An Advanced Weighted Levy Distribution: Statistical Properties and Application

In order to model price variations in market, finance engineers may employ the concept of Levy distribution. The slow fall off of  the Levy  distribution model is a good match after price changes. In this paper, a new weighted model is introduced which would be obtained by assigning weights to Levy distribution. This work provides an insight to some basic distributional properties of this distributions such as Moments, moment generating function, Skewness, kurtosis, Shannon’s entropy etc. Maximum likelihood estimation and method of moments are employed to estimate the model parameters. For the purpose of illustration the proposed model would be applied to the real data set. Keywords: Levy distribution, weighted distribution, Maximum likelihood estimation and Shannon’s entropy

Mass Varying Neutrinos in the Sun

In this work we study the phenomenological consequences of the dependence of mass varying neutrinos on the neutrino density in the Sun, which we precisely compute in each point along the neutrino trajectory. We find that a generic characteristic of these scenarios is that they establish a connection between the effective Delta m^2 in the Sun and the absolute neutrino mass scale. This does not lead to any new allowed region in the oscillation parameter space. On the contrary, due to this effect, the description of solar neutrino data worsens for large absolute mass. As a consequence a lower bound on the level of degeneracy can be derived from the combined analysis of the solar and KamLAND data. In particular this implies that the analysis favours normal over inverted mass orderings. These results, in combination with a positive independent determination of the absolute neutrino mass, can be used as a test of these scenarios together with a precise determination of the energy dependence of the survival probability of solar neutrinos, in particular for low energies.Comment: 15 pages, 4 figures; final version: typos corrected, references added, matches published versio

Simultaneous intent prediction and state estimation using an intent-driven intrinsic coordinate model

The motion of an object (e.g. ship, jet, pedestrian, bird, drone, etc.) is usually governed by premeditated actions as per an underlying intent, for instance reaching a destination. In this paper, we introduce a novel intent-driven dynamical model based on a continuous-time intrinsic coordinate model. By combining this model with particle filtering, a seamless approach for jointly predicting the destination and estimating the state of a highly manoeuvrable object is developed. We examine the proposed inference technique using real data with different measurement models to demonstrate its efficacy. In particular, we show that the introduced approach can be a flexible and competitive alternative, in terms of prediction and estimation performance, to other existing methods for various measurement models including nonlinear ones

Sequential Dynamic Leadership Inference Using Bayesian Monte Carlo Methods

Hierarchy and leadership interactions commonly occur in animal groups, crowds of people and in vehicle motions. Such interactions are often affected by one or more individuals who possess key domain information (e.g. final destination, environmental constraints and best routes) or pertinent traits (e.g. better navigation, sensing and decision making capabilities) compared with the rest of the group. This paper presents a framework for the automatic identification of group structure and leadership from noisy sensory observations of tracked groups. Accordingly, a new leader-follower model is developed which assumes the dynamics of the group to be a multivariate Ornstein–Uhlenbeck process with the designated leader(s) drifting to the destination and followers reverting to the leaders’ state. Sequential Monte Carlo (SMC) approaches, and specifically the sequential Markov chain Monte Carlo (SMCMC) approach, are adopted to infer, probabilistically, the evolving leadership structure. A Rao-Blackwellisation scheme is employed such that the kinematic state of the objects in the group is inferred in closed form by Kalman filtering. Experiments show that the proposed techniques can successfully determine the leadership structures in challenging scenarios with a corresponding enhancement in tracking accuracy through direct consideration of the leadership interactions of the group

Stabilising touch interactions in cockpits, aerospace, and vibrating environments

© Springer International Publishing AG, part of Springer Nature 2018. Incorporating touch screen interaction into cockpit flight systems is increasingly gaining traction given its several potential advantages to design as well as usability to pilots. However, perturbations to the user input are prevalent in such environments due to vibrations, turbulence and high accelerations. This poses particular challenges for interacting with displays in the cockpit, for example, accidental activation during turbulence or high levels of distraction from the primary task of airplane control to accomplish selection tasks. On the other hand, predictive displays have emerged as a solution to minimize the effort as well as cognitive, visual and physical workload associated with using in-vehicle displays under perturbations, induced by road and driving conditions. This technology employs gesture tracking in 3D and potentially eye-gaze as well as other sensory data to substantially facilitate the acquisition (pointing and selection) of an interface component by predicting the item the user intents to select on the display, early in the movements towards the screen. A key aspect is utilising principled Bayesian modelling to incorporate and treat the present perturbation, thus, it is a software-based solution that showed promising results when applied to automotive applications. This paper explores the potential of applying this technology to applications in aerospace and vibrating environments in general and presents design recommendations for such an approach to enhance interactions accuracy as well as safety

Bayesian Intent Prediction in Object Tracking Using Bridging Distributions.

In several application areas, such as human computer interaction, surveillance and defence, determining the intent of a tracked object enables systems to aid the user/operator and facilitate effective, possibly automated, decision making. In this paper, we propose a probabilistic inference approach that permits the prediction, well in advance, of the intended destination of a tracked object and its future trajectory. Within the framework introduced here, the observed partial track of the object is modeled as being part of a Markov bridge terminating at its destination, since the target path, albeit random, must end at the intended endpoint. This captures the underlying long term dependencies in the trajectory, as dictated by the object intent. By determining the likelihood of the partial track being drawn from a particular constructed bridge, the probability of each of a number of possible destinations is evaluated. These bridges can also be employed to produce refined estimates of the latent system state (e.g., object position, velocity, etc.), predict its future values (up until reaching the designated endpoint) and estimate the time of arrival. This is shown to lead to a low complexity Kalman-filter-based implementation of the inference routine, where any linear Gaussian motion model, including the destination reverting ones, can be applied. Free hand pointing gestures data collected in an instrumented vehicle and synthetic trajectories of a vessel heading toward multiple possible harbors are utilized to demonstrate the effectiveness of the proposed approach

Detection of malicious intent in non-cooperative drone surveillance

In this paper, a Bayesian approach is proposed for the early detection of a drone threatening or anomalous behaviour in a surveyed region. This is in relation to revealing, as early as possible, the drone intent to either leave a geographical area where it is authorised to fly (e.g. to conduct inspection work) or reach a prohibited zone (e.g. runway protection zones at airports or a critical infrastructure site). The inference here is based on the noisy sensory observations of the target state from a non-cooperative surveillance system such as a radar. Data from Aveillant's Gamekeeper radar from a live drone trial is used to illustrate the efficacy of the introduced approach

Cu(OAc)2 as a green promoter for one-pot synthesis of 2-amino-4,6-diarylpyridine- 3-carbonitrile as antibacterial agents

The extensive use of antimicrobial drugs and their resistance against bacterial infections have led to discover new antimicrobial compounds. In this study, we wish to report, one-pot synthesis of 2-amino-3-cyanopyridine derivatives (1a-14a). These compounds were synthesized in the presence of Cu(OAc)2 as a highly effective heterogeneous acid catalyst. Here we evaluated the antimicrobial activities of these compounds against different species of microorganisms including gram positive and gram negative bacteria as well as fungi. Standard antimicrobial methods include disc diffusion and Broth microdilution method according to the protocol of the Clinical and Laboratory Standards Institute (CLSI). Synthesis of 2-amino-3-cyanopyridine derivatives were done via reaction of aromatic aldehydes, acetophenone derivatives, malononitrile and ammonium acetate in the presence of Cu(OAc)2 under reflux condition. The results show compound 2-amino-6-(4-chlorophenyl)-4-phenylnicotinonitrile (10a) had the best antimicrobial efficacy toward C. albicans, E. faecalis, P. aeroginosa and E. coli. In conclusion, comparing the structure and activity of the compounds (10a), this compound with the presence of Cl residue at para-position of phenyl ring improves the antibacterial and antifungal activity.   Bull. Chem. Soc. Ethiop. 2020, 34(1), 149-156. DOI: https://dx.doi.org/10.4314/bcse.v34i1.1

Antibiogram of pharyngeal isolates of children with pharyngotonsillitis in a specialist hospital in Gusau, North-Western Nigeria

Pharyngotonsillitis is one of the common childhood infections caused by bacteria in 30 to 40% of cases. Bacterial causes are important due to the non suppurative sequalae caused by Streptococcus pyogenes and also associated complications. These microorganisms undergo constant changes and antibiotic resistance have been reported. Objective: To document organisms isolated from throat swab microscopy and culture with their antibiotic susceptibility pattern in children diagnosed with pharyngotonsillitis. Methodology: This was a retrospective analysis of throat swabs microscopy, culture and sensitivity results of children aged 0-13 years with a diagnosis of pharyngotonsillitis over a four-year period. Results: Of the 144 results reviewed; 120 samples yielded 122 isolates, giving a culture positive yield of 83.3%. Males were 81 (56.2%) with a M:F ratio of 1.3:1. Majority of the children were under fives (58.3%). Gram positive organisms were 118 (96.7%), with Streptococcus pyogenes being the commonest organism isolated (79.5%), followed by Staphylococcus aureus (13.9%). Gentamicin (85.0%), Ofloxacin (64.2%) and Augmentin (51.7%) had the highest susceptibility rate, while the least was seen with Cefixime, Tetracycline, Levofloxacin and Netillin. Streptococcus pyogenes and Staphylococcus aureuswere susceptible to Gentamicin and Ofloxacin, while all the Streptococcus pneumoniae were susceptible to Gentamicin. Multi drug resistance was seen with Providencia spp and Serratia marcescens. Conclusion: Streptococcus pyogenes was the commonest organism and Gentamicin, Ofloxacin and Augmentin were the antibiotics with the highest susceptibility. Gram negative organisms display high rate of multidrug resistance. Gentamicin could be considered as an option or an adjunct in the treatment of pharyngotonsilliti

Driver and Passenger Identification from Smartphone Data

The objective of this paper is twofold. First, it presents a brief overview of existing driver and passenger identification or recognition approaches which rely on smartphone data. This includes listing the typically available sensory measurements and highlighting a few key practical considerations for automotive settings. Second, a simple identification method that utilises the smartphone inertial measurements and, possibly, doors signal is proposed. It is based on analysing the user behaviour during entry, namely the direction of turning, and extracting relevant salient features, which are distinctive depending on the side of entry to the vehicle. This is followed by applying a suitable classifier and decision criterion. Experimental data is shown to demonstrate the usefulness and effectiveness of the introduced probabilistic, low-complexity, identification technique.Jaguar Land Rover under the Centre for Advanced Photonics and Electronics (CAPE) agreement