pwc-archive/papers-with-abstracts huggingface dataset:
Multi-dimensional extensions of the Hegselmann-Krause model
Authors: Giulia De Pasquale, Maria Elena Valcher
Date: 2022-04-18 00:00:00
Tasks: model

In this paper, we consider two multi-dimensional Hagselmann-Krause (HK) models for opinion dynamics. The two models describe how individuals adjust their opinions on multiple topics, based on the influence of their peers. The models differ in the criterion according to which individuals decide whom they want to be influenced from. In the average-based model, individuals compare their average opinions on the various topics with those of the other individuals and interact only with those individuals whose average opinions lie within a confidence interval. For this model, we provide an alternative proof for the contractivity of the range of opinions and show that the agents' opinions reach consensus/clustering if and only if their average opinions do so. In the uniform affinity model agents compare their opinions on every single topic and influence each other only if, topic-wise, such opinions do not differ more than a given tolerance. We identify conditions under which the uniform affinity model enjoys the order-preservation property topic-wise and we prove that the global range of opinions (and hence the range of opinions on every single topic) are nonincreasing.
Hybrid Transformer Network for Different Horizons-based Enriched Wind Speed Forecasting
Authors: Dr. M. Madhiarasan, Prof. Partha Pratim Roy
Date: 2022-04-07 00:00:00

Highly accurate different horizon-based wind speed forecasting facilitates a better modern power system. This paper proposed a novel astute hybrid wind speed forecasting model and applied it to different horizons. The proposed hybrid forecasting model decomposes the original wind speed data into IMFs (Intrinsic Mode Function) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). We fed the obtained subseries from ICEEMDAN to the transformer network. Each transformer network computes the forecast subseries and then passes to the fusion phase. Get the primary wind speed forecasting from the fusion of individual transformer network forecast subseries. Estimate the residual error values and predict errors using a multilayer perceptron neural network. The forecast error is added to the primary forecast wind speed to leverage the high accuracy of wind speed forecasting. Comparative analysis with real-time Kethanur, India wind farm dataset results reveals the proposed ICEEMDAN-TNF-MLPN-RECS hybrid model's superior performance with MAE=1.7096*10^-07, MAPE=2.8416*10^-06, MRE=2.8416*10^-08, MSE=5.0206*10^-14, and RMSE=2.2407*10^-07 for case study 1 and MAE=6.1565*10^-07, MAPE=9.5005*10^-06, MRE=9.5005*10^-08, MSE=8.9289*10^-13, and RMSE=9.4493*10^-07 for case study 2 enriched wind speed forecasting than state-of-the-art methods and reduces the burden on the power system engineer.
Self-organized critical dynamics of RNA virus evolution
Authors: Xiaofei Ge, Kaichao You, Zeren Tan, Hedong Hou, Yang Tian, Pei Sun
Date: 2022-04-19 00:00:00

RNA virus (e.g., SARS-CoV-2) evolves in a complex manner. Studying RNA virus evolution is vital for understanding molecular evolution and medicine development. Scientists lack, however, general frameworks to characterize the dynamics of RNA virus evolution directly from empirical data and identify potential physical laws. To fill this gap, we present a theory to characterize the RNA virus evolution as a physical system with absorbing states and avalanche behaviors. This approach maps accessible biological data (e.g., phylogenetic tree and infection) to a general stochastic process of RNA virus infection and evolution, enabling researchers to verify potential self-organized criticality underlying RNA virus evolution. We apply our framework to SARS-CoV-2, the virus accounting for the global epidemic of COVID-19. We find that SARS-CoV-2 exhibits scale-invariant avalanches as mean-field theory predictions. The observed scaling relation, universal collapse, and slowly decaying auto-correlation suggest a self-organized critical dynamics of SARS-CoV-2 evolution. Interestingly, the lineages that emerge from critical evolution processes coincidentally match with threatening lineages of SARS-CoV-2 (e.g., the Delta virus). We anticipate our approach to be a general formalism to portray RNA virus evolution and help identify potential virus lineages to be concerned.
Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics
Authors: Pan Du, Xiaozhi Zhu, Jian-Xun Wang
Date: 2022-04-11 00:00:00
Tasks: Uncertainty Quantification

Optimization and uncertainty quantification have been playing an increasingly important role in computational hemodynamics. However, existing methods based on principled modeling and classic numerical techniques have faced significant challenges, particularly when it comes to complex 3D patient-specific shapes in the real world. First, it is notoriously challenging to parameterize the input space of arbitrarily complex 3-D geometries. Second, the process often involves massive forward simulations, which are extremely computationally demanding or even infeasible. We propose a novel deep learning surrogate modeling solution to address these challenges and enable rapid hemodynamic predictions. Specifically, a statistical generative model for 3-D patient-specific shapes is developed based on a small set of baseline patient-specific geometries. An unsupervised shape correspondence solution is used to enable geometric morphing and scalable shape synthesis statistically. Moreover, a simulation routine is developed for automatic data generation by automatic meshing, boundary setting, simulation, and post-processing. An efficient supervised learning solution is proposed to map the geometric inputs to the hemodynamics predictions in latent spaces. Numerical studies on aortic flows are conducted to demonstrate the effectiveness and merit of the proposed techniques.
Transparency and Policymaking with Endogenous Information Provision
Authors: Hanzhe Li
Date: 2022-04-19 00:00:00

How does the politician's reputation concern affect information provision when the information is endogenously provided by a biased lobbyist? I develop a model to study this problem and show that the answer depends on the transparency design. When the lobbyist's preference is publicly known, the politician's reputation concern induces the lobbyist to provide more information. When the lobbyist's preference is unknown, the politician's reputation concern may induce the lobbyist to provide less information. One implication of the result is that given transparent preferences, the transparency of decision consequences can impede information provision by moderating the politician's reputational incentive.
Semantics meets attractiveness: Choice by salience
Authors: Alfio Giarlotta, Angelo Petralia, Stephen Watson
Date: 2022-04-19 00:00:00

We describe a context-sensitive model of choice, in which the selection process is shaped not only by the attractiveness of items but also by their semantics ('salience'). All items are ranked according to a relation of salience, and a linear order is associated to each item. The selection of a single element from a menu is justified by one of the linear orders indexed by the most salient items in the menu. The general model provides a structured explanation for any observed behavior, and allows us to to model the 'moodiness' of a decision maker, which is typical of choices requiring as many distinct rationales as items. Asymptotically all choices are moody. We single out a model of linear salience, in which the salience order is transitive and complete, and characterize it by a behavioral property, called WARP(S). Choices rationalizable by linear salience can only exhibit non-conflicting violations of WARP. We also provide numerical estimates, which show the high selectivity of this testable model of bounded rationality.
Data Provision to an Informed Seller
Authors: Shota Ichihashi, Alex Smolin
Date: 2022-04-19 00:00:00

A monopoly seller is privately and imperfectly informed about the buyer's value of the product. The seller uses information to price discriminate the buyer. A designer offers a mechanism that provides the seller with additional information based on the seller's report about her type. We establish the impossibility of screening for welfare purposes, i.e., the designer can attain any implementable combination of buyer surplus and seller profit by providing the same signal to all seller types. We use this result to characterize the set of implementable welfare outcomes, study the seller's incentive to acquire third-party data, and demonstrate the trade-off between buyer surplus and efficiency.
Markovian Persuasion with Stochastic Revelations
Authors: Ehud Lehrer, Dimitry Shaiderman
Date: 2022-04-19 00:00:00

In the classical Bayesian persuasion model an informed player and an uninformed one engage in a static interaction. The informed player, the sender, knows the state of nature, while the uninformed one, the receiver, does not. The informed player partially shares his private information with the receiver and the latter then, based on her belief about the state, takes an action. This action, together with the state of nature, determines the utility of both players. This paper analyzes a dynamic Bayesian persuasion model where the state of nature evolves according to a Markovian law. Here, the sender always knows the realized state, while the receiver randomly gets to know it. We discuss the value of the sender when he becomes more and more patient and its relation to the revelation rate, namely the probability at which the true state is revealed to the receiver at any stage.
Adaptable Semantic Compression and Resource Allocation for Task-Oriented Communications
Authors: Chuanhong Liu, Caili Guo, Yang Yang, Nan Jiang
Date: 2022-04-19 00:00:00
Tasks: Semantic Compression

Task-oriented communication is a new paradigm that aims at providing efficient connectivity for accomplishing intelligent tasks rather than the reception of every transmitted bit. In this paper, a deep learning-based task-oriented communication architecture is proposed where the user extracts, compresses and transmits semantics in an end-to-end (E2E) manner. Furthermore, an approach is proposed to compress the semantics according to their importance relevant to the task, namely, adaptable semantic compression (ASC). Assuming a delay-intolerant system, supporting multiple users indicates a problem that executing with the higher compression ratio requires fewer channel resources but leads to the distortion of semantics, while executing with the lower compression ratio requires more channel resources and thus may lead to a transmission failure due to delay constraint. To solve the problem, both compression ratio and resource allocation are optimized for the task-oriented communication system to maximize the success probability of tasks. Specifically, due to the nonconvexity of the problem, we propose a compression ratio and resource allocation (CRRA) algorithm by separating the problem into two subproblems and solving iteratively to obtain the convergent solution. Furthermore, considering the scenarios where users have various service levels, a compression ratio, resource allocation, and user selection (CRRAUS) algorithm is proposed to deal with the problem. In CRRAUS, users are adaptively selected to complete the corresponding intelligent tasks based on branch and bound method at the expense of higher algorithm complexity compared with CRRA. Simulation results show that the proposed CRRA and CRRAUS algorithms can obtain at least 15% and 10% success gains over baseline algorithms, respectively.
Channel estimation for double IRS assisted broadband single-user SISO communication
Authors: Vishnu Karthikeya Gorty
Date: 2022-04-19 00:00:00

In this paper, two Intelligent reflecting surfaces (double IRS) assisted single-user single input single output (SISO) communication system is considered. The cascaded channels (mobile user (MU)$\rightarrow$IRS-1$\rightarrow$base station (BS), MU$\rightarrow$IRS-2$\rightarrow$BS and MU$\rightarrow$IRS-1$\rightarrow$IRS-2$\rightarrow$BS channels) are estimated under Bayesian setting. Here, the goal is to evaluate the performance of the estimator in case of MU$\rightarrow$IRS-1$\rightarrow$BS and MU$\rightarrow$IRS-2$\rightarrow$BS channel links using Bayesian Cramer-Rao lower bound (CRLB). Without the knowledge of closed form pdf of inner product of circularly symmetric complex Gaussian (CSCG) random vectors, we cannot obtain the fisher information. Hence, by numerical computation we obtain the Bayesian CRLB. In the simulation results, we show that we can approximate the pdf of the inner product of CSCG random vectors by a Rayleigh distribution by increasing the number of elements on the IRS, which is analogous to Central Limit Theorem (CLT). Also, the results convey that the mean squared error (MSE) almost matches with the Bayesian CRLB.
Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on Signal Strength represented by Antenna Gain Array Manifold
Authors: Wencong Xu, Yandong Li, Bingshu Chen, Yue Hu, Jianxu Li, Zijing Zeng
Date: 2022-04-19 00:00:00

Inspection robots are widely used in the field of smart grid monitoring in substations, and partial discharge (PD) is an important sign of the insulation state of equipments. PD direction of arrival (DOA) algorithms using conventional beamforming and time difference of arrival (TDOA) require large-scale antenna arrays and high computational complexity, which make them difficult to implement on inspection robots. To address this problem, a novel directional multiple signal classification (Dir-MUSIC) algorithm for PD direction finding based on signal strength is proposed, and a miniaturized directional spiral antenna circular array is designed in this paper. First, the Dir-MUSIC algorithm is derived based on the array manifold characteristics. This method uses strength intensity information rather than the TDOA information, which could reduce the computational difficulty and the requirement of array size. Second, the effects of signal-to-noise ratio (SNR) and array manifold error on the performance of the algorithm are discussed through simulations in detail. Then according to the positioning requirements, the antenna array and its arrangement are developed, optimized, and simulation results suggested that the algorithm has reliable direction-finding performance in the form of 6 elements. Finally, the effectiveness of the algorithm is tested by using the designed spiral circular array in real scenarios. The experimental results show that the PD direction-finding error is 3.39{\deg}, which can meet the need for Partial discharge DOA estimation using inspection robots in substations.
Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism
Authors: Navid Ayoobi, Elnaz Banan Sadeghian
Date: 2022-04-19 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Motor Imagery, Saliency Detection

Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG) signals can enhance the performance of the brain-computer interface (BCI) system and reduce the computational burden required for processing lengthy MI-EEG signals. In this paper, we propose an unsupervised method based on the self-attention mechanism to detect the salient intervals of MI-EEG signals automatically. Our suggested method can be used as a preprocessing step within any BCI algorithm to enhance its performance. The effectiveness of the suggested method is evaluated on the most widely used BCI algorithm, the common spatial pattern (CSP) algorithm, using dataset 2a from BCI competition IV. The results indicate that the proposed method can effectively prune MI-EEG signals and significantly enhance the performance of the CSP algorithm in terms of classification accuracy.
A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder
Authors: Navid Ayoobi, Elnaz Banan Sadeghian
Date: 2022-04-19 00:00:00
Tasks: Brain Computer Interface, Motor Imagery

A calibration procedure is required in motor imagery-based brain-computer interface (MI-BCI) to tune the system for new users. This procedure is time-consuming and prevents na\"ive users from using the system immediately. Developing a subject-independent MI-BCI system to reduce the calibration phase is still challenging due to the subject-dependent characteristics of the MI signals. Many algorithms based on machine learning and deep learning have been developed to extract high-level features from the MI signals to improve the subject-to-subject generalization of a BCI system. However, these methods are based on supervised learning and extract features useful for discriminating various MI signals. Hence, these approaches cannot find the common underlying patterns in the MI signals and their generalization level is limited. This paper proposes a subject-independent MI-BCI based on a supervised autoencoder (SAE) to circumvent the calibration phase. The suggested framework is validated on dataset 2a from BCI competition IV. The simulation results show that our SISAE model outperforms the conventional and widely used BCI algorithms, common spatial and filter bank common spatial patterns, in terms of the mean Kappa value, in eight out of nine subjects.
DFRC with Improved Communication-Sensing Trade-off via Private Subcarrier Permutations and Pairing with Antennas
Authors: Zhaoyi Xu, Athina P. Petropulu
Date: 2022-04-19 00:00:00

Dual function radar communication (DFRC) systems can achieve significant improvements in spectrum efficiency, system complexity and energy efficiency, and are attracting a lot of attention for next generation wireless system design. This paper considers DFRC systems using MIMO radar with a sparse transmit array, transmitting OFDM waveforms, and assigning shared and private subcarriers to active transmit antennas. Subcarrier sharing allows antennas to modulate data symbols onto the same subcarriers and enables high communication rate, while the use of private subcarriers trades-off communication rate for sensing performance by enabling the formulation of a virtual array with larger aperture than the physical receive array. We propose to exploit the permutation of private subcarriers among the available subcarriers and the pairing between active antennas and private subcarriers to recover some of the communication rate loss. Exploiting the $1$-sparse property of private subcarriers, we also propose a low complexity algorithm to identify private subcarriers and detect the antenna-subcarrier pairing.
Evaluating Digital Sine wave Generator Using Analog Metrics
Authors: HoseinAli Jafari Abeshoori, Seyed Javad Azhari
Date: 2022-04-15 00:00:00

This work aims to relate comparison metrics for both Direct Digital Synthesizers (DDS) and their analog counterparts. The proposed metrics are Total Harmonic Distortion (THD) and maximum absolute error. Error is theoretically formulated into closed forms for known systematic parameters of DDS, sample rate, and bit counts. By the use of Matlab scripting, the system model is simulated for a wide range of parameters sweep.
Short time quaternion quadratic phase Fourier transform and its uncertainty principles
Authors: Bivek Gupta, Amit K. Verma
Date: 2022-04-03 00:00:00
Tasks: Relation

In this paper, we extend the quadratic phase Fourier transform of a complex valued functions to that of the quaternion valued functions of two variables. We call it the quaternion quadratic phase Fourier transform (QQPFT). Based on the relation between the QQPFT and the quaternion Fourier transform (QFT) we obtain the sharp Hausdorff-Young inequality for QQPFT. We define the short time quaternion quadratic phase Fourier transform (STQQPFT) and explore some of its properties including inner product relation and inversion formula. We find its relation with that of the 2D quaternion ambiguity function and the quaternion Wigner-Ville distribution associated with QQPFT and obtain the Lieb's uncertainty and entropy uncertainty principles for these three transforms.
Regulation of stem cell dynamics through volume exclusion
Authors: Rodrigo García-Tejera, Linus Schumacher, Ramon Grima
Date: 2022-04-19 00:00:00

Maintenance and regeneration of adult tissues rely on the self-renewal of stem cells. Regeneration without over-proliferation requires precise regulation of the stem cell proliferation and differentiation rates. The nature of such regulatory mechanisms in different tissues, and how to incorporate them in models of stem cell population dynamics, is incompletely understood. The critical birth-death (CBD) process is widely used to model stem cell populations, capturing key phenomena, such as scaling laws in clone size distributions. However, the CBD process neglects regulatory mechanisms. Here, we propose the birth-death process with volume exclusion (vBD), a variation of the birth-death process that considers crowding effects, such as may arise due to limited space in a stem cell niche. While the deterministic rate equations predict a single non-trivial attracting steady state, the master equation predicts extinction and transient distributions of stem cell numbers with three possible behaviours: long-lived quasi-steady state, and short-lived bimodal or unimodal distributions. In all cases, we approximate solutions to the vBD master equation using a renormalised system-size expansion, quasi-steady state approximation and the WKB method. Our study suggests that the size distribution of a stem cell population bears signatures that are useful to detect negative feedback mediated via volume exclusion.
Is the Contralateral Delay Activity (CDA) a robust neural correlate for Visual Working Memory (VWM) tasks? A reproducibility study
Authors: Yannick Roy, Jocelyn Faubert
Date: 2022-04-18 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Visual working memory (VWM) allows us to actively store, update and manipulate visual information surrounding us. While the underlying neural mechanisms of VWM remain unclear, contralateral delay activity (CDA), a sustained negativity over the hemisphere contralateral to the positions of visual items to be remembered, is often used to study VWM. To investigate if the CDA is a robust neural correlate for VWM tasks, we reproduced eight CDA-related studies with a publicly accessible EEG dataset. We used the raw EEG data from these eight studies and analyzed all of them with the same basic pipeline to extract CDA. We were able to reproduce the results from all the studies and show that with a basic automated EEG pipeline we can extract a clear CDA signal. We share insights from the trends observed across the studies and raise some questions about the CDA decay and the CDA during the recall phase, which surprisingly, none of the eight studies did address. Finally, we also provide reproducibility recommendations based on our experience and challenges in reproducing these studies.