pwc-archive/papers-with-abstracts huggingface dataset:
Characterization of citizens using word2vec and latent topic analysis in a large set of tweets
Authors: Vargas-Calderón Vladimir, Camargo Jorge
Date: 2019-04-15 00:00:00
Tasks: BIG-bench Machine Learning

With the increasing use of the Internet and mobile devices, social networks
are becoming the most used media to communicate citizens' ideas and thoughts.
This information is very useful to identify communities with common ideas based
on what they publish in the network. This paper presents a method to
automatically detect city communities based on machine learning techniques
applied to a set of tweets from Bogot\'a's citizens. An analysis was performed
in a collection of 2,634,176 tweets gathered from Twitter in a period of six
months. Results show that the proposed method is an interesting tool to
characterize a city population based on a machine learning methods and text
analytics.
Software Tools for Big Data Resources in Family Names Dictionaries
Authors: Adam Rambousek, Harry Parkin, Ales Horak
Date: 2019-04-02 00:00:00
Tasks: Management

This paper describes the design and development of specific software tools
used during the creation of Family Names in Britain and Ireland (FaNBI)
research project, started by the University of the West of England in 2010 and
finished successfully in 2016. First, the overview of the project and
methodology is provided. Next section contains the description of dictionary
management tools and software tools to combine input data resources.
Automatic Target Detection for Sparse Hyperspectral Images
Authors: Ahmad W. Bitar, Jean-Philippe Ovarlez, Loong-Fah Cheong, Ali Chehab
Date: 2019-04-14 00:00:00

In this work, a novel target detector for hyperspectral imagery is developed. The detector is independent on the unknown covariance matrix, behaves well in large dimensions, distributional free, invariant to atmospheric effects, and does not require a background dictionary to be constructed. Based on a modification of the robust principal component analysis (RPCA), a given hyperspectral image (HSI) is regarded as being made up of the sum of a low-rank background HSI and a sparse target HSI that contains the targets based on a pre-learned target dictionary specified by the user. The sparse component is directly used for the detection, that is, the targets are simply detected at the non-zero entries of the sparse target HSI. Hence, a novel target detector is developed, which is simply a sparse HSI generated automatically from the original HSI, but containing only the targets with the background is suppressed. The detector is evaluated on real experiments, and the results of which demonstrate its effectiveness for hyperspectral target detection especially when the targets are well matched to the surroundings.
Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery
Authors: Ahmad W. Bitar, Loong-Fah Cheong, Jean-Philippe Ovarlez
Date: 2017-11-24 00:00:00

Given a target prior information, our goal is to propose a method for
automatically separating targets of interests from the background in
hyperspectral imagery. More precisely, we regard the given hyperspectral image
(HSI) as being made up of the sum of low-rank background HSI and a sparse
target HSI that contains the targets based on a pre-learned target dictionary
constructed from some online spectral libraries. Based on the proposed method,
two strategies are briefly outlined and evaluated to realize the target
detection on both synthetic and real experiments.
Evolving Deep Neural Networks by Multi-objective Particle Swarm Optimization for Image Classification
Authors: Bin Wang, Yanan sun, Bing Xue, Mengjie Zhang
Date: 2019-03-21 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

In recent years, convolutional neural networks (CNNs) have become deeper in
order to achieve better classification accuracy in image classification.
However, it is difficult to deploy the state-of-the-art deep CNNs for
industrial use due to the difficulty of manually fine-tuning the
hyperparameters and the trade-off between classification accuracy and
computational cost. This paper proposes a novel multi-objective optimization
method for evolving state-of-the-art deep CNNs in real-life applications, which
automatically evolves the non-dominant solutions at the Pareto front. Three
major contributions are made: Firstly, a new encoding strategy is designed to
encode one of the best state-of-the-art CNNs; With the classification accuracy
and the number of floating point operations as the two objectives, a
multi-objective particle swarm optimization method is developed to evolve the
non-dominant solutions; Last but not least, a new infrastructure is designed to
boost the experiments by concurrently running the experiments on multiple GPUs
across multiple machines, and a Python library is developed and released to
manage the infrastructure. The experimental results demonstrate that the
non-dominant solutions found by the proposed algorithm form a clear Pareto
front, and the proposed infrastructure is able to almost linearly reduce the
running time.
Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry
Authors: Yueqin Li, Ata Mahjoubfar, Claire Lifan Chen, Kayvan Reza Niazi, Li Pei, Bahram Jalali
Date: 2019-04-09 00:00:00
Tasks: Computational Efficiency, Deep Learning, General Classification, speech-recognition, Speech Recognition

Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments based on the photonic time stretch have established record real-time measurement throughput in spectroscopy, optical coherence tomography, and imaging flow cytometry. These extreme-throughput instruments generate approximately 1 Tbit/s of continuous measurement data and have led to the discovery of rare phenomena in nonlinear and complex systems as well as new types of biomedical instruments. Owing to the abundance of data they generate, time-stretch instruments are a natural fit to deep learning classification. Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time-stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood. Such a technology holds promise for early detection of primary cancer or metastasis. Here we describe a new deep learning pipeline, which entirely avoids the slow and computationally costly signal processing and feature extraction steps by a convolutional neural network that directly operates on the measured signals. The improvement in computational efficiency enables low-latency inference and makes this pipeline suitable for cell sorting via deep learning. Our neural network takes less than a few milliseconds to classify the cells, fast enough to provide a decision to a cell sorter for real-time separation of individual target cells. We demonstrate the applicability of our new method in the classification of OT-II white blood cells and SW-480 epithelial cancer cells with more than 95% accuracy in a label-free fashion.
Deep Learning for Power System Security Assessment
Authors: José-María Hidalgo-Arteaga, Fiodar Hancharou, Florian Thams, Spyros Chatzivasileiadis
Date: 2019-03-31 00:00:00
Tasks: Deep Learning

Security assessment is among the most fundamental functions of power system
operator. The sheer complexity of power systems exceeding a few buses, however,
makes it an extremely computationally demanding task. The emergence of deep
learning methods that are able to handle immense amounts of data, and infer
valuable information appears as a promising alternative. This paper has two
main contributions. First, inspired by the remarkable performance of
convolutional neural networks for image processing, we represent for the first
time power system snapshots as 2-dimensional images, thus taking advantage of
the wide range of deep learning methods available for image processing. Second,
we train deep neural networks on a large database for the NESTA 162-bus system
to assess both N-1 security and small-signal stability. We find that our
approach is over 255 times faster than a standard small-signal stability
assessment, and it can correctly determine unsafe points with over 99%
accuracy.
Deep Learning on Mobile Devices - A Review
Authors: Yunbin Deng
Date: 2019-03-21 00:00:00
Tasks: Cloud Computing, Deep Learning, Quantization

Recent breakthroughs in deep learning and artificial intelligence
technologies have enabled numerous mobile applications. While traditional
computation paradigms rely on mobile sensing and cloud computing, deep learning
implemented on mobile devices provides several advantages. These advantages
include low communication bandwidth, small cloud computing resource cost, quick
response time, and improved data privacy. Research and development of deep
learning on mobile and embedded devices has recently attracted much attention.
This paper provides a timely review of this fast-paced field to give the
researcher, engineer, practitioner, and graduate student a quick grasp on the
recent advancements of deep learning on mobile devices. In this paper, we
discuss hardware architectures for mobile deep learning, including Field
Programmable Gate Arrays, Application Specific Integrated Circuit, and recent
mobile Graphic Processing Units. We present Size, Weight, Area and Power
considerations and their relation to algorithm optimizations, such as
quantization, pruning, compression, and approximations that simplify
computation while retaining performance accuracy. We cover existing systems and
give a state-of-the-industry review of TensorFlow, MXNet, Mobile AI Compute
Engine, and Paddle-mobile deep learning platform. We discuss resources for
mobile deep learning practitioners, including tools, libraries, models, and
performance benchmarks. We present applications of various mobile sensing
modalities to industries, ranging from robotics, healthcare and multi-media,
biometrics to autonomous drive and defense. We address the key deep learning
challenges to overcome, including low quality data, and small
training/adaptation data sets. In addition, the review provides numerous
citations and links to existing code bases implementing various technologies.
Mining Closed Strict Episodes
Authors: Nikolaj Tatti, Boris Cule
Date: 2019-04-14 00:00:00

Discovering patterns in a sequence is an important aspect of data mining. One
popular choice of such patterns are episodes, patterns in sequential data
describing events that often occur in the vicinity of each other. Episodes also
enforce in which order the events are allowed to occur.
  In this work we introduce a technique for discovering closed episodes.
Adopting existing approaches for discovering traditional patterns, such as
closed itemsets, to episodes is not straightforward. First of all, we cannot
define a unique closure based on frequency because an episode may have several
closed superepisodes. Moreover, to define a closedness concept for episodes we
need a subset relationship between episodes, which is not trivial to define.
  We approach these problems by introducing strict episodes. We argue that this
class is general enough, and at the same time we are able to define a natural
subset relationship within it and use it efficiently. In order to mine closed
episodes we define an auxiliary closure operator. We show that this closure
satisfies the needed properties so that we can use the existing framework for
mining closed patterns. Discovering the true closed episodes can be done as a
post-processing step. We combine these observations into an efficient mining
algorithm and demonstrate empirically its performance in practice.
Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear Regression Method
Authors: Qibin Zhao, Cesar F. Caiafa, Danilo P. Mandic, Zenas C. Chao, Yasuo Nagasaka, Naotaka Fujii, Liqing Zhang, Andrzej Cichocki
Date: 2012-07-05 00:00:00
Tasks: regression

A new generalized multilinear regression model, termed the Higher-Order
Partial Least Squares (HOPLS), is introduced with the aim to predict a tensor
(multiway array) $\tensor{Y}$ from a tensor $\tensor{X}$ through projecting the
data onto the latent space and performing regression on the corresponding
latent variables. HOPLS differs substantially from other regression models in
that it explains the data by a sum of orthogonal Tucker tensors, while the
number of orthogonal loadings serves as a parameter to control model complexity
and prevent overfitting. The low dimensional latent space is optimized
sequentially via a deflation operation, yielding the best joint subspace
approximation for both $\tensor{X}$ and $\tensor{Y}$. Instead of decomposing
$\tensor{X}$ and $\tensor{Y}$ individually, higher order singular value
decomposition on a newly defined generalized cross-covariance tensor is
employed to optimize the orthogonal loadings. A systematic comparison on both
synthetic data and real-world decoding of 3D movement trajectories from
electrocorticogram (ECoG) signals demonstrate the advantages of HOPLS over the
existing methods in terms of better predictive ability, suitability to handle
small sample sizes, and robustness to noise.
LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms
Authors: Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya
Date: 2019-04-20 00:00:00
Tasks: BIG-bench Machine Learning, Second-order methods

LIBS2ML is a library based on scalable second order learning algorithms for
solving large-scale problems, i.e., big data problems in machine learning.
LIBS2ML has been developed using MEX files, i.e., C++ with MATLAB/Octave
interface to take the advantage of both the worlds, i.e., faster learning using
C++ and easy I/O using MATLAB. Most of the available libraries are either in
MATLAB/Python/R which are very slow and not suitable for large-scale learning,
or are in C/C++ which does not have easy ways to take input and display
results. So LIBS2ML is completely unique due to its focus on the scalable
second order methods, the hot research topic, and being based on MEX files.
Thus it provides researchers a comprehensive environment to evaluate their
ideas and it also provides machine learning practitioners an effective tool to
deal with the large-scale learning problems. LIBS2ML is an open-source, highly
efficient, extensible, scalable, readable, portable and easy to use library.
The library can be downloaded from the URL:
\url{https://github.com/jmdvinodjmd/LIBS2ML}.
Data-Driven Neuron Allocation for Scale Aggregation Networks
Authors: Yi Li, Zhanghui Kuang, Yimin Chen, Wayne Zhang
Date: 2019-04-20 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Classification, object-detection, Object Detection

Successful visual recognition networks benefit from aggregating information
spanning from a wide range of scales. Previous research has investigated
information fusion of connected layers or multiple branches in a block, seeking
to strengthen the power of multi-scale representations. Despite their great
successes, existing practices often allocate the neurons for each scale
manually, and keep the same ratio in all aggregation blocks of an entire
network, rendering suboptimal performance. In this paper, we propose to learn
the neuron allocation for aggregating multi-scale information in different
building blocks of a deep network. The most informative output neurons in each
block are preserved while others are discarded, and thus neurons for multiple
scales are competitively and adaptively allocated. Our scale aggregation
network (ScaleNet) is constructed by repeating a scale aggregation (SA) block
that concatenates feature maps at a wide range of scales. Feature maps for each
scale are generated by a stack of downsampling, convolution and upsampling
operations. The data-driven neuron allocation and SA block achieve strong
representational power at the cost of considerably low computational
complexity. The proposed ScaleNet, by replacing all 3x3 convolutions in ResNet
with our SA blocks, achieves better performance than ResNet and its outstanding
variants like ResNeXt and SE-ResNet, in the same computational complexity. On
ImageNet classification, ScaleNets absolutely reduce the top-1 error rate of
ResNets by 1.12 (101 layers) and 1.82 (50 layers). On COCO object detection,
ScaleNets absolutely improve the mmAP with backbone of ResNets by 3.6 (101
layers) and 4.6 (50 layers) on Faster RCNN, respectively. Code and models are
released at https://github.com/Eli-YiLi/ScaleNet.
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
Authors: Peng Zhang, Fuhao Zou, Zhiwen Wu, Nengli Dai, Skarpness Mark, Michael Fu, Juan Zhao, Kai Li
Date: 2019-04-22 00:00:00
Tasks: Diversity, Face Anti-Spoofing

Face Anti-spoofing gains increased attentions recently in both academic and
industrial fields. With the emergence of various CNN based solutions, the
multi-modal(RGB, depth and IR) methods based CNN showed better performance than
single modal classifiers. However, there is a need for improving the
performance and reducing the complexity. Therefore, an extreme light network
architecture(FeatherNet A/B) is proposed with a streaming module which fixes
the weakness of Global Average Pooling and uses less parameters. Our single
FeatherNet trained by depth image only, provides a higher baseline with 0.00168
ACER, 0.35M parameters and 83M FLOPS. Furthermore, a novel fusion procedure
with ``ensemble + cascade'' structure is presented to satisfy the performance
preferred use cases. Meanwhile, the MMFD dataset is collected to provide more
attacks and diversity to gain better generalization. We use the fusion method
in the Face Anti-spoofing Attack Detection Challenge@CVPR2019 and got the
result of 0.0013(ACER), 0.999(TPR@FPR=10e-2), 0.998(TPR@FPR=10e-3) and
0.9814(TPR@FPR=10e-4).
Deep Anchored Convolutional Neural Networks
Authors: Jiahui Huang, Kshitij Dwivedi, Gemma Roig
Date: 2019-04-22 00:00:00

Convolutional Neural Networks (CNNs) have been proven to be extremely
successful at solving computer vision tasks. State-of-the-art methods favor
such deep network architectures for its accuracy performance, with the cost of
having massive number of parameters and high weights redundancy. Previous works
have studied how to prune such CNNs weights. In this paper, we go to another
extreme and analyze the performance of a network stacked with a single
convolution kernel across layers, as well as other weights sharing techniques.
We name it Deep Anchored Convolutional Neural Network (DACNN). Sharing the same
kernel weights across layers allows to reduce the model size tremendously, more
precisely, the network is compressed in memory by a factor of L, where L is the
desired depth of the network, disregarding the fully connected layer for
prediction. The number of parameters in DACNN barely increases as the network
grows deeper, which allows us to build deep DACNNs without any concern about
memory costs. We also introduce a partial shared weights network (DACNN-mix) as
well as an easy-plug-in module, coined regulators, to boost the performance of
our architecture. We validated our idea on 3 datasets: CIFAR-10, CIFAR-100 and
SVHN. Our results show that we can save massive amounts of memory with our
model, while maintaining a high accuracy performance.
Stochastic Region Pooling: Make Attention More Expressive
Authors: Mingnan Luo, Guihua Wen, Yang Hu, Dan Dai, Yingxue Xu
Date: 2019-04-22 00:00:00
Tasks: Diversity

Global Average Pooling (GAP) is used by default on the channel-wise attention
mechanism to extract channel descriptors. However, the simple global
aggregation method of GAP is easy to make the channel descriptors have
homogeneity, which weakens the detail distinction between feature maps, thus
affecting the performance of the attention mechanism. In this work, we propose
a novel method for channel-wise attention network, called Stochastic Region
Pooling (SRP), which makes the channel descriptors more representative and
diversity by encouraging the feature map to have more or wider important
feature responses. Also, SRP is the general method for the attention mechanisms
without any additional parameters or computation. It can be widely applied to
attention networks without modifying the network structure. Experimental
results on image recognition datasets including CIAFR-10/100, ImageNet and
three Fine-grained datasets (CUB-200-2011, Stanford Cars and Stanford Dogs)
show that SRP brings the significant improvements of the performance over
efficient CNNs and achieves the state-of-the-art results.
Attention Augmented Convolutional Networks
Authors: Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, Quoc V. Le
Date: 2019-04-22 00:00:00
Proceeding: ICCV 2019 10
Tasks: General Classification, image-classification, Image Classification, object-detection, Object Detection

Convolutional networks have been the paradigm of choice in many computer vision applications. The convolution operation however has a significant weakness in that it only operates on a local neighborhood, thus missing global information. Self-attention, on the other hand, has emerged as a recent advance to capture long range interactions, but has mostly been applied to sequence modeling and generative modeling tasks. In this paper, we consider the use of self-attention for discriminative visual tasks as an alternative to convolutions. We introduce a novel two-dimensional relative self-attention mechanism that proves competitive in replacing convolutions as a stand-alone computational primitive for image classification. We find in control experiments that the best results are obtained when combining both convolutions and self-attention. We therefore propose to augment convolutional operators with this self-attention mechanism by concatenating convolutional feature maps with a set of feature maps produced via self-attention. Extensive experiments show that Attention Augmentation leads to consistent improvements in image classification on ImageNet and object detection on COCO across many different models and scales, including ResNets and a state-of-the art mobile constrained network, while keeping the number of parameters similar. In particular, our method achieves a $1.3\%$ top-1 accuracy improvement on ImageNet classification over a ResNet50 baseline and outperforms other attention mechanisms for images such as Squeeze-and-Excitation. It also achieves an improvement of 1.4 mAP in COCO Object Detection on top of a RetinaNet baseline.
Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids
Authors: Despoina Paschalidou, Ali Osman Ulusoy, Andreas Geiger
Date: 2019-04-22 00:00:00
Proceeding: CVPR 2019 6
Tasks: Reinforcement Learning

Abstracting complex 3D shapes with parsimonious part-based representations
has been a long standing goal in computer vision. This paper presents a
learning-based solution to this problem which goes beyond the traditional 3D
cuboid representation by exploiting superquadrics as atomic elements. We
demonstrate that superquadrics lead to more expressive 3D scene parses while
being easier to learn than 3D cuboid representations. Moreover, we provide an
analytical solution to the Chamfer loss which avoids the need for computational
expensive reinforcement learning or iterative prediction. Our model learns to
parse 3D objects into consistent superquadric representations without
supervision. Results on various ShapeNet categories as well as the SURREAL
human body dataset demonstrate the flexibility of our model in capturing fine
details and complex poses that could not have been modelled using cuboids.