Association of Aquatic Insects to Macrophytes in an Agricultural Drainage Ditch

The aquatic insects associated with five species of aquatic macrophytes were collected and identified from a drainage ditch in Le Suer County. A total of 21,160 specimens from eight orders were recovered with Diptera being the dominant. Tests of association, using the Coefficient of Community and Percent Similarity revealed a unique community associated with Potamogeton nodosus. Further, the authors found that the same information generated from the study could have been accomplished without the detailed taxonomy

Quantifying Hydrologic Pathway and Source Connectivity Dynamics in Tile Drainage: Implications for Phosphorus Concentrations

Flowpathways and source water connectivity dynamics are widely recognized to affect tile-drainage water quality. In this study, we developed and evaluated a framework that couples event-based hydrograph recession and specific conductance end-member mixing analysis (SC-EMMA) to provide a more robust framework for quantifying both flow pathway dynamics and source connectivity of drainage water in tile-drained landscapes. High-frequency (30-min) flow and conductivity data were collected from an edge-of-field tile main located in northwestern Ohio, and the newly developed framework was applied for data collected in water year 2019. Multiple linear regression (MLR) analysis was used to evaluate the impact of pathway-connectivity dynamics on flow-weighted mean dissolved reactive P (DRP) concentrations, which were collected as part of the USDA-ARS edge-of-field monitoring network. The hydrograph recession and SC-EMMA results highlighted intra- and interevent differences between quick (preferential) flow and new (precipitation) water transported during events, challenging a common assumption that new water reflects drainage through preferential flow paths. The analysis of hydrologic flow pathways demonstrated matrix–macropore exchange (Qquick-old), preferential flow of new water (Qquick-new), slow flow of old water (Qslow-old), and slow flow of new water (Qslow-new) contributed 9, 39, 42, and 10% to tile discharge, on average, with interevent variability. Matrix water that is transported to tile drains via macropore flowpaths was found to be activated throughout the year, even under drier antecedent conditions, suggesting that matrix–macropore exchange was more sensitive to within-event hydrological processes as compared with antecedent conditions. The MLR results highlighted that pathway-connectivity hydrograph fractions improved prediction of DRP concentrations, although improvement may be more pronounced in landscapes with higher rates of matrix–macropore exchange

Demographic Disparities in 1-to-Many Facial Identification

Most studies to date that have examined demographic variations in face recognition accuracy have analyzed 1-to-1 matching accuracy, using images that could be described as "government ID quality". This paper analyzes the accuracy of 1-to-many facial identification across demographic groups, and in the presence of blur and reduced resolution in the probe image as might occur in "surveillance camera quality" images. Cumulative match characteristic curves(CMC) are not appropriate for comparing propensity for rank-one recognition errors across demographics, and so we introduce three metrics for this: (1) d' metric between mated and non-mated score distributions, (2) absolute score difference between thresholds in the high-similarity tail of the non-mated and the low-similarity tail of the mated distribution, and (3) distribution of (mated - non-mated rank one scores) across the set of probe images. We find that demographic variation in 1-to-many accuracy does not entirely follow what has been observed in 1-to-1 matching accuracy. Also, different from 1-to-1 accuracy, demographic comparison of 1-to-many accuracy can be affected by different numbers of identities and images across demographics. Finally, we show that increased blur in the probe image, or reduced resolution of the face in the probe image, can significantly increase the false positive identification rate. And we show that the demographic variation in these high blur or low resolution conditions is much larger for male/ female than for African-American / Caucasian. The point that 1-to-many accuracy can potentially collapse in the context of processing "surveillance camera quality" probe images against a "government ID quality" gallery is an important one.Comment: 9 pages, 8 figures, Conference submissio

Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts

There exist very few ways to isolate cognitive processes, historically defined via highly controlled laboratory studies, in more ecologically valid contexts. Specifically, it remains unclear as to what extent patterns of neural activity observed under such constraints actually manifest outside the laboratory in a manner that can be used to make an accurate inference about the latent state, associated cognitive process, or proximal behavior of the individual. Improving our understanding of when and how specific patterns of neural activity manifest in ecologically valid scenarios would provide validation for laboratory-based approaches that study similar neural phenomena in isolation and meaningful insight into the latent states that occur during complex tasks. We argue that domain generalization methods from the brain-computer interface community have the potential to address this challenge. We previously used such an approach to decode phasic neural responses associated with visual target discrimination. Here, we extend that work to more tonic phenomena such as internal latent states. We use data from two highly controlled laboratory paradigms to train two separate domain-generalized models. We apply the trained models to an ecologically valid paradigm in which participants performed multiple, concurrent driving-related tasks. Using the pretrained models, we derive estimates of the underlying latent state and associated patterns of neural activity. Importantly, as the patterns of neural activity change along the axis defined by the original training data, we find changes in behavior and task performance consistent with the observations from the original, laboratory paradigms. We argue that these results lend ecological validity to those experimental designs and provide a methodology for understanding the relationship between observed neural activity and behavior during complex tasks

Consistency and Accuracy of CelebA Attribute Values

We report the first systematic analysis of the experimental foundations of facial attribute classification.Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with >= 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially random consistency. Of 5,068 duplicate face appearances in CelebA, attributes have contradicting values on from 10 to 860 of the 5,068 duplicates. Manual audit of a subset of CelebA estimates error rates as high as 40% for (no beard=false), even though the labeling consistency experiment indicates that no beard could be assigned with >= 95% consistency. Selecting the mouth slightly open (MSO) for deeper analysis, we estimate the error rate for (MSO=true) at about 20% and (MSO=false) at about 2%. A corrected version of the MSO attribute values enables learning a model that achieves higher accuracy than previously reported for MSO. Corrected values for CelebA MSO are available at https:// github.com/ HaiyuWu/ CelebAMSO

Assessing Intra-Event Phosphorus Dynamics in Drainage Water Using Phosphate Stable Oxygen Isotopes

Quantifying fluxes and pathways of dissolved reactive phosphorus (DRP) in tile-drained landscapes has been hampered by a lack of measurements that are sensitive to P fate and transport processes. One potential tool to help understand these dynamics is the oxygen isotope signature of phosphate (δ18OPO4); however, its potential benefits and limitations are not well understood for intra-event dynamics at the field scale. The objectives of this study were to quantify intra-event variability of δ18OPO4 signatures in tile drainage water and assess the efficacy of δ18OPO4 to elucidate mechanisms and flow pathways controlling DRP transport to tile drains. We collected water samples during a summer storm event from a subsurface (tile)-drained field located in west-central Ohio and analyzed for δ18OPO4 of DRP. Supplementary water quality measurements, hydrologic modeling, and soil temperature data were used to help understand intra-event δ18OPO4 dynamics. Results of the soil extraction analysis from our study site highlight that the soil water-extractable P (WEP) pool was not in equilibrium with long-term, temperature-dependent water isotope values. This result suggests that P-rich soils may, at least partially, retain their original source signature, which has significant implications for identifying hotspots of P delivery in watershed-scale applications. Results of the storm event analysis highlight that equilibration of leached DRP in soil water creates a gradient between isotopic compositions of pre-event shallow subsurface sources, pre-event deep subsurface sources, and the WEP tied up in surface soils. The current study represents the first intra-event analysis of δ18OPO4 and highlights the potential for phosphate oxygen isotopes as a novel tool to improve understanding of P fate and transport in artificially drained agroecosystems

Our Deep CNN Face Matchers Have Developed Achromatopsia

Modern deep CNN face matchers are trained on datasets containing color images. We show that such matchers achieve essentially the same accuracy on the grayscale or the color version of a set of test images. We then consider possible causes for deep CNN face matchers ``not seeing color''. Popular web-scraped face datasets actually have 30 to 60\% of their identities with one or more grayscale images. We analyze whether this grayscale element in the training set impacts the accuracy achieved, and conclude that it does not. Further, we show that even with a 100\% grayscale training set, comparable accuracy is achieved on color or grayscale test images. Then we show that the skin region of an individual's images in a web-scraped training set exhibit significant variation in their mapping to color space. This suggests that color, at least for web-scraped, in-the-wild face datasets, carries limited identity-related information for training state-of-the-art matchers. Finally, we verify that comparable accuracy is achieved from training using single-channel grayscale images, implying that a larger dataset can be used within the same memory limit, with a less computationally intensive early layer