Now you see me (CME): Concept-based model extraction

Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering DNN-based approaches is improving their explainability. In this work we present CME: a concept-based model extraction framework, used for analysing DNN models via concept-based extracted models. Using two case studies (dSprites, and Caltech UCSD Birds), we demonstrate how CME can be used to (i) analyse the concept information learned by a DNN model (ii) analyse how a DNN uses this concept information when predicting output labels (iii) identify key concept information that can further improve DNN predictive performance (for one of the case studies, we showed how model accuracy can be improved by over 14%, using only 30% of the available concepts)

Now you see me (CME): Concept-based model extraction

Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering DNN-based approaches is improving their explainability. In this work we present CME: a concept-based model extraction framework, used for analysing DNN models via concept-based extracted models. Using two case studies (dSprites, and Caltech UCSD Birds), we demonstrate how CME can be used to (i) analyse the concept information learned by a DNN model (ii) analyse how a DNN uses this concept information when predicting output labels (iii) identify key concept information that can further improve DNN predictive performance (for one of the case studies, we showed how model accuracy can be improved by over 14%, using only 30% of the available concepts)

The measurement of 90Zr (y, 2n) 88Zr and 90Zr (y, np) 8 8Y cross sections

The Zr target was irradiated with betatron bremsstrahlung of different end-point energies

Structural Inductive Biases in Emergent Communication

In order to communicate, humans flatten a complex representation of ideas and their attributes into a single word or a sentence. We investigate the impact of representation learning in artificial agents by developing graph referential games. We empirically show that agents parametrized by graph neural networks develop a more compositional language compared to bag-of-words and sequence models, which allows them to systematically generalize to new combinations of familiar features.Comment: The first two authors contributed equally. Poster presented at CogSci 202

Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations

Recent work has suggested post-hoc explainers might be ineffective for detecting spurious correlations in Deep Neural Networks (DNNs). However, we show there are serious weaknesses with the existing evaluation frameworks for this setting. Previously proposed metrics are extremely difficult to interpret and are not directly comparable between explainer methods. To alleviate these constraints, we propose a new evaluation methodology, Explainer Divergence Scores (EDS), grounded in an information theory approach to evaluate explainers. EDS is easy to interpret and naturally comparable across explainers. We use our methodology to compare the detection performance of three different explainers - feature attribution methods, influential examples and concept extraction, on two different image datasets. We discover post-hoc explainers often contain substantial information about a DNN’s dependence on spurious artifacts, but in ways often imperceptible to human users. This suggests the need for new techniques that can use this information to better detect a DNN’s reliance on spurious correlations

Molecular genetic identification of skeletal remains from the Second World War Konfin I mass grave in Slovenia

This paper describes molecular genetic identification of one third of the skeletal remains of 88 victims of postwar (June 1945) killings found in the Konfin I mass grave in Slovenia. Living relatives were traced for 36 victims. We analyzed 84 right femurs and compared their genetic profiles to the genetic material of living relatives. We cleaned the bones, removed surface contamination, and ground the bones into powder. Prior to DNA isolation using Biorobot EZ1 (Qiagen), the powder was decalcified. The nuclear DNA of the samples was quantified using the real-time polymerase chain reaction method. We extracted 0.8 to 100 ng DNA/g of bone powder from 82 bones. Autosomal genetic profiles and Y-chromosome haplotypes were obtained from 98% of the bones, and mitochondrial DNA (mtDNA) haplotypes from 95% of the bones for the HVI region and from 98% of the bones for the HVII region. Genetic profiles of the nuclear and mtDNA were determined for reference persons. For traceability in the event of contamination, we created an elimination database including genetic profiles of the nuclear and mtDNA of all persons that had been in contact with the skeletal remains. When comparing genetic profiles, we matched 28 of the 84 bones analyzed with living relatives (brothers, sisters, sons, daughters, nephews, or cousins). The statistical analyses showed a high confidence of correct identification for all 28 victims in the Konfin I mass grave (posterior probability ranged from 99.9% to more than 99.999999%)

Enzymatic Degradation of PrPSc by a Protease Secreted from Aeropyrum pernix K1

BACKGROUND: An R30 fraction from the growth medium of Aeropyrum pernix was analyzed for the protease that can digest the pathological prion protein isoform (PrP(Sc)) from different species (human, bovine, deer and mouse). METHODOLOGY/PRINCIPAL FINDINGS: Degradation of the PrP(Sc) isoform by the R30 fraction and the purified protease was evaluated using the 6H4 anti-PrP monoclonal antibody. Fragments from the N-terminal and C-terminal of PrP(Sc) were also monitored by Western blotting using the EB8 anti-PrP monoclonal antibody, and by dot blotting using the C7/5 anti-PrP monoclonal antibody, respectively. For detection of smaller peptides from incomplete digestion of PrP(Sc), the EB8 monoclonal antibody was used after precipitation with sodium phosphotungstate. Characterization of the purified active protease from the R30 fraction was achieved, through purification by fast protein liquid chromatography, and identification by tandem mass spectrometry the serine metalloprotease pernisine. SDS-PAGE and zymography show the purified pernisine plus its proregion with a molecular weight of ca. 45 kDa, and the mature purified pernisine as ca. 23 kDa. The purified pernisine was active between 58 °C and 99 °C, and between pH 3.5 and 8.0. The temperature and pH optima of the enzymatic activity of the purified pernisine in the presence of 1 mM CaCl(2) were 105 °C ± 0.5 °C and pH 6.5 ± 0.2, respectively. CONCLUSIONS/SIGNIFICANCE: Our study has identified and characterized pernisine as a thermostable serine metalloprotease that is secreted from A. pernix and that can digest the pathological prion protein PrP(Sc)