pwc-archive/papers-with-abstracts huggingface dataset:
Fairness-Regularized DLMP-Based Bilevel Transactive Energy Mechanism in Distribution Systems
Date: 2018-10-11 00:00:00
Tasks: Fairness

Distribution locational marginal pricing (DLMP) can adversely affect users in
a grid-constrained transactive distribution system market (DSM) that are at a
distance away from the substation, requiring longer paths to connect to the
substation. When the grid operates closer to its physical limits in terms of
line capacities and voltage deviations, these users are more likely to cause
grid violations than others in the vicinity of the substation. Conversely,
increased energy consumption by users near the substation can choke off the
supply to those at the grid s extremities. This research describes a novel
mechanism to charge users in a more equitable manner, by regularizing the
distribution system operator (DSO)s social welfare based objective function
with a fairness criterion. The Jains index of fairness is used in this context
and corresponding fairness DLMP component is derived. The overall problem
entails the maximization of the regularized objective within a set of linear
constraints. The constraints that ensure that the grids voltage and line power
limits are not violated. Constrained optimization is carried out in an
iterative manner through dual decomposition where the dual variables and unit
costs are incrementally updated by the DSO using the augmented Lagrangian
method (ALM). Simulations reported in this study confirm the effectiveness of
the proposed approach.
Stochastic Revealed Preferences with Measurement Error
Date: 2020-09-18 00:00:00
Tasks: Experimental Design

A long-standing question about consumer behavior is whether individuals'
observed purchase decisions satisfy the revealed preference (RP) axioms of the
utility maximization theory (UMT). Researchers using survey or experimental
panel data sets on prices and consumption to answer this question face the
well-known problem of measurement error. We show that ignoring measurement
error in the RP approach may lead to overrejection of the UMT. To solve this
problem, we propose a new statistical RP framework for consumption panel data
sets that allows for testing the UMT in the presence of measurement error. Our
test is applicable to all consumer models that can be characterized by their
first-order conditions. Our approach is nonparametric, allows for unrestricted
heterogeneity in preferences, and requires only a centering condition on
measurement error. We develop two applications that provide new evidence about
the UMT. First, we find support in a survey data set for the dynamic and
time-consistent UMT in single-individual households, in the presence of
\emph{nonclassical} measurement error in consumption. In the second
application, we cannot reject the static UMT in a widely used experimental data
set in which measurement error in prices is assumed to be the result of price
misperception due to the experimental design. The first finding stands in
contrast to the conclusions drawn from the deterministic RP test of Browning
(1989). The second finding reverses the conclusions drawn from the
deterministic RP test of Afriat (1967) and Varian (1982).
On the Properties of Gromov Matrices and their Applications in Network Inference
Date: 2019-03-22 00:00:00

The spanning tree heuristic is a commonly adopted procedure in network
inference and estimation. It allows one to generalize an inference method
developed for trees, which is usually based on a statistically rigorous
approach, to a heuristic procedure for general graphs by (usually randomly)
choosing a spanning tree in the graph to apply the approach developed for
trees. However, there are an intractable number of spanning trees in a dense
graph. In this paper, we represent a weighted tree with a matrix, which we call
a Gromov matrix. We propose a method that constructs a family of Gromov
matrices using convex combinations, which can be used for inference and
estimation instead of a randomly selected spanning tree. This procedure
increases the size of the candidate set and hence enhances the performance of
the classical spanning tree heuristic. On the other hand, our new scheme is
based on simple algebraic constructions using matrices, and hence is still
computationally tractable. We discuss some applications on network inference
and estimation to demonstrate the usefulness of the proposed method.
Deriving mesoscopic models of collective behaviour for finite populations
Date: 2019-02-20 00:00:00

Animal groups exhibit emergent properties that are a consequence of local
interactions. Linking individual-level behaviour to coarse-grained descriptions
of animal groups has been a question of fundamental interest. Here, we present
two complementary approaches to deriving coarse-grained descriptions of
collective behaviour at so-called mesoscopic scales, which account for the
stochasticity arising from the finite sizes of animal groups. We construct
stochastic differential equations (SDEs) for a coarse-grained variable that
describes the order/consensus within a group. The first method of construction
is based on van Kampen's system-size expansion of transition rates. The second
method employs Gillespie's chemical Langevin equations. We apply these two
methods to two microscopic models from the literature, in which organisms
stochastically interact and choose between two directions/choices of foraging.
These `binary-choice' models differ only in the types of interactions between
individuals, with one assuming simple pair-wise interactions, and the other
incorporating higher-order effects. In both cases, the derived mesoscopic SDEs
have multiplicative, or state-dependent, noise. However, the different models
demonstrate the contrasting effects of noise: increasing order in the pair-wise
interaction model, whilst reducing order in the higher-order interaction model.
Although both methods yield identical SDEs for such binary-choice, or
one-dimensional, systems, the relative tractability of the chemical Langevin
approach is beneficial in generalizations to higher-dimensions. In summary,
this book chapter provides a pedagogical review of two complementary methods to
construct mesoscopic descriptions from microscopic rules and demonstrates how
resultant multiplicative noise can have counter-intuitive effects on shaping
collective behaviour.
Frequency Synchronization for Uplink Massive MIMO with Adaptive MUI Suppression in Angle-domain
Date: 2018-10-12 00:00:00

In this paper, we develop a novel angle-domain adaptive filtering
(ADAF)-based frequency synchronization method for the uplink of a massive
multiple-input multiple-output (MIMO) multiuser network, which is applicable
for users with either separate or overlapped angle-of-arrival (AoA) regions.
For each user, we first introduce the angle-constraining matrix (ACM), which
consists of a set of selected match-filter (MF) beamformers pointing to the
AoAs of the interested user. Then, the adaptive beamformer can be acquired by
appropriately designing the ADAF vectors. Such beamformer can achieve two-stage
adaptive multiuser interference (MUI) suppression, i.e., inherently suppressing
the MUI from non-overlapping users by ACM in the first stage and substantially
mitigating the MUI from adjacent overlapping users by ADAF vector in the second
stage. For both separate and mutually overlapping users, the carrier frequency
offset (CFO) estimation and subsequent data detection can be performed
independently for each user, which significantly reduces computational
complexity. Moreover, ADAF is rather robust to imperfect AoA knowledge and side
cluster of scatterers, making itself promising for multiuser uplink
transmission. We also analyze the performance of CFO estimation and obtain an
approximated closed-form expression of mean-squared error (MSE). The
effectiveness of the proposed method and its superiority over the existing
methods are demonstrated by numerical results.
Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers
Authors: Zou Jie, Li Dan, Kanoulas Evangelos
Date: 2018-10-12 00:00:00
Tasks: Active Learning

The goal of a technology-assisted review is to achieve high recall with low
human effort. Continuous active learning algorithms have demonstrated good
performance in locating the majority of relevant documents in a collection,
however their performance is reaching a plateau when 80\%-90\% of them has been
found. Finding the last few relevant documents typically requires exhaustively
reviewing the collection. In this paper, we propose a novel method to identify
these last few, but significant, documents efficiently. Our method makes the
hypothesis that entities carry vital information in documents, and that
reviewers can answer questions about the presence or absence of an entity in
the missing relevance documents. Based on this we devise a sequential Bayesian
search method that selects the optimal sequence of questions to ask. The
experimental results show that our proposed method can greatly improve
performance requiring less reviewing effort.
A Survey: Non-Orthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC
Authors: Mehmood Alam, Qi Zhang
Date: 2018-10-12 00:00:00
Tasks: compressed sensing, Compressive Sensing

One objective of the 5G communication system and beyond is to support massive machine type of communication (mMTC) to propel the fast growth of diverse Internet of Things use cases. The mMTC aims to provide connectivity to tens of billions sensor nodes. The dramatic increase of sensor devices and massive connectivity impose critical challenges for the network to handle the enormous control signaling overhead with limited radio resource. Non-Orthogonal Multiple Access (NOMA) is a new paradigm shift in the design of multiple user detection and multiple access. NOMA with compressive sensing based multiuser detection is one of the promising candidates to address the challenges of mMTC. The survey article aims at providing an overview of the current state-of-art research work in various compressive sensing based techniques that enable NOMA. We present characteristics of different algorithms and compare their pros and cons, thereby provide useful insights for researchers to make further contributions in NOMA using compressive sensing techniques.
Multi-omic Network Regression: Methodology, Tool and Case Study
Date: 2018-10-12 00:00:00
Tasks: regression

The analysis of biological networks is characterized by the definition of
precise linear constraints used to cumulatively reduce the solution space of
the computed states of a multi-omic (for instance metabolic, transcriptomic and
proteomic) model. In this paper, we attempt, for the first time, to combine
metabolic modelling and networked Cox regression, using the metabolic model of
the bacterium Helicobacter Pylori. This enables a platform both for
quantitative analysis of networked regression, but also testing the findings
from network regression (a list of significant vectors and their networked
relationships) on in vivo transcriptomic data. Data generated from the model,
using flux balance analysis to construct a Pareto front, specifically, a
trade-off of Oxygen exchange and growth rate and a trade-off of Carbon Dioxide
exchange and growth rate, is analysed and then the model is used to quantify
the success of the analysis. It was found that using the analysis,
reconstruction of the initial data was considerably more successful than a pure
noise alternative. Our methodological approach is quite general and it could be
of interest for the wider community of complex networks researchers; it is
implemented in a software tool, MoNeRe, which is freely available through the
Github platform.
Covert Communication with A Full-Duplex Receiver Based on Channel Distribution Information
Date: 2018-11-10 00:00:00

In this work, we consider a system of covert communication with the aid of a
full-duplex (FD) receiver to enhance the performance in a more realistic
scenario, i.e., only the channel distribution information (CDI) rather than
channel state information (CSI) is known to a warden. Our work shows that
transmitting random AN can improve the covert communication with the infinite
blocklength. Specifically, we jointly design the optimal transmit power and AN
power by minimizing the outage probability at Bob, and we find that the outage
probability decreases and then increases as the maximum allowable AN power
increases. Intuitively, once AN exceeds an optimal value, the performance will
become worse because of the self-interference. The simulation results also show
that the performance behaviors of CDI and CSI are different. When Willie only
knows CDI, there is an optimal AN power that minimizes Bob's outage
probability. However, when Willie knows CSI, the outage probability
monotonically decreases with AN power.
Nonlinear cancer chemotherapy: modelling the Norton-Simon hypothesis
Date: 2018-10-15 00:00:00

A fundamental model of tumor growth in the presence of cytotoxic
chemotherapeutic agents is formulated. The model allows to study the role of
the Norton-Simon hypothesis in the context of dose-dense chemotherapy.
Dose-dense protocols aim at reducing the period between courses of chemotherapy
from three weeks to two weeks, in order to avoid tumor regrowth between cycles.
We address the conditions under which these protocols might be more or less
beneficial in comparison to less dense settings, depending on the sensitivity
of the tumor cells to the cytotoxic drugs. The effects of varying other
parameters of the protocol, as for example the duration of each continuous drug
infusion, are also inspected. We believe that the present model might serve as
a foundation for the development of more sophisticated models for cancer
chemotherapy.
Dynamic Difficulty Awareness Training for Continuous Emotion Prediction
Authors: Zhang Zixing, Han Jing, Coutinho Eduardo, Schuller Björn
Date: 2018-09-26 00:00:00
Tasks: BIG-bench Machine Learning, Information Retrieval, Prediction, Retrieval

Time-continuous emotion prediction has become an increasingly compelling task
in machine learning. Considerable efforts have been made to advance the
performance of these systems. Nonetheless, the main focus has been the
development of more sophisticated models and the incorporation of different
expressive modalities (e. g., speech, face, and physiology). In this paper,
motivated by the benefit of difficulty awareness in a human learning procedure,
we propose a novel machine learning framework, namely, Dynamic Difficulty
Awareness Training (DDAT), which sheds fresh light on the research -- directly
exploiting the difficulties in learning to boost the machine learning process.
The DDAT framework consists of two stages: information retrieval and
information exploitation. In the first stage, we make use of the reconstruction
error of input features or the annotation uncertainty to estimate the
difficulty of learning specific information. The obtained difficulty level is
then used in tandem with original features to update the model input in a
second learning stage with the expectation that the model can learn to focus on
high difficulty regions of the learning process. We perform extensive
experiments on a benchmark database (RECOLA) to evaluate the effectiveness of
the proposed framework. The experimental results show that our approach
outperforms related baselines as well as other well-established time-continuous
emotion prediction systems, which suggests that dynamically integrating the
difficulty information for neural networks can help enhance the learning
process.
Robust Joint Estimation of Multi-Microphone Signal Model Parameters
Date: 2018-10-12 00:00:00

One of the biggest challenges in multi-microphone applications is the
estimation of the parameters of the signal model such as the power spectral
densities (PSDs) of the sources, the early (relative) acoustic transfer
functions of the sources with respect to the microphones, the PSD of late
reverberation, and the PSDs of microphone-self noise. Typically, the existing
methods estimate subsets of the aforementioned parameters and assume some of
the other parameters to be known a priori. This may result in inconsistencies
and inaccurately estimated parameters and potential performance degradation in
the applications using these estimated parameters. So far, there is no method
to jointly estimate all the aforementioned parameters. In this paper, we
propose a robust method for jointly estimating all the aforementioned
parameters using confirmatory factor analysis. The estimation accuracy of the
signal-model parameters thus obtained outperforms existing methods in most
cases. We experimentally show significant performance gains in several
multi-microphone applications over state-of-the-art methods.
The Broad Consequences of Narrow Banking
Date: 2018-10-12 00:00:00

We investigate the macroeconomic consequences of narrow banking in the
context of stock-flow consistent models. We begin with an extension of the
Goodwin-Keen model incorporating time deposits, government bills, cash, and
central bank reserves to the base model with loans and demand deposits and use
it to describe a fractional reserve banking system. We then characterize narrow
banking by a full reserve requirement on demand deposits and describe the
resulting separation between the payment system and lending functions of the
resulting banking sector. By way of numerical examples, we explore the
properties of fractional and full reserve versions of the model and compare
their asymptotic properties. We find that narrow banking does not lead to any
loss in economic growth when the models converge to a finite equilibrium, while
allowing for more direct monitoring and prevention of financial breakdowns in
the case of explosive asymptotic behaviour.
Formal Concept Analysis with Many-sorted Attributes
Authors: Kent Robert E., Brady John
Date: 2018-10-12 00:00:00

This paper unites two problem-solving traditions in computer science: (1)
constraint-based reasoning, and (2) formal concept analysis. For basic
definitions and properties of networks of constraints, we follow the
foundational approach of Montanari and Rossi. This paper advocates distributed
relations as a more semantic version of networks of constraints. The theory
developed here uses the theory of formal concept analysis, pioneered by Rudolf
Wille and his colleagues, as a key for unlocking the hidden semantic structure
within distributed relations. Conversely, this paper offers distributed
relations as a seamless many-sorted extension to the formal contexts of formal
concept analysis. Some of the intuitions underlying our approach were discussed
in a preliminary fashion by Freuder and Wallace.
Measuring Swampiness: Quantifying Chaos in Large Heterogeneous Data Repositories
Authors: Jung Luann, Whitaker Brendan, Chard Kyle, Elmore Aaron
Date: 2018-10-13 00:00:00
Tasks: Clustering

As scientific data repositories and filesystems grow in size and complexity,
they become increasingly disorganized. The coupling of massive quantities of
data with poor organization makes it challenging for scientists to locate and
utilize relevant data, thus slowing the process of analyzing data of interest.
To address these issues, we explore an automated clustering approach for
quantifying the organization of data repositories. Our parallel pipeline
processes heterogeneous filetypes (e.g., text and tabular data), automatically
clusters files based on content and metadata similarities, and computes a novel
"cleanliness" score from the resulting clustering. We demonstrate the
generation and accuracy of our cleanliness measure using both synthetic and
real datasets, and conclude that it is more consistent than other potential
cleanliness measures.
A cholera mathematical model with vaccination and the biggest outbreak of world's history
Date: 2018-10-13 00:00:00

We propose and analyse a mathematical model for cholera considering
vaccination. We show that the model is epidemiologically and mathematically
well posed and prove the existence and uniqueness of disease-free and endemic
equilibrium points. The basic reproduction number is determined and the local
asymptotic stability of equilibria is studied. The biggest cholera outbreak of
world's history began on 27th April 2017, in Yemen. Between 27th April 2017 and
15th April 2018 there were 2275 deaths due to this epidemic. A vaccination
campaign began on 6th May 2018 and ended on 15th May 2018. We show that our
model is able to describe well this outbreak. Moreover, we prove that the
number of infected individuals would have been much lower provided the
vaccination campaign had begun earlier.
Time Synchronization in Wireless Sensor Networks based on Newtons Adaptive Algorithm
Date: 2018-10-13 00:00:00

This paper proposes a novel time synchronization protocol inspired by the
adaptive Newton search algorithm. The clock model of nodes are modeled as an
adaptive filter and a pairwise steady state and convergence analyses are
presented. A protocol is derived from the derived algorithm and compared
experimentally to Gradient Descent Synchronization(GraDeS) protocol and the
Average Proportional Integral Synchronization (AvgPISync) protocol.
Experimental results showed similar error performance as AvgPISync but far
outperformed both protocols in terms of convergence time. The protocol is
lightweight, robust and simple to implement.
Using generalized estimating equations to estimate nonlinear models with spatial data
Date: 2018-10-13 00:00:00

In this paper, we study estimation of nonlinear models with cross sectional
data using two-step generalized estimating equations (GEE) in the quasi-maximum
likelihood estimation (QMLE) framework. In the interest of improving
efficiency, we propose a grouping estimator to account for the potential
spatial correlation in the underlying innovations. We use a Poisson model and a
Negative Binomial II model for count data and a Probit model for binary
response data to demonstrate the GEE procedure. Under mild weak dependency
assumptions, results on estimation consistency and asymptotic normality are
provided. Monte Carlo simulations show efficiency gain of our approach in
comparison of different estimation methods for count data and binary response
data. Finally we apply the GEE approach to study the determinants of the inflow
foreign direct investment (FDI) to China.
Delay-Constrained Covert Communications with A Full-Duplex Receiver
Date: 2019-02-20 00:00:00

In this work, we consider delay-constrained covert communications with the
aid of a full-duplex (FD) receiver. Without delay constraints, it has been
shown that the transmit power of artificial noise (AN) at the FD receiver
should be random in order to enhance covert communications. In this work, we
show that transmitting AN with a fixed power indeed improve covert
communications with delay constraints, since in a limited time period the
warden cannot exactly learn its received power. This explicitly shows one
benefit of considering practical delay constraints in the context of covert
communications. We analyze the optimal transmit power of AN for either fixed or
globally optimized transmit power of covert information, based on which we also
determine the specific condition under which transmitting AN by the FD receiver
can aid covert communications and a larger transmit power of AN always leads to
better covert communication performance.