pwc-archive/papers-with-abstracts huggingface dataset:
Tractable Data Enriched Distributionally Robust Chance-Constrained CVR
Authors: Qianzhi Zhang, Fankun Bu, Yi Guo, Zhaoyu Wang
Date: 2022-07-07 00:00:00
Tasks: GPR

This paper proposes a tractable distributionally robust chance-constrained conservation voltage reduction (DRCC-CVR) method with enriched data-based ambiguity set in unbalanced three-phase distribution systems. The increasing penetration of distributed renewable energy not only brings clean power but also challenges the voltage regulation and energy-saving performance of CVR by introducing high uncertainties to distribution systems. In most cases, the conventional robust optimization methods for CVR only provide conservative solutions. To better consider the impacts of load and PV generation uncertainties on CVR implementation in distribution systems and provide less conservative solutions, this paper develops a data-based DRCC-CVR model with tractable reformulation and data enrichment method. Even though the uncertainties of load and photovoltaic (PV) can be captured by data, the availability of smart meters (SMs) and micro-phasor measurement units (PMUs) is restricted by cost budget. The limited data access may hinder the performance of the proposed DRCC-CVR. Thus, we further present a data enrichment method to statistically recover the high-resolution load and PV generation data from low-resolution data with Gaussian Process Regression (GPR) and Markov Chain (MC) models, which can be used to construct a data-based moment ambiguity set of uncertainty distributions for the proposed DRCC-CVR. Finally, the nonlinear power flow and voltage dependant load models and DRCC with moment-based ambiguity set are reformulated to be computationally tractable and tested on a real distribution feeder in Midwest U. S. to validate the effectiveness and robustness of the proposed method.
New perspectives on transient stability between grid-following and grid-forming VSCs
Authors: Kaizhe Zhang
Date: 2022-07-07 00:00:00

The grid-following and grid-forming controls in voltage-source converters are considered as different operation modes and the synchronization mechanism of them are studied separately. In this article, the intrinsic relationships between gridfollowing and grid-forming controlled converters are established as follows: 1) the proportional gain of PLL is in inverse proportion to damping; 2) the integral gain of PLL is similar to integral droop; 3) PLL has no practical inertia but acts like grid-forming control in zero inertia cases. Further, a general stability-enhanced method combining damping and inertia is proposed, and the modified energy function is obtained to estimate the region of attraction for the system. Finally, these findings are corroborated by simulation tests with an intuitive conclusion.
Fuel savings through missed approach maneuvers based on aircraft reinjection
Authors: María Carmona, Rafael Casado, Aurelio Bermúdez, Miguel Pérez Francisco, Pablo Boronat, Carlos T. Calafate
Date: 2022-07-07 00:00:00

Humanity is facing global challenges related to climate change, along with an energetic crisis that urgently requires optimizing any process or system able to improve global economic conditions. Taking fuel as an example, its prices are among the highest increases detected in goods of general use. In this sense, initiatives to mitigate fuel consumption are both welcome and necessary. In the aerial transportation area, fuel costs are critical to the economic viability of companies, and so urgent measures should be adopted to avoid any unnecessary increase of operational costs. In this work we study the costs involved in a standard procedure following a missed approach. In addition, we study the improvements achieved with a fast reinjection scheme proposed in a prior work. Experimental results show that, for a standard A320 aircraft, fuel savings ranging from 55% to 90% can be achieved through our proposed method.
Consistent discretization of finite/fixed-time controllers
Authors: Andrey Polyakov, Denis Efimov, Xubin Ping
Date: 2022-07-07 00:00:00

The paper proposes an algorithm for a discretization (sampled-time implementation) of a homogeneous control preserving the finite-time and nearly fixed-time stability property of the original (sampling-free) system. The sampling period is assumed to be constant. Both single-input and multiple-input cases are considered. The robustness (Input-to-State Stability) of the obtained sampled-time control system is studied as well. Theoretical results are supported by numerical simulations.
Control of Oscillatory Temperature Field in a Building via Damping Assignment to Nonlinear Koopman Mode
Authors: Yoshihiko Susuki, Kohei Eto, Naoto Hiramatsu, Atsushi Ishigame
Date: 2022-07-07 00:00:00

This paper addresses a control problem on air-conditioning systems in buildings that is regarded as a control practice of nonlinear distributed-parameter systems. Specifically, we consider the design of a controller for suppressing an oscillatory response of in-room temperature field. The main idea in this paper is to apply the emergent theory of Koopman operator and Koopman mode decomposition for nonlinear systems, and to formulate a technique of damping assignment to a nonlinear Koopman mode in a fully data-driven manner. Its effectiveness is examined by numerical simulations guided by measurement of a practical room space.
Impact of Internal Algebraic Variable Treatment on Transient Stability Simulation Performance
Authors: Hantao Cui
Date: 2022-07-06 00:00:00

It is a general notion that, in transient stability simulations, reducing the number of algebraic variables for the differential-algebraic equations (DAE) can improve the simulation performance. Many simulation programs split algebraic variables internal to a dynamic model from the full DAE and evaluate them outside each iterative step, using results from the previous iteration. The updated internal variables are then treated as constants when solving for the current iteration. This letter discusses how such a split formulation can impact simulation performance. Case studies using various systems with synchronous generator and converter models demonstrate the impact of the split on the convergence pattern and simulation performance.
Clustering of Excursion Sets in Financial Market
Authors: M. Shadmangohar, S. M. S. Movahed
Date: 2022-07-07 00:00:00
Tasks: Clustering

Relying on the excursion set theory, we compute the number density of local extrema and crossing statistics versus the threshold for the stock market indices. Comparing the number density of excursion sets calculated numerically with the theoretical prediction for the Gaussian process confirmed that all data sets used in this paper have a surplus (almost lack) value of local extrema (up-crossing) density at low (high) thresholds almost around the mean value implying universal properties for stock indices. We estimate the clustering of geometrical measures based on the excess probability of finding the pairs of excursion sets, which clarify well statistical coherency between markets located in the same geographical region. The cross-correlation of excursion sets between various markets is also considered to construct the matrix of agglomerative hierarchical clustering. Our results demonstrate that the peak statistics is more capable of capturing blocks. Incorporating the partitioning approach, we implement the Singular Value Decomposition on the matrix containing the maximum value of unweighted Two-Point Correlation Function of peaks and up-crossing to compute the similarity measure. Our results support that excursion sets are more sensitive than standard measures to elucidate the existence of {\it a priori} crisis.
The Variations of SIkJalpha Model for COVID-19 Forecasting and Scenario Projections
Authors: Ajitesh Srivastava
Date: 2022-07-06 00:00:00

We proposed the SIkJalpha model at the beginning of the COVID-19 pandemic (early 2020). Since then, as the pandemic evolved, more complexities were added to capture crucial factors and variables that can assist with projecting desired future scenarios. Throughout the pandemic, multi-model collaborative efforts have been organized to predict short-term outcomes (cases, deaths, and hospitalizations) of COVID-19 and long-term scenario projections. We have been participating in five such efforts. This paper presents the evolution of the SIkJalpha model and its many versions that have been used to submit to these collaborative efforts since the beginning of the pandemic. Specifically, we show that the SIkJalpha model is an approximation of a class of epidemiological models. We demonstrate how the model can be used to incorporate various complexities, including under-reporting, multiple variants, waning of immunity, and contact rates, and to generate probabilistic outputs.
Degrees of Freedom and Information Criteria for the Synthetic Control Method
Authors: Guillaume Allaire Pouliot, Zhen Xie
Date: 2022-07-06 00:00:00
Tasks: Model Selection

We provide an analytical characterization of the model flexibility of the synthetic control method (SCM) in the familiar form of degrees of freedom. We obtain estimable information criteria. These may be used to circumvent cross-validation when selecting either the weighting matrix in the SCM with covariates, or the tuning parameter in model averaging or penalized variants of SCM. We assess the impact of car license rationing in Tianjin and make a novel use of SCM; while a natural match is available, it and other donors are noisy, inviting the use of SCM to average over approximately matching donors. The very large number of candidate donors calls for model averaging or penalized variants of SCM and, with short pre-treatment series, model selection per information criteria outperforms that per cross-validation.
Homo economicus to model human behavior is ethically doubtful and mathematically inconsistent
Authors: M. Lunkenheimer, A. Kracklauer, G. Klinkova, M. Grabinski
Date: 2022-07-05 00:00:00
Tasks: Ethics

In many models in economics or business a dominantly self-interested homo economicus is assumed. Unfortunately (or fortunately), humans are in general not homines economici as e.g. the ultimatum game shows. This leads to the fact that all these models are at least doubtful. Moreover, economists started to set a quantitative value for the feeling of social justice, altruism, or envy and the like to execute utilitarian calculation. Besides being ethically doubtful, it delivers an explanation in hindsight with little predicting power. We use examples from game theory to show its arbitrariness. It is even possible that a stable Nash equilibrium can be calculated while it does not exist at all, due to the wide differences in human values. Finally, we show that assigned numbers for envy or altruism and the like do not build a field (in a mathematical sense). As there is no homomorphism to real numbers or a subset of it, any calculation is generally invalid or arbitrary. There is no (easy) way to fix the problem. One has to go back to ethical concepts like the categorical imperative or use at most semi quantitative approaches like considering knaves and knights. Mathematically one can only speculate whether e.g. surreal numbers can make ethics calculable.
Asset Trading in Continuous Time: A Cautionary Tale
Authors: William R. Zame
Date: 2022-07-07 00:00:00

The continuous time model of dynamic asset trading is the central model of modern finance. Because trading cannot in fact take place at every moment of time, it would seem desirable to show that the continuous time model can be viewed as the limit of models in which trading can occur only at (many) discrete moments of time. This paper demonstrates that, if we take terminal wealth constraints and self-financing constraints as seriously in the discrete model as in the continuous model, then the continuous trading model need not be the limit of discrete trading models. This raises serious foundational questions about the continuous time model.
Rawlsian Assignments
Authors: Tom Demeulemeester, Juan S. Pereyra
Date: 2022-07-06 00:00:00
Tasks: Fairness

We study the assignment of indivisible goods to individuals without monetary transfers. Previous literature has mainly focused on efficiency and individually fair assignments; consequently, egalitarian concerns have been overlooked. Drawing inspiration from the allocation of apartments in housing cooperatives, where families prioritize egalitarianism in assignments, we introduce the concept of Rawlsian assignment. We demonstrate the uniqueness, efficiency and anonymity of the Rawlsian rule. Our findings are validated using cooperative housing preference data, showing significant improvements in egalitarian outcomes over both the probabilistic serial rule and the currently employed rule.
Private Information Acquisition and Preemption: a Strategic Wald Problem
Authors: Guo Bai
Date: 2022-07-06 00:00:00

This paper studies a dynamic information acquisition model with payoff externalities. Two players can acquire costly information about an unknown state before taking a safe or risky action. Both information and the action taken are private. The first player to take the risky action has an advantage but whether the risky action is profitable depends on the state. The players face the tradeoff between being first and being right. In equilibrium, for different priors, there exist three kinds of randomisation: when the players are pessimistic, they enter the competition randomly; when the players are less pessimistic, they acquire information and then randomly stop; when the players are relatively optimistic, they randomly take an action without acquiring information.
Automated Classification of General Movements in Infants Using a Two-stream Spatiotemporal Fusion Network
Authors: Yuki Hashimoto, Akira Furui, Koji Shimatani, Maura Casadio, Paolo Moretti, Pietro Morasso, Toshio Tsuji
Date: 2022-07-04 00:00:00
Tasks: Classification

The assessment of general movements (GMs) in infants is a useful tool in the early diagnosis of neurodevelopmental disorders. However, its evaluation in clinical practice relies on visual inspection by experts, and an automated solution is eagerly awaited. Recently, video-based GMs classification has attracted attention, but this approach would be strongly affected by irrelevant information, such as background clutter in the video. Furthermore, for reliability, it is necessary to properly extract the spatiotemporal features of infants during GMs. In this study, we propose an automated GMs classification method, which consists of preprocessing networks that remove unnecessary background information from GMs videos and adjust the infant's body position, and a subsequent motion classification network based on a two-stream structure. The proposed method can efficiently extract the essential spatiotemporal features for GMs classification while preventing overfitting to irrelevant information for different recording environments. We validated the proposed method using videos obtained from 100 infants. The experimental results demonstrate that the proposed method outperforms several baseline models and the existing methods.
Highly Efficient Waveform Design and Hybrid Duplex for Joint Communication and Sensing
Authors: Yihua Ma, Zhifeng Yuan, Shuqiang Xia, Guanghui Yu, Liujun Hu
Date: 2022-07-07 00:00:00

Joint communication and sensing (JCAS) is a very promising 6G technology, which attracts more and more research attention. Compared with communication, radar has many unique features in terms of waveform design criteria, self-interference cancellation (SIC), aperture-dependent resolution, and virtual aperture. This paper proposes a novel waveform design named max-aperture radar slicing (MaRS) to gain a large time-frequency aperture, which is generated by orthogonal frequency division multiplexing (OFDM) and occupies only a tiny fraction of OFDM resources. The proposed MaRS keeps the radar advantages of constant modulus, zero auto-correlation sequence, and simple SIC. As MaRS consumes much less resources, conventional processing methods fail, and novel angle-Doppler map based methods are proposed to obtain the range-velocity-angle information from MaRS echos and strong clutters. To avoid complex full-duplex communication, this paper proposes a hybrid-duplex JCAS scheme composed of half-duplex communication and full-duplex radar. The half-duplex communication antenna array is reused, and a small sensing-dedicated antenna array is added. Using these two arrays, a large space-domain sensing aperture is virtually formed to greatly improve the angle resolution. The numerical results show that the proposed MaRS and hybrid duplex can achieve a high sensing resolution with only 0.4% OFDM resources, which reduces the overheads of conventional methods to less than one tenth.
Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification
Authors: Guanxiong Shen, Junqing Zhang, Alan Marshall, Mikko Valkama, Joseph Cavallaro
Date: 2022-07-06 00:00:00
Tasks: Data Augmentation

Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by the intrinsic hardware impairments. State-of-the-art neural networks have been adopted for RFFI. However, many neural networks, e.g., multilayer perceptron (MLP) and convolutional neural network (CNN), require fixed-size input data. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the RFFI performance in such scenarios is rarely investigated. In this paper, we analyze the reason why MLP- and CNN-based RFFI systems are constrained by the input size. To overcome this, we propose four neural networks that can process signals of variable lengths, namely flatten-free CNN, long short-term memory (LSTM) network, gated recurrent unit (GRU) network and transformer. We adopt data augmentation during training which can significantly improve the model's robustness to noise. We compare two augmentation schemes, namely offline and online augmentation. The results show the online one performs better. During the inference, a multi-packet inference approach is further leveraged to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low-SNR classification accuracy by up to 50% and the multi-packet inference approach can further increase the accuracy by over 20%.
Towards Receiver-Agnostic and Collaborative Radio Frequency Fingerprint Identification
Authors: Guanxiong Shen, Junqing Zhang, Alan Marshall, Roger Woods, Joseph Cavallaro, Liquan Chen
Date: 2022-07-06 00:00:00
Tasks: Collaborative Inference

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique, which exploits the hardware characteristics of the RF front-end as device identifiers. RFFI is implemented in the wireless receiver and acts to extract the transmitter impairments and then perform classification. The receiver hardware impairments will actually interfere with the feature extraction process, but its effect and mitigation have not been comprehensively studied. In this paper, we propose a receiver-agnostic RFFI system that is not sensitive to the changes in receiver characteristics; it is implemented by employing adversarial training to learn the receiver-independent features. Moreover, when there are multiple receivers, this functionality can perform collaborative inference to enhance classification accuracy. Finally, we show how it is possible to leverage fine-tuning for further improvement with fewer collected signals. To validate the approach, we have conducted extensive experimental evaluation by applying the approach to a LoRaWAN case study involving ten LoRa devices and 20 software-defined radio (SDR) receivers. The results show that receiver-agnostic training enables the trained neural network to become robust to changes in receiver characteristics. The collaborative inference improves classification accuracy by up to 20% beyond a single-receiver RFFI system and fine-tuning can bring a 40% improvement for under-performing receivers.
Minimizing the Repayment Cost of Federal Student Loans
Authors: Paolo Guasoni, Yu-Jui Huang
Date: 2022-07-07 00:00:00

Federal student loans are fixed-rate debt contracts with three main special features: (i) borrowers can use income-driven schemes to make payments proportional to their income above subsistence, (ii) after several years of good standing, the remaining balance is forgiven but taxed as ordinary income, and (iii) accrued interest is simple, i.e., not capitalized. For a very small loan, the cost-minimizing repayment strategy dictates maximum payments until full repayment, forgoing both income-driven schemes and forgiveness. For a very large loan, the minimal payments allowed by income-driven schemes are optimal. For intermediate balances, the optimal repayment strategy may entail an initial period of minimum payments to exploit the non-capitalization of accrued interest, but when the principal is being reimbursed maximal payments always precede minimum payments. Income-driven schemes and simple accrued interest mostly benefit borrowers with very large balances.
The coalescent in finite populations with arbitrary, fixed structure
Authors: Benjamin Allen, Alex McAvoy
Date: 2022-07-06 00:00:00

The coalescent is a stochastic process representing ancestral lineages in a population undergoing neutral genetic drift. Originally defined for a well-mixed population, the coalescent has been adapted in various ways to accommodate spatial, age, and class structure, along with other features of real-world populations. To further extend the range of population structures to which coalescent theory applies, we formulate a coalescent process for a broad class of neutral drift models with arbitrary -- but fixed -- spatial, age, sex, and class structure, haploid or diploid genetics, and any fixed mating pattern. Here, the coalescent is represented as a random sequence of mappings $\mathcal{C} = \left(C_t\right)_{t=0}^\infty$ from a finite set $G$ to itself. The set $G$ represents the ``sites'' (in individuals, in particular locations and/or classes) at which these alleles can live. The state of the coalescent, $C_t:G \to G$, maps each site $g \in G$ to the site containing $g$'s ancestor, $t$ time-steps into the past. Using this representation, we define and analyze coalescence time, coalescence branch length, mutations prior to coalescence, and stationary probabilities of identity-by-descent and identity-by-state. For low mutation, we provide a recipe for computing identity-by-descent and identity-by-state probabilities via the coalescent. Applying our results to a diploid population with arbitrary sex ratio $r$, we find that measures of genetic dissimilarity, among any set of sites, are scaled by $4r(1-r)$ relative to the even sex ratio case.