pwc-archive/papers-with-abstracts huggingface dataset:
Multi-band Reconfigurable Holographic Surface Based ISAC Systems: Design and Optimization
Authors: Jingzhi Hu, Zhe Chen, Jun Luo
Date: 2023-03-28 00:00:00
Tasks: ISAC

Metamaterial-based reconfigurable holographic surfaces (RHSs) have been proposed as novel cost-efficient antenna arrays, which are promising for improving the positioning and communication performance of integrated sensing and communications (ISAC) systems. However, due to the high frequency selectivity of the metamaterial elements, RHSs face challenges in supporting ultra-wide bandwidth (UWB), which significantly limits the positioning precision. In this paper, to avoid the physical limitations of UWB RHS while enhancing the performance of RHS-based ISAC systems, we propose a multi-band (MB) based ISAC system. We analyze its positioning precision and propose an efficient algorithm to optimize the large number of variables in analog and digital beamforming. Through comparison with benchmark results, simulation results verify the efficiency of our proposed system and algorithm, and show that the system achieves $42\%$ less positioning error, which reduces $82\%$ communication capacity loss.
The Fundamental Limitations of Learning Linear-Quadratic Regulators
Authors: Bruce D. Lee, Ingvar Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni
Date: 2023-03-27 00:00:00
Tasks: valid

We present a local minimax lower bound on the excess cost of designing a linear-quadratic controller from offline data. The bound is valid for any offline exploration policy that consists of a stabilizing controller and an energy bounded exploratory input. The derivation leverages a relaxation of the minimax estimation problem to Bayesian estimation, and an application of Van Trees' inequality. We show that the bound aligns with system-theoretic intuition. In particular, we demonstrate that the lower bound increases when the optimal control objective value increases. We also show that the lower bound increases when the system is poorly excitable, as characterized by the spectrum of the controllability gramian of the system mapping the noise to the state and the $\mathcal{H}_\infty$ norm of the system mapping the input to the state. We further show that for some classes of systems, the lower bound may be exponential in the state dimension, demonstrating exponential sample complexity for learning the linear-quadratic regulator offline.
A New Index based on Power Splitting Indices for Predicting Proper Time of Controlled Islanding
Authors: Hamzeh Davarikia, Faycal Znidi, Masoud Barati, Heena Rathore
Date: 2023-03-27 00:00:00

In the event of large disturbances, the practice of controlled islanding is used as a last resort to prevent cascading outages. The application of the strategy at the right time is crucial to maintaining system security. A controlled islanding strategy may be deployed efficiently at the right time by predicting the time of uncontrolled system splitting. The purpose of this study is to predict the appropriate islanding time to prevent catastrophic blackout and uncontrolled islanding based on existing relationships between coherent generator groups. A new instability index is derived from the proximity of inter-area oscillations to power splitting indices. Power splitting indices are derived using synchronization coefficients, which recognize the conditions in the system that warrant controlled islanding. The critical values of indices are calculated in offline mode using simulation data from IEEE 39-Buses, and their online performance is evaluated following a controlled islanding strategy. Through the introduction of these indices, system degradation can be effectively evaluated, and blackouts can be predicted early and prevented by controlled islanding at the right time.
GNN-Assisted Phase Space Integration with Application to Atomistics
Authors: Shashank Saxena, Jan-Hendrik Bastek, Miguel Spinola, Prateek Gupta, Dennis M. Kochmann
Date: 2023-03-20 00:00:00
Tasks: Computational Efficiency

Overcoming the time scale limitations of atomistics can be achieved by switching from the state-space representation of Molecular Dynamics (MD) to a statistical-mechanics-based representation in phase space, where approximations such as maximum-entropy or Gaussian phase packets (GPP) evolve the atomistic ensemble in a time-coarsened fashion. In practice, this requires the computation of expensive high-dimensional integrals over all of phase space of an atomistic ensemble. This, in turn, is commonly accomplished efficiently by low-order numerical quadrature. We show that numerical quadrature in this context, unfortunately, comes with a set of inherent problems, which corrupt the accuracy of simulations -- especially when dealing with crystal lattices with imperfections. As a remedy, we demonstrate that Graph Neural Networks, trained on Monte-Carlo data, can serve as a replacement for commonly used numerical quadrature rules, overcoming their deficiencies and significantly improving the accuracy. This is showcased by three benchmarks: the thermal expansion of copper, the martensitic phase transition of iron, and the energy of grain boundaries. We illustrate the benefits of the proposed technique over classically used third- and fifth-order Gaussian quadrature, we highlight the impact on time-coarsened atomistic predictions, and we discuss the computational efficiency. The latter is of general importance when performing frequent evaluation of phase space or other high-dimensional integrals, which is why the proposed framework promises applications beyond the scope of atomistics.
Cellular EXchange Imaging (CEXI): Evaluation of a diffusion model including water exchange in cells using numerical phantoms of permeable spheres
Authors: Rémy Gardier, Juan Luis Villarreal Haro, Erick J. Canales-Rodriguez, Ileana O. Jelescu, Gabriel Girard, Jonathan Rafael-Patino, Jean-Philippe Thiran
Date: 2023-03-28 00:00:00
Tasks: Diffusion  MRI

Purpose: Biophysical models of diffusion MRI have been developed to characterize microstructure in various tissues, but existing models are not suitable for tissue composed of permeable spherical cells. In this study we introduce Cellular Exchange Imaging (CEXI), a model tailored for permeable spherical cells, and compares its performance to a related Ball \& Sphere (BS) model that neglects permeability. Methods: We generated DW-MRI signals using Monte-Carlo simulations with a PGSE sequence in numerical substrates made of spherical cells and their extracellular space for a range of membrane permeability. From these signals, the properties of the substrates were inferred using both BS and CEXI models. Results: CEXI outperformed the impermeable model by providing more stable estimates cell size and intracellular volume fraction that were diffusion time-independent. Notably, CEXI accurately estimated the exchange time for low to moderate permeability levels previously reported in other studies ($\kappa<25\mu m/s$). However, in highly permeable substrates ($\kappa=50\mu m/s$), the estimated parameters were less stable, particularly the diffusion coefficients. Conclusion: This study highlights the importance of modeling the exchange time to accurately quantify microstructure properties in permeable cellular substrates. Future studies should evaluate CEXI in clinical applications such as lymph nodes, investigate exchange time as a potential biomarker of tumor severity, and develop more appropriate tissue models that account for anisotropic diffusion and highly permeable membranes.
Multiple-level Green Noise Mask Design for Practical Fourier Phase Retrieval
Authors: Qiuliang Ye, Bingo Wing-Kuen Ling, Li-Wen Wang, Daniel Pak-Kong Lun
Date: 2023-03-28 00:00:00
Tasks: Retrieval

Phase retrieval, a long-established challenge for recovering a complex-valued signal from its Fourier intensity measurements, has attracted significant interest because of its far-flung applications in optical imaging. To enhance accuracy, researchers introduce extra constraints to the measuring procedure by including a random aperture mask in the optical path that randomly modulates the light projected on the target object and gives the coded diffraction patterns (CDP). It is known that random masks are non-bandlimited and can lead to considerable high-frequency components in the Fourier intensity measurements. These high-frequency components can be beyond the Nyquist frequency of the optical system and are thus ignored by the phase retrieval optimization algorithms, resulting in degraded reconstruction performances. Recently, our team developed a binary green noise masking scheme that can significantly reduce the high-frequency components in the measurement. However, the scheme cannot be extended to generate multiple-level aperture masks. This paper proposes a two-stage optimization algorithm to generate multi-level random masks named $\textit{OptMask}$ that can also significantly reduce high-frequency components in the measurements but achieve higher accuracy than the binary masking scheme. Extensive experiments on a practical optical platform were conducted. The results demonstrate the superiority and practicality of the proposed $\textit{OptMask}$ over the existing masking schemes for CDP phase retrieval.
Entropy of financial time series due to the shock of war
Authors: Ewa A. Drzazga-Szczȩśniak, Piotr Szczepanik, Adam Z. Kaczmarek, Dominik Szczȩśniak
Date: 2023-03-15 00:00:00
Tasks: Time Series

The concept of entropy is not uniquely relevant to the statistical mechanics but among others it can play pivotal role in the analysis of a time series, particularly the stock market data. In this area sudden events are especially interesting as they describe abrupt data changes which may have long-lasting effects. Here, we investigate the impact of such events on the entropy of financial time series. As a case study we assume data of polish stock market in the context of its main cumulative index. This index is discussed for the finite time periods before and after outbreak of the 2022 Russian invasion of Ukraine, acting as the sudden event. The analysis allows us to validate the entropy-based methodology in assessing market changes as driven by the extreme external factors. We show that qualitative features of market changes can be captured quantitatively in terms of the entropy. In addition to that, the magnitude of the impact is analysed over various time periods in terms of the introduced entropic index. To this end, the present work also attempts to answer whether or not the recent war can be considered as a reason or at least catalyst to the current economic crisis.
The Value of Information and Circular Settings
Authors: Stefan Behringer, Roman V. Belavkin
Date: 2023-03-28 00:00:00

We present a universal concept for the Value of Information (VoI), based on the works of Claude Shannon's and Ruslan Stratonovich that can take into account very general preferences of the agents and results in a single number. As such it is convenient for applications and also has desirable properties for decision theory and demand analysis. The Shannon/Stratonovich VoI concept is compared to alternatives and applied in examples. In particular we apply the concept to a circular spatial structure well known from many economic models and allow for various economic transport costs.
Study on the risk-informed heuristic of decision-making on the restoration of defaulted corporation networks
Authors: Jiajia Xia
Date: 2023-03-28 00:00:00
Tasks: Decision Making

Government-run (Government-led) restoration has become a common and effective approach to the mitigation of financial risks triggered by corporation credit defaults. However, in practice, it is often challenging to come up with the optimal plan of those restorations, due to the massive search space associated with defaulted corporation networks (DCNs), as well as the dynamic and looped interdependence among the recovery of those individual corporations. To address such a challenge, this paper proposes an array of viable heuristics of the decision-making that drives those restoration campaigns. To examine their applicability and measure their performance, those heuristics have been applied to two real-work DCNs that consists of 100 listed Chinese A-share companies, whose restoration has been modelled based on the 2021 financial data, in the wake of randomly generated default scenarios. The corresponding simulation outcome of the case-study shows that the restoration of the DCNs would be significantly influenced by the different heuristics adopted, and in particular, the system-oriented heuristic is revealed to be significantly outperforming those individual corporation-oriented ones. Therefore, such a research has further highlighted that the interdependence-induced risk propagation shall be accounted for by the decision-makers, whereby a prompt and effective restoration campaign of DCNs could be shaped.
Redeeming Falsifiability?
Authors: Mark Whitmeyer, Kun Zhang
Date: 2023-03-28 00:00:00

We revisit Popper's falsifiability criterion. A tester hires a potential expert to produce a theory, offering payments contingent on the observed performance of the theory. We argue that if the informed expert can acquire additional information, falsifiability does have the power to identify worthless theories.
AD-YOLO: You Look Only Once in Training Multiple Sound Event Localization and Detection
Authors: Jin Sob Kim, Hyun Joon Park, WooSeok Shin, Sung Won Han
Date: 2023-03-28 00:00:00
Tasks: Direction of Arrival Estimation, Sound Event Detection, Sound Event Localization and Detection

Sound event localization and detection (SELD) combines the identification of sound events with the corresponding directions of arrival (DOA). Recently, event-oriented track output formats have been adopted to solve this problem; however, they still have limited generalization toward real-world problems in an unknown polyphony environment. To address the issue, we proposed an angular-distance-based multiple SELD (AD-YOLO), which is an adaptation of the "You Only Look Once" algorithm for SELD. The AD-YOLO format allows the model to learn sound occurrences location-sensitively by assigning class responsibility to DOA predictions. Hence, the format enables the model to handle the polyphony problem, regardless of the number of sound overlaps. We evaluated AD-YOLO on DCASE 2020-2022 challenge Task 3 datasets using four SELD objective metrics. The experimental results show that AD-YOLO achieved outstanding performance overall and also accomplished robustness in class-homogeneous polyphony environments.
Displacement field calculation of large-scale structures using computer vision with physical constraints
Authors: Yapeng Guo, Peng Zhong, Yi Zhuo, Fanzeng Meng, Hao Di, Shunlong Li
Date: 2023-03-28 00:00:00
Tasks: Image Stitching, Template Matching

Because of the advantages of easy deployment, low cost and non-contact, computer vision-based structural displacement acquisition technique has received wide attention and research in recent years. However, the displacement field acquisition of large-scale structures is a challenging topic due to the contradiction of camera field of view and resolution. This paper presents a large-scale structural displacement field calculation framework with integrated computer vision and physical constraints using only one camera. Firstly, the full-field image of the large-scale structure is obtained by processing the multi-view image using image stitching technique; secondly, the full-field image is meshed and the node displacements are calculated using an improved template matching method; and finally, the non-node displacements are described using shape functions considering physical constraints. The developed framework was validated using a scaled bridge model and evaluated by the proposed evaluation index for displacement field calculation accuracy. This paper can provide an effective way to obtain displacement fields of large-scale structures efficiently and cost-effectively.
Imbalance Knowledge-Driven Multi-modal Network for Land-Cover Semantic Segmentation Using Images and LiDAR Point Clouds
Authors: Yameng Wang, Yi Wan, Yongjun Zhang, Bin Zhang, Zhi Gao
Date: 2023-03-28 00:00:00
Tasks: Semantic Segmentation

Despite the good results that have been achieved in unimodal segmentation, the inherent limitations of individual data increase the difficulty of achieving breakthroughs in performance. For that reason, multi-modal learning is increasingly being explored within the field of remote sensing. The present multi-modal methods usually map high-dimensional features to low-dimensional spaces as a preprocess before feature extraction to address the nonnegligible domain gap, which inevitably leads to information loss. To address this issue, in this paper we present our novel Imbalance Knowledge-Driven Multi-modal Network (IKD-Net) to extract features from raw multi-modal heterogeneous data directly. IKD-Net is capable of mining imbalance information across modalities while utilizing a strong modal to drive the feature map refinement of the weaker ones in the global and categorical perspectives by way of two sophisticated plug-and-play modules: the Global Knowledge-Guided (GKG) and Class Knowledge-Guided (CKG) gated modules. The whole network then is optimized using a holistic loss function. While we were developing IKD-Net, we also established a new dataset called the National Agriculture Imagery Program and 3D Elevation Program Combined dataset in California (N3C-California), which provides a particular benchmark for multi-modal joint segmentation tasks. In our experiments, IKD-Net outperformed the benchmarks and state-of-the-art methods both in the N3C-California and the small-scale ISPRS Vaihingen dataset. IKD-Net has been ranked first on the real-time leaderboard for the GRSS DFC 2018 challenge evaluation until this paper's submission.
DiffULD: Diffusive Universal Lesion Detection
Authors: Peiang Zhao, Han Li, Ruiyang Jin, S. Kevin Zhou
Date: 2023-03-28 00:00:00
Tasks: Computed Tomography (CT), Lesion Detection, object-detection, Object Detection

Universal Lesion Detection (ULD) in computed tomography (CT) plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by anchor-based detection designs, but they have inherent drawbacks due to the use of anchors: i) Insufficient training targets and ii) Difficulties in anchor design. Diffusion probability models (DPM) have demonstrated outstanding capabilities in many vision tasks. Many DPM-based approaches achieve great success in natural image object detection without using anchors. But they are still ineffective for ULD due to the insufficient training targets. In this paper, we propose a novel ULD method, DiffULD, which utilizes DPM for lesion detection. To tackle the negative effect triggered by insufficient targets, we introduce a novel center-aligned bounding box padding strategy that provides additional high-quality training targets yet avoids significant performance deterioration. DiffULD is inherently advanced in locating lesions with diverse sizes and shapes since it can predict with arbitrary boxes. Experiments on the benchmark dataset DeepLesion show the superiority of DiffULD when compared to state-of-the-art ULD approaches.
Chloride Ion Erosion of Pre-Stressed Concrete Bridges in Cold Regions
Authors: Hongtao Cui, Yi Zhuo, Dongyuan Ke, Zhonglong Li, Shunlong Li
Date: 2023-03-28 00:00:00

The erosion of chloride ions in concrete bridges will accelerate the corrosion of reinforcement, which is an important reason for the decline of bridge durability. The erosion process of chloride ion, especially deicing salt solution in cold regions, is complex and has many influencing factors. It is very important to use accurate and effective methods to analyze the chloride ion erosion process in concrete. In this study, the pre-stressed concrete bridge retired in the cold region was taken as the research object, and the specimens from the whole bridge are obtained by the method of core drilling sampling. The concentration of chloride ion was measured at different depths of the specimens. The process of chloride ion erosion was simulated in two-dimensional space through COMSOL multi-physical field simulation, and compared with the measured results. The simulation method proposed in this paper has good reliability and accuracy.
Local track irregularity identification based on multi-sensor time-frequency features of high-speed railway bridge accelerations
Authors: Ye Mo, Yi Zhuo, Shunlong Li
Date: 2023-03-28 00:00:00

Shortwave track diseases are generally reflected in the form of local track irregularity. Such diseases will greatly impact the train-track-bridge interaction (TTBI) dynamic system, seriously affecting train safety. Therefore, a method is proposed to detect and localize local track irregularities based on multis-sensor time-frequency features of high-speed railway bridge accelerations. Continuous wavelet transform (CWT) is used to analyze the multi-sensor accelerations of railway bridges. Moreover, time-frequency features based on the sum of wavelet coefficients are proposed, considering the influence of the distance from the measurement points to the local irregularity on the recognition accuracy. Then, the multi-domain features are utilized to recognize deteriorated railway locations. A simply-supported high-speed railway bridge traversed by a railway train is adopted as a numerical simulation. Comparative studies are conducted to investigate the influence of vehicle speeds and the location of local track irregularity on the algorithm. Numerical simulation results show that the proposed algorithm can detect and locate local track irregularity accurately and is robust to vehicle speeds.
Estimation of Scalar Field Distribution in the Fourier Domain
Authors: Alex S. Leong, Alexei T. Skvortsov
Date: 2023-03-28 00:00:00
Tasks: Autonomous Vehicles

In this paper we consider the problem of estimation of scalar field distribution (e.g. pollutant, moisture, temperature) from noisy measurements collected by unmanned autonomous vehicles such as UAVs. The field is modelled as a sum of Fourier components/modes, where the number of modes retained and estimated determines in a natural way the approximation quality. An algorithm for estimating the modes using an online optimization approach is presented, under the assumption that the noisy measurements are quantized. The algorithm can also estimate time-varying fields through the introduction of a forgetting factor. Simulation studies demonstrate the effectiveness of the proposed approach.
A minimal model coupling communicable and non-communicable diseases
Authors: M. Marvá, E. Venturino, M. C. Vera
Date: 2023-03-28 00:00:00

This work presents a model combining the simplest communicable and non-communicable disease models. The latter is, by far, the leading cause of sickness and death in the World, and introduces basal heterogeneity in populations where communicable diseases evolve. The model can be interpreted as a risk-structured model, another way of accounting for population heterogeneity. Our results show that considering the non-communicable disease (in the end, heterogeneous populations) allows the communicable disease to become endemic even if the basic reproduction number is less than $1$. This feature is known as subcritical bifurcation. Furthermore, ignoring the non-communicable disease dynamics results in overestimating the reproduction number and, thus, giving wrong information about the actual number of infected individuals. We calculate sensitivity indices and derive interesting epidemic-control information.