pwc-archive/papers-with-abstracts huggingface dataset:
MMC-Based Distributed Maximum Power Point Tracking for Photovoltaic Systems
Date: 2020-04-27 00:00:00
Tasks: Model Predictive Control, Point Tracking

This paper proposes a novel topology for grid connected photovoltaic (PV)
system based on modular multilevel converter (MMC). In this topology, a PV
array is connected to capacitors of each submodule (SM) of the MMC through a
DC-DC boost converter with maximum power point tracking (MPPT) control. This
topology will maximize the efficiency of the system in the case of partial
shading conditions, as it can regulate the SM capacitor voltages independently
from each other to realize distributed MPPT. A model predictive control is used
to track the AC output current, balance the SMs capacitor voltages, and to
mitigate the circulating current. The proposed PV generation topology with 7
level MMC system validity has been verified by simulations via MATLAB/Simulink
toolbox under normal operation, partial shading and PV array failure.
Dynamic Beveridge Curve Accounting
Date: 2020-02-28 00:00:00

We develop a dynamic decomposition of the empirical Beveridge curve, i.e.,
the level of vacancies conditional on unemployment. Using a standard model, we
show that three factors can shift the Beveridge curve: reduced-form matching
efficiency, changes in the job separation rate, and out-of-steady-state
dynamics. We find that the shift in the Beveridge curve during and after the
Great Recession was due to all three factors, and each factor taken separately
had a large effect. Comparing the pre-2010 period to the post-2010 period, a
fall in matching efficiency and out-of-steady-state dynamics both pushed the
curve upward, while the changes in the separation rate pushed the curve
downward. The net effect was the observed upward shift in vacancies given
unemployment. In previous recessions changes in matching efficiency were
relatively unimportant, while dynamics and the separation rate had more impact.
Thus, the unusual feature of the Great Recession was the deterioration in
matching efficiency, while separations and dynamics have played significant,
partially offsetting roles in most downturns. The importance of these latter
two margins contrasts with much of the literature, which abstracts from one or
both of them. We show that these factors affect the slope of the empirical
Beveridge curve, an important quantity in recent welfare analyses estimating
the natural rate of unemployment.
InfiniWolf: Energy Efficient Smart Bracelet for Edge Computing with Dual Source Energy Harvesting
Authors: Michele Magno, Xiaying Wang, Manuel Eggimann, Lukas Cavigelli, Luca Benini
Date: 2020-02-28 00:00:00
Tasks: Edge-computing

This work presents InfiniWolf, a novel multi-sensor smartwatch that can achieve self-sustainability exploiting thermal and solar energy harvesting, performing computationally high demanding tasks. The smartwatch embeds both a System-on-Chip (SoC) with an ARM Cortex-M processor and Bluetooth Low Energy (BLE) and Mr. Wolf, an open-hardware RISC-V based parallel ultra-low-power processor that boosts the processing capabilities on board by more than one order of magnitude, while also increasing energy efficiency. We demonstrate its functionality based on a sample application scenario performing stress detection with multi-layer artificial neural networks on a wearable multi-sensor bracelet. Experimental results show the benefits in terms of energy efficiency and latency of Mr. Wolf over an ARM Cortex-M4F micro-controllers and the possibility, under specific assumptions, to be self-sustainable using thermal and solar energy harvesting while performing up to 24 stress classifications per minute in indoor conditions.
Generalized Rational Variable Projection With Application in ECG Compression
Date: 2020-02-28 00:00:00

In this paper we develop an adaptive transform-domain technique based on
rational function systems. It is of general importance in several areas of
signal theory, including filter design, transfer function approximation, system
identification, control theory etc. The construction of the proposed method is
discussed in the framework of a general mathematical model called variable
projection. First we generalize this method by adding dimension type free
parameters. Then we deal with the optimization problem of the free parameters.
To this order, based on the well-known particle swarm optimization (PSO)
algorithm, we develop the multi-dimensional hyperbolic PSO algorithm. It is
designed especially for the rational transforms in question. As a result, the
system along with its dimension is dynamically optimized during the process.
The main motivation was to increase the adaptivity while keeping the
computational complexity manageable. We note that the proposed method is of
general nature. As a case study the problem of electrocardiogram (ECG) signal
compression is discussed. By means of comparison tests performed on the
PhysioNet MIT-BIH Arrhythmia database we demonstrate that our method
outperforms other transformation techniques.
Preliminary Report: Cerebral blood flow mediates the relationship between progesterone and perceived stress symptoms among female club athletes after mild traumatic brain injury
Date: 2020-02-28 00:00:00

Female athletes are severely understudied in the field of concussion
research, despite higher prevalence for injuries and tendency to have longer
recovery time. Hormonal fluctuations due to normal menstrual cycle (MC) or
hormonal contraceptive (HC) use have been shown to impact both post-injury
symptoms and neuroimaging measures, but have not been accounted for in
concussion studies. In this preliminary study, we compared arterial spin
labeling measured cerebral blood flow (CBF) between concussed female club
athletes 3-10 days post injury (mTBI) and demographic, HC/MC matched controls
(CON). We test whether CBF mediates the relationship between progesterone
levels in blood and post-injury symptoms, which may be evidence for
progesterone`s role in neuroprotection. We found a significant three-way
relationship between progesterone, CBF and perceived stress score (PSS) in the
left middle temporal gyrus. Higher progesterone was associated with lower (more
normative) PSS, as well as higher (more normative) CBF. CBF mediates 100% of
the relationship between progesterone and PSS (Sobel`s p-value=0.017). These
findings suggest progesterone may have a neuroprotective role after concussion
and highlight the importance of controlling for the effects of sex hormones in
future concussion studies.
Time of arrival imaging: The proof of concept for a novel medical imaging modality
Date: 2020-02-28 00:00:00
Tasks: Anatomy, Image Reconstruction

It has been shown that with the use of ultra-wideband (UWB) electromagnetic
signal and time of arrival (ToA) principle, it is possible to locate medical
implants given the permittivity distribution of the body. We propose a new
imaging modality using the reverse process to acquire permittivity
distributions as a surrogate of human anatomy. In the proposed systems, the
locations of the signal source, receiver, and signal shapes are assumed to be
known exactly. The measured data is recorded as the time it takes for the
signal to travel from the signal source to the signal receiver. The
finite-difference-time-domain (FDTD) method is used for the modeling of signal
propagation within the phantom, which is used for both simulation and image
reconstruction. Image reconstruction is achieved using linear regression on the
training pairs, which includes randomly generated images and its corresponding
arrival times generated using the FDTD approach. The linear weights of the
training images are generated to minimize the difference between the arrival
time of the reconstruction image and the measured arrival time. A simulation
study using UWB signal with the central frequency of 300 MHz and the
Shepp-Logan phantom was carried out. Ten-picosecond timing resolution is used
for the simulation and image reconstruction. The quantitative difference
between the arrival times of the phantom and the reconstructed image reduced
with an increased iteration number. The quantitative error of the reconstructed
image reached below 10% after 900 iterations, and 8.4% after 1200 iterations.
With additional post-smoothing to suppress the introduced noise pattern through
reconstruction, 6.5% error was achieved. In this paper, an approach that
utilizes the ToA principle to achieve transmission imaging with radio waves is
proposed and validated using a simulation study.
Transformers for Limit Order Books
Authors: James Wallbridge
Date: 2020-02-29 00:00:00

We introduce a new deep learning architecture for predicting price movements from limit order books. This architecture uses a causal convolutional network for feature extraction in combination with masked self-attention to update features based on relevant contextual information. This architecture is shown to significantly outperform existing architectures such as those using convolutional networks (CNN) and Long-Short Term Memory (LSTM) establishing a new state-of-the-art benchmark for the FI-2010 dataset.
Generalized correlation based Imaging for satellites
Date: 2020-02-29 00:00:00

We consider imaging of fast moving small objects in space, such as low earth
orbit satellites. The imaging system consists of ground based, asynchronous
sources of radiation and several passive receivers above the dense atmosphere.
We use the cross correlation of the received signals to reduce distortions from
ambient medium fluctuations. Imaging with correlations also has the advantage
of not requiring any knowledge about the probing pulse and depends weakly on
the emitter positions. We account for the target's orbital velocity by
introducing the necessary Doppler compensation. We show that over limited
imaging regions, a constant Doppler factor can be used, resulting in an
efficient data structure for the correlations of the recorded signals. We then
investigate and analyze different imaging methods using the cross-correlation
data structure. Specifically, we show that using a generalized two point
migration of the cross correlation data, the top eigenvector of the migrated
data matrix provides superior image resolution compared to the usual
single-point migration scheme. We carry out a theoretical analysis that
illustrates the role of the two point migration methods as well as that of the
inverse aperture in improving resolution. Extensive numerical simulations
support the theoretical results and assess the scope of the imaging
methodology.
Zero-Forcing Max-Power Beamforming for Hybrid mmWave Full-Duplex MIMO Systems
Date: 2020-02-29 00:00:00
Tasks: Benchmarking

Full-duplex (FD) systems gained enormous attention because of the potential to double the spectral efficiency. In the context of 5G technology, FD systems operating at millimeter-wave (mmWave) frequencies become one of the most promising solutions to further increase the spectral efficiency and reduce the latency. However, such systems are vulnerable to the self-interference (SI) that significantly degrades the performance. To overcome this shortcoming, analog-only beamforming techniques have been developed to mitigate the SI. Because of the huge power consumption, systems operating at mmWave frequencies beamform the power by only tunning the phase shifters while maintaining constant amplitudes. Such a hardware constraint, known as the constant amplitude (CA) constraint, severely limits the system performance. In this work, we propose a digital and analog hybrid beamforming design that completely eliminates the SI while substantially minimizing the losses imposed by the CA constraint. Further, we develop a fully-digital beamforming design and derive the upper bound for the spectral efficiency as benchmarking tools to quantify the losses of our proposed hybrid design.
On the Sample Complexity of Data-Driven Inference of the $\mathcal{L}_2$-gain
Date: 2020-05-29 00:00:00

Lately, data-driven control has become a widespread area of research. A few
recent big-data based approaches for data-driven control of nonlinear systems
try to use classical input-output techniques to design controllers for systems
for which only a finite number of (input-output) samples are known. These
methods focus on using the given data to compute bounds on the
$\mathcal{L}_2$-gain or on the shortage of passivity from finite input-output
data, allowing for the application of the small gain theorem or the feedback
theorem for passive systems. One question regarding these methods asks about
their sample complexity, namely how many input-output samples are needed to get
an approximation of the operator norm or of the shortage of passivity. We show
that the number of samples needed to estimate the operator norm of a system is
roughly the same as the number of samples required to approximate the system in
the operator norm.
Identification of Random Coefficient Latent Utility Models
Date: 2020-02-29 00:00:00

This paper provides nonparametric identification results for random
coefficient distributions in perturbed utility models. We cover discrete and
continuous choice models. We establish identification using variation in mean
quantities, and the results apply when an analyst observes aggregate demands
but not whether goods are chosen together. We require exclusion restrictions
and independence between random slope coefficients and random intercepts. We do
not require regressors to have large supports or parametric assumptions.
Modelling techniques for biomolecular networks
Authors: Gerhard Mayer
Date: 2020-02-29 00:00:00

First we shortly review the different kinds of network modelling methods for systems biology with an emphasis on the different subtypes of logical models, which we review in more detail. Then we show the advantages of Boolean networks models over more mechanistic modelling types like differential equation techniques. Then follows an overlook about connections between different kinds of models and how they can be converted to each other. We also give a short overview about the mathematical frameworks for modelling of logical networks and list available software packages for logical modelling. Then we give an overview about the available standards and ontologies for storing such logical systems biology models and their results. In the end we give a short review about the difference between quantitative and qualitative models and describe the mathematics that specifically deals with qualitative modelling.
Mass spectrometry for semi-experimental protein structure determination and modeling
Authors: Gerhard Mayer
Date: 2020-02-29 00:00:00
Tasks: Protein Structure Prediction

The structure of proteins is essential for its function. The determination of protein structures is possible by experimental or predicted by computational methods, but also a combination of both approaches is possible. Here, first an overview about experimental structure determination methods with their pros and cons is given. Then we describe how mass spectrometry is useful for semi-experimental integrative protein structure determination. We review the methodology and describe software programs supporting such integrated protein structure prediction approaches, making use of distance constraints got from mass spectrometry cross-linking experiments
Weak Texture Information Map Guided Image Super-resolution with Deep Residual Networks
Date: 2020-03-18 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Single image super-resolution (SISR) is an image processing task which
obtains high-resolution (HR) image from a low-resolution (LR) image. Recently,
due to the capability in feature extraction, a series of deep learning methods
have brought important crucial improvement for SISR. However, we observe that
no matter how deeper the networks are designed, they usually do not have good
generalization ability, which leads to the fact that almost all of existing SR
methods have poor performances on restoration of the weak texture details. To
solve these problems, we propose a weak texture information map guided image
super-resolution with deep residual networks. It contains three sub-networks,
one main network which extracts the main features and fuses weak texture
details, another two auxiliary networks extract the weak texture details fallen
in the main network. Two part of networks work cooperatively, the auxiliary
networks predict and integrates week texture information into the main network,
which is conducive to the main network learning more inconspicuous details.
Experiments results demonstrate that our method's performs achieve the
state-of-the-art quantitatively. Specifically, the image super-resolution
results of our method own more weak texture details.
Wireless User-Generic Ear EEG
Date: 2020-04-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Rhythm

In the past few years it has been demonstrated that electroencephalography
(EEG) can be recorded from inside the ear (in-ear EEG). To open the door to
low-profile earpieces as wearable brain-computer interfaces (BCIs), this work
presents a practical in-ear EEG device based on multiple dry electrodes, a
user-generic design, and a lightweight wireless interface for streaming data
and device programming. The earpiece is designed for improved ear canal contact
across a wide population of users and is fabricated in a low-cost and scalable
manufacturing process based on standard techniques such as vacuum
forming,plasma-treatment, and spray coating. A 2.5x2.5 cm^2 wireless recording
module is designed to record and stream data wirelessly to a host computer.
Performance was evaluated on three human subjects over three months and
compared with clinical-grade wet scalp EEG recordings. Recordings of
spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, and the
auditory steady-state response (ASSR), are presented. This is the first
wireless in-ear EEG to our knowledge to incorporate a dry multielectrode,
user-generic design. The user-generic ear EEG recorded a mean alpha modulation
of 2.17, outperforming the state-of-the-art in dry electrode in-ear EEG
systems.
An AGI Modifying Its Utility Function in Violation of the Orthogonality Thesis
Authors: James D. Miller, Roman Yampolskiy, Olle Häggström
Date: 2020-01-31 00:00:00

An artificial general intelligence (AGI) might have an instrumental drive to modify its utility function to improve its ability to cooperate, bargain, promise, threaten, and resist and engage in blackmail. Such an AGI would necessarily have a utility function that was at least partially observable and that was influenced by how other agents chose to interact with it. This instrumental drive would conflict with the orthogonality thesis since the modifications would be influenced by the AGI's intelligence. AGIs in highly competitive environments might converge to having nearly the same utility function, one optimized to favorably influencing other agents through game theory.
Cleaner Production in Optimized Multivariate Networks: Operations Management through a Roll of Dice
Authors: Amit K Chattopadhyay, Biswajit Debnath, Rihab El-Hassani, Sadhan Kumar Ghosh, Rahul Baidya
Date: 2020-02-20 00:00:00
Tasks: Management

The importance of supply chain management in analyzing and later catalyzing economic expectations while simultaneously prioritizing cleaner production aspects is a vital component of modern finance. Such predictions, though, are often known to be less than accurate due to the ubiquitous uncertainty plaguing most business decisions. Starting from a multi-dimensional cost function defining the sustainability of the supply chain (SC) kernel, this article outlines a 4-component SC module - environmental, demand, economic, and social uncertainties - each ranked according to its individual weight. Our mathematical model then assesses the viability of a sustainable business by first ranking the potentially stochastic variables in order of their subjective importance, and then optimizing the cost kernel, defined from a utility function. The model will then identify conditions (as equations) validating the sustainability of a business venture. The ranking is initially obtained from an Analytical Hierarchical Process; the resultant weighted cost function is then optimized to analyze the impact of market uncertainty based on our supply chain model. Model predictions are then ratified against SME data to emphasize the importance of cleaner production in business strategies.
Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems with Joint Transmit and Reflective Beamforming
Authors: Hailiang Xie, Jie Xu, Ya-Feng Liu
Date: 2020-02-28 00:00:00
Tasks: Fairness

This paper investigates an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) system with several multi-antenna base stations (BSs) each communicating with a single-antenna user, in which an IRS is dedicatedly deployed for assisting the wireless transmission and suppressing the inter-cell interference. Under this setup, we jointly optimize the coordinated transmit beamforming at the BSs and the reflective beamforming at the IRS, for the purpose of maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) at the users, subject to the individual maximum transmit power at the BSs and the reflection constraints at the IRS. To solve the non-convex problem, we first present an exact-alternating-optimization design to optimize the transmit and reflective beamforming vectors in an alternating manner, in which the transmit and reflective beamforming optimization subproblems are solved exactly by using the technique of semi-definite relaxation (SDR). However, it has high computational complexity and may lead to compromised performance due to the uncertainty of randomization in SDR. To avoid these drawbacks, we further propose an inexact-alternating-optimization design, in which the transmit and reflective beamforming optimization subproblems are solved inexactly based on the principle of successive convex approximation (SCA). In addition, to further reduce the complexity, we propose a low-complexity inexact-alternating-optimization design, in which the reflective beamforming optimization subproblem is solved more inexactly. Numerical results show that the significant performance gains achieved by the proposed three designs against benchmark schemes. Moreover, the inexact-alternating-optimization designs outperform the exact-alternating-optimization one in terms of both the achieved min-weighted-SINR value and the computational complexity.