pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Mode Spatial Signal Processor with Rainbow-like Fast Beam Training and Wideband Communications using True-Time-Delay Arrays
Authors: Chung-Ching Lin, Chase Puglisi, Veljko Boljanovic, Han Yan, Erfan Ghaderi, Jayce Gaddis, Qiuyan Xu, Sreeni Poolakkal, Danijela Cabric, Subhanshu Gupta
Date: 2022-01-08 00:00:00
Tasks: Unity

Initial access in millimeter-wave (mmW) wireless is critical toward successful realization of the fifth-generation (5G) wireless networks and beyond. Limited bandwidth in existing standards and use of phase-shifters in analog/hybrid phased-antenna arrays (PAA) are not suited for these emerging standards demanding low-latency direction finding. This work proposes a reconfigurable true-time-delay (TTD) based spatial signal processor (SSP) with frequency-division beam training methodology and wideband beam-squint less data communications. Discrete-time delay compensated clocking technique is used to support 800~MHz bandwidth with a large unity-gain bandwidth ring-amplifier (RAMP)-based signal combiner. To extensively characterize the proposed SSP across different SSP modes and frequency-angle pairs, an automated testbed is developed using computer-vision techniques that significantly speeds up the testing progress and minimize possible human errors. Using seven levels of time-interleaving for each of the 4 antenna elements, the TTD SSP has a delay range of 3.8 ns over 800 MHz and achieves unique frequency-to-angle mapping in the beamtraining mode with nearly 12 dB frequency-independent gain in the beamforming mode. The SSP is prototyped in 65nm CMOS with an area of 1.98mm$^2$ consuming only 29 mW excluding buffers. Further, an error vector magnitude (EVM) of 9.8% is realized for 16-QAM modulation at a speed of 122.8 Mb/s.
Embedded Model Predictive Control Using Robust Penalty Method
Authors: Abhijith Sharma, Chaitanya Jugade, Shreya Yawalkar, Vaishali Patne, Deepak Ingole, Dayaram Sonawane
Date: 2022-01-07 00:00:00
Tasks: Model Predictive Control

Model predictive control (MPC) has become a hot cake technology for various applications due to its ability to handle multi-input multi-output systems with physical constraints. The optimization solvers require considerable time, limiting their embedded implementation for real-time control. To overcome the bottleneck of traditional quadratic programming (QP) solvers, this paper proposes a robust penalty method (RPM) to solve an optimization problem in a linear MPC. The main idea of RPM is to solve an unconstrained QP problem using Broyden Fletcher Goldfarb Shannon (BFGS) algorithm. The beauty of this method is that it can find optimal solutions even if initial conditions are in an infeasible region, which makes it robust. Moreover, the RPM is computationally inexpensive as compared to the traditional QP solvers. The proposed RPM is implemented on resource-limited embedded hardware (STM32 microcontroller), and its performance is validated with a case study of a citation aircraft control problem. We show the hardware-in-the-loop co-simulation results of the proposed RPM and compared them with the active set method (ASM) and interior point method (IPM) QP solvers. The performance of MPC with the aforementioned solvers is compared by considering the optimality, time complexity, and ease of hardware implementation. Presented results show that the proposed RPM gives the same optimality as ASM and IPM, and outperforms them in terms of speed.
Tunable electronic properties of germanene and two-dimensional group-III phosphides heterobilayers
Authors: Md. Rayid Hasan Mojumder
Date: 2022-01-07 00:00:00
Tasks: Band Gap

In this research work, the 2D structure of the germanene layer is compounded with 2D group-III phosphides: AlP and GaP. The planar structure of AlP and low-buckled GaP have been taken to form the bilayer patterns. In each case, three stacking patterns are considered, and their relaxed interlayer distance and binding energy have been reported. The binding energy being around in the range between ~150 to 210 meV shows the existence of weak van der Waals interactions between the layers. The heterostructures containing germanene and these two phosphides show an opening of a large indirect bandgap of magnitude range of ~200 meV to 600 meV, which can be tuned by changing interlayer distance and by incorporating bi-axial compressive and tensile strain. Although their normal bandgap, which significantly changes with SOC, is an indirect one, whilst tunning the interlayer distance band gap jumps from unsymmetrical point to symmetrical Dirac cones and becomes direct on K points. The charge carrier mostly concentrates on the p-orbitals of the germanene in the conduction regions; thus, the electrical properties of germanene will be retained, and the carrier will provide a much faster device response property. The absence of the phosphides influence makes them the intended substrate for growing the germanene layer on top of that. Again, due to the bandgap at Dirac cones being opened and jumps between the Dirac cones and band gap changes with SOC tropological insulator can be formed, and Quantum Spin Hall effect may exist.
Immersed boundary approach to biofilm spread on surfaces
Authors: Ana Carpio, Rafael Gonzalez-Albaladejo
Date: 2022-01-09 00:00:00

We propose a computational framework to study the growth and spread of bacterial biofilms on interfaces, as well as the action of antibiotics on them. Bacterial membranes are represented by boundaries immersed in a fluid matrix and subject to interaction forces. Growth, division and death of bacterial cells follow dynamic energy budget rules, in response to variations in environmental concentrations of nutrients, toxicants and substances released by the cells. In this way, we create, destroy and enlarge boundaries, either spherical or rod-like. Appropriate forces represent details of the interaction between cells, and the interaction with the environment. Numerical simulations illustrate the evolution of top views and diametral slices of small biofilm seeds, as well as the action of antibiotics. We show that cocktails of antibiotics targeting active and dormant cells can entirely eradicate a biofilm.
Higher Order Dynamic Mode Decomposition: from Fluid Dynamics to Heart Disease Analysis
Authors: Nourelhouda Groun, Maria Villalba-Orero, Enrique Lara-Pezzi, Eusebio Valero, Jesus Garicano-Mena, Soledad Le Clainche
Date: 2022-01-09 00:00:00

In this work, we study in detail the performance of Higher Order Dynamic Mode Decomposition (HODMD) technique when applied to echocardiography images. HODMD is a data-driven method generally used in fluid dynamics and in the analysis of complex non-linear dynamical systems modeling several complex industrial applications. In this paper we apply HODMD, for the first time to the authors knowledge, for patterns recognition in echocardiography, specifically, echocardiography data taken from several mice, either in healthy conditions or afflicted by different cardiac diseases. We exploit the HODMD advantageous properties in dynamics identification and noise cleaning to identify the relevant frequencies and coherent patterns for each one of the diseases. The echocardiography datasets consist of video loops taken with respect to a long axis view (LAX) and a short axis view (SAX), where each video loop covers at least three cardiac cycles, formed by (at most) 300 frames each (called snapshots). The proposed algorithm, using only a maximum quantity of 200 snapshots, was able to capture two branches of frequencies, representing the heart rate and respiratory rate. Additionally, the algorithm provided a number of modes, which represent the dominant features and patterns in the different echocardiography images, also related to the heart and the lung. Six datasets were analyzed: one echocardiography taken from a healthy subject and five different sets of echocardiography taken from subjects with either Diabetic Cardiomyopathy, Obesity, SFSR4 Hypertrophy, TAC Hypertrophy or Myocardial Infarction. The results show that HODMD is robust and a suitable tool to identify characteristic patterns able to classify the different pathologies studied.
Data-driven approach in a compartmental epidemic model to assess undocumented infections
Authors: Guilherme S. Costa, Wesley Cota, Silvio C. Ferreira
Date: 2022-01-10 00:00:00
Tasks: Time Series, Time Series Analysis

Nowcasting and forecasting of epidemic spreading rely on incidence series of reported cases to derive the fundamental epidemiological parameters for a given pathogen. Two relevant drawbacks for predictions are the unknown fractions of undocumented cases and levels of nonpharmacological interventions, which span highly heterogeneously across different places and times. We describe a simple data-driven approach using a compartmental model including asymptomatic and presymptomatic contagions that allows to estimate both the level of undocumented infections and the value of effective reproductive number R t from time series of reported cases, deaths, and epidemiological parameters. The method was applied to epidemic series for COVID-19 across different municipalities in Brazil allowing to estimate the heterogeneity level of under-reporting across different places. The reproductive number derived within the current framework is little sensitive to both diagnosis and infection rates during the asymptomatic states. The methods described here can be extended to more general cases if data is available and adapted to other epidemiological approaches and surveillance data.
Effects of infection fatality ratio and social contact matrices on vaccine prioritization strategies
Authors: Arthur Schulenburg, Wesley Cota, Guilherme S. Costa, Silvio C. Ferreira
Date: 2022-01-08 00:00:00

Effective strategies of vaccine prioritization are essential to mitigate the impacts of severe infectious diseases. We investigate the role of infection fatality ratio (IFR) and social contact matrices on vaccination prioritization using a compartmental epidemic model fueled by real-world data of different diseases and countries. Our study confirms that massive and early vaccination is extremely effective to reduce the disease fatality if the contagion is mitigated, but the effectiveness is increasingly reduced as vaccination beginning delays in an uncontrolled epidemiological scenario. The optimal and least effective prioritization strategies depend non-linearly on epidemiological variables. Regions of the epidemiological parameter space, in which prioritizing the most vulnerable population is more effective than the most contagious individuals, depend strongly on the IFR age profile being, for example, substantially broader for COVID-19 in comparison with seasonal influenza. Demographics and social contact matrices deform the phase diagrams but do not alter their qualitative shapes.
A machine learning search for optimal GARCH parameters
Authors: Luke De Clerk, Sergey Savl'ev
Date: 2022-01-10 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

Here, we use Machine Learning (ML) algorithms to update and improve the efficiencies of fitting GARCH model parameters to empirical data. We employ an Artificial Neural Network (ANN) to predict the parameters of these models. We present a fitting algorithm for GARCH-normal(1,1) models to predict one of the model's parameters, $\alpha_1$ and then use the analytical expressions for the fourth order standardised moment, $\Gamma_4$ and the unconditional second order moment, $\sigma^2$ to fit the other two parameters; $\beta_1$ and $\alpha_0$, respectively. The speed of fitting of the parameters and quick implementation of this approach allows for real time tracking of GARCH parameters. We further show that different inputs to the ANN namely, higher order standardised moments and the autocovariance of time series can be used for fitting model parameters using the ANN, but not always with the same level of accuracy.
StableSims: Optimizing MakerDAO Liquidations 2.0 Incentives via Agent-Based Modeling
Authors: Andrew Kirillov, Sehyun Chung
Date: 2022-01-10 00:00:00

The StableSims project set out to determine optimal parameters for the new auction mechanism, Liquidations 2.0, used by MakerDAO, a protocol built on Ethereum offering a decentralized, collateralized stablecoin called Dai. We developed an agent-based simulation that emulates both the Maker protocol smart contract logic, and how profit-motivated agents ("keepers") will act in the real world when faced with decisions such as liquidating "vaults" (collateralized debt positions) and bidding on collateral auctions. This research focuses on the incentive structure introduced in Liquidations 2.0, which implements both a constant fee (tip) and a fee proportional to vault size (chip) paid to keepers that liquidate vaults or restart stale collateral auctions. We sought to minimize the amount paid in incentives while maximizing the speed with which undercollateralized vaults were liquidated. Our findings indicate that it is more cost-effective to increase the constant fee, as opposed to the proportional fee, in order to decrease the time it takes for keepers to liquidate vaults.
Economic Integration and Agglomeration of Multinational Production with Transfer Pricing
Authors: Hayato Kato, Hirofumi Okoshi
Date: 2022-01-09 00:00:00

Do low corporate taxes always favor multinational production over economic integration? We propose a two-country model in which multinationals choose the locations of production plants and foreign distribution affiliates and shift profits between them through transfer prices. With high trade costs, plants are concentrated in the low-tax country; surprisingly, this pattern reverses with low trade costs. Indeed, economic integration has a non-monotonic impact: falling trade costs first decrease and then increase the plant share in the high-tax country, which we empirically confirm. Moreover, allowing for transfer pricing makes tax competition tougher and international coordination on transfer-pricing regulation can be beneficial.
TANK meets Diaz-Alejandro: Household heterogeneity, non-homothetic preferences & policy design
Authors: Santiago Camara
Date: 2022-01-09 00:00:00

This paper studies the role of households' heterogeneity in access to financial markets and the consumption of commodity goods in the transmission of foreign shocks. First, I use survey data from Uruguay to show that low income households have poor to no access to savings technology while spending a significant share of their income on commodity-based goods. Second, I construct a Two-Agent New Keynesian (TANK) small open economy model with two main features: (i) limited access to financial markets, and (ii) non-homothetic preferences over commodity goods. I show how these features shape aggregate dynamics and amplify foreign shocks. Additionally, I argue that these features introduce a redistribution channel for monetary policy and a rationale for "fear-of-floating" exchange rate regimes. Lastly, I study the design of optimal policy regimes and find that households have opposing preferences a over monetary and fiscal rules.
Simultaneous Optimal Transport
Authors: Ruodu Wang, Zhenyuan Zhang
Date: 2022-01-10 00:00:00

We propose a general framework of mass transport between vector-valued measures, which will be called simultaneous optimal transport (SOT). The new framework is motivated by the need to transport resources of different types simultaneously, i.e., in single trips, from specified origins to destinations; similarly, in economic matching, one needs to couple two groups, e.g., buyers and sellers, by equating supplies and demands of different goods at the same time. The mathematical structure of simultaneous transport is very different from the classic setting of optimal transport, leading to many new challenges. The Monge and Kantorovich formulations are contrasted and connected. Existence conditions and duality formulas are established. More interestingly, by connecting SOT to a natural relaxation of martingale optimal transport (MOT), we introduce the MOT-SOT parity, which allows for explicit solutions of SOT in many interesting cases.
A study on bribery networks with a focus on harassment bribery and ways to control corruption
Authors: Chanchal Pramanik
Date: 2022-01-08 00:00:00

The paper focuses on the bribery network emphasizing harassment bribery. A bribery network ends with the police officer whose utility from the bribe is positive and the approving officer in the network. The persistent nature of corruption is due to colluding behavior of the bribery networks. The probability of detection of bribery incidents will help in improving controlling corruption in society. The asymmetric form of punishment and award equivalent to the amount of punishment to the network can enhance the probability of detection of harassment bribery $(p_{h})$ and thus increasing the probability of detection of overall bribery $(p_{h} \in p)$.
The component-wise egalitarian Myerson value for Network Games
Authors: Surajit Borkotokey, Sujata Goala, Niharika Kakoty, Parishmita Boruah
Date: 2022-01-08 00:00:00

We introduce the component-wise egalitarian Myerson value for network games. This new value being a convex combination of the Myerson value and the component-wise equal division rule is a player-based allocation rule. In network games under the cooperative framework, the Myerson value is an extreme example of marginalism, while the equal division rule signifies egalitarianism. In the proposed component-wise egalitarian Myerson value, a convexity parameter combines these two attributes and determines the degree of solidarity to the players. Here, by solidarity, we mean the mutual support or compensation among the players in a network. We provide three axiomatic characterizations of the value. Further, we propose an implementation mechanism for the component-wise egalitarian Myerson value under subgame perfect Nash equilibrium.
Data Processing of Functional Optical Microscopy for Neuroscience
Authors: Hadas Benisty, Alexander Song, Gal Mishne, Adam S. Charles
Date: 2022-01-10 00:00:00

Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. In this review, we cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.
Nonasymptotic performance analysis of ESPRIT and spatial-smoothing ESPRIT
Authors: Zai Yang
Date: 2022-01-10 00:00:00

This paper is concerned with the problem of frequency estimation from multiple-snapshot data. It is well-known that ESPRIT (and spatial-smoothing ESPRIT in presence of coherent sources or given limited snapshots) can locate the true frequencies if either the number of snapshots or the signal-to-noise ratio (SNR) approaches infinity. In this paper, we analyze the nonasymptotic performance of ESPRIT and spatial-smoothing ESPRIT with finitely many snapshots and finite SNR. We show that the absolute frequency estimation error of ESPRIT (or spatial-smoothing ESPRIT) is bounded from above by $C\frac{\max(\sigma, \sigma^2)}{\sqrt{L}}$ with overwhelming probability, where $\sigma^2$ denotes the Gaussian noise variance, $L$ is the number of snapshots and $C$ is a coefficient independent of $L$ and $\sigma^2$, if and only if the true frequencies can be localized by ESPRIT (or spatial-smoothing ESPRIT) without noise or with infinitely many snapshots. Our results are obtained by deriving new matrix perturbation bounds and generalizing the classical Schur product theorem, which may be of independent interest. Extensions to MUSIC and SS-MUSIC are also made. Numerical results are provided corroborating our analysis.
$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar
Authors: Yanlong Qiu, Jiaxi Zhang, Yanjiao Chen, Jin Zhang, Bo Ji
Date: 2022-01-10 00:00:00
Tasks: Autonomous Driving

Millimeter-wave (mmWave) radars have found applications in a wide range of domains, including human tracking, health monitoring, and autonomous driving, for their unobtrusive nature and high range accuracy. These capabilities, however, if used for malicious purposes, could also result in serious security and privacy issues. For example, a user's daily life could be secretly monitored by a spy radar. Hence, there is a strong urge to develop systems that can detect and locate such spy radars. In this paper, we propose $Radar^2$, a practical system for passive spy radar detection and localization using a single commercial off-the-shelf (COTS) mmWave radar. Specifically, we propose a novel \textit{Frequency Component Detection} method to detect the existence of mmWave signals, distinguish between mmWave radar and WiGig signals using a waveform classifier based on a convolutional neural network (CNN), and localize spy radars using triangulation based on the detector's observations at multiple anchor points. Not only does $Radar^2$ work for different types of mmWave radar, but it can also detect and localize multiple radars simultaneously. Finally, we performed extensive experiments to evaluate the effectiveness and robustness of $Radar^2$ in various settings. Our evaluation results show that the radar detection rate is above 96$\%$ and the localization error is within 0.3m. The results also reveal that $Radar^2$ is robust against various environmental factors (e.g., room layout and human activities).
Impact of NOMA and CoMP Implementation Order on the Performance of Ultra-Dense Networks
Authors: Akhileswar Chowdary, Garima Chopra, Abhinav Kumar, Linga Reddy Cenkeramaddi
Date: 2022-01-10 00:00:00

Non-orthogonal multiple access (NOMA) is a promising multiple access technology to improve the throughput and spectral efficiency of the users for 5G and beyond cellular networks. Similarly, coordinated multi-point transmission and reception (CoMP) is an existing technology to improve the coverage of cell-edge users. Hence, NOMA along with CoMP can potentially enhance the throughput and coverage of the users. However, the order of implementation of CoMP and NOMA can have a significant impact on the system performance of Ultra-dense networks (UDNs). Motivated by this, we study the performance of the CoMP and NOMA based UDN by proposing two kinds of user grouping and pairing schemes that differ in the order in which CoMP and NOMA are performed for a group of users. Detailed simulation results are presented comparing the proposed schemes with the state-of-the-art systems with varying user and base station densities. Through numerical results, we show that the proposed schemes can be used to achieve a suitable coverage-throughout trade-off in UDNs.