pwc-archive/papers-with-abstracts huggingface dataset:
Response of Selective Attention in Middle Temporal Area
Authors: Linda Wang
Date: 2019-04-16 00:00:00

The primary visual cortex processes a large amount of visual information,
however, due to its large receptive fields, when multiple stimuli fall within
one receptive field, there are computational problems. To solve this problem,
the visual system uses selective attention, which allocates resources to a
specific spatial location, to attend to one of the stimuli in the receptive
field. During this process, the center and width of the attending receptive
field change. The model presented in the paper, which is extended and altered
from Bobier et al., simulates the selective attention between the primary
visual cortex, V1, and middle temporal (MT) area. The responses of the MT
columns, which encode the target stimulus, are compared to the results of an
experiment conducted by Womelsdorf et al. on the receptive field shift and
shrinkage in macaque MT area from selective attention. Based on the results,
the responses in the MT area are similar to the Gaussian shaped receptive
fields found in the experiment. As well, the responses of the MT columns are
also measured for accuracy of representing the target visual stimulus and is
found to represent the stimulus with a root mean squared error around 0.17 to
0.18. The paper also explores varying model parameters, such as the membrane
time constant and maximum firing rates, and how those affect the measurement.
This model is a start to modeling the responses of selective attention, however
there are still improvements that can be made to better compare with the
experiment, produce more accurate responses and incorporate more biologically
plausible features.
A Comprehensive Study of Alzheimer's Disease Classification Using Convolutional Neural Networks
Authors: Ziqiang Guan, Ritesh Kumar, Yi Ren Fung, Yeahuay Wu, Madalina Fiterau
Date: 2019-04-16 00:00:00
Tasks: General Classification

A plethora of deep learning models have been developed for the task of
Alzheimer's disease classification from brain MRI scans. Many of these models
report high performance, achieving three-class classification accuracy of up to
95%. However, it is common for these studies to draw performance comparisons
between models that are trained on different subsets of a dataset or use
varying imaging preprocessing techniques, making it difficult to objectively
assess model performance. Furthermore, many of these works do not provide
details such as hyperparameters, the specific MRI scans used, or their source
code, making it difficult to replicate their experiments. To address these
concerns, we present a comprehensive study of some of the deep learning methods
and architectures on the full set of images available from ADNI. We find that,
(1) classification using 3D models gives an improvement of 1% in our setup, at
the cost of significantly longer training time and more computation power, (2)
with our dataset, pre-training yields minimal ($<0.5\%$) improvement in model
performance, (3) most popular convolutional neural network models yield similar
performance when compared to each other. Lastly, we briefly compare the effects
of two image preprocessing programs: FreeSurfer and Clinica, and find that the
spatially normalized and segmented outputs from Clinica increased the accuracy
of model prediction from 63% to 89% when compared to FreeSurfer images.
Expediting TTS Synthesis with Adversarial Vocoding
Authors: Paarth Neekhara, Chris Donahue, Miller Puckette, Shlomo Dubnov, Julian McAuley
Date: 2019-04-16 00:00:00
Tasks: text-to-speech, Text to Speech

Recent approaches in text-to-speech (TTS) synthesis employ neural network strategies to vocode perceptually-informed spectrogram representations directly into listenable waveforms. Such vocoding procedures create a computational bottleneck in modern TTS pipelines. We propose an alternative approach which utilizes generative adversarial networks (GANs) to learn mappings from perceptually-informed spectrograms to simple magnitude spectrograms which can be heuristically vocoded. Through a user study, we show that our approach significantly outperforms na\"ive vocoding strategies while being hundreds of times faster than neural network vocoders used in state-of-the-art TTS systems. We also show that our method can be used to achieve state-of-the-art results in unsupervised synthesis of individual words of speech.
Beyond Correlation: A Path-Invariant Measure for Seismogram Similarity
Authors: Joshua Dickey, Brett Borghetti, William Junek, Richard Martin
Date: 2019-04-16 00:00:00
Tasks: Template Matching, Triplet

Similarity search is a popular technique for seismic signal processing, with template matching, matched filters and subspace detectors being utilized for a wide variety of tasks, including both signal detection and source discrimination. Traditionally, these techniques rely on the cross-correlation function as the basis for measuring similarity. Unfortunately, seismogram correlation is dominated by path effects, essentially requiring a distinct waveform template along each path of interest. To address this limitation, we propose a novel measure of seismogram similarity that is explicitly invariant to path. Using Earthscope's USArray experiment, a path-rich dataset of 207,291 regional seismograms across 8,452 unique events is constructed, and then employed via the batch-hard triplet loss function, to train a deep convolutional neural network which maps raw seismograms to a low dimensional embedding space, where nearness on the space corresponds to nearness of source function, regardless of path or recording instrumentation. This path-agnostic embedding space forms a new representation for seismograms, characterized by robust, source-specific features, which we show to be useful for performing both pairwise event association as well as template-based source discrimination with a single template.
PL-NMF: Parallel Locality-Optimized Non-negative Matrix Factorization
Authors: Gordon E. Moon, Aravind Sukumaran-Rajam, Srinivasan Parthasarathy, P. Sadayappan
Date: 2019-04-16 00:00:00
Tasks: Dimensionality Reduction, Recommendation Systems

Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised
dimension reduction used in a wide range of applications, including topic
modeling, recommender systems and bioinformatics. Due to the compute-intensive
nature of applications that must perform repeated NMF, several parallel
implementations have been developed in the past. However, existing parallel NMF
algorithms have not addressed data locality optimizations, which are critical
for high performance since data movement costs greatly exceed the cost of
arithmetic/logic operations on current computer systems. In this paper, we
devise a parallel NMF algorithm based on the HALS (Hierarchical Alternating
Least Squares) scheme that incorporates algorithmic transformations to enhance
data locality. Efficient realizations of the algorithm on multi-core CPUs and
GPUs are developed, demonstrating significant performance improvement over
existing state-of-the-art parallel NMF algorithms.
Audio-Visual Model Distillation Using Acoustic Images
Authors: Andrés F. Pérez, Valentina Sanguineti, Pietro Morerio, Vittorio Murino
Date: 2019-04-16 00:00:00
Tasks: Action Recognition, Audio Classification, General Classification, model, Retrieval, Temporal Action Localization

In this paper, we investigate how to learn rich and robust feature representations for audio classification from visual data and acoustic images, a novel audio data modality. Former models learn audio representations from raw signals or spectral data acquired by a single microphone, with remarkable results in classification and retrieval. However, such representations are not so robust towards variable environmental sound conditions. We tackle this drawback by exploiting a new multimodal labeled action recognition dataset acquired by a hybrid audio-visual sensor that provides RGB video, raw audio signals, and spatialized acoustic data, also known as acoustic images, where the visual and acoustic images are aligned in space and synchronized in time. Using this richer information, we train audio deep learning models in a teacher-student fashion. In particular, we distill knowledge into audio networks from both visual and acoustic image teachers. Our experiments suggest that the learned representations are more powerful and have better generalization capabilities than the features learned from models trained using just single-microphone audio data.
IAN: Combining Generative Adversarial Networks for Imaginative Face Generation
Authors: Abdullah Hamdi, Bernard Ghanem
Date: 2019-04-16 00:00:00
Tasks: Face Generation

Generative Adversarial Networks (GANs) have gained momentum for their ability
to model image distributions. They learn to emulate the training set and that
enables sampling from that domain and using the knowledge learned for useful
applications. Several methods proposed enhancing GANs, including regularizing
the loss with some feature matching. We seek to push GANs beyond the data in
the training and try to explore unseen territory in the image manifold. We
first propose a new regularizer for GAN based on K-nearest neighbor (K-NN)
selective feature matching to a target set Y in high-level feature space,
during the adversarial training of GAN on the base set X, and we call this
novel model K-GAN. We show that minimizing the added term follows from
cross-entropy minimization between the distributions of GAN and the set Y.
Then, We introduce a cascaded framework for GANs that try to address the task
of imagining a new distribution that combines the base set X and target set Y
by cascading sampling GANs with translation GANs, and we dub the cascade of
such GANs as the Imaginative Adversarial Network (IAN). We conduct an objective
and subjective evaluation for different IAN setups in the addressed task and
show some useful applications for these IANs, like manifold traversing and
creative face generation for characters' design in movies or video games.
Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptation
Authors: Chia-Hsuan Lee, Yun-Nung Chen, Hung-Yi Lee
Date: 2019-04-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Domain Adaptation, Question Answering, speech-recognition, Speech Recognition, Spoken Language Understanding

Spoken question answering (SQA) is challenging due to complex reasoning on
top of the spoken documents. The recent studies have also shown the
catastrophic impact of automatic speech recognition (ASR) errors on SQA.
Therefore, this work proposes to mitigate the ASR errors by aligning the
mismatch between ASR hypotheses and their corresponding reference
transcriptions. An adversarial model is applied to this domain adaptation task,
which forces the model to learn domain-invariant features the QA model can
effectively utilize in order to improve the SQA results. The experiments
successfully demonstrate the effectiveness of our proposed model, and the
results are better than the previous best model by 2% EM score.
Histopathologic Image Processing: A Review
Authors: Jonathan de Matos, Alceu de Souza Britto Jr., Luiz E. S. Oliveira, Alessandro L. Koerich
Date: 2019-04-16 00:00:00
Tasks: Cancer Classification

Histopathologic Images (HI) are the gold standard for evaluation of some
tumors. However, the analysis of such images is challenging even for
experienced pathologists, resulting in problems of inter and intra observer.
Besides that, the analysis is time and resource consuming. One of the ways to
accelerate such an analysis is by using Computer Aided Diagnosis systems. In
this work we present a literature review about the computing techniques to
process HI, including shallow and deep methods. We cover the most common tasks
for processing HI such as segmentation, feature extraction, unsupervised
learning and supervised learning. A dataset section show some datasets found
during the literature review. We also bring a study case of breast cancer
classification using a mix of deep and shallow machine learning methods. The
proposed method obtained an accuracy of 91% in the best case, outperforming the
compared baseline of the dataset.
Usage of Decision Support Systems for Conflicts Modelling during Information Operations Recognition
Authors: Oleh Andriichuk, Vitaliy Tsyganok, Dmitry Lande, Oleg Chertov, Yaroslava Porplenko
Date: 2019-04-16 00:00:00

Application of decision support systems for conflict modeling in information
operations recognition is presented. An information operation is considered as
a complex weakly structured system. The model of conflict between two subjects
is proposed based on the second-order rank reflexive model. The method is
described for construction of the design pattern for knowledge bases of
decision support systems. In the talk, the methodology is proposed for using of
decision support systems for modeling of conflicts in information operations
recognition based on the use of expert knowledge and content monitoring.
Processing-In-Memory Acceleration of Convolutional Neural Networks for Energy-Efficiency, and Power-Intermittency Resilience
Authors: Arman Roohi, Shaahin Angizi, Deliang Fan, Ronald F. DeMara
Date: 2019-04-16 00:00:00

Herein, a bit-wise Convolutional Neural Network (CNN) in-memory accelerator
is implemented using Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM)
computational sub-arrays. It utilizes a novel AND-Accumulation method capable
of significantly-reduced energy consumption within convolutional layers and
performs various low bit-width CNN inference operations entirely within MRAM.
Power-intermittence resiliency is also enhanced by retaining the partial state
information needed to maintain computational forward-progress, which is
advantageous for battery-less IoT nodes. Simulation results indicate
$\sim$5.4$\times$ higher energy-efficiency and 9$\times$ speedup over
ReRAM-based acceleration, or roughly $\sim$9.7$\times$ higher energy-efficiency
and 13.5$\times$ speedup over recent CMOS-only approaches, while maintaining
inference accuracy comparable to baseline designs.
An Analysis of Speech Enhancement and Recognition Losses in Limited Resources Multi-talker Single Channel Audio-Visual ASR
Authors: Luca Pasa, Giovanni Morrone, Leonardo Badino
Date: 2019-04-16 00:00:00
Tasks: Speech Enhancement

In this paper, we analyzed how audio-visual speech enhancement can help to perform the ASR task in a cocktail party scenario. Therefore we considered two simple end-to-end LSTM-based models that perform single-channel audio-visual speech enhancement and phone recognition respectively. Then, we studied how the two models interact, and how to train them jointly affects the final result. We analyzed different training strategies that reveal some interesting and unexpected behaviors. The experiments show that during optimization of the ASR task the speech enhancement capability of the model significantly decreases and vice-versa. Nevertheless the joint optimization of the two tasks shows a remarkable drop of the Phone Error Rate (PER) compared to the audio-visual baseline models trained only to perform phone recognition. We analyzed the behaviors of the proposed models by using two limited-size datasets, and in particular we used the mixed-speech versions of GRID and TCD-TIMIT.
Discovering Episodes with Compact Minimal Windows
Authors: Nikolaj Tatti
Date: 2019-04-15 00:00:00

Discovering the most interesting patterns is the key problem in the field of
pattern mining. While ranking or selecting patterns is well-studied for
itemsets it is surprisingly under-researched for other, more complex, pattern
types.
  In this paper we propose a new quality measure for episodes. An episode is
essentially a set of events with possible restrictions on the order of events.
We say that an episode is significant if its occurrence is abnormally compact,
that is, only few gap events occur between the actual episode events, when
compared to the expected length according to the independence model. We can
apply this measure as a post-pruning step by first discovering frequent
episodes and then rank them according to this measure.
  In order to compute the score we will need to compute the mean and the
variance according to the independence model. As a main technical contribution
we introduce a technique that allows us to compute these values. Such a task is
surprisingly complex and in order to solve it we develop intricate finite state
machines that allow us to compute the needed statistics. We also show that
asymptotically our score can be interpreted as a P-value. In our experiments we
demonstrate that despite its intricacy our ranking is fast: we can rank tens of
thousands episodes in seconds. Our experiments with text data demonstrate that
our measure ranks interpretable episodes high.
OCKELM+: Kernel Extreme Learning Machine based One-class Classification using Privileged Information (or KOC+: Kernel Ridge Regression or Least Square SVM with zero bias based One-class Classification using Privileged Information)
Authors: Chandan Gautam, Aruna Tiwari, M. Tanveer
Date: 2019-04-13 00:00:00
Tasks: General Classification, Novelty Detection, One-Class Classification, One-class classifier

Kernel method-based one-class classifier is mainly used for outlier or
novelty detection. In this letter, kernel ridge regression (KRR) based
one-class classifier (KOC) has been extended for learning using privileged
information (LUPI). LUPI-based KOC method is referred to as KOC+. This
privileged information is available as a feature with the dataset but only for
training (not for testing). KOC+ utilizes the privileged information
differently compared to normal feature information by using a so-called
correction function. Privileged information helps KOC+ in achieving better
generalization performance which is exhibited in this letter by testing the
classifiers with and without privileged information. Existing and proposed
classifiers are evaluated on the datasets from UCI machine learning repository
and also on MNIST dataset. Moreover, experimental results evince the advantage
of KOC+ over KOC and support vector machine (SVM) based one-class classifiers.
RL-Based User Association and Resource Allocation for Multi-UAV enabled MEC
Authors: Liang Wang, Peiqiu Huang, Kezhi Wang, Guopeng Zhang, Lei Zhang, Nauman Aslam, Kun Yang
Date: 2019-04-08 00:00:00
Tasks: Edge-computing, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, multi-unmanned aerial vehicle (UAV) enabled mobile edge
computing (MEC), i.e., UAVE is studied, where several UAVs are deployed as
flying MEC platform to provide computing resource to ground user equipments
(UEs). Compared to the traditional fixed location MEC, UAV enabled MEC (i.e.,
UAVE) is particular useful in case of temporary events, emergency situations
and on-demand services, due to its high flexibility, low cost and easy
deployment features. However, operation of UAVE faces several challenges, two
of which are how to achieve both 1) the association between multiple UEs and
UAVs and 2) the resource allocation from UAVs to UEs, while minimizing the
energy consumption for all the UEs. To address this, we formulate the above
problem into a mixed integer nonlinear programming (MINLP), which is difficult
to be solved in general, especially in the large-scale scenario. We then
propose a Reinforcement Learning (RL)-based user Association and resource
Allocation (RLAA) algorithm to tackle this problem efficiently and effectively.
Numerical results show that the proposed RLAA can achieve the optimal
performance with comparison to the exhaustive search in small scale, and have
considerable performance gain over other typical algorithms in large-scale
cases.
NEXT: A Neural Network Framework for Next POI Recommendation
Authors: Zhiqian Zhang, Chenliang Li, Zhiyong Wu, Aixin Sun, Dengpan Ye, Xiangyang Luo
Date: 2017-04-15 00:00:00
Tasks: Representation Learning

The task of next POI recommendation has been studied extensively in recent
years. However, developing an unified recommendation framework to incorporate
multiple factors associated with both POIs and users remains challenging,
because of the heterogeneity nature of these information. Further, effective
mechanisms to handle cold-start and endow the system with interpretability are
also difficult topics. Inspired by the recent success of neural networks in
many areas, in this paper, we present a simple but effective neural network
framework for next POI recommendation, named NEXT. NEXT is an unified framework
to learn the hidden intent regarding user's next move, by incorporating
different factors in an unified manner. Specifically, in NEXT, we incorporate
meta-data information and two kinds of temporal contexts (i.e., time interval
and visit time). To leverage sequential relations and geographical influence,
we propose to adopt DeepWalk, a network representation learning technique, to
encode such knowledge. We evaluate the effectiveness of NEXT against
state-of-the-art alternatives and neural networks based solutions. Experimental
results over three publicly available datasets demonstrate that NEXT
significantly outperforms baselines in real-time next POI recommendation.
Further experiments demonstrate the superiority of NEXT in handling cold-start.
More importantly, we show that NEXT provides meaningful explanation of the
dimensions in hidden intent space.
Multi-Touch Attribution in Online Advertising with Survival Theory
Authors: Ya Zhang, Yi Wei, Jianbiao Ren
Date: 2015-01-01 00:00:00
Proceeding: 2014 IEEE International Conference on Data Mining 2015 1

Multi-touch attribution, which allows distributing the credit to all related advertisements based on their corresponding contributions, has recently become an important research topic in digital advertising. Traditionally, rule-based attribution models have been used in practice. The drawback of such rule-based models lies in the fact that the rules are not derived form the data but only based on simple intuition. With the ever enhanced capability to tracking advertisement and users' interaction with the advertisement, data-driven multi-touch attribution models, which attempt to infer the contribution from user interaction data, become an important research direction. We here propose a new data-driven attribution model based on survival theory. By adopting a probabilistic framework, one key advantage of the proposed model is that it is able to remove the presentation biases inherit to most of the other attribution models. In addition to model the attribution, the proposed model is also able to predict user's 'conversion' probability. We validate the proposed method with a real-world data set obtained from a operational commercial advertising monitoring company. Experiment results have shown that the proposed method is quite promising in both conversion prediction and attribution.