pwc-archive/papers-with-abstracts huggingface dataset:
Protein escape at the ribosomal exit tunnel: Effects of native interactions, tunnel length and macromolecular crowding
Authors: Phuong Thuy Bui, Trinh Xuan Hoang
Date: 2018-07-26 00:00:00

How fast a post-translational nascent protein escapes from the ribosomal exit tunnel is relevant to its folding and protection against aggregation. Here, by using Langevin molecular dynamics, we show that non-local native interactions help decreasing the escape time, and foldable proteins generally escape much faster than same-length self-repulsive homopolymers at low temperatures. The escape process, however, is slowed down by the local interactions that stabilize the {\alpha}-helices. The escape time is found to increase with both the tunnel length and the concentration of macromolecular crowders outside the tunnel. We show that a simple diffusion model described by the Smoluchowski equation with an effective linear potential can be used to map out the escape time distribution for various tunnel lengths and various crowder concentrations. The consistency between the simulation data and the diffusion model however is found only for the tunnel length smaller than a cross-over length of 90 {\AA} to 110 {\AA}, above which the escape time increases much faster with the tunnel length. It is suggested that the length of ribosomal exit tunnel has been selected by evolution to facilitate both the efficient folding and efficient escape of single domain proteins. We show that macromolecular crowders lead to an increase of the escape time, and attractive crowders are unfavorable for the folding of nascent polypeptide.
Fitting a recurrent dynamical neural network to neural spiking data: Tackling with the sigmoidal gain function issues
Date: 2018-07-26 00:00:00

This is a continuation of a recent study on the modeling of the information
coding in sensory system in the brain. The data from a sensory neurons are
available as discrete spike timings with no amplitude information. In the
simulations, these are generated from a data generator model which has certain
differences from the model being estimated. The model under consideration is
simpler than the one used as a data generator as it has no sigmoidal gain
function parameters. This choice is based on a suggestion from a recent study
which states that inclusion of gain functions to the estimation algorithm
increases issues such as parameter confounding which leads to performance
degradation issues like increased or unpredictable variance changes with
different stimuli configurations. To resolve this issue we consider a more
generic model that has no sigmoidal gain functions to be estimated. Like that
of the first research, we applied a Fourier series stimulus to both data
generator and the estimated network. In order to test the performance of the
proposed scheme, we have repeated the simulations with different sample sizes,
stimulus component counts, amplitude and base frequencies. Mean values of
estimation are presented as tables and the statistical analysis results are
presented in graphical forms. In addition, since we do not have any true
parameter data (for the estimated model) we compare the firing rate responses
of both data generator and estimated models to different stimuli. It appeared
that the responses of both models are almost the same.
Fast cosine transform for FCC lattices
Date: 2018-07-26 00:00:00

Voxel representation and processing is an important issue in a broad spectrum
of applications. E.g., 3D imaging in biomedical engineering applications, video
game development and volumetric displays are often based on data representation
by voxels. By replacing the standard sampling lattice with a face-centered
lattice one can obtain the same sampling density with less sampling points and
reduce aliasing error, as well. We introduce an analog of the discrete cosine
transform for the facecentered lattice relying on multivariate Chebyshev
polynomials. A fast algorithm for this transform is deduced based on algebraic
signal processing theory and the rich geometry of the special unitary Lie group
of degree four.
Two-Step Estimation and Inference with Possibly Many Included Covariates
Date: 2018-07-26 00:00:00
Tasks: valid, Vocal Bursts Valence Prediction

We study the implications of including many covariates in a first-step
estimate entering a two-step estimation procedure. We find that a first order
bias emerges when the number of \textit{included} covariates is "large"
relative to the square-root of sample size, rendering standard inference
procedures invalid. We show that the jackknife is able to estimate this "many
covariates" bias consistently, thereby delivering a new automatic
bias-corrected two-step point estimator. The jackknife also consistently
estimates the standard error of the original two-step point estimator. For
inference, we develop a valid post-bias-correction bootstrap approximation that
accounts for the additional variability introduced by the jackknife
bias-correction. We find that the jackknife bias-corrected point estimator and
the bootstrap post-bias-correction inference perform excellent in simulations,
offering important improvements over conventional two-step point estimators and
inference procedures, which are not robust to including many covariates. We
apply our results to an array of distinct treatment effect, policy evaluation,
and other applied microeconomics settings. In particular, we discuss production
function and marginal treatment effect estimation in detail.
Key Borrowers Detection by Long-Range Interactions
Date: 2018-06-15 00:00:00

We propose a new method for assessing agents' influence in financial network
structures, which takes into consideration the intensity of interactions. A
distinctive feature of this approach is that it considers not only direct
interactions of agents of the first level and indirect interactions of the
second level, but also long-range indirect interactions. At the same time we
take into account the attributes of agents as well as the possibility of impact
to a single agent from a group of other agents. This approach helps us to
identify systemically important elements which cannot be detected by classical
centrality measures or other indices. The proposed method was used to analyze
the banking foreign claims for the end of 1Q 2015. Under the approach, two
types of key borrowers were detected: a) major players with high ratings and
positive credit history; b) intermediary players, which have a great scale of
financial activities through the organization of favorable investment
conditions and positive business climate.
SVD-based Visualisation and Approximation for Time Series Data in Smart Energy Systems
Authors: Khoshrou Abdolrahman, Dorsman Andre B., Pauwels Eric. J.
Date: 2018-07-11 00:00:00
Tasks: Time Series, Time Series Analysis

Many time series in smart energy systems exhibit two different timescales. On
the one hand there are patterns linked to daily human activities. On the other
hand, there are relatively slow trends linked to seasonal variations. In this
paper we interpret these time series as matrices, to be visualized as images.
This approach has two advantages: First of all, interpreting such time series
as images enables one to visually integrate across the image and makes it
therefore easier to spot subtle or faint features. Second, the matrix
interpretation also grants elucidation of the underlying structure using
well-established matrix decomposition methods. We will illustrate both these
aspects for data obtained from the German day-ahead market.
A simple device for microinjections, manipulations and measurements using an electromorphological chip under microinterferometric control of the interface and membrane processes at the thickness range of 5-1000 nm at different angles
Authors: O. V. Gradov, P. A. Nasirov, A. A. Scrynnic, A. G. Jablokov
Date: 2018-07-23 00:00:00

Micromanipulations, perfusions and measurements performed using glass microelectrodes filled with an electrolyte is a conventional technique for experimental morphological and membrane electrophysiological studies at a single cell and membrane surface level. The typical (effective) diameter of the end of the glass microelectrode is from 500 up to less than 100 nm, which prevents one from observing it using a standard optical microscope in accordance with the optical resolution criteria, since the diameter less than 500 nm is indistinguishable within the interference zone. Microprocessor programming of the puller (microforge) that provides pulling and tearing allows to obtain in certain regimes the adjusted diameter and shape of the micropipette tip, although this result is not fully controlled due to the above limitations. In this connection it is necessary to design the control devices for the micropipette tips both at the preparation and operation stages (intracellular or extracellular insertion). This method also should provide visualization of the processes occurring upon interaction of the microelectrode tip with the cell in real time, depending on the electrode type and state, which allows to level the artifacts arising with the systematic error frequency from the uncontrolled operation of the micropipette tip after different ways of the microelectrode filling with the electrolyte. We propose an installation scheme that solves the above problems by means of introducing an interferometric device for microscopic control of the microelectrode and micromanipulator or microperfusor, for the first time for a given type of optical instruments combined with the interferometric optical scheme.
Can graphene bilayers be the membrane mimetic materials? "Ion channels" in graphene-based nanostructures
Authors: Oleg V. Gradov, Margaret A. Gradova
Date: 2018-07-23 00:00:00

The prospects of application of graphene and related structures as the membrane mimetic materials, capable of reproducing several biomembrane functions up to the certain limit, are analyzed in the series of our papers. This paper considers the possibility of the ion channel function modeling using graphene and its derivatives. The physical mechanisms providing selective permeability for different membrane mimetic materials, as well as the limits of the adequate simulation of the transport, catalytic, sensing and electrogenic properties of the cell membrane ion channels using bilayered graphene-based structures are discussed.
Data-Oriented Algorithm for Real-Time Estimation of Flow Rates and Flow Directions in a Water Distribution Network
Authors: Dumora Christophe  LaBRI, IMB, Auber David  LaBRI, Bigot Jérémie  IMB, Couallier Vincent  IMB, Leclerc Cyril  LyRE
Date: 2018-07-25 00:00:00
Tasks: Management

The aim of this paper is to present how data collected from a water
distribution network (WDN) can be used to reconstruct flow rate and flow
direction all over the network to enhance knowledge and detection of unforeseen
events. The methodological approach consists in modeling the WDN and all
available sensor data related to the management of such a network in the form
of a flow network graph G = (V, E, s, t, c), with V a set of nodes, E a set of
edges whose elements are ordered pairs of distinct nodes, s a source node, t a
sink node and c a capacity function on edges. Our objective is to reconstruct a
real-valued function f(u,v): VxV => R on all the edges E in VxV from partial
observations on a small number of nodes V = {1, ..., n}. This reconstruction
method consists in a data-driven Ford-Fulkerson maximum-flow problem in a
multi-source, multi-sink context using a constrained bidirectional
breadth-first search based on Edmonds-Karp method. The innovative approach is
its application in the context of smart cities to operate from sensor data,
structural data from a geographical information system (GIS) and consumption
estimates.
Performance Analysis of Integrated Power-Line/Visible-Light Communication Systems with AF Relaying
Date: 2018-07-26 00:00:00

Reliable data transmissions and offering better mobility to the end user can
be achieved by integrating different communication systems. In this paper, we
investigate the performance of a cascaded indoor power line communication
(PLC)/visible light communication (VLC) system with the presence of an
amplify-and-forward (AF) relay. Using the pre-installed infrastructure of
electricity wiring networks gives the advantage to use PLC as a backbone for
VLCs. The performance of the proposed hybrid system is discussed in terms of
the average capacity. A mathematical method is developed for this network to
formulate the capacity by exploiting the statistical properties of both the PLC
and VLC channels. The derived analytical expressions are validated by Monte
Carlo simulations. The results showed that there is a considerable improvement
in the performance of the hybrid system as the relay gain increases whereas it
deteriorates with increasing the end-to-end distance. A comparison between the
performance of a parallel hybrid/PLC and hybrid systems is also provided. It is
found that the hybrid/PLC system outperforms the hybrid one. However, the user
mobility offered by the latter system remains the main advantage over the
former approach.
Global optimization using Gaussian Processes to estimate biological parameters from image data
Date: 2018-07-26 00:00:00
Tasks: Gaussian Processes, global-optimization, parameter estimation

Parameter estimation is a major challenge in computational modeling of
biological processes. This is especially the case in image-based modeling where
the inherently quantitative output of the model is measured against image data,
which is typically noisy and non-quantitative. In addition, these models can
have a high computational cost, limiting the number of feasible simulations,
and therefore rendering most traditional parameter estimation methods
unsuitable. In this paper, we present a pipeline that uses Gaussian process
learning to estimate biological parameters from noisy, non-quantitative image
data when the model has a high computational cost. This approach is first
successfully tested on a parametric function with the goal of retrieving the
original parameters. We then apply it to estimating parameters in a biological
setting by fitting artificial in-situ hybridization (ISH) data of the
developing murine limb bud. We expect that this method will be of use in a
variety of modeling scenarios where quantitative data is missing and the use of
standard parameter estimation approaches in biological modeling is prohibited
by the computational cost of the model.
Visual Display and Retrieval of Music Information
Authors: Valle Rafael
Date: 2018-07-26 00:00:00
Tasks: Data Visualization, Information Retrieval, Music Information Retrieval, Retrieval

This paper describes computational methods for the visual display and
analysis of music information. We provide a concise description of software,
music descriptors and data visualization techniques commonly used in music
information retrieval. Finally, we provide use cases where the described
software, descriptors and visualizations are showcased.
Inter-Numerology Interference Analysis for 5G and Beyond
Date: 2018-07-26 00:00:00

One of the defining characteristics of 5G is the flexibility it offers for
supporting different services and communication scenarios. For this purpose,
usage of multiple numerologies has been proposed by the 3rd Generation
Partnership Project (3GPP). The flexibility provided by multi-numerology system
comes at the cost of additional interference, known as inter-numerology
interference (INI). This paper comprehensively explains the primary cause of
INI, and then identifies and describes the factors affecting the amount of INI
experienced by each numerology in the system. These factors include subcarrier
spacing, number of used subcarriers, power offset, windowing operations and
guard bands.
Ripple oscillations in the left temporal neocortex are associated with impaired verbal episodic memory encoding
Date: 2018-07-26 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Background: We sought to determine if ripple oscillations (80-120Hz),
detected in intracranial EEG (iEEG) recordings of epilepsy patients, correlate
with an enhancement or disruption of verbal episodic memory encoding. Methods:
We defined ripple and spike events in depth iEEG recordings during list
learning in 107 patients with focal epilepsy. We used logistic regression
models (LRMs) to investigate the relationship between the occurrence of ripple
and spike events during word presentation and the odds of successful word
recall following a distractor epoch, and included the seizure onset zone (SOZ)
as a covariate in the LRMs. Results: We detected events during 58,312 word
presentation trials from 7,630 unique electrode sites. The probability of
ripple on spike (RonS) events was increased in the seizure onset zone (SOZ,
p<0.04). In the left temporal neocortex RonS events during word presentation
corresponded with a decrease in the odds ratio (OR) of successful recall,
however this effect only met significance in the SOZ (OR of word recall 0.71,
95% CI: 0.59-0.85, n=158 events, adaptive Hochberg p<0.01). Ripple on
oscillation events (RonO) that occurred in the left temporal neocortex non-SOZ
also correlated with decreased odds of successful recall (OR 0.52, 95% CI:
0.34-0.80, n=140, adaptive Hochberg , p<0.01). Spikes and RonS that occurred
during word presentation in the left middle temporal gyrus during word
presentation correlated with the most significant decrease in the odds of
successful recall, irrespective of the location of the SOZ (adaptive Hochberg,
p<0.01). Conclusion: Ripples and spikes generated in left temporal neocortex
are associated with impaired verbal episodic memory encoding.
A new and stable estimation method of country economic fitness and product complexity
Date: 2018-10-12 00:00:00

We present a new metric estimating fitness of countries and complexity of
products by exploiting a non-linear non-homogeneous map applied to the publicly
available information on the goods exported by a country. The non homogeneous
terms guarantee both convergence and stability. After a suitable rescaling of
the relevant quantities, the non homogeneous terms are eventually set to zero
so that this new metric is parameter free. This new map almost reproduces the
results of the original homogeneous metrics already defined in literature and
allows for an approximate analytic solution in case of actual binarized
matrices based on the Revealed Comparative Advantage (RCA) indicator. This
solution is connected with a new quantity describing the neighborhood of nodes
in bipartite graphs, representing in this work the relations between countries
and exported products. Moreover, we define the new indicator of country
net-efficiency quantifying how a country efficiently invests in capabilities
able to generate innovative complex high quality products. Eventually, we
demonstrate analytically the local convergence of the algorithm involved.
High linear low noise amplifier based on self-biasing multiple gated transistors
Date: 2018-06-10 00:00:00

Noise level frequently set the basic limit on the smallest signal. New noise
reduction technology and amplifiers voltage-noise density, yet still offer high
speed, high accuracy, and low power solution. Low noise amplifiers always play
a significant role in RF technology. Hence in this paper, high linear low noise
amplifier (LNA) using cascade self-biased multiple gated transistors (MGTR) is
presented. The proposed system is covering 0.9 to 2.4 GHz applications. To
verify the functionality of the proposed LNA as a bottleneck of RF technology,
a cascade LNA without MGTR is implemented and synthesized. The comparison has
been done with the single-gate LNA. From the synthesized result, proposed LNA
obtained 10 dBm third-order input intercept point (IIP3) in compare with
single-gate LNA at 9dB gain. The proposed LNA is implemented in 90 nm CMOS
technology and reported 13 dBm IIP3, 1.9 dB NF and 9 dB gain while consuming
7.9 mW from 2 V supply.
Mathematical Toolbox and its application in the Development of Laboratory Scale Vertical Axis Wind Turbine
Date: 2018-06-10 00:00:00

A wind turbine works with the principle of extracting energy from the wind to
generate electricity. The power generated is directly proportional to the wind
speed available. There are two major types of wind turbine design namely the
horizontal and vertical axis wind turbine depending on the orientation of the
turbine rotor and its generator. This paper deals with the design of vertical
turbine due to its advantage of operating at a low wind speed over that of the
horizontal turbine. The analysis of change in the parameters of a vertical axis
wind turbine is investigated to get the optimized way in which the rotor of the
turbine is to be designed. This is done through modeling and simulation of the
turbine using various parameters in the MATLAB/SIMULINK environment. A
graphical user interface is created for a generic model of vertical axis wind
turbine that is used to determine its parameters.
Multirate 5G Downlink Performance Comparison for f-OFDM and w-OFDM Schemes with Different Numerologies
Date: 2018-07-26 00:00:00

One of the main open problems for next generation wireless networks, is to
find the new OFDM-based waveform to be used in 5G. The new modulation scheme
must primarily be able to achieve higher spectral efficiency than its
predecessor. The main 3GPP's candidate is a new version of OFDM, called
Filtered Orthogonal Frequency-Division Multiplexing (f-OFDM), which is similar
to OFDM but with additional filtering in order to reduce Out-Of-Band (OOB)
emissions and to obtain a better spectral-localization. Another option is
windowed-OFDM (w-OFDM), which is basically a classical OFDM scheme where each
symbol is windowed and overlapped in the time domain. In this paper we compare
classic OFDM signals using Cyclic Prefix (CP-OFDM) with f-OFDM and w-OFDM, each
one with multiple parametric options and numerologies. A multirate transmitter
simultaneously operating with multiple numerologies is considered, where the
transmitted sub-bands must be up-sampled and interpolated in order to generate
the composite numerical signal fed to the Digital to Analog Converter (DAC).
Finally, we discuss advantages and disadvantages of the various schemes.
Towards Identifying the Systems-Level Primitives of Cortex by In-Circuit Testing
Date: 2018-07-26 00:00:00

The hypothesis considered here is that cognition is based on a small set of
systems-level computational primitives that are defined at a level higher than
single neurons. It is pointed out that for one such set of primitives, whose
quantitative effectiveness has been demonstrated by analysis and computer
simulation, emerging technologies for stimulation and recording are making it
possible to test directly whether cortex is capable of performing them.
Smart Analytical Signature Verification For DSP Applications
Date: 2018-06-10 00:00:00
Tasks: Dimensionality Reduction

Signature verification is an authentication technique that considers
handwritten signature as a biometric. From a biometric perspective this project
made use of automatic means through an integration of intelligent algorithms to
perform signal enhancement function such as filtering and smoothing for
optimization in conventional biometric systems. A handwritten signature is a 1D
Daubechies wavelet signal (db4) that utilizes Discrete Wavelet Transform (DWT)
and Discrete Cosine Transform (DCT) collectively to create a feature dataset
with d-dimensional space. In the proposed work the statistical features
characteristics are extracted from each particular signature per data source.
Two databases called Signature Verification Competition (SVC) 2004 database and
SUBCORPUS 100 MCYT Bimodal database are used to cooperate with the design
algorithm. Furthermore dimension reduction technique is applied to the large
feature vectors. A system model is trained and evaluated using the support
vector machine (SVM) classifier algorithm. Hence an equal error rate (EER) of
8.7 percent and an average correct verification rate of 91.3 percent