pwc-archive/papers-with-abstracts huggingface dataset:
Nonlocal Diffusions and The Quantum Black-Scholes Equation: Modelling the Market Fear Factor
Date: 2018-06-27 00:00:00

In this paper, we establish a link between quantum stochastic processes, and
nonlocal diffusions. We demonstrate how the non-commutative Black-Scholes
equation of Accardi & Boukas (Luigi Accardi, Andreas Boukas, 'The Quantum
Black-Scholes Equation', Jun 2007, available at arXiv:0706.1300v1) can be
written in integral form. This enables the application of the Monte-Carlo
methods adapted to McKean stochastic differential equations (H. P. McKean, 'A
class of Markov processes associated with nonlinear parabolic equations', Proc.
Natl. Acad. Sci. U.S.A., 56(6):1907-1911, 1966) for the simulation of
solutions. We show how unitary transformations can be applied to classical
Black-Scholes systems to introduce novel quantum effects. These have a simple
economic interpretation as a market `fear factor', whereby recent market
turbulence causes an increase in volatility going forward, that is not linked
to either the local volatility function or an additional stochastic variable.
Lastly, we extend this system to 2 variables, and consider Quantum models for
bid-offer spread dynamics.
Semantic Compression of Episodic Memories
Date: 2018-06-20 00:00:00
Tasks: Decision Making, Semantic Compression

Storing knowledge of an agent's environment in the form of a probabilistic
generative model has been established as a crucial ingredient in a multitude of
cognitive tasks. Perception has been formalised as probabilistic inference over
the state of latent variables, whereas in decision making the model of the
environment is used to predict likely consequences of actions. Such generative
models have earlier been proposed to underlie semantic memory but it remained
unclear if this model also underlies the efficient storage of experiences in
episodic memory. We formalise the compression of episodes in the normative
framework of information theory and argue that semantic memory provides the
distortion function for compression of experiences. Recent advances and
insights from machine learning allow us to approximate semantic compression in
naturalistic domains and contrast the resulting deviations in compressed
episodes with memory errors observed in the experimental literature on human
memory.
Mean-Variance Efficiency of Optimal Power and Logarithmic Utility Portfolios
Date: 2019-05-21 00:00:00

We derive new results related to the portfolio choice problem for power and
logarithmic utilities. Assuming that the portfolio returns follow an
approximate log-normal distribution, the closed-form expressions of the optimal
portfolio weights are obtained for both utility functions. Moreover, we prove
that both optimal portfolios belong to the set of mean-variance feasible
portfolios and establish necessary and sufficient conditions such that they are
mean-variance efficient. Furthermore, an application to the stock market is
presented and the behavior of the optimal portfolio is discussed for different
values of the relative risk aversion coefficient. It turns out that the
assumption of log-normality does not seem to be a strong restriction.
Towards Automated Single Channel Source Separation using Neural Networks
Date: 2018-06-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Many applications of single channel source separation (SCSS) including
automatic speech recognition (ASR), hearing aids etc. require an estimation of
only one source from a mixture of many sources. Treating this special case as a
regular SCSS problem where in all constituent sources are given equal priority
in terms of reconstruction may result in a suboptimal separation performance.
In this paper, we tackle the one source separation problem by suitably
modifying the orthodox SCSS framework and focus only on one source at a time.
The proposed approach is a generic framework that can be applied to any
existing SCSS algorithm, improves performance, and scales well when there are
more than two sources in the mixture unlike most existing SCSS methods.
Additionally, existing SCSS algorithms rely on fine hyper-parameter tuning
hence making them difficult to use in practice. Our framework takes a step
towards automatic tuning of the hyper-parameters thereby making our method
better suited for the mixture to be separated and thus practically more useful.
We test our framework on a neural network based algorithm and the results show
an improved performance in terms of SDR and SAR.
Behavior-based evaluation of session satisfaction
Authors: Liu Chao, Zheng Zhenzhen, Jia Jinkang
Date: 2018-06-22 00:00:00

Nowadays, web search becomes more and more popular all over the world. Many
researchers and developers have done lots of studies on behaviors of search
users. In practice, the full understanding of these behaviors can not only help
to evaluate the usefulness of newly-developed ranking algorithms and other
changes of search engine, but also to guide the growth direction of search
engine. As far as we know, most of past work are mainly focused on single
search evaluation, which do promote the rapid development of search engine in
early stage. However,these page-level behaviors are so limited that can no
longer give explicit feedbacks on minor changes of the search engine. We think
that it will be more accurate and sensitive when more information on search
session are provided. In this paper, a session level evaluation method is
proposed. The session-level features are retrieved and carefully analyzed. Some
linear and non-linear features which can reflect the final degree of
satisfaction are chosen and adopted in evaluation models. A two-layer hybrid
evaluation model with different granularity, which can achieve good precision
and recall, is designed and trained. Lots of real experiments are evaluated by
the model, the result shows it achieved a higher accuracy performance than
traditional page-level evaluation metrics. Furthermore, for practical
application, it is important to interpret the reason of each session's
satisfaction judgement. In all, a session-level evaluation model with improved
performance and well capability on interpretation is proposed and applied in
real practice in search engine companies.
Modeling the correlated activity of neural populations: A review
Date: 2018-06-21 00:00:00

The principles of neural encoding and computations are inherently collective
and usually involve large populations of interacting neurons with highly
correlated activities. While theories of neural function have long recognized
the importance of collective effects in populations of neurons, only in the
past two decades has it become possible to record from many cells
simulatenously using advanced experimental techniques with single-spike
resolution, and to relate these correlations to function and behaviour. This
review focuses on the modeling and inference approaches that have been recently
developed to describe the correlated spiking activity of populations of
neurons. We cover a variety of models describing correlations between pairs of
neurons as well as between larger groups, synchronous or delayed in time, with
or without the explicit influence of the stimulus, and including or not latent
variables. We discuss the advantages and drawbacks or each method, as well as
the computational challenges related to their application to recordings of ever
larger populations.
Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search
Authors: Tallis Marcelo, Yadav Pranjul
Date: 2018-05-25 00:00:00
Tasks: BIG-bench Machine Learning

In online internet advertising, machine learning models are widely used to
compute the likelihood of a user engaging with product related advertisements.
However, the performance of traditional machine learning models is often
impacted due to variations in user and advertiser behavior. For example, search
engine traffic for florists usually tends to peak around Valentine's day,
Mother's day, etc. To overcome, this challenge, in this manuscript we propose
three models which are able to incorporate the effects arising due to
variations in product demand. The proposed models are a combination of product
demand features, specialized data sampling methodologies and ensemble
techniques. We demonstrate the performance of our proposed models on datasets
obtained from a real-world setting. Our results show that the proposed models
more accurately predict the outcome of users interactions with product related
advertisements while simultaneously being robust to fluctuations in user and
advertiser behaviors.
Towards Energy Harvesting Powered Sensor Networks for Cyber-Physical Systems
Date: 2018-06-13 00:00:00

The concept of sensor networks (WSNs) has become an important component of
the recently proposed cyber-physical systems (CPSs) and Internet of Things
which can connect the physical world with embedded software systems. Energy-
harvesting (EH) as an enabling technology applied in SNs can heavily reduce the
installation and maintenance cost as well as increase system life, flexibility,
and scalability as the EH is subject to the important "energy interruption"
problem where system tasks can be significantly disrupted by the EH outputs.
This problem not only affects the system modeling but also relates to the
cross-layer network design and schedulability. This paper discusses the
EH-powered CPS architecture and some theoretical problems. In particular, we
propose a framework for fully EH-powered CPS, within which essential topics are
discussed, such as EH software architecture, EH models, and real-time
communication. Different EH models for CPSs (e.g., fully EH-powered and
partially EH-powered) are addressed. Future research directions are briefly
discussed in the end.
New expressions on the performance of a novel multi-hop relay-assisted hybrid FSO / RF communication system with receive diversity
Date: 2018-11-20 00:00:00
Tasks: Diversity

In this paper a novel multi-hop relay-assisted hybrid Free Space Optical /
Radio Frequency (FSO / RF) communication system is presented, in which a mobile
user is connected to the Base Station via a multi-hop relay-assisted hybrid FSO
/ RF link with receive diversity. In this structure, received signal at each
relay is demodulated and forwarded. This is the first time that in a multi-hop
hybrid FSO / RF system, receive diversity is used. Bit Error Rate (BER) and
Outage Probability (P_out) are investigated as system performance criteria. New
exact and asymptotic expressions are derived for these criteria, and MATLAB
simulations are provided to verify the obtained results. For the first time
impact of number of receive antennas and number of relays on the performance of
such a structure is investigated. Results indicate that proposed structure has
low dependence on number of receive antennas; therefore, in the proposed
structure use of single receive antenna has the same performance as the
multi-antenna, while low complexity and power consumption. The proposed
structure shows independent performance at moderate and strong atmospheric
turbulence regimes. Hence, it does not require adaptive processing to match
itself according to atmospheric turbulence condition, therefore is cost
effective and particularly suitable for urban areas that encounter frequent
changes in atmospheric turbulence.
The transmission of uncertainty shocks on income inequality: State-level evidence from the United States
Date: 2018-06-21 00:00:00

In this paper, we explore the relationship between state-level household
income inequality and macroeconomic uncertainty in the United States. Using a
novel large-scale macroeconometric model, we shed light on regional disparities
of inequality responses to a national uncertainty shock. The results suggest
that income inequality decreases in most states, with a pronounced degree of
heterogeneity in terms of shapes and magnitudes of the dynamic responses. By
contrast, some few states, mostly located in the West and South census region,
display increasing levels of income inequality over time. We find that this
directional pattern in responses is mainly driven by the income composition and
labor market fundamentals. In addition, forecast error variance decompositions
allow for a quantitative assessment of the importance of uncertainty shocks in
explaining income inequality. The findings highlight that volatility shocks
account for a considerable fraction of forecast error variance for most states
considered. Finally, a regression-based analysis sheds light on the driving
forces behind differences in state-specific inequality responses.
Sparse Sensing with Semi-Coprime Arrays
Date: 2018-06-21 00:00:00
Tasks: Direction of Arrival Estimation

A semi-coprime array (SCA) interleaves two undersampled uniform linear arrays
(ULAs) and a $Q$ element standard ULA. The undersampling factors of the first
two arrays are $QM$ and $QN$ respectively where $M$ and $N$ are coprime. The
resulting non-uniform linear array is highly sparse. Taking the minimum of the
absolute values of the conventional beampatterns of the three arrays results in
a beampattern free of grating lobes. The SCA offers more savings in the number
of sensors than other popular sparse arrays like coprime arrays, nested arrays,
and minimum redundant arrays. Also, the SCA exhibits better side lobe patterns
than other sparse arrays. An example of direction of arrival estimation with
the SCA illustrates SCA's promising potential in reducing number of sensors,
decreasing system cost and complexity in various signal sensing and processing
applications.
What Makes An Asset Useful?
Date: 2018-06-21 00:00:00
Tasks: Time Series, Time Series Analysis

Given a new candidate asset represented as a time series of returns, how
should a quantitative investment manager be thinking about assessing its
usefulness? This is a key qualitative question inherent to the investment
process which we aim to make precise. We argue that the usefulness of an asset
can only be determined relative to a reference universe of assets and/or
benchmarks the investment manager already has access to or would like to
diversify away from, for instance, standard risk factors, common trading styles
and other assets. We identify four features that the time series of returns of
an asset should exhibit for the asset to be useful to an investment manager,
two primary and two secondary. As primary criteria, we propose that the new
asset should provide sufficient incremental diversification to the reference
universe of assets/benchmarks, and its returns time series should be
sufficiently predictable. As secondary criteria, we propose that the new asset
should mitigate tail risk, and the new asset should be suitable for passive
investment (e.g. buy-and-hold or short-and-hold). We discuss how to quantify
incremental diversification, returns predictability, impact on tail risk, and
suitability for passive investment, and for each criterion, we provide a
scalable algorithmic test of usefulness.
Thermodynamic efficiency of contagions: A statistical mechanical analysis of the SIS epidemic model
Date: 2018-10-22 00:00:00

We present a novel approach to the study of epidemics on networks as
thermodynamic phenomena, considering the thermodynamic efficiency of
contagions, considered as distributed computational processes. Modelling SIS
dynamics on a contact network statistical-mechanically, we follow the Maximum
Entropy principle to obtain steady state distributions and derive, under
certain assumptions, relevant thermodynamic quantities both analytically and
numerically. In particular, we obtain closed form solutions for some cases,
while interpreting key epidemic variables, such as the reproductive ratio $R_0$
of a SIS model, in a statistical mechanical setting. On the other hand, we
consider configuration and free entropy, as well as the Fisher Information, in
the epidemiological context. This allowed us to identify criticality and
distinct phases of epidemic processes. For each of the considered thermodynamic
quantities, we compare the analytical solutions informed by the Maximum Entropy
principle with the numerical estimates for SIS epidemics simulated on
Watts-Strogatz random graphs.
Upgrade of the Analog Integrator for EAST Device
Date: 2018-06-22 00:00:00

Integrators are fundamental instruments to recover differential signals from
magnetic probes in Experimental Advanced Superconducting Tokamak (EAST)
experiments. A kind of difference integrator is introduced which has the same
structure as the standard difference amplifier. The linear fitting method is
used for determining the effective drift slope, then the plasma control system
(PCS) use the drift slope to rectify the integration signal in real time. A new
integrator controller was developed, which uses an ARM micro-controller and the
lightweight IP protocol stack to realize the network control. The tests show
that the upgraded integrator works well, its CMRR is high up to 125 dB when the
common voltage is 1.5 V, and the processed integration drift is about 200 uVs
/1000 s.
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation
Date: 2018-06-22 00:00:00
Tasks: Quantization

We study distributed average consensus problems in multi-agent systems with
directed communication links that are subject to quantized information flow.
The goal of distributed average consensus is for the nodes, each associated
with some initial value, to obtain the average (or some value close to the
average) of these initial values. In this paper, we present and analyze a
distributed averaging algorithm which operates exclusively with quantized
values (specifically, the information stored, processed and exchanged between
neighboring agents is subject to deterministic uniform quantization) and relies
on event-driven updates (e.g., to reduce energy consumption, communication
bandwidth, network congestion, and/or processor usage). We characterize the
properties of the proposed distributed averaging protocol on quantized values
and show that its execution, on any time-invariant and strongly connected
digraph, will allow all agents to reach, in finite time, a common consensus
value represented as the ratio of two integer that is equal to the exact
average. We conclude with examples that illustrate the operation, performance,
and potential advantages of the proposed algorithm.
Innovative method for reducing uninformative calls in non-invasive prenatal testing
Date: 2018-06-22 00:00:00
Tasks: Specificity

Non-invasive prenatal testing or NIPT is currently among the top researched
topic in obstetric care. While the performance of the current state-of-the-art
NIPT solutions achieve high sensitivity and specificity, they still struggle
with a considerable number of samples that cannot be concluded with certainty.
Such uninformative results are often subject to repeated blood sampling and
re-analysis, usually after two weeks, and this period may cause a stress to the
future mothers as well as increase the overall cost of the test. We propose a
supplementary method to traditional z-scores to reduce the number of such
uninformative calls. The method is based on a novel analysis of the length
profile of circulating cell free DNA which compares the change in such profiles
when random-based and length-based elimination of some fragments is performed.
The proposed method is not as accurate as the standard z-score; however, our
results suggest that combination of these two independent methods correctly
resolves a substantial portion of healthy samples with an uninformative result.
Additionally, we discuss how the proposed method can be used to identify
maternal aberrations, thus reducing the risk of false positive and false
negative calls.
  Keywords: Next-generation sequencing, Cell-free DNA, Uninformative result,
Method, Trisomy, Prenatal testing
Generalizing the first-difference correlated random walk for marine animal movement data
Date: 2018-06-22 00:00:00
Tasks: Management

Animal telemetry data are often analysed with discrete time movement models
assuming rotation in the movement. These models are defined with equidistant
distant time steps. However, telemetry data from marine animals are observed
irregularly. To account for irregular data, a time-irregularised
first-difference correlated random walk model with drift is introduced. The
model generalizes the commonly used first-difference correlated random walk
with regular time steps by allowing irregular time steps, including a drift
term, and by allowing different autocorrelation in the two coordinates. The
model is applied to data from a ringed seal collected through the Argos
satellite system, and is compared to related movement models through
simulations. Accounting for irregular data in the movement model results in
accurate parameter estimates and reconstruction of movement paths. Measured by
distance, the introduced model can provide more accurate movement paths than
the regular time counterpart. Extracting accurate movement paths from uncertain
telemetry data is important for evaluating space use patterns for marine
animals, which in turn is crucial for management. Further, handling irregular
data directly in the movement model allows efficient simultaneous analysis of
several animals.
Foreign Object Detection and Quantification of Fat Content Using A Novel Multiplexing Electric Field Sensor
Date: 2018-06-22 00:00:00
Tasks: object-detection, Object Detection

There is an ever growing need to ensure the quality of food and assess
specific quality parameters in all the links of the food chain, ranging from
processing, distribution and retail to preparing food. Various imaging and
sensing technologies, including X-ray imaging, ultrasound, and near infrared
reflectance spectroscopy have been applied to the problem. Cost and other
constraints restrict the application of some of these technologies. In this
study we test a novel Multiplexing Electric Field Sensor (MEFS), an approach
that allows for a completely non-invasive and non-destructive testing approach.
Our experiments demonstrate the reliable detection of certain foreign objects
and provide evidence that this sensor technology has the capability of
measuring fat content in minced meat. Given the fact that this technology can
already be deployed at very low cost, low maintenance and in various different
form factors, we conclude that this type of MEFS is an extremely promising
technology for addressing specific food quality issues.
Role of helicity in DNA hairping folding dynamics
Date: 2018-06-22 00:00:00

We study hairpin folding dynamics by means of extensive computer simulations,
with particular attention paid to the influence of helicity on the folding time
$\tau$. We find that the dynamical exponent $\alpha$ of the anomalous scaling
$\tau \sim N^\alpha$ for a hairpin with length N changes from 1.6 ($1+\nu$) to
1.2 ($2\nu$) in three dimensions, when duplex helicity is removed. The relation
$\alpha = 2\nu$ in rotationless hairpin folding is further verified in two
dimensions ($\nu = 0.75$), and for a ghost-chain ($\nu = 0.5$). This, to our
knowledge, is the first observation of the theoretical lower bound on $\alpha$,
which was predicted earlier on the basis of energy conservation for polymer
translocation through a pore. Our findings suggest that the folding dynamics in
long helical chains is governed by the duplex dynamics, contrasting the earlier
understanding based on the stem-flower picture of unpaired segments. We propose
a scaling argument for $\alpha = 1+\nu$ in helical chains, assuming that duplex
relaxation required for orientational positioning of the next pair of bases is
the rate-limiting process.