pwc-archive/papers-with-abstracts huggingface dataset:
Metrics matter, a Formal comment on Ward et al Plos-One 2016 paper : Is decoupling GDP growth from environmental impact possible?
Authors: Hervé Bercegol, Paul E. Brockway
Date: 2024-01-22 00:00:00
Tasks: valid

The Ward et al. (2016) Plos-One paper is an important, heavily-cited paper in the decoupling literature. The authors present evidence of 1990-2015 growth in material and energy consumption and GDP at a world level, and for selected countries. They find only relative decoupling has occurred, leading to their central claim that future absolute decoupling is implausible. However, the authors have made two key errors in their collected data: GDP data is in current prices which includes inflation, and their global material use data is the total mass of fossil energy materials. Strictly, GDP data should be in constant prices to allow for its comparison over time, and material inputs to an economy should be the sum of mineral raw materials. Amending for these errors, we find much smaller levels of energy-GDP relative decoupling, and no materials-GDP decoupling at all at a global level. We check these new results by adding data for 1900-1990 to provide a longer time series, and find consistently low (and even no) levels of global relative decoupling of material use. The central claim for materials over the implausibility of future absolute decoupling therefore not only remains valid but is reinforced by the corrected datasets.
Analyzing the Impact of Financial Inclusion on Economic Growth in Bangladesh
Authors: Ganapati Kumar Biswas
Date: 2024-01-21 00:00:00
Tasks: Time Series

Financial inclusion is touted one of the principal drivers for economic growth for an economy. The study aims to explore the impact of financial inclusion on economic growth in Bangladesh. In my study, I used the number of loan accounts as the proxy for financial inclusion. Using time series data from spans from 2004-2021, the study revealed that there exists a long-run relationship between GDP, financial inclusion, and other macroeconomic variables in Bangladesh. The study also found that financial inclusion had a positive impact on economic growth of Bangladesh during the study period. Therefore, the policymakers and the central bank of Bangladesh as the apex authority of financial system should promote financial inclusion activities to achieve sustainable economic growth.
Fake Google restaurant reviews and the implications for consumers and restaurants
Authors: Shawn Berry
Date: 2024-01-20 00:00:00
Tasks: Decision Making

The use of online reviews to aid with purchase decisions is popular among consumers as it is a simple heuristic tool based on the reported experiences of other consumers. However, not all online reviews are written by real consumers or reflect actual experiences, and present implications for consumers and businesses. This study examines the effects of fake online reviews written by artificial intelligence (AI) on consumer decision making. Respondents were surveyed about their attitudes and habits concerning online reviews using an online questionnaire (n=351), and participated in a restaurant choice experiment using varying proportions of fake and real reviews. While the findings confirm prior studies, new insights are gained about the confusion for consumers and consequences for businesses when reviews written by AI are believed rather than real reviews. The study presents a fake review detection model using logistic regression modeling to score and flag reviews as a solution.
An income-based approach to modeling commuting distance in the Toronto area
Authors: Shawn Berry
Date: 2024-01-20 00:00:00

The purpose of this article is to propose a novel model of the effects of changes in shelter and driving costs on car commuting distances in the overheated Toronto housing market from 2011 to 2016. The model borrows from theoretical concepts of microeconomics and urban geography to examine the Toronto housing market. Using 2011 and 2016 Census data for census metropolitan areas (CMAs) and census agglomerations (CAs) in Southern Ontario and computed driving costs, the model of car commuting distance is based on variables of allocation of monthly household income to monthly shelter costs and driving costs as a function of the car driving distance to Toronto. Using this model, we can predict the effect on car commuting distance due to changes in any of the variables. The model also offers an explanation for communities of Toronto car commuters beyond a driving radius that we might expect for daily commuting. The model confirms that increases in shelter costs in the Toronto housing market from 2011 to 2016 have forced the boundaries of feasible housing locations outward, and forced households to move farther away, thus increasing car commuting distance.
Long-term Effects of India's Childhood Immunization Program on Earnings and Consumption Expenditure: Comment
Authors: David Roodman
Date: 2024-01-20 00:00:00
Tasks: Survey

Summan, Nandi, and Bloom (2023), hereafter SNB, find that exposure during infancy to India's Universal Immunization Programme (UIP) increased wages and per-capita household expenditure in early adulthood. SNB regress these outcomes on a treatment indicator that depends upon year and district of birth while controlling for age at follow-up. Because year of birth and age are nearly collinear, SNB's identifying variation does not come from the staggered introduction of the UIP, but rather from the progression of time during the follow-up period. Within the 12-month follow-up period, those interviewed later were more likely to have been treated and, on average, reported higher wages and household expenditure. Wages and household expenditure, however, rose by at least as much in a control group composed of people too old to have been exposed as infants to the UIP as in the treated group. SNB's results are best explained by inflation, economic growth, and non-random survey sequencing during the follow-up survey period.
Unambiguous Efficiency of Random Allocations
Authors: Eun Jeong Heo, Vikram Manjunath, Samson Alva
Date: 2024-01-22 00:00:00
Tasks: Fairness

In the problem of allocating indivisible objects via lottery, a social planner often knows only agents' ordinal preferences over objects, but not their complete preferences over lotteries. Such an informationally constrained planner cannot distinguish between different utility profiles that induce the same rankings over the objects. In this context, we ask what it means to adjudge an allocation as efficient. We introduce the concept of unambiguous efficiency, which guarantees no further Pareto improvement regardless of how agents' ordinal preferences extend to lotteries. We compare this concept with the predominant formulation of efficiency in the random allocation literature and explore some structural properties. As an application to mechanism design, we characterize the class of efficient and strategy-proof ordinal mechanisms that satisfy certain regularity conditions.
Coevolution of Resource and Strategies in Common-Pool Resource Dilemmas: A Coupled Human-Environmental System Model
Authors: Chengyi Tu, Renfei Chen, Ying Fan, YongLiang Yang
Date: 2024-01-20 00:00:00

Common-pool resource governance requires users to cooperate and avoid overexploitation, but defection and free-riding often undermine cooperation. We model a human-environmental system that integrates dynamics of resource and users' strategies. The resource follows a logistic function that depends on natural growth rate, carrying capacity, and extraction rates of cooperators and defectors. The users' strategies evolve according to different processes that capture effects of payoff, resource, and noise. We analyze the feedback between resource availability and strategic adaptation, and explores the conditions for the emergence and maintenance of cooperation. We find different processes lead to different regimes of equilibrium solutions and resource levels depending on the parameter configuration and initial conditions. We also show that some processes can enhance the sustainability of the resource by making the users more responsive to the resource scarcity. The paper advances the understanding of human-environmental system and offers insights for resource governance policies and interventions.
ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter
Authors: Yi-Chiao Wu, Dejan Marković, Steven Krenn, Israel D. Gebru, Alexander Richard
Date: 2024-01-22 00:00:00
Tasks: Generative Adversarial Network

Although recent mainstream waveform-domain end-to-end (E2E) neural audio codecs achieve impressive coded audio quality with a very low bitrate, the quality gap between the coded and natural audio is still significant. A generative adversarial network (GAN) training is usually required for these E2E neural codecs because of the difficulty of direct phase modeling. However, such adversarial learning hinders these codecs from preserving the original phase information. To achieve human-level naturalness with a reasonable bitrate, preserve the original phase, and get rid of the tricky and opaque GAN training, we develop a score-based diffusion post-filter (SPF) in the complex spectral domain and combine our previous AudioDec with the SPF to propose ScoreDec, which can be trained using only spectral and score-matching losses. Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information.
ATFusion: An Alternate Cross-Attention Transformer Network for Infrared and Visible Image Fusion
Authors: Han Yan, Songlei Xiong, Long Wang, Lihua Jian, Gemine Vivone
Date: 2024-01-22 00:00:00
Tasks: Infrared And Visible Image Fusion

The fusion of infrared and visible images is essential in remote sensing applications, as it combines the thermal information of infrared images with the detailed texture of visible images for more accurate analysis in tasks like environmental monitoring, target detection, and disaster management. The current fusion methods based on Transformer techniques for infrared and visible (IV) images have exhibited promising performance. However, the attention mechanism of the previous Transformer-based methods was prone to extract common information from source images without considering the discrepancy information, which limited fusion performance. In this paper, by reevaluating the cross-attention mechanism, we propose an alternate Transformer fusion network (ATFusion) to fuse IV images. Our ATFusion consists of one discrepancy information injection module (DIIM) and two alternate common information injection modules (ACIIM). The DIIM is designed by modifying the vanilla cross-attention mechanism, which can promote the extraction of the discrepancy information of the source images. Meanwhile, the ACIIM is devised by alternately using the vanilla cross-attention mechanism, which can fully mine common information and integrate long dependencies. Moreover, the successful training of ATFusion is facilitated by a proposed segmented pixel loss function, which provides a good trade-off for texture detail and salient structure preservation. The qualitative and quantitative results on public datasets indicate our ATFusion is effective and superior compared to other state-of-the-art methods.
Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding
Authors: Yichi Zhang, Zhihao Duan, Ming Lu, Dandan Ding, Fengqing Zhu, Zhan Ma
Date: 2024-01-21 00:00:00
Tasks: Clustering, Image Compression, MS-SSIM, Quantization, SSIM

While convolution and self-attention are extensively used in learned image compression (LIC) for transform coding, this paper proposes an alternative called Contextual Clustering based LIC (CLIC) which primarily relies on clustering operations and local attention for correlation characterization and compact representation of an image. As seen, CLIC expands the receptive field into the entire image for intra-cluster feature aggregation. Afterward, features are reordered to their original spatial positions to pass through the local attention units for inter-cluster embedding. Additionally, we introduce the Guided Post-Quantization Filtering (GuidedPQF) into CLIC, effectively mitigating the propagation and accumulation of quantization errors at the initial decoding stage. Extensive experiments demonstrate the superior performance of CLIC over state-of-the-art works: when optimized using MSE, it outperforms VVC by about 10% BD-Rate in three widely-used benchmark datasets; when optimized using MS-SSIM, it saves more than 50% BD-Rate over VVC. Our CLIC offers a new way to generate compact representations for image compression, which also provides a novel direction along the line of LIC development.
Image detection using combinatorial auction
Authors: Simon Anuk, Tamir Bendory, Amichai Painsky
Date: 2024-01-21 00:00:00

This paper studies the optimal solution of the classical problem of detecting the location of multiple image occurrences in a two-dimensional, noisy measurement. Assuming the image occurrences do not overlap, we formulate this task as a constrained maximum likelihood optimization problem. We show that the maximum likelihood estimator is equivalent to an instance of the winner determination problem from the field of combinatorial auction and that the solution can be obtained by searching over a binary tree. We then design a pruning mechanism that significantly accelerates the runtime of the search. We demonstrate on simulations and electron microscopy data sets that the proposed algorithm provides accurate detection in challenging regimes of high noise levels and densely packed image occurrences.
RoTIR: Rotation-Equivariant Network and Transformers for Fish Scale Image Registration
Authors: Ruixiong Wang, Alin Achim, Renata Raele-Rolfe, Qiao Tong, Dylan Bergen, Chrissy Hammond, Stephen Cross
Date: 2024-01-20 00:00:00
Tasks: Deep Learning, Image Registration

Image registration is an essential process for aligning features of interest from multiple images. With the recent development of deep learning techniques, image registration approaches have advanced to a new level. In this work, we present 'Rotation-Equivariant network and Transformers for Image Registration' (RoTIR), a deep-learning-based method for the alignment of fish scale images captured by light microscopy. This approach overcomes the challenge of arbitrary rotation and translation detection, as well as the absence of ground truth data. We employ feature-matching approaches based on Transformers and general E(2)-equivariant steerable CNNs for model creation. Besides, an artificial training dataset is employed for semi-supervised learning. Results show RoTIR successfully achieves the goal of fish scale image registration.
Susceptibility of Adversarial Attack on Medical Image Segmentation Models
Authors: Zhongxuan Wang, Leo Xu
Date: 2024-01-20 00:00:00
Tasks: Adversarial Attack, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

The nature of deep neural networks has given rise to a variety of attacks, but little work has been done to address the effect of adversarial attacks on segmentation models trained on MRI datasets. In light of the grave consequences that such attacks could cause, we explore four models from the U-Net family and examine their responses to the Fast Gradient Sign Method (FGSM) attack. We conduct FGSM attacks on each of them and experiment with various schemes to conduct the attacks. In this paper, we find that medical imaging segmentation models are indeed vulnerable to adversarial attacks and that there is a negligible correlation between parameter size and adversarial attack success. Furthermore, we show that using a different loss function than the one used for training yields higher adversarial attack success, contrary to what the FGSM authors suggested. In future efforts, we will conduct the experiments detailed in this paper with more segmentation models and different attacks. We will also attempt to find ways to counteract the attacks by using model ensembles or special data augmentations. Our code is available at https://github.com/ZhongxuanWang/adv_attk
Detection of Thermal Events by Semi-Supervised Learning for Tokamak First Wall Safety
Authors: Christian Staron, Hervé Le Borgne, Raphaël Mitteau, Erwan Grelier, Nicolas Allezard
Date: 2024-01-19 00:00:00
Tasks: object-detection, Object Detection, Semi-Supervised Object Detection

This paper explores a semi-supervised object detection approach to detect hot spots on the internal wall of Tokamaks. A huge amount of data is produced during an experimental campaign by the infrared (IR) viewing systems used to monitor the inner thermal shields during machine operation. The amount of data to be processed and analysed is such that protecting the first wall is an overwhelming job. Automatizing this job with artificial intelligence (AI) is an attractive solution, but AI requires large labelled databases which are not readily available for Tokamak walls. Semi-supervised learning (SSL) is a possible solution to being able to train deep learning models with a small amount of labelled data and a large amount of unlabelled data. SSL is explored as a possible tool to rapidly adapt a model trained on an experimental campaign A of Tokamak WEST to a new experimental campaign B by using labelled data from campaign A, a little labelled data from campaign B and a lot of unlabelled data from campaign B. Model performances are evaluated on two labelled datasets and two methods including semi-supervised learning. Semi-supervised learning increased the mAP metric by over six percentage points on the first smaller scale database and over four percentage points on the second larger scale dataset depending on the employed method.
Efficient Resource Allocation and User Association in NOMA-Enabled Vehicular-Aided HetNets with High Altitude Platforms
Authors: Ali Nauman, Mashael Maashi, Hend K. Alkahtani, Fahd N. Al-Wesabi, Nojood O Aljehane, Mohammed Assiri, Sara Saadeldeen Ibrahim, Wali Ullah Khan
Date: 2024-01-22 00:00:00

The increasing demand for massive connectivity and high data rates has made the efficient use of existing spectrum resources an increasingly challenging problem. Non-orthogonal multiple access (NOMA) is a potential solution for future heterogeneous networks (HetNets) due to its high capacity and spectrum efficiency. In this study, we analyze an uplink NOMA-enabled vehicular-aided HetNet, where multiple vehicular user equipment (VUEs) share the access link spectrum, and a high-altitude platform (HAP) communicates with roadside units (RSUs) through a backhaul communication link. We propose an improved algorithm for user association that selects VUEs for HAPs based on channel coefficient ratios and terrestrial VUEs based on a caching-state backhaul communication link. The joint optimization problems aim to maximize a utility function that considers VUE transmission rates and cross-tier interference while meeting the constraints of backhaul transmission rates and QoS requirements of each VUE. The joint resource allocation optimization problem consists of three sub-problems: bandwidth allocation, user association, and transmission power allocation. We derive a closed-form solution for bandwidth allocation and solve the transmission power allocation sub-problem iteratively using Taylor expansion to transform a non-convex term into a convex one. Our proposed three-stage iterative algorithm for resource allocation integrates all three sub-problems and is shown to be effective through simulation results. Specifically, the results demonstrate that our solution achieves performance improvements over existing approaches.
Fully Differentiable Ray Tracing via Discontinuity Smoothing for Radio Network Optimization
Authors: Jerome Eertmans, Laurent Jacques, Claude Oestges
Date: 2024-01-22 00:00:00

Recently, Differentiable Ray Tracing has been successfully applied in the field of wireless communications for learning radio materials or optimizing the transmitter orientation. However, in the frame of gradient based optimization, obstruction of the rays by objects can cause sudden variations in the related objective functions or create entire regions where the gradient is zero. As these issues can dramatically impact convergence, this paper presents a novel Ray Tracing framework that is fully differentiable with respect to any scene parameter, but also provides a loss function continuous everywhere, thanks to specific local smoothing techniques. Previously non-continuous functions are replaced by a smoothing function, that can be exchanged with any function having similar properties. This function is also configurable via a parameter that determines how smooth the approximation should be. The present method is applied on a basic one-transmitter-multi-receiver scenario, and shows that it can successfully find the optimal solution. As a complementary resource, a 2D Python library, DiffeRT2d, is provided in Open Access, with examples and a comprehensive documentation.
Optimizing performance in elastic optical networks using advanced reconfigurable optical add-drop multiplexers: A novel design approach and comprehensive analysis
Authors: Faranak Khosravi, Mehdi Shadaram
Date: 2024-01-22 00:00:00

Network operators diversify service offerings and enhance network efficiency by leveraging bandwidth-variable transceivers and colorless flexible-grid reconfigurable optical add-drop multiplexers (ROADMs). Nonetheless, the paradigm shift from rigid to elastic optical networks (EONs) has affected several key parameters, including bit rate, center frequency spacing, modulation format, and optical reach. This study investigated the transformative impact of emerging technologies on the design and structure of optical network architectures, including spectrally efficient multicarrier systems and bandwidth-variable wavelength-selective switches. A cost-effective ROADM architecture applying an order-based connecting approach was introduced, which presented a high connectivity level and a blockage probability of less than 10-4. When this architecture was implemented in the EON, the data transportation rate was 1 Tb/s. This outcome successfully accommodated a 20 % surge in traffic demand, while the optimized network architecture significantly improved fiber utilization by 3.4 %. Consequently, this study contributed a practical and efficient solution for implementing flexible optical networks, effectively addressing current concerns and propelling the optical communication system sector forward.
Battery-Free Sensor Array for Wireless Multi-Depth In-Situ Sensing
Authors: Hongzhi Guo, Adam Kamrath
Date: 2024-01-21 00:00:00

Underground in-situ sensing plays a vital role in precision agriculture and infrastructure monitoring. While existing sensing systems utilize wires to connect an array of sensors at various depths for spatial-temporal data collection, wireless underground sensor networks offer a cable-free alternative. However, these wireless sensors are typically battery-powered, necessitating periodic recharging or replacement. This paper proposes a battery-free sensor array which can be used for wireless multi-depth in-situ sensing. Utilizing Near Field Communication (NFC)-which can penetrate soil with negligible signal power loss-this sensor array can form a virtual magnetic waveguide, achieving long communication ranges. An analytical model has been developed to offer insights and determine optimal design parameters. Moreover, a prototype, constructed using off-the-shelf NFC sensors, was tested to validate the proposed concept. While this system is primarily designed for underground applications, it holds potential for other multi-depth in-situ sensing scenarios, including underwater environments.