pwc-archive/papers-with-abstracts huggingface dataset:
Segmentation of Scanning Tunneling Microscopy Images Using Variational Methods and Empirical Wavelets
Authors: Bui Kevin, Fauman Jacob, Kes David, Torres Mandiola Leticia, Ciomaga Adina, Salazar Ricardo, Bertozzi L. Andrea, Gilles Jerome, Guttentag I. Andrew, Weiss S. Paul
Date: 2018-04-24 00:00:00
Tasks: Clustering

In the fields of nanoscience and nanotechnology, it is important to be able
to functionalize surfaces chemically for a wide variety of applications.
Scanning tunneling microscopes (STMs) are important instruments in this area
used to measure the surface structure and chemistry with better than molecular
resolution. Self-assembly is frequently used to create monolayers that redefine
the surface chemistry in just a single-molecule-thick layer. Indeed, STM images
reveal rich information about the structure of self-assembled monolayers since
they convey chemical and physical properties of the studied material.
  In order to assist in and to enhance the analysis of STM and other images, we
propose and demonstrate an image-processing framework that produces two image
segmentations: one is based on intensities (apparent heights in STM images) and
the other is based on textural patterns. The proposed framework begins with a
cartoon+texture decomposition, which separates an image into its cartoon and
texture components. Afterward, the cartoon image is segmented by a modified
multiphase version of the local Chan-Vese model, while the texture image is
segmented by a combination of 2D empirical wavelet transform and a clustering
algorithm. Overall, our proposed framework contains several new features,
specifically in presenting a new application of cartoon+texture decomposition
and of the empirical wavelet transforms and in developing a specialized
framework to segment STM images and other data. To demonstrate the potential of
our approach, we apply it to actual STM images of cyanide monolayers on
Au\{111\} and present their corresponding segmentation results.
SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers
Authors: Farhad Nooralahzadeh, Lilja Øvrelid, Jan Tore Lønning
Date: 2018-04-24 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, General Classification, Relation, Relation Classification, Relation Extraction

This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7
on Semantic Relation Extraction and Classification in Scientific Papers. First
we extract the shortest dependency path (sdp) between two entities, then we
introduce a convolutional neural network (CNN) which takes the shortest
dependency path embeddings as input and performs relation classification with
differing objectives for each subtask of the shared task. This approach
achieved overall F1 scores of 76.7 and 83.2 for relation classification on
clean and noisy data, respectively. Furthermore, for combined relation
extraction and classification on clean data, it obtained F1 scores of 37.4 and
33.6 for each phase. Our system ranks 3rd in all three sub-tasks of the shared
task.
Mask-aware Photorealistic Face Attribute Manipulation
Authors: Ruoqi Sun, Chen Huang, Jianping Shi, Lizhuang Ma
Date: 2018-04-24 00:00:00
Tasks: Attribute, Face Recognition, Generative Adversarial Network

The task of face attribute manipulation has found increasing applications,
but still remains challeng- ing with the requirement of editing the attributes
of a face image while preserving its unique details. In this paper, we choose
to combine the Variational AutoEncoder (VAE) and Generative Adversarial Network
(GAN) for photorealistic image genera- tion. We propose an effective method to
modify a modest amount of pixels in the feature maps of an encoder, changing
the attribute strength contin- uously without hindering global information. Our
training objectives of VAE and GAN are reinforced by the supervision of face
recognition loss and cy- cle consistency loss for faithful preservation of face
details. Moreover, we generate facial masks to en- force background
consistency, which allows our training to focus on manipulating the foreground
face rather than background. Experimental results demonstrate our method,
called Mask-Adversarial AutoEncoder (M-AAE), can generate high-quality images
with changing attributes and outperforms prior methods in detail preservation.
Assessing Language Models with Scaling Properties
Authors: Shuntaro Takahashi, Kumiko Tanaka-Ishii
Date: 2018-04-24 00:00:00

Language models have primarily been evaluated with perplexity. While
perplexity quantifies the most comprehensible prediction performance, it does
not provide qualitative information on the success or failure of models.
Another approach for evaluating language models is thus proposed, using the
scaling properties of natural language. Five such tests are considered, with
the first two accounting for the vocabulary population and the other three for
the long memory of natural language. The following models were evaluated with
these tests: n-grams, probabilistic context-free grammar (PCFG), Simon and
Pitman-Yor (PY) processes, hierarchical PY, and neural language models. Only
the neural language models exhibit the long memory properties of natural
language, but to a limited degree. The effectiveness of every test of these
models is also discussed.
Data-driven Summarization of Scientific Articles
Authors: Nikola I. Nikolov, Michael Pfeiffer, Richard H. R. Hahnloser
Date: 2018-04-24 00:00:00
Tasks: Articles, Sentence, Sentence Summarization, Text Summarization

Data-driven approaches to sequence-to-sequence modelling have been
successfully applied to short text summarization of news articles. Such models
are typically trained on input-summary pairs consisting of only a single or a
few sentences, partially due to limited availability of multi-sentence training
data. Here, we propose to use scientific articles as a new milestone for text
summarization: large-scale training data come almost for free with two types of
high-quality summaries at different levels - the title and the abstract. We
generate two novel multi-sentence summarization datasets from scientific
articles and test the suitability of a wide range of existing extractive and
abstractive neural network-based summarization approaches. Our analysis
demonstrates that scientific papers are suitable for data-driven text
summarization. Our results could serve as valuable benchmarks for scaling
sequence-to-sequence models to very long sequences.
Assessment of Deep Convolutional Neural Networks for Road Surface Classification
Authors: Marcus Nolte, Nikita Kister, Markus Maurer
Date: 2018-04-24 00:00:00
Tasks: Classification, Friction, General Classification

When parameterizing vehicle control algorithms for stability or trajectory
control, the road-tire friction coefficient is an essential model parameter
when it comes to control performance. One major impact on the friction
coefficient is the condition of the road surface. A camera-based,
forward-looking classification of the road-surface helps enabling an early
parametrization of vehicle control algorithms. In this paper, we train and
compare two different Deep Convolutional Neural Network models, regarding their
application for road friction estimation and describe the challenges for
training the classifier in terms of available training data and the
construction of suitable datasets.
Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation
Authors: Patrick Follmann, Rebecca König, Philipp Härtinger, Michael Klostermann
Date: 2018-04-24 00:00:00
Tasks: Amodal Instance Segmentation, Data Augmentation, Instance Segmentation, Segmentation, Semantic Segmentation

Semantic amodal segmentation is a recently proposed extension to
instance-aware segmentation that includes the prediction of the invisible
region of each object instance. We present the first all-in-one end-to-end
trainable model for semantic amodal segmentation that predicts the amodal
instance masks as well as their visible and invisible part in a single forward
pass. In a detailed analysis, we provide experiments to show which architecture
choices are beneficial for an all-in-one amodal segmentation model. On the COCO
amodal dataset, our model outperforms the current baseline for amodal
segmentation by a large margin. To further evaluate our model, we provide two
new datasets with ground truth for semantic amodal segmentation, D2S amodal and
COCOA cls. For both datasets, our model provides a strong baseline performance.
Using special data augmentation techniques, we show that amodal segmentation on
D2S amodal is possible with reasonable performance, even without providing
amodal training data.
Spatiotemporal Learning of Dynamic Gestures from 3D Point Cloud Data
Authors: Joshua Owoyemi, Koichi Hashimoto
Date: 2018-04-24 00:00:00
Tasks: Data Augmentation, Scene Understanding

In this paper, we demonstrate an end-to-end spatiotemporal gesture learning
approach for 3D point cloud data using a new gestures dataset of point clouds
acquired from a 3D sensor. Nine classes of gestures were learned from gestures
sample data. We mapped point cloud data into dense occupancy grids, then time
steps of the occupancy grids are used as inputs into a 3D convolutional neural
network which learns the spatiotemporal features in the data without explicit
modeling of gesture dynamics. We also introduced a 3D region of interest
jittering approach for point cloud data augmentation. This resulted in an
increased classification accuracy of up to 10% when the augmented data is added
to the original training data. The developed model is able to classify gestures
from the dataset with 84.44% accuracy. We propose that point cloud data will be
a more viable data type for scene understanding and motion recognition, as 3D
sensors become ubiquitous in years to come.
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Authors: Alex Kendall, Yarin Gal, Roberto Cipolla
Date: 2017-05-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: Deep Learning, General Classification, Instance Segmentation, Multi-Task Learning, regression, Semantic Segmentation

Numerous deep learning applications benefit from multi-task learning with
multiple regression and classification objectives. In this paper we make the
observation that the performance of such systems is strongly dependent on the
relative weighting between each task's loss. Tuning these weights by hand is a
difficult and expensive process, making multi-task learning prohibitive in
practice. We propose a principled approach to multi-task deep learning which
weighs multiple loss functions by considering the homoscedastic uncertainty of
each task. This allows us to simultaneously learn various quantities with
different units or scales in both classification and regression settings. We
demonstrate our model learning per-pixel depth regression, semantic and
instance segmentation from a monocular input image. Perhaps surprisingly, we
show our model can learn multi-task weightings and outperform separate models
trained individually on each task.
DeepEmo: Learning and Enriching Pattern-Based Emotion Representations
Authors: Elvis Saravia, Hsien-Chi Toby Liu, Yi-Shin Chen
Date: 2018-04-24 00:00:00
Tasks: Emotion Recognition, Word Embeddings

We propose a graph-based mechanism to extract rich-emotion bearing patterns,
which fosters a deeper analysis of online emotional expressions, from a corpus.
The patterns are then enriched with word embeddings and evaluated through
several emotion recognition tasks. Moreover, we conduct analysis on the
emotion-oriented patterns to demonstrate its applicability and to explore its
properties. Our experimental results demonstrate that the proposed techniques
outperform most state-of-the-art emotion recognition techniques.
Integrating Multiplicative Features into Supervised Distributional Methods for Lexical Entailment
Authors: Tu Vu, Vered Shwartz
Date: 2018-04-24 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Lexical Entailment

Supervised distributional methods are applied successfully in lexical
entailment, but recent work questioned whether these methods actually learn a
relation between two words. Specifically, Levy et al. (2015) claimed that
linear classifiers learn only separate properties of each word. We suggest a
cheap and easy way to boost the performance of these methods by integrating
multiplicative features into commonly used representations. We provide an
extensive evaluation with different classifiers and evaluation setups, and
suggest a suitable evaluation setup for the task, eliminating biases existing
in previous ones.
Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance
Authors: Albert Haque, Michelle Guo, Alexandre Alahi, Serena Yeung, Zelun Luo, Alisha Rege, Jeffrey Jopling, Lance Downing, William Beninati, Amit Singh, Terry Platchek, Arnold Milstein, Li Fei-Fei
Date: 2017-08-01 00:00:00

One in twenty-five patients admitted to a hospital will suffer from a
hospital acquired infection. If we can intelligently track healthcare staff,
patients, and visitors, we can better understand the sources of such
infections. We envision a smart hospital capable of increasing operational
efficiency and improving patient care with less spending. In this paper, we
propose a non-intrusive vision-based system for tracking people's activity in
hospitals. We evaluate our method for the problem of measuring hand hygiene
compliance. Empirically, our method outperforms existing solutions such as
proximity-based techniques and covert in-person observational studies. We
present intuitive, qualitative results that analyze human movement patterns and
conduct spatial analytics which convey our method's interpretability. This work
is a step towards a computer-vision based smart hospital and demonstrates
promising results for reducing hospital acquired infections.
Measuring the Intrinsic Dimension of Objective Landscapes
Authors: Chunyuan Li, Heerad Farkhoor, Rosanne Liu, Jason Yosinski
Date: 2018-04-24 00:00:00
Proceeding: ICLR 2018 1

Many recently trained neural networks employ large numbers of parameters to
achieve good performance. One may intuitively use the number of parameters
required as a rough gauge of the difficulty of a problem. But how accurate are
such notions? How many parameters are really needed? In this paper we attempt
to answer this question by training networks not in their native parameter
space, but instead in a smaller, randomly oriented subspace. We slowly increase
the dimension of this subspace, note at which dimension solutions first appear,
and define this to be the intrinsic dimension of the objective landscape. The
approach is simple to implement, computationally tractable, and produces
several suggestive conclusions. Many problems have smaller intrinsic dimensions
than one might suspect, and the intrinsic dimension for a given dataset varies
little across a family of models with vastly different sizes. This latter
result has the profound implication that once a parameter space is large enough
to solve a problem, extra parameters serve directly to increase the
dimensionality of the solution manifold. Intrinsic dimension allows some
quantitative comparison of problem difficulty across supervised, reinforcement,
and other types of learning where we conclude, for example, that solving the
inverted pendulum problem is 100 times easier than classifying digits from
MNIST, and playing Atari Pong from pixels is about as hard as classifying
CIFAR-10. In addition to providing new cartography of the objective landscapes
wandered by parameterized models, the method is a simple technique for
constructively obtaining an upper bound on the minimum description length of a
solution. A byproduct of this construction is a simple approach for compressing
networks, in some cases by more than 100 times.
Matlab Implementation of Machine Vision Algorithm on Ballast Degradation Evaluation
Authors: Zixu Zhao
Date: 2018-04-24 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

America has a massive railway system. As of 2006, U.S. freight railroads have
140,490 route- miles of standard gauge, but maintaining such a huge system and
eliminating any dangers, like reduced track stability and poor drainage, caused
by railway ballast degradation require huge amount of labor. The traditional
way to quantify the degradation of ballast is to use an index called Fouling
Index (FI) through ballast sampling and sieve analysis. However, determining
the FI values in lab is very time-consuming and laborious, but with the help of
recent development in the field of computer vision, a novel method for a
potential machine-vison based ballast inspection system can be employed that
can hopefully replace the traditional mechanical method. The new machine-vision
approach analyses the images of the in-service ballasts, and then utilizes
image segmentation algorithm to get ballast segments. By comparing the segment
results and their corresponding FI values, this novel method produces a
machine-vision-based index that has the best-fit relation with FI. The
implementation details of how this algorithm works are discussed in this
report.
Representation Tradeoffs for Hyperbolic Embeddings
Authors: Christopher De Sa, Albert Gu, Christopher Ré, Frederic Sala
Date: 2018-04-10 00:00:00
Proceeding: ICML 2018 7

Hyperbolic embeddings offer excellent quality with few dimensions when
embedding hierarchical data structures like synonym or type hierarchies. Given
a tree, we give a combinatorial construction that embeds the tree in hyperbolic
space with arbitrarily low distortion without using optimization. On WordNet,
our combinatorial embedding obtains a mean-average-precision of 0.989 with only
two dimensions, while Nickel et al.'s recent construction obtains 0.87 using
200 dimensions. We provide upper and lower bounds that allow us to characterize
the precision-dimensionality tradeoff inherent in any hyperbolic embedding. To
embed general metric spaces, we propose a hyperbolic generalization of
multidimensional scaling (h-MDS). We show how to perform exact recovery of
hyperbolic points from distances, provide a perturbation analysis, and give a
recovery result that allows us to reduce dimensionality. The h-MDS approach
offers consistently low distortion even with few dimensions across several
datasets. Finally, we extract lessons from the algorithms and theory above to
design a PyTorch-based implementation that can handle incomplete information
and is scalable.
Measuring and Computing Database Inconsistency via Repairs
Authors: Leopoldo Bertossi
Date: 2018-04-24 00:00:00

We propose a generic numerical measure of inconsistency of a database with
respect to a set of integrity constraints. It is based on an abstract repair
semantics. A particular inconsistency measure associated to cardinality-repairs
is investigated; and we show that it can be computed via answer-set programs.
  Keywords: Integrity constraints in databases, inconsistent databases,
database repairs, inconsistency measure.
Learning Manifolds from Non-stationary Streaming Data
Authors: Suchismit Mahapatra, Varun Chandola
Date: 2018-04-24 00:00:00
Tasks: Dimensionality Reduction, GPR, valid

Streaming adaptations of manifold learning based dimensionality reduction methods, such as Isomap, are based on the assumption that a small initial batch of observations is enough for exact learning of the manifold, while remaining streaming data instances can be cheaply mapped to this manifold. However, there are no theoretical results to show that this core assumption is valid. Moreover, such methods typically assume that the underlying data distribution is stationary. Such methods are not equipped to detect, or handle, sudden changes or gradual drifts in the distribution that may occur when the data is streaming. We present theoretical results to show that the quality of a manifold asymptotically converges as the size of data increases. We then show that a Gaussian Process Regression (GPR) model, that uses a manifold-specific kernel function and is trained on an initial batch of sufficient size, can closely approximate the state-of-art streaming Isomap algorithms. The predictive variance obtained from the GPR prediction is then shown to be an effective detector of changes in the underlying data distribution. Results on several synthetic and real data sets show that the resulting algorithm can effectively learn lower dimensional representation of high dimensional data in a streaming setting, while identifying shifts in the generative distribution.
Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps
Authors: Koushik Nagasubramanian, Sarah Jones, Asheesh K. Singh, Arti Singh, Baskar Ganapathysubramanian, Soumik Sarkar
Date: 2018-04-24 00:00:00
Tasks: Classification, General Classification

Our overarching goal is to develop an accurate and explainable model for
plant disease identification using hyperspectral data. Charcoal rot is a soil
borne fungal disease that affects the yield of soybean crops worldwide.
Hyperspectral images were captured at 240 different wavelengths in the range of
383 - 1032 nm. We developed a 3D Convolutional Neural Network model for soybean
charcoal rot disease identification. Our model has classification accuracy of
95.73\% and an infected class F1 score of 0.87. We infer the trained model
using saliency map and visualize the most sensitive pixel locations that enable
classification. The sensitivity of individual wavelengths for classification
was also determined using the saliency map visualization. We identify the most
sensitive wavelength as 733 nm using the saliency map visualization. Since the
most sensitive wavelength is in the Near Infrared Region(700 - 1000 nm) of the
electromagnetic spectrum, which is also the commonly used spectrum region for
determining the vegetation health of the plant, we were more confident in the
predictions using our model.
Structured SUMCOR Multiview Canonical Correlation Analysis for Large-Scale Data
Authors: Charilaos I. Kanatsoulis, Xiao Fu, Nicholas D. Sidiropoulos, Mingyi Hong
Date: 2018-04-24 00:00:00

The sum-of-correlations (SUMCOR) formulation of generalized canonical
correlation analysis (GCCA) seeks highly correlated low-dimensional
representations of different views via maximizing pairwise latent similarity of
the views. SUMCOR is considered arguably the most natural extension of
classical two-view CCA to the multiview case, and thus has numerous
applications in signal processing and data analytics. Recent work has proposed
effective algorithms for handling the SUMCOR problem at very large scale.
However, the existing scalable algorithms cannot incorporate structural
regularization and prior information -- which are critical for good performance
in real-world applications. In this work, we propose a new computational
framework for large-scale SUMCOR GCCA that can easily incorporate a suite of
structural regularizers which are frequently used in data analytics. The
updates of the proposed algorithm are lightweight and the memory complexity is
also low. In addition, the proposed algorithm can be readily implemented in a
parallel fashion. We show that the proposed algorithm converges to a
Karush-Kuhn-Tucker (KKT) point of the regularized SUMCOR problem. Judiciously
designed simulations and real-data experiments are employed to demonstrate the
effectiveness of the proposed algorithm.