A Multi-Layer Three Dimensional Superconducting Nanowire Photon Detector

Here we propose a new design paradigm for a superconducting nanowire single photon detector that uses a multi-layer architecture that places the electric leads beneath the nanowires. This allows for a very large number of detector elements, which we will call pixels in analogy to a conventional CCD camera, to be placed in close proximity. This leads to significantly better photon number resolution than current single and multi-nanowire meanders, while maintaining similar detection areas. We discuss the reset time of the pixels and how the design can be modified to avoid the latching failure seen in extremely short superconducting nanowires. These advantages give a multi-layer superconducting number-resolving photon detector significant advantages over the current design paradigm of long superconducting nanowire meanders. Such advantages are desirable in a wide array of photonics applications.Comment: 12 pages, 6 figure

Quantifying How Coastal Flooding and Stormwater Runoff Drive Spatiotemporal Variability in Carbon and Nutrient Processing in Urban Aquatic Ecosystems

Coastal river networks alter the transport and transformation of dissolved organic carbon (DOC) and dissolved organic matter (DOM), which can vary in concentration and composition across spatiotemporal scales. Given climate-induced shifts in rainfall and tidal variation in low-lying coastal regions, there is an increasing need to quantify effects of flooding on biogeochemical cycling. Specifically, urban flooding is becoming increasingly common due to biophysical alterations to hydrology from urbanization and climate change. Urban ecosystems have been characterized as having a distinct biogeochemistry compared to other systems, largely due to increased frequency and magnitude of riverine and coastal flooding. Consequently, the role of stormwater runoff and tides on DOC, DOM, and nutrient concentration, composition, and biological processing are highly variable. In order to better understand the biogeochemical consequences of urban flooding, it is important to consider the interactions between surface and subsurface environments to hydroclimatic drivers of flood frequency and magnitude. The process of urbanization can significantly alter DOC and DOM regimes by influencing the timing and delivery of fresh versus saltwater to coastal waterways. DOM composition can be quantified using fluorescent DOM (fDOM) properties that indicate relative source in mixed waterways. However, the quantity and composition of DOM varies widely across spatiotemporal scales, particularly in coastal drainages. Further, most research on DOC and DOM in urban aquatic systems to-date has not been done in low-lying coastal areas, despite the majority of the world\u27s cities residing in coastal regions. Thus, quantifying changes in the hydrologic and land use drivers of DOM source and composition change is needed to understand its role in metabolic processing and export to downstream water bodies. This dissertation research examines various biophysical and climatic drivers of runoff and coastal flooding and their relative influences on carbon and nutrient biogeochemical cycling in multiple urban aquatic ecosystems across multiple cities. In Chapter 2, I evaluated how time-variable interactions among water source contributions from freshwater and saltwater influence DOC and nutrient concentrations and DOM composition in urban canals connected to the ocean. Using a combined isotopic-fluorescent DOM (fDOM) tracer approach, we created a Bayesian Monte-Carlo (BMC) mixing model to estimate fractional contributions of marine water, rainwater, stormwater, and groundwater to three coastal canals of Miami, FL. We found that loading of terrestrially sourced DOC and DOM is pulsed to urban canals and shunted downstream and supplemented by microbially sourced DOM during the wet season at high tide. These results provide an important addition of pulsed groundwater contributions to the Pulse-Shunt concept to explain DOM transport versus processing in coastal urban ecosystems. In Chapter 3 of my dissertation, I compared the bioavailability of DOM contributions across urban canals, we found that interactions between stormwater runoff and tidal amplitude increased the bioavailability of DOM and were positively correlated with predominantly humic-like components in the wet season and protein-like components in the dry season. Additionally, increases in recently produced DOM, indicated by tryptophan-like fluorescence, corresponded with elevated concentrations of indicators of human waste (i.e., E. coli, enterococci) from groundwater inputs. These results suggest evidence of an urban priming effect in which labile autochthonous DOM from urban sources can drive microbial processing of DOM in coastal waterways. In Chapter 4, I examined the role of hydrologic and landscape variables on the water column versus whole system nutrient uptake capacity of urban wetlands. Compared to non-urban wetlands, nutrient uptake in the water column represents a significant portion of total uptake, particularly in small urbanized wetlands of short water residence time and supplemented with labile, proteinaceous DOM. Our results suggest that increases in the stoichiometric availability of labile organic carbon can stimulate sequestration of NO3- and SRP in nutrient polluted or nutrient limited urban wetlands. Finally, in Chapter 5 I investigated how urbanization has impacted the spatiotemporal variability of high discharge events in urban and non-urban watersheds across regional climates. In light of the original Pulse-Shunt Concept, I introduce the urban flow-shunt flood-pulse concept to better explain how spatiotemporal synchrony of urban stream discharge occurs following extreme precipitation events across regional climates. Together, this research shows that significant transformation of DOC, DOM, and nutrients occur within urban aquatic systems and such processes are influenced by the source, magnitude, and timing of water contributions to coastal environments

The When (and How) of Intergroup Competition and Discrimination: Distinguishing the Contributions of Competitive Perceptions and Motivations

A new framework is proposed to examine the effects of intergroup competition on discrimination by assessing the influence of participants’ subjective construal of potentially competitive events. It posits that competitive intergroup perceptions (CIP; the perception that one’s ingroup and another group(s) are attempting to gain a reward or desired outcome at the expense of each other) and competitive intergroup motivations (CIM; the desire for one’s ingroup to acquire more of a reward than the other group(s)) are related but distinct constructs. This distinction implies that CIP and CIM should be strongly related, but not to the point of suggesting they are the same variable. A distinction between CIP and CIM also implies that both constructs can be elicited and experimentally manipulated independently of each other. Most importantly, this distinction implies that both constructs will have unique influences on intergroup behaviour. Although this approach has not been systematically investigated previously, the intergroup relations literature suggests two potential explanations by which CIP and CIM may lead to discrimination: i) CIP and CIM have unique, additive effects on intergroup discrimination (the independence perspective); and ii) CIM is the primary contributor to discrimination, such that CIM is more strongly related with discriminatory behaviour than CIP, and that CIP leads to discriminatory behaviour only when CIM is strong (the motivational perspective). These ideas were examined in three studies that assessed and/or manipulated self-reported CIP and CIM within an intergroup context, then assessed discriminatory intentions or behaviour towards a relevant outgroup. The results of these studies collectively supported the construct validity of the proposed framework: CIP and CIM were positively and non-redundantly related with each other, affected to differing degrees by experimental manipulations that were designed for each variable, and had generally distinct influences on intergroup behaviour. Studies 1-3 generally attested to the primary role of CIM over CIP in predicting intergroup discrimination; however, Studies 2-3 illustrated that experimentally-augmented levels of CIM did not lead to very strong discriminatory behaviour without high levels of CIP. The proposed framework may be instrumental in generating more thorough insights on the processes and social consequences of competitive group dynamics

An easy subexponential bound for online chain partitioning

Bosek and Krawczyk exhibited an online algorithm for partitioning an online poset of width $w$ into $w^{14\lg w}$ chains. We improve this to $w^{6.5 \lg w + 7}$ with a simpler and shorter proof by combining the work of Bosek & Krawczyk with work of Kierstead & Smith on First-Fit chain partitioning of ladder-free posets. We also provide examples illustrating the limits of our approach.Comment: 23 pages, 11 figure

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