pwc-archive/papers-with-abstracts huggingface dataset:
Energy-Efficient Hybrid Precoding for Massive MIMO mmWave Systems With a Fully-Adaptive-Connected Structure
Date: 2019-07-23 00:00:00

This paper investigates the hybrid precoding design in millimeter-wave
(mmWave) systems with a fully-adaptive-connected precoding structure, where a
switch-controlled connection is deployed between every antenna and every radio
frequency (RF) chain. To maximally enhance the energy efficiency (EE) of hybrid
precoding under this structure, the joint optimization of switch-controlled
connections and the hybrid precoders is formulated as a large-scale
mixed-integer non-convex problem with high-dimensional power constraints. To
efficiently solve such a challenging problem, we first decouple it into a
continuous hybrid precoding (CHP) subproblem. Then, with the hybrid precoder
obtained from the CHP subproblem, the original problem can be equivalently
reformulated as a discrete connection-state (DCS) problem with only 0-1 integer
variables. For the CHP subproblem, we propose an alternating hybrid precoding
(AHP) algorithm. Then, with the hybrid precoder provided by the AHP algorithm,
we develop a matching assisted fully-adaptive hybrid precoding (MA-FAHP)
algorithm to solve the DCS problem. It is theoretically shown that the proposed
MA-FAHP algorithm always converges to a stable solution with the polynomial
complexity. Finally, simulation results demonstrate that the superior
performance of the proposed MA-FAHP algorithm in terms of EE and beampattern.
A Note on Universal Bilinear Portfolios
Authors: Alex Garivaltis
Date: 2019-07-23 00:00:00

This note provides a neat and enjoyable expansion and application of the magnificent Ordentlich-Cover theory of "universal portfolios." I generalize Cover's benchmark of the best constant-rebalanced portfolio (or 1-linear trading strategy) in hindsight by considering the best bilinear trading strategy determined in hindsight for the realized sequence of asset prices. A bilinear trading strategy is a mini two-period active strategy whose final capital growth factor is linear separately in each period's gross return vector for the asset market. I apply Cover's ingenious (1991) performance-weighted averaging technique to construct a universal bilinear portfolio that is guaranteed (uniformly for all possible market behavior) to compound its money at the same asymptotic rate as the best bilinear trading strategy in hindsight. Thus, the universal bilinear portfolio asymptotically dominates the original (1-linear) universal portfolio in the same technical sense that Cover's universal portfolios asymptotically dominate all constant-rebalanced portfolios and all buy-and-hold strategies. In fact, like so many Russian dolls, one can get carried away and use these ideas to construct an endless hierarchy of ever more dominant $H$-linear universal portfolios.
Using biochemical circuits to approximately compute log-likelihood ratio for detecting persistent signals
Authors: Chun Tung Chou
Date: 2019-07-23 00:00:00

Given that biochemical circuits can process information by using analog computation, a question is: What can biochemical circuits compute? This paper considers the problem of using biochemical circuits to distinguish persistent signals from transient ones. We define a statistical detection problem over a reaction pathway consisting of three species: an inducer, a transcription factor (TF) and a gene promoter, where the inducer can activate the TF and an active TF can bind to the gene promoter. We model the pathway using the chemical master equation so the counts of bound promoters over time is a stochastic signal. We consider the problem of using the continuous-time stochastic signal of the counts of bound promoters to infer whether the inducer signal is persistent or not. We use statistical detection theory to derive the solution to this detection problem, which is to compute the log-likelihood ratio of observing a persistent signal to a transient one. We then show, using time-scale separation and other assumptions, that this log-likelihood ratio can be approximately computed by using the continuous-time signals of the number of active TF molecules and the number of bound promoters when the input is persistent. Finally, we show that the coherent feedforward gene circuits can be used to approximately compute this log-likelihood ratio when the inducer signal is persistent.
Prosumage of solar electricity: tariff design, capacity investments, and power system effects
Date: 2019-07-23 00:00:00

We analyze how tariff design incentivizes households to invest in residential
photovoltaic and battery systems, and explore selected power sector effects. To
this end, we apply an open-source power system model featuring prosumage agents
to German 2030 scenarios. Results show that lower feed-in tariffs substantially
reduce investments in photovoltaics, yet optimal battery sizing and
self-generation are relatively robust. With increasing fixed parts of retail
tariffs, optimal battery capacities and self-generation are smaller, and
households contribute more to non-energy power sector costs. When choosing
tariff designs, policy makers should not aim to (dis-)incentivize prosumage as
such, but balance effects on renewable capacity expansion and system cost
contribution.
Rebuttal of "On Nonparametric Identification of Treatment Effects in Duration Models"
Date: 2019-07-20 00:00:00

In their IZA Discussion Paper 10247, Johansson and Lee claim that the main
result (Proposition 3) in Abbring and Van den Berg (2003b) does not hold. We
show that their claim is incorrect. At a certain point within their line of
reasoning, they make a rather basic error while transforming one random
variable into another random variable, and this leads them to draw incorrect
conclusions. As a result, their paper can be discarded.
Spatially Informed Independent Vector Analysis
Date: 2020-01-16 00:00:00
Tasks: blind source separation

We present a Maximum A Posteriori (MAP) derivation of the Independent Vector
Analysis (IVA) algorithm, a blind source separation algorithm, by incorporating
a prior over the demixing matrices, relying on a free-field model. In this way,
the outer permutation ambiguity of IVA is avoided. The resulting MAP
optimization problem is solved by deriving majorize-minimize update rules to
achieve convergence speed comparable to the well-known auxiliary function IVA
algorithm. The performance of the proposed algorithm is investigated and
compared to a benchmark algorithm using real measurements.
Voltage control of DC networks: robustness for unknown ZIP-loads
Date: 2019-07-23 00:00:00

In this paper we propose a new passivity-based control technique for DC power
networks comprising the so-called ZIP-loads, i.e., nonlinear loads with the
parallel combination of unknown constant impedance (Z), current (I) and power
(P) components. More precisely, we propose a novel passifying input and a
storage function based on the so-called mixed potential function introduced by
Brayton and Moser, leading to a novel passivity property with output
port-variable equal to the first time derivative of the voltage. Differently
from the existing results in the literature, where restrictive (sufficient)
conditions on Z, P and the voltage reference are assumed to be satisfied, we
establish a passivity property for every positive voltage reference and every
type of load. Consequently, we develop a new decentralized passivity-based
control scheme that is robust with respect to the uncertainty affecting the
ZIP-loads.
Sustainable Business Models: A Review
Date: 2019-07-23 00:00:00
Tasks: Management, Marketing

The concept of the sustainable business model describes the rationale of how
an organization creates, delivers, and captures value, in economic, social,
cultural, or other contexts, in a sustainable way. The process of sustainable
business model construction forms an innovative part of a business strategy.
Different industries and businesses have utilized sustainable business models
concept to satisfy their economic, environmental, and social goals
simultaneously. However, the success, popularity, and progress of sustainable
business models in different application domains are not clear. To explore this
issue, this research provides a comprehensive review of sustainable business
models literature in various application areas. Notable sustainable business
models are identified and further classified in fourteen unique categories, and
in every category, the progress -- either failure or success -- has been
reviewed, and the research gaps are discussed. Taxonomy of the applications
includes innovation, management and marketing, entrepreneurship, energy,
fashion, healthcare, agri-food, supply chain management, circular economy,
developing countries, engineering, construction and real estate, mobility and
transportation, and hospitality. The key contribution of this study is that it
provides an insight into the state of the art of sustainable business models in
the various application areas and future research directions. This paper
concludes that popularity and the success rate of sustainable business models
in all application domains have been increased along with the increasing use of
advanced technologies.
Optimal auctions for networked markets with externalities
Date: 2019-07-23 00:00:00

Motivated by the problem of market power in electricity markets, we
introduced in previous works a mechanism for simplified markets of two agents
with linear cost. In standard procurement auctions, the market power resulting
from the quadratic transmission losses allows the producers to bid above their
true values, which are their production cost. The mechanism proposed in the
previous paper optimally reduces the producers' margin to the society's
benefit. In this paper, we extend those results to a more general market made
of a finite number of agents with piecewise linear cost functions, which makes
the problem more difficult, but simultaneously more realistic. We show that the
methodology works for a large class of externalities. We also provide an
algorithm to solve the principal allocation problem. Our contribution provides
a benchmark to assess the sub-optimality of the mechanisms used in practice.
Ex-ante dynamic network tariffs for transmission cost recovery
Date: 2019-11-11 00:00:00

This paper proposes a novel tariff scheme and a new optimization framework in
order to address the recovery of fixed investment costs in transmission network
planning, particularly against rising demand elasticity. At the moment, ex-post
network tariffs are utilized in addition to congestion revenues to fully
recover network costs, which often leads to over/under fixed cost recovery,
thus increasing the investment risk. Furthermore, in the case of agents with
elastic market curves, ex-post tariffs can cause several inefficiencies, such
as mistrustful bidding to exploit ex-post schemes, imperfect information in
applied costs and cleared quantities, and negative surplus for marginal
generators and consumers. These problems are exacerbated by the increasing
price-elasticity of demand, caused for example by the diffusion of demand
response technologies. To address these issues, we design a dynamic ex-ante
tariff scheme that explicitly accounts for the effect of tariffs in the
longterm network planning problem and in the underlying market clearing
process. Using linearization techniques and a novel reformulation of the
congestion rent, the long-term network planning problem is reformulated as a
single mixed-integer linear problem which returns the combined optimal values
of network expansion and associated tariffs, while accounting for price-elastic
agents and lumpy investments. The advantages of the proposed approach in terms
of cost recovery, market equilibrium and increased social welfare are discussed
qualitatively and are validated in numerical case studies.
Next-generation Wireless Solutions for the Smart Factory, Smart Vehicles, the Smart Grid and Smart Cities
Date: 2019-07-23 00:00:00
Tasks: Survey

5G wireless systems will extend mobile communication services beyond mobile
telephony, mobile broadband, and massive machine-type communication into new
application domains, namely the so-called vertical domains including the smart
factory, smart vehicles, smart grid, smart city, etc. Supporting these vertical
domains comes with demanding requirements: high-availability, high-reliability,
low-latency, and in some cases, high-accuracy positioning. In this survey, we
first identify the potential key performance requirements of 5G communication
in support of automation in the vertical domains and highlight the 5G enabling
technologies conceived for meeting these requirements. We then discuss the key
challenges faced both by industry and academia which have to be addressed in
order to support automation in the vertical domains. We also provide a survey
of the related research dedicated to automation in the vertical domains.
Finally, our vision of 6G wireless systems is discussed briefly.
AdaNN: Adaptive Neural Network-based Equalizer via Online Semi-supervised Learning
Date: 2020-04-10 00:00:00
Tasks: Data Augmentation

The demand for high speed data transmission has increased rapidly, leading to
advanced optical communication techniques. In the past few years, multiple
equalizers based on neural network (NN) have been proposed to recover signal
from nonlinear distortions. However, previous experiments mainly focused on
achieving low bit error rate (BER) on certain dataset with an offline-trained
NN, neglecting the generalization ability of NN-based equalizer when the
properties of optical link change. The development of efficient online training
scheme is urgently needed. In this paper, we've proposed an adaptive online
training scheme, which can fine-tune parameters of NN-based equalizer without
the help of an online training sequence. By introducing data augmentation and
virtual adversarial training, the convergence speed has been accelerated by 4.5
times, compared with decision-directed self-training. The proposed adaptive
NN-based equalizer is called "AdaNN". Its BER has been evaluated under two
scenarios: a 56 Gb/s PAM4-modulated VCSEL-MMF optical link (100-m), and a 32
Gbaud 16QAM-modulated Nyquist-WDM system (960-km SSMF). In our experiments,
with the help of AdaNN, BER values can be quickly stabilized below 1e-3 after
trained with 10^5 unlabeled symbols. AdaNN shows great performance improvement
compared with non-adaptive NN and conventional MLSE.
Inferring long-range interactions between immune and tumor cells -- pitfalls and (partial) solutions
Date: 2019-07-24 00:00:00

Upcoming immunotherapies for cancer treatment rely on the ability of the
immune system to detect and eliminate tumors in the body. A highly simplified
version of this process can be studied in a Petri dish: starting with a random
distribution of immune and tumor cells, it can be observed in detail how
individual immune cells migrate towards nearby tumor cells, establish contact,
and attack. Nevertheless, it remains unclear whether the immune cells find
their targets by chance, or if they approach them 'on purpose', using remote
sensing mechanisms such as chemotaxis. In this work, we present methods to
infer the strength and range of long-range cell-cell interactions from
time-lapse recorded cell trajectories, using a maximum likelihood method to fit
the model parameters. First, we model the interactions as a distance-dependent
'force' that attracts immune cells towards their nearest tumor cell. While this
approach correctly recovers the interaction parameters of simulated cells with
constant migration properties, it detects spurious interactions in the case of
independent cells that spontaneously change their migration behavior over time.
We therefore use an alternative approach that models the interactions by
distance-dependent probabilities for positive and negative turning angles of
the migrating immune cell. We demonstrate that the latter approach finds the
correct interaction parameters even with temporally switching cell migration.
Stay hydrated: Basolateral fluids shaping tissues
Authors: Markus Schliffka, Jean-Léon Maître
Date: 2019-07-24 00:00:00

During development, embryos perform a mesmerizing choreography, which is crucial for the correct shaping, positioning and function of all organs. The cellular properties powering animal morphogenesis have been the focus of much attention. On the other hand, much less consideration has been given to the invisible engine constituted by the intercellular fluid. Cells are immersed in fluid, of which the composition and physical properties have a considerable impact on development. In this review, we revisit recent studies from the perspective of the fluid, focusing on basolateral fluid compartments and taking the early mouse and zebrafish embryos as models. These examples illustrate how the hydration levels of tissues are spatio-temporally controlled and influence embryonic development.
A Nonlinear Perron-Frobenius Approach for Stability and Consensus of Discrete-Time Multi-Agent Systems
Date: 2019-07-24 00:00:00

In this paper we propose a novel method to establish stability and, in
addition, convergence to a consensus state for a class of discrete-time
Multi-Agent System (MAS) evolving according to nonlinear heterogeneous local
interaction rules which is not based on Lyapunov function arguments. In
particular, we focus on a class of discrete-time MASs whose global dynamics can
be represented by sub-homogeneous and order-preserving nonlinear maps. This
paper directly generalizes results for sub-homogeneous and order-preserving
linear maps which are shown to be the counterpart to stochastic matrices thanks
to nonlinear Perron-Frobenius theory. We provide sufficient conditions on the
structure of local interaction rules among agents to establish convergence to a
fixed point and study the consensus problem in this generalized framework as a
particular case. Examples to show the effectiveness of the method are provided
to corroborate the theoretical analysis.
Early Anomaly Detection in Power Systems Based on Random Matrix Theory
Date: 2019-07-21 00:00:00
Tasks: Anomaly Detection

It is important for detecting the anomaly in power systems before it expands
and causes serious faults such as power failures or system blackout. With the
deployments of phasor measurement units (PMUs), massive amounts of
synchrophasor measurements are collected, which makes it possible for the
real-time situation awareness of the entire system. In this paper, based on
random matrix theory (RMT), a data-driven approach is proposed for anomaly
detection in power systems. First, spatio-temporal data set is formulated by
arranging high-dimensional synchrophasor measurements in chronological order.
Based on the Ring Law in RMT for the empirical spectral analysis of
`signal+noise' matrix, the mean spectral radius (MSR) is introduced to indicate
the system states from the macroscopic perspective. In order to realize anomaly
declare automatically, an anomaly indicator based on the MSR is designed and
the corresponding confidence level $1-\alpha$ is calculated. The proposed
approach is capable of detecting the anomaly in an early phase and robust
against random fluctuations and measuring errors. Cases on the synthetic data
generated from IEEE 300-bus, 118-bus and 57-bus test systems validate the
effectiveness and advantages of the approach.
Modelling the Memory of Turbulence-Induced Beam Wander
Date: 2019-07-22 00:00:00

One of the major challenges for long range, high speed Free-Space Optical
(FSO) communication is turbulence induced beam wander. Beam wander causes
fluctuations in the received intensity as well as crosstalk in mode division
multiplexed systems. Existing models for beam wander make use of probability
distributions and long term averages and are not able to accurately model
time-dependent intensity fluctuations such as deep fading, where the received
intensity is too low to maintain reliable communication for an extended period
of time. In this work we present an elegant new memory model which models the
behaviour of beam wander induced intensity fluctuations with the unique
capability to accurately simulate deep fading. This is invaluable for the
development of optimised error correction coding and digital signal processing
in order to improve the throughput and reliability of FSO systems.
Development of a Real-time Indoor Location System using Bluetooth Low Energy Technology and Deep Learning to Facilitate Clinical Applications
Date: 2020-03-26 00:00:00

An indoor, real-time location system (RTLS) can benefit both hospitals and
patients by improving clinical efficiency through data-driven optimization of
procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy
and robustness because Bluetooth signal strength is subject to fluctuation. We
developed a machine learning-based solution using a Long Short-Term Memory
(LSTM) network followed by a Multilayer Perceptron classifier and a posterior
constraint algorithm to improve RTLS performance. Training and validation
datasets showed that most machine learning models perform well in classifying
individual location zones, although LSTM was most reliable. However, when faced
with data indicating cross-zone trajectories, all models showed erratic zone
switching. Thus, we implemented a history-based posterior constraint algorithm
to reduce the variability in exchange for a slight decrease in responsiveness.
This network increases robustness at the expense of latency. When latency is
less of a concern, we computed the latency-corrected accuracy which is 100% for
our testing data, significantly improved from LSTM without constraint which is
96.2%. The balance between robustness and responsiveness can be considered and
adjusted on a case-by-case basis, according to the specific needs of downstream
clinical applications. This system was deployed and validated in an academic
medical center. Industry best practices enabled system scaling without
substantial compromises to performance or cost.
Accurate Angular Inference for 802.11ad Devices Using Beam-Specific Measurements
Date: 2019-07-24 00:00:00

Due to their sparsity, 60GHz channels are characterized by a few dominant
paths. Knowing the angular information of their dominant paths, we can develop
various applications, such as the prediction of link performance and the
tracking of an 802.11ad device. Although they are equipped with phased arrays,
the angular inference for 802.11ad devices is still challenging due to their
limited number of RF chains and limited phase control capabilities. Considering
the beam sweeping operation and the high communication bandwidth of 802.11ad
devices, we propose variation-based angle estimation (VAE), called VAE-CIR, by
utilizing beam-specific channel impulse responses (CIRs) measured under
different beams and the directional gains of the corresponding beams to infer
the angular information of dominant paths. Unlike state-of-the-arts, VAE-CIR
exploits the variations between different beam-specific CIRs, instead of their
absolute values, for angular inference. To evaluate the performance of VAE-CIR,
we generate the beam-specific CIRs by simulating the beam sweeping of 802.11ad
devices with the beam patterns measured on off-the-shelf 802.11ad devices. The
60GHz channel is generated via a ray-tracing simulator and the CIRs are
extracted via channel estimation based on Golay sequences. Through experiments
in various scenarios, we demonstrate the effectiveness of VAE-CIR and its
superiority to existing angular inference schemes for 802.11ad devices.