pwc-archive/papers-with-abstracts huggingface dataset:
Transcriptomic Causal Networks identified patterns of differential gene regulation in human brain from Schizophrenia cases versus controls
Date: 2019-08-20 00:00:00

Common and complex traits are the consequence of the interaction and
regulation of multiple genes simultaneously, which work in a coordinated way.
However, the vast majority of studies focus on the differential expression of
one individual gene at a time. Here, we aim to provide insight into the
underlying relationships of the genes expressed in the human brain in cases
with schizophrenia (SCZ) and controls. We introduced a novel approach to
identify differential gene regulatory patterns and identify a set of essential
genes in the brain tissue. Our method integrates genetic, transcriptomic, and
Hi-C data and generates a transcriptomic-causal network. Employing this
approach for analysis of RNA-seq data from CommonMind Consortium, we identified
differential regulatory patterns for SCZ cases and control groups to unveil the
mechanisms that control the transcription of the genes in the human brain. Our
analysis identified modules with a high number of SCZ-associated genes as well
as assessing the relationship of the hubs with their down-stream genes in both,
cases and controls. In addition, the results identified essential genes for
brain function and suggested new genes putatively related to SCZ.
Power-Efficient Resource Allocation in Massive MIMO Aided Cloud RANs
Date: 2019-08-20 00:00:00

This paper considers the power-efficient resource allocation problem in a
cloud radio access network (C-RAN). The C-RAN architecture consists of a set of
base-band units (BBUs) which are connected to a set of radio remote heads
(RRHs) equipped with massive multiple input multiple output (MIMO), via
fronthaul links with limited capacity. We formulate the power-efficient
optimization problem in C-RANs as a joint resource allocation problem in order
to jointly allocate the RRH and transmit power to each user, and fronthaul
links and BBUs assign to active RRHs while satisfying the minimum required rate
of each user. To solve this non-convex optimization problem we suggest
iterative algorithm with two-step based on the complementary geometric
programming (CGP) and the successive convex approximation (SCA). The simulation
results indicate that our proposed scheme can significantly reduce the total
transmission power by switching off the under-utilized RRHs.
How kinesin waits for ATP affects the nucleotide and load dependence of the stepping kinetics
Authors: Ryota Takaki, Mauro L. Mugnai, Yonathan Goldtzvik, D. Thirumalai
Date: 2019-08-20 00:00:00

Dimeric molecular motors walk on polar tracks by binding and hydrolyzing one ATP per step. Despite tremendous progress, the waiting state for ATP binding in the well-studied kinesin that walks on microtubule (MT), remains controversial. One experiment suggests that in the waiting state both heads are bound to the MT, while the other shows that ATP binds to the leading head after the partner head detaches. To discriminate between these two scenarios, we developed a theory to calculate accurately several experimentally measurable quantities as a function of ATP concentration and resistive force. In particular, we predict that measurement of the randomness parameter could discriminate between the two scenarios for the waiting state of kinesin, thereby resolving this standing controversy.
A physiological model of the inflammatory-thermal-pain-cardiovascular interactions during a pathogen challenge
Date: 2019-08-13 00:00:00

Uncontrolled, excessive production of pro-inflammatory mediators from immune
cells and traumatized tissues can cause systemic inflammatory issues like
sepsis, one of the ten leading causes of death in the United States and one of
the three leading causes of death in the intensive care unit. Understanding the
effects of inflammation on the autonomic control system can improve a patient's
chance of recovery after an inflammatory event such as surgery. Though the
effects of the autonomic response on the inflammatory system are well defined,
there remains a gap in understanding the reverse response. Specifically, the
impact of the inflammatory response on the autonomic control system remains
unknown. In this study, we investigate hypothesized interactions of the
inflammatory system with the thermal and cardiovascular regulatory systems in
response to an endotoxin challenge using mathematical modeling. We calibrate
the model to data from two independent studies: a) of the inflammatory response
in healthy young men and b) a comparative study of the inflammatory response
between mice and humans. Simulation analysis is used to explore how the model
responds to pathological input and treatment, specifically antibiotics,
antipyretics, vasopressors, and combination therapy. Our findings show that
multimodal treatment that simultaneously targets both the pathogen and the
infection symptoms gives the most favorable recovery outcome.
Mapping the bacterial ways of life
Date: 2020-03-27 00:00:00

The rise in the availability of bacterial genomes defines a need for
synthesis: abstracting from individual taxa, to see larger patterns of
bacterial lifestyles across systems. A key concept for such synthesis in
ecology is the niche, the set of capabilities that enables a population's
persistence and defines its impact on the environment. The set of possible
niches forms the niche space, a conceptual space delineating ways in which
persistence in a system is possible. Here we use manifold learning to map the
space of metabolic networks representing thousands of bacterial genera. The
results reveal a metabolic niche space with a complex branching geometry, whose
branches constitute major strategies spanning life in different habitats and
hosts. We further demonstrate that communities from similar ecosystem types map
to characteristic regions of this new functional coordinate system, permitting
ecological descriptions of microbiomes in terms of large scale metabolic roles
that may be filled.
Myopic robust index tracking with Bregman divergence
Authors: Spiridon Penev, Pavel Shevchenko, Wei Wu
Date: 2019-08-21 00:00:00
Tasks: Asset Management, Management

Index tracking is a popular form of asset management. Typically, a quadratic function is used to define the tracking error of a portfolio and the look back approach is applied to solve the index tracking problem. We argue that a forward looking approach is more suitable, whereby the tracking error is expressed as expectation of a function of the difference between the returns of the index and of the portfolio. We also assume that there is an uncertainty in the distribution of the assets, hence a robust version of the optimization problem needs to be adopted. We use Bregman divergence in describing the deviation between the nominal and actual distribution of the components of the index. In this scenario, we derive the optimal robust index tracking strategy in a semi-analytical form as a solution of a system of nonlinear equations. Several numerical results are presented that allow us to compare the performance of this robust strategy with the optimal non-robust strategy. We show that, especially during market downturns, the robust strategy can be very advantageous.
Assessment of protein assembly prediction in CASP13
Date: 2019-08-21 00:00:00
Tasks: Prediction

We present the assembly category assessment in the 13th edition of the CASP
community-wide experiment. For the second time, protein assemblies constitute
an independent assessment category. Compared to the last edition we see a clear
uptake in participation, more oligomeric targets released, and consistent,
albeit modest, improvement of the predictions quality. Looking at the tertiary
structure predictions we observe that ignoring the oligomeric state of the
targets hinders modelling success. We also note that some contact prediction
groups successfully predicted homomeric interfacial contacts, though it appears
that these predictions were not used for assembly modelling. Homology modelling
with sizeable human intervention appears to form the basis of the assembly
prediction techniques in this round of CASP. Future developments should see
more integrated approaches to modelling where multiple subunits are a natural
part of the modelling process, which would benefit the structure prediction
field as a whole.
A false data injection attack method for generator dynamic state estimation
Date: 2019-08-18 00:00:00
Tasks: State Estimation

Accurate and reliable dynamic state quantities of generators are very
important for real-time monitoring and control of the power system. The
emergence of cyber attacks has brought new challenges to the state estimation
of generators. Especially, false data injection (FDI) attacks deteriorate the
accuracy of state estimation by injecting the false data into the measurement
device. In this regard, this paper proposes for the first time an FDI attack
model based on the dynamic state estimation of generators. Firstly, Taylor's
formula was used to linearize the generator's measurement equation. Secondly,
according to the principle that the measurement residuals before and after the
FDI attack are equal, the expressions of the attack vectors were established,
and they were applied to the measurement quantities to avoid the conventional
bad data detection. Thereby, the FDI attacks were successfully implemented.
Then, three attack scenarios were set according to the degree of the FDI
attacks, and they were tested by the cubature Kalman filter (CKF) and the
robust cubature Kalman filter (RCKF). Finally, the simulation results of the
IEEE 9-bus system and the New England 16-machine 68-bus system verify the
effectiveness of the proposed FDI attacks.
Detection-averse optimal and receding-horizon control for Markov decision processes
Date: 2019-08-21 00:00:00

In this paper, we consider a Markov decision process (MDP), where the ego
agent has a nominal objective to pursue while needs to hide its state from
detection by an adversary. After formulating the problem, we first propose a
value iteration (VI) approach to solve it. To overcome the "curse of
dimensionality" and thus gain scalability to larger-sized problems, we then
propose a receding-horizon optimization (RHO) approach to obtain approximate
solutions. We use examples to illustrate and compare the VI and RHO approaches,
and to show the potential of our problem formulation for practical
applications.
CNN-Based Segmentation of the Cardiac Chambers and Great Vessels in Non-Contrast-Enhanced Cardiac CT
Date: 2019-08-21 00:00:00

Quantification of cardiac structures in non-contrast CT (NCCT) could improve
cardiovascular risk stratification. However, setting a manual reference to
train a fully convolutional network (FCN) for automatic segmentation of NCCT
images is hardly feasible, and an FCN trained on coronary CT angiography (CCTA)
images would not generalize to NCCT. Therefore, we propose to train an FCN with
virtual non-contrast (VNC) images from a dual-layer detector CT scanner and a
reference standard obtained on perfectly aligned CCTA images.
Signal denoising based on the Schr\"odinger operator's eigenspectrum and a curvature constraint
Date: 2019-08-21 00:00:00
Tasks: Denoising

Recently, a new Signal processing method, named Semi-Classical Signal
Analysis (SCSA), has been proposed for denoising Magnetic Resonance
Spectroscopy (MRS) signals. It is based on the Schr\"odinger Operator's
eigenspectrum. It allows an efficient noise reduction while preserving MRS
signal's peaks. In this paper, we propose to extend this approach to different
signals, in particular pulse shaped signals, by including an optimization that
considers curvature constraints. The performance of the method is measured by
analyzing noisy signal data and comparing with other denoising methods. Results
indicate that the proposed method not only produces good denoising performance
but also guarantees the peaks are well preserved in the denoising process.
Event-Triggered Output Synchronization of Heterogeneous Nonlinear Multi-Agents
Date: 2019-08-21 00:00:00

This paper addresses the output synchronization problem for heterogeneous
nonlinear multi-agent systems with distributed event-based controllers.
Employing the two-step synchronization process, we first outline the
distributed event-triggered consensus controllers for linear reference models
under a directed communication topology. It is further shown that the
subsequent triggering instants are based on intermittent communication.
Secondly, by using certain input-to-state stability (ISS) property, we design
an event-triggered perturbed output regulation controller for each nonlinear
multi-agent. The ISS technique used in this paper is based on the milder
condition that each agent has a certain ISS property from input (actuator)
disturbance to state rather than measurement (sensor) disturbance to state.
With the two-step design, the objective of output synchronization is
successfully achieved with Zeno behavior avoided.
Relationship between optimal portfolios which can maximize and minimize the expected return
Authors: Takashi Shinzato
Date: 2019-08-21 00:00:00
Tasks: Portfolio Optimization

In recent years, the evaluation of the minimal investment risk of the quenched disordered system of a portfolio optimization problem and the investment concentration of the optimal portfolio has been actively investigated using the analysis methods of statistical mechanical informatics. However, the work to date has not sufficiently compared the optimal portfolios of different portfolio optimization problems. Therefore, in this paper, we use the Lagrange undetermined multiplier method and replica analysis to examine the relationship between the optimal portfolios of the expected return maximization problem and the expected return minimization problem with constraints of budget and investment risk. In particular, we derive the mean square error and the correlation coefficient of the optimal portfolios of these maximization and minimization problems as functions of a variable (the degree of risk tolerance) that can characterize the feasible subspace defined by the two constraints.
A Doubly Corrected Robust Variance Estimator for Linear GMM
Date: 2020-05-29 00:00:00

We propose a new finite sample corrected variance estimator for the linear
generalized method of moments (GMM) including the one-step, two-step, and
iterated estimators. Our formula additionally corrects for the
over-identification bias in variance estimation on top of the commonly used
finite sample correction of Windmeijer (2005) which corrects for the bias from
estimating the efficient weight matrix, so is doubly corrected. An important
feature of the proposed double correction is that it automatically provides
robustness to misspecification of the moment condition. In contrast, the
conventional variance estimator and the Windmeijer correction are inconsistent
under misspecification. That is, the proposed double correction formula
provides a convenient way to obtain improved inference under correct
specification and robustness against misspecification at the same time.
A complex net of intertwined complements: Measuring interdimensional dependence among the poor
Date: 2019-08-21 00:00:00

The choice of appropriate measures of deprivation, identification and
aggregation of poverty has been a challenge for many years. The works of Sen,
Atkinson and others have been the cornerstone for most of the literature on
poverty measuring. Recent contributions have focused in what we now know as
multidimensional poverty measuring. Current aggregation and identification
measures for multidimensional poverty make the implicit assumption that
dimensions are independent of each other, thus ignoring the natural dependence
between them. In this article a variant of the usual method of deprivation
measuring is presented. It allows the existence of the forementioned
connections by drawing from geometric and networking notions. This new
methodology relies on previous identification and aggregation methods, but with
small modifications to prevent arbitrary manipulations. It is also proved that
this measure still complies with the axiomatic framework of its predecessor.
Moreover, the general form of latter can be considered a particular case of
this new measure, although this identification is not unique.
Evolution of specialized microbial cooperation in dynamic fluids
Date: 2020-01-30 00:00:00

Here, we study the evolution of specialization using realistic computer
simulations of bacteria that secrete two public goods in a dynamic fluid.
Through this first principles approach, we find physical factors such as
diffusion, flow patterns, and decay rates are as influential as fitness
economics in governing the evolution of community structure, to the extent that
when mechanical factors are taken into account, (1) Generalist communities can
resist becoming specialists, despite the invasion fitness of specialization,
(2) Generalist and specialists can both resist cheaters despite the invasion
fitness of free-riding, (3) Multiple community structures can coexist despite
the opposing force of competitive exclusion. Our results emphasize the role of
spatial assortment and physical forces on niche partitioning and the evolution
of diverse community structures.
An Experimental Protocol to Derive and Validate a Quantum Model of Decision-Making
Date: 2019-07-30 00:00:00
Tasks: Decision Making

This study utilises an experiment famous in quantum physics, the
Stern-Gerlach experiment, to inform the structure of an experimental protocol
from which a quantum cognitive decision model can be developed. The
'quantumness' of this model is tested by computing a discrete
quasi-probabilistic Wigner function. Based on theory from quantum physics, our
hypothesis is that the Stern-Gerlach protocol will admit negative values in the
Wigner function, thus signalling that the cognitive decision model is quantum.
A crowdsourced experiment of two images was used to collect decisions around
three questions related to image trustworthiness. The resultant data was used
to instantiate the quantum model and compute the Wigner function. Negative
values in the Wigner functions of both images were encountered, thus
substantiating our hypothesis. Findings also revealed that the quantum
cognitive model was a more accurate predictor of decisions when compared to
predictions computed using Bayes' rule.
The effects of ecological selection on species diversity and trait distribution: predictions and an empirical test
Authors: DeMalach Niv, Po-Ju Ke, Tadashi Fukami
Date: 2019-08-21 00:00:00
Tasks: Attribute, Diversity

Ecological selection is a major driver of community assembly. Selection is classified as stabilizing when species with intermediate trait values gain the highest reproductive success, whereas selection is considered directional when fitness is highest for species with extreme trait values. Previous studies have investigated the effects of different selection types on trait distribution, but the effects of selection on species diversity have remained unclear. Here, we propose a framework for inferring the type and strength of selection by studying species diversity and trait distribution together against null expectations. We use a simulation model to confirm our prediction that directional selection should lead to lower species diversity than stabilizing selection despite a similar effect on trait community-weighted variance. We apply the framework to a mesocosm system of annual plants to test whether differences in species diversity between two habitats that vary in productivity are related to differences in selection on seed mass. We show that, in both habitats, species diversity was lower than the null expectation, but that species diversity was lower in the more productive habitat. We attribute this difference to strong directional selection for large-seeded species in the productive habitat as indicated by trait community-weighted-mean being higher and community-weighted variance being lower than the null expectations. In the less productive habitat, we found that community-weighted variance was higher than expected by chance, suggesting that seed mass could be a driver of niche partitioning under such conditions. Altogether, our results suggest that viewing species diversity and trait distribution as interrelated patterns driven by the same process, ecological selection, is helpful in understanding community assembly.
Constrained Thompson Sampling for Real-Time Electricity Pricing with Grid Reliability Constraints
Date: 2020-06-17 00:00:00
Tasks: Thompson Sampling

We consider the problem of an aggregator attempting to learn customers' load
flexibility models while implementing a load shaping program by means of
broadcasting daily dispatch signals. We adopt a multi-armed bandit formulation
to account for the stochastic and unknown nature of customers' responses to
dispatch signals. We propose a constrained Thompson sampling heuristic,
Con-TS-RTP, that accounts for various possible aggregator objectives (e.g., to
reduce demand at peak hours, integrate more intermittent renewable generation,
track a desired daily load profile, etc) and takes into account the operational
constraints of a distribution system to avoid potential grid failures as a
result of uncertainty in the customers' response. We provide a discussion on
the regret bounds for our algorithm as well as a discussion on the operational
reliability of the distribution system's constraints being upheld throughout
the learning process.
Decision-facilitating information in hidden-action setups: An agent-based approach
Date: 2020-04-13 00:00:00

The hidden-action model captures a fundamental problem of principal-agent
theory and provides an optimal sharing rule when only the outcome but not the
effort can be observed. However, the hidden-action model builds on various
explicit and also implicit assumptions about the information of the contracting
parties. This paper relaxes key assumptions regarding the availability of
information included in the hidden-action model in order to study whether and,
if so, how fast the optimal sharing rule is achieved and how this is affected
by the various types of information employed in the principal-agent relation.
Our analysis particularly focuses on information about the environment and
about feasible actions for the agent. We follow an approach to transfer
closed-form mathematical models into agent-based computational models and show
that the extent of information about feasible options to carry out a task only
has an impact on performance if decision makers are well informed about the
environment, and that the decision whether to perform exploration or
exploitation when searching for new feasible options only affects performance
in specific situations. Having good information about the environment, on the
contrary, appears to be crucial in almost all situations.
Simple Thermal Noise Estimation of Switched Capacitor Circuits Based on OTAs -- Part I: Amplifiers with Capacitive Feedback
Date: 2019-08-21 00:00:00
Tasks: Noise Estimation

This paper presents a simple method for estimating the thermal noise voltage
variance in passive and active switched-capacitor (SC) circuits using
operational transconductance amplifiers (OTA). The proposed method is based on
the Bode theorem for passive network which is extended to active circuits based
on OTAs with capacitive feedback. It allows for a precise estimation of the
thermal noise voltage variance by simple inspection of three equivalent
circuits avoiding the calculation of any transfer functions nor integrals. In
this Part I, the method is applied to SC amplifiers and track&hold circuits and
successfully validated by means of transient noise simulations. Part II extends
the application of the method to integrators and active SC filters.