pwc-archive/papers-with-abstracts huggingface dataset:
Provably High-Quality Solutions for the Liquid Medical Oxygen Allocation Problem
Authors: Lejun Zhou, Lavanya Marla, Varun Gupta, Ankur Mani
Date: 2022-12-11 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Oxygen is an essential life-saving medicine used in several indications at all levels of healthcare. During the COVID-19 pandemic, the demand for liquid medical oxygen (LMO) has increased significantly due to the occurrence of lung infections in many patients. However, many countries and regions are not prepared for the emergence of this phenomenon, and the limited supply of LMO has resulted in unsatisfied usage needs in many regions. In this paper, we formulated a linear programming model with the objective to minimize the unsatisfied demand given the constraints of supply and transportation capacity. The decision variables are how much LMO should be transferred from a place to another at each time interval using a specific number of vehicles. Multiple storage points are added into the network to allow for more flexible allocation strategies. The proposed model is implemented in India with real-world LMO supply and demand data as a case study. Compared to the manually designed allocation strategy, the proposed model successfully reduces the unsatisfied demand.
Security Defense of Large Scale Networks Under False Data Injection Attacks: An Attack Detection Scheduling Approach
Authors: Yuhan Suo, Senchun Chai, Runqi Chai, Zhong-Hua Pang, Yuanqing Xia, Guo-Ping Liu
Date: 2022-12-11 00:00:00
Tasks: Combinatorial Optimization, Scheduling

In large-scale networks, communication links between nodes are easily injected with false data by adversaries. This paper proposes a novel security defense strategy from the perspective of attack detection scheduling to ensure the security of the network. Based on the proposed strategy, each sensor can directly exclude suspicious sensors from its neighboring set. First, the problem of selecting suspicious sensors is formulated as a combinatorial optimization problem, which is non-deterministic polynomial-time hard (NP-hard). To solve this problem, the original function is transformed into a submodular function. Then, we propose an attack detection scheduling algorithm based on the sequential submodular optimization theory, which incorporates \emph{expert problem} to better utilize historical information to guide the sensor selection task at the current moment. For different attack strategies, theoretical results show that the average optimization rate of the proposed algorithm has a lower bound, and the error expectation is bounded. In addition, under two kinds of insecurity conditions, the proposed algorithm can guarantee the security of the entire network from the perspective of the augmented estimation error. Finally, the effectiveness of the developed method is verified by the numerical simulation and practical experiment.
Transmit power policies for stochastic stabilisation of multi-link wireless networked control systems
Authors: Alejandro I. Maass, Dragan Nesic, Romain Postoyan, Vineeth S. Varma, Samson Lasaulce
Date: 2022-12-11 00:00:00

Transmit power control is one of the most important issues in wireless networks, where nodes typically operate on limited battery power. Reducing communicating power consumption is essential for both economic and ecologic reasons. In fact, transmitting at unnecessarily high power not only reduces node lifetime, but also introduces excessive interference and electromagnetic pollution. Existing work in the wireless community mostly focus on designing transmit power policies by taking into account communication aspects like quality of service or network capacity. Wireless networked control systems (WNCSs), on the other hand, have different and specific needs such as stability, which require transmit power policies adapted to the control context. Transmit power design in the control community has recently attracted much attention, and available works mostly consider linear systems or specific classes of non-linear systems with a single-link view of the system. In this paper, we propose a framework for the design of stabilising transmit power levels that applies to much larger classes of non-linear plants, controllers, and multi-link setting. By exploiting the fact that channel success probabilities are related to transmit power in a non-linear fashion, we first derive closed-loop stability conditions that relate channel probabilities with transmission rate. Next, we combine these results together with well-known and realistic interference models to provide a design methodology for stabilising transmit power in non-linear and multi-link WNCSs.
Task modules Partitioning, Scheduling and Floorplanning for Partially Dynamically Reconfigurable Systems Based on Modern Heterogeneous FPGAs
Authors: Bo Ding, Jinglei Huang, Junpeng Wang, Qi Xu, Song Chen, Yi Kang
Date: 2022-12-11 00:00:00
Tasks: Scheduling

Modern field programmable gate array(FPGA) can be partially dynamically reconfigurable with heterogeneous resources distributed on the chip. And FPGA-based partially dynamically reconfigurable system(FPGA-PDRS) can be used to accelerate computing and improve computing flexibility. However, the traditional design of FPGA-PDRS is based on manual design. Implementing the automation of FPGA-PDRS needs to solve the problems of task modules partitioning, scheduling, and floorplanning on heterogeneous resources. Existing works only partly solve problems for the automation process of FPGA-PDRS or model homogeneous resource for FPGA-PDRS. To better solve the problems in the automation process of FPGA-PDRS and narrow the gap between algorithm and application, in this paper, we propose a complete workflow including three parts, pre-processing to generate the list of task modules candidate shapes according to the resources requirements, exploration process to search the solution of task modules partitioning, scheduling, and floorplanning, and post-optimization to improve the success rate of floorplan. Experimental results show that, compared with state-of-the-art work, the proposed complete workflow can improve performance by 18.7\%, reduce communication cost by 8.6\%, on average, with improving the resources reuse rate of the heterogeneous resources on the chip. And based on the solution generated by the exploration process, the post-optimization can improve the success rate of the floorplan by 14\%.
Railway Scheduling Using Boolean Satisfiability Modulo Simulations
Authors: Tomáš Kolárik, Stefan Ratschan
Date: 2022-12-11 00:00:00
Tasks: Scheduling

Railway scheduling is a problem that exhibits both non-trivial discrete and continuous behavior. In this paper, we simulate train networks at a low level, where a number of timing and ordering constraints can appear. We model this problem using a combination of SAT and ordinary differential equations (SAT modulo ODE). In addition, we adapt our existing method for solving such problems in such a way that the resulting solver is competitive with methods based on dedicated railway simulators while being more general and extensible.
High Resolution Near Field Microwave Imaging using Loaded Circular Aperture Probe
Authors: Zubair Akhter, MS Rahman, M Aboukhousa
Date: 2022-12-10 00:00:00
Tasks: Sensitivity, Vocal Bursts Intensity Prediction

Circular aperture probes have been successfully used for high-resolution near-field microwave imaging. It has been established that circular aperture probes could conceivably provide higher image fidelity compared to rectangular aperture probes used conventionally for near-field imaging. In this paper, it is proposed to further enhance the near-field imaging resolution and sensitivity by loading the circular aperture with a resonant iris. The proposed probe herein operates in the X-band frequency range and exhibits very localized near-field distribution at the opening of the iris. Consequently, its imaging resolution and sensitivity are enhanced compared to the conventional aperture probes operating over the same frequency band. The imaging capability of the proposed probe is analyzed using 3D electromagnetic simulation, and its performance is validated experimentally. The efficacy of the proposed probe for high-resolution imaging is demonstrated by practical imaging dielectric and metallic samples. Furthermore, the obtained images using the proposed probe are compared to those acquired using conventional circular and rectangular aperture probes. It will be demonstrated that the proposed probe provides higher sensitivity and resolution compared to the conventional aperture probes.
Power Loss Modelling of GaN HEMT based 3L ANPC Three Phase Inverter for different PWM Techniques
Authors: M. Cacciato, G. Aiello, F. Gennaro, S. Mita, D. Patti, G. Scelba, A. Sujeeth
Date: 2022-12-10 00:00:00

The paper presents a straightforward modelling approach to compute the power loss distribution in GaN HEMT based three phase and three level (3L) active neutral point clamped (ANPC) inverters, for different pulse width modulated techniques. Conduction and switching losses averaged over each PWM switching period are analytically computed by starting from the operating conditions of the AC load and data of GaN power devices. The accuracy of the proposed analytical approach is evaluated through a circuit based power electronics simulation tool, applied to different carrier-based PWM strategies.
Protocol selection for second-order consensus against disturbance
Authors: Jiamin Wang, Liqi Zhou, Dong Zhang, Jian Liu, Yuanshi Zheng
Date: 2022-12-10 00:00:00

Noticing that both the absolute and relative velocity protocols can solve the second-order consensus of multi-agent systems, this paper aims to investigate which of the above two protocols has better anti-disturbance capability, in which the anti-disturbance capability is measured by the L2 gain from the disturbance to the consensus error. More specifically, by the orthogonal transformation technique, the analytic expression of the L2 gain of the second-order multi-agent system with absolute velocity protocol is firstly derived, followed by the counterpart with relative velocity protocol. It is shown that both the L2 gains for absolute and relative velocity protocols are determined only by the minimum non-zero eigenvalue of Laplacian matrix and the tunable gains of the state and velocity. Then, we establish the graph conditions to tell which protocol has better anti-disturbance capability. Moreover, we propose a two-step scheme to improve the anti-disturbance capability of second-order multi-agent systems. Finally, simulations are given to illustrate the effectiveness of our findings.
Design, Application and Evaluation of a Multi Agent System in the Logistics Domain
Authors: Juliane Fischer, Christian Lieberoth-Leden, Johannes Fottner, Birgit Vogel-Heuser
Date: 2022-12-09 00:00:00

The increasing demand for flexibility of automated production systems also affects the automated material flow systems (aMFS) they contain and demands reconfigurable systems. However, the centralized control concept usually applied in aMFS hinders an easy adaptation, as the entire control software has to be re-tested, when manually changing sub-parts of the control. As adaption and subsequent testing are a time-consuming task, concepts for splitting the control from one centralized to multiple, decentralized control nodes are required. Therefore, this paper presents a holistic agent-based control concept for aMFS, whereby the system is divided into so-called automated material flow modules (aMFM), each being controlled by a dedicated module agent. The concept allows the reconfiguration of aMFS, consisting of heterogeneous, stationary aMFM, during runtime. Furthermore, it includes aspects such as uniform agent knowledge bases through metamodel-based development, a communication ontology considering different information types and properties, strategic route optimization in decentralized control architecture and a visualization concept to make decisions of the module agents comprehensible to operators and maintenance staff. The evaluation of the concept is performed by means of material flow simulations as well as a prototypical implementation on a lab-sized demonstrator.
Analytical characterization of RIS-aided terahertz links in the presence of beam misalignment
Authors: Evangelos N. Papasotiriou, Sotiris Droulias, Angeliki Alexiou
Date: 2022-12-12 00:00:00

The terahertz (THz) frequency band has recently attracted considerable attention in wireless communications as potential candidate for providing the necessary high bandwidth for demanding applications. With increasing frequency, however, the communication link becomes more vulnerable to blockage and pathloss increases. While both effects can be mitigated with the judicious utilization of directional beams and Reconfigurable Intelligent Surfaces (RISs), high directivity could potentially increase the probability of undesired misalignment between the beam that is steered by the RIS, and the user. It is therefore crucial to characterize and understand the stochastic behavior of misalignment in RIS-aided THz links. In this work, beam misalignment in RIS-aided links is studied theoretically and analytical models are derived, the validity of which is verified through numerical calculations. It is demonstrated that there is a distinction in the stochastic behavior of misalignment between pointing errors that occur on the steering plane or normally to the steering plane, with direct consequences on the link robustness on misalignment. The analytical models capture the impact of misalignment under these qualitatively different conditions and provide the necessary tools for assessing the stochastic RIS performance with respect to crucial link parameters, such as the transmitter's beam width, the transmitter-RIS distance, the RIS-receiver distance, and the steering angle of the RIS.
Ensemble reweighting using Cryo-EM particles
Authors: Wai Shing Tang, David Silva-Sánchez, Julian Giraldo-Barreto, Bob Carpenter, Sonya Hanson, Alex H. Barnett, Erik H. Thiede, Pilar Cossio
Date: 2022-12-10 00:00:00

Cryo-electron microscopy (cryo-EM) has recently become a premier method for obtaining high-resolution structures of biological macromolecules. However, it is limited to biomolecular samples with low conformational heterogeneity, where all the conformations can be well-sampled at many projection angles. While cryo-EM technically provides single-molecule data for heterogeneous molecules, most existing reconstruction tools cannot extract the full distribution of possible molecular configurations. To overcome these limitations, we build on a prior Bayesian approach and develop an ensemble refinement framework that estimates the ensemble density from a set of cryo-EM particles by reweighting a prior ensemble of conformations, e.g., from molecular dynamics simulations or structure prediction tools. Our work is a general approach to recovering the equilibrium probability density of the biomolecule directly in conformational space from single-molecule data. To validate the framework, we study the extraction of state populations and free energies for a simple toy model and from synthetic cryo-EM images of a simulated protein that explores multiple folded and unfolded conformations.
Logs with zeros? Some problems and solutions
Authors: Jiafeng Chen, Jonathan Roth
Date: 2022-12-12 00:00:00

When studying an outcome $Y$ that is weakly-positive but can equal zero (e.g. earnings), researchers frequently estimate an average treatment effect (ATE) for a "log-like" transformation that behaves like $\log(Y)$ for large $Y$ but is defined at zero (e.g. $\log(1+Y)$, $\mathrm{arcsinh}(Y)$). We argue that ATEs for log-like transformations should not be interpreted as approximating percentage effects, since unlike a percentage, they depend on the units of the outcome. In fact, we show that if the treatment affects the extensive margin, one can obtain a treatment effect of any magnitude simply by re-scaling the units of $Y$ before taking the log-like transformation. This arbitrary unit-dependence arises because an individual-level percentage effect is not well-defined for individuals whose outcome changes from zero to non-zero when receiving treatment, and the units of the outcome implicitly determine how much weight the ATE for a log-like transformation places on the extensive margin. We further establish a trilemma: when the outcome can equal zero, there is no treatment effect parameter that is an average of individual-level treatment effects, unit-invariant, and point-identified. We discuss several alternative approaches that may be sensible in settings with an intensive and extensive margin, including (i) expressing the ATE in levels as a percentage (e.g. using Poisson regression), (ii) explicitly calibrating the value placed on the intensive and extensive margins, and (iii) estimating separate effects for the two margins (e.g. using Lee bounds). We illustrate these approaches in three empirical applications.
Dominant Drivers of National Inflation
Authors: Jan Ditzen, Francesco Ravazzolo
Date: 2022-12-12 00:00:00
Tasks: Model Selection

For western economies a long-forgotten phenomenon is on the horizon: rising inflation rates. We propose a novel approach christened D2ML to identify drivers of national inflation. D2ML combines machine learning for model selection with time dependent data and graphical models to estimate the inverse of the covariance matrix, which is then used to identify dominant drivers. Using a dataset of 33 countries, we find that the US inflation rate and oil prices are dominant drivers of national inflation rates. For a more general framework, we carry out Monte Carlo simulations to show that our estimator correctly identifies dominant drivers.
Robust Inference in High Dimensional Linear Model with Cluster Dependence
Authors: Ng Cheuk Fai
Date: 2022-12-11 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Cluster standard error (Liang and Zeger, 1986) is widely used by empirical researchers to account for cluster dependence in linear model. It is well known that this standard error is biased. We show that the bias does not vanish under high dimensional asymptotics by revisiting Chesher and Jewitt (1987)'s approach. An alternative leave-cluster-out crossfit (LCOC) estimator that is unbiased, consistent and robust to cluster dependence is provided under high dimensional setting introduced by Cattaneo, Jansson and Newey (2018). Since LCOC estimator nests the leave-one-out crossfit estimator of Kline, Saggio and Solvsten (2019), the two papers are unified. Monte Carlo comparisons are provided to give insights on its finite sample properties. The LCOC estimator is then applied to Angrist and Lavy's (2009) study of the effects of high school achievement award and Donohue III and Levitt's (2001) study of the impact of abortion on crime.
Why Does GDP Move Before G? It's all in the Measurement
Authors: Edoardo Briganti, Victor Sellemi
Date: 2022-12-12 00:00:00
Tasks: All

We find that the early impact of defense news shocks on GDP is mainly due to a rise in business inventories, as contractors ramp up production for new defense contracts. These contracts do not affect government spending (G) until payment-on-delivery, which occurs 2-3 quarters later. Novel data on defense procurement obligations reveals that contract awards Granger-cause VAR-identified shocks to G, but not defense news shocks. This implies that the early GDP response relative to G is largely driven by the delayed accounting of defense contracts, while VAR-identified shocks to G miss the impact on inventories, resulting in lower multiplier estimates.
Research on College Students' Innovation and Entrepreneurship Education from The Perspective of Artificial Intelligence Knowledge-Based Crowdsourcing
Authors: Yufei Xie, Xiang Liu, Qizhong Yuan
Date: 2022-12-12 00:00:00

Based on the practical process of innovation and entrepreneurship education for college students in the author's university, this study analyzes and deconstructs the key concepts of AI knowledge-based crowdsourcing on the basis of literature research, and analyzes the objective fitting needs of combining AI knowledge-based crowdsourcing with college students' innovation and entrepreneurship education practice through a survey and research of a random sample of college students, and verifies that college students' knowledge and application of AI knowledge-based crowdsourcing in the learning and practice of innovation and entrepreneurship The study also verifies the awareness and application of AI knowledge-based crowdsourcing knowledge by university students in the learning and practice of innovation and entrepreneurship.
Decentralized lending and its users: Insights from Compound
Authors: Kanis Saengchote
Date: 2022-12-12 00:00:00
Tasks: Management

Permissionless blockchains offer an information environment where users can interact privately without fear of censorship. Financial services can be programmatically coded via smart contracts to automate transactions without the need for human intervention or knowing user identity. This new paradigm is known as decentralized finance (DeFi). We investigate Compound (a leading DeFi lending protocol) to show how it works in this novel information environment, who its users are, and what factors determine their participation. On-chain transaction data shows that loan durations are short (31 days on average), and many users borrow to support leveraged investment strategies (yield farming). We show that systemic risk in DeFi arises from concentration and interconnection, and how traditional risk management practices can be challenging for DeFi.
Exploring non-residential technology adoption: an empirical analysis of factors associated with the adoption of photovoltaic systems by municipal authorities in Germany
Authors: Maren Springsklee, Fabian Scheller
Date: 2022-12-10 00:00:00
Tasks: Decision Making

This research article explores potential influencing factors of solar photovoltaic (PV) system adoption by municipal authorities in Germany in the year 2019. We derive seven hypothesized relationships from the empirical literature on residential PV adoption, organizational technology adoption, and sustainability policy adoption by local governments, and apply a twofold empirical approach to examine them. First, we explore the associations of a set of explanatory variables on the installed capacity of adopter municipalities (N=223) in an OLS model. Second, we use a logit model to analyze whether the identified relationships are also apparent between adopter and non-adopter municipalities (N=423). Our findings suggest that fiscal capacity (measured by per capita debt and per capita tax revenue) and peer effects (measured by the pre-existing installed capacity) are positively associated with both the installed capacity and adoption. Furthermore, we find that institutional capacity (measured by the presence of a municipal utility) and environmental concern (measured by the share of green party votes) are positively associated with municipal PV adoption. Economic factors (measured by solar irradiation) show a significant positive but small effect in both regression models. No evidence was found to support the influence of political will. Results for the role of municipal characteristics are mixed, although the population size was consistently positively associated with municipal PV adoption and installed capacity. Our results support previous studies on PV system adoption determinants and offer a starting point for additional research on non-residential decision-making and PV adoption.
Sequential Cursed Equilibrium
Authors: Shani Cohen, Shengwu Li
Date: 2022-12-12 00:00:00
Tasks: Form

We propose an extensive-form solution concept, with players that neglect information from hypothetical events, but make inferences from observed events. Our concept modifies cursed equilibrium (Eyster and Rabin, 2005), and allows that players can be 'cursed about' endogenous information.