pwc-archive/papers-with-abstracts huggingface dataset:
Characterising brain network topologies: a dynamic analysis approach using heat kernels
Date: 2016-03-22 00:00:00
Tasks: Specificity

Network theory provides a principled abstraction of the human brain: reducing
a complex system into a simpler representation from which to investigate brain
organisation. Recent advancement in the neuroimaging field are towards
representing brain connectivity as a dynamic process in order to gain a deeper
understanding of how the brain is organised for information transport. In this
paper we propose a network modelling approach based on the heat kernel to
capture the process of heat diffusion in complex networks. By applying the heat
kernel to structural brain networks, we define new features which quantify
change in energy flow. Identifying suitable features which can classify
networks between cohorts is useful towards understanding the effect of disease
on brain architecture. We demonstrate the discriminative power of heat kernel
features in both synthetic and clinical preterm data. By generating an
extensive range of synthetic networks with varying density and randomisation,
we investigate how heat flows in the networks in relation to changes in network
topology. We demonstrate that our proposed features provide a metric of network
efficiency and may be indicative of organisational principles commonly
associated with, for example, small-world architecture. In addition, we show
the potential of these features to characterise and classify between network
topologies. We further demonstrate our methodology in a clinical setting by
applying it to a large cohort of preterm babies scanned at term equivalent age
from which diffusion networks were computed. We show that our heat kernel
features are able to successfully predict motor function measured at two years
of age (sensitivity, specificity, F-score, accuracy = 75.0, 82.5, 78.6, 82.3%,
respectively.
First Order BSPDEs in higher dimension for optimal control problems
Date: 2018-10-30 00:00:00

The paper studies the First Order BSPDEs (Backward Stochastic Partial
Differential Equations) suggested earlier for a case of multidimensional state
domain with a boundary. These equations represent analogs of
Hamilton-Jacobi-Bellman equations and allow to construct the value function for
stochastic optimal control problems with unspecified dynamics where the
underlying processes do not necessarily satisfy stochastic differential
equations of a known kind with a given structure. The problems considered arise
in financial modelling.
A Unifying Framework for the Identification of Motor Primitives
Date: 2016-03-22 00:00:00

A long-standing hypothesis in neuroscience is that the central nervous system
accomplishes complex motor behaviors through the combination of a small number
of motor primitives. Many studies in the last couples of decades have
identified motor primitives at the kinematic, kinetic, and electromyographic
level, thus supporting modularity at different levels of organization in the
motor system. However, these studies relied on heterogeneous definitions of
motor primitives and on different algorithms for their identification. Standard
unsupervised learning algorithms such as principal component analysis,
independent component analysis, and non-negative matrix factorization, or more
advanced techniques involving the estimation of temporal delays of the relevant
mixture components have been applied. This plurality of algorithms has made
difficult to compare and interpret results obtained across different studies.
Moreover, how the different definitions of motor primitives relate to each
other has never been examined systematically. Here we propose a comprehensive
framework for the definition of different types of motor primitives and a
single algorithm for their identification. By embedding smoothness priors and
specific constraints in the underlying generative model, the algorithm can
identify many different types of motor primitives. We assessed the
identification performance of the algorithm both on simulated data sets, for
which the properties of the primitives and of the corresponding combination
parameters were known, and on experimental electromyographic and kinematic data
sets, collected from human subjects accomplishing goal-oriented and rhythmic
motor tasks. The identification accuracy of the new algorithm was typically
equal or better than the accuracy of other unsupervised learning algorithms
used previously for the identification of the same types of primitives.
The behavioural aspect of green technology investments: a general positive model in the context of heterogeneous agents
Date: 2016-03-22 00:00:00
Tasks: Decision Making, Diversity

Studies report that firms do not invest in cost-effective green technologies.
While economic barriers can explain parts of the gap, behavioural aspects cause
further under-valuation. This could be partly due to systematic deviations of
decision-making agents' perceptions from normative benchmarks, and partly due
to their diversity. This paper combines available behavioural knowledge into a
simple model of technology adoption. Firms are modelled as heterogeneous agents
with different behavioural responses. To quantify the gap, the model simulates
their investment decisions from different theoretical perspectives. While
relevant parameters are uncertain at the micro-level, using distributed agent
perspectives provides a realistic representation of the macro adoption rate.
The model is calibrated using audit data for proposed investments in energy
efficient electric motors. The inclusion of behavioural factors reduces
significantly expected adoption rates: from 81% using a normative optimisation
perspective, down to 20% using a behavioural perspective. The effectiveness of
various policies is tested.
Perspective: Speculative role of Tmp21 mediated protein secretory pathway during endoplasmic reticulum (ER) stress induced chronic inflammation
Date: 2016-03-22 00:00:00

By deploying myelofibrosis as the disease context, I wish to propose that
increased availability of Tmp21 (an NFAT gene target) induces aberrant protein
secretion from the ER contributing to pathological consequences, which has not
been elucidated before. Primary myelofibrosis is now mainly considered as an
advanced stage of BCR-ABL1 negative myeloproliferative neoplasms (MPN), which
otherwise include polycythemia vera and essential thrombocythemia.
Myelofibrosis is defined by an increased insoluble collagen fiber deposition in
the bone marrow2 and harbors chronic inflammation as an important component in
disease progression.
Aligning 415 519 proteins in less than two hours on PC
Date: 2016-03-22 00:00:00
Tasks: Multiple Sequence Alignment, Vocal Bursts Valence Prediction

Rapid development of modern sequencing platforms enabled an unprecedented
growth of protein families databases. The abundance of sets composed of
hundreds of thousands sequences is a great challenge for multiple sequence
alignment algorithms. In the article we introduce FAMSA, a new progressive
algorithm designed for fast and accurate alignment of thousands of protein
sequences. Its features include the utilisation of longest common subsequence
measure for determining pairwise similarities, a novel method of gap costs
evaluation, and a new iterative refinement scheme. Importantly, its
implementation is highly optimised and parallelised to make the most of modern
computer platforms. Thanks to the above, quality indicators, namely
sum-of-pairs and total-column scores, show FAMSA to be superior to competing
algorithms like Clustal Omega or MAFFT for datasets exceeding a few thousand of
sequences. The quality does not compromise time and memory requirements which
are an order of magnitude lower than that of existing solutions. For example, a
family of 415 519 sequences was analysed in less than two hours and required
only 8GB of RAM.
  FAMSA is freely available at http://sun.aei.polsl.pl/REFRESH/famsa.
Profiling vs. Time vs. Content: What does Matter for Top-k Publication Recommendation based on Twitter Profiles? - An Extended Technical Report
Authors: Nishioka Chifumi, Scherp Ansgar
Date: 2016-04-18 00:00:00
Tasks: Recommendation Systems

So far it is unclear how different factors of a scientific publication
recommender system based on users' tweets have an influence on the
recommendation performance. We examine three different factors, namely
profiling method, temporal decay, and richness of content. Regarding profiling,
we compare CF-IDF that replaces terms in TF-IDF by semantic concepts, HCF-IDF
as novel hierarchical variant of CF-IDF, and topic modeling. As temporal decay
functions, we apply sliding window and exponential decay. In terms of the
richness of content, we compare recommendations using both full-texts and
titles of publications and using only titles. Overall, the three factors make
twelve recommendation strategies. We have conducted an online experiment with
123 participants and compared the strategies in a within-group design. The best
recommendations are achieved by the strategy combining CF-IDF, sliding window,
and with full-texts. However, the strategies using the novel HCF-IDF profiling
method achieve similar results with just using the titles of the publications.
Therefore, HCF-IDF can make recommendations when only short and sparse data is
available.
Differentially Expressed Functional Connectivity Networks with K-partite Graph Topology
Date: 2016-03-23 00:00:00
Tasks: Functional Connectivity

Emerging brain network studies suggest that interactions between various
distributed neuronal populations may be characterized by an organized complex
topological structure. Many brain diseases are associated with altered
topological patterns of brain connectivity. Therefore, a key inquiry of
connectivity analysis is to identify network-level differentially expressed
connections that have low false positive rates, sufficient statistical power,
and high reproducibility. In this paper, we propose a novel statistical
approach to fulfill this goal by leveraging the topological structure of
differentially expressed functional connections or edges in a graphical
representation. We propose a new algorithm to automatically detect the latent
topology of a k-partite graph structure, and we also provide statistical
inferential techniques to test the detected topology. We evaluate our new
methods via extensive numerical studies. We also apply our new approach to
resting state fMRI data (24 cases and 18 controls) for Parkinson's disease
research. The detected connectivity network biomaker with the k-partite graph
topological structure reveals underlying neural features distinguishing
Parkinson's disease patients from healthy control subjects.
Modeling multi-particle complexes in stochastic chemical systems
Date: 2016-03-23 00:00:00

Large complexes of classical particles play central roles in biology, in
polymer physics, and in other disciplines. However, physics currently lacks
mathematical methods for describing such complexes in terms of component
particles, interaction energies, and assembly rules. Here we describe a Fock
space structure that addresses this need, as well as diagrammatic methods that
facilitate the use of this formalism. These methods can dramatically simplify
the equations governing both equilibrium and non-equilibrium stochastic
chemical systems. A mathematical relationship between the set of all complexes
and a list of rules for complex assembly is also identified.
Web Data Knowledge Extraction
Authors: Tirado Juan M., Serban Ovidiu, Guo Qiang, Yoneki Eiko
Date: 2016-03-24 00:00:00

A constantly growing amount of information is available through the web.
Unfortunately, extracting useful content from this massive amount of data still
remains an open issue. The lack of standard data models and structures forces
developers to create adhoc solutions from the scratch. The figure of the expert
is still needed in many situations where developers do not have the correct
background knowledge. This forces developers to spend time acquiring the needed
background from the expert. In other directions, there are promising solutions
employing machine learning techniques. However, increasing accuracy requires an
increase in system complexity that cannot be endured in many projects. In this
work, we approach the web knowledge extraction problem using an expertcentric
methodology. This methodology defines a set of configurable, extendible and
independent components that permit the reutilisation of large pieces of code
among projects. Our methodology differs from similar solutions in its
expert-driven design. This design, makes it possible for subject-matter expert
to drive the knowledge extraction for a given set of documents. Additionally,
we propose the utilization of machine assisted solutions that guide the expert
during this process. To demonstrate the capabilities of our methodology, we
present a real use case scenario in which public procurement data is extracted
from the web-based repositories of several public institutions across Europe.
We provide insightful details about the challenges we had to deal with in this
use case and additional discussions about how to apply our methodology.
A Note on the Optimal Dividends Paid in a Foreign Currency
Date: 2016-03-24 00:00:00

We consider an insurance entity endowed with an initial capital and a surplus
process modelled as a Brownian motion with drift. It is assumed that the
company seeks to maximise the cumulated value of expected discounted dividends,
which are declared or paid in a foreign currency. The currency fluctuation is
modelled as a L\'evy process. We consider both cases: restricted and
unrestricted dividend payments. It turns out that the value function and the
optimal strategy can be calculated explicitly.
Preliminary Evidence -- Diagnosed Alzheimer's Disease But Not MCI Affects Working Memory Capacity - 0.7 of 2.7 Memory Slots is Lost
Date: 2016-03-24 00:00:00
Tasks: Management

Recently it was shown explicitly that free recall consists of two stages: the
first few recalls empty working memory (narrowly defined) and a second stage, a
reactivation stage, concludes the recall (Tarnow, 2015). It was also shown that
the serial position curve changes in mild Alzheimer's disease, lowered total
recall and lessened primacy, are similar to second stage recall and different
from recall from working memory. The Tarnow Unchunkable Test (TUT, Tarnow,
2013) uses double integer items to separate out only the first stage, the
emptying of working memory, by making it difficult to reactivate items due to
the lack of intra-item relationships. Here it is shown that subject TUT selects
out diagnosed Alzheimer's Disease but not MCI. On average, diagnosed
Alzheimer's Disease is correlated with a loss of 0.7 memory slots (out of an
average of 2.7 slots). The identification of a lost memory slot may have
implications for improved stage definitions of Alzheimer's disease and for
remediation therapy via working memory capacity management. In conjunction with
the Alzheimer's disease process map, it may also be useful to identify the
exact location of working memory.
On clustering financial time series: a need for distances between dependent random variables
Date: 2016-03-25 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

The following working document summarizes our work on the clustering of
financial time series. It was written for a workshop on information geometry
and its application for image and signal processing. This workshop brought
several experts in pure and applied mathematics together with applied
researchers from medical imaging, radar signal processing and finance. The
authors belong to the latter group. This document was written as a long
introduction to further development of geometric tools in financial
applications such as risk or portfolio analysis. Indeed, risk and portfolio
analysis essentially rely on covariance matrices. Besides that the Gaussian
assumption is known to be inaccurate, covariance matrices are difficult to
estimate from empirical data. To filter noise from the empirical estimate,
Mantegna proposed using hierarchical clustering. In this work, we first show
that this procedure is statistically consistent. Then, we propose to use
clustering with a much broader application than the filtering of empirical
covariance matrices from the estimate correlation coefficients. To be able to
do that, we need to obtain distances between the financial time series that
incorporate all the available information in these cross-dependent random
processes.
Emergent Transport Properties of Molecular Motor Ensemble Affected by Single Motor Mutations
Date: 2016-03-25 00:00:00

Intracellular transport is an essential function in eucaryotic cells,
facilitated by motor proteins - proteins converting chemical energy into
kinetic energy. It is known that motor proteins work in teams enabling
unidirectional and bidirectional transport of intracellular cargo over long
distances. Disruptions of the underlying transport mechanisms, often caused by
mutations that alter single motor characteristics, are known to cause
neurodegenerative diseases. For example, phosphorylation of kinesin motor
domain at the serine residue is implicated in Huntington's disease, with a
recent study of phosphorylated and phosphomimetic serine residues indicating
lowered single motor stalling forces. In this article we report the effects of
mutations of this nature on transport properties of cargo carried by multiple
wild-type and mutant motors. Results indicate that mutants with altered stall
forces might determine the average velocity and run-length even when they are
outnumbered by wild type motors in the ensemble. It is shown that mutants gain
a competitive advantage and lead to an increase in expected run-length when
load on the cargo is in the vicinity of the mutant's stalling force or a
multiple of its stalling force. A separate contribution of this article is the
development of a semi-analytic method to analyze transport of cargo by multiple
motors of multiple types. The technique determines transition rates between
various relative configurations of motors carrying the cargo using the
transition rates between various absolute configurations. This enables exact
computation of average velocity and run-length. It can also be used to
introduce alterations of various single motor parameters to model a mutation
and to deduce effects of such alterations on the transport of a common cargo by
multiple motors. Our method is easily implementable and we provide a software
package for general use.
GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model
Date: 2016-03-26 00:00:00
Tasks: Bayesian Inference, CPU, GPU, Time Series, Time Series Analysis

The realized stochastic volatility (RSV) model that utilizes the realized
volatility as additional information has been proposed to infer volatility of
financial time series. We consider the Bayesian inference of the RSV model by
the Hybrid Monte Carlo (HMC) algorithm. The HMC algorithm can be parallelized
and thus performed on the GPU for speedup. The GPU code is developed with CUDA
Fortran. We compare the computational time in performing the HMC algorithm on
GPU (GTX 760) and CPU (Intel i7-4770 3.4GHz) and find that the GPU can be up to
17 times faster than the CPU. We also code the program with OpenACC and find
that appropriate coding can achieve the similar speedup with CUDA Fortran.
Likelihood-based inference of B-cell clonal families
Date: 2016-06-16 00:00:00
Tasks: Clustering

The human immune system depends on a highly diverse collection of
antibody-making B cells. B cell receptor sequence diversity is generated by a
random recombination process called "rearrangement" forming progenitor B cells,
then a Darwinian process of lineage diversification and selection called
"affinity maturation." The resulting receptors can be sequenced in high
throughput for research and diagnostics. Such a collection of sequences
contains a mixture of various lineages, each of which may be quite numerous, or
may consist of only a single member. As a step to understanding the process and
result of this diversification, one may wish to reconstruct lineage membership,
i.e. to cluster sampled sequences according to which came from the same
rearrangement events. We call this clustering problem "clonal family
inference." In this paper we describe and validate a likelihood-based framework
for clonal family inference based on a multi-hidden Markov Model (multi-HMM)
framework for B cell receptor sequences. We describe an agglomerative algorithm
to find a maximum likelihood clustering, two approximate algorithms with
various trade-offs of speed versus accuracy, and a third, fast algorithm for
finding specific lineages. We show that under simulation these algorithms
greatly improve upon existing clonal family inference methods, and that they
also give significantly different clusters than previous methods when applied
to two real data sets.
Conjoint axiomatization of the Choquet integral for heterogeneous product sets
Date: 2016-03-26 00:00:00

We propose an axiomatization of the Choquet integral model for the general
case of a heterogeneous product set $X = X_1 \times \ldots \times X_n$. In MCDA
elements of $X$ are interpreted as alternatives, characterized by criteria
taking values from the sets $X_i$. Previous axiomatizations of the Choquet
integral have been given for particular cases $X = Y^n$ and $X = \mathbb{R}^n$.
However, within multicriteria context such identicalness, hence
commensurateness, of criteria cannot be assumed a priori. This constitutes the
major difference of this paper from the earlier axiomatizations. In particular,
the notion of "comonotonicity" cannot be used in a heterogeneous structure, as
there does not exist a "built-in" order between elements of sets $X_i$ and
$X_j$. However, such an order is implied by the representation model. Our
approach does not assume commensurateness of criteria. We construct the
representation and study its uniqueness properties.
Robust Optimization of Credit Portfolios
Date: 2016-03-27 00:00:00

We introduce a dynamic credit portfolio framework where optimal investment
strategies are robust against misspecifications of the reference credit model.
The risk-averse investor models his fear of credit risk misspecification by
considering a set of plausible alternatives whose expected log likelihood
ratios are penalized. We provide an explicit characterization of the optimal
robust bond investment strategy, in terms of default state dependent value
functions associated with the max-min robust optimization criterion. The value
functions can be obtained as the solutions of a recursive system of HJB
equations. We show that each HJB equation is equivalent to a suitably truncated
equation admitting a unique bounded regular solution. The truncation technique
relies on estimates for the solution of the master HJB equation that we
establish.
Trading Strategies Generated by Lyapunov Functions
Date: 2016-03-27 00:00:00
Tasks: Descriptive

Functional portfolio generation, initiated by E.R. Fernholz almost twenty
years ago, is a methodology for constructing trading strategies with controlled
behavior. It is based on very weak and descriptive assumptions on the
covariation structure of the underlying market model, and needs no estimation
of model parameters. In this paper, the corresponding generating functions $G$
are interpreted as Lyapunov functions for the vector process $\mu(\cdot)$ of
market weights; that is, via the property that $G(\mu(\cdot))$ is a
supermartingale under an appropriate change of measure. This point of view
unifies, generalizes, and simplifies several existing results, and allows the
formulation of conditions under which it is possible to outperform the market
portfolio over appropriate time-horizons. From a probabilistic point of view,
the present paper yields results concerning the interplay of stochastic
discount factors and concave transformations of semimartingales on compact
domains.