pwc-archive/papers-with-abstracts huggingface dataset:
What drives the European carbon market? Macroeconomic factors and forecasts
Authors: Andrea Bastianin, Elisabetta Mirto, Yan Qin, Luca Rossini
Date: 2024-02-07 00:00:00

Putting a price on carbon -- with taxes or developing carbon markets -- is a widely used policy measure to achieve the target of net-zero emissions by 2050. This paper tackles the issue of producing point, direction-of-change, and density forecasts for the monthly real price of carbon within the EU Emissions Trading Scheme (EU ETS). We aim to uncover supply- and demand-side forces that can contribute to improving the prediction accuracy of models at short- and medium-term horizons. We show that a simple Bayesian Vector Autoregressive (BVAR) model, augmented with either one or two factors capturing a set of predictors affecting the price of carbon, provides substantial accuracy gains over a wide set of benchmark forecasts, including survey expectations and forecasts made available by data providers. We extend the study to verified emissions and demonstrate that, in this case, adding stochastic volatility can further improve the forecasting performance of a single-factor BVAR model. We rely on emissions and price forecasts to build market monitoring tools that track demand and price pressure in the EU ETS market. Our results are relevant for policymakers and market practitioners interested in monitoring the carbon market dynamics.
Monthly GDP nowcasting with Machine Learning and Unstructured Data
Authors: Juan Tenorio, Wilder Perez
Date: 2024-02-06 00:00:00
Tasks: Decision Making

In the dynamic landscape of continuous change, Machine Learning (ML) "nowcasting" models offer a distinct advantage for informed decision-making in both public and private sectors. This study introduces ML-based GDP growth projection models for monthly rates in Peru, integrating structured macroeconomic indicators with high-frequency unstructured sentiment variables. Analyzing data from January 2007 to May 2023, encompassing 91 leading economic indicators, the study evaluates six ML algorithms to identify optimal predictors. Findings highlight the superior predictive capability of ML models using unstructured data, particularly Gradient Boosting Machine, LASSO, and Elastic Net, exhibiting a 20% to 25% reduction in prediction errors compared to traditional AR and Dynamic Factor Models (DFM). This enhanced performance is attributed to better handling of data of ML models in high-uncertainty periods, such as economic crises.
Healthcare Quality by Specialists under a Mixed Compensation System: an Empirical Analysis
Authors: Damien Echevin, Bernard Fortin, Aristide Houndetoungan
Date: 2024-02-06 00:00:00

We analyze the effects of a mixed compensation (MC) scheme for specialists on the quality of their healthcare services. We exploit a reform implemented in Quebec (Canada) in 1999. The government introduced a payment mechanism combining a per diem with a reduced fee per clinical service. Using a large patient/physician panel dataset, we estimate a multi-state multi-spell hazard model analogous to a difference-in-differences approach. We compute quality indicators from our model. Our results suggest that the reform reduced the quality of MC specialist services measured by the risk of re-hospitalization and mortality after discharge. These effects vary across specialties.
Meritocracy and Its Discontents: Long-run Effects of Repeated School Admission Reforms
Authors: Chiaki Moriguchi, Yusuke Narita, Mari Tanaka
Date: 2024-02-06 00:00:00

What happens if selective colleges change their admission policies? We study this question by analyzing the world's first implementation of nationally centralized meritocratic admissions in the early twentieth century. We find a persistent meritocracy-equity tradeoff. Compared to the decentralized system, the centralized system admitted more high-achievers and produced more occupational elites (such as top income earners) decades later in the labor market. This gain came at a distributional cost, however. Meritocratic centralization also increased the number of urban-born elites relative to rural-born ones, undermining equal access to higher education and career advancement.
Effects of carbon pricing and other climate policies on CO2 emissions
Authors: Emanuel Kohlscheen, Richhild Moessner, Elod Takats
Date: 2024-02-06 00:00:00

We provide ex-post empirical analysis of the effects of climate policies on carbon dioxide emissions at the aggregate national level. Our results are based on a comprehensive database of 121 countries. As climate policies we examine carbon taxes and emissions trading systems (ETS), as well as the overall stringency of climate policies. We use dynamic panel regressions, controlling for macroeconomic factors such as economic development, GDP growth, urbanisation, as well as the energy mix. We find that higher carbon taxes and prices of permits in ETS reduce carbon emissions. An increase in carbon taxes by $10 per ton of CO2 reduces CO2 emissions per capita by 1.3% in the short run and by 4.6% in the long run.
Bride Kidnapping and Informal Governance Institutions
Authors: Zachary Porreca
Date: 2024-02-05 00:00:00
Tasks: Survey

Bride kidnapping is a form of forced marriage in which a woman is taken against her will and coerced into accepting marriage with her captor. Post-Soviet Kyrgyzstan has seen a large increase in the prominence of this practice alongside a revitalization of traditional values and culture. As part of this resurgence of Kyrgyz identity and culture, the central government has formalized the authority of councils of elders called aksakals as an arbitrator for local dispute resolution -- guided by informal principles of tradition and cultural norm adherence. Bride kidnapping falls within the domain of aksakal authority. In this study, I leverage data from a nationally representative survey and specify a latent class nested logit model of mens' marriage modality choice to analyze the impacts that aksakal governance has on the decision to kidnap. Based on value assessment questions on the survey, men are assigned to a probability distribution over latent class membership. Utility function parameters for each potential marriage modality are estimated for each latent class of men. Results suggest that living under aksakal governance makes men 9% more likely to obtain a wife through bride capture, with men substituting kidnapping for choice marriage modalities such as elopement and standard love marriages.
Progressive unsupervised domain adaptation for ASR using ensemble models and multi-stage training
Authors: Rehan Ahmad, Muhammad Umar Farooq, Thomas Hain
Date: 2024-02-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Domain Adaptation, speech-recognition, Speech Recognition, Unsupervised Domain Adaptation

In Automatic Speech Recognition (ASR), teacher-student (T/S) training has shown to perform well for domain adaptation with small amount of training data. However, adaption without ground-truth labels is still challenging. A previous study has shown the effectiveness of using ensemble teacher models in T/S training for unsupervised domain adaptation (UDA) but its performance still lags behind compared to the model trained on in-domain data. This paper proposes a method to yield better UDA by training multi-stage students with ensemble teacher models. Initially, multiple teacher models are trained on labelled data from read and meeting domains. These teachers are used to train a student model on unlabelled out-of-domain telephone speech data. To improve the adaptation, subsequent student models are trained sequentially considering previously trained model as their teacher. Experiments are conducted with three teachers trained on AMI, WSJ and LibriSpeech and three stages of students on SwitchBoard data. Results shown on eval00 test set show significant WER improvement with multi-stage training with an absolute gain of 9.8%, 7.7% and 3.3% at each stage.
Arbitrary Scale Super-Resolution Assisted Lunar Crater Detection in Satellite Images
Authors: Atal Tewari, Nitin Khanna
Date: 2024-02-07 00:00:00
Tasks: Super-Resolution

Craters are one of the most studied planetary features used for different scientific analyses, such as estimation of surface age and surface processes. Satellite images utilized for crater detection often have low resolution (LR) due to hardware constraints and transmission time. Super-resolution (SR) is a practical and cost-effective solution; however, most SR approaches work on fixed integer scale factors, i.e., a single model can generate images of a specific resolution. In practical applications, SR on multiple scales provides various levels of detail, but training for each scale is resource-intensive. Therefore, this paper proposes a system for crater detection assisted with an arbitrary scale super-resolution approach (i.e., a single model can be used for multiple scale factors) for the lunar surface. Our work is composed of two subsystems. The first sub-system employs an arbitrary scale SR approach to generate super-resolved images of multiple resolutions. Subsequently, the second sub-system passes super-resolved images of multiple resolutions to a deep learning-based crater detection framework for identifying craters on the lunar surface. Employed arbitrary scale SR approach is based on a combination of convolution and transformer modules. For the crater detection sub-system, we utilize the Mask-RCNN framework. Using SR images of multiple resolutions, the proposed system detects 13.47% more craters from the ground truth than the craters detected using only the LR images. Further, in complex crater settings, specifically in overlapping and degraded craters, 11.84% and 15.01% more craters are detected as compared to the crater detection networks using only the LR images. The proposed system also leads to better localization performance, 3.19% IoU increment compared to the LR images
Perceptually Equivalent Resolution in Handheld Devices for Streaming Bandwidth Saving
Authors: Mateo Cámara, César Díaz, Juan Casal, Jorge Ruano, Narciso García
Date: 2024-02-07 00:00:00

We present the description, results, and analysis of the experiments conducted to find the equivalent resolution associated with handheld devices. That is, the resolution from which users stop perceiving quality improvements if better resolutions are presented to them in such devices. Thus, it is the maximum resolution that it is worth considering for generating and delivering video, as long as sequences are not too intensively compressed. Therefore, the detection of the equivalent resolutions allows for notable savings in bandwidth consumption. Subjective assessments have been carried out on fifty subjects using a set of video sequences of very different nature and four handheld devices with a broad range of screen dimensions. The results prove that the equivalent resolution in current handheld devices is 720p as higher resolutions are not valued by users.
Subjective performance evaluation of bitrate allocation strategies for MPEG and JPEG Pleno point cloud compression
Authors: Davi Lazzarotto, Michela Testolina, Touradj Ebrahimi
Date: 2024-02-07 00:00:00

The recent rise in interest in point clouds as an imaging modality has motivated standardization groups such as MPEG and JPEG Pleno to launch activities aiming at developing compression standards for point clouds. Lossy compression usually introduces visual artifacts that negatively impact the perceived quality of media, which can only be reliably measured through subjective visual quality assessment experiments. While MPEG standards have been subjectively evaluated in previous studies on multiple occasions, no work has yet assessed the performance of the recent JPEG Pleno standard in comparison to them. In this study, a comprehensive performance evaluation of MPEG and JPEG Pleno standards for point cloud compression is conducted. The impact of different configuration parameters on the performance of the codecs is first analyzed with the help of objective quality metrics. The results from this analysis are used to define three rate allocation strategies for each codec, which are employed to compress a set of point clouds at four target rates. The set of distorted point clouds is then subjectively evaluated following two subjective quality assessment protocols. Finally, the obtained results are used to compare the performance of these compression standards and draw insights about best coding practices.
Application-Layer FEC Scheme Configuration Optimization via Hybrid Simulated Annealing
Authors: César Díaz, Julián Cabrera, Fernando Jaureguizar, Narciso García
Date: 2024-02-07 00:00:00

An optimization technique based on an adapted combination of simulated annealing (SA) and tabu search (TS) is presented. This method aims at finding near-optimal unequal error protection (UEP) application-layer FEC code configurations. This approach is intended to smartly protect audio and video transmission over IP networks when hard time restrictions apply. The considered code is a UEP version of the widely-used Pro-MPEG COP3 codes enabling the use of several matrices of dissimilar size and thus of unequal recovery capability. Finding the optimal configuration frequently requires the evaluation of a large solution space. So, to fulfill the imposed constraints, SA is adapted to the specifics of the scenario. In particular, the annealing schedule is conditioned by the real-time restrictions. Furthermore, solution neighborhood structures are determined by a proposed definition of distance between protection configurations, which, jointly with TS, conditions the selection of candidate solutions. Experimental results show a significantly improved performance of the optimization process, which invariably fulfills imposed timing constraints, at the expense of a very low distortion increase, when compared to using exhaustive search. These results allow the use of UEP Pro-MPEG COP3 codes for protecting video and audio transmission, which distinctly outperforms the standard code in a wide range of scenarios.
A Video-Aware FEC-Based Unequal Loss Protection System for Video Streaming over RTP
Authors: César Díaz, Julián Cabrera, Fernando Jaureguizar, Narciso García
Date: 2024-02-07 00:00:00

A video-aware unequal loss protection (ULP) system for protecting RTP video streaming in bursty packet loss networks is proposed. Considering the relevance of the frame, the state of the channel, and the bitrate constraints of the protection bitstream, our algorithm selects in real time the most suitable frames to be protected through forward error protection (FEC) techniques. It benefits from a wise RTP encapsulation that allows working at a frame level without requiring any further process than that of parsing RTP headers. This makes our system straightforward and fast, perfectly suitable to be included in commercial video streaming servers. Simulation results show how our technique outperforms other proposed ULP schemes.
Streamlined Hybrid Annotation Framework using Scalable Codestream for Bandwidth-Restricted UAV Object Detection
Authors: Karim El Khoury, Tiffanie Godelaine, Simon Delvaux, Sebastien Lugan, Benoit Macq
Date: 2024-02-07 00:00:00
Tasks: Decision Making, object-detection, Object Detection

Emergency response missions depend on the fast relay of visual information, a task to which unmanned aerial vehicles are well adapted. However, the effective use of unmanned aerial vehicles is often compromised by bandwidth limitations that impede fast data transmission, thereby delaying the quick decision-making necessary in emergency situations. To address these challenges, this paper presents a streamlined hybrid annotation framework that utilizes the JPEG 2000 compression algorithm to facilitate object detection under limited bandwidth. The proposed framework employs a fine-tuned deep learning network for initial image annotation at lower resolutions and uses JPEG 2000's scalable codestream to selectively enhance the image resolution in critical areas that require human expert annotation. We show that our proposed hybrid framework reduces the response time by a factor of 34 in emergency situations compared to a baseline approach.
On the Modelling of Ship Wakes in S-Band SAR Images and an Application to Ship Identification
Authors: Kamirul Kamirul, Odysseas Pappas, Igor G. Rizaev, Alin Achim
Date: 2024-02-06 00:00:00
Tasks: Dielectric Constant

We present a novel ship wake simulation system for generating S-band Synthetic Aperture Radar (SAR) images, and demonstrate the use of such imagery for the classification of ships based on their wake signatures via a deep learning approach. Ship wakes are modeled through the linear superposition of wind-induced sea elevation and the Kelvin wakes model of a moving ship. Our SAR imaging simulation takes into account frequency-dependent radar parameters, i.e., the complex dielectric constant ($\varepsilon$) and the relaxation rate ($\mu$) of seawater. The former was determined through the Debye model while the latter was estimated for S-band SAR based on pre-existing values for the L, C, and X-bands. The results show good agreement between simulated and real imagery upon visual inspection. The results of implementing different training strategies are also reported, showcasing a notable improvement in accuracy of classifier achieved by integrating real and simulated SAR images during the training.
Transformer based Endmember Fusion with Spatial Context for Hyperspectral Unmixing
Authors: R. M. K. L. Ratnayake, D. M. U. P. Sumanasekara, H. M. K. D. Wickramathilaka, G. M. R. I. Godaliyadda, M. P. B. Ekanayake, H. M. V. R. Herath
Date: 2024-02-06 00:00:00
Tasks: Hyperspectral Unmixing

In recent years, transformer-based deep learning networks have gained popularity in Hyperspectral (HS) unmixing applications due to their superior performance. The attention mechanism within transformers facilitates input-dependent weighting and enhances contextual awareness during training. Drawing inspiration from this, we propose a novel attention-based Hyperspectral Unmixing algorithm called Transformer-based Endmember Fusion with Spatial Context for Hyperspectral Unmixing (FusionNet). This network leverages an ensemble of endmembers for initial guidance, effectively addressing the issue of relying on a single initialization. This approach helps avoid suboptimal results that many algorithms encounter due to their dependence on a singular starting point. The FusionNet incorporates a Pixel Contextualizer (PC), introducing contextual awareness into abundance prediction by considering neighborhood pixels. Unlike Convolutional Neural Networks (CNNs) and traditional Transformer-based approaches, which are constrained by specific kernel or window shapes, the Fusion network offers flexibility in choosing any arbitrary configuration of the neighborhood. We conducted a comparative analysis between the FusionNet algorithm and eight state-of-the-art algorithms using three widely recognized real datasets and one synthetic dataset. The results demonstrate that FusionNet offers competitive performance compared to the other algorithms.
A Comprehensive Approach to Diagnosing Temporomandibular Joint Diseases: AI-driven TMD Diagnostic System
Authors: Y. Gua, C. T. Kong, D. D Zhangc, Y. J Baid, J. K. H. Tsoia, Hua Huangc, Y. Q. Dengc, Y. M Zhue
Date: 2024-02-05 00:00:00
Tasks: Diagnostic, Segmentation

AI-driven TMD diagnostic system uses AI segmentation method to diagnose Temporomandibular Joint Disorders (TMD). By using segmentation, three important parts: temporal bone, temporomandibular joint (TMJ) disc and the condyle can be identified. The location and the size of each segment are used as the basic information to determine if the patient has a high chance of having Temporomandibular Joint Disorders (TMD).
Artificial Intelligence in Image-based Cardiovascular Disease Analysis: A Comprehensive Survey and Future Outlook
Authors: Xin Wang, Hongtu Zhu
Date: 2024-02-04 00:00:00

Recent advancements in Artificial Intelligence (AI) have significantly influenced the field of Cardiovascular Disease (CVD) analysis, particularly in image-based diagnostics. Our paper presents an extensive review of AI applications in image-based CVD analysis, offering insights into its current state and future potential. We systematically categorize the literature based on the primary anatomical structures related to CVD, dividing them into non-vessel structures (such as ventricles and atria) and vessel structures (including the aorta and coronary arteries). This categorization provides a structured approach to explore various imaging modalities like Magnetic Resonance Imaging (MRI), which are commonly used in CVD research. Our review encompasses these modalities, giving a broad perspective on the diverse imaging techniques integrated with AI for CVD analysis. Additionally, we compile a list of publicly accessible cardiac image datasets and code repositories, intending to support research reproducibility and facilitate data and algorithm sharing within the community. We conclude with an examination of the challenges and limitations inherent in current AI-based CVD analysis methods and suggest directions for future research to overcome these hurdles.
Measurements and Analysis of Temporal and Spatial Variability of WiFi Exposure Levels in the 2.4 GHz Frequency Band
Authors: Marta Fernandez, David Guerra, Unai Gil, Inigo Trigo, Ivan Pena, Amaia Arrinda
Date: 2024-02-07 00:00:00

This paper presents an evaluation of the WiFi exposure levels inside the university in the 2.4 GHz frequency band. The selected environment is the typical scenario where WiFi exposure concerns have increased in the last years, since a Wireless Local Area Network is deployed close to the users. Measurements of 1 h and 24 h of duration were performed to assess the temporal and spatial variability of the signal. Two instruments were employed, a spectrum analyzer appropriate configured for recording accurate and realistic samples and an exposimeter. A detailed description of the equipment, the measurement procedure and data analysis is provided in order to allow the reproducibility of these types of measurements. Finally, a comparison of the WiFi levels obtained by other authors is presented, concluding that all these methods are useful for determining WiFi exposure distribution, but if more accurate results are required, professional equipment appropriately configured should be used.
Collaborative Computing in Non-Terrestrial Networks: A Multi-Time-Scale Deep Reinforcement Learning Approach
Authors: Yang Cao, Shao-Yu Lien, Ying-Chang Liang, Dusit Niyato, Xuemin, Shen
Date: 2024-02-07 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning

Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks (NTNs) has been the most promising paradigm to enable global coverage. The limited computing capabilities on LEO satellites however render tackling resource optimization within a short duration a critical challenge. Although the sufficient computing capabilities of the ground infrastructures can be utilized to assist the LEO satellite, different time-scale control cycles and coupling decisions between the space- and ground-segments still obstruct the joint optimization design for computing agents at different segments. To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles. Specifically, the UE updates its policy toward improving value functions of both the satellite and UE, while the LEO satellite only performs finite-step rollout for decision-makings based on the reference decision trajectory provided by the UE. Most importantly, rigorous analysis to guarantee the performance convergence of the proposed scheme is provided. Comprehensive simulations are conducted to justify the effectiveness of the proposed scheme in balancing the transmission performance and computational complexity.