pwc-archive/papers-with-abstracts huggingface dataset:
Learning Occupational Task-Shares Dynamics for the Future of Work
Authors: Subhro Das, Sebastian Steffen, Wyatt Clarke, Prabhat Reddy, Erik Brynjolfsson, Martin Fleming
Date: 2020-01-28 00:00:00

The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and low wage occupations. Notably, big data and AI have risen significantly among high wage occupations since 2012 and 2016, respectively. We built an ARIMA model to predict future occupational task demands and showcase several relevant examples in Healthcare, Administration, and IT. Such task demands predictions across occupations will play a pivotal role in retraining the workforce of the future.
FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains
Authors: Jeanine Harb, Nicolas Rébéna, Raphaël Chosidow, Grégoire Roblin, Roman Potarusov, Hatem Hajri
Date: 2020-02-05 00:00:00
Tasks: Self-Driving Cars

In the realm of autonomous transportation, there have been many initiatives for open-sourcing self-driving cars datasets, but much less for alternative methods of transportation such as trains. In this paper, we aim to bridge the gap by introducing FRSign, a large-scale and accurate dataset for vision-based railway traffic light detection and recognition. Our recordings were made on selected running trains in France and benefited from carefully hand-labeled annotations. An illustrative dataset which corresponds to ten percent of the acquired data to date is published in open source with the paper. It contains more than 100,000 images illustrating six types of French railway traffic lights and their possible color combinations, together with the relevant information regarding their acquisition such as date, time, sensor parameters, and bounding boxes. This dataset is published in open-source at the address \url{https://frsign.irt-systemx.fr}. We compare, analyze various properties of the dataset and provide metrics to express its variability. We also discuss specific challenges and particularities related to autonomous trains in comparison to autonomous cars.
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations
Authors: Michał Kuźba, Przemysław Biecek
Date: 2020-02-07 00:00:00
Tasks: Chatbot, Explainable artificial intelligence, model

Recently we see a rising number of methods in the field of eXplainable Artificial Intelligence. To our surprise, their development is driven by model developers rather than a study of needs for human end users. The analysis of needs, if done, takes the form of an A/B test rather than a study of open questions. To answer the question "What would a human operator like to ask the ML model?" we propose a conversational system explaining decisions of the predictive model. In this experiment, we developed a chatbot called dr_ant to talk about machine learning model trained to predict survival odds on Titanic. People can talk with dr_ant about different aspects of the model to understand the rationale behind its predictions. Having collected a corpus of 1000+ dialogues, we analyse the most common types of questions that users would like to ask. To our knowledge, it is the first study which uses a conversational system to collect the needs of human operators from the interactive and iterative dialogue explorations of a predictive model.
Unsupervised Separation of Native and Loanwords for Malayalam and Telugu
Authors: Sridhama Prakhya, Deepak P
Date: 2020-02-12 00:00:00
Tasks: Translation

Quite often, words from one language are adopted within a different language without translation; these words appear in transliterated form in text written in the latter language. This phenomenon is particularly widespread within Indian languages where many words are loaned from English. In this paper, we address the task of identifying loanwords automatically and in an unsupervised manner, from large datasets of words from agglutinative Dravidian languages. We target two specific languages from the Dravidian family, viz., Malayalam and Telugu. Based on familiarity with the languages, we outline an observation that native words in both these languages tend to be characterized by a much more versatile stem - stem being a shorthand to denote the subword sequence formed by the first few characters of the word - than words that are loaned from other languages. We harness this observation to build an objective function and an iterative optimization formulation to optimize for it, yielding a scoring of each word's nativeness in the process. Through an extensive empirical analysis over real-world datasets from both Malayalam and Telugu, we illustrate the effectiveness of our method in quantifying nativeness effectively over available baselines for the task.
Ten Research Challenge Areas in Data Science
Authors: Jeannette M. Wing
Date: 2020-01-27 00:00:00

Although data science builds on knowledge from computer science, mathematics, statistics, and other disciplines, data science is a unique field with many mysteries to unlock: challenging scientific questions and pressing questions of societal importance. This article starts with meta-questions about data science as a discipline and then elaborates on ten ideas for the basis of a research agenda for data science.
Public Authorities as Defendants: Using Bayesian Networks to determine the Likelihood of Success for Negligence claims in the wake of Oakden
Authors: Scott McLachlan, Evangelia Kyrimi, Norman Fenton
Date: 2020-02-01 00:00:00

Several countries are currently investigating issues of neglect, poor quality care and abuse in the aged care sector. In most cases it is the State who license and monitor aged care providers, which frequently introduces a serious conflict of interest because the State also operate many of the facilities where our most vulnerable peoples are cared for. Where issues are raised with the standard of care being provided, the State are seen by many as a deep-pockets defendant and become the target of high-value lawsuits. This paper draws on cases and circumstances from one jurisdiction based on the English legal tradition, Australia, and proposes a Bayesian solution capable of determining probability for success for citizen plaintiffs who bring negligence claims against a public authority defendant. Use of a Bayesian network trained on case audit data shows that even when the plaintiff case meets all requirements for a successful negligence litigation, success is not often assured. Only in around one-fifth of these cases does the plaintiff succeed against a public authority as defendant.
Fast Adaptive RNN Encoder-Decoder for Anomaly Detection in SMD Assembly Machine
Authors: YeongHyeon Park, Il Dong Yun
Date: 2018-10-22 00:00:00
Proceeding: MDPI 2018 10
Tasks: Anomaly Detection, Decoder

Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. In this paper, we proposed a fast adaptive anomaly detection model based on a Recurrent Neural Network (RNN) Encoder–Decoder with operating machine sounds. RNN Encoder–Decoder has a structure very similar to Auto-Encoder (AE), but the former has significantly reduced parameters compared to the latter because of its rolled structure. Thus, the RNN Encoder–Decoder only requires a short training process for fast adaptation. The anomaly detection model decides abnormality based on Euclidean distance between generated sequences and observed sequence from machine sounds. Experimental evaluation was conducted on a set of dataset from the SMD assembly machine. Results showed cutting-edge performance with fast adaptation. View Full-Text
Preprocessing Method for Performance Enhancement in CNN-based STEMI Detection from 12-lead ECG
Authors: YeongHyeon Park, Il Dong Yun, Si-Hyuck Kang
Date: 2019-07-24 00:00:00
Proceeding: IEEE Access 2019 7
Tasks: Specificity

ST elevation myocardial infarction (STEMI) is an acute life-threatening disease. It shows a high mortality risk when a patient is not timely treated within the golden time, prompt diagnosis with limited information such as electrocardiogram (ECG) is crucial. However, previous studies among physicians and paramedics have shown that the accuracy of STEMI diagnosis by the ECG is not sufficient. Thus, we propose a detecting algorithm based on a convolutional neural network (CNN) for detecting the STEMI on 12-lead ECG in order to support physicians, especially in an emergency room. We mostly focus on enhancing the detecting performance using a preprocessing technique. First, we reduce the noise of ECG using a notch filter and high-pass filter. We also segment pulses from ECG to focus on the ST segment. We use 96 normal and 179 STEMI records provided by Seoul National University Bundang Hospital (SNUBH) for the experiment. The sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve are increased from 0.685, 0.350, and 0.526 to 0.932, 0.896, and 0.943, respectively, depending on the preprocessing technique. As our result shows, the proposed method is effective to enhance STEIM detecting performance. Also, the proposed algorithm would be expected to help timely and the accurate diagnosis of STEMI in clinical practices.
Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems
Authors: Xiuhong Wei, Chen Hu, Linglong Dai
Date: 2019-10-28 00:00:00

Millimeter-wave massive multiple-input multiple-output (MIMO) can use a lens antenna array to considerably reduce the number of radio frequency (RF) chains, but channel estimation is challenging due to the number of RF chains is much smaller than that of antennas. By exploiting the sparsity of beamspace channels, the beamspace channel estimation can be formulated as a sparse signal recovery problem, which can be solved by the classical iterative algorithm named approximate message passing (AMP), and its corresponding version learned AMP (LAMP) realized by a deep neural network (DNN). However, these existing schemes cannot achieve satisfactory estimation accuracy. To improve the channel estimation performance, we propose a prior-aided Gaussian mixture LAMP (GM-LAMP) based beamspace channel estimation scheme in this paper. Specifically, based on the prior information that beamspace channel elements can be modeled by the Gaussian mixture distribution, we first derive a new shrinkage function to refine the AMP algorithm. Then, by replacing the original shrinkage function in the LAMP network with the derived Gaussian mixture shrinkage function, a prior-aided GM-LAMP network is developed to estimate the beamspace channel more accurately. Simulation results by using both the theoretical channel model and the ray-tracing based channel dataset show that, the proposed GM-LAMP network can achieve better channel estimation accuracy.
On the Adaptability of Unsupervised CNN-Based Deformable Image Registration to Unseen Image Domains
Authors: Ferrante, E., Oktay, O., Glocker, B., and Milone, D. H.
Date: 2018-09-01 00:00:00
Proceeding: 不知道 2018 9
Tasks: Image Registration, Medical Image Analysis, One-Shot Learning, Transfer Learning, valid

Deformable image registration is a fundamental problem in medical image analysis. During the last years, several methods based on deep convolutional neural networks (CNN) proved to be highly accurate to perform this task. These models achieved state-of-the-art accuracy while drastically reducing the required computational time, but mainly focusing on images of specific organs and modalities. To date, no work has reported on how these models adapt across different domains. In this work, we ask the question: can we use CNN-based registration models to spatially align images coming from a domain different than the one/s used at training time? We explore the adaptability of CNN-based image registration to different organs/modalities. We employ a fully convolutional architecture trained following an unsupervised approach. We consider a simple transfer learning strategy to study the generalisation of such model to unseen target domains, and devise a one-shot learning scheme taking advantage of the unsupervised nature of the proposed method. Evaluation on two publicly available datasets of X-Ray lung images and cardiac cine magnetic resonance sequences is provided. Our experiments suggest that models learned in different domains can be transferred at the expense of a decrease in performance, and that oneshot learning in the context of unsupervised CNN-based registration is a valid alternative to achieve consistent registration performance when only a pair of images from the target domain is available.
Context-Aware Cross-Attention for Skeleton-Based Human Action Recognition
Authors: Yanbo Fan, Shuchen Weng, Yong Zhang, Boxin Shi, Yi Zhang
Date: 2020-01-20 00:00:00
Proceeding: IEEE Access ( Volume: 8 ) 2020 1
Tasks: Action Recognition, Computational Efficiency, Skeleton Based Action Recognition, Temporal Action Localization

Skeleton-based human action recognition is becoming popular due to its computational efficiency and robustness. Since not all skeleton joints are informative for action recognition, attention mechanisms are adopted to extract informative joints and suppress the influence of irrelevant ones. However, existing attention frameworks usually ignore helpful scenario context information. In this paper, we propose a cross-attention module that consists of a self-attention branch and a cross-attention branch for skeleton-based action recognition. It helps to extract joints that are not only more informative but also highly correlated to the corresponding scenario context information. Moreover, the cross-attention module maintains input variables’ size and can be flexibly incorporated into many existing frameworks without breaking their behaviors. To facilitate end-to-end training, we further develop a scenario context information extraction branch to extract context information from raw RGB video directly. We conduct comprehensive experiments on the NTU RGB+D and the Kinetics databases, and experimental results demonstrate the correctness and effectiveness of the proposed model.
On Bonus Based Exploration Methods In The Arcade Learning Environment
Authors: Adrien Ali Taiga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Atari Games, Montezuma's Revenge, Reinforcement Learning

Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration problems such as Montezuma's Revenge (Bellemare et al., 2016). Recently, bonus-based exploration methods, which explore by augmenting the environment reward, have reached above-human average performance on such domains. In this paper we reassess popular bonus-based exploration methods within a common evaluation framework. We combine Rainbow (Hessel et al., 2018) with different exploration bonuses and evaluate its performance on Montezuma's Revenge, Bellemare et al.'s set of hard of exploration games with sparse rewards, and the whole Atari 2600 suite. We find that while exploration bonuses lead to higher score on Montezuma's Revenge they do not provide meaningful gains over the simpler epsilon-greedy scheme. In fact, we find that methods that perform best on that game often underperform epsilon-greedy on easy exploration Atari 2600 games. We find that our conclusions remain valid even when hyperparameters are tuned for these easy-exploration games. Finally, we find that none of the methods surveyed benefit from additional training samples (1 billion frames, versus Rainbow's 200 million) on Bellemare et al.'s hard exploration games. Our results suggest that recent gains in Montezuma's Revenge may be better attributed to architecture change, rather than better exploration schemes; and that the real pace of progress in exploration research for Atari 2600 games may have been obfuscated by good results on a single domain.
Symmetric Nonnegative Matrix Factorization for Graph Clustering
Authors: Da Kuang, Chris Ding, Haesun Park
Date: 2012-02-27 00:00:00
Proceeding: SDM 2012 2
Tasks: Clustering, Graph Clustering, Spectral Graph Clustering

Nonnegative matrix factorization (NMF) provides a lower rank approximation of a nonnegative matrix, and has been successfully used as a clustering method. In this paper, we offer some conceptual understanding for the capabilities and shortcomings of NMF as a clustering method. Then, we propose Symmetric NMF (SymNMF) as a general framework for graph clustering, which inherits the advantages of NMF by enforcing nonnegativity on the clustering assignment matrix. Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative). We compare SymNMF with the widely-used spectral clustering methods, and give an intuitive explanation of why SymNMF captures the cluster structure embedded in the graph representation more naturally. In addition, we develop a Newton-like algorithm that exploits second-order information efficiently, so as to show the feasibility of SymNMF as a practical framework for graph clustering. Our experiments on artificial graph data, text data, and image data demonstrate the substantially enhanced clustering quality of SymNMF over spectral clustering and NMF. Therefore, SymNMF is able to achieve better clustering results on both linear and nonlinear manifolds, and serves as a potential basis for many extensions and applications.
Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive Convolution
Authors: Chuhua Xian, Dongjiu Zhang, Chengkai Dai, Charlie C. L. Wang
Date: 2020-02-12 00:00:00
Tasks: Decoder, Deep Learning, Super-Resolution

Using the raw data from consumer-level RGB-D cameras as input, we propose a deep-learning based approach to efficiently generate RGB-D images with completed information in high resolution. To process the input images in low resolution with missing regions, new operators for adaptive convolution are introduced in our deep-learning network that consists of three cascaded modules -- the completion module, the refinement module and the super-resolution module. The completion module is based on an architecture of encoder-decoder, where the features of input raw RGB-D will be automatically extracted by the encoding layers of a deep neural-network. The decoding layers are applied to reconstruct the completed depth map, which is followed by a refinement module to sharpen the boundary of different regions. For the super-resolution module, we generate RGB-D images in high resolution by multiple layers for feature extraction and a layer for up-sampling. Benefited from the adaptive convolution operators newly proposed in this paper, our results outperform the existing deep-learning based approaches for RGB-D image complete and super-resolution. As an end-to-end approach, high fidelity RGB-D images can be generated efficiently at the rate of around 21 frames per second.
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function
Authors: Lingkai Kong, Molei Tao
Date: 2020-02-14 00:00:00
Proceeding: NeurIPS 2020 12

This article suggests that deterministic Gradient Descent, which does not use any stochastic gradient approximation, can still exhibit stochastic behaviors. In particular, it shows that if the objective function exhibit multiscale behaviors, then in a large learning rate regime which only resolves the macroscopic but not the microscopic details of the objective, the deterministic GD dynamics can become chaotic and convergent not to a local minimizer but to a statistical distribution. A sufficient condition is also established for approximating this long-time statistical limit by a rescaled Gibbs distribution. Both theoretical and numerical demonstrations are provided, and the theoretical part relies on the construction of a stochastic map that uses bounded noise (as opposed to discretized diffusions).
The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence
Authors: Gary Marcus
Date: 2020-02-14 00:00:00
Tasks: BIG-bench Machine Learning

Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.
Transformer on a Diet
Authors: Chenguang Wang, Zihao Ye, Aston Zhang, Zheng Zhang, Alexander J. Smola
Date: 2020-02-14 00:00:00
Tasks: Language Modeling, Language Modelling

Transformer has been widely used thanks to its ability to capture sequence information in an efficient way. However, recent developments, such as BERT and GPT-2, deliver only heavy architectures with a focus on effectiveness. In this paper, we explore three carefully-designed light Transformer architectures to figure out whether the Transformer with less computations could produce competitive results. Experimental results on language model benchmark datasets hint that such trade-off is promising, and the light Transformer reduces 70% parameters at best, while obtains competitive perplexity compared to standard Transformer. The source code is publicly available.
Learning models of quantum systems from experiments
Authors: Antonio A. Gentile, Brian Flynn, Sebastian Knauer, Nathan Wiebe, Stefano Paesani, Christopher E. Granade, John G. Rarity, Raffaele Santagati, Anthony Laing
Date: 2020-02-14 00:00:00

An isolated system of interacting quantum particles is described by a Hamiltonian operator. Hamiltonian models underpin the study and analysis of physical and chemical processes throughout science and industry, so it is crucial they are faithful to the system they represent. However, formulating and testing Hamiltonian models of quantum systems from experimental data is difficult because it is impossible to directly observe which interactions the quantum system is subject to. Here, we propose and demonstrate an approach to retrieving a Hamiltonian model from experiments, using unsupervised machine learning. We test our methods experimentally on an electron spin in a nitrogen-vacancy interacting with its spin bath environment, and numerically, finding success rates up to 86%. By building agents capable of learning science, which recover meaningful representations, we can gain further insight on the physics of quantum systems.