pwc-archive/papers-with-abstracts huggingface dataset:
Fitting Dynamically Misspecified Models: An Optimal Transportation Approach
Authors: Jean-Jacques Forneron, Zhongjun Qu
Date: 2024-12-28 00:00:00
Tasks: parameter estimation, State Space Models

This paper considers filtering, parameter estimation, and testing for potentially dynamically misspecified state-space models. When dynamics are misspecified, filtered values of state variables often do not satisfy model restrictions, making them hard to interpret, and parameter estimates may fail to characterize the dynamics of filtered variables. To address this, a sequential optimal transportation approach is used to generate a model-consistent sample by mapping observations from a flexible reduced-form to the structural conditional distribution iteratively. Filtered series from the generated sample are model-consistent. Specializing to linear processes, a closed-form Optimal Transport Filtering algorithm is derived. Minimizing the discrepancy between generated and actual observations defines an Optimal Transport Estimator. Its large sample properties are derived. A specification test determines if the model can reproduce the sample path, or if the discrepancy is statistically significant. Empirical applications to trend-cycle decomposition, DSGE models, and affine term structure models illustrate the methodology and the results.
Consumption Periods in Advance Selling Auctions: Evidence from US Timber Market
Authors: Shosuke Noguchi, Suguru Otani
Date: 2024-12-28 00:00:00
Tasks: counterfactual, Sensitivity

This study investigates products sold before consumption and examines how the duration of the consumption periods and the choice of selling mechanism influence sellers' revenue. Using empirical data from timber auctions, we identify buyers' tendency to delay consumption to resolve payoff uncertainty and reveal heterogeneous motivations among buyers. Through structural estimation, we uncover key parameters for each buyer type, including sensitivity to realized payoffs and consumption-related costs. Leveraging these estimates, we perform counterfactual analyses to propose revenue-enhancing consumption periods and selling mechanisms. Our findings suggest that extending the consumption periods is likely to increase revenue, with the magnitude depending on the selling mechanism, the composition of buyer types, and the number of interested buyers.
The impact of China's economic growth on poverty alleviation: From absolute to relative poverty
Authors: Yixun Kang, Ying Li
Date: 2024-12-28 00:00:00

This paper investigates the extent to which China's economic growth and development influence poverty levels, focusing on the dichotomy between absolute and relative poverty. Leveraging data from sources like the World Bank, Statista, and Macrotrends, and employing economic frameworks such as the Lewis Model, Poverty Headcount Ratio, and Gini Coefficient, the study examines China's transformation from combating absolute poverty to addressing relative poverty. The findings highlight that robust economic growth from 2011 to 2022, driven by urban development and rural infrastructure investments, successfully eradicated absolute poverty and elevated rural incomes. However, this progress also exacerbated income inequality, as evidenced by a rising Gini Coefficient, complicating efforts to alleviate relative poverty. Through multidimensional analyses encompassing regional disparities, migration patterns, educational access, and societal factors, the paper underscores the dual impact of economic development on poverty alleviation. It concludes by advocating for policies that balance economic growth with equitable resource distribution to tackle persistent relative poverty and foster sustainable development.
Measurement of coupling development level of new infrastructure investment and digital transformation and its temporal and spatial evolution trend
Authors: Sanglin Zhao, Jikang Cao
Date: 2024-12-26 00:00:00

Based on the coupling mechanism between new infrastructure investment level and digital transformation level, a comprehensive index system is constructed. By using entropy weight method, coupling coordination evaluation model, exploratory spatial data analysis (ESDA) and standard deviation ellipse model, the coupling coordination development level of new infrastructure investment level and digital transformation level in 31 provinces and cities in China from 2014 to 2021 is calculated, and its spatial agglomeration level and spatial-temporal evolution characteristics are analyzed.
Digital transformation: A systematic review and bibliometric analysis from the corporate finance perspective
Authors: Ping Zhang, Yiru Wang
Date: 2024-12-13 00:00:00

Digital transformation significantly impacts firm investment, financing, and value enhancement. A systematic investigation from the corporate finance perspective has not yet been formed. This paper combines bibliometric and content analysis methods to systematically review the evolutionary trend, status quo, hotspots and overall structure of research in digital transformation from 2011 to 2024. The study reveals an emerging and rapidly growing focus on digital transformation research, particularly in developed countries. We categorize the literature into three areas according to bibliometric clustering: the measurements (qualitative and quantitative), impact factors (internal and external), and the economic consequences (investment, financing, and firm value). These areas are divided into ten sub-branches, with a detailed literature review. We also review the existing theories related to digital transformation, identify the current gaps in these papers, and provide directions for future research on each sub-branches.
Comparative Analysis of 2D and 3D ResNet Architectures for IDH and MGMT Mutation Detection in Glioma Patients
Authors: Danial Elyassirad, Benyamin Gheiji, Mahsa Vatanparast, Amir Mahmoud Ahmadzadeh, Neda Kamandi, Amirmohammad Soleimanian, Sara Salehi, Shahriar Faghani
Date: 2024-12-30 00:00:00
Tasks: Prognosis

Gliomas are the most common cause of mortality among primary brain tumors. Molecular markers, including Isocitrate Dehydrogenase (IDH) and O[6]-methylguanine-DNA methyltransferase (MGMT) influence treatment responses and prognosis. Deep learning (DL) models may provide a non-invasive method for predicting the status of these molecular markers. To achieve non-invasive determination of gene mutations in glioma patients, we compare 2D and 3D ResNet models to predict IDH and MGMT status, using T1, post-contrast T1, and FLAIR MRI sequences. USCF glioma dataset was used, which contains 495 patients with known IDH and 410 patients with known MGMT status. The dataset was divided into training (60%), tuning (20%), and test (20%) subsets at the patient level. The 2D models take axial, coronal, and sagittal tumor slices as three separate models. To ensemble the 2D predictions the three different views were combined using logistic regression. Various ResNet architectures (ResNet10, 18, 34, 50, 101, 152) were trained. For the 3D approach, we incorporated the entire brain tumor volume in the ResNet10, 18, and 34 models. After optimizing each model, the models with the lowest tuning loss were selected for further evaluation on the separate test sets. The best-performing models in IDH prediction were the 2D ResNet50, achieving a test area under the receiver operating characteristic curve (AUROC) of 0.9096, and the 3D ResNet34, which reached a test AUROC of 0.8999. For MGMT status prediction, the 2D ResNet152 achieved a test AUROC of 0.6168; however, all 3D models yielded AUROCs less than 0.5. Overall, the study indicated that both 2D and 3D models showed high predictive value for IDH prediction, with slightly better performance in 2D models.
FlameGS: Reconstruct flame light field via Gaussian Splatting
Authors: Yunhao Shui, Fuhao Zhang, Can Gao, Hao Xue, Zhiyin Ma, Gang Xun, Xuesong Li
Date: 2024-12-24 00:00:00

To address the time-consuming and computationally intensive issues of traditional ART algorithms for flame combustion diagnosis, inspired by flame simulation technology, we propose a novel representation method for flames. By modeling the luminous process of flames and utilizing 2D projection images for supervision, our experimental validation shows that this model achieves an average structural similarity index of 0.96 between actual images and predicted 2D projections, along with a Peak Signal-to-Noise Ratio of 39.05. Additionally, it saves approximately 34 times the computation time and about 10 times the memory compared to traditional algorithms.
Quantum Implicit Neural Compression
Authors: Takuya Fujihashi, Toshiaki Koike-Akino
Date: 2024-12-19 00:00:00
Tasks: Data Compression, Image Compression

Signal compression based on implicit neural representation (INR) is an emerging technique to represent multimedia signals with a small number of bits. While INR-based signal compression achieves high-quality reconstruction for relatively low-resolution signals, the accuracy of high-frequency details is significantly degraded with a small model. To improve the compression efficiency of INR, we introduce quantum INR (quINR), which leverages the exponentially rich expressivity of quantum neural networks for data compression. Evaluations using some benchmark datasets show that the proposed quINR-based compression could improve rate-distortion performance in image compression compared with traditional codecs and classic INR-based coding methods, up to 1.2dB gain.
Sparse Array Sensor Selection in ISAC with Identifiability Guarantees
Authors: Robin Rajamäki, Piya Pal
Date: 2024-12-30 00:00:00
Tasks: ISAC

This paper investigates array geometry and waveform design for integrated sensing and communications (ISAC) employing sensor selection. We consider ISAC via index modulation, where various subsets of transmit (Tx) sensors are used for both communications and monostatic active sensing. The set of Tx subarrays make up a codebook, whose cardinality we maximize (for communications) subject to guaranteeing a desired target identifiability (for sensing). To characterize the size of this novel optimal codebook, we derive first upper and lower bounds, which are tight in case of the canonical uniform linear array (ULA) and any nonredundant array. We show that the ULA achieves a large codebook - comparable to the size of the conventional unconstrained case - as satisfying the identifiability constraint only requires including two specific sensors in each Tx subarray (codeword). In contrast, nonredundant arrays, which have the largest identifiability for a given number of physical sensors, only have a single admissible codeword, rendering them ineffectual for communications via sensor selection alone. The results serve as a step towards an analytical understanding of the limits of sensor selection in ISAC and the fundamental trade-offs therein.
Sequential Maximum-Likelihood Estimation of Wideband Polynomial-Phase Signals on Sensor Array
Authors: Kaleb Debre, Tai Fei, Marius Pesavento
Date: 2024-12-30 00:00:00

This paper presents a novel sequential estimator for the direction-of-arrival and polynomial coefficients of wideband polynomial-phase signals impinging on a sensor array. Addressing the computational challenges of Maximum-likelihood estimation for this problem, we propose a method leveraging random sampling consensus (RANSAC) applied to the time-frequency spatial signatures of sources. Our approach supports multiple sources and higher-order polynomials by employing coherent array processing and sequential approximations of the Maximum-likelihood cost function. We also propose a low-complexity variant that estimates source directions via angular domain random sampling. Numerical evaluations demonstrate that the proposed methods achieve Cram\'er-Rao bounds in challenging multi-source scenarios, including closely spaced time-frequency spatial signatures, highlighting their suitability for advanced radar signal processing applications.
Generative AI Empowered Semantic Feature Multiple Access (SFMA) Over Wireless Networks
Authors: Jiaxiang Wang, Yinchao Yang, Zhaohui Yang, Chongwen Huang, Mingzhe Chen, Zhaoyang Zhang, Mohammad Shikh-Bahaei
Date: 2024-12-30 00:00:00
Tasks: Semantic Communication, Video Frame Interpolation

This paper investigates a novel generative artificial intelligence (GAI) empowered multi-user semantic communication system called semantic feature multiple access (SFMA) for video transmission, which comprises a base station (BS) and paired users. The BS generates and combines semantic information of several frames simultaneously requested by paired users into a single signal. Users recover their frames from this combined signal and input the recovered frames into a GAI-based video frame interpolation model to generate the intermediate frame. To optimize transmission rates and temporal gaps between simultaneously transmitted frames, we formulate an optimization problem to maximize the system sum rate while minimizing temporal gaps. Since the standard signal-to-interference-plus-noise ratio (SINR) equation does not accurately capture the performance of our semantic communication system, we introduce a weight parameter into the SINR equation to better represent the system's performance. Due to its dependence on transmit power, we propose a three-step solution. First, we develop a user pairing algorithm that pairs two users with the highest preference value, a weighted combination of semantic transmission rate and temporal gap. Second, we optimize inter-group power allocation by formulating an optimization problem that allocates proper transmit power across all user groups to maximize system sum rates while satisfying each user's minimum rate requirement. Third, we address intra-group power allocation to enhance each user's performance. Simulation results demonstrate that our method improves transmission rates by up to 24.8%, 45.8%, and 66.1% compared to fixed-power non-orthogonal multiple access (F-NOMA), orthogonal joint source-channel coding (O-JSCC), and orthogonal frequency division multiple access (OFDMA), respectively.
Generative Deep Synthesis of MIMO Sensing Waveforms with Desired Transmit Beampattern
Authors: Vesa Saarinen, Robin Rajamäki, Visa Koivunen
Date: 2024-12-30 00:00:00
Tasks: Generative Adversarial Network

This paper develops a generative deep learning model for the synthesis of multiple-input multiple-output (MIMO) active sensing waveforms with desired properties, including constant modulus and a user-defined beampattern. The proposed approach is capable synthesizing unique phase codes of on-the-fly, which has the potential to reduce interference between co-existing active sensing systems and facilitate Low Probability of Intercept/Low Probability of Detection (LPI/LPD) radar operation. The paper extends our earlier work on synthesis of approximately orthogonal MIMO phase codes by introducing flexible control over the transmit beampatterns. The developed machine learning method employs a conditional Wasserstein Generative Adversarial Network (GAN) structure. The main benefits of the method are its ability to discover new waveforms on-demand (post training) and generate demanding beampatterns at lower computational complexity compared to structured optimization approaches.
Movable Antennas Enabled ISAC Systems: Fundamentals, Opportunities, and Future Directions
Authors: Zhendong Li, Jianle Ba, Zhou Su, Jinyuan Huang, Haixia Peng, Wen Chen, Linkang Du, Tom H. Luan
Date: 2024-12-30 00:00:00
Tasks: Integrated sensing and communication, ISAC

The movable antenna (MA)-enabled integrated sensing and communication (ISAC) system attracts widespread attention as an innovative framework. The ISAC system integrates sensing and communication functions, achieving resource sharing across various domains, significantly enhancing communication and sensing performance, and promoting the intelligent interconnection of everything. Meanwhile, MA utilizes the spatial variations of wireless channels by dynamically adjusting the positions of MA elements at the transmitter and receiver to improve the channel and further enhance the performance of the ISAC systems. In this paper, we first outline the fundamental principles of MA and introduce the application scenarios of MA-enabled ISAC systems. Then, we summarize the advantages of MA-enabled ISAC systems in enhancing spectral efficiency, achieving flexible and precise beamforming, and making the signal coverage range adjustable. Besides, a specific case is studied to show the performance gains in terms of transmit power that MA brings to ISAC systems. Finally, we discuss the challenges of MA-enabled ISAC and future research directions, aiming to provide insights for future research on MA-enabled ISAC systems.
Secure Wireless Communications via Frequency Diverse Arrays
Authors: Zhenqiao Cheng, Chongjun Ouyang, Xingqi Zhang
Date: 2024-12-29 00:00:00

A novel frequency diverse array (FDA)-assisted secure transmission framework is proposed, which leverages additional frequency offsets to enhance physical layer security. Specifically, an FDA-assisted wiretap channel is considered, where the transmit beamforming and frequency offsets at each antenna are jointly optimized. A novel alternating optimization-based method is introduced to address the non-convex problem of secure transmission, focusing on minimizing transmit power and maximizing the secrecy rate. Numerical results are provided to demonstrate the superiority of the FDA-based framework compared to systems employing traditional phased array antennas in secure transmission.
Cooperative ISAC-empowered Low-Altitude Economy
Authors: Jun Tang, Yiming Yu, Cunhua Pan, Hong Ren, Dongming Wang, Jiangzhou Wang, Xiaohu You
Date: 2024-12-29 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation, Tensor Decomposition

This paper proposes a cooperative integrated sensing and communication (ISAC) scheme for the low-altitude sensing scenario, aiming at estimating the parameters of the unmanned aerial vehicles (UAVs) and enhancing the sensing performance via cooperation. The proposed scheme consists of two stages. In Stage I, we formulate the monostatic parameter estimation problem via using a tensor decomposition model. By leveraging the Vandermonde structure of the factor matrix, a spatial smoothing tensor decomposition scheme is introduced to estimate the UAVs' parameters. To further reduce the computational complexity, we design a reduced-dimensional (RD) angle of arrival (AoA) estimation algorithm based on generalized Rayleigh quotient (GRQ). In Stage II, the positions and true velocities of the UAVs are determined through the data fusion across multiple base stations (BSs). Specifically, we first develop a false removing minimum spanning tree (MST)-based data association method to accurately match the BSs' parameter estimations to the same UAV. Then, a Pareto optimality method and a residual weighting scheme are developed to facilitate the position and velocity estimation, respectively. We further extend our approach to the dual-polarized system. Simulation results validate the effectiveness of the proposed schemes in comparison to the conventional techniques.
Two-Timescale Design for AP Mode Selection of Cooperative ISAC Networks
Authors: Zhichu Ren, Cunhua Pan, Hong Ren, Dongming Wang, Lexi Xu, Jiangzhou Wang
Date: 2024-12-29 00:00:00
Tasks: Integrated sensing and communication, ISAC

As an emerging technology, cooperative bi-static integrated sensing and communication (ISAC) is promising to achieve high-precision sensing, high-rate communication as well as self-interference (SI) avoidance. This paper investigates the two-timescale design for access point (AP) mode selection to realize the full potential of the cooperative bi-static ISAC network with low system overhead, where the beamforming at the APs is adapted to the rapidly-changing instantaneous channel state information (CSI), while the AP mode is adapted to the slowly-changing statistical CSI. We first apply the minimum mean square error (MMSE) estimator to estimate the channel between the APs and the channels from the APs to the user equipments (UEs). Then we adopt the low-complexity maximum ratio transmission (MRT) beamforming and the maximum ratio combining (MRC) detector, and derive the closed-form expressions of the communication rate and the sensing signal-to-interference-plus-noise-ratio (SINR). We formulate a non-convex integer optimization problem to maximize the minimum sensing SINR under the communication quality of service (QoS) constraints. McCormick envelope relaxation and successive convex approximation (SCA) techniques are applied to solve the challenging non-convex integer optimization problem. Simulation results validate the closed-form expressions and prove the convergence and effectiveness of the proposed AP mode selection scheme.
Resource Allocation for ISAC Networks with Application to Target Tracking
Authors: Lu Wang, Luis F. Abanto-Leon
Date: 2024-12-29 00:00:00
Tasks: Integrated sensing and communication, ISAC

Future 6G networks are expected to empower communication systems by integrating sensing capabilities, resulting in integrated sensing and communication (ISAC) systems. However, this integration may exacerbate the data traffic congestion in existing communication systems due to limited resources. Therefore, the resources of ISAC systems must be carefully allocated to ensure high performance. Given the increasing demands for both sensing and communication services, current methods are inadequate for tracking targets frequently in every frame while simultaneously communicating with users. To address this gap, this work formulates an optimization problem that jointly allocates resources in the time, frequency, power, and spatial domains for targets and users, accounting for the movement of targets and time-varying communication channels. Specifically, we minimize the trace of posterior Cram\'er-Rao bound for target tracking subject to communication throughput and resource allocation constraints. To solve this non-convex problem, we develop a block coordinate descent (BCD) algorithm based on the penalty method, successive convex approximation (SCA), and one-dimensional search. Simulation results demonstrate the validity of the proposed algorithm and the performance trade-off between sensing and communication.
Machine Learning-Enabled Multidimensional Data Utilization Through Multi-Resonance Architecture: A Pathway to Enhanced Accuracy in Biosensing
Authors: Majid Aalizadeh, Morteza Azmoudeh Afshar, Xudong Fan
Date: 2024-12-28 00:00:00
Tasks: Parameter Prediction

A novel framework is proposed that combines multi-resonance biosensors with machine learning (ML) to significantly enhance the accuracy of parameter prediction in biosensing. Unlike traditional single-resonance systems, which are limited to one-dimensional datasets, this approach leverages multi-dimensional data generated by a custom-designed nanostructure, a periodic array of silicon nanorods with a triangular cross-section over an aluminum reflector. High bulk sensitivity values are achieved for this multi-resonant structure, with certain resonant peaks reaching up to 1706 nm/RIU. The field analysis reveals Mie resonances as the physical reason behind the peaks. The predictive power of multiple resonant peaks from transverse magnetic (TM) and transverse electric (TE) polarizations is evaluated using Ridge Regression modeling. Systematic analysis reveals that incorporating multiple resonances yields up to three orders of magnitude improvement in refractive index detection precision compared to single-peak analyses. This precision enhancement is achieved without modifications to the biosensor hardware, highlighting the potential of data-centric strategies in biosensing. The findings establish a new paradigm in biosensing, demonstrating that the synergy between multi-resonance data acquisition and ML-based analysis can significantly enhance detection accuracy. This study provides a scalable pathway for advancing high-precision biosensing technologies.