pwc-archive/papers-with-abstracts huggingface dataset:
GARCH option valuation with long-run and short-run volatility components: A novel framework ensuring positive variance
Authors: Luca Vincenzo Ballestra, Enzo D'Innocenzo, Christian Tezza
Date: 2024-10-18 00:00:00

Christoffersen, Jacobs, Ornthanalai, and Wang (2008) (CJOW) proposed an improved Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model for valuing European options, where the return volatility is comprised of two distinct components. Empirical studies indicate that the model developed by CJOW outperforms widely-used single-component GARCH models and provides a superior fit to options data than models that combine conditional heteroskedasticity with Poisson-normal jumps. However, a significant limitation of this model is that it allows the variance process to become negative. Oh and Park [2023] partially addressed this issue by developing a related model, yet the positivity of the volatility components is not guaranteed, both theoretically and empirically. In this paper we introduce a new GARCH model that improves upon the models by CJOW and Oh and Park [2023], ensuring the positivity of the return volatility. In comparison to the two earlier GARCH approaches, our novel methodology shows comparable in-sample performance on returns data and superior performance on S&P500 options data.
Identification of a Rank-dependent Peer Effect Model
Authors: Eyo I. Herstad, Myungkou Shin
Date: 2024-10-18 00:00:00
Tasks: model

We develop a model that captures peer effect heterogeneity by modeling the endogenous spillover to be linear in ordered peer outcomes. Unlike the canonical linear-in-means model, our approach accounts for the distribution of peer outcomes as well as the size of peer groups. Under a minimal condition, our model admits a unique equilibrium and is therefore tractable and identified. Simulations show our estimator has good finite sample performance. Finally, we apply our model to educational data from Norway, finding that higher-performing friends disproportionately drive GPA spillovers. Our framework provides new insights into the structure of peer effects beyond aggregate measures.
The Economic Consequences of Being Widowed by War: A Life-Cycle Perspective
Authors: Sebastian T. Braun, Jan Stuhler
Date: 2024-10-20 00:00:00

Despite millions of war widows worldwide, little is known about the economic consequences of being widowed by war. We use life history data from West Germany to show that war widowhood increased women's employment immediately after World War II but led to lower employment rates later in life. War widows, therefore, carried a double burden of employment and childcare while their children were young but left the workforce when their children reached adulthood. We show that the design of compensation policies likely explains this counterintuitive life-cycle pattern and examine potential spillovers to the next generation.
Risk Premia in the Bitcoin Market
Authors: Caio Almeida, Maria Grith, Ratmir Miftachov, Zijin Wang
Date: 2024-10-19 00:00:00

Based on options and realized returns, we analyze risk premia in the Bitcoin market through the lens of the Pricing Kernel (PK). We identify that: 1) The projected PK into Bitcoin returns is W-shaped and steep in the negative returns region; 2) Negative Bitcoin returns account for 33% of the total Bitcoin index premium (BP) in contrast to 70% of S&P500 equity premium explained by negative returns. Applying a novel clustering algorithm to the collection of estimated Bitcoin risk-neutral densities, we find that risk premia vary over time as a function of two distinct market volatility regimes. In the low-volatility regime, the PK projection is steeper for negative returns. It has a more pronounced W-shape than the unconditional one, implying particularly high BP for both extreme positive and negative returns and a high Variance Risk Premium (VRP). In high-volatility states, the BP attributable to positive and negative returns is more balanced, and the VRP is lower. Overall, Bitcoin investors are more worried about variance and downside risk in low-volatility states.
Feedback strategies in the market with uncertainties
Authors: Mustapha Nyenye Issah
Date: 2024-10-21 00:00:00

We explore how dynamic entry deterrence operates through feedback strategies in markets experiencing stochastic demand fluctuations. The incumbent firm, aware of its own cost structure, can deter a potential competitor by strategically adjusting prices. The potential entrant faces a one-time, irreversible decision to enter the market, incurring a fixed cost, with profits determined by market conditions and the incumbent's hidden type. Market demand follows a Chan-Karolyi-Longstaff-Sanders Brownian motion. If the demand is low, the threat of entry diminishes, making deterrence less advantageous. In equilibrium, a weak incumbent may be incentivized to reveal its type by raising prices. We derive an optimal equilibrium using path integral control, where the entrant enters once demand reaches a high enough level, and the weak incumbent mixes strategies between revealing itself when demand is sufficiently low.
Analysis of short-run and long-run marginal costs of generation in the power market
Authors: Shamim Homaei, Simon Roussanaly, Asgeir Tomasgard
Date: 2024-10-21 00:00:00

In power markets, understanding the cost dynamics of electricity generation is crucial. The complexity of price formation in the power system arises from its diverse attributes, such as various generator types, each characterized by its specific fixed and variable costs as well as different lifetimes. In this paper, we adopt an approach that investigates both long-run marginal cost (LRMC) and short-run marginal cost (SRMC) in a perfect competition market. According to economic theory, marginal pricing serves as an effective method for determining the generation cost of electricity. This paper presents a capacity expansion model designed to evaluate the marginal cost of electricity generation, encompassing both long-term and short-term perspectives. Following a parametric analysis and the calculation of LRMCs, this study investigates the allocation of investment costs across various time periods and how these costs factor into the LRMC to ensure cost recovery. Additionally, an exploration of SRMCs reveals the conditions under which LRMCs and SRMCs converge or diverge. We observe that when there is a disparity between LRMC and SRMC, setting electricity generation prices equal to SRMCs does not ensure the complete recovery of investment and operational costs. This phenomenon holds implications for market reliability and challenges the pricing strategies that rely solely on SRMCs. Furthermore, our investigation highlighted the significance of addressing degeneracy in the power market modeling. Primal degeneracy in the SRMC model can result in multiple values for the dual variable representing SRMC. This multiplicity of values creates ambiguity regarding the precise SRMC value, making it challenging to ascertain the correct estimation. As a result, resolving degeneracy will ensure the reliability of the SRMC value, consequently enhancing the robustness and credibility of our analysis.
Peace in the Face of Uncertainty: Resource Allocation with Stochastic Armaments
Authors: Sarah Taylor
Date: 2024-10-18 00:00:00

This paper examines a government's strategic resource allocation choices when facing an opposing group whose military power is uncertain. We investigate how this uncertainty affects the government's decision to divide resources in a way that either guarantees peace, despite unresolved uncertainty, or risks conflict. We find that under low uncertainty, the government prefers distributions which ensure peace, while under high uncertainty, they are willing to risk war. When uncertainty is low, the government's allocation is decreasing in uncertainty. When uncertainty is high it is increasing. The latter leads to an increased probability of fighting and falling total welfare.
Continuous Speech Synthesis using per-token Latent Diffusion
Authors: Arnon Turetzky, Nimrod Shabtay, Slava Shechtman, Hagai Aronowitz, David Haws, Ron Hoory, Avihu Dekel
Date: 2024-10-21 00:00:00
Tasks: Image Generation, Quantization, Speech Synthesis, text-to-speech, Text to Speech

The success of autoregressive transformer models with discrete tokens has inspired quantization-based approaches for continuous modalities, though these often limit reconstruction quality. We therefore introduce SALAD, a per-token latent diffusion model for zero-shot text-to-speech, that operates on continuous representations. SALAD builds upon the recently proposed expressive diffusion head for image generation, and extends it to generate variable-length outputs. Our approach utilizes semantic tokens for providing contextual information and determining the stopping condition. We suggest three continuous variants for our method, extending popular discrete speech synthesis techniques. Additionally, we implement discrete baselines for each variant and conduct a comparative analysis of discrete versus continuous speech modeling techniques. Our results demonstrate that both continuous and discrete approaches are highly competent, and that SALAD achieves a superior intelligibility score while obtaining speech quality and speaker similarity on par with the ground-truth audio.
Independent Feature Enhanced Crossmodal Fusion for Match-Mismatch Classification of Speech Stimulus and EEG Response
Authors: Shitong Fan, Wenbo Wang, Feiyang Xiao, Shiheng Zhang, Qiaoxi Zhu, Jian Guan
Date: 2024-10-19 00:00:00
Tasks: EEG, Eeg Decoding

It is crucial for auditory attention decoding to classify matched and mismatched speech stimuli with corresponding EEG responses by exploring their relationship. However, existing methods often adopt two independent networks to encode speech stimulus and EEG response, which neglect the relationship between these signals from the two modalities. In this paper, we propose an independent feature enhanced crossmodal fusion model (IFE-CF) for match-mismatch classification, which leverages the fusion feature of the speech stimulus and the EEG response to achieve auditory EEG decoding. Specifically, our IFE-CF contains a crossmodal encoder to encode the speech stimulus and the EEG response with a two-branch structure connected via crossmodal attention mechanism in the encoding process, a multi-channel fusion module to fuse features of two modalities by aggregating the interaction feature obtained from the crossmodal encoder and the independent feature obtained from the speech stimulus and EEG response, and a predictor to give the matching result. In addition, the causal mask is introduced to consider the time delay of the speech-EEG pair in the crossmodal encoder, which further enhances the feature representation for match-mismatch classification. Experiments demonstrate our method's effectiveness with better classification accuracy, as compared with the baseline of the Auditory EEG Decoding Challenge 2023.
A Unified Framework for Collecting Text-to-Speech Synthesis Datasets for 22 Indian Languages
Authors: Sujitha Sathiyamoorthy, N Mohana, Anusha Prakash, Hema A Murthy
Date: 2024-10-18 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

The performance of a text-to-speech (TTS) synthesis model depends on various factors, of which the quality of the training data is of utmost importance. Millions of data are collected around the globe for various languages, but resources for Indian languages are few. Although there are many efforts involved in data collection, a common set of protocols for data collection becomes necessary for building TTS systems in Indian languages primarily because of the need for a uniform development of TTS systems across languages. In this paper, we present our learnings on data collection efforts' for Indic languages over 15 years. These databases have been used in unit selection synthesis, hidden Markov model based, and end-to-end frameworks, and for generating prosodically rich TTS systems. The most significant feature of the data collected is that data purity enables building high-quality TTS systems with a comparatively small dataset compared to that of European/Chinese languages.
Variable Rate Learned Wavelet Video Coding using Temporal Layer Adaptivity
Authors: Anna Meyer, André Kaup
Date: 2024-10-21 00:00:00

Learned wavelet video coders provide an explainable framework by performing discrete wavelet transforms in temporal, horizontal, and vertical dimensions. With a temporal transform based on motion-compensated temporal filtering (MCTF), spatial and temporal scalability is obtained. In this paper, we introduce variable rate support and a mechanism for quality adaption to different temporal layers for a higher coding efficiency. Moreover, we propose a multi-stage training strategy that allows training with multiple temporal layers. Our experiments demonstrate Bj{\o}ntegaard Delta bitrate savings of at least -32% compared to a learned MCTF model without these extensions. Training and inference code is available at: https://github.com/FAU-LMS/Learned-pMCTF.
Continuous Wavelet Transformation and VGG16 Deep Neural Network for Stress Classification in PPG Signals
Authors: Yasin Hasanpoor, Bahram Tarvirdizadeh, Khalil Alipour, Mohammad Ghamari
Date: 2024-10-17 00:00:00
Tasks: Classification

Our research introduces a groundbreaking approach to stress classification through Photoplethysmogram (PPG) signals. By combining Continuous Wavelet Transformation (CWT) with the proven VGG16 classifier, our method enhances stress assessment accuracy and reliability. Previous studies highlighted the importance of physiological signal analysis, yet precise stress classification remains a challenge. Our approach addresses this by incorporating robust data preprocessing with a Kalman filter and a sophisticated neural network architecture. Experimental results showcase exceptional performance, achieving a maximum training accuracy of 98% and maintaining an impressive average training accuracy of 96% across diverse stress scenarios. These results demonstrate the practicality and promise of our method in advancing stress monitoring systems and stress alarm sensors, contributing significantly to stress classification.
FACMIC: Federated Adaptative CLIP Model for Medical Image Classification
Authors: Yihang Wu, Christian Desrosiers, Ahmad Chaddad
Date: 2024-10-08 00:00:00
Tasks: Domain Adaptation, Federated Learning, image-classification, Image Classification, Medical Image Analysis, Medical Image Classification

Federated learning (FL) has emerged as a promising approach to medical image analysis that allows deep model training using decentralized data while ensuring data privacy. However, in the field of FL, communication cost plays a critical role in evaluating the performance of the model. Thus, transferring vision foundation models can be particularly challenging due to the significant resource costs involved. In this paper, we introduce a federated adaptive Contrastive Language Image Pretraining CLIP model designed for classification tasks. We employ a light-weight and efficient feature attention module for CLIP that selects suitable features for each client's data. Additionally, we propose a domain adaptation technique to reduce differences in data distribution between clients. Experimental results on four publicly available datasets demonstrate the superior performance of FACMIC in dealing with real-world and multisource medical imaging data. Our codes are available at https://github.com/AIPMLab/FACMIC.
Deep Domain Isolation and Sample Clustered Federated Learning for Semantic Segmentation
Authors: Matthis Manthe, Carole Lartizien, Stefan Duffner
Date: 2024-10-04 00:00:00
Tasks: Federated Learning, Semantic Segmentation

Empirical studies show that federated learning exhibits convergence issues in Non Independent and Identically Distributed (IID) setups. However, these studies only focus on label distribution shifts, or concept shifts (e.g. ambiguous tasks). In this paper, we explore for the first time the effect of covariate shifts between participants' data in 2D segmentation tasks, showing an impact way less serious than label shifts but still present on convergence. Moreover, current Personalized (PFL) and Clustered (CFL) Federated Learning methods intrinsically assume the homogeneity of the dataset of each participant and its consistency with future test samples by operating at the client level. We introduce a more general and realistic framework where each participant owns a mixture of multiple underlying feature domain distributions. To diagnose such pathological feature distributions affecting a model being trained in a federated fashion, we develop Deep Domain Isolation (DDI) to isolate image domains directly in the gradient space of the model. A federated Gaussian Mixture Model is fit to the sample gradients of each class, while the results are combined with spectral clustering on the server side to isolate decentralized sample-level domains. We leverage this clustering algorithm through a Sample Clustered Federated Learning (SCFL) framework, performing standard federated learning of several independent models, one for each decentralized image domain. Finally, we train a classifier enabling to associate a test sample to its corresponding domain cluster at inference time, offering a final set of models that are agnostic to any assumptions on the test distribution of each participant. We validate our approach on a toy segmentation dataset as well as different partitionings of a combination of Cityscapes and GTA5 datasets using an EfficientVIT-B0 model, showing a significant performance gain compared to other approaches. Our code is available at https://github.com/MatthisManthe/DDI_SCFL .
Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging
Authors: Jiwon Youn, Dong Woo Kang, Hyun Kook Lim, Mansu Kim
Date: 2024-10-03 00:00:00
Tasks: Functional Connectivity

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory and cognitive decline, affecting millions worldwide. Diagnosing AD is challenging due to its heterogeneous nature and variable progression. This study introduces a novel brain-aware readout layer (BA readout layer) for Graph Neural Networks (GNNs), designed to improve interpretability and predictive accuracy in neuroimaging for early AD diagnosis. By clustering brain regions based on functional connectivity and node embedding, this layer improves the GNN's capability to capture complex brain network characteristics. We analyzed neuroimaging data from 383 participants, including both cognitively normal and preclinical AD individuals, using T1-weighted MRI, resting-state fMRI, and FBB-PET to construct brain graphs. Our results show that GNNs with the BA readout layer significantly outperform traditional models in predicting the Preclinical Alzheimer's Cognitive Composite (PACC) score, demonstrating higher robustness and stability. The adaptive BA readout layer also offers enhanced interpretability by highlighting task-specific brain regions critical to cognitive functions impacted by AD. These findings suggest that our approach provides a valuable tool for the early diagnosis and analysis of Alzheimer's disease.
Explaining an image classifier with a generative model conditioned by uncertainty
Authors: Adrien LeCoz, Stéphane Herbin, Faouzi Adjed
Date: 2024-10-02 00:00:00

We propose to condition a generative model by a given image classifier uncertainty in order to analyze and explain its behavior. Preliminary experiments on synthetic data and a corrupted version of MNIST dataset illustrate the idea.
Characterizing the Effect of Electrode Shift & Sensor Reapplication on Common sEMG Features in Lower Limb Muscles
Authors: Fraser Douglas, Mona Pei, Calvin Kuo
Date: 2024-10-21 00:00:00
Tasks: Attribute

This study investigates the impact of electrode shift and sensor reapplication on common surface electromyography (sEMG) features in lower limb muscles, factors which have, thus far, precluded clinicians from being able to attribute inter-session changes in sEMG signal properties to physiological changes in patients under the context of stroke recovery monitoring. To explore these inter-session errors, we recruited 12 healthy participants to perform a selection of isometric and dynamic exercises seen within stroke assessment sessions while instrumented with high-density sEMG (HDsEMG) arrays on the gastrocnemius medialis, tibialis anterior, semitendinosus, and tensor fascia latae. Between exercise sets, the electrode arrays were intentionally shifted and reapplied to quantify errors in signal features, using 3D scanning equipment to extract the ground truth shift performed. Results revealed that while frequency-domain features (mean, median, and peak frequency) demonstrated high resilience to the inter-session changes, the time-domain features (integrated EMG and max envelope amplitude) showed a greater, yet predictable, variability. In all, these findings suggest that should we be able to quantify placement shift, this can support direct inter-session feature comparisons, improving the reliability of sEMG-based stroke recovery assessments and offering insights for improving remote stroke rehabilitation technologies.
PuLsE: Accurate and Robust Ultrasound-based Continuous Heart-Rate Monitoring on a Wrist-Worn IoT Device
Authors: Marco Giordano, Christoph Leitner, Christian Vogt, Luca Benini, Michele Magno
Date: 2024-10-21 00:00:00

This work explores the feasibility of employing ultrasound (US) US technology in a wrist-worn IoT device for low-power, high-fidelity heart-rate (HR) extraction. US offers deep tissue penetration and can monitor pulsatile arterial blood flow in large vessels and the surrounding tissue, potentially improving robustness and accuracy compared to PPG. We present an IoT wearable system prototype utilizing a commercial microcontroller MCU employing the onboard ADC to capture high frequency US signals and an innovative low-power US pulser. An envelope filter lowers the bandwidth of the US signal by a factor of >5x, reducing the system's acquisition requirements without compromising accuracy (correlation coefficient between HR extracted from enveloped and raw signals, r(92)=0.99, p<0.001). The full signal processing pipeline is ported to fixed point arithmetic for increased energy efficiency and runs entirely onboard. The system has an average power consumption of 5.8mW, competitive with PPG based systems, and the HR extraction algorithm requires only 68kB of RAM and 71ms of processing time on an ARM Cortex-M4 MCU. The system is estimated to run continuously for more than 7 days on a smartwatch battery. To accurately evaluate the proposed circuit and algorithm and identify the anatomical location on the wrist with the highest accuracy for HR extraction, we collected a dataset from 10 healthy adults at three different wrist positions. The dataset comprises roughly 5 hours of HR data with an average of 80.6+-16.3 bpm. During recording, we synchronized the established ECG gold standard with our US-based method. The comparisons yields a Pearson correlation coefficient of r(92)=0.99, p<0.001 and a mean error of 0.69+-1.99 bpm in the lateral wrist position near the radial artery. The dataset and code have been open-sourced at https://github.com/mgiordy/Ultrasound-Heart-Rate