False discovery rate regression: an application to neural synchrony detection in primary visual cortex

Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global false-discovery-rate analysis. But this may be inappropriate for many of today's large-scale screening problems, where auxiliary information about each test is often available, and where a combined analysis can lead to poorly calibrated error rates within different subsets of the experiment. To address this issue, we introduce an approach called false-discovery-rate regression that directly uses this auxiliary information to inform the outcome of each test. The method can be motivated by a two-groups model in which covariates are allowed to influence the local false discovery rate, or equivalently, the posterior probability that a given observation is a signal. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the paper

Overpumping Leads to California Groundwater Arsenic Threat

Water resources are being challenged to meet domestic, agricultural, and industrial needs. To complement finite surface water supplies that are being stressed by changes in precipitation and increased demand, groundwater is increasingly being used. Sustaining groundwater use requires considering both water quantity and quality. A unique challenge for groundwater use, as compared with surface water, is the presence of naturally occurring contaminants within aquifer sediments, which can enter the water supply. Here we find that recent groundwater pumping, observed through land subsidence, results in an increase in aquifer arsenic concentrations in the San Joaquin Valley of California. By comparison, historic groundwater pumping shows no link to current groundwater arsenic concentrations. Our results support the premise that arsenic can reside within pore water of clay strata within aquifers and is released due to overpumping. We provide a quantitative model for using subsidence as an indicator of arsenic concentrations correlated with groundwater pumping

The effect of conversational agent skill on user behavior during deception

Conversational agents (CAs) are an integral component of many personal and business interactions. Many recent advancements in CA technology have attempted to make these interactions more natural and human-like. However, it is currently unclear how human-like traits in a CA impact the way users respond to questions from the CA. In some applications where CAs may be used, detecting deception is important. Design elements that make CA interactions more human-like may induce undesired strategic behaviors from human deceivers to mask their deception. To better understand this interaction, this research investigates the effect of conversational skill—that is, the ability of the CA to mimic human conversation—from CAs on behavioral indicators of deception. Our results show that cues of deception vary depending on CA conversational skill, and that increased conversational skill leads to users engaging in strategic behaviors that are detrimental to deception detection. This finding suggests that for applications in which it is desirable to detect when individuals are lying, the pursuit of more human-like interactions may be counter-productive

Developing a measure of adversarial thinking in social engineering scenarios

Social engineering is a major issue for organizations. In this paper, we propose that increasing adversarial thinking can improve individual resistance to social engineering attacks. We formalize our understanding of adversarial thinking using Utility Theory. Next a measure of adversarial thinking in a text-based context. Lastly the paper reports on two studies that demonstrate the effectiveness of the newly developed measure. We show that the measure of adversarial thinking has variability, can be manipulated with training, and that it is not influenced significantly by priming. The paper also shows that social engineering training has an influence on adversarial thinking and that practicing against an adversarial conversational agent has a positive influence on adversarial thinking

The Influence of Conversational Agents on Socially Desirable Responding

Conversational agents (CAs) are becoming an increasingly common component in many information systems. The ubiquity of CAs in cell phones, entertainment systems, and messaging applications has led to a growing need to understand how design choices made when developing CAs influence user interactions. In this study, we explore the use case of CAs that gather potentially sensitive information from people-”for example, in a medical interview. Using a laboratory experiment, we examine the influence of CA responsiveness and embodiment on the answers people give in response to sensitive and non-sensitive questions. The results show that for sensitive questions, the responsiveness of the CA increased the social desirability of the responses given by participants

Stray Light Modeling of the James Webb Space Telescope (JWST) Integrated Science Instrument Module (ISIM)

This paper describes an integrated stray light model of each Science Instrument (SI) in the Integrated Science Instrument Module (ISIM) of the James Webb Space Telescope (JWST) and the Optical Telescope Element Simulator (OSIM), the light source used to characterize the performance of ISIM in cryogenic-vacuum tests at the Goddard Space Flight Center (GSFC). We present three cases where this stray light model was integral to solving questions that arose during the testing campaign - 1) ghosting and coherent diffraction from hardware surfaces in the Near Infrared Imager and Slitless Spectrograph (NIRISS) GR700XD grism mode, 2) ghost spots in the Near Infrared Camera (NIRCam) GRISM modes, and 3) scattering from knife edges of the NIRCam focal plane array masks

A near infrared frequency comb for Y+J band astronomical spectroscopy

Radial velocity (RV) surveys supported by high precision wavelength references (notably ThAr lamps and I2 cells) have successfully identified hundreds of exoplanets; however, as the search for exoplanets moves to cooler, lower mass stars, the optimum wave band for observation for these objects moves into the near infrared (NIR) and new wavelength standards are required. To address this need we are following up our successful deployment of an H band(1.45-1.7{\mu}m) laser frequency comb based wavelength reference with a comb working in the Y and J bands (0.98-1.3{\mu}m). This comb will be optimized for use with a 50,000 resolution NIR spectrograph such as the Penn State Habitable Zone Planet Finder. We present design and performance details of the current Y+J band comb.Comment: Submitted to SPIE, conference proceedings 845