pwc-archive/papers-with-abstracts huggingface dataset:
Two characterizations of the dense rank
Authors: José Luis García-Lapresta, Miguel Martínez-Panero
Date: 2023-06-30 00:00:00
Tasks: Position

In this paper, we have considered the dense rank for assigning positions to alternatives in weak orders. If we arrange the alternatives in tiers (i.e., indifference classes), the dense rank assigns position 1 to all the alternatives in the top tier, 2 to all the alternatives in the second tier, and so on. We have proposed a formal framework to analyze the dense rank when compared to other well-known position operators such as the standard, modified and fractional ranks. As the main results, we have provided two different axiomatic characterizations which determine the dense rank by considering position invariance conditions along horizontal extensions (duplication), as well as through vertical reductions and movements (truncation, and upwards or downwards independency).
On the Behavior of the Payoff Amounts in Simple Interest Loans in Arbitrage-Free Markets
Authors: Fausto Di Biase, Stefano Di Rocco, Alessandra Ortolano, Maurizio Parton
Date: 2023-06-30 00:00:00

The Consumer Financial Protection Bureau defines the notion of payoff amount as the amount that has to be payed at a particular time in order to completely pay off the debt, in case the lender intends to pay off the loan early, way before the last installment is due (CFPB 2020). This amount is well-understood for loans at compound interest, but much less so when simple interest is used. Recently, Aretusi and Mari (2018) have proposed a formula for the payoff amount for loans at simple interest. We assume that the payoff amounts are established contractually at time zero, whence the requirement that no arbitrage may arise this way The first goal of this paper is to study this new formula and derive it within a model of a loan market in which loans are bought and sold at simple interest, interest rates change over time, and no arbitrage opportunities exist. The second goal is to show that this formula exhibits a behaviour rather different from the one which occurs when compound interest is used. Indeed, we show that, if the installments are constant and if the interest rate is greater than a critical value (which depends on the number of installments), then the sequence of the payoff amounts is increasing before a certain critical time, and will start decreasing only thereafter. We also show that the critical value is decreasing as a function of the number of installments. For two installments, the critical value is equal to the golden section. The third goal is to introduce a more efficient polynomial notation, which encodes a basic tenet of the subject: Each amount of money is embedded in a time position (to wit: The time when it is due). The model of a loan market we propose is naturally linked to this new notation.
Recurring Auctions with Costly Entry: Theory and Evidence
Authors: Shanglyu Deng, Qiyao Zhou
Date: 2023-06-30 00:00:00

Recurring auctions are ubiquitous for selling durable assets like artworks and homes, with follow-up auctions held for unsold items. We investigate such auctions theoretically and empirically. Theoretical analysis demonstrates that recurring auctions outperform single-round auctions when buyers face entry costs, enhancing efficiency and revenue due to sorted entry of potential buyers. Optimal reserve price sequences are characterized. Empirical findings from home foreclosure auctions in China reveal significant annual gains in efficiency (3.40 billion USD, 16.60%) and revenue (2.97 billion USD, 15.92%) using recurring auctions compared to single-round auctions. Implementing optimal reserve prices can further improve efficiency (3.35%) and revenue (3.06%).
Enhancing the EEG Speech Match Mismatch Tasks With Word Boundaries
Authors: Akshara Soman, Vidhi Sinha, Sriram Ganapathy
Date: 2023-07-01 00:00:00
Tasks: EEG, Sentence

Recent studies have shown that the underlying neural mechanisms of human speech comprehension can be analyzed using a match-mismatch classification of the speech stimulus and the neural response. However, such studies have been conducted for fixed-duration segments without accounting for the discrete processing of speech in the brain. In this work, we establish that word boundary information plays a significant role in sentence processing by relating EEG to its speech input. We process the speech and the EEG signals using a network of convolution layers. Then, a word boundary-based average pooling is performed on the representations, and the inter-word context is incorporated using a recurrent layer. The experiments show that the modeling accuracy can be significantly improved (match-mismatch classification accuracy) to 93% on a publicly available speech-EEG data set, while previous efforts achieved an accuracy of 65-75% for this task.
Anisotropic Fanning Aware Low-Rank Tensor Approximation Based Tractography
Authors: Johannes Grün, Jonah Sieg, Thomas Schultz
Date: 2023-07-03 00:00:00

Low-rank higher-order tensor approximation has been used successfully to extract discrete directions for tractography from continuous fiber orientation density functions (fODFs). However, while it accounts for fiber crossings, it has so far ignored fanning, which has led to incomplete reconstructions. In this work, we integrate an anisotropic model of fanning based on the Bingham distribution into a recently proposed tractography method that performs low-rank approximation with an Unscented Kalman Filter. Our technical contributions include an initialization scheme for the new parameters, which is based on the Hessian of the low-rank approximation, pre-integration of the required convolution integrals to reduce the computational effort, and representation of the required 3D rotations with quaternions. Results on 12 subjects from the Human Connectome Project confirm that, in almost all considered tracts, our extended model significantly increases completeness of the reconstruction, while reducing excess, at acceptable additional computational cost. Its results are also more accurate than those from a simpler, isotropic fanning model that is based on Watson distributions.
Sulcal Pattern Matching with the Wasserstein Distance
Authors: Zijian Chen, Soumya Das, Moo K. Chung
Date: 2023-07-01 00:00:00
Tasks: Image Registration

We present the unified computational framework for modeling the sulcal patterns of human brain obtained from the magnetic resonance images. The Wasserstein distance is used to align the sulcal patterns nonlinearly. These patterns are topologically different across subjects making the pattern matching a challenge. We work out the mathematical details and develop the gradient descent algorithms for estimating the deformation field. We further quantify the image registration performance. This method is applied in identifying the differences between male and female sulcal patterns.
Spatio-Temporal Classification of Lung Ventilation Patterns using 3D EIT Images: A General Approach for Individualized Lung Function Evaluation
Authors: Shuzhe Chen, Li Li, Zhichao Lin, Ke Zhang, Ying Gong, Lu Wang, Xu Wu, Maokun Li, Yuanlin Song, Fan Yang, Shenheng Xu
Date: 2023-07-01 00:00:00

The Pulmonary Function Test (PFT) is an widely utilized and rigorous classification test for lung function evaluation, serving as a comprehensive tool for lung diagnosis. Meanwhile, Electrical Impedance Tomography (EIT) is a rapidly advancing clinical technique that visualizes conductivity distribution induced by ventilation. EIT provides additional spatial and temporal information on lung ventilation beyond traditional PFT. However, relying solely on conventional isolated interpretations of PFT results and EIT images overlooks the continuous dynamic aspects of lung ventilation. This study aims to classify lung ventilation patterns by extracting spatial and temporal features from the 3D EIT image series. The study uses a Variational Autoencoder network with a MultiRes block to compress the spatial distribution in a 3D image into a one-dimensional vector. These vectors are then concatenated to create a feature map for the exhibition of temporal features. A simple convolutional neural network is used for classification. Data collected from 137 subjects were finally used for training. The model is validated by ten-fold and leave-one-out cross-validation first. The accuracy and sensitivity of normal ventilation mode are 0.95 and 1.00, and the f1-score is 0.94. Furthermore, we check the reliability and feasibility of the proposed pipeline by testing it on newly recruited nine subjects. Our results show that the pipeline correctly predicts the ventilation mode of 8 out of 9 subjects. The study demonstrates the potential of using image series for lung ventilation mode classification, providing a feasible method for patient prescreening and presenting an alternative form of PFT.
A Motion Assessment Method for Reference Stack Selection in Fetal Brain MRI Reconstruction Based on Tensor Rank Approximation
Authors: Haoan Xu, Wen Shi, Jiwei Sun, Tianshu Zheng, Cong Sun, Sun Yi, Guangbin Wang, Dan Wu
Date: 2023-06-30 00:00:00
Tasks: MRI Reconstruction, Sensitivity, Super-Resolution

Purpose: Slice-to-volume registration and super-resolution reconstruction (SVR-SRR) is commonly used to generate 3D volumes of the fetal brain from 2D stacks of slices acquired in multiple orientations. A critical initial step in this pipeline is to select one stack with the minimum motion as a reference for registration. An accurate and unbiased motion assessment (MA) is thus crucial for successful selection. Methods: We presented a MA method that determines the minimum motion stack based on 3D low-rank approximation using CANDECOMP/PARAFAC (CP) decomposition. Compared to the current 2D singular value decomposition (SVD) based method that requires flattening stacks into matrices to obtain ranks, in which the spatial information is lost, the CP-based method can factorize 3D stack into low-rank and sparse components in a computationally efficient manner. The difference between the original stack and its low-rank approximation was proposed as the motion indicator. Results: Compared to SVD-based methods, our proposed CP-based MA demonstrated higher sensitivity in detecting small motion with a lower baseline bias. Experiments on randomly simulated motion illustrated that the proposed CP method achieved a higher success rate of 95.45% in identifying the minimum motion stack, compared to SVD-based method with a success rate of 58.18%. We further demonstrated that combining CP-based MA with existing SRR-SVR pipeline significantly improved 3D volume reconstruction. Conclusion: The proposed CP-based MA method showed superior performance compared to SVD-based methods with higher sensitivity to motion, success rate, and lower baseline bias, and can be used as a prior step to improve fetal brain reconstruction.
Guided Deep Generative Model-based Spatial Regularization for Multiband Imaging Inverse Problems
Authors: Min Zhao, Nicolas Dobigeon, Jie Chen
Date: 2023-06-29 00:00:00
Tasks: Image Inpainting

When adopting a model-based formulation, solving inverse problems encountered in multiband imaging requires to define spatial and spectral regularizations. In most of the works of the literature, spectral information is extracted from the observations directly to derive data-driven spectral priors. Conversely, the choice of the spatial regularization often boils down to the use of conventional penalizations (e.g., total variation) promoting expected features of the reconstructed image (e.g., piecewise constant). In this work, we propose a generic framework able to capitalize on an auxiliary acquisition of high spatial resolution to derive tailored data-driven spatial regularizations. This approach leverages on the ability of deep learning to extract high level features. More precisely, the regularization is conceived as a deep generative network able to encode spatial semantic features contained in this auxiliary image of high spatial resolution. To illustrate the versatility of this approach, it is instantiated to conduct two particular tasks, namely multiband image fusion and multiband image inpainting. Experimental results obtained on these two tasks demonstrate the benefit of this class of informed regularizations when compared to more conventional ones.
Energy Beamforming for RF Wireless Power Transfer with Dynamic Metasurface Antennas
Authors: Amirhossein Azarbahram, Onel L. A. Lopez, Richard D. Souza, Rui Zhang, Matti Latva-aho
Date: 2023-07-03 00:00:00

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for charging the Internet of Things. Practical RF-WPT systems usually require energy beamforming (EB), which can compensate for the severe propagation loss by directing beams toward the devices. The EB flexibility depends on the transmitter architecture, existing a trade-off between cost/complexity and degrees of freedom. Thus, simpler architectures such as dynamic metasurface antennas (DMAs) are gaining attention. Herein, we consider an RF-WPT system with a transmit DMA for meeting the energy harvesting requirements of multiple devices and formulate an optimization problem for the minimum-power design. First, we provide a mathematical model to capture the frequency-dependant signal propagation effect in the DMA architecture. Next, we propose a solution based on semi-definite programming and alternating optimization. Results show that a DMA-based structure can outperform a fully-digital implementation and that the required transmit power decreases with the antenna array size, while it increases and remains almost constant with frequency in DMA and FD, respectively.
Waveform Optimization and Beam Focusing for Near-field Wireless Power Transfer with Dynamic Metasurface Antennas and Non-linear Energy Harvesters
Authors: Amirhossein Azarbahram, Onel L. A. López, Matti Latva-aho
Date: 2023-07-03 00:00:00

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for future wireless systems. However, the low power transfer efficiency (PTE) is a critical challenge for practical implementations. One of the main inefficiency sources is the power consumption and loss introduced by key components such as high-power amplifier (HPA) and rectenna, thus they must be carefully considered for PTE optimization. Herein, we consider a near-field RF-WPT system with a dynamic metasurface antenna (DMA) at the transmitter and non-linear energy harvesters. We provide a mathematical framework to calculate the power consumption and harvested power from multi-tone signal transmissions. Based on this, we propose an approach relying on alternating optimization and successive convex approximation for waveform optimization and beam focusing to minimize power consumption while meeting energy harvesting requirements. Numerical results show that increasing the number of transmit tones reduces the power consumption by leveraging the rectifier's non-linearity more efficiently. Moreover, they demonstrate that increasing the antenna length improves the performance, while DMA outperforms fully-digital architecture in terms of power consumption. Finally, our results verify that the transmitter focuses the energy on receivers located in the near-field, while energy beams are formed in the receivers' direction in the far-field region.
Robust Beamforming Design for Fairness-Aware Energy Efficiency Maximization in RIS-Assisted mmWave Communications
Authors: Ahmed Magbool, Vaibhav Kumar, Mark F. Flanagan
Date: 2023-07-03 00:00:00
Tasks: Fairness

Users in millimeter-wave (mmWave) systems often exhibit diverse channel strengths, which can negatively impact user fairness in resource allocation. Moreover, exact channel state information (CSI) may not be available at the transmitter, rendering suboptimal resource allocation. In this paper, we address these issues within the context of energy efficiency maximization in RIS-assisted mmWave systems. We first derive a tractable lower bound on the achievable sum rate, taking into account CSI errors. Subsequently, we formulate the optimization problem, targeting maximizing the system energy efficiency while maintaining a minimum Jain's fairness index controlled by a tunable design parameter. The optimization problem is very challenging due to the coupling of the optimization variables in the objective function and the fairness constraint, as well as the existence of non-convex equality and fractional constraints. To solve the optimization problem, we employ the penalty dual decomposition method, together with a projected gradient ascent based alternating optimization procedure. Simulation results demonstrate that the proposed algorithm can achieve an optimal energy efficiency for a prescribed Jain's fairness index. In addition, adjusting the fairness design parameter can yield a favorable trade-off between energy efficiency and user fairness compared to methods that exclusively focus on optimizing one of these metrics.
Greedy Selection for Heterogeneous Sensors
Authors: Kaushani Majumder, SibiRaj B. Pillai, Satish Mulleti
Date: 2023-07-03 00:00:00
Tasks: Quantization

Simultaneous operation of all sensors in a large-scale sensor network is power-consuming and computationally expensive. Hence, it is desirable to select fewer sensors. A greedy algorithm is widely used for sensor selection in homogeneous networks with a theoretical worst-case performance of (1-1/e) ~ 63% of the optimal performance when optimizing submodular metrics. For heterogeneous sensor networks (HSNs) comprising multiple sets of sensors, most of the existing sensor selection methods optimize the performance constrained by a budget on the total value of the selected sensors. However, in many applications, the number of sensors to select from each set is known apriori, and solutions are not well-explored. For this problem, we propose a joint greedy heterogeneous sensor selection algorithm. Theoretically, we show that the worst-case performance of the proposed algorithm is bounded to 50% of the optimum for submodular cost metrics. In the special case of HSNs with two sensor networks, the performance guarantee can be improved to 63% when the number of sensors to select from one set is much smaller than the other. To validate our results experimentally, we propose a submodular metric based on the frame potential measure that considers both the correlation among the sensor measurements and their heterogeneity. We prove theoretical bounds for the mean squared error of the solution when this performance metric is used. We validate our results through simulation experiments considering both linear and non-linear measurement models corrupted by additive noise and quantization errors. Our experiments show that the proposed algorithm results in 4-10 dB lower error than existing methods.
Dynamic Range Improvement in Bistatic Backscatter Communication Using Distributed MIMO
Authors: Ahmet Kaplan, Joao Vieira, Erik G. Larsson
Date: 2023-06-30 00:00:00

Backscatter communication (BSC) is a promising solution for Internet-of-Things (IoT) connections due to its low-complexity, low-cost, and energy-efficient solution for sensors. There are several network infrastructure setups that can be used for BSC with IoT nodes/passive devices. One of them is a bistatic setup where there is a need for high dynamic range and high-resolution analog-to-digital converters at the reader side. In this paper, we investigate a bistatic BSC setup with multiple antennas. We propose a novel algorithm to suppress direct link interference between the carrier emitter (CE) and the reader using beamforming into the nullspace of the CE-reader direct link to decrease the dynamic range of the system and increase the detection performance of the backscatter device (BSD). Further, we derive a Neyman-Pearson (NP) test and an exact closed-form expression for its performance in the detection of the BSD. Finally, simulation results show that the dynamic range of the system is significantly decreased and the detection performance of the BSD is increased by the proposed algorithm compared to a system not using beamforming in the CE, which could then be used in a host of different practical fields such as agriculture, transportation, factories, hospitals, smart cities, and smart homes.
A BP Method for Track-Before-Detect
Authors: Mingchao Liang, Thomas Kropfreiter, Florian Meyer
Date: 2023-07-03 00:00:00

Tracking an unknown number of low-observable objects is notoriously challenging. This letter proposes a sequential Bayesian estimation method based on the track-before-detect (TBD) approach. In TBD, raw sensor measurements are directly used by the tracking algorithm without any preprocessing. Our proposed method is based on a new statistical model that introduces a new object hypothesis for each data cell of the raw sensor measurements. It allows objects to interact and contribute to more than one data cell. Based on the factor graph representing our statistical model, we derive the message passing equations of the proposed belief propagation (BP) method for TBD. Approximations are applied to certain BP messages to reduce computational complexity and improve scalability. In a simulation experiment, our proposed BP-based TBD method outperforms two other state-of-the-art TBD methods.
Coverage Enhancement Strategy in WMSNs Based on a Novel Swarm Intelligence Algorithm: Army Ant Search Optimizer
Authors: Yindi Yao, Qin Wen, Yanpeng Cui, Feng Zhao, Bozhan Zhao, Yaoping Zeng
Date: 2023-07-03 00:00:00

As one of the most crucial scenarios of the Internet of Things (IoT), wireless multimedia sensor networks (WMSNs) pay more attention to the information-intensive data (e.g., audio, video, image) for remote environments. The area coverage reflects the perception of WMSNs to the surrounding environment, where a good coverage effect can ensure effective data collection. Given the harsh and complex physical environment of WMSNs, which easily form the sensing overlapping regions and coverage holes by random deployment. The intention of our research is to deal with the optimization problem of maximizing the coverage rate in WMSNs. By proving the NP-hard of the coverage enhancement of WMSNs, inspired by the predation behavior of army ants, this article proposes a novel swarm intelligence (SI) technology army ant search optimizer (AASO) to solve the above problem, which is implemented by five operators: army ant and prey initialization, recruited by prey, attack prey, update prey, and build ant bridge. The simulation results demonstrate that the optimizer shows good performance in terms of exploration and exploitation on benchmark suites when compared to other representative SI algorithms. More importantly, coverage enhancement AASO-based in WMSNs has better merits in terms of coverage effect when compared to existing approaches.
Discrete Army Ant Search Optimizer-Based Target Coverage Enhancement in Directional Sensor Networks
Authors: Yindi Yao, Qin Wen, Yanpeng Cui, Bozhan Zhao
Date: 2023-07-03 00:00:00

Coverage of interest points is one of the most critical issues in directional sensor networks. However, considering the remote or inhospitable environment and the limitation of the perspective of directional sensors, it is easy to form perception blind after random deployment. The intension of our research is to deal with the bound-constrained optimization problem of maximizing the coverage of target points. A coverage enhancement strategy based on a discrete army ant search optimizer (DAASO) is proposed to solve the above problem, which is inspired by the biological habits of army ants. A set of experiments are conducted using different sensor parameters. Experimental results verify the effectiveness of the DAASO in coverage effect when compared to the existing methods.