Miracle-dz/UniFAR-FTUs huggingface dataset:
Is It Safe for Patients With Cardiac Channelopathies to Undergo Routine Dental Care? Experience From a Single‐Center Study

Background Brugada syndrome and long‐QT syndrome may account for at least one third of unexplained sudden cardiac deaths. Dental care in patients with cardiac channelopathies is challenging because of the potential risk of life‐threatening events. We hypothesized that the use of local dental anesthesia with lidocaine with and without epinephrine is safe and does not result in life‐threatening arrhythmias in patients with channelopathies. Methods and Results We performed a randomized, double‐blind pilot trial comparing the use of 2% lidocaine without a vasoconstrictor and with 1:100 000 epinephrine in 2 sessions of restorative dental treatment with a washout period of 7 days (crossover trial). Twenty‐eight–hour Holter monitoring was performed, and 12‐lead electrocardiography, digital sphygmomanometry, and anxiety scale assessments were also conducted at 3 time points. Fifty‐six dental procedures were performed in 28 patients (18 women, 10 men) with cardiac channelopathies: 16 (57.1%) had long‐QT syndrome, and 12 (42.9%) had Brugada syndrome; 11 (39.3%) of patients had an implantable defibrillator. The mean age was 45.9±15.9 years. The maximum heart rate increased after the use of epinephrine during the anesthesia period from 82.1 to 85.8 beats per minute (P=0.008). In patients with long‐QT syndrome, the median corrected QT was higher, from 450.1 to 465.4 ms (P=0.009) at the end of anesthesia in patients in whom epinephrine was used. The other measurements showed no statistically significant differences. No life‐threatening arrhythmias occurred during dental treatment. Conclusions The use of local dental anesthesia with lidocaine, regardless of the use of a vasoconstrictor, did not result in life‐threatening arrhythmias and appears to be safe in stable patients with cardiac channelopathies. Clinical Trial Registration URL: http://www.clinicaltrials. gov. Unique identifier: NCT03182777.
Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization

Keeping a vehicle well-localized within a prebuilt-map is at the core of any autonomous vehicle navigation system. In this work, we show that both standard SIR sampling and rejection-based optimal sampling are suitable for efficient (10 to 20 ms) real-time pose tracking without feature detection that is using raw point clouds from a 3D LiDAR. Motivated by the large amount of information captured by these sensors, we perform a systematic statistical analysis of how many points are actually required to reach an optimal ratio between efficiency and positioning accuracy. Furthermore, initialization from adverse conditions, e.g., poor GPS signal in urban canyons, we also identify the optimal particle filter settings required to ensure convergence. Our findings include that a decimation factor between 100 and 200 on incoming point clouds provides a large savings in computational cost with a negligible loss in localization accuracy for a VLP-16 scanner. Furthermore, an initial density of ∼2 particles/m2 is required to achieve 100% convergence success for large-scale (∼100,000 m2), outdoor global localization without any additional hint from GPS or magnetic field sensors. All implementations have been released as open-source software.
Use of 1H NMR to Detect the Percentage of Pure Fruit Juices in Blends

The consumption of high-nutritional-value juice blends is increasing worldwide and, considering the large market volume, fraud and adulteration represent an ongoing problem. Therefore, advanced anti-fraud tools are needed. This study aims to verify the potential of 1H NMR combined with partial least squares regression (PLS) to determine the relative percentage of pure fruit juices in commercial blends. Apple, orange, pineapple, and pomegranate juices were selected to set up an experimental plan and then mixed in different proportions according to a central composite design (CCD). NOESY (nuclear Overhauser enhancement spectroscopy) experiments that suppress the water signal were used. Considering the high complexity of the spectra, it was necessary to pretreat and then analyze by chemometric tools the large amount of information contained in the raw data. PLS analysis was performed using venetian-blind internal cross-validation, and the model was established using different chemometric indicators (RMSEC, RMSECV, RMSEP, R2CAL, R2CV, R2PRED). PLS produced the best model, using five factors explaining 94.51 and 88.62% of the total variance in X and Y, respectively. The present work shows the feasibility and advantages of using 1H NMR spectral data in combination with multivariate analysis to develop and optimize calibration models potentially useful for detecting fruit juice adulteration.
A Novel Method for Estimating Distances from a Robot to Humans Using Egocentric RGB Camera

Estimating distances between people and robots plays a crucial role in understanding social Human–Robot Interaction (HRI) from an egocentric view. It is a key step if robots should engage in social interactions, and to collaborate with people as part of human–robot teams. For distance estimation between a person and a robot, different sensors can be employed, and the number of challenges to be addressed by the distance estimation methods rise with the simplicity of the technology of a sensor. In the case of estimating distances using individual images from a single camera in a egocentric position, it is often required that individuals in the scene are facing the camera, do not occlude each other, and are fairly visible so specific facial or body features can be identified. In this paper, we propose a novel method for estimating distances between a robot and people using single images from a single egocentric camera. The method is based on previously proven 2D pose estimation, which allows partial occlusions, cluttered background, and relatively low resolution. The method estimates distance with respect to the camera based on the Euclidean distance between ear and torso of people in the image plane. Ear and torso characteristic points has been selected based on their relatively high visibility regardless of a person orientation and a certain degree of uniformity with regard to the age and gender. Experimental validation demonstrates effectiveness of the proposed method.
The Hunt for Party Discipline in Congress

We analyze party discipline in the House of Representatives between 1947 and 1998. The effects of party pressures can be represented in a spatial model by allowing each party to have its own cutting line on roll call votes. Adding a second cutting line makes, at best, a marginal improvement over the standard single-line model. Analysis of legislators who switch parties shows, however, that party discipline is manifest in the location of the legislator's ideal point. In contrast to our approach, we find that the Snyder-Groseclose method of estimating the influence of party discipline is biased toward exaggerating party effects.
Image fusion based on multi-scale soft morphological filters

A novel image fusion scheme is proposed based on the soft mathematical morphology.First,this scheme performs a multi-scale soft morphological open-close filter to get the low-frequency smooth images for the source images.Second,the multi-scale soft top-hat and bottom-hat transforms are applied to extract the image detail features smaller than the current scale.Finally,an image fusion is performed for the low-frequency smooth images and the multi-scale high-frequency detail images.The fused image is formed with a morphological reconstruction.The fusion experiments on the multi-focus images and the IR visible images indicate that this scheme outperforms the conventional image fusion based on multi-scale standard morphological filters(MSMF). Our fusion scheme is especially suitable for image fusion process with noises.
One-Year Follow-Up of Cognitive-Behavioral Therapy for Obese Individuals With Binge Eating Disorder

The results of a 1-year posttreatment follow-up of 93 obese women diagnosed as having binge eating disorder (BED) and treated with group cognitive-behavioral therapy (CBT) followed by weight loss treatment are described. The group as a whole maintained both reductions in binge eating and abstinence rates fairly well. However, they regained the weight lost during treatment. Those who stopped binge eating during CBT maintained a weight loss of 4.0 kg over the follow-up period. In contrast, those who continued to binge gained 3.6 kg. Twenty-six percent of those abstinent after CBT met criteria for BED at follow-up and had gained weight, whereas the remaining 74% had lost weight. Stopping binge eating appears critical to sustained weight loss in BED.
Robust Location Tracking Using a Double Layered Particle Filter

The location awareness is an important part of many ubiquitous computing systems, but a perfect location system does not exist yet in spite of many researches. Among various location tracking systems, we choose the RFID system due to its wide applications. However, the sensed RSSI signal is too sensitive to the direction of a RFID reader antenna, the orientation of a RFID tag, the human interference, and the propagation media situation. So, the existing location tracking method in spite of using the particle filter is not working well. To overcome this shortcoming, we suggest a robust location tracking method with a double layered structure, where the first layer coarsely estimates a tag's location in the block level using a regression technique or the SVM classifier and the second layer precisely computes the tag's location, velocity and direction using the particle filter technique. Its layered structure improves the location tracking performance by restricting the moving degree of hidden variables. Many extensive experiments show that the proposed location tracking method is so precise and robust to be a good choice for implementing the location estimation of a person or an object in the ubiquitous computing. We also validate the usefulness of the proposed location tracking method by implementing it for a real-time people monitoring system in a noisy and complicate workplace.
Social Representations Theory

Abstract This article argues that the theory of social representations can give valuable contributions to media research. It offers a new theory-based approach for studying how the media and citizens socially represent societal and political issues colouring our age, or some specific time period. Two fundamental communicative mechanisms – anchoring and objectification – are posited by the theory. These mechanisms, with a set of subcategories, are presented and it is shown how they can be used as conceptual analytical tools in empirical analysis. Concrete examples are given from a study on climate change and the media.
Heterogeneous Data Exchange Based on XML

This article brings forward the model of heterogeneous data exchange based on XML and presents the mapping mode between XML and RDB on the base of analyzing the technique of heterogeneous data exchange.In the end,take the test question databases for example,establish the middle ware to interchange the data between the heterogeneous databases by using Java Servlet.
Social Support in Marriage

The current study examines the role of perceived adequacy of social support provided by spouses for both marital and individual functioning. Married individuals from a college sample (N = 177) recorded the adequacy of specific supportive behaviors provided by the spouse on a daily basis for 7 days. Perceived support adequacy was correlated in the expected direction with marital quality, depressive symptomatology, and perceived stress. Furthermore, hierarchical multiple regressions indicated that perceived support adequacy accounts for significant unique variance in marital quality, depressive symptomatology, and perceived stress, even after controlling for social desirability. Discussion focuses on limitations of the study and implications of the findings for clinical work with couples.
High-Speed SiGe HBT BiCMOS Circuits for Communication and Radar Transceivers

To my family, friends, and loved ones. iii ACKNOWLEDGEMENTS This dissertation would not have been completed without the help of many individuals. I am indebted to my advisor, Professor John D. Cressler, for all the opportunities, confidence, guidance, encouragement, patience, and freedom that he has given me. His dedication to research, devotion to students, and passion for life are truly inspirational. Without the support from my loving family, I would not be here today. I owe so much to my parents, Chun-Chun and Hsih-Hsiung, for the sacrifices they made. No words can begin to describe my deepest gratitude. I can only say "We have done it! " My sisters, Sandy, Tina, and Annie, get special thanks for putting up with me. You are always there for me. Your advice and criticism are always taken to heart. My dearest Winda, you have continued to inspire me even half a world away. Thank you for your patience. I look forward to the next chapter of our lives together.
An intention-activation account of residual switch costs

Residual switch costs are performance costs associated with a shift of task that persist even when there is ample time to prepare in advance for the new task. I present a mixture-model approach for evaluating the contributions of two possible causes of residual switch costs: (1) failures to take advantage of opportunities for advance preparation, and (2) limitations to the completeness of task-set reconfiguration attainable by fully endogenous means. The proposed intention-activation hypothesis of failures to engage in advance preparation is shown to provide a coherent account of the influences of a variety of factors on residual switch costs. Two new experiments tested predictions of the hypothesis regarding the effects of task duration and of time on task on the incidence of preparatory failures.
Review of Vehicle Distance Measurement Based on Monocular Vision

Moving vehicle distance measurement is one of the key technology for road satefy,and computer vision provides computational model of human vision.Based on the theory of domestic and foreign vehicle distance measurements,by comparing advantages and disadvantages of monocular vision with what of stereo vision in machine vision,we summarize domestic vehicle distance measurements based on monocular vision,and provide the corresponding principle and location model.At last,we give the merits and demerits of each method,and prospect the future work.
Applications of multispectral/hyperspectral imaging technologies in military

The principle and characteristics of multispectral and hyperspectral imaging technologies are discussed,and the difference and relationship between them,as well as other technical concepts,are also explained.Typical applications of such technologies on military are introduced.The advantages and existing state are presented.The technical level of imaging spectrometers and related technologies are then introduced.Finally,the development trend of multispectral and hyperspectral imaging technologies is expected.
Next Generation University Library Information Systems Based on Cooperative Learning

ABSTRACT An integrated library information system is a resource planning system for a library, used to track resources owned, bills paid, orders made, and patrons who have borrowed. In our research, we focused on university library information systems (ULISs). We identified an important research question regarding their main limitation in offering intelligent help to the students in their documentation/learning. We identified the importance of the endowment of ULISs with artificial intelligence. In this article, we analyzed different aspects related to the presence of computational intelligence in ULISs and intelligence of ULISs. Finally, we proposed a complex next generation ULIS based on a hybrid cooperative learning, being able to offer an intelligent help for personalized learning of students. We defined some novel paradigms in the context of a novel kind of cooperative hybrid personalized learning, such as learning role and sub-role; and learning intelligence level.
On the relation between face and object recognition in developmental prosopagnosia: Systematic association but no dissociation

There is an ongoing debate about whether face recognition and object recognition constitute separate domains. Clarification of this issue can have important theoretical implications as face recognition is often used as a prime example of domain-specificity in mind and brain. An important source of input to this debate comes from studies of individuals with developmental prosopagnosia, suggesting that face recognition can be selectively impaired. We put the selectivity-hypothesis to test by assessing the performance of 10 subjects with developmental prosopagnosia on demanding tests of visual object processing involving both regular and degraded drawings. None of the individuals exhibited a dissociation between face and object recognition, and as a group they were significantly more affected by degradation of objects than control participants. Importantly, we also find positive correlations between the severity of the face recognition impairment and the degree of impaired performance with degraded objects. This suggests that the face and object deficits are systematically related rather than coincidental. We conclude that at present, there is no strong evidence in the literature on developmental prosopagnosia supporting domain-specific accounts of face recognition.
Terrain intervisibility-believe it or not?

Intervisibility displays serve a valuable function. But to build and use these systems wisely, it's good to understand the errors and limitations involved. At LMFS, we have integrated digital maps from other companies into avionics systems such as Army Special Operations Aircraft and MH53J, and have analyzed the errors associated with the use of level 1 and level 2 terrain elevation data from National Imagery and Mapping Agency (NIMA). We have also developed a number of realtime intervisibility and probabilistic intervisibility functions using a hybrid of multi-resolution techniques and algorithms to obtain the best results possible for a given set of computer resources. This paper explores some of the problems, solutions, and human/machine interface considerations associated with the generation and use of intervisibility.
A Face Recognition Method Based on Principal Component Analysis and Support Vector Machine

Based on the high performance of support vector machine(SVM) in tackling small sample size, high dimension and its good generalization, this paper proposed a face recognition method based on principal component analysis(PCA) and SVM. The PCA is used to reduce the dimension and extract the feature, then the SVM combined with the nearest distance classifier is used for classification. The ORL face database was used to test the proposed method. The experiment result shows that the method is effective.
Near infrared CCD imaging of hemodynamics

This paper presents preliminary results of near infrared CCD imaging of hemodynamics with inexpensive system components. The motivation behind this is the requirement of a low-cost and easy to use system capable of aiding the physician in daily assessment of venous disorders such as chronic vein insufficiency (CVI) and deep vein thrombosis (DVT). Photoplethysmographic (PPG) systems with optical sensors are in wide use for these tests today. They essentially measure the blood volume in the top skin layers. Another method that has been described for imaging of veins is near infrared photography. The presented method combines the two. A monochrome CCD camera equipped with a >700 nm filter is mounted on a tripod. A constant indirect illumination is applied, then an exercise is performed by the patient. The camera takes a sequence of pictures of the perfusion area of interest, i.e. the lower leg. Digital filtering and sequence evaluation results in image-mapped data about venous reflow time, venous pump power and other relevant parameters. From this, conclusions can be taken as to the reason and location of disorders.
CRFs Based Recognition of Chinese Verb-Object Collocation

A new method to recognize the Chinese verb-object collocation is proposed on the basis of the conditional random fields(CRFs) model.The CRFs based model is examined with verb subcategorization features,context features,and features of their combination.The experiments are carried on two different Chinese word segmentation and part-of-speech tagging settings,with part-of-speech filtering rules to optimize the experiment.The results show that the best performance is 87.40% in F-score over Tsinghua Chinese Treebank,and 74.70% in F-score over the segmentation and part-of-speech tagging scheme of Peking University.Experimental results show that CRF model is effective in recognizing Chinese verb-object collocation automatically.
The Faddeev Equation and Essential Spectrum of a Hamiltonian in Fock Space

A Hamiltonian (model operator) $H$ associated to a quantum system describing three particles in interaction, without conservation of the number of particles, is considered. The Faddeev type system of equations for eigenvectors of $H$ is constructed. The essential spectrum of $H$ is described by the spectrum of the channel operator.
On Why Artificial Neural Networks should be Developmental.

Biological brains are capable of general learning without super- vision. This is learning across mutiple domains without interference. Unlike artificial neural networks, in real brains, learned information is not purely en- coded in real-valued weights but instead it is resides in many neural aspects. Such aspects include, dendritic and axonal morphology, number and location of synapses, synaptic strengths and the internal state of neural components. Natural evolution has come up with extraordinary 'programs' for neurons that allow them to build learning systems through group activity. The neuron is the 'brain within the brain'. We argue that evolving neural developmental programs which when executed continuously build, shape and adjust neural networks is a promising direction for future research. We discuss aspects of neu- roscience that are important, and examine a model that incorporates many of these features that has been applied to a number of problems: wumpus world, checkers and maze solving.
A Virtual Network Laboratory Based on VMware and GNS3

The VMware and GNS3 can simulate virtual computers and virtual network equipments.After analyzing the principles of connection between the two,a method to realize co-simulations in a single computer has been proposed.By bridging to virtual network adapters in the host computer,the communication between VMware and GNS3 can be realized indirectly.Besides,a typical network topology has been constructed,in which the Zebra routing package has been tested,and dynamic routing has been implemented.The feasibility of the virtual network laboratory built by VMware and GNS3 has been verified.
Hierarchical Approach for Deriving a Reproducible LU factorization on GPUs

We propose a reproducible variant of the unblocked LU factorization for graphics processor units (GPUs). For this purpose, we provide Level-1/2 BLAS kernels that deliver correctly-rounded and reproducible results for the dot (inner) product, vector scaling, and the matrix-vector product. In addition, we draw a strategy to enhance the accuracy of the triangular solve via inexpensive iterative refinement. Following a bottom-up approach, we finally construct a reproducible implementation of the LU factorization for GPUs, which can easily accommodate partial pivoting for stability and be eventually integrated into a (blocked) high performance and stable algorithm for the LU factorization.