pwc-archive/papers-with-abstracts huggingface dataset:
Coevolutionary patterns caused by prey selection
Date: 2020-05-19 00:00:00

Many theoretical models have been formulated to better understand the
coevolutionary patterns that emerge from antagonistic interactions. These
models usually assume that the attacks by the exploiters are random, so the
effect of victim selection by exploiters on coevolutionary patterns remains
unexplored. Here we analytically studied the payoff for predators and prey
under coevolution assuming that every individual predator can attack only a
small number of prey any given time, considering two scenarios: (i) predation
occurs at random; (ii) predators select prey according to phenotype matching.
We also develop an individual based model to verify the robustness of our
analytical prediction. We show that both scenarios result in well known similar
coevolutionary patterns if population sizes are sufficiently high: symmetrical
coevolutionary branching and symmetrical coevolutionary cycling (Red Queen
dynamics). However, for small population sizes, prey selection can cause
unexpected coevolutionary patterns. One is the breaking of symmetry of the
coevolutionary pattern, where the phenotypes evolve towards one of two
evolutionarily stable patterns. As population size increases, the phenotypes
oscillate between these two values in a novel form of Red Queen dynamics, the
episodic reversal between the two stable patterns. Thus, prey selection causes
prey phenotypes to evolve towards more extreme values, which reduces the
fitness of both predators and prey, increasing the likelihood of extinction.
Predicting protein secondary structure with Neural Machine Translation
Authors: Evan Weissburg, Ian Bulovic
Date: 2018-09-24 00:00:00
Tasks: Machine Translation, Prediction, Protein Secondary Structure Prediction, Translation

We present analysis of a novel tool for protein secondary structure prediction using the recently-investigated Neural Machine Translation framework. The tool provides a fast and accurate folding prediction based on primary structure with subsecond prediction time even for batched inputs. We hypothesize that Neural Machine Translation can improve upon current predictive accuracy by better encoding complex relationships between nearby but non-adjacent amino acids. We overview our modifications to the framework in order to improve accuracy on protein sequences. We report 65.9% Q3 accuracy and analyze the strengths and weaknesses of our predictive model.
Fast Signal Recovery from Saturated Measurements by Linear Loss and Nonconvex Penalties
Authors: He Fan, Huang Xiaolin, Liu Yipeng, Yan Ming
Date: 2018-09-12 00:00:00
Tasks: Compressive Sensing

Sign information is the key to overcoming the inevitable saturation error in
compressive sensing systems, which causes information loss and results in bias.
For sparse signal recovery from saturation, we propose to use a linear loss to
improve the effectiveness from existing methods that utilize hard
constraints/hinge loss for sign consistency. Due to the use of linear loss, an
analytical solution in the update progress is obtained, and some nonconvex
penalties are applicable, e.g., the minimax concave penalty, the $\ell_0$ norm,
and the sorted $\ell_1$ norm. Theoretical analysis reveals that the estimation
error can still be bounded. Generally, with linear loss and nonconvex
penalties, the recovery performance is significantly improved, and the
computational time is largely saved, which is verified by the numerical
experiments.
Robustness in the Optimization of Risk Measures
Date: 2018-09-25 00:00:00
Tasks: Management

We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk measurement related optimization problem is robust, which we call "robustness against optimization". The new notion is studied for various classes of risk measures and expected utility and loss functions. Motivated by practical issues from financial regulation, special attention is given to the two most widely used risk measures in the industry, Value-at-Risk (VaR) and Expected Shortfall (ES). We establish that for a class of general optimization problems, VaR leads to non-robust optimizers whereas convex risk measures generally lead to robust ones. Our results offer extra insight on the ongoing discussion about the comparative advantages of VaR and ES in banking and insurance regulation. Our notion of robustness is conceptually different from the field of robust optimization, to which some interesting links are derived.
Asynchronous stochastic price pump
Date: 2018-09-25 00:00:00

We propose a model for equity trading in a population of agents where each
agent acts to achieve his or her target stock-to-bond ratio, and, as a feedback
mechanism, follows a market adaptive strategy. In this model only a fraction of
agents participates in buying and selling stock during a trading period, while
the rest of the group accepts the newly set price. Using numerical simulations
we show that the stochastic process settles on a stationary regime for the
returns. The mean return can be greater or less than the return on the bond and
it is determined by the parameters of the adaptive mechanism. When the number
of interacting agents is fixed, the distribution of the returns follows the
log-normal density. In this case, we give an analytic formula for the mean rate
of return in terms of the rate of change of agents' risk levels and confirm the
formula by numerical simulations. However, when the number of interacting
agents per period is random, the distribution of returns can significantly
deviate from the log-normal, especially as the variance of the distribution for
the number of interacting agents increases.
Graph Variogram: A novel tool to measure spatial stationarity
Date: 2018-09-25 00:00:00

Irregularly sampling a spatially stationary random field does not yield a
graph stationary signal in general. Based on this observation, we build a
definition of graph stationarity based on intrinsic stationarity, a less
restrictive definition of classical stationarity. We introduce the concept of
graph variogram, a novel tool for measuring spatial intrinsic stationarity at
local and global scales for irregularly sampled signals by selecting subgraphs
of local neighborhoods. Graph variograms are extensions of variograms used for
signals defined on continuous Euclidean space. Our experiments with
intrinsically stationary signals sampled on a graph, demonstrate that graph
variograms yield estimates with small bias of true theoretical models, while
being robust to sampling variation of the space.
Derivatives pricing using signature payoffs
Date: 2018-09-25 00:00:00

We introduce signature payoffs, a family of path-dependent derivatives that
are given in terms of the signature of the price path of the underlying asset.
We show that these derivatives are dense in the space of continuous payoffs, a
result that is exploited to quickly price arbitrary continuous payoffs. This
approach to pricing derivatives is then tested with European options, American
options, Asian options, lookback options and variance swaps. As we show,
signature payoffs can be used to price these derivatives with very high
accuracy.
Context in Synthetic Biology: Memory Effects of Environments with Mono-molecular Reactions
Date: 2019-01-11 00:00:00

Synthetic biology aims at designing modular genetic circuits that can be
assembled according to the desired function. When embedded in a cell, a circuit
module becomes a small subnetwork within a larger environmental network, and
its dynamics is therefore affected by potentially unknown interactions with the
environment. It is well-known that the presence of the environment not only
causes extrinsic noise but also memory effects, which means that the dynamics
of the subnetwork is affected by its past states via a memory function that is
characteristic of the environment. We study several generic scenarios for the
coupling between a small module and a larger environment, with the environment
consisting of a chain of mono-molecular reactions. By mapping the dynamics of
this coupled system onto random walks, we are able to give exact analytical
expressions for the arising memory functions. Hence, our results give insights
into the possible types of memory functions and thereby help to better predict
subnetwork dynamics.
No Arbitrage in Continuous Financial Markets
Date: 2020-02-12 00:00:00

We derive integral tests for the existence and absence of arbitrage in a
financial market with one risky asset which is either modeled as stochastic
exponential of an Ito process or a positive diffusion with Markov switching. In
particular, we derive conditions for the existence of the minimal martingale
measure. We also show that for Markov switching models the minimal martingale
measure preserves the independence of the noise and we study how the minimal
martingale measure can be modified to change the structure of the switching
mechanism. Our main mathematical tools are new criteria for the martingale and
strict local martingale property of certain stochastic exponentials.
A model of adaptive, market behavior generating positive returns, volatility and system risk
Date: 2018-09-25 00:00:00

We describe a simple model for speculative trading based on adaptive behavior
of economic agents.The adaptive behavior is expressed through a feedback
mechanism for changing agents' stock-to-bond ratios, depending on the past
performance of their portfolios.The stock price is set according to the
demand-supply for the asset derived from the agents' target risk levels. Using
the methodology of agent-based modeling we show that agents, acting
endogenously and adaptively, create a persistent price bubble. The price
dynamics generated by the trading process does not reveal any singularities,
however the process is accompanied by growing aggregated risk that indicates
increasing likelihood of a crash.
Sparse Recovery and Dictionary Learning from Nonlinear Compressive Measurements
Date: 2019-09-10 00:00:00
Tasks: Denoising, Dictionary Learning

Sparse coding and dictionary learning are popular techniques for linear
inverse problems such as denoising or inpainting. However in many cases, the
measurement process is nonlinear, for example for clipped, quantized or 1-bit
measurements. These problems have often been addressed by solving constrained
sparse coding problems, which can be difficult to solve, and assuming that the
sparsifying dictionary is known and fixed. Here we propose a simple and unified
framework to deal with nonlinear measurements. We propose a cost function that
minimizes the distance to a convex feasibility set, which models our knowledge
about the nonlinear measurement. This provides an unconstrained, convex, and
differentiable cost function that is simple to optimize, and generalizes the
linear least squares cost commonly used in sparse coding. We then propose
proximal based sparse coding and dictionary learning algorithms, that are able
to learn directly from nonlinearly corrupted signals. We show how the proposed
framework and algorithms can be applied to clipped, quantized and 1-bit data.
Physical Uplink Control Channel Design for 5G New Radio
Date: 2018-09-25 00:00:00

The next generation wireless communication system, 5G, or New Radio (NR) will
provide access to information and sharing of data anywhere, anytime by various
users and applications with diverse multi-dimensional requirements. Physical
Uplink Control Channel (PUCCH), which is mainly utilized to convey Uplink
Control Information (UCI), is a fundamental building component to enable NR
system. Compared to Long Term Evolution (LTE), more flexible PUCCH structure is
specified in NR, aiming to support diverse applications and use cases. This
paper describes the design principles of various NR PUCCH formats and the
underlying physical structures. Further, extensive simulation results are
presented to explain the considerations behind the NR PUCCH design.
Development of spatial suppression surrounding the focus of visual attention
Authors: Wong-Kee-You Audrey M. B., Tsotsos John K., Adler Scott A.
Date: 2018-09-17 00:00:00

The capacity to filter out irrelevant information from our environment is
critical to efficient processing. Yet, during development, when building a
knowledge base of the world is occurring, the ability to selectively allocate
attentional resources is limited (e.g., Amso & Scerif, 2015). In adulthood,
research has demonstrated that surrounding the spatial location of attentional
focus is a suppressive field, resulting from top-down attention promoting the
processing of relevant stimuli and inhibiting surrounding distractors (e.g.,
Hopf et al., 2006). It is not fully known, however, whether this phenomenon
manifests in development. In the current study, we examined whether spatial
suppression surrounding the focus of visual attention is exhibited in
developmental age groups. Participants between 12 and 27 years of age exhibited
spatial suppression surrounding their focus of visual attention. Their accuracy
increased as a function of the separation distance between a spatially cued
(and attended) target and a second target, suggesting that a ring of
suppression surrounded the attended target. When a central cue was instead
presented and therefore attention was no longer spatially cued, surround
suppression was not observed, indicating that our initial findings of
suppression were indeed related to the focus of attention. Attentional surround
suppression was not observed in 8- to 11-years-olds, even with a longer spatial
cue presentation time, demonstrating that the lack of the effect at these ages
is not due to slowed attentional feedback processes. Our findings demonstrate
that top-down attentional processes are still immature until approximately 12
years of age, and that they continue to be refined throughout adolescence,
converging well with previous research on attentional development.
Tutorial: Neuromorphic spiking neural networks for temporal learning
Date: 2018-09-11 00:00:00
Tasks: Prediction

Spiking neural networks (SNN) as time-dependent hypotheses consisting of
spiking nodes (neurons) and directed edges (synapses) are believed to offer
unique solutions to reward prediction tasks and the related feedback that are
classified as reinforcement learning. Generally, temporal difference (TD)
learning renders it possible to optimize a model network to predict the delayed
reward in an ad hoc manner. Neuromorphic SNNs--networks built using dedicated
hardware--particularly leverage such TD learning for not only reward prediction
but also temporal sequence prediction in a physical time domain. In this
tutorial, such learning in a physical time domain is referred to as temporal
learning to distinguish it from conventional TD learning-based methods that
generally involve algorithmic (rather than physical) time. This tutorial
addresses neuromorphic SNNs for temporal learning from the scratch. It first
concerns general characteristics of SNNs including spiking neurons and
information coding schemes and then moves on to temporal learning including its
general concept, feasible algorithms, and their association with
neurophysiological learning rules that have intensively been enriched for the
last few decades.
Extended opportunity cost model to find near equilibrium electricity prices under non-convexities
Date: 2018-09-25 00:00:00

This paper finds near equilibrium prices for electricity markets with
nonconvexities due to binary variables, in order to reduce the market
participants' opportunity costs, such as generators' unrecovered costs. The
opportunity cost is defined as the difference between the profit when the
instructions of the market operator are followed and when the market
participants can freely make their own decisions based on the market prices. We
use the minimum complementarity approximation to the minimum total opportunity
cost (MTOC) model, from previous research, with tests on a much more realistic
unit commitment (UC) model than in previous research, including features such
as reserve requirements, ramping constraints, and minimum up and down times.
The developed model incorporates flexible price responsive demand, as in
previous research, but since not all demand is price responsive, we consider
the more realistic case that total demand is a mixture of fixed and flexible.
Another improvement over previous MTOC research is computational: whereas the
previous research had nonconvex terms among the objective function's continuous
variables, we convert the objective to an equivalent form that contains only
linear and convex quadratic terms in the continuous variables. We compare the
unit commitment model with the standard social welfare optimization version of
UC, in a series of sensitivity analyses, varying flexible demand to represent
varying degrees of future penetration of electric vehicles and smart
appliances, different ratios of generation availability, and different values
of transmission line capacities to consider possible congestion. The minimum
total opportunity cost and social welfare solutions are mostly very close in
different scenarios, except in some extreme cases.
Learning Consumer and Producer Embeddings for User-Generated Content Recommendation
Authors: Kang Wang-Cheng, McAuley Julian
Date: 2018-09-25 00:00:00
Tasks: Collaborative Filtering

User-Generated Content (UGC) is at the core of web applications where users
can both produce and consume content. This differs from traditional e-Commerce
domains where content producers and consumers are usually from two separate
groups. In this work, we propose a method CPRec (consumer and producer based
recommendation), for recommending content on UGC-based platforms. Specifically,
we learn a core embedding for each user and two transformation matrices to
project the user's core embedding into two 'role' embeddings (i.e., a producer
and consumer role). We model each interaction by the ternary relation between
the consumer, the consumed item, and its producer. Empirical studies on two
large-scale UGC applications show that our method outperforms standard
collaborative filtering methods as well as recent methods that model producer
information via item features.
La pr\'ediction des int\'er\^ets des utilisateurs pour la RI contextuelle et la recommandation d'amis dans un environnement mobile
Authors: Sassi Imen Ben
Date: 2018-09-25 00:00:00

The emergence of smartphones has given mobile computing access to everyday
reality. More specifically, the context modeling offers users an effective way
to customize search results and even the recommended elements by limiting the
data space. Moreover, in recent years, many social sites have embraced the
notion of context in their recommendations. Indeed, with the availability of
mobile devices, these new mobile sites have the advantage of providing users
with more relevant elements based on their current situations. Thus, we
introduce a new approach of contextual IR in a mobile environment. We offer a
hand, an approach called SA-IRI based on the prediction of users' interests,
from DBpedia, given their current situations. This approach applies the
technique of associative classification in order to enrich the users' queries.
Secondly, we introduce an approach of communities discovering, called
Foaf-A-Walk, combining the random walk technique and the Foaf modeling, for
friend recommendation.
Towards Subject and Diagnostic Identifiability in the Alzheimer's Disease Spectrum based on Functional Connectomes
Date: 2018-09-25 00:00:00
Tasks: Diagnostic, Functional Connectivity

Alzheimer's disease (AD) is the only major cause of mortality in the world
without an effective disease modifying treatment. Evidence supporting the so
called disconnection hypothesis suggests that functional connectivity
biomarkers may have clinical potential for early detection of AD. However,
known issues with low test-retest reliability and signal to noise in functional
connectivity may prevent accuracy and subsequent predictive capacity. We
validate the utility of a novel principal component based diagnostic
identifiability framework to increase separation in functional connectivity
across the Alzheimer's spectrum by identifying and reconstructing FC using only
AD sensitive components or connectivity modes. We show that this framework (1)
increases test-retest correspondence and (2) allows for better separation, in
functional connectivity, of diagnostic groups both at the whole brain and
individual resting state network level. Finally, we evaluate a posteriori the
association between connectivity mode weights with longitudinal neurocognitive
outcomes.
Error Reduction Network for DBLSTM-based Voice Conversion
Date: 2018-09-26 00:00:00
Tasks: Voice Conversion

So far, many of the deep learning approaches for voice conversion produce
good quality speech by using a large amount of training data. This paper
presents a Deep Bidirectional Long Short-Term Memory (DBLSTM) based voice
conversion framework that can work with a limited amount of training data. We
propose to implement a DBLSTM based average model that is trained with data
from many speakers. Then, we propose to perform adaptation with a limited
amount of target data. Last but not least, we propose an error reduction
network that can improve the voice conversion quality even further. The
proposed framework is motivated by three observations. Firstly, DBLSTM can
achieve a remarkable voice conversion by considering the long-term dependencies
of the speech utterance. Secondly, DBLSTM based average model can be easily
adapted with a small amount of data, to achieve a speech that sounds closer to
the target. Thirdly, an error reduction network can be trained with a small
amount of training data, and can improve the conversion quality effectively.
The experiments show that the proposed voice conversion framework is flexible
to work with limited training data and outperforms the traditional frameworks
in both objective and subjective evaluations.
Multivariate Stochastic Volatility Model with Realized Volatilities and Pairwise Realized Correlations
Date: 2019-03-13 00:00:00

Although stochastic volatility and GARCH (generalized autoregressive
conditional heteroscedasticity) models have successfully described the
volatility dynamics of univariate asset returns, extending them to the
multivariate models with dynamic correlations has been difficult due to several
major problems. First, there are too many parameters to estimate if available
data are only daily returns, which results in unstable estimates. One solution
to this problem is to incorporate additional observations based on intraday
asset returns, such as realized covariances. Second, since multivariate asset
returns are not synchronously traded, we have to use the largest time intervals
such that all asset returns are observed in order to compute the realized
covariance matrices. However, in this study, we fail to make full use of the
available intraday informations when there are less frequently traded assets.
Third, it is not straightforward to guarantee that the estimated (and the
realized) covariance matrices are positive definite. Our contributions are the
following: (1) we obtain the stable parameter estimates for the dynamic
correlation models using the realized measures, (2) we make full use of
intraday informations by using pairwise realized correlations, (3) the
covariance matrices are guaranteed to be positive definite, (4) we avoid the
arbitrariness of the ordering of asset returns, (5) we propose the flexible
correlation structure model (e.g., such as setting some correlations to be zero
if necessary), and (6) the parsimonious specification for the leverage effect
is proposed. Our proposed models are applied to the daily returns of nine U.S.
stocks with their realized volatilities and pairwise realized correlations and
are shown to outperform the existing models with respect to portfolio
performances.