pwc-archive/papers-with-abstracts huggingface dataset:
Flux cost functions and optimal metabolic states
Authors: Wolfram Liebermeister
Date: 2018-01-17 00:00:00

The metabolic fluxes in cells follow physical, biochemical, and economic principles. Some flux balance analysis (FBA) methods trade flux benefit against flux cost. However, if flux cost functions are linear and meant to describe underlying enzyme costs, this entails that enzyme efficiencies are constant and ignores the interplay between fluxes, metabolite concentrations and enzyme levels in cells. Here I introduce realistic flux cost functions that describe an "overhead cost", namely the minimum enzyme and metabolite cost associated with the fluxes in a kinetic model. These flux cost functions have general mathematical properties. Enzymatic flux cost functions, which represent enzyme costs, scale proportionally with the flux profile and are concave on the flux polytope. Kinetic flux cost functions represent the sum of enzyme and metabolite costs. If two flux profiles are superimposed, their different demands for metabolite concentrations cause an extra compromise cost, which makes flux cost functions strictly concave in almost all cases. When fluxes change their direction, the enzymatic cost jumps abruptly. Here I propose two methods for flux modelling: Flux Cost Minimisation, a nonlinear variant of FBA with flux minimisation, and Flux Benefit Optimisation, a nonlinear variant of FBA with molecular crowding. The optimal flux profiles, at a given flux benefit, are vertices of the flux polytope. Linear approximations of enzymatic flux cost can be used in FBA. In contrast to flux costs chosen ad hoc, these functions reflect the enzyme kinetics and extracellular concentrations in realistic kinetic models. Based on enzymatic flux costs, we can describe the cell growth rate as a convex function on the flux polytope and derive growth-optimal metabolic states and statistical distributions for the fluxes in cell populations.
Part 1: Training Sets & ASG Transforms
Authors: Rilwan Adewoyin
Date: 2017-12-15 00:00:00
Tasks: Meta-Learning

In this paper, I discuss a method to tackle the issues arising from the small data-sets available to data-scientists when building price predictive algorithms that use monthly/quarterly macro-financial indicators. I approach this by training separate classifiers on the equivalent dataset from a range of countries. Using these classifiers, a three level meta learning algorithm (MLA) is developed. I develop a transform, ASG, to create a country agnostic proxy for the macro-financial indicators. Using these proposed methods, I investigate the degree to which a predictive algorithm for the US 5Y bond price, predominantly using macro-financial indicators, can outperform an identical algorithm which only uses statistics deriving from previous price. This was an undergraduate project, subsequently the research was not exhaustive.
Evaluating the role of risk networks on risk identification, classification and emergence
Date: 2018-01-17 00:00:00
Tasks: Management

Modern society heavily relies on strongly connected, socio-technical systems.
As a result, distinct risks threatening the operation of individual systems can
no longer be treated in isolation. Consequently, risk experts are actively
seeking for ways to relax the risk independence assumption that undermines
typical risk management models. Prominent work has advocated the use of risk
networks as a way forward. Yet, the inevitable biases introduced during the
generation of these survey-based risk networks limit our ability to examine
their topology, and in turn challenge the utility of the very notion of a risk
network. To alleviate these concerns, we proposed an alternative methodology
for generating weighted risk networks. We subsequently applied this methodology
to an empirical dataset of financial data. This paper reports our findings on
the study of the topology of the resulting risk network. We observed a modular
topology, and reasoned on its use as a robust risk classification framework.
Using these modules, we highlight a tendency of specialization during the risk
identification process, with some firms being solely focused on a subset of the
available risk classes. Finally, we considered the independent and systemic
impact of some risks and attributed possible mismatches to their emerging
nature.
CryptoRuble: From Russia with Love
Date: 2018-01-17 00:00:00

We discuss Russia's underlying motives for issuing its government-backed
cryptocurrency, CryptoRuble, and the implications thereof and of other
likely-soon-forthcoming government-issued cryptocurrencies to some stakeholders
(populace, governments, economy, finance, etc.), existing decentralized
cryptocurrencies (such as Bitcoin and Ethereum), as well as the future of the
world monetary system (the role of the U.S. therein and a necessity for the
U.S. to issue CryptoDollar), including a future algorithmic universal world
currency that may also emerge. We further provide a comprehensive list of
references on cryptocurrencies.
A model of dynamic stability of H3K9me3 heterochromatin to explain the resistance to reprogramming of differentiated cells
Date: 2017-12-24 00:00:00

Despite their dynamic nature, certain chromatin marks must be maintained over
the long term. This is particulary true for histone 3 lysine 9 (H3K9)
trimethylation, that is involved in the maintenance of healthy differentiated
cellular states by preventing inappropriate gene expression, and has been
recently identified as the most efficient barrier to cellular reprogramming in
nuclear transfer experiments. We propose that the capacity of the enzymes
SUV39H1/2 to rebind to a minor fraction of their products, either directly or
via HP1, contributes to the solidity of this mark through (i) a positive
feedback involved in its establishment by the mutual enforcement of H3K9me3 and
SUV39H1/2 and then (ii) a negative feedback sufficient to strongly stabilize
H3K9me3 heterochromatin in post-mitotic cells by generating local enzyme
concentrations capable of counteracting transient bursts of demethylation. This
model does not require direct molecular interactions with adjacent nucleosomes
and is favoured by a series of additional mechanisms including (i) the
protection of chromatin-bound SUV39H1/2 from the turnovers of soluble proteins,
which can explain the uncoupling between the cellular contents in SUV39H1 mRNA
and protein; (ii) the cooperative dependence on the local density of the
H3K9me3 of HP1-dependent heterochomatin condensation and, dispensably (iii)
restricted enzyme exchanges with chromocenters confining the reactive bursts of
SUV39H1/2 in heterochromatin. This mechanism illustrates how seemingly static
epigenetic states can be firmly maintained by dynamic and reversible
modifications.
Polylogarithmic equilibrium treatment of molecular aggregation and critical concentrations
Date: 2017-12-24 00:00:00

A full equilibrium treatment of molecular aggregation is presented for
prototypes of 1D and 3D aggregates, with and without nucleation. By skipping
complex kinetic parameters like aggregate size-dependent diffusion, the
equilibrium treatment allows to predict directly time-independent quantities
such as critical concentrations. The relationships between the macroscopic
equilibrium constants for the different paths are first established by
statistical corrections and so as to comply with the detailed balance
constraints imposed by nucleation, and the composition of the mixture resulting
from homogeneous aggregation is then analyzed using the polylogarithm function.
Several critical concentrations are distinguished: the residual monomer
concentation in equilibrium (RMC) and the critical nucleation concentration
(CNC), that is the threshold concentration of total subunits necessary for
initiating aggregation. When increasing the concentration of total subunits,
the RMC converges more strongly to its asymptotic value, the equilibrium
constant of depolymerization, for 3D aggregates and in case of nucleation. The
CNC moderately depends on the number of subunits in the nucleus, but sharply
increases with the difference between the equilibrium constants of
polymerization and nucleation. As the RMC and CNC can be numerically but not
analytically determined, ansatz equations connecting them to thermodynamic
parameters are proposed.
The macroeconomics determinants of default of the borrowers: The case of Moroccan bank
Date: 2018-03-28 00:00:00

This article aims to explore an empirical approach to analyze the
macroeconomicsdeterminants of default of borrowers. For this purpose, we have
measured the impact of the adverse economic conditions on the degradation of
the credit portfolio quality.In our paper, we have shed more light on the
question of the aggravation of default rate. For this, we have undertaken
econometric modeling of the default rate distribution of a Moroccan bank while
we inspired from some studies carried out. Our findings demonstrate that the
decline in the economic situation has a positive impact on default of
borrowers. Hence, the bank also has responsibility for monitoring the adverse
economic conditions.
A Pipeline for Post-Crisis Twitter Data Acquisition
Authors: Kejriwal Mayank, Gu Yao
Date: 2018-01-17 00:00:00
Tasks: Active Learning, Benchmarking, Feature Engineering, Management

Due to instant availability of data on social media platforms like Twitter,
and advances in machine learning and data management technology, real-time
crisis informatics has emerged as a prolific research area in the last decade.
Although several benchmarks are now available, especially on portals like
CrisisLex, an important, practical problem that has not been addressed thus far
is the rapid acquisition and benchmarking of data from free, publicly available
streams like the Twitter API. In this paper, we present ongoing work on a
pipeline for facilitating immediate post-crisis data collection, curation and
relevance filtering from the Twitter API. The pipeline is minimally supervised,
alleviating the need for feature engineering by including a judicious mix of
data preprocessing and fast text embeddings, along with an active learning
framework. We illustrate the utility of the pipeline by describing a recent
case study wherein it was used to collect and analyze millions of tweets in the
immediate aftermath of the Las Vegas shootings.
Unsupervised Hashtag Retrieval and Visualization for Crisis Informatics
Authors: Gu Yao, Kejriwal Mayank
Date: 2018-01-18 00:00:00
Tasks: Dimensionality Reduction, Retrieval

In social media like Twitter, hashtags carry a lot of semantic information
and can be easily distinguished from the main text. Exploring and visualizing
the space of hashtags in a meaningful way can offer important insights into a
dataset, especially in crisis situations. In this demonstration paper, we
present a functioning prototype, HashViz, that ingests a corpus of tweets
collected in the aftermath of a crisis situation (such as the Las Vegas
shootings) and uses the fastText bag-of-tricks semantic embedding algorithm
(from Facebook Research) to embed words and hashtags into a vector space.
Hashtag vectors obtained in this way can be visualized using the t-SNE
dimensionality reduction algorithm in 2D. Although multiple Twitter
visualization platforms exist, HashViz is distinguished by being simple,
scalable, interactive and portable enough to be deployed on a server for
million-tweet corpora collected in the aftermath of arbitrary disasters,
without special-purpose installation, technical expertise, manual supervision
or costly software or infrastructure investment. Although simple, we show that
HashViz offers an intuitive way to summarize, and gain insight into, a
developing crisis situation. HashViz is also completely unsupervised, requiring
no manual inputs to go from a raw corpus to a visualization and search
interface. Using the recent Las Vegas mass shooting massacre as a case study,
we illustrate the potential of HashViz using only a web browser on the client
side.
An Efficient Data-aided Synchronization in L-DACS1 for Aeronautical Communications
Date: 2018-01-18 00:00:00
Tasks: Management

L-band Digital Aeronautical Communication System type-1 (L-DACS1) is an
emerging standard that aims at enhancing air traffic management (ATM) by
transitioning the traditional analog aeronautical communication systems to the
superior and highly efficient digital domain. L-DACS1 employs modern and
efficient orthogonal frequency division multiplexing (OFDM) modulation
technique to achieve more efficient and higher data rate in comparison to the
existing aeronautical communication systems. However, the performance of OFDM
systems is very sensitive to synchronization errors. L-DACS1 transmission is in
the L-band aeronautical channels that suffer from large interference and large
Doppler shifts, which makes the synchronization for L-DACS more challenging.
This paper proposes a novel computationally efficient synchronization method
for L-DACS1 systems that offers robust performance. Through simulation, the
proposed method is shown to provide accurate symbol timing offset (STO)
estimation as well as fractional carrier frequency offset (CFO) estimation in a
range of aeronautical channels. In particular, it can yield excellent
synchronization performance in the face of a large carrier frequency offset.
A New Peak Detection Method for Single or Three-Phase Unbalanced Sinusoidal Signals
Date: 2018-01-18 00:00:00

In this paper, a fast amplitude detection method for the single or
three-phase unbalanced sinusoidal is reported. The proposed method is a method
of the amplitude detection for a single phase or three phase unbalanced
sinusoidal signal, based on detecting of the pulse width corresponding to the
peak amplitude. The detection period is the half period of the input signal.
This method is independent of the three-phase signal's unbalance and phase
sequence. The proposed method was verified through experiments for the single
or three-phase unbalanced signals.
Large-Scale Simulation of Multi-Asset Ising Financial Markets
Date: 2018-01-18 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

We perform a large-scale simulation of an Ising-based financial market model
that includes 300 asset time series. The financial system simulated by the
model shows a fat-tailed return distribution and volatility clustering and
exhibits unstable periods indicated by the volatility index measured as the
average of absolute-returns. Moreover, we determine that the cumulative risk
fraction, which measures the system risk, changes at high volatility periods.
We also calculate the inverse participation ratio (IPR) and its higher-power
version, IPR6, from the absolute-return cross-correlation matrix. Finally, we
show that the IPR and IPR6 also change at high volatility periods.
Noise-induced stabilization and fixation in fluctuating environment
Date: 2018-01-30 00:00:00

The dynamics of a two-species community of $N$ competing individuals is
considered, with an emphasis on the role of environmental variations that
affect coherently the fitness of entire populations. The chance of fixation of
a mutant population (and, in particular, of a single mutant) is calculated as a
function of the mean relative fitness of the mutant, the amplitude of fitness
variations and their typical duration. We emphasize the distinction between the
case of local competition and the case of global competition; in the latter a
noise-induced stabilization mechanism yields a higher chance of fixation for a
single mutant. The distinction becomes dramatic in the weak selection phase,
where the chance of fixation for a single deleterious mutant is an
$N$-independent constant for global competition and decays like $(\ln N)^{-1}$
in the local competition case. The possibility of long-term persistence of
${\cal O} (N)$ suboptimal (and extinction-prone) populations is discussed, as
well as its relevance to stochastic tunneling between fitness peaks.
Image Enhancement and Noise Reduction Using Modified Delay-Multiply-and-Sum Beamformer: Application to Medical Photoacoustic Imaging
Date: 2018-01-18 00:00:00
Tasks: Breast Cancer Detection, Image Enhancement

Photoacoustic imaging (PAI) is an emerging biomedical imaging modality
capable of providing both high contrast and high resolution of optical and
UltraSound (US) imaging. When a short duration laser pulse illuminates the
tissue as a target of imaging, tissue induces US waves and detected waves can
be used to reconstruct optical absorption distribution. Since receiving part of
PA consists of US waves, a large number of beamforming algorithms in US imaging
can be applied on PA imaging. Delay-and-Sum (DAS) is the most common
beamforming algorithm in US imaging. However, make use of DAS beamformer leads
to low resolution images and large scale of off-axis signals contribution. To
address these problems a new paradigm namely Delay-Multiply-and-Sum (DMAS),
which was used as a reconstruction algorithm in confocal microwave imaging for
breast cancer detection, was introduced for US imaging. Consequently, DMAS was
used in PA imaging systems and it was shown this algorithm results in
resolution enhancement and sidelobe degrading. However, in presence of high
level of noise the reconstructed image still suffers from high contribution of
noise. In this paper, a modified version of DMAS beamforming algorithm is
proposed based on DAS inside DMAS formula expansion. The quantitative and
qualitative results show that proposed method results in more noise reduction
and resolution enhancement in expense of contrast degrading. For the
simulation, two-point target, along with lateral variation in two depths of
imaging are employed and it is evaluated under high level of noise in imaging
medium. Proposed algorithm in compare to DMAS, results in reduction of lateral
valley for about 19 dB followed by more distinguished two-point target.
Moreover, levels of sidelobe are reduced for about 25 dB.
A Digital Predistortion Scheme Exploiting Degrees-of-Freedom for Massive MIMO Systems
Date: 2018-01-18 00:00:00

The primary source of nonlinear distortion in wireless transmitters is the
power amplifier (PA). Conventional digital predistortion (DPD) schemes use
high-order polynomials to accurately approximate and compensate for the
nonlinearity of the PA. This is not practical for scaling to tens or hundreds
of PAs in massive multiple-input multiple-output (MIMO) systems. There is more
than one candidate precoding matrix in a massive MIMO system because of the
excess degrees-of-freedom (DoFs), and each precoding matrix requires a
different DPD polynomial order to compensate for the PA nonlinearity. This
paper proposes a low-order DPD method achieved by exploiting massive DoFs of
next-generation front ends. We propose a novel indirect learning structure
which adapts the channel and PA distortion iteratively by cascading adaptive
zero forcing precoding and DPD. Our solution uses a 3rd order polynomial to
achieve the same performance as the conventional DPD using an 11th order
polynomial for a 100x10 massive MIMO configuration. Experimental results show a
70% reduction in computational complexity, enabling ultra-low latency
communications.
A closed-form formula for pricing bonds between coupon payments
Date: 2018-04-16 00:00:00
Tasks: Form

We derive a closed-form formula for computing bond prices between coupon
payments. Our results cover both the `Treasury' and the `Street' pricing
methods used by sovereign and corporate issuers. We apply our formulas to two
UK gilts, the 8% Treasury Gilt 2015, and the 0.5% Treasury Gilt 2022, and show
that we can obtain the dirty price of these bonds at any date with a minimum of
calculations, and without intensive computational resources.
Conditions for wave trains in spiking neural networks
Date: 2019-09-23 00:00:00

Spatiotemporal patterns such as traveling waves are frequently observed in
recordings of neural activity. The mechanisms underlying the generation of such
patterns are largely unknown. Previous studies have investigated the existence
and uniqueness of different types of waves or bumps of activity using
neural-field models, phenomenological coarse-grained descriptions of
neural-network dynamics. But it remains unclear how these insights can be
transferred to more biologically realistic networks of spiking neurons, where
individual neurons fire irregularly. Here, we employ mean-field theory to
reduce a microscopic model of leaky integrate-and-fire (LIF) neurons with
distance-dependent connectivity to an effective neural-field model. In contrast
to existing phenomenological descriptions, the dynamics in this neural-field
model depends on the mean and the variance in the synaptic input, both
determining the amplitude and the temporal structure of the resulting effective
coupling kernel. For the neural-field model we employ liner stability analysis
to derive conditions for the existence of spatial and temporal oscillations and
wave trains, that is, temporally and spatially periodic traveling waves. We
first prove that wave trains cannot occur in a single homogeneous population of
neurons, irrespective of the form of distance dependence of the connection
probability. Compatible with the architecture of cortical neural networks, wave
trains emerge in two-population networks of excitatory and inhibitory neurons
as a combination of delay-induced temporal oscillations and spatial
oscillations due to distance-dependent connectivity profiles. Finally, we
demonstrate quantitative agreement between predictions of the analytically
tractable neural-field model and numerical simulations of both networks of
nonlinear rate-based units and networks of LIF neurons.
Double Stage Delay Multiply and Sum Beamforming Algorithm Applied to Ultrasound Medical Imaging
Date: 2018-01-18 00:00:00

In Ultrasound (US) imaging, Delay and Sum (DAS) is the most common
beamformer, but it leads to low quality images. Delay Multiply and Sum (DMAS)
was introduced to address this problem. However, the reconstructed images using
DMAS still suffer from level of sidelobes and low noise suppression. In this
paper, a novel beamforming algorithm is introduced based on the expansion of
DMAS formula. It is shown that there is a DAS algebra inside the expansion, and
it is proposed to use DMAS instead of the DAS algebra. The introduced method,
namely Double Stage DMAS (DS-DMAS), is evaluated numerically and
experimentally. The quantitative results indicate that DS-DMAS results in about
25% lower level of sidelobes compared to DMAS. Moreover, the introduced method
leads to 23%, 22% and 43% improvement in Signal-to-Noise Ratio,
Full-Width-Half-Maximum and Contrast Ratio, respectively, in comparison with
DMAS beamformer.