pwc-archive/papers-with-abstracts huggingface dataset:
Weber's law based perception and the stability of animal groups
Date: 2019-04-18 00:00:00
Tasks: Deblurring, Image Deblurring, Position

Group living animals form aggregations and flocks that remain cohesive in
spite of internal movements of individuals. This is possible because individual
group members repeatedly adjust their position and motion in response to the
position and motion of other group members. Here we develop a theoretical
approach to address the question, what general features -- if any -- underlie
the interaction rules that mediate group stability in animals of all species?
We do so by considering how the spatial organisation of a group would change in
the complete absence of interactions. Without interactions, a group would
disperse in a way that can be easily characterised in terms of Fick's diffusion
equations. We can hence address the inverse theoretical problem of finding the
individual-level interaction responses that are required to counterbalance
diffusion and to preserve group stability. We show that an individual-level
response to neighbour densities in the form of Weber's law (a 'universal' law
describing the functioning of the sensory systems of animals of all species)
results in an 'anti-diffusion' term at the group level. On short time scales,
this anti-diffusion restores the initial group configuration in a way which is
reminiscent of methods for image deblurring in image processing. We also show
that any non-homogeneous, spatial density distribution can be preserved over
time if individual movement patterns have the form of a Weber's law response.
Weber's law describes the fundamental functioning of perceptual systems. Our
study indicates that it is also a necessary -- but not sufficient -- feature of
collective interactions in stable animal groups.
A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers
Authors: Ben-Porat Omer, Tennenholtz Moshe
Date: 2018-10-18 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Fairness, Recommendation Systems

We introduce a game-theoretic approach to the study of recommendation systems
with strategic content providers. Such systems should be fair and stable.
Showing that traditional approaches fail to satisfy these requirements, we
propose the Shapley mediator. We show that the Shapley mediator fulfills the
fairness and stability requirements, runs in linear time, and is the only
economically efficient mechanism satisfying these properties.
A geometric attractor mechanism for self-organization of entorhinal grid modules
Date: 2019-03-11 00:00:00

Grid cells in the medial entorhinal cortex (MEC) respond when an animal
occupies a periodic lattice of "grid fields" in the environment. The grids are
organized in modules with spatial periods, or scales, clustered around discrete
values separated by ratios in the range 1.2--2.0. We propose a mechanism that
produces this modular structure through dynamical self-organization in the MEC.
In attractor network models of grid formation, the grid scale of a single
module is set by the distance of recurrent inhibition between neurons. We show
that the MEC forms a hierarchy of discrete modules if a smooth increase in
inhibition distance along its dorso-ventral axis is accompanied by excitatory
interactions along this axis. Moreover, constant scale ratios between
successive modules arise through geometric relationships between triangular
grids and have values that fall within the observed range. We discuss how
interactions required by our model might be tested experimentally.
Giving Text Analytics a Boost
Authors: Polig Raphael, Atasu Kubilay, Chiticariu Laura, Hagleitner Christoph, Hofstee H. Peter, Reiss Frederick R., Sitaridi Eva, Zhu Huaiyu
Date: 2018-04-25 00:00:00

The amount of textual data has reached a new scale and continues to grow at
an unprecedented rate. IBM's SystemT software is a powerful text analytics
system, which offers a query-based interface to reveal the valuable information
that lies within these mounds of data. However, traditional server
architectures are not capable of analyzing the so-called "Big Data" in an
efficient way, despite the high memory bandwidth that is available. We show
that by using a streaming hardware accelerator implemented in reconfigurable
logic, the throughput rates of the SystemT's information extraction queries can
be improved by an order of magnitude. We present how such a system can be
deployed by extending SystemT's existing compilation flow and by using a
multi-threaded communication interface that can efficiently use the bandwidth
of the accelerator.
Light interception modelling using unstructured LiDAR data in avocado orchards
Date: 2018-05-30 00:00:00

In commercial fruit farming, managing the light distribution through canopies
is important because the amount and distribution of solar energy that is
harvested by each tree impacts the production of fruit quantity and quality. It
is therefore an important characteristic to measure and ultimately to control
with pruning. We present a solar-geometric model to estimate light interception
in individual avocado (Persea americana) trees, that is designed to scale to
whole-orchard scanning, ultimately to inform pruning decisions. The geometry of
individual trees was measured using LiDAR and represented by point clouds. A
discrete energy distribution model of the hemispherical sky was synthesised
using public weather records. The light from each sky node was then ray traced,
applying a radiation absorption model where rays pass the point cloud
representation of the tree. The model was validated using ceptometer energy
measurements at the canopy floor, and model parameters were optimised by
analysing the error between modelled and measured energies. The model was shown
to perform well qualitatively well through visual comparison with tree shadows
in photographs, and quantitatively well with R^2 = 0.854, suggesting it is
suitable to use in the context of agricultural decision support systems, in
future work.
Safe Driving Capacity of Autonomous Vehicles
Date: 2018-05-26 00:00:00
Tasks: Autonomous Vehicles

An excellent self-driving car is expected to take its passengers safely and
efficiently from one place to another. However, different ways of defining
safety and efficiency may significantly affect the conclusion we make. In this
paper, we give formal definitions to the safe state of a road and safe state of
a vehicle using the syntax of linear temporal logic (LTL). We then propose the
concept of safe driving throughput (SDT) and safe driving capacity (SDC) which
measure the amount of vehicles in the safe state on a road. We analyze how SDT
is affected by different factors. We show the analytic difference of SDC
between the road with perception-based vehicles (PBV) and the road with
cooperative-based vehicles (CBV). We claim that through proper design, the SDC
of the road filled with PBVs will be upper-bounded by the SDC of the road
filled with CBVs.
Behavior stability and individual differences in Pavlovian extended conditioning
Date: 2020-04-22 00:00:00

How stable and general is behavior once maximum learning is reached? To
answer this question and understand post-acquisition behavior and its related
individual differences, we propose a psychological principle that naturally
extends associative models of Pavlovian conditioning to a dynamical oscillatory
model where subjects have a greater memory capacity than usually postulated,
but with greater forecast uncertainty. This results in a greater resistance to
learning in the first few sessions followed by an over-optimal response peak
and a sequence of progressively damped response oscillations. We detected the
first peak and trough of the new learning curve in our data, but their
dispersion was too large to also check the presence of oscillations with
smaller amplitude. We ran an unusually long experiment with 32 rats over 3960
trials, where we excluded habituation and other well-known phenomena as sources
of variability in the subjects' performance. Using the data of this and another
Pavlovian experiment by Harris et al. (2015), as an illustration of the
principle we tested the theory against the basic associative single-cue
Rescorla-Wagner (RW) model. We found evidence that the RW model is the best
nonlinear regression to data only for a minority of the subjects, while its
dynamical extension can explain the almost totality of data with strong to very
strong evidence. Finally, an analysis of short-scale fluctuations of individual
responses showed that they are described by random white noise, in contrast
with the colored-noise findings in human performance.
Compressed Sensing ECG using Restricted Boltzmann Machines
Date: 2018-05-30 00:00:00
Tasks: compressed sensing

Recently, it has been shown that compressed sensing (CS) has the potential to
lower energy consumption in wireless electrocardiogram (ECG) systems. By
reducing the number of acquired measurements, the communication burden is
decreased and energy is saved. In this paper, we aim at further reducing the
number of necessary measurements to achieve faithful reconstruction by
exploiting the representational power of restricted Boltzmann machines (RBMs)
to model the probability distribution of the sparsity pattern of ECG signals.
The motivation for using this approach is to capture the higher-order
statistical dependencies between the coefficients of the ECG sparse
representation, which in turn, leads to superior reconstruction accuracy and
reduction in the number of measurements, as it is shown via experiments.
Comparing Alternatives to Measure the Impact of DDoS Attack Announcements on Target Stock Prices
Date: 2018-05-30 00:00:00

The attack intensity of distributed denial of service (DDoS) attacks is
increasing every year. Botnets based on internet of things (IOT) devices are
now being used to conduct DDoS attacks. The estimation of direct and indirect
economic damages caused by these attacks is a complex problem. One of the
indirect damage of a DDoS attack can be on the market value of the victim firm.
In this article we analyze the impact of 45 different DDoS attack announcements
on victim's stock prices. We find that previous studies have a mixed conclusion
on the impact of DDoS attack announcements on the victim's stock price. Hence,
in this article we evaluate this impact using three different approaches and
compare the results. In the first approach, we use the assume the cumulative
abnormal returns to be normally distributed and test the hypothesis that a DDoS
attack announcement has no impact on the victim's stock price. In the latter
two methods, we do not assume a distribution and use the empirical distribution
of cumulative abnormal returns to test the hypothesis. We find that the
assumption of cumulative abnormal returns being normally distributed leads to
overestimation/underestimation of the impact. Finally, we analyze the impact of
DDoS attack announcement on victim's stock price in each of the 45 cases and
present our results.
Muscle Activity Analysis using Higher-Order Tensor Models: Application to Muscle Synergy Identification
Date: 2019-03-23 00:00:00
Tasks: Electromyography (EMG), Tensor Decomposition

Higher-order tensor decompositions have hardly been used in muscle activity
analysis despite multichannel electromyography (EMG) datasets naturally
occurring as multi-way structures. Here, we seek to demonstrate and discuss the
potential of tensor decompositions as a framework to estimate muscle synergies
from $3^{rd}$-order EMG tensors built by stacking repetitions of multi-channel
EMG for several tasks. We compare the two most widespread tensor decomposition
models -- Parallel Factor Analysis (PARAFAC) and Tucker -- in muscle synergy
analysis of the wrist's three main Degree of Freedoms (DoFs) using the public
first Ninapro database. Furthermore, we proposed a constrained Tucker
decomposition (consTD) method for efficient synergy extraction building on the
power of tensor decompositions. This method is proposed as a direct novel
approach for shared and task-specific synergy estimation from two
biomechanically related tasks. Our approach is compared with the current
standard approach of repetitively applying non-negative matrix factorisation
(NMF) to a series of movements. The results show that the consTD method is
suitable for synergy extraction compared to PARAFAC and Tucker. Moreover,
exploiting the multi-way structure of muscle activity, the proposed methods
successfully identified shared and task-specific synergies for all three DoFs
tensors. These were found to be robust to disarrangement with regard to
task-repetition information, unlike the commonly used NMF. In summary, we
demonstrate how to use tensors to characterise muscle activity and develop a
new consTD method for muscle synergy extraction that could be used for shared
and task-specific synergies identification. We expect that this study will pave
the way for the development of novel muscle activity analysis methods based on
higher-order techniques.
Time-varying Rotational Inverted Pendulum Control using Fuzzy Approach
Date: 2018-05-31 00:00:00

In this paper, a nonlinear rotational inverted pendulum with time-varying
parameters is controlled using the indirect adaptive fuzzy controller design.
This type of controller is chosen because this particular system performance is
highly sensitive to unavoidable unknown model changes. So, a conventional
controller is firstly designed through feedback linearization method, and
applied to the system. Feedback linearization method here is used for two
purposes; to attain an approximation of necessary system dynamics and to assess
the performance of the proposed adaptive fuzzy controller by comparing the
results of both adaptive fuzzy and feedback linearization controllers. An
indirect adaptive fuzzy controller, resistant to parameter variations is then
proposed. The general structure of the adaptive controller is specified in the
first stage. In the second stage, its parameters are regulated with the aid of
two fuzzy systems. Lyapunov stability theorem is used to regulate the system
parameters such that the closed loop system is stabilized and zero tracking
error is attained. Finally, the results of the proposed and the conventional
approaches are compared. Results showed that the adaptive fuzzy controller
performed more efficiently than the classical controller, with existing
parameters variations.
Load Restoration Methodology Considering Renewable Energies and Combined Heat and Power Systems
Date: 2018-05-31 00:00:00

Outages and faults cause problems in interconnected power system with huge
economic consequences in modern societies. In the power system blackouts, black
start resources such as micro combined heat and power (CHP) systems and
renewable energies, due to their self-start ability, are one of the solutions
to restore power system as quickly as possible. In this paper, we propose a
model for power system restoration considering CHP systems and renewable energy
sources as being available in blackout states. We define a control variable
representing a level of balance between the distance and importance of loads
according to the importance and urgency of the affected customer. Dynamic power
flow is considered in order to find feasible sequence and combination of loads
for load restoration.
Design of Voltage Pulse Control Module for Free Space Measurement-Device-Independent Quantum Key Distribution
Date: 2018-06-20 00:00:00

Measurement-Device-Independent Quantum Key Distribution (MDIQKD) protocol has
been proved that it is unaffected by all hacking attacks, and ensures the
security of information theory even when the performance of single-photon
detectors is not ideal. Fiber channel has been used by the previous MDIQKD
experimental device. However, the signal attenuation increases exponentially as
the transmission distance increases. In order to overcome this, we regard free
space as the channel of signal transmission, and the signal attenuation
increases square as the transmission distance increases (regardless of the
atmospheric scattering), which can effectively reduce the signal attenuation
trend. In order to implement the free space MDIQKD experiments, a modulation
module is needed to modulate the wide pulse chopping, decoy-state,
normalization, phase encoding and time encoding. In this paper, we present the
design of the Voltage Pulse Control Module for the free space MDIQKD.
Range Variation Monitoring with Wireless Two-Way Interferometry (Wi-Wi)
Date: 2018-10-30 00:00:00
Tasks: Vocal Bursts Valence Prediction

We demonstrated a simple technique for monitoring range variation with
millimeter-precision between two remote sites using off-the-shelf wireless
communication modules. The need for the flexible positioning of wireless
devices is significantly increasing as more devices are being connected and new
services are being developed that require devices to collaborate with one
another. We showed that one can monitor the distance variation by analyzing the
propagation delay of the wireless communication signal between devices. We
previously reported a technique for synchronizing clocks with picosecond
precision by monitoring the time variation of two rubidium clocks located at
remote sites. Precise measurement of the propagation time variation was
necessary for precise synchronization of the clocks, and we used this
information to estimate the distance with high precision. In a localized
situation, our technique makes it easy to implement a millimeter-precision
measurement system. Furthermore, it is less complex in terms of system design
and can be a low-cost alternative to existing systems that require precise
position measurement. We envision that this demonstrated protocol will be
implemented in wireless communication chips and microprocessing units.
Chromosomal rearrangements as a source of new gene formation in Drosophila yakuba
Date: 2019-08-15 00:00:00
Tasks: Open-Ended Question Answering

The origins of new genes are among the most fundamental questions in
evolutionary biology. Our understanding of the ways that new genetic material
appears and how that genetic material shapes population variation remains
incomplete. De novo genes and duplicate genes are a key source of new genetic
material on which selection acts. To better understand the origins of these new
gene sequences, we explored the ways that structural variation might alter
expression patterns and form novel transcripts. We provide evidence that
chromosomal rearrangements are a source of novel genetic variation that
facilitates the formation of de novo genes in Drosophila. We identify 52 cases
of de novo gene formation created by chromosomal rearrangements in 14 strains
of D. yakuba. These new genes inherit transcription start signals and open
reading frames when the 5' end of existing genes are combined with previously
untranscribed regions. Such new genes would appear with novel peptide
sequences, without the necessity for secondary transitions from non-coding RNA
to protein. This mechanism of new peptide formations contrasts with canonical
theory of de novo gene progression requiring non-coding intermediaries that
must acquire new mutations prior to loss via pseudogenization. Hence, these
mutations offer a means to de novo gene creation and protein sequence formation
in a single mutational step, answering a long standing open question concerning
new gene formation. We further identify gene expression changes to 134 existing
genes, indicating that these mutations can alter gene regulation. Population
variability for chromosomal rearrangements is considerable, with 2368
rearrangements observed across 14 inbred lines. More rearrangements were
identified on the X chromosome than any of the autosomes, suggesting the X is
more susceptible to chromosome alterations.
New expression on the performance of a novel multi-hop relay-assisted hybrid FSO / RF communication system
Date: 2018-05-22 00:00:00

In this paper a novel multi-hop relay-assisted hybrid FSO / RF system is
presented. It is assumed that direct RF connection between mobile users and
source Base Station is impossible, therefore a relay connects RF users to the
source Base Station via a FSO link. Source and destination Base Stations are
connected via a multi-hop relay-assisted hybrid FSO / RF link. FSO link is
investigated over moderate to saturate regimes of atmospheric turbulence. Also
RF link is assumed to have Rayleigh fading. For the first time, new exact and
asymptotic expressions are derived in closed-form for Bit Error Rate (BER) and
the Outage Probability (P_out), of the proposed system. MATLAB simulations are
performed to validate obtained analytical results. Results indicate that the
proposed structure has low dependence on the number of users, therefore, the
proposed structure is suitable for cells which encounter different populations
during a day, because there is little performance difference between systems
with different number of users. Also the proposed structure, at Negative
exponential atmospheric turbulence has small dependence on the number of
relays, but this dependence is a bit more for Gamma-Gamma atmospheric
turbulence. Therefore, the proposed structure increases capacity whereas
maintaining performance of the system.
Low-Complexity Linear Precoding for Secure Spatial Modulation
Date: 2018-05-28 00:00:00

In this work, we investigate linear precoding for secure spatial modulation.
With secure spatial modulation, the achievable secrecy rate does not have an
easy-to-compute mathematical expression, and hence, has to be evaluated
numerically, which leads to high complexity in the optimal precoder design. To
address this issue, an accurate and analytical approximation of the secrecy
rate is derived in this work. Using this approximation as the objective
function, two low-complexity linear precoding methods based on gradient descend
(GD) and successive convex approximation (SCA) are proposed. The GD-based
method has much lower complexity but usually converges to a local optimum. On
the other hand, the SCA-based method uses semi-definite relaxation to deal with
the non-convexity in the precoder optimization problem and achieves
near-optimal solution. Compared with the existing GD-based precoder design in
the literature that directly uses the exact and numerically evaluated secrecy
capacity as the objective function, the two proposed designs have significantly
lower complexity. Our SCA-based design even achieves a higher secrecy rate than
the existing GD-based design.
Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes
Date: 2018-12-21 00:00:00
Tasks: Gaussian Processes

In this paper, we address the estimation of a time-varying spatial field of
received signal strength (RSS) by relying on measurements from randomly placed
and not very accurate sensors. We employ a radio propagation model where the
path loss exponent and the transmitted power are unknown with Gaussian priors
whose hyper-parameters are estimated by applying the empirical Bayes method. We
consider the locations of the sensors to be imperfectly known, which entails
that they represent another source of error in the model. The propagation model
includes shadowing which is considered to be a zero-mean Gaussian process where
the correlation of attenuation between two spatial points is quantified by an
exponential function of the distance between the points. The location of the
transmitter is also unknown and estimated from the data with a weighted
centroid approach. We propose to estimate time-varying RSS fields by a
recursive Bayesian method and crowdsourcing. The method is based on Gaussian
processes (GP), and it produces the joint distribution of the spatial field.
Further, it summarizes all the acquired information by keeping the size of the
needed memory bounded. We also present the Bayesian Cram\'er-Rao bound (BCRB)
of the estimated parameters. Finally, we illustrate the performance of our
method with experimental results on synthetic data sets.