pwc-archive/papers-with-abstracts huggingface dataset:
Collaborative planning and optimization for electric-thermal-hydrogen-coupled energy systems with portfolio selection of the complete hydrogen energy chain
Authors: Xinning Yi, Tianguang Lu, Yixiao Li, Qian Ai, Ran Hao
Date: 2023-11-14 00:00:00

Under the global low-carbon target, the uneven spatiotemporal distribution of renewable energy resources exacerbates the uncertainty and seasonal power imbalance. Additionally, the issue of an incomplete hydrogen energy chain is widely overlooked in planning models, which hinders the complete analysis of the role of hydrogen in energy systems. Therefore, this paper proposes a high-resolution collaborative planning model for electricity-thermal-hydrogen-coupled energy systems considering both the spatiotemporal distribution characteristics of renewable energy resources and the multi-scale bottom-to-top investment strategy for the complete hydrogen energy chain. Considering the high-resolution system operation flexibility, this paper proposes a hydrogen chain-based fast clustering optimization method that can handle high-dimensional data and multi-time scale operation characteristics. The model optimizes the geographical distribution and capacity configuration of the Northeast China energy system in 2050, with hourly operational characteristics. The planning optimization covered single-energy devices, multi-energy-coupled conversion devices, and electric-hydrogen transmission networks. Last but not least, this paper thoroughly examines the optimal portfolio selection of different hydrogen technologies based on the differences in cost, flexibility, and efficiency. In the Pareto analysis, the proposed model reduces CO2 emissions by 60% with a competitive cost. This paper provides a zero-carbon pathway for multi-energy systems with a cost 4% less than the social cost of carbon $44.6/ton, and the integration of the complete hydrogen energy chain reduces the renewable energy curtailment by 97.0%. Besides, the portfolio selection results indicate that the system favors the SOEC with the highest energy efficiency and the PEMFC with the fastest dynamic response when achieving zero-carbon emissions
Synchrophasor Data Anomaly Detection on Grid Edge by 5G Communication and Adjacent Compute
Authors: Chuan Qin, Dexin Wang, Kishan Prudhvi Guddanti, Xiaoyuan Fan, Zhangshuan Hou
Date: 2023-11-13 00:00:00
Tasks: Anomaly Detection

The fifth-generation mobile communication (5G) technology offers opportunities to enhance the real-time monitoring of grids. The 5G-enabled phasor measurement units (PMUs) feature flexible positioning and cost-effective long-term maintenance without the constraints of fixing wires. This paper is the first to demonstrate the applicability of 5G in PMU communication, and the experiment was carried out at Verizon non-standalone test-bed at Pacific Northwest National Laboratory (PNNL) Advanced Wireless Communication lab. The performance of the 5G-enabled PMU communication setup is reviewed and discussed in this paper, and a generalized dynamic linear model (GDLM) based real-time synchrophasor data anomaly detection use-case is presented. Last but not least, the practicability of implementing 5G for wide-area protection strategies is explored and discussed by analyzing the experimental results.
Strong exponential stability of switched impulsive systems with mode-constrained switching
Authors: Alexis J. Vallarella, José Luis Mancilla-Aguilar, Hernan Haimovich
Date: 2023-11-13 00:00:00

Strong stability, defined by bounds that decay not only over time but also with the number of impulses, has been established as a requirement to ensure robustness properties for impulsive systems with respect to inputs or disturbances. Most existing results, however, only consider weak stability, where the bounds only decay with time. In this paper, we provide a method for calculating the maximum overshoot and the decay rate for strong global uniform exponential stability bounds for nonlinear switched impulsive systems. We consider the scenario of mode-constrained switching where not all transitions between subsystems are allowed, and where subsystems may exhibit unstable dynamics in the flow and/or jump maps. Based on direct and reverse mode-dependent average dwell-time and activation-time constraints, we derive stability bounds that can be improved by considering longer switching sequences for computation. We provide an example that shows how the results can be employed to ensure the stability robustness of nonlinear systems that admit a global state weak linearization.
Providing a Periodic Control Solution for Balance Control While Standing Using a Pendulum-Based Approach
Authors: Golnoush Shahraki, Majid Sadrzadeh, Elyas Irankhah
Date: 2023-11-13 00:00:00

The stability of standing in humans is a complex process that leads to maintaining the upright position against external disturbances. Balance control during standing is of vital importance for humans in daily life. An issue that is still not clearly understood is which control mechanism the central nervous system uses to maintain stability. In the rehabilitation of standing function, the coordination pattern between the angles of the leg joint of a healthy person should be restored. For example, one of the rehabilitation methods is functional electrical stimulation. In the work that was mainly done in the control of standing balance with functional electrical stimulation, the problem of the optimal pattern using the phase space was not mentioned at all, and a series of predetermined desired curves were assigned to the joints, and the controller only used these curves. followed, while the origin of these curves are not real patterns. Therefore, the main goal of this project is to design a periodic controller based on phase space. In such a way that a mapping related to standing is detected first, then a feedback controller is designed so that it is activated only when the system state space curves find a significant distance from the detected mapping, then the feedback controller is activated, and it adjusts the control signal so that the system state space curves come close to the detected mapping.
The optimal resolution level of a protein is an emergent property of its structure and dynamics
Authors: Raffaele Fiorentini, Thomas Tarenzi, Giovanni Mattiotti, Raffaello Potestio
Date: 2023-11-14 00:00:00
Tasks: Dimensionality Reduction

Molecular dynamics simulations provide a wealth of data whose in-depth analysis can be computationally demanding and, sometimes, even unnecessary. Dimensionality reduction techniques are thus routinely employed to simplify and improve the interpretation of trajectories focusing on specific subsets of the system's atoms; a still open problem, in this context, is to determine the optimal resolution level of the molecule, i.e. the smallest number of atoms needed to preserve the largest information content from the full atomistic trajectory. Here, we introduce the protein optimal resolution identification method (PROPRE), an unsupervised approach built on information theory principles that determines the smallest number of atoms that need to be retained in order to attain a synthetic yet informative description of a protein. By applying the method to a protein dataset and two particular case studies, we show that this number is typically between 1.5 and 2 times the amount of residues in a protein; furthermore, the degree of structural variability of the system influences the optimal resolution level importantly, in that a broader range of large-scale conformations correlates with fewer retained sites. The PROPRE method is implemented in efficient and user-friendly python scripts, which are made available for download on a github repository.
Comparison of model selection techniques for seafloor scattering statistics
Authors: Derek R Olson, Marc Geilhufe
Date: 2023-11-14 00:00:00
Tasks: Model Selection

In quantitative analysis of seafloor imagery, it is common to model the collection of individual pixel intensities scattered by the seafloor as a random variable with a given statistical distribution. There is a considerable literature on statistical models for seafloor scattering, mostly focused on areas with statistically homogeneous properties (i.e. exhibiting spatial stationarity). For more complex seafloors, the pixel intensity distribution is more appropriately modeled using a mixture of simple distributions. For very complex seafloors, fitting 3 or more mixture components makes physical sense, but the statistical model becomes much more complex in these cases. Therefore, picking the number of components of the mixture model is a decision that must be made, using a priori information, or using a data driven approach. However, this information is time consuming to collect, and depends on the skill and experience of the human. Therefore, a data-driven approach is advantageous to use, and is explored in this work. Criteria for choosing a model always need to balance the trade-off for the best fit for the data on the one hand and the model complexity on the other hand. In this work, we compare several statistical model selection criteria, e.g., the Bayesian information criterion. Examples are given for SAS data collected by an autonomous underwater vehicle in a rocky environment off the coast of Bergen, Norway using data from the HISAS-1032 synthetic aperture sonar system.
Estimating Conditional Value-at-Risk with Nonstationary Quantile Predictive Regression Models
Authors: Christis Katsouris
Date: 2023-11-14 00:00:00
Tasks: regression

This paper develops an asymptotic distribution theory for an endogenous instrumentation approach in quantile predictive regressions when both generated covariates and persistent predictors are used. The generated covariates are obtained from an auxiliary quantile predictive regression model and the statistical problem of interest is the robust estimation and inference of the parameters that correspond to the primary quantile predictive regression in which this generated covariate is added to the set of nonstationary regressors. We find that the proposed doubly IVX corrected estimator is robust to the abstract degree of persistence regardless of the presence of generated regressor obtained from the first stage procedure. The asymptotic properties of the two-stage IVX estimator such as mixed Gaussianity are established while the asymptotic covariance matrix is adjusted to account for the first-step estimation error.
Consensus and Disagreement: Information Aggregation under (not so) Naive Learning
Authors: Abhijit Banerjee, Olivier Compte
Date: 2023-11-14 00:00:00

We explore a model of non-Bayesian information aggregation in networks. Agents non-cooperatively choose among Friedkin-Johnsen type aggregation rules to maximize payoffs. The DeGroot rule is chosen in equilibrium if and only if there is noiseless information transmission, leading to consensus. With noisy transmission, while some disagreement is inevitable, the optimal choice of rule amplifies the disagreement: even with little noise, individuals place substantial weight on their own initial opinion in every period, exacerbating the disagreement. We use this framework to think about equilibrium versus socially efficient choice of rules and its connection to polarization of opinions across groups.
Strategic Waiting in Centralized Matching: Daycare Assignment
Authors: Kan Kuno
Date: 2023-11-14 00:00:00
Tasks: counterfactual

In centralized assignment mechanisms, unassigned participants are often placed on waitlists to improve their chances in future rounds. However, I demonstrate that this practice may bring unintended welfare consequences on the participants, using data from the Japanese daycare system. The prioritization of waitlisted applicants introduces a dynamic incentive for applicants to manipulate their priority by strategically choosing to be waitlisted to secure positions at more selective daycares. I show that 30.0% of applicants do not list safety options when they initially apply and that those who benefit from additional priority are 8.1 percentage points more likely to avoid listing safety options than those who do not. Given the prevalence of such strategic waiting, I estimate a structural model of daycare choice that extends Agarwal and Somaini (2018) to a two-period model allowing for reapplication. By simulating a scenario without waitlist priority, I find that the current priority functions as a redistributive mechanism: through its abolition, early starters (age 0) experience a 34.1% decrease in welfare and a 1.7 percentage point increase in the likelihood of being waitlisted, whereas late starters (age 1) experience a 29.0% increase in welfare and a 6.9 percentage point decrease in the likelihood of being waitlisted.
Considering Risk Aversion in Economic Evaluation: A Rank Dependent Approach
Authors: Jacob Smith
Date: 2023-11-14 00:00:00

This paper presents a method for incorporating risk aversion into existing decision tree models used in economic evaluations. The method involves applying a probability weighting function based on rank dependent utility theory to reduced lotteries in the decision tree model. This adaptation embodies the fact that different decision makers can observe the same decision tree model structure but come to different conclusions about the optimal treatment. The proposed solution to this problem is to compensate risk-averse decision makers to use the efficient technology that they are reluctant to adopt.
Mustango: Toward Controllable Text-to-Music Generation
Authors: Jan Melechovsky, Zixun Guo, Deepanway Ghosal, Navonil Majumder, Dorien Herremans, Soujanya Poria
Date: 2023-11-14 00:00:00
Tasks: Data Augmentation, Denoising, Information Retrieval, Music Generation, Music Information Retrieval, Text-to-Music Generation

The quality of the text-to-music models has reached new heights due to recent advancements in diffusion models. The controllability of various musical aspects, however, has barely been explored. In this paper, we propose Mustango: a music-domain-knowledge-inspired text-to-music system based on diffusion. Mustango aims to control the generated music, not only with general text captions, but with more rich captions that can include specific instructions related to chords, beats, tempo, and key. At the core of Mustango is MuNet, a Music-Domain-Knowledge-Informed UNet guidance module that steers the generated music to include the music-specific conditions, which we predict from the text prompt, as well as the general text embedding, during the reverse diffusion process. To overcome the limited availability of open datasets of music with text captions, we propose a novel data augmentation method that includes altering the harmonic, rhythmic, and dynamic aspects of music audio and using state-of-the-art Music Information Retrieval methods to extract the music features which will then be appended to the existing descriptions in text format. We release the resulting MusicBench dataset which contains over 52K instances and includes music-theory-based descriptions in the caption text. Through extensive experiments, we show that the quality of the music generated by Mustango is state-of-the-art, and the controllability through music-specific text prompts greatly outperforms other models such as MusicGen and AudioLDM2.
Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks
Authors: Yang Gao, Zhuang Xiong, Shanshan Shan, Yin Liu, Pengfei Rong, Min Li, Alan H Wilman, G. Bruce Pike, Feng Liu, Hongfu Sun
Date: 2023-11-14 00:00:00

Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms.
Maximum Eigenvalue Detection based Spectrum Sensing in RIS-aided System with Correlated Fading
Authors: Nikhilsingh Parihar, Praful D. Mankar, Sachin Chaudhari
Date: 2023-11-14 00:00:00

Robust spectrum sensing is crucial for facilitating opportunistic spectrum utilization for secondary users (SU) in the absense of primary users (PU). However, propagation environment factors such as multi-path fading, shadowing, and lack of line of sight (LoS) often adversely affect detection performance. To deal with these issues, this paper focuses on utilizing reconfigurable intelligent surfaces (RIS) to improve spectrum sensing in the scenario wherein both the multi-path fading and noise are correlated. In particular, to leverage the spatially correlated fading, we propose to use maximum eigenvalue detection (MED) for spectrum sensing. We first derive exact distributions of test statistics, i.e., the largest eigenvalue of the sample covariance matrix, observed under the null and signal present hypothesis. Next, utilizing these results, we present the exact closed-form expressions for the false alarm and detection probabilities. In addition, we also optimally configure the phase shift matrix of RIS such that the mean of the test statistics is maximized, thus improving the detection performance. Our numerical analysis demonstrates that the MED's receiving operating characteristic (ROC) curve improves with increased RIS elements, SNR, and the utilization of statistically optimal configured RIS.
Joint Location Sensing and Channel Estimation for IRS-Aided mmWave ISAC Systems
Authors: Zijian Chen, Ming-Min Zhao, Min Li, Fan Xu, Qingqing Wu, Min-Jian Zhao
Date: 2023-11-14 00:00:00
Tasks: Bayesian Inference, Integrated sensing and communication, ISAC, POS

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly sense the target/scatterer/user positions as well as estimate the sensing and communication (SAC) channels in the considered system, we propose a two-phase transmission scheme, where the coarse and refined sensing/channel estimation (CE) results are respectively obtained in the first phase (using scanning-based IRS reflection coefficients) and second phase (using optimized IRS reflection coefficients). For each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is developed to solve the considered joint location sensing and CE problem. The proposed algorithm effectively exploits the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the overall performance. Based on the estimation results from the first phase, we formulate a Cram\'{e}r-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem. Simulation results are provided to verify the superiority of the proposed transmission scheme and associated algorithms.
Passive Human Sensing Enhanced by Reconfigurable Intelligent Surface: Opportunities and Challenges
Authors: Xinyu Li, Jian Wei You, Ze Gu, Qian Ma, Long Chen, Jingyuan Zhang, Shi Jin, Tie Jun Cui
Date: 2023-11-14 00:00:00
Tasks: Activity Recognition

Reconfigurable intelligent surfaces (RISs) have flexible and exceptional performance in manipulating electromagnetic waves and customizing wireless channels. These capabilities enable them to provide a plethora of valuable activity-related information for promoting wireless human sensing. In this article, we present a comprehensive review of passive human sensing using radio frequency signals with the assistance of RISs. Specifically, we first introduce fundamental principles and physical platform of RISs. Subsequently, based on the specific applications, we categorize the state-of-the-art human sensing techniques into three types, including human imaging,localization, and activity recognition. Meanwhile, we would also investigate the benefits that RISs bring to these applications. Furthermore, we explore the application of RISs in human micro-motion sensing, and propose a vital signs monitoring system enhanced by RISs. Experimental results are presented to demonstrate the promising potential of RISs in sensing vital signs for manipulating individuals. Finally, we discuss the technical challenges and opportunities in this field.
Undesignable RNA Structure Identification via Rival Structure Generation and Structure Decomposition
Authors: Tianshuo Zhou, Wei Yu Tang, David H. Mathews, Liang Huang
Date: 2023-11-14 00:00:00

RNA design is the search for a sequence or set of sequences that will fold into predefined structures, also known as the inverse problem of RNA folding. While numerous RNA design methods have been invented to find sequences capable of folding into a target structure, little attention has been given to the identification of undesignable structures according to the minimum free energy (MFE) criterion under the Turner model. In this paper, we address this gap by first introducing mathematical theorems outlining sufficient conditions for recognizing undesignable structures, then proposing efficient algorithms, guided by these theorems, to verify the undesignability of RNA structures. Through the application of these theorems and algorithms to the Eterna100 puzzles, we demonstrate the ability to efficiently establish that 15 of the puzzles indeed fall within the category of undesignable structures. In addition, we provide specific insights from the study of undesignability, in the hope that it will enable more understanding of RNA folding and RNA design.