in Fukuoka City, Japan

Objectives We examined whether the weekly number of newspaper articles reporting on influenza was related to the incidence of influenza in a large city. Design Prospective, non-randomised, observational study. Setting Registry data of influenza cases in Fukuoka City, Japan. Participants A total of 83 613 cases of influenza cases that occurred between October 1999 and March 2007 in Fukuoka City, Japan. Main outcome measure A linear model with autoregressive time series errors was fitted to time series data on the incidence of influenza and the accumulated number of influenza-related newspaper articles with different time lags in Fukuoka City, Japan. In order to obtain further evidence that the number of newspaper articles a week with specific time lags is related to the incidence of influenza, Granger causality was also tested. Results Of the 16 models including ‘number of newspaper articles’ with different time lags between 2 and 17 weeks (xt-2 to t-17), the β coefficients of ‘number of newspaper articles’ at time lags between t-5 and t-13 were significant. However, the β coefficients of ‘number of newspaper articles’ that are significant with respect to the Granger causality tests (p<0.05) were the weekly number of newspaper articles at time lags between t-6 and t-10 (time shift of 10 weeks, β=−0.301, p<0.01; time shift of 9 weeks, β=−0.200, p<0.01; time shift of 8 weeks, β=−0.156, p<0.01; time shift of 7 weeks, β=−0.122, p<0.05; time shift of 6 weeks, β=−0.113, p<0.05). Conclusions We found that the number of newspaper articles reporting on influenza in a week was related to the incidence of influenza 6–10 weeks after media coverage in a large city in Japan.
Graph-based features for supervised link prediction

The growing ubiquity of social networks has spurred research in link prediction, which aims to predict new connections based on existing ones in the network. The 2011 IJCNN Social Network challenge asked participants to separate real edges from fake in a set of 8960 edges sampled from an anonymized, directed graph depicting a subset of relationships on Flickr. Our method incorporates 94 distinct graph features, used as input for classification with Random Forests. We present a three-pronged approach to the link prediction task, along with several novel variations on established similarity metrics. We discuss the challenges of processing a graph with more than a million nodes. We found that the best classification results were achieved through the combination of a large number of features that model different aspects of the graph structure. Our method achieved an area under the receiver-operator characteristic (ROC) curve of 0.9695, the 2nd best overall score in the competition and the best score which did not de-anonymize the dataset.
A Review on Helmet Detection by using Image Processing and Convolutional Neural Networks

The main application of helmet detection is in traffic roads where accidents are more. Even though various measures are taken by government, it is not followed correctly by the motorcyclists, so several smart techniques should be employed. Construction industry and power substation suffer a lot of fatalities because of negligence in wearing safety helmets, hence there is a need of surveillance system that is capable of detecting helmets and preventing the deaths. A more sophisticated computer vision model that encompasses image processing, machine learning, Convolutional neural networks(CNN), classifiers such as support vector machine (SVM), ViBe background modeling algorithm, a relevant data set containing helmets; caps; Histogram of Oriented Gradients(HOG) features and number of other techniques would solve the problem. Methods like COCO model, HOG descriptors, Hough transforms are used to detect objects. Optical character recognition(OCR), Haar like feature is used to detect number plates.
Iterative Annotation Transformation with Predict-Self Reestimation for Chinese Word Segmentation

In this paper we first describe the technology of automatic annotation transformation, which is based on the annotation adaptation algorithm (Jiang et al., 2009). It can automatically transform a human-annotated corpus from one annotation guideline to another. We then propose two optimization strategies, iterative training and predict-self reestimation, to further improve the accuracy of annotation guideline transformation. Experiments on Chinese word segmentation show that, the iterative training strategy together with predict-self reestimation brings significant improvement over the simple annotation transformation baseline, and leads to classifiers with significantly higher accuracy and several times faster processing than annotation adaptation does. On the Penn Chinese Treebank 5.0, it achieves an F-measure of 98.43%, significantly outperforms previous works although using a single classifier with only local features.
Nonconvex Low-Rank Matrix Recovery with Arbitrary Outliers via Median-Truncated Gradient Descent

Recent work has demonstrated the effectiveness of gradient descent for directly recovering the factors of low-rank matrices from random linear measurements in a globally convergent manner when initialized properly. However, the performance of existing algorithms is highly sensitive in the presence of outliers that may take arbitrary values. In this paper, we propose a truncated gradient descent algorithm to improve the robustness against outliers, where the truncation is performed to rule out the contributions of samples that deviate significantly from the sample median of measurement residuals adaptively in each iteration. We demonstrate that, when initialized in a basin of attraction close to the ground truth, the proposed algorithm converges to the ground truth at a linear rate for the Gaussian measurement model with a near-optimal number of measurements, even when a constant fraction of the measurements are arbitrarily corrupted. In addition, we propose a new truncated spectral method that ensures an initialization in the basin of attraction at slightly higher requirements. We finally provide numerical experiments to validate the superior performance of the proposed approach.
Integrating SLAM into gas distribution mapping

In this paper we consider the problem of creating a spatial representation of a gas distribution in an environment using a mobile robot equipped with gas sensors. The gas distribution mapping method used models the information content of a given measurement about the average concentration distribution with respect to the point of measurement. In this paper, we present an extension which can consider the uncertainty about the robot’s position in the gas distribution mapping. We present a preliminary result where a mobile robot equipped with gas sensors creates a map of a large indoor environment, using both spatial and olfactory information.