pwc-archive/papers-with-abstracts huggingface dataset:
Supporting GNSS Baseband Using Smartphone IMU and Ultra-Tight Integration
Authors: Yiran Luo, You Li, Jin Wang, Naser El-Sheimy
Date: 2021-11-04 00:00:00

A great surge in the development of global navigation satellite systems (GNSS) excavates the potential for prosperity in many state-of-the-art technologies, e.g., autonomous ground vehicle navigation. Nevertheless, the GNSS is vulnerable to various ground interferences, which significantly break down the continuity of the navigation system. Meanwhile, the GNSS-based next-generation navigation devices are being developed to be smaller, more low-cost, and lightweight, as the commercial market forecasts. This work aims to answer whether the smartphone inertial measurement unit (IMU) is sufficient to support the GNSS baseband. Thus, a cascaded ultra-tightly coupled GNSS/inertial navigation system (INS) technique, where consumer-level smartphone sensors are used, is applied to improve the baseband of GNSS software-defined radios (SDRs). A Doppler value is predicted based on an integrated extended Kalman filter (EKF) navigator where the pseudorange-state-based measurements of GNSS and INS are fused. It is used to assist numerically controlled oscillators (NCOs) in the GNSS baseband. Then, an ultra-tight integration platform is built with the upgraded GNSS SDR, of which baseband processing is integrated with INS mechanization. Finally, tracking and carrier-based positioning performances are assessed in the proposed platform for the smartphone-IMU-aided GNSS baseband via kinematic field tests. The experimental results prove that extra hardware with only a few dollars instead of more expensive ones can improve the GNSS baseband efficiently.
Clustering-based Multicast Scheme for UAV Networks
Authors: Hao Song, Lingjia Liu, Bodong Shang, Scott Pudlewski, Elizabeth Serena Bentley
Date: 2021-11-03 00:00:00
Tasks: Clustering, Collision Avoidance

When an unmanned aerial vehicle (UAV) network is utilized as an aerial small base station (BS), like a relay deployed far away from macro BSs, existing multicast methods based on acknowledgement (ACK) feedback and retransmissions may encounter severe delay and signaling overhead due to hostile wireless environments caused by a long-distance propagation and numerous UAVs. In this paper, a novel multicast scheme is designed for UAV networks serving as an aerial small BS, where a UAV experiencing a packet loss will request the packet from other UAVs in the same cluster rather than relying on retransmissions of BSs. The technical details of the introduced multicast scheme are designed with the carrier sense multiple access with collision avoidance (CSMA/CA) protocol for practicability and without loss of generality. Then, the Poisson cluster process is employed to model UAV networks to capture their dynamic network topology, based on which distance distributions are derived using tools of stochastic geometry for analytical tractability. Additionally, critical performance indicators of the designed multicast scheme are analyzed. Through extensive simulation studies, the superiority of the designed multicast scheme is demonstrated and the system design insight related to the proper number of clusters is revealed.
Real-Time Simulation of Level 1, Level 2, and Level 3 Electric Vehicle Charging Systems
Authors: Li Bao, Lingling Fan, Zhixin Miao
Date: 2021-11-03 00:00:00

A charging system is required to convert ac electricity from the grid to dc electricity to charge an electric vehicle (EV) battery. According to the Society of Automatic Engineers (SAE) standard, EV chargers can be divided into three levels based on power rating: Level 1, Level 2, and Level 3. This paper investigates the circuit topologies and control principles of EV charging systems at each level. Three high-fidelity testbeds of EV charging systems for a 10 kWh battery are designed and implemented in real-time digital simulator RT-Lab. The testbeds include modeling details such as switching of semiconductors. Twenty-five minutes real-time simulation is conducted for each testbed. Detailed dynamic performance of the circuits and the controls at every stage are presented to demonstrate the charging process. All three level EV charging systems employ high-frequency transformer embedded dual active bridge (DAB) dc/dc converter to regulate battery side dc voltage and current. Hence, average model-based linear system analysis is given to configure the parameters of the phase shift control adopted by the DAB dc/dc converter. In addition, power factor control (PFC) that is employed for Level 1 and Level 2 single-phase ac charging systems, three-phase voltage source converter control that is employed for Level 3 three-phase ac charging systems, are all analyzed. The three testbeds, with their detailed circuit parameters and control parameters presented, can be used as reference testbeds for EV grid integration research.
Smart sensors network for accurate indirect heat accounting in apartment buildings
Authors: Yves Stauffer, Fabio Saba, Rafael E. Carrillo, Max Boegli, Andrea Malengo, Andreas Hutter
Date: 2021-11-03 00:00:00

A new method for accurate indirect heat accounting in apartment buildings has been recently developed by the Centre Suisse d'Electronique et de Microtechnique (CSEM). It is based on a data driven approach aimed to the smart networking of any type of indirect heat allocation devices, which can provide, for each heat delivery point of an apartment building, measurements or estimations of the temperature difference between the heat transfer fluid and the indoor environment. The analysis of the data gathered from the devices installed on the heating bodies, together with the measurements of the overall building heat consumption provided by direct heat metering, allows the evaluation of the characteristic thermal model parameters of heating bodies at actual installation and working conditions. Thus overcoming the negative impact on accuracy of conventional indirect heat accounting due to off-design operation, in which these measurement systems normally operate. The method has been tested on conventional heat cost allocators (HCA), and on innovative smart radiator thermostatic valves developed by CSEM. The evaluations were carried out at the centralized heating system mock-up of the Istituto Nazionale di Ricerca Metrologica (INRIM), and also in a real building in Neuchatel, Switzerland. The method has proven to be an effective tool to improve the accuracy of indirect heat metering systems; compared to conventional HCA systems, the error on the individual heating bill is reduced by 20% to 50%.
A Two-Stage Stochastic Programming Model for Blood Supply Chain Management, Considering Facility Disruption and Service Level
Authors: Mohammad Arani, Mohsen Momenitabar, Zhila Dehdari Ebrahimi, Xian Liu
Date: 2021-11-02 00:00:00
Tasks: Management

In this paper, a blood supply chain network, where the occurrence of disruption might interrupt the flow of Red Blood Cells, is dealt with. In principle, the probability of disruption is not the only property confiding the network, but unprecedented fluctuations in supplies and demands also contribute to the network shortages and outdated blood units. Although the consideration of parameter uncertainties is of paramount importance in the real-world circumstances for a decision-maker, she or he would be willing to monitor the network in a properly broader perspective. Therefore, one of the eminent key performance indicators known as service level turned our attention. To tackle uncertainties in the mentioned network - comprising of the four conventional levels containing donors, blood collection facilities, blood banks, and hospitals - we present a two-stage stochastic programming model. Consequently, a toy-example is randomly generated to validate the proposed model. Furthermore, numerical analysis led us to a comprehensive service level analysis. Finally, potential pathways for future research are suggested.
Proteins -- a celebration of consilience
Authors: Tatjana Škrbić, Trinh Xuan Hoang, Achille Giacometti, Amos Maritan, Jayanth R. Banavar
Date: 2021-11-04 00:00:00

Proteins are the common constituents of all living cells. They are molecular machines that interact with each other as well as with other cell products and carry out a dizzying array of functions with distinction. These interactions follow from their native state structures and therefore understanding sequence-structure relationships is of fundamental importance. What is quite remarkable about proteins is that their understanding necessarily straddles several disciplines. The importance of geometry in defining protein native state structure, the constraints placed on protein behavior by mathematics and physics, the need for proteins to obey the laws of quantum chemistry, and the rich role of evolution and biology all come together in defining protein science. Here we review ideas from the literature and present an interdisciplinary framework that aims to marry ideas from Plato and Darwin and demonstrates an astonishing consilience between disciplines in describing proteins. We discuss the consequences of this framework on protein behavior.
Long anisotropic absolute refractory periods with rapid rise-times to reliable responsiveness
Authors: Shira Sardi, Roni Vardi, Yael Tugendhaft, Anton Sheinin, Amir Goldental, Ido Kanter
Date: 2021-11-04 00:00:00

Refractoriness is a fundamental property of excitable elements, such as neurons, indicating the probability for re-excitation in a given time-lag, and is typically linked to the neuronal hyperpolarization following an evoked spike. Here we measured the refractory periods (RPs) in neuronal cultures and observed that an average anisotropic absolute RP could exceed 10 milliseconds and its tail 20 milliseconds, independent of a large stimulation frequency range. It is an order of magnitude longer than anticipated and comparable with the decaying membrane potential timescale. It is followed by a sharp rise-time (relative RP) of merely ~1 millisecond to complete responsiveness. Extracellular stimulations result in longer absolute RPs than solely intracellular ones, and a pair of extracellular stimulations from two different routes exhibits distinct absolute RPs, depending on their order. Our results indicate that a neuron is an accurate excitable element, where the diverse RPs cannot be attributed solely to the soma and imply fast mutual interactions between different stimulation routes and dendrites. Further elucidation of neuronal computational capabilities and their interplay with adaptation mechanisms is warranted.
Concerted Rolling and Membrane Penetration Revealed by Atomistic Simulations of Antimicrobial Peptides
Authors: Jacob M. Remington, Jonathon B. Ferrell, Jianing Li
Date: 2021-11-03 00:00:00

Short peptides with antimicrobial activity have therapeutic potential for treating bacterial infections. Mechanisms of actions for antimicrobial peptides require binding the biological membrane of their target, which often represents a key mechanistic step. A multitude of data-driven approaches have been developed to predict potential antimicrobial peptide sequences; however, these methods are usually agnostic to the physical interactions between the peptide and the membrane. Towards developing higher throughput screening methodologies, here we use Markov State Modeling and all-atom molecular dynamics simulations to quantify the membrane binding and insertion kinetics of three prototypical and antimicrobial peptides (alpha-helical magainin 2 and PGLa and beta-hairpin tachyplesin 1). By leveraging a set of collective variables that capture the essential physics of the amphiphilic and cationic peptide-membrane interactions we reveal how the slowest kinetic process of membrane insertion is the dynamic rolling of the peptide from a prebound to fully inserted state. These results add critical details to how antimicrobial peptides insert into bacterial membranes.
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Authors: Jeremy Yu, Lu Lu, Xuhui Meng, George Em Karniadakis
Date: 2021-11-01 00:00:00

Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual into the loss function of the neural network, and have been successfully employed to solve diverse forward and inverse PDE problems. However, one disadvantage of the first generation of PINNs is that they usually have limited accuracy even with many training points. Here, we propose a new method, gradient-enhanced physics-informed neural networks (gPINNs), for improving the accuracy and training efficiency of PINNs. gPINNs leverage gradient information of the PDE residual and embed the gradient into the loss function. We tested gPINNs extensively and demonstrated the effectiveness of gPINNs in both forward and inverse PDE problems. Our numerical results show that gPINN performs better than PINN with fewer training points. Furthermore, we combined gPINN with the method of residual-based adaptive refinement (RAR), a method for improving the distribution of training points adaptively during training, to further improve the performance of gPINN, especially in PDEs with solutions that have steep gradients.
Use of low-fidelity models with machine-learning error correction for well placement optimization
Authors: Haoyu Tang, Louis J. Durlofsky
Date: 2021-10-30 00:00:00

Well placement optimization is commonly performed using population-based global stochastic search algorithms. These optimizations are computationally expensive due to the large number of multiphase flow simulations that must be conducted. In this work, we present an optimization framework in which these simulations are performed with low-fidelity (LF) models. These LF models are constructed from the underlying high-fidelity (HF) geomodel using a global transmissibility upscaling procedure. Tree-based machine-learning methods, specifically random forest and light gradient boosting machine, are applied to estimate the error in objective function value (in this case net present value, NPV) associated with the LF models. In the offline (preprocessing) step, preliminary optimizations are performed using LF models, and a clustering procedure is applied to select a representative set of 100--150 well configurations to use for training. HF simulation is then performed for these configurations, and the tree-based models are trained using an appropriate set of features. In the online (runtime) step, optimization with LF models, with the machine-learning correction, is conducted. Differential evolution is used for all optimizations. Results are presented for two example cases involving the placement of vertical wells in 3D bimodal channelized geomodels. We compare the performance of our procedure to optimization using HF models. In the first case, 25 optimization runs are performed with both approaches. Our method provides an overall speedup factor of 46 relative to optimization using HF models, with the best-case NPV within 1% of the HF result. In the second case fewer HF optimization runs are conducted (consistent with actual practice), and the overall speedup factor with our approach is about 8. In this case, the best-case NPV from our procedure exceeds the HF result by 3.8%
VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction
Authors: Arjun D Desai, Beliz Gunel, Batu M Ozturkler, Harris Beg, Shreyas Vasanawala, Brian A Hargreaves, Christopher Ré, John M Pauly, Akshay S Chaudhari
Date: 2021-11-03 00:00:00
Tasks: Data Augmentation, MRI Reconstruction

Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction. However, such models require a large number of fully-sampled ground truth datasets, which are difficult to curate, and are sensitive to distribution drifts. In this work, we propose applying physics-driven data augmentations for consistency training that leverage our domain knowledge of the forward MRI data acquisition process and MRI physics to achieve improved label efficiency and robustness to clinically-relevant distribution drifts. Our approach, termed VORTEX, (1) demonstrates strong improvements over supervised baselines with and without data augmentation in robustness to signal-to-noise ratio change and motion corruption in data-limited regimes; (2) considerably outperforms state-of-the-art purely image-based data augmentation techniques and self-supervised reconstruction methods on both in-distribution and out-of-distribution data; and (3) enables composing heterogeneous image-based and physics-driven data augmentations. Our code is available at https://github.com/ad12/meddlr.
The Gompertz Growth of COVID-19 Outbreaks is Caused by Super-Spreaders
Authors: Francesco Zonta, Andrea Scaiewicz, Michael Levitt
Date: 2021-11-03 00:00:00

In individual SARS-CoV-2 outbreaks, the count of confirmed cases and deaths follow a Gompertz growth function for locations of very different sizes. This lack of dependence on region size leads us to hypothesize that virus spread depends on universal properties of the network of social interactions. We test this hypothesis by simulating the propagation of a virus on networks of different topologies. Our main finding is that Gompertz growth observed for early outbreaks occurs only for a scale-free network, in which nodes with many more neighbors than average are common. These nodes that have very many neighbors are infected early in the outbreak and then spread the infection very rapidly. When these nodes are no longer infectious, the remaining nodes that have most neighbors take over and continue to spread the infection. In this way, the rate of spread is fastest at the very start and slows down immediately. Geometrically it is seen that the "surface" of the epidemic, the number of susceptible nodes in contact with the infected nodes, starts to rapidly decrease very early in the epidemic and as soon as the larger nodes have been infected. In our simulation, the speed and impact of an outbreak depend on three parameters: the average number of contacts each node makes, the probability of being infected by a neighbor, and the probability of recovery. Intelligent interventions to reduce the impact of future outbreaks need to focus on these critical parameters in order to minimize economic and social collateral damage.
Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19
Authors: Yiannis Karavias, Paresh Narayan, Joakim Westerlund
Date: 2021-11-04 00:00:00

Dealing with structural breaks is an important step in most, if not all, empirical economic research. This is particularly true in panel data comprised of many cross-sectional units, such as individuals, firms or countries, which are all affected by major events. The COVID-19 pandemic has affected most sectors of the global economy, and there is by now plenty of evidence to support this. The impact on stock markets is, however, still unclear. The fact that most markets seem to have partly recovered while the pandemic is still ongoing suggests that the relationship between stock returns and COVID-19 has been subject to structural change. It is therefore important to know if a structural break has occurred and, if it has, to infer the date of the break. In the present paper we take this last observation as a source of motivation to develop a new break detection toolbox that is applicable to different sized panels, easy to implement and robust to general forms of unobserved heterogeneity. The toolbox, which is the first of its kind, includes a test for structural change, a break date estimator, and a break date confidence interval. Application to a panel covering 61 countries from January 3 to September 25, 2020, leads to the detection of a structural break that is dated to the first week of April. The effect of COVID-19 is negative before the break and zero thereafter, implying that while markets did react, the reaction was short-lived. A possible explanation for this is the quantitative easing programs announced by central banks all over the world in the second half of March.
occ2vec: A principal approach to representing occupations using natural language processing
Authors: Nicolaj Søndergaard Mühlbach
Date: 2021-11-03 00:00:00
Tasks: Emotional Intelligence

We propose \textbf{occ2vec}, a principal approach to representing occupations, which can be used in matching, predictive and causal modeling, and other economic areas. In particular, we use it to score occupations on any definable characteristic of interest, say the degree of \textquote{greenness}. Using more than 17,000 occupation-specific text descriptors, we transform each occupation into a high-dimensional vector using natural language processing. Similar, we assign a vector to the target characteristic and estimate the occupational degree of this characteristic as the cosine similarity between the vectors. The main advantages of this approach are its universal applicability and verifiability contrary to existing ad-hoc approaches. We extensively validate our approach on several exercises and then use it to estimate the occupational degree of charisma and emotional intelligence (EQ). We find that occupations that score high on these tend to have higher educational requirements. Turning to wages, highly charismatic occupations are either found in the lower or upper tail in the wage distribution. This is not found for EQ, where higher levels of EQ are generally correlated with higher wages.
Managing Innovation in Technical Education: Revisiting the Developmental Strategies of Politeknik Brunei
Authors: Bashir Ahmed Bhuiyan, Mohammad Shahansha Molla, Masud Alam
Date: 2021-11-04 00:00:00
Tasks: Decision Making, Management, Philosophy

The present study aims at exploring the strategies for managing innovation in technical education by using blended learning philosophy and practices with special reference to Politeknik Brunei. Based on literature review and desk research, the study found out salient characteristics, explored constraining factors, elicited strategies of Politeknik Brunei, and suggested some options and a framework for innovations management and development of effective blended teaching and learning. The limiting factors identified are the unwillingness of the top-level management, lack of structural support, dearth of readiness of the stakeholders, the gap between teacher's expectations and changed students characteristics, and blended teaching myopia on the way of effective application of blended learning strategies. Notable suggestions for strategic development are developing wide-angle vision and self-renewal processes, analyzing the environment for needs determination. Clarity of purpose and tasks, technological adaptability, data-driven decision making, prompt feedback, flipped classroom, and development of learning clusters are other dimensions that may go a long way toward innovating teaching-learning and the overall development of an academic institution. Finally, the study suggested important guidelines for applying the strategies and proposed framework for quality blended learning and managing innovations in technical education.
Network analysis regarding international trade network
Authors: Xiufeng Yan, Qi Tang
Date: 2021-11-04 00:00:00

We study the effect of globalization of world economy between 1980 and 2010 by using network analysis technics on trade and GDP data of 71 countries in the world. We draw results distinguishing relatively developing and relatively developed countries during this period of time and point out the standing out economies among the BRICS countries during the years of globalization: within our context of study, China and Russia are the countries that already exhibit developed economy characters, India is next in line but have some unusual features, while Brazil and South Africa still have erratic behaviors