pwc-archive/papers-with-abstracts huggingface dataset:
How ants move: individual and collective scaling properties
Date: 2018-07-25 00:00:00

The motion of social insects is often used a paradigmatic example of complex
adaptive dynamics arising from decentralized individual behavior. In this paper
we revisit the topic of the ruling laws behind burst of activity in ants. The
analysis, done over previously reported data, reconsider the causation arrows,
proposed at individual level, not finding any link between the duration of the
ants' activity and its moving speed. Secondly, synthetic trajectories created
from steps of different ants, demonstrate that a Markov process can explain the
previously reported speed shape profile. Finally we show that as more ants
enter the nest, the faster they move, which implies a collective property.
Overall these results provides a mechanistic explanation for the reported
behavioral laws, and suggest a formal way to further study the collective
properties in these scenarios.
Reduction of a metapopulation genetic model to an effective one island model
Date: 2018-05-27 00:00:00
Tasks: model

We explore a model of metapopulation genetics which is based on a more
ecologically motivated approach than is frequently used in population genetics.
The size of the population is regulated by competition between individuals,
rather than by artificially imposing a fixed population size. The increased
complexity of the model is managed by employing techniques often used in the
physical sciences, namely exploiting time-scale separation to eliminate fast
variables and then constructing an effective model from the slow modes.
Remarkably, an initial model with 2$\mathcal{D}$ variables, where $\mathcal{D}$
is the number of islands in the metapopulation, can be reduced to a model with
a single variable. We analyze this effective model and show that the
predictions for the probability of fixation of the alleles and the mean time to
fixation agree well with those found from numerical simulations of the original
model.
Lagrange regularisation approach to compare nested data sets and determine objectively financial bubbles' inceptions
Date: 2017-07-22 00:00:00
Tasks: Time Series, Time Series Analysis

Inspired by the question of identifying the start time $\tau$ of financial
bubbles, we address the calibration of time series in which the inception of
the latest regime of interest is unknown. By taking into account the tendency
of a given model to overfit data, we introduce the Lagrange regularisation of
the normalised sum of the squared residuals, $\chi^{2}_{np}(\Phi)$, to
endogenously detect the optimal fitting window size := $w^* \in
[\tau:\bar{t}_2]$ that should be used for calibration purposes for a fixed
pseudo present time $\bar{t}_2$. The performance of the Lagrange regularisation
of $\chi^{2}_{np}(\Phi)$ defined as $\chi^{2}_{\lambda (\Phi)}$ is exemplified
on a simple Linear Regression problem with a change point and compared against
the Residual Sum of Squares (RSS) := $\chi^{2}(\Phi)$ and RSS/(N-p):=
$\chi^{2}_{np}(\Phi)$, where $N$ is the sample size and p is the number of
degrees of freedom. Applied to synthetic models of financial bubbles with a
well-defined transition regime and to a number of financial time series (US
S\&P500, Brazil IBovespa and China SSEC Indices), the Lagrange regularisation
of $\chi^{2}_{\lambda}(\Phi)$ is found to provide well-defined reasonable
determinations of the starting times for major bubbles such as the bubbles
ending with the 1987 Black-Monday, the 2008 Sub-prime crisis and minor
speculative bubbles on other Indexes, without any further exogenous
information. It thus allows one to endogenise the determination of the
beginning time of bubbles, a problem that had not received previously a
systematic objective solution.
Using PCA and Factor Analysis for Dimensionality Reduction of Bio-informatics Data
Date: 2017-07-22 00:00:00
Tasks: Dimensionality Reduction

Large volume of Genomics data is produced on daily basis due to the
advancement in sequencing technology. This data is of no value if it is not
properly analysed. Different kinds of analytics are required to extract useful
information from this raw data. Classification, Prediction, Clustering and
Pattern Extraction are useful techniques of data mining. These techniques
require appropriate selection of attributes of data for getting accurate
results. However, Bioinformatics data is high dimensional, usually having
hundreds of attributes. Such large a number of attributes affect the
performance of machine learning algorithms used for classification/prediction.
So, dimensionality reduction techniques are required to reduce the number of
attributes that can be further used for analysis. In this paper, Principal
Component Analysis and Factor Analysis are used for dimensionality reduction of
Bioinformatics data. These techniques were applied on Leukaemia data set and
the number of attributes was reduced from to.
Chance, long tails, and inference: a non-Gaussian, Bayesian theory of vocal learning in songbirds
Date: 2017-07-23 00:00:00
Tasks: Bayesian Inference

Traditional theories of sensorimotor learning posit that animals use sensory
error signals to find the optimal motor command in the face of Gaussian sensory
and motor noise. However, most such theories cannot explain common behavioral
observations, for example that smaller sensory errors are more readily
corrected than larger errors and that large abrupt (but not gradually
introduced) errors lead to weak learning. Here we propose a new theory of
sensorimotor learning that explains these observations. The theory posits that
the animal learns an entire probability distribution of motor commands rather
than trying to arrive at a single optimal command, and that learning arises via
Bayesian inference when new sensory information becomes available. We test this
theory using data from a songbird, the Bengalese finch, that is adapting the
pitch (fundamental frequency) of its song following perturbations of auditory
feedback using miniature headphones. We observe the distribution of the sung
pitches to have long, non-Gaussian tails, which, within our theory, explains
the observed dynamics of learning. Further, the theory makes surprising
predictions about the dynamics of the shape of the pitch distribution, which we
confirm experimentally.
Extended Gini-type measures of risk and variability
Date: 2018-03-20 00:00:00
Tasks: Management, Vocal Bursts Type Prediction

The aim of this paper is to introduce a risk measure that extends the
Gini-type measures of risk and variability, the Extended Gini Shortfall, by
taking risk aversion into consideration. Our risk measure is coherent and
catches variability, an important concept for risk management. The analysis is
made under the Choquet integral representations framework. We expose results
for analytic computation under well-known distribution functions. Furthermore,
we provide a practical application.
Simplification of Markov chains with infinite state space and the mathematical theory of random gene expression bursts
Date: 2017-08-20 00:00:00

Here we develop an effective approach to simplify two-time-scale Markov
chains with infinite state spaces by removal of states with fast leaving rates,
which improves the simplification method of finite Markov chains. We introduce
the concept of fast transition paths and show that the effective transitions of
the reduced chain can be represented as the superposition of the direct
transitions and the indirect transitions via all the fast transition paths.
Furthermore, we apply our simplification approach to the standard Markov model
of single-cell stochastic gene expression and provide a mathematical theory of
random gene expression bursts. We give the precise mathematical conditions for
the bursting kinetics of both mRNAs and proteins. It turns out that random
bursts exactly correspond to the fast transition paths of the Markov model.
This helps us gain a better understanding of the physics behind the bursting
kinetics as an emergent behavior from the fundamental multi-scale biochemical
reaction kinetics of stochastic gene expression.
Tail-Tolerant Distributed Search
Authors: Kraus Naama, Carmel David, Keidar Idit
Date: 2017-07-24 00:00:00

Today's search engines process billions of online user queries a day over
huge collections of data. In order to scale, they distribute query processing
among many nodes, where each node holds and searches over a subset of the index
called shard. Responses from some nodes occasionally fail to arrive within a
reasonable time-interval due to various reasons, such as high server load and
network congestion. Search engines typically need to respond in a timely
manner, and therefore skip such tail latency responses, which causes
degradation in search quality. In this paper, we tackle response misses due to
high tail latencies with the goal of maximizing search quality.
  Search providers today use redundancy in the form of Replication for
mitigating response misses, by constructing multiple copies of each shard and
searching all replicas. This approach is not ideal, as it wastes resources on
searching duplicate data. We propose two strategies to reduce this waste.
First, we propose rSmartRed, an optimal shard selection scheme for replicated
indexes. Second, when feasible, we propose to replace Replication with
Repartition, which constructs independent index instances instead of exact
copies. We analytically prove that rSmartRed's selection is optimal for
Replication, and that Repartition achieves better search quality compared to
Replication. We confirm our results with an empirical study using two
real-world datasets, showing that rSmartRed improves recall compared to
currently used approaches. We additionally show that Repartition improves over
Replication in typical scenarios.
Absence of Typical Haversian System from the Compact Bone of Some Reptile and Bird Species
Date: 2017-07-31 00:00:00
Tasks: Anatomy

Background and Objective: Mammalian compact bone is composed mostly of
Haversian system. Although there are many studies describing the typical
Haversian system in mammals, there are few studies conducted on bones from
non-mammalian species. The objective of the current study was to investigate
the existence of the typical Haversian system in compact bones from reptiles
and birds. Materials and Methods: Femora were collected from geckos, Nile
monitors, sparrows, ducks and geese. Samples were then, fixed in 10%
paraformaldehyde and embedded in paraffin. Paraffin sections were stained with
hematoxylin and eosin or Von Kossa and then examined using light microscopy.
Results: The current study showed different histological structures of compact
bones from studied species and an absence of the typical Haversian system seen
in mammals. The compact bone of geckos was mostly avascular, while Nile monitor
bone was highly vascular. Sparrow bone showed one side vascular and the other
side avascular. The vascularity was represented by primary vascular canals
surrounded by a few irregularly arranged osteocytes inside their lacunae
forming primary osteons. Bone tissue from ducks and geese were similar and were
composed mostly of dense Haversian tissue, while some areas had primary
osteons. Conclusion: The compact bone microstructure in some reptile and bird
species lack the typical Haversian system described in mammals. This will be of
a great importance in developing a better understanding of long bone anatomy
and physiology in different species.
Natural selection in compartmentalized environment with reshuffling
Date: 2019-07-11 00:00:00

The emerging field of high-throughput compartmentalized in vitro evolution is
a promising new approach to protein engineering. In these experiments,
libraries of mutant genotypes are randomly distributed and expressed in
microscopic compartments - droplets of an emulsion. The selection of desirable
variants is performed according to the phenotype of each compartment. The
random partitioning leads to a fraction of compartments receiving more than one
genotype making the whole process a lab implementation of the group selection.
From a practical point of view (where efficient selection is typically sought),
it is important to know the impact of the increase in the mean occupancy of
compartments on the selection efficiency. We carried out a theoretical
investigation of this problem in the context of selection dynamics for an
infinite non-mutating subdivided population that randomly colonizes an infinite
number of patches (compartments) at each reproduction cycle. We derive here an
update equation for any distribution of phenotypes and any value of the mean
occupancy. Using this result, we demonstrate that, for the linear additive
fitness, the best genotype is still selected regardless of the mean occupancy.
Furthermore, the selection process is remarkably resilient to the presence of
multiple genotypes per compartments, and slows down approximately inversely
proportional to the mean occupancy at high values. We extend out results to
more general expressions that cover nonadditive and non-linear fitnesses, as
well non-Poissonian distribution among compartments. Our conclusions may also
apply to natural genetic compartmentalized replicators, such as viruses or
early trans-acting RNA replicators.
Statistical properties and multifractality of Bitcoin
Date: 2018-05-26 00:00:00
Tasks: Time Series, Time Series Analysis

Using 1-min returns of Bitcoin prices, we investigate statistical properties
and multifractality of a Bitcoin time series. We find that the 1-min return
distribution is fat-tailed, and kurtosis largely deviates from the Gaussian
expectation. Although for large sampling periods, kurtosis is anticipated to
approach the Gaussian expectation, we find that convergence to that is very
slow. Skewness is found to be negative at time scales shorter than one day and
becomes consistent with zero at time scales longer than about one week. We also
investigate daily volatility-asymmetry by using GARCH, GJR, and RGARCH models,
and find no evidence of it. On exploring multifractality using multifractal
detrended fluctuation analysis, we find that the Bitcoin time series exhibits
multifractality. The sources of multifractality are investigated, confirming
that both temporal correlation and the fat-tailed distribution contribute to
it. The influence of "Brexit" on June 23, 2016 to GBP--USD exchange rate and
Bitcoin is examined in multifractal properties. We find that, while Brexit
influenced the GBP--USD exchange rate, Bitcoin was robust to Brexit.
Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems
Date: 2017-07-24 00:00:00
Tasks: Time Series, Time Series Analysis

Modern time series gene expression and other omics data sets have enabled
unprecedented resolution of the dynamics of cellular processes such as cell
cycle and response to pharmaceutical compounds. In anticipation of the
proliferation of time series data sets in the near future, we use the Hopfield
model, a recurrent neural network based on spin glasses, to model the dynamics
of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study
some of the rich dynamical properties of these cyclic Hopfield systems,
including the ability of populations of simulated cells to recreate
experimental expression data and the effects of noise on the dynamics. Next, we
use a genetic algorithm to identify sets of genes which, when selectively
inhibited by local external fields representing gene silencing compounds such
as kinase inhibitors, disrupt the encoded cell cycle. We find, for example,
that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa
cells causes simulated cells to accumulate in the M phase. Finally, we suggest
possible improvements and extensions to our model.
Covariations in ecological scaling laws fostered by community dynamics
Date: 2017-10-17 00:00:00

Scaling laws in ecology, intended both as functional relationships among
ecologically-relevant quantities and the probability distributions that
characterize their occurrence, have long attracted the interest of empiricists
and theoreticians. Empirical evidence exists of power laws associated with the
number of species inhabiting an ecosystem, their abundances and traits.
Although their functional form appears to be ubiquitous, empirical scaling
exponents vary with ecosystem type and resource supply rate. The idea that
ecological scaling laws are linked had been entertained before, but the full
extent of macroecological pattern covariations, the role of the constraints
imposed by finite resource supply and a comprehensive empirical verification
are still unexplored. Here, we propose a theoretical scaling framework that
predicts the linkages of several macroecological patterns related to species'
abundances and body sizes. We show that such framework is consistent with the
stationary state statistics of a broad class of resource-limited community
dynamics models, regardless of parametrization and model assumptions. We verify
predicted theoretical covariations by contrasting empirical data and provide
testable hypotheses for yet unexplored patterns. We thus place the observed
variability of ecological scaling exponents into a coherent statistical
framework where patterns in ecology embed constrained fluctuations.
Ecological feedback in quorum-sensing microbial populations can induce heterogeneous production of autoinducers
Date: 2017-07-25 00:00:00

Autoinducers are small signaling molecules that mediate intercellular
communication in microbial populations and trigger coordinated gene expression
via "quorum sensing". Elucidating the mechanisms that control autoinducer
production is, thus, pertinent to understanding collective microbial behavior,
such as virulence and bioluminescence. Recent experiments have shown a
heterogeneous promoter activity of autoinducer synthase genes, suggesting that
some of the isogenic cells in a population might produce autoinducers, whereas
others might not. However, the mechanism underlying this phenotypic
heterogeneity in quorum-sensing microbial populations has remained elusive. In
our theoretical model, cells synthesize and secrete autoinducers into the
environment, up-regulate their production in this self-shaped environment, and
non-producers replicate faster than producers. We show that the coupling
between ecological and population dynamics through quorum sensing can induce
phenotypic heterogeneity in microbial populations, suggesting an alternative
mechanism to stochastic gene expression in bistable gene regulatory circuits.
Ether: Bitcoin's competitor or ally?
Date: 2017-07-25 00:00:00

Although Bitcoin has long been dominant in the crypto scene, it is certainly
not alone. Ether is another cryptocurrency related project that has attracted
an intensive attention because of its additional features. This study seeks to
test whether these cryptocurrencies differ in terms of their volatile and
speculative behaviors, hedge, safe haven and risk diversification properties.
Using different econometric techniques, we show that a) Bitcoin and Ether are
volatile and relatively more responsive to bad news, but the volatility of
Ether is more persistent than that of Bitcoin; b) for both cryptocurrencies,
the exuberance and the collapse of bubbles were identified, but Bitcoin appears
more speculative than Ether; c) there is negative and significant correlation
between Bitcoin/Ether and other assets (S\&P500 stocks, US bonds, oil), which
would indicate that digital currencies can hedge against the price movements of
these assets; d) there is negative tail independence between Bitcoin/Ether and
other financial assets, implying that these cryptocurrencies exhibit the
function of a weak safe haven; and e) The inclusion of Bitcoin/ Ether in a
portfolio improve its efficiency in terms of higher reward-to-risk ratios. But
investors who hold diversified portfolios made of stocks or bonds and Ether may
face losses over bearish regime. In such situation, stock and bond investors
may take a short position on Bitcoin.
Equity Default Clawback Swaps to Implement Venture Banking
Authors: Brian P. Hanley
Date: 2017-07-19 00:00:00

In this theoretical paper, I propose creation of a venture bank, able to multiply the capital of a venture capital firm by at least 47 times, without requiring access to the Federal Reserve or other central bank apart from settlement. This concept rests on obtaining default swap instruments on loans in order to create the capital required, and expand Tier 1 and 2 base capital. Profitability depends on overall portfolio performance, availability of equity default swaps, cost of default swap, and the multiple of original capital (MOC) adopted by the venture bank. A new derivative financial instrument, the equity default swap (EDS), to cover loans made as venture investments. An EDS is similar to a credit default swap (CDS) but with some unique features. The features and operation of these new derivative instruments are outlined along with audit requirements. This instrument would be traded on open-outcry exchanges with special features to ensure orderly operation of the market. It is the creation of public markets for EDSs that makes possible the use of public market pricing to indirectly provide a potential market capitalization for the underlying venture-bank investment. Full coverage insulates the venture-bank from losses in most situations, and multiplies profitability quite dramatically in all scenarios. Ten year returns above 20X are attainable. Further, a new feature for EDS derivatives, a clawback lien, closes out the equity default swap. Here it is optimized at 77%, and is to be paid back to the underwriter at a future date to prevent perverse incentive to deliberately fail. This new feature creates an Equity Default Clawback Swap (EDCS) which can be used safely. This proposal also solves an old problem in banking, because it matches the term of the loan with the term of the investment. I show that the venture-bank investment and the EDCS underwriting business are profitable.
Accurate Chemical Master Equation Solution Using Multi-Finite Buffers
Date: 2017-07-25 00:00:00

The discrete chemical master equation (dCME) provides a fundamental framework
for studying stochasticity in mesoscopic networks. Because of the multi-scale
nature of many networks where reaction rates have large disparity, directly
solving dCMEs is intractable due to the exploding size of the state space. It
is important to truncate the state space effectively with quantified errors, so
accurate solutions can be computed. It is also important to know if all major
probabilistic peaks have been computed. Here we introduce the Accurate CME
(ACME) algorithm for obtaining direct solutions to dCMEs. With multi-finite
buffers for reducing the state space by O(n!), exact steady-state and
time-evolving network probability landscapes can be computed. We further
describe a theoretical framework of aggregating microstates into a smaller
number of macrostates by decomposing a network into independent aggregated
birth and death processes, and give an a priori method for rapidly determining
steady-state truncation errors. The maximal sizes of the finite buffers for a
given error tolerance can also be pre-computed without costly trial solutions
of dCMEs. We show exactly computed probability landscapes of three multi-scale
networks, namely, a 6-node toggle switch, 11-node phage-lambda epigenetic
circuit, and 16-node MAPK cascade network, the latter two with no known
solutions. We also show how probabilities of rare events can be computed from
first-passage times, another class of unsolved problems challenging for
simulation-based techniques due to large separations in time scales. Overall,
the ACME method enables accurate and efficient solutions of the dCME for a
large class of networks.