A Gaussian Process Approach for Extended Object Tracking with Random Shapes and for Dealing with Intractable Likelihoods

Tracking of arbitrarily shaped extended objects is a complex task due to the intractable analytical expression of measurement to object associations. The presence of sensor noise and clutter worsens the situation. Although a significant work has been done on the extended object tracking (EOT) problems, most of the developed methods are restricted by assumptions on the shape of the object such as stick, circle, or other axis-symmetric properties etc. This paper proposes a novel Gaussian process approach for tracking an extended object using a convolution particle filter (CPF). The new approach is shown to track irregularly shaped objects efficiently in presence of measurement noise and clutter. The mean recall and precision values for the shape, calculated by the proposed method on simulated data are around 0.9, respectively, by using 1000 particles

Inelastic neutron scattering in random binary alloys : an augmented space approach

Combining the augmented space representation for phonons with a generalized version of Yonezawa-Matsubara diagrammatic technique, we have set up a formalism to seperate the coherent and incoherent part of the total intensity of thermal neutron scattering from disordered alloys. This is done exacly without taking any recourse to mean-field like approximation (as done previously). The formalism includes disorder in masses, force constants and scattering lengths. Implementation of the formalism to realistic situations is performed by an augmented space Block recursion which calculates entire Green matrix and self energy matrix which in turn is needed to evaluate the coherent and incoherent intensities. we apply the formalism to NiPd and NiPt alloys. Numerical results on coherent and incoherent scattering cross sections are presented along the highest symmetry directions. Finally the incoherent intensities are compared with the CPA and also with experiments.Comment: 18 pages, 13 figure

A learning gaussian process approach for maneuvering target tracking and smoothing

Model-based approaches for target tracking and smoothing estimate the infinite number of possible target trajectories using a finite set of models. This paper proposes a data-driven approach that represents the possible target trajectories using a distribution over an infinite number of functions. Recursive Gaussian process and derivative based Gaussian process approaches for target tracking and smoothing are developed, with online training and parameter learning. The performance evaluation over two highly maneuvering scenarios, shows that the proposed approach provides 80% and 62% performance improvement in the position and 49% and 22% in the velocity estimation, respectively, as compared to the best model-based filter

On the impact of different kernels and training data on a Gaussian process approach for target tracking

The application of multiple target tracking algorithms has exponentially increased during the last two decades. Recently, model-free approaches, such as Gaussian process regression and convolutional neural networks, have been developed for target tracking. This paper presents a simulation-based study on the practical aspects of a very promising and recently proposed Gaussian process method, namely the Gaussian process motion tracker [1]. The paper also provides design guidelines on the various aspects of the above-mentioned tracking method

Transient expression of βC1 protein differentially regulates host genes related to stress response, chloroplast and mitochondrial functions

<p>Abstract</p> <p>Background</p> <p>Geminiviruses are emerging plant pathogens that infect a wide variety of crops including cotton, cassava, vegetables, ornamental plants and cereals. The geminivirus disease complex consists of monopartite begomoviruses that require betasatellites for the expression of disease symptoms. These complexes are widespread throughout the Old World and cause economically important diseases on several crops. A single protein encoded by betasatellites, termed βC1, is a suppressor of gene silencing, inducer of disease symptoms and is possibly involved in virus movement. Studies of the interaction of βC1 with hosts can provide useful insight into virus-host interactions and aid in the development of novel control strategies. We have used the differential display technique to isolate host genes which are differentially regulated upon transient expression of the βC1 protein of chili leaf curl betasatellite (ChLCB) in <it>Nicotiana tabacum</it>.</p> <p>Results</p> <p>Through differential display analysis, eight genes were isolated from <it>Nicotiana tabacum</it>, at two and four days after infitration with βC1 of ChLCB, expressed under the control of the <it>Cauliflower mosaic virus </it>35S promoter. Cloning and sequence analysis of differentially amplified products suggested that these genes were involved in ATP synthesis, and acted as electron carriers for respiration and photosynthesis processes. These differentially expressed genes (DEGs) play an important role in plant growth and development, cell protection, defence processes, replication mechanisms and detoxification responses. Kegg orthology based annotation system analysis of these DEGs demonstrated that one of the genes, coding for polynucleotide nucleotidyl transferase, is involved in purine and pyrimidine metabolic pathways and is an RNA binding protein which is involved in RNA degradation.</p> <p>Conclusion</p> <p>βC1 differentially regulated genes are mostly involved in chloroplast and mitochondrial functions. βC1 also increases the expression of those genes which are involved in purine and pyrimidine metabolism. This information gives a new insight into the interaction of βC1 with the host and can be used to understand host-virus interactions in follow-up studies.</p

Structural modeling of natural citrus products as potential cross-strain inhibitors of Dengue virus

There are four serotypes of Dengue virus and there are existing drugs used against specific serotype. There is no drug that is effective against all strains of this virus. In this research, bioinformatics tools were used to predict the affinity of natural ligands for the glycoprotein E of Dengue virus by considering the conserved domains. Molecular docking studies were carried out by using Autodock 3.0. Computational analysis which showed that two ligands have the potential to inhibit the site in glycoprotein E and control of all strains is now possible by these ligands.Key words: Bioinformatics, multivariate drug designing, Dengue virus, in silico drug for dengue, glycoprotein E, conserved domain

Relating localized nanoparticle resonances to an associated antenna problem

We conceptually unify the description of resonances existing at metallic nanoparticles and optical nanowire antennas. To this end the nanoantenna is treated as a Fabry-Perot resonator with arbitrary semi-nanoparticles forming the terminations. We show that the frequencies of the quasi-static dipolar resonances of these nanoparticles coincide with the frequency where the phase of the complex reflection coefficient of the fundamental propagating plasmon polariton mode at the wire termination amounts to $\pi$. The lowest order Fabry-Perot resonance of the optical wire antenna occurs therefore even for a negligible wire length. This approach can be used either to easily calculate resonance frequencies for arbitrarily shaped nanoparticles or for tuning the resonance of nanoantennas by varying their termination.Comment: Submitted to Phys. Rev.

Caregivers knowledge, practices about childhood diarrhea and pneumonia and their perceptions of lady health worker program; findings from NIGRAAN implementation research project

Background: Despite 60% coverage by Lady Health Worker (LHW) Program, 30% of child deaths in Pakistan are still due to diarrhea and pneumonia. Caregivers are an important stakeholder yet there is little information on their case management practices and utilization of LHW Program. This study explored caregivers’ knowledge and practices about childhood diarrhea and pneumonia and utility of LHW services before and after a supportive supervision intervention.Methods: Cross sectional surveys were conducted with caregivers’ (mothers) pre and post intervention in project NIGRAAN. The intervention aimed to improve LHSs clinical and supervisory skills of lady health supervisors in order to improve LHW performance and ultimately impact caregiver practices. 4250 households were surveyed. Questionnaire was adapted from PDHS 2012-13. Differences between intervention and control groups were assessed using chi square test. P-value of Results: Comparing baseline to end line, there were significant overall improvements in caregivers’knowledge of loose motion (62 to 84%) and dehydration (12 to 18%) as signs and symptoms of childhood diarrhea. There was also a significant overall increase in caregivers’ knowledge of presenting features of pneumonia- i.e. fever (58 to 86%), cough (51 to 61%) and breathing problems (25 to 57%). The proportion of caregivers seeking advice for diarrhea from public sector significantly improved in intervention arm from 20% to 29%. Private sector however remained overall preferred choice for care seeking. There was significant overall improvement in awareness about LHWs functioning (93 to 99%) and household visits (91 to 98%). Actual care seeking from LHWs however stayed low (≤ 0.3%) Conclusion: In order to improve utility and expand coverage of LHW Program interventions aimed at providing supportive supervision have the potential to improve caregiver practices and utilization of available services and decrease childhood deaths due to preventable illnesses

Dual stream spatio-temporal motion fusion with self-attention for action recognition

Human action recognition in diverse and realistic environments is a challenging task. Automatic classification of action and gestures has a significant impact on human-robot interaction and human-machine interaction technologies. Due to the prevalence of complex real-world problems, it is non-trivial to produce a rich representation of actions and to produce an effective categorical distribution of large action classes. Deep convolutional neural networks have obtained great success in this area. Many researchers have proposed deep neural architectures for action recognition while considering the spatial and temporal aspects of the action. This research proposes a dual stream spatiotemporal fusion architecture for human action classification. The spatial and temporal data is fused using an attention mechanism. We investigate two fusion techniques and show that the proposed architecture achieves accurate results with much fewer parameters as compared to the traditional deep neural networks. We achieved 99.1 % absolute accuracy on the UCF-101 test set