pwc-archive/papers-with-abstracts huggingface dataset:
Students Perceptions of Sustainable Universities in Hungary. An Importance-Performance Analysis
Authors: Szabolcs Nagy, Mariann Veresne Somosi
Date: 2022-12-26 00:00:00

In order to succeed, universities are forced to respond to the new challenges in the rapidly changing world. The recently emerging fourth-generation universities should meet sustainability objectives to better serve their students and their communities. It is essential for universities to measure their sustainability performance to capitalise on their core strengths and to overcome their weaknesses. In line with the stakeholder theory, the objective of this study was to investigate students perceptions of university sustainability including their expectations about and satisfaction with the efforts that universities make towards sustainability. This paper proposes a new approach that combines the sustainable university scale, developed by the authors, with the importance-performance analysis to identify key areas of university sustainability. To collect data, an online survey was conducted in Hungary in 2019. The sustainable university scale was found to be a reliable construct to measure different aspects of university sustainability. Results of the importance-performance analysis suggest that students consider Hungarian universities unsustainable. Research findings indicate that Hungarian universities perform poorly in sustainable purchasing and renewable energy use, but their location and their efforts towards separate waste collection are their major competitive advantages. The main domains of university sustainability were also discussed. This study provides university decision-makers and researchers with insightful results supporting the transformation of traditional universities into sustainable, fourth-generation higher education institutions.
Consumer acceptance of the use of artificial intelligence in online shopping: evidence from Hungary
Authors: Szabolcs Nagy, Noemi Hajdu
Date: 2022-12-26 00:00:00

The rapid development of technology has drastically changed the way consumers do their shopping. The volume of global online commerce has significantly been increasing partly due to the recent COVID-19 crisis that has accelerated the expansion of e-commerce. A growing number of webshops integrate Artificial Intelligence (AI), state-of-the-art technology into their stores to improve customer experience, satisfaction and loyalty. However, little research has been done to verify the process of how consumers adopt and use AI-powered webshops. Using the technology acceptance model (TAM) as a theoretical background, this study addresses the question of trust and consumer acceptance of Artificial Intelligence in online retail. An online survey in Hungary was conducted to build a database of 439 respondents for this study. To analyse data, structural equation modelling (SEM) was used. After the respecification of the initial theoretical model, a nested model, which was also based on TAM, was developed and tested. The widely used TAM was found to be a suitable theoretical model for investigating consumer acceptance of the use of Artificial Intelligence in online shopping. Trust was found to be one of the key factors influencing consumer attitudes towards Artificial Intelligence. Perceived usefulness as the other key factor in attitudes and behavioural intention was found to be more important than the perceived ease of use. These findings offer valuable implications for webshop owners to increase customer acceptance
On the notion of measurable utility on a connected and separable topological space: an order isomorphism theorem
Authors: Gianmarco Caldini
Date: 2022-12-15 00:00:00
Tasks: Philosophy

The aim of this article is to define a notion of cardinal utility function called measurable utility and to define it on a connected and separable subset of a weakly ordered topological space. The definition is equivalent to the ones given by Frisch in 1926 and by Shapley in 1975 and postulates axioms on a set of alternatives that allow both to ordinally rank alternatives and to compare their utility differences. After a brief review of the philosophy of utilitarianism and the history of utility theory, the paper introduces the mathematical framework to represent intensity comparisons of utility and proves a list of topological lemmas that will be used in the main result. Finally, the article states and proves a representation theorem for a measurable utility function defined on a connected and separable subset of a weakly ordered topological space equipped with another weak order on its cartesian product. Under some assumptions on the order relations, the main theorem proves existence and uniqueness, up to positive affine transformations, of an order isomorphism with the real line.
A Bertrand duopoly game with differentiated products reconsidered
Authors: Xiaoliang Li, Bo Li
Date: 2023-01-03 00:00:00

In this paper, we explore a dynamic Bertrand duopoly game with differentiated products, where firms are boundedly rational and consumers are assumed to possess an underlying CES utility function. We mainly focus on two distinct degrees of product substitutability. Several tools based on symbolic computations such as the triangular decomposition method and the PCAD method are employed in the analytical investigation of the model. The uniqueness of the non-vanishing equilibrium is proved and rigorous conditions for the local stability of this equilibrium are established for the first time. Most importantly, we find that increasing the substitutability degree or decreasing the product differentiation has an effect of destabilization for our Bertrand model, which is in contrast with the relative conclusions for the Cournot models. This finding could be conducive to the revelation of the essential difference between dynamic Cournot and Bertrand oligopolies with differentiated goods. In the special case of identical marginal costs, we derive that lower degrees of product differentiation mean lower prices, higher supplies, lower profits, and lower social welfare. Furthermore, complex dynamics such as periodic orbits and chaos are reported through our numerical simulations.
Market Design with Distributional Objectives
Authors: Isa E. Hafalir, Fuhito Kojima, M. Bumin Yenmez, Koji Yokote
Date: 2022-12-31 00:00:00
Tasks: Diversity

We provide optimal solutions to an institution that has distributional objectives when choosing from a set of applications based on merit (or priority). For example, in college admissions, administrators may want to admit a diverse class in addition to choosing students with the highest qualifications. We provide a family of choice rules that maximize merit subject to attaining a level of the distributional objective. We study the desirable properties of choice rules in this family and use them to find all subsets of applications on the Pareto frontier of the distributional objective and merit. In addition, we provide two novel characterizations of matroids.
Efficient Market Design with Distributional Objectives
Authors: Isa E. Hafalir, Fuhito Kojima, M. Bumin Yenmez
Date: 2022-12-31 00:00:00

Given an initial matching and a policy objective on the distribution of agent types to institutions, we study the existence of a mechanism that weakly improves the distributional objective and satisfies constrained efficiency, individual rationality, and strategy-proofness. We show that such a mechanism need not exist in general. We introduce a new notion of discrete concavity, which we call pseudo M$^{\natural}$-concavity, and construct a mechanism with the desirable properties when the distributional objective satisfies this notion. We provide several practically relevant distributional objectives that are pseudo M$^{\natural}$-concave.
Frequency-aware Learned Image Compression for Quality Scalability
Authors: Hyomin Choi, Fabien Racape, Shahab Hamidi-Rad, Mateen Ulhaq, Simon Feltman
Date: 2023-01-03 00:00:00
Tasks: Decoder, Image Compression

Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches. We propose a novel NN-based image coding framework that utilizes forward wavelet transforms to decompose the input signal by spatial frequency. Our encoder generates separate bitstreams for each latent representation of low and high frequencies. This enables our decoder to selectively decode bitstreams in a quality-scalable manner. Hence, the decoder can produce an enhanced image by using an enhancement bitstream in addition to the base bitstream. Furthermore, our method is able to enhance only a specific region of interest (ROI) by using a corresponding part of the enhancement latent representation. Our experiments demonstrate that the proposed method shows competitive rate-distortion performance compared to several non-scalable image codecs. We also showcase the effectiveness of our two-level quality scalability, as well as its practicality in ROI quality enhancement.
Developing and deploying deep learning models in brain MRI: a review
Authors: Kunal Aggarwal, Marina Manso Jimeno, Keerthi Sravan Ravi, Gilberto Gonzalez, Sairam Geethanath
Date: 2023-01-03 00:00:00

Magnetic Resonance Imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools have resulted in the rapid increase of DL models and subsequent peer-reviewed publications. However, the rate of deployment in clinical settings is low. Therefore, this review attempts to bring together the ideas from data collection to deployment into the clinic building on the guidelines and principles that accreditation agencies have espoused. We introduce the need for and the role of DL to deliver accessible MRI. This is followed by a brief review of DL examples in the context of neuropathologies. Based on these studies and others, we collate the prerequisites to develop and deploy DL models for brain MRI. We then delve into the guiding principles to practice good machine learning practices in the context of neuroimaging with a focus on explainability. A checklist based on the FDA's good machine learning practices is provided as a summary of these guidelines. Finally, we review the current challenges and future opportunities in DL for brain MRI.
Towards retrospective motion correction and reconstruction for clinical 3D brain MRI protocols with a reference contrast
Authors: Gabrio Rizzuti, Tim Schakel, Niek R. F. Huttinga, Jan Willem Dankbaar, Tristan van Leeuwen, Alessandro Sbrizzi
Date: 2023-01-03 00:00:00

Motion artifacts often spoil the radiological interpretation of MR images, and in the most severe cases the scan needs be repeated, with additional costs for the provider. We discuss the application of a novel 3D retrospective rigid motion correction and reconstruction scheme for MRI, which leverages multiple scans contained in a MR session. Typically, in a multi-contrast MR session, motion does not equally affect all the scans, and some motion-free scans are generally available, so that we can exploit their anatomic similarity. The uncorrupted scan is used as a reference in a generalized rigid-motion registration problem to remove the motion artifacts affecting the corrupted scans. We discuss the potential of the proposed algorithm with a prospective in-vivo study and clinical 3D brain protocols. This framework can be easily incorporated into the existing clinical practice with no disruption to the conventional workflow.
Reduced Reference Quality Assessment for Point Cloud Compression
Authors: Yipeng Liu, Qi Yang, Yiling Xu
Date: 2023-01-03 00:00:00
Tasks: Attribute, Point Cloud Quality Assessment, Quantization

In this paper, we propose a reduced reference (RR) point cloud quality assessment (PCQA) model named R-PCQA to quantify the distortions introduced by the lossy compression. Specifically, we use the attribute and geometry quantization steps of different compression methods (i.e., V-PCC, G-PCC and AVS) to infer the point cloud quality, assuming that the point clouds have no other distortions before compression. First, we analyze the compression distortion of point clouds under separate attribute compression and geometry compression to avoid their mutual masking, for which we consider 5 point clouds as references to generate a compression dataset (PCCQA) containing independent attribute compression and geometry compression samples. Then, we develop the proposed R-PCQA via fitting the relationship between the quantization steps and the perceptual quality. We evaluate the performance of R-PCQA on both the established dataset and another independent dataset. The results demonstrate that the proposed R-PCQA can exhibit reliable performance and high generalization ability.
In-situ monitoring additive manufacturing process with AI edge computing
Authors: Wenkang Zhu, Hui Li, Yikai Zhang, Yuqing Hou, Liwei Chen
Date: 2023-01-02 00:00:00
Tasks: ARC, Edge-computing, Super-Resolution, Video Super-Resolution

In-situ monitoring system can be used to monitor the quality of additive manufacturing (AM) processes. In the case of digital image correlation (DIC) based in-situ monitoring systems, high-speed cameras were used to capture images of high resolutions. This paper proposed a novel in-situ monitoring system to accelerate the process of digital images using artificial intelligence (AI) edge computing board. It built a visual transformer based video super resolution (ViTSR) network to reconstruct high resolution (HR) videos frames. Fully convolutional network (FCN) was used to simultaneously extract the geometric characteristics of molten pool and plasma arc during the AM processes. Compared with 6 state-of-the-art super resolution methods, ViTSR ranks first in terms of peak signal to noise ratio (PSNR). The PSNR of ViTSR for 4x super resolution reached 38.16 dB on test data with input size of 75 pixels x 75 pixels. Inference time of ViTSR and FCN was optimized to 50.97 ms and 67.86 ms on AI edge board after operator fusion and model pruning. The total inference time of the proposed system was 118.83 ms, which meets the requirement of real-time quality monitoring with low cost in-situ monitoring equipment during AM processes. The proposed system achieved an accuracy of 96.34% on the multi-objects extraction task and can be applied to different AM processes.
DIVA: Deep Unfolded Network from Quantum Interactive Patches for Image Restoration
Authors: Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé
Date: 2022-12-31 00:00:00
Tasks: Deblurring, Denoising, Image Deblurring, Image Restoration, Super-Resolution

This paper presents a deep neural network called DIVA unfolding a baseline adaptive denoising algorithm (De-QuIP), relying on the theory of quantum many-body physics. Furthermore, it is shown that with very slight modifications, this network can be enhanced to solve more challenging image restoration tasks such as image deblurring, super-resolution and inpainting. Despite a compact and interpretable (from a physical perspective) architecture, the proposed deep learning network outperforms several recent algorithms from the literature, designed specifically for each task. The key ingredients of the proposed method are on one hand, its ability to handle non-local image structures through the patch-interaction term and the quantum-based Hamiltonian operator, and, on the other hand, its flexibility to adapt the hyperparameters patch-wisely, due to the training process.
Uncertainty in Real-Time Semantic Segmentation on Embedded Systems
Authors: Ethan Goan, Clinton Fookes
Date: 2022-12-20 00:00:00
Tasks: Autonomous Vehicles, Real-Time Semantic Segmentation, regression, Semantic Segmentation

Application for semantic segmentation models in areas such as autonomous vehicles and human computer interaction require real-time predictive capabilities. The challenges of addressing real-time application is amplified by the need to operate on resource constrained hardware. Whilst development of real-time methods for these platforms has increased, these models are unable to sufficiently reason about uncertainty present when applied on embedded real-time systems. This paper addresses this by combining deep feature extraction from pre-trained models with Bayesian regression and moment propagation for uncertainty aware predictions. We demonstrate how the proposed method can yield meaningful epistemic uncertainty on embedded hardware in real-time whilst maintaining predictive performance.
One-shot skill assessment in high-stakes domains with limited data via meta learning
Authors: Erim Yanik, Steven Schwaitzberg, Gene Yang, Xavier Intes, Jack Norfleet, Matthew Hackett, Suvranu De
Date: 2022-12-16 00:00:00
Tasks: Domain Adaptation, Meta-Learning, One-Shot Learning

Deep Learning (DL) has achieved robust competency assessment in various high-stakes fields. However, the applicability of DL models is often hampered by their substantial data requirements and confinement to specific training domains. This prevents them from transitioning to new tasks where data is scarce. Therefore, domain adaptation emerges as a critical element for the practical implementation of DL in real-world scenarios. Herein, we introduce A-VBANet, a novel meta-learning model capable of delivering domain-agnostic skill assessment via one-shot learning. Our methodology has been tested by assessing surgical skills on five laparoscopic and robotic simulators and real-life laparoscopic cholecystectomy. Our model successfully adapted with accuracies up to 99.5% in one-shot and 99.9% in few-shot settings for simulated tasks and 89.7% for laparoscopic cholecystectomy. This study marks the first instance of a domain-agnostic methodology for skill assessment in critical fields setting a precedent for the broad application of DL across diverse real-life domains with limited data.
Message Passing-Based 9-D Cooperative Localization and Navigation with Embedded Particle Flow
Authors: Lukas Wielandner, Erik Leitinger, Florian Meyer, Klaus Witrisal
Date: 2023-01-03 00:00:00

Cooperative localization (CL) is an important technology for innovative services such as location-aware communication networks, modern convenience, and public safety. We consider wireless networks with mobile agents that aim to localize themselves by performing pairwise measurements amongst agents and exchanging their location information. Belief propagation (BP) is a state-of-the-art Bayesian method for CL. In CL, particle-based implementations of BP often are employed that can cope with non-linear measurement models and state dynamics. However, particle-based BP algorithms are known to suffer from particle degeneracy in large and dense networks of mobile agents with high-dimensional states. This paper derives the messages of BP for CL by means of particle flow, leading to the development of a distributed particle-based message-passing algorithm which avoids particle degeneracy. Our combined particle flow-based BP approach allows the calculation of highly accurate proposal distributions for agent states with a minimal number of particles. It outperforms conventional particle-based BP algorithms in terms of accuracy and runtime. Furthermore, we compare the proposed method to a centralized particle flow-based implementation, known as the exact Daum-Huang filter, and to sigma point BP in terms of position accuracy, runtime, and memory requirement versus the network size. We further contrast all methods to the theoretical performance limit provided by the posterior Cram\'er-Rao lower bound (PCRLB). Based on three different scenarios, we demonstrate the superiority of the proposed method.
A Laplacian Gaussian Mixture Model for Surface EMG Signals of Human Arm Activity
Authors: Durgesh Kusuru, Anish C. Turlapaty, Mainak Thakur
Date: 2023-01-03 00:00:00

The probability density function (pdf) of surface Electromyography (sEMG) signals follows any one of the standalone standard distributions: the Gaussian or the Laplacian. Further, the choice of the model is dependent on muscle contraction force (MCF) levels. Hence, a unified model is proposed which explains the statistical nature of sEMG signals at different MCF levels. In this paper, we propose the Laplacian Gaussian Mixture (LGM) model for the signals recorded from upper limbs. This model is able to explain the sEMG signals from different activities corresponding to different MCF levels. The model is tested on different bench-mark sEMG data sets and is validated using both the qualitative and quantitative perspectives. It is determined that for low and medium contraction force levels the proposed mixture model is more accurate than both the Laplacian and the Gaussian models. Whereas for high contraction force level, the LGM model behaves as a Gaussian model. The mixing weights of the LGM model are analyzed and it is observed that for low and medium MCF levels both the mixing weights of LGM model do contribute. Whereas for high contraction force levels the Laplacian weight becomes weaker. The proposed LGM model for sEMG signals from upper limbs explains sEMG signals at different MCF levels. The proposed model helps in improved understanding of statistical nature of sEMG signals and better feature representation in the classification problems.
Modeling and Analysis of OFDM-based 5G/6G Localization under Hardware Impairments
Authors: Hui Chen, Musa Furkan Keskin, Sina Rezaei Aghdam, Hyowon Kim, Simon Lindberg, Andreas Wolfgang, Traian E. Abrudan, Thomas Eriksson, Henk Wymeersch
Date: 2023-01-03 00:00:00

Localization is envisioned as a key enabler to satisfy the requirements of communication and context-aware services in the fifth/sixth generation (5G/6G) communication systems. User localization can be achieved based on delay and angle estimation using uplink/downlink pilot signals. However, hardware impairments (HWIs) (such as phase noise and mutual coupling) distort the signals at both the transmitter and receiver sides and thus affect the localization performance. While this impact can be ignored at lower frequencies with less severe HWIs, and less stringent localization requirements, modeling and analysis efforts are needed for high-frequency bands to assess degradation in localization accuracy due to HWIs. In this work, we model various types of impairments for a mmWave multiple-input-multiple-output communication system and conduct a misspecified Cram\'er-Rao bound analysis to evaluate HWI-induced performance losses in terms of angle/delay estimation and the resulting 3D position/orientation estimation error. We also investigate the effect of individual and overall HWIs on communication in terms of symbol error rate (SER). Our extensive simulation results demonstrate that each type of HWI leads to a different level of degradation in angle and delay estimation performance, and the prominent impairment factors on delay estimation will have a dominant negative effect on SER.