Semi-Informed Multi-Agent Patrol Strategies

The adversarial multi-agent patrol problem is an active research topic with many real-world applications such as physical robots guarding an area and software agents protecting a computer network. In it, agents patrol a graph looking for so-called critical vertices that are subject to attack by adversaries. The agents are unaware of which vertices are subject to attack by adversaries and when they encounter such a vertex they attempt to protect it from being compromised (an adversary must occupy the vertex it targets a certain amount of time for the attack to succeed). Even though the terms adversary and attack are used, the problem domain extends to patrolling a graph for other interesting noncompetitive contexts such as search and rescue. The problem statement adopted in this work is formulated such that agents obtain knowledge of local graph topology and critical vertices over the course of their travels via an API ; there is no global knowledge of the graph or communication between agents. The challenge is to balance exploration, necessary to discover critical vertices, with exploitation, necessary to protect critical vertices from attack. Four types of adversaries were used for experiments, three from previous research – waiting, random, and statistical - and the fourth, a hybrid of those three. Agent strategies for countering each of these adversaries are designed and evaluated. Benchmark graphs and parameter settings from related research will be employed. The proposed research culminates in the design and evaluation of agents to counter these various types of adversaries under a range of conditions. The results of this work are agent strategies in which each agent becomes solely responsible for protecting those critical vertices it discovers. The agents use emergent behavior to minimize successful attacks and maximize the discovery of new critical vertices. A set of seven edge choosing primitives (ECPs) are defined that are combined in different ways to yield a range of agent strategies using the chain of responsibility OOP design pattern. Every permutation of them were tested and measured in order to identify those strategies that perform well. One strategy performed particularly well against all adversaries, graph topology, and other experimental variables. This particular strategy combines ECPs of: A hard-deadline return to covered vertices to counter the random adversary, efficiently checking vertices to see if they are being attacked by the waiting adversary, and random movement to impede the statistical adversary

Assessing socioeconomic health care utilization inequity in Israel: impact of alternative approaches to morbidity adjustment

<p/> <p>Background</p> <p>The ability to accurately detect differential resource use between persons of different socioeconomic status relies on the accuracy of health-needs adjustment measures. This study tests different approaches to morbidity adjustment in explanation of health care utilization inequity.</p> <p>Methods</p> <p>A representative sample was selected of 10 percent (~270,000) adult enrolees of Clalit Health Services, Israel's largest health care organization. The Johns-Hopkins University Adjusted Clinical Groups^®were used to assess each person's overall morbidity burden based on one year's (2009) diagnostic information. The odds of above average health care resource use (primary care visits, specialty visits, diagnostic tests, or hospitalizations) were tested using multivariate logistic regression models, separately adjusting for levels of health-need using data on age and gender, comorbidity (using the Charlson Comorbidity Index), or morbidity burden (using the Adjusted Clinical Groups). Model fit was assessed using tests of the Area Under the Receiver Operating Characteristics Curve and the Akaike Information Criteria.</p> <p>Results</p> <p>Low socioeconomic status was associated with higher morbidity burden (1.5-fold difference). Adjusting for health needs using age and gender or the Charlson index, persons of low socioeconomic status had greater odds of above average resource use for all types of services examined (primary care and specialist visits, diagnostic tests, or hospitalizations). In contrast, after adjustment for overall morbidity burden (using Adjusted Clinical Groups), low socioeconomic status was no longer associated with greater odds of specialty care or diagnostic tests (OR: 0.95, CI: 0.94-0.99; and OR: 0.91, CI: 0.86-0.96, for specialty visits and diagnostic respectively). Tests of model fit showed that adjustment using the comprehensive morbidity burden measure provided a better fit than age and gender or the Charlson Index.</p> <p>Conclusions</p> <p>Identification of socioeconomic differences in health care utilization is an important step in disparity reduction efforts. Adjustment for health-needs using a comprehensive morbidity burden diagnoses-based measure, this study showed relative underutilization in use of specialist and diagnostic services, and thus allowed for identification of inequity in health resources use, which could not be detected with less comprehensive forms of health-needs adjustments.</p

Multi-messenger searches via IceCube’s high-energy neutrinos and gravitational-wave detections of LIGO/Virgo

We summarize initial results for high-energy neutrino counterpart searches coinciding with gravitational-wave events in LIGO/Virgo\u27s GWTC-2 catalog using IceCube\u27s neutrino triggers. We did not find any statistically significant high-energy neutrino counterpart and derived upper limits on the time-integrated neutrino emission on Earth as well as the isotropic equivalent energy emitted in high-energy neutrinos for each event