pwc-archive/papers-with-abstracts huggingface dataset:
Multi-player Multi-armed Bandits for Stable Allocation in Heterogeneous Ad-Hoc Networks
Authors: Sumit J. Darak, Manjesh K. Hanawal
Date: 2018-12-24 00:00:00
Tasks: channel selection, Multi-Armed Bandits

Next generation networks are expected to be ultradense and aim to explore spectrum sharing paradigm that allows users to communicate in licensed, shared as well as unlicensed spectrum. Such ultra-dense networks will incur significant signaling load at base stations leading to a negative effect on spectrum and energy efficiency. To minimize signaling overhead, an adhoc approach is being considered for users communicating in the unlicensed and shared spectrums. For such users, decisions need to be completely decentralized as: 1) No communication between users and signaling from the base station is possible which necessitates independent channel selection at each user. A collision occurs when multiple users transmit simultaneously on the same channel, 2) Channel qualities may be heterogeneous, i.e., they are not same across all users, and moreover, are unknown, and 3) The network could be dynamic where users can enter or leave anytime. We develop a multi-armed bandit based distributed algorithm for static networks and extend it for the dynamic networks. The algorithms aim to achieve stable orthogonal allocation (SOC) in finite time and meet the above three constraints with two novel characteristics: 1) Low complexity narrowband radio compared to wideband radio in existing works, and 2) Epoch-less approach for dynamic networks. We establish a convergence of our algorithms to SOC and validate via extensive simulation experiments.
AVRA: Automatic Visual Ratings of Atrophy from MRI images using Recurrent Convolutional Neural Networks
Authors: Gustav Mårtensson, Daniel Ferreira, Lena Cavallin, J-Sebastian Muehlboeck, Lars-Olof Wahlund, Chunliang Wang, Eric Westman
Date: 2018-12-23 00:00:00

Quantifying the degree of atrophy is done clinically by neuroradiologists
following established visual rating scales. For these assessments to be
reliable the rater requires substantial training and experience, and even then
the rating agreement between two radiologists is not perfect. We have developed
a model we call AVRA (Automatic Visual Ratings of Atrophy) based on machine
learning methods and trained on 2350 visual ratings made by an experienced
neuroradiologist. It provides fast and automatic ratings for Scheltens' scale
of medial temporal atrophy (MTA), the frontal subscale of Pasquier's Global
Cortical Atrophy (GCA-F) scale, and Koedam's scale of Posterior Atrophy (PA).
We demonstrate substantial inter-rater agreement between AVRA's and a
neuroradiologist ratings with Cohen's weighted kappa values of $\kappa_w$ =
0.74/0.72 (MTA left/right), $\kappa_w$ = 0.62 (GCA-F) and $\kappa_w$ = 0.74
(PA), with an inherent intra-rater agreement of $\kappa_w$ = 1. We conclude
that automatic visual ratings of atrophy can potentially have great clinical
and scientific value, and aim to present AVRA as a freely available toolbox.
Epipolar Geometry based Learning of Multi-view Depth and Ego-Motion from Monocular Sequences
Authors: Vignesh Prasad, Dipanjan Das, Brojeshwar Bhowmick
Date: 2018-12-23 00:00:00
Tasks: Visual Odometry

Deep approaches to predict monocular depth and ego-motion have grown in
recent years due to their ability to produce dense depth from monocular images.
The main idea behind them is to optimize the photometric consistency over image
sequences by warping one view into another, similar to direct visual odometry
methods. One major drawback is that these methods infer depth from a single
view, which might not effectively capture the relation between pixels.
Moreover, simply minimizing the photometric loss does not ensure proper pixel
correspondences, which is a key factor for accurate depth and pose estimations.
  In contrast, we propose a 2-view depth network to infer the scene depth from
consecutive frames, thereby learning inter-pixel relationships. To ensure
better correspondences, thereby better geometric understanding, we propose
incorporating epipolar constraints to make the learning more geometrically
sound. We use the Essential matrix obtained using Nist'er's Five Point
Algorithm, to enforce meaningful geometric constraints, rather than using it as
training labels. This allows us to use lesser no. of trainable parameters
compared to state-of-the-art methods. The proposed method results in better
depth images and pose estimates, which capture the scene structure and motion
in a better way. Such a geometrically constrained learning performs
successfully even in cases where simply minimizing the photometric error would
fail.
Whittemore: An embedded domain specific language for causal programming
Authors: Joshua Brulé
Date: 2018-12-21 00:00:00
Tasks: Causal Inference

This paper introduces Whittemore, a language for causal programming. Causal
programming is based on the theory of structural causal models and consists of
two primary operations: identification, which finds formulas that compute
causal queries, and estimation, which applies formulas to transform probability
distributions to other probability distribution. Causal programming provides
abstractions to declare models, queries, and distributions with syntax similar
to standard mathematical notation, and conducts rigorous causal inference,
without requiring detailed knowledge of the underlying algorithms. Examples of
causal inference with real data are provided, along with discussion of the
implementation and possibilities for future extension.
Instant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imaging
Authors: Youngjun Cho, Simon J. Julier, Nadia Bianchi-Berthouze
Date: 2018-12-21 00:00:00
Tasks: Clustering, Mental Stress Detection, Photoplethysmography (PPG), Physiological Computing

Background: A smartphone is a promising tool for daily cardiovascular
measurement and mental stress monitoring. A smartphone camera-based
PhotoPlethysmoGraphy (PPG) and a low-cost thermal camera can be used to create
cheap, convenient and mobile monitoring systems. However, to ensure reliable
monitoring results, a person has to remain still for several minutes while a
measurement is being taken. This is very cumbersome and makes its use in
real-life mobile situations quite impractical.
  Objective: We propose a system which combines PPG and thermography with the
aim of improving cardiovascular signal quality and capturing stress responses
quickly.
  Methods: Using a smartphone camera with a low cost thermal camera added on,
we built a novel system which continuously and reliably measures two different
types of cardiovascular events: i) blood volume pulse and ii)
vasoconstriction/dilation-induced temperature changes of the nose tip. 17
healthy participants, involved in a series of stress-inducing mental workload
tasks, measured their physiological responses to stressors over a short window
of time (20 seconds) immediately after each task. Participants reported their
level of perceived mental stress using a 10-cm Visual Analogue Scale (VAS). We
used normalized K-means clustering to reduce interpersonal differences in the
self-reported ratings. For the instant stress inference task, we built novel
low-level feature sets representing variability of cardiovascular patterns. We
then used the automatic feature learning capability of artificial Neural
Networks (NN) to improve the mapping between the extracted set of features and
the self-reported ratings. We compared our proposed method with existing
hand-engineered features-based machine learning methods.
  Results, Conclusions: ... due to limited space here, we refer to our
manuscript.
Sequential Gating Ensemble Network for Noise Robust Multi-Scale Face Restoration
Authors: Zhibo Chen, Jianxin Lin, Tiankuang Zhou, Feng Wu
Date: 2018-12-19 00:00:00
Tasks: Ensemble Learning, Image Restoration

Face restoration from low resolution and noise is important for applications
of face analysis recognition. However, most existing face restoration models
omit the multiple scale issues in face restoration problem, which is still not
well-solved in research area. In this paper, we propose a Sequential Gating
Ensemble Network (SGEN) for multi-scale noise robust face restoration issue. To
endow the network with multi-scale representation ability, we first employ the
principle of ensemble learning for SGEN network architecture designing. The
SGEN aggregates multi-level base-encoders and base-decoders into the network,
which enables the network to contain multiple scales of receptive field.
Instead of combining these base-en/decoders directly with non-sequential
operations, the SGEN takes base-en/decoders from different levels as sequential
data. Specifically, it is visualized that SGEN learns to sequentially extract
high level information from base-encoders in bottom-up manner and restore low
level information from base-decoders in top-down manner. Besides, we propose to
realize bottom-up and top-down information combination and selection with
Sequential Gating Unit (SGU). The SGU sequentially takes information from two
different levels as inputs and decides the output based on one active input.
Experiment results on benchmark dataset demonstrate that our SGEN is more
effective at multi-scale human face restoration with more image details and
less noise than state-of-the-art image restoration models. Further utilizing
adversarial training scheme, SGEN also produces more visually preferred results
than other models under subjective evaluation.
Automatic Summarization of Natural Language
Authors: Marc Everett Johnson
Date: 2018-12-18 00:00:00
Tasks: Abstractive Text Summarization, Extractive Summarization

Automatic summarization of natural language is a current topic in computer
science research and industry, studied for decades because of its usefulness
across multiple domains. For example, summarization is necessary to create
reviews such as this one. Research and applications have achieved some success
in extractive summarization (where key sentences are curated), however,
abstractive summarization (synthesis and re-stating) is a hard problem and
generally unsolved in computer science. This literature review contrasts
historical progress up through current state of the art, comparing dimensions
such as: extractive vs. abstractive, supervised vs. unsupervised, NLP (Natural
Language Processing) vs Knowledge-based, deep learning vs algorithms,
structured vs. unstructured sources, and measurement metrics such as Rouge and
BLEU. Multiple dimensions are contrasted since current research uses
combinations of approaches as seen in the review matrix. Throughout this
summary, synthesis and critique is provided. This review concludes with
insights for improved abstractive summarization measurement, with surprising
implications for detecting understanding and comprehension in general.
Using Machine Learning for Handover Optimization in Vehicular Fog Computing
Authors: Salman Memon, Muthucumaru Maheswaran
Date: 2018-12-18 00:00:00
Tasks: BIG-bench Machine Learning, Management

Smart mobility management would be an important prerequisite for future fog
computing systems. In this research, we propose a learning-based handover
optimization for the Internet of Vehicles that would assist the smooth
transition of device connections and offloaded tasks between fog nodes. To
accomplish this, we make use of machine learning algorithms to learn from
vehicle interactions with fog nodes. Our approach uses a three-layer
feed-forward neural network to predict the correct fog node at a given location
and time with 99.2 % accuracy on a test set. We also implement a dual stacked
recurrent neural network (RNN) with long short-term memory (LSTM) cells capable
of learning the latency, or cost, associated with these service requests. We
create a simulation in JAMScript using a dataset of real-world vehicle
movements to create a dataset to train these networks. We further propose the
use of this predictive system in a smarter request routing mechanism to
minimize the service interruption during handovers between fog nodes and to
anticipate areas of low coverage through a series of experiments and test the
models' performance on a test set.
Mask-aware networks for crowd counting
Authors: Shengqin Jiang, Xiaobo Lu, Yinjie Lei, Lingqiao Liu
Date: 2018-12-18 00:00:00
Tasks: Crowd Counting, Object, Prediction

Crowd counting problem aims to count the number of objects within an image or a frame in the videos and is usually solved by estimating the density map generated from the object location annotations. The values in the density map, by nature, take two possible states: zero indicating no object around, a non-zero value indicating the existence of objects and the value denoting the local object density. In contrast to traditional methods which do not differentiate the density prediction of these two states, we propose to use a dedicated network branch to predict the object/non-object mask and then combine its prediction with the input image to produce the density map. Our rationale is that the mask prediction could be better modeled as a binary segmentation problem and the difficulty of estimating the density could be reduced if the mask is known. A key to the proposed scheme is the strategy of incorporating the mask prediction into the density map estimator. To this end, we study five possible solutions, and via analysis and experimental validation we identify the most effective one. Through extensive experiments on five public datasets, we demonstrate the superior performance of the proposed approach over the baselines and show that our network could achieve the state-of-the-art performance.
TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training Set
Authors: Moran Rubin, Omer Stein, Nir A. Turko, Yoav Nygate, Darina Roitshtain, Lidor Karako, Itay Barnea, Raja Giryes, Natan T. Shaked
Date: 2018-12-17 00:00:00
Tasks: General Classification, Generative Adversarial Network, image-classification, Image Classification, Medical Image Classification, Transfer Learning

We propose a new deep learning approach for medical imaging that copes with
the problem of a small training set, the main bottleneck of deep learning, and
apply it for classification of healthy and cancer cells acquired by
quantitative phase imaging. The proposed method, called transferring of
pre-trained generative adversarial network (TOP-GAN), is a hybridization
between transfer learning and generative adversarial networks (GANs). Healthy
cells and cancer cells of different metastatic potential have been imaged by
low-coherence off-axis holography. After the acquisition, the optical path
delay maps of the cells have been extracted and directly used as an input to
the deep networks. In order to cope with the small number of classified images,
we have used GANs to train a large number of unclassified images from another
cell type (sperm cells). After this preliminary training, and after
transforming the last layer of the network with new ones, we have designed an
automatic classifier for the correct cell type (healthy/primary
cancer/metastatic cancer) with 90-99% accuracy, although small training sets of
down to several images have been used. These results are better in comparison
to other classic methods that aim at coping with the same problem of a small
training set. We believe that our approach makes the combination of holographic
microscopy and deep learning networks more accessible to the medical field by
enabling a rapid, automatic and accurate classification in stain-free imaging
flow cytometry. Furthermore, our approach is expected to be applicable to many
other medical image classification tasks, suffering from a small training set.
CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild
Authors: Yang Zhang, Hassan Foroosh, Philip David, Boqing Gong
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Adversarial Attack, Object

In this paper, we conduct an intriguing experimental study about the physical adversarial attack on object detectors in the wild. In particular, we learn a camouflage pattern to hide vehicles from being detected by state-of-the-art convolutional neural network based detectors. Our approach alternates between two threads. In the first, we train a neural approximation function to imitate how a simulator applies a camouflage to vehicles and how a vehicle detector performs given images of the camouflaged vehicles. In the second, we minimize the approximated detection score by searching for the optimal camouflage. Experiments show that the learned camouflage can not only hide a vehicle from the image-based detectors under many test cases but also generalizes to different environments, vehicles, and object detectors.
A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation
Authors: Yunyi Liang, Zhiyong Cui, Yu Tian, Huimiao Chen, Yinhai Wang
Date: 2018-01-05 00:00:00
Tasks: Missing Values, State Estimation

This study proposes a deep generative adversarial architecture (GAA) for
network-wide spatial-temporal traffic state estimation. The GAA is able to
combine traffic flow theory with neural networks and thus improve the accuracy
of traffic state estimation. It consists of two Long Short-Term Memory Neural
Networks (LSTM NNs) which capture correlation in time and space among traffic
flow and traffic density. One of the LSTM NNs, called a discriminative network,
aims to maximize the probability of assigning correct labels to both true
traffic state matrices (i.e., traffic flow and traffic density within a given
spatial-temporal area) and the traffic state matrices generated from the other
neural network. The other LSTM NN, called a generative network, aims to
generate traffic state matrices which maximize the probability that the
discriminative network assigns true labels to them. The two LSTM NNs are
trained simultaneously such that the trained generative network can generate
traffic matrices similar to those in the training data set. Given a traffic
state matrix with missing values, we use back-propagation on three defined loss
functions to map the corrupted matrix to a latent space. The mapping vector is
then passed through the pre-trained generative network to estimate the missing
values of the corrupted matrix. The proposed GAA is compared with the existing
Bayesian network approach on loop detector data collected from Seattle,
Washington and that collected from San Diego, California. Experimental results
indicate that the GAA can achieve higher accuracy in traffic state estimation
than the Bayesian network approach.
Learning an Inverse Tone Mapping Network with a Generative Adversarial Regularizer
Authors: Shiyu Ning, Hongteng Xu, Li Song, Rong Xie, Wenjun Zhang
Date: 2018-04-20 00:00:00
Tasks: inverse tone mapping, Inverse-Tone-Mapping, Tone Mapping

Transferring a low-dynamic-range (LDR) image to a high-dynamic-range (HDR)
image, which is the so-called inverse tone mapping (iTM), is an important
imaging technique to improve visual effects of imaging devices. In this paper,
we propose a novel deep learning-based iTM method, which learns an inverse tone
mapping network with a generative adversarial regularizer. In the framework of
alternating optimization, we learn a U-Net-based HDR image generator to
transfer input LDR images to HDR ones, and a simple CNN-based discriminator to
classify the real HDR images and the generated ones. Specifically, when
learning the generator we consider the content-related loss and the generative
adversarial regularizer jointly to improve the stability and the robustness of
the generated HDR images. Using the learned generator as the proposed inverse
tone mapping network, we achieve superior iTM results to the state-of-the-art
methods consistently.
GANs for generating EFT models
Authors: Harold Erbin, Sven Krippendorf
Date: 2018-09-06 00:00:00

We initiate a way of generating models by the computer, satisfying both
experimental and theoretical constraints. In particular, we present a framework
which allows the generation of effective field theories. We use Generative
Adversarial Networks to generate these models and we generate examples which go
beyond the examples known to the machine. As a starting point, we apply this
idea to the generation of supersymmetric field theories. In this case, the
machine knows consistent examples of supersymmetric field theories with a
single field and generates new examples of such theories. In the generated
potentials we find distinct properties, here the number of minima in the scalar
potential, with values not found in the training data. We comment on potential
further applications of this framework.
VelocityGAN: Data-Driven Full-Waveform Inversion Using Conditional Adversarial Networks
Authors: Zhongping Zhang, Yue Wu, Zheng Zhou, Youzuo Lin
Date: 2018-09-26 00:00:00

Acoustic- and elastic-waveform inversion is an important and widely used
method to reconstruct subsurface velocity image. Waveform inversion is a
typical non-linear and ill-posed inverse problem. Existing physics-driven
computational methods for solving waveform inversion suffer from the cycle
skipping and local minima issues, and not to mention solving waveform inversion
is computationally expensive. In this paper, we developed a real-time
data-driven technique, VelocityGAN, to accurately reconstruct subsurface
velocities. Our VelocityGAN is an end-to-end framework which can generate
high-quality velocity images directly from the raw seismic waveform data. A
series of numerical experiments are conducted on the synthetic seismic
reflection data to evaluate the effectiveness and efficiency of VelocityGAN. We
not only compare it with existing physics-driven approaches but also choose
some deep learning frameworks as our data-driven baselines. The experiment
results show that VelocityGAN outperforms the physics-driven waveform inversion
methods and achieves the state-of-the-art performance among data-driven
baselines.
Incorporating GAN for Negative Sampling in Knowledge Representation Learning
Authors: Peifeng Wang, Shuangyin Li, Rong pan
Date: 2018-09-23 00:00:00
Tasks: Link Prediction, Representation Learning

Knowledge representation learning aims at modeling knowledge graph by
encoding entities and relations into a low dimensional space. Most of the
traditional works for knowledge embedding need negative sampling to minimize a
margin-based ranking loss. However, those works construct negative samples
through a random mode, by which the samples are often too trivial to fit the
model efficiently. In this paper, we propose a novel knowledge representation
learning framework based on Generative Adversarial Networks (GAN). In this
GAN-based framework, we take advantage of a generator to obtain high-quality
negative samples. Meanwhile, the discriminator in GAN learns the embeddings of
the entities and relations in knowledge graph. Thus, we can incorporate the
proposed GAN-based framework into various traditional models to improve the
ability of knowledge representation learning. Experimental results show that
our proposed GAN-based framework outperforms baselines on triplets
classification and link prediction tasks.