pwc-archive/papers-with-abstracts huggingface dataset:
State-of-the-art and gaps for deep learning on limited training data in remote sensing
Authors: Ball John E., Anderson Derek T., Wei Pan
Date: 2018-07-11 00:00:00
Tasks: Deep Learning, Transfer Learning

Deep learning usually requires big data, with respect to both volume and
variety. However, most remote sensing applications only have limited training
data, of which a small subset is labeled. Herein, we review three
state-of-the-art approaches in deep learning to combat this challenge. The
first topic is transfer learning, in which some aspects of one domain, e.g.,
features, are transferred to another domain. The next is unsupervised learning,
e.g., autoencoders, which operate on unlabeled data. The last is generative
adversarial networks, which can generate realistic looking data that can fool
the likes of both a deep learning network and human. The aim of this article is
to raise awareness of this dilemma, to direct the reader to existing work and
to highlight current gaps that need solving.
Shared Spectrum for Mobile-Cells Backhaul and Access Link
Date: 2018-07-31 00:00:00

Offloading cellular hotspot regions to small-cells has been the main theme
for the fifth generation of cellular network. One such hotspot is the public
transport which carries a large number of cellular users who frequently receive
low quality of service (QoS) due to vehicular penetration effect (VPE). Hence
installation of mobile-cell (MC) within public transport is seen as a potential
enabler to enhance QoS for commuting users. However, unlike fixed cells, MC
requires wireless backhaul (BH) connectivity along with in-vehicle Access-Link
(AL) communication. These additional wireless links for MC communication will
pose an excessive burden on an already scarce frequency spectrum. Hence, in
this research, we exploit VPE and line-of-sight (LOS) communication to allow
the downlink backhaul (DL-BH) sub-channels to be shared by in-vehicle downlink
access-link (DL-AL) transmission. Our analysis and simulations show that using
the above-mentioned technique, both links maintain high success probability,
especially in regions with low signal to interference ratios.
Insights into Complex Brain Functions Related to Schizophrenia Disorder through Causal Network Analysis
Date: 2019-05-28 00:00:00
Tasks: Diversity, Network Identification

Gene expression represents a fundamental interface between genes and
environment in the development and ongoing plasticity of the human organism.
Individual differences in gene expression are likely to underpin much of human
diversity, including psychiatric illness. Gene expression shows a distinct
regulatory pattern in different tissues. Therefore, brain tissue analysis
provides insights into brain disorder mechanisms. Furthermore, mechanistic
understanding of gene regulatory pattern can be provided through studying the
underlying relationships as a complex network. Identification of brain specific
gene relationships provides a complementary framework in which to tackle the
complex dysregulations that occur in neuropsychiatric and other neurological
disorders. Using a systems approach established in Mendelian randomization and
Bayesian Network, we integrated genetic and transcriptomic data from the
common-mind consortium and identified transcriptomic causal networks in
observational studies. Focusing on Schizophrenia disorder, we identified high
impact genes and revealed their underlying pathways in brain tissue. In
addition, we generated novel hypotheses including genes as causes of the
schizophrenia-associated genes and new genes associated with Schizophrenia.
This approach may facilitate a better understanding of the disease mechanism
that is complementary to molecular experimental studies especially for complex
systems and large-scale data sets.
Modeling joint probability distribution of yield curve parameters
Date: 2018-07-31 00:00:00
Tasks: Time Series, Time Series Analysis

US Yield curve has recently collapsed to its most flattened level since
subprime crisis and is close to the inversion. This fact has gathered attention
of investors around the world and revived the discussion of proper modeling and
forecasting yield curve, since changes in interest rate structure are believed
to represent investors expectations about the future state of economy and have
foreshadowed recessions in the United States. While changes in term structure
of interest rates are relatively easy to interpret they are however very
difficult to model and forecast due to no proper economic theory underlying
such events. Yield curves are usually represented by multivariate sparse time
series, at any point in time infinite dimensional curve is portrayed via
relatively few points in a multivariate space of data and as a consequence
multimodal statistical dependencies behind these curves are relatively hard to
extract and forecast via typical multivariate statistical methods.We propose to
model yield curves via reconstruction of joint probability distribution of
parameters in functional space as a high degree polynomial. Thanks to adoption
of an orthonormal basis, the MSE estimation of coefficients of a given function
is an average over a data sample in the space of functions. Since such
polynomial coefficients are independent and have cumulant-like interpretation
ie.describe corresponding perturbation from an uniform joint distribution, our
approach can also be extended to any d-dimensional space of yield curve
parameters (also in neighboring times) due to controllable accuracy. We believe
that this approach to modeling of local behavior of a sparse multivariate
curved time series can complement prediction from standard models like ARIMA,
that are using long range dependencies, but provide only inaccurate prediction
of probability distribution, often as just Gaussian with constant width.
Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model
Date: 2018-07-31 00:00:00
Tasks: Time Series, Time Series Analysis

Trend and Value are pervasive anomalies, common to all financial markets. We
address the problem of their co-existence and interaction within the framework
of Heterogeneous Agent Based Models (HABM). More specifically, we extend the
Chiarella (1992) model by adding noise traders and a non-linear demand of
fundamentalists. We use Bayesian filtering techniques to calibrate the model on
time series of prices across a variety of asset classes since 1800. The
fundamental value is an output of the calibration, and does not require the use
of an external pricing model. Our extended model reproduces many empirical
observations, including the non-monotonic relation between past trends and
future returns. The destabilizing activity of trend-followers leads to a
qualitative change of mispricing distribution, from unimodal to bimodal,
meaning that some markets tend to be over- (or under-) valued for long periods
of time.
Workflow description to dynamically model \beta-arrestin signaling networks
Date: 2018-07-31 00:00:00

Dynamic models of signaling networks allow the formulation of hypotheses on
the topology and kinetic rate laws characterizing a given molecular network,
in-depth exploration and confrontation with kinetic biological data. Despite
its standardization, dynamic modeling of signaling networks still requires
successive technical steps that need to be carefully performed. Here, we detail
these steps by going through the mathematical and statistical framework. We
explain how it can be applied to the understanding of \beta-arrestin-dependent
signaling networks. We illustrate our methodology through the modeling of
\beta-arrestin recruitment kinetics at the Follicle Stimulating Hormone (FSH)
receptor supported by in-house Bioluminescence Resonance Energy Transfer (BRET)
data.
Panel quantile regressions for estimating and predicting the Value--at--Risk of commodities
Date: 2018-07-31 00:00:00
Tasks: Management, quantile regression

This paper investigates how realized and option implied volatilities are
related to the future quantiles of commodity returns. Whereas realized
volatility measures ex-post uncertainty, volatility implied by option prices
reveals the market's expectation and is often used as an ex-ante measure of the
investor sentiment. Using a flexible panel quantile regression framework, we
show how the future conditional quantiles of commodities returns depend on both
ex-post and ex-ante uncertainty measures. Empirical analysis of the most liquid
commodities covering main sectors including energy, food, agricultural,
precious and industrial metals reveal several important stylized facts about
the data. We document common patterns of the dependence between future quantile
returns and ex-post as well as ex-ante volatilities. We further show that
conditional returns distribution is platykurtic and time-invariant. The
approach can serve as a useful risk management tools for investors interested
in commodity future contracts.
The econometrics of happiness: Are we underestimating the returns to education and income?
Authors: Christopher P Barrington-Leigh
Date: 2018-07-31 00:00:00
Tasks: counterfactual, Descriptive, Econometrics

This paper describes a fundamental and empirically conspicuous problem inherent to surveys of human feelings and opinions in which subjective responses are elicited on numerical scales. The paper also proposes a solution. The problem is a tendency by some individuals -- particularly those with low levels of education -- to simplify the response scale by considering only a subset of possible responses such as the lowest, middle, and highest. In principle, this ``focal value rounding'' (FVR) behavior renders invalid even the weak ordinality assumption often used in analysis of such data. With ``happiness'' or life satisfaction data as an example, descriptive methods and a multinomial logit model both show that the effect is large and that education and, to a lesser extent, income level are predictors of FVR behavior. A model simultaneously accounting for the underlying wellbeing and for the degree of FVR is able to estimate the latent subjective wellbeing, i.e.~the counterfactual full-scale responses for all respondents, the biases associated with traditional estimates, and the fraction of respondents who exhibit FVR. Addressing this problem helps to resolve a longstanding puzzle in the life satisfaction literature, namely that the returns to education, after adjusting for income, appear to be small or negative. Due to the same econometric problem, the marginal utility of income in a subjective wellbeing sense has been consistently underestimated.
Pulling in models of cell migration
Authors: Christian A. Yates, George Chappelle
Date: 2018-07-31 00:00:00

There are numerous scenarios in which populations of cells migrate in crowded environments. Typical examples include wound healing, cancer growth and embryo development. In these crowded environments cells are able to interact with each other in a variety of ways. These include excluded volume interactions, adhesion, repulsion, cell signalling, pushing and pulling. One popular way to understand the behaviour of a group of interacting cells is through an agent-based model (ABM). A typical aim of modellers using such represtations is to elucidate how the microscopic interactions at the cell-level impact on the macroscopic behaviour of the population. The complex cell-cell interactions listed above have also been incorporated into such models; all apart from cell-cell pulling. In this paper we consider this under-represented cell-cell interaction, in which an active cell is able to `pull' a nearby neighbour as it moves. We incorporate a variety of potential cell-cell pulling mechanisms into on- and off-lattice agent-based volume exclusion models of cell movement. For each of these agent-based models we derive a continuum partial differential equation which describes the evolution of the cells at a population-level. We study the agreement between the ABMs and the continuum, population-based models, and compare and contrast a range of ABMs (accounting for the different pulling mechanisms) with each other. We find generally good agreement between the ABMs and the corresponding continuum models that worsens as the agent-based models become more complex. Interestingly, we observe that the partial differential equations that we derive differ significantly, depending on whether they were derived from on- or off-lattice ABMs of pulling. This hints that it is important employ the appropriate ABM when representing pulling cell-cell interactions.
IGLOSS: iterative gapless local similarity search
Date: 2018-07-31 00:00:00
Tasks: Sensitivity

Searching for local sequence patterns is one of the basic tasks in
bioinformatics. Sequence patterns might have structural, functional or some
other relevance, and numerous methods have been developed to detect and analyze
them. These methods often depend on the wealth of information already
collected. The explosion in the number of newly available sequences calls for
novel methods to explore local sequence similarity. We have developed a high
sensitivity web-based iterative local similarity scanner, that finds sequence
patterns similar to a submitted query.
Strictly strategy-proof auctions
Date: 2020-07-25 00:00:00

A strictly strategy-proof mechanism is one that asks agents to use strictly
dominant strategies. In the canonical one-dimensional mechanism design setting
with private values, we show that strict strategy-proofness is equivalent to
strict monotonicity plus the envelope formula, echoing a well-known
characterisation of (weak) strategy-proofness. A consequence is that
strategy-proofness can be made strict by an arbitrarily small modification, so
that strictness is 'essentially for free'.
Quantitative Comparisons of Linked Color Imaging and White-Light Colonoscopy for Colorectal Polyp Analysis
Date: 2018-07-28 00:00:00
Tasks: Diagnostic

The performance of imaging techniques has an important influence on the
clinical diagnostic strategy of colorectal cancer. Linked color imaging (LCI)
by laser endoscopy is a recently developed techniques, and its advantage in
improving the analysis accuracy of colorectal polyps over white-light (WL)
endoscopy has been demonstrated in previous clinical studies. However, there
are no objective criteria to evaluate and compare the aforementioned endoscopy
methods. This paper presents a new criterion, namely entropy of color gradients
image (ECGI), which is based on color gradients distribution and provides a
comprehensive and objective evaluating indicator of the performance of
colorectal images. Our method extracts the color gradient image pairs of 143
colonoscopy polyps in the LCI-PairedColon database, which are generated with WL
and LCI conditions, respectively. Then, we apply the morphological method to
fix the deviation of light-reflecting regions, and the ECGI scores of sample
pairs are calculated. Experimental results show that the average ECGI scores of
LCI images (5.7071) were significantly higher than that of WL (4.6093). This
observation is consistent with the clinical studies. Therefore, the
effectiveness of the proposed criterion is demonstrated.
Real-Time Millimeter-Wave MIMO Channel Sounder for Dynamic Directional Measurements
Date: 2018-07-31 00:00:00

In this paper, we present a novel real-time multiple-input-multiple-output
(MIMO) channel sounder for the 28 GHz band. Until now, most investigations of
the directional characteristics of millimeter-wave channels have used
mechanically rotating horn antennas. In contrast, the sounder presented here is
capable of performing horizontal and vertical beam steering with the help of
phased arrays. Due to its fast beam-switching capability, the proposed sounder
can perform measurements that are directionally resolved both at the
transmitter(TX) and receiver (RX) in 1.44 milliseconds compared to the minutes
or even hours required for rotating horn antenna sounders. This not only
enables measurement of more TX-RX locations for a better statistical validity
but also allows to perform directional analysis in dynamic environments. The
short measurement time combined with the high phase stability limits the phase
drift between TX and RX, enabling phase-coherent sounding of all beam pairs
even when TX and RX have no cabled connection for synchronization without any
delay ambiguity. Furthermore, the phase stability over time enables complex RX
waveform averaging to improve the signal to noise ratio during high path loss
measurements. The paper discusses both the system design as well as the
measurements performed for verification of the sounder performance.
Furthermore, we present sample results from double directional measurements in
dynamic environments.
Network models in neuroscience
Date: 2018-07-31 00:00:00

From interacting cellular components to networks of neurons and neural
systems, interconnected units comprise a fundamental organizing principle of
the nervous system. Understanding how their patterns of connections and
interactions give rise to the many functions of the nervous system is a primary
goal of neuroscience. Recently, this pursuit has begun to benefit from the
development of new mathematical tools that can relate a system's architecture
to its dynamics and function. These tools, which are known collectively as
network science, have been used with increasing success to build models of
neural systems across spatial scales and species. Here we discuss the nature of
network models in neuroscience. We begin with a review of model theory from a
philosophical perspective to inform our view of networks as models of complex
systems in general, and of the brain in particular. We then summarize the types
of models that are frequently studied in network neuroscience along three
primary dimensions: from data representations to first-principles theory, from
biophysical realism to functional phenomenology, and from elementary
descriptions to coarse-grained approximations. We then consider ways to
validate these models, focusing on approaches that perturb a system to probe
its function. We close with a description of important frontiers in the
construction of network models and their relevance for understanding
increasingly complex functions of neural systems.
Delay-Performance Tradeoffs in Causal Microphone Array Processing
Date: 2018-07-31 00:00:00

In real-time listening enhancement applications, such as hearing aid signal
processing, sounds must be processed with no more than a few milliseconds of
delay to sound natural to the listener. Listening devices can achieve better
performance with lower delay by using microphone arrays to filter acoustic
signals in both space and time. Here, we analyze the tradeoff between delay and
squared-error performance of causal multichannel Wiener filters for microphone
array noise reduction. We compute exact expressions for the delay-error curves
in two special cases and present experimental results from real-world
microphone array recordings. We find that delay-performance characteristics are
determined by both the spatial and temporal correlation structures of the
signals.
Gaussian stochastic volatility models: Scaling regimes, large deviations, and moment explosions
Date: 2019-06-14 00:00:00

In this paper, we establish sample path large and moderate deviation
principles for log-price processes in Gaussian stochastic volatility models,
and study the asymptotic behavior of exit probabilities, call pricing
functions, and the implied volatility. In addition, we prove that if the
volatility function in an uncorrelated Gaussian model grows faster than
linearly, then, for the asset price process, all the moments of order greater
than one are infinite. Similar moment explosion results are obtained for
correlated models.
Code-Switching Detection with Data-Augmented Acoustic and Language Models
Authors: Yılmaz Emre, Heuvel Henk van den, van Leeuwen David A.
Date: 2018-07-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Text Generation

In this paper, we investigate the code-switching detection performance of a
code-switching (CS) automatic speech recognition (ASR) system with
data-augmented acoustic and language models. We focus on the recognition of
Frisian-Dutch radio broadcasts where one of the mixed languages, namely
Frisian, is under-resourced. Recently, we have explored how the acoustic
modeling (AM) can benefit from monolingual speech data belonging to the
high-resourced mixed language. For this purpose, we have trained
state-of-the-art AMs on a significantly increased amount of CS speech by
applying automatic transcription and monolingual Dutch speech. Moreover, we
have improved the language model (LM) by creating CS text in various ways
including text generation using recurrent LMs trained on existing CS text.
Motivated by the significantly improved CS ASR performance, we delve into the
CS detection performance of the same ASR system in this work by reporting CS
detection accuracies together with a detailed detection error analysis.
The ISRS GN Model, an Efficient Tool in Modeling Ultra-Wideband Transmission in Point-to-Point and Network Scenarios
Date: 2018-07-30 00:00:00

An analytical model to estimate nonlinear performance in ultra-wideband
optical transmission networks is presented. The model accurately accounts for
inter-channel stimulated Raman scattering, variably loaded fibre spans and is
validated through C+L band simulations for uniform and probabilistically shaped
64-QAM.
Rational Optimization for Nonlinear Reconstruction with Approximate $\ell_0$ Penalization
Date: 2018-12-21 00:00:00

Recovering nonlinearly degraded signal in the presence of noise is a
challenging problem. In this work, this problem is tackled by minimizing the
sum of a non convex least-squares fit criterion and a penalty term. We assume
that the nonlinearity of the model can be accounted for by a rational function.
In addition, we suppose that the signal to be sought is sparse and a rational
approximation of the $\ell_0$ pseudo-norm thus constitutes a suitable
penalization. The resulting composite cost function belongs to the broad class
of semi-algebraic functions. To find a globally optimal solution to such an
optimization problem, it can be transformed into a generalized moment problem,
for which a hierarchy of semidefinite programming relaxations can be built.
Global optimality comes at the expense of an increased dimension and, to
overcome computational limitations concerning the number of involved variables,
the structure of the problem has to be carefully addressed. A situation of
practical interest is when the nonlinear model consists of a convolutive
transform followed by a componentwise nonlinear rational saturation. We then
propose to use a sparse relaxation able to deal with up to several hundreds of
optimized variables. In contrast with the naive approach consisting of
linearizing the model, our experiments show that the proposed approach offers
good performance.
Stable memory with unstable synapses
Date: 2019-02-24 00:00:00
Tasks: Retrieval

What is the physiological basis of long-term memory? The prevailing view in
neuroscience attributes changes in synaptic efficacy to memory acquisition.
This view implies that stable memories correspond to stable connectivity
patterns. However, an increasing body of experimental evidence points to
significant, activity-independent dynamics in synaptic strengths. Motivated by
these observations, we explore the possibility of memory storage within a
global component of network connectivity, while individual connections
fluctuate. We find a simple and general principle, stemming from stability
arguments, that links eigenvalues in the complex plane to memories.
Specifically, imaginary-coded memories are more resilient to noise and
homeostatic plasticity than their real-coded counterparts. Memory
representations are stored as time-varying attractors in neural state-space and
support associative retrieval of learned information. Our results suggest a
link between the properties of learning rules and those of network-level memory
representations, and point at measurable signatures to be sought in
experimental data.