pwc-archive/papers-with-abstracts huggingface dataset:
Cholinergic switch between two types of slow waves in cerebral cortex
Date: 2019-11-28 00:00:00
Tasks: Vocal Bursts Valence Prediction

Sleep slow waves are known to participate in memory consolidation, yet slow
waves occurring under anesthesia present no positive effects on memory. Here,
we shed light onto this paradox, based on a combination of extracellular
recordings in vivo, in vitro, and computational models. We find two types of
slow waves, based on analyzing the temporal patterns of successive slow-wave
events. The first type is consistently observed in natural slow-wave sleep,
while the second is shown to be ubiquitous under anesthesia. Network models of
spiking neurons predict that the two slow wave types emerge due to a different
gain on inhibitory vs excitatory cells and that different levels of
spike-frequency adaptation in excitatory cells can account for dynamical
distinctions between the two types. This prediction was tested in vitro by
varying adaptation strength using an agonist of acetylcholine receptors, which
demonstrated a neuromodulatory switch between the two types of slow waves.
Finally, we show that the first type of slow-wave dynamics is more sensitive to
external stimuli, which can explain how slow waves in sleep and anesthesia
differentially affect memory consolidation, as well as provide a link between
slow-wave dynamics and memory diseases.
Omega-Regular Objectives in Model-Free Reinforcement Learning
Authors: Hahn Ernst Moritz, Perez Mateo, Schewe Sven, Somenzi Fabio, Trivedi Ashutosh, Wojtczak Dominik
Date: 2018-09-26 00:00:00
Tasks: model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We provide the first solution for model-free reinforcement learning of
{\omega}-regular objectives for Markov decision processes (MDPs). We present a
constructive reduction from the almost-sure satisfaction of {\omega}-regular
objectives to an almost- sure reachability problem and extend this technique to
learning how to control an unknown model so that the chance of satisfying the
objective is maximized. A key feature of our technique is the compilation of
{\omega}-regular properties into limit- deterministic Buechi automata instead
of the traditional Rabin automata; this choice sidesteps difficulties that have
marred previous proposals. Our approach allows us to apply model-free,
off-the-shelf reinforcement learning algorithms to compute optimal strategies
from the observations of the MDP. We present an experimental evaluation of our
technique on benchmark learning problems.
The competitive exclusion principle in stochastic environments
Date: 2019-09-20 00:00:00

In its simplest form, the competitive exclusion principle states that a
number of species competing for a smaller number of resources cannot coexist.
However, it has been observed empirically that in some settings it is possible
to have coexistence. One example is Hutchinson's `paradox of the plankton'.
This is an instance where a large number of phytoplankton species coexist while
competing for a very limited number of resources. Both experimental and
theoretical studies have shown that temporal fluctuations of the environment
can facilitate coexistence for competing species. Hutchinson conjectured that
one can get coexistence because nonequilibrium conditions would make it
possible for different species to be favored by the environment at different
times.
  In this paper we show in various settings how a variable (stochastic)
environment enables a set of competing species limited by a smaller number of
resources or other density dependent factors to coexist. If the environmental
fluctuations are modeled by white noise, and the per-capita growth rates of the
competitors depend linearly on the resources, we prove that there is
competitive exclusion. However, if either the dependence between the growth
rates and the resources is not linear or the white noise term is nonlinear we
show that coexistence on fewer resources than species is possible. Even more
surprisingly, if the temporal environmental variation comes from switching the
environment at random times between a finite number of possible states, it is
possible for all species to coexist even if the growth rates depend linearly on
the resources. We show in an example (a variant of which first appeared in
Benaim and Lobry '16) that, contrary to Hutchinson's explanation, one can
switch between two environments in which the same species is favored and still
get coexistence.
Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines
Authors: Sainct Rémi, Feau Cyril, Martinez Jean-Marc, Garnier Josselin
Date: 2018-09-25 00:00:00
Tasks: Active Learning, Binary Classification

Fragility curves which express the failure probability of a structure, or
critical components, as function of a loading intensity measure are nowadays
widely used (i) in Seismic Probabilistic Risk Assessment studies, (ii) to
evaluate impact of construction details on the structural performance of
installations under seismic excitations or under other loading sources such as
wind. To avoid the use of parametric models such as lognormal model to estimate
fragility curves from a reduced number of numerical calculations, a methodology
based on Support Vector Machines coupled with an active learning algorithm is
proposed in this paper. In practice, input excitation is reduced to some
relevant parameters and, given these parameters, SVMs are used for a binary
classification of the structural responses relative to a limit threshold of
exceedance. Since the output is not only binary, this is a score, a
probabilistic interpretation of the output is exploited to estimate very
efficiently fragility curves as score functions or as functions of classical
seismic intensity measures.
The logic of uncertainty as a logic of experience and chance and the co~event-based Bayes' theorem
Date: 2018-09-28 00:00:00

The logic of uncertainty is not the logic of experience and as well as it is
not the logic of chance. It is the logic of experience and chance. Experience
and chance are two inseparable poles. These are two dual reflections of one
essence, which is called co~event. The theory of experience and chance is the
theory of co~events. To study the co~events, it is not enough to study the
experience and to study the chance. For this, it is necessary to study the
experience and chance as a single entire, a co~event. In other words, it is
necessary to study their interaction within a co~event. The new co~event
axiomatics and the theory of co~events following from it were created precisely
for these purposes. In this work, I am going to demonstrate the effectiveness
of the new theory of co~events in a studying the logic of uncertainty. I will
do this by the example of a co~event splitting of the logic of the Bayesian
scheme, which has a long history of fierce debates between Bayesianists and
frequentists. I hope the logic of the theory of experience and chance will make
its modest contribution to the application of these old dual debaters.
Impact of 3D UWB Antenna Radiation Pattern on Air-to-Ground Drone Connectivity
Date: 2018-12-17 00:00:00

Three dimensional (3D) radiation pattern of an antenna mounted at a drone can
significantly influence the air-to-ground (A2G) link quality. Even when a drone
transmitter is very close to a ground receiver, if the antenna orientations are
not aligned properly, a significant degradation can be observed in the received
signal power at the receiver. To characterize such effects for a
doughnut-shaped antenna radiation pattern, using an ultra-wideband (UWB)
transmitter at the drone and a UWB receiver at the ground, we carry out A2G
channel measurements to capture the link quality at the ground receiver for
various link distances, drone heights, and antenna orientations. We develop a
simple analytical model to approximate the influence of 3D antenna patterns on
the received signal strength (RSS), which show reasonable agreement with
measurements despite the simplicity of the model and the complicated 3D
radiation from the UWB antennas. We also explore how the signal strength can be
improved when multiple antennas with different orientations are utilized at
transmitter/receiver.
A Partition Theorem for a Randomly Selected Large Population
Authors: Arni S. R. Srinivasa Rao
Date: 2018-10-02 00:00:00

We state and prove a theorem on the partitioning of a randomly selected large population into stationary and non-stationary components by using a property of stationary population identity. Applications of this theorem for practical purposes is summarized at the end.
Topological exploration of artificial neuronal network dynamics
Date: 2019-01-28 00:00:00

One of the paramount challenges in neuroscience is to understand the dynamics
of individual neurons and how they give rise to network dynamics when
interconnected. Historically, researchers have resorted to graph theory,
statistics, and statistical mechanics to describe the spatiotemporal structure
of such network dynamics. Our novel approach employs tools from algebraic
topology to characterize the global properties of network structure and
dynamics.
  We propose a method based on persistent homology to automatically classify
network dynamics using topological features of spaces built from various
spike-train distances. We investigate the efficacy of our method by simulating
activity in three small artificial neural networks with different sets of
parameters, giving rise to dynamics that can be classified into four regimes.
We then compute three measures of spike train similarity and use persistent
homology to extract topological features that are fundamentally different from
those used in traditional methods. Our results show that a machine learning
classifier trained on these features can accurately predict the regime of the
network it was trained on and also generalize to other networks that were not
presented during training. Moreover, we demonstrate that using features
extracted from multiple spike-train distances systematically improves the
performance of our method.
Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging
Date: 2019-03-25 00:00:00
Tasks: Depth Estimation

Depth sensing is useful in a variety of applications that range from
augmented reality to robotics. Time-of-flight (TOF) cameras are appealing
because they obtain dense depth measurements with minimal latency. However, for
many battery-powered devices, the illumination source of a TOF camera is power
hungry and can limit the battery life of the device. To address this issue, we
present an algorithm that lowers the power for depth sensing by reducing the
usage of the TOF camera and estimating depth maps using concurrently collected
images. Our technique also adaptively controls the TOF camera and enables it
when an accurate depth map cannot be estimated. To ensure that the overall
system power for depth sensing is reduced, we design our algorithm to run on a
low power embedded platform, where it outputs 640x480 depth maps at 30 frames
per second. We evaluate our approach on several RGB-D datasets, where it
produces depth maps with an overall mean relative error of 0.96% and reduces
the usage of the TOF camera by 85%. When used with commercial TOF cameras, we
estimate that our algorithm can lower the total power for depth sensing by up
to 73%.
A Mechanical Instability in Planar Epithelial Monolayers Leads to Cell Extrusion
Authors: Satoru Okuda, Koichi Fujimoto
Date: 2018-10-03 00:00:00
Tasks: 3D geometry

In cell extrusion, a cell embedded in an epithelial monolayer loses its apical or basal surface and is subsequently squeezed out of the monolayer by neighboring cells. Cell extrusions occur during apoptosis, epithelial-mesenchymal transition, or pre-cancerous cell invasion. They play important roles in embryogenesis, homeostasis, carcinogenesis, and many other biological processes. Although many of the molecular factors involved in cell extrusion are known, little is known about the mechanical basis of cell extrusion. We used a three-dimensional (3D) vertex model to investigate the mechanical stability of cells arranged in a monolayer with 3D foam geometry. We found that when the cells composing the monolayer have homogeneous mechanical properties, cells are extruded from the monolayer when the symmetry of the 3D geometry is broken due to an increase in cell density or a decrease in the number of topological neighbors around single cells. Those results suggest that mechanical instability inherent in the 3D foam geometry of epithelial monolayers is sufficient to drive epithelial cell extrusion. In the situation where cells in the monolayer actively generate contractile or adhesive forces under the control of intrinsic genetic programs, the forces act to break the symmetry of the monolayer, leading to cell extrusion that is directed to the apical or basal side of the monolayer by the balance of contractile and adhesive forces on the apical and basal sides. Although our analyses are based on a simple mechanical model, our results are in accordance with observations of epithelial monolayers {\it in vivo} and consistently explain cell extrusions under a wide range of physiological and pathophysiological conditions. Our results illustrate the importance of a mechanical understanding of cell extrusion and provide a basis by which to link molecular regulation to physical processes.
Topological Connectedness and Behavioral Assumptions on Preferences: A Two-Way Relationship
Date: 2018-10-25 00:00:00
Tasks: Relation, Vocal Bursts Valence Prediction

This paper offers a comprehensive treatment of the question as to whether a
binary relation can be consistent (transitive) without being decisive
(complete), or decisive without being consistent, or simultaneously
inconsistent or indecisive, in the presence of a continuity hypothesis that is,
in principle, non-testable. It identifies topological connectedness of the
(choice) set over which the continuous binary relation is defined as being
crucial to this question. Referring to the two-way relationship as the
Eilenberg-Sonnenschein (ES) research program, it presents four synthetic, and
complete, characterizations of connectedness, and its natural extensions; and
two consequences that only stem from it. The six theorems are novel to both the
economic and the mathematical literature: they generalize pioneering results of
Eilenberg (1941), Sonnenschein (1965), Schmeidler (1971) and Sen (1969), and
are relevant to several applied contexts, as well as to ongoing theoretical
work.
Optimal control of protein copy number
Authors: Steven Blaber, David A. Sivak
Date: 2018-10-04 00:00:00

Cell-cell communication is often achieved by secreted signaling molecules that bind membrane-bound receptors. A common class of such receptors are G-protein coupled receptors, where extracellular binding induces changes on the membrane affinity near the receptor for certain cytosolic proteins, effectively altering their chemical potential. We analyze the minimum-dissipation schedules for dynamically changing chemical potential to induce steady-state changes in protein copy-number distributions, and illustrate with analytic solutions for linear chemical reaction networks. Protocols that change chemical potential on biologically relevant timescales are experimentally accessible using optogenetic manipulations, and our framework provides non-trivial predictions about functional dynamical cell-cell interactions.
Separating the effects of experimental noise from inherent system variability in voltammetry: the $[$Fe(CN)$_6]^{3-/ 4-}$ process
Date: 2018-09-18 00:00:00

Recently, we have introduced the use of techniques drawn from Bayesian
statistics to recover kinetic and thermodynamic parameters from voltammetric
data, and were able to show that the technique of large amplitude ac
voltammetry yielded significantly more accurate parameter values than the
equivalent dc approach. In this paper we build on this work to show that this
approach allows us, for the first time, to separate the effects of random
experimental noise and inherent system variability in voltammetric experiments.
We analyse ten repeated experimental data sets for the $[$Fe(CN)$_6]^{3-/ 4-}$
process, again using large-amplitude ac cyclic voltammetry. In each of the ten
cases we are able to obtain an extremely good fit to the experimental data and
obtain very narrow distributions of the recovered parameters governing both the
faradaic (the reversible formal faradaic potential, $E_0$, the standard
heterogeneous charge transfer rate constant $k_0$, and the charge transfer
coefficient $\alpha$) and non-faradaic terms (uncompensated resistance, $R_u$,
and double layer capacitance, $C_{dl}$). We then employ hierarchical Bayesian
methods to recover the underlying "hyperdistribution" of the faradaic and
non-faradaic parameters, showing that in general the variation between the
experimental data sets is significantly greater than suggested by individual
experiments, except for $\alpha$ where the inter-experiment variation was
relatively minor. Correlations between pairs of parameters are provided, and
for example, reveal a weak link between $k_0$ and $C_{dl}$ (surface activity of
a glassy carbon electrode surface). Finally, we discuss the implications of our
findings for voltammetric experiments more generally.
PatternListener: Cracking Android Pattern Lock Using Acoustic Signals
Date: 2018-10-12 00:00:00

Pattern lock has been widely used for authentication to protect user privacy
on mobile devices (e.g., smartphones and tablets). Given its pervasive usage,
the compromise of pattern lock could lead to serious consequences. Several
attacks have been constructed to crack the lock. However, these approaches
require the attackers to either be physically close to the target device or be
able to manipulate the network facilities (e.g., WiFi hotspots) used by the
victims. Therefore, the effectiveness of the attacks is significantly impacted
by the environment of mobile devices. Also, these attacks are not scalable
since they cannot easily infer unlock patterns of a large number of devices.
  Motivated by an observation that fingertip motions on the screen of a mobile
device can be captured by analyzing surrounding acoustic signals on it, we
propose PatternListener, a novel acoustic attack that cracks pattern lock by
analyzing imperceptible acoustic signals reflected by the fingertip. It
leverages speakers and microphones of the victim's device to play imperceptible
audio and record the acoustic signals reflected by the fingertip. In
particular, it infers each unlock pattern by analyzing individual lines that
compose the pattern and are the trajectories of the fingertip. We propose
several algorithms to construct signal segments according to the captured
signals for each line and infer possible candidates of each individual line
according to the signal segments. Finally, we map all line candidates into grid
patterns and thereby obtain the candidates of the entire unlock pattern. We
implement a PatternListener prototype by using off-the-shelf smartphones and
thoroughly evaluate it using 130 unique patterns. The real experimental results
demonstrate that PatternListener can successfully exploit over 90% patterns
within five attempts.
Measuring Football Players' On-the-ball Contributions From Passes During Games
Authors: Bransen Lotte, Van Haaren Jan
Date: 2018-09-13 00:00:00

Several performance metrics for quantifying the in-game performances of
individual football players have been proposed in recent years. Although the
majority of the on-the-ball actions during games constitutes of passes, many of
the currently available metrics focus on measuring the quality of shots only.
To help bridge this gap, we propose a novel approach to measure players'
on-the-ball contributions from passes during games. Our proposed approach
measures the expected impact of each pass on the scoreline.
Estimation of Parameters in Avian Movement Models
Date: 2018-10-15 00:00:00

The knowledge of the movement of animals is important and necessary for
ecologists to do further analysis such as exploring the animal migration route.
A novel method which is based on the state space modeling has been proposed to
track the bird, where the VHF transmitter is attached to the bird to emit the
signal and several towers with antenna arrays installed on its top are built to
receive the signal. The method consists of two parts, the first one is called
movement model which accounts for prediction of the dynamic movement of the
target, and the second part is the measurement model which links the target's
state variables to the available measurements data, the measurement includes
the time when the signal was detected, the ID of the antenna array which
detected the signal and integers between 0 and 255, the integers are
proportional to the strength of received signal. The extended Kalman filter is
then applied to estimate the location of the target with combing the movement
model and measurement model. In the movement model, several parameters with
positive values are deployed to define the change of the state variables with
time, these parameters reflect the relationship of the state variables at
current time and next time. In this paper, a method based on the maximum
likelihood estimation is proposed to estimate the appropriate values for these
unknown constant variables with given measurement data, and a kite is applied
to demonstrate the validity of the proposed method. Furthermore, the unscented
transformation is applied in Kalman filter to achieve more accurate estimation
of the target's states.
Super-Replication of the Best Pairs Trade in Hindsight
Authors: Alex Garivaltis
Date: 2018-10-04 00:00:00

This paper derives a robust on-line equity trading algorithm that achieves the greatest possible percentage of the final wealth of the best pairs rebalancing rule in hindsight. A pairs rebalancing rule chooses some pair of stocks in the market and then perpetually executes rebalancing trades so as to maintain a target fraction of wealth in each of the two. After each discrete market fluctuation, a pairs rebalancing rule will sell a precise amount of the outperforming stock and put the proceeds into the underperforming stock. Under typical conditions, in hindsight one can find pairs rebalancing rules that would have spectacularly beaten the market. Our trading strategy, which extends Ordentlich and Cover's (1998) "max-min universal portfolio," guarantees to achieve an acceptable percentage of the hindsight-optimized wealth, a percentage which tends to zero at a slow (polynomial) rate. This means that on a long enough investment horizon, the trader can enforce a compound-annual growth rate that is arbitrarily close to that of the best pairs rebalancing rule in hindsight. The strategy will "beat the market asymptotically" if there turns out to exist a pairs rebalancing rule that grows capital at a higher asymptotic rate than the market index. The advantages of our algorithm over the Ordentlich and Cover (1998) strategy are twofold. First, their strategy is impossible to compute in practice. Second, in considering the more modest benchmark (instead of the best all-stock rebalancing rule in hindsight), we reduce the "cost of universality" and achieve a higher learning rate.