pwc-archive/papers-with-abstracts huggingface dataset:
Nonparametric Analysis of Random Utility Models Robust to Nontransitive Preferences
Authors: Wilfried Youmbi
Date: 2024-06-20 00:00:00

The Random Utility Model (RUM) is the gold standard in describing the behavior of a population of consumers. The RUM operates under the assumption of transitivity in consumers' preference relationships, but the empirical literature has regularly documented its violation. In this paper, I introduce the Random Preference Model (RPM), a novel framework for understanding the choice behavior in a population akin to RUMs, which preserves monotonicity and accommodates nontransitive behaviors. The primary objective is to test the null hypothesis that a population of rational consumers generates cross-sectional demand distributions without imposing constraints on the unobserved heterogeneity or the number of goods. I analyze data from the UK Family Expenditure Survey and find evidence that contradicts RUMs and supports RPMs. These findings underscore RPMs' flexibility and capacity to explain a wider spectrum of consumer behaviors compared to RUMs. This paper generalizes the stochastic revealed preference methodology of McFadden & Richter (1990) for finite choice sets to settings with nontransitive and possibly nonconvex preference relations.
Combining Combined Forecasts: a Network Approach
Authors: Marcos R. Fernandes
Date: 2024-06-19 00:00:00
Tasks: Decision Making

This study investigates the practice of experts aggregating forecasts before informing a decision-maker. The significance of this subject extends to various contexts where experts inform their assessments to a decision-maker following discussions with peers. My findings show that, irrespective of the information structure, aggregation rules introduce no bias to decision-making in expected terms. Nevertheless, the concern revolves around variance. In situations where experts are equally precise, and pair-wise correlation of forecasts is the same across all pairs of experts, the network structure plays a pivotal role in decision-making variance. For classical structures, I show that star networks exhibit the highest variance, contrasting with $d$-regular networks that achieve zero variance, emphasizing their efficiency. Additionally, by employing the Poisson random graph model under the assumptions of a large network size and a small connection probability, the results indicate that both the expected Network Bias and its variance converge to zero as the network size becomes sufficiently large. These insights enhance the understanding of decision-making under different information, network structures and aggregation rules. They enrich the literature on combining forecasts by exploring the effects of prior network communication on decision-making.
Decoding Vocal Articulations from Acoustic Latent Representations
Authors: Mateo Cámara, Fernando Marcos, José Luis Blanco
Date: 2024-06-20 00:00:00

We present a novel neural encoder system for acoustic-to-articulatory inversion. We leverage the Pink Trombone voice synthesizer that reveals articulatory parameters (e.g tongue position and vocal cord configuration). Our system is designed to identify the articulatory features responsible for producing specific acoustic characteristics contained in a neural latent representation. To generate the necessary latent embeddings, we employed two main methodologies. The first was a self-supervised variational autoencoder trained from scratch to reconstruct the input signal at the decoder stage. We conditioned its bottleneck layer with a subnetwork called the "projector," which decodes the voice synthesizer's parameters. The second methodology utilized two pretrained models: EnCodec and Wav2Vec. They eliminate the need to train the encoding process from scratch, allowing us to focus on training the projector network. This approach aimed to explore the potential of these existing models in the context of acoustic-to-articulatory inversion. By reusing the pretrained models, we significantly simplified the data processing pipeline, increasing efficiency and reducing computational overhead. The primary goal of our project was to demonstrate that these neural architectures can effectively encapsulate both acoustic and articulatory features. This prediction-based approach is much faster than traditional methods focused on acoustic feature-based parameter optimization. We validated our models by predicting six different parameters and evaluating them with objective and ViSQOL subjective-equivalent metric using both synthesizer- and human-generated sounds. The results show that the predicted parameters can generate human-like vowel sounds when input into the synthesizer. We provide the dataset, code, and detailed findings to support future research in this field.
Rule-based outlier detection of AI-generated anatomy segmentations
Authors: Deepa Krishnaswamy, Vamsi Krishna Thiriveedhi, Cosmin Ciausu, David Clunie, Steve Pieper, Ron Kikinis, Andrey Fedorov
Date: 2024-06-20 00:00:00
Tasks: Anatomy, Outlier Detection

There is a dire need for medical imaging datasets with accompanying annotations to perform downstream patient analysis. However, it is difficult to manually generate these annotations, due to the time-consuming nature, and the variability in clinical conventions. Artificial intelligence has been adopted in the field as a potential method to annotate these large datasets, however, a lack of expert annotations or ground truth can inhibit the adoption of these annotations. We recently made a dataset publicly available including annotations and extracted features of up to 104 organs for the National Lung Screening Trial using the TotalSegmentator method. However, the released dataset does not include expert-derived annotations or an assessment of the accuracy of the segmentations, limiting its usefulness. We propose the development of heuristics to assess the quality of the segmentations, providing methods to measure the consistency of the annotations and a comparison of results to the literature. We make our code and related materials publicly available at https://github.com/ImagingDataCommons/CloudSegmentatorResults and interactive tools at https://huggingface.co/spaces/ImagingDataCommons/CloudSegmentatorResults.
Learning Binary Color Filter Arrays with Trainable Hard Thresholding
Authors: Cemre Omer Ayna, Bahadir Kursat Gunturk, Ali Cafer Gurbuz
Date: 2024-06-20 00:00:00
Tasks: channel selection, Demosaicking

Color Filter Arrays (CFA) are optical filters in digital cameras that capture specific color channels. Current commercial CFAs are hand-crafted patterns with different physical and application-specific considerations. This study proposes a binary CFA learning module based on hard thresholding with a deep learning-based demosaicing network in a joint architecture. Unlike most existing learnable CFAs that learn a channel from the whole color spectrum or linearly combine available digital colors, this method learns a binary channel selection, resulting in CFAs that are practical and physically implementable to digital cameras. The binary selection is based on adapting the hard thresholding operation into neural networks via a straight-through estimator, and therefore it is named HardMax. This paper includes the background on the CFA design problem, the description of the HardMax method, and the performance evaluation results. The evaluation of the proposed method includes tests for different demosaicing models, color configurations, filter sizes, and a comparison with existing methods in various reconstruction metrics. The proposed approach is tested with Kodak and BSDS500 datasets and provides higher reconstruction performance than hand-crafted or alternative learned binary filters.
CLAMP: Majorized Plug-and-Play for Coherent 3D LIDAR Imaging
Authors: Tony G. Allen, David J. Rabb, Gregery T. Buzzard, Charles A. Bouman
Date: 2024-06-19 00:00:00
Tasks: Computational Efficiency, Image Reconstruction

Coherent lidar uses a chirped laser pulse for 3D imaging of distant targets. However, existing coherent lidar image reconstruction methods do not account for the system's aperture, resulting in sub-optimal resolution. Moreover, these methods use majorization-minimization for computational efficiency, but do so without a theoretical treatment of convergence. In this paper, we present Coherent Lidar Aperture Modeled Plug-and-Play (CLAMP) for multi-look coherent lidar image reconstruction. CLAMP uses multi-agent consensus equilibrium (a form of PnP) to combine a neural network denoiser with an accurate surrogate forward model of coherent lidar. Additionally, CLAMP introduces a computationally efficient FFT-based method to account for the system's aperture to improve resolution of reconstructed images. Furthermore, we formalize the use of majorization-minimization in consensus optimization problems and prove convergence to the exact consensus equilibrium solution. Finally, we apply CLAMP to synthetic and measured data to demonstrate its effectiveness in producing high-resolution, speckle-free, 3D imagery.
Advancements in Orthopaedic Arm Segmentation: A Comprehensive Review
Authors: Abhishek Swami, Snehal Farande, Atharv Patil, Atharva Parle, Vivekanand Mane, Prathamesh Thorat
Date: 2024-06-19 00:00:00
Tasks: Edge Detection, object-detection, Object Detection

The most recent advances in medical imaging that have transformed diagnosis, especially in the case of interpreting X-ray images, are actively involved in the healthcare sector. The advent of digital image processing technology and the implementation of deep learning models such as Convolutional Neural Networks (CNNs) have made the analysis of X-rays much more accurate and efficient. In this article, some essential techniques such as edge detection, region-growing technique, and thresholding approach, and the deep learning models such as variants of YOLOv8-which is the best object detection and segmentation framework-are reviewed. We further investigate that the traditional image processing techniques like segmentation are very much simple and provides the alternative to the advanced methods as well. Our review gives useful knowledge on the practical usage of the innovative and traditional approaches of manual X-ray interpretation. The discovered information will help professionals and researchers to gain more profound knowledge in digital interpretation techniques in medical imaging.
A square cross-section FOV rotational CL (SC-CL) and its analytical reconstruction method
Authors: Xiang Zou, Wuliang Shi, Muge Du, Yuxiang Xing
Date: 2024-06-18 00:00:00

Rotational computed laminography (CL) has broad application potential in three-dimensional imaging of plate-like objects, as it only needs x-ray to pass through the tested object in the thickness direction during the imaging process. In this study, a square cross-section FOV rotational CL (SC-CL) was proposed. Then, the FDK-type analytical reconstruction algorithm applicable to the SC-CL was derived. On this basis, the proposed method was validated through numerical experiments.
LLM4CP: Adapting Large Language Models for Channel Prediction
Authors: Boxun Liu, Xuanyu Liu, Shijian Gao, Xiang Cheng, Liuqing Yang
Date: 2024-06-20 00:00:00
Tasks: Prediction, Time Series Analysis, Transfer Learning

Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction method (LLM4CP) to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM, preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves SOTA prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.
A tensor model for the calibration of air-coupled ultrasonic sensor arrays in 3D imaging
Authors: Raphael Müller, Gianni Allevato, Matthias Rutsch, Christoph Haugwitz, Tianyi Liu, Mario Kupnik, Marius Pesavento
Date: 2024-06-20 00:00:00

Arrays of ultrasonic sensors are capable of 3D imaging in air and an affordable supplement to other sensing modalities, such as radar, lidar, and camera, i.e. in heterogeneous sensing systems. However, manufacturing tolerances of air-coupled ultrasonic sensors may lead to amplitude and phase deviations. Together with artifacts from imperfect knowledge of the array geometry, there are numerous factors that can impair the imaging performance of an array. We propose a reference-based calibration method to overcome possible limitations. First, we introduce a novel tensor signal model to capture the characteristics of piezoelectric ultrasonic transducers (PUTs) and the underlying multidimensional nature of a multiple-input multiple-output (MIMO) sensor array. Second, we formulate and solve an optimization problem based on this model to obtain the calibrated parameters of the array. Third, we assess both our model and the commonly used analytic model using real data from a 3D imaging experiment. The experiment reveals that our array response model we learned with calibration data yields an imaging performance similar to that of the analytic array model, which requires perfect array geometry information.
Single Channel-based Motor Imagery Classification using Fisher's Ratio and Pearson Correlation
Authors: Sonal Santosh Baberwal, Tomas Ward, Shirley Coyle
Date: 2024-06-20 00:00:00
Tasks: Classification, Motor Imagery

Motor imagery-based BCI systems have been promising and gaining popularity in rehabilitation and Activities of daily life(ADL). Despite this, the technology is still emerging and has not yet been outside the laboratory constraints. Channel reduction is one contributing avenue to make these systems part of ADL. Although Motor Imagery classification heavily depends on spatial factors, single channel-based classification remains an avenue to be explored thoroughly. Since Fisher's ratio and Pearson Correlation are powerful measures actively used in the domain, we propose an integrated framework (FRPC integrated framework) that integrates Fisher's Ratio to select the best channel and Pearson correlation to select optimal filter banks and extract spectral and temporal features respectively. The framework is tested for a 2-class motor imagery classification on 2 open-source datasets and 1 collected dataset and compared with state-of-art work. Apart from implementing the framework, this study also explores the most optimal channel among all the subjects and later explores classes where the single-channel framework is efficient.
Efficient Design and Implementation of Fast-Convolution-Based Variable-Bandwidth Filters
Authors: Oksana Moryakova, Håkan Johansson
Date: 2024-06-20 00:00:00

This paper introduces an efficient design approach for a fast-convolution-based variable-bandwidth (VBW) filter. The proposed approach is based on a hybrid of frequency sampling and optimization (HFSO), that offers significant computational complexity reduction compared to existing solutions for a given performance. The paper provides a design procedure based on minimax optimization to obtain the minimum complexity of the overall filter. A design example includes a comparison of the proposed design-based VBW filter and time-domain designed VBW filters implemented in the time domain and in the frequency domain. It is shown that not only the implementation complexity can be reduced but also the design complexity by excluding any computations when the bandwidth of the filter is adjusted. Moreover, memory requirements are also decreased compared to the existing frequency-domain implementations.
Efficient Transmission Scheme for LEO Satellite-Based NB-IoT: A Data-Driven Perspective
Authors: Ayush Kumar Dwivedi, Houcine Chougrani, Sachin Chaudhari, Neeraj Varshney, Symeon Chatzinotas
Date: 2024-06-20 00:00:00
Tasks: Change Detection

This study analyses the medium access control (MAC) layer aspects of a low-Earth-orbit (LEO) satellite-based Internet of Things (IoT) network. A transmission scheme based on change detection is proposed to accommodate more users within the network and improve energy efficiency. Machine learning (ML) algorithms are also proposed to reduce the payload size by leveraging the correlation among the sensed parameters. Real-world data from an IoT testbed deployed for a smart city application is utilised to analyse the performance regarding collision probability, effective data received and average battery lifetime. The findings reveal that the traffic pattern, post-implementation of the proposed scheme, differs from the commonly assumed Poisson traffic, thus proving the effectiveness of having IoT data from actual deployment. It is demonstrated that the transmission scheme facilitates accommodating more devices while targeting a specific collision probability. Considering the link budget for a direct access NB-IoT scenario, more data is effectively offloaded to the server within the limited visibility of LEO satellites. The average battery lifetimes are also demonstrated to increase by many folds by using the proposed access schemes and ML algorithms.
Groupwise Deformable Registration of Diffusion Tensor Cardiovascular Magnetic Resonance: Disentangling Diffusion Contrast, Respiratory and Cardiac Motions
Authors: Fanwen Wang, Yihao Luo, Ke Wen, Jiahao Huang, Pedro F. Ferreira, Yaqing Luo, Yinzhe Wu, Camila Munoz, Dudley J. Pennell, Andrew D. Scott, Sonia Nielles-Vallespin, Guang Yang
Date: 2024-06-19 00:00:00

Diffusion tensor based cardiovascular magnetic resonance (DT-CMR) offers a non-invasive method to visualize the myocardial microstructure. With the assumption that the heart is stationary, frames are acquired with multiple repetitions for different diffusion encoding directions. However, motion from poor breath-holding and imprecise cardiac triggering complicates DT-CMR analysis, further challenged by its inherently low SNR, varied contrasts, and diffusion induced textures. Our solution is a novel framework employing groupwise registration with an implicit template to isolate respiratory and cardiac motions, while a tensor-embedded branch preserves diffusion contrast textures. We have devised a loss refinement tailored for non-linear least squares fitting and low SNR conditions. Additionally, we introduce new physics-based and clinical metrics for performance evaluation. Access code and supplementary materials at: https://github.com/ayanglab/DTCMR-Reg
Using Geometrical information to Measure the Vibration of A Swaying Millimeter-wave Radar
Authors: Chengyao Tang, Yongpeng Dai, Zhi Li, Tian Jin
Date: 2024-06-19 00:00:00

This paper presents two new, simple yet effective approaches to measure the vibration of a swaying millimeter-wave radar (mmRadar) utilizing geometrical information. Specifically, for the planar vibrations, we firstly establish an equation based on the area difference between the swaying mmRadar and the reference objects at different moments, which enables the quantification of planar displacement. Secondly, volume differences are also utilized with the same idea, achieving the self-vibration measurement of a swaying mmRadar for spatial vibrations. Experimental results confirm the effectiveness of our methods, demonstrating its capability to estimate both the amplitude and a crude direction of the mmRadar's self-vibration.
An Efficient yet High-Performance Method for Precise Radar-Based Imaging of Human Hand Poses
Authors: Johanna Bräunig, Vanessa Wirth, Marc Stamminger, Ingrid Ullmann, Martin Vossiek
Date: 2024-06-19 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation

Contactless hand pose estimation requires sensors that provide precise spatial information and low computational complexity for real-time processing. Unlike vision-based systems, radar offers lighting independence and direct motion assessments. Yet, there is limited research balancing real-time constraints, suitable frame rates for motion evaluations, and the need for precise 3D data. To address this, we extend the ultra-efficient two-tone hand imaging method from our prior work to a three-tone approach. Maintaining high frame rates and real-time constraints, this approach significantly enhances reconstruction accuracy and precision. We assess these measures by evaluating reconstruction results for different hand poses obtained by an imaging radar. Accuracy is assessed against ground truth from a spatially calibrated photogrammetry setup, while precision is measured using 3D-printed hand poses. The results emphasize the method's great potential for future radar-based hand sensing.
A Hybrid Intelligent System for Protection of Transmission Lines Connected to PV Farms based on Linear Trends
Authors: Pallav Kumar Bera, Samita Rani Pani, Can Isik, Ramesh C. Bansal
Date: 2024-06-19 00:00:00
Tasks: feature selection

Conventional relays face challenges for transmission lines connected to inverter-based resources (IBRs). In this article, a single-ended intelligent protection of the transmission line in the zone between the grid and the PV farm is suggested. The method employs a fuzzy logic and random forest (RF)-based hybrid system to detect faults based on combined linear trend attributes of the 3-phase currents. The fault location is determined and the faulty phase is detected. RF feature selection is used to obtain the optimal linear trend feature. The performance of the methodology is examined for abnormal events such as faults, capacitor and load-switching operations simulated in PSCAD/EMTDC on IEEE 9-bus system obtained by varying various fault and switching parameters. Additionally, when validating the suggested strategy, consideration is given to the effects of conditions such as the presence of double circuit lines, PV capacity, sampling rate, data window length, noise, high impedance faults, CT saturation, compensation devices, evolving and cross-country faults, and far-end and near-end faults. The findings indicate that the suggested strategy can be used to deal with a variety of system configurations and situations while still safeguarding such complex power transmission networks.