pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of free recall dynamics of an abstract working memory model
Date: 2020-03-17 00:00:00

This paper analyzes the free recall dynamics of a working memory model. Free
recalling is the reactivation of a stored pattern in the memory in the absence
of the pattern. Our free recall model is based on an abstract model of a
modular neural network composed on $N$ modules, hypercolumns, each of which is
a bundle of minicolumns. This paper considers a network of $N$ modules, each
consisting of two minicolumns, over a complete graph topology. We analyze the
free recall dynamics assuming a constant, and homogeneous coupling between the
network modules. We obtain a sufficient condition for synchronization of
network's minicolumns whose activities are positively correlated. Furthermore,
for the synchronized network, the bifurcation analysis of one module is
presented. This analysis gives a necessary condition for having a stable limit
cycle as the attractor of each module. The latter implies recalling a stored
pattern. Numerical results are provided to verify the theoretical analysis.
Inequality in Turkey: Looking Beyond Growth
Date: 2019-10-25 00:00:00

This paper investigates the relationships between economic growth, investment
in human capital and income equality in Turkey. The conclusion drawn based on
the data from the OECD and the World Bank suggests that economic growth can
improve income equality depending on the expenditures undertaken by the
government. As opposed to the standard view that economic growth and income
inequality are positively related, the findings of this paper suggest that
other factors such as education and healthcare spending are also driving
factors of income inequality in Turkey. The proven positive impact of
investment in education and health care on income equality could aid
policymakers who aim to achieve fairer income equality and economic growth, in
investment decisions.
Adaptive blind audio source extraction supervised by dominant speaker identification using x-vectors
Date: 2019-10-25 00:00:00
Tasks: Speaker Identification

We propose a novel algorithm for adaptive blind audio source extraction. The
proposed method is based on independent vector analysis and utilizes the
auxiliary function optimization to achieve high convergence speed. The
algorithm is partially supervised by a pilot signal related to the source of
interest (SOI), which ensures that the method correctly extracts the utterance
of the desired speaker. The pilot is based on the identification of a dominant
speaker in the mixture using x-vectors. The properties of the x-vectors
computed in the presence of cross-talk are experimentally analyzed. The
proposed approach is verified in a scenario with a moving SOI, static
interfering speaker, and environmental noise.
Innovative Telecommunications Training through Flexible Radio Platforms
Date: 2019-10-25 00:00:00

The ever-changing telecommunication industry is in severe need of a
highly-skilled workforce to shape and deploy future generation communication
systems. This article presents an innovative telecommunication training that is
designed to satisfy this need. The training focuses on hardware layers of the
open systems interconnection model. It integrates theory, numerical modeling,
and hardware implementation to ensure a complete and long-lasting
understanding. The key telecommunication concepts that are covered in the
fundamental training phase are detailed along with best teaching practices. In
addition, the methods that enrich the learning experience, such as gamified
micro-tasks and interactive use of daily telecommunication devices, are
featured. The project development case studies that cultivate creative thinking
and scientific interest are highlighted. Also, a well-established guideline to
compose the teaching environment that emphasizes hands-on experience is
provided. Therefore, the presented training can be exemplary to other
institutions that share the same mission to educate the distinguished engineers
of the future.
Sparsity and Stability for Minimum-Variance Portfolios
Authors: Sven Husmann, Antoniya Shivarova, Rick Steinert
Date: 2019-10-25 00:00:00

The popularity of modern portfolio theory has decreased among practitioners because of its unfavorable out-of-sample performance. Estimation errors tend to affect the optimal weight calculation noticeably, especially when a large number of assets is considered. To overcome these issues, many methods have been proposed in recent years, although most only address a small set of practically relevant questions related to portfolio allocation. This study therefore sheds light on different covariance estimation techniques, combines them with sparse model approaches, and includes a turnover constraint that induces stability. We use two datasets - comprising 319 and 100 companies of the S&P 500, respectively - to create a realistic and reproducible data foundation for our empirical study. To the best of our knowledge, this study is the first to show that it is possible to maintain the low-risk profile of efficient estimation methods while automatically selecting only a subset of assets and further inducing low portfolio turnover. Moreover, we provide evidence that using the LASSO as the sparsity-generating model is insufficient to lower turnover when the involved tuning parameter can change over time.
Optimal Resource Allocation in Wireless Control Systems via Deep Policy Gradient
Date: 2019-10-25 00:00:00
Tasks: Deep Reinforcement Learning, Policy Gradient Methods

In wireless control systems, remote control of plants is achieved through
closing of the control loop over a wireless channel. As wireless communication
is noisy and subject to packet dropouts, proper allocation of limited
resources, e.g. transmission power, across plants is critical for maintaining
reliable operation. In this paper, we formulate the design of an optimal
resource allocation policy that uses current plant states and wireless channel
states to assign resources used to send control actuation information back to
plants. While this problem is challenging due to its infinite dimensionality
and need for explicit system model and state knowledge, we propose the use of
deep reinforcement learning techniques to find neural network-based resource
allocation policies. In particular, we use model-free policy gradient methods
to directly learn continuous power allocation policies without knowledge of
plant dynamics or communication models. Numerical simulations demonstrate the
strong performance of learned policies relative to baseline resource allocation
methods in settings where state information is available both with and without
noise.
Change of drift in one-dimensional diffusions
Authors: Sascha Desmettre, Gunther Leobacher, L. C. G. Rogers
Date: 2019-10-25 00:00:00

It is generally understood that a given one-dimensional diffusion may be transformed by Cameron-Martin-Girsanov measure change into another one-dimensional diffusion with the same volatility but a different drift. But to achieve this we have to know that the change-of-measure local martingale that we write down is a true martingale; we provide a complete characterization of when this happens. This is then used to discuss absence of arbitrage in a generalized Heston model including the case where the Feller condition for the volatility process is violated.
sEMG-Based Natural Control Interface for a Variable Stiffness Transradial Hand Prosthesis
Date: 2019-10-25 00:00:00
Tasks: Position

We propose, implement and evaluate a natural human-machine control interface
for a variable stiffness transradial hand prosthesis that achieves
tele-impedance control through surface electromyography (sEMG) signals. This
interface, together with variable stiffness actuation (VSA), enables an amputee
to modulate the impedance of the prosthetic limb to properly match the
requirements of a task, while performing activities of daily living. Both the
desired position and stiffness references are estimated through sEMG signals
and used to control the VSA hand prosthesis. In particular, regulation of hand
impedance is managed through the impedance measurements of the intact upper
arm; this control takes place naturally and automatically as the amputee
interacts with the environment, while position of the hand prosthesis is
regulated intentionally by the amputee through the estimated position of the
shoulder. The proposed approach is advantageous, since the impedance regulation
takes place naturally without requiring amputees' attention and diminishing
their functional capability. Consequently, the proposed interface is easy to
use, does not require long training periods or interferes with the control of
intact body segments. The performance of the approach is evaluated through
human subject experiments where adequate estimation of references and
independent control of position and stiffness are demonstrated.
Sum-Product Networks for Robust Automatic Speaker Identification
Date: 2020-08-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speaker Identification, Speaker Verification, speech-recognition, Speech Recognition

We introduce sum-product networks (SPNs) for robust speech processing through
a simple robust automatic speaker identification (ASI) task. SPNs are deep
probabilistic graphical models capable of answering multiple probabilistic
queries. We show that SPNs are able to remain robust by using the marginal
probability density function (PDF) of the spectral features that reliably
represent speech. Though current SPN toolkits and learning algorithms are in
their infancy, we aim to show that SPNs have the potential to become a useful
tool for robust speech processing in the future. SPN speaker models are
evaluated here on real-world non-stationary and coloured noise sources at
multiple signal-to-noise ratio (SNR) levels. In terms of ASI accuracy, we find
that SPN speaker models are more robust than two recent convolutional neural
network (CNN)-based ASI systems. Additionally, SPN speaker models consist of
significantly fewer parameters than their CNN-based counterparts. The results
indicate that SPN speaker models could be a robust, parameter-efficient
alternative for ASI. Additionally, this work demonstrates that SPNs have
potential in related tasks, such as robust automatic speech recognition (ASR)
and automatic speaker verification (ASV). Availability: The SPN ASI system is
available at https://github.com/anicolson/SPN-ASI.
Compensation of Charging Station Overload via On-road Mobile Energy Storage Scheduling
Date: 2019-10-26 00:00:00
Tasks: Scheduling

Supported by the technical development of electric battery and charging
facilities, plug-in electric vehicle (PEV) has the potential to be mobile
energy storage (MES) for energy delivery from resourceful charging stations
(RCSs) to limited-capacity charging stations (LCSs). In this paper, we study
the problem of using on-road PEVs as MESs for energy compensation service to
compensate charging station (CS) overload. A price-incentive scheme is proposed
for power system operator (PSO) to stimulate on-road MESs fulfilling energy
compensation tasks. The price-service interaction between the PSO and MESs is
characterized as a one-leader, multiple-follower Stackelberg game. The PSO acts
as a leader to schedule on-road MESs by posting service price and on-road MESs
respond to the price by choosing their service amount. The existence and
uniqueness of the Stackelberg equilibrium are validated, and an algorithm is
developed to find the equilibrium. Simulation results show the effectiveness of
the proposed scheme in utility optimization and overload mitigation.
Gene expression and pathway bioinformatics analysis detect a potential predictive value of MAP3K8 in thyroid cancer progression
Authors: Valentina Di Salvatore, Fiorenza Gianì, Giulia Russo, Marzio Pennisi, Pasqualino Malandrino, Francesco Frasca, Francesco Pappalardo
Date: 2019-10-26 00:00:00
Tasks: Prognosis

Thyroid cancer is the commonest endocrine malignancy. Mutation in the BRAF serine/threonine kinase is the most frequent genetic alteration in thyroid cancer. Target therapy for advanced and poorly differentiated thyroid carcinomas include BRAF pathway inhibitors. Here, we evaluated the role of MAP3K8 expression as a potential driver of resistance to BRAF inhibition in thyroid cancer. By analyzing Gene Expression Omnibus data repository, across all thyroid cancer histotypes, we found that MAP3K8 is up-regulated in poorly differentiated thyroid carcinomas and its expression is related to a stem cell like phenotype and a poorer prognosis and survival. Taken together these data unravel a novel mechanism for thyroid cancer progression and chemo-resistance and confirm previous results obtained in cultured thyroid cancer stem cells
Image to Image Translation based on Convolutional Neural Network Approach for Speech Declipping
Date: 2019-10-26 00:00:00
Tasks: Image-to-Image Translation, Translation

Clipping, as a current nonlinear distortion, often occurs due to the limited
dynamic range of audio recorders. It degrades the speech quality and
intelligibility and adversely affects the performances of speech and speaker
recognitions. In this paper, we focus on enhancement of clipped speech by using
a fully convolutional neural network as U-Net. Motivated by the idea of
image-to-image translation, we propose a declipping approach, namely U-Net
declipper in which the magnitude spectrum images of clipped signals are
translated to the corresponding images of clean ones. The experimental results
show that the proposed approach outperforms other declipping methods in terms
of both quality and intelligibility measures, especially in severe clipping
cases. Moreover, the superior performance of the U-Net declipper over the
well-known declipping methods is verified in additive Gaussian noise
conditions.
Price mediated contagion through capital ratio requirements with VWAP liquidation prices
Authors: Tathagata Banerjee, Zachary Feinstein
Date: 2019-10-26 00:00:00

We develop a framework for price-mediated contagion in financial systems where banks are forced to liquidate assets to satisfy a risk-weight based capital adequacy requirement. In constructing this modeling framework, we introduce a two-tier pricing structure: the volume weighted average price that is obtained by any bank liquidating assets and the terminal mark-to-market price used to account for all assets held at the end of the clearing process. We consider the case of multiple illiquid assets and develop conditions for the existence and uniqueness of clearing prices. We provide a closed-form representation for the sensitivity of these clearing prices to the system parameters, and use this result to quantify: (1) the cost of regulation, in stress scenarios, faced by the system as a whole and the individual banks, and (2) the value of providing bailouts to consider when such notions are financially advisable. Numerical case studies are provided to study the application of this model to data.
Almost Quasi-linear Utilities in Disguise: Positive-representation An Extension of Roberts' Theorem
Date: 2019-10-26 00:00:00

This work deals with the implementation of social choice rules using dominant
strategies for unrestricted preferences. The seminal Gibbard-Satterthwaite
theorem shows that only few unappealing social choice rules can be implemented
unless we assume some restrictions on the preferences or allow monetary
transfers. When monetary transfers are allowed and quasi-linear utilities
w.r.t. money are assumed, Vickrey-Clarke-Groves (VCG) mechanisms were shown to
implement any affine-maximizer, and by the work of Roberts, only
affine-maximizers can be implemented whenever the type sets of the agents are
rich enough.
  In this work, we generalize these results and define a new class of
preferences: Preferences which are positive-represented by a quasi-linear
utility. That is, agents whose preference on a subspace of the outcomes can be
modeled using a quasi-linear utility. We show that the characterization of VCG
mechanisms as the incentive-compatible mechanisms extends naturally to this
domain. Our result follows from a simple reduction to the characterization of
VCG mechanisms. Hence, we see our result more as a fuller more correct version
of the VCG characterization.
  This work also highlights a common misconception in the community attributing
the VCG result to the usage of transferable utility. Our result shows that the
incentive-compatibility of the VCG mechanisms does not rely on money being a
common denominator, but rather on the ability of the designer to fine the
agents on a continuous (maybe agent-specific) scale.
  We think these two insights, considering the utility as a representation and
not as the preference itself (which is common in the economic community) and
considering utilities which represent the preference only for the relevant
domain, would turn out to fruitful in other domains as well.
Symbolic Controller Synthesis for B\"uchi Specifications on Stochastic Systems
Date: 2020-02-14 00:00:00
Tasks: Fairness

We consider the policy synthesis problem for continuous-state controlled
Markov processes evolving in discrete time, when the specification is given as
a B\"uchi condition (visit a set of states infinitely often). We decompose
computation of the maximal probability of satisfying the B\"uchi condition into
two steps. The first step is to compute the maximal qualitative winning set,
from where the B\"uchi condition can be enforced with probability one. The
second step is to find the maximal probability of reaching the already computed
qualitative winning set. In contrast with finite-state models, we show that
such a computation only gives a lower bound on the maximal probability where
the gap can be non-zero.
  In this paper we focus on approximating the qualitative winning set, while
pointing out that the existing approaches for unbounded reachability
computation can solve the second step. We provide an abstraction-based
technique to approximate the qualitative winning set by simultaneously using an
over- and under-approximation of the probabilistic transition relation. Since
we are interested in qualitative properties, the abstraction is
non-probabilistic; instead, the probabilistic transitions are assumed to be
under the control of a (fair) adversary. Thus, we reduce the original policy
synthesis problem to a B\"uchi game under a fairness assumption and
characterize upper and lower bounds on winning sets as nested fixed point
expressions in the $\mu$-calculus. This characterization immediately provides a
symbolic algorithm scheme. Further, a winning strategy computed on the abstract
game can be refined to a policy on the controlled Markov process.
  We describe a concrete abstraction procedure and demonstrate our algorithm on
two case studies.
An Optimal Detector for Hierarchical $2^n$ QAM
Date: 2019-10-28 00:00:00

A practical and scalable, optimal detection algorithm is introduced for
hierarchical Gray-coded $2^n$ QAM for application in software defined
communications. The introduced optimal detection algorithm is based on
successive interference cancellation and is equivalent to a successive
approximation analog to digital conversion (SAR-ADC). The performance of the
optimal detector is discussed in additive white Gaussian noise channels. The
proposed detection algorithm needs much less storage space than the
conventional optimal detection algorithm followed by table lookup.
A Bin Encoding Training of a Spiking Neural Network-based Voice Activity Detection
Date: 2019-10-28 00:00:00
Tasks: Action Detection, Activity Detection

Advances of deep learning for Artificial Neural Networks(ANNs) have led to
significant improvements in the performance of digital signal processing
systems implemented on digital chips. Although recent progress in low-power
chips is remarkable, neuromorphic chips that run Spiking Neural Networks (SNNs)
based applications offer an even lower power consumption, as a consequence of
the ensuing sparse spike-based coding scheme. In this work, we develop a
SNN-based Voice Activity Detection (VAD) system that belongs to the building
blocks of any audio and speech processing system. We propose to use the bin
encoding, a novel method to convert log mel filterbank bins of single-time
frames into spike patterns. We integrate the proposed scheme in a bilayer
spiking architecture which was evaluated on the QUT-NOISE-TIMIT corpus. Our
approach shows that SNNs enable an ultra low-power implementation of a VAD
classifier that consumes only 3.8$\mu$W, while achieving state-of-the-art
performance.
The coherent feed-forward loop acts as an efficient information transmitting motif
Date: 2020-01-27 00:00:00

We present a theoretical formalism to study steady state information
transmission in type 1 coherent feed-forward loop motif with an additive signal
integration mechanism. Our construct allows a two-step cascade to be slowly
transformed into a bifurcation network via a feed-forward loop which is a
prominent network motif. Utilizing a Gaussian framework, we show that the
feed-forward loop motif harnesses the maximum amount of Shannon mutual
information fractions constructed between the input signaling species and the
final gene product and also between the two downstream gene products. Our
findings may explain why a feed-forward loop is naturally selected by the force
of evolution as a motif in bacterial transcription control network while the
two-step cascade and bifurcation network are not.
Low-Complexity Leakage-based Secure Precise Wireless Transmission with Hybrid Beamforming
Date: 2019-10-28 00:00:00

In conventional secure precise wireless transmission (SPWT), fully digital
beamforming (FDB) has a high secrecy performance in transmit antenna system,
but results in a huge RF-chain circuit budget for medium-scale and large-scale
systems. To reduce the complexity, this letter considers a hybrid digital and
analog (HDA) structure with random frequency mapped into the RF-chains to
achieve SPWT. Then, a hybrid SPWT scheme based on maximizing
signal-to-leakage-and-noise ratio (SLNR) and
artificial-noise-to-leakage-and-noise ratio (ANLNR) (M-SLNR-ANLNR) is proposed.
Compared to the FDB scheme, the proposed scheme reduces the circuit budget with
low computational complexity and comparable secrecy performance.
Testing Forecast Rationality for Measures of Central Tendency
Authors: Timo Dimitriadis, Andrew J. Patton, Patrick W. Schmidt
Date: 2019-10-28 00:00:00
Tasks: Survey

Rational respondents to economic surveys may report as a point forecast any measure of the central tendency of their (possibly latent) predictive distribution, for example the mean, median, mode, or any convex combination thereof. We propose tests of forecast rationality when the measure of central tendency used by the respondent is unknown. We overcome an identification problem that arises when the measures of central tendency are equal or in a local neighborhood of each other, as is the case for (exactly or nearly) symmetric distributions. As a building block, we also present novel tests for the rationality of mode forecasts. We apply our tests to income forecasts from the Federal Reserve Bank of New York's Survey of Consumer Expectations. We find these forecasts are rationalizable as mode forecasts, but not as mean or median forecasts. We also find heterogeneity in the measure of centrality used by respondents when stratifying the sample by past income, age, job stability, and survey experience.