Emulator-based global sensitivity analysis for flow-like landslide run-out models

Landslide run-out modeling involves various uncertainties originating from model input data. It is therefore desirable to assess the model's sensitivity. A global sensitivity analysis that is capable of exploring the entire input space and accounts for all interactions, often remains limited due to computational challenges resulting from a large number of necessary model runs. We address this research gap by integrating Gaussian process emulation into landslide run-out modeling and apply it to the open-source simulation tool r.avaflow. The feasibility and efficiency of our approach is illustrated based on the 2017 Bondo landslide event. The sensitivity of aggregated model outputs, such as the apparent friction angle, impact area, as well as spatially resolved maximum flow height and velocity, to the dry-Coulomb friction coefficient, turbulent friction coefficient and the release volume are studied. The results of first-order effects are consistent with previous results of common one-at-a-time sensitivity analyses. In addition to that, our approach allows to rigorously investigate interactions. Strong interactions are detected on the margins of the flow path where the expectation and variation of maximum flow height and velocity are small. The interactions generally become weak with increasing variation of maximum flow height and velocity. Besides, there are stronger interactions between the two friction coefficients than between the release volume and each friction coefficient. In the future, it is promising to extend the approach for other computationally expensive tasks like uncertainty quantification, model calibration, and smart early warning

A Graphene Oxide-based Fluorescence Assay for Sensitive Detection of DNA Exonuclease Enzymatic Activity

The 3′–5′ exonuclease enzyme plays a dominant role in multiple pivotal physiological activities, such as DNA replication and repair processes. In this study, we designed a sensitive graphene oxide (GO)-based probe for the detection of exonuclease enzymatic activity. In the absence of Exo III, the strong π–π interaction between the fluorophore-tagged DNA and GO causes the efficient fluorescence quenching via a fluorescence resonance energy transfer (FRET). In contrast, in the presence of Exo III, the fluorophore-tagged 3′-hydroxyl termini of the DNA probe was digested by Exo III to set the fluorophore free from adsorption when GO was introduced, causing an inefficient fluorescence quenching. As a result, the fluorescence intensity of the sensor was found to be proportional to the concentration of Exo III; towards the detection of Exo III, this simple GO-based probe demonstrated a highly sensitive and selective linear response in the low detection range from 0.01 U mL−1 to 0.5 U mL−1 and with the limit of detection (LOD) of 0.001 U mL−1. Compared with other fluorescent probes, this assay exhibited superior sensitivity and selectivity in both buffer and fetal bovine serum samples, in addition to being cost effective and having a simple setup

An Empirical Comparison of Accounting Information Representations (Research-in-Progress)

Information is often multidimensional, dynamic, and difficult to communicate using traditional representations such as verbal descriptions or even graphics. Taking a distributed cognition perspective and integrating several theories of visualization, this study formulates a theoretical model to examine the effects of information representation on a classic business decision-making task: bankruptcy prediction. The preliminary results from a laboratory experiment show that animated representations lead more accurate decisions than static graphs. The findings indicate that animation facilitates identification of the flows and problems in operating and financing processes, thereby improving subjects’ assessment of firm health

A Design Study of an Animated System for Representing Financial Ratios

Computer visualizations are all around us. In this paper we describe a design process in which we explore the development of a new visualization to aid managerial decision making. The ultimate goal of our design effort is to develop a visualization that allows for presenting most of the critical financial ratios used to describe a firm’s activity on a single computer display and dynamically. In doing so, we hope to enable managers to develop holistic and intuitive appreciations of such matters as how a business changes through time, how the flows of resources in healthy businesses differ from those in trouble, and how decisions about one aspect of a business affect others

Discovering Galaxy Features via Dataset Distillation

In many applications, Neural Nets (NNs) have classification performance on par or even exceeding human capacity. Moreover, it is likely that NNs leverage underlying features that might differ from those humans perceive to classify. Can we "reverse-engineer" pertinent features to enhance our scientific understanding? Here, we apply this idea to the notoriously difficult task of galaxy classification: NNs have reached high performance for this task, but what does a neural net (NN) "see" when it classifies galaxies? Are there morphological features that the human eye might overlook that could help with the task and provide new insights? Can we visualize tracers of early evolution, or additionally incorporated spectral data? We present a novel way to summarize and visualize galaxy morphology through the lens of neural networks, leveraging Dataset Distillation, a recent deep-learning methodology with the primary objective to distill knowledge from a large dataset and condense it into a compact synthetic dataset, such that a model trained on this synthetic dataset achieves performance comparable to a model trained on the full dataset. We curate a class-balanced, medium-size high-confidence version of the Galaxy Zoo 2 dataset, and proceed with dataset distillation from our accurate NN-classifier to create synthesized prototypical images of galaxy morphological features, demonstrating its effectiveness. Of independent interest, we introduce a self-adaptive version of the state-of-the-art Matching Trajectory algorithm to automate the distillation process, and show enhanced performance on computer vision benchmarks.Comment: Accepted to NeurIPS Workshop on Machine Learning and the Physical Sciences, 202

Towards standard plane prediction of fetal head ultrasound with domain adaption

Fetal Standard Plane (SP) acquisition is a key step in ultrasound based assessment of fetal health. The task detects an ultrasound (US) image with predefined anatomy. However, it requires skill to acquire a good SP in practice, and trainees and occasional users of ultrasound devices can find this challenging. In this work, we consider the task of automatically predicting the fetal head SP from the video approaching the SP. We adopt a domain transfer learning approach that maps the encoded spatial and temporal features of video in the source domain to the spatial representations of the desired SP image in the target domain, together with adversarial training to preserve the quality of the resulting image. Experimental results show that the predicted head plane is plausible and consistent with the anatomical features expected in a real SP. The proposed approach is motivated to support non-experts to find and analyse a trans-ventricular (TV) plane but could also be generalized to other planes, trimesters, and ultrasound imaging tasks for which standard planes are defined

Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch

We propose Rockmate to control the memory requirements when training PyTorch DNN models. Rockmate is an automatic tool that starts from the model code and generates an equivalent model, using a predefined amount of memory for activations, at the cost of a few re-computations. Rockmate automatically detects the structure of computational and data dependencies and rewrites the initial model as a sequence of complex blocks. We show that such a structure is widespread and can be found in many models in the literature (Transformer based models, ResNet, RegNets,...). This structure allows us to solve the problem in a fast and efficient way, using an adaptation of Checkmate (too slow on the whole model but general) at the level of individual blocks and an adaptation of Rotor (fast but limited to sequential models) at the level of the sequence itself. We show through experiments on many models that Rockmate is as fast as Rotor and as efficient as Checkmate, and that it allows in many cases to obtain a significantly lower memory consumption for activations (by a factor of 2 to 5) for a rather negligible overhead (of the order of 10% to 20%). Rockmate is open source and available at https://github.com/topal-team/rockmate