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Pseudo-Anosov dilatations and the Johnson filtration [article]

Justin Malestein, Andrew Putman
2015 arXiv   pre-print
Answering a question of Farb-Leininger-Margalit, we give explicit lower bounds for the dilatations of pseudo-Anosov mapping classes lying in the kth term of the Johnson filtration of the mapping class  ...  Sloan Foundation Johnson filtration. The Johnson filtration is an important sequence of subgroups of Mod(Σ).  ...  Dilatation in the Johnson filtration. If H < Mod(Σ) is a subgroup, then define Spec(H) = {ln(λ(f )) | f ∈ H is pseudo-Anosov} ⊂ R >0 .  ... 
arXiv:1307.6226v3 fatcat:t72m47f3djhfnaxzncm6d47fsu

Bootstrap Your Own Correspondences [article]

Mohamed El Banani, Justin Johnson
2021 arXiv   pre-print
Geometric feature extraction is a crucial component of point cloud registration pipelines. Recent work has demonstrated how supervised learning can be leveraged to learn better and more compact 3D features. However, those approaches' reliance on ground-truth annotation limits their scalability. We propose BYOC: a self-supervised approach that learns visual and geometric features from RGB-D video without relying on ground-truth pose or correspondence. Our key observation is that
more » ... zed CNNs readily provide us with good correspondences; allowing us to bootstrap the learning of both visual and geometric features. Our approach combines classic ideas from point cloud registration with more recent representation learning approaches. We evaluate our approach on indoor scene datasets and find that our method outperforms traditional and learned descriptors, while being competitive with current state-of-the-art supervised approaches.
arXiv:2106.00677v1 fatcat:dktciv56lnezfcf5uuciu5pcf4

Crisis Resolution and Home Treatment in Mental Health Edited by Sonia Johnson, Justin Needle, Jonathan P. Bindman & Graham Thornicroft. Cambridge Medicine. 2008. £29.99 (pb). 336pp. ISBN: 9780521678759

Christine Dean
2009 British Journal of Psychiatry  
doi:10.1192/bjp.bp.108.060194 fatcat:hox4g4oiare4rkpzs4m7xfsfyy

Inverting and Understanding Object Detectors [article]

Ang Cao, Justin Johnson
2021 arXiv   pre-print
As a core problem in computer vision, the performance of object detection has improved drastically in the past few years. Despite their impressive performance, object detectors suffer from a lack of interpretability. Visualization techniques have been developed and widely applied to introspect the decisions made by other kinds of deep learning models; however, visualizing object detectors has been underexplored. In this paper, we propose using inversion as a primary tool to understand modern
more » ... ect detectors and develop an optimization-based approach to layout inversion, allowing us to generate synthetic images recognized by trained detectors as containing a desired configuration of objects. We reveal intriguing properties of detectors by applying our layout inversion technique to a variety of modern object detectors, and further investigate them via validation experiments: they rely on qualitatively different features for classification and regression; they learn canonical motifs of commonly co-occurring objects; they use diff erent visual cues to recognize objects of varying sizes. We hope our insights can help practitioners improve object detectors.
arXiv:2106.13933v1 fatcat:whdaoliyjbee3co6d2vgmfdaxm

Mesh R-CNN [article]

Georgia Gkioxari, Jitendra Malik, Justin Johnson
2020 arXiv   pre-print
Rapid advances in 2D perception have led to systems that accurately detect objects in real-world images. However, these systems make predictions in 2D, ignoring the 3D structure of the world. Concurrently, advances in 3D shape prediction have mostly focused on synthetic benchmarks and isolated objects. We unify advances in these two areas. We propose a system that detects objects in real-world images and produces a triangle mesh giving the full 3D shape of each detected object. Our system,
more » ... d Mesh R-CNN, augments Mask R-CNN with a mesh prediction branch that outputs meshes with varying topological structure by first predicting coarse voxel representations which are converted to meshes and refined with a graph convolution network operating over the mesh's vertices and edges. We validate our mesh prediction branch on ShapeNet, where we outperform prior work on single-image shape prediction. We then deploy our full Mesh R-CNN system on Pix3D, where we jointly detect objects and predict their 3D shapes.
arXiv:1906.02739v2 fatcat:4gk4jyeqhrffdga65wfcxcjje4

Temporal Reasoning via Audio Question Answering [article]

Haytham M. Fayek, Justin Johnson
2019 arXiv   pre-print
Fayek and Justin Johnson Abstract-Multimodal question answering tasks can be used as proxy tasks to study systems that can perceive and reason about the world.  ... 
arXiv:1911.09655v1 fatcat:znxap2kwkjbbdlvdhai2i573pi

Image Generation from Scene Graphs [article]

Justin Johnson, Agrim Gupta, Li Fei-Fei
2018 arXiv   pre-print
To truly understand the visual world our models should be able not only to recognize images but also generate them. To this end, there has been exciting recent progress on generating images from natural language descriptions. These methods give stunning results on limited domains such as descriptions of birds or flowers, but struggle to faithfully reproduce complex sentences with many objects and relationships. To overcome this limitation we propose a method for generating images from scene
more » ... hs, enabling explicitly reasoning about objects and their relationships. Our model uses graph convolution to process input graphs, computes a scene layout by predicting bounding boxes and segmentation masks for objects, and converts the layout to an image with a cascaded refinement network. The network is trained adversarially against a pair of discriminators to ensure realistic outputs. We validate our approach on Visual Genome and COCO-Stuff, where qualitative results, ablations, and user studies demonstrate our method's ability to generate complex images with multiple objects.
arXiv:1804.01622v1 fatcat:5vbjvmq2qjfnnihkkxnidgno3i

Pseudo-Anosov dilatations and the Johnson filtration

Justin Malestein, Andrew Putman
2016 Groups, Geometry, and Dynamics  
Answering a question of Farb-Leininger-Margalit, we give explicit lower bounds for the dilatations of pseudo-Anosov mapping classes lying in the k th term of the Johnson filtration of the mapping class  ...  Sloan Foundation Johnson filtration. The Johnson filtration is an important sequence of subgroups of Mod(Σ).  ...  Dilatation in the Johnson filtration. If H < Mod(Σ) is a subgroup, then define Spec(H) = {ln(λ(f )) | f ∈ H is pseudo-Anosov} ⊂ R >0 .  ... 
doi:10.4171/ggd/365 fatcat:sce2bxf7wja4dlnslwizwky7fa

Multiproduct Cournot Oligopoly

Justin P. Johnson, David P. Myatt
2005 Social Science Research Network  
Our specification generalizes that in Johnson and Myatt (2003) .  ...  We follow earlier work (Johnson and Myatt, 2003) by taking an "upgrades approach."  ... 
doi:10.2139/ssrn.467461 fatcat:c6ji3jwyqbelzf23yr2er3jfta

DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer [article]

Joseph Suarez, Justin Johnson, Fei-Fei Li
2018 arXiv   pre-print
., 2017) and IEP (Johnson et al., 2017) as the basis of the first visual question answering (VQA) architectures successful on CLEVR (Johnson et al., 2016) .  ... 
arXiv:1803.11361v1 fatcat:bsvx23qxavf6zfq3dtiw2z2gya

HiDDeN: Hiding Data With Deep Networks [article]

Jiren Zhu, Russell Kaplan, Justin Johnson, Li Fei-Fei
2018 arXiv   pre-print
Recent work has shown that deep neural networks are highly sensitive to tiny perturbations of input images, giving rise to adversarial examples. Though this property is usually considered a weakness of learned models, we explore whether it can be beneficial. We find that neural networks can learn to use invisible perturbations to encode a rich amount of useful information. In fact, one can exploit this capability for the task of data hiding. We jointly train encoder and decoder networks, where
more » ... iven an input message and cover image, the encoder produces a visually indistinguishable encoded image, from which the decoder can recover the original message. We show that these encodings are competitive with existing data hiding algorithms, and further that they can be made robust to noise: our models learn to reconstruct hidden information in an encoded image despite the presence of Gaussian blurring, pixel-wise dropout, cropping, and JPEG compression. Even though JPEG is non-differentiable, we show that a robust model can be trained using differentiable approximations. Finally, we demonstrate that adversarial training improves the visual quality of encoded images.
arXiv:1807.09937v1 fatcat:6wvra4awzbhonghsfnxdonnry4

Unplanned Purchases and Retail Competition

Justin P. Johnson
2013 Social Science Research Network  
doi:10.2139/ssrn.2319929 fatcat:mdy5ftdnafcfvbcham2hn4mhri

Targeted Advertising and Advertising Avoidance

Justin P. Johnson
2011 Social Science Research Network  
Johnson and Myatt (2006) consider the effects of information release, with an emphasize on monopoly.  ...  targeting opportunities, whereas this is mostly absent from the other work (targeting is most central in Van Zandt (2004) ). 12 See Meurer and Stahl (1994) , Moscarini and Ottaviani (2001) and Johnson  ... 
doi:10.2139/ssrn.2018938 fatcat:6bo2yjpouzanhajiyle7pbsfbq

Characterization of a Thick Ozone Layer in Mars' Past [article]

Justin Deighan, Robert E Johnson
2013 arXiv   pre-print
Johnson et al. 2009; Tian et al. 2010 ).  ...  Johnson et al. 2008; Kasting 1997; Squyres & Kasting 1994) .  ... 
arXiv:1303.3826v1 fatcat:vyhw3iog4vg3vdjqobnze4mbie

The Agency Model and MFN Clauses

Justin P. Johnson
2013 Social Science Research Network  
I provide a general analysis of vertical relations that are intermediated either with wholesale prices or with revenue-sharing contracts. Although revenuesharing does not eliminate double markups, it nonetheless tends to lower retail prices. Revenue-sharing is extremely attractive to the firm that is able to set the revenue shares, but often makes the other firm worse off. These results hold even when there is imperfect competition at both layers of the supply chain. I also show that retail
more » ... e-parity restrictions raise industry prices. These results explain why many online retailers have adopted the "agency model" (in which they set revenue shares and suppliers set retail prices) and price-parity clauses. Finally, in an extension that considers private bargaining, I show that unobservable delegations can have equilibrium effects, and identify how pricing discretion can improve a firm's bargaining position.
doi:10.2139/ssrn.2217849 fatcat:ueyajvtix5c2liauxdvdsc2uli
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