How many dimensions are required to find an adversarial example?

Past work exploring adversarial vulnerability have focused on situations where an adversary can perturb all dimensions of model input. On the other hand, a range of recent works consider the case where either (i) an adversary can perturb a limited number of input parameters or (ii) a subset of modalities in a multimodal problem. In both of these cases, adversarial examples are effectively constrained to a subspace $V$ in the ambient input space $\mathcal{X}$. Motivated by this, in this work we investigate how adversarial vulnerability depends on $\dim(V)$. In particular, we show that the adversarial success of standard PGD attacks with $\ell^p$ norm constraints behaves like a monotonically increasing function of $\epsilon (\frac{\dim(V)}{\dim \mathcal{X}})^{\frac{1}{q}}$ where $\epsilon$ is the perturbation budget and $\frac{1}{p} + \frac{1}{q} =1$, provided $p > 1$ (the case $p=1$ presents additional subtleties which we analyze in some detail). This functional form can be easily derived from a simple toy linear model, and as such our results land further credence to arguments that adversarial examples are endemic to locally linear models on high dimensional spaces.Comment: Comments welcome! V2: minor edits for clarit

Modelling of long-term along-fault flow of CO2 from a natural reservoir

Geological sequestration of CO2 requires the presence of at least one competent seal above the storage reservoir to ensure containment of the stored CO2. Most of the considered storage sites are overlain by low-permeability evaporites or mudrocks that form competent seals in the absence of defects. Potential defects are formed by man-made well penetrations (necessary for exploration and appraisal, and injection) as well as (for mudrocks) natural or injection-induced fracture systems through the caprock. These defects need to be de-risked during site selection and characterisation. A European ACT-sponsored research consortium, DETECT, developed an integrated characterisation and risk assessment toolkit for natural fault/fracture pathways. In this paper we describe the DETECT experimental-modelling workflow, which aims to be predictive for fault-related leakage quantification, and its application to a field case example for validation. The workflow combines laboratory experiments to obtain single-fracture stress-sensitive permeabilities; single-fracture modelling for stress-sensitive relative permeabilities and capillary pressures; fracture network characterisation and modelling for the caprock(s); upscaling of properties and constitutive functions in fracture networks; and full compositional flow modelling at field scale. We focus the paper on the application of the workflow to the Green River Site in Utah. This is a rare case of leakage from a natural CO2 reservoir, where CO2 (dissolved or gaseous) migrates along two fault zones to the surface. This site provides a unique opportunity to understand CO2 leakage mechanisms and volumes along faults, because of its extensive characterisation including a large dataset of present-day CO2 surface flux measurements as well as historical records of CO2 leakage in the form of travertine mounds. When applied to this site, our methodology predicts leakage locations accurately and, within an order of magnitude, leakage rates correctly without extensive history matching. Subsequent history matching achieves accurate leak rate matches within a-priori uncertainty ranges for model input parameters

Evaluating the effects of antimicrobial drug use on the ecology of antimicrobial resistance and microbial community structure in beef feedlot cattle

IntroductionUse of antimicrobial drugs (AMDs) in food producing animals has received increasing scrutiny because of concerns about antimicrobial resistance (AMR) that might affect consumers. Previously, investigations regarding AMR have focused largely on phenotypes of selected pathogens and indicator bacteria, such as Salmonella enterica or Escherichia coli. However, genes conferring AMR are known to be distributed and shared throughout microbial communities. The objectives of this study were to employ target-enriched metagenomic sequencing and 16S rRNA gene amplicon sequencing to investigate the effects of AMD use, in the context of other management and environmental factors, on the resistome and microbiome in beef feedlot cattle.MethodsThis study leveraged samples collected during a previous longitudinal study of cattle at beef feedlots in Canada. This included fecal samples collected from randomly selected individual cattle, as well as composite-fecal samples from randomly selected pens of cattle. All AMD use was recorded and characterized across different drug classes using animal defined daily dose (ADD) metrics.ResultsOverall, fecal resistome composition was dominated by genes conferring resistance to tetracycline and macrolide-lincosamide-streptogramin (MLS) drug classes. The diversity of bacterial phyla was greater early in the feeding period and decreased over time in the feedlot. This decrease in diversity occurred concurrently as the microbiome represented in different individuals and different pens shifted toward a similar composition dominated by Proteobacteria and Firmicutes. Some antimicrobial drug exposures in individuals and groups were associated with explaining a statistically significant proportion of the variance in the resistome, but the amount of variance explained by these important factors was very small (<0.6% variance each), and smaller than associations with other factors measured in this study such as time and feedlot ID. Time in the feedlot was associated with greater changes in the resistome for both individual animals and composite pen-floor samples, although the proportion of the variance associated with this factor was small (2.4% and 1.2%, respectively).DiscussionResults of this study are consistent with other investigations showing that, compared to other factors, AMD exposures did not have strong effects on antimicrobial resistance or the fecal microbial ecology of beef cattle

ICML 2023 Topological Deep Learning Challenge : Design and Results

This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The challenge attracted twenty-eight qualifying submissions in its two-month duration. This paper describes the design of the challenge and summarizes its main findings

Modelling of long-term along-fault flow of CO2 from a natural reservoir

Geological sequestration of CO2 requires knowledge of the flow properties of fault-related fracture networks in the low-permeability shale caprocks that overly most of the considered storage sites. A safe, sustainable and economical storage operation requires a profound understanding of these risks, recognising that quantification is challenging due to the many length and timescales involved and the very limited availability of data. The Green River site in Utah is a rare case of leakage from a natural CO2 reservoir, where CO2 (dissolved or gaseous) migrates along two fault zones to the surface. This provides a unique opportunity to understand CO2 leakage mechanisms and volumes along faults. A successful modelling of measured leakage rates will provide confidence in modelling approaches and will help select safe storage sites, de-risk storage operations and guide containment monitoring. Here, we present an integrated workflow to model the measured leakage rates and locations at this site. We combine laboratory experiments to obtain single-fracture stress-sensitive permeabilities; single-fracture modelling for stress-sensitive relative permeabilities and capillary pressures; fracture network characterisation and modelling for the primary and secondary caprocks; upscaling of properties and constitutive functions in fracture networks; and full compositional flow modelling at field scale modelling. Our results predict locations accurately and, within an order of magnitude, leakage rates correctly without extensive history matching. Subsequent history matching achieves accurate leak rate matches within a-priori uncertainty ranges for model input parameters

Modelling of long-term along-fault flow of CO₂ from a natural reservoir

As part of the European ACT-sponsored research consortium, DETECT, we developed an integrated characterisation and risk assessment toolkit for natural fault/fracture pathways. In this paper, we describe the DETECT experimental-modelling workflow, which aims to be predictive for fault-related leakage quantification, and its application to a field case example for validation. The workflow combines laboratory experiments to obtain single-fracture stress-dependent permeabilities; single-fracture modelling for stress-dependent relative permeabilities and capillary pressures; fracture network characterisation and modelling for the caprock(s); upscaling of properties and constitutive functions in fracture networks; and full compositional flow modelling at field scale. Here we focus on the application of the workflow to the Green River site in Utah. This is a rare case of leakage from a shallow natural CO2 reservoir, where CO2 (dissolved or gaseous) migrates along two fault zones to the surface. This site provides a unique opportunity to understand CO2 leakage mechanisms and volumes along faults, because of its extensive characterisation including a large dataset of present-day CO2 surface flux measurements as well as historical records of CO2 leakage in the form of travertine mounds. When applied to this site, our methodology predicts leakage locations accurately and, within an order of magnitude, leakage rates correctly without extensive history matching. Subsequent history matching achieves accurate leak rate matches within a-priori uncertainty ranges for model input parameters

Characterization of the Microbial Resistome in Conventional and "Raised Without Antibiotics" Beef and Dairy Production Systems.

Metagenomic investigations have the potential to provide unprecedented insights into microbial ecologies, such as those relating to antimicrobial resistance (AMR). We characterized the microbial resistome in livestock operations raising cattle conventionally (CONV) or without antibiotic exposures (RWA) using shotgun metagenomics. Samples of feces, wastewater from catchment basins, and soil where wastewater was applied were collected from CONV and RWA feedlot and dairy farms. After DNA extraction and sequencing, shotgun metagenomic reads were aligned to reference databases for identification of bacteria (Kraken) and antibiotic resistance genes (ARGs) accessions (MEGARes). Differences in microbial resistomes were found across farms with different production practices (CONV vs. RWA), types of cattle (beef vs. dairy), and types of sample (feces vs. wastewater vs. soil). Feces had the greatest number of ARGs per sample (mean = 118 and 79 in CONV and RWA, respectively), with tetracycline efflux pumps, macrolide phosphotransferases, and aminoglycoside nucleotidyltransferases mechanisms of resistance more abundant in CONV than in RWA feces. Tetracycline and macrolide-lincosamide-streptogramin classes of resistance were more abundant in feedlot cattle than in dairy cow feces, whereas the β-lactam class was more abundant in dairy cow feces. Lack of congruence between ARGs and microbial communities (procrustes analysis) suggested that other factors (e.g., location of farms, cattle source, management practices, diet, horizontal ARGs transfer, and co-selection of resistance), in addition to antimicrobial use, could have impacted resistome profiles. For that reason, we could not establish a cause-effect relationship between antimicrobial use and AMR, although ARGs in feces and effluents were associated with drug classes used to treat animals according to farms' records (tetracyclines and macrolides in feedlots, β-lactams in dairies), whereas ARGs in soil were dominated by multidrug resistance. Characterization of the "resistance potential" of animal-derived and environmental samples is the first step toward incorporating metagenomic approaches into AMR surveillance in agricultural systems. Further research is needed to assess the public-health risk associated with different microbial resistomes

ICML 2023 Topological Deep Learning Challenge:Design and Results

This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The challenge attracted twenty-eight qualifying submissions in its two month duration. This paper describes the design of the challenge and summarizes its main findings.</p