Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a pixel-wise reconstruction error based on an $\ell^p$ distance. This procedure, however, leads to large residuals whenever the reconstruction encompasses slight localization inaccuracies around edges. It also fails to reveal defective regions that have been visually altered when intensity values stay roughly consistent. We show that these problems prevent these approaches from being applied to complex real-world scenarios and that it cannot be easily avoided by employing more elaborate architectures such as variational or feature matching autoencoders. We propose to use a perceptual loss function based on structural similarity which examines inter-dependencies between local image regions, taking into account luminance, contrast and structural information, instead of simply comparing single pixel values. It achieves significant performance gains on a challenging real-world dataset of nanofibrous materials and a novel dataset of two woven fabrics over the state of the art approaches for unsupervised defect segmentation that use pixel-wise reconstruction error metrics

Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings

We introduce a powerful student-teacher framework for the challenging problem of unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution images. Student networks are trained to regress the output of a descriptive teacher network that was pretrained on a large dataset of patches from natural images. This circumvents the need for prior data annotation. Anomalies are detected when the outputs of the student networks differ from that of the teacher network. This happens when they fail to generalize outside the manifold of anomaly-free training data. The intrinsic uncertainty in the student networks is used as an additional scoring function that indicates anomalies. We compare our method to a large number of existing deep learning based methods for unsupervised anomaly detection. Our experiments demonstrate improvements over state-of-the-art methods on a number of real-world datasets, including the recently introduced MVTec Anomaly Detection dataset that was specifically designed to benchmark anomaly segmentation algorithms.Comment: Accepted to CVPR 202

Efficient Algorithms for Semiclassical Quantum Dynamics

We study the well-known Herman-Kluk propagator in order to calculate approximate solutions to the time--dependent semiclassical Schroedinger equation in high dimensions. We derive a discretisation scheme and provide its approximation properties. This scheme, as well as other semiclassical methods, are of an intrinsically parallel nature. This allows for the design of highly efficient algorithms employing state of the art parallelization and vectorization techniques.Non UBCUnreviewedAuthor affiliation: Technische Universität MünchenGraduat

The Gcn2 Regulator Yih1 Interacts with the Cyclin Dependent Kinase Cdc28 and Promotes Cell Cycle Progression through G2/M in Budding Yeast

<div><p>The <i>Saccharomyces cerevisiae</i> protein Yih1, when overexpressed, inhibits the eIF2 alpha kinase Gcn2 by competing for Gcn1 binding. However, deletion of <i>YIH1</i> has no detectable effect on Gcn2 activity, suggesting that Yih1 is not a general inhibitor of Gcn2, and has no phenotypic defect identified so far. Thus, its physiological role is largely unknown. Here, we show that Yih1 is involved in the cell cycle. Yeast lacking Yih1 displays morphological patterns and DNA content indicative of a delay in the G2/M phases of the cell cycle, and this phenotype is independent of Gcn1 and Gcn2. Accordingly, the levels of phosphorylated eIF2α, which show a cell cycle-dependent fluctuation, are not altered in cells devoid of Yih1. We present several lines of evidence indicating that Yih1 is in a complex with Cdc28. Yih1 pulls down endogenous Cdc28 <i>in vivo</i> and this interaction is enhanced when Cdc28 is active, suggesting that Yih1 modulates the function of Cdc28 in specific stages of the cell cycle. We also demonstrate, by Bimolecular Fluorescence Complementation, that endogenous Yih1 and Cdc28 interact with each other, confirming Yih1 as a <i>bona fide</i> Cdc28 binding partner. Amino acid substitutions within helix H2 of the RWD domain of Yih1 enhance Yih1-Cdc28 association. Overexpression of this mutant, but not of wild type Yih1, leads to a phenotype similar to that of <i>YIH1</i> deletion, supporting the view that Yih1 is involved through Cdc28 in the regulation of the cell cycle. We further show that IMPACT, the mammalian homologue of Yih1, interacts with CDK1, the mammalian counterpart of Cdc28, indicating that the involvement with the cell cycle is conserved. Together, these data provide insights into the cellular function of Yih1/IMPACT, and provide the basis for future studies on the role of this protein in the cell cycle.</p></div

Cdc28 co-precipitates with GST-Yih1.

<p><b>(A)</b><i>In vivo</i> GST-pull-down assay. <i>yih1Δ</i> strains (MSY-Y2) expressing GST-Yih1 or GST alone from the galactose inducible promoter were grown to log-phase and harvested. Equal amounts of WCEs (2 mg) were subjected to glutathione-mediated GST pull-down assays. The precipitates (100% of the bound proteins – right-panel) and the input (1/100^th of the input – left panel) were assessed by immunoblot to detect the indicated proteins. <b>(B)</b> GST-Yih1 purified from <i>E</i>. <i>coli</i> co-precipitates endogenous Cdc28 from yeast WCEs. Full-length Yih1 fused to GST or GST alone were expressed in <i>E</i>. <i>coli</i>, purified, immobilized on glutathione-Sepharose beads and incubated with equal amounts of glutathione-Sepharose pre-cleared WCEs (1.25 mg) prepared from a <i>yih1Δ</i> strain (MSY-Y2). After extensive washes the precipitates (100% of the bound proteins) and the input (1/50^th of the input) were analyzed by immunoblot to detect the indicated proteins. The Ponceau staining of the membrane is shown (lower panel). One representative blot from two independent experiments performed in duplicate is shown.</p

Evidence that GST-Yih1 preferentially binds to the Cdc28 active complex.

<p><i>yih1Δ</i> cells (MSY-Y2) expressing Yih1 fused to GST from a galactose-inducible promoter were grown to log-phase in S medium containing galactose as carbon source (SGal). Cells were synchronized with α-factor, released into fresh SD media and samples were collected at the indicated times. <b>(A)</b> Representative histograms of DNA content (PI staining) of arrested (G1 – time 0) and released cells measured by flow cytometry. The distribution of cells in G1 (1C), S and G2/M (2C), analyzed with the Flowjo software, 9.3.3 version is shown. <b>(B)</b> Representative immunoblot of <i>in vivo</i> GST-pull-down assays. The collected cells were promptly harvested and equal amounts of proteins (1 mg) were subjected to glutathione-mediated pull-down assays. All the precipitated material (upper-panels) and 2% of the input (lower-panels) were subjected to immunoblots to detect GST proteins and Cdc28. As a negative control, GST alone was expressed in asynchronous <i>yih1Δ</i> cultures, pulled-down and analyzed as above. <b>(C)</b> The relative amount of Cdc28 bound to GST-Yih1 was determined with data from B using the NIH image J software. The amount of precipitated Cdc28 was normalized to the levels of precipitated GST-Yih1. Blue, yellow and pink shaded boxes indicate the estimated G1, S and G2/M cell cycle stages, respectively. Data represent mean ±S.E. of three independent experiments.</p

Mammalian IMPACT forms a complex with Cdc28 and CDK1.

<p><b>(A)</b> Mammalian IMPACT expressed in yeast precipitates Cdc28. Two different transformants of <i>yih1Δ</i> strain (BY4741) expressing either GST-IMPACT or GST alone from a galactose inducible promoter were grown to log phase in SGal. WCEs were prepared and equivalent amounts of protein (1 mg) were subjected to GST-pull-down assays. The precipitated complexes were analyzed by immunoblot for the indicated proteins. The input lanes (left-panel) contained 4% of the WCEs used in the assay. <b>(B)</b> CDK1 co-precipitates with Flag-tagged IMPACT in N2a cells. Undifferentiated mouse N2a cells were transfected with a plasmid expressing IMPACT fused to Flag or with the vector alone (pFLAG). Cell lysates were cleared with protein-A agarose and subjected to immunoprecipitation with anti-Flag antibodies (M2-Flag-Resin). All the precipitated material and 1% of the input material were subjected to immunoblot to detect Flag-IMPACT, CDK1, and GAPDH as negative control.</p

Phosphorylation of eIF2α along the cell cycle.

<p>Exponentially growing wild type (MSY-WT2) and <i>yih1Δ</i> (MSY-Y2) cells were arrested in G1 with α-factor and released into fresh SD media. Samples were collected at the indicated time intervals. <b>(A)</b> WCEs of these cultures and of an asynchronous culture (AS) were subjected to immunoblot analysis, using antibodies against eIF2α phosphorylated on Ser-51 (eIF2α-P) and against total eIF2α. <b>(B)</b> Immunoblot signals from three independent blots as shown in (A) were quantified using the NIH Image J software, the ratios between eIF2α-P and eIF2α were determined and the results were normalized using the value of the ratio of the asynchronous cells (AS); values represent means ± S.E.. <b>(C)</b> Representative histograms. Flow cytometry analysis of DNA content of the samples used in (A). Blue, yellow and pink shaded boxes indicate the estimated G1, S and G2/M cell cycle stages, respectively.</p

The abnormal cell cycle phenotype of <i>yih1Δ</i> cells does not depend on Gcn2 or Gcn1.

<p><b>(A)</b> Representative histograms of DNA content of asynchronous cultures: wild type (H1511), and <i>yih1Δ</i> (ESY11001b), <i>gcn2Δ</i> (H2557) and <i>gcn1Δ</i> (H2556) single mutants, and <i>yih1Δ;gcn2Δ</i> (ESY10075aa) or <i>yih1Δ</i>;<i>gcn1Δ</i> (ESY10075aa) double mutants, grown to log-phase in YPD, stained with PI and analyzed by flow cytometry; the distribution of cells in G1 (1C), S or G2/M (2C) is shown. <b>(B)</b> The proportion of cells in G1 (1C), S or G2/M (2C), as determined from (A) is given as percentage of the total cell number, quantified using the flow cytometry gates depicted in A with the cell-cycle tool (Watson model) of Flowjo software, 9.3.3 version. Data are presented as means ± S.E. (<i>error bars</i>) of three independent experiments. * <i>p</i>< 0.05 (ANOVA).</p