pwc-archive/papers-with-abstracts huggingface dataset:
International Trade Network: Country centrality and COVID-19 pandemic
Authors: Roberto Antonietti, Paolo Falbo, Fulvio Fontini, Rosanna Grassi, Giorgio Rizzini
Date: 2021-07-30 00:00:00
Tasks: Community Detection

International trade is based on a set of complex relationships between different countries that can be modelled as an extremely dense network of interconnected agents. On the one hand, this network might favour the economic growth of countries, but on the other, it can also favour the diffusion of diseases, like the COVID-19. In this paper, we study whether, and to what extent, the topology of the trade network can explain the rate of COVID-19 diffusion and mortality across countries. We compute the countries' centrality measures and we apply the community detection methodology based on communicability distance. Then, we use these measures as focal regressors in a negative binomial regression framework. In doing so, we also compare the effect of different measures of centrality. Our results show that the number of infections and fatalities are larger in countries with a higher centrality in the global trade network.
Observer-based switched-linear system identification
Authors: Fethi Bencherki, Semiha Türkay, Hüseyin Akçay
Date: 2021-07-30 00:00:00
Tasks: Denoising

In this paper, we present a methodology to identify discrete-time state-space switched linear systems (SLSs) from input-output measurements. Continuous-state is not assumed to be measured. The key step is a deadbeat observer based transformation to a switched auto-regressive with exogenous input (SARX) model. This transformation reduces the state-space identification problem to a SARX model estimation problem. Overfitting issues are tackled. The switch and parameter identifiability and the persistence of excitation conditions on the inputs are discussed in detail. The discrete-states are identified in the observer domain by solving a non-convex sparse optimization problem. A clustering algorithm reveals the discrete-states under mild assumptions on the system structure and the dwell times. The switching sequence is estimated from the input-output data by the multi-variable output error state space (MOESP) algorithm and a variant modified from it. A convex relaxation of the sparse optimization problem yields the block basis pursuit denoising (BBPDN) algorithm. Theoretical findings are supported by means of a detailed numerical example. In this example, the proposed methodology is also compared to another identification scheme in hybrid systems literature.
Towards a Sustainable Power Grid: Stochastic Hierarchical Planning for High Renewable Integration
Authors: Semih Atakan, Harsha Gangammanavar, Suvrajeet Sen
Date: 2021-07-30 00:00:00
Tasks: Stochastic Optimization

Driven by ambitious renewable portfolio standards, large-scale inclusion of variable energy resources (such as wind and solar) are expected to introduce unprecedented levels of uncertainty into power system operations. The current practice of operations planning with deterministic optimization models may be ill-suited for a future with abundant uncertainty. To overcome the potential reliability and economic challenges, we present a stochastic hierarchical planning (SHP) framework. This framework captures operations at day-ahead, short-term and hour-ahead timescales, along with the interactions between the stochastic processes and decisions. In contrast to earlier studies where stochastic optimization of individual problems (e.g., unit commitment, economic dispatch) have been studied, this paper studies an integrated framework of \emph{planning under uncertainty}, where stochastic optimization models are stitched together in a hierarchical setting which parallels the deterministic hierarchical planning approach that is widely adopted in the power industry. Our experiments, based on the NREL-118 dataset, reveal that under high renewable penetration, significant operational improvements can be expected by transitioning to the SHP paradigm. In particular, the computational results show that significant improvements can be achieved in several metrics, including system reliability, environmental sustainability, and system economics, solely by making a strategic choice to adopt the new SHP paradigm.
Adaptive Optimization of Autonomous Vehicle Computational Resources for Performance and Energy Improvement
Authors: Saurabh Jambotkar, Longxiang Guo, Yunyi Jia
Date: 2021-02-13 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

Autonomous vehicles usually consume a large amount of computational power for their operations, especially for the tasks of sensing and perception with artificial intelligence algorithms. Such a computation may not only cost a significant amount of energy but also cause performance issues when the onboard computational resources are limited. To address this issue, this paper proposes an adaptive optimization method to online allocate the onboard computational resources of an autonomous vehicle amongst multiple vehicular subsystems depending on the contexts of the situations that the vehicle is facing. Different autonomous driving scenarios were designed to validate the proposed approach and the results showed that it could help improve the overall performance and energy consumption of autonomous vehicles compared to existing computational arrangement.
Optimal Variable Speed Limit Control Strategy on Freeway Segments under Fog Conditions
Authors: Ben Zhai, Yanli Wang, Wenxuan Wang, Bing Wu
Date: 2021-07-30 00:00:00

Fog is a critical external factor that threatens traffic safety on freeways. Variable speed limit (VSL) control can effectively harmonize vehicle speed and improve safety. However, most existing weather-related VSL controllers are limited to adapt to the dynamic traffic environment. This study developed optimal VSL control strategy under fog conditions with fully consideration of factors that affect traffic safety risks. The crash risk under fog conditions was estimated using a crash risk prediction model based on Bayesian logistic regression. The traffic flow with VSL control was simulated by a modified cell transmission model (MCTM). The optimal factors of VSL control were obtained by solving an optimization problem that coordinated safety and mobility with the help of the genetic algorithm. An example of I-405 in California, USA was designed to simulate and evaluate the effects of the proposed VSL control strategy. The optimal VSL control factors under fog conditions were compared with sunny conditions, and different placements of VSL signs were evaluated. Results showed that the optimal VSL control strategy under fog conditions changed the speed limit more cautiously. The VSL control under fog conditions in this study effectively reduced crash risks without significantly increasing travel time, which is up to 37.15% reduction of risks and only 0.48% increase of total travel time. The proposed VSL control strategy is expected to be of great use in the development of VSL systems to enhance freeway safety under fog conditions.
LPV Delay-Dependent Sampled-Data Output-Feedback Control of Fueling in Spark Ignition Engines
Authors: Shahin Tasoujian, Karolos Grigoriadis, Matthew Franchek
Date: 2021-07-29 00:00:00
Tasks: Scheduling

We propose a delay-dependent sampled-data output-feedback LPV control technique to address the air-fuel ratio (AFR) regulation problem in spark ignition (SI) engines. AFR control and advanced fueling strategies are essential for maximizing fuel economy while minimizing harmful exhaust emissions. The fuel path of the SI engine, as well as the three-way catalyst (TWC) simplified dynamics, have been captured by a continuous-time linear parameter-varying (LPV) system with varying time delay, where the system dynamics rely on the engine speed, defined as the system's scheduling parameter. The interconnection of the continuous-time plant and a digital controller through analog-to-digital and digital-to-analog converter devices forms a hybrid closed-loop configuration. Therefore, in order to benefit from continuous-time control synthesis tools, the input-delay method has been employed to transform the hybrid closed-loop system into the continuous-time domain with system inherent time delay and an additional delay imposed by the mapping approach. The designed sampled-data gain scheduled output-feedback controller seeks to establish the closed-loop asymptotic stability and a prescribed level of performance for the LPV system with an arbitrarily varying time delay and varying sampling time, where the synthesis results are provided in a convex linear matrix inequality (LMI) constraint setting. Finally, several closed-loop simulation scenarios are conducted, and comparisons are provided to demonstrate the proposed methodology's performance in achieving precise reference AFR tracking and disturbance attenuation.
Comparing cars with apples? Identifying the appropriate benchmark countries for relative ecological pollution rankings and international learning
Authors: Dominik Hartmann, Diogo Ferraz, Mayra Bezerra, Andreas Pyka, Flavio L. Pinheiro
Date: 2021-07-29 00:00:00

Research in Data Envelopment Analysis has created rankings of the ecological efficiency of countries' economies. At the same time, research in economic complexity has provided new methods to depict productive structures and has analyzed how economic diversification and sophistication affect environmental pollution indicators. However, no research so far has compared the ecological efficiency of countries with similar productive structures and levels of economic complexity, combining the strengths of both approaches. In this article, we use data on 774 different types of exports, CO2 emissions, and the ecological footprint of 99 countries to create a relative ecological pollution ranking (REPR). Moreover, we use methods from network science to reveal a benchmark network of the best learning partners based on country pairs with a large extent of export similarity, yet significant differences in pollution values. This is important because it helps to reveal adequate benchmark countries for efficiency improvements and cleaner production, considering that countries may specialize in substantially different types of economic activities. Finally, the article (i) illustrates large efficiency improvements within current global output levels, (ii) helps to identify countries that can best learn from each other, and (iii) improves the information base in international negotiations for the sake of a clean global production system.
Semiparametric Estimation of Long-Term Treatment Effects
Authors: Jiafeng Chen, David M. Ritzwoller
Date: 2021-07-30 00:00:00

Long-term outcomes of experimental evaluations are necessarily observed after long delays. We develop semiparametric methods for combining the short-term outcomes of experiments with observational measurements of short-term and long-term outcomes, in order to estimate long-term treatment effects. We characterize semiparametric efficiency bounds for various instances of this problem. These calculations facilitate the construction of several estimators. We analyze the finite-sample performance of these estimators with a simulation calibrated to data from an evaluation of the long-term effects of a poverty alleviation program.
Spousal Occupational Sorting and COVID-19 Incidence: Evidence from the United States
Authors: Egor Malkov
Date: 2021-07-29 00:00:00

How do matching of spouses and the nature of work jointly shape the distribution of COVID-19 health risks? To address this question, I study the association between the incidence of COVID-19 and the degree of spousal sorting into occupations that differ by contact intensity at the workplace. The mechanism, that I explore, implies that the higher degree of positive spousal sorting mitigates intra-household contagion and this translates into a smaller number of individuals exposed to COVID-19 risk. Using the U.S. data at the state level, I argue that spousal sorting is an important factor for understanding the disparities in the prevalence of COVID-19 during the early stages of the pandemic. First, I document that it creates about two-thirds of the U.S. dual-earner couples that are exposed to higher COVID-19 health risk due to within-household transmission. Moreover, I uncover substantial heterogeneity in the degree of spousal sorting by state. Next, for the first week of April 2020, I estimate that a one standard deviation increase in the measure of spousal sorting is associated with a 30% reduction in the total number of cases per 100000 inhabitants and a 39.3% decline in the total number of deaths per 100000 inhabitants. Furthermore, I find substantial temporal heterogeneity as the coefficients decline in magnitude over time. My results speak to the importance of policies that allow mitigating intra-household contagion.
Reconciling revealed and stated measures for willingness to pay in recreation by building a probability model
Authors: Edoh Y. Amiran, Joni S. James Charles
Date: 2021-07-29 00:00:00

The consumers' willingness to pay plays an important role in economic theory and in setting policy. For a market, this function can often be estimated from observed behavior -- preferences are revealed. However, economists would like to measure consumers' willingness to pay for some goods where this can only be measured through stated valuation. Confirmed convergence of valuations based on stated preferences as compared to valuations based on revealed preferences is rare, and it is important to establish circumstances under which one can expect such convergence. By building a simple probabilistic model for the consumers' likelihood of travel, we provide an approach that should make comparing stated and revealed preferences easier in cases where the preference is tied to travel or some other behavior whose cost can be measured. We implemented this approach in a pilot study and found an estimate of willingness to pay for visiting an environmentally enhanced recreational site based on actual travel in good agreement with an estimate based on a survey using stated preferences. To use the probabilistic model we used population statistics to adjust for the relevant duration and thus compare stated and revealed responses.
On-Line Audio-to-Lyrics Alignment Based on a Reference Performance
Authors: Charles Brazier, Gerhard Widmer
Date: 2021-07-30 00:00:00
Tasks: Specificity

Audio-to-lyrics alignment has become an increasingly active research task in MIR, supported by the emergence of several open-source datasets of audio recordings with word-level lyrics annotations. However, there are still a number of open problems, such as a lack of robustness in the face of severe duration mismatches between audio and lyrics representation; a certain degree of language-specificity caused by acoustic differences across languages; and the fact that most successful methods in the field are not suited to work in real-time. Real-time lyrics alignment (tracking) would have many useful applications, such as fully automated subtitle display in live concerts and opera. In this work, we describe the first real-time-capable audio-to-lyrics alignment pipeline that is able to robustly track the lyrics of different languages, without additional language information. The proposed model predicts, for each audio frame, a probability vector over (European) phoneme classes, using a very small temporal context, and aligns this vector with a phoneme posteriogram matrix computed beforehand from another recording of the same work, which serves as a reference and a proxy to the written-out lyrics. We evaluate our system's tracking accuracy on the challenging genre of classical opera. Finally, robustness to out-of-training languages is demonstrated in an experiment on Jingju (Beijing opera).
Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition
Authors: Qinqin Yang, Yanhong Lin, Jiechao Wang, Jianfeng Bao, Xiaoyin Wang, Lingceng Ma, Zihan Zhou, Qizhi Yang, Shuhui Cai, Hongjian He, Congbo Cai, Jiyang Dong, Jingliang Cheng, Zhong Chen, Jianhui Zhong
Date: 2021-07-30 00:00:00

Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI). However, the challenge brought by domain gap between synthetic and real data is usually encountered, especially under complex experimental conditions. In this study, by combining Bloch simulation and general MRI models, we propose a framework for addressing the lack of training data in supervised learning scenarios, termed MOST-DL. A challenging application is demonstrated to verify the proposed framework and achieve motion-robust T2 mapping using single-shot overlapping-echo acquisition. We decompose the process into two main steps: (1) calibrationless parallel reconstruction for ultra-fast pulse sequence and (2) intra-shot motion correction for T2 mapping. To bridge the domain gap, realistic textures from a public database and various imperfection simulations were explored. The neural network was first trained with pure synthetic data and then evaluated with in vivo human brain. Both simulation and in vivo experiments show that the MOST-DL method significantly reduces ghosting and motion artifacts in T2 maps in the presence of unpredictable subject movement and has the potential to be applied to motion-prone patients in the clinic.
Deep Learning Framework for Hybrid Analog-Digital Signal Processing in mmWave Massive-MIMO Systems
Authors: Alireza Morsali, Afshin Haghighat, Benoit Champagne
Date: 2021-07-30 00:00:00

Hybrid analog-digital signal processing (HSP) is an enabling technology to harvest the potential of millimeter-wave (mmWave) massive-MIMO communications. In this paper, we present a general deep learning (DL) framework for efficient design and implementation of HSP-based massive-MIMO systems. Exploiting the fact that any complex matrix can be written as a scaled sum of two matrices with unit-modulus entries, a novel analog deep neural network (ADNN) structure is first developed which can be implemented with common radio frequency (RF) components. This structure is then embedded into an extended hybrid analog-digital deep neural network (HDNN) architecture which facilitates the implementation of mmWave massive-MIMO systems while improving their performance. In particular, the proposed HDNN architecture enables HSP-based massive-MIMO transceivers to approximate any desired transmitter and receiver mapping with arbitrary precision. To demonstrate the capabilities of the proposed DL framework, we present a new HDNN-based beamformer design that can achieve the same performance as fully-digital beamforming, with reduced number of RF chains. Finally, simulation results are presented confirming the superiority of the proposed HDNN design over existing hybrid beamforming schemes.
New trends in indoor positioning based on WiFi and machine learning: A systematic review
Authors: Vladimir Bellavista-Parent, Joaquín Torres-Sospedra, Antoni Perez-Navarro
Date: 2021-07-29 00:00:00
Tasks: BIG-bench Machine Learning, Position

Currently there is no standard indoor positioning system, similar to outdoor GPS. However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so. But what are the most commonly used techniques in machine learning? What accuracy do they achieve? Where have they been tested? This article presents a systematic review of works between 2019 and 2021 that use WiFi as the signal for positioning and machine learning models to estimate indoor position. 64 papers have been identified as relevant, which have been systematically analyzed for a better understanding of the current situation in different aspects. The results show that indoor positioning based on WiFi trends use neural network-based models, evaluated in empirical experiments. Despite this, many works still conduct an assessment in small areas, which can influence the goodness of the results presented.
Potential 3rd COVID Wave in Mumbai: Scenario Analysis
Authors: Sandeep Juneja, Daksh Mittal
Date: 2021-07-30 00:00:00

The second wave of Covid-19 that started in mid-February 2021 in Mumbai is now subsiding. Increasingly the focus amongst the policy makers and general public is on the potential third wave. Due to uncertainties regarding emergence of new variants and reinfections, instead of projecting our best guess scenario, in this report we conduct an extensive scenario analysis for Mumbai and track peak fatalities in the coming months in each of these scenarios. Our key conclusions are - As per our model, about 80% of Mumbai population has been exposed to Covid-19 by June 1, 2021. Under the assumption that all who are exposed have immunity against further infection, it is unlikely that Mumbai would see a large third wave. It is the reinfections that may lead to a large wave. Reinfections could occur because of declining antibodies amongst the infected as well as by variants that can break through the immunity provided by prior infections. Even under a reasonably pessimistic scenario we observe the resulting peak to be no larger than that under the second wave. We further observe that under the scenario where the reinfections are mild so that they affect the fatality figures negligibly, where the new variants (beyond the existing delta variant) have a mild impact, as the city opens up, we observe a small wave in the coming months. However, if by then the vaccine coverage is extensive, this wave will be barely noticeable. We also plot $R_t$, the infection growth rate at time $t$, and highlight some interesting observations.
Actin reorganization throughout the cell cycle mediated by motor proteins
Authors: Maria-Veronica Ciocanel, Aravind Chandrasekaran, Carli Mager, Qin Ni, Garegin Papoian, Adriana Dawes
Date: 2021-07-30 00:00:00

Cortical actin networks are highly dynamic and play critical roles in shaping the mechanical properties of cells. The actin cytoskeleton undergoes significant reorganization over the course of the cell cycle, when cortical actin transitions between open patched meshworks, homogeneous distributions, and aligned bundles. Several types of myosin motor proteins, characterized by different kinetic parameters, have been involved in this reorganization of actin filaments. Given the limitations in studying the interactions of actin with myosin in vivo, we propose stochastic agent-based model simulations and develop a set of data analysis measures to assess how myosin motor proteins mediate various actin organizations. In particular, we identify individual motor parameters, such as motor binding rate and step size, that generate actin networks with different levels of contractility and different patterns of myosin motor localization. In simulations where two motor populations with distinct kinetic parameters interact with the same actin network, we find that motors may act in a complementary way, by tuning the actin network organization, or in an antagonistic way, where one motor emerges as dominant. This modeling and data analysis framework also uncovers parameter regimes where spatial segregation between motor populations is achieved. By allowing for changes in kinetic rates during the actin-myosin dynamic simulations, our work suggests that certain actin-myosin organizations may require additional regulation beyond mediation by motor proteins in order to reconfigure the cytoskeleton network on experimentally-observed timescales.