pwc-archive/papers-with-abstracts huggingface dataset:
PromID: human promoter prediction by deep learning
Authors: Ramzan Umarov, Hiroyuki Kuwahara, Yu Li, Xin Gao, Victor Solovyev
Date: 2018-10-02 00:00:00
Tasks: Deep Learning, Prediction

Computational identification of promoters is notoriously difficult as human
genes often have unique promoter sequences that provide regulation of
transcription and interaction with transcription initiation complex. While
there are many attempts to develop computational promoter identification
methods, we have no reliable tool to analyze long genomic sequences. In this
work we further develop our deep learning approach that was relatively
successful to discriminate short promoter and non-promoter sequences. Instead
of focusing on the classification accuracy, in this work we predict the exact
positions of the TSS inside the genomic sequences testing every possible
location. We studied human promoters to find effective regions for
discrimination and built corresponding deep learning models. These models use
adaptively constructed negative set which iteratively improves the models
discriminative ability. The developed promoter identification models
significantly outperform the previously developed promoter prediction programs
by considerably reducing the number of false positive predictions. The best
model we have built has recall 0.76, precision 0.77 and MCC 0.76, while the
next best tool FPROM achieved precision 0.48 and MCC 0.60 for the recall of
0.75. Our method is available at http://www.cbrc.kaust.edu.sa/PromID/.
Efficient Fastest-Path Computations in Road Maps
Authors: Renjie Chen, Craig Gotsman
Date: 2018-10-02 00:00:00

In the age of real-time online traffic information and GPS-enabled devices,
fastest-path computations between two points in a road network modeled as a
directed graph, where each directed edge is weighted by a "travel time" value,
are becoming a standard feature of many navigation-related applications. To
support this, very efficient computation of these paths in very large road
networks is critical. Fastest paths may be computed as minimal-cost paths in a
weighted directed graph, but traditional minimal-cost path algorithms based on
variants of the classic Dijkstra algorithm do not scale well, as in the worst
case they may traverse the entire graph. A common improvement, which can
dramatically reduce the number of traversed graph vertices, is the A*
algorithm, which requires a good heuristic lower bound on the minimal cost. We
introduce a simple, but very effective, heuristic function based on a small
number of values assigned to each graph vertex. The values are based on graph
separators and computed efficiently in a preprocessing stage. We present
experimental results demonstrating that our heuristic provides estimates of the
minimal cost which are superior to those of other heuristics. Our experiments
show that when used in the A* algorithm, this heuristic can reduce the number
of vertices traversed by an order of magnitude compared to other heuristics.
Heuristic Optimization of Electrical Energy Systems: Refined Metrics to Compare the Solutions
Authors: Gianfranco Chicco, Andrea Mazza
Date: 2018-10-02 00:00:00

Many optimization problems admit a number of local optima, among which there
is the global optimum. For these problems, various heuristic optimization
methods have been proposed. Comparing the results of these solvers requires the
definition of suitable metrics. In the electrical energy systems literature,
simple metrics such as best value obtained, the mean value, the median or the
standard deviation of the solutions are still used. However, the comparisons
carried out with these metrics are rather weak, and on these bases a somehow
uncontrolled proliferation of heuristic solvers is taking place. This paper
addresses the overall issue of understanding the reasons of this proliferation,
showing a conceptual scheme that indicates how the assessment of the best
solver may result in the unlimited formulation of new solvers. Moreover, this
paper shows how the use of more refined metrics defined to compare the
optimization result, associated with the definition of appropriate benchmarks,
may make the comparisons among the solvers more robust. The proposed metrics
are based on the concept of first-order stochastic dominance and are defined
for the cases in which: (i) the globally optimal solution can be found (for
testing purposes); and (ii) the number of possible solutions is so large that
practically it cannot be guaranteed that the global optimum has been found.
Illustrative examples are provided for a typical problem in the electrical
energy systems area-distribution network reconfiguration. The conceptual
results obtained are generally valid to compare the results of other
optimization problems.
LIT: Block-wise Intermediate Representation Training for Model Compression
Authors: Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia
Date: 2018-10-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Knowledge Distillation, Model Compression

Knowledge distillation (KD) is a popular method for reducing the
computational overhead of deep network inference, in which the output of a
teacher model is used to train a smaller, faster student model. Hint training
(i.e., FitNets) extends KD by regressing a student model's intermediate
representation to a teacher model's intermediate representation. In this work,
we introduce bLock-wise Intermediate representation Training (LIT), a novel
model compression technique that extends the use of intermediate
representations in deep network compression, outperforming KD and hint
training. LIT has two key ideas: 1) LIT trains a student of the same width (but
shallower depth) as the teacher by directly comparing the intermediate
representations, and 2) LIT uses the intermediate representation from the
previous block in the teacher model as an input to the current student block
during training, avoiding unstable intermediate representations in the student
network. We show that LIT provides substantial reductions in network depth
without loss in accuracy -- for example, LIT can compress a ResNeXt-110 to a
ResNeXt-20 (5.5x) on CIFAR10 and a VDCNN-29 to a VDCNN-9 (3.2x) on Amazon
Reviews without loss in accuracy, outperforming KD and hint training in network
size for a given accuracy. We also show that applying LIT to identical
student/teacher architectures increases the accuracy of the student model above
the teacher model, outperforming the recently-proposed Born Again Networks
procedure on ResNet, ResNeXt, and VDCNN. Finally, we show that LIT can
effectively compress GAN generators, which are not supported in the KD
framework because GANs output pixels as opposed to probabilities.
Predicting Factuality of Reporting and Bias of News Media Sources
Authors: Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, Preslav Nakov
Date: 2018-10-02 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Articles, Fact Checking

We present a study on predicting the factuality of reporting and bias of news
media. While previous work has focused on studying the veracity of claims or
documents, here we are interested in characterizing entire news media. These
are under-studied but arguably important research problems, both in their own
right and as a prior for fact-checking systems. We experiment with a large list
of news websites and with a rich set of features derived from (i) a sample of
articles from the target news medium, (ii) its Wikipedia page, (iii) its
Twitter account, (iv) the structure of its URL, and (v) information about the
Web traffic it attracts. The experimental results show sizable performance
gains over the baselines, and confirm the importance of each feature type.
Multi-directional Geodesic Neural Networks via Equivariant Convolution
Authors: Adrien Poulenard, Maks Ovsjanikov
Date: 2018-10-01 00:00:00

We propose a novel approach for performing convolution of signals on curved
surfaces and show its utility in a variety of geometric deep learning
applications. Key to our construction is the notion of directional functions
defined on the surface, which extend the classic real-valued signals and which
can be naturally convolved with with real-valued template functions. As a
result, rather than trying to fix a canonical orientation or only keeping the
maximal response across all alignments of a 2D template at every point of the
surface, as done in previous works, we show how information across all
rotations can be kept across different layers of the neural network. Our
construction, which we call multi-directional geodesic convolution, or
directional convolution for short, allows, in particular, to propagate and
relate directional information across layers and thus different regions on the
shape. We first define directional convolution in the continuous setting, prove
its key properties and then show how it can be implemented in practice, for
shapes represented as triangle meshes. We evaluate directional convolution in a
wide variety of learning scenarios ranging from classification of signals on
surfaces, to shape segmentation and shape matching, where we show a significant
improvement over several baselines.
Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico
Authors: B. Yuan, Y. J. Tan, M. K. Mudunuru, O. E. Marcillo, A. A. Delorey, P. M. Roberts, J. D. Webster, C. N. L. Gammans, S. Karra, G. D. Guthrie, P. A. Johnson
Date: 2018-10-01 00:00:00

We present an approach based on machine learning (ML) to distinguish eruption
and precursory signals of Chimay\'{o} geyser (New Mexico, USA) under noisy
environments. This geyser can be considered as a natural analog of
$\mathrm{CO}_2$ intrusion into shallow water aquifers. By studying this geyser,
we can understand upwelling of $\mathrm{CO}_2$-rich fluids from depth, which
has relevance to leak monitoring in a $\mathrm{CO}_2$ sequestration project. ML
methods such as Random Forests (RF) are known to be robust multi-class
classifiers and perform well under unfavorable noisy conditions. However, the
extent of the RF method's accuracy is poorly understood for this
$\mathrm{CO}_2$-driven geysering application. The current study aims to
quantify the performance of RF-classifiers to discern the geyser state. Towards
this goal, we first present the data collected from the seismometer that is
installed near the Chimay\'{o} geyser. The seismic signals collected at this
site contain different types of noises such as daily temperature variations,
seasonal trends, animal movement near the geyser, and human activity. First, we
filter the signals from these noises by combining the Butterworth-Highpass
filter and an Autoregressive method in a multi-level fashion. We show that by
combining these filtering techniques, in a hierarchical fashion, leads to
reduction in the noise in the seismic data without removing the precursors and
eruption event signals. We then use RF on the filtered data to classify the
state of geyser into three classes -- remnant noise, precursor, and eruption
states. We show that the classification accuracy using RF on the filtered data
is greater than 90\%.These aspects make the proposed ML framework attractive
for event discrimination and signal enhancement under noisy conditions, with
strong potential for application to monitoring leaks in $\mathrm{CO}_2$
sequestration.
PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data
Authors: Patrick Schwab, Walter Karlen
Date: 2018-10-01 00:00:00

Parkinson's disease is a neurodegenerative disease that can affect a person's
movement, speech, dexterity, and cognition. Clinicians primarily diagnose
Parkinson's disease by performing a clinical assessment of symptoms. However,
misdiagnoses are common. One factor that contributes to misdiagnoses is that
the symptoms of Parkinson's disease may not be prominent at the time the
clinical assessment is performed. Here, we present a machine-learning approach
towards distinguishing between people with and without Parkinson's disease
using long-term data from smartphone-based walking, voice, tapping and memory
tests. We demonstrate that our attentive deep-learning models achieve
significant improvements in predictive performance over strong baselines (area
under the receiver operating characteristic curve = 0.85) in data from a cohort
of 1853 participants. We also show that our models identify meaningful features
in the input data. Our results confirm that smartphone data collected over
extended periods of time could in the future potentially be used as a digital
biomarker for the diagnosis of Parkinson's disease.
A Multi-Face Challenging Dataset for Robust Face Recognition
Authors: Shiv Ram Dubey, Snehasis Mukherjee
Date: 2018-09-30 00:00:00
Tasks: Face Recognition, Robust Face Recognition

Face recognition in images is an active area of interest among the computer
vision researchers. However, recognizing human face in an unconstrained
environment, is a relatively less-explored area of research. Multiple face
recognition in unconstrained environment is a challenging task, due to the
variation of view-point, scale, pose, illumination and expression of the face
images. Partial occlusion of faces makes the recognition task even more
challenging. The contribution of this paper is two-folds: introducing a
challenging multiface dataset (i.e., IIITS MFace Dataset) for face recognition
in unconstrained environment and evaluating the performance of state-of-the-art
hand-designed and deep learning based face descriptors on the dataset. The
proposed IIITS MFace dataset contains faces with challenges like pose
variation, occlusion, mask, spectacle, expressions, change of illumination,
etc. We experiment with several state-of-the-art face descriptors, including
recent deep learning based face descriptors like VGGFace, and compare with the
existing benchmark face datasets. Results of the experiments clearly show that
the difficulty level of the proposed dataset is much higher compared to the
benchmark datasets.
Robust PCA via Outlier Pursuit
Authors: Huan Xu, Constantine Caramanis, Sujay Sanghavi
Date: 2010-10-20 00:00:00
Proceeding: NeurIPS 2010 12
Tasks: Collaborative Filtering, Dimensionality Reduction, Matrix Completion

Singular Value Decomposition (and Principal Component Analysis) is one of the
most widely used techniques for dimensionality reduction: successful and
efficiently computable, it is nevertheless plagued by a well-known,
well-documented sensitivity to outliers. Recent work has considered the setting
where each point has a few arbitrarily corrupted components. Yet, in
applications of SVD or PCA such as robust collaborative filtering or
bioinformatics, malicious agents, defective genes, or simply corrupted or
contaminated experiments may effectively yield entire points that are
completely corrupted.
  We present an efficient convex optimization-based algorithm we call Outlier
Pursuit, that under some mild assumptions on the uncorrupted points (satisfied,
e.g., by the standard generative assumption in PCA problems) recovers the exact
optimal low-dimensional subspace, and identifies the corrupted points. Such
identification of corrupted points that do not conform to the low-dimensional
approximation, is of paramount interest in bioinformatics and financial
applications, and beyond. Our techniques involve matrix decomposition using
nuclear norm minimization, however, our results, setup, and approach,
necessarily differ considerably from the existing line of work in matrix
completion and matrix decomposition, since we develop an approach to recover
the correct column space of the uncorrupted matrix, rather than the exact
matrix itself. In any problem where one seeks to recover a structure rather
than the exact initial matrices, techniques developed thus far relying on
certificates of optimality, will fail. We present an important extension of
these methods, that allows the treatment of such problems.
Finite size effects for spiking neural networks with spatially dependent coupling
Authors: Siwei Qiu, Carson Chow
Date: 2018-05-09 00:00:00

We study finite-size fluctuations in a network of spiking deterministic
neurons coupled with non-uniform synaptic coupling. We generalize a previously
developed theory of finite size effects for uniform globally coupled neurons.
In the uniform case, mean field theory is well defined by averaging over the
network as the number of neurons in the network goes to infinity. However, for
nonuniform coupling it is no longer possible to average over the entire network
if we are interested in fluctuations at a particular location within the
network. We show that if the coupling function approaches a continuous function
in the infinite system size limit then an average over a local neighborhood can
be defined such that mean field theory is well defined for a spatially
dependent field. We then derive a perturbation expansion in the inverse system
size around the mean field limit for the covariance of the input to a neuron
(synaptic drive) and firing rate fluctuations due to dynamical deterministic
finite-size effects.
AlphaSeq: Sequence Discovery with Deep Reinforcement Learning
Authors: Yulin Shao, Soung Chang Liew, Taotao Wang
Date: 2018-09-26 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Sequences play an important role in many applications and systems. Discovering sequences with desired properties has long been an interesting intellectual pursuit. This paper puts forth a new paradigm, AlphaSeq, to discover desired sequences algorithmically using deep reinforcement learning (DRL) techniques. AlphaSeq treats the sequence discovery problem as an episodic symbol-filling game, in which a player fills symbols in the vacant positions of a sequence set sequentially during an episode of the game. Each episode ends with a completely-filled sequence set, upon which a reward is given based on the desirability of the sequence set. AlphaSeq models the game as a Markov Decision Process (MDP), and adapts the DRL framework of AlphaGo to solve the MDP. Sequences discovered improve progressively as AlphaSeq, starting as a novice, learns to become an expert game player through many episodes of game playing. Compared with traditional sequence construction by mathematical tools, AlphaSeq is particularly suitable for problems with complex objectives intractable to mathematical analysis. We demonstrate the searching capabilities of AlphaSeq in two applications: 1) AlphaSeq successfully rediscovers a set of ideal complementary codes that can zero-force all potential interferences in multi-carrier CDMA systems. 2) AlphaSeq discovers new sequences that triple the signal-to-interference ratio -- benchmarked against the well-known Legendre sequence -- of a mismatched filter estimator in pulse compression radar systems.
Learning to Interpret Satellite Images Using Wikipedia
Authors: Evan Sheehan, Burak Uzkent, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon
Date: 2018-09-19 00:00:00
Tasks: Articles

Despite recent progress in computer vision, fine-grained interpretation of
satellite images remains challenging because of a lack of labeled training
data. To overcome this limitation, we propose using Wikipedia as a previously
untapped source of rich, georeferenced textual information with global
coverage. We construct a novel large-scale, multi-modal dataset by pairing
geo-referenced Wikipedia articles with satellite imagery of their corresponding
locations. To prove the efficacy of this dataset, we focus on the African
continent and train a deep network to classify images based on labels extracted
from articles. We then fine-tune the model on a human annotated dataset and
demonstrate that this weak form of supervision can drastically reduce the
quantity of human annotated labels and time required for downstream tasks.
Caulking the Leakage Effect in MEEG Source Connectivity Analysis
Authors: Deirel Paz-Linares, Eduardo Gonzalez-Moreira, Eduardo Martinez-Montes, Pedro A. Valdes-Hernandez, Jorge Bosch-Bayard, Maria Luisa Bringas-Vega, Pedro A. Valdes-Sosa
Date: 2018-09-28 00:00:00
Tasks: Connectivity Estimation, Time Series Analysis

Simplistic estimation of neural connectivity in MEEG sensor space is
impossible due to volume conduction. The only viable alternative is to carry
out connectivity estimation in source space. Among the neuroscience community
this is claimed to be impossible or misleading due to Leakage: linear mixing of
the reconstructed sources. To address this problematic we propose a novel
solution method that caulks the Leakage in MEEG source activity and
connectivity estimates: BC-VARETA. It is based on a joint estimation of source
activity and connectivity in the frequency domain representation of MEEG time
series. To achieve this, we go beyond current methods that assume a fixed
gaussian graphical model for source connectivity. In contrast we estimate this
graphical model in a Bayesian framework by placing priors on it, which allows
for highly optimized computations of the connectivity, via a new procedure
based on the local quadratic approximation under quite general prior models. A
further contribution of this paper is the rigorous definition of leakage via
the Spatial Dispersion Measure and Earth Movers Distance based on the geodesic
distances over the cortical manifold. Both measures are extended for the first
time to quantify Connectivity Leakage by defining them on the cartesian product
of cortical manifolds. Using these measures, we show that BC-VARETA outperforms
most state of the art inverse solvers by several orders of magnitude.
Weakly Supervised Object Detection in Artworks
Authors: Nicolas Gonthier, Yann Gousseau, Said Ladjal, Olivier Bonfait
Date: 2018-10-05 00:00:00
Proceeding: ECCV 2018 Workshop Computer Vision for Art Analysis - VISART 2018 2018 10
Tasks: Multiple Instance Learning, Object, object-detection, Object Detection, Weakly Supervised Object Detection

We propose a method for the weakly supervised detection of objects in
paintings. At training time, only image-level annotations are needed. This,
combined with the efficiency of our multiple-instance learning method, enables
one to learn new classes on-the-fly from globally annotated databases, avoiding
the tedious task of manually marking objects. We show on several databases that
dropping the instance-level annotations only yields mild performance losses. We
also introduce a new database, IconArt, on which we perform detection
experiments on classes that could not be learned on photographs, such as Jesus
Child or Saint Sebastian. To the best of our knowledge, these are the first
experiments dealing with the automatic (and in our case weakly supervised)
detection of iconographic elements in paintings. We believe that such a method
is of great benefit for helping art historians to explore large digital
databases.