pwc-archive/papers-with-abstracts huggingface dataset:
Wireless Power Transfer in Cooperative DF Relaying Networks with Log-Normal Fading
Date: 2018-06-25 00:00:00

Energy-harvesting and wireless power transfer in cooperative relaying
networks have recently attracted a considerable amount of research attention.
Most of the existing work on this topic however focuses on Rayleigh fading
channels which represents outdoor environments. Unlike these studies, in this
paper we analyze the performance of wireless power transfer in two-hop
decode-and-forward (DF) cooperative relaying systems in indoor channels
characterized by log-normal fading. Three well-known energy-harvesting
protocols are considered in our evaluations: a) time-switching relaying (TSR),
b) power-splitting relaying (PSR) and c) ideal relaying receiver (IRR). The
performance is evaluated in terms of the ergodic outage probability for which
we derive accurate analytical expressions for the three systems under
consideration. Results reveal that careful selection of the energy-harvesting
time and power-splitting factors in the TSR- and PSR-based system are important
to optimize performance. It is also presented that the optimized PSR system has
near-ideal performance and that increasing the source transmit power and/or the
energy-harvester efficiency can further improve performance.
A Design Space Exploration (DSE) on Non-Invasive Sensing of Bladder Filling Using Near Infrared Spectroscopy (NIRS)
Date: 2018-06-25 00:00:00

Urinary Incontinence (UI) is a widespread medical condition that affects one
person from every three or four Americans. Near-Infrared Spectroscopy (NIRS) is
a non-invasive under-study method for bladder filling sensation that can
enhance the life quality of UI patients by finding the optimal voiding time.
However, the application of NIRS to bladder volume sensing can be quite
challenging due to three major obstacles: non-adequate traversal depth of NIR
wavelengths, robustness and power efficiency requirements of the application,
and low power transmission rate of NIR wavelengths. This work provides a Design
Space Exploration (DSE) through the effect of various design parameters on NIRS
applicability for bladder volume sensing. We investigate the impact of 7
different wavelengths from 650-950 nm, 16 possible detector-source distances,
and 6 different sensation depths. The results of our work can be used as a
guideline through optimal design and implementation of NIRS for bladder filling
sensation.
Deterministic X-ray Bragg coherent diffraction imaging as a seed for subsequent iterative reconstruction
Date: 2018-06-21 00:00:00

Coherent diffractive imaging (CDI), using both X-rays and electrons, has made
extremely rapid progress over the past two decades. The associated
reconstruction algorithms are typically iterative, and seeded with a crude
first estimate. A deterministic method for Bragg Coherent Diffraction Imaging
(Pavlov et al., Sci. Rep. 7, 1132 (2017)) is used as a more refined starting
point for a shrink-wrap iterative reconstruction procedure. The appropriate
comparison with the autocorrelation function as a starting point is performed.
Real-space and Fourier-space error metrics are used to analyse the convergence
of the reconstruction procedure for noisy and noise-free simulated data. Our
results suggest that the use of deterministic-CDI reconstructions, as a seed
for subsequent iterative-CDI refinement, may boost the speed and degree of
convergence compared to the cruder seeds that are currently commonly used. We
also highlight the utility of monitoring multiple error metrics in the context
of iterative refinement.
Testability of instrument validity under continuous endogenous variables
Date: 2019-10-21 00:00:00
Tasks: Causal Inference

Instrumental variables have informed the research on causal inference in
economics over the last century. Despite their ubiquity and decided usefulness,
the current consensus in the literature is that the validity of an instrument
cannot be tested, particularly when the treatment is continuous. This note
addresses this issue in two ways. As a first contribution, it presents a proof
confirming the conjecture in Pearl (1995), showing that the validity of an
instrument indeed cannot be tested in the most general case when the endogenous
variable is continuous. However, as a second contribution, it shows that
already weak restrictions on the instrumental variable model reestablish
theoretical testability. Continuity is already enough. Monotonicity introduces
further testable implications.
Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding
Date: 2018-07-20 00:00:00
Tasks: EEG, Eeg Decoding, Electroencephalogram (EEG), Transfer Learning

When it comes to the classification of brain signals in real-life
applications, the training and the prediction data are often described by
different distributions. Furthermore, diverse data sets, e.g., recorded from
various subjects or tasks, can even exhibit distinct feature spaces. The fact
that data that have to be classified are often only available in small amounts
reinforces the need for techniques to generalize learned information, as
performances of brain-computer interfaces (BCIs) are enhanced by increasing
quantity of available data. In this paper, we apply transfer learning to a
framework based on deep convolutional neural networks (deep ConvNets) to prove
the transferability of learned patterns in error-related brain signals across
different tasks. The experiments described in this paper demonstrate the
usefulness of transfer learning, especially improving performances when only
little data can be used to distinguish between erroneous and correct
realization of a task. This effect could be delimited from a transfer of merely
general brain signal characteristics, underlining the transfer of
error-specific information. Furthermore, we could extract similar patterns in
time-frequency analyses in identical channels, leading to selective high signal
correlations between the two different paradigms. Classification on the
intracranial data yields in median accuracies up to $(81.50 \pm 9.49)\,\%$.
Decoding on only $10\%$ of the data without pre-training reaches performances
of $(54.76 \pm 3.56)\,\%$, compared to $(64.95 \pm 0.79)\,\%$ with
pre-training.
Traveling waves appear and disappear in unison with produced speech
Date: 2018-06-25 00:00:00

In Rapela (2016) we reported traveling waves (TWs) on electrocorticographic
(ECoG) recordings from an epileptic subject over speech processing brain
regions, while the subject rhythmically produced consonant-vowel syllables
(CVSs). In Rapela (2017) we showed that TWs are precisely synchronized, in
dynamical systems terms, to these productions. Here we show that TWs do not
occur continuously, but tend to appear before the initiation of CVSs and tend
to disappear before their termination. During moments of silence, between
productions of CVSs, TWs tend to reverse direction. To our knowledge, this is
the first study showing TWs related to the production of speech and, more
generally, the first report of behavioral correlates of mesoscale TWs in
behaving humans.
Point-identification in multivariate nonseparable triangular models
Date: 2018-06-25 00:00:00

In this article we introduce a general nonparametric point-identification
result for nonseparable triangular models with a multivariate first- and second
stage. Based on this we prove point-identification of Hedonic models with
multivariate heterogeneity and endogenous observable characteristics, extending
and complementing identification results from the literature which all require
exogeneity. As an additional application of our theoretical result, we show
that the BLP model (Berry et al. 1995) can also be identified without index
restrictions.
Automatic Feature Selection in Markov State Models Using Genetic Algorithm
Date: 2018-06-25 00:00:00
Tasks: Diversity, feature selection

Markov State Models (MSMs) are a powerful framework to reproduce the
long-time conformational dynamics of biomolecules using a set of short
Molecular Dynamics (MD) simulations. However, precise kinetics predictions of
MSMs heavily rely on the features selected to describe the system. Despite the
importance of feature selection for large system, determining an optimal set of
features remains a difficult unsolved problem. Here, we introduce an automatic
approach to optimize feature selection based on genetic algorithms (GA), which
adaptively evolves the most fitted solution according to natural selection
laws. The power of the GA-based method is illustrated on long atomistic folding
simulations of four proteins, varying in length from 28 to 80 residues. Due to
the diversity of tested proteins, we expect that our method will be extensible
to other proteins and drive MSM building to a more objective protocol.
Use of full-scale hybrid horizontal tubular photobioreactors to process agricultural runoff
Date: 2018-06-26 00:00:00

Diffuse pollution in rural areas due to agricultural run-off is a widespread
and difficult problem to address due to the vast areas affected. Drainage
channels do receive these polluted waters, but its introduction in the
conventional treatment network is unfeasible. Within this context,
microalgae-based treatment systems could be used as alternative treatment
plants. In this study, a new design of semi-closed (hybrid) tubular horizontal
photobioreactor (HTH-PBR) with low energy requirements has been evaluated for
microalgae cultivation at full-scale (8.5 m3), using agricultural runoff as
feedstock. This novel system was tested in batch and continuous mode during a
total of 4 and 135 days. Considering a full-scale application in an
agricultural context, a batch test was carried out to evaluate the performance
of the system. An increase of 22% in the biomass concentration in 4 days was
registered, and all nutrients were consumed during the first two days. In the
continuous experiment (December- April), a productivity between 2-14 g TSS/m3d
was reached in winter, whereas values up to 76.4 g TSS/m3d were reached at the
end of the study in spring, despite the low nutrients concentration in the
feedstock. The elimination of emerging contaminants was also evaluated,
obtaining the highest removals for the fragrances tonalide and galaxolide (73%
and 68%), and the antiinflamatory diclofenac (61%).
Fund Characteristics and Performances of Socially Responsible Mutual Funds: Do ESG Ratings Play a Role?
Authors: Nandita Das, Bernadette Ruf, Swarn Chatterjee, Aman Sunder
Date: 2018-06-26 00:00:00

This paper examines the risk-adjusted performance and differential fund flows for socially responsible mutual funds (SRMF). The results show that SRMF rated high on ESG, perform better than lower rated ESG funds during the period of economic crisis. The findings also show that low ESG rated SRMF had higher differential cash-flows than high rated ESG funds except for the period of economic down turn. The findings are of interest to financial advisors, investors, mutual fund managers, and researchers on how SRMF performance responds to periods of economic downturn and expansion
Multiple Scattering Media Imaging via End-to-End Neural Network
Date: 2018-06-20 00:00:00
Tasks: Object, Retrieval

Recovering the image of an object from its phaseless speckle pattern is
difficult. Let alone the transmission matrix is unknown in multiple scattering
media imaging. Double phase retrieval is a recently proposed efficient method
which recovers the unknown object from its phaseless measurements by two steps
with phase retrieval.
  In this paper, we combine the two steps in double phase retrieval and
construct an end-to-end neural network called TCNN(Transforming Convolutional
Neural Network) which directly learns the relationship between the phaseless
measurements and the object. TCNN contains a special layer called transform
layer which aims to be a bridge between different transform domains. Tested by
the empirical data provided in\cite{Metzler2017Coherent}, images can be
recovered by TCNN with comparable quality compared with state-of-the-art
methods. Not only the transmission matrix needn't to be calculated but also the
time to recover the object can be hugely reduced once the parameters of TCNN
are stable.
Segmentation of TCD Cerebral Blood Flow Velocity Recordings
Date: 2018-06-24 00:00:00
Tasks: Classification

A binary beat-by-beat classification algorithm for cerebral blood flow
velocity (CBFV) recordings based on amplitude, spectral and morphological
features is presented. The classification difference between 15 manually and
algorithmically annotated CBFV records is around 5%.
Real time state monitoring and fault diagnosis system for motor based on LabVIEW
Date: 2018-06-25 00:00:00
Tasks: Fault Diagnosis

Motor is the most widely used production equipment in industrial field. In
order to realize the real-time state monitoring and multi-fault pre-diagnosis
of three-phase motor, this paper presents a design of three-phase motor state
monitoring and fault diagnosis system based on LabVIEW.
  The multi-dimensional vibration acceleration, rotational speed, temperature,
current and voltage signals of the motor are collected with NI cDAQ acquisition
equipment in real time and high speed. At the same time, the model of motor
health state and fault state is established. The order analysis algorithm is
used to analyze the data at an advanced level, and the diagnosis and
classification of different fault types are realized. The system is equipped
with multi-channel acquisition, display, analysis and storage. Combined with
the current cloud transmission technology, we will back up the data to the
cloud to be used by other terminals.
Hybrid Power-Line/Wireless Communication Systems For Indoor Applications
Date: 2018-06-26 00:00:00

The high attenuation which increases as frequency increases in power line
communication systems (PLC) makes achieving and sustaining high data rates over
such channels a challenging task. In most communication systems, relays are
normally deployed to overcome the effect of signal attenuation and to achieve
reliable transmissions between users. Amplifyand- forward (AF) and
decode-and-forward (DF) relays are often used. In contrast to the existing work
on this topic which advocate utilizing relays either between PLC links or
between wireless links, the authors of this paper focus on using AF relaying
for in-home PLC and wireless systems where one PLC user wants to share data
with one wireless user which has no access to the PLC network. We refer to this
system as the Hybrid approach. A mathematical method is developed for this
network to formulate the capacity of the system by exploiting the statistical
properties of both PLC and wireless channels. Monte Carlo simulations are used
throughout this paper to validate the analytical results. The impact of several
system parameters is investigated in this paper. For the sake of comparison, we
also provide the performance analysis and results for a PLC only network. The
results showed that the performance of the hybrid system enhances as we
increase the relay gain and deteriorates as the end-to-end distance increases.
It is also found that the PLC only scheme may outperform the hybrid one if the
relay gain is relatively small; however, the user mobility offered by the
hybrid system remains the main advantage over the PLC only system.
Low Photon Count Phase Retrieval Using Deep Learning
Date: 2018-06-25 00:00:00
Tasks: Deep Learning, Retrieval

Imaging systems' performance at low light intensity is affected by shot
noise, which becomes increasingly strong as the power of the light source
decreases. In this paper we experimentally demonstrate the use of deep neural
networks to recover objects illuminated with weak light and demonstrate better
performance than with the classical Gerchberg-Saxton phase retrieval algorithm
for equivalent signal over noise ratio. Prior knowledge about the object is
implicitly contained in the training data set and feature detection is possible
for a signal over noise ratio close to one. We apply this principle to a phase
retrieval problem and show successful recovery of the object's most salient
features with as little as one photon per detector pixel on average in the
illumination beam. We also show that the phase reconstruction is significantly
improved by training the neural network with an initial estimate of the object,
as opposed as training it with the raw intensity measurement.
AlertMix: A Big Data platform for multi-source streaming data
Authors: Singhal Ayush, Pant Rakesh, Sinha Pradeep
Date: 2018-06-18 00:00:00
Tasks: Fraud Detection

The demand for stream processing is increasing at an unprecedented rate. Big
data is no longer limited to processing of big volumes of data. In most
real-world scenarios, the need for processing stream data as it comes can only
meet the business needs. It is required for trading, fraud detection, system
monitoring, product maintenance and of course social media data such as Twitter
and YouTube videos. In such cases, a "too late architecture" that focuses on
batch processing cannot realize the use cases. In this article, we present an
end to end Big data platform called AlertMix for processing multi-source
streaming data. Its architecture and how various Big data technologies are
utilized are explained in this work. We present the performance of our platform
on real live streaming data which is currently handled by the platform.
Embodying probabilistic inference in biochemical circuits
Date: 2018-06-26 00:00:00

Probabilistic inference provides a language for describing how organisms may
learn from and adapt to their environment. The computations needed to implement
probabilistic inference often require specific representations, akin to having
the suitable data structures for implementing certain algorithms in computer
programming. Yet it is unclear how such representations can be instantiated in
the stochastic, parallel-running biochemical machinery found in cells (such as
single-celled organisms). Here, we show how representations for supporting
inference in Markov models can be embodied in cellular circuits, by combining a
concentration-dependent scheme for encoding probabilities with a mechanism for
directional counting. We show how the logic of protein production and
degradation constrains the computation we set out to implement. We argue that
this process by which an abstract computation is shaped by its biochemical
realization strikes a compromise between "rationalistic" information-processing
perspectives and alternative approaches that emphasize embodiment.
Vector OFDM Transmission over Non-Gaussian Power Line Communication Channels
Date: 2018-06-26 00:00:00

Most of the recent power line communication (PLC) systems and standards, both
narrow-band and broadband, are based on orthogonal frequency-division
multiplexing (OFDM). This multiplexing scheme however suffers from the high
peak-to-average power ratio (PAPR) which can considerably impact the energy
efficiency, size and cost of PLC modems as well as cause electromagnetic
compatibility (EMC) issues. This paper investigates the performance of vector
OFDM (VOFDM), which has inherently better PAPR properties, over non-Gaussian
broadband PLC channels equipped with two nonlinear preprocessors at the
receiver. In addition, the low PAPR property of the VOFDM system is exploited
to further enhance the efficiency of the nonlinear preprocessors. The
achievable gains are studied in terms of the complementary cumulative
distribution function of the PAPR, probability of noise detection error and the
signal-to-noise ratio at the output of the nonlinear preprocessors. For
comparison's sake, the performance of conventional OFDM systems is also
presented throughout the paper. Results reveal that the proposed system is able
to provide up to 2 dB saving in the transmit power relative to the conventional
OFDM under same system conditions, which eventually also translates into a
system that is more resilient to EMC limits, reduced cost and size of PLC
modems. It is also shown that the achievable gains become more significant as
the vector block (VB) size of the VOFDM system is increased.
von Mises Tapering: A Circular Data Windowing
Date: 2018-06-27 00:00:00

Continuous standard windowing is revisited and a new taper shape is
introduced, which is based on the normal circular distribution by von Mises.
Continuous-time windows are considered and their spectra obtained. A brief
comparison with classical window families is performed in terms of their
spectral properties. These windows can be used as an alternative in spectral
analysis.
Independent Deeply Learned Matrix Analysis for Multichannel Audio Source Separation
Date: 2018-06-27 00:00:00
Tasks: Audio Source Separation

In this paper, we address a multichannel audio source separation task and
propose a new efficient method called independent deeply learned matrix
analysis (IDLMA). IDLMA estimates the demixing matrix in a blind manner and
updates the time-frequency structures of each source using a pretrained deep
neural network (DNN). Also, we introduce a complex Student's t-distribution as
a generalized source generative model including both complex Gaussian and
Cauchy distributions. Experiments are conducted using music signals with a
training dataset, and the results show the validity of the proposed method in
terms of separation accuracy and computational cost.
400 Gbps Dual-polarisation Non-linear Frequency-division Multiplexed Transmission with b-Modulation
Date: 2018-06-27 00:00:00

We demonstrate, for the first time, a b-modulated dual-polarisation NFDM
transmission in simulation, achieving a record net data rate of 400 Gbps (SE of
7.2 bit/s/Hz) over 960 km. The proposed scheme shows 1 dB Q-factor improvement
over qc-modulation scheme.
A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP
Date: 2018-06-27 00:00:00
Tasks: Brain Computer Interface

Humans perform remarkably well in many cognitive tasks including pattern
recognition. However, the neuronal mechanisms underlying this process are not
well understood. Nevertheless, artificial neural networks, inspired in brain
circuits, have been designed and used to tackle spatio-temporal pattern
recognition tasks. In this paper we present a multineuronal spike pattern
detection structure able to autonomously implement online learning and
recognition of parallel spike sequences (i.e., sequences of pulses belonging to
different neurons/neural ensembles). The operating principle of this structure
is based on two spiking/synaptic neurocomputational characteristics: spike
latency, that enables neurons to fire spikes with a certain delay and
heterosynaptic plasticity, that allows the own regulation of synaptic weights.
From the perspective of the information representation, the structure allows
mapping a spatio-temporal stimulus into a multidimensional, temporal, feature
space. In this space, the parameter coordinate and the time at which a neuron
fires represent one specific feature. In this sense, each feature can be
considered to span a single temporal axis. We applied our proposed scheme to
experimental data obtained from a motor inhibitory cognitive task. The test
exhibits good classification performance, indicating the adequateness of our
approach. In addition to its effectiveness, its simplicity and low
computational cost suggest a large scale implementation for real time
recognition applications in several areas, such as brain computer interface,
personal biometrics authentication or early detection of diseases.