pwc-archive/papers-with-abstracts huggingface dataset:
Variable Neighborhood Search Algorithms for the multi-depot dial-a-ride problem with heterogeneous vehicles and users
Authors: Paolo Detti, Garazi Zabalo Manrique de Lara
Date: 2016-11-16 00:00:00

In this work, a study on Variable Neighborhood Search algorithms for
multi-depot dial-a-ride problems is presented. In dial-a-ride problems patients
need to be transported from pre-specified pickup locations to pre-specified
delivery locations, under different considerations. The addressed problem
presents several constraints and features, such as heterogeneous vehicles,
distributed in different depots, and heterogeneous patients. The aim is of
minimizing the total routing cost, while respecting time-window, ride-time,
capacity and route duration constraints. The objective of the study is of
determining the best algorithm configuration in terms of initial solution,
neighborhood and local search procedures. At this aim, two different procedures
for the computation of an initial solution, six different type of neighborhoods
and five local search procedures, where only intra-route changes are made, have
been considered and compared.
  We have also evaluated an "adjusting procedure" that aims to produce feasible
solutions from infeasible solutions with small constraints violations. The
different VNS algorithms have been tested on instances from literature as well
as on random instances arising from a real-world healthcare application.
The Effects of Relative Importance of User Constraints in Cloud of Things Resource Discovery: A Case Study
Authors: Luiz H. Nunes, Julio C. Estrella, Alexandre C. B. Delbem, Charith Perera, Stephan Reiff-Marganiec
Date: 2016-11-16 00:00:00
Tasks: Cloud Computing

Over the last few years, the number of smart objects connected to the
Internet has grown exponentially in comparison to the number of services and
applications. The integration between Cloud Computing and Internet of Things,
named as Cloud of Things, plays a key role in managing the connected things,
their data and services. One of the main challenges in Cloud of Things is the
resource discovery of the smart objects and their reuse in different contexts.
Most of the existent work uses some kind of multi-criteria decision analysis
algorithm to perform the resource discovery, but do not evaluate the impact
that the user constraints has in the final solution. In this paper, we analyse
the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses
algorithms and the impact of user constraints on them. We evaluated the quality
of the proposed solutions using the Pareto-optimality concept.
An Analysis of Tournament Structure
Authors: Nhien Pham Hoang Bao, Hiroyuki Iida
Date: 2016-11-16 00:00:00

This paper explores a novel way for analyzing the tournament structures to
find a best suitable one for the tournament under consideration. It concerns
about three aspects such as tournament conducting cost, competitiveness
development and ranking precision. It then proposes a new method using progress
tree to detect potential throwaway matches. The analysis performed using the
proposed method reveals the strengths and weaknesses of tournament structures.
As a conclusion, single elimination is best if we want to qualify one winner
only, all matches conducted are exciting in term of competitiveness. Double
elimination with proper seeding system is a better choice if we want to qualify
more winners. A reasonable number of extra matches need to be conducted in
exchange of being able to qualify top four winners. Round-robin gives reliable
ranking precision for all participants. However, its conduction cost is very
high, and it fails to maintain competitiveness development.
Relational Multi-Manifold Co-Clustering
Authors: Ping Li, Jiajun Bu, Chun Chen, Zhanying He, Deng Cai
Date: 2016-11-16 00:00:00
Tasks: Clustering, Ensemble Learning

Co-clustering targets on grouping the samples (e.g., documents, users) and
the features (e.g., words, ratings) simultaneously. It employs the dual
relation and the bilateral information between the samples and features. In
many realworld applications, data usually reside on a submanifold of the
ambient Euclidean space, but it is nontrivial to estimate the intrinsic
manifold of the data space in a principled way. In this study, we focus on
improving the co-clustering performance via manifold ensemble learning, which
is able to maximally approximate the intrinsic manifolds of both the sample and
feature spaces. To achieve this, we develop a novel co-clustering algorithm
called Relational Multi-manifold Co-clustering (RMC) based on symmetric
nonnegative matrix tri-factorization, which decomposes the relational data
matrix into three submatrices. This method considers the intertype relationship
revealed by the relational data matrix, and also the intra-type information
reflected by the affinity matrices encoded on the sample and feature data
distributions. Specifically, we assume the intrinsic manifold of the sample or
feature space lies in a convex hull of some pre-defined candidate manifolds. We
want to learn a convex combination of them to maximally approach the desired
intrinsic manifold. To optimize the objective function, the multiplicative
rules are utilized to update the submatrices alternatively. Besides, both the
entropic mirror descent algorithm and the coordinate descent algorithm are
exploited to learn the manifold coefficient vector. Extensive experiments on
documents, images and gene expression data sets have demonstrated the
superiority of the proposed algorithm compared to other well-established
methods.
A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data
Authors: Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar
Date: 2016-11-16 00:00:00
Tasks: ICU Admission

Critically ill patients in regular wards are vulnerable to unanticipated
clinical dete- rioration which requires timely transfer to the intensive care
unit (ICU). To allow for risk scoring and patient monitoring in such a setting,
we develop a novel Semi- Markov Switching Linear Gaussian Model (SSLGM) for the
inpatients' physiol- ogy. The model captures the patients' latent clinical
states and their corresponding observable lab tests and vital signs. We present
an efficient unsupervised learn- ing algorithm that capitalizes on the
informatively censored data in the electronic health records (EHR) to learn the
parameters of the SSLGM; the learned model is then used to assess the new
inpatients' risk for clinical deterioration in an online fashion, allowing for
timely ICU admission. Experiments conducted on a het- erogeneous cohort of
6,094 patients admitted to a large academic medical center show that the
proposed model significantly outperforms the currently deployed risk scores
such as Rothman index, MEWS, SOFA and APACHE.
A Proximal Stochastic Quasi-Newton Algorithm
Authors: Luo Luo, Zihao Chen, Zhihua Zhang, Wu-Jun Li
Date: 2016-01-31 00:00:00

In this paper, we discuss the problem of minimizing the sum of two convex
functions: a smooth function plus a non-smooth function. Further, the smooth
part can be expressed by the average of a large number of smooth component
functions, and the non-smooth part is equipped with a simple proximal mapping.
We propose a proximal stochastic second-order method, which is efficient and
scalable. It incorporates the Hessian in the smooth part of the function and
exploits multistage scheme to reduce the variance of the stochastic gradient.
We prove that our method can achieve linear rate of convergence.
Relation Schema Induction using Tensor Factorization with Side Information
Authors: Madhav Nimishakavi, Uday Singh Saini, Partha Talukdar
Date: 2016-05-12 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Open Information Extraction, Relation

Given a set of documents from a specific domain (e.g., medical research
journals), how do we automatically build a Knowledge Graph (KG) for that
domain? Automatic identification of relations and their schemas, i.e., type
signature of arguments of relations (e.g., undergo(Patient, Surgery)), is an
important first step towards this goal. We refer to this problem as Relation
Schema Induction (RSI). In this paper, we propose Schema Induction using
Coupled Tensor Factorization (SICTF), a novel tensor factorization method for
relation schema induction. SICTF factorizes Open Information Extraction
(OpenIE) triples extracted from a domain corpus along with additional side
information in a principled way to induce relation schemas. To the best of our
knowledge, this is the first application of tensor factorization for the RSI
problem. Through extensive experiments on multiple real-world datasets, we find
that SICTF is not only more accurate than state-of-the-art baselines, but also
significantly faster (about 14x faster).
Training Spiking Deep Networks for Neuromorphic Hardware
Authors: Eric Hunsberger, Chris Eliasmith
Date: 2016-11-16 00:00:00

We describe a method to train spiking deep networks that can be run using
leaky integrate-and-fire (LIF) neurons, achieving state-of-the-art results for
spiking LIF networks on five datasets, including the large ImageNet ILSVRC-2012
benchmark. Our method for transforming deep artificial neural networks into
spiking networks is scalable and works with a wide range of neural
nonlinearities. We achieve these results by softening the neural response
function, such that its derivative remains bounded, and by training the network
with noise to provide robustness against the variability introduced by spikes.
Our analysis shows that implementations of these networks on neuromorphic
hardware will be many times more power-efficient than the equivalent
non-spiking networks on traditional hardware.
Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media Text
Authors: Raj Nath Patel, Prakash B. Pimpale, M Sasikumar
Date: 2016-11-15 00:00:00
Tasks: Language Modeling, Language Modelling, POS, POS Tagging, TAG

This paper describes Centre for Development of Advanced Computing's (CDACM)
submission to the shared task-'Tool Contest on POS tagging for Code-Mixed
Indian Social Media (Facebook, Twitter, and Whatsapp) Text', collocated with
ICON-2016. The shared task was to predict Part of Speech (POS) tag at word
level for a given text. The code-mixed text is generated mostly on social media
by multilingual users. The presence of the multilingual words,
transliterations, and spelling variations make such content linguistically
complex. In this paper, we propose an approach to POS tag code-mixed social
media text using Recurrent Neural Network Language Model (RNN-LM) architecture.
We submitted the results for Hindi-English (hi-en), Bengali-English (bn-en),
and Telugu-English (te-en) code-mixed data.
S3Pool: Pooling with Stochastic Spatial Sampling
Authors: Shuangfei Zhai, Hui Wu, Abhishek Kumar, Yu Cheng, Yongxi Lu, Zhongfei Zhang, Rogerio Feris
Date: 2016-11-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Data Augmentation, image-classification, Image Classification

Feature pooling layers (e.g., max pooling) in convolutional neural networks
(CNNs) serve the dual purpose of providing increasingly abstract
representations as well as yielding computational savings in subsequent
convolutional layers. We view the pooling operation in CNNs as a two-step
procedure: first, a pooling window (e.g., $2\times 2$) slides over the feature
map with stride one which leaves the spatial resolution intact, and second,
downsampling is performed by selecting one pixel from each non-overlapping
pooling window in an often uniform and deterministic (e.g., top-left) manner.
Our starting point in this work is the observation that this regularly spaced
downsampling arising from non-overlapping windows, although intuitive from a
signal processing perspective (which has the goal of signal reconstruction), is
not necessarily optimal for \emph{learning} (where the goal is to generalize).
We study this aspect and propose a novel pooling strategy with stochastic
spatial sampling (S3Pool), where the regular downsampling is replaced by a more
general stochastic version. We observe that this general stochasticity acts as
a strong regularizer, and can also be seen as doing implicit data augmentation
by introducing distortions in the feature maps. We further introduce a
mechanism to control the amount of distortion to suit different datasets and
architectures. To demonstrate the effectiveness of the proposed approach, we
perform extensive experiments on several popular image classification
benchmarks, observing excellent improvements over baseline models. Experimental
code is available at https://github.com/Shuangfei/s3pool.
Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery
Authors: Mahtab J. Fard, Sattar Ameri, Ratna B. Chinnam, Abhilash K. Pandya, Michael D. Klein, R. Darin Ellis
Date: 2016-11-16 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

Evaluating surgeon skill has predominantly been a subjective task.
Development of objective methods for surgical skill assessment are of increased
interest. Recently, with technological advances such as robotic-assisted
minimally invasive surgery (RMIS), new opportunities for objective and
automated assessment frameworks have arisen. In this paper, we applied machine
learning methods to automatically evaluate performance of the surgeon in RMIS.
Six important movement features were used in the evaluation including
completion time, path length, depth perception, speed, smoothness and
curvature. Different classification methods applied to discriminate expert and
novice surgeons. We test our method on real surgical data for suturing task and
compare the classification result with the ground truth data (obtained by
manual labeling). The experimental results show that the proposed framework can
classify surgical skill level with relatively high accuracy of 85.7%. This
study demonstrates the ability of machine learning methods to automatically
classify expert and novice surgeons using movement features for different RMIS
tasks. Due to the simplicity and generalizability of the introduced
classification method, it is easy to implement in existing trainers.
Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation
Authors: Yu-An Chung, Shao-Wen Yang, Hsuan-Tien Lin
Date: 2016-11-16 00:00:00
Tasks: Classification, Deep Learning, General Classification, Object Recognition

While deep neural networks have succeeded in several visual applications, such as object recognition, detection, and localization, by reaching very high classification accuracies, it is important to note that many real-world applications demand varying costs for different types of misclassification errors, thus requiring cost-sensitive classification algorithms. Current models of deep neural networks for cost-sensitive classification are restricted to some specific network structures and limited depth. In this paper, we propose a novel framework that can be applied to deep neural networks with any structure to facilitate their learning of meaningful representations for cost-sensitive classification problems. Furthermore, the framework allows end-to-end training of deeper networks directly. The framework is designed by augmenting auxiliary neurons to the output of each hidden layer for layer-wise cost estimation, and including the total estimation loss within the optimization objective. Experimental results on public benchmark visual data sets with two cost information settings demonstrate that the proposed framework outperforms state-of-the-art cost-sensitive deep learning models.
Convergence rate of stochastic k-means
Authors: Cheng Tang, Claire Monteleoni
Date: 2016-11-16 00:00:00
Tasks: Clustering

We analyze online \cite{BottouBengio} and mini-batch \cite{Sculley} $k$-means
variants. Both scale up the widely used $k$-means algorithm via stochastic
approximation, and have become popular for large-scale clustering and
unsupervised feature learning. We show, for the first time, that starting with
any initial solution, they converge to a "local optimum" at rate
$O(\frac{1}{t})$ (in terms of the $k$-means objective) under general
conditions. In addition, we show if the dataset is clusterable, when
initialized with a simple and scalable seeding algorithm, mini-batch $k$-means
converges to an optimal $k$-means solution at rate $O(\frac{1}{t})$ with high
probability. The $k$-means objective is non-convex and non-differentiable: we
exploit ideas from recent work on stochastic gradient descent for non-convex
problems \cite{ge:sgd_tensor, balsubramani13} by providing a novel
characterization of the trajectory of $k$-means algorithm on its solution
space, and circumvent the non-differentiability problem via geometric insights
about $k$-means update.
Chinese/English mixed Character Segmentation as Semantic Segmentation
Authors: Huabin Zheng, Jingyu Wang, Zhengjie Huang, Yang Yang, Rong pan
Date: 2016-11-07 00:00:00
Tasks: Diversity, Optical Character Recognition (OCR), Position, Segmentation, Semantic Segmentation

OCR character segmentation for multilingual printed documents is difficult
due to the diversity of different linguistic characters. Previous approaches
mainly focus on monolingual texts and are not suitable for multilingual-lingual
cases. In this work, we particularly tackle the Chinese/English mixed case by
reframing it as a semantic segmentation problem. We take advantage of the
successful architecture called fully convolutional networks (FCN) in the field
of semantic segmentation. Given a wide enough receptive field, FCN can utilize
the necessary context around a horizontal position to determinate whether this
is a splitting point or not. As a deep neural architecture, FCN can
automatically learn useful features from raw text line images. Although trained
on synthesized samples with simulated random disturbance, our FCN model
generalizes well to real-world samples. The experimental results show that our
model significantly outperforms the previous methods.
Classifier comparison using precision
Authors: Lovedeep Gondara
Date: 2016-09-29 00:00:00

New proposed models are often compared to state-of-the-art using statistical
significance testing. Literature is scarce for classifier comparison using
metrics other than accuracy. We present a survey of statistical methods that
can be used for classifier comparison using precision, accounting for
inter-precision correlation arising from use of same dataset. Comparisons are
made using per-class precision and methods presented to test global null
hypothesis of an overall model comparison. Comparisons are extended to multiple
multi-class classifiers and to models using cross validation or its variants.
Partial Bayesian update to precision is introduced when population prevalence
of a class is known. Applications to compare deep architectures are studied.
Bayesian Exploration: Incentivizing Exploration in Bayesian Games
Authors: Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu
Date: 2016-02-24 00:00:00

We consider a ubiquitous scenario in the Internet economy when individual decision-makers (henceforth, agents) both produce and consume information as they make strategic choices in an uncertain environment. This creates a three-way tradeoff between exploration (trying out insufficiently explored alternatives to help others in the future), exploitation (making optimal decisions given the information discovered by other agents), and incentives of the agents (who are myopically interested in exploitation, while preferring the others to explore). We posit a principal who controls the flow of information from agents that came before, and strives to coordinate the agents towards a socially optimal balance between exploration and exploitation, not using any monetary transfers. The goal is to design a recommendation policy for the principal which respects agents' incentives and minimizes a suitable notion of regret. We extend prior work in this direction to allow the agents to interact with one another in a shared environment: at each time step, multiple agents arrive to play a Bayesian game, receive recommendations, choose their actions, receive their payoffs, and then leave the game forever. The agents now face two sources of uncertainty: the actions of the other agents and the parameters of the uncertain game environment. Our main contribution is to show that the principal can achieve constant regret when the utilities are deterministic (where the constant depends on the prior distribution, but not on the time horizon), and logarithmic regret when the utilities are stochastic. As a key technical tool, we introduce the concept of explorable actions, the actions which some incentive-compatible policy can recommend with non-zero probability. We show how the principal can identify (and explore) all explorable actions, and use the revealed information to perform optimally.
Learning Dexterous Manipulation Policies from Experience and Imitation
Authors: Vikash Kumar, Abhishek Gupta, Emanuel Todorov, Sergey Levine
Date: 2016-11-15 00:00:00

We explore learning-based approaches for feedback control of a dexterous
five-finger hand performing non-prehensile manipulation. First, we learn local
controllers that are able to perform the task starting at a predefined initial
state. These controllers are constructed using trajectory optimization with
respect to locally-linear time-varying models learned directly from sensor
data. In some cases, we initialize the optimizer with human demonstrations
collected via teleoperation in a virtual environment. We demonstrate that such
controllers can perform the task robustly, both in simulation and on the
physical platform, for a limited range of initial conditions around the trained
starting state. We then consider two interpolation methods for generalizing to
a wider range of initial conditions: deep learning, and nearest neighbors. We
find that nearest neighbors achieve higher performance. Nevertheless, the
neural network has its advantages: it uses only tactile and proprioceptive
feedback but no visual feedback about the object (i.e. it performs the task
blind) and learns a time-invariant policy. In contrast, the nearest neighbors
method switches between time-varying local controllers based on the proximity
of initial object states sensed via motion capture. While both generalization
methods leave room for improvement, our work shows that (i) local
trajectory-based controllers for complex non-prehensile manipulation tasks can
be constructed from surprisingly small amounts of training data, and (ii)
collections of such controllers can be interpolated to form more global
controllers. Results are summarized in the supplementary video:
https://youtu.be/E0wmO6deqjo
Harnessing Deep Neural Networks with Logic Rules
Authors: Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, Eric Xing
Date: 2016-03-21 00:00:00
Proceeding: ACL 2016 8
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Sentiment Analysis

Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models. We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and RNNs) with declarative first-order logic rules. Specifically, we develop an iterative distillation method that transfers the structured information of logic rules into the weights of neural networks. We deploy the framework on a CNN for sentiment analysis, and an RNN for named entity recognition. With a few highly intuitive rules, we obtain substantial improvements and achieve state-of-the-art or comparable results to previous best-performing systems.