pwc-archive/papers-with-abstracts huggingface dataset:
Artificial Stupidity
Authors: Michael Falk
Date: 2020-07-02 00:00:00

Public debate about AI is dominated by Frankenstein Syndrome, the fear that AI will become superhuman and escape human control. Although superintelligence is certainly a possibility, the interest it excites can distract the public from a more imminent concern: the rise of Artificial Stupidity (AS). This article discusses the roots of Frankenstein Syndrome in Mary Shelley's famous novel of 1818. It then provides a philosophical framework for analysing the stupidity of artificial agents, demonstrating that modern intelligent systems can be seen to suffer from 'stupidity of judgement'. Finally it identifies an alternative literary tradition that exposes the perils and benefits of AS. In the writings of Edmund Spenser, Jonathan Swift and E.T.A. Hoffmann, ASs replace, oppress or seduce their human users. More optimistically, Joseph Furphy and Laurence Sterne imagine ASs that can serve human intellect as maps or as pipes. These writers provide a strong counternarrative to the myths that currently drive the AI debate. They identify ways in which even stupid artificial agents can evade human control, for instance by appealing to stereotypes or distancing us from reality. And they underscore the continuing importance of the literary imagination in an increasingly automated society.
Group Equivariant Deep Reinforcement Learning
Authors: Arnab Kumar Mondal, Pratheeksha Nair, Kaleem Siddiqi
Date: 2020-07-01 00:00:00
Tasks: Deep Reinforcement Learning, Inductive Bias, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In Reinforcement Learning (RL), Convolutional Neural Networks(CNNs) have been successfully applied as function approximators in Deep Q-Learning algorithms, which seek to learn action-value functions and policies in various environments. However, to date, there has been little work on the learning of symmetry-transformation equivariant representations of the input environment state. In this paper, we propose the use of Equivariant CNNs to train RL agents and study their inductive bias for transformation equivariant Q-value approximation. We demonstrate that equivariant architectures can dramatically enhance the performance and sample efficiency of RL agents in a highly symmetric environment while requiring fewer parameters. Additionally, we show that they are robust to changes in the environment caused by affine transformations.
Difference-Based Deep Learning Framework for Stress Predictions in Heterogeneous Media
Authors: Haotian Feng, Pavana Prabhakar
Date: 2020-07-01 00:00:00

Stress analysis of heterogeneous media, like composite materials, using Finite Element Analysis (FEA) has become commonplace in design and analysis. However, determining stress distributions in heterogeneous media using FEA can be computationally expensive in situations like optimization and multi-scaling. To address this, we utilize Deep Learning for developing a set of novel Difference-based Neural Network (DiNN) frameworks based on engineering and statistics knowledge to determine stress distribution in heterogeneous media, for the first time, with special focus on discontinuous domains that manifest high stress concentrations. The novelty of our approach is that instead of directly using several FEA model geometries and stresses as inputs for training a Neural Network, as typically done previously, we focus on highlighting the differences in stress distribution between different input samples for improving the accuracy of prediction in heterogeneous media. We evaluate the performance of DiNN frameworks by considering different types of geometric models that are commonly used in the analysis of composite materials, including volume fraction and spatial randomness. Results show that the DiNN structures significantly enhance the accuracy of stress prediction compared to existing structures, especially for composite models with random volume fraction when localized high stress concentrations are present.
Subject-Aware Contrastive Learning for Biosignals
Authors: Joseph Y. Cheng, Hanlin Goh, Kaan Dogrusoz, Oncel Tuzel, Erdrin Azemi
Date: 2020-06-30 00:00:00
Tasks: Anomaly Detection, Contrastive Learning, Data Augmentation, EEG, EEG 4 classes, Eeg Decoding, EEG Left/Right hand, Electroencephalogram (EEG), Person Identification, Self-Supervised Learning, Time Series, Time Series Analysis

Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100). To handle these challenges, we propose a self-supervised approach based on contrastive learning to model biosignals with a reduced reliance on labeled data and with fewer subjects. In this regime of limited labels and subjects, intersubject variability negatively impacts model performance. Thus, we introduce subject-aware learning through (1) a subject-specific contrastive loss, and (2) an adversarial training to promote subject-invariance during the self-supervised learning. We also develop a number of time-series data augmentation techniques to be used with the contrastive loss for biosignals. Our method is evaluated on publicly available datasets of two different biosignals with different tasks: EEG decoding and ECG anomaly detection. The embeddings learned using self-supervision yield competitive classification results compared to entirely supervised methods. We show that subject-invariance improves representation quality for these tasks, and observe that subject-specific loss increases performance when fine-tuning with supervised labels.
A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem
Authors: Han Zhang, Jialin Liu, Xin Yao
Date: 2020-06-27 00:00:00
Tasks: Decision Making, Management

The reliable facility location problem (RFLP) is an important research topic of operational research and plays a vital role in the decision-making and management of modern supply chain and logistics. Through solving RFLP, the decision-maker can obtain reliable location decisions under the risk of facilities' disruptions or failures. In this paper, we propose a novel model for the RFLP. Instead of assuming allocating a fixed number of facilities to each customer as in the existing works, we set the number of allocated facilities as an independent variable in our proposed model, which makes our model closer to the scenarios in real life but more difficult to be solved by traditional methods. To handle it, we propose EAMLS, a hybrid evolutionary algorithm, which combines a memorable local search (MLS) method and an evolutionary algorithm (EA). Additionally, a novel metric called l3-value is proposed to assist the analysis of the algorithm's convergence speed and exam the process of evolution. The experimental results show the effectiveness and superior performance of our EAMLS, compared to a CPLEX solver and a Genetic Algorithm (GA), on large-scale problems.
Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net
Authors: Dominik Müller, Iñaki Soto Rey, Frank Kramer
Date: 2020-06-24 00:00:00
Tasks: Data Augmentation, Image Segmentation, Medical Image Analysis, Segmentation, Semantic Segmentation

The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. Due to rising skepticism towards the sensitivity of RT-PCR as screening method, medical imaging like computed tomography offers great potential as alternative. For this reason, automated image segmentation is highly desired as clinical decision support for quantitative assessment and disease monitoring. However, publicly available COVID-19 imaging data is limited which leads to overfitting of traditional approaches. To address this problem, we propose an innovative automated segmentation pipeline for COVID-19 infected regions, which is able to handle small datasets by utilization as variant databases. Our method focuses on on-the-fly generation of unique and random image patches for training by performing several preprocessing methods and exploiting extensive data augmentation. For further reduction of the overfitting risk, we implemented a standard 3D U-Net architecture instead of new or computational complex neural network architectures. Through a 5-fold cross-validation on 20 CT scans of COVID-19 patients, we were able to develop a highly accurate as well as robust segmentation model for lungs and COVID-19 infected regions without overfitting on the limited data. Our method achieved Dice similarity coefficients of 0.956 for lungs and 0.761 for infection. We demonstrated that the proposed method outperforms related approaches, advances the state-of-the-art for COVID-19 segmentation and improves medical image analysis with limited data. The code and model are available under the following link: https://github.com/frankkramer-lab/covid19.MIScnn
Self-supervised edge features for improved Graph Neural Network training
Authors: Arijit Sehanobish, Neal G. Ravindra, David van Dijk
Date: 2020-06-23 00:00:00
Tasks: General Classification, Graph Attention, Graph Neural Network, Link Prediction, Node Classification

Graph Neural Networks (GNN) have been extensively used to extract meaningful representations from graph structured data and to perform predictive tasks such as node classification and link prediction. In recent years, there has been a lot of work incorporating edge features along with node features for prediction tasks. One of the main difficulties in using edge features is that they are often handcrafted, hard to get, specific to a particular domain, and may contain redundant information. In this work, we present a framework for creating new edge features, applicable to any domain, via a combination of self-supervised and unsupervised learning. In addition to this, we use Forman-Ricci curvature as an additional edge feature to encapsulate the local geometry of the graph. We then encode our edge features via a Set Transformer and combine them with node features extracted from popular GNN architectures for node classification in an end-to-end training scheme. We validate our work on three biological datasets comprising of single-cell RNA sequencing data of neurological disease, \textit{in vitro} SARS-CoV-2 infection, and human COVID-19 patients. We demonstrate that our method achieves better performance on node classification tasks over baseline Graph Attention Network (GAT) and Graph Convolutional Network (GCN) models. Furthermore, given the attention mechanism on edge and node features, we are able to interpret the cell types and genes that determine the course and severity of COVID-19, contributing to a growing list of potential disease biomarkers and therapeutic targets.
Graph Neural Network Based Coarse-Grained Mapping Prediction
Authors: Zhiheng Li, Geemi P. Wellawatte, Maghesree Chakraborty, Heta A. Gandhi, Chenliang Xu, Andrew D. White
Date: 2020-06-24 00:00:00
Tasks: Clustering, Graph Neural Network, graph partitioning, Metric Learning, Open-Ended Question Answering, Prediction

The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this choice and there is a need for theory. The current state-of-the art method is mapping operators manually selected by experts. In this work, we demonstrate an automated approach by viewing this problem as supervised learning where we seek to reproduce the mapping operators produced by experts. We present a graph neural network based CG mapping predictor called DEEP SUPERVISED GRAPH PARTITIONING MODEL(DSGPM) that treats mapping operators as a graph segmentation problem. DSGPM is trained on a novel dataset, Human-annotated Mappings (HAM), consisting of 1,206 molecules with expert annotated mapping operators. HAM can be used to facilitate further research in this area. Our model uses a novel metric learning objective to produce high-quality atomic features that are used in spectral clustering. The results show that the DSGPM outperforms state-of-the-art methods in the field of graph segmentation. Finally, we find that predicted CG mapping operators indeed result in good CG MD models when used in simulation.
Recursion and evolution: Part II
Authors: Alexandros Arvanitakis
Date: 2020-06-12 00:00:00

We examine the question of whether it is possible for a diagonalizing system, to learn to use environmental reward and punishment as an information, in order to appropriately adapt. More specifically, we study the possiblity of such a system, to learn to use diagonalization on the basis of a rewarding function. Relevant phenomena regarding memory are also investigated.
Exploring the BERT Cross-Lingual Transferability: a Case Study in Reading Comprehension
Authors: Konovalov V. P., Gulyaev P. A., Sorokin A. A., Kuratov Y. M., Burtsev M. S.
Date: 2020-06-17 00:00:00
Proceeding: Proceedings of the International Conference “Dialogue 2020” 2020 6
Tasks: NER, POS, Question Answering, Reading Comprehension

Multilingual BERT has been shown to generalize well in a zero-shot cross-lingual setting. This generalization was measured on POS and NER tasks. We explore the multilingual BERT cross-language transferability on the reading comprehension task. We compare different modes of training of the question-answering model for a non-English language using both English and language-specific data. We demonstrate that the model based on multilingual BERT is slightly behind the monolingual BERT-based on Russian data, however, it achieves comparable results with the language-specific variant on Chinese. We also show that training jointly on English data and additional 10,000 monolingual samples allows it to reach the performance comparable to the one trained on monolingual data only.
Tagging and parsing of multidomain collections
Authors: Alexey Sorokin, Ivan Smurov, Denis Kirianov
Date: 2020-06-17 00:00:00
Proceeding: Proceedings of the International Conference “Dialogue 2020” 2020 6
Tasks: Dependency Parsing, Language Modeling, Language Modelling, Lemmatization, Morphological Tagging

In this paper we describe our submission to GramEval2020 competition on morphological tagging, lemmatization and dependency parsing.
Our model uses biaffine attention over the BERT representations. The main feature of our work is the extensive usage of language model, tagger and parser fine-tuning on several distinct genres and the implementation of genre classifier. To deal with dataset idiosyncrasies we also extensively apply handwritten rules.
Our model took second place in the overall model performance scoring 90.8 aggregate measure over all 4 tasks.
SPARQL query generation for complex question answering with BERT and BiLSTM-based model
Authors: Evseev D. A., Arkhipov M. Yu.
Date: 2020-06-17 00:00:00
Proceeding: Proceedings of the International Conference “Dialogue 2020” 2020 6
Tasks: Knowledge Base Question Answering, Question Answering, Semantic Parsing, Triplet

In this paper we describe question answering system for answering of complex questions over Wikidata knowledge base. Unlike simple questions, which require extraction of single fact from the knowledge base, complex questions are based on more than one triplet and need logical or comparative reasoning. The proposed question answering system translates a natural language question into a query in SPARQL language, execution of which gives an answer. The system includes the models which define the SPARQL query template corresponding to the question and then fill the slots in the template with entities, relations and numerical values. For entity detection we use BERT-based sequence labelling model. Ranking of candidate relations is performed in two steps with BiLSTM and BERT-based models. The proposed models are the first solution for LC-QUAD2.0 dataset. The system is capable of answering complex questions which involve comparative or boolean reasoning.
Interpolated corrected curvature measures for polygonal surfaces
Authors: Jacques-Olivier Lachaud, Pascal Romon, Boris Thibert, David Coeurjolly
Date: 2020-07-09 00:00:00
Proceeding: Computer Graphics Forum (Proceedings of Symposium on Geometry Processing) 2020 7

A consistent and yet practically accurate definition of curvature onto polyhedral meshes remains an open problem. We pro- pose a new framework to define curvature measures, based on the Corrected Normal Current, which generalizes the normal cycle: it uncouples the positional information of the polyhedral mesh from its geometric normal vector field, and the user can freely choose the corrected normal vector field at vertices for curvature computations. A smooth surface is then built in the Grassmannian R^3xS^2 by simply interpolating the given normal vector field. Curvature measures are then computed using the usual Lipschitz–Killing forms, and we provide closed-form formulas per triangle. We prove a stability result with respect to perturbations of positions and normals. Our approach provides a natural scale-space for all curvature estimations, where the scale is given by the radius of the measuring ball. We show on experiments how this method outperforms state-of-the-art methods on clean and noisy data, and even achieves pointwise convergence on difficult polyhedral meshes like digital surfaces. The framework is also well suited to curvature computations using normal map information.
Super 4PCS Fast Global Pointcloud Registration via Smart Indexing
Authors: Nicolas Mellado, Dror Aiger, Niloy J. Mitra
Date: 2014-08-23 00:00:00
Proceeding: Computer Graphics Forum 2014 8
Tasks: Point Cloud Registration

Data acquisition in large‐scale scenes regularly involves accumulating information across multiple scans. A common approach is to locally align scan pairs using Iterative Closest Point (ICP) algorithm (or its variants), but requires static scenes and small motion between scan pairs. This prevents accumulating data across multiple scan sessions and/or different acquisition modalities (e.g., stereo, depth scans). Alternatively, one can use a global registration algorithm allowing scans to be in arbitrary initial poses. The state‐of‐the‐art global registration algorithm, 4PCS, however has a quadratic time complexity in the number of data points. This vastly limits its applicability to acquisition of large environments. We present Super 4PCS for global pointcloud registration that is optimal, i.e., runs in linear time (in the number of data points) and is also output sensitive in the complexity of the alignment problem based on the (unknown) overlap across scan pairs. Technically, we map the algorithm as an ‘instance problem’ and solve it efficiently using a smart indexing data organization. The algorithm is simple, memory‐efficient, and fast. We demonstrate that Super 4PCS results in significant speedup over alternative approaches and allows unstructured efficient acquisition of scenes at scales previously not possible. Complete source code and datasets are available for research use at http://geometry.cs.ucl.ac.uk/projects/2014/super4PCS/.
Efficient reachability analysis of parametric linear hybrid systems with time-triggered transitions
Authors: Marcelo Forets, Daniel Freire, Christian Schilling
Date: 2020-06-22 00:00:00

Efficiently handling time-triggered and possibly nondeterministic switches for hybrid systems reachability is a challenging task. In this paper we present an approach based on conservative set-based enclosure of the dynamics that can handle systems with uncertain parameters and inputs, where the uncertainties are bound to given intervals. The method is evaluated on the plant model of an experimental electro-mechanical braking system with periodic controller. In this model, the fast-switching controller dynamics requires simulation time scales of the order of nanoseconds. Accurate set-based computations for relatively large time horizons are known to be expensive. However, by appropriately decoupling the time variable with respect to the spatial variables, and enclosing the uncertain parameters using interval matrix maps acting on zonotopes, we show that the computation time can be lowered to 5000 times faster with respect to previous works. This is a step forward in formal verification of hybrid systems because reduced run-times allow engineers to introduce more expressiveness in their models with a relatively inexpensive computational cost.
WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans
Authors: Laetitia Hebert, Tosif Ahamed, Antonio C Costa, Liam O'Shaughnessy, Greg J. Stephens
Date: 2020-07-10 00:00:00
Proceeding: Plos Computational Biology 2021 4
Tasks: 2D Pose Estimation, Animal Pose Estimation, Image Generation, Pose Estimation

An important model system for understanding genes, neurons and behavior, the nematode worm C. elegans naturally moves through a variety of complex postures, for which estimation from video data is challenging. We introduce an open-source Python package, WormPose, for 2D pose estimation in C. elegans , including self-occluded, coiled shapes. We leverage advances in machine vision afforded from convolutional neural networks and introduce a synthetic yet realistic generative model for images of worm posture, thus avoiding the need for human-labeled training. WormPose is effective and adaptable for imaging conditions across worm tracking efforts. We quantify pose estimation using synthetic data as well as N2 and mutant worms in on-food conditions. We further demonstrate WormPose by analyzing long (~10 hour), fast-sampled (~30 Hz) recordings of on-food N2 worms to provide a posture-scale analysis of roaming/dwelling behaviors.
POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation
Authors: Thomas Berg, Peter N. Belhumeur
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Attribute, Face Verification, Fine-Grained Visual Categorization

From a set of images in a particular domain, labeled with part locations and class, we present a method to automatically learn a large and diverse set of highly discriminative intermediate features that we call Part-based One-vs-One Features (POOFs). Each of these features specializes in discrimination between two particular classes based on the appearance at a particular part. We demonstrate the particular usefulness of these features for fine-grained visual categorization with new state-of-the-art results on bird species identification using the Caltech UCSD Birds (CUB) dataset and parity with the best existing results in face verification on the Labeled Faces in the Wild (LFW) dataset. Finally, we demonstrate the particular advantage of POOFs when training data is scarce.