pwc-archive/papers-with-abstracts huggingface dataset:
High Dimensional Random Walks Can Appear Low Dimensional: Application to Influenza H3N2 Evolution
Date: 2017-07-27 00:00:00

One important feature of the mammalian immune system is the highly specific
binding of antigens to antibodies. Antibodies generated in response to one
infection may also provide some level of cross immunity to other infections.
One model to describe this cross immunity is the notion of antigenic space,
which assigns each antibody and each virus a point in $\mathbb{R}^n$. Past
studies have suggested the dimensionality of antigenic space, $n$, may be
small. In this study we show that data from hemagglutination assays suggest a
high dimensional random walk (or self avoiding random random walk). The
discrepancy between our result and prior studies is due to the fact that random
walks can appear low dimensional according to a variety of analyses. including
principal component analysis (PCA) and multidimensional scaling (MDS).
Illusions - a model of mind
Date: 2017-07-28 00:00:00
Tasks: model

Recognizing that all mental processes have to be unfree and passive, we
develop a model of behavior and perceptions. We shall see how misleading our
intuition is and shall understand how consciousness arises.
Explicit expressions for European option pricing under a generalized skew normal distribution
Date: 2017-07-30 00:00:00
Tasks: Sensitivity

Under a generalized skew normal distribution we consider the problem of
European option pricing. Existence of the martingale measure is proved. An
explicit expression for a given European option price is presented in terms of
the cumulative distribution function of the univariate skew normal and the
bivariate standard normal distributions. Some special cases are investigated in
a greater detail. To carry out the sensitivity of the option price to the skew
parameters, numerical methods are applied. Some concluding remarks and further
works are given. The results obtained are extensions of the results provided by
[4].
Spurious memory in non-equilibrium stochastic models of imitative behavior
Date: 2017-07-31 00:00:00
Tasks: Time Series, Time Series Analysis

The origin of the long-range memory in the non-equilibrium systems is still
an open problem as the phenomenon can be reproduced using models based on
Markov processes. In these cases a notion of spurious memory is introduced. A
good example of Markov processes with spurious memory is stochastic process
driven by a non-linear stochastic differential equation (SDE). This example is
at odds with models built using fractional Brownian motion (fBm). We analyze
differences between these two cases seeking to establish possible empirical
tests of the origin of the observed long-range memory. We investigate
probability density functions (PDFs) of burst and inter-burst duration in
numerically obtained time series and compare with the results of fBm. Our
analysis confirms that the characteristic feature of the processes described by
a one-dimensional SDE is the power-law exponent $3/2$ of the burst or
inter-burst duration PDF. This property of stochastic processes might be used
to detect spurious memory in various non-equilibrium systems, where observed
macroscopic behavior can be derived from the imitative interactions of agents.
On a robust risk measurement approach for capital determination errors minimization
Authors: Marcelo Brutti Righi, Fernanda Maria Müller, Marlon Ruoso Moresco
Date: 2017-07-31 00:00:00

We propose a robust risk measurement approach that minimizes the expectation of overestimation plus underestimation costs. We consider uncertainty by taking the supremum over a collection of probability measures, relating our approach to dual sets in the representation of coherent risk measures. We provide results that guarantee the existence of a solution and explore the properties of minimizer and minimum as risk and deviation measures, respectively. An empirical illustration is carried out to demonstrate the use of our approach in capital determination.
Learning Audio - Sheet Music Correspondences for Score Identification and Offline Alignment
Authors: Dorfer Matthias, Arzt Andreas, Widmer Gerhard
Date: 2017-07-31 00:00:00

This work addresses the problem of matching short excerpts of audio with
their respective counterparts in sheet music images. We show how to employ
neural network-based cross-modality embedding spaces for solving the following
two sheet music-related tasks: retrieving the correct piece of sheet music from
a database when given a music audio as a search query; and aligning an audio
recording of a piece with the corresponding images of sheet music. We
demonstrate the feasibility of this in experiments on classical piano music by
five different composers (Bach, Haydn, Mozart, Beethoven and Chopin), and
additionally provide a discussion on why we expect multi-modal neural networks
to be a fruitful paradigm for dealing with sheet music and audio at the same
time.
Deep Discrete Supervised Hashing
Authors: Jiang Qing-Yuan, Cui Xue, Li Wu-Jun
Date: 2017-07-31 00:00:00
Tasks: Deep Hashing, Image Retrieval, Retrieval

Hashing has been widely used for large-scale search due to its low storage
cost and fast query speed. By using supervised information, supervised hashing
can significantly outperform unsupervised hashing. Recently, discrete
supervised hashing and deep hashing are two representative progresses in
supervised hashing. On one hand, hashing is essentially a discrete optimization
problem. Hence, utilizing supervised information to directly guide discrete
(binary) coding procedure can avoid sub-optimal solution and improve the
accuracy. On the other hand, deep hashing, which integrates deep feature
learning and hash-code learning into an end-to-end architecture, can enhance
the feedback between feature learning and hash-code learning. The key in
discrete supervised hashing is to adopt supervised information to directly
guide the discrete coding procedure in hashing. The key in deep hashing is to
adopt the supervised information to directly guide the deep feature learning
procedure. However, there have not existed works which can use the supervised
information to directly guide both discrete coding procedure and deep feature
learning procedure in the same framework. In this paper, we propose a novel
deep hashing method, called deep discrete supervised hashing (DDSH), to address
this problem. DDSH is the first deep hashing method which can utilize
supervised information to directly guide both discrete coding procedure and
deep feature learning procedure, and thus enhance the feedback between these
two important procedures. Experiments on three real datasets show that DDSH can
outperform other state-of-the-art baselines, including both discrete hashing
and deep hashing baselines, for image retrieval.
Sparse Structural Approach for Rating Transitions
Authors: Volodymyr Perederiy
Date: 2017-07-31 00:00:00

In banking practice, rating transition matrices have become the standard approach of deriving multi-year probabilities of default (PDs) from one-year PDs, the latter normally being available from Basel ratings. Rating transition matrices have gained in importance with the newly adopted IFRS 9 accounting standard. Here, the multi-year PDs can be used to calculate the so-called expected credit losses (ECL) over the entire lifetime of relevant credit assets. A typical approach for estimating the rating transition matrices relies on calculating empirical rating migration counts and frequencies from rating history data. For small portfolios, however, this approach often leads to zero counts and high count volatility, which makes the estimations unreliable and unstable, and can also produce counter-intuitive prediction patterns such as non-parallel/crossing forward PD patterns. This paper proposes a structural model which overcomes these problems. We make a plausible assumption of an underlying autoregressive mean-reverting ability-to-pay process. With only three parameters, this sparse process can well describe an entire typical rating transition matrix, provided the one-year PDs of the rating classes are specified. The transition probabilities produced by the structural approach are well-behaved by design. The approach significantly reduces the statistical degrees of freedom of the estimated transition probabilities, which makes the rating transition matrix more reliable for small portfolios. The approach can be applied to data with as few as 50 observed rating transitions. Moreover, the approach can be efficiently applied to data consisting of continuous PDs (prior to rating discretization). In the IFRS 9 context, the approach offers an additional merit: it can easily account for the macroeconomic adjustments, which are required by the IFRS 9 accounting standard.
The differential geometry of perceptual similarity
Date: 2017-09-26 00:00:00

Human similarity judgments are inconsistent with Euclidean, Hamming,
Mahalanobis, and the majority of measures used in the extensive literatures on
similarity and dissimilarity. From intrinsic properties of brain circuitry, we
derive principles of perceptual metrics, showing their conformance to
Riemannian geometry. As a demonstration of their utility, the perceptual
metrics are shown to outperform JPEG compression. Unlike machine-learning
approaches, the outperformance uses no statistics, and no learning. Beyond the
incidental application to compression, the metrics offer broad explanatory
accounts of empirical perceptual findings such as Tverskys triangle inequality
violations, contradictory human judgments of identical stimuli such as speech
sounds, and a broad range of other phenomena on percepts and concepts that may
initially appear unrelated. The findings constitute a set of fundamental
principles underlying perceptual similarity.
Sequential Sampling for CGMY Processes via Decomposition of their Time Changes
Date: 2018-08-22 00:00:00

We present a new and easy-to-implement sequential sampling method for CGMY
processes with either finite or infinite variation, exploiting the time change
representation of the CGMY model and a decomposition of its time change. We
find that the time change can be decomposed into two independent components.
While the first component is a \emph{finite} \emph{generalized gamma
convolution} process whose increments can be sampled by either the exact double
CFTP ("coupling from the past") method or an approximation scheme with high
speed and accuracy, the second component can easily be made arbitrarily small
in the $L^1$ sense. Simulation results show that the proposed method is
advantageous over two existing methods under a model calibrated to historical
option price data.
Mean and Variance of Phylogenetic Trees
Date: 2018-05-10 00:00:00
Tasks: Diversity

We describe the use of the Frechet mean and variance in the
Billera-Holmes-Vogtmann (BHV) treespace to summarize and explore the diversity
of a set of phylogenetic trees. We show that the Frechet mean is comparable to
other summary methods, and, despite its stickiness property, is more likely to
be binary than the majority-rules consensus tree. We show that the Frechet
variance is faster and more precise than commonly used variance measures. The
Frechet mean and variance are more theoretically justified, and more robust,
than previous estimates of this type, and can be estimated reasonably
efficiently, providing a foundation for building more advanced statistical
methods and leading to applications such as mean hypothesis testing.
Probabilistic models of individual and collective animal behavior
Date: 2017-07-31 00:00:00
Tasks: Model Selection

Recent developments in automated tracking allow uninterrupted,
high-resolution recording of animal trajectories, sometimes coupled with the
identification of stereotyped changes of body pose or other behaviors of
interest. Analysis and interpretation of such data represents a challenge: the
timing of animal behaviors may be stochastic and modulated by kinematic
variables, by the interaction with the environment or with the conspecifics
within the animal group, and dependent on internal cognitive or behavioral
state of the individual. Existing models for collective motion typically fail
to incorporate the discrete, stochastic, and internal-state-dependent aspects
of behavior, while models focusing on individual animal behavior typically
ignore the spatial aspects of the problem. Here we propose a probabilistic
modeling framework to address this gap. Each animal can switch stochastically
between different behavioral states, with each state resulting in a possibly
different law of motion through space. Switching rates for behavioral
transitions can depend in a very general way, which we seek to identify from
data, on the effects of the environment as well as the interaction between the
animals. We represent the switching dynamics as a Generalized Linear Model and
show that: (i) forward simulation of multiple interacting animals is possible
using a variant of the Gillespie's Stochastic Simulation Algorithm; (ii)
formulated properly, the maximum likelihood inference of switching rate
functions is tractably solvable by gradient descent; (iii) model selection can
be used to identify factors that modulate behavioral state switching and to
appropriately adjust model complexity to data. To illustrate our framework, we
apply it to two synthetic models of animal motion and to real zebrafish
tracking data.
Control-stopping Games for Market Microstructure and Beyond
Date: 2019-03-19 00:00:00

In this paper, we present a family of a control-stopping games which arise
naturally in equilibrium-based models of market microstructure, as well as in
other models with strategic buyers and sellers. A distinctive feature of this
family of games is the fact that the agents do not have any exogenously given
fundamental value for the asset, and they deduce the value of their position
from the bid and ask prices posted by other agents (i.e. they are pure
speculators). As a result, in such a game, the reward function of each agent,
at the time of stopping, depends directly on the controls of other players. The
equilibrium problem leads naturally to a system of coupled control-stopping
problems (or, equivalently, Reflected Backward Stochastic Differential
Equations (RBSDEs)), in which the individual reward functions (or, reflecting
barriers) depend on the value functions (or, solution components) of other
agents. The resulting system, in general, presents multiple mathematical
challenges due to the non-standard form of coupling (or, reflection). In the
present case, this system is also complicated by the fact that the continuous
controls of the agents, describing their posted bid and ask prices, are
constrained to take values in a discrete grid. The latter feature reflects the
presence of a positive tick size in the market, and it creates additional
discontinuities in the agents reward functions (or, reflecting barriers).
Herein, we prove the existence of a solution to the associated system in a
special Markovian framework, provide numerical examples, and discuss the
potential applications.
WAND: A 128-channel, closed-loop, wireless artifact-free neuromodulation device
Date: 2018-05-29 00:00:00

Closed-loop neuromodulation systems aim to treat a variety of neurological
conditions by dynamically delivering and adjusting therapeutic electrical
stimulation in response to a patient's neural state, recorded in real-time.
Existing systems are limited by low channel counts, lack of algorithmic
flexibility, and distortion of recorded signals from large, persistent
stimulation artifacts. Here, we describe a device that enables new research
applications requiring high-throughput data streaming, low-latency biosignal
processing, and truly simultaneous sensing and stimulation. The Wireless
Artifact-free Neuromodulation Device (WAND) is a miniaturized, wireless neural
interface capable of recording and stimulating on 128 channels with on-board
processing to fully cancel stimulation artifacts, detect neural biomarkers, and
automatically adjust stimulation parameters in a closed-loop fashion. It
combines custom application specific integrated circuits (ASICs), an on-board
FPGA, and a low-power bidirectional radio. We validate wireless, long-term
recordings of local field potentials (LFP) and real-time cancellation of
stimulation artifacts in a behaving nonhuman primate (NHP). We use WAND to
demonstrate a closed-loop stimulation paradigm to disrupt movement preparatory
activity during a delayed-reach task in a NHP in vivo. This wireless device,
leveraging custom ASICs for both neural recording and electrical stimulation
modalities, makes possible a neural interface platform technology to
significantly advance both neuroscientific discovery and preclinical
investigations of stimulation-based therapeutic interventions.
Identification of Essential Proteins Using Induced Stars in Protein-Protein Interaction Networks
Date: 2018-03-14 00:00:00

In this work, we propose a novel centrality metric, referred to as star
centrality, which incorporates information from the closed neighborhood of a
node, rather than solely from the node itself, when calculating its topological
importance. More specifically, we focus on degree centrality and show that in
the complex protein-protein interaction networks it is a naive metric that can
lead to misclassifying protein importance. For our extension of degree
centrality when considering stars, we derive its computational complexity,
provide a mathematical formulation, and propose two approximation algorithms
that are shown to be efficient in practice. We portray the success of this new
metric in protein-protein interaction networks when predicting protein
essentiality in several organisms, including the well-studied Saccharomyces
cerevisiae, Helicobacter pylori, and Caenorhabditis elegans, where star
centrality is shown to significantly outperform other nodal centrality metrics
at detecting essential proteins. We also analyze the average and worst case
performance of the two approximation algorithms in practice, and show that they
are viable options for computing star centrality in very large-scale
protein-protein interaction networks, such as the human proteome, where exact
methodologies are bound to be time and memory intensive.
Control of Functional Connectivity in Cerebral Cortex by Basal Ganglia Mediated Synchronization
Date: 2020-04-10 00:00:00
Tasks: Functional Connectivity

Since the earliest electroencephalography experiments, large scale
oscillations have been observed in the mammalian brain. More recently, they
have been identified not only in the cerebral cortex and thalamus, but
pervasively in the healthy basal ganglia. The basal ganglia mediated
synchronization model, introduced here, implicates these oscillations in the
combination of cortical association mechanisms with stimulus-response and
reinforcement mechanisms in the basal ganglia. In the core mechanism of the
model, oscillatory patterns in cortex are selected by and routed through the
basal ganglia to the thalamus phase-coherently, then circulated back to widely
separated areas of cortex, synchronizing those areas and functionally
connecting them. Corticostriatal and striatonigral conduction delays are
crucial to this mechanism, and evidence suggests that these delays are
unusually long, and unusually varied, in arrangements that might facilitate
learning of useful time alignments and associated resonant frequencies. Other
structural arrangements in the basal ganglia show further specialization for
this role, with convergence in the inputs from cortex, and divergence in many
of the return paths to cortex, that systematically reflect corticocortical
anatomical connectivity. The basal ganglia also target the dopaminergic,
cholinergic, and serotonergic centers of the brainstem and basal forebrain, and
the reticular nucleus of the thalamus, structures broadly implicated in the
modulation of oscillatory network activity and expressions of plasticity. By
learning to coordinate these various output channels, the basal ganglia are
positioned to facilitate and synchronize activity in selected areas of cortex,
broadly impart selective receptivity, attenuate and disconnect interfering
activity, and recurrently process the resulting patterns of activity,
channeling cognition and promoting goal [...]
Interactions of Fungi with Concrete: Significant Importance for Bio-Based Self-Healing Concrete
Authors: Jing Luo, Xiaobo Chen, Jada Crump, Hui Zhou, David G. Davies, Guangwen Zhou, Ning Zhang, Congrui Jin
Date: 2017-08-04 00:00:00
Tasks: X-Ray Diffraction (XRD)

The goal of this study is to explore a new self-healing concept in which fungi are used as a self-healing agent to promote calcium mineral precipitation to fill the cracks in concrete. An initial screening of different species of fungi has been conducted. Fungal growth medium was overlaid onto cured concrete plate. Mycelial discs were aseptically deposited at the plate center. The results showed that, due to the dissolving of Ca(OH)2 from concrete, the pH of the growth medium increased from its original value of 6.5 to 13.0. Despite the drastic pH increase, Trichoderma reesei (ATCC13631) spores germinated into hyphal mycelium and grew equally well with or without concrete. X-ray diffraction (XRD) and scanning electron microscope (SEM) confirmed that the crystals precipitated on the fungal hyphae were composed of calcite. These results indicate that T. reesei has great potential to be used in bio-based self-healing concrete for sustainable infrastructure.
Predicting potential treatments for complex diseases based on miRNA and tissue specificity
Date: 2017-06-30 00:00:00
Tasks: Literature Mining, Specificity

Drug repositioning, that is finding new uses for existing drugs to treat more
patients. Cumulative studies demonstrate that the mature miRNAs as well as
their precursors can be targeted by small molecular drugs. At the same time,
human diseases result from the disordered interplay of tissue- and cell
lineage-specific processes. However, few computational researches predict
drug-disease potential relationships based on miRNA data and tissue
specificity. Therefore, based on miRNA data and the tissue specificity of
diseases, we propose a new method named as miTS to predict the potential
treatments for diseases. Firstly, based on miRNAs data, target genes and
information of FDA approved drugs, we evaluate the relationships between miRNAs
and drugs in the tissue-specific PPI network. Then, we construct a tripartite
network: drug-miRNA-disease Finally, we obtain the potential drug-disease
associations based on the tripartite network. In this paper, we take breast
cancer as case study and focus on the top-30 predicted drugs. 25 of them
(83.3%) are found having known connections with breast cancer in CTD benchmark
and the other 5 drugs are potential drugs for breast cancer. We further
evaluate the 5 newly predicted drugs from clinical records, literature mining,
KEGG pathways enrichment analysis and overlapping genes between enriched
pathways. For each of the 5 new drugs, strongly supported evidences can be
found in three or more aspects. In particular, Regorafenib has 15 overlapping
KEGG pathways with breast cancer and their p-values are all very small. In
addition, whether in the literature curation or clinical validation,
Regorafenib has a strong correlation with breast cancer. All the facts show
that Regorafenib is likely to be a truly effective drug, worthy of our further
study. It further follows that our method miTS is effective and practical for
predicting new drug indications.