pwc-archive/papers-with-abstracts huggingface dataset:
Unbiased Shape Compactness for Segmentation
Authors: Jose Dolz, Ismail Ben Ayed, Christian Desrosiers
Date: 2017-04-28 00:00:00
Tasks: Segmentation

We propose to constrain segmentation functionals with a dimensionless,
unbiased and position-independent shape compactness prior, which we solve
efficiently with an alternating direction method of multipliers (ADMM).
Involving a squared sum of pairwise potentials, our prior results in a
challenging high-order optimization problem, which involves dense (fully
connected) graphs. We split the problem into a sequence of easier sub-problems,
each performed efficiently at each iteration: (i) a sparse-matrix inversion
based on Woodbury identity, (ii) a closed-form solution of a cubic equation and
(iii) a graph-cut update of a sub-modular pairwise sub-problem with a sparse
graph. We deploy our prior in an energy minimization, in conjunction with a
supervised classifier term based on CNNs and standard regularization
constraints. We demonstrate the usefulness of our energy in several medical
applications. In particular, we report comprehensive evaluations of our fully
automated algorithm over 40 subjects, showing a competitive performance for the
challenging task of abdominal aorta segmentation in MRI.
Rise of the humanbot
Authors: Ricard Sole
Date: 2017-05-16 00:00:00

The accelerated path of technological development, particularly at the
interface between hardware and biology has been suggested as evidence for
future major technological breakthroughs associated to our potential to
overcome biological constraints. This includes the potential of becoming
immortal, having expanded cognitive capacities thanks to hardware implants or
the creation of intelligent machines. Here I argue that several relevant
evolutionary and structural constraints might prevent achieving most (if not
all) these innovations. Instead, the coming future will bring novelties that
will challenge many other aspects of our life and that can be seen as other
feasible singularities. One particularly important one has to do with the
evolving interactions between humans and non-intelligent robots capable of
learning and communication. Here I argue that a long term interaction can lead
to a new class of "agent" (the humanbot). The way shared memories get tangled
over time will inevitably have important consequences for both sides of the
pair, whose identity as separated entities might become blurred and ultimately
vanish. Understanding such hybrid systems requires a second-order neuroscience
approach while posing serious conceptual challenges, including the definition
of consciousness.
Low-dimensional Data Embedding via Robust Ranking
Authors: Ehsan Amid, Nikos Vlassis, Manfred K. Warmuth
Date: 2016-11-30 00:00:00
Tasks: Triplet

We describe a new method called t-ETE for finding a low-dimensional embedding
of a set of objects in Euclidean space. We formulate the embedding problem as a
joint ranking problem over a set of triplets, where each triplet captures the
relative similarities between three objects in the set. By exploiting recent
advances in robust ranking, t-ETE produces high-quality embeddings even in the
presence of a significant amount of noise and better preserves local scale than
known methods, such as t-STE and t-SNE. In particular, our method produces
significantly better results than t-SNE on signature datasets while also being
faster to compute.
Optimal Surface Segmentation with Convex Priors in Irregularly Sampled Space
Authors: Abhay Shah, Michael D. Abramoff, Xiaodong Wu
Date: 2016-11-09 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Super-Resolution

Optimal surface segmentation is a state-of-the-art method used for
segmentation of multiple globally optimal surfaces in volumetric datasets. The
method is widely used in numerous medical image segmentation applications.
However, nodes in the graph based optimal surface segmentation method typically
encode uniformly distributed orthogonal voxels of the volume. Thus the
segmentation cannot attain an accuracy greater than a single unit voxel, i.e.
the distance between two adjoining nodes in graph space. Segmentation accuracy
higher than a unit voxel is achievable by exploiting partial volume information
in the voxels which shall result in non-equidistant spacing between adjoining
graph nodes. This paper reports a generalized graph based multiple surface
segmentation method with convex priors which can optimally segment the target
surfaces in an irregularly sampled space. The proposed method allows
non-equidistant spacing between the adjoining graph nodes to achieve subvoxel
segmentation accuracy by utilizing the partial volume information in the
voxels. The partial volume information in the voxels is exploited by computing
a displacement field from the original volume data to identify the
subvoxel-accurate centers within each voxel resulting in non-equidistant
spacing between the adjoining graph nodes. The smoothness of each surface
modeled as a convex constraint governs the connectivity and regularity of the
surface. We employ an edge-based graph representation to incorporate the
necessary constraints and the globally optimal solution is obtained by
computing a minimum s-t cut. The proposed method was validated on 10
intravascular multi-frame ultrasound image datasets for subvoxel segmentation
accuracy. In all cases, the approach yielded highly accurate results. Our
approach can be readily extended to higher-dimensional segmentations.
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
Authors: Subarna Tripathi, Gokce Dane, Byeongkeun Kang, Vasudev Bhaskaran, Truong Nguyen
Date: 2017-05-16 00:00:00
Tasks: Face Detection, image-classification, Image Classification, Object, object-detection, Object Detection, Quantization

Deep convolutional Neural Networks (CNN) are the state-of-the-art performers
for object detection task. It is well known that object detection requires more
computation and memory than image classification. Thus the consolidation of a
CNN-based object detection for an embedded system is more challenging. In this
work, we propose LCDet, a fully-convolutional neural network for generic object
detection that aims to work in embedded systems. We design and develop an
end-to-end TensorFlow(TF)-based model. Additionally, we employ 8-bit
quantization on the learned weights. We use face detection as a use case. Our
TF-Slim based network can predict different faces of different shapes and sizes
in a single forward pass. Our experimental results show that the proposed
method achieves comparative accuracy comparing with state-of-the-art CNN-based
face detection methods, while reducing the model size by 3x and memory-BW by
~4x comparing with one of the best real-time CNN-based object detector such as
YOLO. TF 8-bit quantized model provides additional 4x memory reduction while
keeping the accuracy as good as the floating point model. The proposed model
thus becomes amenable for embedded implementations.
Ethical Artificial Intelligence - An Open Question
Authors: Alice Pavaloiu, Utku Kose
Date: 2017-05-16 00:00:00
Tasks: Ethics, Open-Ended Question Answering

Artificial Intelligence (AI) is an effective science which employs strong
enough approaches, methods, and techniques to solve unsolvable real world based
problems. Because of its unstoppable rise towards the future, there are also
some discussions about its ethics and safety. Shaping an AI friendly
environment for people and a people friendly environment for AI can be a
possible answer for finding a shared context of values for both humans and
robots. In this context, objective of this paper is to address the ethical
issues of AI and explore the moral dilemmas that arise from ethical algorithms,
from pre set or acquired values. In addition, the paper will also focus on the
subject of AI safety. As general, the paper will briefly analyze the concerns
and potential solutions to solving the ethical issues presented and increase
readers awareness on AI safety as another related research interest.
Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
Authors: Michael Gastegger, Jörg Behler, Philipp Marquetand
Date: 2017-05-16 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency, Philosophy

Machine learning has emerged as an invaluable tool in many research areas. In
the present work, we harness this power to predict highly accurate molecular
infrared spectra with unprecedented computational efficiency. To account for
vibrational anharmonic and dynamical effects -- typically neglected by
conventional quantum chemistry approaches -- we base our machine learning
strategy on ab initio molecular dynamics simulations. While these simulations
are usually extremely time consuming even for small molecules, we overcome
these limitations by leveraging the power of a variety of machine learning
techniques, not only accelerating simulations by several orders of magnitude,
but also greatly extending the size of systems that can be treated. To this
end, we develop a molecular dipole moment model based on environment dependent
neural network charges and combine it with the neural network potentials of
Behler and Parrinello. Contrary to the prevalent big data philosophy, we are
able to obtain very accurate machine learning models for the prediction of
infrared spectra based on only a few hundreds of electronic structure reference
points. This is made possible through the introduction of a fully automated
sampling scheme and the use of molecular forces during neural network potential
training. We demonstrate the power of our machine learning approach by applying
it to model the infrared spectra of a methanol molecule, n-alkanes containing
up to 200 atoms and the protonated alanine tripeptide, which at the same time
represents the first application of machine learning techniques to simulate the
dynamics of a peptide. In all these case studies we find excellent agreement
between the infrared spectra predicted via machine learning models and the
respective theoretical and experimental spectra.
What's In A Patch, II: Visualizing generic surfaces
Authors: Benjamin S. Kunsberg, Daniel Niels Holtmann-Rice, Steven W. Zucker
Date: 2017-05-16 00:00:00

We continue the development of a linear algebraic framework for the
shape-from-shading problem, exploiting the manner in which tensors arise when
scalar (e.g. image) and vector (e.g. surface normal) fields are differentiated
multiple times. In this paper we apply that framework to develop Taylor
expansions of the normal field and build a boot-strapping algorithm to find
these polynomial surface solutions (under any light source) consistent with a
given patch to arbitrary order. A generic constraint on the image derivatives
restricts these solutions to a 2-D subspace, plus an unknown rotation matrix.
The parameters for the subspace and rotation matrix encapsulate the ambiguity
in the shading problem.
What's In A Patch, I: Tensors, Differential Geometry and Statistical Shading Analysis
Authors: Daniel Niels Holtmann-Rice, Benjamin S. Kunsberg, Steven W. Zucker
Date: 2017-05-16 00:00:00

We develop a linear algebraic framework for the shape-from-shading problem,
because tensors arise when scalar (e.g. image) and vector (e.g. surface normal)
fields are differentiated multiple times. The work is in two parts. In this
first part we investigate when image derivatives exhibit invariance to changing
illumination by calculating the statistics of image derivatives under general
distributions on the light source. We computationally validate the hypothesis
that image orientations (derivatives) provide increased invariance to
illumination by showing (for a Lambertian model) that a shape-from-shading
algorithm matching gradients instead of intensities provides more accurate
reconstructions when illumination is incorrectly estimated under a flatness
prior.
Static Gesture Recognition using Leap Motion
Authors: Babak Toghiani-Rizi, Christofer Lind, Maria Svensson, Marcus Windmark
Date: 2017-05-16 00:00:00
Tasks: BIG-bench Machine Learning, Gesture Recognition

In this report, an automated bartender system was developed for making orders
in a bar using hand gestures. The gesture recognition of the system was
developed using Machine Learning techniques, where the model was trained to
classify gestures using collected data. The final model used in the system
reached an average accuracy of 95%. The system raised ethical concerns both in
terms of user interaction and having such a system in a real world scenario,
but it could initially work as a complement to a real bartender.
Predictive Business Process Monitoring with LSTM Neural Networks
Authors: Niek Tax, Ilya Verenich, Marcello La Rosa, Marlon Dumas
Date: 2016-12-07 00:00:00
Tasks: Multivariate Time Series Forecasting, Predictive Process Monitoring, Time Series Prediction

Predictive business process monitoring methods exploit logs of completed
cases of a process in order to make predictions about running cases thereof.
Existing methods in this space are tailor-made for specific prediction tasks.
Moreover, their relative accuracy is highly sensitive to the dataset at hand,
thus requiring users to engage in trial-and-error and tuning when applying them
in a specific setting. This paper investigates Long Short-Term Memory (LSTM)
neural networks as an approach to build consistently accurate models for a wide
range of predictive process monitoring tasks. First, we show that LSTMs
outperform existing techniques to predict the next event of a running case and
its timestamp. Next, we show how to use models for predicting the next task in
order to predict the full continuation of a running case. Finally, we apply the
same approach to predict the remaining time, and show that this approach
outperforms existing tailor-made methods.
The Imprecisions of Precision Measures in Process Mining
Authors: Niek Tax, Xixi Lu, Natalia Sidorova, Dirk Fahland, Wil M. P. van der Aalst
Date: 2017-05-03 00:00:00

In process mining, precision measures are used to quantify how much a process
model overapproximates the behavior seen in an event log. Although several
measures have been proposed throughout the years, no research has been done to
validate whether these measures achieve the intended aim of quantifying
over-approximation in a consistent way for all models and logs. This paper
fills this gap by postulating a number of axioms for quantifying precision
consistently for any log and any model. Further, we show through
counter-examples that none of the existing measures consistently quantifies
precision.
Real-Time Adaptive Image Compression
Authors: Oren Rippel, Lubomir Bourdev
Date: 2017-05-16 00:00:00
Proceeding: ICML 2017 8
Tasks: GPU, Image Compression

We present a machine learning-based approach to lossy image compression which
outperforms all existing codecs, while running in real-time.
  Our algorithm typically produces files 2.5 times smaller than JPEG and JPEG
2000, 2 times smaller than WebP, and 1.7 times smaller than BPG on datasets of
generic images across all quality levels. At the same time, our codec is
designed to be lightweight and deployable: for example, it can encode or decode
the Kodak dataset in around 10ms per image on GPU.
  Our architecture is an autoencoder featuring pyramidal analysis, an adaptive
coding module, and regularization of the expected codelength. We also
supplement our approach with adversarial training specialized towards use in a
compression setting: this enables us to produce visually pleasing
reconstructions for very low bitrates.
Mining Local Process Models
Authors: Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
Date: 2016-06-20 00:00:00
Tasks: Model Discovery, Sequential Pattern Mining

In this paper we describe a method to discover frequent behavioral patterns
in event logs. We express these patterns as \emph{local process models}. Local
process model mining can be positioned in-between process discovery and episode
/ sequential pattern mining. The technique presented in this paper is able to
learn behavioral patterns involving sequential composition, concurrency, choice
and loop, like in process mining. However, we do not look at start-to-end
models, which distinguishes our approach from process discovery and creates a
link to episode / sequential pattern mining. We propose an incremental
procedure for building local process models capturing frequent patterns based
on so-called process trees. We propose five quality dimensions and
corresponding metrics for local process models, given an event log. We show
monotonicity properties for some quality dimensions, enabling a speedup of
local process model discovery through pruning. We demonstrate through a real
life case study that mining local patterns allows us to get insights in
processes where regular start-to-end process discovery techniques are only able
to learn unstructured, flower-like, models.
The Incremental Multiresolution Matrix Factorization Algorithm
Authors: Vamsi K. Ithapu, Risi Kondor, Sterling C. Johnson, Vikas Singh
Date: 2017-05-16 00:00:00
Proceeding: CVPR 2017 7

Multiresolution analysis and matrix factorization are foundational tools in
computer vision. In this work, we study the interface between these two
distinct topics and obtain techniques to uncover hierarchical block structure
in symmetric matrices -- an important aspect in the success of many vision
problems. Our new algorithm, the incremental multiresolution matrix
factorization, uncovers such structure one feature at a time, and hence scales
well to large matrices. We describe how this multiscale analysis goes much
farther than what a direct global factorization of the data can identify. We
evaluate the efficacy of the resulting factorizations for relative leveraging
within regression tasks using medical imaging data. We also use the
factorization on representations learned by popular deep networks, providing
evidence of their ability to infer semantic relationships even when they are
not explicitly trained to do so. We show that this algorithm can be used as an
exploratory tool to improve the network architecture, and within numerous other
settings in vision.
Ensemble Sales Forecasting Study in Semiconductor Industry
Authors: Qiuping Xu, Vikas Sharma
Date: 2017-04-28 00:00:00
Tasks: CPU, Time Series Analysis

Sales forecasting plays a prominent role in business planning and business
strategy. The value and importance of advance information is a cornerstone of
planning activity, and a well-set forecast goal can guide sale-force more
efficiently. In this paper CPU sales forecasting of Intel Corporation, a
multinational semiconductor industry, was considered. Past sale, future
booking, exchange rates, Gross domestic product (GDP) forecasting, seasonality
and other indicators were innovatively incorporated into the quantitative
modeling. Benefit from the recent advances in computation power and software
development, millions of models built upon multiple regressions, time series
analysis, random forest and boosting tree were executed in parallel. The models
with smaller validation errors were selected to form the ensemble model. To
better capture the distinct characteristics, forecasting models were
implemented at lead time and lines of business level. The moving windows
validation process automatically selected the models which closely represent
current market condition. The weekly cadence forecasting schema allowed the
model to response effectively to market fluctuation. Generic variable
importance analysis was also developed to increase the model interpretability.
Rather than assuming fixed distribution, this non-parametric permutation
variable importance analysis provided a general framework across methods to
evaluate the variable importance. This variable importance framework can
further extend to classification problem by modifying the mean absolute
percentage error(MAPE) into misclassify error. Please find the demo code at :
https://github.com/qx0731/ensemble_forecast_methods
Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models
Authors: Felix Mannhardt, Niek Tax
Date: 2017-04-11 00:00:00
Tasks: Clustering

Process mining analyzes business processes based on events stored in event
logs. However, some recorded events may correspond to activities on a very low
level of abstraction. When events are recorded on a too low level of
granularity, process discovery methods tend to generate overgeneralizing
process models. Grouping low-level events to higher level activities, i.e.,
event abstraction, can be used to discover better process models. Existing
event abstraction methods are mainly based on common sub-sequences and
clustering techniques. In this paper, we propose to first discover local
process models and then use those models to lift the event log to a higher
level of abstraction. Our conjecture is that process models discovered on the
obtained high-level event log return process models of higher quality: their
fitness and precision scores are more balanced. We show this with preliminary
results on several real-life event logs.
Obstacle Avoidance Using Stereo Camera
Authors: Akkas Uddin Haque, Ashkan Nejadpak
Date: 2017-05-11 00:00:00

In this paper we present a novel method for obstacle avoidance using the
stereo camera. The conventional obstacle avoidance methods and their
limitations are discussed. A new algorithm is developed for the real-time
obstacle avoidance which responds faster to unexpected obstacles. In this
approach the depth map is divided into optimized number of regions and the
minimum depth at each section is assigned as the depth of that region. A fuzzy
controller is designed to create the drive commands for the robot/quadcopter.
The system was tested on multiple paths with different obstacles and the
results demonstrated the high accuracy of the developed system.
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization
Authors: Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden, Michel Verhaegen
Date: 2016-04-01 00:00:00
Tasks: Bayesian Optimization, Thompson Sampling

Bayesian optimization through Gaussian process regression is an effective
method of optimizing an unknown function for which every measurement is
expensive. It approximates the objective function and then recommends a new
measurement point to try out. This recommendation is usually selected by
optimizing a given acquisition function. After a sufficient number of
measurements, a recommendation about the maximum is made. However, a key
realization is that the maximum of a Gaussian process is not a deterministic
point, but a random variable with a distribution of its own. This distribution
cannot be calculated analytically. Our main contribution is an algorithm,
inspired by sequential Monte Carlo samplers, that approximates this maximum
distribution. Subsequently, by taking samples from this distribution, we enable
Thompson sampling to be applied to (armed-bandit) optimization problems with a
continuous input space. All this is done without requiring the optimization of
a nonlinear acquisition function. Experiments have shown that the resulting
optimization method has a competitive performance at keeping the cumulative
regret limited.