pwc-archive/papers-with-abstracts huggingface dataset:
Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features
Authors: Kaisar Kushibar, Sergi Valverde, Sandra Gonzalez-Villa, Jose Bernal, Mariano Cabezas, Arnau Oliver, Xavier Llado
Date: 2017-09-26 00:00:00
Tasks: Segmentation

Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI)
has attracted the interest of the research community for a long time because
morphological changes in these structures are related to different
neurodegenerative disorders. However, manual segmentation of these structures
can be tedious and prone to variability, highlighting the need for robust
automated segmentation methods. In this paper, we present a novel convolutional
neural network based approach for accurate segmentation of the sub-cortical
brain structures that combines both convolutional and prior spatial features
for improving the segmentation accuracy. In order to increase the accuracy of
the automated segmentation, we propose to train the network using a restricted
sample selection to force the network to learn the most difficult parts of the
structures. We evaluate the accuracy of the proposed method on the public
MICCAI 2012 challenge and IBSR 18 datasets, comparing it with different
available state-of-the-art methods and other recently proposed deep learning
approaches. On the MICCAI 2012 dataset, our method shows an excellent
performance comparable to the best challenge participant strategy, while
performing significantly better than state-of-the-art techniques such as
FreeSurfer and FIRST. On the IBSR 18 dataset, our method also exhibits a
significant increase in the performance with respect to not only FreeSurfer and
FIRST, but also comparable or better results than other recent deep learning
approaches. Moreover, our experiments show that both the addition of the
spatial priors and the restricted sampling strategy have a significant effect
on the accuracy of the proposed method. In order to encourage the
reproducibility and the use of the proposed method, a public version of our
approach is available to download for the neuroimaging community.
MDP environments for the OpenAI Gym
Authors: Andreas Kirsch
Date: 2017-09-26 00:00:00
Tasks: OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The OpenAI Gym provides researchers and enthusiasts with simple to use
environments for reinforcement learning. Even the simplest environment have a
level of complexity that can obfuscate the inner workings of RL approaches and
make debugging difficult. This whitepaper describes a Python framework that
makes it very easy to create simple Markov-Decision-Process environments
programmatically by specifying state transitions and rewards of deterministic
and non-deterministic MDPs in a domain-specific language in Python. It then
presents results and visualizations created with this MDP framework.
AutoEncoder by Forest
Authors: Ji Feng, Zhi-Hua Zhou
Date: 2017-09-26 00:00:00

Auto-encoding is an important task which is typically realized by deep neural
networks (DNNs) such as convolutional neural networks (CNN). In this paper, we
propose EncoderForest (abbrv. eForest), the first tree ensemble based
auto-encoder. We present a procedure for enabling forests to do backward
reconstruction by utilizing the equivalent classes defined by decision paths of
the trees, and demonstrate its usage in both supervised and unsupervised
setting. Experiments show that, compared with DNN autoencoders, eForest is able
to obtain lower reconstruction error with fast training speed, while the model
itself is reusable and damage-tolerable.
Multi-layer Visualization for Medical Mixed Reality
Authors: Séverine Habert, Ma Meng, Pascal Fallavollita, Nassir Navab
Date: 2017-09-26 00:00:00
Tasks: Mixed Reality

Medical Mixed Reality helps surgeons to contextualize intraoperative data
with video of the surgical scene. Nonetheless, the surgical scene and
anatomical target are often occluded by surgical instruments and surgeon hands.
In this paper and to our knowledge, we propose a multi-layer visualization in
Medical Mixed Reality solution which subtly improves a surgeon's visualization
by making transparent the occluding objects. As an example scenario, we use an
augmented reality C-arm fluoroscope device. A video image is created using a
volumetric-based image synthesization technique and stereo-RGBD cameras mounted
on the C-arm. From this synthesized view, the background which is occluded by
the surgical instruments and surgeon hands is recovered by modifying the
volumetric-based image synthesization technique. The occluding objects can,
therefore, become transparent over the surgical scene. Experimentation with
different augmented reality scenarios yield results demonstrating that the
background of the surgical scenes can be recovered with accuracy between
45%-99%. In conclusion, we presented a solution that a Mixed Reality solution
for medicine, providing transparency to objects occluding the surgical scene.
This work is also the first application of volumetric field for Diminished
Reality/ Mixed Reality.
Quantifying the relation between performance and success in soccer
Authors: Luca Pappalardo, Paolo Cintia
Date: 2017-05-02 00:00:00
Tasks: BIG-bench Machine Learning, Relation

The availability of massive data about sports activities offers nowadays the
opportunity to quantify the relation between performance and success. In this
study, we analyze more than 6,000 games and 10 million events in six European
leagues and investigate this relation in soccer competitions. We discover that
a team's position in a competition's final ranking is significantly related to
its typical performance, as described by a set of technical features extracted
from the soccer data. Moreover we find that, while victory and defeats can be
explained by the team's performance during a game, it is difficult to detect
draws by using a machine learning approach. We then simulate the outcomes of an
entire season of each league only relying on technical data, i.e. excluding the
goals scored, exploiting a machine learning model trained on data from past
seasons. The simulation produces a team ranking (the PC ranking) which is close
to the actual ranking, suggesting that a complex systems' view on soccer has
the potential of revealing hidden patterns regarding the relation between
performance and success.
UBSegNet: Unified Biometric Region of Interest Segmentation Network
Authors: Ranjeet Ranjan Jha, Daksh Thapar, Shreyas Malakarjun Patil, Aditya Nigam
Date: 2017-09-26 00:00:00
Tasks: General Classification, Management, Region Proposal

Digital human identity management, can now be seen as a social necessity, as
it is essentially required in almost every public sector such as, financial
inclusions, security, banking, social networking e.t.c. Hence, in today's
rampantly emerging world with so many adversarial entities, relying on a single
biometric trait is being too optimistic. In this paper, we have proposed a
novel end-to-end, Unified Biometric ROI Segmentation Network (UBSegNet), for
extracting region of interest from five different biometric traits viz. face,
iris, palm, knuckle and 4-slap fingerprint. The architecture of the proposed
UBSegNet consists of two stages: (i) Trait classification and (ii) Trait
localization. For these stages, we have used a state of the art region based
convolutional neural network (RCNN), comprising of three major parts namely
convolutional layers, region proposal network (RPN) along with classification
and regression heads. The model has been evaluated over various huge publicly
available biometric databases. To the best of our knowledge this is the first
unified architecture proposed, segmenting multiple biometric traits. It has
been tested over around 5000 * 5 = 25,000 images (5000 images per trait) and
produces very good results. Our work on unified biometric segmentation, opens
up the vast opportunities in the field of multiple biometric traits based
authentication systems.
Telling Cause from Effect using MDL-based Local and Global Regression
Authors: Alexander Marx, Jilles Vreeken
Date: 2017-09-26 00:00:00
Tasks: regression

We consider the fundamental problem of inferring the causal direction between
two univariate numeric random variables $X$ and $Y$ from observational data.
The two-variable case is especially difficult to solve since it is not possible
to use standard conditional independence tests between the variables.
  To tackle this problem, we follow an information theoretic approach based on
Kolmogorov complexity and use the Minimum Description Length (MDL) principle to
provide a practical solution. In particular, we propose a compression scheme to
encode local and global functional relations using MDL-based regression. We
infer $X$ causes $Y$ in case it is shorter to describe $Y$ as a function of $X$
than the inverse direction. In addition, we introduce Slope, an efficient
linear-time algorithm that through thorough empirical evaluation on both
synthetic and real world data we show outperforms the state of the art by a
wide margin.
First Steps Toward Camera Model Identification with Convolutional Neural Networks
Authors: Luca Bondi, Luca Baroffio, David Güera, Paolo Bestagini, Edward J. Delp, Stefano Tubaro
Date: 2016-03-03 00:00:00
Tasks: General Classification, Image Forensics

Detecting the camera model used to shoot a picture enables to solve a wide
series of forensic problems, from copyright infringement to ownership
attribution. For this reason, the forensic community has developed a set of
camera model identification algorithms that exploit characteristic traces left
on acquired images by the processing pipelines specific of each camera model.
In this paper, we investigate a novel approach to solve camera model
identification problem. Specifically, we propose a data-driven algorithm based
on convolutional neural networks, which learns features characterizing each
camera model directly from the acquired pictures. Results on a well-known
dataset of 18 camera models show that: (i) the proposed method outperforms
up-to-date state-of-the-art algorithms on classification of 64x64 color image
patches; (ii) features learned by the proposed network generalize to camera
models never used for training.
An enhanced method to compute the similarity between concepts of ontology
Authors: Noreddine Gherabi, Abdelhadi Daoui, Abderrahim Marzouk
Date: 2017-09-26 00:00:00
Tasks: Information Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity

With the use of ontologies in several domains such as semantic web,
information retrieval, artificial intelligence, the concept of similarity
measuring has become a very important domain of research. Therefore, in the
current paper, we propose our method of similarity measuring which uses the
Dijkstra algorithm to define and compute the shortest path. Then, we use this
one to compute the semantic distance between two concepts defined in the same
hierarchy of ontology. Afterward, we base on this result to compute the
semantic similarity. Finally, we present an experimental comparison between our
method and other methods of similarity measuring.
Generating Sentences by Editing Prototypes
Authors: Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, Percy Liang
Date: 2017-09-26 00:00:00
Proceeding: TACL 2018 1
Tasks: Language Modeling, Language Modelling, Sentence, Sentence Similarity

We propose a new generative model of sentences that first samples a prototype
sentence from the training corpus and then edits it into a new sentence.
Compared to traditional models that generate from scratch either left-to-right
or by first sampling a latent sentence vector, our prototype-then-edit model
improves perplexity on language modeling and generates higher quality outputs
according to human evaluation. Furthermore, the model gives rise to a latent
edit vector that captures interpretable semantics such as sentence similarity
and sentence-level analogies.
Learning to Label Affordances from Simulated and Real Data
Authors: Timo Lüddecke, Florentin Wörgötter
Date: 2017-09-26 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

An autonomous robot should be able to evaluate the affordances that are
offered by a given situation. Here we address this problem by designing a
system that can densely predict affordances given only a single 2D RGB image.
This is achieved with a convolutional neural network (ResNet), which we combine
with refinement modules recently proposed for addressing semantic image
segmentation. We define a novel cost function, which is able to handle
(potentially multiple) affordances of objects and their parts in a pixel-wise
manner even in the case of incomplete data. We perform qualitative as well as
quantitative evaluations with simulated and real data assessing 15 different
affordances. In general, we find that affordances, which are well-enough
represented in the training data, are correctly recognized with a substantial
fraction of correctly assigned pixels. Furthermore, we show that our model
outperforms several baselines. Hence, this method can give clear action
guidelines for a robot.
Robust non-local means filter for ultrasound image denoising
Authors: Hamid Reza Shahdoosti
Date: 2017-09-26 00:00:00
Tasks: Denoising, Image Denoising

This paper introduces a new approach to non-local means image denoising.
Instead of using all pixels located in the search window for estimating the
value of a pixel, we identify the highly corrupted pixels and assign less
weight to these pixels. This method is called robust non-local means. Numerical
and subjective evaluations using ultrasound images show good performances of
the proposed denoising method in recovering the shape of edges and important
detailed components, in comparison to traditional ultrasound image denoising
methods
The Parallel Algorithm for the 2-D Discrete Wavelet Transform
Authors: David Barina, Pavel Najman, Petr Kleparnik, Michal Kula, Pavel Zemcik
Date: 2017-08-25 00:00:00

The discrete wavelet transform can be found at the heart of many
image-processing algorithms. Until now, the transform on general-purpose
processors (CPUs) was mostly computed using a separable lifting scheme. As the
lifting scheme consists of a small number of operations, it is preferred for
processing using single-core CPUs. However, considering a parallel processing
using multi-core processors, this scheme is inappropriate due to a large number
of steps. On such architectures, the number of steps corresponds to the number
of points that represent the exchange of data. Consequently, these points often
form a performance bottleneck. Our approach appropriately rearranges
calculations inside the transform, and thereby reduces the number of steps. In
other words, we propose a new scheme that is friendly to parallel environments.
When evaluating on multi-core CPUs, we consistently overcome the original
lifting scheme. The evaluation was performed on 61-core Intel Xeon Phi and
8-core Intel Xeon processors.
Morphable Face Models - An Open Framework
Authors: Thomas Gerig, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Lüthi, Sandro Schönborn, Thomas Vetter
Date: 2017-09-25 00:00:00
Tasks: Face Model, Gaussian Processes

In this paper, we present a novel open-source pipeline for face registration
based on Gaussian processes as well as an application to face image analysis.
Non-rigid registration of faces is significant for many applications in
computer vision, such as the construction of 3D Morphable face models (3DMMs).
Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid
deformation models with B-splines and PCA models as examples. GPMM separate
problem specific requirements from the registration algorithm by incorporating
domain-specific adaptions as a prior model. The novelties of this paper are the
following: (i) We present a strategy and modeling technique for face
registration that considers symmetry, multi-scale and spatially-varying
details. The registration is applied to neutral faces and facial expressions.
(ii) We release an open-source software framework for registration and
model-building, demonstrated on the publicly available BU3D-FE database. The
released pipeline also contains an implementation of an Analysis-by-Synthesis
model adaption of 2D face images, tested on the Multi-PIE and LFW database.
This enables the community to reproduce, evaluate and compare the individual
steps of registration to model-building and 3D/2D model fitting. (iii) Along
with the framework release, we publish a new version of the Basel Face Model
(BFM-2017) with an improved age distribution and an additional facial
expression model.
Polysemy Detection in Distributed Representation of Word Sense
Authors: Kana Oomoto, Haruka Oikawa, Eiko Yamamoto, Mitsuo Yoshida, Masayuki Okabe, Kyoji Umemura
Date: 2017-09-26 00:00:00
Tasks: Position

In this paper, we propose a statistical test to determine whether a given
word is used as a polysemic word or not. The statistic of the word in this test
roughly corresponds to the fluctuation in the senses of the neighboring words a
nd the word itself. Even though the sense of a word corresponds to a single
vector, we discuss how polysemy of the words affects the position of vectors.
Finally, we also explain the method to detect this effect.
Active Learning amidst Logical Knowledge
Authors: Emmanouil Antonios Platanios, Ashish Kapoor, Eric Horvitz
Date: 2017-09-26 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Structured Prediction

Structured prediction is ubiquitous in applications of machine learning such
as knowledge extraction and natural language processing. Structure often can be
formulated in terms of logical constraints. We consider the question of how to
perform efficient active learning in the presence of logical constraints among
variables inferred by different classifiers. We propose several methods and
provide theoretical results that demonstrate the inappropriateness of employing
uncertainty guided sampling, a commonly used active learning method.
Furthermore, experiments on ten different datasets demonstrate that the methods
significantly outperform alternatives in practice. The results are of practical
significance in situations where labeled data is scarce.
Learning a Predictive Model for Music Using PULSE
Authors: Jonas Langhabel
Date: 2017-09-26 00:00:00
Tasks: feature selection, model, Music Modeling

Predictive models for music are studied by researchers of algorithmic
composition, the cognitive sciences and machine learning. They serve as base
models for composition, can simulate human prediction and provide a
multidisciplinary application domain for learning algorithms. A particularly
well established and constantly advanced subtask is the prediction of
monophonic melodies. As melodies typically involve non-Markovian dependencies
their prediction requires a capable learning algorithm. In this thesis, I apply
the recent feature discovery and learning method PULSE to the realm of symbolic
music modeling. PULSE is comprised of a feature generating operation and
L1-regularized optimization. These are used to iteratively expand and cull the
feature set, effectively exploring feature spaces that are too large for common
feature selection approaches. I design a general Python framework for PULSE,
propose task-optimized feature generating operations and various
music-theoretically motivated features that are evaluated on a standard corpus
of monophonic folk and chorale melodies. The proposed method significantly
outperforms comparable state-of-the-art models. I further discuss the free
parameters of the learning algorithm and analyze the feature composition of the
learned models. The models learned by PULSE afford an easy inspection and are
musicologically interpreted for the first time.
An In-field Automatic Wheat Disease Diagnosis System
Authors: Jiang Lu, Jie Hu, Guannan Zhao, Fenghua Mei, Chang-Shui Zhang
Date: 2017-09-26 00:00:00
Tasks: Management, Multiple Instance Learning

Crop diseases are responsible for the major production reduction and economic
losses in agricultural industry world- wide. Monitoring for health status of
crops is critical to control the spread of diseases and implement effective
management. This paper presents an in-field automatic wheat disease diagnosis
system based on a weakly super- vised deep learning framework, i.e. deep
multiple instance learning, which achieves an integration of identification for
wheat diseases and localization for disease areas with only image-level
annotation for training images in wild conditions. Furthermore, a new in-field
image dataset for wheat disease, Wheat Disease Database 2017 (WDD2017), is
collected to verify the effectiveness of our system. Under two different
architectures, i.e. VGG-FCN-VD16 and VGG-FCN-S, our system achieves the mean
recognition accuracies of 97.95% and 95.12% respectively over 5-fold
cross-validation on WDD2017, exceeding the results of 93.27% and 73.00% by two
conventional CNN frameworks, i.e. VGG-CNN-VD16 and VGG-CNN-S. Experimental
results demonstrate that the proposed system outperforms conventional CNN
architectures on recognition accuracy under the same amount of parameters,
meanwhile main- taining accurate localization for corresponding disease areas.
Moreover, the proposed system has been packed into a real-time mobile app to
provide support for agricultural disease diagnosis.
UAV and Service Robot Coordination for Indoor Object Search Tasks
Authors: Sandeep Konam, Stephanie Rosenthal, Manuela Veloso
Date: 2017-09-26 00:00:00
Tasks: Object

Our CoBot robots have successfully performed a variety of service tasks in
our multi-building environment including accompanying people to meetings and
delivering objects to offices due to its navigation and localization
capabilities. However, they lack the capability to visually search over desks
and other confined locations for an object of interest. Conversely, an
inexpensive GPS-denied quadcopter platform such as the Parrot ARDrone 2.0 could
perform this object search task if it had access to reasonable localization. In
this paper, we propose the concept of coordination between CoBot and the Parrot
ARDrone 2.0 to perform service-based object search tasks, in which CoBot
localizes and navigates to the general search areas carrying the ARDrone and
the ARDrone searches locally for objects. We propose a vision-based moving
target navigation algorithm that enables the ARDrone to localize with respect
to CoBot, search for objects, and return to the CoBot for future searches. We
demonstrate our algorithm in indoor environments on several search
trajectories.