pwc-archive/papers-with-abstracts huggingface dataset:
Evolving methods for rational de novo design of functional RNA molecules
Date: 2019-05-10 00:00:00

Artificial RNA molecules with novel functionality have many applications in
synthetic biology, pharmacy and white biotechnology. The de novo design of such
devices using computational methods and prediction tools is a
resource-efficient alternative to experimental screening and selection
pipelines. In this review, we describe methods common to many such
computational approaches, thoroughly dissect these methods and highlight open
questions for the individual steps. Initially, it is essential to investigate
the biological target system, the regulatory mechanism that will be exploited,
as well as the desired components in order to define design objectives.
Subsequent computational design is needed to combine the selected components
and to obtain novel functionality. This process can usually be split into
constrained sequence sampling, the formulation of an optimization problem and
an in silico analysis to narrow down the number of candidates with respect to
secondary goals. Finally, experimental analysis is important to check whether
the defined design objectives are indeed met in the target environment and
detailed characterization experiments should be performed to improve the
mechanistic models and detect missing design requirements.
Optimal market making under partial information with general intensities
Authors: Diego Zabaljauregui, Luciano Campi
Date: 2019-02-04 00:00:00

Starting from the Avellaneda-Stoikov framework, we consider a market maker who wants to optimally set bid/ask quotes over a finite time horizon, to maximize her expected utility. The intensities of the orders she receives depend not only on the spreads she quotes, but also on unobservable factors modelled by a hidden Markov chain. We tackle this stochastic control problem under partial information with a model that unifies and generalizes many existing ones under full information, combining several risk metrics and constraints, and using general decreasing intensity functionals. We use stochastic filtering, control and piecewise-deterministic Markov processes theory, to reduce the dimensionality of the problem and characterize the reduced value function as the unique continuous viscosity solution of its dynamic programming equation. We then solve the analogous full information problem and compare the results numerically through a concrete example. We show that the optimal full information spreads are biased when the exact market regime is unknown, and the market maker needs to adjust for additional regime uncertainty in terms of P&L sensitivity and observed order flow volatility. This effect becomes higher, the longer the waiting time in between orders.
DBSCAN for nonlinear equalization in high-capacity multi-carrier optical communications
Date: 2019-01-17 00:00:00
Tasks: Clustering, Vocal Bursts Intensity Prediction

Coherent optical multi-carrier communications have recently dominated
metro-regional and long-haul optical communications. However, the major
obstacle of networks involving coherent multi-carrier signals such as coherent
optical orthogonal frequency-division multiplexing (CO-OFDM) is the
fiber-induced nonlinearity and the parametric noise amplification from cascaded
optical amplifiers which results in significant nonlinear distortion among
subcarriers. Here, we present the first nonlinear equalizer in optical
communications using the traditional Density-Based Spatial Clustering of
Applications with Noise (DBSCAN) algorithm and a novel modified version of
DBSCAN which combines K-means clustering on the noisy un-clustered symbols. For
a 24.72 Gbit/sec differential quaternary phase-shift keying (DQPSK) CO-OFDM
system, the modified DBSCAN can increase the signal quality-factor by up to
2.158 dB compared to linear equalization at 500 km of transmission. The
modified DBSCAN slightly outperforms the traditional DBSCAN, fuzzy-logic
C-means, hierarchical and conventional K-means clustering at high launched
optical powers.
Spectral Efficiency Analysis in Downlink Massive MIMO System for Perfect CSI with Precoding
Date: 2019-02-13 00:00:00

In this paper we first derived the mathematical expression for lower bound
spectral efficiency (SE) calculation for zero-force (ZF), and minimum mean
square error (MMSE). Secondly, we calculated the simulation SE with three
algorithms for ZF and MMSE precoding. We compared the simulation and
theoretical results and found that the theoretical results are 1 to 1.5 bits
less than the simulation values which implied that the theoretical lower bounds
are actually the lower bounds. To achieve the maximum spectral efficiency in
downlink massive MIMO systems we assumed perfect CSI, ZF and MMSE precoding in
this paper. We also considered that the channel has the characteristics of
small and large scale fading (SSF and LSF) as the model is like a practical. We
investigated the effect of different SNR, base station (M) and radius (R) of
the cell on spectral efficiency for simulation and theoretical results. We also
evaluated the performance of SE of each algorithms and precoding schemes for
different configurations. From the results we have observed that algorithms-1
and ZF outperform other algorithms and MMSE. From our investigation we noticed
that the LSF parameters are the most dominated factor in SE in massive MIMO
systems.
Probabilistic Power Flow Calculation using Non-intrusive Low-rank Approximation Method
Date: 2019-01-31 00:00:00

In this paper, a novel non-intrusive probabilistic power flow (PPF) analysis
method based on the low-rank approximation (LRA) is proposed, which can
accurately and efficiently estimate the probabilistic characteristics (e.g.,
mean, variance, probability density function) of the PPF solutions. This method
aims at building up a statistically-equivalent surrogate for the PPF solutions
through a small number of power flow evaluations. By exploiting the retained
tensor-product form of the univariate polynomial basis, a sequential
correction-updating scheme is applied, making the total number of unknowns to
be linear rather than exponential to the number of random inputs. Consequently,
the LRA method is particularly promising for dealing with high-dimensional
problems with a large number of random inputs. Numerical studies on the IEEE
39-bus, 118-bus, and 1354-bus systems show that the proposed method can achieve
accurate probabilistic characteristics of the PPF solutions with much less
computational effort compared to the Monte Carlo simulations. Even compared to
the polynomial chaos expansion method, the LRA method can achieve comparable
accuracy, while the LRA method is more capable of handling higher-dimensional
problems. Moreover, numerical results reveal that the randomness brought about
by the renewable energy resources and loads may inevitably affect the
feasibility of dispatch/planning schemes.
Cortical thickness and functional networks modules by cortical lobes
Date: 2019-06-13 00:00:00

This study aims to investigate topological organization of cortical thickness
and functional networks by cortical lobes. First, I demonstrated modular
organization of these networks by the cortical surface frontal, temporal,
parietal and occipital divisions. Secondly, I mapped the overlapping edges of
cortical thickness and functional networks for positive and negative
correlations. Finally, I showed that overlapping positive edges map onto
within-lobe cortical interactions and negative onto between-lobes interactions.
A Sieve-SMM Estimator for Dynamic Models
Authors: Jean-Jacques Forneron
Date: 2019-02-04 00:00:00

This paper proposes a Sieve Simulated Method of Moments (Sieve-SMM) estimator for the parameters and the distribution of the shocks in nonlinear dynamic models where the likelihood and the moments are not tractable. An important concern with SMM, which matches sample with simulated moments, is that a parametric distribution is required. However, economic quantities that depend on this distribution, such as welfare and asset-prices, can be sensitive to misspecification. The Sieve-SMM estimator addresses this issue by flexibly approximating the distribution of the shocks with a Gaussian and tails mixture sieve. The asymptotic framework provides consistency, rate of convergence and asymptotic normality results, extending existing results to a new framework with more general dynamics and latent variables. An application to asset pricing in a production economy shows a large decline in the estimates of relative risk-aversion, highlighting the empirical relevance of misspecification bias.
Efficient conversion of chemical energy into mechanical work by Hsp70 chaperones
Date: 2019-03-29 00:00:00

Hsp70 molecular chaperones are abundant ATP-dependent nanomachines that
actively reshape non-native, misfolded proteins and assist a wide variety of
essential cellular processes. Here we combine complementary
computational/theoretical approaches to elucidate the structural and
thermodynamic details of the chaperone-induced expansion of a substrate
protein, with a particular emphasis on the critical role played by ATP
hydrolysis. We first determine the conformational free-energy cost of the
substrate expansion due to the binding of multiple chaperones using
coarse-grained molecular simulations. We then exploit this result to implement
a non-equilibrium rate model which estimates the degree of expansion as a
function of the free energy provided by ATP hydrolysis. Our results are in
quantitative agreement with recent single-molecule FRET experiments and
highlight the stark non-equilibrium nature of the process, showing that Hsp70s
are optimized to convert effectively chemical energy into mechanical work close
to physiological conditions.
Modularity-in-Design of Dynamical Network Systems: Retrofit Control Approach
Date: 2020-06-22 00:00:00

In this paper, we develop a modular design method of decentralized
controllers for linear dynamical network systems, where multiple subcontroller
designers aim at individually regulating their local control performance with
accessibility only to their respective subsystem models. First, we derive a
constrained version of the Youla parameterization that characterizes all
retrofit controllers for a single subcontroller, defined as an add-on type
subcontroller that manages a subsystem. The resultant feedback system is kept
robustly stable for any variation in the neighboring subsystems, other than the
subsystem of interest, provided that the original system is stable prior to
implementing the retrofit control. Subsequently, we find out a unique internal
structure of the retrofit controllers, assuming that the interaction input
signal from the neighboring subsystems is measurable. Furthermore, we show that
the simultaneous implementation of multiple retrofit controllers, designed by
individual subcontroller designers, can improve the upper bound of the overall
control performance. Finally, the practical significance of the method is
demonstrated via an illustrative example of frequency regulation using the IEEE
68-bus power system model.
On spatially irregular ordinary differential equations and a pathwise volatility modelling framework
Authors: Ryan McCrickerd
Date: 2019-02-05 00:00:00

This thesis develops a new framework for modelling price processes in finance, such as an equity price or foreign exchange rate. This can be related to the conventional Ito calculus-based framework through the time integral of a price's squared volatility, or `cumulative variance'. In the new framework, corresponding processes are strictly increasing, solve random ordinary differential equations (ODEs), and are composed with geometric Brownian motion. The new framework has no dependence on stochastic calculus, so processes can be studied on a pathwise basis using probability-free ODE techniques and functional analysis. The ODEs considered depend on continuous driving functions which are `spatially irregular', meaning they need not have any spatial regularity properties such as Holder continuity. They are however strictly increasing in time, thus temporally asymmetric. When sensible initial values are chosen, initial value problem (IVP) solutions are also strictly increasing, and the solution set of such IVPs is shown to contain all differentiable bijections on the non-negative reals. This enables the modelling of any non-negative volatility path which is not zero over intervals, via the time derivative of solutions. Despite this generality, new well-posedness results establish the uniqueness of solutions going forwards in time. Motivation to explore this framework comes from its connection with a time-changed Heston volatility model. The framework shows how Heston price processes can converge to a generalisation of the NIG Levy process, and reveals a deeper relationship between integrated CIR processes and the IG process. Within this framework, a `Riemann-Liouville-Heston' martingale model is defined which generalises these relationships to fractional counterparts. This model's implied volatilities are simulated, and exhibit features characteristic of leading volatility models.
Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network
Date: 2019-01-19 00:00:00

The cryptocurrency market is a very huge market without effective
supervision. It is of great importance for investors and regulators to
recognize whether there are market manipulation and its manipulation patterns.
This paper proposes an approach to mine the transaction networks of exchanges
for answering this question.By taking the leaked transaction history of Mt. Gox
Bitcoin exchange as a sample,we first divide the accounts into three categories
according to its characteristic and then construct the transaction history into
three graphs. Many observations and findings are obtained via analyzing the
constructed graphs. To evaluate the influence of the accounts' transaction
behavior on the Bitcoin exchange price,the graphs are reconstructed into series
and reshaped as matrices. By using singular value decomposition (SVD) on the
matrices, we identify many base networks which have a great correlation with
the price fluctuation. When further analyzing the most important accounts in
the base networks, plenty of market manipulation patterns are found. According
to these findings, we conclude that there was serious market manipulation in
Mt. Gox exchange and the cryptocurrency market must strengthen the supervision.
A lifestyle-based model of household neighbourhood location and individual travel mode choice behaviours
Date: 2019-11-28 00:00:00

Issues such as urban sprawl, congestion, oil dependence, climate change and
public health, are prompting urban and transportation planners to turn to land
use and urban design to rein in automobile use. One of the implicit beliefs in
this effort is that the right land-use policies will, in fact, help to reduce
automobile use and increase the use of alternative modes of transportation.
Thus, planners and transport engineers are increasingly viewing land use
policies and lifestyle patterns as a way to manage transportation demand. While
a substantial body of work has looked at the relationship between the built
environment and travel behaviour, as well as the influence of lifestyles and
lifestyle-related decisions on using different travel modes and activity
behaviours, limited work has been done in capturing these effects
simultaneously and also in exploring the effect of intra-household interaction
on individual attitudes and beliefs towards travel and activity behavior, and
their subsequent influence on lifestyles and modality styles. Therefore, for
this study we proposed a framework that captures the concurrent influence of
lifestyles and modality styles on both household-level decisions, such as
neighbourhood location, and individual-level decisions, such as travel mode
choices using a hierarchical Latent Class Choice Model.
Beam Acquisition and Training in Millimeter Wave Networks with Narrowband Pilots
Date: 2019-10-10 00:00:00

This paper studies initial beam acquisition in a millimeter wave network
consisting of multiple access points (APs) and mobile devices. A training
protocol for joint estimation of transmit and receive beams is presented with a
general frame structure consisting of an initial access sub-frame followed by
data transmission sub-frames. During the initial subframe, APs and mobiles
sweep through a set of beams and determine the best transmit and receive beams
via a handshake. All pilot signals are narrowband (tones), and the mobiles are
distinguished by their assigned pilot frequencies. Both non-coherent and
coherent beam estimation methods based on, respectively, power detection and
maximum likelihood (ML) are presented. To avoid exchanging information about
beamforming vectors between APs and mobiles, a local maximum likelihood (LML)
algorithm is also presented. An efficient fast Fourier transform implementation
is proposed for ML and LML to achieve high-resolution. A system-level
optimization is performed in which the frame length, training time, and
training bandwidth are selected to maximize a rate objective taking into
account blockage and mobility. Simulation results based on a realistic network
topology are presented to compare the performance of different estimation
methods and training codebooks, and demonstrate the effectiveness of the
proposed protocol.
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?
Date: 2019-06-13 00:00:00
Tasks: Deep Learning, Transfer Learning

This work deals with the use of emerging deep learning techniques in future
wireless communication networks. It will be shown that data-driven approaches
should not replace, but rather complement traditional design techniques based
on mathematical models.
  Extensive motivation is given for why deep learning based on artificial
neural networks will be an indispensable tool for the design and operation of
future wireless communications networks, and our vision of how artificial
neural networks should be integrated into the architecture of future wireless
communication networks is presented.
  A thorough description of deep learning methodologies is provided, starting
with the general machine learning paradigm, followed by a more in-depth
discussion about deep learning and artificial neural networks, covering the
most widely-used artificial neural network architectures and their training
methods. Deep learning will also be connected to other major learning
frameworks such as reinforcement learning and transfer learning.
  A thorough survey of the literature on deep learning for wireless
communication networks is provided, followed by a detailed description of
several novel case-studies wherein the use of deep learning proves extremely
useful for network design. For each case-study, it will be shown how the use of
(even approximate) mathematical models can significantly reduce the amount of
live data that needs to be acquired/measured to implement data-driven
approaches. For each application, the merits of the proposed approaches will be
demonstrated by a numerical analysis in which the implementation and training
of the artificial neural network used to solve the problem is discussed.
  Finally, concluding remarks describe those that in our opinion are the major
directions for future research in this field.
Generalized Design of Sampling Kernels for 2-D FRI Signals
Date: 2019-02-19 00:00:00

One of the interesting problems in the finite-rate-of-innovation signal
sampling framework is the design of compactly supported sampling kernels. In
this paper, we present a generic framework for designing sampling kernels in
2-D. We consider both separable and nonseparable kernels. The design is carried
out in the frequency domain, where a set of alias cancellation conditions are
imposed on the kernel's frequency response. The Paley-Wiener theorem for 2-D
signals is invoked to arrive at admissible kernels with a compact support. As a
specific case, we show that a certain separable extension of the 1-D design
framework results in 2-D sum-of-modulated-spline (SMS) kernels. Similar to
their 1-D counterparts, the 2-D SMS kernels have the attractive feature of
reproducing a class of 2-D polynomial-modulated exponentials of a desired
order. Also, the support of the kernels is independent of the order. The design
framework is generic and also allows one to design nonseparable sampling
kernels. To this end, we demonstrate the design of a nonseparable kernel and
present simulation results.
Spatial eco-evolutionary feedbacks mediate coexistence in prey-predator systems
Date: 2019-11-15 00:00:00

Eco-evolutionary frameworks can explain certain features of communities in
which ecological and evolutionary processes occur over comparable timescales.
Here, we investigate whether an evolutionary dynamics may interact with the
spatial structure of a prey-predator community in which both species show
limited mobility and predator perceptual ranges are subject to natural
selection. In these conditions, our results unveil an eco-evolutionary feedback
between species spatial mixing and predators perceptual range: different levels
of mixing select for different perceptual ranges, which in turn reshape the
spatial distribution of prey and its interaction with predators. This emergent
pattern of interspecific interactions feeds back to the efficiency of the
various perceptual ranges, thus selecting for new ones. Finally, since
prey-predator mixing is the key factor that regulates the intensity of
predation, we explore the community-level implications of such feedback and
show that it controls both coexistence times and species extinction
probabilities.
Can biological quantum networks solve NP-hard problems?
Date: 2019-05-27 00:00:00
Tasks: Philosophy

There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
  This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
  Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
  On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.