pwc-archive/papers-with-abstracts huggingface dataset:
A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring
Date: 2019-03-21 00:00:00
Tasks: Event Detection, Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM) is a practical method to provide
appliance-level electricity consumption information. Event detection, as an
important part of event-based NILM methods, has a direct impact on the accuracy
of the ultimate load disaggregation results in the entire NILM framework. This
paper presents a hybrid event detection approach for relatively complex
household load datasets that include appliances with long transients, high
fluctuations, and/or near-simultaneous actions. The proposed approach includes
a base algorithm based on moving average change with time limit, and two
auxiliary algorithms based on derivative analysis and filtering analysis. The
structure, steps, and working principle of this approach are described in
detail. The proposed approach does not require additional information about
household appliances, nor does it require any training sets. Case studies on
different datasets are conducted to evaluate the performance of the proposed
approach in comparison with several existing approaches including log
likelihood ratio detector with maxima (LLD-Max) approach, active window-based
(AWB) approach, and generalized likelihood ratio (GLR) approach. Results show
that the proposed approach works well in detecting events in complex household
load datasets and performs better than the existing approaches.
Cache-Aided Massive MIMO: Linear Precoding Design and Performance Analysis
Date: 2019-03-22 00:00:00

In this paper, we propose a novel joint caching and massive multiple-input
multiple-output (MIMO) transmission scheme, referred to as cache-aided massive
MIMO, for advanced downlink cellular communications. In addition to reaping the
conventional advantages of caching and massive MIMO, the proposed scheme also
exploits the side information provided by cached files for interference
cancellation at the receivers. This interference cancellation increases the
degrees of freedom available for precoding design. In addition, the power freed
by the cache-enabled offloading can benefit the transmissions to the users
requesting non-cached files. The resulting performance gains are not possible
if caching and massive MIMO are designed separately. We analyze the performance
of cache-aided massive MIMO for cache-dependent maximum-ratio transmission
(MRT), zero-forcing (ZF) precoding, and regularized zero-forcing (RZF)
precoding. Lower bounds on the ergodic achievable rates are derived in closed
form for MRT and ZF precoding. The ergodic achievable rate of RZF precoding is
obtained for the case when the numbers of transmit antennas and users are large
but their ratio is fixed. Compared to conventional massive MIMO, the proposed
cache-aided massive MIMO scheme achieves a significantly higher ergodic rate
especially when the number of users approaches the number of transmit antennas.
Digital Predistortion for Multiuser Hybrid MIMO at mmWaves
Authors: Alberto Brihuega, Lauri Anttila, Mahmoud Abdelaziz, Fredrik Tufvesson, Mikko Valkama
Date: 2019-03-22 00:00:00

Efficient mitigation of power amplifier (PA) nonlinear distortion in multi-user hybrid precoding based broadband mmWave systems is an open research problem. In this article, we first carry out detailed signal and distortion modeling in broadband multi-user hybrid MIMO systems, with a bank of nonlinear PAs in each subarray, while also take the inevitable crosstalk between the antenna/PA branches into account. Building on the derived models, we then propose an efficient closed-loop (CL) digital predistortion (DPD) solution that requires only a single-input DPD unit per transmit chain or subarray, despite crosstalk, providing substantial complexity-benefit compared to the state-of-the art multi-dimensional DPD solutions. We show that under spatially correlated multipath propagation, each DPD unit can provide linearization towards every intended user, or more generally, towards all spatial directions where coherent propagation is taking place, and that the proposed CL DPD system is robust against crosstalk}. Extensive numerical results building on practical measurement-based mmWave PA models are provided, demonstrating and verifying the excellent linearization performance of the proposed DPD system in different evaluation scenarios.
Mitigation of Through-Wall Distortions of Frontal Radar Images using Denoising Autoencoders
Date: 2019-03-22 00:00:00
Tasks: Denoising

Radar images of humans and other concealed objects are considerably distorted
by attenuation, refraction and multipath clutter in indoor through-wall
environments. While several methods have been proposed for removing target
independent static and dynamic clutter, there still remain considerable
challenges in mitigating target dependent clutter especially when the knowledge
of the exact propagation characteristics or analytical framework is
unavailable. In this work we focus on mitigating wall effects using a machine
learning based solution -- denoising autoencoders -- that does not require
prior information of the wall parameters or room geometry. Instead, the method
relies on the availability of a large volume of training radar images gathered
in through-wall conditions and the corresponding clean images captured in
line-of-sight conditions. During the training phase, the autoencoder learns how
to denoise the corrupted through-wall images in order to resemble the free
space images. We have validated the performance of the proposed solution for
both static and dynamic human subjects. The frontal radar images of static
targets are obtained by processing wideband planar array measurement data with
two-dimensional array and range processing. The frontal radar images of dynamic
targets are simulated using narrowband planar array data processed with
two-dimensional array and Doppler processing. In both simulation and
measurement processes, we incorporate considerable diversity in the target and
propagation conditions. Our experimental results, from both simulation and
measurement data, show that the denoised images are considerably more similar
to the free-space images when compared to the original through-wall images.
A lightweight convolutional neural network for image denoising with fine details preservation capability
Date: 2019-03-22 00:00:00
Tasks: Denoising, Image Denoising, MS-SSIM, SSIM

Image denoising is a fundamental problem in image processing whose primary
objective is to remove the noise while preserving the original image structure.
In this work, we proposed a new architecture for image denoising. We have used
several dense blocks to design our network. Additionally, we have forwarded
feature extracted in the first layer to the input of every transition layer.
Our experimental result suggests that the use of low-level feature helps in
reconstructing better texture. Furthermore, we had trained our network with a
combination of MSE and a differentiable multi-scale structural similarity
index(MS-SSIM). With proper training, our proposed model with a much lower
parameter can outperform other models which were with trained much higher
parameters. We evaluated our algorithm on two grayscale benchmark dataset BSD68
and SET12. Our model had achieved similar PSNR with the current state of the
art methods and most of the time better SSIM than other algorithms.
Cost-effective Energy Monitoring of a Zynq-based Real-time System including dual Gigabit Ethernet
Date: 2019-03-22 00:00:00
Tasks: CPU, Management

The ongoing integration of fine-grained power management features already
established in CPU-driven Systems-on-Chip (SoCs) enables both traditional Field
Programmable Gate Arrays (FPGAs) and, more recently, hybrid Programmable SoCs
(pSoCs) to reach more energy-sensitive application domains (such as, e.g.,
automotive and robotics). By combining a fixed-function multi-core SoC with
flexible, configurable FPGA fabric, the latter can be used to realize
heterogeneous Real-time Systems (RTSs) commonly implementing complex
application-specific architectures with high computation and communication
(I/O) densities. Their dynamic changes in workload, currently active power
saving features and thus power consumption require precise voltage and current
sensing on all relevant supply rails to enable dependable evaluation of the
various power management techniques. In this paper, we propose a low-cost
18-channel 16-bit-resolution measurement (sub-)system capable of 200 kSPS
(kilo-samples per second) for instrumentation of current pSoC development
boards. To this end, we join simultaneously sampling analog-to-digital
converters (ADCs) and analog voltage/current sensing circuitry with a Cortex M7
microcontroller using an SD card for storage. In addition, we propose to
include crucial I/O components such as Ethernet PHYs into the power monitoring
to gain a holistic view on the RTS's temporal behavior covering not only
computation on FPGA and CPUs, but also communication in terms of, e.g.,
reception of sensor values and transmission of actuation signals. We present an
FMC-sized implementation of our measurement system combined with two Gigabit
Ethernet PHYs and one HDMI input. Paired with Xilinx' ZC702 development board,
we are able to synchronously acquire power traces of a Zynq pSoC and the two
PHYs precise enough to identify individual Ethernet frames.
Natural reward as the fundamental macroevolutionary force
Date: 2019-03-22 00:00:00

Darwin's theory of evolution by natural selection does not predict long-term
progress or advancement, nor does it provide a useful way to define or
understand these concepts. Nevertheless, the history of life is marked by major
trends that appear progressive, and seemingly more advanced forms of life have
appeared. To reconcile theory and fact, evolutionists have proposed novel
theories that extend natural selection to levels and time frames not justified
by the original structure of Darwin's theory. To extend evolutionary theory
without violating the most basic tenets of Darwinism, I here identify a
separate struggle and an alternative evolutionary force. Owing to the abundant
free energy in our universe, there is a struggle for supremacy that naturally
rewards those that are first to invent novelties that allow exploitation of
untapped resources. This natural reward comes in form of a temporary monopoly,
which is granted to those who win a competitive race to innovate. By analogy to
human economies, natural selection plays the role of nature's inventor,
gradually fashioning inventions to the situation at hand, while natural reward
plays the role of nature's entrepreneur, choosing which inventions to first
disseminate to large markets. Natural reward leads to progress through a
process of invention-conquest macroevolution, in which the dual forces of
natural selection and natural reward create and disseminate major innovations.
Over vast time frames, natural reward drives the advancement of life by a
process of extinction-replacement megaevolution that releases constraints on
progress and increases the innovativeness of life.
Towards adversarial learning of speaker-invariant representation for speech emotion recognition
Date: 2019-03-22 00:00:00
Tasks: Classification, Emotion Classification, Emotion Recognition, Representation Learning, Speech Emotion Recognition

Speech emotion recognition (SER) has attracted great attention in recent
years due to the high demand for emotionally intelligent speech interfaces.
Deriving speaker-invariant representations for speech emotion recognition is
crucial. In this paper, we propose to apply adversarial training to SER to
learn speaker-invariant representations. Our model consists of three parts: a
representation learning sub-network with time-delay neural network (TDNN) and
LSTM with statistical pooling, an emotion classification network and a speaker
classification network. Both the emotion and speaker classification network
take the output of the representation learning network as input. Two training
strategies are employed: one based on domain adversarial training (DAT) and the
other one based on cross-gradient training (CGT). Besides the conventional data
set, we also evaluate our proposed models on a much larger publicly available
emotion data set with 250 speakers. Evaluation results show that on IEMOCAP,
DAT and CGT provides 5.6% and 7.4% improvement respectively, over a baseline
system without speaker-invariant representation learning on 5-fold cross
validation. On the larger emotion data set, while CGT fails to yield better
results than baseline, DAT can still provide 9.8% relative improvement on a
standalone test set.
The Impact of Renewable Energy Forecasts on Intraday Electricity Prices
Date: 2019-11-18 00:00:00

In this paper we study the impact of errors in wind and solar power forecasts
on intraday electricity prices. We develop a novel econometric model which is
based on day-ahead wholesale auction curves data and errors in wind and solar
power forecasts. The model shifts day-ahead supply curves to calculate intraday
prices. We apply our model to the German EPEX SPOT SE data. Our model
outperforms both linear and non-linear benchmarks. Our study allows us to
conclude that errors in renewable energy forecasts exert a non-linear impact on
intraday prices. We demonstrate that additional wind and solar power capacities
induce non-linear changes in the intraday price volatility. Finally, we comment
on economical and policy implications of our findings.
Identification and Estimation of a Partially Linear Regression Model using Network Data
Authors: Eric Auerbach
Date: 2019-03-22 00:00:00
Tasks: regression

I study a regression model in which one covariate is an unknown function of a latent driver of link formation in a network. Rather than specify and fit a parametric network formation model, I introduce a new method based on matching pairs of agents with similar columns of the squared adjacency matrix, the ijth entry of which contains the number of other agents linked to both agents i and j. The intuition behind this approach is that for a large class of network formation models the columns of the squared adjacency matrix characterize all of the identifiable information about individual linking behavior. In this paper, I describe the model, formalize this intuition, and provide consistent estimators for the parameters of the regression model. Auerbach (2021) considers inference and an application to network peer effects.
Quantifying the impact of network structure on speed and accuracy in collective decision-making
Date: 2019-03-22 00:00:00
Tasks: Decision Making

Found in varied contexts from neurons to ants to fish, binary decision-making
is one of the simplest forms of collective computation. In this process,
information collected by individuals about an uncertain environment is
accumulated to guide behavior at the aggregate scale. We study binary
decision-making dynamics in networks responding to inputs with small
signal-to-noise ratios, looking for quantitative measures of collectivity that
control decision-making performance. We find that decision accuracy is
controlled largely by three factors: the leading eigenvalue of the network
adjacency matrix, the corresponding eigenvector's participation ratio, and
distance from the corresponding symmetry-breaking bifurcation. This allows us
to predict how decision-making performance scales in large networks based on
their spectral properties. Specifically, we explore the effects of localization
caused by the hierarchical assortative structure of a "rich club" topology.
This gives insight into the tradeoffs involved in the higher-order structure
found in living networks performing collective computations.
Connections between spectral properties of asymptotic mappings and solutions to wireless network problems
Authors: Renato Luís Garrido Cavalcante, Qi Liao, Slawomir Stańczak
Date: 2019-03-23 00:00:00

In this study we establish connections between asymptotic functions and properties of solutions to important problems in wireless networks. We start by introducing a class of self-mappings (called asymptotic mappings) constructed with asymptotic functions, and we show that spectral properties of these mappings explain the behavior of solutions to some maxmin utility optimization problems. For example, in a common family of max-min utility power control problems, we prove that the optimal utility as a function of the power available to transmitters is approximately linear in the low power regime. However, as we move away from this regime, there exists a transition point, easily computed from the spectral radius of an asymptotic mapping, from which gains in utility become increasingly marginal. From these results we derive analogous properties of the transmit energy efficiency. In this study we also generalize and unify existing approaches for feasibility analysis in wireless networks. Feasibility problems often reduce to determining the existence of the fixed point of a standard interference mapping, and we show that the spectral radius of an asymptotic mapping provides a necessary and sufficient condition for the existence of such a fixed point. We further present a result that determines whether the fixed point satisfies a constraint given in terms of a monotone norm.
An interim core for normal form games and exchange economies with incomplete information: a correction
Date: 2019-03-23 00:00:00
Tasks: Form

We consider the interim core of normal form cooperative games and exchange
economies with incomplete information based on the partition model. We develop
a solution concept that we can situate roughly between Wilson's coarse core and
Yannelis's private core. We investigate the interim negotiation of contracts
and address the two situations of contract delivery: interim and ex post. Our
solution differs from Wilson's concept because the measurability of strategies
in our solution is postponed until the consumption date (assumed with respect
to the information that will be known by the players at the consumption date).
For interim consumption, our concept differs from Yannelis's private core
because players can negotiate conditional on proper common knowledge events in
our solution, which strengthens the interim aspect of the game, as we will
illustrate with examples.
LiDAL: Light Detection and Localization
Date: 2019-04-26 00:00:00

In this paper, we present the first indoor light-based detection and
localization system that builds on concepts from radio detection and ranging
(radar) making use of the expected growth in the use and adoption of visible
light communication (VLC), which can provide the infrastructure for our LiDAL
system. Our system enables active detection, counting and localization of
people, in addition to being fully compatible with existing VLC systems. In
order to detect human (targets), LiDAL uses the visible light spectrum, it
sends pulses using a VLC transmitter and analyses the reflected signal
collected by a photodetector receiver. Although we examine the use of the
visible spectrum here, LiDAL can be used in the infrared spectrum and other
parts of the light spectrum. We introduce LiDAL with different
transmitter-receiver configurations and optimum detectors considering the
fluctuation of the received reflected signal from the target in the presence of
Gaussian noise. We design an efficient multiple input multiple output (MIMO)
LiDAL system with wide field of view (FOV) single photodetector receiver, and
also design a multiple input single output (MISO) LiDAL system with an imaging
receiver to eliminate the ambiguity in target detection and localization. We
develop models for the human body and its reflections and consider the impact
of the colour and texture of the cloth used as well as the impact of target
mobility. A number of detection and localization methods are developed for our
LiDAL system including cross correaltion, a background subtraction method and a
neural network based method. These methods are considered to distinguish a
mobile target from the ambient reflections due to background obstacles
(furniture) in a realistic indoor environment.
Dynamics of Value-Tracking in Financial Markets
Authors: Nicholas CL Beale, Richard M Gunton, Kutlwano L Bashe, Heather S Battey, Robert S MacKay
Date: 2019-03-23 00:00:00

The efficiency of a modern economy depends on what we call the Value-Tracking Hypothesis: that market prices of key assets broadly track some underlying value. This can be expected if a sufficient weight of market participants are valuation-based traders, buying and selling an asset when its price is, respectively, below and above their well-informed private valuations. Such tracking will never be perfect, and we propose a natural unit of tracking error, the 'deciblack'. We then use a simple discrete-time model to show how large tracking errors can arise if enough market participants are not valuation-based traders, regardless of how much information the valuation-based traders have. We find a threshold above which value-tracking breaks down without any changes in the underlying value of the asset. Because financial markets are increasingly dominated by non-valuation-based traders, assessing how much valuation-based investing is required for reasonable value tracking is of urgent practical interest.
Core Access Hybridization in 5G
Date: 2019-03-24 00:00:00

The fifth generation (5G) cellular communication system will have several
advanced features, majority of which will come into existence for the first
time. According to the ITU recommendations, 5G will provide minimum 3 GB/s end
to end data rates under the static conditions and in the mobile condition the
minimum data rate is 100 Mb/s. The latency will be brought down to 1 ms and the
device densities will increase by many folds. These features cannot be provided
in the pure wireless domain. Certainly, the support of the optical networks is
essential to provide these advanced features in 5G communication. These
features need special architectures. In this article, we provide advanced
optical wireless hybrid architectures for 5G networks. We explain the
importance of this architecture and also show the nodal configuration of these
hybrid networks.
Dynamic intertemporal utility optimization by means of Riccati transformation of Hamilton-Jacobi Bellman equation
Date: 2019-03-24 00:00:00
Tasks: Portfolio Optimization

In this paper we investigate a dynamic stochastic portfolio optimization
problem involving both the expected terminal utility and intertemporal utility
maximization. We solve the problem by means of a solution to a fully nonlinear
evolutionary Hamilton-Jacobi-Bellman (HJB) equation. We propose the so-called
Riccati method for transformation of the fully nonlinear HJB equation into a
quasi-linear parabolic equation with non-local terms involving the
intertemporal utility function. As a numerical method we propose a
semi-implicit scheme in time based on a finite volume approximation in the
spatial variable. By analyzing an explicit traveling wave solution we show that
the numerical method is of the second experimental order of convergence. As a
practical application we compute optimal strategies for a portfolio investment
problem motivated by market financial data of German DAX 30 Index and show the
effect of considering intertemporal utility on optimal portfolio selection.