pwc-archive/papers-with-abstracts huggingface dataset:
On-the-fly Augmented Reality for Orthopaedic Surgery Using a Multi-Modal Fiducial
Authors: Sebastian Andress, Alex Johnson, Mathias Unberath, Alexander Winkler, Kevin Yu, Javad Fotouhi, Simon Weidert, Greg Osgood, Nassir Navab
Date: 2018-01-04 00:00:00
Tasks: Anatomy, Simultaneous Localization and Mapping

Fluoroscopic X-ray guidance is a cornerstone for percutaneous orthopaedic
surgical procedures. However, two-dimensional observations of the
three-dimensional anatomy suffer from the effects of projective simplification.
Consequently, many X-ray images from various orientations need to be acquired
for the surgeon to accurately assess the spatial relations between the
patient's anatomy and the surgical tools. In this paper, we present an
on-the-fly surgical support system that provides guidance using augmented
reality and can be used in quasi-unprepared operating rooms. The proposed
system builds upon a multi-modality marker and simultaneous localization and
mapping technique to co-calibrate an optical see-through head mounted display
to a C-arm fluoroscopy system. Then, annotations on the 2D X-ray images can be
rendered as virtual objects in 3D providing surgical guidance. We
quantitatively evaluate the components of the proposed system, and finally,
design a feasibility study on a semi-anthropomorphic phantom. The accuracy of
our system was comparable to the traditional image-guided technique while
substantially reducing the number of acquired X-ray images as well as procedure
time. Our promising results encourage further research on the interaction
between virtual and real objects, that we believe will directly benefit the
proposed method. Further, we would like to explore the capabilities of our
on-the-fly augmented reality support system in a larger study directed towards
common orthopaedic interventions.
Plan in 2D, execute in 3D: An augmented reality solution for cup placement in total hip arthroplasty
Authors: Javad Fotouhi, Clayton P. Alexander, Mathias Unberath, Giacomo Taylor, Sing Chun Lee, Bernhard Fuerst, Alex Johnson, Greg Osgood, Russell H. Taylor, Harpal Khanuja, Mehran Armand, Nassir Navab
Date: 2018-01-04 00:00:00
Tasks: Translation

Reproducibly achieving proper implant alignment is a critical step in total
hip arthroplasty (THA) procedures that has been shown to substantially affect
patient outcome. In current practice, correct alignment of the acetabular cup
is verified in C-arm X-ray images that are acquired in an anterior-posterior
(AP) view. Favorable surgical outcome is, therefore, heavily dependent on the
surgeon's experience in understanding the 3D orientation of a hemispheric
implant from 2D AP projection images. This work proposes an easy to use
intra-operative component planning system based on two C-arm X-ray images that
is combined with 3D augmented reality (AR) visualization that simplifies
impactor and cup placement according to the planning by providing a real-time
RGBD data overlay. We evaluate the feasibility of our system in a user study
comprising four orthopedic surgeons at the Johns Hopkins Hospital, and also
report errors in translation, anteversion, and abduction as low as 1.98 mm,
1.10 degrees, and 0.53 degrees, respectively. The promising performance of this
AR solution shows that deploying this system could eliminate the need for
excessive radiation, simplify the intervention, and enable reproducibly
accurate placement of acetabular implants.
An Implementation of Back-Propagation Learning on GF11, a Large SIMD Parallel Computer
Authors: Michael Witbrock, Marco Zagha
Date: 2018-01-04 00:00:00
Tasks: Neural Network simulation, text-to-speech, Text to Speech

Current connectionist simulations require huge computational resources. We
describe a neural network simulator for the IBM GF11, an experimental SIMD
machine with 566 processors and a peak arithmetic performance of 11 Gigaflops.
We present our parallel implementation of the backpropagation learning
algorithm, techniques for increasing efficiency, performance measurements on
the NetTalk text-to-speech benchmark, and a performance model for the
simulator. Our simulator currently runs the back-propagation learning algorithm
at 900 million connections per second, where each "connection per second"
includes both a forward and backward pass. This figure was obtained on the
machine when only 356 processors were working; with all 566 processors
operational, our simulation will run at over one billion connections per
second. We conclude that the GF11 is well-suited to neural network simulation,
and we analyze our use of the machine to determine which features are the most
important for high performance.
Who Said What: Modeling Individual Labelers Improves Classification
Authors: Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey E. Hinton
Date: 2017-03-26 00:00:00
Tasks: Classification, General Classification

Data are often labeled by many different experts with each expert only
labeling a small fraction of the data and each data point being labeled by
several experts. This reduces the workload on individual experts and also gives
a better estimate of the unobserved ground truth. When experts disagree, the
standard approaches are to treat the majority opinion as the correct label or
to model the correct label as a distribution. These approaches, however, do not
make any use of potentially valuable information about which expert produced
which label. To make use of this extra information, we propose modeling the
experts individually and then learning averaging weights for combining them,
possibly in sample-specific ways. This allows us to give more weight to more
reliable experts and take advantage of the unique strengths of individual
experts at classifying certain types of data. Here we show that our approach
leads to improvements in computer-aided diagnosis of diabetic retinopathy. We
also show that our method performs better than competing algorithms by Welinder
and Perona (2010), and by Mnih and Hinton (2012). Our work offers an innovative
approach for dealing with the myriad real-world settings that use expert
opinions to define labels for training.
Discrete symbolic optimization and Boltzmann sampling by continuous neural dynamics: Gradient Symbolic Computation
Authors: Paul Tupper, Paul Smolensky, Pyeong Whan Cho
Date: 2018-01-04 00:00:00
Tasks: global-optimization

Gradient Symbolic Computation is proposed as a means of solving discrete
global optimization problems using a neurally plausible continuous stochastic
dynamical system. Gradient symbolic dynamics involves two free parameters that
must be adjusted as a function of time to obtain the global maximizer at the
end of the computation. We provide a summary of what is known about the GSC
dynamics for special cases of settings of the parameters, and also establish
that there is a schedule for the two parameters for which convergence to the
correct answer occurs with high probability. These results put the empirical
results already obtained for GSC on a sound theoretical footing.
Slugbot: An Application of a Novel and Scalable Open Domain Socialbot Framework
Authors: Kevin K. Bowden, Jiaqi Wu, Shereen Oraby, Amita Misra, Marilyn Walker
Date: 2018-01-04 00:00:00
Tasks: Dialogue Management, Information Retrieval, Management, Natural Language Understanding, Retrieval

In this paper we introduce a novel, open domain socialbot for the Amazon
Alexa Prize competition, aimed at carrying on friendly conversations with users
on a variety of topics. We present our modular system, highlighting our
different data sources and how we use the human mind as a model for data
management. Additionally we build and employ natural language understanding and
information retrieval tools and APIs to expand our knowledge bases. We describe
our semistructured, scalable framework for crafting topic-specific dialogue
flows, and give details on our dialogue management schemes and scoring
mechanisms. Finally we briefly evaluate the performance of our system and
observe the challenges that an open domain socialbot faces.
Deep Cross Polarimetric Thermal-to-visible Face Recognition
Authors: Seyed Mehdi Iranmanesh, Ali Dabouei, Hadi Kazemi, Nasser M. Nasrabadi
Date: 2018-01-04 00:00:00
Tasks: Face Recognition

In this paper, we present a deep coupled learning frame- work to address the
problem of matching polarimetric ther- mal face photos against a gallery of
visible faces. Polariza- tion state information of thermal faces provides the
miss- ing textural and geometrics details in the thermal face im- agery which
exist in visible spectrum. we propose a coupled deep neural network
architecture which leverages relatively large visible and thermal datasets to
overcome the problem of overfitting and eventually we train it by a
polarimetric thermal face dataset which is the first of its kind. The pro-
posed architecture is able to make full use of the polari- metric thermal
information to train a deep model compared to the conventional shallow
thermal-to-visible face recogni- tion methods. Proposed coupled deep neural
network also finds global discriminative features in a nonlinear embed- ding
space to relate the polarimetric thermal faces to their corresponding visible
faces. The results show the superior- ity of our method compared to the
state-of-the-art models in cross thermal-to-visible face recognition
algorithms.
A Greedy Search Tree Heuristic for Symbolic Regression
Authors: Fabricio Olivetti de Franca
Date: 2018-01-04 00:00:00
Tasks: regression, Symbolic Regression

Symbolic Regression tries to find a mathematical expression that describes
the relationship of a set of explanatory variables to a measured variable. The
main objective is to find a model that minimizes the error and, optionally,
that also minimizes the expression size. A smaller expression can be seen as an
interpretable model considered a reliable decision model. This is often
performed with Genetic Programming which represents their solution as
expression trees. The shortcoming of this algorithm lies on this representation
that defines a rugged search space and contains expressions of any size and
difficulty. These pose as a challenge to find the optimal solution under
computational constraints. This paper introduces a new data structure, called
Interaction-Transformation (IT), that constrains the search space in order to
exclude a region of larger and more complicated expressions. In order to test
this data structure, it was also introduced an heuristic called SymTree. The
obtained results show evidence that SymTree are capable of obtaining the
optimal solution whenever the target function is within the search space of the
IT data structure and competitive results when it is not. Overall, the
algorithm found a good compromise between accuracy and simplicity for all the
generated models.
PHOENICS: A universal deep Bayesian optimizer
Authors: Florian Häse, Loïc M. Roch, Christoph Kreisbeck, Alán Aspuru-Guzik
Date: 2018-01-04 00:00:00
Tasks: Bayesian Optimization, Density Estimation, Gaussian Processes, global-optimization

In this work we introduce PHOENICS, a probabilistic global optimization
algorithm combining ideas from Bayesian optimization with concepts from
Bayesian kernel density estimation. We propose an inexpensive acquisition
function balancing the explorative and exploitative behavior of the algorithm.
This acquisition function enables intuitive sampling strategies for an
efficient parallel search of global minima. The performance of PHOENICS is
assessed via an exhaustive benchmark study on a set of 15 discrete,
quasi-discrete and continuous multidimensional functions. Unlike optimization
methods based on Gaussian processes (GP) and random forests (RF), we show that
PHOENICS is less sensitive to the nature of the co-domain, and outperforms GP
and RF optimizations. We illustrate the performance of PHOENICS on the
Oregonator, a difficult case-study describing a complex chemical reaction
network. We demonstrate that only PHOENICS was able to reproduce qualitatively
and quantitatively the target dynamic behavior of this nonlinear reaction
dynamics. We recommend PHOENICS for rapid optimization of scalar, possibly
non-convex, black-box unknown objective functions.
Deep Reinforcement Learning based Optimal Control of Hot Water Systems
Authors: Hussain Kazmi, Fahad Mehmood, Stefan Lodeweyckx, Johan Driesen
Date: 2018-01-04 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Energy consumption for hot water production is a major draw in high
efficiency buildings. Optimizing this has typically been approached from a
thermodynamics perspective, decoupled from occupant influence. Furthermore,
optimization usually presupposes existence of a detailed dynamics model for the
hot water system. These assumptions lead to suboptimal energy efficiency in the
real world. In this paper, we present a novel reinforcement learning based
methodology which optimizes hot water production. The proposed methodology is
completely generalizable, and does not require an offline step or human domain
knowledge to build a model for the hot water vessel or the heating element.
Occupant preferences too are learnt on the fly. The proposed system is applied
to a set of 32 houses in the Netherlands where it reduces energy consumption
for hot water production by roughly 20% with no loss of occupant comfort.
Extrapolating, this translates to absolute savings of roughly 200 kWh for a
single household on an annual basis. This performance can be replicated to any
domestic hot water system and optimization objective, given that the fairly
minimal requirements on sensor data are met. With millions of hot water systems
operational worldwide, the proposed framework has the potential to reduce
energy consumption in existing and new systems on a multi Gigawatt-hour scale
in the years to come.
Denoising Adversarial Autoencoders
Authors: Antonia Creswell, Anil Anthony Bharath
Date: 2017-03-03 00:00:00
Tasks: Denoising, General Classification

Unsupervised learning is of growing interest because it unlocks the potential
held in vast amounts of unlabelled data to learn useful representations for
inference. Autoencoders, a form of generative model, may be trained by learning
to reconstruct unlabelled input data from a latent representation space. More
robust representations may be produced by an autoencoder if it learns to
recover clean input samples from corrupted ones. Representations may be further
improved by introducing regularisation during training to shape the
distribution of the encoded data in latent space. We suggest denoising
adversarial autoencoders, which combine denoising and regularisation, shaping
the distribution of latent space using adversarial training. We introduce a
novel analysis that shows how denoising may be incorporated into the training
and sampling of adversarial autoencoders. Experiments are performed to assess
the contributions that denoising makes to the learning of representations for
classification and sample synthesis. Our results suggest that autoencoders
trained using a denoising criterion achieve higher classification performance,
and can synthesise samples that are more consistent with the input data than
those trained without a corruption process.
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior
Authors: Xiang Zhang, Nishant Vishwamitra, Hongxin Hu, Feng Luo
Date: 2017-10-30 00:00:00
Proceeding: ICLR 2018 1

We introduce a new deep convolutional neural network, CrescendoNet, by
stacking simple building blocks without residual connections. Each Crescendo
block contains independent convolution paths with increased depths. The numbers
of convolution layers and parameters are only increased linearly in Crescendo
blocks. In experiments, CrescendoNet with only 15 layers outperforms almost all
networks without residual connections on benchmark datasets, CIFAR10, CIFAR100,
and SVHN. Given sufficient amount of data as in SVHN dataset, CrescendoNet with
15 layers and 4.1M parameters can match the performance of DenseNet-BC with 250
layers and 15.3M parameters. CrescendoNet provides a new way to construct high
performance deep convolutional neural networks without residual connections.
Moreover, through investigating the behavior and performance of subnetworks in
CrescendoNet, we note that the high performance of CrescendoNet may come from
its implicit ensemble behavior, which differs from the FractalNet that is also
a deep convolutional neural network without residual connections. Furthermore,
the independence between paths in CrescendoNet allows us to introduce a new
path-wise training procedure, which can reduce the memory needed for training.
SmartTennisTV: Automatic indexing of tennis videos
Authors: Anurag Ghosh, C. V. Jawahar
Date: 2018-01-04 00:00:00

In this paper, we demonstrate a score based indexing approach for tennis
videos. Given a broadcast tennis video (BTV), we index all the video segments
with their scores to create a navigable and searchable match. Our approach
temporally segments the rallies in the video and then recognizes the scores
from each of the segments, before refining the scores using the knowledge of
the tennis scoring system. We finally build an interface to effortlessly
retrieve and view the relevant video segments by also automatically tagging the
segmented rallies with human accessible tags such as 'fault' and 'deuce'. The
efficiency of our approach is demonstrated on BTV's from two major tennis
tournaments.
Practical Challenges in Explicit Ethical Machine Reasoning
Authors: Louise Dennis, Michael Fisher
Date: 2018-01-04 00:00:00

We examine implemented systems for ethical machine reasoning with a view to
identifying the practical challenges (as opposed to philosophical challenges)
posed by the area. We identify a need for complex ethical machine reasoning not
only to be multi-objective, proactive, and scrutable but that it must draw on
heterogeneous evidential reasoning. We also argue that, in many cases, it needs
to operate in real time and be verifiable. We propose a general architecture
involving a declarative ethical arbiter which draws upon multiple evidential
reasoners each responsible for a particular ethical feature of the system's
environment. We claim that this architecture enables some separation of
concerns among the practical challenges that ethical machine reasoning poses.
What have we learned from deep representations for action recognition?
Authors: Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes, Andrew Zisserman
Date: 2018-01-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Recognition, Temporal Action Localization

As the success of deep models has led to their deployment in all areas of
computer vision, it is increasingly important to understand how these
representations work and what they are capturing. In this paper, we shed light
on deep spatiotemporal representations by visualizing what two-stream models
have learned in order to recognize actions in video. We show that local
detectors for appearance and motion objects arise to form distributed
representations for recognizing human actions. Key observations include the
following. First, cross-stream fusion enables the learning of true
spatiotemporal features rather than simply separate appearance and motion
features. Second, the networks can learn local representations that are highly
class specific, but also generic representations that can serve a range of
classes. Third, throughout the hierarchy of the network, features become more
abstract and show increasing invariance to aspects of the data that are
unimportant to desired distinctions (e.g. motion patterns across various
speeds). Fourth, visualizations can be used not only to shed light on learned
representations, but also to reveal idiosyncracies of training data and to
explain failure cases of the system.
A fully automated framework for lung tumour detection, segmentation and analysis
Authors: Devesh Walawalkar
Date: 2018-01-04 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Early and correct diagnosis is a very important aspect of cancer treatment.
Detection of tumour in Computed Tomography scan is a tedious and tricky task
which requires expert knowledge and a lot of human working hours. As small
human error is present in any work he does, it is possible that a CT scan could
be misdiagnosed causing the patient to become terminal. This paper introduces a
novel fully automated framework which helps to detect and segment tumour, if
present in a lung CT scan series. It also provides useful analysis of the
detected tumour such as its approximate volume, centre location and more. The
framework provides a single click solution which analyses all CT images of a
single patient series in one go. It helps to reduce the work of manually going
through each CT slice and provides quicker and more accurate tumour diagnosis.
It makes use of customized image processing and image segmentation methods, to
detect and segment the prospective tumour region from the CT scan. It then uses
a trained ensemble classifier to correctly classify the segmented region as
being tumour or not. Tumour analysis further computed can then be used to
determine malignity of the tumour. With an accuracy of 98.14%, the implemented
framework can be used in various practical scenarios, capable of eliminating
need of any expert pathologist intervention.
A Nonlinear Kernel Support Matrix Machine for Matrix Learning
Authors: Yunfei Ye
Date: 2017-07-20 00:00:00
Tasks: Generalization Bounds

In many problems of supervised tensor learning (STL), real world data such as
face images or MRI scans are naturally represented as matrices, which are also
called as second order tensors. Most existing classifiers based on tensor
representation, such as support tensor machine (STM) need to solve iteratively
which occupy much time and may suffer from local minima. In this paper, we
present a kernel support matrix machine (KSMM) to perform supervised learning
when data are represented as matrices. KSMM is a general framework for the
construction of matrix-based hyperplane to exploit structural information. We
analyze a unifying optimization problem for which we propose an asymptotically
convergent algorithm. Theoretical analysis for the generalization bounds is
derived based on Rademacher complexity with respect to a probability
distribution. We demonstrate the merits of the proposed method by exhaustive
experiments on both simulation study and a number of real-word datasets from a
variety of application domains.
Image Restoration by Iterative Denoising and Backward Projections
Authors: Tom Tirer, Raja Giryes
Date: 2017-10-18 00:00:00
Tasks: Deblurring, Denoising, Image Deblurring, Image Inpainting, Image Restoration

Inverse problems appear in many applications, such as image deblurring and
inpainting. The common approach to address them is to design a specific
algorithm for each problem. The Plug-and-Play (P&P) framework, which has been
recently introduced, allows solving general inverse problems by leveraging the
impressive capabilities of existing denoising algorithms. While this fresh
strategy has found many applications, a burdensome parameter tuning is often
required in order to obtain high-quality results. In this work, we propose an
alternative method for solving inverse problems using off-the-shelf denoisers,
which requires less parameter tuning. First, we transform a typical cost
function, composed of fidelity and prior terms, into a closely related, novel
optimization problem. Then, we propose an efficient minimization scheme with a
plug-and-play property, i.e., the prior term is handled solely by a denoising
operation. Finally, we present an automatic tuning mechanism to set the
method's parameters. We provide a theoretical analysis of the method, and
empirically demonstrate its competitiveness with task-specific techniques and
the P&P approach for image inpainting and deblurring.