pwc-archive/papers-with-abstracts huggingface dataset:
Strategic Responses to Technological Change: Evidence from Online Labor Markets
Authors: Shun Yiu, Rob Seamans, Manav Raj, Ted Liu
Date: 2024-03-22 00:00:00
Tasks: Language Modelling, Position

In this project, we examine how freelancers changed their strategic positioning on an online work platform following the launch of ChatGPT in November 2022 - a major advance in AI technologies. We document that post-ChatGPT, freelancers bid on fewer jobs and reposition themselves by differentiating their distribution of bids (i.e., job applications) relative to their prior behavior. We disentangle heterogeneity in strategic responses by exploring how exposure to changes in demand or supply shape incumbent repositioning. We find that the launch of ChatGPT was associated with a short-term decrease in labor demand and an increase in labor supply, though these changes vary across work domains. In response to decreases in labor demand, workers changed their horizontal positioning and withdrew from the platform. In response to increases in labor supply, workers were less likely to decrease bidding or reposition horizontally but shifted their vertical position by targeting lower-value jobs. We further show that repositioning is less likely for high-skill freelancers who face greater adjustment costs. This research contributes to our understanding of how and why workers respond to technological change in the context of recent advances in AI technologies.
Teamwork and Spillover Effects in Performance Evaluations
Authors: Enzo Brox, Michael Lechner
Date: 2024-03-22 00:00:00

This article shows how coworker performance affects individual performance evaluation in a teamwork setting at the workplace. We use high-quality data on football matches to measure an important component of individual performance, shooting performance, isolated from collaborative effects. Employing causal machine learning methods, we address the assortative matching of workers and estimate both average and heterogeneous effects. There is substantial evidence for spillover effects in performance evaluations. Coworker shooting performance, meaningfully impacts both, manager decisions and third-party expert evaluations of individual performance. Our results underscore the significant role coworkers play in shaping career advancements and highlight a complementary channel, to productivity gains and learning effects, how coworkers impact career advancement. We characterize the groups of workers that are most and least affected by spillover effects and show that spillover effects are reference point dependent. While positive deviations from a reference point create positive spillover effects, negative deviations are not harmful for coworkers.
The Power of Linear Programming in Sponsored Listings Ranking: Evidence from Field Experiments
Authors: Haihao Lu, Luyang Zhang, Yuting Zhu
Date: 2024-03-21 00:00:00

Sponsored listing is one of the major revenue sources for many prominent online marketplaces, such as Amazon, Walmart, and Alibaba. When consumers visit a marketplace's webpage for a specific item, in addition to that item, the marketplace might also display a ranked listing of sponsored items from various third-party sellers. These sellers are charged an advertisement fee if a user purchases any of the sponsored items from this listing. Determining how to rank these sponsored items for each incoming visit is a crucial challenge for online marketplaces, a problem known as sponsored listings ranking (SLR). The major difficulty of SLR lies in balancing the trade-off between maximizing the overall revenue and recommending high-quality and relevant ranked listings. While a more relevant ranking may result in more purchases and consumer engagement, the marketplace also needs to take account of the potential revenue when making ranking decisions. Due to the latency requirement and historical reasons, many online marketplaces use score-based ranking algorithms for SLR optimization. Alternatively, recent research also discusses obtaining the ranking by solving linear programming (LP). In this paper, we collaborate with a leading online global marketplace and conduct a series of field experiments to compare the performance of the score-based ranking algorithms and the LP-based algorithms. The field experiment lasted for $19$ days, which included $329.3$ million visits in total. We observed that the LP-based approach improved all major metrics by $1.80\%$ of revenue, $1.55\%$ of purchase, and $1.39\%$ of the gross merchandise value (GMV), compared to an extremely-tuned score-based algorithm that was previously used in production by the marketplace.
Risk Propagation in Endogenous Supply Chains
Authors: Andrea Titton
Date: 2024-03-25 00:00:00

This paper investigates the endogenous formation of supply chains and its consequences for disruption propagation. In production networks where upstream risk is highly correlated and supplier relationships are not observable, the marginal risk reduction of adding an additional supplier is low, because this additional supplier's risk is likely to be correlated to that of the firm's existing suppliers. This channel reduces firm incentives to diversify, which gives rise to inefficiently fragile production networks. By solving the social planner problem, I show that, if the risk reduction experienced downstream resulting from upstream diversification were to be internalised by upstream firms, endogenous production networks would be resilient to most levels of risk. Furthermore, I show that imperfect information yields inefficient but more robust supply chains. Despite its stylised form, the model identifies the trade-off firms face when diversifying risk and isolates the mechanism that aggregates these decisions into a production network. Furthermore, it maps the conditions of the trade-off, such as expected profits of the firm or the sourcing costs, to the properties of the production network.
Discounted Subjective Expected Utility in Continuous Time
Authors: Lorenzo Bastianello, Vassili Vergopoulos
Date: 2024-03-22 00:00:00

By embedding uncertainty into time, we obtain a conjoint axiomatic characterization of both Exponential Discounting and Subjective Expected Utility that accommodates arbitrary state and outcome spaces. In doing so, we provide a novel and simple time-interpretation of subjective probability. The subjective probability of an event is calibrated using time discounting.
Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives
Authors: Billel Essaid, Hamza Kheddar, Noureddine Batel, Muhammad E. H. Chowdhury, Abderrahmane Lakas
Date: 2024-03-17 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

Automatic speech recognition (ASR) plays a pivotal role in our daily lives, offering utility not only for interacting with machines but also for facilitating communication for individuals with partial or profound hearing impairments. The process involves receiving the speech signal in analog form, followed by various signal processing algorithms to make it compatible with devices of limited capacities, such as cochlear implants (CIs). Unfortunately, these implants, equipped with a finite number of electrodes, often result in speech distortion during synthesis. Despite efforts by researchers to enhance received speech quality using various state-of-the-art (SOTA) signal processing techniques, challenges persist, especially in scenarios involving multiple sources of speech, environmental noise, and other adverse conditions. The advent of new artificial intelligence (AI) methods has ushered in cutting-edge strategies to address the limitations and difficulties associated with traditional signal processing techniques dedicated to CIs. This review aims to comprehensively cover advancements in CI-based ASR and speech enhancement, among other related aspects. The primary objective is to provide a thorough overview of metrics and datasets, exploring the capabilities of AI algorithms in this biomedical field, and summarizing and commenting on the best results obtained. Additionally, the review will delve into potential applications and suggest future directions to bridge existing research gaps in this domain.
A Method for Target Detection Based on Mmw Radar and Vision Fusion
Authors: Ming Zong, Jiaying Wu, Zhanyu Zhu, Jingen Ni
Date: 2024-03-25 00:00:00

An efficient and accurate traffic monitoring system often takes advantages of multi-sensor detection to ensure the safety of urban traffic, promoting the accuracy and robustness of target detection and tracking. A method for target detection using Radar-Vision Fusion Path Aggregation Fully Convolutional One-Stage Network (RV-PAFCOS) is proposed in this paper, which is extended from Fully Convolutional One-Stage Network (FCOS) by introducing the modules of radar image processing branches, radar-vision fusion and path aggregation. The radar image processing branch mainly focuses on the image modeling based on the spatiotemporal calibration of millimeter-wave (mmw) radar and cameras, taking the conversion of radar point clouds to radar images. The fusion module extracts features of radar and optical images based on the principle of spatial attention stitching criterion. The path aggregation module enhances the reuse of feature layers, combining the positional information of shallow feature maps with deep semantic information, to obtain better detection performance for both large and small targets. Through the experimental analysis, the method proposed in this paper can effectively fuse the mmw radar and vision perceptions, showing good performance in traffic target detection.
Passive Screen-to-Camera Communication
Authors: Seyed Keyarash Ghiasi, Marco Kaldenbach, Marco Zuniga
Date: 2024-03-24 00:00:00

A recent technology known as transparent screens is transforming windows into displays. These smart windows are present in buses, airports and offices. They can remain transparent, as a normal window, or display relevant information that overlays their panoramic views. In this paper, we propose transforming these windows not only into screens but also into wireless transmitters. To achieve this goal, we build upon the research area of screen-to-camera communication. In this area, videos are modified in a way that smartphone cameras can decode data out of them, while this data remains invisible to the viewers. A person sees a normal video, but the camera sees the video plus additional information. In this communication method, one of the biggest disadvantages is the traditional screens' power consumption, more than 80% of which is used to generate light. To solve this, we employ novel transparent screens relying on ambient light to display pictures, hence eliminating the power source. However, this comes at the cost of a lower image quality, since they use variable and out-of-control environment light, instead of generating a constant and strong light by LED panels. Our work, dubbed PassiveCam, overcomes the challenge of creating the first screen-to-camera communication link using passive displays. This paper presents two main contributions. First, we analyze and modify existing screens and encoding methods to embed information reliably in ambient light. Second, we develop an Android App that optimizes the decoding process, obtaining a real-time performance. Our evaluation, which considers a musical application, shows a Packet Success Rate (PSR) of close to 90%. In addition, our real-time application achieves response times of 530 ms and 1071 ms when the camera is static and when it is hand-held, respectively.
PSHop: A Lightweight Feed-Forward Method for 3D Prostate Gland Segmentation
Authors: Yijing Yang, Vasileios Magoulianitis, Jiaxin Yang, Jintang Xue, Masatomo Kaneko, Giovanni Cacciamani, Andre Abreu, Vinay Duddalwar, C. -C. Jay Kuo, Inderbir S. Gill, Chrysostomos Nikias
Date: 2024-03-24 00:00:00
Tasks: 3D Prostate Segmentation, Decoder, Deep Learning, Representation Learning, Segmentation

Automatic prostate segmentation is an important step in computer-aided diagnosis of prostate cancer and treatment planning. Existing methods of prostate segmentation are based on deep learning models which have a large size and lack of transparency which is essential for physicians. In this paper, a new data-driven 3D prostate segmentation method on MRI is proposed, named PSHop. Different from deep learning based methods, the core methodology of PSHop is a feed-forward encoder-decoder system based on successive subspace learning (SSL). It consists of two modules: 1) encoder: fine to coarse unsupervised representation learning with cascaded VoxelHop units, 2) decoder: coarse to fine segmentation prediction with voxel-wise classification and local refinement. Experiments are conducted on the publicly available ISBI-2013 dataset, as well as on a larger private one. Experimental analysis shows that our proposed PSHop is effective, robust and lightweight in the tasks of prostate gland and zonal segmentation, achieving a Dice Similarity Coefficient (DSC) of 0.873 for the gland segmentation task. PSHop achieves a competitive performance comparatively to other deep learning methods, while keeping the model size and inference complexity an order of magnitude smaller.
PCa-RadHop: A Transparent and Lightweight Feed-forward Method for Clinically Significant Prostate Cancer Segmentation
Authors: Vasileios Magoulianitis, Jiaxin Yang, Yijing Yang, Jintang Xue, Masatomo Kaneko, Giovanni Cacciamani, Andre Abreu, Vinay Duddalwar, C. -C. Jay Kuo, Inderbir S. Gill, Chrysostomos Nikias
Date: 2024-03-24 00:00:00
Tasks: Diagnostic

Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as ``black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity.
A Deep Learning Architectures for Kidney Disease Classification
Authors: Muhammad Shoaib Farooq, Ayesha Tariq
Date: 2024-03-23 00:00:00
Tasks: Classification, Deep Learning, image-classification, Image Classification, Transfer Learning

Deep learning has become an extremely powerful tool for complex tasks such as image classification and segmentation. The medical industry often lacks high-quality, balanced datasets, which can be a challenge for deep learning algorithms that need sufficiently large amounts of data to train and increase their performance. This is especially important in the context of kidney issues such as for stones, cysts and tumors. We used deep learning models for this study to classify or detect several types of kidney diseases. We use different classification models, such as VGG-19, (CNNs) Convolutional Neural Networks, ResNet-101, VGG-16, ResNet-50, and DenseNet-169, which can be enhanced through techniques such as classification, segmentation, and transfer learning. These algorithms can help improve model accuracy by allowing them to learn from multiple datasets. This technique has the potential to revolutionize the diagnosis and treatment of kidney problems as it enables more accurate and effective classification of CT-scan images. This may ultimately lead to better patient outcomes and improved overall health outcomes.
Using Super-Resolution Imaging for Recognition of Low-Resolution Blurred License Plates: A Comparative Study of Real-ESRGAN, A-ESRGAN, and StarSRGAN
Authors: Ching-Hsiang Wang
Date: 2024-03-20 00:00:00
Tasks: License Plate Recognition, Super-Resolution

With the robust development of technology, license plate recognition technology can now be properly applied in various scenarios, such as road monitoring, tracking of stolen vehicles, detection at parking lot entrances and exits, and so on. However, the precondition for these applications to function normally is that the license plate must be 'clear' enough to be recognized by the system with the correct license plate number. If the license plate becomes blurred due to some external factors, then the accuracy of recognition will be greatly reduced. Although there are many road surveillance cameras in Taiwan, the quality of most cameras is not good, often leading to the inability to recognize license plate numbers due to low photo resolution. Therefore, this study focuses on using super-resolution technology to process blurred license plates. This study will mainly fine-tune three super-resolution models: Real-ESRGAN, A-ESRGAN, and StarSRGAN, and compare their effectiveness in enhancing the resolution of license plate photos and enabling accurate license plate recognition. By comparing different super-resolution models, it is hoped to find the most suitable model for this task, providing valuable references for future researchers.
FUELVISION: A Multimodal Data Fusion and Multimodel Ensemble Algorithm for Wildfire Fuels Mapping
Authors: Riyaaz Uddien Shaik, Mohamad Alipour, Eric Rowell, Bharathan Balaji, Adam Watts, Ertugrul Taciroglu
Date: 2024-03-19 00:00:00

Accurate assessment of fuel conditions is a prerequisite for fire ignition and behavior prediction, and risk management. The method proposed herein leverages diverse data sources including Landsat-8 optical imagery, Sentinel-1 (C-band) Synthetic Aperture Radar (SAR) imagery, PALSAR (L-band) SAR imagery, and terrain features to capture comprehensive information about fuel types and distributions. An ensemble model was trained to predict landscape-scale fuels such as the 'Scott and Burgan 40' using the as-received Forest Inventory and Analysis (FIA) field survey plot data obtained from the USDA Forest Service. However, this basic approach yielded relatively poor results due to the inadequate amount of training data. Pseudo-labeled and fully synthetic datasets were developed using generative AI approaches to address the limitations of ground truth data availability. These synthetic datasets were used for augmenting the FIA data from California to enhance the robustness and coverage of model training. The use of an ensemble of methods including deep learning neural networks, decision trees, and gradient boosting offered a fuel mapping accuracy of nearly 80\%. Through extensive experimentation and evaluation, the effectiveness of the proposed approach was validated for regions of the 2021 Dixie and Caldor fires. Comparative analyses against high-resolution data from the National Agriculture Imagery Program (NAIP) and timber harvest maps affirmed the robustness and reliability of the proposed approach, which is capable of near-real-time fuel mapping.
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition
Authors: Abhi Kamboj, Minh Do
Date: 2024-03-17 00:00:00
Tasks: Action Recognition, Activity Recognition, Domain Adaptation, Human Activity Recognition, Representation Learning, Sensor Fusion, Transfer Learning

Despite living in a multi-sensory world, most AI models are limited to textual and visual understanding of human motion and behavior. In fact, full situational awareness of human motion could best be understood through a combination of sensors. In this survey we investigate how knowledge can be transferred and utilized amongst modalities for Human Activity/Action Recognition (HAR), i.e. cross-modality transfer learning. We motivate the importance and potential of IMU data and its applicability in cross-modality learning as well as the importance of studying the HAR problem. We categorize HAR related tasks by time and abstractness and then compare various types of multimodal HAR datasets. We also distinguish and expound on many related but inconsistently used terms in the literature, such as transfer learning, domain adaptation, representation learning, sensor fusion, and multimodal learning, and describe how cross-modal learning fits with all these concepts. We then review the literature in IMU-based cross-modal transfer for HAR. The two main approaches for cross-modal transfer are instance-based transfer, where instances of one modality are mapped to another (e.g. knowledge is transferred in the input space), or feature-based transfer, where the model relates the modalities in an intermediate latent space (e.g. knowledge is transferred in the feature space). Finally, we discuss future research directions and applications in cross-modal HAR.
Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images
Authors: Arezoo Borji, Taha-Hossein Hejazi, Abbas Seifi
Date: 2024-03-17 00:00:00
Tasks: Deep Learning

Alzheimer's disease is a progressive neurodegenerative disorder that primarily affects cognitive functions such as memory, thinking, and behavior. In this disease, there is a critical phase, mild cognitive impairment, that is really important to be diagnosed early since some patients with progressive MCI will develop the disease. This study delves into the challenging task of classifying Alzheimer's disease into four distinct groups: control normal (CN), progressive mild cognitive impairment (pMCI), stable mild cognitive impairment (sMCI), and Alzheimer's disease (AD). This classification is based on a thorough examination of PET scan images obtained from the ADNI dataset, which provides a thorough understanding of the disease's progression. Several deep-learning and traditional machine-learning models have been used to detect Alzheimer's disease. In this paper, three deep-learning models, namely VGG16 and AlexNet, and a custom Convolutional neural network (CNN) with 8-fold cross-validation have been used for classification. Finally, an ensemble technique is used to improve the overall result of these models. The results show that using deep-learning models to tell the difference between MCI patients gives an overall average accuracy of 93.13% and an AUC of 94.4%.
HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology
Authors: Xiajun Jiang, Sumeet Vadhavkar, Yubo Ye, Maryam Toloubidokhti, Ryan Missel, Linwei Wang
Date: 2024-03-15 00:00:00
Tasks: Meta-Learning

Personalized virtual heart models have demonstrated increasing potential for clinical use, although the estimation of their parameters given patient-specific data remain a challenge. Traditional physics-based modeling approaches are computationally costly and often neglect the inherent structural errors in these models due to model simplifications and assumptions. Modern deep learning approaches, on the other hand, rely heavily on data supervision and lacks interpretability. In this paper, we present a novel hybrid modeling framework to describe a personalized cardiac digital twin as a combination of a physics-based known expression augmented by neural network modeling of its unknown gap to reality. We then present a novel meta-learning framework to enable the separate identification of both the physics-based and neural components in the hybrid model. We demonstrate the feasibility and generality of this hybrid modeling framework with two examples of instantiations and their proof-of-concept in synthetic experiments.