pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks
Date: 2019-11-21 00:00:00
Tasks: Edge-computing, Scheduling

In this paper, we consider a hybrid mobile edge computing (H-MEC) platform,
which includes ground stations (GSs), ground vehicles (GVs) and unmanned aerial
vehicle (UAVs), all with mobile edge cloud installed to enable user equipments
(UEs) or Internet of thing (IoT) devices with intensive computing tasks to
offload. Our objective is to obtain an online offloading algorithm to minimize
the energy consumption of all the UEs, by jointly optimizing the positions of
GVs and UAVs, user association and resource allocation in real-time, while
considering the dynamic environment. To this end, we propose a hybrid deep
learning based online offloading (H2O) framework where a large-scale path-loss
fuzzy c-means (LSFCM) algorithm is first proposed and used to predict the
optimal positions of GVs and UAVs. Secondly, a fuzzy membership matrix U-based
particle swarm optimization (U-PSO) algorithm is applied to solve the mixed
integer nonlinear programming (MINLP) problems and generate the sample datasets
for the deep neural network (DNN) where the fuzzy membership matrix can capture
the small-scale fading effects and the information of mutual interference.
Thirdly, a DNN with the scheduling layer is introduced to provide user
association and computing resource allocation under the practical latency
requirement of the tasks and limited available computing resource of H-MEC. In
addition, different from traditional DNN predictor, we only input one UE
information to the DNN at one time, which will be suitable for the scenarios
where the number of UE is varying and avoid the curse of dimensionality in DNN.
Estimating Time-Varying Applied Current in the Hodgkin-Huxley Model
Date: 2019-11-21 00:00:00

The classic Hodgkin-Huxley model is widely used for understanding the
electrophysiological dynamics of a single neuron. While applying a constant
current to the system results in a single voltage spike, it is possible to
produce more interesting dynamics by applying time-varying currents, which may
not be experimentally measurable. The aim of this work is to estimate
time-varying applied currents of different deterministic forms given noisy
voltage data. In particular, we utilize an augmented ensemble Kalman filter
with parameter tracking to estimate four different deterministic applied
currents, analyzing how the model dynamics change in each case. We test the
efficiency of the parameter tracking algorithm in this setting by exploring the
effects of changing the standard deviation of the parameter drift and the
frequency of data available on the resulting time-varying applied current
estimates and related uncertainty.
A Fully Convolutional Network for MR Fingerprinting
Date: 2019-11-22 00:00:00
Tasks: Dimensionality Reduction, Magnetic Resonance Fingerprinting

Magnetic Resonance Fingerprinting (MRF) methods typically rely on dictionary
matching to map the temporal MRF signals to quantitative tissue parameters.
These methods suffer from heavy storage and computation requirements as the
dictionary size grows. To address these issues, we proposed an end to end fully
convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly
employ linear dimensionality reduction and then use neural network to project
the data into the tissue parameters manifold space. Experiments on the MAGIC
data demonstrate the effectiveness of the method.
Time-Domain Multi-modal Bone/air Conducted Speech Enhancement
Authors: Cheng Yu, Kuo-Hsuan Hung, Syu-Siang Wang, Szu-Wei Fu, Yu Tsao, Jeih-weih Hung
Date: 2019-11-22 00:00:00
Tasks: Ensemble Learning, Speech Enhancement

Previous studies have proven that integrating video signals, as a complementary modality, can facilitate improved performance for speech enhancement (SE). However, video clips usually contain large amounts of data and pose a high cost in terms of computational resources and thus may complicate the SE system. As an alternative source, a bone-conducted speech signal has a moderate data size while manifesting speech-phoneme structures, and thus complements its air-conducted counterpart. In this study, we propose a novel multi-modal SE structure in the time domain that leverages bone- and air-conducted signals. In addition, we examine two ensemble-learning-based strategies, early fusion (EF) and late fusion (LF), to integrate the two types of speech signals, and adopt a deep learning-based fully convolutional network to conduct the enhancement. The experiment results on the Mandarin corpus indicate that this newly presented multi-modal (integrating bone- and air-conducted signals) SE structure significantly outperforms the single-source SE counterparts (with a bone- or air-conducted signal only) in various speech evaluation metrics. In addition, the adoption of an LF strategy other than an EF in this novel SE multi-modal structure achieves better results.
Dissipativity in economic model predictive control: beyond steady-state optimality
Date: 2019-11-22 00:00:00
Tasks: Model Predictive Control, Survey

This chapter provides a concise survey on different dissipativity conditions
that have appeared in the literature on economic model predictive control and
discusses their decisive role in this context.
6-O-glucose palmitate synthesis with lipase: Investigation of some key parameters
Date: 2019-11-22 00:00:00

Fatty acid sugar esters represent an important class of non-ionic bio-based
surfactants. They can be synthesized from vinyl fatty acids and sugars with
enzyme as a catalyst. Herein, the influence of the solvent, the lipase and the
temperature on a model reaction between vinyl palmitate and glucose via
enzymatic catalysis has been investigated and the reaction conditions
optimized. Full conversion into 6-O-glucose palmitate was reached in 40 hours
in acetonitrile starting from a reactant ratio 1:1, at only 5%-wt loading of
lipase from Candida antarctica B (CALB) without the presence of molecular
sieves.
Subjective Logic-based Identification of Markov Chains and Its Application to CAV's Safety
Date: 2019-11-22 00:00:00

A reliable estimation of the communication chan-nel which connects automated
vehicles is an important steptowards the safety of connected and automated
vehicles. The communication channel is usually modeled as Markov chain with
slowly time-varying transition rates and is identifiedthrough statistics on the
observed state transitions. However, the classical identification approach
lacks a measure on how reliable the identification results, and thus the
channel, are. In this work, we propose an identification method, based onthe
subjective logic theory, which features such a reliability measure in terms of
statistical uncertainty. We demonstrate through simulations that the proposed
method is capable of quickly responding to parameter changes. Furthermore, it
is shown that the transition rates of the Markov chain are tracked with high
accuracy. Finally, we validate our results by a real-world experiment.
Collectivised Pension Investment with Homogeneous Epstein-Zin Preferences
Authors: John Armstrong, Cristin Buescu
Date: 2019-11-22 00:00:00

In a collectivised pension fund, investors agree that any money remaining in the fund when they die can be shared among the survivors. We compute analytically the optimal investment-consumption strategy for a fund of $n$ identical investors with homogeneous Epstein--Zin preferences, investing in the Black--Scholes market in continuous time but consuming in discrete time. Our result holds for arbitrary mortality distributions. We also compute the optimal strategy for an infinite fund of investors, and prove the convergence of the optimal strategy as $n\to \infty$. The proof of convergence shows that effective strategies for inhomogeneous funds can be obtained using the optimal strategies found in this paper for homogeneous funds, using the results of [2]. We find that a constant consumption strategy is suboptimal even for infinite collectives investing in markets where assets provide no return so long as investors are "satisfaction risk-averse." This suggests that annuities and defined benefit investments will always be suboptimal investments. We present numerical results examining the importance of the fund size, $n$, and the market parameters.
Speculative Trading, Prospect Theory and Transaction Costs
Authors: Alex S. L. Tse, Harry Zheng
Date: 2019-11-22 00:00:00
Tasks: Friction

A speculative agent with Prospect Theory preference chooses the optimal time to purchase and then to sell an indivisible risky asset to maximize the expected utility of the round-trip profit net of transaction costs. The optimization problem is formulated as a sequential optimal stopping problem and we provide a complete characterization of the solution. Depending on the preference and market parameters, the optimal strategy can be "buy and hold", "buy low sell high", "buy high sell higher" or "no trading". Behavioral preference and market friction interact in a subtle way which yields surprising implications on the agent's trading patterns. For example, increasing the market entry fee does not necessarily curb speculative trading, but instead it may induce a higher reference point under which the agent becomes more risk-seeking and in turn is more likely to trade.
Asset Price Bubbles in market models with proportional transaction costs
Authors: Francesca Biagini, Thomas Reitsam
Date: 2019-11-22 00:00:00
Tasks: Position

We study asset price bubbles in market models with proportional transaction costs $\lambda\in (0,1)$ and finite time horizon $T$ in the setting of [49]. By following [28], we define the fundamental value $F$ of a risky asset $S$ as the price of a super-replicating portfolio for a position terminating in one unit of the asset and zero cash. We then obtain a dual representation for the fundamental value by using the super-replication theorem of [50]. We say that an asset price has a bubble if its fundamental value differs from the ask-price $(1+\lambda)S$. We investigate the impact of transaction costs on asset price bubbles and show that our model intrinsically includes the birth of a bubble.
One-Bit Radar Processing with Time-Varying Sampling Thresholds
Date: 2019-11-22 00:00:00
Tasks: parameter estimation

Target parameter estimation in active sensing, and particularly radar signal
processing, is a long-standing problem that has been studied extensively. In
this paper, we propose a novel approach for target parameter estimation in
cases where one-bit analog-to-digital-converters (ADCs), also known as signal
comparators with time-varying thresholds, are employed to sample the received
radar signal instead of high-resolution ADCs. The considered problem has
potential applications in the design of inexpensive radar and sensing devices
in civilian applications, and can likely pave the way for future radar systems
employing low-resolution ADCs for faster sampling and high-resolution target
determination. We formulate the target estimation as a multivariate
weighted-least-squares optimization problem that can be solved in a cyclic
manner. Numerical results are provided to exhibit the effectiveness of the
proposed algorithms.
PPSM: A Privacy-Preserving Stackelberg Mechanism: Privacy Guarantees for the Coordination of Sequential Electricity and Gas Markets
Date: 2019-11-22 00:00:00
Tasks: Privacy Preserving

This paper introduces a differentially private mechanism to protect the
information exchanged during the coordination of the sequential market-clearing
of electricity and natural gas systems. The coordination between these
sequential and interdependent markets represents a classic Stackelberg game and
relies on the exchange of sensitive information between the system agents,
including the supply and demand bids in each market or the characteristics of
the systems. The paper is motivated by the observation that traditional
differential privacy mechanisms are unsuitable for the problem of interest: The
perturbation introduced by these mechanisms fundamentally changes the
underlying optimization problem and even leads to unsatisfiable instances. To
remedy such limitation, the paper introduces the Privacy-Preserving Stackelberg
Mechanism (PPSM), a framework that enforces the notions of consistency and
fidelity of the privacy-preserving information to the original problem
objective. The PPSM has strong properties: It complies with the notion of
differential privacy and ensures that the outcomes of the privacy-preserving
coordination mechanisms are close-to-optimality for each agent. The fidelity
property is analyzed by providing theoretical guarantees on the cost of privacy
of PPSM and experimental results on several gas and electricity market
benchmarks based on a real case study demonstrate the effectiveness of the
approach.
Uncovering differential identifiability in network properties of human brain functional connectomes
Date: 2019-11-22 00:00:00

The Identifiability Framework (If) has been shown to improve differential
identifiability (reliability across-sessions and -sites, and differentiability
across-subjects) of functional connectomes for a variety of fMRI tasks. But
having a robust single session/subject functional connectome is just the
starting point to subsequently assess network properties for characterizing
properties of integration, segregation and communicability, among others.
Naturally, one wonders if uncovering identifiability at the connectome level
also uncovers identifiability on the derived network properties. This also
raises the question of where to apply the If framework: on the connectivity
data or directly on each network measurement? Our work answers these questions
by exploring the differential identifiability profiles of network measures when
If is applied on 1) the functional connectomes, and 2) directly on derived
network measurements. Results show that improving across-session reliability of
FCs also improves reliability of derived network measures. We also find that,
for specific network properties, application of If directly on network
properties is more effective. Finally, we discover that applying the framework,
either way, increases task sensitivity of network properties. At a time when
the neuroscientific community is focused on subject-level inferences, this
framework is able to uncover FC fingerprints, which propagates to derived
network properties.
Improved motion correction for functional MRI using an omnibus regression model
Date: 2020-01-21 00:00:00
Tasks: Functional Connectivity, regression

Head motion during functional Magnetic Resonance Imaging acquisition can
significantly contaminate the neural signal and introduce spurious,
distance-dependent changes in signal correlations. This can heavily confound
studies of development, aging, and disease. Previous approaches to suppress
head motion artifacts have involved sequential regression of nuisance
covariates, but this has been shown to reintroduce artifacts. We propose a new
motion correction pipeline using an omnibus regression model that avoids this
problem by simultaneously regressing out multiple artifacts using the best
performing algorithms to estimate each artifact. We quantitatively evaluate its
motion artifact suppression performance against sequential regression pipelines
using a large heterogeneous dataset (n=151) which includes high-motion subjects
and multiple disease phenotypes. The proposed concatenated regression pipeline
significantly reduces the association between head motion and functional
connectivity while significantly outperforming the traditional sequential
regression pipelines in eliminating distance-dependent head motion artifacts.
Coded LoRa Frame Error Rate Analysis
Date: 2019-11-22 00:00:00

In this work, we study the coded frame error rate (FER) of LoRa under
additive white Gaussian noise (AWGN) and under carrier frequency offset (CFO).
To this end, we use existing approximations for the bit error rate (BER) of the
LoRa modulation under AWGN and we present a FER analysis that includes the
channel coding, interleaving, and Gray mapping of the LoRa physical layer. We
also derive the LoRa BER under carrier frequency offset and we present a
corresponding FER analysis. We compare the derived frame error rate expressions
to Monte Carlo simulations to verify their accuracy.
Biological Regulatory Networks are Minimally Frustrated
Date: 2019-11-22 00:00:00

Characterization of the differences between biological and random networks
can reveal the design principles that enable the robust realization of crucial
biological functions including the establishment of different cell types.
Previous studies, focusing on identifying topological features that are present
in biological networks but not in random networks, have, however, provided few
functional insights. We use a Boolean modeling framework and ideas from spin
glass literature to identify functional differences between five real
biological networks and random networks with similar topological features. We
show that minimal frustration is a fundamental property that allows biological
networks to robustly establish cell types and regulate cell fate choice, and
this property can emerge in complex networks via Darwinian evolution. The study
also provides clues regarding how the regulation of cell fate choice can go
awry in a disease like cancer and lead to the emergence of aberrant cell types.
Omega and Sharpe ratio
Authors: Eric Benhamou, Beatrice Guez, Nicolas Paris1
Date: 2019-10-15 00:00:00

Omega ratio, defined as the probability-weighted ratio of gains over losses at a given level of expected return, has been advocated as a better performance indicator compared to Sharpe and Sortino ratio as it depends on the full return distribution and hence encapsulates all information about risk and return. We compute Omega ratio for the normal distribution and show that under some distribution symmetry assumptions, the Omega ratio is oversold as it does not provide any additional information compared to Sharpe ratio. Indeed, for returns that have elliptic distributions, we prove that the optimal portfolio according to Omega ratio is the same as the optimal portfolio according to Sharpe ratio. As elliptic distributions are a weak form of symmetric distributions that generalized Gaussian distributions and encompass many fat tail distributions, this reduces tremendously the potential interest for the Omega ratio.
Design and Autonomous Stabilization of a Ballistically Launched Multirotor
Date: 2020-05-18 00:00:00

Aircraft that can launch ballistically and convert to autonomous, free flying
drones have applications in many areas such as emergency response, defense, and
space exploration, where they can gather critical situational data using
onboard sensors. This paper presents a ballistically launched, autonomously
stabilizing multirotor prototype (SQUID, Streamlined Quick Unfolding
Investigation Drone) with an onboard sensor suite, autonomy pipeline, and
passive aerodynamic stability. We demonstrate autonomous transition from
passive to vision based, active stabilization, confirming the ability of the
multirotor to autonomously stabilize after a ballistic launch in a GPS denied
environment.
Dynamic Control of a Fiber Manufacturing Process using Deep Reinforcement Learning
Date: 2019-11-23 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

This paper presents a model-free deep reinforcement learning (DRL) approach for controlling a fiber drawing system. The custom DRL-based control system predictively regulates fiber diameter and produces a fiber with a desired, constant or non-constant, diameter trajectory, i.e. diameter variation along the fiber length. Physical models of the system are not used. The system was trained and tested on a compact fiber drawing system, which has non-linear delayed dynamics and stochastic behaviors. For a reference trajectory with random step changes, after 1 hour of training, the DRL controller showed the same root mean squared error (RMSE) as an optimized PI controller; after 3 hours of training, it achieved the performance of a quadratic dynamic matrix controller (QDMC). While the PI feedback controller showed 3.5 seconds of time lag in a step response, the DRL controller showed less than a second of time lag. Controller performance tests on trajectories not used in the training process are conducted; for a sine sweep reference trajectory, the DRL controller maintained an RMSE under 40 um up to a frequency of 45 mHz, compared to 25 mHz for QDMC.
New Transceiver Designs for Interleaved Frequency Division Multiple Access
Authors: Soung Chang Liew, Yulin Shao
Date: 2019-11-23 00:00:00

This paper puts forth a class of new transceiver designs for interleaved frequency division multiple access (IFDMA) systems. These transceivers are significantly less complex than conventional IFDMA transceiver. The simple new designs are founded on a key observation that multiplexing and demultiplexing of IFDMA data streams of different sizes are coincident with the IFFTs and FFTs of different sizes embedded within the Cooley-Tukey recursive FFT decomposition scheme. For flexible resource allocation, this paper puts forth a new IFDMA resource allocation framework called Multi-IFDMA, in which a user can be allocated multiple IFDMA streams. Our new transceivers are unified designs in that they can be used in conventional IFDMA as well as multi-IFDMA systems. Two other well-known multiple-access schemes are localized FDMA (LFDMA) and orthogonal FDMA (OFDMA). In terms of flexibility in resource allocation, Multi-IFDMA, LFDMA, and OFDMA are on an equal footing. With our new transceiver designs, however, IFDMA has the following advantages (besides other known advantages not due to our new transceiver designs): 1) IFDMA/Multi-IFDMA transceivers are significantly less complex than LFDMA transceivers; in addition, IFDMA/Multi-IFDMA has better Peak-to-Average Power Ratio (PAPR) than LFDMA; 2) IFDMA/Multi-IFDMA transceivers and OFDMA transceivers are comparable in complexity; but IFDMA/Multi-IFDMA has significantly better PAPR than OFDMA.
Interarea Oscillations & Chimera in Power Systems
Date: 2019-11-23 00:00:00

This paper proposes a novel second order mathematical model in the Kuramoto
framework to simulate and study low frequency oscillations in power systems.
This model facilitates better understanding of the complex dynamics of a power
network. A standard four generator power system with all-to-all connectivity is
considered and results obtained from the proposed model are verified. It is
shown, that the model simulates various properties related to low frequency
oscillations in power systems which presently are obtained through small-signal
analysis. Further, we provide analogy to blackouts in a power grid, by
emulating chimera behavior and thereby discuss bifurcation analysis of the
proposed model.