Survival and Growth Rate of Translocated Freshwater Mussels \u3ci\u3eLampsilis fasciola\u3c/i\u3e and \u3ci\u3eMedionidus conradicus\u3c/i\u3e

Freshwater mussels (Family Unionidae and Margaritiferidae) are a widely threatened group of bivalve molluscs, particularly in the Southeastern United States. Translocation of freshwater mussels is an increasingly common conservation method. However, there are relatively few studies that quantitatively investigate the factors influencing translocation success or failure. In October 2013, hundreds of Medionidus conradicus and Lampsilis fasciola were translocated to the Pigeon and Nolichucky Rivers in Tennessee, with an interim partial survey (June 2014) and a full survey (October 2014). In this study, I analyze this field-collected data to determine the mechanism(s) that currently influence the outcomes of Tennessee mussel translocation. My recommendations for future surveys include open and timely data sharing between investigators and the scientific community at large. Given these data and associated collection methods, a better understanding of freshwater mussel communities and restoration success factors can be identified at lower future costs and facilitate longer-term research. My research recommendations include more frequent, complete surveys, and quantitative analyses at the mussel and community levels. The results of this study have implications for conservation translocation efforts. My results indicate that both L. fasciola and M. conradicus can be successfully translocated to the Pigeon River, if 1) they are translocated to the Pigeon where it has less boulder, cobble and exposed bedrock in favor of more coarse and fine gravel and sand; 2) it had lower peak and average water discharge rates, 3) if some translocations occurred in the spring-early summer, and 4) if the translocated mussels are initially housed in cages or silos. The non-housed mussels were not recovered, primarily due to high water volumes and velocities soon after the beginning of the study. The housed mussels were protected. There is no overall predictability of the water discharge timing and size of the Waterville Hydroelectric Power plant’s dam. A management recommendation is for incremental releases and notification to conservation authorities. Due to significant mortality in the first 8 months of this study, some studies should start in the spring-early summer rather than in October to help translocated mussels survive their first over-winter by having some growth and habitat acclimation underway

Verification of Compartmental Epidemiological Models Using Metamorphic Testing, Model Checking and Visual Analytics

Abstract—Compartmental models in epidemiology are widely used as a means to model disease spread mechanisms and understand how one can best control the disease in case an outbreak of a widespread epidemic occurs. However, a signifi-cant challenge within the community is in the development of approaches that can be used to rigorously verify and validate these models. In this paper, we present an approach to rigorously examine and verify the behavioral properties of compartmen-tal epidemiological models under several common modeling scenarios including birth/death rates and multi-host/pathogen species. Using metamorphic testing, a novel visualization tool and model checking, we build a workflow that provides insights into the functionality of compartmental epidemiological models. Our initial results indicate that metamorphic testing can be used to verify the implementation of these models and provide insights into special conditions where these mathematical models may fail. The visualization front-end allows the end-user to scan through a variety of parameters commonly used in these models to elucidate the conditions under which an epidemic can occur. Further, specifying these models using a process algebra allows one to automatically construct behavioral properties that can be rigorously verified using model checking. Taken together, our approach allows for detecting implementation errors as well as handling conditions under which compartmental epidemiological models may fail to provide insights into disease spread dynamics

Workshop summary: 2019 IEEE/ACM Fourth International Workshop on Metamorphic Testing (MET 2019)

Poon, PL ORCiD: 0000-0003-2840-2418MET is a relatively new workshop on metamorphic testing for academic researchers and industry practitioners. The first international workshop on MET (MET 2016) was co-located with the 38th International Conference on Software Engineering (ICSE 2016) in Austin TX, USA on May 16, 2016. Since then the workshop has become an annual event. This paper reports on the fourth International Workshop on Metamorphic Testing (MET 2019) held in Montréal, Canada on May 26, 2019, as part of the 41st International Conference on Software Engineering (ICSE 2019). We first outline the aims of the workshop, followed by a discussion of its keynote speech and technical program

EpiSpec: A formal specification language for parameterized agent-based models against epidemiological ground truth

Building complex computational models of the spread of epidemics is a problem that has seen renewed interest in recent years. Such models are being used for understanding real-time disease evolution prediction and are also proving useful in the prevention, monitoring and control of contagious diseases. There is a pressing need to ensure reliability of epidemiological models since they are widely used in safety-critical applications. In this paper, we present a new spatio-temporal specification language, EpiSpec, for describing detailed properties of agent-based computational epidemiological models. We describe the formal syntax of EpiSpec and demonstrate its use by describing various spatio-temporal properties related to disease evolution, and propose the use of statistical model checking as an algorithmic technique for verification and validation of large computational epidemiological models

Augmenting Epidemiological Models with Point-Of-Care Diagnostics Data.

Although adoption of newer Point-of-Care (POC) diagnostics is increasing, there is a significant challenge using POC diagnostics data to improve epidemiological models. In this work, we propose a method to process zip-code level POC datasets and apply these processed data to calibrate an epidemiological model. We specifically develop a calibration algorithm using simulated annealing and calibrate a parsimonious equation-based model of modified Susceptible-Infected-Recovered (SIR) dynamics. The results show that parsimonious models are remarkably effective in predicting the dynamics observed in the number of infected patients and our calibration algorithm is sufficiently capable of predicting peak loads observed in POC diagnostics data while staying within reasonable and empirical parameter ranges reported in the literature. Additionally, we explore the future use of the calibrated values by testing the correlation between peak load and population density from Census data. Our results show that linearity assumptions for the relationships among various factors can be misleading, therefore further data sources and analysis are needed to identify relationships between additional parameters and existing calibrated ones. Calibration approaches such as ours can determine the values of newly added parameters along with existing ones and enable policy-makers to make better multi-scale decisions

Work-In-Progress: Testing Autonomous Cyber-Physical Systems Using Fuzzing Features From Convolutional Neural Networks

Autonomous cyber-physical systems rely on modern machine learning methods such as deep neural networks to control their interactions with the physical world. Testing of such intelligent cyberphysical systems is a challenge due to the huge state space associated with high-resolution visual sensory inputs. We demonstrate how fuzzing the input using patterns obtained from the convolutional flters of an unrelated convolutional neural network can be used to test computer vision algorithms implemented in intelligent cyber-physical systems. Our method discovers interesting counterexamples to a pedestrian detection algorithm implemented in the popular OpenCV library. Our approach also unearths counterexamples to the correct behavior of an autonomous car similar to NVIDIA\u27s end-to-end self-driving deep neural net running on the Udacity open-source simulator

Epispec: A Formal Specification Language For Parameterized Agent-Based Models Against Epidemiological Ground Truth

Building complex computational models of the spread of epidemics is a problem that has seen renewed interest in recent years. Such models are being used for understanding real-time disease evolution prediction and are also proving useful in the prevention, monitoring and control of contagious diseases. There is a pressing need to ensure reliability of epidemiological models since they are widely used in safety-critical applications. In this paper, we present a new spatio-temporal specification language, EpiSpec, for describing detailed properties of agent-based computational epidemiological models. We describe the formal syntax of EpiSpec and demonstrate its use by describing various spatio-temporal properties related to disease evolution, and propose the use of statistical model checking as an algorithmic technique for verification and validation of large computational epidemiological models