Method and system for training dynamic nonlinear adaptive filters which have embedded memory

Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6

Linkages between animal and human health sentinel data

INTRODUCTION: In order to identify priorities for building integrated surveillance systems that effectively model and predict human risk of zoonotic diseases, there is a need for improved understanding of the practical options for linking surveillance data of animals and humans. We conducted an analysis of the literature and characterized the linkage between animal and human health data. We discuss the findings in relation to zoonotic surveillance and the linkage of human and animal data. METHODS: The Canary Database, an online bibliographic database of animal-sentinel studies was searched and articles were classified according to four linkage categories. RESULTS: 465 studies were identified and assigned to linkage categories involving: descriptive, analytic, molecular, or no human outcomes of human and animal health. Descriptive linkage was the most common, whereby both animal and human health outcomes were presented, but without quantitative linkage between the two. Rarely, analytic linkage was utilized in which animal data was used to quantitatively predict human risk. The other two categories included molecular linkage, and no human outcomes, which present health outcomes in animals but not humans. DISCUSSION: We found limited use of animal data to quantitatively predict human risk and listed the methods from the literature that performed analytic linkage. The lack of analytic linkage in the literature might not be solely related to technological barriers including access to electronic database, statistical software packages, and Geographical Information System (GIS). Rather, the problem might be from a lack of understanding by researchers of the importance of animal data as a 'sentinel' for human health. Researchers performing zoonotic surveillance should be aware of the value of animal-sentinel approaches for predicting human risk and consider analytic methods for linking animal and human data. Qualitative work needs to be done in order to examine researchers' decisions in linkage strategies between animal and human data

System using leo satellites for centimeter-level navigation

Disclosed herein is a system for rapidly resolving position with centimeter-level accuracy for a mobile or stationary receiver [4]. This is achieved by estimating a set of parameters that are related to the integer cycle ambiguities which arise in tracking the carrier phase of satellite downlinks [5,6]. In the preferred embodiment, the technique involves a navigation receiver [4] simultaneously tracking transmissions [6] from Low Earth Orbit Satellites (LEOS) [2] together with transmissions [5] from GPS navigation satellites [1]. The rapid change in the line-of-sight vectors from the receiver [4] to the LEO signal sources [2], due to the orbital motion of the LEOS, enables the resolution with integrity of the integer cycle ambiguities of the GPS signals [5] as well as parameters related to the integer cycle ambiguity on the LEOS signals [6]. These parameters, once identified, enable real-time centimeter-level positioning of the receiver [4]. In order to achieve high-precision position estimates without the use of specialized electronics such as atomic clocks, the technique accounts for instabilities in the crystal oscillators driving the satellite transmitters, as well as those in the reference [3] and user [4] receivers. In addition, the algorithm accommodates as well as to LEOS that receive signals from ground-based transmitters, then re-transmit frequency-converted signals to the ground

COVID Smell Tracker: A research-based mobile application to study smell loss in subjects with COVID-19

Introduction: Up to 60% of people infected with SARS-CoV-2 report anosmia or ageusia during their disease course. “COVID Smell Tracker” is a smart phone application (app) developed to elucidate the onset, duration and extent of anosmia and ageusia through questionnaires. Methods: “COVID Smell Tracker” is publicly available on smart phone devices (www.covidsmelltracker.org). Users complete surveys around demographics, medical history, COVID status and symptomology. Deidentified responses were collated and analyzed using descriptive statistics. Results: Of the 266 users included, the majority were located in Europe (43%) and North America (33%). Male, Caucasian users were most common (54.9% and 61.7% respectively), followed by Indian (10.5%) and Latino (9.4%). The majority of users reported no COVID testing (63%). 164 users reported COVID-related symptoms, and 57% of them reported olfactory dysfunction. Users who were younger age (p = 0.0003) and with type A and B blood types (p = 0.021) experienced smell loss at higher frequencies. Dysgeusia was associated with 28-34% of patients with concomitant smell loss, versus 6%-9% in users without. Smell loss was described as “sudden” (63%), occurring on average 3 days following the onset of any other symptom. Of those that reported resolution of their smell loss, 50% resolved in 1 week, with 75% resolution reported within 1 month. Conclusions: The results herein correlate with other established COVID-related studies, despite relying on purely volunteered data from participants from around the world. This is the first study to suggest an association of age and blood type with smell loss. Mobile app use offers a novel method for safe, remote and granular insight into those suffering from infectious diseases like COVID-19

Risk factors for human infection with West Nile Virus in Connecticut: a multi-year analysis

<p>Abstract</p> <p>Background</p> <p>The optimal method for early prediction of human West Nile virus (WNV) infection risk remains controversial. We analyzed the predictive utility of risk factor data for human WNV over a six-year period in Connecticut.</p> <p>Results and Discussion</p> <p>Using only environmental variables or animal sentinel data was less predictive than a model that considered all variables. In the final parsimonious model, population density, growing degree-days, temperature, WNV positive mosquitoes, dead birds and WNV positive birds were significant predictors of human infection risk, with an ROC value of 0.75.</p> <p>Conclusion</p> <p>A real-time model using climate, land use, and animal surveillance data to predict WNV risk appears feasible. The dynamic patterns of WNV infection suggest a need to periodically refine such prediction systems.</p> <p>Methods</p> <p>Using multiple logistic regression, the 30-day risk of human WNV infection by town was modeled using environmental variables as well as mosquito and wild bird surveillance.</p