pwc-archive/papers-with-abstracts huggingface dataset:
Cross lingual transfer learning for zero-resource domain adaptation
Date: 2019-10-29 00:00:00
Tasks: Acoustic Modelling, Cross-Lingual Transfer, Domain Adaptation, Transfer Learning

We propose a method for zero-resource domain adaptation of DNN acoustic
models, for use in low-resource situations where the only in-language training
data available may be poorly matched to the intended target domain. Our method
uses a multi-lingual model in which several DNN layers are shared between
languages. This architecture enables domain adaptation transforms learned for
one well-resourced language to be applied to an entirely different low-resource
language. First, to develop the technique we use English as a well-resourced
language and take Spanish to mimic a low-resource language. Experiments in
domain adaptation between the conversational telephone speech (CTS) domain and
broadcast news (BN) domain demonstrate a 29% relative WER improvement on
Spanish BN test data by using only English adaptation data. Second, we
demonstrate the effectiveness of the method for low-resource languages with a
poor match to the well-resourced language. Even in this scenario, the proposed
method achieves relative WER improvements of 18-27% by using solely English
data for domain adaptation. Compared to other related approaches based on
multi-task and multi-condition training, the proposed method is able to better
exploit well-resource language data for improved acoustic modelling of the
low-resource target domain.
A New Atomic Norm for DOA Estimation With Gain-Phase Errors
Authors: Peng Chen, Zhimin Chen, Zhenxin Cao, Xianbin Wang
Date: 2019-10-05 00:00:00

The problem of direction of arrival (DOA) estimation has been studied for decades as an essential technology in enabling radar, wireless communications, and array signal processing related applications. In this paper, the DOA estimation problem in the scenario with gain-phase errors is considered, and a sparse model is formulated by exploiting the signal sparsity in the spatial domain. By proposing a new atomic norm, named as GP-ANM, an optimization method is formulated via deriving a dual norm of GP-ANM. Then, the corresponding semidefinite program (SDP) is given to estimate the DOA efficiently, where the SDP is obtained based on the Schur complement. Moreover, a regularization parameter is obtained theoretically in the convex optimization problem. Simulation results show that the proposed method outperforms the existing methods, including the subspace-based and sparse-based methods in the scenario with gain-phase errors.
A Note on Species Richness and the Variance of Epidemic Severity
Date: 2020-01-20 00:00:00
Tasks: Diversity

The commonly observed negative correlation between the number of species in
an ecological community and disease risk, typically referred to as "the
dilution effect", has received a substantial amount of attention over the past
decade. Attempts to test this relationship experimentally have revealed that,
in addition to the mean disease risk decreasing with species number, so too
does the variance of disease risk. This is referred to as the "variance
reduction effect", and has received relatively little attention in the
disease-diversity literature. Here, we set out to clarify and quantify some of
these relationships in an idealized model of a randomly assembled multi-species
community undergoing an epidemic. We specifically investigate the variance of
the community disease reproductive ratio, a multi-species extension of the
basic reproductive ratio R_0, for a family of random-parameter meta-community
SIR models, and show how the variance of community $R_0$ varies depending on
whether transmission is density or frequency-dependent. We finally outline
areas of further research on how changes in variance affect transmission
dynamics in other systems.
Intelligent Reflecting Surface Aided Multi-User Millimeter-Wave Communications for Coverage Enhancement
Authors: Yashuai Cao, Tiejun Lv, Wei Ni
Date: 2019-10-06 00:00:00

Intelligent reflecting surface (IRS) is envisioned as a promising solution for controlling radio propagation environments in future wireless systems. In this paper, we propose a distributed intelligent reflecting surface (IRS) assisted multi-user millimeter wave (mmWave) system, where IRSs are exploited to enhance the mmWave signal coverage when direct links between base station and users are unavailable. First, a joint active and passive beamforming problem is established for weighted sum-rate maximization. Then, an alternating iterative algorithm with closed-form expressions is proposed to tackle the challenging non-convex problem, thereby decoupling the active and passive beamforming variables. Moreover, we design a constraint relaxation technique to address the unit modulus constraints pertaining to the IRS. Numerical results demonstrate that the distributed IRS can potentially enhance the communication performance of existing wireless systems.
Storage or No Storage: Duopoly Competition Between Renewable Energy Suppliers in a Local Energy Market
Date: 2019-10-13 00:00:00

This paper studies the duopoly competition between renewable energy suppliers
with or without energy storage in a local energy market. The storage investment
brings the benefits of stabilizing renewable energy suppliers' outputs, but it
also leads to substantial investment costs as well as some surprising changes
in the market outcome. To study the equilibrium decisions of storage investment
in the renewable energy suppliers' competition, we model the interactions
between suppliers and consumers using a three-stage game-theoretic model. In
Stage I, at the beginning of the investment horizon, suppliers decide whether
to invest in storage. Once such decisions have been made, in the day-ahead
market of each day, suppliers decide on their bidding prices and quantities in
Stage II, based on which consumers decide the electricity quantity purchased
from each supplier in Stage III. In the real-time market, a supplier is
penalized if his actual generation falls short of his commitment. We
characterize a price-quantity competition equilibrium of Stage II, and we
further characterize a storage-investment equilibrium in Stage I incorporating
electricity-selling revenue and storage cost. Counter-intuitively, we show that
the uncertainty of renewable energy without storage investment can lead to
higher supplier profits compared with the stable generations with storage
investment due to the reduced market competition under random energy
generation. Simulations further illustrate results due to the market
competition. For example, a higher penalty for not meeting the commitment, a
higher storage cost, or a lower consumer demand can sometimes increase a
supplier's profit. We also show that although storage investment can increase a
supplier 's profit, the first-mover supplier who invests in storage may benefit
less than the free-rider competitor who chooses not to invest.
Joint Grid Topology Reconfiguration and Design of Watt-VAR Curves for DERs
Date: 2020-02-22 00:00:00

Operators can now remotely control switches and update the control settings
for voltage regulators and distributed energy resources (DERs), thus unleashing
the network reconfiguration opportunities to improve efficiency. Aligned to
this direction, this work puts forth a comprehensive toolbox of optimization
models leveraging the control capabilities of smart grid assets. We put forth
detailed yet practical models to capture the operation of locally and remotely
controlled regulators, and customize the watt-var DER control curves complying
with the IEEE 1547.8 mandates. Maintaining radiality is a key requirement
germane to various feeder optimization tasks. This requirement is accomplished
here through an intuitive and provably correct formulation. The developed
toolbox is put into action to reconfigure a grid for minimizing losses using
real-world data on a benchmark feeder. The results corroborate that optimal
topologies vary across the day and coordinating DERs and regulators is critical
during periods of steep net load changes.
Absolutely Robust Controllers for Chemical Reaction Networks
Authors: Jinsu Kim, German Enciso
Date: 2019-10-08 00:00:00

In this work, we design a type of controller that consists of adding a specific set of reactions to an existing mass-action chemical reaction network in order to control a target species. This set of reactions is effective for both deterministic and stochastic networks, in the latter case controlling the mean as well as the variance of the target species. We employ a type of network property called absolute concentration robustness (ACR). We provide applications to the control of a multisite phosphorylation model as well as a receptor-ligand signaling system. For this framework, we use the so-called deficiency zero theorem from chemical reaction network theory as well as multiscaling model reduction methods. We show that the target species has approximately Poisson distribution with the desired mean. We further show that ACR controllers can bring robust perfect adaptation to a target species and are complementary to a recently introduced antithetic feedback controller used for stochastic chemical reactions.
Experimental Validation of Feedback Optimization in Power Distribution Grids
Authors: Lukas Ortmann, Adrian Hauswirth, Ivo Caduff, Florian Dörfler, Saverio Bolognani
Date: 2019-10-08 00:00:00
Tasks: State Estimation

We consider the problem of controlling the voltage of a distribution feeder using the reactive power capabilities of inverters. On a real distribution grid, we compare the local Volt/VAr droop control recommended in recent grid codes, a centralized dispatch based on optimal power flow (OPF) programming, and a feedback optimization (FO) controller that we propose. The local droop control yields suboptimal regulation, as predicted analytically. The OPF-based dispatch strategy requires an accurate grid model and measurement of all loads on the feeder in order to achieve proper voltage regulation. However, in the experiment, the OPF-based strategy violates voltage constraints due to inevitable model mismatch and uncertainties. Our proposed FO controller, on the other hand, satisfies the constraints and does not require load measurements or any grid state estimation. The only needed model knowledge is the sensitivity of the voltages with respect to reactive power, which can be obtained from data. As we show, an approximation of these sensitivities is also sufficient, which makes the approach essentially model-free, easy to tune, compatible with the current sensing and control infrastructure, and remarkably robust to measurement noise. We expect these properties to be fundamental features of FO for power systems and not specific to Volt/VAr regulation or to distribution grids.
Experimental Validation of Fully Distributed Peer-to-Peer Optimal Voltage Control with Minimal Model Requirements
Authors: Lukas Ortmann, Alexander Prostejovsky, Kai Heussen, Saverio Bolognani
Date: 2019-10-08 00:00:00

This paper addresses the problem of voltage regulation in a power distribution grid using the reactive power injections of grid-connected power inverters. We first discuss how purely local voltage control schemes cannot regulate the voltages within a desired range under all circumstances and may even yield detrimental control decisions. Communication and, through that, coordination are therefore needed. On the other hand, short-range peer-to-peer communication and knowledge of electric distances between neighbouring controllers are sufficient for this task. We implement such a peer-to-peer controller and test it on a 400~V distribution feeder with asynchronous communication channels, confirming its viability on real-life systems. Finally, we analyze the scalability of this approach with respect to the number of agents on the feeder that participate in the voltage regulation task.
Estimating and decomposing most productive scale size in parallel DEA networks with shared inputs: A case of China's Five-Year Plans
Date: 2019-10-10 00:00:00
Tasks: Management

Attaining the optimal scale size of production systems is an issue frequently
found in the priority questions on management agendas of various types of
organizations. Determining the most productive scale size (MPSS) allows the
decision makers not only to know the best scale size that their systems can
achieve but also to tell the decision makers how to move the inefficient
systems onto the MPSS region. This paper investigates the MPSS concept for
production systems consisting of multiple subsystems connected in parallel.
First, we propose a relational model where the MPSS of the whole system and the
internal subsystems are measured in a single DEA implementation. Then, it is
proved that the MPSS of the system can be decomposed as the weighted sum of the
MPSS of the individual subsystems. The main result is that the system is
overall MPSS if and only if it is MPSS in each subsystem. MPSS decomposition
allows the decision makers to target the non-MPSS subsystems so that the
necessary improvements can be readily suggested. An application of China's
Five-Year Plans (FYPs) with shared inputs is used to show the applicability of
the proposed model for estimating and decomposing MPSS in parallel network DEA.
Industry and Agriculture sectors are selected as two parallel subsystems in the
FYPs. Interesting findings have been noticed. Using the same amount of
resources, the Industry sector had a better economic scale than the Agriculture
sector. Furthermore, the last two FYPs, 11th and 12th, were the perfect two
FYPs among the others.
Political Openness and Armed Conflict: Evidence from Local Councils in Colombia
Date: 2020-08-11 00:00:00
Tasks: regression

In this paper, I empirically investigate how the openness of political
institutions to diverse representation can impact conflict-related violence. By
exploiting plausibly exogenous variations in the number of councillors in
Colombian municipalities, I develop two sets of results. First, regression
discontinuity estimates show that larger municipal councils have a considerably
greater number of political parties with at least one elected representative. I
interpret this result as evidence that larger municipal councils are more open
to diverse political participation. The estimates also reveal that
non-traditional parties are the main beneficiaries of this greater political
openness. Second, regression discontinuity estimates show that political
openness substantially decreases conflict-related violence, namely the killing
of civilian non-combatants. By exploiting plausibly exogenous variations in
local election results, I show that the lower level of political violence stems
from greater participation by parties with close links to armed groups. Using
data about the types of violence employed by these groups, and representation
at higher levels of government, I argue that armed violence has decreased not
because of power-sharing arrangements involving armed groups linked to the
parties with more political representation, but rather because armed groups
with less political power and visibility are deterred from initiating certain
types of violence.
Does inter-organellar proteostasis impact yeast quality and performance during beer fermentation?
Authors: Bianca de Paula Telini, Marcelo Menoncin, Diego Bonatto
Date: 2019-10-09 00:00:00

During beer production, yeast generate ethanol that is exported to the extracellular environment where it accumulates. Depending on the initial carbohydrate concentration in the wort a high concentration of ethanol can be achieved in beer, often higher than 20% (v/v). It is noteworthy that the effects of elevated ethanol concentrations generated during beer fermentation resemble those of heat shock stress, with similar responses observed in both situations, such as the activation of proteostasis and protein quality control mechanisms in different cell compartments, including endoplasmic reticulum (ER), mitochondria, and cytosol. Despite the extensive published molecular and biochemical data regarding the roles of proteostasis in different organelles of yeast cells, little is known about how this mechanism impacts beer fermentation and how different proteostasis mechanisms found in ER, mitochondria, and cytosol communicate with each other during ethanol/fermentative stress. Supporting this integrative view, transcriptome data analysis was applied using publicly available information for a lager yeast strain grown under in beer production conditions. The transcriptome data indicated upregulation of genes that encode chaperones, co-chaperones, unfolded protein response elements in ER and mitochondria, ubiquitin ligases, proteasome components, N-glycosylation quality control pathway proteins, and components of processing bodies (p-bodies) and stress granules (SGs) during lager beer fermentation. Thus, the main purpose of this hypothesis and theory manuscript is to provide a concise picture of how inter-organellar proteostasis mechanisms are connected with one another and with biological processes that may modulate the viability and/or vitality of yeast populations during HG/VHG beer fermentation and serial repitching.
Model predictive control design for dynamical systems learned by Long Short-Term Memory Networks
Date: 2020-08-27 00:00:00
Tasks: Model Predictive Control

This paper analyzes the stability-related properties of Long Short-Term
Memory (LSTM) networks and investigates their use as the model of the plant in
the design of Model Predictive Controllers (MPC). First, sufficient conditions
guaranteeing the Input-to-State stability (ISS) and Incremental Input-to-State
stability (dISS) of LSTM are derived. These properties are then exploited to
design an observer with guaranteed convergence of the state estimate to the
true one. Such observer is then embedded in a MPC scheme solving the tracking
problem. The resulting closed-loop scheme is proved to be asymptotically
stable. The training algorithm and control scheme are tested numerically on the
simulator of a pH reactor, and the reported results confirm the effectiveness
of the proposed approach.
Embedding Lithium-ion Battery Scrapping Criterion and Degradation Model in Optimal Operation of Peak-shaving Energy Storage
Authors: Qingchun Hou, Yanghao Yu, Ershun Du, Hongjie He, Ning Zhang, Chongqing Kang, Guojing Liu, Huan Zhu
Date: 2019-10-04 00:00:00

Lithium-ion battery systems have been used in practical power systems for peak-shaving, demand response, and frequency regulation. However, a lithium-ion battery is degrading while cycling and would be scrapped when the capacity reduces to a certain threshold (e.g. 80%). Such scrapping criterion may not explore the maximum benefit from the battery storage. In this paper, we propose a novel scrapping criterion for peak-shaving energy storage based on battery efficiency, time-of-use price, and arbitrage benefit. A new battery life model with scrapping parameters is then derived using this criterion. Embedded with the life model, an optimal operation method for peak-shaving energy storage system is presented. The results of case study show that the operation method could maximize the benefits of peak-shaving energy storage while delaying battery degradation. Compared with the traditional 80% capacity-based scrapping criterion, our efficiency-based scrapping criterion can significantly improve the lifetime benefit of the battery.
Efficiently and Effectively Recognizing Toricity of Steady State Varieties
Authors: Dima Grigoriev, Alexandru Iosif, Hamid Rahkooy, Thomas Sturm, Andreas Weber
Date: 2019-10-09 00:00:00

We consider the problem of testing whether the points in a complex or real variety with non-zero coordinates form a multiplicative group or, more generally, a coset of a multiplicative group. For the coset case, we study the notion of shifted toric varieties which generalizes the notion of toric varieties. This requires a geometric view on the varieties rather than an algebraic view on the ideals. We present algorithms and computations on 129 models from the BioModels repository testing for group and coset structures over both the complex numbers and the real numbers. Our methods over the complex numbers are based on Gr\"obner basis techniques and binomiality tests. Over the real numbers we use first-order characterizations and employ real quantifier elimination. In combination with suitable prime decompositions and restrictions to subspaces it turns out that almost all models show coset structure. Beyond our practical computations, we give upper bounds on the asymptotic worst-case complexity of the corresponding problems by proposing single exponential algorithms that test complex or real varieties for toricity or shifted toricity. In the positive case, these algorithms produce generating binomials. In addition, we propose an asymptotically fast algorithm for testing membership in a binomial variety over the algebraic closure of the rational numbers.
A Gradual Takeover Strategy of the Active Safety System
Date: 2020-11-12 00:00:00
Tasks: Model Predictive Control

A gradual takeover strategy is proposed, in which the dynamic driving
privilege assignment in real-time and the driving privilege gradual handover
are realized. Firstly, the driving privilege assignment based on the risk level
is achieved. The naturalistic driving data is applied to study the driver
behavior during danger. TTC (time to collision) is defined as an obvious risk
measure, whereas the time before the host vehicle has to brake assuming that
the target vehicle is braking is defined as the potential risk measure, i.e.
the time margin (TM). A risk assessment algorithm is proposed based on the
obvious risk and potential risk. Secondly, the driving privilege gradual
handover is realized. The non-cooperative MPC (model predictive control) is
employed to resolve the conflicts between the driver and active safety system.
The naturalistic driving data are applied to verify the effectiveness of the
risk assessment algorithm, and the risk assessment algorithm performs better
than TTC in the ROC (receiver operating characteristic). It is identified that
the Nash equilibrium of the non-cooperative MPC can be achieved by using a
non-iterative method. The driving privilege gradual handover is realized by
using the confidence matrixes updating. The simulation verification shows that
the gradual takeover strategy can achieve the driving privilege gradual
handover between the driver and active safety system.