pwc-archive/papers-with-abstracts huggingface dataset:
Scan transcription of two-dimensional shapes as an alternative neuromorphic concept
Date: 2018-03-21 00:00:00
Tasks: Vocal Bursts Valence Prediction

Selfridge, along with Sutherland and Marr provided some of the earliest
proposals for how to program computers to recognize shapes. Their emphasis on
filtering for contour features, especially the orientation of boundary
segments, was reinforced by the Nobel Prize winning work of Hubel & Wiesel who
discovered that neurons in primary visual cortex selectively respond as a
function of contour orientation. Countless investigators and theorists have
continued to build on this approach. These models are often described as
neuromorphic, which implies that the computational methods are based on
biologically plausible principles. Recent work from the present lab has
challenged the emphasis on orientation selectivity and the use of neural
network principles. The goal of the present report is not to relitigate those
issues, but to provide an alternative concept for encoding of shape information
that may be useful to neuromorphic modelers.
Beyond activator-inhibitor networks: the generalised Turing mechanism
Date: 2018-03-21 00:00:00

The Turing patterning mechanism is believed to underly the formation of
repetitive structures in development, such as zebrafish stripes and mammalian
digits, but it has proved difficult to isolate the specific biochemical species
responsible for pattern formation. Meanwhile, synthetic biologists have
designed Turing systems for implementation in cell colonies, but none have yet
led to visible patterns in the laboratory. In both cases, the relationship
between underlying chemistry and emergent biology remains mysterious. To help
resolve the mystery, this article asks the question: what kinds of biochemical
systems can generate Turing patterns? We find general conditions for Turing
pattern inception -- the ability to generate unstable patterns from random
noise -- which may lead to the ultimate formation of stable patterns, depending
on biochemical non-linearities. We find that a wide variety of systems can
generate stable Turing patterns, including several which are currently unknown,
such as two-species systems composed of two self-activators, and systems
composed of a short-range inhibitor and a long-range activator. We furthermore
find that systems which are widely believed to generate stable patterns may in
fact only generate unstable patterns, which ultimately converge to
spatially-homogeneous concentrations. Our results suggest that a much wider
variety of systems than is commonly believed could be responsible for observed
patterns in development, or could be good candidates for synthetic patterning
networks.
Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks
Date: 2018-03-21 00:00:00
Tasks: Management

Herbaria worldwide are housing a treasure of 100s of millions of herbarium
specimens, which are increasingly being digitized in recent years and thereby
made more easily accessible to the scientific community. At the same time, deep
learning algorithms are rapidly improving pattern recognition from images and
these techniques are more and more being applied to biological objects. We are
using digital images of herbarium specimens in order to identify taxa and
traits of these collection objects by applying convolutional neural networks
(CNN). Images of the 1000 species most frequently documented by herbarium
specimens on GBIF have been downloaded and combined with morphological trait
data, preprocessed and divided into training and test datasets for species and
trait recognition. Good performance in both domains is promising to use this
approach in future tools supporting taxonomy and natural history collection
management.
A path integral based model for stocks and order dynamics
Date: 2018-03-21 00:00:00

We introduce a model for the short-term dynamics of financial assets based on
an application to finance of quantum gauge theory, developing ideas of Ilinski.
We present a numerical algorithm for the computation of the probability
distribution of prices and compare the results with APPLE stocks prices and the
S&P500 index.
Testing Continuity of a Density via g-order statistics in the Regression Discontinuity Design
Date: 2020-02-11 00:00:00
Tasks: regression, valid

In the regression discontinuity design (RDD), it is common practice to assess
the credibility of the design by testing the continuity of the density of the
running variable at the cut-off, e.g., McCrary (2008). In this paper we propose
an approximate sign test for continuity of a density at a point based on the
so-called g-order statistics, and study its properties under two complementary
asymptotic frameworks. In the first asymptotic framework, the number q of
observations local to the cut-off is fixed as the sample size n diverges to
infinity, while in the second framework q diverges to infinity slowly as n
diverges to infinity. Under both of these frameworks, we show that the test we
propose is asymptotically valid in the sense that it has limiting rejection
probability under the null hypothesis not exceeding the nominal level. More
importantly, the test is easy to implement, asymptotically valid under weaker
conditions than those used by competing methods, and exhibits finite sample
validity under stronger conditions than those needed for its asymptotic
validity. In a simulation study, we find that the approximate sign test
provides good control of the rejection probability under the null hypothesis
while remaining competitive under the alternative hypothesis. We finally apply
our test to the design in Lee (2008), a well-known application of the RDD to
study incumbency advantage.
Use of Genome Information-Based Potentials to Characterize Human Adaptation
Date: 2018-03-21 00:00:00
Tasks: Diversity

As a living information and communications system, the genome encodes
patterns in single nucleotide polymorphisms (SNPs) reflecting human adaption
that optimizes population survival in differing environments. This paper
mathematically models environmentally induced adaptive forces that quantify
changes in the distribution of SNP frequencies between populations. We make
direct connections between biophysical methods (e.g. minimizing genomic free
energy) and concepts in population genetics. Our unbiased computer program
scanned a large set of SNPs in the major histocompatibility complex region, and
flagged an altitude dependency on a SNP associated with response to oxygen
deprivation. The statistical power of our double-blind approach is demonstrated
in the flagging of mathematical functional correlations of SNP
information-based potentials in multiple populations with specific
environmental parameters. Furthermore, our approach provides insights for new
discoveries on the biology of common variants. This paper demonstrates the
power of biophysical modeling of population diversity for better understanding
genome-environment interactions in biological phenomenon.
An Economic Bubble Model and Its First Passage Time
Date: 2018-03-21 00:00:00

We introduce a new diffusion process Xt to describe asset prices within an
economic bubble cycle. The main feature of the process, which differs from
existing models, is the drift term where a mean-reversion is taken based on an
exponential decay of the scaled price. Our study shows the scaling factor on Xt
is crucial for modelling economic bubbles as it mitigates the dependence
structure between the price and parameters in the model. We prove both the
process and its first passage time are well-defined. An efficient calibration
scheme, together with the probability density function for the process are
given. Moreover, by employing the perturbation technique, we deduce the
closed-form density for the downward first passage time, which therefore can be
used in estimating the burst time of an economic bubble. The object of this
study is to understand the asset price dynamics when a financial bubble is
believed to form, and correspondingly provide estimates to the bubble crash
time. Calibration examples on the US dot-com bubble and the 2007 Chinese stock
market crash verify the effectiveness of the model itself. The example on
BitCoin prediction confirms that we can provide meaningful estimate on the
downward probability for asset prices.
Mislearning from Censored Data: The Gambler's Fallacy and Other Correlational Mistakes in Optimal-Stopping Problems
Authors: Kevin He
Date: 2018-03-21 00:00:00

I study endogenous learning dynamics for people who misperceive intertemporal correlations in random sequences. Biased agents face an optimal-stopping problem. They are uncertain about the underlying distribution and learn its parameters from predecessors. Agents stop when early draws are "good enough," so predecessors' experiences contain negative streaks but not positive streaks. When agents wrongly expect systematic reversals (the "gambler's fallacy"), they understate the likelihood of consecutive below-average draws, converge to over-pessimistic beliefs about the distribution's mean, and stop too early. Agents uncertain about the distribution's variance overestimate it to an extent that depends on predecessors' stopping thresholds. I also analyze how other misperceptions of intertemporal correlation interact with endogenous data censoring.
Generalized Optimization of High Capacity Compressive Imaging Systems
Date: 2018-03-22 00:00:00
Tasks: Compressive Sensing, Image Reconstruction, Vocal Bursts Intensity Prediction

One of the greatest challenges in applying compressive sensing (CS) signal
processing techniques to electromagnetic imaging applications is designing a
sensing matrix that has good reconstruction capabilities. Compressive reflector
antennas (CRA) are a class of antennas that have been shown to provide enhanced
image reconstruction performance over traditional reflector antennas (TRA) when
CS techniques are employed. In this paper, we present a unified CRA design
method, which considers both the sensing capacity and efficiency of the
antenna, and can be used for both compressive imaging and multiple-input
multiple-output (MIMO) communication applications. The unified design method is
assessed for a CRA configuration in which dielectric scatterers are added to
the surface of a TRA. The design results demonstrate the ability of the unified
design method to enhance the CS reconstruction capabilities of the CRA.
Causal Inference for Survival Analysis
Date: 2018-03-22 00:00:00
Tasks: Causal Inference, Survival Analysis

In this paper, we propose the use of causal inference techniques for survival
function estimation and prediction for subgroups of the data, upto individual
units. Tree ensemble methods, specifically random forests were modified for
this purpose. A real world healthcare dataset was used with about 1800 patients
with breast cancer, which has multiple patient covariates as well as disease
free survival days (DFS) and a death event binary indicator (y). We use the
type of cancer curative intervention as the treatment variable (T=0 or 1,
binary treatment case in our example). The algorithm is a 2 step approach. In
step 1, we estimate heterogeneous treatment effects using a causalTree with the
DFS as the dependent variable. Next, in step 2, for each selected leaf of the
causalTree with distinctly different average treatment effect (with respect to
survival), we fit a survival forest to all the patients in that leaf, one
forest each for treatment T=0 as well as T=1 to get estimated patient level
survival curves for each treatment (more generally, any model can be used at
this step). Then, we subtract the patient level survival curves to get the
differential survival curve for a given patient, to compare the survival
function as a result of the 2 treatments. The path to a selected leaf also
gives us the combination of patient features and their values which are
causally important for the treatment effect difference at the leaf.
Coverage Enhancement for mmWave Communications using Passive Reflectors
Date: 2018-05-25 00:00:00

Millimeter wave (mmWave) technology is expected to dominate the future 5G
networks mainly due to large spectrum available at these frequencies. However,
coverage deteriorates significantly at mmWave frequencies due to higher path
loss, especially for the non-line-of-sight (NLOS) scenarios. In this work, we
explore the use of passive reflectors for improving mmWave signal coverage in
NLOS indoor areas. Measurements are carried out using the PXI-based mmWave
transceiver platforms from National Instruments operating at 28 GHz, and the
results are compared with the outcomes of ray tracing (RT) simulations in a
similar environment. For both the measurements and RT simulations, different
shapes of metallic passive reflectors are used to observe the coverage (signal
strength) statistics on a receiver grid in an NLOS area. For a square metallic
sheet reflector of size 24 by 24 in and 33 by 33 in , we observe a significant
increase in the received power in the NLOS region, with a median gain of 20 dB
when compared to no reflector case. The cylindrical reflector shows more
uniform coverage on the receiver grid as compared to flat reflectors that are
more directional.
Temporal and Spatial Characteristics of mmWave Propagation Channels for UAVs
Date: 2018-05-25 00:00:00

Unmanned aerial vehicles~(UAVs) are envisioned to be an integral part of
future 5G communication systems. The agile nature of UAVs for serving users at
different locations can help to dynamically optimize coverage and
quality-of-service (QoS) in future networks. %However, there is very limited
literature available for mmWave communications using UAVs. In this work, we
explore the small scale temporal and spatial characteristics of mmWave
air-to-ground~(AG) line-of-sight~(LOS) propagation channels at $28$~GHz in
different environmental scenarios: dense-urban, suburban, rural, and over sea
using omni-directional antennas employing Wireless InSite ray tracing software.
We classify the received multipath components~(MPCs) into persistent and
non-persistent components. The small scale temporal and spatial characteristics
of the AG propagation channel are found to be dependent on the scatterer
properties: number, distribution, and geometry. Additionally, clustering of
MPCs in the time and spatial domain for different environments is found to be
dependent on the scatterer properties and receiver sensitivity. When the height
of the UAV is comparable to the height of the scatterers, we observe large
temporal and angular spreads.
Equilibrium Effects of Intraday Order-Splitting Benchmarks
Date: 2020-03-28 00:00:00

This paper presents a continuous-time model of intraday trading, pricing, and
liquidity with dynamic TWAP and VWAP benchmarks. The model is solved in
closed-form for the competitive equilibrium and also for non-price-taking
equilibria. The intraday trajectories of TWAP trading targets cause predictable
intraday patterns of price pressure, and randomness in VWAP target trajectories
induces additional randomness in intraday price-pressure patterns. TWAP and
VWAP trading both reduce market liquidity and increase price volatility
relative to just terminal trading targets alone. The model is computationally
tractable, which lets us provide a number of numerical illustrations.
A trust-based recommendation method using network diffusion processes
Authors: Chen Ling-Jiao, Gao Jian
Date: 2018-03-22 00:00:00
Tasks: Collaborative Filtering, Diversity

A variety of rating-based recommendation methods have been extensively
studied including the well-known collaborative filtering approaches and some
network diffusion-based methods, however, social trust relations are not
sufficiently considered when making recommendations. In this paper, we
contribute to the literature by proposing a trust-based recommendation method,
named CosRA+T, after integrating the information of trust relations into the
resource-redistribution process. Specifically, a tunable parameter is used to
scale the resources received by trusted users before the redistribution back to
the objects. Interestingly, we find an optimal scaling parameter for the
proposed CosRA+T method to achieve its best recommendation accuracy, and the
optimal value seems to be universal under several evaluation metrics across
different datasets. Moreover, results of extensive experiments on the two
real-world rating datasets with trust relations, Epinions and FriendFeed,
suggest that CosRA+T has a remarkable improvement in overall accuracy,
diversity, and novelty. Our work takes a step towards designing better
recommendation algorithms by employing multiple resources of social network
information.
Large large-trader activity weakens the long memory of limit order markets
Date: 2018-03-22 00:00:00

Using more than 6.7 billions of trades, we explore how the tick-by-tick
dynamics of limit order books depends on the aggregate actions of large
investment funds on a much larger (quarterly) timescale. In particular, we find
that the well-established long memory of market order signs is markedly weaker
when large investment funds trade either in a directional way and even weaker
when their aggregate participation ratio is large. Conversely, we investigate
to what respect a weaker memory of market order signs predicts that an asset is
being actively traded by large funds. Theoretical arguments suggest two simple
mechanisms that contribute to the observed effect: a larger number of active
meta-orders and a modification of the distribution of size of meta-orders.
Empirical evidence suggests that the number of active meta-orders is the most
important contributor to the loss of market order sign memory.
Scaling properties of extreme price fluctuations in Bitcoin markets
Date: 2018-03-22 00:00:00
Tasks: Management, Portfolio Optimization

Detection of power-law behavior and studies of scaling exponents uncover the
characteristics of complexity in many real world phenomena. The complexity of
financial markets has always presented challenging issues and provided
interesting findings, such as the inverse cubic law in the tails of stock price
fluctuation distributions. Motivated by the rise of novel digital assets based
on blockchain technology, we study the distributions of cryptocurrency price
fluctuations. We consider Bitcoin returns over various time intervals and from
multiple digital exchanges, in order to investigate the existence of universal
scaling behavior in the tails, and ascertain whether the scaling exponent
supports the presence of a finite second moment. We provide empirical evidence
on slowly decaying tails in the distributions of returns over multiple time
intervals and different exchanges, corresponding to a power-law. We estimate
the scaling exponent and find an asymptotic power-law behavior with 2 <
{\alpha} < 2.5 suggesting that Bitcoin returns, in addition to being more
volatile, also exhibit heavier tails than stocks, which are known to be around
3. Our results also imply the existence of a finite second moment, thus
providing a fundamental basis for the usage of standard financial theories and
covariance-based techniques in risk management and portfolio optimization
scenarios.
Complex dynamics and development of behavioural individuality
Date: 2018-03-22 00:00:00

Behavioural differences may arise in the absence of genetic or environmental
variation. Chaotic dynamics may influence behavioural development, and so this
among-individual variation. We discuss methods and experimental designs to test
this idea. Ultimately, nonlinear and chaotic behavioural development may
explain much of natural variation.
Survival of the simplest: the cost of complexity in microbial evolution
Date: 2018-03-22 00:00:00

The evolution of microbial and viral organisms often generates clonal
interference, a mode of competition between genetic clades within a population.
In this paper, we show that interference strongly constrains the genetic and
phenotypic complexity of evolving systems. Our analysis uses biophysically
grounded evolutionary models for an organism's quantitative molecular
phenotypes, such as fold stability and enzymatic activity of genes. We find a
generic mode of asexual evolution called phenotypic interference with strong
implications for systems biology: it couples the stability and function of
individual genes to the population's global speed of evolution. This mode
occurs over a wide range of evolutionary parameters appropriate for microbial
populations. It generates selection against genome complexity, because the
fitness cost of mutations increases faster than linearly with the number of
genes. Recombination can generate a distinct mode of sexual evolution that
eliminates the superlinear cost. We show that positive selection can drive a
transition from asexual to facultative sexual evolution, providing a specific,
biophysically grounded scenario for the evolution of sex. In a broader context,
our analysis suggests that the systems biology of microbial organisms is
strongly intertwined with their mode of evolution.
Population Genetics and Evolution
Date: 2018-03-22 00:00:00

These lecture notes introduce key concepts of mathematical population
genetics within the most elementary setting and describe a few recent
applications to microbial evolution experiments. Pointers to the literature for
further reading are provided, and some of the derivations are left as exercises
for the reader.
A spectrum of routing strategies for brain networks
Date: 2018-03-22 00:00:00

Communication of signals among nodes in a complex network poses fundamental
problems of efficiency and cost. Routing of messages along shortest paths
requires global information about the topology, while spreading by diffusion,
which operates according to local topological features, is informationally
"cheap" but inefficient. We introduce a stochastic model for network
communication that combines varying amounts of local and global information
about the network topology. The model generates a continuous spectrum of
dynamics that converge onto shortest-path and random-walk (diffusion)
communication processes at the limiting extremes. We implement the model on two
cohorts of human connectome networks and investigate the effects of varying
amounts of local and global information on the network's communication cost. We
identify routing strategies that approach a (highly efficient) shortest-path
communication process with a relatively small amount of global information.
Moreover, we show that the cost of routing messages from and to hub nodes
varies as a function of the amount of global information driving the system's
dynamics. Finally, we implement the model to identify individual subject
differences from a communication dynamics point of view. The present framework
departs from the classical shortest paths vs. diffusion dichotomy, suggesting
instead that brain networks may exhibit different types of communication
dynamics depending on varying functional demands and the availability of
resources.
The effects of cyclical simulation on the axon hillock diameter of murine intracortical neurons
Date: 2018-03-22 00:00:00

Changes to the axon hillock in frequently firing neurons are known to be
important predictors of early disease states. Studying this phenomenon is
critical to understanding the first insult implicated in multiple
neuro-degenerative disorders. To study these changes we used cyclical
stimulations using micro-electrodes to the axon hillock of mouse intracortical
neurons. Numerical simulation results indicate that axon hillock water
potential fluctuated sinusoidally on high voltage only. Fluctuations in the
amplitude and trend were caused by calcium flow and storage resistance,
respectively. The change in axon hillock-stored water was proportional to the
change rate in water potential. Axon hillock diameter increased with
fluctuations in calcium free media; moreover, it varied slightly under low
voltage conditions. Changes in axon hillock diameter were caused by changes in
water potential, which was determined by subcellular gated channels, media
calcium potential, and other baseline characteristics of neurons.