pwc-archive/papers-with-abstracts huggingface dataset:
Predicting Rankings of Software Verification Competitions
Authors: Mike Czech, Eyke Hüllermeier, Marie-Christine Jakobs, Heike Wehrheim
Date: 2017-03-02 00:00:00

Software verification competitions, such as the annual SV-COMP, evaluate
software verification tools with respect to their effectivity and efficiency.
Typically, the outcome of a competition is a (possibly category-specific)
ranking of the tools. For many applications, such as building portfolio
solvers, it would be desirable to have an idea of the (relative) performance of
verification tools on a given verification task beforehand, i.e., prior to
actually running all tools on the task.
  In this paper, we present a machine learning approach to predicting rankings
of tools on verification tasks. The method builds upon so-called label ranking
algorithms, which we complement with appropriate kernels providing a similarity
measure for verification tasks. Our kernels employ a graph representation for
software source code that mixes elements of control flow and program dependence
graphs with abstract syntax trees. Using data sets from SV-COMP, we demonstrate
our rank prediction technique to generalize well and achieve a rather high
predictive accuracy. In particular, our method outperforms a recently proposed
feature-based approach of Demyanova et al. (when applied to rank predictions).
Wireless Interference Identification with Convolutional Neural Networks
Authors: Malte Schmidt, Dimitri Block, Uwe Meier
Date: 2017-03-02 00:00:00
Tasks: GPU, Management

The steadily growing use of license-free frequency bands requires reliable
coexistence management for deterministic medium utilization. For interference
mitigation, proper wireless interference identification (WII) is essential. In
this work we propose the first WII approach based upon deep convolutional
neural networks (CNNs). The CNN naively learns its features through
self-optimization during an extensive data-driven GPU-based training process.
We propose a CNN example which is based upon sensing snapshots with a limited
duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs
between 15 classes. They represent packet transmissions of IEEE 802.11 b/g,
IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the
2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII
approaches and has a classification accuracy greater than 95% for
signal-to-noise ratio of at least -5 dB.
Mixing Complexity and its Applications to Neural Networks
Authors: Michal Moshkovitz, Naftali Tishby
Date: 2017-03-02 00:00:00

We suggest analyzing neural networks through the prism of space constraints.
We observe that most training algorithms applied in practice use bounded
memory, which enables us to use a new notion introduced in the study of
space-time tradeoffs that we call mixing complexity. This notion was devised in
order to measure the (in)ability to learn using a bounded-memory algorithm. In
this paper we describe how we use mixing complexity to obtain new results on
what can and cannot be learned using neural networks.
Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting
Authors: Wei Zhang, Lei Han, Juanzhen Sun, Hanyang Guo, Jie Dai
Date: 2017-02-15 00:00:00
Tasks: Deep Learning, Feature Engineering

Convective storm nowcasting has attracted substantial attention in various fields. Existing methods under a deep learning framework rely primarily on radar data. Although they perform nowcast storm advection well, it is still challenging to nowcast storm initiation and growth, due to the limitations of the radar observations. This paper describes the first attempt to nowcast storm initiation, growth, and advection simultaneously under a deep learning framework using multi-source meteorological data. To this end, we present a multi-channel 3D-cube successive convolution network (3D-SCN). As real-time re-analysis meteorological data can now provide valuable atmospheric boundary layer thermal dynamic information, which is essential to predict storm initiation and growth, both raw 3D radar and re-analysis data are used directly without any handcraft feature engineering. These data are formulated as multi-channel 3D cubes, to be fed into our network, which are convolved by cross-channel 3D convolutions. By stacking successive convolutional layers without pooling, we build an end-to-end trainable model for nowcasting. Experimental results show that deep learning methods achieve better performance than traditional extrapolation methods. The qualitative analyses of 3D-SCN show encouraging results of nowcasting of storm initiation, growth, and advection.
Survey of reasoning using Neural networks
Authors: Amit Sahu
Date: 2017-02-14 00:00:00
Tasks: Question Answering, Survey

Reason and inference require process as well as memory skills by humans.
Neural networks are able to process tasks like image recognition (better than
humans) but in memory aspects are still limited (by attention mechanism, size).
Recurrent Neural Network (RNN) and it's modified version LSTM are able to solve
small memory contexts, but as context becomes larger than a threshold, it is
difficult to use them. The Solution is to use large external memory. Still, it
poses many challenges like, how to train neural networks for discrete memory
representation, how to describe long term dependencies in sequential data etc.
Most prominent neural architectures for such tasks are Memory networks:
inference components combined with long term memory and Neural Turing Machines:
neural networks using external memory resources. Also, additional techniques
like attention mechanism, end to end gradient descent on discrete memory
representation are needed to support these solutions. Preliminary results of
above neural architectures on simple algorithms (sorting, copying) and Question
Answering (based on story, dialogs) application are comparable with the state
of the art. In this paper, I explain these architectures (in general), the
additional techniques used and the results of their application.
Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs
Authors: Raphael Meier, Urspeter Knecht, Alain Jungo, Roland Wiest, Mauricio Reyes
Date: 2017-03-01 00:00:00
Tasks: Image Segmentation, Semantic Segmentation, Uncertainty Quantification

This paper proposes a novel approach for uncertainty quantification in dense
Conditional Random Fields (CRFs). The presented approach, called
Perturb-and-MPM, enables efficient, approximate sampling from dense multi-label
CRFs via random perturbations. An analytic error analysis was performed which
identified the main cause of approximation error as well as showed that the
error is bounded. Spatial uncertainty maps can be derived from the
Perturb-and-MPM model, which can be used to visualize uncertainty in image
segmentation results. The method is validated on synthetic and clinical
Magnetic Resonance Imaging data. The effectiveness of the approach is
demonstrated on the challenging problem of segmenting the tumor core in
glioblastoma. We found that areas of high uncertainty correspond well to
wrongly segmented image regions. Furthermore, we demonstrate the potential use
of uncertainty maps to refine imaging biomarkers in the case of extent of
resection and residual tumor volume in brain tumor patients.
Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention
Authors: Qinghai Liao, Ming Liu, Lei Tai, Haoyang Ye
Date: 2017-03-02 00:00:00

Fusion of heterogeneous extroceptive sensors is the most effient and
effective way to representing the environment precisely, as it overcomes
various defects of each homogeneous sensor. The rigid transformation (aka.
extrinsic parameters) of heterogeneous sensory systems should be available
before precisely fusing the multisensor information. Researchers have proposed
several approaches to estimating the extrinsic parameters. These approaches
require either auxiliary objects, like chessboards, or extra help from human to
select correspondences. In this paper, we proposed a novel extrinsic
calibration approach for the extrinsic calibration of range and image sensors.
As far as we know, it is the first automatic approach with no requirement of
auxiliary objects or any human interventions. First, we estimate the initial
extrinsic parameters from the individual motion of the range finder and the
camera. Then we extract lines in the image and point-cloud pairs, to refine the
line feature associations by the initial extrinsic parameters. At the end, we
discussed the degenerate case which may lead to the algorithm failure and
validate our approach by simulation. The results indicate high-precision
extrinsic calibration results against the ground-truth.
Introduction to Nonnegative Matrix Factorization
Authors: Nicolas Gillis
Date: 2017-03-02 00:00:00

In this paper, we introduce and provide a short overview of nonnegative
matrix factorization (NMF). Several aspects of NMF are discussed, namely, the
application in hyperspectral imaging, geometry and uniqueness of NMF solutions,
complexity, algorithms, and its link with extended formulations of polyhedra.
In order to put NMF into perspective, the more general problem class of
constrained low-rank matrix approximation problems is first briefly introduced.
TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process
Authors: Sarfaraz Hussein, Robert Gillies, Kunlin Cao, Qi Song, Ulas Bagci
Date: 2017-03-02 00:00:00
Tasks: Data Augmentation, Lung Cancer Diagnosis

Characterization of lung nodules as benign or malignant is one of the most
important tasks in lung cancer diagnosis, staging and treatment planning. While
the variation in the appearance of the nodules remains large, there is a need
for a fast and robust computer aided system. In this work, we propose an
end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for
nodule characterization. First, we use median intensity projection to obtain a
2D patch corresponding to each dimension. The three images are then
concatenated to form a tensor, where the images serve as different channels of
the input image. In order to increase the number of training samples, we
perform data augmentation by scaling, rotating and adding noise to the input
image. The trained network is used to extract features from the input image
followed by a Gaussian Process (GP) regression to obtain the malignancy score.
We also empirically establish the significance of different high level nodule
attributes such as calcification, sphericity and others for malignancy
determination. These attributes are found to be complementary to the deep
multi-view CNN features and a significant improvement over other methods is
obtained.
Mode Regularized Generative Adversarial Networks
Authors: Tong Che, Yan-ran Li, Athul Paul Jacob, Yoshua Bengio, Wenjie Li
Date: 2016-12-07 00:00:00

Although Generative Adversarial Networks achieve state-of-the-art results on
a variety of generative tasks, they are regarded as highly unstable and prone
to miss modes. We argue that these bad behaviors of GANs are due to the very
particular functional shape of the trained discriminators in high dimensional
spaces, which can easily make training stuck or push probability mass in the
wrong direction, towards that of higher concentration than that of the data
generating distribution. We introduce several ways of regularizing the
objective, which can dramatically stabilize the training of GAN models. We also
show that our regularizers can help the fair distribution of probability mass
across the modes of the data generating distribution, during the early phases
of training and thus providing a unified solution to the missing modes problem.
Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US
Authors: Timnit Gebru, Jonathan Krause, Yi-Lun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, Li Fei-Fei
Date: 2017-02-22 00:00:00

The United States spends more than $1B each year on initiatives such as the
American Community Survey (ACS), a labor-intensive door-to-door study that
measures statistics relating to race, gender, education, occupation,
unemployment, and other demographic factors. Although a comprehensive source of
data, the lag between demographic changes and their appearance in the ACS can
exceed half a decade. As digital imagery becomes ubiquitous and machine vision
techniques improve, automated data analysis may provide a cheaper and faster
alternative. Here, we present a method that determines socioeconomic trends
from 50 million images of street scenes, gathered in 200 American cities by
Google Street View cars. Using deep learning-based computer vision techniques,
we determined the make, model, and year of all motor vehicles encountered in
particular neighborhoods. Data from this census of motor vehicles, which
enumerated 22M automobiles in total (8% of all automobiles in the US), was used
to accurately estimate income, race, education, and voting patterns, with
single-precinct resolution. (The average US precinct contains approximately
1000 people.) The resulting associations are surprisingly simple and powerful.
For instance, if the number of sedans encountered during a 15-minute drive
through a city is higher than the number of pickup trucks, the city is likely
to vote for a Democrat during the next Presidential election (88% chance);
otherwise, it is likely to vote Republican (82%). Our results suggest that
automated systems for monitoring demographic trends may effectively complement
labor-intensive approaches, with the potential to detect trends with fine
spatial resolution, in close to real time.
An Online Mechanism for Ridesharing in Autonomous Mobility-on-Demand Systems
Authors: Wen Shen, Cristina V. Lopes, Jacob W. Crandall
Date: 2016-03-07 00:00:00
Tasks: Management

With proper management, Autonomous Mobility-on-Demand (AMoD) systems have
great potential to satisfy the transport demands of urban populations by
providing safe, convenient, and affordable ridesharing services. Meanwhile,
such systems can substantially decrease private car ownership and use, and thus
significantly reduce traffic congestion, energy consumption, and carbon
emissions. To achieve this objective, an AMoD system requires private
information about the demand from passengers. However, due to
self-interestedness, passengers are unlikely to cooperate with the service
providers in this regard. Therefore, an online mechanism is desirable if it
incentivizes passengers to truthfully report their actual demand. For the
purpose of promoting ridesharing, we hereby introduce a posted-price,
integrated online ridesharing mechanism (IORS) that satisfies desirable
properties such as ex-post incentive compatibility, individual rationality, and
budget-balance. Numerical results indicate the competitiveness of IORS compared
with two benchmarks, namely the optimal assignment and an offline,
auction-based mechanism.
Signal-based Bayesian Seismic Monitoring
Authors: David A. Moore, Stuart J. Russell
Date: 2017-03-02 00:00:00
Tasks: Bayesian Inference, Gaussian Processes, Seismic Detection

Detecting weak seismic events from noisy sensors is a difficult perceptual
task. We formulate this task as Bayesian inference and propose a generative
model of seismic events and signals across a network of spatially distributed
stations. Our system, SIGVISA, is the first to directly model seismic
waveforms, allowing it to incorporate a rich representation of the physics
underlying the signal generation process. We use Gaussian processes over
wavelet parameters to predict detailed waveform fluctuations based on
historical events, while degrading smoothly to simple parametric envelopes in
regions with no historical seismicity. Evaluating on data from the western US,
we recover three times as many events as previous work, and reduce mean
location errors by a factor of four while greatly increasing sensitivity to
low-magnitude events.
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
Authors: Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony Price, Daniel Rueckert
Date: 2017-03-01 00:00:00
Tasks: compressed sensing, Dictionary Learning, Image Reconstruction

The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
Inspired by recent advances in deep learning, we propose a framework for
reconstructing MR images from undersampled data using a deep cascade of
convolutional neural networks to accelerate the data acquisition process. We
show that for Cartesian undersampling of 2D cardiac MR images, the proposed
method outperforms the state-of-the-art compressed sensing approaches, such as
dictionary learning-based MRI (DLMRI) reconstruction, in terms of
reconstruction error, perceptual quality and reconstruction speed for both
3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the
method proposed is approximately twice as small, allowing to preserve
anatomical structures more faithfully. Using our method, each image can be
reconstructed in 23 ms, which is fast enough to enable real-time applications.
Change Detection under Global Viewpoint Uncertainty
Authors: Murase Tomoya, Tanaka Kanji
Date: 2017-03-01 00:00:00
Tasks: Change Detection, Motion Detection, Retrieval

This paper addresses the problem of change detection from a novel perspective
of long-term map learning. We are particularly interested in designing an
approach that can scale to large maps and that can function under global
uncertainty in the viewpoint (i.e., GPS-denied situations). Our approach, which
utilizes a compact bag-of-words (BoW) scene model, makes several contributions
to the problem:
  1) Two kinds of prior information are extracted from the view sequence map
and used for change detection. Further, we propose a novel type of prior,
called motion prior, to predict the relative motions of stationary objects and
anomaly ego-motion detection. The proposed prior is also useful for
distinguishing stationary from non-stationary objects.
  2) A small set of good reference images (e.g., 10) are efficiently retrieved
from the view sequence map by employing the recently developed
Bag-of-Local-Convolutional-Features (BoLCF) scene model.
  3) Change detection is reformulated as a scene retrieval over these reference
images to find changed objects using a novel spatial Bag-of-Words (SBoW) scene
model. Evaluations conducted of individual techniques and also their
combinations on a challenging dataset of highly dynamic scenes in the publicly
available Malaga dataset verify their efficacy.
Label Refinement Network for Coarse-to-Fine Semantic Segmentation
Authors: Md Amirul Islam, Shujon Naha, Mrigank Rochan, Neil Bruce, Yang Wang
Date: 2017-03-01 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We consider the problem of semantic image segmentation using deep
convolutional neural networks. We propose a novel network architecture called
the label refinement network that predicts segmentation labels in a
coarse-to-fine fashion at several resolutions. The segmentation labels at a
coarse resolution are used together with convolutional features to obtain finer
resolution segmentation labels. We define loss functions at several stages in
the network to provide supervisions at different stages. Our experimental
results on several standard datasets demonstrate that the proposed model
provides an effective way of producing pixel-wise dense image labeling.
Improving a Credit Scoring Model by Incorporating Bank Statement Derived Features
Authors: Rory P. Bunker, Wenjun Zhang, M. Asif Naeem
Date: 2016-10-30 00:00:00
Tasks: Form

In this paper, we investigate the extent to which features derived from bank
statements provided by loan applicants, and which are not declared on an
application form, can enhance a credit scoring model for a New Zealand lending
company. Exploring the potential of such information to improve credit scoring
models in this manner has not been studied previously. We construct a baseline
model based solely on the existing scoring features obtained from the loan
application form, and a second baseline model based solely on the new bank
statement-derived features. A combined feature model is then created by
augmenting the application form features with the new bank statement derived
features. Our experimental results using ROC analysis show that a combined
feature model performs better than both of the two baseline models, and show
that a number of the bank statement-derived features have value in improving
the credit scoring model. The target data set used for modelling was highly
imbalanced, and Naive Bayes was found to be the best performing model, and
outperformed a number of other classifiers commonly used in credit scoring,
suggesting its potential for future use on highly imbalanced data sets.
Skin cancer reorganization and classification with deep neural network
Authors: Hao Chang
Date: 2017-03-01 00:00:00
Tasks: Boundary Detection, Classification, Diagnostic, General Classification, Lesion Segmentation, Melanoma Diagnosis, Segmentation, Skin Lesion Segmentation, Transfer Learning

As one kind of skin cancer, melanoma is very dangerous. Dermoscopy based
early detection and recarbonization strategy is critical for melanoma therapy.
However, well-trained dermatologists dominant the diagnostic accuracy. In order
to solve this problem, many effort focus on developing automatic image analysis
systems. Here we report a novel strategy based on deep learning technique, and
achieve very high skin lesion segmentation and melanoma diagnosis accuracy: 1)
we build a segmentation neural network (skin_segnn), which achieved very high
lesion boundary detection accuracy; 2) We build another very deep neural
network based on Google inception v3 network (skin_recnn) and its well-trained
weight. The novel designed transfer learning based deep neural network
skin_inceptions_v3_nn helps to achieve a high prediction accuracy.
Boosting Variational Inference
Authors: Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, David B. Dunson
Date: 2016-11-17 00:00:00
Tasks: Variational Inference

Variational inference (VI) provides fast approximations of a Bayesian
posterior in part because it formulates posterior approximation as an
optimization problem: to find the closest distribution to the exact posterior
over some family of distributions. For practical reasons, the family of
distributions in VI is usually constrained so that it does not include the
exact posterior, even as a limit point. Thus, no matter how long VI is run, the
resulting approximation will not approach the exact posterior. We propose to
instead consider a more flexible approximating family consisting of all
possible finite mixtures of a parametric base distribution (e.g., Gaussian).
For efficient inference, we borrow ideas from gradient boosting to develop an
algorithm we call boosting variational inference (BVI). BVI iteratively
improves the current approximation by mixing it with a new component from the
base distribution family and thereby yields progressively more accurate
posterior approximations as more computing time is spent. Unlike a number of
common VI variants including mean-field VI, BVI is able to capture
multimodality, general posterior covariance, and nonstandard posterior shapes.
ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection
Authors: Matt Berseth
Date: 2017-03-01 00:00:00
Tasks: Classification, General Classification, Lesion Classification, Lesion Segmentation, Segmentation

Our system addresses Part 1, Lesion Segmentation and Part 3, Lesion
Classification of the ISIC 2017 challenge. Both algorithms make use of deep
convolutional networks to achieve the challenge objective.