pwc-archive/papers-with-abstracts huggingface dataset:
A High-Performance HOG Extractor on FPGA
Authors: Vinh Ngo, Arnau Casadevall, Marc Codina, David Castells-Rufas, Jordi Carrabina
Date: 2018-01-12 00:00:00
Tasks: Pedestrian Detection, Vocal Bursts Intensity Prediction

Pedestrian detection is one of the key problems in emerging self-driving car
industry. And HOG algorithm has proven to provide good accuracy for pedestrian
detection. There are plenty of research works have been done in accelerating
HOG algorithm on FPGA because of its low-power and high-throughput
characteristics. In this paper, we present a high-performance HOG architecture
for pedestrian detection on a low-cost FPGA platform. It achieves a maximum
throughput of 526 FPS with 640x480 input images, which is 3.25 times faster
than the state of the art design. The accelerator is integrated with SVM-based
prediction in realizing a pedestrian detection system. And the power
consumption of the whole system is comparable with the best existing
implementations.
A Computer Vision Pipeline for Automated Determination of Cardiac Structure and Function and Detection of Disease by Two-Dimensional Echocardiography
Authors: Jeffrey Zhang, Sravani Gajjala, Pulkit Agrawal, Geoffrey H. Tison, Laura A. Hallock, Lauren Beussink-Nelson, Eugene Fan, Mandar A. Aras, ChaRandle Jordan, Kirsten E. Fleischmann, Michelle Melisko, Atif Qasim, Alexei Efros, Sanjiv. J. Shah, Ruzena Bajcsy, Rahul C. Deo
Date: 2017-06-22 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Automated cardiac image interpretation has the potential to transform
clinical practice in multiple ways including enabling low-cost serial
assessment of cardiac function in the primary care and rural setting. We
hypothesized that advances in computer vision could enable building a fully
automated, scalable analysis pipeline for echocardiogram (echo) interpretation.
Our approach entailed: 1) preprocessing; 2) convolutional neural networks (CNN)
for view identification, image segmentation, and phasing of the cardiac cycle;
3) quantification of chamber volumes and left ventricular mass; 4) particle
tracking to compute longitudinal strain; and 5) targeted disease detection.
CNNs accurately identified views (e.g. 99% for apical 4-chamber) and segmented
individual cardiac chambers. Cardiac structure measurements agreed with study
report values (e.g. mean absolute deviations (MAD) of 7.7 mL/kg/m2 for left
ventricular diastolic volume index, 2918 studies). We computed automated
ejection fraction and longitudinal strain measurements (within 2 cohorts),
which agreed with commercial software-derived values [for ejection fraction,
MAD=5.3%, N=3101 studies; for strain, MAD=1.5% (n=197) and 1.6% (n=110)], and
demonstrated applicability to serial monitoring of breast cancer patients for
trastuzumab cardiotoxicity. Overall, we found that, compared to manual
measurements, automated measurements had superior performance across seven
internal consistency metrics with an average increase in the Spearman
correlation coefficient of 0.05 (p=0.02). Finally, we developed disease
detection algorithms for hypertrophic cardiomyopathy and cardiac amyloidosis,
with C-statistics of 0.93 and 0.84, respectively. Our pipeline lays the
groundwork for using automated interpretation to support point-of-care handheld
cardiac ultrasound and large-scale analysis of the millions of echos archived
within healthcare systems.
Toward Metric Indexes for Incremental Insertion and Querying
Authors: Edward Raff, Charles Nicholas
Date: 2018-01-12 00:00:00
Tasks: Malware Analysis

In this work we explore the use of metric index structures, which accelerate
nearest neighbor queries, in the scenario where we need to interleave
insertions and queries during deployment. This use-case is inspired by a
real-life need in malware analysis triage, and is surprisingly understudied.
Existing literature tends to either focus on only final query efficiency, often
does not support incremental insertion, or does not support arbitrary distance
metrics. We modify and improve three algorithms to support our scenario of
incremental insertion and querying with arbitrary metrics, and evaluate them on
multiple datasets and distance metrics while varying the value of $k$ for the
desired number of nearest neighbors. In doing so we determine that our improved
Vantage-Point tree of Minimum-Variance performs best for this scenario.
A Model of Multi-Agent Consensus for Vague and Uncertain Beliefs
Authors: Michael Crosscombe, Jonathan Lawry
Date: 2016-12-11 00:00:00

Consensus formation is investigated for multi-agent systems in which agents'
beliefs are both vague and uncertain. Vagueness is represented by a third truth
state meaning \emph{borderline}. This is combined with a probabilistic model of
uncertainty. A belief combination operator is then proposed which exploits
borderline truth values to enable agents with conflicting beliefs to reach a
compromise. A number of simulation experiments are carried out in which agents
apply this operator in pairwise interactions, under the bounded confidence
restriction that the two agents' beliefs must be sufficiently consistent with
each other before agreement can be reached. As well as studying the consensus
operator in isolation we also investigate scenarios in which agents are
influenced either directly or indirectly by the state of the world. For the
former we conduct simulations which combine consensus formation with belief
updating based on evidence. For the latter we investigate the effect of
assuming that the closer an agent's beliefs are to the truth the more visible
they are in the consensus building process. In all cases applying the consensus
operators results in the population converging to a single shared belief which
is both crisp and certain. Furthermore, simulations which combine consensus
formation with evidential updating converge faster to a shared opinion which is
closer to the actual state of the world than those in which beliefs are only
changed as a result of directly receiving new evidence. Finally, if agent
interactions are guided by belief quality measured as similarity to the true
state of the world, then applying the consensus operator alone results in the
population converging to a high quality shared belief.
Multinomial logistic model for coinfection diagnosis between arbovirus and malaria in Kedougou
Authors: Mor Absa Loum, Marie-Anne Poursat, Abdourahmane Sow, Amadou Sall, Cheikh Loucoubar, Elisabeth Gassiat
Date: 2018-01-12 00:00:00

In tropical regions, populations continue to suffer morbidity and mortality from malaria and arboviral diseases. In Kedougou (Senegal), these illnesses are all endemic due to the climate and its geographical position. The co-circulation of malaria parasites and arboviruses can explain the observation of coinfected cases. Indeed there is strong resemblance in symptoms between these diseases making problematic targeted medical care of coinfected cases. This is due to the fact that the origin of illness is not obviously known. Some cases could be immunized against one or the other of the pathogens, immunity typically acquired with factors like age and exposure as usual for endemic area. Then, coinfection needs to be better diagnosed. Using data collected from patients in Kedougou region, from 2009 to 2013, we adjusted a multinomial logistic model and selected relevant variables in explaining coinfection status. We observed specific sets of variables explaining each of the diseases exclusively and the coinfection. We tested the independence between arboviral and malaria infections and derived coinfection probabilities from the model fitting. In case of a coinfection probability greater than a threshold value to be calibrated on the data, duration of illness above 3 days and age above 10 years-old are mostly indicative of arboviral disease while body temperature higher than 40{\textdegree}C and presence of nausea or vomiting symptoms during the rainy season are mostly indicative of malaria disease.
Towards Arbitrary Noise Augmentation - Deep Learning for Sampling from Arbitrary Probability Distributions
Authors: Felix Horger, Tobias Würfl, Vincent Christlein, Andreas Maier
Date: 2018-01-12 00:00:00

Accurate noise modelling is important for training of deep learning
reconstruction algorithms. While noise models are well known for traditional
imaging techniques, the noise distribution of a novel sensor may be difficult
to determine a priori. Therefore, we propose learning arbitrary noise
distributions. To do so, this paper proposes a fully connected neural network
model to map samples from a uniform distribution to samples of any explicitly
known probability density function. During the training, the Jensen-Shannon
divergence between the distribution of the model's output and the target
distribution is minimized. We experimentally demonstrate that our model
converges towards the desired state. It provides an alternative to existing
sampling methods such as inversion sampling, rejection sampling, Gaussian
mixture models and Markov-Chain-Monte-Carlo. Our model has high sampling
efficiency and is easily applied to any probability distribution, without the
need of further analytical or numerical calculations.
HiNet: Hierarchical Classification with Neural Network
Authors: Zhenzhou Wu, Sean Saito
Date: 2017-05-31 00:00:00
Tasks: Classification, General Classification

Traditionally, classifying large hierarchical labels with more than 10000
distinct traces can only be achieved with flatten labels. Although flatten
labels is feasible, it misses the hierarchical information in the labels.
Hierarchical models like HSVM by \cite{vural2004hierarchical} becomes
impossible to train because of the sheer number of SVMs in the whole
architecture. We developed a hierarchical architecture based on neural networks
that is simple to train. Also, we derived an inference algorithm that can
efficiently infer the MAP (maximum a posteriori) trace guaranteed by our
theorems. Furthermore, the complexity of the model is only $O(n^2)$ compared to
$O(n^h)$ in a flatten model, where $h$ is the height of the hierarchy.
MSDNN: Multi-Scale Deep Neural Network for Salient Object Detection
Authors: Fen Xiao, Wenzheng Deng, Liangchan Peng, Chunhong Cao, Kai Hu, Xieping Gao
Date: 2018-01-12 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Salient object detection is a fundamental problem and has been received a
great deal of attentions in computer vision. Recently deep learning model
became a powerful tool for image feature extraction. In this paper, we propose
a multi-scale deep neural network (MSDNN) for salient object detection. The
proposed model first extracts global high-level features and context
information over the whole source image with recurrent convolutional neural
network (RCNN). Then several stacked deconvolutional layers are adopted to get
the multi-scale feature representation and obtain a series of saliency maps.
Finally, we investigate a fusion convolution module (FCM) to build a final
pixel level saliency map. The proposed model is extensively evaluated on four
salient object detection benchmark datasets. Results show that our deep model
significantly outperforms other 12 state-of-the-art approaches.
Working hard to know your neighbor's margins: Local descriptor learning loss
Authors: Anastasiya Mishchuk, Dmytro Mishkin, Filip Radenovic, Jiri Matas
Date: 2017-05-30 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: GPU, Image Retrieval, Patch Matching, Retrieval

We introduce a novel loss for learning local feature descriptors which is
inspired by the Lowe's matching criterion for SIFT. We show that the proposed
loss that maximizes the distance between the closest positive and closest
negative patch in the batch is better than complex regularization methods; it
works well for both shallow and deep convolution network architectures.
Applying the novel loss to the L2Net CNN architecture results in a compact
descriptor -- it has the same dimensionality as SIFT (128) that shows
state-of-art performance in wide baseline stereo, patch verification and
instance retrieval benchmarks. It is fast, computing a descriptor takes about 1
millisecond on a low-end GPU.
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput Computing
Authors: A. Gomez Ramirez, C. Lara, L. Betev, D. Bilanovic, U. Kebschull, for the ALICE Collaboration
Date: 2018-01-12 00:00:00
Tasks: Intrusion Detection, Malware Detection

Grid computing systems require innovative methods and tools to identify
cybersecurity incidents and perform autonomous actions i.e. without
administrator intervention. They also require methods to isolate and trace job
payload activity in order to protect users and find evidence of malicious
behavior. We introduce an integrated approach of security monitoring via
Security by Isolation with Linux Containers and Deep Learning methods for the
analysis of real time data in Grid jobs running inside virtualized
High-Throughput Computing infrastructure in order to detect and prevent
intrusions. A dataset for malware detection in Grid computing is described. We
show in addition the utilization of generative methods with Recurrent Neural
Networks to improve the collected dataset. We present Arhuaco, a prototype
implementation of the proposed methods. We empirically study the performance of
our technique. The results show that Arhuaco outperforms other methods used in
Intrusion Detection Systems for Grid Computing. The study is carried out in the
ALICE Collaboration Grid, part of the Worldwide LHC Computing Grid.
Stochastic Maximum Likelihood Optimization via Hypernetworks
Authors: Abdul-Saboor Sheikh, Kashif Rasul, Andreas Merentitis, Urs Bergmann
Date: 2017-12-04 00:00:00
Tasks: Classification, General Classification, regression

This work explores maximum likelihood optimization of neural networks through
hypernetworks. A hypernetwork initializes the weights of another network, which
in turn can be employed for typical functional tasks such as regression and
classification. We optimize hypernetworks to directly maximize the conditional
likelihood of target variables given input. Using this approach we obtain
competitive empirical results on regression and classification benchmarks.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Authors: Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter
Date: 2017-06-26 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Emotion Recognition in Conversation, Image Generation, Video Generation

Generative Adversarial Networks (GANs) excel at creating realistic images
with complex models for which maximum likelihood is infeasible. However, the
convergence of GAN training has still not been proved. We propose a two
time-scale update rule (TTUR) for training GANs with stochastic gradient
descent on arbitrary GAN loss functions. TTUR has an individual learning rate
for both the discriminator and the generator. Using the theory of stochastic
approximation, we prove that the TTUR converges under mild assumptions to a
stationary local Nash equilibrium. The convergence carries over to the popular
Adam optimization, for which we prove that it follows the dynamics of a heavy
ball with friction and thus prefers flat minima in the objective landscape. For
the evaluation of the performance of GANs at image generation, we introduce the
"Fr\'echet Inception Distance" (FID) which captures the similarity of generated
images to real ones better than the Inception Score. In experiments, TTUR
improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP)
outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN
Bedrooms, and the One Billion Word Benchmark.
Weakly Supervised One-Shot Detection with Attention Similarity Networks
Authors: Gil Keren, Maximilian Schmitt, Thomas Kehrenberg, Björn Schuller
Date: 2018-01-10 00:00:00
Tasks: One-Shot Learning, Transfer Learning

Neural network models that are not conditioned on class identities were shown
to facilitate knowledge transfer between classes and to be well-suited for
one-shot learning tasks. Following this motivation, we further explore and
establish such models and present a novel neural network architecture for the
task of weakly supervised one-shot detection. Our model is only conditioned on
a single exemplar of an unseen class and a larger target example that may or
may not contain an instance of the same class as the exemplar. By pairing a
Siamese similarity network with an attention mechanism, we design a model that
manages to simultaneously identify and localise instances of classes unseen at
training time. In experiments with datasets from the computer vision and audio
domains, the proposed method considerably outperforms the baseline methods for
the weakly supervised one-shot detection task.
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
Authors: Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, Christian Wachinger
Date: 2018-01-12 00:00:00
Tasks: Brain Segmentation, Decision Making, Segmentation, Translation

Whole brain segmentation from structural magnetic resonance imaging (MRI) is
a prerequisite for most morphological analyses, but is computationally intense
and can therefore delay the availability of image markers after scan
acquisition. We introduce QuickNAT, a fully convolutional, densely connected
neural network that segments a \revision{MRI brain scan} in 20 seconds. To
enable training of the complex network with millions of learnable parameters
using limited annotated data, we propose to first pre-train on auxiliary labels
created from existing segmentation software. Subsequently, the pre-trained
model is fine-tuned on manual labels to rectify errors in auxiliary labels.
With this learning strategy, we are able to use large neuroimaging repositories
without manual annotations for training. In an extensive set of evaluations on
eight datasets that cover a wide age range, pathology, and different scanners,
we demonstrate that QuickNAT achieves superior segmentation accuracy and
reliability in comparison to state-of-the-art methods, while being orders of
magnitude faster. The speed up facilitates processing of large data
repositories and supports translation of imaging biomarkers by making them
available within seconds for fast clinical decision making.
Can Who-Edits-What Predict Edit Survival?
Authors: Ali Batuhan Yardım, Victor Kristof, Lucas Maystre, Matthias Grossglauser
Date: 2018-01-12 00:00:00

As the number of contributors to online peer-production systems grows, it
becomes increasingly important to predict whether the edits that users make
will eventually be beneficial to the project. Existing solutions either rely on
a user reputation system or consist of a highly specialized predictor that is
tailored to a specific peer-production system. In this work, we explore a
different point in the solution space that goes beyond user reputation but does
not involve any content-based feature of the edits. We view each edit as a game
between the editor and the component of the project. We posit that the
probability that an edit is accepted is a function of the editor's skill, of
the difficulty of editing the component and of a user-component interaction
term. Our model is broadly applicable, as it only requires observing data about
who makes an edit, what the edit affects and whether the edit survives or not.
We apply our model on Wikipedia and the Linux kernel, two examples of
large-scale peer-production systems, and we seek to understand whether it can
effectively predict edit survival: in both cases, we provide a positive answer.
Our approach significantly outperforms those based solely on user reputation
and bridges the gap with specialized predictors that use content-based
features. It is simple to implement, computationally inexpensive, and in
addition it enables us to discover interesting structure in the data.
AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience
Authors: Feng-Lin Li, Minghui Qiu, Haiqing Chen, Xiongwei Wang, Xing Gao, Jun Huang, Juwei Ren, Zhongzhou Zhao, Weipeng Zhao, Lei Wang, Guwei Jin, Wei Chu
Date: 2018-01-12 00:00:00
Tasks: Question Answering

We present AliMe Assist, an intelligent assistant designed for creating an
innovative online shopping experience in E-commerce. Based on question
answering (QA), AliMe Assist offers assistance service, customer service, and
chatting service. It is able to take voice and text input, incorporate context
to QA, and support multi-round interaction. Currently, it serves millions of
customer questions per day and is able to address 85% of them. In this paper,
we demonstrate the system, present the underlying techniques, and share our
experience in dealing with real-world QA in the E-commerce field.
Invariant Synthesis for Incomplete Verification Engines
Authors: Daniel Neider, Pranav Garg, P. Madhusudan, Shambwaditya Saha, Daejun Park
Date: 2017-12-15 00:00:00

We propose a framework for synthesizing inductive invariants for incomplete
verification engines, which soundly reduce logical problems in undecidable
theories to decidable theories. Our framework is based on the counter-example
guided inductive synthesis principle (CEGIS) and allows verification engines to
communicate non-provability information to guide invariant synthesis. We show
precisely how the verification engine can compute such non-provability
information and how to build effective learning algorithms when invariants are
expressed as Boolean combinations of a fixed set of predicates. Moreover, we
evaluate our framework in two verification settings, one in which verification
engines need to handle quantified formulas and one in which verification
engines have to reason about heap properties expressed in an expressive but
undecidable separation logic. Our experiments show that our invariant synthesis
framework based on non-provability information can both effectively synthesize
inductive invariants and adequately strengthen contracts across a large suite
of programs.
Cosmic String Detection with Tree-Based Machine Learning
Authors: A. Vafaei Sadr, M. Farhang, S. M. S. Movahed, B. Bassett, M. Kunz
Date: 2018-01-12 00:00:00
Tasks: BIG-bench Machine Learning

We explore the use of random forest and gradient boosting, two powerful
tree-based machine learning algorithms, for the detection of cosmic strings in
maps of the cosmic microwave background (CMB), through their unique
Gott-Kaiser-Stebbins effect on the temperature anisotropies.The information in
the maps is compressed into feature vectors before being passed to the learning
units. The feature vectors contain various statistical measures of processed
CMB maps that boost the cosmic string detectability. Our proposed classifiers,
after training, give results improved over or similar to the claimed
detectability levels of the existing methods for string tension, $G\mu$. They
can make $3\sigma$ detection of strings with $G\mu \gtrsim 2.1\times 10^{-10}$
for noise-free, $0.9'$-resolution CMB observations. The minimum detectable
tension increases to $G\mu \gtrsim 3.0\times 10^{-8}$ for a more realistic, CMB
S4-like (II) strategy, still a significant improvement over the previous
results.