pwc-archive/papers-with-abstracts huggingface dataset:
Design of PI Controller for Automatic Generation Control of Multi Area Interconnected Power System using Bacterial Foraging Optimization
Authors: Naresh Kumari, Nitin Malik, A. N. Jha, Gaddam Mallesham
Date: 2017-01-26 00:00:00

The system comprises of three interconnected power system networks based on
thermal, wind and hydro power generation. The load variation in any one of the
network results in frequency deviation in all the connected systems.The PI
controllers have been connected separately with each system for the frequency
control and the gains (Kp and Ki) of all the controllers have been optimized
along with frequency bias (Bi) and speed regulation parameter (Ri). The
computationally intelligent techniques like bacterial foraging optimization
(BFO) and particle swarm optimization (PSO) have been applied for the tuning of
controller gains along with variable parameters Bi and Ri. The gradient descent
(GD) based conventional method has also been applied for optimizing the
parameters Kp, Ki,Bi and Ri.The frequency responses are obtained with all the
methods. The performance index chosen is the integral square error (ISE). The
settling time, peak overshoot and peak undershoot of all the frequency
responses on applying three optimization techniques have been compared. It has
been observed that the peak overshoot and peak undershoot significantly reduce
with BFO technique followed by the PSO and GD techniques. While obtaining such
optimum response the settling time is increased marginally with bacterial
foraging technique due to large number of mathematical equations used for the
computation in BFO. The comparison of frequency response using three techniques
show the superiority of BFO over the PSO and GD techniques. The designing of
the system and tuning of the parameters with three techniques has been done in
MATLAB/SIMULINK environment.
Ethical Considerations in Artificial Intelligence Courses
Authors: Emanuelle Burton, Judy Goldsmith, Sven Koenig, Benjamin Kuipers, Nicholas Mattei, Toby Walsh
Date: 2017-01-26 00:00:00
Tasks: Ethics

The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses. As instructors we want to develop curriculum that not only prepares students to be artificial intelligence practitioners, but also to understand the moral, ethical, and philosophical impacts that artificial intelligence will have on society. In this article we provide practical case studies and links to resources for use by AI educators. We also provide concrete suggestions on how to integrate AI ethics into a general artificial intelligence course and how to teach a stand-alone artificial intelligence ethics course.
Linear Learning with Sparse Data
Authors: Ofer Dekel
Date: 2016-12-29 00:00:00
Tasks: Translation

Linear predictors are especially useful when the data is high-dimensional and
sparse. One of the standard techniques used to train a linear predictor is the
Averaged Stochastic Gradient Descent (ASGD) algorithm. We present an efficient
implementation of ASGD that avoids dense vector operations. We also describe a
translation invariant extension called Centered Averaged Stochastic Gradient
Descent (CASGD).
The Causal Frame Problem: An Algorithmic Perspective
Authors: Ardavan Salehi Nobandegani, Ioannis N. Psaromiligkos
Date: 2017-01-26 00:00:00
Tasks: Causal Judgment, Philosophy

The Frame Problem (FP) is a puzzle in philosophy of mind and epistemology,
articulated by the Stanford Encyclopedia of Philosophy as follows: "How do we
account for our apparent ability to make decisions on the basis only of what is
relevant to an ongoing situation without having explicitly to consider all that
is not relevant?" In this work, we focus on the causal variant of the FP, the
Causal Frame Problem (CFP). Assuming that a reasoner's mental causal model can
be (implicitly) represented by a causal Bayes net, we first introduce a notion
called Potential Level (PL). PL, in essence, encodes the relative position of a
node with respect to its neighbors in a causal Bayes net. Drawing on the
psychological literature on causal judgment, we substantiate the claim that PL
may bear on how time is encoded in the mind. Using PL, we propose an inference
framework, called the PL-based Inference Framework (PLIF), which permits a
boundedly-rational approach to the CFP to be formally articulated at Marr's
algorithmic level of analysis. We show that our proposed framework, PLIF, is
consistent with a wide range of findings in causal judgment literature, and
that PL and PLIF make a number of predictions, some of which are already
supported by existing findings.
Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case
Authors: Konstantinos Slavakis, Shiva Salsabilian, David S. Wack, Sarah F. Muldoon, Henry E. Baidoo-Williams, Jean M. Vettel, Matthew Cieslak, Scott T. Grafton
Date: 2017-01-26 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

This paper advocates Riemannian multi-manifold modeling in the context of
network-wide non-stationary time-series analysis. Time-series data, collected
sequentially over time and across a network, yield features which are viewed as
points in or close to a union of multiple submanifolds of a Riemannian
manifold, and distinguishing disparate time series amounts to clustering
multiple Riemannian submanifolds. To support the claim that exploiting the
latent Riemannian geometry behind many statistical features of time series is
beneficial to learning from network data, this paper focuses on brain networks
and puts forth two feature-generation schemes for network-wide dynamic time
series. The first is motivated by Granger-causality arguments and uses an
auto-regressive moving average model to map low-rank linear vector subspaces,
spanned by column vectors of appropriately defined observability matrices, to
points into the Grassmann manifold. The second utilizes (non-linear)
dependencies among network nodes by introducing kernel-based partial
correlations to generate points in the manifold of positive-definite matrices.
Capitilizing on recently developed research on clustering Riemannian
submanifolds, an algorithm is provided for distinguishing time series based on
their geometrical properties, revealed within Riemannian feature spaces.
Extensive numerical tests demonstrate that the proposed framework outperforms
classical and state-of-the-art techniques in clustering brain-network
states/structures hidden beneath synthetic fMRI time series and brain-activity
signals generated from real brain-network structural connectivity matrices.
Dynamic time warping distance for message propagation classification in Twitter
Authors: Siwar Jendoubi, Arnaud Martin, Ludovic Liétard, Boutheina Ben Yaghlane, Hend Ben Hadji
Date: 2017-01-26 00:00:00
Tasks: Classification, Dynamic Time Warping, General Classification

Social messages classification is a research domain that has attracted the
attention of many researchers in these last years. Indeed, the social message
is different from ordinary text because it has some special characteristics
like its shortness. Then the development of new approaches for the processing
of the social message is now essential to make its classification more
efficient. In this paper, we are mainly interested in the classification of
social messages based on their spreading on online social networks (OSN). We
proposed a new distance metric based on the Dynamic Time Warping distance and
we use it with the probabilistic and the evidential k Nearest Neighbors (k-NN)
classifiers to classify propagation networks (PrNets) of messages. The
propagation network is a directed acyclic graph (DAG) that is used to record
propagation traces of the message, the traversed links and their types. We
tested the proposed metric with the chosen k-NN classifiers on real world
propagation traces that were collected from Twitter social network and we got
good classification accuracies.
Per-Block-Convex Data Modeling by Accelerated Stochastic Approximation
Authors: Konstantinos Slavakis, Georgios B. Giannakis
Date: 2015-01-29 00:00:00
Tasks: Clustering, Dictionary Learning, Imputation, Tensor Decomposition

Applications involving dictionary learning, non-negative matrix
factorization, subspace clustering, and parallel factor tensor decomposition
tasks motivate well algorithms for per-block-convex and non-smooth optimization
problems. By leveraging the stochastic approximation paradigm and first-order
acceleration schemes, this paper develops an online and modular learning
algorithm for a large class of non-convex data models, where convexity is
manifested only per-block of variables whenever the rest of them are held
fixed. The advocated algorithm incurs computational complexity that scales
linearly with the number of unknowns. Under minimal assumptions on the cost
functions of the composite optimization task, without bounding constraints on
the optimization variables, or any explicit information on bounds of Lipschitz
coefficients, the expected cost evaluated online at the resultant iterates is
provably convergent with quadratic rate to an accumulation point of the
(per-block) minima, while subgradients of the expected cost asymptotically
vanish in the mean-squared sense. The merits of the general approach are
demonstrated in two online learning setups: (i) Robust linear regression using
a sparsity-cognizant total least-squares criterion; and (ii) semi-supervised
dictionary learning for network-wide link load tracking and imputation with
missing entries. Numerical tests on synthetic and real data highlight the
potential of the proposed framework for streaming data analytics by
demonstrating superior performance over block coordinate descent, and reduced
complexity relative to the popular alternating-direction method of multipliers.
Pose Invariant Embedding for Deep Person Re-identification
Authors: Liang Zheng, Yujia Huang, Huchuan Lu, Yi Yang
Date: 2017-01-26 00:00:00
Tasks: Person Re-Identification, Pose Estimation, Retrieval

Pedestrian misalignment, which mainly arises from detector errors and pose
variations, is a critical problem for a robust person re-identification (re-ID)
system. With bad alignment, the background noise will significantly compromise
the feature learning and matching process. To address this problem, this paper
introduces the pose invariant embedding (PIE) as a pedestrian descriptor.
First, in order to align pedestrians to a standard pose, the PoseBox structure
is introduced, which is generated through pose estimation followed by affine
transformations. Second, to reduce the impact of pose estimation errors and
information loss during PoseBox construction, we design a PoseBox fusion (PBF)
CNN architecture that takes the original image, the PoseBox, and the pose
estimation confidence as input. The proposed PIE descriptor is thus defined as
the fully connected layer of the PBF network for the retrieval task.
Experiments are conducted on the Market-1501, CUHK03, and VIPeR datasets. We
show that PoseBox alone yields decent re-ID accuracy and that when integrated
in the PBF network, the learned PIE descriptor produces competitive performance
compared with the state-of-the-art approaches.
Ancestral Causal Inference
Authors: Sara Magliacane, Tom Claassen, Joris M. Mooij
Date: 2016-06-22 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Causal Discovery, Causal Inference

Constraint-based causal discovery from limited data is a notoriously
difficult challenge due to the many borderline independence test decisions.
Several approaches to improve the reliability of the predictions by exploiting
redundancy in the independence information have been proposed recently. Though
promising, existing approaches can still be greatly improved in terms of
accuracy and scalability. We present a novel method that reduces the
combinatorial explosion of the search space by using a more coarse-grained
representation of causal information, drastically reducing computation time.
Additionally, we propose a method to score causal predictions based on their
confidence. Crucially, our implementation also allows one to easily combine
observational and interventional data and to incorporate various types of
available background knowledge. We prove soundness and asymptotic consistency
of our method and demonstrate that it can outperform the state-of-the-art on
synthetic data, achieving a speedup of several orders of magnitude. We
illustrate its practical feasibility by applying it on a challenging protein
data set.
Fast and Accurate Time Series Classification with WEASEL
Authors: Patrick Schäfer, Ulf Leser
Date: 2017-01-26 00:00:00
Tasks: Classification, General Classification, Load Forecasting, Time Series, Time Series Analysis, Time Series Classification

Time series (TS) occur in many scientific and commercial applications,
ranging from earth surveillance to industry automation to the smart grids. An
important type of TS analysis is classification, which can, for instance,
improve energy load forecasting in smart grids by detecting the types of
electronic devices based on their energy consumption profiles recorded by
automatic sensors. Such sensor-driven applications are very often characterized
by (a) very long TS and (b) very large TS datasets needing classification.
However, current methods to time series classification (TSC) cannot cope with
such data volumes at acceptable accuracy; they are either scalable but offer
only inferior classification quality, or they achieve state-of-the-art
classification quality but cannot scale to large data volumes.
  In this paper, we present WEASEL (Word ExtrAction for time SEries
cLassification), a novel TSC method which is both scalable and accurate. Like
other state-of-the-art TSC methods, WEASEL transforms time series into feature
vectors, using a sliding-window approach, which are then analyzed through a
machine learning classifier. The novelty of WEASEL lies in its specific method
for deriving features, resulting in a much smaller yet much more discriminative
feature set. On the popular UCR benchmark of 85 TS datasets, WEASEL is more
accurate than the best current non-ensemble algorithms at orders-of-magnitude
lower classification and training times, and it is almost as accurate as
ensemble classifiers, whose computational complexity makes them inapplicable
even for mid-size datasets. The outstanding robustness of WEASEL is also
confirmed by experiments on two real smart grid datasets, where it
out-of-the-box achieves almost the same accuracy as highly tuned,
domain-specific methods.
Operationalizing Declarative and Procedural Knowledge: a Benchmark on Logic Programming Petri Nets (LPPNs)
Authors: Giovanni Sileno
Date: 2017-01-26 00:00:00
Tasks: Computational Efficiency

Modelling, specifying and reasoning about complex systems requires to process in an integrated fashion declarative and procedural aspects of the target domain. The paper reports on an experiment conducted with a propositional version of Logic Programming Petri Nets (LPPNs), a notation extending Petri Nets with logic programming constructs. Two semantics are presented: a denotational semantics that fully maps the notation to ASP via Event Calculus; and a hybrid operational semantics that process separately the causal mechanisms via Petri nets, and the constraints associated to objects and to events via Answer Set Programming (ASP). These two alternative specifications enable an empirical evaluation in terms of computational efficiency. Experimental results show that the hybrid semantics is more efficient w.r.t. sequences, whereas the two semantics follows the same behaviour w.r.t. branchings (although the denotational one performs better in absolute terms).
Super-resolution Using Constrained Deep Texture Synthesis
Authors: Libin Sun, James Hays
Date: 2017-01-26 00:00:00
Tasks: Image Super-Resolution, Super-Resolution, Texture Synthesis

Hallucinating high frequency image details in single image super-resolution
is a challenging task. Traditional super-resolution methods tend to produce
oversmoothed output images due to the ambiguity in mapping between low and high
resolution patches. We build on recent success in deep learning based texture
synthesis and show that this rich feature space can facilitate successful
transfer and synthesis of high frequency image details to improve the visual
quality of super-resolution results on a wide variety of natural textures and
images.
FPGA Architecture for Deep Learning and its application to Planetary Robotics
Authors: Pranay Gankidi, Jekan Thangavelautham
Date: 2017-01-26 00:00:00
Tasks: CPU, Q-Learning, Reinforcement Learning

Autonomous control systems onboard planetary rovers and spacecraft benefit
from having cognitive capabilities like learning so that they can adapt to
unexpected situations in-situ. Q-learning is a form of reinforcement learning
and it has been efficient in solving certain class of learning problems.
However, embedded systems onboard planetary rovers and spacecraft rarely
implement learning algorithms due to the constraints faced in the field, like
processing power, chip size, convergence rate and costs due to the need for
radiation hardening. These challenges present a compelling need for a portable,
low-power, area efficient hardware accelerator to make learning algorithms
practical onboard space hardware. This paper presents a FPGA implementation of
Q-learning with Artificial Neural Networks (ANN). This method matches the
massive parallelism inherent in neural network software with the fine-grain
parallelism of an FPGA hardware thereby dramatically reducing processing time.
Mars Science Laboratory currently uses Xilinx-Space-grade Virtex FPGA devices
for image processing, pyrotechnic operation control and obstacle avoidance. We
simulate and program our architecture on a Xilinx Virtex 7 FPGA. The
architectural implementation for a single neuron Q-learning and a more complex
Multilayer Perception (MLP) Q-learning accelerator has been demonstrated. The
results show up to a 43-fold speed up by Virtex 7 FPGAs compared to a
conventional Intel i5 2.3 GHz CPU. Finally, we simulate the proposed
architecture using the Symphony simulator and compiler from Xilinx, and
evaluate the performance and power consumption.
Non-Negative Matrix Factorizations for Multiplex Network Analysis
Authors: Vladimir Gligorijevic, Yannis Panagakis, Stefanos Zafeiriou
Date: 2016-12-01 00:00:00
Tasks: Clustering, Community Detection

Networks have been a general tool for representing, analyzing, and modeling
relational data arising in several domains. One of the most important aspect of
network analysis is community detection or network clustering. Until recently,
the major focus have been on discovering community structure in single (i.e.,
monoplex) networks. However, with the advent of relational data with multiple
modalities, multiplex networks, i.e., networks composed of multiple layers
representing different aspects of relations, have emerged. Consequently,
community detection in multiplex network, i.e., detecting clusters of nodes
shared by all layers, has become a new challenge. In this paper, we propose
Network Fusion for Composite Community Extraction (NF-CCE), a new class of
algorithms, based on four different non-negative matrix factorization models,
capable of extracting composite communities in multiplex networks. Each
algorithm works in two steps: first, it finds a non-negative, low-dimensional
feature representation of each network layer; then, it fuses the feature
representation of layers into a common non-negative, low-dimensional feature
representation via collective factorization. The composite clusters are
extracted from the common feature representation. We demonstrate the superior
performance of our algorithms over the state-of-the-art methods on various
types of multiplex networks, including biological, social, economic, citation,
phone communication, and brain multiplex networks.
A Model-based Projection Technique for Segmenting Customers
Authors: Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman
Date: 2017-01-25 00:00:00
Tasks: Marketing, model

We consider the problem of segmenting a large population of customers into
non-overlapping groups with similar preferences, using diverse preference
observations such as purchases, ratings, clicks, etc. over subsets of items. We
focus on the setting where the universe of items is large (ranging from
thousands to millions) and unstructured (lacking well-defined attributes) and
each customer provides observations for only a few items. These data
characteristics limit the applicability of existing techniques in marketing and
machine learning. To overcome these limitations, we propose a model-based
projection technique, which transforms the diverse set of observations into a
more comparable scale and deals with missing data by projecting the transformed
data onto a low-dimensional space. We then cluster the projected data to obtain
the customer segments. Theoretically, we derive precise necessary and
sufficient conditions that guarantee asymptotic recovery of the true customer
segments. Empirically, we demonstrate the speed and performance of our method
in two real-world case studies: (a) 84% improvement in the accuracy of new
movie recommendations on the MovieLens data set and (b) 6% improvement in the
performance of similar item recommendations algorithm on an offline dataset at
eBay. We show that our method outperforms standard latent-class and
demographic-based techniques.
Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding
Authors: Zhengping Che, Yu Cheng, Zhaonan Sun, Yan Liu
Date: 2017-01-25 00:00:00

The widespread availability of electronic health records (EHRs) promises to
usher in the era of personalized medicine. However, the problem of extracting
useful clinical representations from longitudinal EHR data remains challenging.
In this paper, we explore deep neural network models with learned medical
feature embedding to deal with the problems of high dimensionality and
temporality. Specifically, we use a multi-layer convolutional neural network
(CNN) to parameterize the model and is thus able to capture complex non-linear
longitudinal evolution of EHRs. Our model can effectively capture local/short
temporal dependency in EHRs, which is beneficial for risk prediction. To
account for high dimensionality, we use the embedding medical features in the
CNN model which hold the natural medical concepts. Our initial experiments
produce promising results and demonstrate the effectiveness of both the medical
feature embedding and the proposed convolutional neural network in risk
prediction on cohorts of congestive heart failure and diabetes patients
compared with several strong baselines.
Recovering 3D Planar Arrangements from Videos
Authors: Shuai Du, Youyi Zheng
Date: 2017-01-25 00:00:00
Tasks: 3D geometry, 3D Reconstruction

Acquiring 3D geometry of real world objects has various applications in 3D
digitization, such as navigation and content generation in virtual
environments. Image remains one of the most popular media for such visual tasks
due to its simplicity of acquisition. Traditional image-based 3D reconstruction
approaches heavily exploit point-to-point correspondence among multiple images
to estimate camera motion and 3D geometry. Establishing point-to-point
correspondence lies at the center of the 3D reconstruction pipeline, which
however is easily prone to errors. In this paper, we propose an optimization
framework which traces image points using a novel structure-guided dynamic
tracking algorithm and estimates both the camera motion and a 3D structure
model by enforcing a set of planar constraints. The key to our method is a
structure model represented as a set of planes and their arrangements.
Constraints derived from the structure model is used both in the correspondence
establishment stage and the bundle adjustment stage in our reconstruction
pipeline. Experiments show that our algorithm can effectively localize
structure correspondence across dense image frames while faithfully
reconstructing the camera motion and the underlying structured 3D model.