pwc-archive/papers-with-abstracts huggingface dataset:
Google matrix analysis of bi-functional SIGNOR network of protein-protein interactions
Date: 2019-09-24 00:00:00

Directed protein networks with only a few thousand of nodes are rather
complex and do not allow to extract easily the effective influence of one
protein to another taking into account all indirect pathways via the global
network. Furthermore, the different types of activation and inhibition actions
between proteins provide a considerable challenge in the frame work of network
analysis. At the same time these protein interactions are of crucial importance
and at the heart of cellular functioning. We develop the Google matrix analysis
of the protein-protein network from the open public database SIGNOR. The
developed approach takes into account the bi-functional activation or
inhibition nature of interactions between each pair of proteins describing it
in the frame work of Ising-spin matrix transitions. We also apply a recently
developed linear response theory for the Google matrix which highlights a
pathway of proteins whose PageRank probabilities are most sensitive with
respect to two proteins selected for the analysis. This group of proteins is
analyzed by the reduced Google matrix algorithm which allows to determine the
effective interactions between them due to direct and indirect pathways in the
global network. We show that the dominating activation or inhibition function
of each protein can be characterized by its magnetization. The results of this
Google matrix analysis are presented for three examples of selected pairs of
proteins. The developed methods work rapidly and efficiently even for networks
with several million of nodes and can be applied to various biological
networks.
Evaluating the Impacts of Transmission Expansion on Sub-Synchronous Resonance Risk
Date: 2019-09-24 00:00:00

While transmission expansions are planned to have positive impact on
reliability of power grids, they could increase the risk and severity of some
of the detrimental incidents in power grid mainly by virtue of changing system
configuration, consequently electrical distance. This paper aims to evaluate
and quantify the impact of transmission expansion projects on Sub-Synchronous
Resonance (SSR) risk through a two-step approach utilizing outage count index
and Sub-synchronous damping index. A graph-theory based SSR screening tool is
introduced to quantify the outage count associated with all grid contingencies
which results in radial connection between renewable generation resources and
nearby series compensated lines. Moreover, a frequency-scan based damping
analysis is performed to assess the impact of transmission expansion on the
system damping in sub-synchronous frequency range. The proposed approach has
been utilized to evaluate the impact of recently-built transmission expansion
project on SSR risk in a portion of Electric Reliability Council of Texas
(ERCOT) grid.
Resilience of Dynamic Routing in the Face of Recurrent and Random Sensing Faults
Date: 2020-03-13 00:00:00

Feedback dynamic routing is a commonly used control strategy in
transportation systems. This class of control strategies relies on real-time
information about the traffic state in each link. However, such information may
not always be observable due to temporary sensing faults. In this article, we
consider dynamic routing over two parallel routes, where the sensing on each
link is subject to recurrent and random faults. The faults occur and clear
according to a finite-state Markov chain. When the sensing is faulty on a link,
the traffic state on that link appears to be zero to the controller. Building
on the theories of Markov processes and monotone dynamical systems, we derive
lower and upper bounds for the resilience score, i.e. the guaranteed throughput
of the network, in the face of sensing faults by establishing stability
conditions for the network. We use these results to study how a variety of key
parameters affect the resilience score of the network. The main conclusions
are: (i) Sensing faults can reduce throughput and destabilize a nominally
stable network; (ii) A higher failure rate does not necessarily reduce
throughput, and there may exist a worst rate that minimizes throughput; (iii)
Higher correlation between the failure probabilities of two links leads to
greater throughput; (iv) A large difference in capacity between two links can
result in a drop in throughput.
Wavelet invariants for statistically robust multi-reference alignment
Authors: Matthew Hirn, Anna Little
Date: 2019-09-24 00:00:00
Tasks: Retrieval, Translation

We propose a nonlinear, wavelet based signal representation that is translation invariant and robust to both additive noise and random dilations. Motivated by the multi-reference alignment problem and generalizations thereof, we analyze the statistical properties of this representation given a large number of independent corruptions of a target signal. We prove the nonlinear wavelet based representation uniquely defines the power spectrum but allows for an unbiasing procedure that cannot be directly applied to the power spectrum. After unbiasing the representation to remove the effects of the additive noise and random dilations, we recover an approximation of the power spectrum by solving a convex optimization problem, and thus reduce to a phase retrieval problem. Extensive numerical experiments demonstrate the statistical robustness of this approximation procedure.
Sleep Stage Classification Using Bidirectional LSTM in Wearable Multi-sensor Systems
Date: 2019-09-24 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sleep Quality

Understanding the sleep quality and architecture is essential to human
being's health, which is usually represented using multiple sleep stages. A
standard sleep stage determination requires Electroencephalography (EEG)
signals during the expensive and labor-intensive Polysomnography (PSG) test. To
overcome this inconvenience, cardiorespiratory signals are proposed for the
same purpose because of the easy and comfortable acquisition by simplified
devices. In this paper, we leverage our low-cost wearable multi-sensor system
to acquire the cardiorespiratory signals from subjects. Three novel features
are designed during the feature extraction. We then apply a Bi-directional
Recurrent Neural Network architecture with Long Short-term Memory (BLSTM) to
predict the four-class sleep stages. Our prediction accuracy is 80.25% on a
large public dataset (417 subjects), and 80.75% on our 32 enrolled subjects,
respectively. Our results outperform the previous works which either used small
data sets and had the potential over-fitting issues, or used the conventional
machine learning methods on large data sets.
Revisiting neutron propagation-based phase contrast imaging and tomography: use of phase retrieval to amplify the effective degree of brilliance
Authors: David M. Paganin, Morten Sales, Peter M. Kadletz, Winfried Kockelmann, Mario A. Beltran, Henning F. Poulsen, Søren Schmidt
Date: 2019-09-22 00:00:00
Tasks: Retrieval

Propagation-based neutron phase-contrast tomography was demonstrated using the ISIS pulsed spallation source. The proof-of-concept tomogram with Paganin-type phase-retrieval filter applied exhibited an effective net boost of $23\pm 1$ in the signal-to-noise ratio as compared to an attenuation-based tomogram, implying a boost in the effective degree of neutron brilliance of over two orders of magnitude. This comparison is for phase retrieval versus conventional absorption with no additional collimation in place. Expressions are provided for the optimal phase-contrast geometry as well as conditions for the validity of the method. The underpinning theory is derived under the assumption of the sample being composed of a single material. The effective boost in brilliance may be employed to give reduced acquisition time, or may instead be used to keep exposure times fixed while improving the measured contrast.
The converse envelope theorem
Authors: Ludvig Sinander
Date: 2019-09-24 00:00:00

I prove an envelope theorem with a converse: the envelope formula is equivalent to a first-order condition. Like Milgrom and Segal's (2002) envelope theorem, my result requires no structure on the choice set. I use the converse envelope theorem to extend to general outcomes and preferences the canonical result in mechanism design that any increasing allocation is implementable, and apply this to selling information.
Structural Balance via Gradient Flows over Signed Graphs
Date: 2019-09-21 00:00:00

Structural balance is a classic property of signed graphs satisfying Heider's
seminal axioms. Mathematical sociologists have studied balance theory since its
inception in the 1040s. Recent research has focused on the development of
dynamic models explaining the emergence of structural balance. In this paper,
we introduce a novel class of parsimonious dynamic models for structural
balance based on an interpersonal influence process. Our proposed models are
gradient flows of an energy function, called the dissonance function, which
captures the cognitive dissonance arising from violations of Heider's axioms.
Thus, we build a new connection with the literature on energy landscape
minimization. This gradient flow characterization allows us to study the
transient and asymptotic behaviors of our model. We provide mathematical and
numerical results describing the critical points of the dissonance function.
A New Approach to Fair Distribution of Welfare
Date: 2019-09-25 00:00:00
Tasks: Fairness

We consider transferable-utility profit-sharing games that arise from
settings in which agents need to jointly choose one of several alternatives,
and may use transfers to redistribute the welfare generated by the chosen
alternative. One such setting is the Shared-Rental problem, in which students
jointly rent an apartment and need to decide which bedroom to allocate to each
student, depending on the student's preferences. Many solution concepts have
been proposed for such settings, ranging from mechanisms without transfers,
such as Random Priority and the Eating mechanism, to mechanisms with transfers,
such as envy free solutions, the Shapley value, and the Kalai-Smorodinsky
bargaining solution. We seek a solution concept that satisfies three natural
properties, concerning efficiency, fairness and decomposition. We observe that
every solution concept known (to us) fails to satisfy at least one of the three
properties. We present a new solution concept, designed so as to satisfy the
three properties. A certain submodularity condition (which holds in interesting
special cases such as the Shared-Rental setting) implies both existence and
uniqueness of our solution concept.
Three-Dimensional Extended Object Tracking and Shape Learning Using Gaussian Processes
Authors: Murat Kumru, Emre Özkan
Date: 2019-09-25 00:00:00
Tasks: Gaussian Processes, Object, Object Tracking

In this study, we investigate the problem of tracking objects with unknown shapes using three-dimensional (3D) point cloud data. We propose a Gaussian process-based model to jointly estimate object kinematics, including position, orientation and velocities, together with the shape of the object for online and offline applications. We describe the unknown shape by a radial function in 3D, and induce a correlation structure via a Gaussian process. Furthermore, we propose an efficient algorithm to reduce the computational complexity of working with 3D data. This is accomplished by casting the tracking problem into projection planes which are attached to the object's local frame. The resulting algorithms can process 3D point cloud data and accomplish tracking of a dynamic object. Furthermore, they provide analytical expressions for the representation of the object shape in 3D, together with confidence intervals. The confidence intervals, which quantify the uncertainty in the shape estimate, can later be used for solving the gating and association problems inherent in object tracking. The performance of the methods is demonstrated both on simulated and real data. The results are compared with an existing random matrix model, which is commonly used for extended object tracking in the literature.
Aging, computation, and the evolution of neural regeneration processes
Date: 2019-09-25 00:00:00

Metazoans are capable of gathering information from their environments and
respond in predictable ways. These computational tasks are achieved by means of
more or less complex networks of neurons. Task performance must be reliable
over an individual's lifetime and must deal robustly with the finite lifespan
of cells or with connection failure - rendering aging a relevant feature in
this context. How do computations degrade over an organism's lifespan? How
reliable can computations remain throughout? In order to answer these
questions, here we approach the problem under a multiobjective (Pareto)
optimization approach. We consider a population of digital organisms equipped
with a neural network that must solve a given computational task reliably. We
demand that they remain functional (as reliable as possible) for an extended
lifespan. Neural connections are costly (as an associated metabolism in living
beings) and degrade over time. They can also be regenerated at some expense. We
investigate the simultaneous minimization of the metabolic burden (due to
connections and regeneration costs) and the computational error and the
tradeoffs emerging thereof. We show that Pareto optimal designs display a broad
range of potential solutions: from small networks with high regeneration rate,
to larger and redundant circuits that regenerate slowly. The organism's
lifespan and the external damage rates are found to act as an evolutionary
pressure that improve the exploration of the space of solutions and poses
tighter optimality conditions. We also find that large damage rates to the
circuits can constrain the space of the possible and pose organisms to commit
to unique strategies for neural systems maintenance.
Biomagnetic signals recorded during transcranial magnetic stimulation (TMS)-evoked peripheral muscular activity
Authors: Geoffrey Z. Iwata, Yinan Hu, Tilmann Sander, Muthuraman Muthuraman, Venkata Chaitanya Chirumamilla, Sergiu Groppa, Dmitry Budker, Arne Wickenbrock
Date: 2019-09-25 00:00:00
Tasks: Diagnostic, EEG, Electroencephalogram (EEG)

Objective: We present magnetomyograms (MMG) of TMS-evoked movement in a human hand, together with a simultaneous surface electromyograph (EMG) and electroencephalograph (EEG) data. Approach: We combined TMS with non-contact magnetic detection of TMS-evoked muscle activity in peripheral limbs to explore a new diagnostic modality that enhances the utility of TMS as a clinical tool by leveraging technological advances in magnetometry. We recorded measurements in a regular hospital room using an array of optically pumped magnetometers (OPM) inside a portable shield that encompasses only the forearm and hand of the subject. Main Results: The biomagnetic signals recorded in the MMG provide detailed spatial and temporal information that is complementary to that of the electric signal channels. Moreover, we identify features in the magnetic recording beyond those of the EMG. Significance: These results validate the viability of MMG recording with a compact OPM based setup in small-sized magnetic shielding, and provide proof-of-principle for a non-contact data channel for detection and analysis of TMS-evoked muscle activity from peripheral limbs.
MPEG-H Audio for Improving Accessibility in Broadcasting and Streaming
Date: 2019-09-25 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Broadcasting and streaming services still suffer from various levels of
accessibility barriers for a significant portion of the population, limiting
the access to information and culture, and in the most severe cases limiting
the empowerment of people. This paper provides a brief overview of some of the
most common accessibility barriers encountered. It then gives a short
introduction to object-based audio (OBA) production and transport, focusing on
the aspects relevant for lowering accessibility barriers. MPEG-H Audio is used
as a concrete example of an OBA system already deployed. Two example cases
(dialog enhancement and audio description) are used to demonstrate in detail
the simplicity of producing MPEG-H Audio content providing improved
accessibility. Several other possibilities are outlined briefly. We show that
using OBA for broadcasting and streaming content allows offering several
accessibility features in a flexible manner, requiring only small changes to
the existing production workflow, assuming the receiver supports the
functionality.
Automatic Receiver Tracking and Power Channeling for Multi-Transmitter Wireless Power Transfer
Date: 2019-09-23 00:00:00
Tasks: Position

Free positioning of receivers is one of the key requirements for many
wireless power transfer (WPT) applications, required from the end-user point of
view. However, realization of stable and effective wireless power transfer for
freely positioned receivers is technically challenging task because of the
requirement of complex control and tuning. In this paper, we propose a concept
of automatic receiver tracking and power channeling for multi-transmitter WPT
systems using uncoupled transmitter and uncoupled repeaters. Each
transmitter-repeater pair forms an independent power transfer channel providing
an effective link for the power flow from the transmitter to the receiver. The
proposed WPT system is capable of maintaining stable output power with constant
high efficiency regardless of the receiver position and without having any
active control or tuning. The proposed concept is numerically and
experimentally verified by using a four-transmitter WPT system in form of a
linear array. The experimental results show that the efficiency of the proposed
WPT system can reach 94.5\% with a variation less than 2\% against the receiver
position.
Considering pricing and uncertainty in designing a reverse logistics network
Date: 2019-09-17 00:00:00

Companies try to maximize their profits by recovering returned products of
highly uncertain quality and quantity. In this paper, a reverse logistics
network for an Original Equipment Manufacturer (OEM) is presented. Returned
products are selected for remanufacturing or scrapping, based on their quality
and proportional prices are offered to customers. A Mixed Integer Non-linear
Programming (MINLP) model is proposed to determine the location of collection
centers, the optimum price of returned products and the sorting policy. The
risk in the objective function is measured using the Conditional Value at Risk
(CVaR) metric. CVaR measures the risk of an investment in a conservative way by
considering the maximum lost. The results are analyzed for various values of
the risk parameters ({\alpha}, and {\lambda}). These parameters indicate that
considering risk affects prices, the classification of returned products, the
location of collection centers and, consequently, the objective function. The
model performs more conservatively when the weight of the CVaR part ({\lambda})
and the value of the confidence level {\alpha} are increased. The results show
that better profits are obtained when we take CVaR into account.
Distributed Control of Linear Multi-Channel Systems
Date: 2020-03-13 00:00:00

A solution is given to the basic distributed feedback control problem for a
multi-channel linear system assuming only that the system is jointly
controllable, jointly observable and has an associated neighbor graph which is
strongly connected. The solution is an observer-based control system which is
implemented in a distributed manner. Using these ideas, a solution is also
given to the distributed set-point control problem for a multi-channel linear
system in which each and every agent with access to the system is able to
independently adjust its controlled output to any desired set-point value. An
example is given to briefly illustrate how network transmission delays might be
dealt with.
Propaganda, Alternative Media, and Accountability in Fragile Democracies
Authors: Anqi Li, Davin Raiha, Kenneth W. Shotts
Date: 2019-09-26 00:00:00

We develop a model of electoral accountability with mainstream and alternative media. In addition to regular high- and low-competence types, the incumbent may be an aspiring autocrat who controls the mainstream media and will subvert democracy if retained in office. A truthful alternative media can help voters identify and remove these subversive types while re-electing competent leaders. A malicious alternative media, in contrast, spreads false accusations about the incumbent and demotivates policy effort. If the alternative media is very likely be malicious and hence is unreliable, voters ignore it and use only the mainstream media to hold regular incumbents accountable, leaving aspiring autocrats to win re-election via propaganda that portrays them as effective policymakers. When the alternative media's reliability is intermediate, voters heed its warnings about subversive incumbents, but the prospect of being falsely accused demotivates effort by regular incumbents and electoral accountability breaks down.
Rank Constrained Diffeomorphic Density Motion Estimation for Respiratory Correlated Computed Tomography
Date: 2019-09-26 00:00:00
Tasks: Motion Estimation

Motion estimation of organs in a sequence of images is important in numerous
medical imaging applications. The focus of this paper is the analysis of 4D
Respiratory Correlated Computed Tomography (RCCT) Imaging. It is hypothesized
that the quasi-periodic breathing induced motion of organs in the thorax can be
represented by deformations spanning a very low dimension subspace of the full
infinite dimensional space of diffeomorphic transformations. This paper
presents a novel motion estimation algorithm that includes the constraint for
low-rank motion between the different phases of the RCCT images. Low-rank
deformation solutions are necessary for the efficient statistical analysis and
improved treatment planning and delivery. Although the application focus of
this paper is RCCT the algorithm is quite general and applicable to various
motion estimation problems in medical imaging.
Modelling and Dynamic Tracking Control of Industrial Vehicles with Tractor-trailer Structure
Date: 2019-09-26 00:00:00

Existing works on control of tractor-trailers systems only consider the
kinematics model without taking dynamics into account. Also, most of them treat
the issue as a pure control theory problem whose solutions are difficult to
implement. This paper presents a trajectory tracking control approach for a
full-scale industrial tractor-trailers vehicle composed of a car-like tractor
and arbitrary number of passive full trailers. To deal with dynamic effects of
trailing units, a force sensor is innovatively installed at the connection
between the tractor and the first trailer to measure the forces acting on the
tractor. The tractor's dynamic model that explicitly accounts for the measured
forces is derived. A tracking controller that compensates the pulling/pushing
forces in real time and simultaneously drives the system onto desired
trajectories is proposed. The propulsion map between throttle opening and the
propulsion force is proposed to be modeled with a fifth-order polynomial. The
parameters are estimated by fitting experimental data, in order to provide
accurate driving force. Stability of the control algorithm is rigorously proved
by Lyapunov methods. Experiments of full-size vehicles are conducted to
validate the performance of the control approach.