pwc-archive/papers-with-abstracts huggingface dataset:
Stable fixed points of combinatorial threshold-linear networks
Authors: Carina Curto, Jesse Geneson, Katherine Morrison
Date: 2019-08-27 00:00:00

Combinatorial threshold-linear networks (CTLNs) are a special class of recurrent neural networks whose dynamics are tightly controlled by an underlying directed graph. Recurrent networks have long been used as models for associative memory and pattern completion, with stable fixed points playing the role of stored memory patterns in the network. In prior work, we showed that target-free cliques of the graph correspond to stable fixed points of the dynamics, and we conjectured that these are the only stable fixed points possible. In this paper, we prove that the conjecture holds in a variety of special cases, including for networks with very strong inhibition and graphs of size $n \leq 4$. We also provide further evidence for the conjecture by showing that sparse graphs and graphs that are nearly cliques can never support stable fixed points. Finally, we translate some results from extremal combinatorics to obtain an upper bound on the number of stable fixed points of CTLNs in cases where the conjecture holds.
Monolingual and bilingual language networks in healthy subjects using functional MRI and graph theory
Date: 2020-06-15 00:00:00

Pre-surgical language mapping with functional magnetic resonance imaging
(fMRI) is routinely conducted to assist the neurosurgeon in preventing damage
to brain regions responsible for language. Functional differences exist between
the monolingual versus the bilingual brain, whereas clinical fMRI tasks are
typically conducted in a single language. The presence of secondary language
processing mechanisms is a potential source of error in the inferred language
map. From fMRI data of healthy bilingual and monolingual subjects we obtain
language maps as functional networks. Our results show a sub-network "core"
architecture consisting of the Broca's, pre-supplementary motor, and premotor
areas present across all subjects. Wernicke's Area was found to connect to the
"core" to a different extent across groups. The $k$ core centrality measure
shows "core" areas belong to the maximum core while WA and other fROIs vary
across groups. The results may provide a benchmark to preserve equal treatment
outcomes for bilingual patients.
Ultra-broadband local active noise control with remote acoustic sensing
Authors: Tong Xiao, Xiaojun Qiu, Benjamin Halkon
Date: 2019-09-08 00:00:00

One enduring challenge for controlling high frequency sound in local active noise control (ANC) systems is to obtain the acoustic signal at the specific location to be controlled. In some applications such as in ANC headrest systems, it is not practical to install error microphones in a person's ears to provide the user a quiet or optimally acoustically controlled environment. Many virtual error sensing approaches have been proposed to estimate the acoustic signal remotely with the current state-of-the-art method using an array of four microphones and a head tracking system to yield sound reduction up to 1 kHz for a single sound source. In the work reported in this paper, a novel approach of incorporating remote acoustic sensing using a laser Doppler vibrometer into an ANC headrest system is investigated. In this 'virtual ANC headphone' system, a lightweight retro-reflective membrane pick-up is mounted in each synthetic ear of a head and torso simulator to determine the sound in the ear in real-time with minimal invasiveness. The membrane design and the effects of its location on the system performance are explored, the noise spectra in the ears without and with ANC for a variety of relevant primary sound fields are reported, and the performance of the system during head movements is demonstrated. The test results show that at least 10 dB sound attenuation can be realised in the ears over an extended frequency range from (500 Hz to 6 kHz) under a complex sound field and for several common types of synthesised environmental noise, even in the presence of head motion.
Education Projects for Sustainable Development: Evidence from Ural Federal University
Date: 2019-09-21 00:00:00

Sustainable development is a worldwide recognized social and political goal,
discussed in both academic and political discourse and with much research on
the topic related to sustainable development in higher education. Since mental
models are formed more effectively at school age, we propose a new way of
thinking that will help achieve this goal. This paper was written in the
context of Russia, where the topic of sustainable development in education is
poorly developed. The authors used the classical methodology of the case
analysis. The analysis and interpretation of the results were conducted in the
framework of the institutional theory. Presented is the case of Ural Federal
University, which has been working for several years on the creation of a
device for the purification of industrial sewer water in the framework of an
initiative student group. Schoolchildren recently joined the program, and such
projects have been called university-to-school projects. Successful solutions
of inventive tasks contribute to the formation of mental models. This case has
been analyzed in terms of institutionalism, and the authors argue for the
primacy of mental institutions over normative ones during sustainable society
construction. This case study is the first to analyze a partnership between a
Federal University and local schools regarding sustainable education and
proposes a new way of thinking.
Systemic Risk Clustering of China Internet Financial Based on t-SNE Machine Learning Algorithm
Date: 2019-08-30 00:00:00

With the rapid development of Internet finance, a large number of studies
have shown that Internet financial platforms have different financial systemic
risk characteristics when they are subject to macroeconomic shocks or fragile
internal crisis. From the perspective of regional development of Internet
finance, this paper uses t-SNE machine learning algorithm to obtain data mining
of China's Internet finance development index involving 31 provinces and 335
cities and regions. The conclusion of the peak and thick tail characteristics,
then proposed three classification risks of Internet financial systemic risk,
providing more regionally targeted recommendations for the systematic risk of
Internet finance.
Tree-based Synthetic Control Methods: Consequences of moving the US Embassy
Date: 2019-09-09 00:00:00
Tasks: counterfactual, regression

We recast the synthetic controls for evaluating policies as a counterfactual prediction problem and replace its linear regression with a nonparametric model inspired by machine learning. The proposed method enables us to achieve accurate counterfactual predictions and we provide theoretical guarantees. We apply our method to a highly debated policy: the relocation of the US embassy to Jerusalem. In Israel and Palestine, we find that the average number of weekly conflicts has increased by roughly 103\% over 48 weeks since the relocation was announced on December 6, 2017. By using conformal inference and placebo tests, we justify our model and find the increase to be statistically significant.
Perimeter-defense Game on Arbitrary Convex Shapes
Authors: Daigo Shishika, Vijay Kumar
Date: 2019-09-09 00:00:00

This paper studies a variant of multi-player reach-avoid game played between intruders and defenders. The intruder team tries to score by sending as many intruders as possible to the target area, while the defender team tries to minimize this score by intercepting them. Specifically, we consider the case where the defenders are constrained to move on the perimeter of the target area. Since it is challenging to directly solve the multi-player game due to the high dimensionality of the joint state space, we leverage the solutions to smaller scale problems. First, we solve the one vs. one game, for which existing works either rely on numerical approaches or make simplifying assumptions (e.g., circular perimeter, or equal speed). This paper accommodates target areas with any arbitrary convex shapes and provides analytical solution which lends itself to a useful geometric interpretation. We also provide a detailed discussion on the optimality of the derived strategies. Secondly, we solve the two vs. one game to introduce a cooperative pincer maneuver, where a pair of defenders team up to capture an intruder that cannot be captured by either one of the defender individually. Finally, we introduce how the aforementioned building blocks are used in three different assignment-based defense strategies.
Translation Operator in Graph Signal Processing: A Generalized Approach
Date: 2019-11-27 00:00:00
Tasks: Translation

The notion of translation (shift) is straightforward in classical signal
processing, however, it is challenging on an irregular graph structure. In this
work, we present an approach to characterize the translation operator in
various signal domains. By a natural generalization from classical domains, one
can characterize an abstract representation for the graph translation operator.
Then we propose an isometric translation operator in joint time-vertex domain
consistent with the abstract form of translation operators in other domains. We
also demonstrate the connection between this notion and the Schr\"{o}dinger
equation on a dynamic system which intriguingly describes the idea behind
translation on graph.
MAJoRCom: A Dual-Function Radar Communication System Using Index Modulation
Date: 2019-09-10 00:00:00
Tasks: Decoder, Joint Radar-Communication

Dual-function radar communication (DFRC) systems implement both sensing and
communication using the same hardware. Such schemes are often more efficient in
terms of size, power, and cost, over using distinct radar and communication
systems. Since these functionalities share resources such as spectrum, power,
and antennas, DFRC methods typically entail some degradation in both radar and
communication performance. In this work we propose a DFRC scheme based on the
carrier agile phased array radar (CAESAR), which combines frequency and spatial
agility. The proposed DFRC system, referred to as multi-carrier agile joint
radar communication (MAJoRCom), exploits the inherent spatial and spectral
randomness of CAESAR to convey digital messages in the form of index
modulation. The resulting communication scheme naturally coexists with the
radar functionality, and thus does not come at the cost of reduced radar
performance. We analyze the performance of MAJoRCom, quantifying its achievable
bit rate. In addition, we develop a low complexity decoder and a codebook
design approach, which simplify the recovery of the communicated bits. Our
numerical results demonstrate that MAJoRCom is capable of achieving a bit rate
which is comparable to utilizing independent communication modules without
affecting the radar performance, and that our proposed low-complexity decoder
allows the receiver to reliably recover the transmitted symbols with an
affordable computational burden.
Robust data-driven state-feedback design
Date: 2020-10-26 00:00:00

We consider the problem of designing robust state-feedback controllers for
discrete-time linear time-invariant systems, based directly on measured data.
The proposed design procedures require no model knowledge, but only a single
open-loop data trajectory, which may be affected by noise. First, a data-driven
characterization of the uncertain class of closed-loop matrices under
state-feedback is derived. By considering this parametrization in the robust
control framework, we design data-driven state-feedback gains with guarantees
on stability and performance, containing, e.g., the
$\mathcal{H}_\infty$-control problem as a special case. Further, we show how
the proposed framework can be extended to take partial model knowledge into
account. The validity of the proposed approach is illustrated via a numerical
example.
Empirical investigation of state-of-the-art mean reversion strategies for equity markets
Authors: Seung-Hyun Moon, Yong-Hyuk Kim, Byung-Ro Moon
Date: 2019-09-10 00:00:00

Recent studies have shown that online portfolio selection strategies that exploit the mean reversion property can achieve excess return from equity markets. This paper empirically investigates the performance of state-of-the-art mean reversion strategies on real market data. The aims of the study are twofold. The first is to find out why the mean reversion strategies perform extremely well on well-known benchmark datasets, and the second is to test whether or not the mean reversion strategies work well on recent market data. The mean reversion strategies used in this study are the passive aggressive mean reversion (PAMR) strategy, the on-line moving average reversion (OLMAR) strategy, and the transaction cost optimization (TCO) strategies. To test the strategies, we use the historical prices of the stocks that constitute S\&P 500 index over the period from 2000 to 2017 as well as well-known benchmark datasets. Our findings are that the well-known benchmark datasets favor mean reversion strategies, and mean reversion strategies may fail even in favorable market conditions, especially when there exist explicit or implicit transaction costs.
VLC-Based Networking: Feasibility and Challenges
Date: 2019-09-10 00:00:00

Visible-light communication (VLC) has emerged as a prominent technology to
address the radio spectrum shortage. It is characterized by the unlicensed and
unexploited high bandwidth, and provides the system with cost-effective
advantages because of the dual-use of light bulbs for illumination and
communication and the low complexity design. It is considered to be utilized in
various telecommunication systems, including 5G, and represents the key
technology for light-fidelity. To this end, VLC has to be integrated into the
existing telecommunication networks. Therefore, its analysis as a network
technology is momentous. In this article, we consider the feasibility of using
VLC as a network technology and discuss the challenges related to the
implementation of a VLC-based network, as well as the integration of VLC into
existing conventional networks and its inclusion in standards.
Quasi-steady uptake and bacterial community assembly in a mathematical model of soil-phosphorus mobility
Date: 2020-10-26 00:00:00
Tasks: Time Series, Time Series Analysis

We mathematically model the uptake of phosphorus by a soil community
consisting of a plant and two bacterial groups: copiotrophs and oligotrophs.
Four equilibrium states emerge, one for each of the species monopolising the
resource and dominating the community and one with coexistence of all species.
We show that the dynamics are controlled by the ratio of chemical adsorption to
bacterial death permitting either oscillatory states or quasi-steady uptake. We
show how a steady state can emerge which has soil and plant nutrient content
unresponsive to increased fertilization. However, the additional fertilization
supports the copiotrophs leading to community reassembly. Our results
demonstrate the importance of time-series measurements in nutrient uptake
experiments.
A Theory on Formatting Sensory Input for Cognition
Date: 2020-02-28 00:00:00
Tasks: Hippocampus

Over the last few decades, a lot of progress has been made in understanding
different aspects of the brain's ability to form abstract representations, but
a specific mechanism for how they are created and used remains to emerge. Here,
we review recent findings on the subject and we propose a mechanism for the
dynamics of forming abstract representations, where the formation of local
connectivity in neural networks determines the of search terms between the
prefrontal cortex and the hippocampus, as well as the amount of detail that is
transcribed into abstract representations.
Arbitrage-free modeling under Knightian Uncertainty
Date: 2020-04-27 00:00:00

We study the Fundamental Theorem of Asset Pricing for a general financial
market under Knightian Uncertainty. We adopt a functional analytic approach
which require neither specific assumptions on the class of priors $\mathcal{P}$
nor on the structure of the state space. Several aspects of modeling under
Knightian Uncertainty are considered and analyzed. We show the need for a
suitable adaptation of the notion of No Free Lunch with Vanishing Risk and
discuss its relation to the choice of an appropriate filtration. In an abstract
setup, we show that absence of arbitrage is equivalent to the existence of
\emph{approximate} martingale measures sharing the same polar set of
$\mathcal{P}$. We then specialize the results to a discrete-time framework in
order to obtain true martingale measures.
Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems
Authors: Gui Zhou, Cunhua Pan, Hong Ren, Kezhi Wang, Arumugam Nallanathan
Date: 2019-09-10 00:00:00

Intelligent reflecting surface (IRS) has recently been envisioned to offer unprecedented massive multiple-input multiple-output (MIMO)-like gains by deploying large-scale and low-cost passive reflection elements. By adjusting the reflection coefficients, the IRS can change the phase shifts on the impinging electromagnetic waves so that it can smartly reconfigure the signal propagation environment and enhance the power of the desired received signal or suppress the interference signal. In this paper, we consider downlink multigroup multicast communication systems assisted by an IRS. We aim for maximizing the sum rate of all the multicasting groups by the joint optimization of the precoding matrix at the base station (BS) and the reflection coefficients at the IRS under both the power and unit-modulus constraint. To tackle this non-convex problem, we propose two efficient algorithms under the majorization--minimization (MM) algorithm framework. Specifically, a concave lower bound surrogate objective function of each user's rate has been derived firstly, based on which two sets of variables can be updated alternately by solving two corresponding second-order cone programming (SOCP) problems. Then, in order to reduce the computational complexity, we derive another concave lower bound function of each group's rate for each set of variables at every iteration, and obtain the closed-form solutions under these loose surrogate objective functions. Finally, the simulation results demonstrate the benefits in terms of the spectral and energy efficiency of the introduced IRS and the effectiveness in terms of the convergence and complexity of our proposed algorithms.
Virtual Historical Simulation for estimating the conditional VaR of large portfolios
Date: 2019-09-10 00:00:00

In order to estimate the conditional risk of a portfolio's return, two
strategies can be advocated. A multivariate strategy requires estimating a
dynamic model for the vector of risk factors, which is often challenging, when
at all possible, for large portfolios. A univariate approach based on a dynamic
model for the portfolio's return seems more attractive. However, when the
combination of the individual returns is time varying, the portfolio's return
series is typically non stationary which may invalidate statistical inference.
An alternative approach consists in reconstituting a "virtual portfolio", whose
returns are built using the current composition of the portfolio and for which
a stationary dynamic model can be estimated.
  This paper establishes the asymptotic properties of this method, that we call
Virtual Historical Simulation. Numerical illustrations on simulated and real
data are provided.
Decoupling and Matching Strategies for Compact Antenna Arrays
Date: 2019-09-10 00:00:00

Antenna arrays have been used in various applications and have become an
important tool to achieve high spectral efficiency in wireless communications.
Its use brings to the communications system an increase in performance in terms
of capacity and reliability. Recently one of the main communications
architectures that makes use of antenna arrays is the Multiple-Input
Multiple-Output (MIMO) technology. MIMO technology has been applied in antenna
arrays composed of elements in the order of tens to hundreds. In this way, it
is necessary to use the structure of compact antennas that offer all the
necessary robustness to the applied project. In this context, it is important
to revisit the concepts of mutual coupling and impedance matching among antenna
elements in an array. This paper proposes and evaluates three strategies of
joint decoupling and impedance matching networks(DMN) for antenna arrays. The
first method called DMN with Lumped Elements (DMN-LE) performs the decoupling
and impedance matching steps with capacitors and inductors. The second method
is called the Ring Hybrid (DMN-RH). It is utilizes a microstrip line in the
ring form. With this approach is achieved first the decoupling followed by
impedance matching steps. The third method is called Networkless Decoupling and
Matching (NDM). It brings a concept of decoupling without the presence of a
network itself. A comparison of the methods is performed both analytically and
via computer simulations. We conclude that the third method, networkless one,
is an interesting new alternative approach.
Generative Speech Enhancement Based on Cloned Networks
Date: 2019-09-10 00:00:00
Tasks: Speech Enhancement

We propose to implement speech enhancement by the regeneration of clean
speech from a salient representation extracted from the noisy signal. The
network that extracts salient features is trained using a set of weight-sharing
clones of the extractor network. The clones receive mel-frequency spectra of
different noisy versions of the same speech signal as input. By encouraging the
outputs of the clones to be similar for these different input signals, we train
a feature extractor network that is robust to noise. At inference, the salient
features form the input to a WaveNet network that generates a natural and clean
speech signal with the same attributes as the ground-truth clean signal. As the
signal becomes noisier, our system produces natural sounding errors that stay
on the speech manifold, in place of traditional artifacts found in other
systems. Our experiments confirm that our generative enhancement system
provides state-of-the-art enhancement performance within the generative class
of enhancers according to a MUSHRA-like test. The clones based system matches
or outperforms the other systems at each input signal-to-noise (SNR) range with
statistical significance.
Linear Equilibria for Dynamic LQG Games with Asymmetric Information and Dependent Types
Date: 2019-09-11 00:00:00

We consider a non-zero-sum linear quadratic Gaussian (LQG) dynamic game with
asymmetric information. Each player observes privately a noisy version of a
(hidden) state of the world $V$, resulting in dependent private observations.
We study perfect Bayesian equilibria (PBE) for this game with equilibrium
strategies that are linear in players' private estimates of $V$. The main
difficulty arises from the fact that players need to construct estimates on
other players' estimate on $V$, which in turn would imply that an infinite
hierarchy of estimates on estimates needs to be constructed, rendering the
problem unsolvable. We show that this is not the case: each player's estimate
on other players' estimates on $V$ can be summarized into her own estimate on
$V$ and some appropriately defined public information. Based on this finding we
characterize the PBE through a backward/forward algorithm akin to dynamic
programming for the standard LQG control problem. Unlike the standard LQG
problem, however, Kalman filter covariance matrices, as well as some other
required quantities, are observation-dependent and thus cannot be evaluated
off-line through a forward recursion.