pwc-archive/papers-with-abstracts huggingface dataset:
Question Answering via Web Extracted Tables and Pipelined Models
Authors: Bhavya Karki, Fan Hu, Nithin Haridas, Suhail Barot, Zihua Liu, Lucile Callebert, Matthias Grabmair, Anthony Tomasic
Date: 2019-03-17 00:00:00
Tasks: Question Answering, Retrieval, Table Retrieval

In this paper, we describe a dataset and baseline result for a question
answering that utilizes web tables. It contains commonly asked questions on the
web and their corresponding answers found in tables on websites. Our dataset is
novel in that every question is paired with a table of a different signature.
In particular, the dataset contains two classes of tables: entity-instance
tables and the key-value tables. Each QA instance comprises a table of either
kind, a natural language question, and a corresponding structured SQL query. We
build our model by dividing question answering into several tasks, including
table retrieval and question element classification, and conduct experiments to
measure the performance of each task. We extract various features specific to
each task and compose a full pipeline which constructs the SQL query from its
parts. Our work provides qualitative results and error analysis for each task,
and identifies in detail the reasoning required to generate SQL expressions
from natural language questions. This analysis of reasoning informs future
models based on neural machine learning.
Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents
Authors: Amir Behjat, Sharat Chidambaran, Souma Chowdhury
Date: 2019-03-17 00:00:00
Tasks: Collision Avoidance, Diversity, Reinforcement Learning, Reinforcement Learning (RL)

Neuroevolution is a process of training neural networks (NN) through an
evolutionary algorithm, usually to serve as a state-to-action mapping model in
control or reinforcement learning-type problems. This paper builds on the Neuro
Evolution of Augmented Topologies (NEAT) formalism that allows designing
topology and weight evolving NNs. Fundamental advancements are made to the
neuroevolution process to address premature stagnation and convergence issues,
central among which is the incorporation of automated mechanisms to control the
population diversity and average fitness improvement within the neuroevolution
process. Insights into the performance and efficiency of the new algorithm is
obtained by evaluating it on three benchmark problems from the Open AI platform
and an Unmanned Aerial Vehicle (UAV) collision avoidance problem.
tspDB: Time Series Predict DB
Authors: Anish Agarwal, Abdullah Alomar, Devavrat Shah
Date: 2019-03-17 00:00:00
Tasks: Imputation, Prediction, Prediction Intervals, Time Series, Time Series Analysis, Time Series Prediction

A major bottleneck of the current Machine Learning (ML) workflow is the time consuming, error prone engineering required to get data from a datastore or a database (DB) to the point an ML algorithm can be applied to it. Hence, we explore the feasibility of directly integrating prediction functionality on top of a data store or DB. Such a system ideally: (i) provides an intuitive prediction query interface which alleviates the unwieldy data engineering; (ii) provides state-of-the-art statistical accuracy while ensuring incremental model update, low model training time and low latency for making predictions. As the main contribution we explicitly instantiate a proof-of-concept, tspDB, which directly integrates with PostgreSQL. We rigorously test tspDB's statistical and computational performance against the state-of-the-art time series algorithms, including a Long-Short-Term-Memory (LSTM) neural network and DeepAR (industry standard deep learning library by Amazon). Statistically, on standard time series benchmarks, tspDB outperforms LSTM and DeepAR with 1.1-1.3x higher relative accuracy. Computationally, tspDB is 59-62x and 94-95x faster compared to LSTM and DeepAR in terms of median ML model training time and prediction query latency, respectively. Further, compared to PostgreSQL's bulk insert time and its SELECT query latency, tspDB is slower only by 1.3x and 2.6x respectively. That is, tspDB is a real-time prediction system in that its model training / prediction query time is similar to just inserting / reading data from a DB. As an algorithmic contribution, we introduce an incremental multivariate matrix factorization based time series method, which tspDB is built off. We show this method also allows one to produce reliable prediction intervals by accurately estimating the time-varying variance of a time series, thereby addressing an important problem in time series analysis.
The Missing Ingredient in Zero-Shot Neural Machine Translation
Authors: Naveen Arivazhagan, Ankur Bapna, Orhan Firat, Roee Aharoni, Melvin Johnson, Wolfgang Macherey
Date: 2019-03-17 00:00:00
Tasks: Machine Translation, NMT, Translation

Multilingual Neural Machine Translation (NMT) models are capable of
translating between multiple source and target languages. Despite various
approaches to train such models, they have difficulty with zero-shot
translation: translating between language pairs that were not together seen
during training. In this paper we first diagnose why state-of-the-art
multilingual NMT models that rely purely on parameter sharing, fail to
generalize to unseen language pairs. We then propose auxiliary losses on the
NMT encoder that impose representational invariance across languages. Our
simple approach vastly improves zero-shot translation quality without
regressing on supervised directions. For the first time, on WMT14
English-FrenchGerman, we achieve zero-shot performance that is on par with
pivoting. We also demonstrate the easy scalability of our approach to multiple
languages on the IWSLT 2017 shared task.
On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials
Authors: Maryam Aziz, Emilie Kaufmann, Marie-Karelle Riviere
Date: 2019-03-17 00:00:00
Tasks: Thompson Sampling

We study the problem of finding the optimal dosage in early stage clinical trials through the multi-armed bandit lens. We advocate the use of the Thompson Sampling principle, a flexible algorithm that can accommodate different types of monotonicity assumptions on the toxicity and efficacy of the doses. For the simplest version of Thompson Sampling, based on a uniform prior distribution for each dose, we provide finite-time upper bounds on the number of sub-optimal dose selections, which is unprecedented for dose-finding algorithms. Through a large simulation study, we then show that variants of Thompson Sampling based on more sophisticated prior distributions outperform state-of-the-art dose identification algorithms in different types of dose-finding studies that occur in phase I or phase I/II trials.
STNReID : Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification
Authors: Hao Luo, Xing Fan, Chi Zhang, Wei Jiang
Date: 2019-03-17 00:00:00
Tasks: Person Re-Identification

Partial person re-identification (ReID) is a challenging task because only partial information of person images is available for matching target persons. Few studies, especially on deep learning, have focused on matching partial person images with holistic person images. This study presents a novel deep partial ReID framework based on pairwise spatial transformer networks (STNReID), which can be trained on existing holistic person datasets. STNReID includes a spatial transformer network (STN) module and a ReID module. The STN module samples an affined image (a semantically corresponding patch) from the holistic image to match the partial image. The ReID module extracts the features of the holistic, partial, and affined images. Competition (or confrontation) is observed between the STN module and the ReID module, and two-stage training is applied to acquire a strong STNReID for partial ReID. Experimental results show that our STNReID obtains 66.7% and 54.6% rank-1 accuracies on partial ReID and partial iLIDS datasets, respectively. These values are at par with those obtained with state-of-the-art methods.
Bag of Tricks and A Strong Baseline for Deep Person Re-identification
Authors: Hao Luo, Youzhi Gu, Xingyu Liao, Shenqi Lai, Wei Jiang
Date: 2019-03-17 00:00:00
Tasks: Person Re-Identification

This paper explores a simple and efficient baseline for person
re-identification (ReID). Person re-identification (ReID) with deep neural
networks has made progress and achieved high performance in recent years.
However, many state-of-the-arts methods design complex network structure and
concatenate multi-branch features. In the literature, some effective training
tricks are briefly appeared in several papers or source codes. This paper will
collect and evaluate these effective training tricks in person ReID. By
combining these tricks together, the model achieves 94.5% rank-1 and 85.9% mAP
on Market1501 with only using global features. Our codes and models are
available at https://github.com/michuanhaohao/reid-strong-baseline.
Spatiotemporal Filtering for Event-Based Action Recognition
Authors: Rohan Ghosh, Anupam Gupta, Andrei Nakagawa, Alcimar Soares, Nitish Thakor
Date: 2019-03-17 00:00:00
Tasks: Action Recognition, Temporal Action Localization

In this paper, we address the challenging problem of action recognition,
using event-based cameras. To recognise most gestural actions, often higher
temporal precision is required for sampling visual information. Actions are
defined by motion, and therefore, when using event-based cameras it is often
unnecessary to re-sample the entire scene. Neuromorphic, event-based cameras
have presented an alternative to visual information acquisition by
asynchronously time-encoding pixel intensity changes, through temporally
precise spikes (10 micro-second resolution), making them well equipped for
action recognition. However, other challenges exist, which are intrinsic to
event-based imagers, such as higher signal-to-noise ratio, and a
spatiotemporally sparse information. One option is to convert event-data into
frames, but this could result in significant temporal precision loss. In this
work we introduce spatiotemporal filtering in the spike-event domain, as an
alternative way of channeling spatiotemporal information through to a
convolutional neural network. The filters are local spatiotemporal weight
matrices, learned from the spike-event data, in an unsupervised manner. We find
that appropriate spatiotemporal filtering significantly improves CNN
performance beyond state-of-the-art on the event-based DVS Gesture dataset. On
our newly recorded action recognition dataset, our method shows significant
improvement when compared with other, standard ways of generating the
spatiotemporal filters.
A customisable pipeline for continuously harvesting socially-minded Twitter users
Authors: Flavio Primo, Paolo Missier, Alexander Romanovsky, Mickael Figueredo, Nelio Cacho
Date: 2019-03-17 00:00:00

On social media platforms and Twitter in particular, specific classes of
users such as influencers have been given satisfactory operational definitions
in terms of network and content metrics.
  Others, for instance online activists, are not less important but their
characterisation still requires experimenting.
  We make the hypothesis that such interesting users can be found within
temporally and spatially localised contexts, i.e., small but topical fragments
of the network containing interactions about social events or campaigns with a
significant footprint on Twitter.
  To explore this hypothesis, we have designed a continuous user profile
discovery pipeline that produces an ever-growing dataset of user profiles by
harvesting and analysing contexts from the Twitter stream.
  The profiles dataset includes key network and content-based users metrics,
enabling experimentation with user-defined score functions that characterise
specific classes of online users.
  The paper describes the design and implementation of the pipeline and its
empirical evaluation on a case study consisting of healthcare-related campaigns
in the UK, showing how it supports the operational definitions of online
activism, by comparing three experimental ranking functions. The code is
publicly available.
Learning Competitive and Discriminative Reconstructions for Anomaly Detection
Authors: Kai Tian, Shuigeng Zhou, Jianping Fan, Jihong Guan
Date: 2019-03-17 00:00:00
Tasks: Anomaly Detection

Most of the existing methods for anomaly detection use only positive data to
learn the data distribution, thus they usually need a pre-defined threshold at
the detection stage to determine whether a test instance is an outlier.
Unfortunately, a good threshold is vital for the performance and it is really
hard to find an optimal one. In this paper, we take the discriminative
information implied in unlabeled data into consideration and propose a new
method for anomaly detection that can learn the labels of unlabelled data
directly. Our proposed method has an end-to-end architecture with one encoder
and two decoders that are trained to model inliers and outliers' data
distributions in a competitive way. This architecture works in a discriminative
manner without suffering from overfitting, and the training algorithm of our
model is adopted from SGD, thus it is efficient and scalable even for
large-scale datasets. Empirical studies on 7 datasets including KDD99, MNIST,
Caltech-256, and ImageNet etc. show that our model outperforms the
state-of-the-art methods.
Adversarial Attacks on Deep Neural Networks for Time Series Classification
Authors: Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller
Date: 2019-03-17 00:00:00
Tasks: Activity Recognition, Adversarial Attack, Classification, General Classification, Human Activity Recognition, Time Series, Time Series Analysis, Time Series Classification

Time Series Classification (TSC) problems are encountered in many real life
data mining tasks ranging from medicine and security to human activity
recognition and food safety. With the recent success of deep neural networks in
various domains such as computer vision and natural language processing,
researchers started adopting these techniques for solving time series data
mining problems. However, to the best of our knowledge, no previous work has
considered the vulnerability of deep learning models to adversarial time series
examples, which could potentially make them unreliable in situations where the
decision taken by the classifier is crucial such as in medicine and security.
For computer vision problems, such attacks have been shown to be very easy to
perform by altering the image and adding an imperceptible amount of noise to
trick the network into wrongly classifying the input image. Following this line
of work, we propose to leverage existing adversarial attack mechanisms to add a
special noise to the input time series in order to decrease the network's
confidence when classifying instances at test time. Our results reveal that
current state-of-the-art deep learning time series classifiers are vulnerable
to adversarial attacks which can have major consequences in multiple domains
such as food safety and quality assurance.
DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme
Authors: Arunselvan Ramaswamy
Date: 2019-03-17 00:00:00

Distributed descent-based methods are an essential toolset to solving optimization problems in multi-agent system scenarios. Here the agents seek to optimize a global objective function through mutual cooperation. Oftentimes, cooperation is achieved over a wireless communication network that is prone to delays and errors. There are many scenarios wherein the objective function is either non-differentiable or merely observable. In this paper, we present a cross-entropy based distributed stochastic approximation algorithm (SA) that finds a minimum of the objective, using only samples. We call this algorithm Decentralized Simultaneous Perturbation Stochastic Gradient, with Constant Sensitivity Parameters (DSPG). This algorithm is a two fold improvement over the classic Simultaneous Perturbation Stochastic Approximations (SPSA) algorithm. Specifically, DSPG allows for (i) the use of old information from other agents and (ii) easy implementation through the use simple hyper-parameter choices. We analyze the biases and variances that arise due to these two allowances. We show that the biases due to communication delays can be countered by a careful choice of algorithm hyper-parameters. The variance of the gradient estimator and its effect on the rate of convergence is studied. We present numerical results supporting our theory. Finally, we discuss an application to the stochastic consensus problem.
Deep Feature Selection using a Teacher-Student Network
Authors: Ali Mirzaei, Vahid Pourahmadi, Mehran Soltani, Hamid Sheikhzadeh
Date: 2019-03-17 00:00:00
Tasks: Clustering, Dimensionality Reduction, feature selection

High-dimensional data in many machine learning applications leads to
computational and analytical complexities. Feature selection provides an
effective way for solving these problems by removing irrelevant and redundant
features, thus reducing model complexity and improving accuracy and
generalization capability of the model. In this paper, we present a novel
teacher-student feature selection (TSFS) method in which a 'teacher' (a deep
neural network or a complicated dimension reduction method) is first employed
to learn the best representation of data in low dimension. Then a 'student'
network (a simple neural network) is used to perform feature selection by
minimizing the reconstruction error of low dimensional representation. Although
the teacher-student scheme is not new, to the best of our knowledge, it is the
first time that this scheme is employed for feature selection. The proposed
TSFS can be used for both supervised and unsupervised feature selection. This
method is evaluated on different datasets and is compared with state-of-the-art
existing feature selection methods. The results show that TSFS performs better
in terms of classification and clustering accuracies and reconstruction error.
Moreover, experimental evaluations demonstrate a low degree of sensitivity to
parameter selection in the proposed method.
Discriminating Original Region from Duplicated One in Copy-Move Forgery
Authors: Saba Salehi, Ahmad Mahmoodi-Aznaveh
Date: 2019-03-17 00:00:00

Since images are used as evidence in many cases, validation of digital images
is essential. Copy-move forgery is a special kind of manipulation in which some
parts of an image is copied and pasted into another part of the same image.
Various methods have been proposed to detect copy-move forgery, which have
achieved promising results. In previous methods, a binary mask determining the
original and forged region is presented as the final result. However, it is not
specified which part of the mask is the forged region. It should be noted that
discriminating the original region from the duplicated one is not usually
feasible by human visual system(HVS). On the other hand, exact localizing the
forged region can be helpful for automatic forgery detection especially in
combined forgeries. In real-world forgeries, some manipulations are performed
in order to provide a visibly realistic scene. These modifications are usually
applied on the boundary of the duplicated snippets. In this research, the
texture information of the border regions of both the original and copied
patches have been statistically investigated. Based on this analysis, we
propose a method to discriminated copied snippets from original ones. In order
to validate our method, GRIP dataset is utilized since it contains more
realistic forged images which are not easily recognizable by HVS.
Audio De-identification: A New Entity Recognition Task
Authors: Ido Cohn, Itay Laish, Genady Beryozkin, Gang Li, Izhak Shafran, Idan Szpektor, Tzvika Hartman, Avinatan Hassidim, Yossi Matias
Date: 2019-03-17 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), De-identification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, speech-recognition, Speech Recognition

Named Entity Recognition (NER) has been mostly studied in the context of written text. Specifically, NER is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor. In such recordings, audio spans with personal information should be redacted, similar to the redaction of sensitive character spans in de-ID for written text. The application of NER in the context of audio de-identification has yet to be fully investigated. To this end, we define the task of audio de-ID, in which audio spans with entity mentions should be detected. We then present our pipeline for this task, which involves Automatic Speech Recognition (ASR), NER on the transcript text, and text-to-audio alignment. Finally, we introduce a novel metric for audio de-ID and a new evaluation benchmark consisting of a large labeled segment of the Switchboard and Fisher audio datasets and detail our pipeline's results on it.
Reconstructing neuronal anatomy from whole-brain images
Authors: James Gornet, Kannan Umadevi Venkataraju, Arun Narasimhan, Nicholas Turner, Kisuk Lee, H. Sebastian Seung, Pavel Osten, Uygar Sümbül
Date: 2019-03-17 00:00:00
Tasks: Anatomy, Data Augmentation

Reconstructing multiple molecularly defined neurons from individual brains
and across multiple brain regions can reveal organizational principles of the
nervous system. However, high resolution imaging of the whole brain is a
technically challenging and slow process. Recently, oblique light sheet
microscopy has emerged as a rapid imaging method that can provide whole brain
fluorescence microscopy at a voxel size of 0.4 by 0.4 by 2.5 cubic microns. On
the other hand, complex image artifacts due to whole-brain coverage produce
apparent discontinuities in neuronal arbors. Here, we present
connectivity-preserving methods and data augmentation strategies for supervised
learning of neuroanatomy from light microscopy using neural networks. We
quantify the merit of our approach by implementing an end-to-end automated
tracing pipeline. Lastly, we demonstrate a scalable, distributed implementation
that can reconstruct the large datasets that sub-micron whole-brain images
produce.
Responses to a Critique of Artificial Moral Agents
Authors: Adam Poulsen, Michael Anderson, Susan L. Anderson, Ben Byford, Fabio Fossa, Erica L. Neely, Alejandro Rosas, Alan Winfield
Date: 2019-03-17 00:00:00
Tasks: Ethics

The field of machine ethics is concerned with the question of how to embed
ethical behaviors, or a means to determine ethical behaviors, into artificial
intelligence (AI) systems. The goal is to produce artificial moral agents
(AMAs) that are either implicitly ethical (designed to avoid unethical
consequences) or explicitly ethical (designed to behave ethically). Van
Wynsberghe and Robbins' (2018) paper Critiquing the Reasons for Making
Artificial Moral Agents critically addresses the reasons offered by machine
ethicists for pursuing AMA research; this paper, co-authored by machine
ethicists and commentators, aims to contribute to the machine ethics
conversation by responding to that critique. The reasons for developing AMAs
discussed in van Wynsberghe and Robbins (2018) are: it is inevitable that they
will be developed; the prevention of harm; the necessity for public trust; the
prevention of immoral use; such machines are better moral reasoners than
humans, and building these machines would lead to a better understanding of
human morality. In this paper, each co-author addresses those reasons in turn.
In so doing, this paper demonstrates that the reasons critiqued are not shared
by all co-authors; each machine ethicist has their own reasons for researching
AMAs. But while we express a diverse range of views on each of the six reasons
in van Wynsberghe and Robbins' critique, we nevertheless share the opinion that
the scientific study of AMAs has considerable value.