pwc-archive/papers-with-abstracts huggingface dataset:
Good signals gone bad: dynamic signalling with switching efforts
Date: 2017-07-15 00:00:00
Tasks: Vocal Bursts Type Prediction

This paper examines signalling when the sender exerts effort and receives
benefits over time. Receivers only observe a noisy public signal about the
effort, which has no intrinsic value.
  The modelling of signalling in a dynamic context gives rise to novel
equilibrium outcomes. In some equilibria, a sender with a higher cost of effort
exerts strictly more effort than his low-cost counterpart. The low-cost type
can compensate later for initial low effort, but this is not worthwhile for a
high-cost type. The interpretation of a given signal switches endogenously over
time, depending on which type the receivers expect to send it.
  JEL classification: D82, D83, C73.
  Keywords: Dynamic games, signalling , incomplete information
Machine learning application in online lending risk prediction
Date: 2017-07-16 00:00:00
Tasks: BIG-bench Machine Learning, Credit score, Prediction

Online leading has disrupted the traditional consumer banking sector with
more effective loan processing. Risk prediction and monitoring is critical for
the success of the business model. Traditional credit score models fall short
in applying big data technology in building risk model. In this manuscript,
data with various format and size were collected from public website,
third-parties and assembled with client's loan application information data.
Ensemble machine learning models, random forest model and XGBoost model, were
built and trained with the historical transaction data and subsequently tested
with separate data. XGBoost model shows higher K-S value, suggesting better
classification capability in this task. Top 10 important features from the two
models suggest external data such as zhimaScore, multi-platform stacking loans
information, and social network information are important factors in predicting
loan default probability.
Transitions between superstatistical regimes: validity, breakdown and applications
Date: 2017-07-22 00:00:00

Superstatistics is a widely employed tool of non-equilibrium statistical
physics which plays an important role in analysis of hierarchical complex
dynamical systems. Yet, its "canonical" formulation in terms of a single
nuisance parameter is often too restrictive when applied to complex empirical
data. Here we show that a multi-scale generalization of the superstatistics
paradigm is more versatile, allowing to address such pertinent issues as
transmutation of statistics or inter-scale stochastic behavior. To put some
flesh on the bare bones, we provide a numerical evidence for a transition
between two superstatistics regimes, by analyzing high-frequency (minute-tick)
data for share-price returns of seven selected companies. Salient issues, such
as breakdown of superstatistics in fractional diffusion processes or connection
with Brownian subordination are also briefly discussed.
Metabolic plasticity in synthetic lethal mutants: viability at higher cost
Date: 2017-07-16 00:00:00

The most frequent form of pairwise synthetic lethality (SL) in metabolic
networks is known as plasticity synthetic lethality (PSL). It occurs when the
simultaneous inhibition of paired functional and silent metabolic reactions or
genes is lethal, while the default of the functional reaction or gene in the
pair is backed up by the activation of the silent one. Based on a complex
systems approach and by using computational techniques on bacterial
genome-scale metabolic reconstructions, we found that the failure of the
functional PSL partner triggers a critical reorganization of fluxes to ensure
viability in the mutant which not only affects the SL pair but a significant
fraction of other interconnected reactions forming what we call a SL cluster.
Interestingly, SL clusters show a strong entanglement both in terms of silent
coessential reactions, which band together to form backup systems, and of other
functional and silent reactions in the metabolic network. This strong overlap,
also detected at the level of genes, mitigates the acquired vulnerabilities and
increased structural and functional costs that pay for the robustness provided
by essential plasticity. Finally, the participation of coessential reactions
and genes in different SL clusters is very heterogeneous and those at the
intersection of many SL clusters could serve as supertargets for more efficient
drug action in the treatment of complex diseases and to elucidate improved
strategies directed to reduce undesired resistance to chemicals in pathogens.
Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features
Authors: Oramas Sergio, Nieto Oriol, Barbieri Francesco, Serra Xavier
Date: 2017-07-16 00:00:00
Tasks: General Classification, Genre classification, Music Genre Classification

Music genres allow to categorize musical items that share common
characteristics. Although these categories are not mutually exclusive, most
related research is traditionally focused on classifying tracks into a single
class. Furthermore, these categories (e.g., Pop, Rock) tend to be too broad for
certain applications. In this work we aim to expand this task by categorizing
musical items into multiple and fine-grained labels, using three different data
modalities: audio, text, and images. To this end we present MuMu, a new dataset
of more than 31k albums classified into 250 genre classes. For every album we
have collected the cover image, text reviews, and audio tracks. Additionally,
we propose an approach for multi-label genre classification based on the
combination of feature embeddings learned with state-of-the-art deep learning
methodologies. Experiments show major differences between modalities, which not
only introduce new baselines for multi-label genre classification, but also
suggest that combining them yields improved results.
Surplus-invariant risk measures
Date: 2018-05-14 00:00:00

This paper presents a systematic study of the notion of surplus invariance,
which plays a natural and important role in the theory of risk measures and
capital requirements. So far, this notion has been investigated in the setting
of some special spaces of random variables. In this paper we develop a theory
of surplus invariance in its natural framework, namely that of vector lattices.
Besides providing a unifying perspective on the existing literature, we
establish a variety of new results including dual representations and
extensions of surplus-invariant risk measures and structural results for
surplus-invariant acceptance sets. We illustrate the power of the lattice
approach by specifying our results to model spaces with a dominating
probability, including Orlicz spaces, as well as to robust model spaces without
a dominating probability, where the standard topological techniques and
exhaustion arguments cannot be applied.
Mutation in Populations Governed by a Galton-Watson Branching Process
Date: 2018-02-19 00:00:00

A population genetics model based on a multitype branching process, or
equivalently a Galton-Watson branching process for multiple alleles, is pre-
sented. The diffusion limit forward Kolmogorov equation is derived for the case
of neutral mutations. The asymptotic stationary solution is obtained and has
the property that the extant population partitions into subpopulations whose
relative sizes are determined by mutation rates. An approximate time-dependent
solution is obtained in the limit of low mutation rates. This solution has the
property that the system undergoes a rapid transition from a drift-dominated
phase to a mutation-dominated phase in which the distribution collapses onto
the asymptotic stationary distribution. The changeover point of the transition
is determined by the per-generation growth factor and mutation rate. The
approximate solution is confirmed using numerical simulations.
All-atom simulations reveal how single point mutations promote serpin misfolding
Date: 2018-06-18 00:00:00
Tasks: All

Protein misfolding is implicated in many diseases, including the
serpinopathies. For the canonical inhibitory serpin {\alpha}1-antitrypsin
(A1AT), mutations can result in protein deficiencies leading to lung disease,
and misfolded mutants can accumulate in hepatocytes leading to liver disease.
Using all-atom simulations based on the recently developed Bias Functional
algorithm we elucidate how wild-type A1AT folds and how the disease-associated
S (Glu264Val) and Z (Glu342Lys) mutations lead to misfolding. The deleterious Z
mutation disrupts folding at an early stage, while the relatively benign S
mutant shows late stage minor misfolding. A number of suppressor mutations
ameliorate the effects of the Z mutation and simulations on these mutants help
to elucidate the relative roles of steric clashes and electrostatic
interactions in Z misfolding. These results demonstrate a striking correlation
between atomistic events and disease severity and shine light on the mechanisms
driving chains away from their correct folding routes.
Pricing formulae for derivatives in insurance using the Malliavin calculus
Date: 2017-07-17 00:00:00

In this paper we provide a valuation formula for different classes of
actuarial and financial contracts which depend on a general loss process, by
using the Malliavin calculus. In analogy with the celebrated Black-Scholes
formula, we aim at expressing the expected cash flow in terms of a building
block. The former is related to the loss process which is a cumulated sum
indexed by a doubly stochastic Poisson process of claims allowed to be
dependent on the intensity and the jump times of the counting process. For
example, in the context of Stop-Loss contracts the building block is given by
the distribution function of the terminal cumulated loss, taken at the Value at
Risk when computing the Expected Shortfall risk measure.
Environmental stochasticity and the speed of evolution
Date: 2018-01-22 00:00:00

Biological populations are subject to two types of noise: demographic
stochasticity due to fluctuations in the reproductive success of individuals,
and environmental variations that affect coherently the relative fitness of
entire populations. The rate in which the average fitness of a community
increases has been considered so far using models with pure demographic
stochasticity; here we present some theoretical considerations and numerical
results for the general case where environmental variations are taken into
account. When the competition is pairwise, fitness fluctuations are shown to
reduce the speed of evolution, while under global competition the speed
increases due to environmental stochasticity.
Unfolding the innovation system for the development of countries: co-evolution of Science, Technology and Production
Date: 2017-12-27 00:00:00

We show that the space in which scientific, technological and economic
developments interplay with each other can be mathematically shaped using
pioneering multilayer network and complexity techniques. We build the
tri-layered network of human activities (scientific production, patenting, and
industrial production) and study the interactions among them, also taking into
account the possible time delays. Within this construction we can identify
which capabilities and prerequisites are needed to be competitive in a given
activity, and even measure how much time is needed to transform, for instance,
the technological know-how into economic wealth and scientific innovation,
being able to make predictions with a very long time horizon. Quite
unexpectedly, we find empirical evidence that the naive knowledge flow from
science, to patents, to products is not supported by data, being instead
technology the best predictor for industrial and scientific production for the
next decades.
Preliminary Exploration of Formula Embedding for Mathematical Information Retrieval: can mathematical formulae be embedded like a natural language?
Authors: Gao Liangcai, Jiang Zhuoren, Yin Yue, Yuan Ke, Yan Zuoyu, Tang Zhi
Date: 2017-08-29 00:00:00
Tasks: Information Retrieval, Retrieval

While neural network approaches are achieving breakthrough performance in the
natural language related fields, there have been few similar attempts at
mathematical language related tasks. In this study, we explore the potential of
applying neural representation techniques to Mathematical Information Retrieval
(MIR) tasks. In more detail, we first briefly analyze the characteristic
differences between natural language and mathematical language. Then we design
a "symbol2vec" method to learn the vector representations of formula symbols
(numbers, variables, operators, functions, etc.) Finally, we propose a
"formula2vec" based MIR approach and evaluate its performance. Preliminary
experiment results show that there is a promising potential for applying
formula embedding models to mathematical language representation and MIR tasks.
Oscillations in networks of networks stem from adaptive nodes with memory
Date: 2017-07-17 00:00:00

We present an analytical framework that allows the quantitative study of
statistical dynamic properties of networks with adaptive nodes that have memory
and is used to examine the emergence of oscillations in networks with response
failures. The frequency of the oscillations was quantitatively found to
increase with the excitability of the nodes and with the average degree of the
network and to decrease with delays between nodes. For networks of networks,
diverse cluster oscillation modes were found as a function of the topology.
Analytical results are in agreement with large-scale simulations and open the
horizon for understanding network dynamics composed of finite memory nodes as
well as their different phases of activity.
A probabilistic model for learning in cortical microcircuit motifs with data-based divisive inhibition
Date: 2017-07-17 00:00:00
Tasks: blind source separation

Previous theoretical studies on the interaction of excitatory and inhibitory
neurons proposed to model this cortical microcircuit motif as a so-called
Winner-Take-All (WTA) circuit. A recent modeling study however found that the
WTA model is not adequate for data-based softer forms of divisive inhibition as
found in a microcircuit motif in cortical layer 2/3. We investigate here
through theoretical analysis the role of such softer divisive inhibition for
the emergence of computational operations and neural codes under spike-timing
dependent plasticity (STDP). We show that in contrast to WTA models - where the
network activity has been interpreted as probabilistic inference in a
generative mixture distribution - this network dynamics approximates inference
in a noisy-OR-like generative model that explains the network input based on
multiple hidden causes. Furthermore, we show that STDP optimizes the parameters
of this model by approximating online the expectation maximization (EM)
algorithm. This theoretical analysis corroborates a preceding modelling study
which suggested that the learning dynamics of this layer 2/3 microcircuit motif
extracts a specific modular representation of the input and thus performs blind
source separation on the input statistics.
A temporal access code to consciousness?
Date: 2017-07-12 00:00:00

While questions of a functional localization of consciousness in the brain
have been the subject of myriad studies, the idea of a temporal access code as
a specific brain mechanism for consciousness has remained a neglected
possibility. Dresp-Langley and Durup (2009, 2012) proposed a theoretical
approach in terms of a temporal access mechanism for consciousness based on its
two universally recognized properties. Consciousness is limited in processing
capacity and described by a unique processing stream across a single dimension,
which is time. The time ordering function of conscious states is highlighted
and neurobiological theories of the temporal brain activities likely to
underlie such function are discussed, and the properties of the code model are
then introduced. Spatial information is integrated into provisory topological
maps at non-conscious levels through adaptive resonant matching, but does not
form part of the temporal access code as such. The latter, de-correlated from
the spatial code, operates without any need for firing synchrony on the sole
basis of temporal coincidence probabilities in dedicated resonant circuits
through progressively non-arbitrary selection of specific temporal activity
patterns in the continuously developing brain.
An Optimal Stopping Problem Modeling Technical Analysis
Date: 2020-03-27 00:00:00

We present a solution to an optimal stopping problem for a process with a
wide-class of novel dynamics. The dynamics model the support/resistance line
concept from financial technical analysis.
Self-Organization and Chaos in the Metabolism of Hemostasis in a Blood Vessel
Date: 2017-07-14 00:00:00

A mathematical model of the metabolic process of formation of the hemostasis
in a blood-carrying vessel is constructed. As distinct from the earlier
developed model of the multienzyme prostacyclin-thromboxane system of blood,
this model includes, for the first time, the influence of the level of "bad
cholesterol", i.e., low-density lipoproteins (LDLs), on the hemostasis. The
conditions, under which the self-organization of the system appears, and the
modes of autooscillations and chaos in the metabolic process, which affects the
formation of hemostasis and the development of thrombophilia, are found. With
the aid of a phase-parametric diagram, the scenario of their appearance is
studied. The bifurcations of doubling of a period and the transition to chaotic
oscillations as a result of the intermittence are found. The obtained strange
attractors are formed due to a mixing funnel. The full spectra of Lyapunov's
indices, KS-entropies, "predictability horizons", and Lyapunov's dimensions of
strange attractors are calculated. The reasons for a change in the cyclicity of
the given metabolic process, its stability, and the physiological manifestation
in the blood-carrying system are discussed. The role of physiologically active
substances in a decrease in the level of cholesterol in blood vessels is
estimated.
Plunges in the Bombay stock exchange: Characteristics and indicators
Date: 2017-07-18 00:00:00

We study the various sectors of the Bombay Stock Exchange (BSE) for a period
of eight years from January 2006 to March 2014. Using the data of the daily
returns of a period of eight years we investigate the financial cross
correlation co-efficients among the sectors of BSE and Price by Earning (PE)
ratio of BSE Sensex. We show that the behavior of these quantities during
normal periods and during crisis is very different. We show that the PE ratio
shows a particular distinctive trend in the approach to a crash of the
financial market and can therefore be used as an indicator of an impending
catastrophe. We propose that a model of analysis of crashes in a financial
market can be built using two parameters: (i) the PE ratio and (ii) the largest
eigenvalue of the cross correlation matrix.
Power-law tails in the distribution of order imbalance
Date: 2017-07-18 00:00:00

We investigate the probability distribution of order imbalance calculated
from the order flow data of 43 Chinese stocks traded on the Shenzhen Stock
Exchange. Two definitions of order imbalance are considered based on the order
number and the order size. We find that the order imbalance distributions of
individual stocks have power-law tails. However, the tail index fluctuates
remarkably from stock to stock. We also investigate the distributions of
aggregated order imbalance of all stocks at different timescales $\Delta{t}$.
We find no clear trend in the tail index with respect $\Delta{t}$. All the
analyses suggest that the distributions of order imbalance are asymmetric.
Impact and Recovery Process of Mini Flash Crashes: An Empirical Study
Date: 2017-07-18 00:00:00

In an Ultrafast Extreme Event (or Mini Flash Crash), the price of a traded
stock increases or decreases strongly within milliseconds. We present a
detailed study of Ultrafast Extreme Events in stock market data. In contrast to
popular belief, our analysis suggests that most of the Ultrafast Extreme Events
are not primarily due to High Frequency Trading. In at least 60 percent of the
observed Ultrafast Extreme Events, the main cause for the events are large
market orders. In times of financial crisis, large market orders are more
likely which can be linked to the significant increase of Ultrafast Extreme
Events occurrences. Furthermore, we analyze the 100 trades following each
Ultrafast Extreme Events. While we observe a tendency of the prices to
partially recover, less than 40 percent recover completely. On the other hand
we find 25 percent of the Ultrafast Extreme Events to be almost recovered after
only one trade which differs from the usually found price impact of market
orders.
Self-Organization and Fractality in the Metabolic Process of Glycolysis
Date: 2017-07-14 00:00:00

Within a mathematical model, the metabolic process of glycolysis is studied.
The general scheme of glycolysis is considered as a natural result of the
biochemical evolution. By using the theory of dissipative structures, the
conditions of self-organization of the given process are sought. The
autocatalytic processes resulting in the conservation of cyclicity in the
dynamics of the process are determined. The conditions of breaking of the
synchronization of the process, increase in the multiplicity of a cyclicity,
and appearance of chaotic modes are studied. The phase-parametric diagrams of a
cascade of bifurcations, which characterize the transition to chaotic modes
according to the Feigenbaum scenario and the intermittence, are constructed.
The strange attractors formed as a result of the funnel effect are found. The
complete spectra of Lyapunov indices and divergences for the obtained modes are
calculated. The values of KS-entropy, horizons of predictability, and Lyapunov
dimensions of strange attractors are determined. Some conclusions concerning
the structural-functional connections in glycolysis and their influence on the
stability of the metabolic process in a cell are presented.
Brain activity mapping from MEG data via a hierarchical Bayesian algorithm with automatic depth weighting: sensitivity and specificity analysis
Date: 2017-07-15 00:00:00
Tasks: Sensitivity, Specificity

A recently proposed IAS MEG inverse solver algorithm, based on the coupling
of a hierarchical Bayesian model with computationally efficient Krylov subspace
linear solver, has been shown to perform well for both superficial and deep
brain sources. However, a systematic study of its sensitivity and specificity
as a function of the activity location is still missing. We propose novel
statistical protocols to quantify the performance of MEG inverse solvers,
focusing in particular on their sensitivity and specificity in identifying
active brain regions. We use these protocols for a systematic study of the
sensitivity and specificity of the IAS MEG inverse solver, comparing the
performance with three standard inversion methods, wMNE, dSPM, and sLORETA. To
avoid the bias of anecdotal tests towards a particular algorithm, the proposed
protocols are Monte Carlo sampling based, generating an ensemble of activity
patches in each brain region identified in a given atlas. The sensitivity is
measured by how much, on average, the reconstructed activity is concentrated in
the brain region of the simulated active patch. The specificity analysis is
based on Bayes factors, interpreting the estimated current activity as data for
testing the hypothesis that the active brain region is correctly identified,
vs. the hypothesis of any erroneous attribution. The methodology allows the
presence of a single or several simultaneous activity regions, without assuming
the knowledge of the number of active regions. The testing protocols suggest
that the IAS solver performs well in terms of sensitivity and specificity both
with cortical and subcortical activity estimation.