Evaluation of four point cloud similarity measures for the use in autonomous driving

Measuring the similarity between point clouds is required in many areas. In autonomous driving, point clouds for 3D perception are estimated from camera images but these estimations are error-prone. Furthermore, there is a lack of measures for quality quantification using ground truth. In this paper, we derive conditions point cloud comparisons need to fulfill and accordingly evaluate the Chamfer distance, a lower bound of the Gromov Wasserstein metric, and the ratio measure. We show that the ratio measure is not affected by erroneous points and therefore introduce the new measure “average ratio”. All measures are evaluated and compared using exemplary point clouds. We discuss characteristics, advantages and drawbacks with respect to interpretability, noise resistance, environmental representation, and computation.

Feature Extraction and Clustering for Static Video Summarization

Numerous limitations of shot based and content based key frame extraction approaches have encouraged the development of cluster based methods. This work provides OTMW, Optimal Threshold and Maximum Weight clustering method, as a novel cluster based key frame extraction method. The video feature dataset is constructed by computing the color, texture and information complexity features of frame images. An optimization function is developed to compute the optimal clustering threshold. It is constrained by fidelity and ratio measure parameters. We turn to an empirical study on the proposed method in multi-type video key frame extraction tasks and compare it with popular cluster based methods including Mean-shift, DBSCAN, GMM and K-means. OTWM method achieves an average fidelity and ratio of 96.12 and 97.13, respectively. Experimental results demonstrate that OTMW can bring higher fidelity and ratio performance, while still maintaining a competitive performance over other cluster based methods. Overall, the proposed method can accurately extract key frames from multi-type videos.

A New Set of Financial Instruments

In complete markets there are risky assets and a riskless asset. It is assumed that the riskless asset and the risky asset are traded continuously in time and that the market is frictionless. In this paper, we propose a new method for hedging derivatives assuming that a hedger should not always rely on trading existing assets that are used to form a linear portfolio comprised of the risky asset, the riskless asset, and standard derivatives, but rather should design a set of specific, most-suited financial instruments for the hedging problem. We introduce a sequence of new financial instruments best suited for hedging jump-diffusion and stochastic volatility market models. The new instruments we introduce are perpetual derivatives. More specifically, they are options with perpetual maturities. In a financial market where perpetual derivatives are introduced, there is a new set of partial and partial-integro differential equations for pricing derivatives. Our analysis demonstrates that the set of new financial instruments together with a risk measure called the tail-loss ratio measure defined by the new instrument’s return series can be potentially used as an early warning system for a market crash.

The pertinent single-attribute-based classifier for small datasets classification

Classifying a dataset using machine learning algorithms can be a big challenge when the target is a small dataset. The OneR classifier can be used for such cases due to its simplicity and efficiency. In this paper, we revealed the power of a single attribute by introducing the pertinent single-attribute-based-heterogeneity-ratio classifier (SAB-HR) that used a pertinent attribute to classify small datasets. The SAB-HR’s used feature selection method, which used the Heterogeneity-Ratio (H-Ratio) measure to identify the most homogeneous attribute among the other attributes in the set. Our empirical results on 12 benchmark datasets from a UCI machine learning repository showed that the SAB-HR classifier significantly outperformed the classical OneR classifier for small datasets. In addition, using the H-Ratio as a feature selection criterion for selecting the single attribute was more effectual than other traditional criteria, such as Information Gain (IG) and Gain Ratio (GR).

Heart Rate Variability during Face Cooling in Concussed Adolescents

ObjectiveWe measured heart rate variability (HRV) during physiological stimuli in acutely concussed adolescents (CX) and after clinical recovery, and compared with healthy controls (HC).BackgroundConcussion is associated with autonomic dysfunction. Face Cooling (FC) triggers the trigeminal nerve to evoke transient increases in cardiac parasympathetic (PNS) activity.Design/Methods11 CX (14.8 ± 0.9 years, 6 male, 7 days since injury) and 11 HC (16.1 ± 1.1 years, 9 male) participated. We calculated mean heart rate (HR), standard deviation of root mean square (RMSSD, measure of PNS tone) and low-frequency to high-frequency power ratio (LF/HF ratio, measure of sympathetic [SNS] tone) at rest and 3-minute FC test.ResultsCX at Visit 1 and 2 had significantly lesser increase in HR (p = 0.02) and RMSSD (p = 0.038) than HC on FC.ConclusionsThese data show that acutely concussed participants have an attenuated PNS response to physiological stimuli which continues after clinical recovery.

Crossover Assessment of Intraoral and Cuffed Ventilation by Emergency Responders

Objectives A cuffed bag valve mask (BVM) is the most common device used by emergency medical responders to ventilate patients. The BVM can be difficult for users to seal around the patient’s mouth and nose. An intraoral mask (IOM) with snorkel-like design may facilitate quicker and better ventilation particularly under austere conditions. Methods Both a BVM and IOM were utilized by 27 trained emergency medical technicians and paramedics to ventilate a lightly embalmed cadaver. Ventilation efficacy, workload, and usability were assessed for both devices across four study conditions. Results The IOM was superior to the BVM in delivered tidal volume ratio (measure of leak, p < 0.03) and minute ventilation (p < 0.0001). Workload, ergonomic and usability assessments indicated that the IOM facilitated gripping through the reduced hand interface size (p < 0.01), decreased user effort (p < 0.001), and reduced upper limb workload (p = 0.0088). Conclusions In the assessed model, the IOM represented a better choice for airway management than the standard cuffed BVM. An emergency medical device that is intuitive, efficacious and less demanding has the potential to reduce responder stress and improve resuscitation efforts, especially during austere rescue and patient transport.