Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Motion Tracking for Mobile Augmented/Virtual Reality Using Adaptive Visual-Inertial Fusion

In mobile augmented/virtual reality (AR/VR), real-time 6-Degree of Freedom (DoF) motion tracking is essential for the registration between virtual scenes and the real world. However, due to the limited computational capacity of mobile terminals today, the latency between consecutive arriving poses would damage the user experience in mobile AR/VR. Thus, a visual-inertial based real-time motion tracking for mobile AR/VR is proposed in this paper. By means of high frequency and passive outputs from the inertial sensor, the real-time performance of arriving poses for mobile AR/VR is achieved. In addition, to alleviate the jitter phenomenon during the visual-inertial fusion, an adaptive filter framework is established to cope with different motion situations automatically, enabling the real-time 6-DoF motion tracking by balancing the jitter and latency. Besides, the robustness of the traditional visual-only based motion tracking is enhanced, giving rise to a better mobile AR/VR performance when motion blur is encountered. Finally, experiments are carried out to demonstrate the proposed method, and the results show that this work is capable of providing a smooth and robust 6-DoF motion tracking for mobile AR/VR in real-time.
TSD: A Shape Descriptor Based on a Distribution of Tensor Scale Local Orientation

We present tensor scale descriptor (TSD) — a shape descriptor for content-based image retrieval, registration, and analysis. TSD exploits the notion of local structure thickness, orientation, and anisotropy as represented by the largest ellipse centered at each image pixel and within the same homogeneous region. The proposed method uses the normalized histogram of the local orientation (the angle of the ellipse) at regions of high anisotropy and thickness within a certain interval. It is shown that TSD is invariant to rotation and to some reasonable level of scale changes. Experimental results with a fish database are presented to illustrate and validate the method.
Long-term Maintenance of Response Across Multiple Fibromyalgia Symptom Domains in a Randomized Withdrawal Study of Pregabalin

Objective: To determine the incidence and duration of response of clinically meaningful improvements with pregabalin across several key symptoms of fibromyalgia (FM). Methods: This was a post hoc analysis of data from a multicenter, double-blind, placebo-controlled, randomized, withdrawal study, originally designed to evaluate the efficacy of pregabalin monotherapy for durability of effect on FM pain based on pain and Patient Global Impression of Change (PGIC) criteria. Responder criteria for Fibromyalgia Impact Questionnaire total score (≥16-point change), Medical Outcomes Study Sleep Scale Sleep Disturbance subscale (≥15.8-point change), and the 36-item Short-Form Health Survey Vitality scale (≥10-point change) were used to evaluate the incidence and duration of improvements in function, sleep, and fatigue for pregabalin versus placebo among pain and PGIC responders. A composite responder index consisting of pain, PGIC, function, and sleep endpoints was used to explore multidimensional response. Results: Approximately 80% of patients meeting pain and PGIC improvement criteria at randomization had clinically meaningful improvement in fatigue, sleep, or function. Higher proportions of patients in the pregabalin group maintained a clinically meaningful response, and pregabalin-treated patients had a significantly longer time to loss of therapeutic response compared with the placebo group. Composite responder Kaplan-Meier analysis, performed with patients demonstrating clinically meaningful improvements in pain, PGIC, function, and sleep at randomization showed a significantly longer median time to loss of therapeutic response for pregabalin-treated patients. Discussion: The results from this post hoc analysis indicate that pregabalin provides long-term effects across multiple domains of FM (ClinicalTrials.gov registry ID: NCT00151489).
The Profile and Dynamics of RNA Modifications in Animals

More than a hundred distinct modified nucleosides have been identified in RNA, but little is known about their distribution across different organisms, their dynamic nature and their response to cellular and environmental stress. Mass‐spectrometry‐based methods have been at the forefront of identifying and quantifying modified nucleosides. However, they often require synthetic reference standards, which do not exist in the case of many modified nucleosides, and this therefore impedes their analysis. Here we use a metabolic labelling approach to achieve rapid generation of bio‐isotopologues of the complete Caenorhabditis elegans transcriptome and its modifications and use them as reference standards to characterise the RNA modification profile in this multicellular organism through an untargeted liquid‐chromatography tandem high‐resolution mass spectrometry (LC‐HRMS) approach. We furthermore show that several of these RNA modifications have a dynamic response to environmental stress and that, in particular, changes in the tRNA wobble base modification 5‐methoxycarbonylmethyl‐2‐thiouridine (mcm5s2U) lead to codon‐biased gene‐expression changes in starved animals.
A Single-Ended Modified Class-E PA With HD2 Rejection for Low-Power RF Applications

This letter presents a fully integrated single-ended power amplifier for ultralow-power applications. The single-ended configuration is possible due to a choke-inductor less output network capable of rejecting the second-harmonic emission. A switching duty-ratio lower than 50% avoids the impedance transformation output network traditionally required for proper class-E operation. At 2.45 GHz, the PA (including the driver) delivers 1.4 dBm to a <inline-formula> <tex-math notation="LaTeX">$50~ {\Omega }$ </tex-math></inline-formula> load and achieves an overall efficiency of 51% while complying with the spurious emission regulation. The prototype was fabricated in <inline-formula> <tex-math notation="LaTeX">$0.13~ {\mu }\text{m}$ </tex-math></inline-formula> CMOS.
A Neural Architecture for Automated ICD Coding

The International Classification of Diseases (ICD) provides a hierarchy of diagnostic codes for classifying diseases. Medical coding – which assigns a subset of ICD codes to a patient visit – is a mandatory process that is crucial for patient care and billing. Manual coding is time-consuming, expensive, and error prone. In this paper, we build a neural architecture for automated coding. It takes the diagnosis descriptions (DDs) of a patient as inputs and selects the most relevant ICD codes. This architecture contains four major ingredients: (1) tree-of-sequences LSTM encoding of code descriptions (CDs), (2) adversarial learning for reconciling the different writing styles of DDs and CDs, (3) isotonic constraints for incorporating the importance order among the assigned codes, and (4) attentional matching for performing many-to-one and one-to-many mappings from DDs to CDs. We demonstrate the effectiveness of the proposed methods on a clinical datasets with 59K patient visits.
An Architecture Providing Depolarization Ratio Capability for a Multi-Wavelength Raman Lidar: Implementation and First Measurements

A new architecture for the measurement of depolarization produced by atmospheric aerosols with a Raman lidar is presented. The system uses two different telescopes: one for depolarization measurements and another for total-power measurements. The system architecture and principle of operation are described. The first experimental results are also presented, corresponding to a collection of atmospheric conditions over the city of Barcelona.
Cultural contingencies of alcoholism: variations between and within nineteenth-century urban ethnic groups in alcohol-related death rates.

From the 1890 U.S. Census vital statistics compilations are drawn comparisons of adult rates of death from alcohol-related causes (alcoholism, liver diseases) in urban white ethnic groups (defined by birthplace of mother), with specification of sex, sex and marital status, neighborhood status, and sex and neighborhood status. In each of three cities, the Irish have the highest rate, followed by the English, Germans, and those with U.S.-born mothers, while the Italians and especially Russian Jews have extraordinarily low rates. Furthermore, within each of the three largest maternal-origin groups (German, Irish, U.S.), death rates from alcoholism by sex and marital status conform to Durkheim's findings for suicide, with wives contributing a higher proportion of the deaths of married persons than spinsters contribute of the deaths of unmarried persons. The extraordinarily low Irish sex ratio, and the uniform effects of marital status in three ethnic groups, cast doubt on explanations of an Irish propensity to alcoholism which focus on the peculiarities of Irish bachelorhood.
Small animal PET imaging with germanium strip detectors

We have demonstrated a PET imaging system based on double-sided germanium strip detectors. The demonstration achieved an intrinsic position resolution of 1.35 mm FWHM. This performance was achieved with 2 mm orthogonal strips and a depth sensing capability of 0.5 mm based on timing the pulses at either side of the detector. The system could trigger at energies as low as 15 keV without any image degradation. By selecting events within the 2 keV-wide photopeak, events that scattered within the sampling volume were totally eliminated. If a detector is built with 1 mm strips, the position resolution for PET imaging will not be dominated by the position resolution of the instrument, but by the range of the positron in the sample.
Characterizing Dynamic Walking Patterns and Detecting Falls with Wearable Sensors Using Gaussian Process Methods

By incorporating a growing number of sensors and adopting machine learning technologies, wearable devices have recently become a prominent health care application domain. Among the related research topics in this field, one of the most important issues is detecting falls while walking. Since such falls may lead to serious injuries, automatically and promptly detecting them during daily use of smartphones and/or smart watches is a particular need. In this paper, we investigate the use of Gaussian process (GP) methods for characterizing dynamic walking patterns and detecting falls while walking with built-in wearable sensors in smartphones and/or smartwatches. For the task of characterizing dynamic walking patterns in a low-dimensional latent feature space, we propose a novel approach called auto-encoded Gaussian process dynamical model, in which we combine a GP-based state space modeling method with a nonlinear dimensionality reduction method in a unique manner. The Gaussian process methods are fit for this task because one of the most import strengths of the Gaussian process methods is its capability of handling uncertainty in the model parameters. Also for detecting falls while walking, we propose to recycle the latent samples generated in training the auto-encoded Gaussian process dynamical model for GP-based novelty detection, which can lead to an efficient and seamless solution to the detection task. Experimental results show that the combined use of these GP-based methods can yield promising results for characterizing dynamic walking patterns and detecting falls while walking with the wearable sensors.
Shear-induced anisotropic decay of correlations in hard-sphere colloidal glasses

Spatial correlations of microscopic fluctuations are investigated via real-space experiments and computer simulations of colloidal glasses under steady shear. It is shown that while the distribution of one-particle fluctuations is always isotropic regardless of the relative importance of shear as compared to thermal fluctuations, their spatial correlations show a marked sensitivity to the competition between shear-induced and thermally activated relaxation. Correlations are isotropic in the thermally dominated regime, but develop strong anisotropy as shear dominates the dynamics of microscopic fluctuations. We discuss the relevance of this observation for a better understanding of flow heterogeneity in sheared amorphous solids.
Circulating protein Z concentration, PROZ variants, and unexplained cerebral infarction in young and middle-aged adults

Summary Protein Z (PZ) is a vitamin K–dependent plasma protein that exhibits both pro- and anticoagulant properties. Both low and high PZ levels have been linked to ischaemic stroke. Although PZ-lowering gene variants have been found to be less common in ischaemic stroke, the relationship remains unclear. We investigated PZ levels and PROZ variants in a multi-ethnic case-control study of unexplained stroke in participants aged 18 to 64. Plasma PZ was measured in cases (≥2 months post-stroke) and controls. PZ polymorphisms G79A (rs3024735) and A13G (2273971) were genotyped. A combined genetic score (0–4 minor alleles) was created assuming additive effects. A total of 715 individuals (1:1.4 cases:controls) was included. Analyses revealed evidence of a non-linear association. After adjusting for demographic and clinical covariates, PZ levels >2.5 µg/ml (90th %ile) were significantly associated with cryptogenic stroke (OR 2.41 [95 % CI 1.34, 4.34]) as compared with lower levels. Higher genetic score was related to progressively lower levels of PZ, and the presence of four minor alleles was associated with lower odds of stroke (adjusted OR 0.26 [95 % CI 0.07, 0.96]) versus 0 minor alleles. In this multi-ethnic study of young and middle-aged adults, there was evidence of a nonlinear positive association between PZ level and unexplained stroke, with a directionally consistent association for genetic variants related to PZ levels and cryptogenic stroke. These findings support elevated PZ levels as a risk factor for cryptogenic stroke.
Importance sampling the union of rare events with an application to power systems analysis

We consider importance sampling to estimate the probability $\mu$ of a union of $J$ rare events $H_j$ defined by a random variable $\boldsymbol{x}$. The sampler we study has been used in spatial statistics, genomics and combinatorics going back at least to Karp and Luby (1983). It works by sampling one event at random, then sampling $\boldsymbol{x}$ conditionally on that event happening and it constructs an unbiased estimate of $\mu$ by multiplying an inverse moment of the number of occuring events by the union bound. We prove some variance bounds for this sampler. For a sample size of $n$, it has a variance no larger than $\mu(\bar\mu-\mu)/n$ where $\bar\mu$ is the union bound. It also has a coefficient of variation no larger than $\sqrt{(J+J^{-1}-2)/(4n)}$ regardless of the overlap pattern among the $J$ events. Our motivating problem comes from power system reliability, where the phase differences between connected nodes have a joint Gaussian distribution and the $J$ rare events arise from unacceptably large phase differences. In the grid reliability problems even some events defined by $5772$ constraints in $326$ dimensions, with probability below $10^{-22}$, are estimated with a coefficient of variation of about $0.0024$ with only $n=10{,}000$ sample values.
Brivaracetam in the Treatment of Patients with Epilepsy—First Clinical Experiences

Objectives To assess first clinical experiences with brivaracetam (BRV) in the treatment of epilepsies. Methods Data on patients treated with BRV from February to December 2016 and with at least one clinical follow-up were collected from electronic patient records. Data on safety and efficacy were evaluated retrospectively. Results In total, 93 patients were analyzed; 12 (12.9%) received BRV in monotherapy. The mean duration to follow-up was 4.85 months (MD = 4 months; SD = 3.63). Fifty-seven patients had more than one seizure per month at baseline and had a follow-up of more than 4 weeks; the rate of ≥50% responders was 35.1% (n = 20) in this group, of which five (8.8%) patients were newly seizure-free. In 50.5% (47/93), patients were switched from levetiracetam (LEV) to BRV, of which 43 (46.2%) were switched immediately. Adverse events (AE) occurred in 39.8%, with 22.6% experiencing behavioral and 25.8% experiencing non-behavioral AE. LEV-related AE (LEV-AE) were significantly reduced by switching to BRV. The discontinuation of BRV was reported in 26/93 patients (28%); 10 of those were switched back to LEV with an observed reduction of AE in 70%. For clinical reasons, 12 patients received BRV in monotherapy, 75% were seizure–free, and previous LEV-AE improved in 6/9 patients. BRV-related AE occurred in 5/12 cases, and five patients discontinued BRV. Conclusion BRV seems to be a safe, easy, and effective option in the treatment of patients with epilepsy, especially in the treatment of patients who have psychiatric comorbidities and might not be good candidates for LEV treatment. BRV broadens the therapeutic spectrum and facilitates personalized treatment.
Comparative Evaluation of the Loop-Mediated Isothermal Amplification Assay for Detecting Pulmonary Tuberculosis

Background Early detection of tuberculosis (TB) is challenging in resource-poor settings because of limited accessibility to molecular diagnostics. The aim of this study was to evaluate the performance of the loop-mediated isothermal amplification kit (TB-LAMP) for TB diagnosis compared with conventional and molecular tests. Methods A total of 290 consecutive sputum samples were collected from May till September, 2015. All samples were processed using the N-Acetyl-L-cysteine (NALC) NaOH method and tested by smear microscopy, solid and liquid culture, real-time PCR, and TB-LAMP. Results The sensitivity of TB-LAMP for smear-positive and smear-negative samples with culture positivity was 92.0% and 58.8%, respectively. TB-LAMP was positive in 14.9% of TB culture-negative samples; however, all those samples were also positive by real-time PCR. In addition, none of the samples positive for nontuberculous mycobacteria by culture were positive by TB-LAMP. The overall agreement between TB-LAMP and real-time PCR was good; however, the concordance rate was significantly lower for real-time PCR positive samples with Ct values of 30–35. Conclusions TB-LAMP could replace smear microscopy and increase TB diagnostic capacity when Xpert MTB/RIF is not feasible because of poor infrastructure.
Development of a modeling platform for 4.5 kV IGBT power modules

This paper deals with the development of a PSpice based evaluation platform for high power IGBT devices. The purpose of this platform is to provide useful information for the design and the assessment of converter cells for potential high power applications. An extended version of Hefner model is presented for high power, high voltage IGBTs taking into account temperature variation. A parametric analysis is presented in order to depict the effect of each parameter in the model. A set of parameters have been extracted and then verified with static and dynamic comparison of experimental data from 4.5 kV and 2.0 kA Si based IGBT power module (StakPak). Finally, an overall energy loss estimation is presented as function of temperature, load current and collector-emitter voltage.
Advertising in Directed Search ∗

We consider a directed search environment where capacity constrained sellers reach uncoordinated buyers through costly advertising while buyers observed all prices probabilistically. We show that: (i) the equilibrium advertising intensity has an inverted U-shape in market tightness, (ii) the equilibrium advertising intensity is higher under an auction mechanism than under posted pricing, and (iii) the equilibrium price and measure of informed buyers may be positively correlated even in large markets. JEL Classification: E52, E63.
The Modern Art of Reading Computed Tomography Images of the Lungs: Quantitative CT

Lung diseases are increasing in prevalence and overall burden worldwide. To stem the tide, more and more national and international guidelines are recommending the use of various diagnostic algorithms that are disease specific. There is growing consensus among the respiratory community that although patient histories and lung function testing are the minimum required for clinical examinations, these tests alone are not sufficient for disease characterization. Therefore, the use of computed tomography (CT) imaging is increasing used in clinical decision making for lung diseases. Lung diseases affect various components of lung, including the small airways, lung parenchyma, the interstitial space and the pulmonary vasculature. Quantitative CT (QCT) methods are emerging and are increasingly available using commercial software to quantify the underlying disease components, and a growing body of evidence suggests that QCT is an important tool in the clinical setting to help accurately and reproducibly detect where the disease is located in the lung, and to quantify the extent and overall severity for several lung diseases. Furthermore, this growing body of evidence has promoted the use of thoracic QCT to the point that it is now considered by many as an indispensable technology for longitudinal analysis and intervention trials. Many QCT imaging measurements are available to the respiratory physician, and the aim of this review is to introduce and describe pulmonary QCT imaging measurements and methodologies.
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions

Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world. For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved. In order to match real-world conditions this causal knowledge must be learned without access to supervised data. To address this problem we present a novel method that learns to discover objects and model their physical interactions from raw visual images in a purely \emph{unsupervised} fashion. It incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and learn efficiently. On videos of bouncing balls we show the superior modelling capabilities of our method compared to other unsupervised neural approaches that do not incorporate such prior knowledge. We demonstrate its ability to handle occlusion and show that it can extrapolate learned knowledge to scenes with different numbers of objects.