pwc-archive/papers-with-abstracts huggingface dataset:
Learning Graph-Level Representation for Drug Discovery
Authors: Junying Li, Deng Cai, Xiaofei He
Date: 2017-09-12 00:00:00
Tasks: All, Drug Discovery, General Classification, regression

Predicating macroscopic influences of drugs on human body, like efficacy and
toxicity, is a central problem of small-molecule based drug discovery.
Molecules can be represented as an undirected graph, and we can utilize graph
convolution networks to predication molecular properties. However, graph
convolutional networks and other graph neural networks all focus on learning
node-level representation rather than graph-level representation. Previous
works simply sum all feature vectors for all nodes in the graph to obtain the
graph feature vector for drug predication. In this paper, we introduce a dummy
super node that is connected with all nodes in the graph by a directed edge as
the representation of the graph and modify the graph operation to help the
dummy super node learn graph-level feature. Thus, we can handle graph-level
classification and regression in the same way as node-level classification and
regression. In addition, we apply focal loss to address class imbalance in drug
datasets. The experiments on MoleculeNet show that our method can effectively
improve the performance of molecular properties predication.
Bundle Optimization for Multi-aspect Embedding
Authors: Qiong Zeng, Baoquan Chen, Yanir Kleiman, Daniel Cohen-Or, Yangyan Li
Date: 2017-03-29 00:00:00
Tasks: Clustering, image-classification, Image Classification, Semantic Similarity, Semantic Textual Similarity

Understanding semantic similarity among images is the core of a wide range of
computer vision applications. An important step towards this goal is to collect
and learn human perceptions. Interestingly, the semantic context of images is
often ambiguous as images can be perceived with emphasis on different aspects,
which may be contradictory to each other.
  In this paper, we present a method for learning the semantic similarity among
images, inferring their latent aspects and embedding them into multi-spaces
corresponding to their semantic aspects.
  We consider the multi-embedding problem as an optimization function that
evaluates the embedded distances with respect to the qualitative clustering
queries. The key idea of our approach is to collect and embed qualitative
measures that share the same aspects in bundles. To ensure similarity aspect
sharing among multiple measures, image classification queries are presented to,
and solved by users. The collected image clusters are then converted into
bundles of tuples, which are fed into our bundle optimization algorithm that
jointly infers the aspect similarity and multi-aspect embedding. Extensive
experimental results show that our approach significantly outperforms
state-of-the-art multi-embedding approaches on various datasets, and scales
well for large multi-aspect similarity measures.
Process-oriented Iterative Multiple Alignment for Medical Process Mining
Authors: Shuhong Chen, Sen yang, Moliang Zhou, Randall S. Burd, Ivan Marsic
Date: 2017-09-16 00:00:00
Tasks: Data Visualization

Adapted from biological sequence alignment, trace alignment is a process
mining technique used to visualize and analyze workflow data. Any analysis done
with this method, however, is affected by the alignment quality. The best
existing trace alignment techniques use progressive guide-trees to
heuristically approximate the optimal alignment in O(N2L2) time. These
algorithms are heavily dependent on the selected guide-tree metric, often
return sum-of-pairs-score-reducing errors that interfere with interpretation,
and are computationally intensive for large datasets. To alleviate these
issues, we propose process-oriented iterative multiple alignment (PIMA), which
contains specialized optimizations to better handle workflow data. We
demonstrate that PIMA is a flexible framework capable of achieving better
sum-of-pairs score than existing trace alignment algorithms in only O(NL2)
time. We applied PIMA to analyzing medical workflow data, showing how iterative
alignment can better represent the data and facilitate the extraction of
insights from data visualization.
A LBP Based Correspondence Identification Scheme for Multi-view Sensing Network
Authors: Raghavendra Kandukuri
Date: 2017-09-16 00:00:00
Tasks: 3D Reconstruction, Image Retrieval

In this paper, we describes a correspondence identification method between
two-views of regular RGB camera that can be run in real-time. The basic idea is
first applying normalized cross correlation to retrieve a sparse set of
matching pairs from image pair. Then loopy belief propagation scheme is applied
to the the set of possible candidates to densely identify correspondences from
different views. The experiment results demonstrate superb accuracy and
precision that outperform the state-of-the-art in the computer vision field.
Meanwhile, the implementation is simple enough that can be optimized for
real-time performance. We have given the detailed comparison of existing
approaches and show that this method can enable various practical applications
from 3D reconstruction to image search.
To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation
Authors: Noriaki Hirose, Amir Sadeghian, Patrick Goebel, Silvio Savarese
Date: 2017-09-16 00:00:00
Tasks: Anomaly Detection, Navigate, Robot Navigation

It is important for robots to be able to decide whether they can go through a
space or not, as they navigate through a dynamic environment. This capability
can help them avoid injury or serious damage, e.g., as a result of running into
people and obstacles, getting stuck, or falling off an edge. To this end, we
propose an unsupervised and a near-unsupervised method based on Generative
Adversarial Networks (GAN) to classify scenarios as traversable or not based on
visual data. Our method is inspired by the recent success of data-driven
approaches on computer vision problems and anomaly detection, and reduces the
need for vast amounts of negative examples at training time. Collecting
negative data indicating that a robot should not go through a space is
typically hard and dangerous because of collisions, whereas collecting positive
data can be automated and done safely based on the robot's own traveling
experience. We verify the generality and effectiveness of the proposed approach
on a test dataset collected in a previously unseen environment with a mobile
robot. Furthermore, we show that our method can be used to build costmaps (we
call as "GoNoGo" costmaps) for robot path planning using visual data only.
Learning Robust Video Synchronization without Annotations
Authors: Patrick Wieschollek, Ido Freeman, Hendrik P. A. Lensch
Date: 2016-10-19 00:00:00
Tasks: Video Alignment, Video Synchronization

Aligning video sequences is a fundamental yet still unsolved component for a
broad range of applications in computer graphics and vision. Most classical
image processing methods cannot be directly applied to related video problems
due to the high amount of underlying data and their limit to small changes in
appearance. We present a scalable and robust method for computing a non-linear
temporal video alignment. The approach autonomously manages its training data
for learning a meaningful representation in an iterative procedure each time
increasing its own knowledge. It leverages on the nature of the videos
themselves to remove the need for manually created labels. While previous
alignment methods similarly consider weather conditions, season and
illumination, our approach is able to align videos from data recorded months
apart.
Speculate-Correct Error Bounds for k-Nearest Neighbor Classifiers
Authors: Eric Bax, Lingjie Weng, Xu Tian
Date: 2014-10-09 00:00:00

We introduce the speculate-correct method to derive error bounds for local
classifiers. Using it, we show that k nearest neighbor classifiers, in spite of
their famously fractured decision boundaries, have exponential error bounds
with O(sqrt((k + ln n) / n)) error bound range for n in-sample examples.
Grade Prediction with Temporal Course-wise Influence
Authors: Zhiyun Ren, Xia Ning, Huzefa Rangwala
Date: 2017-09-15 00:00:00
Tasks: Prediction

There is a critical need to develop new educational technology applications
that analyze the data collected by universities to ensure that students
graduate in a timely fashion (4 to 6 years); and they are well prepared for
jobs in their respective fields of study. In this paper, we present a novel
approach for analyzing historical educational records from a large, public
university to perform next-term grade prediction; i.e., to estimate the grades
that a student will get in a course that he/she will enroll in the next term.
Accurate next-term grade prediction holds the promise for better student degree
planning, personalized advising and automated interventions to ensure that
students stay on track in their chosen degree program and graduate on time. We
present a factorization-based approach called Matrix Factorization with
Temporal Course-wise Influence that incorporates course-wise influence effects
and temporal effects for grade prediction. In this model, students and courses
are represented in a latent "knowledge" space. The grade of a student on a
course is modeled as the similarity of their latent representation in the
"knowledge" space. Course-wise influence is considered as an additional factor
in the grade prediction. Our experimental results show that the proposed method
outperforms several baseline approaches and infer meaningful patterns between
pairs of courses within academic programs.
Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
Authors: Simon Kallweit, Thomas Müller, Brian McWilliams, Markus Gross, Jan Novák
Date: 2017-09-15 00:00:00
Tasks: GPU

We present a technique for efficiently synthesizing images of atmospheric
clouds using a combination of Monte Carlo integration and neural networks. The
intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming
aerosols make rendering of clouds---e.g. the characteristic silverlining and
the "whiteness" of the inner body---challenging for methods based solely on
Monte Carlo integration or diffusion theory. We approach the problem
differently. Instead of simulating all light transport during rendering, we
pre-learn the spatial and directional distribution of radiant flux from tens of
cloud exemplars. To render a new scene, we sample visible points of the cloud
and, for each, extract a hierarchical 3D descriptor of the cloud geometry with
respect to the shading location and the light source. The descriptor is input
to a deep neural network that predicts the radiance function for each shading
configuration. We make the key observation that progressively feeding the
hierarchical descriptor into the network enhances the network's ability to
learn faster and predict with high accuracy while using few coefficients. We
also employ a block design with residual connections to further improve
performance. A GPU implementation of our method synthesizes images of clouds
that are nearly indistinguishable from the reference solution within seconds
interactively. Our method thus represents a viable solution for applications
such as cloud design and, thanks to its temporal stability, also for
high-quality production of animated content.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Authors: Han Xiao, Kashif Rasul, Roland Vollgraf
Date: 2017-08-25 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning

We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images
of 70,000 fashion products from 10 categories, with 7,000 images per category.
The training set has 60,000 images and the test set has 10,000 images.
Fashion-MNIST is intended to serve as a direct drop-in replacement for the
original MNIST dataset for benchmarking machine learning algorithms, as it
shares the same image size, data format and the structure of training and
testing splits. The dataset is freely available at
https://github.com/zalandoresearch/fashion-mnist
"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts
Authors: Shereen Oraby, Pritam Gundecha, Jalal Mahmud, Mansurul Bhuiyan, Rama Akkiraju
Date: 2017-09-15 00:00:00

Given the increasing popularity of customer service dialogue on Twitter,
analysis of conversation data is essential to understand trends in customer and
agent behavior for the purpose of automating customer service interactions. In
this work, we develop a novel taxonomy of fine-grained "dialogue acts"
frequently observed in customer service, showcasing acts that are more suited
to the domain than the more generic existing taxonomies. Using a sequential
SVM-HMM model, we model conversation flow, predicting the dialogue act of a
given turn in real-time. We characterize differences between customer and agent
behavior in Twitter customer service conversations, and investigate the effect
of testing our system on different customer service industries. Finally, we use
a data-driven approach to predict important conversation outcomes: customer
satisfaction, customer frustration, and overall problem resolution. We show
that the type and location of certain dialogue acts in a conversation have a
significant effect on the probability of desirable and undesirable outcomes,
and present actionable rules based on our findings. The patterns and rules we
derive can be used as guidelines for outcome-driven automated customer service
platforms.
Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue
Authors: Kevin K. Bowden, Shereen Oraby, Jiaqi Wu, Amita Misra, Marilyn Walker
Date: 2017-09-15 00:00:00

The greatest challenges in building sophisticated open-domain conversational
agents arise directly from the potential for ongoing mixed-initiative
multi-turn dialogues, which do not follow a particular plan or pursue a
particular fixed information need. In order to make coherent conversational
contributions in this context, a conversational agent must be able to track the
types and attributes of the entities under discussion in the conversation and
know how they are related. In some cases, the agent can rely on structured
information sources to help identify the relevant semantic relations and
produce a turn, but in other cases, the only content available comes from
search, and it may be unclear which semantic relations hold between the search
results and the discourse context. A further constraint is that the system must
produce its contribution to the ongoing conversation in real-time. This paper
describes our experience building SlugBot for the 2017 Alexa Prize, and
discusses how we leveraged search and structured data from different sources to
help SlugBot produce dialogic turns and carry on conversations whose length
over the semi-finals user evaluation period averaged 8:17 minutes.
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
Authors: Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence
Date: 2017-09-15 00:00:00
Tasks: Gaussian Processes

This article is concerned with learning and stochastic control in physical
systems which contain unknown input signals. These unknown signals are modeled
as Gaussian processes (GP) with certain parametrized covariance structures. The
resulting latent force models (LFMs) can be seen as hybrid models that contain
a first-principles physical model part and a non-parametric GP model part. We
briefly review the statistical inference and learning methods for this kind of
models, introduce stochastic control methodology for the models, and provide
new theoretical observability and controllability results for them.
Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue
Authors: Shereen Oraby, Vrindavan Harrison, Lena Reed, Ernesto Hernandez, Ellen Riloff, Marilyn Walker
Date: 2017-09-15 00:00:00
Proceeding: WS 2016 9
Tasks: Diversity

The use of irony and sarcasm in social media allows us to study them at scale
for the first time. However, their diversity has made it difficult to construct
a high-quality corpus of sarcasm in dialogue. Here, we describe the process of
creating a large- scale, highly-diverse corpus of online debate forums
dialogue, and our novel methods for operationalizing classes of sarcasm in the
form of rhetorical questions and hyperbole. We show that we can use
lexico-syntactic cues to reliably retrieve sarcastic utterances with high
accuracy. To demonstrate the properties and quality of our corpus, we conduct
supervised learning experiments with simple features, and show that we achieve
both higher precision and F than previous work on sarcasm in debate forums
dialogue. We apply a weakly-supervised linguistic pattern learner and
qualitatively analyze the linguistic differences in each class.
Zero-Shot Learning to Manage a Large Number of Place-Specific Compressive Change Classifiers
Authors: Tanaka Kanji
Date: 2017-09-15 00:00:00
Tasks: Change Detection, Memorization, Zero-Shot Learning

With recent progress in large-scale map maintenance and long-term map
learning, the task of change detection on a large-scale map from a visual image
captured by a mobile robot has become a problem of increasing criticality.
Previous approaches for change detection are typically based on image
differencing and require the memorization of a prohibitively large number of
mapped images in the above context. In contrast, this study follows the recent,
efficient paradigm of change-classifier-learning and specifically employs a
collection of place-specific change classifiers. Our change-classifier-learning
algorithm is based on zero-shot learning (ZSL) and represents a place-specific
change classifier by its training examples mined from an external knowledge
base (EKB). The proposed algorithm exhibits several advantages. First, we are
required to memorize only training examples (rather than the classifier
itself), which can be further compressed in the form of bag-of-words (BoW).
Secondly, we can incorporate the most recent map into the classifiers by
straightforwardly adding or deleting a few training examples that correspond to
these classifiers. Thirdly, we can share the BoW vocabulary with other related
task scenarios (e.g., BoW-based self-localization), wherein the vocabulary is
generally designed as a rich, continuously growing, and domain-adaptive
knowledge base. In our contribution, the proposed algorithm is applied and
evaluated on a practical long-term cross-season change detection system that
consists of a large number of place-specific object-level change classifiers.
Road Friction Estimation for Connected Vehicles using Supervised Machine Learning
Authors: Ghazaleh Panahandeh, Erik Ek, Nasser Mohammadiha
Date: 2017-09-15 00:00:00
Tasks: BIG-bench Machine Learning, Descriptive, Friction, General Classification, Prediction

In this paper, the problem of road friction prediction from a fleet of
connected vehicles is investigated. A framework is proposed to predict the road
friction level using both historical friction data from the connected cars and
data from weather stations, and comparative results from different methods are
presented. The problem is formulated as a classification task where the
available data is used to train three machine learning models including
logistic regression, support vector machine, and neural networks to predict the
friction class (slippery or non-slippery) in the future for specific road
segments. In addition to the friction values, which are measured by moving
vehicles, additional parameters such as humidity, temperature, and rainfall are
used to obtain a set of descriptive feature vectors as input to the
classification methods. The proposed prediction models are evaluated for
different prediction horizons (0 to 120 minutes in the future) where the
evaluation shows that the neural networks method leads to more stable results
in different conditions.
General Phase Regularized Reconstruction using Phase Cycling
Authors: Frank Ong, Joseph Cheng, Michael Lustig
Date: 2017-09-15 00:00:00
Tasks: compressed sensing, Image Reconstruction

Purpose: To develop a general phase regularized image reconstruction method,
with applications to partial Fourier imaging, water-fat imaging and flow
imaging.
  Theory and Methods: The problem of enforcing phase constraints in
reconstruction was studied under a regularized inverse problem framework. A
general phase regularized reconstruction algorithm was proposed to enable
various joint reconstruction of partial Fourier imaging, water-fat imaging and
flow imaging, along with parallel imaging (PI) and compressed sensing (CS).
Since phase regularized reconstruction is inherently non-convex and sensitive
to phase wraps in the initial solution, a reconstruction technique, named phase
cycling, was proposed to render the overall algorithm invariant to phase wraps.
The proposed method was applied to retrospectively under-sampled in vivo
datasets and compared with state of the art reconstruction methods.
  Results: Phase cycling reconstructions showed reduction of artifacts compared
to reconstructions with- out phase cycling and achieved similar performances as
state of the art results in partial Fourier, water-fat and divergence-free
regularized flow reconstruction. Joint reconstruction of partial Fourier +
water-fat imaging + PI + CS, and partial Fourier + divergence-free regularized
flow imaging + PI + CS were demonstrated.
  Conclusion: The proposed phase cycling reconstruction provides an alternative
way to perform phase regularized reconstruction, without the need to perform
phase unwrapping. It is robust to the choice of initial solutions and
encourages the joint reconstruction of phase imaging applications.
Modelling Energy Consumption based on Resource Utilization
Authors: Lucas Venezian Povoa, Cesar Marcondes, Hermes Senger
Date: 2017-09-15 00:00:00
Tasks: Management, regression

Power management is an expensive and important issue for large computational
infrastructures such as datacenters, large clusters, and computational grids.
However, measuring energy consumption of scalable systems may be impractical
due to both cost and complexity for deploying power metering devices on a large
number of machines. In this paper, we propose the use of information about
resource utilization (e.g. processor, memory, disk operations, and network
traffic) as proxies for estimating power consumption. We employ machine
learning techniques to estimate power consumption using such information which
are provided by common operating systems. Experiments with linear regression,
regression tree, and multilayer perceptron on data from different hardware
resulted into a model with 99.94\% of accuracy and 6.32 watts of error in the
best case.
Deep Graph Attention Model
Authors: John Boaz Lee, Ryan Rossi, Xiangnan Kong
Date: 2017-09-15 00:00:00
Tasks: General Classification, Graph Attention, Graph Classification, model

Graph classification is a problem with practical applications in many
different domains. Most of the existing methods take the entire graph into
account when calculating graph features. In a graphlet-based approach, for
instance, the entire graph is processed to get the total count of different
graphlets or sub-graphs. In the real-world, however, graphs can be both large
and noisy with discriminative patterns confined to certain regions in the graph
only. In this work, we study the problem of attentional processing for graph
classification. The use of attention allows us to focus on small but
informative parts of the graph, avoiding noise in the rest of the graph. We
present a novel RNN model, called the Graph Attention Model (GAM), that
processes only a portion of the graph by adaptively selecting a sequence of
"interesting" nodes. The model is equipped with an external memory component
which allows it to integrate information gathered from different parts of the
graph. We demonstrate the effectiveness of the model through various
experiments.