pwc-archive/papers-with-abstracts huggingface dataset:
Non-contact photoplethysmogram and instantaneous heart rate estimation from infrared face video
Authors: Natalia Martinez, Martin Bertran, Guillermo Sapiro, Hau-Tieng Wu
Date: 2019-02-14 00:00:00
Tasks: Heart rate estimation

Extracting the instantaneous heart rate (iHR) from face videos has been well
studied in recent years. It is well known that changes in skin color due to
blood flow can be captured using conventional cameras. One of the main
limitations of methods that rely on this principle is the need of an
illumination source. Moreover, they have to be able to operate under different
light conditions. One way to avoid these constraints is using infrared cameras,
allowing the monitoring of iHR under low light conditions. In this work, we
present a simple, principled signal extraction method that recovers the iHR
from infrared face videos. We tested the procedure on 7 participants, for whom
we recorded an electrocardiogram simultaneously with their infrared face video.
We checked that the recovered signal matched the ground truth iHR, showing that
infrared is a promising alternative to conventional video imaging for heart
rate monitoring, especially in low light conditions. Code is available at
https://github.com/natalialmg/IR_iHR
The Many-to-Many Mapping Between the Concordance Correlation Coefficient and the Mean Square Error
Authors: Vedhas Pandit, Björn Schuller
Date: 2019-02-14 00:00:00
Tasks: Sentiment Analysis, Time Series Analysis

We derive the mapping between two of the most pervasive utility functions, the mean square error ($MSE$) and the concordance correlation coefficient (CCC, $\rho_c$). Despite its drawbacks, $MSE$ is one of the most popular performance metrics (and a loss function); along with lately $\rho_c$ in many of the sequence prediction challenges. Despite the ever-growing simultaneous usage, e.g., inter-rater agreement, assay validation, a mapping between the two metrics is missing, till date. While minimisation of $L_p$ norm of the errors or of its positive powers (e.g., $MSE$) is aimed at $\rho_c$ maximisation, we reason the often-witnessed ineffectiveness of this popular loss function with graphical illustrations. The discovered formula uncovers not only the counterintuitive revelation that `$MSE_1<MSE_2$' does not imply `$\rho_{c_1}>\rho_{c_2}$', but also provides the precise range for the $\rho_c$ metric for a given $MSE$. We discover the conditions for $\rho_c$ optimisation for a given $MSE$; and as a logical next step, for a given set of errors. We generalise and discover the conditions for any given $L_p$ norm, for an even p. We present newly discovered, albeit apparent, mathematical paradoxes. The study inspires and anticipates a growing use of $\rho_c$-inspired loss functions e.g., $\left|\frac{MSE}{\sigma_{XY}}\right|$, replacing the traditional $L_p$-norm loss functions in multivariate regressions.
Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks
Authors: Behnoosh Parsa, Ekta U. Samani, Rose Hendrix, Cameron Devine, Shashi M. Singh, Santosh Devasia, Ashis G. Banerjee
Date: 2019-02-14 00:00:00
Tasks: Action Segmentation

Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human-robot collaboration systems for logistics and manufacturing applications. We present a foundational paradigm to address this challenge by formulating the problem as one of action segmentation from RGB-D camera videos. Spatial features are first learned using a deep convolutional model from the video frames, which are then fed sequentially to temporal convolutional networks to semantically segment the frames into a hierarchy of actions, which are either ergonomically safe, require monitoring, or need immediate attention. For performance evaluation, in addition to an open-source kitchen dataset, we collected a new dataset comprising twenty individuals picking up and placing objects of varying weights to and from cabinet and table locations at various heights. Results show very high (87-94)\% F1 overlap scores among the ground truth and predicted frame labels for videos lasting over two minutes and consisting of a large number of actions.
Some Interesting Features of Memristor CNN
Authors: Makoto Itoh
Date: 2019-02-14 00:00:00

In this paper, we introduce some interesting features of a memristor CNN
(Cellular Neural Network). We first show that there is the similarity between
the dynamics of memristors and neurons. That is, some kind of flux-controlled
memristors can not respond to the sinusoidal voltage source quickly, namely,
they can not switch `on' rapidly. Furthermore, these memristors have refractory
period after switch `on', which means that it can not respond to further
sinusoidal inputs until the flux is decreased. We next show that the
memristor-coupled two-cell CNN can exhibit chaotic behavior. In this system,
the memristors switch `off' and `on' at irregular intervals, and the two cells
are connected when either or both of the memristors switches `on'. We then
propose the modified CNN model, which can hold a binary output image, even if
all cells are disconnected and no signal is supplied to the cell after a
certain point of time. However, the modified CNN requires power to maintain the
output image, that is, it is volatile. We next propose a new memristor CNN
model. It can also hold a binary output state (image), even if all cells are
disconnected, and no signal is supplied to the cell, by memristor's switching
behavior. Furthermore, even if we turn off the power of the system during the
computation, it can resume from the previous average output state, since the
memristor CNN has functions of both short-term (volatile) memory and long-term
(non-volatile) memory. The above suspend and resume feature are useful when we
want to save the current state, and continue work later from the previous
state. Finally, we show that the memristor CNN can exhibit interesting
two-dimensional waves, if an inductor is connected to each memristor CNN cell.
Probabilistic Neural Architecture Search
Authors: Francesco Paolo Casale, Jonathan Gordon, Nicolo Fusi
Date: 2019-02-13 00:00:00
Tasks: Neural Architecture Search

In neural architecture search (NAS), the space of neural network
architectures is automatically explored to maximize predictive accuracy for a
given task. Despite the success of recent approaches, most existing methods
cannot be directly applied to large scale problems because of their prohibitive
computational complexity or high memory usage. In this work, we propose a
Probabilistic approach to neural ARchitecture SEarCh (PARSEC) that drastically
reduces memory requirements while maintaining state-of-the-art computational
complexity, making it possible to directly search over more complex
architectures and larger datasets. Our approach only requires as much memory as
is needed to train a single architecture from our search space. This is due to
a memory-efficient sampling procedure wherein we learn a probability
distribution over high-performing neural network architectures. Importantly,
this framework enables us to transfer the distribution of architectures learnt
on smaller problems to larger ones, further reducing the computational cost. We
showcase the advantages of our approach in applications to CIFAR-10 and
ImageNet, where our approach outperforms methods with double its computational
cost and matches the performance of methods with costs that are three orders of
magnitude larger.
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting
Authors: Zhijian Li, Xiyang Luo, Bao Wang, Andrea L. Bertozzi, Jack Xin
Date: 2019-02-13 00:00:00

We study epidemic forecasting on real-world health data by a graph-structured
recurrent neural network (GSRNN). We achieve state-of-the-art forecasting
accuracy on the benchmark CDC dataset. To improve model efficiency, we sparsify
the network weights via transformed-$\ell_1$ penalty and maintain prediction
accuracy at the same level with 70% of the network weights being zero.
Leveraging Newswire Treebanks for Parsing Conversational Data with Argument Scrambling
Authors: Riyaz Ahmad Bhat, Irshad Ahmad Bhat, Dipti Misra Sharma
Date: 2019-02-13 00:00:00

We investigate the problem of parsing conversational data of
morphologically-rich languages such as Hindi where argument scrambling occurs
frequently. We evaluate a state-of-the-art non-linear transition-based parsing
system on a new dataset containing 506 dependency trees for sentences from
Bollywood (Hindi) movie scripts and Twitter posts of Hindi monolingual
speakers. We show that a dependency parser trained on a newswire treebank is
strongly biased towards the canonical structures and degrades when applied to
conversational data. Inspired by Transformational Generative Grammar, we
mitigate the sampling bias by generating all theoretically possible alternative
word orders of a clause from the existing (kernel) structures in the treebank.
Training our parser on canonical and transformed structures improves
performance on conversational data by around 9% LAS over the baseline newswire
parser.
Anytime Tail Averaging
Authors: Nicolas Le Roux
Date: 2019-02-13 00:00:00

Tail averaging consists in averaging the last examples in a stream. Common
techniques either have a memory requirement which grows with the number of
samples to average, are not available at every timestep or do not accomodate
growing windows. We propose two techniques with a low constant memory cost that
perform tail averaging with access to the average at every time step. We also
show how one can improve the accuracy of that average at the cost of increased
memory consumption.
Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal Attention Model
Authors: Sirin Haddad, Meiqing Wu, He Wei, Siew Kei Lam
Date: 2019-02-13 00:00:00
Tasks: Autonomous Driving, Collision Avoidance, Pedestrian Trajectory Prediction, Robot Navigation, Trajectory Prediction

Pedestrian trajectory prediction is essential for collision avoidance in
autonomous driving and robot navigation. However, predicting a pedestrian's
trajectory in crowded environments is non-trivial as it is influenced by other
pedestrians' motion and static structures that are present in the scene. Such
human-human and human-space interactions lead to non-linearities in the
trajectories. In this paper, we present a new spatio-temporal graph based Long
Short-Term Memory (LSTM) network for predicting pedestrian trajectory in
crowded environments, which takes into account the interaction with static
(physical objects) and dynamic (other pedestrians) elements in the scene. Our
results are based on two widely-used datasets to demonstrate that the proposed
method outperforms the state-of-the-art approaches in human trajectory
prediction. In particular, our method leads to a reduction in Average
Displacement Error (ADE) and Final Displacement Error (FDE) of up to 55% and
61% respectively over state-of-the-art approaches.
Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization
Authors: Jorge, Davila-Chacon, Jindong, Liu, Stefan, Wermter
Date: 2019-02-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Sentence, Sound Source Localization, speech-recognition, Speech Recognition

Inspired by the behavior of humans talking in noisy environments, we propose
an embodied embedded cognition approach to improve automatic speech recognition
(ASR) systems for robots in challenging environments, such as with ego noise,
using binaural sound source localization (SSL). The approach is verified by
measuring the impact of SSL with a humanoid robot head on the performance of an
ASR system. More specifically, a robot orients itself toward the angle where
the signal-to-noise ratio (SNR) of speech is maximized for one microphone
before doing an ASR task. First, a spiking neural network inspired by the
midbrain auditory system based on our previous work is applied to calculate the
sound signal angle. Then, a feedforward neural network is used to handle high
levels of ego noise and reverberation in the signal. Finally, the sound signal
is fed into an ASR system. For ASR, we use a system developed by our group and
compare its performance with and without the support from SSL. We test our SSL
and ASR systems on two humanoid platforms with different structural and
material properties. With our approach we halve the sentence error rate with
respect to the common downmixing of both channels. Surprisingly, the ASR
performance is more than two times better when the angle between the humanoid
head and the sound source allows sound waves to be reflected most intensely
from the pinna to the ear microphone, rather than when sound waves arrive
perpendicularly to the membrane.
Optimization problems with low SWaP tactical Computing
Authors: Mee Seong Im, Venkat R. Dasari, Lubjana Beshaj, Dale Shires
Date: 2019-02-13 00:00:00
Tasks: Computational Efficiency, Decision Making

In a resource-constrained, contested environment, computing resources need to
be aware of possible size, weight, and power (SWaP) restrictions. SWaP-aware
computational efficiency depends upon optimization of computational resources
and intelligent time versus efficiency tradeoffs in decision making. In this
paper we address the complexity of various optimization strategies related to
low SWaP computing. Due to these restrictions, only a small subset of less
complicated and fast computable algorithms can be used for tactical, adaptive
computing.
Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Image
Authors: Siyan Tao, Yao Guo, Chuang Zhu, Huang Chen, Yue Zhang, Jie Yang, Jun Liu
Date: 2019-02-13 00:00:00
Tasks: General Classification, Segmentation

In this paper, we propose a novel method for highly efficient follicular
segmentation of thyroid cytopathological WSIs. Firstly, we propose a hybrid
segmentation architecture, which integrates a classifier into Deeplab V3 by
adding a branch. A large amount of the WSI segmentation time is saved by
skipping the irrelevant areas using the classification branch. Secondly, we
merge the low scale fine features into the original atrous spatial pyramid
pooling (ASPP) in Deeplab V3 to accurately represent the details in
cytopathological images. Thirdly, our hybrid model is trained by a
criterion-oriented adaptive loss function, which leads the model converging
much faster. Experimental results on a collection of thyroid patches
demonstrate that the proposed model reaches 80.9% on the segmentation accuracy.
Besides, 93% time is reduced for the WSI segmentation by using our proposed
method, and the WSI-level accuracy achieves 53.4%.
Structured Bayesian Compression for Deep models in mobile enabled devices for connected healthcare
Authors: Sijia Chen, Bin Song, Xiaojiang Du, Nadra Guizani
Date: 2019-02-13 00:00:00
Tasks: Computational Efficiency

Deep Models, typically Deep neural networks, have millions of parameters,
analyze medical data accurately, yet in a time-consuming method. However,
energy cost effectiveness and computational efficiency are important for
prerequisites developing and deploying mobile-enabled devices, the mainstream
trend in connected healthcare.
Improving performance and inference on audio classification tasks using capsule networks
Authors: Royal Jain
Date: 2019-02-13 00:00:00
Tasks: Audio Classification, Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Transfer Learning

Classification of audio samples is an important part of many auditory
systems. Deep learning models based on the Convolutional and the Recurrent
layers are state-of-the-art in many such tasks. In this paper, we approach
audio classification tasks using capsule networks trained by recently proposed
dynamic routing-by-agreement mechanism. We propose an architecture for capsule
networks fit for audio classification tasks and study the impact of various
parameters on classification accuracy. Further, we suggest modifications for
regularization and multi-label classification. We also develop insights into
the data using capsule outputs and show the utility of the learned network for
transfer learning. We perform experiments on 7 datasets of different domains
and sizes and show significant improvements in performance compared to strong
baseline models. To the best of our knowledge, this is the first detailed study
about the application of capsule networks in the audio domain.
On the Convergence of Extended Variational Inference for Non-Gaussian Statistical Models
Authors: Zhanyu Ma, Jalil Taghia, Jun Guo
Date: 2019-02-13 00:00:00
Tasks: Variational Inference

Variational inference (VI) is a widely used framework in Bayesian estimation. For most of the non-Gaussian statistical models, it is infeasible to find an analytically tractable solution to estimate the posterior distributions of the parameters. Recently, an improved framework, namely the extended variational inference (EVI), has been introduced and applied to derive analytically tractable solution by employing lower-bound approximation to the variational objective function. Two conditions required for EVI implementation, namely the weak condition and the strong condition, are discussed and compared in this paper. In practical implementation, the convergence of the EVI depends on the selection of the lower-bound approximation, no matter with the weak condition or the strong condition. In general, two approximation strategies, the single lower-bound (SLB) approximation and the multiple lower-bounds (MLB) approximation, can be applied to carry out the lower-bound approximation. To clarify the differences between the SLB and the MLB, we will also discuss the convergence properties of the aforementioned two approximations. Extensive comparisons are made based on some existing EVI-based non-Gaussian statistical models. Theoretical analysis are conducted to demonstrate the differences between the weak and the strong conditions. Qualitative and quantitative experimental results are presented to show the advantages of the SLB approximation.
On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects
Authors: Linshan Jiang, Rui Tan, Xin Lou, Guosheng Lin
Date: 2019-02-13 00:00:00
Tasks: BIG-bench Machine Learning, Privacy Preserving

The Internet of Things (IoT) will be a main data generation infrastructure
for achieving better system intelligence. This paper considers the design and
implementation of a practical privacy-preserving collaborative learning scheme,
in which a curious learning coordinator trains a better machine learning model
based on the data samples contributed by a number of IoT objects, while the
confidentiality of the raw forms of the training data is protected against the
coordinator. Existing distributed machine learning and data encryption
approaches incur significant computation and communication overhead, rendering
them ill-suited for resource-constrained IoT objects. We study an approach that
applies independent Gaussian random projection at each IoT object to obfuscate
data and trains a deep neural network at the coordinator based on the projected
data from the IoT objects. This approach introduces light computation overhead
to the IoT objects and moves most workload to the coordinator that can have
sufficient computing resources. Although the independent projections performed
by the IoT objects address the potential collusion between the curious
coordinator and some compromised IoT objects, they significantly increase the
complexity of the projected data. In this paper, we leverage the superior
learning capability of deep learning in capturing sophisticated patterns to
maintain good learning performance. Extensive comparative evaluation shows that
this approach outperforms other lightweight approaches that apply additive
noisification for differential privacy and/or support vector machines for
learning in the applications with light data pattern complexities.
Robust Multi-instance Learning with Stable Instances
Authors: Weijia Zhang, Jiuyong Li, Lin Liu
Date: 2019-02-13 00:00:00
Tasks: Causal Inference, image-classification, Image Classification

Multi-instance learning (MIL) deals with tasks where data is represented by a set of bags and each bag is described by a set of instances. Unlike standard supervised learning, only the bag labels are observed whereas the label for each instance is not available to the learner. Previous MIL studies typically follow the i.i.d. assumption, that the training and test samples are independently drawn from the same distribution. However, such assumption is often violated in real-world applications. Efforts have been made towards addressing distribution changes by importance weighting the training data with the density ratio between the training and test samples. Unfortunately, models often need to be trained without seeing the test distributions. In this paper we propose possibly the first framework for addressing distribution change in MIL without requiring access to the unlabeled test data. Our framework builds upon identifying a novel connection between MIL and the potential outcome framework in causal effect estimation. Experimental results on synthetic distribution change datasets, real-world datasets with synthetic distribution biases and real distributional biased image classification datasets validate the effectiveness of our approach.
Predicting Food Security Outcomes Using Convolutional Neural Networks (CNNs) for Satellite Tasking
Authors: Swetava Ganguli, Jared Dunnmon, Darren Hau
Date: 2019-02-13 00:00:00
Tasks: General Classification, Land Cover Classification, Transfer Learning

Obtaining reliable data describing local Food Security Metrics (FSM) at a
granularity that is informative to policy-makers requires expensive and
logistically difficult surveys, particularly in the developing world. We train
a CNN on publicly available satellite data describing land cover classification
and use both transfer learning and direct training to build a model for FSM
prediction purely from satellite imagery data. We then propose efficient
tasking algorithms for high resolution satellite assets via transfer learning,
Markovian search algorithms, and Bayesian networks.
PLIT: An alignment-free computational tool for identification of long non-coding RNAs in plant transcriptomic datasets
Authors: S. Deshpande, J. Shuttleworth, J. Yang, S. Taramonli, M. England
Date: 2019-02-12 00:00:00
Tasks: feature selection

Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a
significant role in several biological processes. RNA-seq based transcriptome
sequencing has been extensively used for identification of lncRNAs. However,
accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring
their characteristic functions in the genome as most coding potential
computation (CPC) tools fail to accurately identify them in transcriptomic
data. Well-known CPC tools such as CPC2, lncScore, CPAT are primarily designed
for prediction of lncRNAs based on the GENCODE, NONCODE and CANTATAdb
databases. The prediction accuracy of these tools often drops when tested on
transcriptomic datasets. This leads to higher false positive results and
inaccuracy in the function annotation process. In this study, we present a
novel tool, PLIT, for the identification of lncRNAs in plants RNA-seq datasets.
PLIT implements a feature selection method based on L1 regularization and
iterative Random Forests (iRF) classification for selection of optimal
features. Based on sequence and codon-bias features, it classifies the RNA-seq
derived FASTA sequences into coding or long non-coding transcripts. Using L1
regularization, 31 optimal features were obtained based on lncRNA and
protein-coding transcripts from 8 plant species. The performance of the tool
was evaluated on 7 plant RNA-seq datasets using 10-fold cross-validation. The
analysis exhibited superior accuracy when evaluated against currently available
state-of-the-art CPC tools.