pwc-archive/papers-with-abstracts huggingface dataset:
On the Importance of Video Action Recognition for Visual Lipreading
Authors: Xinshuo Weng
Date: 2019-03-22 00:00:00
Tasks: Action Recognition, Lipreading, Optical Flow Estimation, Temporal Action Localization

We focus on the word-level visual lipreading, which requires to decode the word from the speaker's video. Recently, many state-of-the-art visual lipreading methods explore the end-to-end trainable deep models, involving the use of 2D convolutional networks (e.g., ResNet) as the front-end visual feature extractor and the sequential model (e.g., Bi-LSTM or Bi-GRU) as the back-end. Although a deep 2D convolution neural network can provide informative image-based features, it ignores the temporal motion existing between the adjacent frames. In this work, we investigate the spatial-temporal capacity power of I3D (Inflated 3D ConvNet) for visual lipreading. We demonstrate that, after pre-trained on the large-scale video action recognition dataset (e.g., Kinetics), our models show a considerable improvement of performance on the task of lipreading. A comparison between a set of video model architectures and input data representation is also reported. Our extensive experiments on LRW shows that a two-stream I3D model with RGB video and optical flow as the inputs achieves the state-of-the-art performance.
Improving Search with Supervised Learning in Trick-Based Card Games
Authors: Christopher Solinas, Douglas Rebstock, Michael Buro
Date: 2019-03-22 00:00:00
Tasks: Card Games

In trick-taking card games, a two-step process of state sampling and
evaluation is widely used to approximate move values. While the evaluation
component is vital, the accuracy of move value estimates is also fundamentally
linked to how well the sampling distribution corresponds the true distribution.
Despite this, recent work in trick-taking card game AI has mainly focused on
improving evaluation algorithms with limited work on improving sampling. In
this paper, we focus on the effect of sampling on the strength of a player and
propose a novel method of sampling more realistic states given move history. In
particular, we use predictions about locations of individual cards made by a
deep neural network --- trained on data from human gameplay - in order to
sample likely worlds for evaluation. This technique, used in conjunction with
Perfect Information Monte Carlo (PIMC) search, provides a substantial increase
in cardplay strength in the popular trick-taking card game of Skat.
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
Authors: Chi Sun, Luyao Huang, Xipeng Qiu
Date: 2019-03-22 00:00:00
Proceeding: NAACL 2019 6
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), General Classification, Natural Language Inference, Question Answering, Sentence, Sentence-Pair Classification, Sentiment Analysis

Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained
opinion polarity towards a specific aspect, is a challenging subtask of
sentiment analysis (SA). In this paper, we construct an auxiliary sentence from
the aspect and convert ABSA to a sentence-pair classification task, such as
question answering (QA) and natural language inference (NLI). We fine-tune the
pre-trained model from BERT and achieve new state-of-the-art results on
SentiHood and SemEval-2014 Task 4 datasets.
Comparison of Hand-held WEMI Target Detection Algorithms
Authors: Connor H. McCurley, James Bocinsky, Alina Zare
Date: 2019-03-22 00:00:00

Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a
number of years in subsurface detection of explosive hazards. While WEMI
sensors have proven effective at localizing objects exhibiting large magnetic
responses, detecting objects lacking or containing very low amounts of
conductive materials can be challenging. In this paper, we compare a number of
target detection algorithms in the literature in terms of detection
performance. In the comparison, methods are tested on two real-world data sets:
one containing relatively low amounts of ground noise pollution, and the other
demonstrating highly-magnetic soil interference. Results are quantitatively
evaluated through receiver-operator characteristic (ROC) curves and are used to
highlight the strengths and weaknesses of the compared approaches in hand-held
explosive hazard detection.
Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games
Authors: Li Zhang, Wei Wang, Shijian Li, Gang Pan
Date: 2019-03-22 00:00:00

Researchers on artificial intelligence have achieved human-level intelligence
in large-scale perfect-information games, but it is still a challenge to
achieve (nearly) optimal results (in other words, an approximate Nash
Equilibrium) in large-scale imperfect-information games (i.e. war games,
football coach or business strategies). Neural Fictitious Self Play (NFSP) is
an effective algorithm for learning approximate Nash equilibrium of
imperfect-information games from self-play without prior domain knowledge.
However, it relies on Deep Q-Network, which is off-line and is hard to converge
in online games with changing opponent strategy, so it can't approach
approximate Nash equilibrium in games with large search scale and deep search
depth. In this paper, we propose Monte Carlo Neural Fictitious Self Play
(MC-NFSP), an algorithm combines Monte Carlo tree search with NFSP, which
greatly improves the performance on large-scale zero-sum imperfect-information
games. Experimentally, we demonstrate that the proposed Monte Carlo Neural
Fictitious Self Play can converge to approximate Nash equilibrium in games with
large-scale search depth while the Neural Fictitious Self Play can't.
Furthermore, we develop Asynchronous Neural Fictitious Self Play (ANFSP). It
use asynchronous and parallel architecture to collect game experience. In
experiments, we show that parallel actor-learners have a further accelerated
and stabilizing effect on training.
Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration
Authors: Cristina González-Gonzalo, Verónica Sánchez-Gutiérrez, Paula Hernández-Martínez, Inés Contreras, Yara T. Lechanteur, Artin Domanian, Bram van Ginneken, Clara I. Sánchez
Date: 2019-03-22 00:00:00

Purpose: To validate the performance of a commercially-available,
CE-certified deep learning (DL) system, RetCAD v.1.3.0 (Thirona, Nijmegen, The
Netherlands), for the joint automatic detection of diabetic retinopathy (DR)
and age-related macular degeneration (AMD) in color fundus (CF) images on a
dataset with mixed presence of eye diseases.
  Methods: Evaluation of joint detection of referable DR and AMD was performed
on a DR-AMD dataset with 600 images acquired during routine clinical practice,
containing referable and non-referable cases of both diseases. Each image was
graded for DR and AMD by an experienced ophthalmologist to establish the
reference standard (RS), and by four independent observers for comparison with
human performance. Validation was furtherly assessed on Messidor (1200 images)
for individual identification of referable DR, and the Age-Related Eye Disease
Study (AREDS) dataset (133821 images) for referable AMD, against the
corresponding RS.
  Results: Regarding joint validation on the DR-AMD dataset, the system
achieved an area under the ROC curve (AUC) of 95.1% for detection of referable
DR (SE=90.1%, SP=90.6%). For referable AMD, the AUC was 94.9% (SE=91.8%,
SP=87.5%). Average human performance for DR was SE=61.5% and SP=97.8%; for AMD,
SE=76.5% and SP=96.1%. Regarding detection of referable DR in Messidor, AUC was
97.5% (SE=92.0%, SP=92.1%); for referable AMD in AREDS, AUC was 92.7%
(SE=85.8%, SP=86.0%).
  Conclusions: The validated system performs comparably to human experts at
simultaneous detection of DR and AMD. This shows that DL systems can facilitate
access to joint screening of eye diseases and become a quick and reliable
support for ophthalmological experts.
Nonmodular architectures of cognitive systems based on active inference
Authors: Manuel Baltieri, Christopher L. Buckley
Date: 2019-03-22 00:00:00

In psychology and neuroscience it is common to describe cognitive systems as
input/output devices where perceptual and motor functions are implemented in a
purely feedforward, open-loop fashion. On this view, perception and action are
often seen as encapsulated modules with limited interaction between them. While
embodied and enactive approaches to cognitive science have challenged the
idealisation of the brain as an input/output device, we argue that even the
more recent attempts to model systems using closed-loop architectures still
heavily rely on a strong separation between motor and perceptual functions.
Previously, we have suggested that the mainstream notion of modularity strongly
resonates with the separation principle of control theory. In this work we
present a minimal model of a sensorimotor loop implementing an architecture
based on the separation principle. We link this to popular formulations of
perception and action in the cognitive sciences, and show its limitations when,
for instance, external forces are not modelled by an agent. These forces can be
seen as variables that an agent cannot directly control, i.e., a perturbation
from the environment or an interference caused by other agents. As an
alternative approach inspired by embodied cognitive science, we then propose a
nonmodular architecture based on the active inference framework. We demonstrate
the robustness of this architecture to unknown external inputs and show that
the mechanism with which this is achieved in linear models is equivalent to
integral control.
Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics
Authors: Hiroaki Adachi, Yoko Kawamura, Keiji Nakagawa, Ryoichi Horisaki, Issei Sato, Satoko Yamaguchi, Katsuhito Fujiu, Kayo Waki, Hiroyuki Noji, Sadao Ota
Date: 2019-03-22 00:00:00
Tasks: General Classification

Imaging flow cytometry shows significant potential for increasing our
understanding of heterogeneous and complex life systems and is useful for
biomedical applications. Ghost cytometry is a recently proposed approach for
directly analyzing compressively measured signals, thereby relieving the
computational bottleneck observed in high-throughput cytometry based on
morphological information. While this image-free approach could distinguish
different cell types using the same fluorescence staining method, further
strict controls are sometimes required to clearly demonstrate that the
classification is based on detailed morphologic analysis. In this study, we
show that ghost cytometry can be used to classify cell populations of the same
type but with different fluorescence distributions in space, supporting the
strength of our image-free approach for morphologic cell analysis.
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes
Authors: Daniel Poh, Stephen Roberts, Martin Tegnér
Date: 2019-03-22 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, Time Series Analysis, Transfer Learning

The non-storability of electricity makes it unique among commodity assets,
and it is an important driver of its price behaviour in secondary financial
markets. The instantaneous and continuous matching of power supply with demand
is a key factor explaining its volatility. During periods of high demand,
costlier generation capabilities are utilised since electricity cannot be
stored and this has the impact of driving prices up very quickly. Furthermore,
the non-storability also complicates physical hedging. Owing to these, the
problem of joint price-quantity risk in electricity markets is a commonly
studied theme.
  We propose using Gaussian Processes (GPs) to tackle this problem since GPs
provide a versatile and elegant non-parametric approach for regression and
time-series modelling. However, GPs scale poorly with the amount of training
data due to a cubic complexity. These considerations suggest that knowledge
transfer between price and load is vital for effective hedging, and that a
computationally efficient method is required. To this end, we use the
coregionalized (or multi-task) sparse GPs which addresses the aforementioned
issues.
  To gauge the performance of our model, we use an average-load strategy as
comparator. The latter is a robust approach commonly used by industry. If the
spot and load are uncorrelated and Gaussian, then hedging with the expected
load will result in the minimum variance position.
  Our main contributions are twofold. Firstly, in developing a coregionalized
sparse GP-based approach for hedging. Secondly, in demonstrating that our
model-based strategy outperforms the comparator, and can thus be employed for
effective hedging in electricity markets.
The invisible power of fairness. How machine learning shapes democracy
Authors: Elena Beretta, Antonio Santangelo, Bruno Lepri, Antonio Vetrò, Juan Carlos De Martin
Date: 2019-03-22 00:00:00
Tasks: BIG-bench Machine Learning, Cultural Vocal Bursts Intensity Prediction, Fairness

Many machine learning systems make extensive use of large amounts of data
regarding human behaviors. Several researchers have found various
discriminatory practices related to the use of human-related machine learning
systems, for example in the field of criminal justice, credit scoring and
advertising. Fair machine learning is therefore emerging as a new field of
study to mitigate biases that are inadvertently incorporated into algorithms.
Data scientists and computer engineers are making various efforts to provide
definitions of fairness. In this paper, we provide an overview of the most
widespread definitions of fairness in the field of machine learning, arguing
that the ideas highlighting each formalization are closely related to different
ideas of justice and to different interpretations of democracy embedded in our
culture. This work intends to analyze the definitions of fairness that have
been proposed to date to interpret the underlying criteria and to relate them
to different ideas of democracy.
Optimal Combination Forecasts on Retail Multi-Dimensional Sales Data
Authors: Luis Roque, Cristina A. C. Fernandes, Tony Silva
Date: 2019-03-22 00:00:00
Tasks: Time Series, Time Series Analysis

Time series data in the retail world are particularly rich in terms of
dimensionality, and these dimensions can be aggregated in groups or
hierarchies. Valuable information is nested in these complex structures, which
helps to predict the aggregated time series data. From a portfolio of brands
under HUUB's monitoring, we selected two to explore their sales behaviour,
leveraging the grouping properties of their product structure. Using
statistical models, namely SARIMA, to forecast each level of the hierarchy, an
optimal combination approach was used to generate more consistent forecasts in
the higher levels. Our results show that the proposed methods can indeed
capture nested information in the more granular series, helping to improve the
forecast accuracy of the aggregated series. The Weighted Least Squares (WLS)
method surpasses all other methods proposed in the study, including the Minimum
Trace (MinT) reconciliation.
Using SMT Solvers to Validate Models for AI Problems
Authors: Andrei Arusoaie, Ionut Pistol
Date: 2019-03-22 00:00:00
Tasks: Scheduling

Artificial Intelligence problems, ranging form planning/scheduling up to game
control, include an essential crucial step: describing a model which accurately
defines the problem's required data, requirements, allowed transitions and
established goals. The ways in which a model can fail are numerous and often
lead to a failure of search strategies to provide a quick, optimal, or even any
solution. This paper proposes using SMT (Satisfiability Modulo Theories)
solvers, such as Z3, to check the validity of a model. We propose two tests:
checking whether a final(goal) state exists in the model's described problem
space and checking whether the transitions described can provide a path from
the identified initial states to any the goal states (meaning a solution has
been found). The advantage of using an SMT solver for AI model checking is that
they substitute actual search strategies and they work over an abstract
representation of the model, that is, a set of logical formulas. Reasoning at
an abstract level is not as expensive as exploring the entire solution space.
SMT solvers use efficient decision procedures which provide proofs for the
logical formulas corresponding to the AI model. A recent addition to Z3 allowed
us to describe sequences of transitions as a recursive function, thus we can
check if a solution can be found in the defined model.
Aggregated Deep Local Features for Remote Sensing Image Retrieval
Authors: Raffaele Imbriaco, Clint Sebastian, Egor Bondarev, Peter H. N. de With
Date: 2019-03-22 00:00:00
Tasks: Dimensionality Reduction, Image Retrieval, Retrieval

Remote Sensing Image Retrieval remains a challenging topic due to the special
nature of Remote Sensing Imagery. Such images contain various different
semantic objects, which clearly complicates the retrieval task. In this paper,
we present an image retrieval pipeline that uses attentive, local convolutional
features and aggregates them using the Vector of Locally Aggregated Descriptors
(VLAD) to produce a global descriptor. We study various system parameters such
as the multiplicative and additive attention mechanisms and descriptor
dimensionality. We propose a query expansion method that requires no external
inputs. Experiments demonstrate that even without training, the local
convolutional features and global representation outperform other systems.
After system tuning, we can achieve state-of-the-art or competitive results.
Furthermore, we observe that our query expansion method increases overall
system performance by about 3%, using only the top-three retrieved images.
Finally, we show how dimensionality reduction produces compact descriptors with
increased retrieval performance and fast retrieval computation times, e.g. 50%
faster than the current systems.
Disentangled Representation Learning in Cardiac Image Analysis
Authors: Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou, Michelle Williams, David Newby, Rohan Dharmakumar, Sotirios A. Tsaftaris
Date: 2019-03-22 00:00:00
Tasks: Anatomy, Computed Tomography (CT), Image Generation, Medical Image Analysis, Representation Learning, Segmentation

Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be interpreted in this way. We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics. Here, we explicitly learn this decomposed (disentangled) representation of imaging data, focusing in particular on cardiac images. We propose Spatial Decomposition Network (SDNet), which factorises 2D medical images into spatial anatomical factors and non-spatial modality factors. We demonstrate that this high-level representation is ideally suited for several medical image analysis tasks, such as semi-supervised segmentation, multi-task segmentation and regression, and image-to-image synthesis. Specifically, we show that our model can match the performance of fully supervised segmentation models, using only a fraction of the labelled images. Critically, we show that our factorised representation also benefits from supervision obtained either when we use auxiliary tasks to train the model in a multi-task setting (e.g. regressing to known cardiac indices), or when aggregating multimodal data from different sources (e.g. pooling together MRI and CT data). To explore the properties of the learned factorisation, we perform latent-space arithmetic and show that we can synthesise CT from MR and vice versa, by swapping the modality factors. We also demonstrate that the factor holding image specific information can be used to predict the input modality with high accuracy. Code will be made available at https://github.com/agis85/anatomy_modality_decomposition.
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages
Authors: Gözde Gül Şahin, Mark Steedman
Date: 2019-03-22 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Data Augmentation, Part-Of-Speech Tagging, Text Augmentation

Neural NLP systems achieve high scores in the presence of sizable training
dataset. Lack of such datasets leads to poor system performances in the case
low-resource languages. We present two simple text augmentation techniques
using dependency trees, inspired from image processing. We crop sentences by
removing dependency links, and we rotate sentences by moving the tree fragments
around the root. We apply these techniques to augment the training sets of
low-resource languages in Universal Dependencies project. We implement a
character-level sequence tagging model and evaluate the augmented datasets on
part-of-speech tagging task. We show that crop and rotate provides improvements
over the models trained with non-augmented data for majority of the languages,
especially for languages with rich case marking systems.
LINSPECTOR: Multilingual Probing Tasks for Word Representations
Authors: Gözde Gül Şahin, Clara Vania, Ilia Kuznetsov, Iryna Gurevych
Date: 2019-03-22 00:00:00
Proceeding: CL 2020 6
Tasks: Dependency Parsing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Inference, POS, POS Tagging, Semantic Role Labeling, TAG, Word Embeddings

Despite an ever growing number of word representation models introduced for a large number of languages, there is a lack of a standardized technique to provide insights into what is captured by these models. Such insights would help the community to get an estimate of the downstream task performance, as well as to design more informed neural architectures, while avoiding extensive experimentation which requires substantial computational resources not all researchers have access to. A recent development in NLP is to use simple classification tasks, also called probing tasks, that test for a single linguistic feature such as part-of-speech. Existing studies mostly focus on exploring the linguistic information encoded by the continuous representations of English text. However, from a typological perspective the morphologically poor English is rather an outlier: the information encoded by the word order and function words in English is often stored on a morphological level in other languages. To address this, we introduce 15 type-level probing tasks such as case marking, possession, word length, morphological tag count and pseudoword identification for 24 languages. We present a reusable methodology for creation and evaluation of such tests in a multilingual setting. We then present experiments on several diverse multilingual word embedding models, in which we relate the probing task performance for a diverse set of languages to a range of five classic NLP tasks: POS-tagging, dependency parsing, semantic role labeling, named entity recognition and natural language inference. We find that a number of probing tests have significantly high positive correlation to the downstream tasks, especially for morphologically rich languages. We show that our tests can be used to explore word embeddings or black-box neural models for linguistic cues in a multilingual setting.