Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models

We present Learning to Explain (LTX), a model-agnostic framework designed for providing post-hoc explanations for vision models. The LTX framework introduces an "explainer" model that generates explanation maps, highlighting the crucial regions that justify the predictions made by the model being explained. To train the explainer, we employ a two-stage process consisting of initial pretraining followed by per-instance finetuning. During both stages of training, we utilize a unique configuration where we compare the explained model's prediction for a masked input with its original prediction for the unmasked input. This approach enables the use of a novel counterfactual objective, which aims to anticipate the model's output using masked versions of the input image. Importantly, the LTX framework is not restricted to a specific model architecture and can provide explanations for both Transformer-based and convolutional models. Through our evaluations, we demonstrate that LTX significantly outperforms the current state-of-the-art in explainability across various metrics

Visual Explanations via Iterated Integrated Attributions

We introduce Iterated Integrated Attributions (IIA) - a generic method for explaining the predictions of vision models. IIA employs iterative integration across the input image, the internal representations generated by the model, and their gradients, yielding precise and focused explanation maps. We demonstrate the effectiveness of IIA through comprehensive evaluations across various tasks, datasets, and network architectures. Our results showcase that IIA produces accurate explanation maps, outperforming other state-of-the-art explanation techniques.Comment: ICCV 202

Deep Integrated Explanations

This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the intermediate representations of the model, coupled with their corresponding gradients. Through an extensive array of both objective and subjective evaluations spanning diverse tasks, datasets, and model configurations, we showcase the efficacy of DIX in generating faithful and accurate explanation maps, while surpassing current state-of-the-art methods.Comment: CIKM 202

Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits

To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants’ later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant–environment interactions by elucidating plants’ temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant–environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity

Using Archived ITS Data to Measure the Operational Benefits of a System-wide Adaptive Ramp Metering System

A System-Wide Adaptive Ramp Metering (SWARM) system has been implemented in the Portland, Oregon metropolitan area, replacing the previous pre-timed ramp-metering system that had been in operation since 1981. SWARM has been deployed on six major corridors and operates during the morning and afternoon peak hours. This report presents results of a before and after evaluation of the performance of two freeway corridors as part of ongoing efforts to measure the benefits of the new SWARM system, as compared to the pre-timed system. The study benefited from using the existing regional data, surveillance and communications infrastructure in addition to a regional data archive system. The evaluation revealed that the operation of the SWARM system, as currently configured in the Portland metropolitan region, produced mixed results when comparing the selected performance metrics to pre-timed operation. For the I-205 corridor, the results were generally positive. In the morning peak period, SWARM operation resulted in an 18.1% decrease in mainline delay and decreased variability in the delay. For the afternoon peak period, improvements were also found (a 7.9% decrease in mainline delay) with the exception of moderately congested days which saw an 4.7% increase in mainline delay. On the OR-217, however, significant increases were found in overall average delay. In the morning peak period, delay increased 34.9% while in the afternoon period delay increased 55.0%. These conclusions, however, must be tempered because of lack of ramp demand data. If an assumption is made that ramp demand changes correspond with the measured freeway VMT changes, it is likely that ramp delay decreased under SWARM operation (i.e. more vehicles were allowed on the freeway which would equate to lower delay for vehicles on the ramps). Another important finding of this evaluation was that implementation of the SWARM algorithm resulted in significantly more data communication failures in the traffic management system. While this outcome is specific to the ODOT communication infrastructure and hardware, it was not anticipated. These communication failures have the potential to impact other traveler information programs that depend on the freeway surveillance data as well as the SWARM algorithm. Finally, one of the intentions of this research project was to encourage ongoing evaluation and continuous improvement of the ramp metering system and, in general, the overall freeway management system. It is clear from the analysis that meter activation times and rates are necessary to evaluate system performance. Incorporating additional logging capabilities into the SWARM system would make it easier to evaluate system operations on an on-going automated basis

Using Archived ITS Data to Measure the Operational Benefits of a System-wide Adaptive Ramp Metering System

System-Wide Adaptive Ramp Metering (SWARM) system has been implemented in the Portland, Oregon metropolitan area, replacing the previous pre-timed ramp-metering system that had been in operation since 1981. SWARM has been deployed on six major corridors and operates during the morning and afternoon peak hours. This report presents results of a before and after evaluation of the performance of two freeway corridors as part of ongoing efforts to measure the benefits of the new SWARM system, as compared to the pre-timed system. The study benefited from using the existing regional data, surveillance and communications infrastructure in addition to a regional data archive system. The evaluation revealed that the operation of the SWARM system, as currently configured in the Portland metropolitan region, produced mixed results when comparing the selected performance metrics to pre-timed operation. For the I-205 corridor, the results were generally positive. In the morning peak period, SWARM operation resulted in an 18.1% decrease in mainline delay and decreased variability in the delay. For the afternoon peak period, improvements were also found (a 7.9 % decrease in mainline delay) with the exception of moderately congested days which saw an 4.7% increase in mainline delay. On the OR-217, however, significant increases were found in overall average delay. In the morning peak period, delay increased 34.9% while in the afternoon period delay increased 55.0%. These conclusions, however, must be tempered because of lack of ramp demand data

Vernonia 2020 Vision: A Plan for the Future

The Vernonia 2020 Vision Plan was an endeavor by the City of Vernonia to involve citizens in long-term resiliency planning and visioning to complement the short-term recovery effort following a 500-year flood in December 2007. Bridges Planning Group facilitated the process, over the course of which residents identified the highest-priority barriers to resiliency and past and present efforts to overcome these barriers. This project was conducted under the supervision of Sy Adler and Ethan Seltzer

Evolution of an Academic Village: Vision for Inner South Portland

This vision of an Academic Village is a culmination of three months of work by 12 students in the Spring 2007 Urban Design Workshop at Portland State University. Our understanding of the Academic Village arose out of an exploration of connections between neighboring academic institutions – Portland State University, Oregon Health and Sciences University on Marquam Hill, and OHSU’s emerging campus on the South Waterfront – and the Corbett and Lair Hill neighborhoods. We discovered that people from these institutions and communities live, work, study, and play in a shared urban space, but that the potential for creating an Academic Village is bounded by the history and physical characteristics of that urban space. We would like to illustrate how an Academic Village can be a major asset to the city of Portland and its residents. By combining the resources and ideals of our academic institutions with the vitality of our urban and community institutions, our vision seeks to bring our academic and community nodes closer together at many different levels. By highlighting how barriers to interaction and connectivity can be overcome, we hope to pave the way for an Academic Village that will embody sustainable ideals, contribute to the economic health of our region, and foster a deeper “sense of place.” This project was conducted under the supervision of Donald J. Stastny and Edward Starkie