pwc-archive/papers-with-abstracts huggingface dataset:
Robust and Accurate Authorship Attribution via Program Normalization
Authors: Yizhen Wang, Mohannad Alhanahnah, Ke Wang, Mihai Christodorescu, Somesh Jha
Date: 2020-07-01 00:00:00
Tasks: Authorship Attribution, Image Classification, Malware Detection

Source code attribution approaches have achieved remarkable accuracy thanks to the rapid advances in deep learning. However, recent studies shed light on their vulnerability to adversarial attacks. In particular, they can be easily deceived by adversaries who attempt to either create a forgery of another author or to mask the original author. To address these emerging issues, we formulate this security challenge into a general threat model, the $\textit{relational adversary}$, that allows an arbitrary number of the semantics-preserving transformations to be applied to an input in any problem space. Our theoretical investigation shows the conditions for robustness and the trade-off between robustness and accuracy in depth. Motivated by these insights, we present a novel learning framework, $\textit{normalize-and-predict}$ ($\textit{N&P}$), that in theory guarantees the robustness of any authorship-attribution approach. We conduct an extensive evaluation of $\textit{N&P}$ in defending two of the latest authorship-attribution approaches against state-of-the-art attack methods. Our evaluation demonstrates that $\textit{N&P}$ improves the accuracy on adversarial inputs by as much as 70% over the vanilla models. More importantly, $\textit{N&P}$ also increases robust accuracy to 45% higher than adversarial training while running over 40 times faster.
Rapid tissue oxygenation mapping from snapshot structured-light images with adversarial deep learning
Authors: Mason T. Chen, Nicholas J. Durr
Date: 2020-07-01 00:00:00

Spatial frequency domain imaging (SFDI) is a powerful technique for mapping tissue oxygen saturation over a wide field of view. However, current SFDI methods either require a sequence of several images with different illumination patterns or, in the case of single snapshot optical properties (SSOP), introduce artifacts and sacrifice accuracy. To avoid this tradeoff, we introduce OxyGAN: a data-driven, content-aware method to estimate tissue oxygenation directly from single structured light images using end-to-end generative adversarial networks. Conventional SFDI is used to obtain ground truth tissue oxygenation maps for ex vivo human esophagi, in vivo hands and feet, and an in vivo pig colon sample under 659 nm and 851 nm sinusoidal illumination. We benchmark OxyGAN by comparing to SSOP and to a two-step hybrid technique that uses a previously-developed deep learning model to predict optical properties followed by a physical model to calculate tissue oxygenation. When tested on human feet, a cross-validated OxyGAN maps tissue oxygenation with an accuracy of 96.5%. When applied to sample types not included in the training set, such as human hands and pig colon, OxyGAN achieves a 93.0% accuracy, demonstrating robustness to various tissue types. On average, OxyGAN outperforms SSOP and a hybrid model in estimating tissue oxygenation by 24.9% and 24.7%, respectively. Lastly, we optimize OxyGAN inference so that oxygenation maps are computed ~10 times faster than previous work, enabling video-rate, 25Hz imaging. Due to its rapid acquisition and processing speed, OxyGAN has the potential to enable real-time, high-fidelity tissue oxygenation mapping that may be useful for many clinical applications.
Bandit Linear Control
Authors: Asaf Cassel, Tomer Koren
Date: 2020-07-01 00:00:00
Proceeding: NeurIPS 2020 12

We consider the problem of controlling a known linear dynamical system under stochastic noise, adversarially chosen costs, and bandit feedback. Unlike the full feedback setting where the entire cost function is revealed after each decision, here only the cost incurred by the learner is observed. We present a new and efficient algorithm that, for strongly convex and smooth costs, obtains regret that grows with the square root of the time horizon $T$. We also give extensions of this result to general convex, possibly non-smooth costs, and to non-stochastic system noise. A key component of our algorithm is a new technique for addressing bandit optimization of loss functions with memory.
In-Distribution Interpretability for Challenging Modalities
Authors: Cosmas Heiß, Ron Levie, Cinjon Resnick, Gitta Kutyniok, Joan Bruna
Date: 2020-07-01 00:00:00
Tasks: Physical Simulations

It is widely recognized that the predictions of deep neural networks are difficult to parse relative to simpler approaches. However, the development of methods to investigate the mode of operation of such models has advanced rapidly in the past few years. Recent work introduced an intuitive framework which utilizes generative models to improve on the meaningfulness of such explanations. In this work, we display the flexibility of this method to interpret diverse and challenging modalities: music and physical simulations of urban environments.
Signature-Based Abduction for Expressive Description Logics -- Technical Report
Authors: Patrick Koopmann, Warren Del-Pinto, Sophie Tourret, Renate A. Schmidt
Date: 2020-07-01 00:00:00

Signature-based abduction aims at building hypotheses over a specified set of names, the signature, that explain an observation relative to some background knowledge. This type of abduction is useful for tasks such as diagnosis, where the vocabulary used for observed symptoms differs from the vocabulary expected to explain those symptoms. We present the first complete method solving signature-based abduction for observations expressed in the expressive description logic ALC, which can include TBox and ABox axioms, thereby solving the knowledge base abduction problem. The method is guaranteed to compute a finite and complete set of hypotheses, and is evaluated on a set of realistic knowledge bases.
Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey
Authors: Samuel Henrique Silva, Peyman Najafirad
Date: 2020-07-01 00:00:00
Tasks: Adversarial Robustness, BIG-bench Machine Learning, Deep Learning, Survey

As we seek to deploy machine learning models beyond virtual and controlled domains, it is critical to analyze not only the accuracy or the fact that it works most of the time, but if such a model is truly robust and reliable. This paper studies strategies to implement adversary robustly trained algorithms towards guaranteeing safety in machine learning algorithms. We provide a taxonomy to classify adversarial attacks and defenses, formulate the Robust Optimization problem in a min-max setting and divide it into 3 subcategories, namely: Adversarial (re)Training, Regularization Approach, and Certified Defenses. We survey the most recent and important results in adversarial example generation, defense mechanisms with adversarial (re)Training as their main defense against perturbations. We also survey mothods that add regularization terms that change the behavior of the gradient, making it harder for attackers to achieve their objective. Alternatively, we've surveyed methods which formally derive certificates of robustness by exactly solving the optimization problem or by approximations using upper or lower bounds. In addition, we discuss the challenges faced by most of the recent algorithms presenting future research perspectives.
Legends: Folklore on Reddit
Authors: Caitrin Armstrong, Derek Ruths
Date: 2020-07-01 00:00:00

In this paper we introduce Reddit legends, a collection of venerated old posts that have become famous on Reddit. To establish the utility of Reddit legends for both computational science/HCI and folkloristics, we investigate two main questions: (1) whether they can be considered folklore, i.e. if they have consistent form, cultural significance, and undergo spontaneous transmission, and (2) whether they can be studied in a systematic manner. Through several subtasks, including the creation of a typology, an analysis of references to Reddit legends, and an examination of some of the textual characteristics of referencing behaviour, we show that Reddit legends can indeed be considered as folklore and that they are amendable to systematic text-based approaches. We discuss how these results will enable future analyses of folklore on Reddit, including tracking subreddit-wide and individual-user behaviour, and the relationship of this behaviour to other cultural markers.
Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images
Authors: Ostap Viniavskyi, Mariia Dobko, Oles Dobosevych
Date: 2020-07-01 00:00:00
Tasks: Relation Network, Segmentation, Semantic Segmentation, Weakly supervised segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Deep Convolutional Neural Networks have proven effective in solving the task of semantic segmentation. However, their efficiency heavily relies on the pixel-level annotations that are expensive to get and often require domain expertise, especially in medical imaging. Weakly supervised semantic segmentation helps to overcome these issues and also provides explainable deep learning models. In this paper, we propose a novel approach to the semantic segmentation of medical chest X-ray images with only image-level class labels as supervision. We improve the disease localization accuracy by combining three approaches as consecutive steps. First, we generate pseudo segmentation labels of abnormal regions in the training images through a supervised classification model enhanced with a regularization procedure. The obtained activation maps are then post-processed and propagated into a second classification model-Inter-pixel Relation Network, which improves the boundaries between different object classes. Finally, the resulting pseudo-labels are used to train a proposed fully supervised segmentation model. We analyze the robustness of the presented method and test its performance on two distinct datasets: PASCAL VOC 2012 and SIIM-ACR Pneumothorax. We achieve significant results in the segmentation on both datasets using only image-level annotations. We show that this approach is applicable to chest X-rays for detecting an anomalous volume of air in the pleural space between the lung and the chest wall. Our code has been made publicly available.
Deep learning-based holographic polarization microscopy
Authors: Tairan Liu, Kevin de Haan, Bijie Bai, Yair Rivenson, Yi Luo, Hongda Wang, David Karalli, Hongxiang Fu, Yibo Zhang, John FitzGerald, Aydogan Ozcan
Date: 2020-07-01 00:00:00
Tasks: Deep Learning, Diagnostic, Medical Diagnosis

Polarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically operate by analyzing images collected from two or more light paths in different states of polarization, which lead to relatively complex optical designs, high system costs or experienced technicians being required. Here, we present a deep learning-based holographic polarization microscope that is capable of obtaining quantitative birefringence retardance and orientation information of specimen from a phase recovered hologram, while only requiring the addition of one polarizer/analyzer pair to an existing holographic imaging system. Using a deep neural network, the reconstructed holographic images from a single state of polarization can be transformed into images equivalent to those captured using a single-shot computational polarized light microscope (SCPLM). Our analysis shows that a trained deep neural network can extract the birefringence information using both the sample specific morphological features as well as the holographic amplitude and phase distribution. To demonstrate the efficacy of this method, we tested it by imaging various birefringent samples including e.g., monosodium urate (MSU) and triamcinolone acetonide (TCA) crystals. Our method achieves similar results to SCPLM both qualitatively and quantitatively, and due to its simpler optical design and significantly larger field-of-view, this method has the potential to expand the access to polarization microscopy and its use for medical diagnosis in resource limited settings.
Build2Vec: Building Representation in Vector Space
Authors: Mahmoud Abdelrahman, Adrian Chong, Clayton Miller
Date: 2020-07-01 00:00:00

In this paper, we represent a methodology of a graph embeddings algorithm that is used to transform labeled property graphs obtained from a Building Information Model (BIM). Industrial Foundation Classes (IFC) is a standard schema for BIM, which is utilized to convert the building data into a graph representation. We used node2Vec with biased random walks to extract semantic similarities between different building components and represent them in a multi-dimensional vector space. A case study implementation is conducted on a net-zero-energy building located at the National University of Singapore (SDE4). This approach shows promising machine learning applications in capturing the semantic relations and similarities of different building objects, more specifically, spatial and spatio-temporal data.
Virtual Testbed for Monocular Visual Navigation of Small Unmanned Aircraft Systems
Authors: Kyung Kim, Robert C. Leishman, Scott L. Nykl
Date: 2020-07-01 00:00:00
Tasks: Monocular Visual Odometry, Visual Navigation, Visual Odometry

Monocular visual navigation methods have seen significant advances in the last decade, recently producing several real-time solutions for autonomously navigating small unmanned aircraft systems without relying on GPS. This is critical for military operations which may involve environments where GPS signals are degraded or denied. However, testing and comparing visual navigation algorithms remains a challenge since visual data is expensive to gather. Conducting flight tests in a virtual environment is an attractive solution prior to committing to outdoor testing. This work presents a virtual testbed for conducting simulated flight tests over real-world terrain and analyzing the real-time performance of visual navigation algorithms at 31 Hz. This tool was created to ultimately find a visual odometry algorithm appropriate for further GPS-denied navigation research on fixed-wing aircraft, even though all of the algorithms were designed for other modalities. This testbed was used to evaluate three current state-of-the-art, open-source monocular visual odometry algorithms on a fixed-wing platform: Direct Sparse Odometry, Semi-Direct Visual Odometry, and ORB-SLAM2 (with loop closures disabled).
Tensor Estimation with Nearly Linear Samples Given Weak Side Information
Authors: Christina Lee Yu, Xumei Xi
Date: 2020-07-01 00:00:00

Tensor completion exhibits an interesting computational-statistical gap in terms of the number of samples needed to perform tensor estimation. While there are only $\Theta(tn)$ degrees of freedom in a $t$-order tensor with $n^t$ entries, the best known polynomial time algorithm requires $O(n^{t/2})$ samples in order to guarantee consistent estimation. In this paper, we show that weak side information is sufficient to reduce the sample complexity to $O(n)$. The side information consists of a weight vector for each of the modes which is not orthogonal to any of the latent factors along that mode; this is significantly weaker than assuming noisy knowledge of the subspaces. We provide an algorithm that utilizes this side information to produce a consistent estimator with $O(n^{1+\kappa})$ samples for any small constant $\kappa > 0$. We also provide experiments on both synthetic and real-world datasets that validate our theoretical insights.
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation
Authors: Songyang Zhang, Han Zhang, Shuguang Cui, Zhi Ding
Date: 2020-07-01 00:00:00
Tasks: Node Classification

Graph convolutional networks (GCN) have been recently utilized to extract the underlying structures of datasets with some labeled data and high-dimensional features. Existing GCNs mostly rely on a first-order Chebyshev approximation of graph wavelet-kernels. Such a generic propagation model does not always suit the various datasets and their features. This work revisits the fundamentals of graph wavelet and explores the utility of signal propagation in the vertex domain to approximate the spectral wavelet-kernels. We first derive the conditions for representing the graph wavelet-kernels via vertex propagation. We next propose alternative propagation models for GCN layers based on Taylor expansions. We further analyze the choices of detailed graph representations for TGCNs. Experiments on citation networks, multimedia datasets and synthetic graphs demonstrate the advantage of Taylor-based GCN (TGCN) in the node classification problems over the traditional GCN methods.
Learning Geocentric Object Pose in Oblique Monocular Images
Authors: Gordon Christie, Rodrigo Rene Rai Munoz Abujder, Kevin Foster, Shea Hagstrom, Gregory D. Hager, Myron Z. Brown
Date: 2020-07-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Earth Observation, Object, object-detection, Object Detection, Optical Flow Estimation, Semantic Segmentation

An object's geocentric pose, defined as the height above ground and orientation with respect to gravity, is a powerful representation of real-world structure for object detection, segmentation, and localization tasks using RGBD images. For close-range vision tasks, height and orientation have been derived directly from stereo-computed depth and more recently from monocular depth predicted by deep networks. For long-range vision tasks such as Earth observation, depth cannot be reliably estimated with monocular images. Inspired by recent work in monocular height above ground prediction and optical flow prediction from static images, we develop an encoding of geocentric pose to address this challenge and train a deep network to compute the representation densely, supervised by publicly available airborne lidar. We exploit these attributes to rectify oblique images and remove observed object parallax to dramatically improve the accuracy of localization and to enable accurate alignment of multiple images taken from very different oblique viewpoints. We demonstrate the value of our approach by extending two large-scale public datasets for semantic segmentation in oblique satellite images. All of our data and code are publicly available.
Molecular Latent Space Simulators
Authors: Hythem Sidky, Wei Chen, Andrew L. Ferguson
Date: 2020-07-01 00:00:00

Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional kinetic models from MD simulation data by performing configurational or dynamical coarse-graining of the state space. The learned kinetic models enable the efficient generation of dynamical trajectories over vastly longer time scales than are accessible by MD, but the discretization of configurational space and/or absence of a means to reconstruct molecular configurations precludes the generation of continuous all-atom molecular trajectories. We propose latent space simulators (LSS) to learn kinetic models for continuous all-atom simulation trajectories by training three deep learning networks to (i) learn the slow collective variables of the molecular system, (ii) propagate the system dynamics within this slow latent space, and (iii) generatively reconstruct molecular configurations. We demonstrate the approach in an application to Trp-cage miniprotein to produce novel ultra-long synthetic folding trajectories that accurately reproduce all-atom molecular structure, thermodynamics, and kinetics at six orders of magnitude lower cost than MD. The dramatically lower cost of trajectory generation enables greatly improved sampling and greatly reduced statistical uncertainties in estimated thermodynamic averages and kinetic rates.
Sequential Transfer in Reinforcement Learning with a Generative Model
Authors: Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli
Date: 2020-07-01 00:00:00
Proceeding: ICML 2020 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We are interested in how to design reinforcement learning agents that provably reduce the sample complexity for learning new tasks by transferring knowledge from previously-solved ones. The availability of solutions to related problems poses a fundamental trade-off: whether to seek policies that are expected to achieve high (yet sub-optimal) performance in the new task immediately or whether to seek information to quickly identify an optimal solution, potentially at the cost of poor initial behavior. In this work, we focus on the second objective when the agent has access to a generative model of state-action pairs. First, given a set of solved tasks containing an approximation of the target one, we design an algorithm that quickly identifies an accurate solution by seeking the state-action pairs that are most informative for this purpose. We derive PAC bounds on its sample complexity which clearly demonstrate the benefits of using this kind of prior knowledge. Then, we show how to learn these approximate tasks sequentially by reducing our transfer setting to a hidden Markov model and employing spectral methods to recover its parameters. Finally, we empirically verify our theoretical findings in simple simulated domains.
Adversarial Example Games
Authors: Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton
Date: 2020-07-01 00:00:00
Proceeding: NeurIPS 2020 12

The existence of adversarial examples capable of fooling trained neural network classifiers calls for a much better understanding of possible attacks to guide the development of safeguards against them. This includes attack methods in the challenging non-interactive blackbox setting, where adversarial attacks are generated without any access, including queries, to the target model. Prior attacks in this setting have relied mainly on algorithmic innovations derived from empirical observations (e.g., that momentum helps), lacking principled transferability guarantees. In this work, we provide a theoretical foundation for crafting transferable adversarial examples to entire hypothesis classes. We introduce Adversarial Example Games (AEG), a framework that models the crafting of adversarial examples as a min-max game between a generator of attacks and a classifier. AEG provides a new way to design adversarial examples by adversarially training a generator and a classifier from a given hypothesis class (e.g., architecture). We prove that this game has an equilibrium, and that the optimal generator is able to craft adversarial examples that can attack any classifier from the corresponding hypothesis class. We demonstrate the efficacy of AEG on the MNIST and CIFAR-10 datasets, outperforming prior state-of-the-art approaches with an average relative improvement of $29.9\%$ and $47.2\%$ against undefended and robust models (Table 2 & 3) respectively.