pwc-archive/papers-with-abstracts huggingface dataset:
A simulated annealing approach to optimal storing in a multi-level warehouse
Authors: Alexander Eckrot, Carina Geldhauser, Jan Jurczyk
Date: 2017-03-25 00:00:00
Tasks: Clustering, Retrieval

We propose a simulated annealing algorithm specifically tailored to optimise
total retrieval times in a multi-level warehouse under complex pre-batched
picking constraints. Experiments on real data from a picker-to-parts order
picking process in the warehouse of a European manufacturer show that optimal
storage assignments do not necessarily display features presumed in heuristics,
such as clustering of positively correlated items or ordering of items by
picking frequency.
  In an experiment run on more than 4000 batched orders with 1 to 150 items per
batch, the storage assignment suggested by the algorithm produces a 21\%
reduction in the total retrieval time with respect to a frequency-based storage
assignment.
Observable dictionary learning for high-dimensional statistical inference
Authors: Lionel Mathelin, Kévin Kasper, Hisham Abou-Kandil
Date: 2017-02-17 00:00:00
Tasks: Dictionary Learning, Vocal Bursts Intensity Prediction

This paper introduces a method for efficiently inferring a high-dimensional
distributed quantity from a few observations. The quantity of interest (QoI) is
approximated in a basis (dictionary) learned from a training set. The
coefficients associated with the approximation of the QoI in the basis are
determined by minimizing the misfit with the observations. To obtain a
probabilistic estimate of the quantity of interest, a Bayesian approach is
employed. The QoI is treated as a random field endowed with a hierarchical
prior distribution so that closed-form expressions can be obtained for the
posterior distribution. The main contribution of the present work lies in the
derivation of \emph{a representation basis consistent with the observation
chain} used to infer the associated coefficients. The resulting dictionary is
then tailored to be both observable by the sensors and accurate in
approximating the posterior mean. An algorithm for deriving such an observable
dictionary is presented. The method is illustrated with the estimation of the
velocity field of an open cavity flow from a handful of wall-mounted point
sensors. Comparison with standard estimation approaches relying on Principal
Component Analysis and K-SVD dictionaries is provided and illustrates the
superior performance of the present approach.
Non-Associative Learning Representation in the Nervous System of the Nematode Caenorhabditis elegans
Authors: Ramin M. Hasani, Magdalena Fuchs, Victoria Beneder, Radu Grosu
Date: 2017-03-18 00:00:00

Caenorhabditis elegans (C. elegans) illustrated remarkable behavioral
plasticities including complex non-associative and associative learning
representations. Understanding the principles of such mechanisms presumably
leads to constructive inspirations for the design of efficient learning
algorithms. In the present study, we postulate a novel approach on modeling
single neurons and synapses to study the mechanisms underlying learning in the
C. elegans nervous system. In this regard, we construct a precise mathematical
model of sensory neurons where we include multi-scale details from genes, ion
channels and ion pumps, together with a dynamic model of synapses comprised of
neurotransmitters and receptors kinetics. We recapitulate mechanosensory
habituation mechanism, a non-associative learning process, in which elements of
the neural network tune their parameters as a result of repeated input stimuli.
Accordingly, we quantitatively demonstrate the roots of such plasticity in the
neuronal and synaptic-level representations. Our findings can potentially give
rise to the development of new bio-inspired learning algorithms.
SIM-CE: An Advanced Simulink Platform for Studying the Brain of Caenorhabditis elegans
Authors: Ramin M. Hasani, Victoria Beneder, Magdalena Fuchs, David Lung, Radu Grosu
Date: 2017-03-18 00:00:00

We introduce SIM-CE, an advanced, user-friendly modeling and simulation
environment in Simulink for performing multi-scale behavioral analysis of the
nervous system of Caenorhabditis elegans (C. elegans). SIM-CE contains an
implementation of the mathematical models of C. elegans's neurons and synapses,
in Simulink, which can be easily extended and particularized by the user. The
Simulink model is able to capture both complex dynamics of ion channels and
additional biophysical detail such as intracellular calcium concentration. We
demonstrate the performance of SIM-CE by carrying out neuronal, synaptic and
neural-circuit-level behavioral simulations. Such environment enables the user
to capture unknown properties of the neural circuits, test hypotheses and
determine the origin of many behavioral plasticities exhibited by the worm.
GP-GAN: Towards Realistic High-Resolution Image Blending
Authors: Huikai Wu, Shuai Zheng, Junge Zhang, Kaiqi Huang
Date: 2017-03-21 00:00:00
Tasks: Conditional Image Generation, Generative Adversarial Network, Image Generation, Vocal Bursts Intensity Prediction

It is common but challenging to address high-resolution image blending in the automatic photo editing application. In this paper, we would like to focus on solving the problem of high-resolution image blending, where the composite images are provided. We propose a framework called Gaussian-Poisson Generative Adversarial Network (GP-GAN) to leverage the strengths of the classical gradient-based approach and Generative Adversarial Networks. To the best of our knowledge, it's the first work that explores the capability of GANs in high-resolution image blending task. Concretely, we propose Gaussian-Poisson Equation to formulate the high-resolution image blending problem, which is a joint optimization constrained by the gradient and color information. Inspired by the prior works, we obtain gradient information via applying gradient filters. To generate the color information, we propose a Blending GAN to learn the mapping between the composite images and the well-blended ones. Compared to the alternative methods, our approach can deliver high-resolution, realistic images with fewer bleedings and unpleasant artifacts. Experiments confirm that our approach achieves the state-of-the-art performance on Transient Attributes dataset. A user study on Amazon Mechanical Turk finds that the majority of workers are in favor of the proposed method.
Simplifying the Bible and Wikipedia Using Statistical Machine Translation
Authors: Yohan Jo
Date: 2017-03-25 00:00:00
Tasks: Articles, Language Modeling, Language Modelling, Machine Translation, Text Simplification, Translation

I started this work with the hope of generating a text synthesizer (like a
musical synthesizer) that can imitate certain linguistic styles. Most of the
report focuses on text simplification using statistical machine translation
(SMT) techniques. I applied MOSES to a parallel corpus of the Bible (King James
Version and Easy-to-Read Version) and that of Wikipedia articles (normal and
simplified). I report the importance of the three main components of
SMT---phrase translation, language model, and recording---by changing their
weights and comparing the resulting quality of simplified text in terms of
METEOR and BLEU. Toward the end of the report will be presented some examples
of text "synthesized" into the King James style.
Modeling Scalability of Distributed Machine Learning
Authors: Alexander Ulanov, Andrey Simanovsky, Manish Marwah
Date: 2016-10-20 00:00:00
Tasks: BIG-bench Machine Learning

Present day machine learning is computationally intensive and processes large
amounts of data. It is implemented in a distributed fashion in order to address
these scalability issues. The work is parallelized across a number of computing
nodes. It is usually hard to estimate in advance how many nodes to use for a
particular workload. We propose a simple framework for estimating the
scalability of distributed machine learning algorithms. We measure the
scalability by means of the speedup an algorithm achieves with more nodes. We
propose time complexity models for gradient descent and graphical model
inference. We validate our models with experiments on deep learning training
and belief propagation. This framework was used to study the scalability of
machine learning algorithms in Apache Spark.
Rapid-Rate: A Framework for Semi-supervised Real-time Sentiment Trend Detection in Unstructured Big Data
Authors: Vineet John
Date: 2017-03-23 00:00:00
Tasks: Document Classification, General Classification

Commercial establishments like restaurants, service centres and retailers
have several sources of customer feedback about products and services, most of
which need not be as structured as rated reviews provided by services like
Yelp, or Amazon, in terms of sentiment conveyed. For instance, Amazon provides
a fine-grained score on a numeric scale for product reviews. Some sources,
however, like social media (Twitter, Facebook), mailing lists (Google Groups)
and forums (Quora) contain text data that is much more voluminous, but
unstructured and unlabelled. It might be in the best interests of a business
establishment to assess the general sentiment towards their brand on these
platforms as well. This text could be pipelined into a system with a built-in
prediction model, with the objective of generating real-time graphs on opinion
and sentiment trends. Although such tasks like the one described about have
been explored with respect to document classification problems in the past, the
implementation described in this paper, by virtue of learning a continuous
function rather than a discrete one, offers a lot more depth of insight as
compared to document classification approaches. This study aims to explore the
validity of such a continuous function predicting model to quantify sentiment
about an entity, without the additional overhead of manual labelling, and
computational preprocessing & feature extraction. This research project also
aims to design and implement a re-usable document regression pipeline as a
framework, Rapid-Rate, that can be used to predict document scores in
real-time.
Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition
Authors: Chi Nhan Duong, Kha Gia Quach, Khoa Luu, T. Hoang Ngan Le, Marios Savvides
Date: 2017-03-24 00:00:00
Proceeding: ICCV 2017 10
Tasks: Age-Invariant Face Recognition, Density Estimation, Face Recognition, Face Verification, MORPH

Modeling the long-term facial aging process is extremely challenging due to
the presence of large and non-linear variations during the face development
stages. In order to efficiently address the problem, this work first decomposes
the aging process into multiple short-term stages. Then, a novel generative
probabilistic model, named Temporal Non-Volume Preserving (TNVP)
transformation, is presented to model the facial aging process at each stage.
Unlike Generative Adversarial Networks (GANs), which requires an empirical
balance threshold, and Restricted Boltzmann Machines (RBM), an intractable
model, our proposed TNVP approach guarantees a tractable density function,
exact inference and evaluation for embedding the feature transformations
between faces in consecutive stages. Our model shows its advantages not only in
capturing the non-linear age related variance in each stage but also producing
a smooth synthesis in age progression across faces. Our approach can model any
face in the wild provided with only four basic landmark points. Moreover, the
structure can be transformed into a deep convolutional network while keeping
the advantages of probabilistic models with tractable log-likelihood density
estimation. Our method is evaluated in both terms of synthesizing
age-progressed faces and cross-age face verification and consistently shows the
state-of-the-art results in various face aging databases, i.e. FG-NET, MORPH,
AginG Faces in the Wild (AGFW), and Cross-Age Celebrity Dataset (CACD). A
large-scale face verification on Megaface challenge 1 is also performed to
further show the advantages of our proposed approach.
Low Precision Neural Networks using Subband Decomposition
Authors: Sek Chai, Aswin Raghavan, David Zhang, Mohamed Amer, Tim Shields
Date: 2017-03-24 00:00:00

Large-scale deep neural networks (DNN) have been successfully used in a
number of tasks from image recognition to natural language processing. They are
trained using large training sets on large models, making them computationally
and memory intensive. As such, there is much interest in research development
for faster training and test time. In this paper, we present a unique approach
using lower precision weights for more efficient and faster training phase. We
separate imagery into different frequency bands (e.g. with different
information content) such that the neural net can better learn using less bits.
We present this approach as a complement existing methods such as pruning
network connections and encoding learning weights. We show results where this
approach supports more stable learning with 2-4X reduction in precision with
17X reduction in DNN parameters.
Deep Residual Learning for Instrument Segmentation in Robotic Surgery
Authors: Daniil Pakhomov, Vittal Premachandran, Max Allan, Mahdi Azizian, Nassir Navab
Date: 2017-03-24 00:00:00
Tasks: Pose Estimation, Segmentation

Detection, tracking, and pose estimation of surgical instruments are crucial
tasks for computer assistance during minimally invasive robotic surgery. In the
majority of cases, the first step is the automatic segmentation of surgical
tools. Prior work has focused on binary segmentation, where the objective is to
label every pixel in an image as tool or background. We improve upon previous
work in two major ways. First, we leverage recent techniques such as deep
residual learning and dilated convolutions to advance binary-segmentation
performance. Second, we extend the approach to multi-class segmentation, which
lets us segment different parts of the tool, in addition to background. We
demonstrate the performance of this method on the MICCAI Endoscopic Vision
Challenge Robotic Instruments dataset.
Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior
Authors: Alex C. Rollins, Jacob Schrum
Date: 2017-03-24 00:00:00

Previous research using evolutionary computation in Multi-Agent Systems
indicates that assigning fitness based on team vs.\ individual behavior has a
strong impact on the ability of evolved teams of artificial agents to exhibit
teamwork in challenging tasks. However, such research only made use of
single-objective evolution. In contrast, when a multiobjective evolutionary
algorithm is used, populations can be subject to individual-level objectives,
team-level objectives, or combinations of the two. This paper explores the
performance of cooperatively coevolved teams of agents controlled by artificial
neural networks subject to these types of objectives. Specifically, predator
agents are evolved to capture scripted prey agents in a torus-shaped grid
world. Because of the tension between individual and team behaviors, multiple
modes of behavior can be useful, and thus the effect of modular neural networks
is also explored. Results demonstrate that fitness rewarding individual
behavior is superior to fitness rewarding team behavior, despite being applied
to a cooperative task. However, the use of networks with multiple modules
allows predators to discover intelligent behavior, regardless of which type of
objectives are used.
YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video
Authors: Esteban Real, Jonathon Shlens, Stefano Mazzocchi, Xin Pan, Vincent Vanhoucke
Date: 2017-02-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: General Classification, object-detection, Object Detection, Video Object Detection

We introduce a new large-scale data set of video URLs with densely-sampled
object bounding box annotations called YouTube-BoundingBoxes (YT-BB). The data
set consists of approximately 380,000 video segments about 19s long,
automatically selected to feature objects in natural settings without editing
or post-processing, with a recording quality often akin to that of a hand-held
cell phone camera. The objects represent a subset of the MS COCO label set. All
video segments were human-annotated with high-precision classification labels
and bounding boxes at 1 frame per second. The use of a cascade of increasingly
precise human annotations ensures a label accuracy above 95% for every class
and tight bounding boxes. Finally, we train and evaluate well-known deep
network architectures and report baseline figures for per-frame classification
and localization to provide a point of comparison for future work. We also
demonstrate how the temporal contiguity of video can potentially be used to
improve such inferences. Please see the PDF file to find the URL to download
the data. We hope the availability of such large curated corpus will spur new
advances in video object detection and tracking.
Does Weather Matter? Causal Analysis of TV Logs
Authors: Shi Zong, Branislav Kveton, Shlomo Berkovsky, Azin Ashkan, Nikos Vlassis, Zheng Wen
Date: 2017-01-25 00:00:00
Tasks: BIG-bench Machine Learning

Weather affects our mood and behaviors, and many aspects of our life. When it
is sunny, most people become happier; but when it rains, some people get
depressed. Despite this evidence and the abundance of data, weather has mostly
been overlooked in the machine learning and data science research. This work
presents a causal analysis of how weather affects TV watching patterns. We show
that some weather attributes, such as pressure and precipitation, cause major
changes in TV watching patterns. To the best of our knowledge, this is the
first large-scale causal study of the impact of weather on TV watching
patterns.
Crowdsourcing Universal Part-Of-Speech Tags for Code-Switching
Authors: Victor Soto, Julia Hirschberg
Date: 2017-03-24 00:00:00

Code-switching is the phenomenon by which bilingual speakers switch between
multiple languages during communication. The importance of developing language
technologies for codeswitching data is immense, given the large populations
that routinely code-switch. High-quality linguistic annotations are extremely
valuable for any NLP task, and performance is often limited by the amount of
high-quality labeled data. However, little such data exists for code-switching.
In this paper, we describe crowd-sourcing universal part-of-speech tags for the
Miami Bangor Corpus of Spanish-English code-switched speech. We split the
annotation task into three subtasks: one in which a subset of tokens are
labeled automatically, one in which questions are specifically designed to
disambiguate a subset of high frequency words, and a more general cascaded
approach for the remaining data in which questions are displayed to the worker
following a decision tree structure. Each subtask is extended and adapted for a
multilingual setting and the universal tagset. The quality of the annotation
process is measured using hidden check questions annotated with gold labels.
The overall agreement between gold standard labels and the majority vote is
between 0.95 and 0.96 for just three labels and the average recall across
part-of-speech tags is between 0.87 and 0.99, depending on the task.
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks
Authors: Shuai Xiao, Junchi Yan, Mehrdad Farajtabar, Le Song, Xiaokang Yang, Hongyuan Zha
Date: 2017-03-24 00:00:00
Tasks: Point Processes, Time Series, Time Series Analysis

A variety of real-world processes (over networks) produce sequences of data
whose complex temporal dynamics need to be studied. More especially, the event
timestamps can carry important information about the underlying network
dynamics, which otherwise are not available from the time-series evenly sampled
from continuous signals. Moreover, in most complex processes, event sequences
and evenly-sampled times series data can interact with each other, which
renders joint modeling of those two sources of data necessary. To tackle the
above problems, in this paper, we utilize the rich framework of (temporal)
point processes to model event data and timely update its intensity function by
the synergic twin Recurrent Neural Networks (RNNs). In the proposed
architecture, the intensity function is synergistically modulated by one RNN
with asynchronous events as input and another RNN with time series as input.
Furthermore, to enhance the interpretability of the model, the attention
mechanism for the neural point process is introduced. The whole model with
event type and timestamp prediction output layers can be trained end-to-end and
allows a black-box treatment for modeling the intensity. We substantiate the
superiority of our model in synthetic data and three real-world benchmark
datasets.
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models
Authors: Kaspar Märtens, Michalis K. Titsias, Christopher Yau
Date: 2017-03-24 00:00:00
Tasks: Bayesian Inference

Bayesian inference for factorial hidden Markov models is challenging due to
the exponentially sized latent variable space. Standard Monte Carlo samplers
can have difficulties effectively exploring the posterior landscape and are
often restricted to exploration around localised regions that depend on
initialisation. We introduce a general purpose ensemble Markov Chain Monte
Carlo (MCMC) technique to improve on existing poorly mixing samplers. This is
achieved by combining parallel tempering and an auxiliary variable scheme to
exchange information between the chains in an efficient way. The latter
exploits a genetic algorithm within an augmented Gibbs sampler. We compare our
technique with various existing samplers in a simulation study as well as in a
cancer genomics application, demonstrating the improvements obtained by our
augmented ensemble approach.
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
Authors: Martin Vallières, Emily Kay-Rivest, Léo Jean Perrin, Xavier Liem, Christophe Furstoss, Hugo J. W. L. Aerts, Nader Khaouam, Phuc Felix Nguyen-Tan, Chang-Shu Wang, Khalil Sultanem, Jan Seuntjens, Issam El Naqa
Date: 2017-03-24 00:00:00

Quantitative extraction of high-dimensional mineable data from medical images
is a process known as radiomics. Radiomics is foreseen as an essential
prognostic tool for cancer risk assessment and the quantification of
intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying
tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET
and CT images of 300 patients from four different cohorts were analyzed for the
risk assessment of locoregional recurrences (LR) and distant metastases (DM) in
head-and-neck cancer. Prediction models combining radiomic and clinical
variables were constructed via random forests and imbalance-adjustment
strategies using two of the four cohorts. Independent validation of the
prediction and prognostic performance of the models was carried out on the
other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88).
Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the
potential of radiomics for assessing the risk of specific tumour outcomes using
multiple stratification groups. This could have important clinical impact,
notably by allowing for a better personalization of chemo-radiation treatments
for head-and-neck cancer patients from different risk groups.