Coastal Groundwater Watch: A Citizen Science Project - Report No. 477

The goals of this study were to utilize citizen scientists in groundwater research in a coastal community where groundwater plays a large role in sustainable water resources management, and assess the extent of groundwater and marine inundation in response to future sea-level rise scenarios. A total of 7 citizen scientists participated in the study by measuring water levels from 15 groundwater monitoring wells using water level meters once a week over a 10-week period. Automated water level loggers were deployed in three of the same wells to assess the quality of the data collected by the citizen scientists. Additional water level loggers were deployed in other groundwater monitoring wells to increase the amount of water level data collected across the island. Several methods were used to assess agreement (i.e., validity) between water level measurements collected by citizen scientists and automated water level loggers. Scatter plots showed that data did not significantly deviate from the line of linearity, suggesting that the data collected by the citizen scientists were comparable to the data collected by automated water level loggers. The Pearson correlation coefficient was greater than 0.9 for all plots that revealed a linear correlation between measurements from different methods. The Bland-Altman method was also used to evaluate the validity of measurements by assessing agreement between measurements from citizen scientists and automated water level loggers. The intraclass correlation coefficient (ICC) and the concordance correlation coefficient (CCC) were used to assess reliability of measurements of water levels from citizen scientists. The values for the ICC and CCC were greater than 0.95 indicating excellent agreement. These values demonstrate that environmental data collected by citizen scientists can be trustworthy. A pretest-posttest survey design and a focus group were used to examine how participants perceived the citizen science project, and how participation as a citizen scientist influenced the participants’ knowledge about water resources and stormwater flooding. Qualitative data suggest that citizen scientists improved their knowledge about groundwater systems on the island. Additionally, the citizen scientists found the project to be enriching and beneficial to their understanding of issues facing the island (e.g., storm water flooding). The groundwater data from both the citizen scientists and automated water level loggers were used to calibrate a numerical groundwater model that characterized the baseline conditions of the water table on the island. Impacts of projected sea-level rise ranging from 0.2 m to 1.4 m on the baseline water table were then simulated under steady state conditions. Finally, geospatial techniques were used to estimate the proportion of land that would be lost to marine inundation and groundwater inundation under identical sea-level rise scenarios. Results indicate that marine and groundwater inundation would have comparable effects on the island, with between 7 and 22% of the land being lost under sea-level rise scenarios of 0.2 to 1.2 m. At extreme sea-level rise scenarios (1.4 m), the effects of groundwater inundation are far much greater than those of marine inundation (with losses of 28% for marine inundation and 40% for groundwater inundation). As a consequence, groundwater inundation may therefore play an important role in future discussions about how climate change and sea-level rise may impact groundwater resources in coastal communities. Involving community residents in scientific research such as the project described in this report may therefore be an effective way for positively engaging with residents about important environmental issues such as climate change, sea-level rise and groundwater resources

On the universality of the scaling of fluctuations in traffic on complex networks

We study the scaling of fluctuations with the mean of traffic in complex networks using a model where the arrival and departure of "packets" follow exponential distributions, and the processing capability of nodes is either unlimited or finite. The model presents a wide variety of exponents between 1/2 and 1 for this scaling, revealing their dependence on the few parameters considered, and questioning the existence of universality classes. We also report the experimental scaling of the fluctuations in the Internet for the Abilene backbone network. We found scaling exponents between 0.71 and 0.86 that do not fit with the exponent 1/2 reported in the literature.Comment: 4 pages, 4 figure

Few-Shot Bayesian Imitation Learning with Logical Program Policies

Humans can learn many novel tasks from a very small number (1--5) of demonstrations, in stark contrast to the data requirements of nearly tabula rasa deep learning methods. We propose an expressive class of policies, a strong but general prior, and a learning algorithm that, together, can learn interesting policies from very few examples. We represent policies as logical combinations of programs drawn from a domain-specific language (DSL), define a prior over policies with a probabilistic grammar, and derive an approximate Bayesian inference algorithm to learn policies from demonstrations. In experiments, we study five strategy games played on a 2D grid with one shared DSL. After a few demonstrations of each game, the inferred policies generalize to new game instances that differ substantially from the demonstrations. Our policy learning is 20--1,000x more data efficient than convolutional and fully convolutional policy learning and many orders of magnitude more computationally efficient than vanilla program induction. We argue that the proposed method is an apt choice for tasks that have scarce training data and feature significant, structured variation between task instances.Comment: AAAI 202

Multiparametric MR imaging for detection of clinically significant prostate cancer: a validation cohort study with transperineal template prostate mapping as the reference standard.

PURPOSE: To evaluate the diagnostic performance of multiparametric (MP) magnetic resonance (MR) imaging for prostate cancer detection by using transperineal template prostate mapping (TTPM) biopsies as the reference standard and to determine the potential ability of MP MR imaging to identify clinically significant prostate cancer. MATERIALS AND METHODS: Institutional review board exemption was granted by the local research ethics committee for this retrospective study. Included were 64 men (mean age, 62 years [range, 40-76]; mean prostate-specific antigen, 8.2 ng/mL [8.2 μg/L] [range, 2.1-43 ng/mL]), 51 with biopsy-proved cancer and 13 suspected of having clinically significant cancer that was biopsy negative or without prior biopsy. MP MR imaging included T2-weighted, dynamic contrast-enhanced and diffusion-weighted imaging (1.5 T, pelvic phased-array coil). Three radiologists independently reviewed images and were blinded to results of biopsy. Two-by-two tables were derived by using sectors of analysis of four quadrants, two lobes, and one whole prostate. Primary target definition for clinically significant disease necessary to be present within a sector of analysis on TTPM for that sector to be deemed positive was set at Gleason score of 3+4 or more and/or cancer core length involvement of 4 mm or more. Sensitivity, negative predictive value, and negative likelihood ratio were calculated to determine ability of MP MR imaging to rule out cancer. Specificity, positive predictive value, positive likelihood ratio, accuracy (overall fraction correct), and area under receiver operating characteristic curves were also calculated. RESULTS: Twenty-eight percent (71 of 256) of sectors had clinically significant cancer by primary endpoint definition. For primary endpoint definition (≥ 4 mm and/or Gleason score ≥ 3+4), sensitivity, negative predictive value, and negative likelihood ratios were 58%-73%, 84%-89%, and 0.3-0.5, respectively. Specificity, positive predictive value, and positive likelihood ratios were 71%-84%, 49%-63%, and 2.-3.44, respectively. Area under the curve values were 0.73-0.84. CONCLUSION: Results of this study indicate that MP MR imaging has a high negative predictive value to rule out clinically significant prostate cancer and may potentially have clinical use in diagnostic pathways of men at risk