pwc-archive/papers-with-abstracts huggingface dataset:
Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings
Authors: Tiger W. Lin, Anup Das, Giri P. Krishnan, Maxim Bazhenov, Terrence J. Sejnowski
Date: 2017-06-08 00:00:00
Tasks: Connectivity Estimation, Functional Connectivity

With our ability to record more neurons simultaneously, making sense of these
data is a challenge. Functional connectivity is one popular way to study the
relationship between multiple neural signals. Correlation-based methods are a
set of currently well-used techniques for functional connectivity estimation.
However, due to explaining away and unobserved common inputs (Stevenson et al.,
2008), they produce spurious connections. The general linear model (GLM), which
models spikes trains as Poisson processes (Okatan et al., 2005; Truccolo et
al., 2005; Pillow et al., 2008), avoids these confounds. We develop here a new
class of methods by using differential signals based on simulated intracellular
voltage recordings. It is equivalent to a regularized AR(2) model. We also
expand the method to simulated local field potential (LFP) recordings and
calcium imaging. In all of our simulated data, the differential
covariance-based methods achieved better or similar performance to the GLM
method and required fewer data samples. This new class of methods provides
alternative ways to analyze neural signals.
FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-gram Language Model
Authors: Deniz Yuret
Date: 2012-05-24 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Sentence, Text Simplification, Translation, Word Sense Disambiguation

Lexical substitutes have found use in areas such as paraphrasing, text
simplification, machine translation, word sense disambiguation, and part of
speech induction. However the computational complexity of accurately
identifying the most likely substitutes for a word has made large scale
experiments difficult. In this paper I introduce a new search algorithm,
FASTSUBS, that is guaranteed to find the K most likely lexical substitutes for
a given word in a sentence based on an n-gram language model. The computation
is sub-linear in both K and the vocabulary size V. An implementation of the
algorithm and a dataset with the top 100 substitutes of each token in the WSJ
section of the Penn Treebank are available at http://goo.gl/jzKH0.
DSNet for Real-Time Driving Scene Semantic Segmentation
Authors: Wenfu Wang, Zhijie Pan
Date: 2018-12-06 00:00:00
Tasks: Autonomous Driving, Decision Making, Segmentation, Semantic Segmentation

We focus on the very challenging task of semantic segmentation for autonomous driving system. It must deliver decent semantic segmentation result for traffic critical objects real-time. In this paper, we propose a very efficient yet powerful deep neural network for driving scene semantic segmentation termed as Driving Segmentation Network (DSNet). DSNet achieves state-of-the-art balance between accuracy and inference speed through efficient units and architecture design inspired by ShuffleNet V2 and ENet. More importantly, DSNet highlights classes most critical with driving decision making through our novel Driving Importance-weighted Loss. We evaluate DSNet on Cityscapes dataset, our DSNet achieves 71.8% mean Intersection-over-Union (IoU) on validation set and 69.3% on test set. Class-wise IoU scores show that Driving Importance-weighted Loss could improve most driving critical classes by a large margin. Compared with ENet, DSNet is 18.9% more accurate and 1.1+ times faster which implies great potential for autonomous driving application.
Speaker Adaptation for End-to-End CTC Models
Authors: Ke Li, Jinyu Li, Yong Zhao, Kshitiz Kumar, Yifan Gong
Date: 2019-01-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Multi-Task Learning, speech-recognition, Speech Recognition

We propose two approaches for speaker adaptation in end-to-end (E2E)
automatic speech recognition systems. One is Kullback-Leibler divergence (KLD)
regularization and the other is multi-task learning (MTL). Both approaches aim
to address the data sparsity especially output target sparsity issue of speaker
adaptation in E2E systems. The KLD regularization adapts a model by forcing the
output distribution from the adapted model to be close to the unadapted one.
The MTL utilizes a jointly trained auxiliary task to improve the performance of
the main task. We investigated our approaches on E2E connectionist temporal
classification (CTC) models with three different types of output units.
Experiments on the Microsoft short message dictation task demonstrated that MTL
outperforms KLD regularization. In particular, the MTL adaptation obtained
8.8\% and 4.0\% relative word error rate reductions (WERRs) for supervised and
unsupervised adaptations for the word CTC model, and 9.6% and 3.8% relative
WERRs for the mix-unit CTC model, respectively.
A Distance Map Regularized CNN for Cardiac Cine MR Image Segmentation
Authors: Shusil Dangi, Cristian Linte, Ziv Yaniv
Date: 2019-01-04 00:00:00
Tasks: Decoder, Image Segmentation, Multi-Task Learning, Segmentation, Semantic Segmentation

Cardiac image segmentation is a critical process for generating personalized
models of the heart and for quantifying cardiac performance parameters. Several
convolutional neural network (CNN) architectures have been proposed to segment
the heart chambers from cardiac cine MR images. Here we propose a multi-task
learning (MTL)-based regularization framework for cardiac MR image
segmentation. The network is trained to perform the main task of semantic
segmentation, along with a simultaneous, auxiliary task of pixel-wise distance
map regression. The proposed distance map regularizer is a decoder network
added to the bottleneck layer of an existing CNN architecture, facilitating the
network to learn robust global features. The regularizer block is removed after
training, so that the original number of network parameters does not change. We
show that the proposed regularization method improves both binary and
multi-class segmentation performance over the corresponding state-of-the-art
CNN architectures on two publicly available cardiac cine MRI datasets,
obtaining average dice coefficient of 0.84$\pm$0.03 and 0.91$\pm$0.04,
respectively. Furthermore, we also demonstrate improved generalization
performance of the distance map regularized network on cross-dataset
segmentation, showing as much as 42% improvement in myocardium Dice coefficient
from 0.56$\pm$0.28 to 0.80$\pm$0.14.
Solving Markov Decision Processes with Reachability Characterization from Mean First Passage Times
Authors: Shoubhik Debnath, Lantao Liu, Gaurav Sukhatme
Date: 2019-01-04 00:00:00
Tasks: Decision Making

A new mechanism for efficiently solving the Markov decision processes (MDPs)
is proposed in this paper. We introduce the notion of reachability landscape
where we use the Mean First Passage Time (MFPT) as a means to characterize the
reachability of every state in the state space. We show that such reachability
characterization very well assesses the importance of states and thus provides
a natural basis for effectively prioritizing states and approximating policies.
Built on such a novel observation, we design two new algorithms -- Mean First
Passage Time based Value Iteration (MFPT-VI) and Mean First Passage Time based
Policy Iteration (MFPT-PI) -- that have been modified from the state-of-the-art
solution methods. To validate our design, we have performed numerical
evaluations in robotic decision-making scenarios, by comparing the proposed new
methods with corresponding classic baseline mechanisms. The evaluation results
showed that MFPT-VI and MFPT-PI have outperformed the state-of-the-art
solutions in terms of both practical runtime and number of iterations. Aside
from the advantage of fast convergence, this new solution method is intuitively
easy to understand and practically simple to implement.
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study
Authors: Xurong Li, Shouling Ji, Meng Han, Juntao Ji, Zhenyu Ren, Yushan Liu, Chunming Wu
Date: 2019-01-04 00:00:00
Tasks: General Classification, image-classification, Image Classification, Pornography Detection, Semantic Segmentation

Deep learning has been broadly leveraged by major cloud providers, such as Google, AWS and Baidu, to offer various computer vision related services including image classification, object identification, illegal image detection, etc. While recent works extensively demonstrated that deep learning classification models are vulnerable to adversarial examples, cloud-based image detection models, which are more complicated than classifiers, may also have similar security concern but not get enough attention yet. In this paper, we mainly focus on the security issues of real-world cloud-based image detectors. Specifically, (1) based on effective semantic segmentation, we propose four attacks to generate semantics-aware adversarial examples via only interacting with black-box APIs; and (2) we make the first attempt to conduct an extensive empirical study of black-box attacks against real-world cloud-based image detectors. Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100%, and the semantic segmentation based attacks have a success rate over 90% among different detection services, such as violence, politician, and pornography detection. We also proposed several possible defense strategies for these security challenges in the real-life situation.
Fully Convolutional Deep Network Architectures for Automatic Short Glass Fiber Semantic Segmentation from CT scans
Authors: Tomasz Konopczyński, Danish Rathore, Jitendra Rathore, Thorben Kröger, Lei Zheng, Christoph S. Garbe, Simone Carmignato, Jürgen Hesser
Date: 2019-01-04 00:00:00
Tasks: Semantic Segmentation

We present the first attempt to perform short glass fiber semantic
segmentation from X-ray computed tomography volumetric datasets at medium (3.9
{\mu}m isotropic) and low (8.3 {\mu}m isotropic) resolution using deep learning
architectures. We performed experiments on both synthetic and real CT scans and
evaluated deep fully convolutional architectures with both 2D and 3D kernels.
Our artificial neural networks outperform existing methods at both medium and
low resolution scans.
Reference Setup for Quantitative Comparison of Segmentation Techniques for Short Glass Fiber CT Data
Authors: Tomasz Konopczyński, Jitendra Rathore, Thorben Kröger, Lei Zheng, Christoph S. Garbe, Simone Carmignato, Jürgen Hesser
Date: 2019-01-04 00:00:00
Tasks: Computed Tomography (CT)

Comparing different algorithms for segmenting glass fibers in industrial
computed tomography (CT) scans is difficult due to the absence of a standard
reference dataset. In this work, we introduce a set of annotated scans of
short-fiber reinforced polymers (SFRP) as well as synthetically created CT
volume data together with the evaluation metrics. We suggest both the metrics
and this data set as a reference for studying the performance of different
algorithms. The real scans were acquired by a Nikon MCT225 X-ray CT system. The
simulated scans were created by the use of an in-house computational model and
third-party commercial software. For both types of data, corresponding ground
truth annotations have been prepared, including hand annotations for the real
scans and STL models for the synthetic scans. Additionally, a Hessian-based
Frangi vesselness filter for fiber segmentation has been implemented and
open-sourced to serve as a reference for comparisons.
Learning Sound Event Classifiers from Web Audio with Noisy Labels
Authors: Eduardo Fonseca, Manoj Plakal, Daniel P. W. Ellis, Frederic Font, Xavier Favory, Xavier Serra
Date: 2019-01-04 00:00:00
Tasks: General Classification, Sound Event Detection

As sound event classification moves towards larger datasets, issues of label
noise become inevitable. Web sites can supply large volumes of user-contributed
audio and metadata, but inferring labels from this metadata introduces errors
due to unreliable inputs, and limitations in the mapping. There is, however,
little research into the impact of these errors. To foster the investigation of
label noise in sound event classification we present FSDnoisy18k, a dataset
containing 42.5 hours of audio across 20 sound classes, including a small
amount of manually-labeled data and a larger quantity of real-world noisy data.
We characterize the label noise empirically, and provide a CNN baseline system.
Experiments suggest that training with large amounts of noisy data can
outperform training with smaller amounts of carefully-labeled data. We also
show that noise-robust loss functions can be effective in improving performance
in presence of corrupted labels.
Aspect Category Detection via Topic-Attention Network
Authors: Sajad Movahedi, Erfan Ghadery, Heshaam Faili, Azadeh Shakery
Date: 2019-01-04 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Category Detection, Sentence, Sentiment Analysis

The e-commerce has started a new trend in natural language processing through sentiment analysis of user-generated reviews. Different consumers have different concerns about various aspects of a specific product or service. Aspect category detection, as a subtask of aspect-based sentiment analysis, tackles the problem of categorizing a given review sentence into a set of pre-defined aspect categories. In recent years, deep learning approaches have brought revolutionary advances in multiple branches of natural language processing including sentiment analysis. In this paper, we propose a deep neural network method based on attention mechanism to identify different aspect categories of a given review sentence. Our model utilizes several attentions with different topic contexts, enabling it to attend to different parts of a review sentence based on different topics. Experimental results on two datasets in the restaurant domain released by SemEval workshop demonstrates that our approach outperforms existing methods on both datasets. Visualization of the topic attention weights shows the effectiveness of our model in identifying words related to different topics.
Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video
Authors: Xiaohui Huang, Pan He, Anand Rangarajan, Sanjay Ranka
Date: 2019-01-04 00:00:00
Tasks: Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking

In Intelligent Transportation System, real-time systems that monitor and
analyze road users become increasingly critical as we march toward the smart
city era. Vision-based frameworks for Object Detection, Multiple Object
Tracking, and Traffic Near Accident Detection are important applications of
Intelligent Transportation System, particularly in video surveillance and etc.
Although deep neural networks have recently achieved great success in many
computer vision tasks, a uniformed framework for all the three tasks is still
challenging where the challenges multiply from demand for real-time
performance, complex urban setting, highly dynamic traffic event, and many
traffic movements. In this paper, we propose a two-stream Convolutional Network
architecture that performs real-time detection, tracking, and near accident
detection of road users in traffic video data. The two-stream model consists of
a spatial stream network for Object Detection and a temporal stream network to
leverage motion features for Multiple Object Tracking. We detect near accidents
by incorporating appearance features and motion features from two-stream
networks. Using aerial videos, we propose a Traffic Near Accident Dataset
(TNAD) covering various types of traffic interactions that is suitable for
vision-based traffic analysis tasks. Our experiments demonstrate the advantage
of our framework with an overall competitive qualitative and quantitative
performance at high frame rates on the TNAD dataset.
Adaptive Density Estimation for Generative Models
Authors: Thomas Lucas, Konstantin Shmelkov, Karteek Alahari, Cordelia Schmid, Jakob Verbeek
Date: 2019-01-04 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Decoder, Density Estimation

Unsupervised learning of generative models has seen tremendous progress over recent years, in particular due to generative adversarial networks (GANs), variational autoencoders, and flow-based models. GANs have dramatically improved sample quality, but suffer from two drawbacks: (i) they mode-drop, i.e., do not cover the full support of the train data, and (ii) they do not allow for likelihood evaluations on held-out data. In contrast, likelihood-based training encourages models to cover the full support of the train data, but yields poorer samples. These mutual shortcomings can in principle be addressed by training generative latent variable models in a hybrid adversarial-likelihood manner. However, we show that commonly made parametric assumptions create a conflict between them, making successful hybrid models non trivial. As a solution, we propose to use deep invertible transformations in the latent variable decoder. This approach allows for likelihood computations in image space, is more efficient than fully invertible models, and can take full advantage of adversarial training. We show that our model significantly improves over existing hybrid models: offering GAN-like samples, IS and FID scores that are competitive with fully adversarial models, and improved likelihood scores.
Introduction to Voice Presentation Attack Detection and Recent Advances
Authors: Md Sahidullah, Hector Delgado, Massimiliano Todisco, Tomi Kinnunen, Nicholas Evans, Junichi Yamagishi, Kong-Aik Lee
Date: 2019-01-04 00:00:00
Tasks: Benchmarking, Speaker Recognition

Over the past few years significant progress has been made in the field of
presentation attack detection (PAD) for automatic speaker recognition (ASV).
This includes the development of new speech corpora, standard evaluation
protocols and advancements in front-end feature extraction and back-end
classifiers. The use of standard databases and evaluation protocols has enabled
for the first time the meaningful benchmarking of different PAD solutions. This
chapter summarises the progress, with a focus on studies completed in the last
three years. The article presents a summary of findings and lessons learned
from two ASVspoof challenges, the first community-led benchmarking efforts.
These show that ASV PAD remains an unsolved problem and that further attention
is required to develop generalised PAD solutions which have potential to detect
diverse and previously unseen spoofing attacks.
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
Authors: Xiangxiang Chu, Bo Zhang, Ruijun Xu, Hailong Ma
Date: 2019-01-04 00:00:00
Tasks: image-classification, Image Classification, Neural Architecture Search, Super-Resolution

Fabricating neural models for a wide range of mobile devices demands for a
specific design of networks due to highly constrained resources. Both evolution
algorithms (EA) and reinforced learning methods (RL) have been dedicated to
solve neural architecture search problems. However, these combinations usually
concentrate on a single objective such as the error rate of image
classification. They also fail to harness the very benefits from both sides. In
this paper, we present a new multi-objective oriented algorithm called MoreMNAS
(Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search) by
leveraging good virtues from both EA and RL. In particular, we incorporate a
variant of multi-objective genetic algorithm NSGA-II, in which the search space
is composed of various cells so that crossovers and mutations can be performed
at the cell level. Moreover, reinforced control is mixed with a natural
mutating process to regulate arbitrary mutation, maintaining a delicate balance
between exploration and exploitation. Therefore, not only does our method
prevent the searched models from degrading during the evolution process, but it
also makes better use of learned knowledge. Our experiments conducted in
Super-resolution domain (SR) deliver rivalling models compared to some
state-of-the-art methods with fewer FLOPS.
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
Authors: Marie-Julie Rakotosaona, Vittorio La Barbera, Paul Guerrero, Niloy J. Mitra, Maks Ovsjanikov
Date: 2019-01-04 00:00:00
Tasks: Denoising, Surface Reconstruction

Point clouds obtained with 3D scanners or by image-based reconstruction techniques are often corrupted with significant amount of noise and outliers. Traditional methods for point cloud denoising largely rely on local surface fitting (e.g., jets or MLS surfaces), local or non-local averaging, or on statistical assumptions about the underlying noise model. In contrast, we develop a simple data-driven method for removing outliers and reducing noise in unordered point clouds. We base our approach on a deep learning architecture adapted from PCPNet, which was recently proposed for estimating local 3D shape properties in point clouds. Our method first classifies and discards outlier samples, and then estimates correction vectors that project noisy points onto the original clean surfaces. The approach is efficient and robust to varying amounts of noise and outliers, while being able to handle large densely-sampled point clouds. In our extensive evaluation, both on synthesic and real data, we show an increased robustness to strong noise levels compared to various state-of-the-art methods, enabling accurate surface reconstruction from extremely noisy real data obtained by range scans. Finally, the simplicity and universality of our approach makes it very easy to integrate in any existing geometry processing pipeline.
An Insight into the Dynamics and State Space Modelling of a 3-D Quadrotor
Authors: Rahul Vigneswaran K, Soman Kp
Date: 2019-01-04 00:00:00

Drones have gained popularity in a wide range of field ranging from aerial
photography, aerial mapping, and investigation of electric power lines. Every
drone that we know today is carrying out some kind of control algorithm at the
low level in order to manoeuvre itself around. For the quadrotor to either
control itself autonomously or to develop a high-level user interface for us to
control it, we need to understand the basic mathematics behind how it
functions. This paper aims to explain the mathematical modelling of the
dynamics of a 3 Dimensional quadrotor. As it may seem like a trivial task, it
plays a vital role in how we control the drone. Also, additional effort has
been taken to explain the transformations of the drone's frame of reference to
the inertial frame of reference.