pwc-archive/papers-with-abstracts huggingface dataset:
On the networked architecture of genotype spaces and its critical effects on molecular evolution
Date: 2018-04-18 00:00:00

Evolutionary dynamics is often viewed as a subtle process of change
accumulation that causes a divergence among organisms and their genomes.
However, this interpretation is an inheritance of a gradualistic view that has
been challenged at the macroevolutionary, ecological, and molecular level.
Actually, when the complex architecture of genotype spaces is taken into
account, the evolutionary dynamics of molecular populations becomes
intrinsically non-uniform, sharing deep qualitative and quantitative
similarities with slowly driven physical systems: non-linear responses
analogous to critical transitions, sudden state changes, or hysteresis, among
others. Furthermore, the phenotypic plasticity inherent to genotypes transforms
classical fitness landscapes into multiscapes where adaptation in response to
an environmental change may be very fast. The quantitative nature of adaptive
molecular processes is deeply dependent on a networks-of-networks multilayered
structure of the map from genotype to function that we begin to unveil.
On the Capacity of Fading Channels with Peak and Average Power Constraints at Low SNR
Date: 2018-11-19 00:00:00

The capacity of fading channels under peak and average power constraints in
the low-SNR regime is investigated. We show that the capacity scales
essentially as ${C \approx A \ \text{SNR} \int_{1- \frac{1}{A}}^1
F^{-1}\left(t\right)dt}$, where $A$ is the peak to average power ratio (PAPR),
and $F(\cdot)$ is the cumulative distribution function of the fading channel.
  We also prove that an On-Off power scheme is sufficient to asymptotically
achieve the capacity. Furthermore, by considering the variable PAPR scenario,
we generalize the scalability of the capacity and derive the asymptotic
expression for the capacity in the low-SNR regime.
Accounting Noise and the Pricing of CoCos
Date: 2018-04-18 00:00:00

Contingent Convertible bonds (CoCos) are debt instruments that convert into
equity or are written down in times of distress. Existing pricing models assume
conversion triggers based on market prices and on the assumption that markets
can always observe all relevant firm information. But all Cocos issued so far
have triggers based on accounting ratios and/or regulatory intervention. We
incorporate that markets receive information through noisy accounting reports
issued at discrete time instants, which allows us to distinguish between market
and accounting values, and between automatic triggers and regulator-mandated
conversions. Our second contribution is to incorporate that coupon payments are
contingent too: their payment is conditional on the Maximum Distributable
Amount not being exceeded. We examine the impact of CoCo design parameters,
asset volatility and accounting noise on the price of a CoCo; and investigate
the interaction between CoCo design features, the capital structure of the
issuing bank and their implications for risk taking and investment incentives.
Finally, we use our model to explain the crash in CoCo prices after Deutsche
Bank's profit warning in February 2016.
Highly Relevant Routing Recommendation Systems for Handling Few Data Using MDL Principle and Embedded Relevance Boosting Factors
Authors: Puspitaningrum Diyah, Prasetya I. S. W. B., Wicaksono P. A.
Date: 2018-06-13 00:00:00
Tasks: Classification, General Classification, Recommendation Systems, Sentiment Analysis, Sentiment Classification

A route recommendation system can provide better recommendation if it also
takes collected user reviews into account, e.g. places that generally get
positive reviews may be preferred. However, to classify sentiment, many
classification algorithms existing today suffer in handling small data items
such as short written reviews. In this paper we propose a model for a strongly
relevant route recommendation system that is based on an MDL-based (Minimum
Description Length) sentiment classification and show that such a system is
capable of handling small data items (short user reviews). Another highlight of
the model is the inclusion of a set of boosting factors in the relevance
calculation to improve the relevance in any recommendation system that
implements the model.
Model-based clustering of multi-tissue gene expression data
Date: 2018-04-18 00:00:00
Tasks: Clustering

Recently, it has become feasible to generate large-scale, multi-tissue gene
expression data, where expression profiles are obtained from multiple tissues
or organs sampled from dozens to hundreds of individuals. When traditional
clustering methods are applied to this type of data, important information is
lost, because they either require all tissues to be analyzed independently,
ignoring dependencies and similarities between tissues, or to merge tissues in
a single, monolithic dataset, ignoring individual characteristics of tissues.
We developed a Bayesian model-based multi-tissue clustering algorithm, revamp,
which can incorporate prior information on physiological tissue similarity, and
which results in a set of clusters, each consisting of a core set of genes
conserved across tissues as well as differential sets of genes specific to one
or more subsets of tissues. Using data from seven vascular and metabolic
tissues from over 100 individuals in the STockholm Atherosclerosis Gene
Expression (STAGE) study, we demonstrate that multi-tissue clusters inferred by
revamp are more enriched for tissue-dependent protein-protein interactions
compared to alternative approaches. We further demonstrate that revamp results
in easily interpretable multi-tissue gene expression associations to key
coronary artery disease processes and clinical phenotypes in the STAGE
individuals. Revamp is implemented in the Lemon-Tree software, available at
https://github.com/eb00/lemon-tree
Seven rules to avoid the tragedy of the commons
Date: 2018-04-19 00:00:00

Cooperation among self-interested players in a social dilemma is fragile and
easily interrupted by mistakes. In this work, we study the repeated $n$-person
public-goods game and search for a strategy that forms a cooperative Nash
equilibrium in the presence of implementation error with a guarantee that the
resulting payoff will be no less than any of the co-players'. By enumerating
strategic possibilities for $n=3$, we show that such a strategy indeed exists
when its memory length $m$ equals three. It means that a deterministic strategy
can be publicly employed to stabilize cooperation against error with avoiding
the risk of being exploited. We furthermore show that, for general $n$-person
public-goods game, $m \geq n$ is necessary to satisfy the above criteria.
The determinants of bank loan recovery rates in good times and bad - new evidence
Date: 2018-04-19 00:00:00

We find that factors explaining bank loan recovery rates vary depending on
the state of the economic cycle. Our modeling approach incorporates a two-state
Markov switching mechanism as a proxy for the latent credit cycle, helping to
explain differences in observed recovery rates over time. We are able to
demonstrate how the probability of default and certain loan-specific and other
variables hold different explanatory power with respect to recovery rates over
`good' and `bad' times in the credit cycle. That is, the relationship between
recovery rates and certain loan characteristics, firm characteristics and the
probability of default differs depending on underlying credit market
conditions. This holds important implications for modelling capital retention,
particularly in terms of countercyclicality.
Interplay of spatial dynamics and local adaptation shapes species lifetime distributions and species-area relationships
Date: 2019-02-12 00:00:00

The distributions of species lifetimes and species in space are related,
since species with good local survival chances have more time to colonize new
habitats and species inhabiting large areas have higher chances to survive
local disturbances. Yet, both distributions have been discussed in mostly
separate communities. Here, we study both patterns simultaneously using a
spatially explicit, evolutionary community assembly approach. We present and
investigate a metacommunity model, consisting of a grid of patches, where each
patch contains a local food web. Species survival depends on predation and
competition interactions, which in turn depend on species body masses as the
key traits. The system evolves due to the migration of species to neighboring
patches, the addition of new species as modifications of existing species, and
local extinction events. The structure of each local food web thus emerges in a
self-organized manner as the highly non-trivial outcome of the relative time
scales of these processes. Our model generates a large variety of complex,
multi-trophic networks and therefore serves as a powerful tool to investigate
ecosystems on long temporal and large spatial scales. We find that the observed
lifetime distributions and species-area relations resemble power laws over
appropriately chosen parameter ranges and thus agree qualitatively with
empirical findings. Moreover, we observe strong finite-size effects, and a
dependence of the relationships on the trophic level of the species. By
comparing our results to simple neutral models found in the literature, we
identify the features that are responsible for the values of the exponents.
Performance Analysis of Massive MIMO Multi-Way Relay Networks with Low-Resolution ADCs
Date: 2019-09-19 00:00:00
Tasks: Quantization

High power consumption and hardware cost are two barriers for practical
massive multiple-input multiple-output (mMIMO) systems. A promising solution is
to employ low-resolution analog-to-digital converters (ADCs). In this paper, we
consider a general mMIMO multi-way relaying system with a multi-level mixed-ADC
architecture, in which each antenna is connected to an ADC pair of an arbitrary
resolution. By leveraging on Bussgang's decomposition theorem and Lloyd-Max
algorithm for quantization, tight closed-form approximations are derived for
the average achievable rates of zero-forcing (ZF) relaying considering both
perfect and imperfect channel state information (CSI). To conquer the
challenges caused by multi-way relaying, the complicated ZF beam-forming
matrix, and the general mixed-ADC structure, we develop a novel method for the
achievable rate analysis using the singular-value decomposition (SVD) for
Gaussian matrices, distributions of the singular values of Gaussian matrices,
and properties of Haar matrices. The results explicitly show the achievable
rate behavior in terms of the user and relay transmit powers and the numbers of
relay antennas and users. Most importantly, it quantifies the performance
degradation caused by low-resolution ADCs and channel estimation error. We
demonstrate that the average achievable rate has an almost linear relation with
the square of the average of quantization coefficients pertaining to the ADC
resolution profile.
Classes of elementary function solutions to the CEV model. I
Date: 2018-04-19 00:00:00

The CEV model subsumes some of the previous option pricing models. An
important parameter in the model is the parameter b, the elasticity of
volatility. For b=0, b=-1/2, and b=-1 the CEV model reduces respectively to the
BSM model, the square-root model of Cox and Ross, and the Bachelier model. Both
in the case of the BSM model and in the case of the CEV model it has become
traditional to begin a discussion of option pricing by starting with the
vanilla European calls and puts. In the case of BSM model simpler solutions are
the log and power solutions. These contracts, despite the simplicity of their
mathematical description, are attracting increasing attention as a trading
instrument. Similar simple solutions have not been studied so far in a
systematic fashion for the CEV model. We use Kovacic's algorithm to derive, for
all half-integer values of b, all solutions "in quadratures" of the CEV
ordinary differential equation. These solutions give rise, by separation of
variables, to simple solutions to the CEV partial differential equation. In
particular, when b=...,-5/2,-2,-3/2,-1, 1, 3/2, 2, 5/2,..., we obtain four
classes of denumerably infinite elementary function solutions, when b=-1/2 and
b=1/2 we obtain two classes of denumerably infinite elementary function
solutions, whereas, when b=0 we find two elementary function solutions. In the
derived solutions we have also dispensed with the unnecessary assumption made
in the the BSM model asserting that the underlying asset pays no dividends
during the life of the option.
Smartphone-based point-of-care lipid blood test performance evaluation compared with a clinical diagnostic laboratory method
Date: 2018-04-19 00:00:00
Tasks: Diagnostic

Managing blood lipid levels is important for the treatment and prevention of
diabetes, cardiovascular disease, and obesity. An easy-to-use, portable lipid
blood test will accelerate more frequent testing by patients and at-risk
populations. We used smartphone systems that are already familiar to many
people. Because smartphone systems can be carried around everywhere, blood can
be measured easily and frequently. We compared the results of lipid tests with
those of existing clinical diagnostic laboratory methods. We found that
smartphone-based point-of-care lipid blood tests are as accurate as
hospital-grade laboratory tests. Our system will be useful for those who need
to manage blood lipid levels to motivate them to track and control their
behavior.
Optimal investment with transient price impact
Date: 2018-04-19 00:00:00
Tasks: Position

We introduce a price impact model which accounts for finite market depth,
tightness and resilience. Its coupled bid- and ask-price dynamics induce convex
liquidity costs. We provide existence of an optimal solution to the classical
problem of maximizing expected utility from terminal liquidation wealth at a
finite planning horizon. In the specific case when market uncertainty is
generated by an arithmetic Brownian motion with drift and the investor exhibits
constant absolute risk aversion, we show that the resulting singular optimal
stochastic control problem readily reduces to a deterministic optimal tracking
problem of the optimal frictionless constant Merton portfolio in the presence
of convex costs. Rather than studying the associated Hamilton-Jacobi-Bellmann
PDE, we exploit convex analytic and calculus of variations techniques allowing
us to construct the solution explicitly and to describe the free boundaries of
the action- and non-action regions in the underlying state space. As expected,
it is optimal to trade towards the frictionless Merton position, taking into
account the initial bid-ask spread as well as the optimal liquidation of the
accrued position when approaching terminal time. It turns out that this leads
to a surprisingly rich phenomenology of possible trajectories for the optimal
share holdings.
Volterra Kernel Identification using Regularized Orthonormal Basis Functions
Date: 2018-04-20 00:00:00

The Volterra series is a powerful tool in modelling a broad range of
nonlinear dynamic systems. However, due to its nonparametric nature, the number
of parameters in the series increases rapidly with memory length and series
order, with the uncertainty in resulting model estimates increasing
accordingly. In this paper, we propose an identification method where the
Volterra kernels are estimated indirectly through orthonormal basis function
expansions, with regularization applied directly to the expansion coefficients
to reduce variance in the final model estimate and provide access to useful
models at previously unfeasible series orders. The higher dimensional kernel
expansions are regularized using a method that allows smoothness and decay to
be imposed on the entire hyper-surface. Numerical examples demonstrate improved
Volterra series estimation up to the 4th order using the regularized basis
function method.
Orbital Angular Momentum for Wireless Communications
Date: 2018-04-20 00:00:00

As the traditional resources (frequency, time, space, etc.) are efficiently
utilized, it becomes more and more challenging to satisfy the ever-lasting
capacity-growing and users-boosting demand in wireless networks. Recently, the
electromagnetic (EM) wave was found to possess not only linear momentum, but
also angular momentum. The orbital angular momentum (OAM) is a kind of
wavefront with helical phase. The OAM-based vortex wave has different
topological charges, which are orthogonal to each other, bridging a new way for
multiple access in wireless communications. In this article, we introduce the
fundamental theory of OAM and the OAM based wireless communications. The
research challenges regarding OAM signal generation, OAM beam converging, and
OAM signal reception are discussed. Further, we propose a new multiuser access
with different OAM-modes in wireless networks, where multiple OAM-modes are
used as a new orthogonal dimension for interference avoidance. Simulation
results reveal the inherent property of OAM waves and show that OAM based radio
transmission can significantly increase the spectrum efficiency in wireless
networks.
The Role-Relevance Model for Enhanced Semantic Targeting in Unstructured Text
Authors: George Christopher A., Ozdemir Onur, Fournelle Connie, Moore Kendra E.
Date: 2018-04-29 00:00:00

Personalized search provides a potentially powerful tool, however, it is
limited due to the large number of roles that a person has: parent, employee,
consumer, etc. We present the role-relevance algorithm: a search technique that
favors search results relevant to the user's current role. The role-relevance
algorithm uses three factors to score documents: (1) the number of keywords
each document contains; (2) each document's geographic relevance to the user's
role (if applicable); and (3) each document's topical relevance to the user's
role (if applicable). Topical relevance is assessed using a novel extension to
Latent Dirichlet Allocation (LDA) that allows standard LDA to score document
relevance to user-defined topics. Overall results on a pre-labeled corpus show
an average improvement in search precision of approximately 20% compared to
keyword search alone.
Membrane Trafficking in the Yeast Saccharomyces cerevisiae Model
Date: 2018-04-20 00:00:00

The yeast Saccharomyces cerevisiae is one of the best characterized
eukaryotic models. The secretory pathway was the first trafficking pathway
clearly understood mainly thanks to the work done in the laboratory of Randy
Schekman in the 1980s. They have isolated yeast sec mutants unable to secrete
an extracellular enzyme and these SEC genes were identified as encoding key
effectors of the secretory machinery. For this work, the 2013 Nobel Prize in
Physiology and Medicine has been awarded to Randy Schekman; the prize is shared
with James Rothman and Thomas S{\"u}dhof. Here, we present the different
trafficking pathways of yeast S. cerevisiae. At the Golgi apparatus newly
synthesized proteins are sorted between those transported to the plasma
membrane (PM), or the external medium, via the exocytosis or secretory pathway
(SEC), and those targeted to the vacuole either through endosomes (vacuolar
protein sorting or VPS pathway) or directly (alkaline phosphatase or ALP
pathway). Plasma membrane proteins can be internalized by endocytosis (END) and
transported to endosomes where they are sorted between those targeted for
vacuolar degradation and those redirected to the Golgi (recycling or RCY
pathway). Studies in yeast S. cerevisiae allowed the identification of most of
the known effectors, protein complexes, and trafficking pathways in eukaryotic
cells, and most of them are conserved among eukaryotes.
Benchmarking Top-K Keyword and Top-K Document Processing with T${}^2$K${}^2$ and T${}^2$K${}^2$D${}^2$
Authors: Truica Ciprian-Octavian  UPB, Darmont Jérôme  ERIC, Boicea Alexandru  UPB, Radulescu Florin  UPB
Date: 2018-04-20 00:00:00
Tasks: Benchmarking

Top-k keyword and top-k document extraction are very popular text analysis
techniques. Top-k keywords and documents are often computed on-the-fly, but
they exploit weighted vocabularies that are costly to build. To compare
competing weighting schemes and database implementations, benchmarking is
customary. To the best of our knowledge, no benchmark currently addresses these
problems. Hence, in this paper, we present T${}^2$K${}^2$, a top-k keywords and
documents benchmark, and its decision support-oriented evolution
T${}^2$K${}^2$D${}^2$. Both benchmarks feature a real tweet dataset and queries
with various complexities and selectivities. They help evaluate weighting
schemes and database implementations in terms of computing performance. To
illustrate our bench-marks' relevance and genericity, we successfully ran
performance tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand,
and on different relational (Oracle, PostgreSQL) and document-oriented
(MongoDB) database implementations, on the other hand.
Bias-variance tradeoff in MIMO channel estimation
Date: 2018-04-26 00:00:00

Channel estimation is challenging in multi-antenna communication systems,
because of the large number of parameters to estimate. It is possible to
facilitate this task by using a physical model describing the multiple paths
constituting the channel, in the hope of reducing the number of unknowns in the
problem. Adjusting the number of estimated paths leads to a bias-variance
tradeoff. This paper explores this tradeoff, aiming to find the optimal number
of paths to estimate. Moreover, the approach based on a physical model is
compared to the classical least squares and Bayesian techniques. Finally, the
impact of channel estimation error on the system data rate is assessed.
Fourth order compact scheme for option pricing under Merton and Kou jump-diffusion models
Date: 2018-04-20 00:00:00

In this article, a three-time levels compact scheme is proposed to solve the
partial integro-differential equation governing the option prices under
jump-diffusion models. In the proposed compact scheme, the second derivative
approximation of unknowns is approximated by the value of unknowns and their
first derivative approximations which allow us to obtain a tri-diagonal system
of linear equations for the fully discrete problem. Moreover, consistency and
stability of the proposed compact scheme are proved. Due to the low regularity
of typical initial conditions, the smoothing operator is employed to ensure the
fourth-order convergence rate. Numerical illustrations for pricing European
options under Merton and Kou jump-diffusion models are presented to validate
the theoretical results.
Reliable Low Latency Wireless Communication Enabling Industrial Mobile Control and Safety Applications
Date: 2018-04-20 00:00:00

Advanced industrial applications for human-machine interaction such as
augmented reality support for maintenance works or mobile control panels for
operating production facility set high demands on underlying wireless
connectivity so- lution. Based on 802.11 standard, this paper proposes a
concept of a new system, which is capable of those requirements. For increasing
reliability, an agile triple-band (2.4 GHz, 5 GHz and 60 GHz) communication
system can be used. In order to deal with latency and deterministic channel
access, PHY and MAC techniques such as new waveforms or hybrid MAC schemes are
investigated. Integration of precise localization introduces new possibilities
for safety-critical applications.