pwc-archive/papers-with-abstracts huggingface dataset:
Bitcoin and Shadow Exchange Rates
Authors: Yanan Niu, Ilja Kantorovitch
Date: 2024-10-29 00:00:00

This research expands the existing literature on Bitcoin (BTC) price misalignments by incorporating transaction-level data from a peer-to-peer (P2P) exchange, LocalBitcoins.com (LB). It examines how broader economic and regulatory factors influence cryptocurrency markets and highlights the role of cryptocurrencies in facilitating international capital movements. By constructing shadow exchange rates (SERs) for national currencies against the US dollar based on BTC prices, we calculate discrepancies between these SERs and their official exchange rates (OERs), referred to as BTC premiums. We analyze various factors driving the BTC premiums on LB, including those sourced from the BTC blockchain, mainstream centralized BTC exchanges, and international capital transfer channels. Unlike in centralized markets, our results indicate that the microstructure of the BTC blockchain does not correlate with BTC premiums in the P2P market. Regarding frictions from international capital transfers, we interpret remittance costs as indicators of inefficiencies in traditional capital transfer systems. For constrained currencies subject to severe capital controls and managed exchange rate regimes, increased transaction costs in conventional currency exchange channels almost entirely translate into higher BTC premiums. Additionally, our analysis suggests that BTC premiums can serve as short-term predictors of future exchange rate depreciation for unconstrained currencies.
Why is it so hard to find a job now? Enter Ghost Jobs
Authors: Hunter Ng
Date: 2024-10-29 00:00:00

This study investigates the emerging phenomenon of "ghost hiring" or "ghost jobs", where employers advertise job openings without intending to fill them. Using a novel dataset from Glassdoor and employing a LLM-BERT technique, I find that up to 21% of job ads may be ghost jobs, and this is particularly prevalent in specialized industries and in larger firms. The trend could be due to the low marginal cost of posting additional job ads and to maintain a pipeline of talents. After adjusting for yearly trends, I find that ghost jobs can explain the recent disconnect in the Beveridge Curve in the past fifteen years. The results show that policy-makers should be aware of such a practice as it causes significant job fatigue and distorts market signals.
Safety and Security Dynamics in Gulf Cooperation Council (GCC) Countries: A Machine Learning Approach to Forecasting Security Trends
Authors: Mahdi Goldani
Date: 2024-10-28 00:00:00
Tasks: feature selection

The GCC region includes Saudi Arabia, UAE, Bahrain, Kuwait, Qatar, and Oman, which are of critical geopolitical and economic importance, being rich in oil and positioned along vital maritime routes. However, the region faces complex security challenges, ranging from traditional threats like interstate conflicts to nontraditional risks such as cyber-attacks, piracy, and environmental concerns. This study investigates the safety and security index for six GCC countries using machine learning techniques, specifically XGBoost, to forecast security trends for the next five years. Data from the Global Peace Index and World Bank development indicators were employed to construct the model. Key indicators related to economic, political, and environmental factors were selected using the Edit Distance on Real Sequence feature selection method. The model demonstrated high accuracy, with a mean absolute percentage error of less than 10% across all countries. The results indicate that Bahrain and Saudi Arabia are likely to experience improvements in their safety and security indexes. At the same time, Kuwait and Oman may face challenges in maintaining their current levels of security. The findings suggest that economic diversification, environmental sustainability, and social stability are critical for ensuring long-term security in the region. This study provides valuable insights for policymakers in designing proactive strategies to address emerging security threats.
American Views About Election Fraud in 2024
Authors: Mitchell Linegar, R. Michael Alvarez
Date: 2024-10-25 00:00:00

What are the opinions of American registered voters about election fraud and types of election fraud as we head into the final stages of the 2024 Presidential election? In this paper we use data from an online national survey of 2,211 U.S. registered voters interviewed between June 26 - July 3, 2024. Respondents were asked how common they thought that ten different types of election fraud might be in the U.S. In our analysis, we show that substantial proportions of U.S. registered voters believe that these types of election fraud are common. Our multivariate analysis shows that partisanship correlates strongly with endorsement of types of election fraud, with Republicans consistently more likely to state that types of election fraud are common, even when we control for a wide variety of other factors. We also find that conspiratorial thinking is strongly correlated with belief in the occurrence of types of election fraud, even when we control for partisanship. Our results reported in this paper provide important data regarding how American registered voters perceive the prevalence of types of election fraud, just months before the 2024 Presidential election.
Strategic communication of narratives
Authors: Gerrit Bauch, Manuel Foerster
Date: 2024-10-30 00:00:00

We conceptualize the communication of narratives as a cheap-talk game under model uncertainty. The sender has private information about the true data generating process of publicly observable data. The receiver is uncertain about how to interpret the data, but aware of the sender's incentives to strategically provide interpretations ("narratives") in her favor. We consider a general class of decision rules under ambiguity resolving the receiver's ignorance of the true data generating process, including maximum likelihood expected utility. The set of equilibria is characterized by a positive integer $N$: there is an equilibrium that induces $n$ different actions for each $1\leq n \leq N$. The diverting power of the sender is weaker than with a na\"ive receiver being unaware of the sender's incentives. Surprisingly, the receiver sometimes prefers to be na\"ive.
Markov Stochastic Choice
Authors: Kremena Valkanova
Date: 2024-10-29 00:00:00
Tasks: Decision Making

We examine the effect of item arrangement on choices using a novel decision-making model based on the Markovian exploration of choice sets. This model is inspired by experimental evidence suggesting that the decision-making process involves sequential search through rapid stochastic pairwise comparisons. Our findings show that decision-makers following a reversible process are unaffected by item rearrangements, and further demonstrate that this property can be inferred from their choice behavior. Additionally, we provide a characterization of the class of Markovian models in which the agent makes all possible pairwise comparisons with positive probability. The intersection of reversible models and those allowing all pairwise comparisons is observationally equivalent to the well-known Luce model. Finally, we characterize the class of Markovian models for which the initial fixation does not impact the final choice and show that choice data reveals the existence and composition of consideration sets.
Weighted Garbling
Authors: Daehyun Kim, Ichiro Obara
Date: 2024-10-29 00:00:00

We introduce and develop an information order for experiments based on a generalized notion of garbling called weighted garbling. An experiment is more informative than another in this order if the latter experiment is obtained by a weighted garbling of the former. This notion can be shown to be equivalent to a regular garbling conditional on some event for the former experiment. We also characterize this order in terms of posterior beliefs and show that it only depends on the support of posterior beliefs, not their distribution. Our main results are two characterizations of the weighted-garbling order based on some decision problems. For static Bayesian decision problems, one experiment is more informative than another in the weighted-garbling order if and only if a decision maker's value of information (i.e., the difference in the optimal expected payoffs with and without an experiment) from the former is guaranteed to be some fraction of the value of information from the latter for any decision problem. When the weighted garbling is a regular garbling, this lower bound reduces to the value of information itself as the fraction becomes one, thus generalizing the result in Blackwell (1951, 1953). We also consider a class of stopping time problems where the state of nature changes over time according to a hidden Markov process, and a patient decision maker can conduct the same experiment as many times as she wants without any cost before making a one-time decision. We show that an experiment is more informative than another in the weighted-garbling order if and only if the decision maker achieves a weakly higher expected payoff for any problem with a regular prior belief in this class.
Cough-E: A multimodal, privacy-preserving cough detection algorithm for the edge
Authors: Stefano Albini, Lara Orlandic, Jonathan Dan, Jérôme Thevenot, Tomas Teijeiro, Denisa Andreea Constantinescu, David Atienza
Date: 2024-10-31 00:00:00
Tasks: Model Optimization, Privacy Preserving

Continuous cough monitors can greatly aid doctors in home monitoring and treatment of respiratory diseases. Although many algorithms have been proposed, they still face limitations in data privacy and short-term monitoring. Edge-AI offers a promising solution by processing privacy-sensitive data near the source, but challenges arise in deploying resource-intensive algorithms on constrained devices. From a suitable selection of audio and kinematic signals, our methodology aims at the optimal selection of features via Recursive Feature Elimination with Cross-Validation (RFECV), which exploits the explainability of the selected XGB model. Additionally, it analyzes the use of Mel spectrogram features, instead of the more common MFCC. Moreover, a set of hyperparameters for a multimodal implementation of the classifier is explored. Finally, it evaluates the performance based on clinically relevant event-based metrics. We apply our methodology to develop Cough-E, an energy-efficient, multimodal and edge AI cough detection algorithm. It exploits audio and kinematic data in two distinct classifiers, jointly cooperating for a balanced energy and performance trade-off. We demonstrate that our algorithm can be executed in real-time on an ARM Cortex M33 microcontroller. Cough-E achieves a 70.56\% energy saving when compared to the audio-only approach, at the cost of a 1.26\% relative performance drop, resulting in a 0.78 F1-score. Both Cough-E and the edge-aware model optimization methodology are publicly available as open-source code. This approach demonstrates the benefits of the proposed hardware-aware methodology to enable privacy-preserving cough monitors on the edge, paving the way to efficient cough monitoring.
Novel View Acoustic Parameter Estimation
Authors: Ricardo Falcon-Perez, Ruohan Gao, Gregor Mueckl, Sebastia V. Amengual Gari, Ishwarya Ananthabhotla
Date: 2024-10-31 00:00:00
Tasks: 3D geometry, Image-to-Image Translation, parameter estimation, Parameter Prediction

The task of Novel View Acoustic Synthesis (NVAS) - generating Room Impulse Responses (RIRs) for unseen source and receiver positions in a scene - has recently gained traction, especially given its relevance to Augmented Reality (AR) and Virtual Reality (VR) development. However, many of these efforts suffer from similar limitations: they infer RIRs in the time domain, which prove challenging to optimize; they focus on scenes with simple, single-room geometries; they infer only single-channel, directionally-independent acoustic characteristics; and they require inputs, such as 3D geometry meshes with material properties, that may be impractical to obtain for on-device applications. On the other hand, research suggests that sample-wise accuracy of RIRs is not required for perceptual plausibility in AR and VR. Standard acoustic parameters like Clarity Index (C50) or Reverberation Time (T60) have been shown to capably describe pertinent characteristics of the RIRs, especially late reverberation. To address these gaps, this paper introduces a new task centered on estimating spatially distributed acoustic parameters that can be then used to condition a simple reverberator for arbitrary source and receiver positions. The approach is modelled as an image-to-image translation task, which translates 2D floormaps of a scene into 2D heatmaps of acoustic parameters. We introduce a new, large-scale dataset of 1000 scenes consisting of complex, multi-room apartment conditions, and show that our method outperforms statistical baselines significantly. Moreover, we show that the method also works for directionally-dependent (i.e. beamformed) parameter prediction. Finally, the proposed method operates on very limited information, requiring only a broad outline of the scene and a single RIR at inference time.
Simultaneous Diarization and Separation of Meetings through the Integration of Statistical Mixture Models
Authors: Tobias Cord-Landwehr, Christoph Boeddeker, Reinhold Haeb-Umbach
Date: 2024-10-28 00:00:00
Tasks: Speech Enhancement

We propose an approach for simultaneous diarization and separation of meeting data. It consists of a complex Angular Central Gaussian Mixture Model (cACGMM) for speech source separation, and a von-Mises-Fisher Mixture Model (VMFMM) for diarization in a joint statistical framework. Through the integration, both spatial and spectral information are exploited for diarization and separation. We also develop a method for counting the number of active speakers in a segment of a meeting to support block-wise processing. While the total number of speakers in a meeting may be known, it is usually not known on a per-segment level. With the proposed speaker counting, joint diarization and source separation can be done segment-by-segment, and the permutation problem across segments is solved, thus allowing for block-online processing in the future. Experimental results on the LibriCSS meeting corpus show that the integrated approach outperforms a cascaded approach of diarization and speech enhancement in terms of WER, both on a per-segment and on a per-meeting level.
UAV-based detection of landmines using infrared thermography
Authors: Muhammad Umair Akram Butt, Zaighum Naveed, Usama Javed
Date: 2024-10-31 00:00:00
Tasks: Landmine

Landmines remain a pervasive threat in conflict-affected regions worldwide, exacting a toll on innocent lives. Shockingly, every 95 minutes, another individual becomes a victim of these lethal explosive devices (Landmines Monitor 2022 2022), with a significant proportion being innocent civilians. Current methods for landmine detection suffer from inefficiency, high costs, and risks to the operator and system integrity. In this paper, we present a novel, efficient, safe, and cost-effective approach to unearth these hidden dangers. Our proposed method integrates an unmanned aerial vehicle (UAV) with a thermal camera to capture high-resolution images of minefields. These images are subsequently transmitted to a base computer, where a state-of-the-art image processing algorithm is applied to identify the presence of landmines. Notably, our solution performs exceptionally well, particularly during evening hours, achieving an impressive detection accuracy of nearly 88%. These results exhibit great promise when compared to existing methods constrained by their design limitations.
Inter-Camera Color Correction for Multispectral Imaging with Camera Arrays Using a Consensus Image
Authors: Katja Kossira, Jürgen Seiler, André Kaup
Date: 2024-10-30 00:00:00

This paper introduces a novel method for inter-camera color calibration for multispectral imaging with camera arrays using a consensus image. Capturing images using multispectral camera arrays has gained importance in medical, agricultural, and environmental processes. Due to fabrication differences, noise, or device altering, varying pixel sensitivities occur, influencing classification processes. Therefore, color calibration between the cameras is necessary. In existing methods, one of the camera images is chosen and considered as a reference, ignoring the color information of all other recordings. Our new approach does not just take one image as reference, but uses statistical information such as the location parameter to generate a consensus image as basis for calibration. This way, we managed to improve the PSNR values for the linear regression color correction algorithm by 1.15 dB and the improved color difference (iCID) values by 2.81.
Enhancing Image Resolution: A Simulation Study and Sensitivity Analysis of System Parameters for Resourcesat-3S/3SA
Authors: Ankur Garg, Meenakshi Sarkar, S. M. Moorthi, Debajyoti Dhar
Date: 2024-10-30 00:00:00
Tasks: Sensitivity, Super-Resolution

Resourcesat-3S/3SA, an upcoming Indian satellite, is designed with Aft and Fore payloads capturing stereo images at look angles of -5deg and 26deg, respectively. Operating at 632.6 km altitude, it features a panchromatic (PAN) band offering a Ground Sampling Distance (GSD) of 1.25 meters and a 60 km swath. To balance swath width and resolution, an Instantaneous Geometric Field of View (IGFOV) of 2.5 meters is maintained while ensuring a 1.25-meter GSD both along and across track. Along-track sampling is achieved through precise timing, while across-track accuracy is ensured by using two staggered pixel arrays. Signal-to-Noise Ratio (SNR) is enhanced through Time Delay and Integration (TDI), employing two five-stage subarrays spaced 80 {\mu}m apart along the track, with a 4 {\mu}m (0.5 pixel) stagger in the across-track direction to achieve 1.25-meter resolution. To further boost resolution, the satellite employs super-resolution (SR), combining multiple low-resolution captures using sub-pixel shifts to produce high-resolution images. This technique, effective when images contain aliased high-frequency details, reconstructs full-resolution imagery using phase information from multiple observations, and has been successfully applied in remote sensing missions like SPOT-5, SkySat, and DubaiSat-1. A Monte Carlo simulation explores the factors influencing the resolution in Resourcesat-3S/3SA, with sensitivity analysis highlighting key impacts. The simulation methodology is broadly applicable to other remote sensing missions, optimizing SR for enhanced image clarity and resolution in future satellite systems.
Fast Hyperspectral Neutron Tomography
Authors: Mohammad Samin Nur Chowdhury, Diyu Yang, Shimin Tang, Singanallur V. Venkatakrishnan, Hassina Z. Bilheux, Gregery T. Buzzard, Charles A. Bouman
Date: 2024-10-29 00:00:00

Hyperspectral neutron computed tomography is a tomographic imaging technique in which thousands of wavelength-specific neutron radiographs are measured for each tomographic view. In conventional hyperspectral reconstruction, data from each neutron wavelength bin are reconstructed separately, which is extremely time-consuming. These reconstructions often suffer from poor quality due to low signal-to-noise ratios. Consequently, material decomposition based on these reconstructions tends to produce inaccurate estimates of the material spectra and erroneous volumetric material separation. In this paper, we present two novel algorithms for processing hyperspectral neutron data: fast hyperspectral reconstruction and fast material decomposition. Both algorithms rely on a subspace decomposition procedure that transforms hyperspectral views into low-dimensional projection views within an intermediate subspace, where tomographic reconstruction is performed. The use of subspace decomposition dramatically reduces reconstruction time while reducing both noise and reconstruction artifacts. We apply our algorithms to both simulated and measured neutron data and demonstrate that they reduce computation and improve the quality of the results relative to conventional methods.
Quality Analysis of the Coding Bitrate Tradeoff Between Geometry and Attributes for Colored Point Clouds
Authors: Joao Prazeres, Rafael Rodrigues, Manuela Pereira, Antonio M. G. Pinheiro
Date: 2024-10-28 00:00:00
Tasks: Attribute

Typically, point cloud encoders allocate a similar bitrate for geometry and attributes (usually RGB color components) information coding. This paper reports a quality study considering different coding bitrate tradeoff between geometry and attributes. A set of five point clouds, representing different characteristics and types of content was encoded with the MPEG standard Geometry Point Cloud Compression (G-PCC), using octree to encode geometry information, and both the Region Adaptive Hierarchical Transform and the Prediction Lifting transform for attributes. Furthermore, the JPEG Pleno Point Cloud Verification Model was also tested. Five different attributes/geometry bitrate tradeoffs were considered, notably 70%/30%, 60%/40%, 50%/50%, 40%/60%, 30%/70%. Three point cloud objective metrics were selected to assess the quality of the reconstructed point clouds, notably the PSNR YUV, the Point Cloud Quality Metric, and GraphSIM. Furthermore, for each encoder, the Bjonteegaard Deltas were computed for each tradeoff, using the 50%/50% tradeoff as a reference. The reported results indicate that using a higher bitrate allocation for attribute encoding usually yields slightly better results.
MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing Dataset and Benchmark for Text-to-Image Generation
Authors: Jialin Luo, Yuanzhi Wang, Ziqi Gu, Yide Qiu, Shuaizhen Yao, Fuyun Wang, Chunyan Xu, Wenhua Zhang, Dan Wang, Zhen Cui
Date: 2024-10-26 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

Recently, the diffusion-based generative paradigm has achieved impressive general image generation capabilities with text prompts due to its accurate distribution modeling and stable training process. However, generating diverse remote sensing (RS) images that are tremendously different from general images in terms of scale and perspective remains a formidable challenge due to the lack of a comprehensive remote sensing image generation dataset with various modalities, ground sample distances (GSD), and scenes. In this paper, we propose a Multi-modal, Multi-GSD, Multi-scene Remote Sensing (MMM-RS) dataset and benchmark for text-to-image generation in diverse remote sensing scenarios. Specifically, we first collect nine publicly available RS datasets and conduct standardization for all samples. To bridge RS images to textual semantic information, we utilize a large-scale pretrained vision-language model to automatically output text prompts and perform hand-crafted rectification, resulting in information-rich text-image pairs (including multi-modal images). In particular, we design some methods to obtain the images with different GSD and various environments (e.g., low-light, foggy) in a single sample. With extensive manual screening and refining annotations, we ultimately obtain a MMM-RS dataset that comprises approximately 2.1 million text-image pairs. Extensive experimental results verify that our proposed MMM-RS dataset allows off-the-shelf diffusion models to generate diverse RS images across various modalities, scenes, weather conditions, and GSD. The dataset is available at https://github.com/ljl5261/MMM-RS.