pwc-archive/papers-with-abstracts huggingface dataset:
A Dynamic Penalty Function Approach for Constraints-Handling in Reinforcement Learning
Authors: Haeun Yoo, Victor M. Zavala, Jay H. Lee
Date: 2020-12-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Reinforcement learning (RL) is attracting attention as an effective way to solve sequential optimization problems that involve high dimensional state/action space and stochastic uncertainties. Many such problems involve constraints expressed by inequality constraints. This study focuses on using RL to solve constrained optimal control problems. Most RL application studies have dealt with inequality constraints by adding soft penalty terms for violating the constraints to the reward function. However, while training neural networks to learn the value (or Q) function, one can run into computational issues caused by the sharp change in the function value at the constraint boundary due to the large penalty imposed. This difficulty during training can lead to convergence problems and ultimately lead to poor closed-loop performance. To address this issue, this study proposes a dynamic penalty (DP) approach where the penalty factor is gradually and systematically increased during training as the iteration episodes proceed. We first examine the ability of a neural network to represent a value function when uniform, linear, or DP functions are added to prevent constraint violation. The agent trained by a Deep Q Network (DQN) algorithm with the DP function approach was compared with agents with other constant penalty functions in a simple vehicle control problem. Results show that the proposed approach can improve the neural network approximation accuracy and provide faster convergence when close to a solution.
Algorithmic Recourse in the Wild: Understanding the Impact of Data and Model Shifts
Authors: Kaivalya Rawal, Ece Kamar, Himabindu Lakkaraju
Date: 2020-12-22 00:00:00

As predictive models are increasingly being deployed to make a variety of consequential decisions, there is a growing emphasis on designing algorithms that can provide recourse to affected individuals. Existing recourse algorithms function under the assumption that the underlying predictive model does not change. However, models are regularly updated in practice for several reasons including data distribution shifts. In this work, we make the first attempt at understanding how model updates resulting from data distribution shifts impact the algorithmic recourses generated by state-of-the-art algorithms. We carry out a rigorous theoretical and empirical analysis to address the above question. Our theoretical results establish a lower bound on the probability of recourse invalidation due to model shifts, and show the existence of a tradeoff between this invalidation probability and typical notions of "cost" minimized by modern recourse generation algorithms. We experiment with multiple synthetic and real world datasets, capturing different kinds of distribution shifts including temporal shifts, geospatial shifts, and shifts due to data correction. These experiments demonstrate that model updation due to all the aforementioned distribution shifts can potentially invalidate recourses generated by state-of-the-art algorithms. Our findings thus not only expose previously unknown flaws in the current recourse generation paradigm, but also pave the way for fundamentally rethinking the design and development of recourse generation algorithms.
Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer
Authors: Wei Cui, Wei Yu
Date: 2020-12-22 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes a novel scalable reinforcement learning approach for simultaneous routing and spectrum access in wireless ad-hoc networks. In most previous works on reinforcement learning for network optimization, the network topology is assumed to be fixed, and a different agent is trained for each transmission node -- this limits scalability and generalizability. Further, routing and spectrum access are typically treated as separate tasks. Moreover, the optimization objective is usually a cumulative metric along the route, e.g., number of hops or delay. In this paper, we account for the physical-layer signal-to-interference-plus-noise ratio (SINR) in a wireless network and further show that bottleneck objective such as the minimum SINR along the route can also be optimized effectively using reinforcement learning. Specifically, we propose a scalable approach in which a single agent is associated with each flow and makes routing and spectrum access decisions as it moves along the frontier nodes. The agent is trained according to the physical-layer characteristics of the environment using a novel rewarding scheme based on the Monte Carlo estimation of the future bottleneck SINR. It learns to avoid interference by intelligently making joint routing and spectrum allocation decisions based on the geographical location information of the neighbouring nodes.
Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization
Authors: Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike, Kento Uemura, Hiroki Arimura
Date: 2020-12-22 00:00:00
Tasks: counterfactual, Counterfactual Explanation, Decision Making

Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result. Given a perturbation vector, a user can interpret it as an "action" for obtaining one's desired decision result. In practice, however, showing only a perturbation vector is often insufficient for users to execute the action. The reason is that if there is an asymmetric interaction among features, such as causality, the total cost of the action is expected to depend on the order of changing features. Therefore, practical CE methods are required to provide an appropriate order of changing features in addition to a perturbation vector. For this purpose, we propose a new framework called Ordered Counterfactual Explanation (OrdCE). We introduce a new objective function that evaluates a pair of an action and an order based on feature interaction. To extract an optimal pair, we propose a mixed-integer linear optimization approach with our objective function. Numerical experiments on real datasets demonstrated the effectiveness of our OrdCE in comparison with unordered CE methods.
Towards an Automatic System for Extracting Planar Orientations from Software Generated Point Clouds
Authors: J. Kissi-Ameyaw, K. McIsaac, X. Wang, G. R. Osinski
Date: 2020-12-22 00:00:00

In geology, a key activity is the characterisation of geological structures (surface formation topology and rock units) using Planar Orientation measurements such as Strike, Dip and Dip Direction. In general these measurements are collected manually using basic equipment; usually a compass/clinometer and a backboard, recorded on a map by hand. Various computing techniques and technologies, such as Lidar, have been utilised in order to automate this process and update the collection paradigm for these types of measurements. Techniques such as Structure from Motion (SfM) reconstruct of scenes and objects by generating a point cloud from input images, with detailed reconstruction possible on the decimetre scale. SfM-type techniques provide advantages in areas of cost and usability in more varied environmental conditions, while sacrificing the extreme levels of data fidelity. Here is presented a methodology of data acquisition and a Machine Learning-based software system: GeoStructure, developed to automate the measurement of orientation measurements. Rather than deriving measurements using a method applied to the input images, such as the Hough Transform, this method takes measurements directly from the reconstructed point cloud surfaces. Point cloud noise is mitigated using a Mahalanobis distance implementation. Significant structure is characterised using a k-nearest neighbour region growing algorithm, and final surface orientations are quantified using the plane, and normal direction cosines.
SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction
Authors: P. J. "Eddie'' Edwards, Dimitris Psychogyios, Stefanie Speidel, Lena Maier-Hein, Danail Stoyanov
Date: 2020-12-22 00:00:00
Tasks: 3D Reconstruction, Anatomy

In computer vision, reference datasets have been highly successful in promoting algorithmic development in stereo reconstruction. Surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surfaces and the presence of blood and smoke. Publicly available datasets have been produced using CT and either phantom images or biological tissue samples covering a relatively small region of the endoscope field-of-view. We present a stereo-endoscopic reconstruction validation dataset based on CT (SERV-CT). Two {\it ex vivo} small porcine full torso cadavers were placed within the view of the endoscope with both the endoscope and target anatomy visible in the CT scan. Orientation of the endoscope was manually aligned to the stereoscopic view. Reference disparities and occlusions were calculated for 8 stereo pairs from each sample. For the second sample an RGB surface was acquired to aid alignment of smooth, featureless surfaces. Repeated manual alignments showed an RMS disparity accuracy of ~2 pixels and a depth accuracy of ~2mm. The reference dataset includes endoscope image pairs with corresponding calibration, disparities, depths and occlusions covering the majority of the endoscopic image and a range of tissue types. Smooth specular surfaces and images with significant variation of depth are included. We assessed the performance of various stereo algorithms from online available repositories. There is a significant variation between algorithms, highlighting some of the challenges of surgical endoscopic images. The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths with coverage over the majority of the endoscopic images. This complements existing resources well and we hope will aid the development of surgical endoscopic anatomical reconstruction algorithms.
MailLeak: Obfuscation-Robust Character Extraction Using Transfer Learning
Authors: Wei Wang, Emily Sallenback, Zeyu Ning, Hugues Nelson Iradukunda, Wenxi Lu, Qingquan Zhang, Ting Zhu
Date: 2020-12-22 00:00:00
Tasks: Transfer Learning

The following work presents a new algorithm for character recognition from obfuscated images. The presented method is an example of a potential threat to current postal services. This paper both analyzes the efficiency of the given algorithm and suggests countermeasures to prevent such threats from occurring.
Power-SLIC: Fast Superpixel Segmentations by Diagrams
Authors: Maximilian Fiedler, Andreas Alpers
Date: 2020-12-22 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

Superpixel algorithms grouping pixels with similar color and other low-level properties are increasingly used for pre-processing in image segmentation. In recent years, a focus has been placed on developing geometric superpixel methods that facilitate the extraction and analysis of geometric image features. Diagram-based superpixel methods are important among the geometric methods as they generate compact and sparsely representable superpixels. Introducing generalized balanced power diagrams to the field of superpixels, we propose a diagram method called Power-SLIC. Power-SLIC is the first geometric superpixel method to generate piecewise quadratic boundaries. Its speed, competitive with fast state-of-the-art methods, is unprecedented for diagram approaches. Extensive computational experiments show that Power-SLIC outperforms existing diagram approaches in boundary recall, under segmentation error, achievable segmentation accuracy, and compression quality. Moreover, Power-SLIC is robust to Gaussian noise.
Self-Progressing Robust Training
Authors: Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das
Date: 2020-12-22 00:00:00
Tasks: Adversarial Robustness

Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems. Current robust training methods such as adversarial training explicitly uses an "attack" (e.g., $\ell_{\infty}$-norm bounded perturbation) to generate adversarial examples during model training for improving adversarial robustness. In this paper, we take a different perspective and propose a new framework called SPROUT, self-progressing robust training. During model training, SPROUT progressively adjusts training label distribution via our proposed parametrized label smoothing technique, making training free of attack generation and more scalable. We also motivate SPROUT using a general formulation based on vicinity risk minimization, which includes many robust training methods as special cases. Compared with state-of-the-art adversarial training methods (PGD-l_inf and TRADES) under l_inf-norm bounded attacks and various invariance tests, SPROUT consistently attains superior performance and is more scalable to large neural networks. Our results shed new light on scalable, effective and attack-independent robust training methods.
Estimating the Impact of Weather on Agriculture
Authors: Jeffrey D. Michler, Anna Josephson, Talip Kilic, Siobhan Murray
Date: 2020-12-22 00:00:00
Tasks: Survey

This paper quantifies the significance and magnitude of the effect of measurement error in remote sensing weather data in the analysis of smallholder agricultural productivity. The analysis leverages 17 rounds of nationally-representative, panel household survey data from six countries in Sub-Saharan Africa. These data are spatially-linked with a range of geospatial weather data sources and related metrics. We provide systematic evidence on measurement error introduced by 1) different methods used to obfuscate the exact GPS coordinates of households, 2) different metrics used to quantify precipitation and temperature, and 3) different remote sensing measurement technologies. First, we find no discernible effect of measurement error introduced by different obfuscation methods. Second, we find that simple weather metrics, such as total seasonal rainfall and mean daily temperature, outperform more complex metrics, such as deviations in rainfall from the long-run average or growing degree days, in a broad range of settings. Finally, we find substantial amounts of measurement error based on remote sensing product. In extreme cases, data drawn from different remote sensing products result in opposite signs for coefficients on weather metrics, meaning that precipitation or temperature draw from one product purportedly increases crop output while the same metrics drawn from a different product purportedly reduces crop output. We conclude with a set of six best practices for researchers looking to combine remote sensing weather data with socioeconomic survey data.
Deep Multi-attribute Graph Representation Learning on Protein Structures
Authors: Tian Xia, Wei-Shinn Ku
Date: 2020-12-22 00:00:00
Tasks: Attribute, Graph Neural Network, Graph Representation Learning, Representation Learning

Graphs as a type of data structure have recently attracted significant attention. Representation learning of geometric graphs has achieved great success in many fields including molecular, social, and financial networks. It is natural to present proteins as graphs in which nodes represent the residues and edges represent the pairwise interactions between residues. However, 3D protein structures have rarely been studied as graphs directly. The challenges include: 1) Proteins are complex macromolecules composed of thousands of atoms making them much harder to model than micro-molecules. 2) Capturing the long-range pairwise relations for protein structure modeling remains under-explored. 3) Few studies have focused on learning the different attributes of proteins together. To address the above challenges, we propose a new graph neural network architecture to represent the proteins as 3D graphs and predict both distance geometric graph representation and dihedral geometric graph representation together. This gives a significant advantage because this network opens a new path from the sequence to structure. We conducted extensive experiments on four different datasets and demonstrated the effectiveness of the proposed method.
Bounding the Complexity of Formally Verifying Neural Networks: A Geometric Approach
Authors: James Ferlez, Yasser Shoukry
Date: 2020-12-22 00:00:00

In this paper, we consider the computational complexity of formally verifying the behavior of Rectified Linear Unit (ReLU) Neural Networks (NNs), where verification entails determining whether the NN satisfies convex polytopic specifications. Specifically, we show that for two different NN architectures -- shallow NNs and Two-Level Lattice (TLL) NNs -- the verification problem with (convex) polytopic constraints is polynomial in the number of neurons in the NN to be verified, when all other aspects of the verification problem held fixed. We achieve these complexity results by exhibiting explicit (but similar) verification algorithms for each type of architecture. Both algorithms efficiently translate the NN parameters into a partitioning of the NN's input space by means of hyperplanes; this has the effect of partitioning the original verification problem into polynomially many sub-verification problems derived from the geometry of the neurons. We show that these sub-problems may be chosen so that the NN is purely affine within each, and hence each sub-problem is solvable in polynomial time by means of a Linear Program (LP). Thus, a polynomial-time algorithm for the original verification problem can be obtained using known algorithms for enumerating the regions in a hyperplane arrangement. Finally, we adapt our proposed algorithms to the verification of dynamical systems, specifically when these NN architectures are used as state-feedback controllers for LTI systems. We further evaluate the viability of this approach numerically.
Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding
Authors: Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen, Walter Chang, Leo Anthony Celi
Date: 2020-12-22 00:00:00
Tasks: document understanding

Acronyms are the short forms of longer phrases and they are frequently used in writing, especially scholarly writing, to save space and facilitate the communication of information. As such, every text understanding tool should be capable of recognizing acronyms in text (i.e., acronym identification) and also finding their correct meaning (i.e., acronym disambiguation). As most of the prior works on these tasks are restricted to the biomedical domain and use unsupervised methods or models trained on limited datasets, they fail to perform well for scientific document understanding. To push forward research in this direction, we have organized two shared task for acronym identification and acronym disambiguation in scientific documents, named AI@SDU and AD@SDU, respectively. The two shared tasks have attracted 52 and 43 participants, respectively. While the submitted systems make substantial improvements compared to the existing baselines, there are still far from the human-level performance. This paper reviews the two shared tasks and the prominent participating systems for each of them.
Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery
Authors: Qian Wang, Toby P. Breckon
Date: 2020-12-21 00:00:00
Tasks: 3D Semantic Segmentation, Computed Tomography (CT), object-detection, Object Detection, Semantic Segmentation

Automatic prohibited object detection within 2D/3D X-ray Computed Tomography (CT) has been studied in literature to enhance the aviation security screening at checkpoints. Deep Convolutional Neural Networks (CNN) have demonstrated superior performance in 2D X-ray imagery. However, there exists very limited proof of how deep neural networks perform in materials detection within volumetric 3D CT baggage screening imagery. We attempt to close this gap by applying Deep Neural Networks in 3D contraband substance detection based on their material signatures. Specifically, we formulate it as a 3D semantic segmentation problem to identify material types for all voxels based on which contraband materials can be detected. To this end, we firstly investigate 3D CNN based semantic segmentation algorithms such as 3D U-Net and its variants. In contrast to the original dense representation form of volumetric 3D CT data, we propose to convert the CT volumes into sparse point clouds which allows the use of point cloud processing approaches such as PointNet++ towards more efficient processing. Experimental results on a publicly available dataset (NEU ATR) demonstrate the effectiveness of both 3D U-Net and PointNet++ in materials detection in 3D CT imagery for baggage security screening.
RealFormer: Transformer Likes Residual Attention
Authors: Ruining He, Anirudh Ravula, Bhargav Kanagal, Joshua Ainslie
Date: 2020-12-21 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Language Modeling, Language Modelling, Linguistic Acceptability, Machine Translation, Masked Language Modeling, Natural Language Inference, Natural Questions, Paraphrase Identification, Semantic Textual Similarity, Sentiment Analysis, Translation

Transformer is the backbone of modern NLP models. In this paper, we propose RealFormer, a simple and generic technique to create Residual Attention Layer Transformer networks that significantly outperform the canonical Transformer and its variants (BERT, ETC, etc.) on a wide spectrum of tasks including Masked Language Modeling, GLUE, SQuAD, Neural Machine Translation, WikiHop, HotpotQA, Natural Questions, and OpenKP. We also observe empirically that RealFormer stabilizes training and leads to models with sparser attention. Source code and pre-trained checkpoints for RealFormer can be found at https://github.com/google-research/google-research/tree/master/realformer.
Training DNNs in O(1) memory with MEM-DFA using Random Matrices
Authors: Tien Chu, Kamil Mykitiuk, Miron Szewczyk, Adam Wiktor, Zbigniew Wojna
Date: 2020-12-21 00:00:00

This work presents a method for reducing memory consumption to a constant complexity when training deep neural networks. The algorithm is based on the more biologically plausible alternatives of the backpropagation (BP): direct feedback alignment (DFA) and feedback alignment (FA), which use random matrices to propagate error. The proposed method, memory-efficient direct feedback alignment (MEM-DFA), uses higher independence of layers in DFA and allows avoiding storing at once all activation vectors, unlike standard BP, FA, and DFA. Thus, our algorithm's memory usage is constant regardless of the number of layers in a neural network. The method increases the computational cost only by a constant factor of one extra forward pass. The MEM-DFA, BP, FA, and DFA were evaluated along with their memory profiles on MNIST and CIFAR-10 datasets on various neural network models. Our experiments agree with our theoretical results and show a significant decrease in the memory cost of MEM-DFA compared to the other algorithms.