pwc-archive/papers-with-abstracts huggingface dataset:
Latency Minimization for Multiuser Computation Offloading in Fog-Radio Access Networks
Authors: Wei zhang, Shafei Wang, Ye Pan, Qiang Li, Jingran Lin, Xiaoxiao Wu
Date: 2019-07-20 00:00:00

This paper considers computation offloading in fog-radio access networks (F-RAN), where multiple user equipments (UEs) offload their computation tasks to the F-RAN through a number of fog nodes. Each UE can choose one of the fog nodes to offload its task, and each fog node may serve multiple UEs. Depending on the computation burden at the fog nodes, the tasks may be computed by the fog nodes or further offloaded to the cloud via capacity-limited fronthaul links. To compute all UEs' tasks as fast as possible, joint optimization of UE-Fog association, radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs. This min-max problem is formulated as a mixed integer nonlinear program (MINP). We first show that the MINP can be reformulated as a continuous optimization problem, and then employ the majorization minimization (MM) approach to find a solution. The MM approach that we develop is unconventional in that -- each MM subproblem can be solved inexactly with the same provable convergence guarantee as the conventional exact MM, thereby reducing the complexity of each MM iteration. In addition, we also consider a cooperative offloading model, where the fog nodes compress-and-forward their received signals to the cloud. Under this model, a similar min-max latency optimization problem is formulated and tackled again by the inexact MM approach. Simulation results show that the proposed algorithms outperform some heuristic offloading strategies, and that the cooperative offloading can better exploit the transmission diversity to attain better latency performance than the non-cooperative one.
Radio Frequency Fingerprint Identification Based on Denoising Autoencoders
Date: 2019-07-20 00:00:00
Tasks: Denoising

Radio Frequency Fingerprinting (RFF) is one of the promising passive
authentication approaches for improving the security of the Internet of Things
(IoT). However, with the proliferation of low-power IoT devices, it becomes
imperative to improve the identification accuracy at low SNR scenarios. To
address this problem, this paper proposes a general Denoising AutoEncoder
(DAE)-based model for deep learning RFF techniques. Besides, a partially
stacking method is designed to appropriately combine the semi-steady and
steady-state RFFs of ZigBee devices. The proposed Partially Stacking-based
Convolutional DAE (PSC-DAE) aims at reconstructing a high-SNR signal as well as
device identification. Experimental results demonstrate that compared to
Convolutional Neural Network (CNN), PSCDAE can improve the identification
accuracy by 14% to 23.5% at low SNRs (from -10 dB to 5 dB) under Additive White
Gaussian Noise (AWGN) corrupted channels. Even at SNR = 10 dB, the
identification accuracy is as high as 97.5%.
A Stock Market Model Based on CAPM and Market Size
Authors: Andrey Sarantsev, Blessing Ofori-Atta, Brandon Flores
Date: 2019-07-21 00:00:00

We introduce a new system of stochastic differential equations which models dependence of market beta and unsystematic risk upon size, measured by market capitalization. We fit our model using size deciles data from Kenneth French's data library. This model is somewhat similar to generalized volatility-stabilized models in (Pal, 2011; Pickova, 2013). The novelty of our work is twofold. First, we take into account the difference between price and total returns (in other words, between market size and wealth processes). Second, we work with actual market data. We study the long-term properties of this system of equations, and reproduce observed linearity of the capital distribution curve. Our model has two modifications: for price returns and for equity premium. Somewhat surprisingly, they exhibit the same fit, with very similar coefficients. In the Appendix, we analyze size-based real-world index funds.
Short-term Electric Load Forecasting Using TensorFlow and Deep Auto-Encoders
Date: 2019-07-21 00:00:00
Tasks: Load Forecasting

This paper conducts research on the short-term electric load forecast method
under the background of big data. It builds a new electric load forecast model
based on Deep Auto-Encoder Networks (DAENs), which takes into account
multidimensional load-related data sets including historical load value,
temperature, day type, etc. A new distributed short-term load forecast method
based on TensorFlow and DAENs is therefore proposed, with an algorithm
flowchart designed. This method overcomes the shortcomings of traditional
neural network methods, such as over-fitting, slow convergence and local
optimum, etc. Case study results show that the proposed method has obvious
advantages in prediction accuracy, stability, and expansibility compared with
those based on traditional neural networks. Thus, this model can better meet
the demands of short-term electric load forecasting under big data scenario.
Capacitive Sensor Based 2D Subsurface Imaging Technology for Non Destructive Evaluation of Building Surfaces
Date: 2019-07-22 00:00:00

Understanding the underlying structure of building surfaces like walls and
floors is essential when carrying out building maintenance and modification
work. To facilitate such work, this paper introduces a capacitive sensor-based
technology which can conduct non-destructive evaluation of building surfaces.
The novelty of this sensor is that it can generate a real-time 2D subsurface
image which can be used to understand structure beneath the top surface. Finite
Element Analysis (FEA) simulations are done to understand the best sensor head
configuration that gives optimum results. Hardware and software components are
custom-built to facilitate real-time imaging capability. The sensor is
validated by laboratory tests, which revealed the ability of the proposed
capacitive sensing technology to see through common building materials like
wood and concrete. The 2D image generated by the sensor is found to be useful
in understanding the subsurface structure beneath the top surface.
An equilibrium model for ribosome competition
Authors: Pascal S. Rogalla, Timothy J. Rudge, Luca Ciandrini
Date: 2019-07-22 00:00:00
Tasks: model, Translation

The number of ribosomes in a cell is considered as limiting, and gene expression is thus largely determined by their cellular concentration. In this work we develop a toy model to study the trade-off between the ribosomal supply and the demand of the translation machinery, dictated by the composition of the transcript pool. Our equilibrium framework is useful to highlight qualitative behaviours and new means of gene expression regulation determined by the fine balance of this trade-off. We also speculate on the possible impact of these mechanisms on cellular physiology.
A model for integrating the effects of multiple simultaneous stressors on marine systems
Date: 2019-07-22 00:00:00

While much has been learnt about the impacts of specific stressors on
individual marine organisms, considerable debate exists over the nature and
impact of multiple simultaneous stressors on both individual species and marine
ecosystems. We describe a modelling tool (OSIRIS) for integrating the effects
of multiple simultaneous stressors. The model is relatively computationally
light, and demonstrated using a coarse-grained, non-spatial and simplified
representation of a temperate marine ecosystem. This version is capable of
reproducing a wide range of dynamic responses.Results indicate the degree to
which interactions are synergistic is crucial in determining sensitivity to
forcing, particularly for the higher trophic levels, which can respond
non-linearly to stronger forcing. Stronger synergistic interactions sensitize
the system to variability in forcing, and combinations of stronger forcing,
noise and synergies between effects are particularly potent. This work also
underlines the significant potential risk incurred in treating stressors on
ecosystems as individual and additive.
X-model: further development and possible modifications
Date: 2019-07-22 00:00:00
Tasks: model

Despite its critical importance, the famous X-model elaborated by Ziel and
Steinert (2016) has neither bin been widely studied nor further developed. And
yet, the possibilities to improve the model are as numerous as the fields it
can be applied to. The present paper takes advantage of a technique proposed by
Coulon et al. (2014) to enhance the X-model. Instead of using the wholesale
supply and demand curves as inputs for the model, we rely on the transformed
versions of these curves with a perfectly inelastic demand. As a result,
computational requirements of our X-model reduce and its forecasting power
increases substantially. Moreover, our X-model becomes more robust towards
outliers present in the initial auction curves data.
Generalized statistical arbitrage concepts and related gain strategies
Authors: Christian Rein, Ludger Rüschendorf, Thorsten Schmidt
Date: 2019-07-22 00:00:00

Generalized statistical arbitrage concepts are introduced corresponding to trading strategies which yield positive gains on average in a class of scenarios rather than almost surely. The relevant scenarios or market states are specified via an information system given by a $\sigma$-algebra and so this notion contains classical arbitrage as a special case. It also covers the notion of statistical arbitrage introduced in Bondarenko (2003). Relaxing these notions further we introduce generalized profitable strategies which include also static or semi-static strategies. Under standard no-arbitrage there may exist generalized gain strategies yielding positive gains on average under the specified scenarios. In the first part of the paper we characterize these generalized statistical no-arbitrage notions. In the second part of the paper we construct several profitable generalized strategies with respect to various choices of the information system. In particular, we consider several forms of embedded binomial strategies and follow-the-trend strategies as well as partition-type strategies. We study and compare their behaviour on simulated data. Additionally, we find good performance on market data of these simple strategies which makes them profitable candidates for real applications.
A bundle framework for observer design on smooth manifolds with symmetry
Authors: Anant A. Joshi, D. H. S. Maithripala, Ravi N. Banavar
Date: 2019-07-22 00:00:00

The article presents a bundle framework for nonlinear observer design on a manifold with a Lie group action. The group action on the manifold decomposes the manifold to a quotient structure and an orbit space, and the problem of observer design for the entire system gets decomposed to a design over the orbit (the group space) and a design over the quotient space. The emphasis throughout the article is on presenting an overarching geometric structure; the special case when the group action is free is given special emphasis. Gradient based observer design on a Lie group is given explicit attention. The concepts developed are illustrated by applying them on well known examples, which include the action of $\mathbb{SO}(3)$ on $\mathbb{R}^3 \setminus \{0\}$ and the simultaneous localisation and mapping (SLAM) problem.
Complex order control for improved loop-shaping in precision positioning
Date: 2019-07-22 00:00:00

This paper presents a complex order filter developed and subsequently
integrated into a PID-based controller design. The nonlinear filter is designed
with reset elements to have describing function based frequency response
similar to that of a linear (practically non-implementable) complex order
filter. This allows for a design which has a negative gain slope and a
corresponding positive phase slope as desired from a loop-shaping
controller-design perspective. This approach enables improvement in precision
tracking without compromising the bandwidth or stability requirements. The
proposed designs are tested on a planar precision positioning stage and
performance compared with PID and other state-of-the-art reset based
controllers to showcase the advantages of this filter.
ML Estimation and CRBs for Reverberation, Speech and Noise PSDs in Rank-Deficient Noise-Field
Date: 2020-01-27 00:00:00
Tasks: Blocking

Speech communication systems are prone to performance degradation in
reverberant and noisy acoustic environments. Dereverberation and noise
reduction algorithms typically require several model parameters, e.g. the
speech, reverberation and noise power spectral densities (PSDs). A commonly
used assumption is that the noise PSD matrix is known. However, in practical
acoustic scenarios, the noise PSD matrix is unknown and should be estimated
along with the speech and reverberation PSDs. In this paper, we consider the
case of rank-deficient noise PSD matrix, which arises when the noise signal
consists of multiple directional interference sources, whose number is less
than the number of microphones. We derive two closed-form maximum likelihood
estimators (MLEs). The first is a non-blocking-based estimator which jointly
estimates the speech, reverberation and noise PSDs, and the second is a
blocking-based estimator, which first blocks the speech signal and then jointly
estimates the reverberation and noise PSDs. Both estimators are analytically
compared and analyzed, and mean square errors (MSEs) expressions are derived.
Furthermore, Cramer-Rao Bounds (CRBs) on the estimated PSDs are derived. The
proposed estimators are examined using both simulation and real reverberant and
noisy signals, demonstrating the advantage of the proposed method compared to
competing estimators.
Spike-timing-dependent plasticity with axonal delay tunes networks of Izhikevich neurons to the edge of synchronization transition with scale-free avalanches
Date: 2019-07-22 00:00:00

Critical brain hypothesis has been intensively studied both in experimental
and theoretical neuroscience over the past two decades. However, some important
questions still remain: (i) What is the critical point the brain operates at?
(ii) What is the regulatory mechanism that brings about and maintains such a
critical state? (iii) The critical state is characterized by scale-invariant
behavior which is seemingly at odds with definitive brain oscillations? In this
work we consider a biologically motivated model of Izhikevich neuronal network
with chemical synapses interacting via spike-timingdependent plasticity (STDP)
as well as axonal time delay. Under generic and physiologically relevant
conditions we show that the system is organized and maintained around a
synchronization transition point as opposed to an activity transition point
associated with an absorbing state phase transition. However, such a state
exhibits experimentally relevant signs of critical dynamics including
scale-free avalanches with finite-size scaling as well as branching ratios.
While the system displays stochastic oscillations with highly correlated
fluctuations, it also displays dominant frequency modes seen as sharp peaks in
the power spectrum. The role of STDP as well as time delay is crucial in
achieving and maintaining such critical dynamics, while the role of inhibition
is not as crucial. In this way we provide definitive answers to all three
questions posed above. We also show that one can achieve supercritical or
subcritical dynamics if one changes the average time delay associated with
axonal conduction.
SpliceCombo: A Hybrid Technique efficiently use for Principal Component Analysis of Splice Site Prediction
Date: 2019-07-19 00:00:00
Tasks: feature selection, Prediction, Sensitivity, Specificity, Splice Site Prediction

The primary step in search of the gene prediction is an identification of the
coding region from genomic DNA sequence. Gene structure in the case of a
eukaryotic organism is composed of promoter, intron, start codon, exons, stop
codon, etc. Splice site prediction, which separates the junction between exon
and intron, though the sequence beside. The splice sites have huge
preservation, however, the precision of the tool exhibits less than 90%. The
main objective of this work to exhibits a hybrid technique that efficiently
improves the existing gene recognition technique. Therefore to enhance the
identification of splice sites, the respective algorithm needs to be improved.
Over the last decade, the researcher paid more attention to improve the
accuracy of a predicted model in this domain. Our proposed method, SpliceCombo
involves three stages. At initial stage, which considers the principal
Component Analysis, based on the feature extracted. In the intermediate stage,
i.e.,, the second stage Case- Based Reasoning is done, i.e., feature selection.
The third stage uses support vector machine based along with polynomial kernel
function for final classification. In comparison with other methods, the
proposed SpliceCombo model outperforms other prediction models with respect to
prediction accuracies. Particularly for donor splice site the methodology
exhibits sensitivity is 97.25% accurate and specificity is 97.46% accurate. For
acceptor Splice Site the sensitivity is 96.51% and Specificity is 94.48%
correct.
Deep Fault Diagnosis for Rotating Machinery with Scarce Labeled Samples
Date: 2019-07-13 00:00:00
Tasks: Diagnostic, Fault Diagnosis

Early and accurately detecting faults in rotating machinery is crucial for
operation safety of the modern manufacturing system. In this paper, we proposed
a novel Deep fault diagnosis (DFD) method for rotating machinery with scarce
labeled samples. DFD tackles the challenging problem by transferring knowledge
from shallow models, which is based on the idea that shallow models trained
with different hand-crafted features can reveal the latent prior knowledge and
diagnostic expertise and have good generalization ability even with scarce
labeled samples. DFD can be divided into three phases. First, a spectrogram of
the raw vibration signal is calculated by applying a Short-time Fourier
transform (STFT). From those spectrograms, discriminative time-frequency domain
features can be extracted and used to form a feature pool. Then, several
candidate Support vector machine (SVM) models are trained with different
combinations of features in the feature pool with scarce labeled samples. By
evaluating the pretrained SVM models on the validation set, the most
discriminative features and best-performed SVM models can be selected, which
are used to make predictions on the unlabeled samples. The predicted labels
reserve the expert knowledge originally carried by the SVM model. They are
combined together with the scarce fine labeled samples to form an Augmented
training set (ATS). Finally, a novel 2D deep Convolutional neural network (CNN)
model is trained on the ATS to learn more discriminative features and a better
classifier. Experimental results on two fault diagnosis datasets demonstrate
the effectiveness of the proposed DFD, which achieves better performance than
SVM models and the vanilla deep CNN model trained on scarce labeled samples.
Moreover, it is computationally efficient and is promising for real-time
rotating machinery fault diagnosis.
Improving Brain Magnetic Resonance Image MRI Segmentation via a Novel Algorithm based on Genetic and Regional Growth
Date: 2019-07-22 00:00:00
Tasks: Anatomy, Image Segmentation, MRI segmentation, Segmentation, Semantic Segmentation

Background: Regarding the importance of right diagnosis in medical
applications, various methods have been exploited for processing medical images
solar. The method of segmentation is used to analyze anal to miscall structures
in medical imaging. Objective: This study describes a new method for brain
Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on
genetic and regional growth. Methods: Among medical imaging methods, brains MRI
segmentation is important due to the high contrast of non-intrusive soft tissue
and high spatial resolution. Size variations of brain tissues are often
accompanied by various diseases such as Alzheimers disease. As our knowledge
about the relationship between various brain diseases and deviation of brain
anatomy increases, MRI segmentation is exploited as the first step in early
diagnosis. In this paper, the regional growth method and auto-mate selection of
initial points by genetic algorithm are used to introduce a new method for MRI
segmentation. Primary pixels and similarity criterion are automatically by
genetic algorithms to maximize the accuracy and validity in image segmentation.
Results: By using genetic algorithms and defining the fixed function of image
segmentation, the initial points for the algorithm were found. The proposed
algorithms are applied to the images and results are manually selected by
regional growth in which the initial points were compared. The results showed
that the proposed algorithm could reduce segmentation error effectively.
Conclusion: The study concluded that the proposed algorithm could reduce
segmentation error effectively and help us to diagnose brain diseases.
An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing
Date: 2019-07-17 00:00:00
Tasks: Action Detection, Activity Recognition, EEG, Electroencephalogram (EEG), Human Activity Recognition

Brain computer interfaces (BCI) using EEG, fNIRS and body motion (MoCap) data
are getting more attention due to the fact that fNIRS and MoCap are not prone
to movement artifacts similar to other brain imaging techniques such as EEG.
Advancements in deep learning (neural networks) would allow the use of raw data
for efficient feature extraction without any pre-/post-processing. In this
work, we are performing human activity recognition (BCI classification task)
for 5 activity classes using an end-to-end (deep) neural network (NN) (from
input all the way to the output) on raw fNIRS, EEG and MoCap data. Our core
contribution is focused on applying an end-to-end NN model without any
pre-/post-processing on the data. The entire NN model is being trained using
backpropagation algorithm. Our end-to-end model is composed of a four-layered
MLP: input layer, two hidden layers (using fully connected (dense) layer, batch
normalization and leaky-RELU as non-linearity and activation function), and
output layer using softmax. We have reached minimum 90\% accuracy on the test
dataset for the classification task on 10 subjects data and 5 classes of
activity.
Development and Applicability of Online Passivity Enforced Wide-Band Multi-Port Equivalents For Hybrid Transient Simulation
Date: 2019-07-17 00:00:00

This paper presents a method for developing single and multi-port frequency
dependent network equivalent (FDNE) based on a passivity enforced online
recursive least squares identification algorithm, which identifies the input
admittance matrix in z-domain. Furthermore, with the proposed architecture, a
real-time hybrid model of the reduced power system is developed that integrate
transient stability analysis and FDNE. Main advantages of the proposed
architecture are, it identifies the FDNE even with unknown network parameters
in the frequency range of interest, and yet can be implemented directly due to
discrete formulation while maintaining desired accuracy, stability, and
passivity conditions. The accuracy and characteristics of the proposed method
are verified by implementing on two-area, IEEE 39 and 68 bus power system
models.