pwc-archive/papers-with-abstracts huggingface dataset:
What sets Verified Users apart? Insights, Analysis and Prediction of Verified Users on Twitter
Authors: Indraneil Paul, Abhinav Khattar, Shaan Chopra, Ponnurangam Kumaraguru, Manish Gupta
Date: 2019-03-12 00:00:00

Social network and publishing platforms, such as Twitter, support the concept
of a secret proprietary verification process, for handles they deem worthy of
platform-wide public interest. In line with significant prior work which
suggests that possessing such a status symbolizes enhanced credibility in the
eyes of the platform audience, a verified badge is clearly coveted among public
figures and brands. What are less obvious are the inner workings of the
verification process and what being verified represents. This lack of clarity,
coupled with the flak that Twitter received by extending aforementioned status
to political extremists in 2017, backed Twitter into publicly admitting that
the process and what the status represented needed to be rethought.
  With this in mind, we seek to unravel the aspects of a user's profile which
likely engender or preclude verification. The aim of the paper is two-fold:
First, we test if discerning the verification status of a handle from profile
metadata and content features is feasible. Second, we unravel the features
which have the greatest bearing on a handle's verification status. We collected
a dataset consisting of profile metadata of all 231,235 verified
English-speaking users (as of July 2018), a control sample of 175,930
non-verified English-speaking users and all their 494 million tweets over a one
year collection period. Our proposed models are able to reliably identify
verification status (Area under curve AUC > 99%). We show that number of public
list memberships, presence of neutral sentiment in tweets and an authoritative
language style are the most pertinent predictors of verification status.
  To the best of our knowledge, this work represents the first attempt at
discerning and classifying verification worthy users on Twitter.
QuickStop: A Markov Optimal Stopping Approach for Quickest Misinformation Detection
Authors: Honghao Wei, Xiaohan Kang, Weina Wang, Lei Ying
Date: 2019-03-04 00:00:00
Tasks: Misinformation

This paper combines data-driven and model-driven methods for real-time misinformation detection. Our algorithm, named QuickStop, is an optimal stopping algorithm based on a probabilistic information spreading model obtained from labeled data. The algorithm consists of an offline machine learning algorithm for learning the probabilistic information spreading model and an online optimal stopping algorithm to detect misinformation. The online detection algorithm has both low computational and memory complexities. Our numerical evaluations with a real-world dataset show that QuickStop outperforms existing misinformation detection algorithms in terms of both accuracy and detection time (number of observations needed for detection). Our evaluations with synthetic data further show that QuickStop is robust to (offline) learning errors.
conLSH: Context based Locality Sensitive Hashing for Mapping of noisy SMRT Reads
Authors: Angana Chakraborty, Sanghamitra Bandyopadhyay
Date: 2019-03-11 00:00:00

Single Molecule Real-Time (SMRT) sequencing is a recent advancement of Next
Gen technology developed by Pacific Bio (PacBio). It comes with an explosion of
long and noisy reads demanding cutting edge research to get most out of it. To
deal with the high error probability of SMRT data, a novel contextual Locality
Sensitive Hashing (conLSH) based algorithm is proposed in this article, which
can effectively align the noisy SMRT reads to the reference genome. Here,
sequences are hashed together based not only on their closeness, but also on
similarity of context. The algorithm has $\mathcal{O}(n^{\rho+1})$ space
requirement, where $n$ is the number of sequences in the corpus and $\rho$ is a
constant. The indexing time and querying time are bounded by $\mathcal{O}(
\frac{n^{\rho+1} \cdot \ln n}{\ln \frac{1}{P_2}})$ and $\mathcal{O}(n^\rho)$
respectively, where $P_2 > 0$, is a probability value. This algorithm is
particularly useful for retrieving similar sequences, a widely used task in
biology. The proposed conLSH based aligner is compared with rHAT, popularly
used for aligning SMRT reads, and is found to comprehensively beat it in speed
as well as in memory requirements. In particular, it takes approximately
$24.2\%$ less processing time, while saving about $70.3\%$ in peak memory
requirement for H.sapiens PacBio dataset.
Goal-Directed Behavior under Variational Predictive Coding: Dynamic Organization of Visual Attention and Working Memory
Authors: Minju Jung, Takazumi Matsumoto, Jun Tani
Date: 2019-03-12 00:00:00
Tasks: Bayesian Inference

Mental simulation is a critical cognitive function for goal-directed behavior
because it is essential for assessing actions and their consequences. When a
self-generated or externally specified goal is given, a sequence of actions
that is most likely to attain that goal is selected among other candidates via
mental simulation. Therefore, better mental simulation leads to better
goal-directed action planning. However, developing a mental simulation model is
challenging because it requires knowledge of self and the environment. The
current paper studies how adequate goal-directed action plans of robots can be
mentally generated by dynamically organizing top-down visual attention and
visual working memory. For this purpose, we propose a neural network model
based on variational Bayes predictive coding, where goal-directed action
planning is formulated by Bayesian inference of latent intentional space. Our
experimental results showed that cognitively meaningful competencies, such as
autonomous top-down attention to the robot end effector (its hand) as well as
dynamic organization of occlusion-free visual working memory, emerged.
Furthermore, our analysis of comparative experiments indicated that
introduction of visual working memory and the inference mechanism using
variational Bayes predictive coding significantly improve the performance in
planning adequate goal-directed actions.
Hierarchical Autoregressive Image Models with Auxiliary Decoders
Authors: Jeffrey De Fauw, Sander Dieleman, Karen Simonyan
Date: 2019-03-06 00:00:00

Autoregressive generative models of images tend to be biased towards capturing local structure, and as a result they often produce samples which are lacking in terms of large-scale coherence. To address this, we propose two methods to learn discrete representations of images which abstract away local detail. We show that autoregressive models conditioned on these representations can produce high-fidelity reconstructions of images, and that we can train autoregressive priors on these representations that produce samples with large-scale coherence. We can recursively apply the learning procedure, yielding a hierarchy of progressively more abstract image representations. We train hierarchical class-conditional autoregressive models on the ImageNet dataset and demonstrate that they are able to generate realistic images at resolutions of 128$\times$128 and 256$\times$256 pixels. We also perform a human evaluation study comparing our models with both adversarial and likelihood-based state-of-the-art generative models.
Real-Time Boiler Control Optimization with Machine Learning
Authors: Yukun Ding, Yiyu Shi
Date: 2019-03-07 00:00:00
Tasks: BIG-bench Machine Learning, FLUE

In coal-fired power plants, it is critical to improve the operational
efficiency of boilers for sustainability. In this work, we formulate real-time
boiler control as an optimization problem that looks for the best distribution
of temperature in different zones and oxygen content from the flue to improve
the boiler's stability and energy efficiency. We employ an efficient algorithm
by integrating appropriate machine learning and optimization techniques. We
obtain a large dataset collected from a real boiler for more than two months
from our industry partner, and conduct extensive experiments to demonstrate the
effectiveness and efficiency of the proposed algorithm.
Cascaded Projection: End-to-End Network Compression and Acceleration
Authors: Breton Minnehan, Andreas Savakis
Date: 2019-03-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Neural Network Compression

We propose a data-driven approach for deep convolutional neural network
compression that achieves high accuracy with high throughput and low memory
requirements. Current network compression methods either find a low-rank
factorization of the features that requires more memory, or select only a
subset of features by pruning entire filter channels. We propose the Cascaded
Projection (CaP) compression method that projects the output and input filter
channels of successive layers to a unified low dimensional space based on a
low-rank projection. We optimize the projection to minimize classification loss
and the difference between the next layer's features in the compressed and
uncompressed networks. To solve this non-convex optimization problem we propose
a new optimization method of a proxy matrix using backpropagation and
Stochastic Gradient Descent (SGD) with geometric constraints. Our cascaded
projection approach leads to improvements in all critical areas of network
compression: high accuracy, low memory consumption, low parameter count and
high processing speed. The proposed CaP method demonstrates state-of-the-art
results compressing VGG16 and ResNet networks with over 4x reduction in the
number of computations and excellent performance in top-5 accuracy on the
ImageNet dataset before and after fine-tuning.
An Efficient Augmented Lagrangian Based Method for Constrained Lasso
Authors: Zengde Deng, Anthony Man-Cho So
Date: 2019-03-12 00:00:00
Tasks: BIG-bench Machine Learning, regression, Variable Selection

Variable selection is one of the most important tasks in statistics and
machine learning. To incorporate more prior information about the regression
coefficients, the constrained Lasso model has been proposed in the literature.
In this paper, we present an inexact augmented Lagrangian method to solve the
Lasso problem with linear equality constraints. By fully exploiting
second-order sparsity of the problem, we are able to greatly reduce the
computational cost and obtain highly efficient implementations. Furthermore,
numerical results on both synthetic data and real data show that our algorithm
is superior to existing first-order methods in terms of both running time and
solution accuracy.
Efficient Optimization of Echo State Networks for Time Series Datasets
Authors: Jacob Reinier Maat, Nikos Gianniotis, Pavlos Protopapas
Date: 2019-03-12 00:00:00
Tasks: Astronomy, Bayesian Optimization, Time Series, Time Series Analysis

Echo State Networks (ESNs) are recurrent neural networks that only train
their output layer, thereby precluding the need to backpropagate gradients
through time, which leads to significant computational gains. Nevertheless, a
common issue in ESNs is determining its hyperparameters, which are crucial in
instantiating a well performing reservoir, but are often set manually or using
heuristics. In this work we optimize the ESN hyperparameters using Bayesian
optimization which, given a limited budget of function evaluations, outperforms
a grid search strategy. In the context of large volumes of time series data,
such as light curves in the field of astronomy, we can further reduce the
optimization cost of ESNs. In particular, we wish to avoid tuning
hyperparameters per individual time series as this is costly; instead, we want
to find ESNs with hyperparameters that perform well not just on individual time
series but rather on groups of similar time series without sacrificing
predictive performance significantly. This naturally leads to a notion of
clusters, where each cluster is represented by an ESN tuned to model a group of
time series of similar temporal behavior. We demonstrate this approach both on
synthetic datasets and real world light curves from the MACHO survey. We show
that our approach results in a significant reduction in the number of ESN
models required to model a whole dataset, while retaining predictive
performance for the series in each cluster.
Guiding High-Performance SAT Solvers with Unsat-Core Predictions
Authors: Daniel Selsam, Nikolaj Bjørner
Date: 2019-03-12 00:00:00
Tasks: Scheduling, Vocal Bursts Intensity Prediction

The NeuroSAT neural network architecture was recently introduced for predicting properties of propositional formulae. When trained to predict the satisfiability of toy problems, it was shown to find solutions and unsatisfiable cores on its own. However, the authors saw "no obvious path" to using the architecture to improve the state-of-the-art. In this work, we train a simplified NeuroSAT architecture to directly predict the unsatisfiable cores of real problems. We modify several high-performance SAT solvers to periodically replace their variable activity scores with NeuroSAT's prediction of how likely the variables are to appear in an unsatisfiable core. The modified MiniSat solves 10% more problems on SAT-COMP 2018 within the standard 5,000 second timeout than the original does. The modified Glucose solves 11% more problems than the original, while the modified Z3 solves 6% more. The gains are even greater when the training is specialized for a specific distribution of problems; on a benchmark of hard problems from a scheduling domain, the modified Glucose solves 20% more problems than the original does within a one-hour timeout. Our results demonstrate that NeuroSAT can provide effective guidance to high-performance SAT solvers on real problems.
Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks
Authors: Ian A. D. Williamson, Tyler W. Hughes, Momchil Minkov, Ben Bartlett, Sunil Pai, Shanhui Fan
Date: 2019-03-12 00:00:00

We introduce an electro-optic hardware platform for nonlinear activation functions in optical neural networks. The optical-to-optical nonlinearity operates by converting a small portion of the input optical signal into an analog electric signal, which is used to intensity-modulate the original optical signal with no reduction in processing speed. Our scheme allows for complete nonlinear on-off contrast in transmission at relatively low optical power thresholds and eliminates the requirement of having additional optical sources between each layer of the network. Moreover, the activation function is reconfigurable via electrical bias, allowing it to be programmed or trained to synthesize a variety of nonlinear responses. Using numerical simulations, we demonstrate that this activation function significantly improves the expressiveness of optical neural networks, allowing them to perform well on two benchmark machine learning tasks: learning a multi-input exclusive-OR (XOR) logic function and classification of images of handwritten numbers from the MNIST dataset. The addition of the nonlinear activation function improves test accuracy on the MNIST task from 85% to 94%.
AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks
Authors: Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das
Date: 2019-03-11 00:00:00
Tasks: Deep Learning

The power budget for embedded hardware implementations of Deep Learning
algorithms can be extremely tight. To address implementation challenges in such
domains, new design paradigms, like Approximate Computing, have drawn
significant attention. Approximate Computing exploits the innate
error-resilience of Deep Learning algorithms, a property that makes them
amenable for deployment on low-power computing platforms. This paper describes
an Approximate Computing design methodology, AX-DBN, for an architecture
belonging to the class of stochastic Deep Learning algorithms known as Deep
Belief Networks (DBNs). Specifically, we consider procedures for efficiently
implementing the Discriminative Deep Belief Network (DDBN), a stochastic neural
network which is used for classification tasks, extending Approximation
Computing from the analysis of deterministic to stochastic neural networks. For
the purpose of optimizing the DDBN for hardware implementations, we explore the
use of: (a)Limited precision of neurons and functional approximations of
activation functions; (b) Criticality analysis to identify nodes in the network
which can operate at reduced precision while allowing the network to maintain
target accuracy levels; and (c) A greedy search methodology with incremental
retraining to determine the optimal reduction in precision for all neurons to
maximize power savings. Using the AX-DBN methodology proposed in this paper, we
present experimental results across several network architectures that show
significant power savings under a user-specified accuracy loss constraint with
respect to ideal full precision implementations.
The Truth and Nothing but the Truth: Multimodal Analysis for Deception Detection
Authors: Mimansa Jaiswal, Sairam Tabibu, Rajiv Bajpai
Date: 2019-03-11 00:00:00
Tasks: Deception Detection, Facial Action Unit Detection, Lexical Analysis

We propose a data-driven method for automatic deception detection in
real-life trial data using visual and verbal cues. Using OpenFace with facial
action unit recognition, we analyze the movement of facial features of the
witness when posed with questions and the acoustic patterns using OpenSmile. We
then perform a lexical analysis on the spoken words, emphasizing the use of
pauses and utterance breaks, feeding that to a Support Vector Machine to test
deceit or truth prediction. We then try out a method to incorporate
utterance-based fusion of visual and lexical analysis, using string based
matching.
Practical Semantic Parsing for Spoken Language Understanding
Authors: Marco Damonte, Rahul Goel, Tagyoung Chung
Date: 2019-03-11 00:00:00
Proceeding: NAACL 2019 6
Tasks: Multi-Task Learning, Question Answering, Semantic Parsing, Spoken Language Understanding, Transfer Learning

Executable semantic parsing is the task of converting natural language
utterances into logical forms that can be directly used as queries to get a
response. We build a transfer learning framework for executable semantic
parsing. We show that the framework is effective for Question Answering (Q&A)
as well as for Spoken Language Understanding (SLU). We further investigate the
case where a parser on a new domain can be learned by exploiting data on other
domains, either via multi-task learning between the target domain and an
auxiliary domain or via pre-training on the auxiliary domain and fine-tuning on
the target domain. With either flavor of transfer learning, we are able to
improve performance on most domains; we experiment with public data sets such
as Overnight and NLmaps as well as with commercial SLU data. The experiments
carried out on data sets that are different in nature show how executable
semantic parsing can unify different areas of NLP such as Q&A and SLU.
Context-Aware Learning for Neural Machine Translation
Authors: Sébastien Jean, Kyunghyun Cho
Date: 2019-03-12 00:00:00
Tasks: Machine Translation, Translation

Interest in larger-context neural machine translation, including
document-level and multi-modal translation, has been growing. Multiple works
have proposed new network architectures or evaluation schemes, but potentially
helpful context is still sometimes ignored by larger-context translation
models. In this paper, we propose a novel learning algorithm that explicitly
encourages a neural translation model to take into account additional context
using a multilevel pair-wise ranking loss. We evaluate the proposed learning
algorithm with a transformer-based larger-context translation system on
document-level translation. By comparing performance using actual and random
contexts, we show that a model trained with the proposed algorithm is more
sensitive to the additional context.
Syllable-based Neural Named Entity Recognition for Myanmar Language
Authors: Hsu Myat Mo, Khin Mar Soe
Date: 2019-03-12 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named Entity Recognition (NER) for Myanmar Language is essential to Myanmar
natural language processing research work. In this work, NER for Myanmar
language is treated as a sequence tagging problem and the effectiveness of deep
neural networks on NER for Myanmar language has been investigated. Experiments
are performed by applying deep neural network architectures on syllable level
Myanmar contexts. Very first manually annotated NER corpus for Myanmar language
is also constructed and proposed. In developing our in-house NER corpus,
sentences from online news website and also sentences supported from
ALT-Parallel-Corpus are also used. This ALT corpus is one part of the Asian
Language Treebank (ALT) project under ASEAN IVO. This paper contributes the
first evaluation of neural network models on NER task for Myanmar language. The
experimental results show that those neural sequence models can produce
promising results compared to the baseline CRF model. Among those neural
architectures, bidirectional LSTM network added CRF layer above gives the
highest F-score value. This work also aims to discover the effectiveness of
neural network approaches to Myanmar textual processing as well as to promote
further researches on this understudied language.
Character Eyes: Seeing Language through Character-Level Taggers
Authors: Yuval Pinter, Marc Marone, Jacob Eisenstein
Date: 2019-03-12 00:00:00
Proceeding: WS 2019 8
Tasks: POS

Character-level models have been used extensively in recent years in NLP
tasks as both supplements and replacements for closed-vocabulary token-level
word representations. In one popular architecture, character-level LSTMs are
used to feed token representations into a sequence tagger predicting
token-level annotations such as part-of-speech (POS) tags. In this work, we
examine the behavior of POS taggers across languages from the perspective of
individual hidden units within the character LSTM. We aggregate the behavior of
these units into language-level metrics which quantify the challenges that
taggers face on languages with different morphological properties, and identify
links between synthesis and affixation preference and emergent behavior of the
hidden tagger layer. In a comparative experiment, we show how modifying the
balance between forward and backward hidden units affects model arrangement and
performance in these types of languages.
Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling
Authors: Peidong Wang, Ke Tan, DeLiang Wang
Date: 2019-03-11 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

Monaural speech enhancement has made dramatic advances since the introduction
of deep learning a few years ago. Although enhanced speech has been
demonstrated to have better intelligibility and quality for human listeners,
feeding it directly to automatic speech recognition (ASR) systems trained with
noisy speech has not produced expected improvements in ASR performance. The
lack of an enhancement benefit on recognition, or the gap between monaural
speech enhancement and recognition, is often attributed to speech distortions
introduced in the enhancement process. In this study, we analyze the distortion
problem, compare different acoustic models, and investigate a
distortion-independent training scheme for monaural speech recognition.
Experimental results suggest that distortion-independent acoustic modeling is
able to overcome the distortion problem. Such an acoustic model can also work
with speech enhancement models different from the one used during training.
Moreover, the models investigated in this paper outperform the previous best
system on the CHiME-2 corpus.