pwc-archive/papers-with-abstracts huggingface dataset:
Perceptual implications of simplifying geometrical acoustics models for Ambisonics-based binaural reverberation
Authors: Vincent Martin, Isaac Engel, Lorenzo Picinali
Date: 2024-11-15 00:00:00

Different methods can be employed to render virtual reverberation, often requiring substantial information about the room's geometry and the acoustic characteristics of the surfaces. However, fully comprehensive approaches that account for all aspects of a given environment may be computationally costly and redundant from a perceptual standpoint. For these methods, achieving a trade-off between perceptual authenticity and model's complexity becomes a relevant challenge. This study investigates this compromise through the use of geometrical acoustics to render Ambisonics-based binaural reverberation. Its precision is determined, among other factors, by its fidelity to the room's geometry and to the acoustic properties of its materials. The purpose of this study is to investigate the impact of simplifying the room geometry and the frequency resolution of absorption coefficients on the perception of reverberation within a virtual sound scene. Several decimated models based on a single room were perceptually evaluated using the a multi-stimulus comparison method. Additionally, these differences were numerically assessed through the calculation of acoustic parameters of the reverberation. According to numerical and perceptual evaluations, lowering the frequency resolution of absorption coefficients can have a significant impact on the perception of reverberation, while a less notable impact was observed when decimating the geometry of the model.
Channel-Adaptive Wireless Image Semantic Transmission with Learnable Prompts
Authors: Liang Zhang, Danlan Huang, Xinyi Zhou, Feng Ding, Sheng Wu, Zhiqing Wei
Date: 2024-11-15 00:00:00
Tasks: Image Reconstruction

Recent developments in Deep learning based Joint Source-Channel Coding (DeepJSCC) have demonstrated impressive capabilities within wireless semantic communications system. However, existing DeepJSCC methodologies exhibit limited generalization ability across varying channel conditions, necessitating the preparation of multiple models. Optimal performance is only attained when the channel status during testing aligns precisely with the training channel status, which is very inconvenient for real-life applications. In this paper, we introduce a novel DeepJSCC framework, termed Prompt JSCC (PJSCC), which incorporates a learnable prompt to implicitly integrate the physical channel state into the transmission system. Specifically, the Channel State Prompt (CSP) module is devised to generate prompts based on diverse SNR and channel distribution models. Through the interaction of latent image features with channel features derived from the CSP module, the DeepJSCC process dynamically adapts to varying channel conditions without necessitating retraining. Comparative analyses against leading DeepJSCC methodologies and traditional separate coding approaches reveal that the proposed PJSCC achieves optimal image reconstruction performance across different SNR settings and various channel models, as assessed by Peak Signal-to-Noise Ratio (PSNR) and Learning-based Perceptual Image Patch Similarity (LPIPS) metrics. Furthermore, in real-world scenarios, PJSCC shows excellent memory efficiency and scalability, rendering it readily deployable on resource-constrained platforms to facilitate semantic communications.
A Multi-Scale Spatial-Temporal Network for Wireless Video Transmission
Authors: Xinyi Zhou, Danlan Huang, Zhixin Qi, Liang Zhang, Ting Jiang
Date: 2024-11-15 00:00:00
Tasks: Decoder, Semantic Communication

Deep joint source-channel coding (DeepJSCC) has shown promise in wireless transmission of text, speech, and images within the realm of semantic communication. However, wireless video transmission presents greater challenges due to the difficulty of extracting and compactly representing both spatial and temporal features, as well as its significant bandwidth and computational resource requirements. In response, we propose a novel video DeepJSCC (VDJSCC) approach to enable end-to-end video transmission over a wireless channel. Our approach involves the design of a multi-scale vision Transformer encoder and decoder to effectively capture spatial-temporal representations over long-term frames. Additionally, we propose a dynamic token selection module to mask less semantically important tokens from spatial or temporal dimensions, allowing for content-adaptive variable-length video coding by adjusting the token keep ratio. Experimental results demonstrate the effectiveness of our VDJSCC approach compared to digital schemes that use separate source and channel codes, as well as other DeepJSCC schemes, in terms of reconstruction quality and bandwidth reduction.
A Geometric Algebra Framework for a Multidimensional Analytic Signal
Authors: K. James Sangston
Date: 2024-11-15 00:00:00

This work examines the problem of extending the one-dimensional analytic signal, which is ubiquitous throughout signal processing, to higher dimensional signals. Bulow et al. and Felsberg et al. have previously used techniques from Clifford algebra and analysis to extend the one-dimensional analytic signal to higher dimensions. However, each author sets forth a different definition of a multidimensional analytic signal. Herein we follow an observation of Brackx et al. and adopt a general definition of an analytic signal that encompasses both the hypercomplex signal of Bulow et al. and the monogenic signal of Felsberg et al. within the same mathematical framework. The crux of our approach is captured by the following statement: A multidimensional analytic signal is generated by an idempotent. We develop this notion more specifically using examples from geometric algebra.
A Random Process Model Useful for Describing Radar Clutter
Authors: K. James Sangston
Date: 2024-11-15 00:00:00

We use the theory of Bernstein functions, completely monotonic functions, and Levy processes to define a positive random process $\tau(t)$. For radar clutter one may think of $\tau(t)$ as the instantaneous power of the scattered radar signal that is described by a compound-Gaussian model. Thus the results herein give a mechanism for defining and simulating a compound-Gaussian random process that can be used in various radar studies. We give several examples of the sample paths of this process.
Holographic MIMO for Next Generation Non-Terrestrial Networks: Motivation, Opportunities, and Challenges
Authors: Giovanni Iacovelli, Chandan Kumar Sheemar, Wali Ullah Khan, Asad Mahmood, George C. Alexandropoulos, Jorge Querol, Symeon Chatzinotas
Date: 2024-11-15 00:00:00

In this article, we propose the integration of the Holographic Multiple Input Multiple Output (HMIMO) as a transformative solution for next generation Non-Terrestrial Networks (NTNs), addressing key challenges, such as high hardware costs, launch expenses, and energy inefficiency. Traditional NTNs are constrained by the financial and operational limitations posed by bulky, costly antenna systems, alongside the complexities of maintaining effective communications in space. HMIMO offers a novel approach utilizing compact and lightweight arrays of densely packed radiating elements with real-time reconfiguration capabilities, thus, capable of optimizing system performance under dynamic conditions such as varying orbital dynamics and Doppler shifts. By replacing conventional antenna systems with HMIMO, the complexity and cost of satellite manufacturing and launch can be substantially reduced, enabling more streamlined and cost-effective satellite designs. This advancement holds significant potential to democratize space communications, making them accessible to a broader range of stakeholders, including smaller nations and commercial enterprises. Moreover, the inherent capabilities of HMIMO in enhancing energy efficiency, scalability, and adaptability position this technology as a key enabler of new use cases and sustainable satellite operations.
Affine Frequency Division Multiplexing with Index Modulation: Full Diversity Condition, Performance Analysis, and Low-Complexity Detection
Authors: Yiwei Tao, Miaowen Wen, Yao Ge, Jun Li, Ertugrul Basar, Naofal Al-Dhahir
Date: 2024-11-15 00:00:00
Tasks: Diversity

Affine frequency division multiplexing (AFDM) is a novel modulation technique based on chirp signals that has been recently proposed as an effective solution for highly reliable communications in high-mobility scenarios. In this paper, we focus on the design of robust index modulation (IM) schemes under the multiple-antenna AFDM transmission framework. To this end, the cyclic delay diversity (CDD) technique is employed to harvest the transmit diversity gain. As a result, we propose two novel AFDM-IM schemes with transmit diversity, termed as CDD-AFDM-IM-I and CDD-AFDM-IM-II. We analyze the full diversity conditions and parameter settings of the proposed CDD-AFDM-IM schemes for both integer and fractional Doppler cases over linear time-varying (LTV) channels. Moreover, we prove that IM enables AFDM to have stronger diversity protection when the full diversity condition is not satisfied. Asymptotically tight upper bounds on the average bit error rates (BERs) of the proposed schemes with maximum-likelihood (ML) detection are derived in closed-form. Furthermore, we propose a low-complexity double-layer message passing (DLMP) algorithm for practical large-dimensional signal detection in the proposed CDD-AFDM-IM systems. Comparison with existing detections shows that the proposed DLMP algorithm achieves a better tradeoff between the BER performance and the computational complexity. Finally, BER simulation results confirm that our proposed CDD-AFDM-IM schemes with both the ML and DLMP detections outperform the benchmark schemes over the LTV channels.
Virtual Array for Dual Function MIMO Radar Communication Systems using OTFS Waveforms
Authors: Kailong Wang, Athina Petropulu
Date: 2024-11-14 00:00:00

A MIMO dual-function radar communication (DFRC) system transmitting orthogonal time frequency space (OTFS) waveforms is considered. A key advantage of MIMO radar is its ability to create a virtual array, achieving higher sensing resolution than the physical receive array. In this paper, we propose a novel approach to construct a virtual array for the system under consideration. The transmit antennas can use the Doppler-delay (DD) domain bins in a shared fashion. A number of Time-Frequency (TF) bins, referred to as private bins, are exclusively assigned to specific transmit antennas. The TF signals received on the private bins are orthogonal and thus can be used to synthesize a virtual array, which, combined with coarse knowledge of radar parameters (i.e., angle, range, and velocity), enables high-resolution estimation of those parameters. The introduction of $N_p$ private bins necessitates a reduction in DD domain symbols, thereby reducing the data rate of each transmit antenna by $N_p-1$. However, even a small number of private bins is sufficient to achieve significant sensing gains with minimal communication rate loss.
Epidemic outbreaks in structured host populations
Authors: Horst R Thieme
Date: 2024-11-14 00:00:00

For a heterogeneous host population, the basic reproduction number of an infectious disease, $\cR_0$, is defined as the spectral radius of the next generation operator (NGO). The threshold properties of the basic reproduction number are typically established by imposing conditions that make $\cR_0$ an eigenvalue of the NGO associated with a positive eigenvector and a positive eigenfunctional (eigenvector of the dual of the NGO). More general results can be obtained by imposing conditions that associate $\cR_0$ just with a positive eigenfunctional. The next generation operator is conveniently expressed by a measure kernel or a Feller kernel which enables the use of analytic rather than functional analytic methods.
Modelling Population-Level Hes1 Dynamics: Insights from a Multi-Framework Approach
Authors: Gesina Menz, Stefan Engblom
Date: 2024-11-14 00:00:00

Mathematical models of living cells have been successively refined with advancements in experimental techniques. A main concern is striking a balance between modelling power and the tractability of the associated mathematical analysis. In this work we model the dynamics for the transcription factor Hairy and enhancer of split-1 (Hes1), whose expression oscillates during neural development, and which critically enables stable fate decision in the embryonic brain. We design, parametrise, and analyse a detailed spatial model using ordinary differential equations (ODEs) over a grid capturing both transient oscillatory behaviour and fate decision on a population-level. We also investigate the relationship between this ODE model and a more realistic grid-based model involving intrinsic noise using mostly directly biologically motivated parameters. While we focus specifically on Hes1 in neural development, the approach of linking deterministic and stochastic grid-based models shows promise in modelling various biological processes taking place in a cell population. In this context, our work stresses the importance of the interpretability of complex computational models into a framework which is amenable to mathematical analysis.
Some remarks on the effect of risk sharing and diversification for infinite mean risks
Authors: Alfred Müller
Date: 2024-11-15 00:00:00

The basic principle of any version of insurance is the paradigm that exchanging risk by sharing it in a pool is beneficial for the participants. In case of independent risks with a finite mean this is the case for risk averse decision makers. The situation may be very different in case of infinite mean models. In that case it is known that risk sharing may have a negative effect, which is sometimes called the nondiversification trap. This phenomenon is well known for infinite mean stable distributions. In a series of recent papers similar results for infinite mean Pareto and Fr\'echet distributions have been obtained. We further investigate this property by showing that many of these results can be obtained as special cases of a simple result demonstrating that this holds for any distribution that is more skewed than a Cauchy distribution. We also relate this to the situation of deadly catastrophic risks, where we assume a positive probability for an infinite value. That case gives a very simple intuition why this phenomenon can occur for such catastrophic risks. We also mention several open problems and conjectures in this context.
Deciphering SCN2A: A comprehensive review of rodent models of Scn2a dysfunction
Authors: Katelin E. J Scott, Maria F. Hermosillo Arrieta, Aislinn J. Williams
Date: 2024-11-15 00:00:00

SCN2A encodes the alpha subunit of the voltage-gated sodium channel Nav1.2, which is involved in action potential initiation and backpropagation in glutamatergic neurons. Mutations in the gene lead to SCN2A-related disorders, which are highly heterogeneous and manifest in a variety of diagnoses, such as self-limited familial and non-familial infantile epilepsy (SeLFNIE, previously benign familial infantile seizures or BFNIS), epileptic encephalopathies (EE), infantile spasms, ataxia, autism spectrum disorder (ASD), intellectual disability (ID), and schizophrenia. Researchers have attempted to elucidate the complexity of the SCN2A gene and Nav1.2 channel function through the use and development of non-human mammalian models. These models have been invaluable to uncovering the molecular underpinnings of why changes in SCN2A lead to such a heterogenous group of disorders. This review aims to evaluate and compare the published rodent models to consolidate findings, identify limitations, and highlight future research directions.
Can EEG resting state data benefit data-driven approaches for motor-imagery decoding?
Authors: Rishan Mehta, Param Rajpura, Yogesh Kumar Meena
Date: 2024-10-28 00:00:00
Tasks: EEG, EEG Signal Classification, Functional Connectivity, Motor Imagery, User Identification

Resting-state EEG data in neuroscience research serve as reliable markers for user identification and reveal individual-specific traits. Despite this, the use of resting-state data in EEG classification models is limited. In this work, we propose a feature concatenation approach to enhance decoding models' generalization by integrating resting-state EEG, aiming to improve motor imagery BCI performance and develop a user-generalized model. Using feature concatenation, we combine the EEGNet model, a standard convolutional neural network for EEG signal classification, with functional connectivity measures derived from resting-state EEG data. The findings suggest that although grounded in neuroscience with data-driven learning, the concatenation approach has limited benefits for generalizing models in within-user and across-user scenarios. While an improvement in mean accuracy for within-user scenarios is observed on two datasets, concatenation doesn't benefit across-user scenarios when compared with random data concatenation. The findings indicate the necessity of further investigation on the model interpretability and the effect of random data concatenation on model robustness.
Using sequence data to study spatial scales of interactions driving spread of Highly Pathogenic Avian Influenza in Great Britain
Authors: Anna Gamża, Sam Lycett, Aeron Sanchez, Rowland Kao
Date: 2024-11-15 00:00:00

H5N1 highly pathogenic avian influenza (HPAI) has been recently circulating in previously unseen patterns. As the underlying causes are uncertain, we need a better understanding of the drivers of virus circulation, as they underpin the spread, and inform about the impact of HPAI. We combine infection and bird population data, using Random Forest models to quantify the relationship between spatially aggregated values of possible infection risk factors and viral phylogenetic data from HPAI outbreaks to define scales of interactions that influence virus circulation. As genetic proximity is correlated to transmission proximity, the genetic relationships of sequences and relevant variables indicate the scale and resolution of interactions driving transmission. We used H5N1 Sequences from Great Britain divided into two periods: Dec 2020-May 2022, and Jun-Oct 2022. Final model with selected spatial scales shows that for the first period game bird abundance and geographical distance were the strongest predictors of genetic distance, and for the second period, these were geographical distance, collection date and farm count. Variables related to infections have different spatial scales for analysed periods, while variables describing the environment have more consistent scales. The differences in predictors for the periods bears further investigation, however may be related to differences in the virus, areas of spread, or seasonal factors. This is the first phylogenetic study indicating possible role for game birds in the circulation of HPAI and supports experimental studies but must be viewed with caution as our study does not indicate direct causation. We show that the scale of spatial aggregation is variable dependent and reflects the scale of underlying processes. In the future our findings may be used to study dynamics of HPAI and design targeted surveillance and intervention strategies.
Gamma Distribution for Equilibrium Analysis of Discrete Stochastic Logistic Population Models
Authors: HaiYan Wang
Date: 2024-11-15 00:00:00

Stochastic models play an essential role in accounting for the variability and unpredictability seen in real-world. This paper focuses on the application of the gamma distribution to analysis of the stationary distributions of populations governed by the discrete stochastic logistic equation at equilibrium. It is well known that the population dynamics of deterministic logistic models are dependent on the range of intrinsic growth rate. In this paper, we identify the same feasible range of the intrinsic growth rate for the stochastic model at equilibrium and establish explicit mathematical relation among the parameters of the gamma distribution and the stochastic models. We analyze the biological implications of these relationships, with particular emphasis on how the shape and scale parameters of the gamma distribution reflect population dynamics at equilibrium. These mathematical relations describe the impact of the variance of the stochastic perturbation on the intrinsic growth rate, and, in particular, reveal that there are two branches of the intrinsic growth rates representing alternative stable states at equilibrium.
Deep Hedging Bermudan Swaptions
Authors: Kenjiro Oya
Date: 2024-11-15 00:00:00

Abstract This paper proposes a novel approach to Bermudan swaption hedging by applying the deep hedging framework to address limitations of traditional arbitrage-free methods. Conventional methods assume ideal conditions, such as zero transaction costs, perfect liquidity, and continuous-time hedging, which often differ from real market environments. This discrepancy can lead to residual profit and loss (P&L), resulting in two primary issues. First, residual P&L may prevent achieving the initial model price, especially with improper parameter settings, potentially causing a negative P&L trend and significant financial impacts. Second, controlling the distribution of residual P&L to mitigate downside risk is challenging, as hedged positions may become curve gamma-short, making them vulnerable to large interest rate movements. The deep hedging approach enables flexible selection of convex risk measures and hedge strategies, allowing for improved residual P&L management. This study also addresses challenges in applying the deep hedging approach to Bermudan swaptions, such as efficient arbitrage-free market scenario generation and managing early exercise conditions. Additionally, we introduce a unique "Option Spread Hedge" strategy, which allows for robust hedging and provides intuitive interpretability. Numerical analysis results demonstrate the effectiveness of our approach.
Portfolio Optimization with Feedback Strategies Based on Artificial Neural Networks
Authors: Yaacov Kopeliovich, Michael Pokojovy
Date: 2024-11-15 00:00:00
Tasks: Portfolio Optimization

With the recent advancements in machine learning (ML), artificial neural networks (ANN) are starting to play an increasingly important role in quantitative finance. Dynamic portfolio optimization is among many problems that have significantly benefited from a wider adoption of deep learning (DL). While most existing research has primarily focused on how DL can alleviate the curse of dimensionality when solving the Hamilton-Jacobi-Bellman (HJB) equation, some very recent developments propose to forego derivation and solution of HJB in favor of empirical utility maximization over dynamic allocation strategies expressed through ANN. In addition to being simple and transparent, this approach is universally applicable, as it is essentially agnostic about market dynamics. To showcase the method, we apply it to optimal portfolio allocation between a cash account and the S&P 500 index modeled using geometric Brownian motion or the Heston model. In both cases, the results are demonstrated to be on par with those under the theoretical optimal weights assuming isoelastic utility and real-time rebalancing. A set of R codes for a broad class of stochastic volatility models are provided as a supplement.