pwc-archive/papers-with-abstracts huggingface dataset:
New indicators for assessing the quality of in silico produced biomolecules: the case study of the aptamer-Angiopoietin-2 complex
Date: 2018-07-19 00:00:00
Tasks: Specificity

Computational procedures to foresee the 3D structure of aptamers are in
continuous progress. They constitute a crucial input to research, mainly when
the crystallographic counterpart of the structures in silico produced is not
present. At now, many codes are able to perform structure and binding
prediction, although their ability in scoring the results remains rather weak.
In this paper, we propose a novel procedure to complement the ranking outcomes
of free docking code, by applying it to a set of anti-angiopoietin aptamers,
whose performances are known. We rank the in silico produced configurations,
adopting a maximum likelihood estimate, based on their topological and
electrical properties. From the analysis, two principal kinds of conformers are
identified, whose ability to mimick the binding features of the natural
receptor is discussed. The procedure is easily generalizable to many biological
biomolecules, useful for increasing chances of success in designing
high-specificity biosensors (aptasensors).
Quantifying Volatility Reduction in German Day-ahead Spot Market in the Period 2006 through 2016
Date: 2018-07-19 00:00:00
Tasks: Time Series, Time Series Analysis

In Europe, Germany is taking the lead in the switch from the conventional to
renewable energy. This poses new challenges as wind and solar energy are
fundamentally intermittent, weather-dependent and less predictable. It is
therefore of considerable interest to investigate the evolution of price
volatility in this post-transition era. There are a number of reasons, however,
that makes the practical studies difficult. For instance, EPEX prices can be
zero or negative. Consequently, the standard approach in financial time series
analysis to switch to logarithmic measures is inapplicable. Furthermore, in
contrast to the stock market prices which are only available for trading days,
EPEX prices cover the whole year, including weekends and holidays. Accordingly,
there is a lot of underlying variability in the data which has nothing to do
with volatility, but simply reflects diurnal activity patterns. An important
distinction of the present work is the application of matrix decomposition
techniques, namely the singular value decomposition (SVD), for defining an
alternative notion of volatility. This approach is systematically more robust
toward outliers and also the diurnal patterns. Our observations show that the
day-ahead market is becoming less volatile in recent years.
QoS and Coverage Aware Dynamic High Density Vehicle Platooning (HDVP)
Date: 2018-07-19 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In a self-driving environment, vehicles communicate with each other to create
a closely spaced multiple vehicle strings on a highway, i.e., high-density
vehicle platooning (HDVP). In this paper, we address the Cellular Vehicle to
Everything (C-V2X) quality of service (QoS) and radio coverage issues for HDVP
and propose a dynamic platooning mechanism taking into account the change of
coverage condition, the road capacity, medium access control (MAC) and spectrum
reuse while at the same time guaranteeing the stringent QoS requirements in
terms of latency and reliability.
Eigenspace-Based Minimum Variance Combined with Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging
Date: 2018-07-18 00:00:00

In Photoacoustic imaging, Delay-and-Sum (DAS) algorithm is the most commonly
used beamformer. However, it leads to a low resolution and high level of
sidelobes. Delay-Multiply-and-Sum (DMAS) was introduced to provide lower
sidelobes compared to DAS. In this paper, to improve the resolution and
sidelobes of DMAS, a novel beamformer is introduced using Eigenspace-Based
Minimum Variance (EIBMV) method combined with DMAS, namely EIBMV-DMAS. It is
shown that expanding the DMAS algebra leads to several terms which can be
interpreted as DAS. Using the EIBMV adaptive beamforming instead of the
existing DAS (inside the DMAS algebra expansion) is proposed to improve the
image quality. EIBMV-DMAS is evaluated numerically and experimentally. It is
shown that EIBMV-DMAS outperforms DAS, DMAS and EIBMV in terms of resolution
and sidelobes. In particular, at the depth of 11 mm of the experimental images,
EIBMV-DMAS results in about 113 dB and 50 dB sidelobe reduction, compared to
DMAS and EIBMV, respectively. At the depth of 7 mm, for the experimental
images, the quantitative results indicate that EIBMV-DMAS leads to improvement
in Signal-to-Noise Ratio (SNR) of about 75% and 34%, compared to DMAS and
EIBMV, respectively.
A Capsule based Approach for Polyphonic Sound Event Detection
Date: 2018-07-23 00:00:00
Tasks: Event Detection, Sound Event Detection

Polyphonic sound event detection (polyphonic SED) is an interesting but
challenging task due to the concurrence of multiple sound events. Recently, SED
methods based on convolutional neural networks (CNN) and recurrent neural
networks (RNN) have shown promising performance. Generally, CNN are designed
for local feature extraction while RNN are used to model the temporal
dependency among these local features. Despite their success, it is still
insufficient for existing deep learning techniques to separate individual sound
event from their mixture, largely due to the overlapping characteristic of
features. Motivated by the success of Capsule Networks (CapsNet), we propose a
more suitable capsule based approach for polyphonic SED. Specifically, several
capsule layers are designed to effectively select representative frequency
bands for each individual sound event. The temporal dependency of capsule's
outputs is then modeled by a RNN. And a dynamic threshold method is proposed
for making the final decision based on RNN outputs. Experiments on the TUT-SED
Synthetic 2016 dataset show that the proposed approach obtains an F1-score of
68.8% and an error rate of 0.45, outperforming the previous state-of-the-art
method of 66.4% and 0.48, respectively.
Robust Oil-spill Forensics and Petroleum Source Differentiation using Quantized Peak Topography Maps
Date: 2018-07-19 00:00:00

Identification and classification of environmental forensics, with the
petroleum forensics as the main application, requires an effective technology
or method to distinguish between the closely located forensics as they share
many main biomarkers. Two-dimensional gas chromatography is one of these
technologies with which a petroleum forensic is separated into its chemical
compounds, resulting in a three-dimensional image, GCXGC image. Therefore,
distinguishing between two petroleum forensics is equivalent to the comparison
between their corresponding GCXGC images. In this paper, we present a
technique, called Quantized Peak Topography Map (QPTM), which results in a
better separation between the GCXGC images. We validate our proposed method on
a model dataset, consisting of thirtyfour GCXGC images, extracted from the
different parts of the world.
Chromatin Laser Imaging Reveals Abnormal Nuclear Changes for Early Cancer Detection
Date: 2018-07-19 00:00:00

We developed and applied rapid scanning laser-emission microscopy to detect
abnormal changes in cell nuclei for early diagnosis of cancer and cancer
precursors. Regulation of chromatins is essential for genetic development and
normal cell functions, while abnormal nuclear changes may lead to many
diseases, in particular, cancer. The capability to detect abnormal changes in
apparently normal tissues at a stage earlier than tumor development is critical
for cancer prevention. Here we report using LEM to analyze colonic tissues from
mice at-risk for colon cancer by detecting prepolyp nuclear abnormality. By
imaging the lasing emissions from chromatins, we discovered that, despite the
absence of observable lesions, polyps, or tumors under stereoscope, high-fat
mice exhibited significantly lower lasing thresholds than low-fat mice. The low
lasing threshold is, in fact, very similar to that of adenomas and is caused by
abnormal cell proliferation and chromatin deregulation that can potentially
lead to cancer. Our findings suggest that conventional methods, such as
colonoscopy, may be insufficient to reveal hidden or early tumors under
development. We envision that this work will provide new insights into LEM for
early tumor detection in clinical diagnosis and fundamental biological and
biomedical research of chromatin changes at the biomolecular level of cancer
development.
StimDust: A mm-scale implantable wireless precision neural stimulator with ultrasonic power and communication
Date: 2020-02-15 00:00:00
Tasks: Diagnostic

Neural stimulation is a powerful technique for modulating physiological
functions and for writing information into the nervous system as part of
brain-machine interfaces. Current clinically approved neural stimulators
require batteries and are many cubic centimetres in size -- typically much
larger than their intended targets. We present a complete wireless neural
stimulation system consisting of a 1.7 mm3 wireless, batteryless, leadless
implantable stimulator (the "mote"), an ultrasonic wireless link for power and
bi-directional communication, and a hand-held external transceiver. The mote
consists of a piezoceramic transducer, an energy storage capacitor, and a
stimulator integrated circuit (IC). The IC harvests ultrasonic power with high
efficiency, decodes stimulation parameter downlink data, and generates
current-controlled stimulation pulses. Stimulation parameters are time-encoded
on the fly through the wireless link rather than being programmed and stored on
the mote, reducing power consumption and on-chip memory requirements and
enabling complex stimulation protocols with high-temporal resolution and
low-latency feedback for use in closed-loop stimulation. Uplink data indicates
whether the mote is currently stimulating; it is encoded by the mote via
backscatter modulation and is demodulated at the external transceiver. We show
that the mote operates at an acoustic intensity that is 7.8% of the FDA limit
for diagnostic ultrasound and characterize the acoustic wireless link's
robustness to expected real-world misalignment. We demonstrate the in vivo
performance of the system with motes acutely implanted with a cuff on the
sciatic nerve of anesthetized rats and show highly repeatable stimulation
across a wide range of physiological responses.
General theory for stochastic admixture graphs and F-statistics
Date: 2018-07-19 00:00:00

We provide a general mathematical framework based on the theory of graphical
models to study admixture graphs. Admixture graphs are used to describe the
ancestral relationships between past and present populations, allowing for
population merges and migration events, by means of gene flow. We give various
mathematical properties of admixture graphs with particular focus on properties
of the so-called $F$-statistics. Also the Wright-Fisher model is studied and a
general expression for the loss of heterozygosity is derived.
Distributed Power Control Schemes for In-Band Full-Duplex Energy Harvesting Wireless Networks
Date: 2018-07-19 00:00:00

This paper studies two power control problems in energy harvesting wireless
networks where one hybrid base station (HBS) and all user equipments (UEs) are
operating in in-band full-duplex mode. We consider minimizing the aggregate
power subject to the quality of service requirement constraint, and maximizing
the aggregate throughput. We address these two problems by proposing two
distributed power control schemes for controlling the uplink transmit power by
the UEs and the downlink energy harvesting signal power by the HBS. In our
proposed schemes, the HBS updates the downlink transmit power level of the
energy-harvesting signal so that each UE is enabled to harvest its required
energy for powering the operating circuit and transmitting its uplink
information signal with the power level determined by the proposed schemes. We
show that our proposed power control schemes converge to their corresponding
unique fixed points starting from any arbitrary initial transmit power. We will
show that our proposed schemes well address the stated problems, which is also
demonstrated by our extensive simulation results.
Critique of the Fox-Lu model for Hodgkin-Huxley fluctuations in neuron ion channels
Date: 2018-07-28 00:00:00

Using a well known result that every FP equation has an antecedent Langevin
equation LE Fox and Lu proposed such a description for ion channels in 1994.
Their contraction followed the works of van Kampen and of T. Kurtz. The
contraction produces a diffusion term with a state dependent diffusion matrix,
D, that arises from the coupling matrix, S, in the LE. This S connected the
noise terms to the channel subunit variables in the LE. Fox and Lu and many
others later on observed that SS = D. Since D was determined by the contraction
of the MC equations into the FP equation, this left the problem of determining
the square root matrix, S, for every time step of the simulation. Since this is
time consuming, Fox and Lu introduced simplified models not requiring the
square root of a matrix. Subsequently, numerous studies were published that
showed the several shortcomings of these simplified models. In 2011, Goldwyn et
al. [6] rediscovered the overlooked original matrix dependent approach in the
Fox-Lu 1994 paper. They showed that it produced results in very good agreement
with the MC results. In 1991, Fox and Keizer [7] wrote a paper on an unrelated
topic that utilized the work of van Kampen and of Kurtz. In that work the
connection between D and S is SST = D. ST is the adjoint (transpose) of S. D
remains a positive definite symmetric matrix but S need not be. Fox has
reproduced the 2012 results of Orio and Soudry for potassium channels and has
also found in closed form the solution for the more complicated sodium
channels. The square root problem generally must be done numerically, but the
Cholesky is always doable in closed form. Thereby the S matrix for sodium is
given explicitly for the first time in this paper
Near-Optimal Distributed Estimation for a Network of Sensing Units Operating Under Communication Constraints
Date: 2018-07-19 00:00:00
Tasks: State Estimation

We study the problem of distributed state estimation in a network of sensing
units that can exchange their measurements but the rate of communication
between the units is constrained. The units collect noisy, possibly only
partial observations of the unknown state; they are assisted by a relay center
which can communicate at a higher rate and schedules the exchange of
measurements between the units. We consider the task of minimizing the total
mean-square estimation error of the network while promoting balance between the
individual units' performances. This problem is formulated as the maximization
of a monotone objective function subject to a cardinality constraint. By
leveraging the notion of weak submodularity, we develop an efficient greedy
algorithm for the proposed formulation and show that the greedy algorithm
achieves a constant factor approximation of the optimal objective. Our
extensive simulation studies illustrate the efficacy of the proposed
formulation and the greedy algorithm.
Modelling of consciousness and interpretation of quantum mechanics
Date: 2018-07-20 00:00:00
Tasks: Decision Making, Unity

I start from the fundamental principles of non-relativistic quantum
mechanics, without probability, and interpret them using the notion of
coexistence: a quantum state can be read, not uniquely, as a coexistence of
other quantum states, which are pairwise orthogonal. In this formalism, I prove
that a conscious observer is necessarily a physical object that can memorize
local events by setting one of its parts in an exactly specified constant
quantum state (hypotheses H1, H2 and H3). Then I define the probability of a
future event as the proportion of initial observers, all identical, who will
actually experience that event. It then becomes possible to establish the usual
results of quantum mechanics. Furthermore, I detail the link between
probabilities and relative frequencies. Additionally, I study the biological
feasibility of this modelling of observer's mind.
  The second part of this paper completes the neuronal description of the mind
functions, based on current neuroscientific knowledge. It provides a model that
is compatible with the assumptions of the first part and consistent with our
daily conscious experience. In particular, it develops a model of
self-consciousness based on an explicit use of the random component of neuron
behaviour; according to the first part, that random is in fact the coexistence
of a multiplicity of possibilities. So, when the mind measures the random part
of certain neurons in the brain, he goes himself within each of these
possibilities. The mind has a decision-making component that is active in this
situation, appearing then as the cause of the choice of this possibility among
all the others. This models the self-consciousness which then ensures the unity
of our conscious experience by equating this experience with ``what the ego is
conscious about''. The conclusion details the points that remain to be
developed.
A novel filter for accurate estimation of fluid pressure and fluid velocity using poroelastography
Date: 2018-07-20 00:00:00
Tasks: Prognosis

Fluid pressure and fluid velocity carry important information for cancer
diagnosis, prognosis and treatment. Recent work has demonstrated that
estimation of these parameters is theoretically possible using ultrasound
poroelastography. However, accurate estimation of these parameters requires
high quality axial and lateral strain estimates from noisy ultrasound radio
frequency (RF) data. In this paper, we propose a filtering technique combining
two efficient filters for removal of noise from strain images, i.e., Kalman and
nonlinear complex diffusion filters (NCDF). Our proposed filter is based on a
novel noise model, which takes into consideration both additive and amplitude
modulation noise in the estimated strains. Using finite element and ultrasound
simulations, we demonstrate that the proposed filtering technique can
significantly improve image quality of lateral strain elastograms along with
fluid pressure and velocity elastograms. Technical feasibility of the proposed
method on an in vivo set of data is also demonstrated. Our results show that
the CNRe of the lateral strain, fluid pressure and fluid velocity as estimated
using the proposed technique is higher by at least 10.9%, 51.3% and 334.4% when
compared to the results obtained using a Kalman filter only, by at least 8.9%,
27.6% and 219.5% when compared to the results obtained using a NCDF only and by
at least 152.3%, 1278% and 742% when compared to the results obtained using a
median filter only for all SNRs considered in this study.
Stability in EMU
Date: 2018-07-20 00:00:00

The public debt and deficit ceilings of the Maastricht Treaty are the subject
of recurring controversy. First, there is debate about the role and impact of
these criteria in the initial phase of the introduction of the single currency.
Secondly, it must be specified how these will then be applied, in a permanent
regime, when the single currency is well established.
Semantic Document Clustering on Named Entity Features
Authors: Cao Tru H., Ngo Vuong M., Hong Dung T., Quan Tho T.
Date: 2018-07-20 00:00:00
Tasks: Clustering

Keyword-based information processing has limitations due to simple treatment
of words. In this paper, we introduce named entities as objectives into
document clustering, which are the key elements defining document semantics and
in many cases are of user concerns. First, the traditional keyword-based vector
space model is adapted with vectors defined over spaces of entity names, types,
name-type pairs, and identifiers, instead of keywords. Then, hierarchical
document clustering can be performed using the similarity measure defined as
the cosines of the vectors representing documents. Experimental results are
presented and discussed. Clustering documents by information of named entities
could be useful for managing web-based learning materials with respect to
related objects.
Semiparametric Inference and Lower Bounds for Real Elliptically Symmetric Distributions
Date: 2018-10-15 00:00:00

This paper has a twofold goal. The first aim is to provide a deeper
understanding of the family of the Real Elliptically Symmetric (RES)
distributions by investigating their intrinsic semiparametric nature. The
second aim is to derive a semiparametric lower bound for the estimation of the
parametric component of the model. The RES distributions represent a
semiparametric model where the parametric part is given by the mean vector and
by the scatter matrix while the non-parametric, infinite-dimensional, part is
represented by the density generator. Since, in practical applications, we are
often interested only in the estimation of the parametric component, the
density generator can be considered as nuisance. The first part of the paper is
dedicated to conveniently place the RES distributions in the framework of the
semiparametric group models. The second part of the paper, building on the
mathematical tools previously introduced, the Constrained Semiparametric
Cram\'{e}r-Rao Bound (CSCRB) for the estimation of the mean vector and of the
constrained scatter matrix of a RES distributed random vector is introduced.
The CSCRB provides a lower bound on the Mean Squared Error (MSE) of any robust
$M$-estimator of mean vector and scatter matrix when no a-priori information on
the density generator is available. A closed form expression for the CSCRB is
derived. Finally, in simulations, we assess the statistical efficiency of the
Tyler's and Huber's scatter matrix $M$-estimators with respect to the CSCRB.
Differential cellular contractility as a mechanism for stiffness sensing
Date: 2018-07-20 00:00:00

The ability of cells to sense and respond to the mechanical properties of
their environments is fundamental to a range of cellular behaviours, with
substrate stiffness increasingly being found to be a key signalling factor.
Although active contractility of the cytoskeleton is clearly necessary for
stiffness sensing in cells, the physical mechanisms connecting contractility
with mechanosensing and molecular conformational change are not well
understood. Here we present a contractility-driven mechanism for linking
changes in substrate stiffness with internal conformational changes. Cellular
contractility is often assumed to imply an associated compressive strain. We
show, however, that where the contractility is non-uniform, localized areas of
internal stretch can be generated as stiffer substrates are encountered. This
suggests a physical mechanism for the stretch-activation of mechanotransductive
molecules on stiffer substrates. Importantly, the areas of internal stretch are
not co-localized with those traditionally associated with stiffness sensing,
e.g. focal adhesions, supporting recent experimental results on whole-cell
mechanically-driven mechanotransduction. Considering cellular shape we show
that aspect ratio acts as an additional control parameter, so that the onset of
positive strain moves to higher stiffness values in elliptical cells.