Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Feature-based and Direct Methods for Semi-dense Real-time Visual SLAM from Stereo Cameras

For autonomous navigation of Micro Aerial Vehicles (MAVs), a reliable and efficient state estimation is of utmost importance. Due to their low weight and size, cameras are often used as sensors on MAVs and can cover a wide field of applications, such as simultaneous localization and mapping (SLAM), obstacle detection or 3D reconstruction. Specific applications for cameras on MAVs are for example position control, obstacle avoidance, and autonomous navigation and mapping, showing the flexible possibilities a camera as a sensor offers. Multiple different tasks can be performed using a single sensor. For state estimation with visual SLAM, our MAV is equipped with six fish eye cameras, covering an omnidirectional field of view. In this thesis, a novel approach for estimating the egomotion of MAVs using visual SLAM from stereo cameras is presented, which combines feature-based matching with semi-dense direct image alignment. Recent visual SLAM methods can be classified into either feature-based methods or direct methods. While feature-based methods rely on sparse image features only, direct methods use the entire image for tracking. As feature-based methods keep a sparse representation of the image, they are inherently faster than direct methods. Direct methods, however, estimate the motion based on all information of the image and are thus more accurate. By combining both paradigms we are able to combine their strengths. We are not only able to efficiently estimate the pose of our MAV with a high frame rate, but also reconstruct a semi-dense map of the environment online, using direct tracking. The major challenges are the strong radial distortions of the employed fish eye lenses and the real-time requirements for reliable navigation on MAVs. We evaluate our method on the popular KITTI dataset, on the EuRoC dataset and on our MAV as well. In experiments we show, that our approach accurately estimates the camera motion and reconstructs a globally consistent semi-dense map of the environment. Quantitative results show, that our approach outperforms other state-of-the-art methods on the KITTI and on the EuRoC dataset. Due to the strong radial distortion of the fish eye lenses and a non-rigid stereo setup, our approach shows less accurate results on our MAV dataset, but still achieves an average absolute trajectory error (ATE) of 0.8m.
ACTION OF LOBELINE ON INTRATHORACIC RECEPTORS: A COMPARISON WITH PHENYLDIGUANIDE, SEROTONIN AND VERATRIDINE

Small amounts of lobeline sulfate (15 µg/kg) given intravenously to a cat anesthetized with α-chloralose cause hypotension, bradycardia and apnea due to a pharmacological stimulation of sensory receptors within the thorax. Cardiovascular and respiratory effects are obtained when the drug is injected into the pulmonary vascular bed, but only cardiovascular effects are elicited from the coronary vascular bed. In over half the experimental animals, on injection of the median effective dose of lobeline, the degree of hypotension was greater than the hypertension that subsequently occurred due to stimulation of the carotid and aortic chemoreceptors. The magnitude of the hypotensive response is related to the level of the arterial pressure and is greatly diminished under barbiturate anesthesia. The characteristics of this early response triad elicited by lobeline are contrasted with those of the responses obtained with phenyldiguanide, serotonin and veratridine which are superficially similar. On the injection of just maximal doses of these drugs into the right atria of cats, lobeline is seen to act consistently 1 second earlier than phenyldiguanide and serotonin. When injected into the left ventricle, the latent period between injection and response is the same. The response to diguanide and serotonin but not that to lobeline and veratridine is blocked by 2-naphthyl-guanidine. The response to lobeline alone is blocked by tetraethylammonium and pentobarbital. Whereas phenyldiguanide has little activity in the dog, lobeline is equi-potent in the dog and cat. It is proposed that lobeline in small doses stimulates sensory receptors in the pulmonary circulation that are pharmacologically and possibly anatomically distinct from those for the amidines and veratridine. It also stimulates receptors that are pharmacologically distinct from those for the amidines and veratridine in the coronary circulation.
Formation of the Sensitive Impurity 1,3,5-Triamino-2-chloro-4,6-dinitrobenzene in Pilot Plant TATB Production

A thermally stable, insensitive, high-explosive 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) is manufactured by amination of 1,3,5-trichloro-2,4,6-trinitrobenzene in toluene with NH3 gas. An orange–red, sensitive impurity, 1,3,5-triamino-2-chloro-4,6-dinitrobenzene (TACDNB), was found forming with product TATB in the pilot-plant process. This impurity was isolated and characterized by single-crystal XRD, NMR, FTIR, DSC, and sensitivity test. The chemical reactivity of this impurity with aqueous sodium hydroxide solution was also studied. The process anomalies which led to the formation of this impurity with product TATB were investigated. This TACDNB impurity was formed due to amination of 1,3,5,6-tetrachloro-2,4-dinitrobenzene (T4) when excess NH3 gas was fed into the reactor.
Waveactivity at ionospheric heights above theAndes Mountains detected from FORMOSAT-3/COSMIC GPS radio occultation data

An estimation of the ionospheric wave activity, derived from 4 years of FORMOSAT-3/ COSMIC GPS (Taiwan’s Formosa Satellite Mission 3/Constellation Observing System for Meteorology—Global Positioning System) radio occultation electron density data, is presented. A systematic enhancement at the eastern side of the Andes range with respect to the western side is observed. A fitting method to remove the wavelike component from each measured profile and estimate the wave activity is described. The differential effect introduced by the action of orography on the generation, to the eastern side of the Andes, of mountain waves, deep convection waves, or even secondary waves aloft after momentum deposition in the middle atmosphere, is suggested.
ELF: High-Performance In-band Network Measurement

—Over the past decades, active measurements have been used to gain a deep and broad understanding of routing, latency, packet loss, etc. Unfortunately, typical active measurements are ill-suited for elucidating the performance of individual application ﬂows due to route changes, load balancing, transient queues, and other dynamic effects. Recent efforts have identiﬁed in-band measurement, in which probes are injected into an existing application ﬂow, as a promising approach for gaining insight into network behaviors that affect application ﬂows. However, the use of libpcap by these efforts poses signiﬁcant performance bottlenecks and is at odds with high-ﬁdelity measurements. In this paper, we explore a new implementation pathway for in-band application ﬂow monitoring: the extended Berkeley Packet Filter (eBPF), which enables safe programs to be run within the OS kernel. We develop an eBPF-based in-band ﬂow monitoring tool called ELF that sends hop-limited probes within an existing ﬂow. We compare the performance of our eBPF-based approach with the use of libpcap, ﬁnding that libpcap introduces undesirable high variability into the probe emission process. We illustrate the potential of ELF by monitoring hourly Network Diagnostic Tool (NDT) throughput measurements to 12 Measurement Lab destinations for one week. We observe that at least 90% of routers traversed by the in-band probes respond positively, with no apparent rate limiting. We examine how the hop-by-hop evolution of network queues is exposed using ELF in-band probes, illustrate the impact of mid-ﬂow route changes, and show that load balancing may inequitably affect throughput.
Uncertainty Estimation in Hydrogeological Forecasting with Neural Networks: Impact of Spatial Distribution of Rainfalls and Random Initialization of the Model

Neural networks are used to forecast hydrogeological risks, such as droughts and floods. However, uncertainties generated by these models are difficult to assess, possibly leading to a low use of these solutions by water managers. These uncertainties are the result of three sources: input data, model architecture and parameters and their initialization. The aim of the study is, first, to calibrate a model to predict Champagne chalk groundwater level at Vailly (Grand-Est, France), and, second, to estimate related uncertainties, linked both to the spatial distribution of rainfalls and to the parameter initialization. The parameter uncertainties are assessed following a previous methodology, using nine mixed probability density functions (pdf), thus creating models of correctness. Spatial distribution of rainfall uncertainty is generated by swapping three rainfall inputs and then observing dispersion of 27 model outputs. This uncertainty is incorporated into models of correctness. We show that, in this case study, an ensemble model of 40 different initializations is sufficient to estimate parameter uncertainty while preserving quality. Logistic, Gumbel and Raised Cosine laws fit the distribution of increasing and decreasing groundwater levels well, which then allows the establishment of models of correctness. These models of correctness provide a confidence interval associated with the forecasts, with an arbitrary degree of confidence chosen by the user. These methodologies have proved to have significant advantages: the rigorous design of the neural network model has allowed the realisation of models able to generalize outside of the range of the data used for training. Furthermore, it is possible to flexibly choose the confidence index according to the hydrological configuration (e.g., recession or rising water table).
Is high-fidelity simulation based education effective in enhancing learning and performance among undergraduate medical students?

Background: Simulation is an all-inclusive term about incorporating an educational approach that links a learner to a situated environment for the purpose of learning. The efficacy of simulation in undergraduate medical education is now well established as it reduces the number of safety concerns in managing the patients. It helps learners to try out and acquire new knowledge and confidence for effective management of patients. The study was done to analyse the effectiveness of high-fidelity simulation (HFS) based education in enhancement of knowledge and performance for management of acute trauma in terms of self-assessment scores. Methods: It was a pre-test and post-test mediated quasi-experimental time series study which involved 347 undergraduate medical students. During simulation sessions, students were divided into groups and their acquisition of knowledge and performance were evaluated individually with self-assessed pre-test and post-test scores. Mean, Standard Deviation and Standard Error of Mean were used for quantitative analysis and Repeated Measure of Analysis of Variance (ANOVA) was used for comparison. Friedman test was used for assessment of individual components in simulation. The scores were compared to note the difference in knowledge and performance. P value < 0.001 was considered to be statistically significant. Results: The study showed that learning and performance had progressively improved with each session of simulation in the management of acute trauma. European Journal of Molecular & Clinical Medicine ISSN 2515-8260 Volume 07, Issue 10, 202
Use of Genetic Markers to Assess Pedigrees of Grape Cultivars and Breeding Program Selections

In a plant breeding program, an accurate understanding of pedigrees provides useful guidance for future hybridizations. However, plant breeder records occasionally contain errors which may mislead future breeding efforts, and there is considerable value in independently testing reported pedigrees. In this project, SSR markers were used to confirm or correct pedigrees for grape varieties from the Cornell breeding program and a few key varieties used as parents. As expected, most (20 of 24) reported pedigrees were confirmed. Recognizing a heritable null allele from Ontario, at VVMD25, was necessary for parent progeny analysis of several varieties. Suffolk Red and Glenora were shown to be progeny of Black Kishmish and not Black Monukka. The advanced selection NY63.0970. 07 is a hybrid of Pinot noir, not Gamay, x Chancellor. Vignoles (Ravat 51) is not descended from the reported parentage of Seibel 6905 x Pinot noir.
Determinants of Startup Funding: The Interaction between Web Attention and Culture

Technology empowers entrepreneurs to pursue alternative funding through platforms like crowdfunding. This research explores significant startup funding factors using Crunchbase. Controlling for common factors (acquisition/fundingrounds/IPO), the research uniquely focuses on web attention the visibility on social media and its impact on funding. It also examines the moderating influence of startup’s home country culture (individualism/collectivism). Findings show stronger positive impact of web attention on startup funding for collectivist countries. While individualistic investors value personal goals, collectivists value collaborative goals inclinations that align with crowdfunding behavior. Therefore while increasing web attention, crowdfunding efforts can be targeted towards collectivist countries.
Single cell high-throughput qRT-PCR protocol

Single cell high-throughput qRT-PCR protocol combines high sensitivity technique of single cell qPCR with high-throughput qPCR technology that can generate data from 96 samples and 96 genes in a single experiment. It can be adapted for various sample types- cell culture, tissue samples and extracted RNA (10 pg) and measured on traditional qPCR and high-throughput qPCR platforms. The workflow is comprised of four steps – cell lysis, reverse transcription, pre-amplification and qPCR. Key features of this protocol are; processing low input samples directly to reverse transcription without RNA extraction which minimizes sample loss, pre-amplification enables amplification of cDNA from single cells to detectable levels for qPCR and measuring up to 400 genes from a single cell sample/10 pg of RNA (starting material). Robust, reproducible and versatile this protocol can be adapted to several upstream and downstream techniques.
Modeled source apportionment of black carbon particles coated with a light-scattering shell

Abstract. The Aethalometer model has been used widely for estimating the contributions of fossil fuel emissions and biomass burning to equivalent black carbon (eBC). The calculation is based on measured absorption Ångström exponents (αabs). The interpretation of αabs is ambiguous since it is well known that it not only depends on the dominant absorber but also on the size and internal structure of the particles, core size, and shell thickness. In this work the uncertainties of the Aethalometer-model-derived apparent fractions of absorption by eBC from fossil fuel and biomass burning are evaluated with a core–shell Mie model. Biomass-burning fractions (BB(%)) were calculated for pure and coated single BC particles for lognormal unimodal and bimodal size distributions of BC cores coated with ammonium sulfate, a scattering-only material. BB(%) was very seldom 0 % even though BC was the only absorbing material in the simulations. The shape of size distribution plays an important role. Narrow size distributions result in higher αabs and BB(%) values than wide size distributions. The sensitivity of αabs and BB(%) to variations in shell volume fractions is the highest for accumulation-mode particles. This is important because that is where the largest aerosol mass is. For the interpretation of absorption Ångström exponents it would be very good to measure BC size distributions and shell thicknesses together with the wavelength dependency of absorption.
Eddies in motion: visualizing boundary-layer turbulence above an open boreal peatland using UAS thermal videos

Abstract. High-resolution thermal infrared (TIR) imaging is opening up new vistas in biosphere–atmosphere heat exchange studies. The rapidly developing unmanned aerial systems (UASs) and specially designed cameras offer opportunities for TIR survey with increasingly high resolution, reduced geometric and radiometric noise, and prolonged flight times. A state-of-the-art science platform is assembled using a Matrice 210 V2 drone equipped with a Zenmuse XT2 thermal camera and deployed over a pristine boreal peatland with the aim of testing its performance in a heterogeneous sedge-fen ecosystem. The study utilizes the capability of the UAS platform to hover for prolonged times (about 20 min) at a height of 500 m a.g. l. while recording high frame rate (30 Hz) TIR videos of an area of ca. 430 × 340 m. A methodology is developed to derive thermal signatures of near-ground coherent turbulent structures impinging on the land surface, surface temperature spectra, and heat fluxes from the retrieved videos. The size, orientation, and movement of the coherent structures are computed from the surface temperature maps, and their dependency on atmospheric conditions is examined. A range of spectral and wavelet-based approaches are used to infer the properties of the dominant turbulent scene structures. A ground-based eddy-covariance system and an in situ meteorological setup are used for reference.
Towards A Reliable Ground-Truth For Biased Language Detection

Reference texts such as encyclopedias and news articles can manifest biased language when objective reporting is substituted by subjective writing. Existing methods to detect bias mostly rely on annotated data to train machine learning models. However, low annotator agreement and comparability is a substantial drawback in available media bias corpora. To evaluate data collection options, we collect and compare labels obtained from two popular crowdsourcing platforms. Our results demonstrate the existing crowdsourcing approaches' lack of data quality, underlining the need for a trained expert framework to gather a more reliable dataset. By creating such a framework and gathering a first dataset, we are able to improve Krippendorff's a = 0.144 (crowdsourcing labels) to a = 0.419 (expert labels). We conclude that detailed annotator training increases data quality, improving the performance of existing bias detection systems. We will continue to extend our dataset in the future.
Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy

Abstract. Data-driven flow-forecasting models, such as artificial neural networks (ANNs), are increasingly featured in research for their potential use in operational riverine flood warning systems. However, the distributions of observed flow data are imbalanced, resulting in poor prediction accuracy on high flows in terms of both amplitude and timing error. Resampling and ensemble techniques have been shown to improve model performance on imbalanced datasets. However, the efficacy of these methods (individually or combined) has not been explicitly evaluated for improving high-flow forecasts. In this research, we systematically evaluate and compare three resampling methods, random undersampling (RUS), random oversampling (ROS), and the synthetic minority oversampling technique for regression (SMOTER), and four ensemble techniques, randomised weights and biases, bagging, adaptive boosting (AdaBoost), and least-squares boosting (LSBoost), on their ability to improve high stage prediction accuracy using ANNs. These methods are implemented both independently and in combined hybrid techniques, where the resampling methods are embedded within the ensemble methods. This systematic approach for embedding resampling methods is a novel contribution. This research presents the first analysis of the effects of combining these methods on high stage prediction accuracy. Data from two Canadian watersheds (the Bow River in Alberta and the Don River in Ontario), representing distinct hydrological systems, are used as the basis for the comparison of the methods. The models are evaluated on overall performance and on typical and high stage subsets. The results of this research indicate that resampling produces marginal improvements to high stage prediction accuracy, whereas ensemble methods produce more substantial improvements, with or without resampling. Many of the techniques used produced an asymmetric trade-off between typical and high stage performance; reduction of high stage error resulted in disproportionately larger error on a typical stage. The methods proposed in this study highlight the diversity-in-learning concept and help support future studies on adapting ensemble algorithms for resampling. This research contains many of the first instances of such methods for flow forecasting and, moreover, their efficacy in addressing the imbalance problem and heteroscedasticity, which are commonly observed in high-flow and flood-forecasting models.
Comparing Methods for Handling Missing Cost and Outcome Data in Clinical Trial-based Cost-effectiveness Analysis

OBJECTIVES: This study compares methods for handling missing data to conduct cost-effectiveness analysis in the context of a clinical study.METHODS: A long-term clinical trial with staggered recruitment was used as a case study (EVRA, NIHR-HTA project 11/129/197). Patients had between 1 year and 5.5 years (median 3 years) of follow-up under “early” or “deferred” treatment. The methods compared were Complete-Case-Analysis (CCA), multiple imputation using linear regression (MILR) and using predictive mean matching (MIPMM), Bayesian parametric approach using the R package missingHE (BPA) and repeated measures mixed model (RMM). The outcomes were total mean costs and total mean quality-adjusted life years (QALYs) at different time horizons (1 year, 3 years and 5 years). RESULTS: All methods found no statistically significant difference in cost at the 5% level in all years, and all methods found statistically significantly greater mean QALY at year 1. At year 3, BPA showed a statistically significant difference while other method did not at later time horizons. Standard errors differed substantially between the methods employed. CONCLUSION: MIPMM seemed to perform better than MILM, confirming findings from simulation studies. RMM and BPA might be feasible options, though they did not perform better than MIPMM in this dataset. Further simulation studies and applications should continue to compare these methods.
Where and Why Do Submarine Canyons Remain Connected to the Shore During Sea‐Level Rise? Insights From Global Topographic Analysis and Bayesian Regression

The efficiency of sediment routing from land to the ocean depends on the position of submarine canyon heads with regard to terrestrial sediment sources. We aim to identify the main controls on whether a submarine canyon head remains connected to terrestrial sediment input during Holocene sea‐level rise. Globally, we identified 798 canyon heads that are currently located at the 120m‐depth contour (the Last Glacial Maximum shoreline) and 183 canyon heads that are connected to the shore (within a distance of 6 km) during the present‐day highstand. Regional hotspots of shore‐connected canyons are the Mediterranean active margin and the Pacific coast of Central and South America. We used 34 terrestrial and marine predictor variables to predict shore‐connected canyon occurrence using Bayesian regression. Our analysis shows that steep and narrow shelves facilitate canyon‐head connectivity to the shore. Moreover, shore‐connected canyons occur preferentially along active margins characterized by resistant bedrock and high river‐water discharge.