Interpreting Deep Visual Representations via Network Dissection

The success of recent deep convolutional neural networks (CNNs) depends on learning hidden representations that can summarize the important factors of variation behind the data. However, CNNs often criticized as being black boxes that lack interpretability, since they have millions of unexplained model parameters. In this work, we describe Network Dissection, a method that interprets networks by providing labels for the units of their deep visual representations. The proposed method quantifies the interpretability of CNN representations by evaluating the alignment between individual hidden units and a set of visual semantic concepts. By identifying the best alignments, units are given human interpretable labels across a range of objects, parts, scenes, textures, materials, and colors. The method reveals that deep representations are more transparent and interpretable than expected: we find that representations are significantly more interpretable than they would be under a random equivalently powerful basis. We apply the method to interpret and compare the latent representations of various network architectures trained to solve different supervised and self-supervised training tasks. We then examine factors affecting the network interpretability such as the number of the training iterations, regularizations, different initializations, and the network depth and width. Finally we show that the interpreted units can be used to provide explicit explanations of a prediction given by a CNN for an image. Our results highlight that interpretability is an important property of deep neural networks that provides new insights into their hierarchical structure.Comment: *B. Zhou and D. Bau contributed equally to this work. 15 pages, 27 figure

Morphological word structure in English and Swedish : the evidence from prosody

Trubetzkoy's recognition of a delimitative function of phonology, serving to signal boundaries between morphological units, is expressed in terms of alignment constraints in Optimality Theory, where the relevant constraints require specific morphological boundaries to coincide with phonological structure (Trubetzkoy 1936, 1939, McCarthy & Prince 1993). The approach pursued in the present article is to investigate the distribution of phonological boundary signals to gain insight into the criteria underlying morphological analysis. The evidence from English and Swedish suggests that necessary and sufficient conditions for word-internal morphological analysis concern the recognizability of head constituents, which include the rightmost members of compounds and head affixes. The claim is that the stability of word-internal boundary effects in historical perspective cannot in general be sufficiently explained in terms of memorization and imitation of phonological word form. Rather, these effects indicate a morphological parsing mechanism based on the recognition of word-internal head constituents. Head affixes can be shown to contrast systematically with modifying affixes with respect to syntactic function, semantic content, and prosodic properties. That is, head affixes, which cannot be omitted, often lack inherent meaning and have relatively unmarked boundaries, which can be obscured entirely under specific phonological conditions. By contrast, modifying affixes, which can be omitted, consistently have inherent meaning and have stronger boundaries, which resist prosodic fusion in all phonological contexts. While these correlations are hardly specific to English and Swedish it remains to be investigated to which extent they hold cross-linguistically. The observation that some of the constituents identified on the basis of prosodic evidence lack inherent meaning raises the issue of compositionality. I will argue that certain systematic aspects of word meaning cannot be captured with reference to the syntagmatic level, but require reference to the paradigmatic level instead. The assumption is then that there are two dimensions of morphological analysis: syntagmatic analysis, which centers on the criteria for decomposing words in terms of labelled constituents, and paradigmatic analysis, which centers on the criteria for establishing relations among (whole) words in the mental lexicon. While meaning is intrinsically connected with paradigmatic analysis (e.g. base relations, oppositeness) it is not essential to syntagmatic analysis

A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data

Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context

A Convolutional Neural Network (CNN) based Pill Image Retrieval System

Several works have been done in the area of image retrieval systems, and many are still trying to provide improvements for a better model for retrieving said images. Image segmentation using clustering techniques is one of the most used approaches. There are various clustering methods available, but the non-linear k-means clustering technique is the most used method. In the following research, a model of retrieving images using a non-linear classifier aided with a convolutional neural network is proposed. Both algorithms were exploited and paired in terms of feature extraction and classification. Comprehensive evaluations over a dataset containing over 7,000 pill images of 1,000 pill types obtained from the National Library of Medicine database demonstrate significant success during the data classification using the proposed model

Toxicity Of Potassium Permanganate And Potassium Peroxymonosulfate Controlled Released Biodegradable Polymer To The Non-Target Organism Daphnia Magna

Potassium permanganate (KMnO4) and potassium peroxymonosulfate (Oxone®) are chemical oxidants widely used in natural surface waters and wastewaters for the control and removal of pathogens and the prevention of disease caused by bacteria. However, KMnO4 and Oxone® have the ability to be toxic to aquatic life. Moreover, there is limited information within literature about the toxicity of these oxidizing agents to non-target organisms such as the microcrustacean Daphnia magna (D. magna). The purpose of this study was to evaluate the acute toxicity of KMnO4 and Oxone® to D. magna. The focus for this work is to determine the toxic effects on non-target organisms using the U.S. EPA approved Whole Effluent Toxicity (WET) Test performed with D. magna. Controlled Release Biodegradable Polymers (CRBP) were produced by encapsulating KMnO4 and Oxone® in polycaprolactone (PCL) per U.S. Patent #8,519,061. The CRBPs have previously been shown to be effective at reducing bacteria levels for Escherichia coli (E. coli), Enterococci, and total coliform in contaminated water. It was hypothesized that the CRBP could be a potential treatment technology to treat natural surface waters without toxicity to aquatic life. The CRBPs were tested at 0.1g KMnO4 encapsulated in 0.5g PCL and 0.3g Oxone® encapsulated in 0.5g PCL and released for 24h, 48h and 72h in 100-1000ml of natural surface test water to determine the toxic effects to D. magna. The results showed that the WET Test using D. magna demonstrated Oxone® CRBP can cause severe toxic effects to non-target organisms when released at any length of time, hence indicating potential damaging effects on higher level aquatic life that may receive effluents treated with this strong oxidant. Further results show that the KMnO4 CRBP caused minimal acute toxic effect in D. magna when released at longer lengths of time. These findings show KMnO4 CRBP exhibit promising minimal toxic effects on non-target organisms during environmental remediation

Guidelines for Organizing Art Exhibitions on Addiction and Recovery

Outlines the Innovators Combating Substance Abuse program's model for exhibiting art by those in addiction recovery as a way to offer insight into substance abuse and recovery. With lessons learned and submissions, selection, and installation guidelines

The structure of random ellipsoid packings

Disordered packings of ellipsoidal particles are an important model for disordered granular matter and can shed light on geometric features and structural transitions in granular matter. In this thesis, the structure of experimental ellipsoid packings is analyzed in terms of contact numbers and measures from mathematical morphometry to characterize of Voronoi cell shapes. Jammed ellipsoid packings are prepared by vertical shaking of loose configurations in a cylindrical container. For approximately 50 realizations with packing fractions between 0.54 and 0.70 and aspect ratios from 0.40 to 0.97, tomographic images are recorded, from which positions and orientations of the ellipsoids are reconstructed. Contact numbers as well as discrete approximations of generalized Voronoi diagrams are extracted. The shape of the Voronoi cells is quantified by isotropy indexes b,r,s,n based on Minkowski tensors. In terms of the Voronoi cells, the behavior for jammed ellipsoids differs from that of spheres; the Voronoi Cells of spheres become isotropic with increasing packing fraction, whereas the shape of the Voronoi Cells of ellipsoids with high aspect ratio remains approximately constant. Contact numbers are discussed in the context of the jamming paradigm and it is found that the frictional ellipsoid packings are hyperstatic, i.e. have more contacts than are required for mechanical stability. It is observed, that the contact numbers of jammed ellipsoid packings predominantly depend on the packing fraction, but also a weaker dependence on the aspect ratio and the friction coefficient is found. The achieved packing fractions in the experiments lie within upper and lower limits expected from DEM simulations of jammed ellipsoid packings. Finally, the results are compared to Monte Carlo and Molecular Dynamics data of unjammed equilibrium ellipsoid ensembles. The Voronoi cell shapes of equilibrium ensembles of ellipsoidal particles with a low aspect ratio become more anisotropic by increasing the packing fraction, while the cell shape of particles with large aspect ratios does the opposite. The experimental jammed packings are always more anisotropic than the corresponding densest equilibrium configuration