pwc-archive/papers-with-abstracts huggingface dataset:
Demand Prediction for Electric Vehicle Sharing
Authors: Man Luo, Hongkai Wen, Yi Luo, Bowen Du, Konstantin Klemmer, Hong-Ming Zhu
Date: 2019-03-10 00:00:00
Tasks: Decision Making, Prediction

Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the globe. Many car sharing service providers as well as automobile manufacturers are entering this competition by expanding both their EV fleets and renting/returning station networks, aiming to seize a share of the market and bring car sharing to the zero emissions level. During their fast expansion, one fundamental determinant for success is the capability of dynamically predicting the demand of stations. In this paper we propose a novel demand prediction approach, which is able to model the dynamics of the system and predict demand accordingly. We use a local temporal encoding process to handle the available historical data at individual stations, and a spatial encoding process to take correlations between stations into account with graph convolutional neural networks. The encoded features are fed to a prediction network, which forecasts both the long-term expected demand of the stations. We evaluate the proposed approach on real-world data collected from a major EV sharing platform. Experimental results demonstrate that our approach significantly outperforms the state of the art.
Algorithms for an Efficient Tensor Biclustering
Authors: Andriantsiory Dina Faneva, Mustapha Lebbah, Hanane Azzag, Gaël Beck
Date: 2019-03-10 00:00:00

Consider a data set collected by (individuals-features) pairs in different
times. It can be represented as a tensor of three dimensions (Individuals,
features and times). The tensor biclustering problem computes a subset of
individuals and a subset of features whose signal trajectories over time lie in
a low-dimensional subspace, modeling similarity among the signal trajectories
while allowing different scalings across different individuals or different
features. This approach are based on spectral decomposition in order to build
the desired biclusters. We evaluate the quality of the results from each
algorithms with both synthetic and real data set.
Knowledge compilation languages as proof systems
Authors: Florent Capelli
Date: 2019-03-10 00:00:00

In this paper, we study proof systems in the sense of Cook-Reckhow for
problems that are higher in the polynomial hierarchy than coNP, in particular,
#SAT and maxSAT. We start by explaining how the notion of Cook-Reckhow proof
systems can be apply to these problems and show how one can twist existing
languages in knowledge compilation such as decision DNNF so that they can be
seen as proof systems for problems such as #SAT and maxSAT.
Just-Enough Interaction Approach to Knee MRI Segmentation: Data from the Osteoarthritis Initiative
Authors: Satyananda Kashyap, Honghai Zhang, Milan Sonka
Date: 2019-03-10 00:00:00
Tasks: MRI segmentation, Segmentation

State-of-the-art automated segmentation algorithms are not 100\% accurate
especially when segmenting difficult to interpret datasets like those with
severe osteoarthritis (OA). We present a novel interactive method called
just-enough interaction (JEI), which adds a fast correction step to the
automated layered optimal graph segmentation of multiple objects and surfaces
(LOGISMOS). After LOGISMOS segmentation in knee MRI, the JEI user interaction
does not modify boundary surfaces of the bones and cartilages directly. Local
costs of underlying graph nodes are modified instead and the graph is
re-optimized, providing globally optimal corrected results. Significant
performance improvement ($p \ll 0.001$) was observed when comparing
JEI-corrected results to the automated. The algorithm was extended from 3D JEI
to longitudinal multi-3D (4D) JEI allowing simultaneous visualization and
interaction of multiple-time points of the same patient.
Venue Suggestion Using Social-Centric Scores
Authors: Mohammad Aliannejadi, Fabio Crestani
Date: 2018-03-22 00:00:00

User modeling is a very important task for making relevant suggestions of
venues to the users. These suggestions are often based on matching the venues'
features with the users' preferences, which can be collected from previously
visited locations. In this paper, we present a set of relevance scores for
making personalized suggestions of points of interest. These scores model each
user by focusing on the different types of information extracted from venues
that they have previously visited. In particular, we focus on scores extracted
from social information available on location-based social networks. Our
experiments, conducted on the dataset of the TREC Contextual Suggestion Track,
show that social scores are more effective than scores based venues' content.
Deep Reinforcement Learning of Volume-guided Progressive View Inpainting for 3D Point Scene Completion from a Single Depth Image
Authors: Xiaoguang Han, Zhaoxuan Zhang, Dong Du, Mingdai Yang, Jingming Yu, Pan Pan, Xin Yang, Ligang Liu, Zixiang Xiong, Shuguang Cui
Date: 2019-03-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deep Reinforcement Learning, Reinforcement Learning

We present a deep reinforcement learning method of progressive view
inpainting for 3D point scene completion under volume guidance, achieving
high-quality scene reconstruction from only a single depth image with severe
occlusion. Our approach is end-to-end, consisting of three modules: 3D scene
volume reconstruction, 2D depth map inpainting, and multi-view selection for
completion. Given a single depth image, our method first goes through the 3D
volume branch to obtain a volumetric scene reconstruction as a guide to the
next view inpainting step, which attempts to make up the missing information;
the third step involves projecting the volume under the same view of the input,
concatenating them to complete the current view depth, and integrating all
depth into the point cloud. Since the occluded areas are unavailable, we resort
to a deep Q-Network to glance around and pick the next best view for large hole
completion progressively until a scene is adequately reconstructed while
guaranteeing validity. All steps are learned jointly to achieve robust and
consistent results. We perform qualitative and quantitative evaluations with
extensive experiments on the SUNCG data, obtaining better results than the
state of the art.
$β^3$-IRT: A New Item Response Model and its Applications
Authors: Yu Chen, Telmo Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter Flach
Date: 2019-03-10 00:00:00

Item Response Theory (IRT) aims to assess latent abilities of respondents based on the correctness of their answers in aptitude test items with different difficulty levels. In this paper, we propose the $\beta^3$-IRT model, which models continuous responses and can generate a much enriched family of Item Characteristic Curve (ICC). In experiments we applied the proposed model to data from an online exam platform, and show our model outperforms a more standard 2PL-ND model on all datasets. Furthermore, we show how to apply $\beta^3$-IRT to assess the ability of machine learning classifiers. This novel application results in a new metric for evaluating the quality of the classifier's probability estimates, based on the inferred difficulty and discrimination of data instances.
Optimal Collusion-Free Teaching
Authors: David Kirkpatrick, Hans U. Simon, Sandra Zilles
Date: 2019-03-10 00:00:00

Formal models of learning from teachers need to respect certain criteria to
avoid collusion. The most commonly accepted notion of collusion-freeness was
proposed by Goldman and Mathias (1996), and various teaching models obeying
their criterion have been studied. For each model $M$ and each concept class
$\mathcal{C}$, a parameter $M$-$\mathrm{TD}(\mathcal{C})$ refers to the
teaching dimension of concept class $\mathcal{C}$ in model $M$---defined to be
the number of examples required for teaching a concept, in the worst case over
all concepts in $\mathcal{C}$.
  This paper introduces a new model of teaching, called no-clash teaching,
together with the corresponding parameter $\mathrm{NCTD}(\mathcal{C})$.
No-clash teaching is provably optimal in the strong sense that, given any
concept class $\mathcal{C}$ and any model $M$ obeying Goldman and Mathias's
collusion-freeness criterion, one obtains $\mathrm{NCTD}(\mathcal{C})\le
M$-$\mathrm{TD}(\mathcal{C})$. We also study a corresponding notion
$\mathrm{NCTD}^+$ for the case of learning from positive data only, establish
useful bounds on $\mathrm{NCTD}$ and $\mathrm{NCTD}^+$, and discuss relations
of these parameters to the VC-dimension and to sample compression.
  In addition to formulating an optimal model of collusion-free teaching, our
main results are on the computational complexity of deciding whether
$\mathrm{NCTD}^+(\mathcal{C})=k$ (or $\mathrm{NCTD}(\mathcal{C})=k$) for given
$\mathcal{C}$ and $k$. We show some such decision problems to be equivalent to
the existence question for certain constrained matchings in bipartite graphs.
Our NP-hardness results for the latter are of independent interest in the study
of constrained graph matchings.
Multinomial Random Forest: Toward Consistency and Privacy-Preservation
Authors: Yiming Li, Jiawang Bai, Jiawei Li, Xue Yang, Yong Jiang, Chun Li, Shu-Tao Xia
Date: 2019-03-10 00:00:00
Tasks: General Classification

Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze the \emph{consistency} and \emph{privacy-preservation}. Instead of deterministic greedy split rule or with simple randomness, the MRF adopts two impurity-based multinomial distributions to randomly select a split feature and a split value respectively. Theoretically, we prove the consistency of the proposed MRF and analyze its privacy-preservation within the framework of differential privacy. We also demonstrate with multiple datasets that its performance is on par with the standard RF. To the best of our knowledge, MRF is the first consistent RF variant that has comparable performance to the standard RF.
Efficiently Reusing Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: Methodology Study
Authors: Honghan Wu, Karen Hodgson, Sue Dyson, Katherine I. Morley, Zina M. Ibrahim, Ehtesham Iqbal, Robert Stewart, Richard JB Dobson, Cathie Sudlow
Date: 2019-03-10 00:00:00

Background: Many efforts have been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to construct comprehensive patient profiles for delivering better health-care. Reusing NLP models in new settings, however, remains cumbersome - requiring validation and/or retraining on new data iteratively to achieve convergent results. Objective: The aim of this work is to minimize the effort involved in reusing NLP models on free-text medical records. Methods: We formally define and analyse the model adaptation problem in phenotype-mention identification tasks. We identify "duplicate waste" and "imbalance waste", which collectively impede efficient model reuse. We propose a phenotype embedding based approach to minimize these sources of waste without the need for labelled data from new settings. Results: We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype-mention identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% (duplicate waste), i.e. phenotype mentions without the need for validation and model retraining, and with very good performance (93-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% (imbalance waste), i.e. the effort required in "blind" model-adaptation approaches. Conclusions: Adapting pre-trained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype-mention embedding approach is an effective way to model language patterns for phenotype-mention identification tasks and that its use can guide efficient NLP model reuse.
From Low-Level Events to Activities -- A Session-Based Approach (Extended Version)
Authors: Massimiliano de Leoni, Safa Dundar
Date: 2019-03-10 00:00:00
Tasks: Clustering

Process-Mining techniques aim to use event data about past executions to gain insight into how processes are executed. While these techniques are proven to be very valuable, they are less successful to reach their goal if the process is flexible and, hence, events can potentially occur in any order. Furthermore, information systems can record events at very low level, which do not match the high-level concepts known at business level. Without abstracting sequences of events to high-level concepts, the results of applying process mining (e.g., discovered models) easily become very complex and difficult to interpret, which ultimately means that they are of little use. A large body of research exists on event abstraction but typically a large amount of domain knowledge is required to be fed in, which is often not readily available. Other abstraction techniques are unsupervised, which give lower accuracy. This paper puts forward a technique that requires limited domain knowledge that can be easily provided. Traces are divided in sessions, and each session is abstracted as one single high-level activity execution. The abstraction is based on a combination of automatic clustering and visualization methods. The technique was assessed on two case studies that evidently exhibits a large amount of behavior. The results clearly illustrate the benefits of the abstraction to convey knowledge to stakeholders.
Uncertainty Propagation in Deep Neural Network Using Active Subspace
Authors: Weiqi Ji, Zhuyin Ren, Chung K. Law
Date: 2019-03-10 00:00:00

The inputs of deep neural network (DNN) from real-world data usually come with uncertainties. Yet, it is challenging to propagate the uncertainty in the input features to the DNN predictions at a low computational cost. This work employs a gradient-based subspace method and response surface technique to accelerate the uncertainty propagation in DNN. Specifically, the active subspace method is employed to identify the most important subspace in the input features using the gradient of the DNN output to the inputs. Then the response surface within that low-dimensional subspace can be efficiently built, and the uncertainty of the prediction can be acquired by evaluating the computationally cheap response surface instead of the DNN models. In addition, the subspace can help explain the adversarial examples. The approach is demonstrated in MNIST datasets with a convolutional neural network. Code is available at: https://github.com/jiweiqi/nnsubspace.
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations
Authors: Astrid Dahl, Edwin V. Bonilla
Date: 2019-03-10 00:00:00
Tasks: Gaussian Processes, Variational Inference

We consider multi-task regression models where observations are assumed to be a linear combination of several latent node and weight functions, all drawn from Gaussian process (GP) priors that allow nonzero covariance between grouped latent functions. We show that when these grouped functions are conditionally independent given a group-dependent pivot, it is possible to parameterize the prior through sparse Cholesky factors directly, hence avoiding their computation during inference. Furthermore, we establish that kernels that are multiplicatively separable over input points give rise to such sparse parameterizations naturally without any additional assumptions. Finally, we extend the use of these sparse structures to approximate posteriors within variational inference, further improving scalability on the number of functions. We test our approach on multi-task datasets concerning distributed solar forecasting and show that it outperforms several multi-task GP baselines and that our sparse specifications achieve the same or better accuracy than non-sparse counterparts.
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches
Authors: Philip John Gorinski, Honghan Wu, Claire Grover, Richard Tobin, Conn Talbot, Heather Whalley, Cathie Sudlow, William Whiteley, Beatrice Alex
Date: 2019-03-10 00:00:00
Tasks: BIG-bench Machine Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning

This work investigates multiple approaches to Named Entity Recognition (NER) for text in Electronic Health Record (EHR) data. In particular, we look into the application of (i) rule-based, (ii) deep learning and (iii) transfer learning systems for the task of NER on brain imaging reports with a focus on records from patients with stroke. We explore the strengths and weaknesses of each approach, develop rules and train on a common dataset, and evaluate each system's performance on common test sets of Scottish radiology reports from two sources (brain imaging reports in ESS -- Edinburgh Stroke Study data collected by NHS Lothian as well as radiology reports created in NHS Tayside). Our comparison shows that a hand-crafted system is the most accurate way to automatically label EHR, but machine learning approaches can provide a feasible alternative where resources for a manual system are not readily available.
Improving Humanness of Virtual Agents and Users' Cooperation through Emotions
Authors: Moojan Ghafurian, Neil Budnarain, Jesse Hoey
Date: 2019-03-10 00:00:00

In this paper, we analyze the performance of an agent developed according to
a well-accepted appraisal theory of human emotion with respect to how it
modulates play in the context of a social dilemma. We ask if the agent will be
capable of generating interactions that are considered to be more human than
machine-like. We conduct an experiment with 117 participants and show how
participants rate our agent on dimensions of human-uniqueness (which separates
humans from animals) and human-nature (which separates humans from machines).
We show that our appraisal theoretic agent is perceived to be more human-like
than baseline models, by significantly improving both human-nature and
human-uniqueness aspects of the intelligent agent. We also show that perception
of humanness positively affects enjoyment and cooperation in the social
dilemma.
Deep Griffin-Lim Iteration
Authors: Yoshiki Masuyama, Kohei Yatabe, Yuma Koizumi, Yasuhiro Oikawa, Noboru Harada
Date: 2019-03-10 00:00:00

This paper presents a novel phase reconstruction method (only from a given
amplitude spectrogram) by combining a signal-processing-based approach and a
deep neural network (DNN). To retrieve a time-domain signal from its amplitude
spectrogram, the corresponding phase is required. One of the popular phase
reconstruction methods is the Griffin-Lim algorithm (GLA), which is based on
the redundancy of the short-time Fourier transform. However, GLA often involves
many iterations and produces low-quality signals owing to the lack of prior
knowledge of the target signal. In order to address these issues, in this
study, we propose an architecture which stacks a sub-block including two
GLA-inspired fixed layers and a DNN. The number of stacked sub-blocks is
adjustable, and we can trade the performance and computational load based on
requirements of applications. The effectiveness of the proposed method is
investigated by reconstructing phases from amplitude spectrograms of speeches.
Domain Randomization for Active Pose Estimation
Authors: Xinyi Ren, Jianlan Luo, Eugen Solowjow, Juan Aparicio Ojea, Abhishek Gupta, Aviv Tamar, Pieter Abbeel
Date: 2019-03-10 00:00:00
Tasks: Pose Estimation, State Estimation

Accurate state estimation is a fundamental component of robotic control. In
robotic manipulation tasks, as is our focus in this work, state estimation is
essential for identifying the positions of objects in the scene, forming the
basis of the manipulation plan. However, pose estimation typically requires
expensive 3D cameras or additional instrumentation such as fiducial markers to
perform accurately. Recently, Tobin et al.~introduced an approach to pose
estimation based on domain randomization, where a neural network is trained to
predict pose directly from a 2D image of the scene. The network is trained on
computer-generated images with a high variation in textures and lighting,
thereby generalizing to real-world images. In this work, we investigate how to
improve the accuracy of domain randomization based pose estimation. Our main
idea is that active perception -- moving the robot to get a better estimate of
pose -- can be trained in simulation and transferred to real using domain
randomization. In our approach, the robot trains in a domain-randomized
simulation how to estimate pose from a \emph{sequence} of images. We show that
our approach can significantly improve the accuracy of standard pose estimation
in several scenarios: when the robot holding an object moves, when reference
objects are moved in the scene, or when the camera is moved around the object.
Rethinking System Health Management
Authors: Edward Balaban, Stephen B. Johnson, Mykel J. Kochenderfer
Date: 2019-03-10 00:00:00
Tasks: Decision Making, Management, Scheduling

Health management of complex dynamic systems has traditionally evolved
separately from automated control, planning, and scheduling (generally referred
to in the paper as decision making). A goal of Integrated System Health
Management has been to enable coordination between system health management and
decision making, although successful practical implementations have remained
limited. This paper proposes that, rather than being treated as connected, yet
distinct entities, system health management and decision making should be
unified in their formulations. Enabled by advances in modeling and computing,
we argue that the unified approach will increase a system's operational
effectiveness and may also lead to a lower overall system complexity. We
overview the prevalent system health management methodology and illustrate its
limitations through numerical examples. We then describe the proposed
unification approach and show how it accommodates the typical system health
management concepts.
Rolling-Shutter-Aware Differential SfM and Image Rectification
Authors: Bingbing Zhuang, Loong-Fah Cheong, Gim Hee Lee
Date: 2019-03-10 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Reconstruction, Optical Flow Estimation, Pose Estimation

In this paper, we develop a modified differential Structure from Motion (SfM) algorithm that can estimate relative pose from two consecutive frames despite of Rolling Shutter (RS) artifacts. In particular, we show that under constant velocity assumption, the errors induced by the rolling shutter effect can be easily rectified by a linear scaling operation on each optical flow. We further propose a 9-point algorithm to recover the relative pose of a rolling shutter camera that undergoes constant acceleration motion. We demonstrate that the dense depth maps recovered from the relative pose of the RS camera can be used in a RS-aware warping for image rectification to recover high-quality Global Shutter (GS) images. Experiments on both synthetic and real RS images show that our RS-aware differential SfM algorithm produces more accurate results on relative pose estimation and 3D reconstruction from images distorted by RS effect compared to standard SfM algorithms that assume a GS camera model. We also demonstrate that our RS-aware warping for image rectification method outperforms state-of-the-art commercial software products, i.e. Adobe After Effects and Apple Imovie, at removing RS artifacts.