Measuring the Environmental Impact of Embedded/Bankfull Culverts

Streams are dynamic systems constantly changing over time. The health of a stream system is tied to the amount of sedimentation occurring in a stream. The addition of roadway culverts into a stream has been shown to cause erosion and sedimentation problems if the culvert does not meet stream characteristics of slope, bankfull/width, and channel orientation. This has led to the design of embedded/bankfull culverts. In the State of Ohio, the Ohio Environmental Protection Agency and the United States Army Corps of Engineers now require the Ohio Transportation to install embedded/bankfull culverts at all stream locations during new roadway construction. A 2008 preliminary study at Cleveland State University, showed an embedded/bankfull culvert can cost an additional 31.5 higher over traditional culvert installations. Though this design has been accepted by regulatory agencies, there has been little research to determine if embedded/bankfull culverts minimize the change in sedimentation patterns, or if embedded/bankful culverts minimize disruption to environment surrounding the culvert. This study developed a decision tree approach to determine which existing testing methods are applicable to studying culverts, and then applied this decision tree to select tests for studying embedded/bankful culverts in the state of Ohio. 63 culverts were visited and surveyed. Sediment and water samples were collected and analyzed. Data was collected on particle-size distribution, total organic carbon, total suspended solids, and turbidity. It was discovered through the field studies that many of the culverts surveyed in Ohio are not operating as embedded/bankfull culverts.The change is sedimentation patterns were compared to length, slope, diameter, and shear stress in the culvert. Some correlations were found between the change in sedimentation patterns and the physical characteristics of the culvert. All of the correlations found were in functioning culverts. More habitat data is needed to determine the effects on

Uncovering Intermediate Variables in Transformers using Circuit Probing

Neural network models have achieved high performance on a wide variety of complex tasks, but the algorithms that they implement are notoriously difficult to interpret. In order to understand these algorithms, it is often necessary to hypothesize intermediate variables involved in the network's computation. For example, does a language model depend on particular syntactic properties when generating a sentence? However, existing analysis tools make it difficult to test hypotheses of this type. We propose a new analysis technique -- circuit probing -- that automatically uncovers low-level circuits that compute hypothesized intermediate variables. This enables causal analysis through targeted ablation at the level of model parameters. We apply this method to models trained on simple arithmetic tasks, demonstrating its effectiveness at (1) deciphering the algorithms that models have learned, (2) revealing modular structure within a model, and (3) tracking the development of circuits over training. We compare circuit probing to other methods across these three experiments, and find it on par or more effective than existing analysis methods. Finally, we demonstrate circuit probing on a real-world use case, uncovering circuits that are responsible for subject-verb agreement and reflexive anaphora in GPT2-Small and Medium

NeuroSurgeon: A Toolkit for Subnetwork Analysis

Despite recent advances in the field of explainability, much remains unknown about the algorithms that neural networks learn to represent. Recent work has attempted to understand trained models by decomposing them into functional circuits (Csord\'as et al., 2020; Lepori et al., 2023). To advance this research, we developed NeuroSurgeon, a python library that can be used to discover and manipulate subnetworks within models in the Huggingface Transformers library (Wolf et al., 2019). NeuroSurgeon is freely available at https://github.com/mlepori1/NeuroSurgeon