pwc-archive/papers-with-abstracts huggingface dataset:
A Time-Series Distribution Test System Based on Real Utility Data
Date: 2020-01-29 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we provide a time-series distribution test system. This test
system is a fully observable distribution grid in Midwest U.S. with smart
meters (SM) installed at all end users. Our goal is to share a real U.S.
distribution grid model without modification. This grid model is comprehensive
and representative since it consists of both overhead lines and underground
cables, and it has standard distribution grid components such as capacitor
banks, line switches, substation transformers with load tap changer and
secondary distribution transformers. An important uniqueness of this grid model
is it has one-year smart meter measurements at all nodes, thus bridging the gap
between existing test feeders and quasi-static time-series based distribution
system analysis.
Supervised and Semi-Supervised Learning for MIMO Blind Detection with Low-Resolution ADCs
Date: 2019-06-10 00:00:00

The use of low-resolution analog-to-digital converters (ADCs) is considered
to be an effective technique to reduce the power consumption and hardware
complexity of wireless transceivers. However, in systems with low-resolution
ADCs, obtaining channel state information (CSI) is difficult due to significant
distortions in the received signals. The primary motivation of this paper is to
show that learning techniques can mitigate the impact of CSI unavailability. We
study the blind detection problem in multiple-input-multiple-output (MIMO)
systems with low-resolution ADCs using learning approaches. Two methods, which
employ a sequence of pilot symbol vectors as the initial training data, are
proposed. The first method exploits the use of a cyclic redundancy check (CRC)
to obtain more training data, which helps improve the detection accuracy. The
second method is based on the perspective that the to-be-decoded data can
itself assist the learning process, so no further training information is
required except the pilot sequence. For the case of 1-bit ADCs, we provide a
performance analysis of the vector error rate for the proposed methods. Based
on the analytical results, a criterion for designing transmitted signals is
also presented. Simulation results show that the proposed methods outperform
existing techniques and are also more robust.
Altruism in populations at the extinction transition
Date: 2020-04-13 00:00:00

We study the evolution of cooperation as a birth-death process in spatially
extended populations. The benefit from the altruistic behavior of a cooperator
is implemented by decreasing the death rate of its direct neighbors. The cost
of cooperation is the increase of a cooperator's death rate proportional to the
number of its neighbors. When cooperation has higher cost than benefit,
cooperators disappear. Then the dynamics reduces to the contact process with
defectors as the single particle type. Increasing the benefit-cost ratio above
1, the extinction transition of the contact process splits into a set of
nonequilibrium transitions between four regimes when increasing the baseline
death rate $p$ as a control parameter: (i) defection only, (ii) coexistence,
(iii) cooperation only, (iv) extinction. We investigate the transitions between
these regimes. As the main result, we find that full cooperation is established
at the extinction transition as long as benefit is strictly larger than cost.
Qualitatively identical phase diagrams are obtained for populations on square
lattices and in pair approximation. Spatial correlations with nearest neighbors
only are thus sufficient for sustained cooperation.
Multi-Level Taxonomy and Critical Review of Eco-Routing Methods
Date: 2019-12-15 00:00:00

Routing decisions are initially based on minimizing travel time.
Nevertheless, eco-routing considers the environmental aspect (e.g. emissions,
fuel, and exposure) and was introduced to replace the initial routing concept
to mitigate the undesirable impact of transportation systems on the
environment. This review paper aims to provide a four level taxonomy and map
eco-routing studies to the proposed taxonomy. Furthermore, the strengths and
weaknesses of the presented models are summarized. In the literature reviewed,
special emphasis was given to the role of vehicle connectivity in eco-routing.
The main findings include: the microscopic level of aggregation of the flow and
emission/fuel models was rarely employed for large case studies due to the
associated complexity; one objective was optimized at a time for a majority of
the studies; and all of the reviewed studies were applied in a centralized
routing system environment.
Beam Learning -- Using Machine Learning for Finding Beam Directions
Date: 2019-06-11 00:00:00
Tasks: BIG-bench Machine Learning, Multi-Armed Bandits

Beamforming is the key enabler for wireless communications in the mmWave
bands. 802.11ad and WiGig are wireless technologies that currently use the 60
GHz unlicensed mmWave spectrum via beamforming techniques. It is likely that 5G
systems will be considered for 60GHz unlicensed spectrum (apart from other
unlicensed bands) deployments and hence must co-exist with 802.11ad and WiGig.
3GPP is taking steps towards achieving the same and the standardization for
this is underway. The first step to achieve this co-existence is to find the
interference-free directions, in other words identify the directions in which
the nodes using these incumbent technologies are communicating and eliminate
those directions from further communications. Such a mechanism can help to
exploit the spatial holes rather than avoid communications even when only a few
spatial directions are used by incumbents. Such a mechanism trivially increases
the throughput of the proposed 5G systems. However, since the incumbent
technologies may be unknown to the 5G mmWave nodes and their behavior may also
be unknown apriori (for instance, parameters such as duty cycle, power levels,
CSMA parameter used by 802.11ad are unknown to the 5G nodes), this spatial
direction finding must be performed in a blind manner. In this paper, we use
multi-armed bandits-based algorithms, a variant of machine learning algorithms,
to blindly detect the beam directions (both along azimuth and elevation i.e.,
3D-beamforming) used for communication by the incumbents. This work paves the
way for combining the powerful of machine learning algorithms into 5G
unlicensed mmWave systems. Numerical results show the superior performance of
these algorithms over techniques that are commonly employed in such blind
settings.
Extending Deep Learning Models for Limit Order Books to Quantile Regression
Date: 2019-06-11 00:00:00
Tasks: quantile regression, regression, Time Series, Time Series Analysis

We showcase how Quantile Regression (QR) can be applied to forecast financial
returns using Limit Order Books (LOBs), the canonical data source of
high-frequency financial time-series. We develop a deep learning architecture
that simultaneously models the return quantiles for both buy and sell
positions. We test our model over millions of LOB updates across multiple
different instruments on the London Stock Exchange. Our results suggest that
the proposed network not only delivers excellent performance but also provides
improved prediction robustness by combining quantile estimates.
Bayesian Estimation of Economic Simulation Models using Neural Networks
Date: 2019-06-11 00:00:00

Recent advances in computing power and the potential to make more realistic
assumptions due to increased flexibility have led to the increased prevalence
of simulation models in economics. While models of this class, and particularly
agent-based models, are able to replicate a number of empirically-observed
stylised facts not easily recovered by more traditional alternatives, such
models remain notoriously difficult to estimate due to their lack of tractable
likelihood functions. While the estimation literature continues to grow,
existing attempts have approached the problem primarily from a frequentist
perspective, with the Bayesian estimation literature remaining comparatively
less developed. For this reason, we introduce a Bayesian estimation protocol
that makes use of deep neural networks to construct an approximation to the
likelihood, which we then benchmark against a prominent alternative from the
existing literature. Overall, we find that our proposed methodology
consistently results in more accurate estimates in a variety of settings,
including the estimation of financial heterogeneous agent models and the
identification of changes in dynamics occurring in models incorporating
structural breaks.
Screening by changes in stereotypical behavior during cell motility
Authors: Luke Tweedy, Patrick Witzel, Doris Heinrich, Robert H. Insall, Robert G. Endres
Date: 2019-06-11 00:00:00

Stereotyped behaviors are series of postures that show very little variability between repeats. They have been used to classify the dynamics of individuals, groups and species without reference to the lower-level mechanisms that drive them. Stereotypes are easily identified in animals due to strong constraints on the number, shape, and relative positions of anatomical features, such as limbs, that may be used as landmarks for posture identification. In contrast, the identification of stereotypes in single cells poses a significant challenge as the cell lacks these landmark features, and finding constraints on cell shape is a non-trivial task. Here, we use the maximum caliber variational method to build a minimal model of cell behavior during migration. Without reference to biochemical details, we are able to make behavioral predictions over timescales of minutes using only changes in cell shape over timescales of seconds. We use drug treatment and genetics to demonstrate that maximum caliber descriptors can discriminate between healthy and aberrant migration, thereby showing potential applications for maximum caliber methods in automated disease screening, for example in the identification of behaviors associated with cancer metastasis.
Ergodicity-breaking reveals time optimal decision making in humans
Date: 2020-09-25 00:00:00
Tasks: Decision Making

Ergodicity describes an equivalence between the expectation value and the
time average of observables. Applied to human behaviour, ergodic theories of
decision-making reveal how individuals should tolerate risk in different
environments. To optimise wealth over time, agents should adapt their utility
function according to the dynamical setting they face. Linear utility is
optimal for additive dynamics, whereas logarithmic utility is optimal for
multiplicative dynamics. Whether humans approximate time optimal behavior
across different dynamics is unknown. Here we compare the effects of additive
versus multiplicative gamble dynamics on risky choice. We show that utility
functions are modulated by gamble dynamics in ways not explained by prevailing
economic theory. Instead, as predicted by time optimality, risk aversion
increases under multiplicative dynamics, distributing close to the values that
maximise the time average growth of wealth. We suggest that our findings
motivate a need for explicitly grounding theories of decision-making on ergodic
considerations.
Optimum LoRaWAN Configuration Under Wi-SUN Interference
Date: 2019-12-11 00:00:00

Smart Utility Networks (SUN) rely on the Wireless-SUN (Wi-SUN) specification
for years. Recently practitioners and researchers have considered Low-Power
Wide-Area Networks (LPWAN) like LoRaWAN for SUN applications. With distinct
technologies deployed in the same area and sharing unlicensed bands, one can
expect these networks to interfere with one another. This paper builds over a
LoRaWAN model to optimize network parameters while accounting for
inter-technology interference. Our analytic model accounts for the interference
LoRaWAN receives from IEEE 802.15.4g networks, which forms the bottom layers of
Wi-SUN systems. We derive closed-form equations for the expected reliability of
LoRaWAN in such scenarios. We set the model parameters with data from real
measurements of the interplay among the technologies. Finally, we propose two
optimization algorithms to determine the best LoRaWAN configurations, given a
targeted minimum reliability level. The algorithms maximize either
communication range or the number of users given constraints on the minimum
number of users, minimum communication range, and minimum reliability. We
validate the models and algorithms through numerical analysis and simulations.
The proposed methods are useful tools for planning interference-limited
networks with requirements of minimum reliability.
Generalized Beta Prime Distribution: Stochastic Model of Economic Exchange and Properties of Inequality Indices
Date: 2019-06-11 00:00:00

We argue that a stochastic model of economic exchange, whose steady-state
distribution is a Generalized Beta Prime (also known as GB2), and some unique
properties of the latter, are the reason for GB2's success in describing
wealth/income distributions. We use housing sale prices as a proxy to
wealth/income distribution to numerically illustrate this point. We also
explore parametric limits of the distribution to do so analytically. We discuss
parametric properties of the inequality indices -- Gini, Hoover, Theil T and
Theil L -- vis-a-vis those of GB2 and introduce a new inequality index, which
serves a similar purpose. We argue that Hoover and Theil L are more appropriate
measures for distributions with power-law dependencies, especially fat tails,
such as GB2.
Relaxed random walks at scale
Date: 2019-11-14 00:00:00

Relaxed random walk (RRW) models of trait evolution introduce branch-specific
rate multipliers to modulate the variance of a standard Brownian diffusion
process along a phylogeny and more accurately model overdispersed biological
data. Increased taxonomic sampling challenges inference under RRWs as the
number of unknown parameters grows with the number of taxa. To solve this
problem, we present a scalable method to efficiently fit RRWs and infer this
branch-specific variation in a Bayesian framework. We develop a Hamiltonian
Monte Carlo (HMC) sampler to approximate the high-dimensional, correlated
posterior that exploits a closed-form evaluation of the gradient of the trait
data log-likelihood with respect to all branch-rate multipliers simultaneously.
Our gradient calculation achieves computational complexity that scales only
linearly with the number of taxa under study. We compare the efficiency of our
HMC sampler to the previously standard univariable Metropolis-Hastings approach
while studying the spatial emergence of the West Nile virus in North America in
the early 2000s. Our method achieves an over 300-fold speed-increase over the
univariable approach. Additionally, we demonstrate the scalability of our
method by applying the RRW to study the correlation between five mammalian life
history traits in a phylogenetic tree with 3650 tips.
Energy Efficient Massive MIMO Array Configurations
Date: 2019-06-12 00:00:00

The high spectral efficiency of massive MIMO (Multiple Input Multiple Output)
is mainly achieved through the exploitation of spatial multiplexing, i.e. by
using a high number of MIMO layers that are applied simultaneously to many
users. The power consumption of a massive MIMO base station is determined by
the hardware driving a high number of antenna ports and elements. This paper
focuses on practical deployment situations with varying user load. During hours
with low number of users a certain significant part of hardware power
consumption would remain with conventional massive MIMO processing, while the
full potential of spectral efficiency cannot be exploited due to the low number
of users, resulting in low power efficiency and cost. We investigate the impact
of different hybrid array architectures on spectral efficiency, average user
throughput and power consumption and show how to design a massive MIMO system
with significantly improved energy efficiency for a given target scenario,
while maintaining a targeted service quality.
Frequency and Time Domain Analysis ofsRNA-based Treatment for Inflammatory BowelDisease
Authors: Arman Karshenas, Joseph Windo, Bhuvana, Jonathan Stocks, Adrian Kozhevnikov, Jhanna Kryukova, Eleanor Beard, Laurel Constanti Crosby, Max Taylor
Date: 2019-06-12 00:00:00

The validity of a complex reaction pathway proposed to treat Inflammatory Bowel Disease (IBD) was verified by a comprehensive time and frequency domain analysis. The model was taken to the frequency domain to study the effect and the significance of the negative feedback loop introduced by the reaction pathways. It could be shown that such proposed probiotics have very interesting potentials that could be used extensively in the near future.
Selecting stock pairs for pairs trading while incorporating lead-lag relationship
Date: 2019-12-31 00:00:00

Pairs Trading is carried out in the financial market to earn huge profits
from known equilibrium relation between pairs of stock. In financial markets,
seldom it is seen that stock pairs are correlated at particular lead or lag.
This lead-lag relationship has been empirically studied in various financial
markets. Earlier research works have suggested various measures for identifying
the best pairs for pairs trading, but they do not consider this lead-lag
effect. The present study proposes a new distance measure which incorporates
the lead-lag relationship between the stocks while selecting the best pairs for
pairs trading. Further, the lead-lag value between the stocks is allowed to
vary continuously over time. The proposed measures importance has been
show-cased through experimentation on two different datasets, one corresponding
to Indian companies and another corresponding to American companies. When the
proposed measure is clubbed with SSD measure, i.e., when pairs are identified
through optimising both these measures, then the selected pairs consistently
generate the best profit, as compared to all other measures. Finally, possible
generalisation and extension of the proposed distance measure have been
discussed.
Preferred Design of Hierarchical Distribution Matching
Date: 2019-06-10 00:00:00

Distribution matching and dematching (DM/invDM) are key functions in
probabilistic shaping (PS). Recently techniques for low complexity
implementation of DM/invDM have been well studied. Our previously proposed
hierarchical DM (HiDM) is one of the good candidates, with capacity-approaching
performance with reasonable hardware resources. Though we explained the recipe
of HiDM construction with a small example having a short DM word length, there
might still be difficulties to expand it to longer DM word lengths. To improve
the reproducibility of our work, this paper explains the key parameters in an
HiDM having a DM word length more than 100 symbols.
Markov-modulated continuous-time Markov chains to identify site- and branch-specific evolutionary variation
Date: 2019-06-12 00:00:00

Markov models of character substitution on phylogenies form the foundation of
phylogenetic inference frameworks. Early models made the simplifying assumption
that the substitution process is homogeneous over time and across sites in the
molecular sequence alignment. While standard practice adopts extensions that
accommodate heterogeneity of substitution rates across sites, heterogeneity in
the process over time in a site-specific manner remains frequently overlooked.
This is problematic, as evolutionary processes that act at the molecular level
are highly variable, subjecting different sites to different selective
constraints over time, impacting their substitution behaviour. We propose
incorporating time variability through Markov-modulated models (MMMs) that
allow the substitution process (including relative character exchange rates as
well as the overall substitution rate) that models the evolution at an
individual site to vary across lineages. We implement a general MMM framework
in BEAST, a popular Bayesian phylogenetic inference software package, allowing
researchers to compose a wide range of MMMs through flexible XML specification.
Using examples from bacterial, viral and plastid genome evolution, we show that
MMMs impact phylogenetic tree estimation and can substantially improve model
fit compared to standard substitution models. Through simulations, we show that
marginal likelihood estimation accurately identifies the generative model and
does not systematically prefer the more parameter-rich MMMs. In order to
mitigate the increased computational demands associated with MMMs, our
implementation exploits recently developed updates to BEAGLE, a
high-performance computational library for phylogenetic inference.
Exploring Bayesian approaches to eQTL mapping through probabilistic programming
Date: 2019-06-12 00:00:00
Tasks: Probabilistic Programming, regression

The discovery of genomic polymorphisms influencing gene expression (also
known as expression quantitative trait loci or eQTLs) can be formulated as a
sparse Bayesian multivariate/multiple regression problem. An important aspect
in the development of such models is the implementation of bespoke inference
methodologies, a process which can become quite laborious, when multiple
candidate models are being considered. We describe automatic, black-box
inference in such models using Stan, a popular probabilistic programming
language. The utilisation of systems like Stan can facilitate model prototyping
and testing, thus accelerating the data modelling process. The code described
in this chapter can be found at https://github.com/dvav/eQTLBookChapter.
Modeling Generalized Rate-Distortion Functions
Date: 2019-06-12 00:00:00
Tasks: Informativeness, Profile Generation

Many multimedia applications require precise understanding of the
rate-distortion characteristics measured by the function relating visual
quality to media attributes, for which we term it the generalized
rate-distortion (GRD) function. In this study, we explore the GRD behavior of
compressed digital videos in a three-dimensional space of bitrate, resolution,
and viewing device/condition. Our analysis on a large-scale video dataset
reveals that empirical parametric models are systematically biased while
exhaustive search methods require excessive computation time to depict the GRD
surfaces. By exploiting the properties that all GRD functions share, we develop
an Robust Axial-Monotonic Clough-Tocher (RAMCT) interpolation method to model
the GRD function. This model allows us to accurately reconstruct the complete
GRD function of a source video content from a moderate number of measurements.
To further reduce the computational cost, we present a novel sampling scheme
based on a probabilistic model and an information measure. The proposed
sampling method constructs a sequence of quality queries by minimizing the
overall informativeness in the remaining samples. Experimental results show
that the proposed algorithm significantly outperforms state-of-the-art
approaches in accuracy and efficiency. Finally, we demonstrate the usage of the
proposed model in three applications: rate-distortion curve prediction,
per-title encoding profile generation, and video encoder comparison.
The Case for Long-Only Agnostic Allocation Portfolios
Date: 2019-06-12 00:00:00

We advocate the use of Agnostic Allocation for the construction of long-only
portfolios of stocks. We show that Agnostic Allocation Portfolios (AAPs) are a
special member of a family of risk-based portfolios that are able to mitigate
certain extreme features (excess concentration, high turnover, strong exposure
to low-risk factors) of classical portfolio construction methods, while
achieving similar performance. AAPs thus represent a very attractive
alternative risk-based portfolio construction framework that can be implemented
in different situations, with or without an active trading signal.