pwc-archive/papers-with-abstracts huggingface dataset:
Neural Network Architectures for Location Estimation in the Internet of Things
Authors: Ihsan Ullah, Robert Malaney, Shihao Yan
Date: 2020-10-19 00:00:00
Tasks: Efficient Neural Network

Artificial Intelligence (AI) solutions for wireless location estimation are likely to prevail in many real-world scenarios. In this work, we demonstrate for the first time how the Cramer-Rao upper bound on localization accuracy can facilitate efficient neural-network solutions for wireless location estimation. In particular, we demonstrate how the number of neurons for the network can be intelligently chosen, leading to AI location solutions that are not time-consuming to run and less likely to be plagued by over-fitting. Experimental verification of our approach is provided. Our new algorithms are directly applicable to location estimates in many scenarios including the Internet of Things, and vehicular networks where vehicular GPS coordinates are unreliable or need verifying. Our work represents the first successful AI solution for a communication problem whose neural-network design is based on fundamental information-theoretic constructs. We anticipate our approach will be useful for a wide range of communication problems beyond location estimation.
Influencing Competition Through Shelf Design
Authors: Francisco Cisternas, Wee Chaimanowong, Alan Montgomery, Timothy Derdenger
Date: 2020-10-19 00:00:00

Shelf design decisions strongly influence product demand. In particular, placing products in desirable locations increases demand. This primary effect on shelf position is clear, but there is a secondary effect based on the relative positioning of nearby products. Intuitively, products located next to each other are more likely to be compared having positive and negative effects. On the one hand, locations closer to relatively strong products will be undesirable, as these strong products will draw demand from others -- an effect that is stronger for those in close proximity. On the other hand, because strong products tend to attract more traffic, locations closer to them elicit high consumer attention by increased visibility. Modifying the GEV class of models to allow demand to be moderated by competitors' proximity, these two effects emerge naturally. We found that although the competition effect is usually stronger, it is not always the dominating effect. Shelf displays can achieve higher profits by exploiting the relative influence on competition from shelf design to shift demand to higher profitability products. In the paper towel category, we found profitability differences of up to 7\% and displays with 3\% higher gross profits over the best shelf design present in our data.
Frequency-Hopping MIMO Radar-Based Communications: An Overview
Authors: Kai Wu, J. Andrew Zhang, Xiaojing Huang, Y. Jay Guo
Date: 2020-10-19 00:00:00

Enabled by the advancement in radio frequency technologies, the convergence of radar and communication systems becomes increasingly promising and is envisioned as a key feature of future 6G networks. Recently, the frequency-hopping (FH) MIMO radar is introduced to underlay dual-function radar-communication (DFRC) systems. Superior to many previous radar-centric DFRC designs, the symbol rate of FH-MIMO radar-based DFRC (FH-MIMO DFRC) can exceed the radar pulse repetition frequency. However, many practical issues, particularly those regarding effective data communications, are unexplored/unsolved. To promote the awareness and general understanding of the novel DFRC, this article is devoted to providing a timely introduction of FH-MIMO DFRC. We comprehensively review many essential aspects of the novel DFRC: channel/signal models, signaling strategies, modulation/demodulation processing and channel estimation methods, to name a few. We also highlight major remaining issues in FH-MIMO DFRC and suggest potential solutions to shed light on future research works.
Equilibrium price in intraday electricity markets
Authors: René Aid, Andrea Cosso, Huyên Pham
Date: 2020-10-19 00:00:00

We formulate an equilibrium model of intraday trading in electricity markets. Agents face balancing constraints between their customers consumption plus intraday sales and their production plus intraday purchases. They have continuously updated forecast of their customers consumption at maturity with decreasing volatility error. Forecasts are prone to idiosyncratic noise as well as common noise (weather). Agents production capacities are subject to independent random outages, which are each modelled by a Markov chain. The equilibrium price is defined as the price that minimises trading cost plus imbalance cost of each agent and satisfies the usual market clearing condition. Existence and uniqueness of the equilibrium are proved, and we show that the equilibrium price and the optimal trading strategies are martingales. The main economic insights are the following. (i) When there is no uncertainty on generation, it is shown that the market price is a convex combination of forecasted marginal cost of each agent, with deterministic weights. Furthermore, the equilibrium market price follows Almgren and Chriss's model and we identify the fundamental part as well as the permanent market impact. It turns out that heterogeneity across agents is a necessary condition for the Samuelson's effect to hold. (ii) When there is production uncertainty, the price volatility becomes stochastic but converges to the case without production uncertainty when the number of agents increases to infinity. Further, on a two-agent case, we show that the potential outages of a low marginal cost producer reduces her sales position.
A Scalable Nyquist Stability Criterion with Application to Power System Small-Signal Stability
Authors: Joakim Björk, Karl Henrik Johansson
Date: 2020-10-19 00:00:00

A decentralized stability criterion is derived for a power system with heterogeneous subsystems. A condition for frequency stability and stability of interarea modes is derived using the generalized Nyquist criterion. The resulting scalable Nyquist stability criterion requires only locally available information and gives a priori stability guarantees for connecting new subsystems to an arbitrarily large network. The method can be applied to a general set of agents. For instance, agents with time-delays, nonminimum phase actuators or even unstable dynamics. The scalable Nyquist criterion makes no distinction between nodes with or without synchronous inertia, making it easy to include converter-interfaced renewable energy in the analysis. The method is validated on a detailed nonlinear power system model with frequency droop provided by hydro governors assisted by wind power.
Poisson Image Deconvolution by a Plug-and-Play Quantum Denoising Scheme
Authors: Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé
Date: 2020-10-19 00:00:00
Tasks: Denoising, Image Deconvolution

This paper introduces a new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme based on a recently proposed denoiser using the Schroedinger equation's solutions of quantum physics. The efficiency of the proposed algorithm is evaluated for Poisson image deconvolution, which is very common for imaging applications, such as, for example, limited photon acquisition. Numerical results show the superiority of the proposed scheme compared to recent state-of-the-art techniques, for both low and high signal-to-noise-ratio scenarios. This performance gain is mostly explained by the flexibility of the embedded quantum denoiser for different types of noise affecting the observations.
Data-driven sparse sensor placement based on A-optimal design of experiment with ADMM
Date: 2020-10-19 00:00:00

The present study proposes a sensor selection method based on the proximal
splitting algorithm and the A-optimal design of experiment using the
alternating direction method of multipliers (ADMM) algorithm. The performance
of the proposed method was evaluated with a random sensor problem and compared
with the previously proposed methods such as the greedy method and the convex
relaxation. The performance of the proposed method is better than an existing
method in terms of the A-optimality criterion. In addition, the proposed method
requires longer computational time than the greedy method but it is quite
shorter than the convex relaxation in large-scale problems. The proposed method
was applied to the data-driven sparse-sensor-selection problem. A data set
adopted is the NOAA OISST V2 mean sea surface temperature set. At the number of
sensors larger than that of the latent state variables, the proposed method
showed similar and better performances compared with previously proposed
methods in terms of the A-optimality criterion and reconstruction error.
Altered structural balance of resting-state networks in autism
Authors: Z. Moradimanesh, R. Khosrowabadi, M. Eshaghi Gordji, G. R. Jafari
Date: 2020-10-06 00:00:00

What makes a network complex, in addition to its size, is the interconnected interactions between elements, disruption of which inevitably results in dysfunction. Likewise, the brain networks' complexity arises from interactions beyond pair connections, as it is simplistic to assume that in complex networks state of a link is independently determined only according to its two constituting nodes. This is particularly of note in genetically complex brain impairments, such as the autism spectrum disorder (ASD), which has a surprising heterogeneity in manifestations with no clear-cut neuropathology. Accordingly, structural balance theory (SBT) affirms that in real-world signed networks, a link is remarkably influenced by each of its two nodes' interactions with the third node within a triadic interrelationship. Thus, it is plausible to ask whether ASD is associated with altered structural balance resulting from atypical triadic interactions. In other words, it is the abnormal interplay of positive and negative interactions that matters in ASD, besides and beyond hypo (hyper) pair connectivity. To address this question, we explore triadic interactions based on SBT in the weighted signed resting-state functional magnetic resonance imaging networks of participants with ASD relative to healthy controls (CON). We demonstrate that balanced triads are overrepresented in the ASD and CON networks while unbalanced triads are underrepresented, providing first-time empirical evidence for the strong notion of structural balance on the brain networks. We further analyze the frequency and energy distributions of different triads and suggest an alternative description for the reduced functional integration and segregation in the ASD brain networks. Moreover, results reveal that the scale of change in the whole-brain networks' energy is more narrow in the ASD networks during development.
Continuous monitoring of cognitive load using advanced computerized analysis of brain signals during virtual simulator training for laparoscopic surgery, reflects laparoscopic dexterity. A comparative study using a novel wireless device
Authors: Maxim Bez, Neta B. Maimon, Denis Ddobot, Lior Molcho, Nathan Intrator, Eli Kakiashvilli, Amitai Bickel
Date: 2020-10-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Simulation-based training is an effective tool for acquiring practical skills, specifically to train new surgeons in a controlled and hazard-free environment, it is however important to measure participants cognitive load to decide whether they are ready to go into a real surgery. In the present study we measured performance on a surgery simulator of medical students and interns, while their brain activity was monitored by a mobile EEG device. 38 medical studentswere underwent 3 experiments undergoing a task with Simbionix simulator, while their brain activity was measured using a single-channel EEG device (Aurora by Neurosteer). On each experiment, participants performed 3 repeats of a simulator task using laparoscopic hands. The retention between tasks was different on each experiment, to examine changes in performance and cognitive load biomarkers that occur during the task or as a results of night sleep consolidation. The participants behavioral performance improved with trial repetition in all 3 experiments. In Exps. 1 & 2, the theta band activity significantly decreased with better individual performance, as exhibited by some of the behavioral measurements of the simulator. The novel VC9 biomarker (previously shown to correlate with cognitive load), exhibited a significant decrease with better individual performance shown by all behavioral measurements. In correspondence with previous research, theta decreased with lower cognitive load and higher performance and the novel biomarker, VC9, showed higher sensitivity to load changes. Together, these measurements might be for neuroimaging assessment of cognitive load while performing simulator laparoscopic tasks. This could potentially be expanded to evaluate efficacy of different medical simulations to provide more efficient training to medical staff and to measure cognitive and mental load in real laparoscopic surgeries.
Neuronal graphs: a graph theory primer for microscopic, functional networks of neurons recorded by Calcium imaging
Date: 2020-10-19 00:00:00

Connected networks are a fundamental structure of neurobiology. Understanding
these networks will help us elucidate the neural mechanisms of computation.
Mathematically speaking these networks are `graphs' - structures containing
objects that are connected. In neuroscience, the objects could be regions of
the brain, e.g. fMRI data, or be individual neurons, e.g. calcium imaging with
fluorescence microscopy. The formal study of graphs, graph theory, can provide
neuroscientists with a large bank of algorithms for exploring networks. Graph
theory has already been applied in a variety of ways to fMRI data but, more
recently, has begun to be applied at the scales of neurons, e.g. from
functional calcium imaging. In this primer we explain the basics of graph
theory and relate them to features of microscopic functional networks of
neurons from calcium imaging - neuronal graphs. We explore recent examples of
graph theory applied to calcium imaging and we highlight some areas where
researchers new to the field could go awry.
Geometric framework to predict structure from function in neural networks
Authors: Tirthabir Biswas, James E. Fitzgerald
Date: 2020-10-19 00:00:00

Neural computation in biological and artificial networks relies on the nonlinear summation of many inputs. The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function, but quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of threshold-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate the solution space of all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. A generalization accounting for noise further reveals that the solution space geometry can undergo topological transitions as the allowed error increases, which could provide insight into both neuroscience and machine learning. We ultimately use this geometric characterization to derive certainty conditions guaranteeing a non-zero synapse between neurons. Our theoretical framework could thus be applied to neural activity data to make rigorous anatomical predictions that follow generally from the model architecture.
Safety-Critical Protective Systems and Margins of Safety
Date: 2020-10-19 00:00:00

The design and operation of protective systems is an essential engineering
responsibility. Ensuring public safety, while essential, must be accomplished
at a feasible cost and within government regulation. Hence, protective system
design and operational decisions must be evaluated with respect to benefit
(both enterprise profit and social benefit) and cost (both enterprise and
social costs).
  Analytical arguments are made that establish the economic relationship
between protective system margins of safety, regulatory authority, and the
calculus of negligence. Within this risk-based analytical framework, protection
efficacy is explored. In particular, the risk-economics of margins of safety
are examined by identifying the reference efficacy with respect to which
margins of safety are measured. Engineering design and operations decisions
intended to improve protection efficacy can, thus, be gauged as the degree to
which they advance a risk-based margin of safety.
  Finally, our analytical framework is exercised to show how risk-based margins
of safety reveal the relationship between uncertain costs and regulatory
activity focused on ensuring public welfare that is backstopped by liability in
the event of catastrophe. How both prescriptive and performance based
regulations influence margins of safety with respect to protective system
innovation can be identified here.
Effects of demographic and weather parameters on COVID-19 basic reproduction number
Authors: Igor Salom, Andjela Rodic, Ognjen Milicevic, Dusan Zigic, Magdalena Djordjevic, Marko Djordjevic
Date: 2020-10-19 00:00:00

Timely prediction of the COVID-19 progression is not possible without a comprehensive understanding of environmental factors that may affect the infection transmissibility. Studies addressing parameters that may influence COVID-19 progression relied on either the total numbers of detected cases and similar proxies and/or a small number of analyzed factors, including analysis of regions that display a narrow range of these parameters. We here apply a novel approach, exploiting widespread growth regimes in COVID-19 detected case counts. By applying nonlinear dynamics methods to the exponential regime, we extract basic reproductive number R0 (i.e., the measure of COVID-19 inherent biological transmissibility), applying to the completely naive population in the absence of social distancing, for 118 different countries. We then use bioinformatics methods to systematically collect data on a large number of demographics and weather parameters from these countries, and seek their correlations with the rate of COVID-19 spread. In addition to some of the already reported tendencies, we show a number of both novel results and those that help settle existing disputes: the absence of dependence on wind speed and air pressure, negative correlation with precipitation; significant positive correlation with society development level (human development index) irrespective of testing policies, and percent of the urban population, but an absence of correlation with population density per se. We find a strong positive correlation of transmissibility on alcohol consumption, and the absence of correlation on refugee numbers, contrary to some widespread beliefs. Significant tendencies with health-related factors are reported, including a detailed analysis of the blood type group showing consistent tendencies on Rh factor, and a strong positive correlation of transmissibility with cholesterol levels.
Low-frequency Selection Switch based Cell-to-Cell Battery Voltage Equalizer with Reduced Switch Count
Authors: Shimul K Dam, Vinod John
Date: 2020-10-19 00:00:00

A selection switch based cell-to-cell voltage equalizer requires only one dual-port dc-dc converter shared by all the cells. A cell-to-cell voltage equalizer is proposed that utilizes a capacitively level-shifted Cuk converter and low-frequency cell selection switches. The absence of isolation transformer and diodes in the equalizer leads to high efficiency, and the use of low-frequency selection switches significantly reduces the cost of the drive circuits. A low-frequency cell selection network is proposed using bipolar voltage buses, where the switch count is almost half, compared to the existing low-frequency cell-to-cell equalizers for the case of a large number of cells. A novel approach for cell voltage recovery compensation is proposed, which reduces the number of operations of the selection switches and the equalization time. The proposed equalizer is implemented with relays and verified with an 8-cell Li-ion stack. The developed prototype shows the efficiency of over 90\% and good voltage balancing performance during charging, discharging, and varying load conditions. Experimental results also show about one order of magnitude reduction in the number of relay switchings and a significant reduction in equalization time using the proposed voltage compensation.
Vehicular Visible Light Positioning for Collision Avoidance and Platooning: A Survey
Authors: Burak Soner, Merve Karakas, Utku Noyan, Furkan Sahbaz, Sinem Coleri
Date: 2020-10-19 00:00:00
Tasks: Autonomous Driving, Collision Avoidance

Relative vehicle positioning methods can contribute to safer and more efficient autonomous driving by enabling collision avoidance and platooning applications. For full automation, these applications require cm-level positioning accuracy and greater than 50 Hz update rate. Since sensor-based methods (e.g., LIDAR, cameras) have not been able to reliably satisfy these requirements under all conditions so far, complementary methods are sought. Recently, positioning based on visible light communication signals from vehicle head/tail LED lights (VLP) has shown significant promise as a complementary method attaining cm-level accuracy and near-kHz rate in realistic driving scenarios. Vehicular VLP methods measure relative bearing (angle) or range (distance) of transmitters (i.e., head/tail lights) based on received signals from on-board photodiodes and estimate transmitter relative positions based on those measurements. In this survey, we first review existing vehicular VLP methods and propose a new method that advances the state-of-the-art in positioning performance. Next, we analyze the theoretical and simulated performance of all methods in realistic driving scenarios under challenging noise and weather conditions, real asymmetric light beam patterns and different vehicle dimensions and light placements. Our simulation results show that the newly proposed VLP method is the overall best performer, and can indeed satisfy the accuracy and rate requirements for localization in collision avoidance and platooning applications within practical constraints. Finally, we discuss remaining open challenges that are faced for the deployment of VLP solutions in the automotive sector and further research questions.
Circular Convolution and Product Theorem for Affine Discrete Fractional Fourier Transform
Authors: Amir R. Nafchi, Eric Hamke, Cristina Pereyra, Ramiro Jordan
Date: 2020-10-19 00:00:00

The Fractional Fourier Transform is a ubiquitous signal processing tool in basic and applied sciences. The Fractional Fourier Transform generalizes every property and application of the Fourier Transform. Despite the practical importance of the discrete fractional Fourier transform, its applications in digital communications have been elusive. The convolution property of the discrete Fourier transform plays a vital role in designing multi-carrier modulation systems. Here we report a closed-form affine discrete fractional Fourier transform and we show the circular convolution property for it. The proposed approach is versatile and generalizes the discrete Fourier transform and can find applications in Fourier based signal processing tools.