Multi-Layered Oxygen Tension Maps of the Retina

Retinal hypoxia is associated with many retinal diseases, such as diabetic retinopathy. Current retinal research suggests that retinal hypoxia appears prior to the onset of diabetic retinopathy. The preliminary association of retinal hypoxia to the early stages of diabetic retinopathy is stimulating the development of new technologies to measure the oxygen content of retinal tissue. Frequency domain phosphoresence lifetime imaging (PLI) is a promising technology that enables the mapping of the oxygen content across the entire retina in the form of two-dimensional images. The two-dimensional images generated from the PLI process are a spatial mapping of the retinal tissue’s oxygen tension. Currently, the phosphorescent based oxygen tension PLI measurements contain contaminating auto-fluorescent signals in addition to the desired phosphorescent signals. These auto-fluorescent signals artificially inflate the oxygen tension readings due to the nature of fluorescent signals in phosphorescent imaging. Additionally, the maps generated through PLI appear to contain oxygen tension information from both the retinal vasculature and the choroidal vasculature. The choroidal vasculature is situated directly behind the retina and can have a different oxygen tension value than the retinal vasculature. This research enhanced the PLI system by mathematically eliminating the contaminating auto-fluorescent signals and investigated the methods aimed at separating the PO2s of the retinal and choroidal vasculature beds. In addition, the application of the enhanced PLI technology to the investigation of retinal oxygen changes in a rat model of type I diabetes yielded results that suggest a hyperoxic to hypoxic trend prior to the onset of diabetic retinopathy

FlowSifter: A Counting Automata Approach to Layer 7 Field Extraction for Deep Flow Inspection

Abstract—In this paper, we introduce FlowSifter, a systematic framework for online application protocol field extraction. FlowSifter introduces a new grammar model Counting Regular Grammars (CRG) and a corresponding automata model Counting Automata (CA). The CRG and CA models add counters with update functions and transition guards to regular grammars and finite state automata. These additions give CRGs and CAs the ability to parse and extract fields from context sensitive application protocols. These additions also facilitate fast and stackless approximate parsing of recursive structures. These new grammar models enable FlowSifter to generate optimized Layer 7 field extractors from simple extraction specifications. In our experiments, we compare FlowSifter against both BinPAC and UltraPAC, which are the freely available state of the art field extractors. Our experiments show that when compared to UltraPAC parsers, FlowSifter extractors run 84 % faster and use 12 % of the memory. I

Replication of direction selectivity in retinal ganglion cells via simulated artificial neural networks

A primary task of the retina is to frequency encode light absorption into action potentials. Using simulated artificial neural networks (SANN), the direction selective component of these electrical signals was replicated. The SANN was constructed to mimic the retinal architecture and was trained using published extracellular recordings from ganglion cells. Analysis of the trained model supported the hypothesis that Direction Selectivity arises from asymmetric connections among retinal cells. This project contributes to development of a biology-inspired artificial vision solution

GIS-based incident forecasting.

The current population growth and urban expansion in Victoria, Australia is increasing the need for effective fire control through the planning and allocation of fire safety resources. This project assisted the Country Fire Authority (CFA) by modeling emergency incidents in Victoria, forecasting future incident patterns, and identifying high fire risk regions. This task was accomplished by developing a prediction tool using Geographical Information Systems. In addition to assisting the CFA with fire suppression planning, this method may aid other institutions and communities by establishing a new standard for fire risk assessment

Automated Exposure Notification for COVID-19

The authors were among the 70+ in-person and virtual participants in the October 2021 ImPACT 2021 workshop. This final report has been heavily influenced by the discussion at that workshop.Private Automated Contact Tracing (PACT) was a collaborative team and effort formed during the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic. PACT’s mission was to enhance contact tracing in pandemic response by designing exposure-detection functions in personal digital communication devices that have maximal public health utility while preserving privacy. PACT had four major lines of effort: proximity detection efficacy, privacy, public health integration, and public health efficacy. In support of these lines of effort, PACT executed several cross-layer activities that helped demonstrate public health efficacy. These included prototype development and demonstrations; system analysis; data collection and experimentation; and large-scale deployment support. PACT convened two scientific workshops relating to privacy-preserving AEN: one virtual workshop in April 2020 and a second hybrid workshop in October 2021. This report is an outcome of the second workshop and serves as PACT’s final report. It seeks to explain and discuss the use of automated exposure notification during the COVID-19 pandemic and to provide some recommendations for those who may try to design and deploy similar technologies in future pandemics.IBM Research, the U.S. Defense Advanced Research Projects Agency (DARPA), and the U.S. Centers for Disease Control and Prevention (CDC)