pwc-archive/papers-with-abstracts huggingface dataset:
Performance Analysis of Robust Stable PID Controllers Using Dominant Pole Placement for SOPTD Process Models
Authors: Saptarshi Das, Kaushik Halder, Amitava Gupta
Date: 2018-01-28 00:00:00

This paper derives new formulations for designing dominant pole placement
based proportional-integral-derivative (PID) controllers to handle second order
processes with time delays (SOPTD). Previously, similar attempts have been made
for pole placement in delay-free systems. The presence of the time delay term
manifests itself as a higher order system with variable number of interlaced
poles and zeros upon Pade approximation, which makes it difficult to achieve
precise pole placement control. We here report the analytical expressions to
constrain the closed loop dominant and non-dominant poles at the desired
locations in the complex s-plane, using a third order Pade approximation for
the delay term. However, invariance of the closed loop performance with
different time delay approximation has also been verified using increasing
order of Pade, representing a closed to reality higher order delay dynamics.
The choice of the nature of non-dominant poles e.g. all being complex, real or
a combination of them modifies the characteristic equation and influences the
achievable stability regions. The effect of different types of non-dominant
poles and the corresponding stability regions are obtained for nine test-bench
processes indicating different levels of open-loop damping and lag to delay
ratio. Next, we investigate which expression yields a wider stability region in
the design parameter space by using Monte Carlo simulations while uniformly
sampling a chosen design parameter space. Various time and frequency domain
control performance parameters are investigated next, as well as their
deviations with uncertain process parameters, using thousands of Monte Carlo
simulations, around the robust stable solution for each of the nine test-bench
processes.
Evolving Chaos: Identifying New Attractors of the Generalised Lorenz Family
Authors: Indranil Pan, Saptarshi Das
Date: 2018-01-28 00:00:00

In a recent paper, we presented an intelligent evolutionary search technique
through genetic programming (GP) for finding new analytical expressions of
nonlinear dynamical systems, similar to the classical Lorenz attractor's which
also exhibit chaotic behaviour in the phase space. In this paper, we extend our
previous finding to explore yet another gallery of new chaotic attractors which
are derived from the original Lorenz system of equations. Compared to the
previous exploration with sinusoidal type transcendental nonlinearity, here we
focus on only cross-product and higher-power type nonlinearities in the three
state equations. We here report over 150 different structures of chaotic
attractors along with their one set of parameter values, phase space dynamics
and the Largest Lyapunov Exponents (LLE). The expressions of these new
Lorenz-like nonlinear dynamical systems have been automatically evolved through
multi-gene genetic programming (MGGP). In the past two decades, there have been
many claims of designing new chaotic attractors as an incremental extension of
the Lorenz family. We provide here a large family of chaotic systems whose
structure closely resemble the original Lorenz system but with drastically
different phase space dynamics. This advances the state of the art knowledge of
discovering new chaotic systems which can find application in many real-world
problems. This work may also find its archival value in future in the domain of
new chaotic system discovery.
SVM via Saddle Point Optimization: New Bounds and Distributed Algorithms
Authors: Yifei Jin, Lingxiao Huang, Jian Li
Date: 2017-05-20 00:00:00

We study two important SVM variants: hard-margin SVM (for linearly separable
cases) and $\nu$-SVM (for linearly non-separable cases). We propose new
algorithms from the perspective of saddle point optimization. Our algorithms
achieve $(1-\epsilon)$-approximations with running time $\tilde{O}(nd+n\sqrt{d
/ \epsilon})$ for both variants, where $n$ is the number of points and $d$ is
the dimensionality. To the best of our knowledge, the current best algorithm
for $\nu$-SVM is based on quadratic programming approach which requires
$\Omega(n^2 d)$ time in worst case~\cite{joachims1998making,platt199912}. In
the paper, we provide the first nearly linear time algorithm for $\nu$-SVM. The
current best algorithm for hard margin SVM achieved by Gilbert
algorithm~\cite{gartner2009coresets} requires $O(nd / \epsilon )$ time. Our
algorithm improves the running time by a factor of $\sqrt{d}/\sqrt{\epsilon}$.
Moreover, our algorithms can be implemented in the distributed settings
naturally. We prove that our algorithms require $\tilde{O}(k(d
+\sqrt{d/\epsilon}))$ communication cost, where $k$ is the number of clients,
which almost matches the theoretical lower bound. Numerical experiments support
our theory and show that our algorithms converge faster on high dimensional,
large and dense data sets, as compared to previous methods.
Characterisation of (Sub)sequential Rational Functions over a General Class Monoids
Authors: Stefan Gerdjikov
Date: 2018-01-28 00:00:00
Tasks: Relation

In this technical report we describe a general class of monoids for which
(sub)sequential rational can be characterised in terms of a congruence relation
in the flavour of Myhill-Nerode relation. The class of monoids that we consider
can be described in terms of natural algebraic axioms, contains the free
monoids, groups, the tropical monoid, and is closed under Cartesian.
Run, skeleton, run: skeletal model in a physics-based simulation
Authors: Mikhail Pavlov, Sergey Kolesnikov, Sergey M. Plis
Date: 2017-11-18 00:00:00
Tasks: Navigate, Policy Gradient Methods, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we present our approach to solve a physics-based reinforcement
learning challenge "Learning to Run" with objective to train
physiologically-based human model to navigate a complex obstacle course as
quickly as possible. The environment is computationally expensive, has a
high-dimensional continuous action space and is stochastic. We benchmark state
of the art policy-gradient methods and test several improvements, such as layer
normalization, parameter noise, action and state reflecting, to stabilize
training and improve its sample-efficiency. We found that the Deep
Deterministic Policy Gradient method is the most efficient method for this
environment and the improvements we have introduced help to stabilize training.
Learned models are able to generalize to new physical scenarios, e.g. different
obstacle courses.
Pose Guided Person Image Generation
Authors: Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc van Gool
Date: 2017-05-25 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Gesture-to-Gesture Translation, Image Generation, Pose Transfer

This paper proposes the novel Pose Guided Person Generation Network (PG$^2$)
that allows to synthesize person images in arbitrary poses, based on an image
of that person and a novel pose. Our generation framework PG$^2$ utilizes the
pose information explicitly and consists of two key stages: pose integration
and image refinement. In the first stage the condition image and the target
pose are fed into a U-Net-like network to generate an initial but coarse image
of the person with the target pose. The second stage then refines the initial
and blurry result by training a U-Net-like generator in an adversarial way.
Extensive experimental results on both 128$\times$64 re-identification images
and 256$\times$256 fashion photos show that our model generates high-quality
person images with convincing details.
Algorithmic Linearly Constrained Gaussian Processes
Authors: Markus Lange-Hegermann
Date: 2018-01-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Gaussian Processes

We algorithmically construct multi-output Gaussian process priors which
satisfy linear differential equations. Our approach attempts to parametrize all
solutions of the equations using Gr\"obner bases. If successful, a push forward
Gaussian process along the paramerization is the desired prior. We consider
several examples from physics, geomathematics and control, among them the full
inhomogeneous system of Maxwell's equations. By bringing together stochastic
learning and computer algebra in a novel way, we combine noisy observations
with precise algebraic computations.
Siamese Neural Networks with Random Forest for detecting duplicate question pairs
Authors: Ameya Godbole, Aman Dalmia, Sunil Kumar Sahu
Date: 2018-01-22 00:00:00
Tasks: BIG-bench Machine Learning

Determining whether two given questions are semantically similar is a fairly
challenging task given the different structures and forms that the questions
can take. In this paper, we use Gated Recurrent Units(GRU) in combination with
other highly used machine learning algorithms like Random Forest, Adaboost and
SVM for the similarity prediction task on a dataset released by Quora,
consisting of about 400k labeled question pairs. We got the best result by
using the Siamese adaptation of a Bidirectional GRU with a Random Forest
classifier, which landed us among the top 24% in the competition Quora Question
Pairs hosted on Kaggle.
Marketing Analytics: Methods, Practice, Implementation, and Links to Other Fields
Authors: Stephen L. France, Sanjoy Ghose
Date: 2018-01-28 00:00:00
Tasks: Marketing

Marketing analytics is a diverse field, with both academic researchers and
practitioners coming from a range of backgrounds including marketing, expert
systems, statistics, and operations research. This paper provides an
integrative review at the boundary of these areas. The aim is to give
researchers in the intelligent and expert systems community the opportunity to
gain a broad view of the marketing analytics area and provide a starting point
for future interdisciplinary collaboration. The topics of visualization,
segmentation, and class prediction are featured. Links between the disciplines
are emphasized. For each of these topics, a historical overview is given,
starting with initial work in the 1960s and carrying through to the present
day. Recent innovations for modern, large, and complex "big data" sets are
described. Practical implementation advice is given, along with a directory of
open source R routines for implementing marketing analytics techniques.
Contextual Multi-Scale Region Convolutional 3D Network for Activity Detection
Authors: Yancheng Bai, Huijuan Xu, Kate Saenko, Bernard Ghanem
Date: 2018-01-28 00:00:00
Tasks: Action Detection, Activity Detection

Activity detection is a fundamental problem in computer vision. Detecting
activities of different temporal scales is particularly challenging. In this
paper, we propose the contextual multi-scale region convolutional 3D network
(CMS-RC3D) for activity detection. To deal with the inherent temporal scale
variability of activity instances, the temporal feature pyramid is used to
represent activities of different temporal scales. On each level of the
temporal feature pyramid, an activity proposal detector and an activity
classifier are learned to detect activities of specific temporal scales.
Temporal contextual information is fused into activity classifiers for better
recognition. More importantly, the entire model at all levels can be trained
end-to-end. Our CMS-RC3D detector can deal with activities at all temporal
scale ranges with only a single pass through the backbone network. We test our
detector on two public activity detection benchmarks, THUMOS14 and ActivityNet.
Extensive experiments show that the proposed CMS-RC3D detector outperforms
state-of-the-art methods on THUMOS14 by a substantial margin and achieves
comparable results on ActivityNet despite using a shallow feature extractor.
CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing
Authors: Kai Xu, Fengbo Ren
Date: 2016-12-15 00:00:00
Tasks: Compressive Sensing, GPU, Video Compressive Sensing

This paper addresses the real-time encoding-decoding problem for
high-frame-rate video compressive sensing (CS). Unlike prior works that perform
reconstruction using iterative optimization-based approaches, we propose a
non-iterative model, named "CSVideoNet". CSVideoNet directly learns the inverse
mapping of CS and reconstructs the original input in a single forward
propagation. To overcome the limitations of existing CS cameras, we propose a
multi-rate CNN and a synthesizing RNN to improve the trade-off between
compression ratio (CR) and spatial-temporal resolution of the reconstructed
videos. The experiment results demonstrate that CSVideoNet significantly
outperforms the state-of-the-art approaches. With no pre/post-processing, we
achieve 25dB PSNR recovery quality at 100x CR, with a frame rate of 125 fps on
a Titan X GPU. Due to the feedforward and high-data-concurrency natures of
CSVideoNet, it can take advantage of GPU acceleration to achieve three orders
of magnitude speed-up over conventional iterative-based approaches. We share
the source code at https://github.com/PSCLab-ASU/CSVideoNet.
Bayesian Nonparametric Modeling of Driver Behavior using HDP Split-Merge Sampling Algorithm
Authors: Vadim Smolyakov, Julian Straub, Sue Zheng, John W. Fisher III
Date: 2018-01-27 00:00:00
Tasks: Position

Modern vehicles are equipped with increasingly complex sensors. These sensors
generate large volumes of data that provide opportunities for modeling and
analysis. Here, we are interested in exploiting this data to learn aspects of
behaviors and the road network associated with individual drivers. Our dataset
is collected on a standard vehicle used to commute to work and for personal
trips. A Hidden Markov Model (HMM) trained on the GPS position and orientation
data is utilized to compress the large amount of position information into a
small amount of road segment states. Each state has a set of observations, i.e.
car signals, associated with it that are quantized and modeled as draws from a
Hierarchical Dirichlet Process (HDP). The inference for the topic distributions
is carried out using HDP split-merge sampling algorithm. The topic
distributions over joint quantized car signals characterize the driving
situation in the respective road state. In a novel manner, we demonstrate how
the sparsity of the personal road network of a driver in conjunction with a
hierarchical topic model allows data driven predictions about destinations as
well as likely road conditions.
Adaptive Scan Gibbs Sampler for Large Scale Inference Problems
Authors: Vadim Smolyakov, Qiang Liu, John W. Fisher III
Date: 2018-01-27 00:00:00

For large scale on-line inference problems the update strategy is critical
for performance. We derive an adaptive scan Gibbs sampler that optimizes the
update frequency by selecting an optimum mini-batch size. We demonstrate
performance of our adaptive batch-size Gibbs sampler by comparing it against
the collapsed Gibbs sampler for Bayesian Lasso, Dirichlet Process Mixture
Models (DPMM) and Latent Dirichlet Allocation (LDA) graphical models.
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions
Authors: Maria-Florina Balcan, Hongyang Zhang
Date: 2017-03-22 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Active Learning

We provide new results for noise-tolerant and sample-efficient learning
algorithms under $s$-concave distributions. The new class of $s$-concave
distributions is a broad and natural generalization of log-concavity, and
includes many important additional distributions, e.g., the Pareto distribution
and $t$-distribution. This class has been studied in the context of efficient
sampling, integration, and optimization, but much remains unknown about the
geometry of this class of distributions and their applications in the context
of learning. The challenge is that unlike the commonly used distributions in
learning (uniform or more generally log-concave distributions), this broader
class is not closed under the marginalization operator and many such
distributions are fat-tailed. In this work, we introduce new convex geometry
tools to study the properties of $s$-concave distributions and use these
properties to provide bounds on quantities of interest to learning including
the probability of disagreement between two halfspaces, disagreement outside a
band, and the disagreement coefficient. We use these results to significantly
generalize prior results for margin-based active learning, disagreement-based
active learning, and passive learning of intersections of halfspaces. Our
analysis of geometric properties of $s$-concave distributions might be of
independent interest to optimization more broadly.
Meshed Up: Learnt Error Correction in 3D Reconstructions
Authors: Michael Tanner, Stefan Saftescu, Alex Bewley, Paul Newman
Date: 2018-01-27 00:00:00
Tasks: Image Reconstruction

Dense reconstructions often contain errors that prior work has so far
minimised using high quality sensors and regularising the output. Nevertheless,
errors still persist. This paper proposes a machine learning technique to
identify errors in three dimensional (3D) meshes. Beyond simply identifying
errors, our method quantifies both the magnitude and the direction of depth
estimate errors when viewing the scene. This enables us to improve the
reconstruction accuracy.
  We train a suitably deep network architecture with two 3D meshes: a
high-quality laser reconstruction, and a lower quality stereo image
reconstruction. The network predicts the amount of error in the lower quality
reconstruction with respect to the high-quality one, having only view the
former through its input. We evaluate our approach by correcting
two-dimensional (2D) inverse-depth images extracted from the 3D model, and show
that our method improves the quality of these depth reconstructions by up to a
relative 10% RMSE.
Scalable Mutual Information Estimation using Dependence Graphs
Authors: Morteza Noshad, Yu Zeng, Alfred O. Hero III
Date: 2018-01-27 00:00:00
Tasks: Information Plane, Mutual Information Estimation

The Mutual Information (MI) is an often used measure of dependency between
two random variables utilized in information theory, statistics and machine
learning. Recently several MI estimators have been proposed that can achieve
parametric MSE convergence rate. However, most of the previously proposed
estimators have the high computational complexity of at least $O(N^2)$. We
propose a unified method for empirical non-parametric estimation of general MI
function between random vectors in $\mathbb{R}^d$ based on $N$ i.i.d. samples.
The reduced complexity MI estimator, called the ensemble dependency graph
estimator (EDGE), combines randomized locality sensitive hashing (LSH),
dependency graphs, and ensemble bias-reduction methods. We prove that EDGE
achieves optimal computational complexity $O(N)$, and can achieve the optimal
parametric MSE rate of $O(1/N)$ if the density is $d$ times differentiable. To
the best of our knowledge EDGE is the first non-parametric MI estimator that
can achieve parametric MSE rates with linear time complexity. We illustrate the
utility of EDGE for the analysis of the information plane (IP) in deep
learning. Using EDGE we shed light on a controversy on whether or not the
compression property of information bottleneck (IB) in fact holds for ReLu and
other rectification functions in deep neural networks (DNN).
Robust Multi-subspace Analysis Using Novel Column L0-norm Constrained Matrix Factorization
Authors: Binghui Wang, Chuang Lin
Date: 2018-01-27 00:00:00
Tasks: Clustering

We study the underlying structure of data (approximately) generated from a
union of independent subspaces. Traditional methods learn only one subspace,
failing to discover the multi-subspace structure, while state-of-the-art
methods analyze the multi-subspace structure using data themselves as the
dictionary, which cannot offer the explicit basis to span each subspace and are
sensitive to errors via an indirect representation. Additionally, they also
suffer from a high computational complexity, being quadratic or cubic to the
sample size. To tackle all these problems, we propose a method, called Matrix
Factorization with Column L0-norm constraint (MFC0), that can simultaneously
learn the basis for each subspace, generate a direct sparse representation for
each data sample, as well as removing errors in the data in an efficient way.
Furthermore, we develop a first-order alternating direction algorithm, whose
computational complexity is linear to the sample size, to stably and
effectively solve the nonconvex objective function and non- smooth l0-norm
constraint of MFC0. Experimental results on both synthetic and real-world
datasets demonstrate that besides the superiority over traditional and
state-of-the-art methods for subspace clustering, data reconstruction, error
correction, MFC0 also shows its uniqueness for multi-subspace basis learning
and direct sparse representation.
A Stochastic Trust Region Algorithm Based on Careful Step Normalization
Authors: Frank E. Curtis, Katya Scheinberg, Rui Shi
Date: 2017-12-29 00:00:00

An algorithm is proposed for solving stochastic and finite sum minimization
problems. Based on a trust region methodology, the algorithm employs normalized
steps, at least as long as the norms of the stochastic gradient estimates are
within a specified interval. The complete algorithm---which dynamically chooses
whether or not to employ normalized steps---is proved to have convergence
guarantees that are similar to those possessed by a traditional stochastic
gradient approach under various sets of conditions related to the accuracy of
the stochastic gradient estimates and choice of stepsize sequence. The results
of numerical experiments are presented when the method is employed to minimize
convex and nonconvex machine learning test problems. These results illustrate
that the method can outperform a traditional stochastic gradient approach.
Feature Based Framework to Detect Diseases, Tumor, and Bleeding in Wireless Capsule Endoscopy
Authors: Omid Haji Maghsoudi, Mahdi Alizadeh
Date: 2018-01-27 00:00:00
Tasks: 3D Reconstruction

Studying animal locomotion improves our understanding of motor control and
aids in the treatment of motor impairment. Mice are a premier model of human
disease and are the model system of choice for much of basic neuroscience. High
frame rates (250 Hz) are needed to quantify the kinematics of these running
rodents. Manual tracking, especially for multiple markers, becomes
time-consuming and impossible. Therefore, an automated method is necessary. We
propose a method to track the paws of the animal in the following manner:
first, segmenting all the possible paws based on color; second, classifying the
segmented objects using a support vector machine (SVM) and neural network (NN);
third, classifying the objects using the kinematic features of the running
animal, coupled with texture features from earlier frames; and finally,
detecting and handling collisions to assure the correctness of labelled paws.
The proposed method is validated in sixty 1,000 frame video sequences (4
seconds) captured by four cameras from five mice. The total sensitivity for
tracking of the front and hind paw is 99.70% using the SVM classifier and
99.76% using the NN classifier. In addition, we show the feasibility of 3D
reconstruction using the four camera system.