pwc-archive/papers-with-abstracts huggingface dataset:
Anticipating epileptic seizures through the analysis of EEG synchronization as a data classification problem
Authors: Paolo Detti, Garazi Zabalo Manrique de Lara, Renato Bruni, Marco Pranzo, Francesco Sarnari
Date: 2018-01-24 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification

Epilepsy is a neurological disorder arising from anomalies of the electrical
activity in the brain, affecting about 0.5--0.8\% of the world population.
Several studies investigated the relationship between seizures and brainwave
synchronization patterns, pursuing the possibility of identifying interictal,
preictal, ictal and postictal states. In this work, we introduce a graph-based
model of the brain interactions developed to study synchronization patterns in
the electroencephalogram (EEG) signals. The aim is to develop a
patient-specific approach, also for a real-time use, for the prediction of
epileptic seizures' occurrences. Different synchronization measures of the EEG
signals and easily computable functions able to capture in real-time the
variations of EEG synchronization have been considered. Both standard and
ad-hoc classification algorithms have been developed and used. Results on scalp
EEG signals show that this simple and computationally viable processing is able
to highlight the changes in the synchronization corresponding to the preictal
state.
Mask R-CNN
Authors: Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick
Date: 2017-03-20 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Instance Segmentation, Human Part Segmentation, Instance Segmentation, Keypoint Detection, Keypoint Estimation, Multi-Human Parsing, Multi-Person Pose Estimation, Multi-tissue Nucleus Segmentation, Nuclear Segmentation, Object, Object Detection, Object Localization, Object Segmentation, Panoptic Segmentation, Pose Estimation, Real-Time Object Detection, Segmentation, Semantic Segmentation

We present a conceptually simple, flexible, and general framework for object
instance segmentation. Our approach efficiently detects objects in an image
while simultaneously generating a high-quality segmentation mask for each
instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a
branch for predicting an object mask in parallel with the existing branch for
bounding box recognition. Mask R-CNN is simple to train and adds only a small
overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to
generalize to other tasks, e.g., allowing us to estimate human poses in the
same framework. We show top results in all three tracks of the COCO suite of
challenges, including instance segmentation, bounding-box object detection, and
person keypoint detection. Without bells and whistles, Mask R-CNN outperforms
all existing, single-model entries on every task, including the COCO 2016
challenge winners. We hope our simple and effective approach will serve as a
solid baseline and help ease future research in instance-level recognition.
Code has been made available at: https://github.com/facebookresearch/Detectron
Opinion Dynamics with Varying Susceptibility to Persuasion
Authors: Rediet Abebe, Jon Kleinberg, David Parkes, Charalampos E. Tsourakakis
Date: 2018-01-24 00:00:00

A long line of work in social psychology has studied variations in people's
susceptibility to persuasion -- the extent to which they are willing to modify
their opinions on a topic. This body of literature suggests an interesting
perspective on theoretical models of opinion formation by interacting parties
in a network: in addition to considering interventions that directly modify
people's intrinsic opinions, it is also natural to consider interventions that
modify people's susceptibility to persuasion. In this work, we adopt a popular
model for social opinion dynamics, and we formalize the opinion maximization
and minimization problems where interventions happen at the level of
susceptibility.
  We show that modeling interventions at the level of susceptibility lead to an
interesting family of new questions in network opinion dynamics. We find that
the questions are quite different depending on whether there is an overall
budget constraining the number of agents we can target or not. We give a
polynomial-time algorithm for finding the optimal target-set to optimize the
sum of opinions when there are no budget constraints on the size of the
target-set. We show that this problem is NP-hard when there is a budget, and
that the objective function is neither submodular nor supermodular. Finally, we
propose a heuristic for the budgeted opinion optimization and show its efficacy
at finding target-sets that optimize the sum of opinions compared on real world
networks, including a Twitter network with real opinion estimates.
Improving Review Representations with User Attention and Product Attention for Sentiment Classification
Authors: Zhen Wu, Xin-yu Dai, Cunyan Yin, Shu-Jian Huang, Jia-Jun Chen
Date: 2018-01-24 00:00:00
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

Neural network methods have achieved great success in reviews sentiment
classification. Recently, some works achieved improvement by incorporating user
and product information to generate a review representation. However, in
reviews, we observe that some words or sentences show strong user's preference,
and some others tend to indicate product's characteristic. The two kinds of
information play different roles in determining the sentiment label of a
review. Therefore, it is not reasonable to encode user and product information
together into one representation. In this paper, we propose a novel framework
to encode user and product information. Firstly, we apply two individual
hierarchical neural networks to generate two representations, with user
attention or with product attention. Then, we design a combined strategy to
make full use of the two representations for training and final prediction. The
experimental results show that our model obviously outperforms other
state-of-the-art methods on IMDB and Yelp datasets. Through the visualization
of attention over words related to user or product, we validate our observation
mentioned above.
Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering
Authors: Zhe Wang, Xiaoyi Liu, Liangjian Chen, Li-Min Wang, Yu Qiao, Xiaohui Xie, Charless Fowlkes
Date: 2018-01-24 00:00:00
Tasks: Multiple-choice, POS, Question Answering, TAG, Triplet, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering (VQA) is of significant interest due to its
potential to be a strong test of image understanding systems and to probe the
connection between language and vision. Despite much recent progress, general
VQA is far from a solved problem. In this paper, we focus on the VQA
multiple-choice task, and provide some good practices for designing an
effective VQA model that can capture language-vision interactions and perform
joint reasoning. We explore mechanisms of incorporating part-of-speech (POS)
tag guided attention, convolutional n-grams, triplet attention interactions
between the image, question and candidate answer, and structured learning for
triplets based on image-question pairs. We evaluate our models on two popular
datasets: Visual7W and VQA Real Multiple Choice. Our final model achieves the
state-of-the-art performance of 68.2% on Visual7W, and a very competitive
performance of 69.6% on the test-standard split of VQA Real Multiple Choice.
Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks
Authors: Sepidehsadat Hosseini, Seok Hee Lee, Nam Ik Cho
Date: 2018-01-24 00:00:00
Tasks: Emotion Recognition, Face Detection

Since the convolutional neural network (CNN) is be- lieved to find right
features for a given problem, the study of hand-crafted features is somewhat
neglected these days. In this paper, we show that finding an appropriate
feature for the given problem may be still important as they can en- hance the
performance of CNN-based algorithms. Specif- ically, we show that feeding an
appropriate feature to the CNN enhances its performance in some face related
works such as age/gender estimation, face detection and emotion recognition. We
use Gabor filter bank responses for these tasks, feeding them to the CNN along
with the input image. The stack of image and Gabor responses can be fed to the
CNN as a tensor input, or as a fused image which is a weighted sum of image and
Gabor responses. The Gabor filter parameters can also be tuned depending on the
given problem, for increasing the performance. From the extensive experiments,
it is shown that the proposed methods provide better performance than the
conventional CNN-based methods that use only the input images.
Learning a Dilated Residual Network for SAR Image Despeckling
Authors: Qiang Zhang, Qiangqiang Yuan, Jie Li, Zhen Yang, Xiaoshuang Ma
Date: 2017-09-09 00:00:00
Tasks: Sar Image Despeckling

In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated convolutions, which can both enlarge the receptive field and
maintain the filter size and layer depth with a lightweight structure. In
addition, skip connections and residual learning strategy are added to the
despeckling model to maintain the image details and reduce the vanishing
gradient problem. Compared with the traditional despeckling methods, the
proposed method shows superior performance over the state-of-the-art methods on
both quantitative and visual assessments, especially for strong speckle noise.
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data
Authors: Hugh Chen, Scott Lundberg, Su-In Lee
Date: 2018-01-23 00:00:00
Tasks: Representation Learning, Time Series, Time Series Analysis

Time series data constitutes a distinct and growing problem in machine
learning. As the corpus of time series data grows larger, deep models that
simultaneously learn features and classify with these features can be
intractable or suboptimal. In this paper, we present feature learning via long
short term memory (LSTM) networks and prediction via gradient boosting trees
(XGB). Focusing on the consequential setting of electronic health record data,
we predict the occurrence of hypoxemia five minutes into the future based on
past features. We make two observations: 1) long short term memory networks are
effective at capturing long term dependencies based on a single feature and 2)
gradient boosting trees are capable of tractably combining a large number of
features including static features like height and weight. With these
observations in mind, we generate features by performing "supervised"
representation learning with LSTM networks. Augmenting the original XGB model
with these features gives significantly better performance than either
individual method.
Non-ergodic Complexity of Convex Proximal Inertial Gradient Descents
Authors: Tao Sun, Linbo Qiao, Dongsheng Li
Date: 2018-01-23 00:00:00

The proximal inertial gradient descent is efficient for the composite minimization and applicable for broad of machine learning problems. In this paper, we revisit the computational complexity of this algorithm and present other novel results, especially on the convergence rates of the objective function values. The non-ergodic O(1/k) rate is proved for proximal inertial gradient descent with constant stepzise when the objective function is coercive. When the objective function fails to promise coercivity, we prove the sublinear rate with diminishing inertial parameters. In the case that the objective function satisfies optimal strong convexity condition (which is much weaker than the strong convexity), the linear convergence is proved with much larger and general stepsize than previous literature. We also extend our results to the multi-block version and present the computational complexity. Both cyclic and stochastic index selection strategies are considered.
Accelerating Deep Learning with Memcomputing
Authors: Haik Manukian, Fabio L. Traversa, Massimiliano Di Ventra
Date: 2018-01-01 00:00:00
Tasks: Deep Learning

Restricted Boltzmann machines (RBMs) and their extensions, called
'deep-belief networks', are powerful neural networks that have found
applications in the fields of machine learning and artificial intelligence. The
standard way to training these models resorts to an iterative unsupervised
procedure based on Gibbs sampling, called 'contrastive divergence' (CD), and
additional supervised tuning via back-propagation. However, this procedure has
been shown not to follow any gradient and can lead to suboptimal solutions. In
this paper, we show an efficient alternative to CD by means of simulations of
digital memcomputing machines (DMMs). We test our approach on pattern
recognition using a modified version of the MNIST data set. DMMs sample
effectively the vast phase space given by the model distribution of the RBM,
and provide a very good approximation close to the optimum. This efficient
search significantly reduces the number of pretraining iterations necessary to
achieve a given level of accuracy, as well as a total performance gain over CD.
In fact, the acceleration of pretraining achieved by simulating DMMs is
comparable to, in number of iterations, the recently reported hardware
application of the quantum annealing method on the same network and data set.
Notably, however, DMMs perform far better than the reported quantum annealing
results in terms of quality of the training. We also compare our method to
advances in supervised training, like batch-normalization and rectifiers, that
work to reduce the advantage of pretraining. We find that the memcomputing
method still maintains a quality advantage ($>1\%$ in accuracy, and a $20\%$
reduction in error rate) over these approaches. Furthermore, our method is
agnostic about the connectivity of the network. Therefore, it can be extended
to train full Boltzmann machines, and even deep networks at once.
Dynamic Graph CNN for Learning on Point Clouds
Authors: Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon
Date: 2018-01-24 00:00:00
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, 3D Semantic Segmentation, Few-Shot 3D Point Cloud Classification, Point Cloud Classification, Point Cloud Segmentation, Semantic Segmentation, Supervised Only 3D Point Cloud Classification

Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks including ModelNet40, ShapeNetPart, and S3DIS.
3D Scanning: A Comprehensive Survey
Authors: Morteza Daneshmand, Ahmed Helmi, Egils Avots, Fatemeh Noroozi, Fatih Alisinanoglu, Hasan Sait Arslan, Jelena Gorbova, Rain Eric Haamer, Cagri Ozcinar, Gholamreza Anbarjafari
Date: 2018-01-24 00:00:00
Tasks: Outlier Detection, Survey

This paper provides an overview of 3D scanning methodologies and technologies
proposed in the existing scientific and industrial literature. Throughout the
paper, various types of the related techniques are reviewed, which consist,
mainly, of close-range, aerial, structure-from-motion and terrestrial
photogrammetry, and mobile, terrestrial and airborne laser scanning, as well as
time-of-flight, structured-light and phase-comparison methods, along with
comparative and combinational studies, the latter being intended to help make a
clearer distinction on the relevance and reliability of the possible choices.
Moreover, outlier detection and surface fitting procedures are discussed
concisely, which are necessary post-processing stages.
Algorithmic Bio-surveillance For Precise Spatio-temporal Prediction of Zoonotic Emergence
Authors: Jaideep Dhanoa, Balaji Manicassamy, Ishanu Chattopadhyay
Date: 2018-01-23 00:00:00

Viral zoonoses have emerged as the key drivers of recent pandemics. Human
infection by zoonotic viruses are either spillover events -- isolated
infections that fail to cause a widespread contagion -- or species jumps, where
successful adaptation to the new host leads to a pandemic. Despite expensive
bio-surveillance efforts, historically emergence response has been reactive,
and post-hoc. Here we use machine inference to demonstrate a high accuracy
predictive bio-surveillance capability, designed to pro-actively localize an
impending species jump via automated interrogation of massive sequence
databases of viral proteins. Our results suggest that a jump might not purely
be the result of an isolated unfortunate cross-infection localized in space and
time; there are subtle yet detectable patterns of genotypic changes
accumulating in the global viral population leading up to emergence. Using tens
of thousands of protein sequences simultaneously, we train models that track
maximum achievable accuracy for disambiguating host tropism from the primary
structure of surface proteins, and show that the inverse classification
accuracy is a quantitative indicator of jump risk. We validate our claim in the
context of the 2009 swine flu outbreak, and the 2004 emergence of H5N1
subspecies of Influenza A from avian reservoirs; illustrating that
interrogation of the global viral population can unambiguously track a near
monotonic risk elevation over several preceding years leading to eventual
emergence.
Vietnamese Open Information Extraction
Authors: Diem Truong, Duc-Thuan Vo, U. T Nguyen
Date: 2018-01-23 00:00:00
Tasks: Dependency Parsing, Open Information Extraction, Sentence

Open information extraction (OIE) is the process to extract relations and
their arguments automatically from textual documents without the need to
restrict the search to predefined relations. In recent years, several OIE
systems for the English language have been created but there is not any system
for the Vietnamese language. In this paper, we propose a method of OIE for
Vietnamese using a clause-based approach. Accordingly, we exploit Vietnamese
dependency parsing using grammar clauses that strives to consider all possible
relations in a sentence. The corresponding clause types are identified by their
propositions as extractable relations based on their grammatical functions of
constituents. As a result, our system is the first OIE system named vnOIE for
the Vietnamese language that can generate open relations and their arguments
from Vietnamese text with highly scalable extraction while being domain
independent. Experimental results show that our OIE system achieves promising
results with a precision of 83.71%.
Multiple scan data association by convex variational inference
Authors: Jason L. Williams, Roslyn A. Lau
Date: 2016-07-27 00:00:00
Tasks: Variational Inference

Data association, the reasoning over correspondence between targets and
measurements, is a problem of fundamental importance in target tracking.
Recently, belief propagation (BP) has emerged as a promising method for
estimating the marginal probabilities of measurement to target association,
providing fast, accurate estimates. The excellent performance of BP in the
particular formulation used may be attributed to the convexity of the
underlying free energy which it implicitly optimises. This paper studies
multiple scan data association problems, i.e., problems that reason over
correspondence between targets and several sets of measurements, which may
correspond to different sensors or different time steps. We find that the
multiple scan extension of the single scan BP formulation is non-convex and
demonstrate the undesirable behaviour that can result. A convex free energy is
constructed using the recently proposed fractional free energy (FFE). A
convergent, BP-like algorithm is provided for the single scan FFE, and employed
in optimising the multiple scan free energy using primal-dual coordinate
ascent. Finally, based on a variational interpretation of joint probabilistic
data association (JPDA), we develop a sequential variant of the algorithm that
is similar to JPDA, but retains consistency constraints from prior scans. The
performance of the proposed methods is demonstrated on a bearings only target
localisation problem.
The WiLI benchmark dataset for written language identification
Authors: Martin Thoma
Date: 2018-01-23 00:00:00
Tasks: General Classification, Language Identification

This paper describes the WiLI-2018 benchmark dataset for monolingual written
natural language identification. WiLI-2018 is a publicly available, free of
charge dataset of short text extracts from Wikipedia. It contains 1000
paragraphs of 235 languages, totaling in 23500 paragraphs. WiLI is a
classification dataset: Given an unknown paragraph written in one dominant
language, it has to be decided which language it is.
Multi-label Pixelwise Classification for Reconstruction of Large-scale Urban Areas
Authors: Yuanlie He, Sudhir Mudur, Charalambos Poullis
Date: 2017-09-21 00:00:00
Tasks: Classification, General Classification, Object

Object classification is one of the many holy grails in computer vision and
as such has resulted in a very large number of algorithms being proposed
already. Specifically in recent years there has been considerable progress in
this area primarily due to the increased efficiency and accessibility of deep
learning techniques. In fact, for single-label object classification [i.e. only
one object present in the image] the state-of-the-art techniques employ deep
neural networks and are reporting very close to human-like performance. There
are specialized applications in which single-label object-level classification
will not suffice; for example in cases where the image contains multiple
intertwined objects of different labels.
  In this paper, we address the complex problem of multi-label pixelwise
classification. We present our distinct solution based on a convolutional
neural network (CNN) for performing multi-label pixelwise classification and
its application to large-scale urban reconstruction. A supervised learning
approach is followed for training a 13-layer CNN using both LiDAR and satellite
images. An empirical study has been conducted to determine the hyperparameters
which result in the optimal performance of the CNN. Scale invariance is
introduced by training the network on five different scales of the input and
labeled data. This results in six pixelwise classifications for each different
scale. An SVM is then trained to map the six pixelwise classifications into a
single-label. Lastly, we refine boundary pixel labels using graph-cuts for
maximum a-posteriori (MAP) estimation with Markov Random Field (MRF) priors.
The resulting pixelwise classification is then used to accurately extract and
reconstruct the buildings in large-scale urban areas. The proposed approach has
been extensively tested and the results are reported.