pwc-archive/papers-with-abstracts huggingface dataset:
Fast Quasi-Optimal Power Flow of Flexible DC Traction Power Systems
Authors: Zhanhe Li, Xiaoqian Li, Yingdong Wei, Chao Lu, Xuelian Bai
Date: 2022-11-05 00:00:00
Tasks: Computational Efficiency, Mathematical Proofs

This paper proposes a quasi-optimal power flow (OPF) algorithm for flexible DC traction power systems (TPSs). Near-optimal solutions can be solved with high computational efficiency by the proposed quasi-OPF. Unlike conventional OPF utilizing mathematical optimization algorithms, the proposed quasi-OPF adopts analytical mapping from load information to near-optimal solutions, hence considerably accelerating the computation. First, we study the mechanism and physical meaning interpretation of conventional OPF based on a new modeling method and successfully interpret the mechanism of conventional OPF in flexible DC TPSs. Then, the analytical mapping from load information to near-optimal solutions is obtained inspired by the mechanism of conventional OPF, and the quasi-OPF algorithm is designed based on the mapping. Since the mapping is based on simple arithmetic, the quasi-OPF algorithm can solve OPF with much less execution time, achieving subsecond level calculation and a speed-up of 57 times compared to conventional OPF. The effectiveness is verified by mathematical proofs and a case study with Beijing Metro Line 13. It provides an insight into the mechanism and physical meaning of OPF, and is a powerful tool for flexible DC TPSs to analyze the effects of coordinated control, design real-time control strategies, and solve operational problems in planning.
Cyber-physical interdependent restoration scheduling for active distribution network via ad hoc wireless communication
Authors: Chongyu Wang, Mingyu Yan, Kaiyuan Pang, Fushuan Wen, Fei Teng
Date: 2022-11-05 00:00:00
Tasks: Scheduling

This paper proposes a post-disaster cyber-physical interdependent restoration scheduling (CPIRS) framework for active distribution networks (ADN) where the simultaneous damages on cyber and physical networks are considered. The ad hoc wireless device-to-device (D2D) communication is leveraged, for the first time, to establish cyber networks instantly after the disaster to support ADN restoration. The repair and operation crew dispatching, the remote-controlled network reconfiguration and the system operation with DERs can be effectively coordinated under the cyber-physical interactions. The uncertain outputs of renewable energy resources (RESs) are represented by budget-constrained polyhedral uncertainty sets. Through implementing linearization techniques on disjunctive expressions, a monolithic mixed-integer linear programming (MILP) based two-stage robust optimization model is formulated and subsequently solved by a customized column-and-constraint generation (C&CG) algorithm. Numerical results on the IEEE 123-node distribution system demonstrate the effectiveness and superiorities of the proposed CPIRS method for ADN.
Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach
Authors: Yang Li, Ruinong Wang, Yuanzheng Li, Meng Zhang, Chao Long
Date: 2022-11-02 00:00:00
Tasks: Deep Reinforcement Learning, Federated Learning, Prediction, Privacy Preserving, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional centralized forecasting methods raise concerns regarding data privacy-preserving and data islands problem. To handle the data privacy and openness, we propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting, called federated deep reinforcement learning (FedDRL). Firstly, this paper uses the deep deterministic policy gradient (DDPG) algorithm as the basic forecasting model to improve prediction accuracy. Secondly, we integrate the DDPG forecasting model into the framework of federated learning. The designed FedDRL can obtain an accurate prediction model in a decentralized way by sharing model parameters instead of sharing private data which can avoid sensitive privacy issues. The simulation results show that the proposed FedDRL outperforms the traditional prediction methods in terms of forecasting accuracy. More importantly, while ensuring the forecasting performance, FedDRL can effectively protect the data privacy and relieve the communication pressure compared with the traditional centralized forecasting method. In addition, a simulation with different federated learning parameters is conducted to confirm the robustness of the proposed scheme.
De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection
Authors: Ziyi Yin, Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Mathias Louboutin, Felix J. Herrmann
Date: 2022-10-07 00:00:00
Tasks: Seismic Imaging

Geological carbon storage represents one of the few truly scalable technologies capable of reducing the CO2 concentration in the atmosphere. While this technology has the potential to scale, its success hinges on our ability to mitigate its risks. An important aspect of risk mitigation concerns assurances that the injected CO2 remains within the storage complex. Amongst the different monitoring modalities, seismic imaging stands out with its ability to attain high resolution and high fidelity images. However, these superior features come, unfortunately, at prohibitive costs and time-intensive efforts potentially rendering extensive seismic monitoring undesirable. To overcome this shortcoming, we present a methodology where time-lapse images are created by inverting non-replicated time-lapse monitoring data jointly. By no longer insisting on replication of the surveys to obtain high fidelity time-lapse images and differences, extreme costs and time-consuming labor are averted. To demonstrate our approach, hundreds of noisy time-lapse seismic datasets are simulated that contain imprints of regular CO2 plumes and irregular plumes that leak. These time-lapse datasets are subsequently inverted to produce time-lapse difference images used to train a deep neural classifier. The testing results show that the classifier is capable of detecting CO2 leakage automatically on unseen data and with a reasonable accuracy.
A Survey on Quantum Reinforcement Learning
Authors: Nico Meyer, Christian Ufrecht, Maniraman Periyasamy, Daniel D. Scherer, Axel Plinge, Christopher Mutschler
Date: 2022-11-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Survey

Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on quantum reinforcement learning - our interpretation of this term will be clarified below - we put particular emphasis on recent developments. With a focus on already available noisy intermediate-scale quantum devices, these include variational quantum circuits acting as function approximators in an otherwise classical reinforcement learning setting. In addition, we survey quantum reinforcement learning algorithms based on future fault-tolerant hardware, some of which come with a provable quantum advantage. We provide both a birds-eye-view of the field, as well as summaries and reviews for selected parts of the literature.
Dynamic Estimates Of The Arrow-Pratt Absolute And Relative Risk Aversion Coefficients
Authors: George Samartzis, Nikitas Pittis
Date: 2022-11-07 00:00:00

We derive a closed-form expression capturing the degree of Relative Risk Aversion (RRA) of investors for non-"fair" lotteries. We argue that our formula is superior to earlier methods that have been proposed, as it is a function of only three variables. Namely, the Treasury yields, the returns and the market capitalization of a specific market index. Our formula, is tested on CAC 40, EURO, S&P 500 and STOXX 600, with respect to the market capitalization of each index, for different time periods. We deduce that the investors in these markets exhibit Decreasing Absolute Risk Aversion (DARA) through all the different time periods that we consider, while the degree of RRA has altered between being constant, decreasing or increasing. Furthermore, we propose a simple and intuitive way to measure the degree to which a wrong assumption with respect to the utility function of an investor will affect the structure of his portfolio. Our method is built on a two asset portfolio framework. Namely, a portfolio consisting of one risky and one risk-free asset. Applying our method, the empirical findings indicate that the weight invested in the risky asset varies substantially even among utility functions with similar characteristics.
Shadow prices and optimal cost in economic applications
Authors: Nikolay Khabarov, Alexey Smirnov, Michael Obersteiner
Date: 2022-11-07 00:00:00

Shadow prices are well understood and are widely used in economic applications. However, there are limits to where shadow prices can be applied assuming their natural interpretation and the fact that they reflect the first order optimality conditions (FOC). In this paper, we present a simple ad-hoc example demonstrating that marginal cost associated with exercising an optimal control may exceed the respective cost estimated from a ratio of shadow prices. Moreover, such cost estimation through shadow prices is arbitrary and depends on a particular (mathematically equivalent) formulation of the optimization problem. These facts render a ratio of shadow prices irrelevant to estimation of optimal marginal cost. The provided illustrative optimization problem links to a similar approach of calculating social cost of carbon (SCC) in the widely used dynamic integrated model of climate and the economy (DICE).
Prospects and Challenges for Sustainable Tourism: Evidence from South Asian Countries
Authors: Janifar Alam, Quazi Nur Alam, Abu Kalam
Date: 2022-11-07 00:00:00
Tasks: Articles, Marketing

Tourism is one of the world's fastest expanding businesses, as well as a significant source of foreign exchange profits and jobs. The research is based on secondary sources. The facts and information were primarily gathered and analyzed from various published papers and articles. The study goals are to illustrate the current scenario of tourism industry in south Asia, classifies the restraints and recommends helpful key developments to achieve sustainable tourism consequently. The study revealed that major challenges of sustainable tourism in south Asian region are lack of infrastructure facilities, modern and sufficient recreation facilities, security and safety, proper training and HR, proper planning from government, marketing and information, product development, tourism awareness, security and safety, and political instability etc. The study also provides some suggestive measures that for the long-term growth of regional tourism, the government should establish and implement policies involving public and private investment and collaboration.
The debt aversion survey module: An experimentally validated tool to measure individual debt aversion
Authors: David Albrecht, Thomas Meissner
Date: 2022-11-04 00:00:00
Tasks: Survey

We develop an experimentally validated, short and easy-to-use survey module for measuring individual debt aversion. To this end, we first estimate debt aversion on an individual level, using choice data from Meissner and Albrecht (2022). This data also contains responses to a large set of debt aversion survey items, consisting of existing items from the literature and novel items developed for this study. Out of these, we identify a survey module comprising two qualitative survey items to best predict debt aversion in the incentivized experiment.
Coordination through ambiguous language
Authors: Michele Crescenzi
Date: 2022-11-07 00:00:00

We provide a syntactic construction of correlated equilibrium. For any finite game, we study how players coordinate their play on a signal by means of a public strategy whose instructions are expressed in some natural language. Language can be ambiguous in that different players may assign different truth values to the very same formula in the same state of the world. We model ambiguity using the player-dependent logic of Halpern and Kets (2015). We show that, absent any ambiguity, self-enforcing coordination always induces a correlated equilibrium of the underlying game. When language ambiguity is allowed, self-enforcing coordination strategies induce subjective correlated equilibria.
Strategies in deterministic totally-ordered-time games
Authors: Tomohiko Kawamori
Date: 2022-11-07 00:00:00

We consider deterministic totally-ordered-time games. We present three axioms for strategies. We show that for any tuple of strategies that satisfy the axioms, there exists a unique complete history that is consistent with the strategy tuple.
Arbitrage from a Bayesian's Perspective
Authors: Ayan Bhattacharya
Date: 2022-11-07 00:00:00

This paper builds a model of interactive belief hierarchies to derive the conditions under which judging an arbitrage opportunity requires Bayesian market participants to exercise their higher-order beliefs. As a Bayesian, an agent must carry a complete recursion of priors over the uncertainty about future asset payouts, the strategies employed by other market participants that are aggregated in the price, other market participants' beliefs about the agent's strategy, other market participants beliefs about what the agent believes their strategies to be, and so on ad infinitum. Defining this infinite recursion of priors -- the belief hierarchy so to speak -- along with how they update gives the Bayesian decision problem equivalent to the standard asset pricing formulation of the question. The main results of the paper show that an arbitrage trade arises only when an agent updates his recursion of priors about the strategies and beliefs employed by other market participants. The paper thus connects the foundations of finance to the foundations of game theory by identifying a bridge from market arbitrage to market participant belief hierarchies.
On Existence of alpha-Core Solutions for Games with Finite or Infinite Players
Authors: Qi-Qing Song, Min Guo
Date: 2022-11-06 00:00:00

This gives two existence results of alpha-core solutions by introducing P-open conditions and strong P-open conditions into games without ordered preferences. The existence of alpha-core solutions is obtained for games with infinite-players. Secondly, it provides a short proof of Kajii's (Journal of Economic Theory 56, 194-205, 1992) existence theorem for alpha-core solutions, further, the Kajii's theorem is equivalent to the Browder fixed point theorem. In addition, the obtained existence results can include many typical results for alpha-core solutions and some recent existence results as special cases.
Discovery through Trial Balloons
Authors: Eitan Sapiro-Gheiler
Date: 2022-11-04 00:00:00

A principal and an agent face symmetric uncertainty about the value of two correlated projects for the agent. The principal chooses which project values to publicly discover and makes a proposal to the agent, who accepts if and only if the expected sum of values is positive. We characterize optimal discovery for various principal preferences: maximizing the probability of the grand bundle, of having at least one project approved, and of a weighted combination of projects. Our results highlight the usefulness of trial balloons: projects which are ex-ante disfavored but have higher variance than a more favored alternative. Discovering disfavored projects may be optimal even when their variance is lower than that of the alternative, so long as their disfavorability is neither too large nor too small. These conclusions rationalize the inclusion of controversial policies in omnibus bills and the presence of moonshot projects in organizations.
Multipath Interference Suppression of Amplitude-Modulated Continuous Wave Scanning LiDAR Based on Bayesian-Optimized XGBoost Ensemble
Authors: Sunghyun Lee, Yoonseop Lim, Wookhyeon Kwon, Yonghwa Park
Date: 2022-11-07 00:00:00

This paper proposes a novel multipath interference (MPI) suppression algorithm based on Bayesian-optimized extreme gradient boosting (XGBoost) ensemble to reduce MPI error in amplitude-modulated continuous wave (AMCW) scanning light detection and ranging (LiDAR). Contrast to this paper, many previous research works have focused on the MPI suppression in conventional AMCW time-of-flight (ToF) sensors with flash type illumination sources. However, the mitigated MPI error of these previous works still remains cm-scale due to the inherent limitation of illumination source and lack of MPI data. Meanwhile, since there exist few previous works for coaxial type AMCW scanning LiDAR, the MPI in such LiDAR still has not been validated. To achieve mm-scale MPI error mitigation regarding aforementioned issues, this paper proposes a MPI error correction algorithm based on Bayesian-optimized XGBoost ensemble and its implementation in coaxial type AMCW scanning LiDAR. To train the XGBoost ensemble, the MPI synthetic dataset generated by customized simulation is used in this paper. According to validation results, the mean absolute error (MAE) of MPI error originally 9.8 mm can be reduced to less than 2 mm by Bayesian-optimized XGBoost in simulation dataset. Such precise MPI mitigation results are also maintained in real object scenes. Specifically, the MAE of MPI error in measurement condition similar with public dataset is reduced to 2.8 mm, which is extremely low compared to other previous works.
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression
Authors: Junqi Shi, Ming Lu, Zhan Ma
Date: 2022-11-05 00:00:00
Tasks: image-classification, Image Classification, Image Compression, Quantization, Super-Resolution

Quantizing a floating-point neural network to its fixed-point representation is crucial for Learned Image Compression (LIC) because it improves decoding consistency for interoperability and reduces space-time complexity for implementation. Existing solutions often have to retrain the network for model quantization, which is time-consuming and impractical to some extent. This work suggests using Post-Training Quantization (PTQ) to process pretrained, off-the-shelf LIC models. We theoretically prove that minimizing quantization-induced mean square error (MSE) of model parameters (e.g., weight, bias, and activation) in PTQ is sub-optimal for compression tasks and thus develop a novel Rate-Distortion (R-D) Optimized PTQ (RDO-PTQ) to best retain the compression performance. Given a LIC model, RDO-PTQ layer-wisely determines the quantization parameters to transform the original floating-point parameters in 32-bit precision (FP32) to fixed-point ones at 8-bit precision (INT8), for which a tiny calibration image set is compressed in optimization to minimize R-D loss. Experiments reveal the outstanding efficiency of the proposed method on different LICs, showing the closest coding performance to their floating-point counterparts. Our method is a lightweight and plug-and-play approach without retraining model parameters but just adjusting quantization parameters, which is attractive to practitioners. Such an RDO-PTQ is a task-oriented PTQ scheme, which is then extended to quantize popular super-resolution and image classification models with negligible performance loss, further evidencing the generalization of our methodology. Related materials will be released at https://njuvision.github.io/RDO-PTQ.
Channel Tracking for RIS-aided mmWave Communications Under High Mobility Scenarios
Authors: Yu Liu, Ming Chen, Cunhua Pan, Yijin Pan, Yinlu Wang, Yaoming Huang, Tianyang Cao, Jiangzhou Wang
Date: 2022-11-07 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The emerging reconfigurable intelligent surface (RIS) technology is promising for applications in the millimeter wave (mmWave) communication systems to effectively compensate for propagation loss or tackle the blockage issue. Considering the high mobility of users in realistic scenarios, it is essential to adjust the phase shifts in real time to align the beam towards the mobile users, which requires to frequently estimate the channel state information. Hence, it is imperative to design efficient channel tracking schemes to avoid the complex channel estimation procedure. In this paper, we develop a novel channel tracking scheme with two advantages over conventional schemes. First, our tracking scheme is based on the cascaded angles at the RIS instead of the accurate angle values, which is more practical. Second, it can be employed under a more general setting where the noise can be non-Gaussian. Simulation results show the high tracking accuracy of our proposed scheme, and validate the superiority to the existing EKF-based tracking scheme.
Spectrally Limited Periodic Waveforms for HF OTHR Applications
Authors: Yuri Abramovich, Dan Dickey, Victor Abramovich
Date: 2022-11-07 00:00:00

The problem of a constant modulus (CM) continuous wave (CW) waveform design with the thumb-tack ambiguity function that meets the NTIA RSEC requirements is addressed. The ad-hoc and alternating projection techniques are proposed to modify the spectrum of a prototype waveform to meet the NTIA RSEC requirements, retaining the "thumb-tack" property of the original waveform ambiguity function. Periodic binary frequency shift keying (BFSK), Costas FSK, and noise-like waveforms are modified to meet CM and NTIA RSEC requirements. Introduced examples demonstrate the spectrum modification consequences and the proposed technique's efficiency for generating the CM spectrum-controlled waveforms with the thumb-tack ambiguity function.
Contact-Free Simultaneous Sensing of Human Heart Rate and Canine Breathing Rate for Animal Assisted Interactions
Authors: Timothy Holder, Mushfiqur Rahman, Emily Summers, David Roberts, Chau-Wai Wong, Alper Bozkurt
Date: 2022-11-07 00:00:00

Animal Assisted Interventions (AAIs) involve pleasant interactions between humans and animals and can potentially benefit both types of participants. Research in this field may help to uncover universal insights about cross-species bonding, dynamic affect detection, and the influence of environmental factors on dyadic interactions. However, experiments evaluating these outcomes are limited to methodologies that are qualitative, subjective, and cumbersome due to the ergonomic challenges related to attaching sensors to the body. Current approaches in AAIs also face challenges when translating beyond controlled clinical environments or research contexts. These also often neglect the measurements from the animal throughout the interaction. Here, we present our preliminary effort toward a contact-free approach to facilitate AAI assessment via the physiological sensing of humans and canines using consumer-grade cameras. This initial effort focuses on verifying the technological feasibility of remotely sensing the heart rate signal of the human subject and the breathing rate signal of the dog subject while they are interacting. Small amounts of motion such as patting and involuntary body shaking or movement can be tolerated with our custom designed vision-based algorithms. The experimental results show that the physiological measurements obtained by our algorithms were consistent with those provided by the standard reference devices. With further validation and expansion to other physiological parameters, the presented approach offers great promise for many scenarios from the AAI research space to veterinary, surgical, and clinical applications.