pwc-archive/papers-with-abstracts huggingface dataset:
Reconfigurable Intelligent Surface Enabled Joint Backscattering and Communication
Authors: Jinqiu Zhao, Jia Ye Shuaishuai Guo, Zhiquan Bai, Di Zhou, Abeer Mohamed
Date: 2023-08-21 00:00:00

Reconfigurable intelligent surface (RIS) as an essential topic in the sixth-generation (6G) communications aims to enhance communication performance or mitigate undesired transmission. However, the controllability of each reflecting element on RIS also enables it to act as a passive backscatter device (BD) and transmit its information to reader devices. In this paper, we propose a RIS-enabled joint backscattering and communication (JBAC) system, where the backscatter communication coexists with the primary communication and occupies no extra spectrum. Specifically, the RIS modifies its reflecting pattern to act as a passive BD and reflect its own information back to the base station (BS) in the backscatter communication, while helping the primary communication from the BS to the users simultaneously. We further present an iterative active beamforming and reflecting pattern design to maximize the user average transmission rate of the primary communication and the goodput of the backscatter communication by solving the formulated multi-objective optimization problem (MOOP). Numerical results fully uncover the impacts of the number of reflecting elements and the reflecting patterns on the system performance, and demonstrate the effectiveness of the proposed scheme. Important practical implementation remarks have also been discussed.
Channel sensing for holographic interference surfaces based on the principle of interferometry
Authors: Jindiao Huang, Yuyao Wu, Haifan Yin, Yuhao Zhang, Ruikun Zhang
Date: 2023-08-20 00:00:00

The Holographic Interference Surface (HIS) provides a new paradigm for building a more cost-effective wireless communication architecture. In this paper, we derive the principles of holographic interference theory for electromagnetic wave reception and transmission, whereby the optical holography is extended to communication holography and a channel sensing architecture for holographic interference surfaces is established. Unlike the traditional pilot-based channel estimation approaches, the proposed architecture circumvents the complicated processes like filtering, analog to digital conversion (ADC), down conversion. Instead, it relies on interfering the object waves with a pre-designed reference wave, and therefore reduces the hardware complexity and requires less time-frequency resources for channel estimation. To address the self-interference problem in the holographic recording process, we propose a phase shifting-based interference suppression (PSIS) method according to the structural characteristics of communication hologram and interference composition. We then propose a Prony-based multi-user channel segmentation (PMCS) algorithm to acquire the channel state information (CSI). Our theoretical analysis shows that the estimation error of the PMCS algorithm converges to zero when the number of HIS units is large enough. Simulation results show that under the holographic architecture, our proposed algorithm can accurately estimate the CSI in multi-user scenarios.
Realizing In-Memory Baseband Processing for Ultra-Fast and Energy-Efficient 6G
Authors: Qunsong Zeng, Jiawei Liu, Mingrui Jiang, Jun Lan, Yi Gong, Zhongrui Wang, Yida Li, Can Li, Jim Ignowski, Kaibin Huang
Date: 2023-08-19 00:00:00

To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultra-fast and energy-efficient baseband processors. Traditional complementary metal-oxide-semiconductor (CMOS)-based baseband processors face two challenges in transistor scaling and the von Neumann bottleneck. To address these challenges, in-memory computing-based baseband processors using resistive random-access memory (RRAM) present an attractive solution. In this paper, we propose and demonstrate RRAM-implemented in-memory baseband processing for the widely adopted multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) air interface. Its key feature is to execute the key operations, including discrete Fourier transform (DFT) and MIMO detection using linear minimum mean square error (L-MMSE) and zero forcing (ZF), in one-step. In addition, RRAM-based channel estimation module is proposed and discussed. By prototyping and simulations, we demonstrate the feasibility of RRAM-based full-fledged communication system in hardware, and reveal it can outperform state-of-the-art baseband processors with a gain of 91.2$\times$ in latency and 671$\times$ in energy efficiency by large-scale simulations. Our results pave a potential pathway for RRAM-based in-memory computing to be implemented in the era of the sixth generation (6G) mobile communications.
Stochastic Geometry Analysis of a New GSCM with Dual Visibility Regions
Authors: Anish Pradhan, Harpreet S. Dhillon, Fredrik Tufvesson, Andreas F. Molisch
Date: 2023-08-18 00:00:00
Tasks: Point Processes

The geometry-based stochastic channel models (GSCM), which can describe realistic channel impulse responses, often rely on the existence of both {\em local} and {\em far} scatterers. However, their visibility from both the base station (BS) and mobile station (MS) depends on their relative heights and positions. For example, the condition of visibility of a scatterer from the perspective of a BS is different from that of an MS and depends on the height of the scatterer. To capture this, we propose a novel GSCM where each scatterer has dual disk visibility regions (VRs) centered on itself for both BS and MS, with their radii being our model parameters. Our model consists of {\em short} and {\em tall} scatterers, which are both modeled using independent inhomogeneous Poisson point processes (IPPPs) having distinct dual VRs. We also introduce a probability parameter to account for the varying visibility of tall scatterers from different MSs, effectively emulating their noncontiguous VRs. Using stochastic geometry, we derive the probability mass function (PMF) of the number of multipath components (MPCs), the marginal and joint distance distributions for an active scatterer, the mean time of arrival (ToA), and the mean received power through non-line-of-sight (NLoS) paths for our proposed model. By selecting appropriate model parameters, the propagation characteristics of our GSCM are demonstrated to closely emulate those of the COST-259 model.
Novel LoS $β-γ$ THz Channel Unifying Molecular Re-radiation Manifestations
Authors: Anish Pradhan, J. Kartheek Devineni, Andreas F. Molisch, Harpreet S. Dhillon
Date: 2023-08-18 00:00:00

This paper introduces a novel line-of-sight (LoS) $\beta-\gamma$ terahertz (THz) channel model that closely mirrors physical reality by considering radiation trapping. Our channel model provides an exhaustive modeling of the physical phenomena including the amount of re-radiation available at the receiver, parametrized by $\beta$, and the balance between scattering and noise contributions, parametrized by $\gamma$, respectively. Our findings indicate a nontrivial relationship between average limiting received signal-to-noise ratio (SNR) and distance emphasizing the significance of $\gamma$ in THz system design. We further propose new maximum likelihood (ML) thresholds for pulse amplitude modulation (PAM) and quadrature amplitude modulation (QAM) schemes, resulting in analytical symbol error rate (SER) expressions that account for different noise variances across constellation points. The results confirm that the analytical SER closely matches the true simulated SER when using an optimal detector. As expected, under maximum molecular re-radiation, the true SER is shown to be lower than that produced by a suboptimal detector that assumes equal noise variances.
Signal Processing Based Antenna Pattern Characterization for MIMO Systems
Authors: Chandan Kumar Sheemar, Jorge Querol, Symeon Chatzinotas
Date: 2023-08-18 00:00:00

Sophisticated antenna technologies are constantly evolving to meet the escalating data demands projected for 6G and future networks. The characterization of these emerging antenna systems poses challenges that necessitate a reevaluation of conventional techniques, which rely solely on simple measurements conducted in advanced anechoic chambers. In this study, our objective is to introduce a novel endeavour for antenna pattern characterization (APC) in next-generation multiple-input-multiple-output (MIMO) systems by utilizing the potential of signal processing tools. In contrast to traditional methods that struggle with multi-path scenarios and require specialized equipment for measurements, we endeavour to estimate the antenna pattern by exploiting information from both line-of-sight (LoS) and non-LoS contributions. This approach enables antenna pattern characterization in complex environments without the need for anechoic chambers, resulting in substantial cost savings. Furthermore, it grants a much wider research community the ability to independently perform APC for emerging complex 6G antenna systems, without relying on anechoic chambers. Simulation results demonstrate the efficacy of the proposed novel approach in accurately estimating the true antenna pattern.
Liquid Crystal-Based RIS for VLC Transmitters: Performance Analysis, Challenges, and Opportunities
Authors: Sylvester Aboagye, Telex M. N. Ngatched, Alain R. Ndjiongue, Octavia A. Dobre, Hyundong Shin
Date: 2023-08-18 00:00:00
Tasks: Diversity

This article presents a novel approach of using reconfigurable intelligent surfaces (RISs) in the transmitter of indoor visible light communication (VLC) systems to enhance data rate uniformity and maintain adequate illumination. In this approach, a liquid crystal (LC)-based RIS is placed in front of the LED arrays of the transmitter to form an LC-based RIS-enabled VLC transmitter. This RIS-enabled transmitter is able to perform new functions such as transmit light steering and amplification and demonstrates very high data rate and illumination performance when compared with traditional VLC transmitters with circular and distributed LED arrays and the more recent angle diversity transmitter. Simulation results reveal the strong potential of LC-based RIS-aided transmitters in satisfying the joint illumination and communication needs of indoor VLC systems and positions VLC as a critical essential block for next generation communication networks. Several challenging and exciting issues related to the realization of such transmitters are discussed.
Order-of-mutation effects on cancer progression: models for myeloproliferative neoplasm
Authors: Yue Wang, Blerta Shtylla, Tom Chou
Date: 2023-08-19 00:00:00
Tasks: Prognosis

We develop a modeling framework for cancer progression that distinguishes the order of two possible mutations. Recent observations and information on myeloproliferative neoplasms are analyzed within our framework. In some patients with myeloproliferative neoplasms, two genetic mutations can be found, JAK2 V617F and TET2. Whether or not one mutation is present will influence how the other subsequent mutation affects the regulation of gene expression. When both mutations are present, the order of their occurrence has been shown to influence disease progression and prognosis. In this paper, we propose a nonlinear ordinary differential equation (ODE) and Markov chain models to explain the non-additive and non-commutative clinical observations with respect to different orders of mutations: gene expression patterns, proportions of cells with different mutations, and ages at diagnosis. We also propose potential experiments measurements that can be used to verify our models.
Canonical Cortical Field Theories
Authors: Gerald K. Cooray, Vernon Cooray, Karl Friston
Date: 2023-08-21 00:00:00

We characterise the dynamics of neuronal activity, in terms of field theory, using neural units placed on a 2D-lattice modelling the cortical surface. The electrical activity of neuronal units was analysed with the aim of deriving a neural field model with a simple functional form that still able to predict or reproduce empirical findings. Each neural unit was modelled using a neural mass and the accompanying field theory was derived in the continuum limit. The field theory comprised coupled (real) Klein-Gordon fields, where predictions of the model fall within the range of experimental findings. These predictions included the frequency spectrum of electric activity measured from the cortex, which was derived using an equipartition of energy over eigenfunctions of the neural fields. Moreover, the neural field model was invariant, within a set of parameters, to the dynamical system used to model each neuronal mass. Specifically, topologically equivalent dynamical systems resulted in the same neural field model when connected in a lattice; indicating that the fields derived could be read as a canonical cortical field theory. We specifically investigated non-dispersive fields that provide a structure for the coding (or representation) of afferent information. Further elaboration of the ensuing neural field theory, including the effect of dispersive forces, could be of importance in the understanding of the cortical processing of information.
Linking fast and slow: the case for generative models
Authors: Johan Medrano, Karl J. Friston, Peter Zeidman
Date: 2023-08-21 00:00:00
Tasks: Bayesian Inference

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multi-scale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.
Deep Learning Architecture for Motor Imaged Words
Authors: Vimal W, Akshansh Gupta
Date: 2023-08-08 00:00:00
Tasks: Brain Computer Interface, Deep Learning, Denoising, Dimensionality Reduction, EEG, Motor Imagery

The notion of a Brain-Computer Interface system is the acquisition of signals from the brain, processing them, and translating them into commands. The study concentrated on a specific sort of brain signal known as Motor Imagery EEG signals, which are activated in the brain without any external stimulus of the needed motor activities in relation to the signal. The signals are further processed using complicated signal processing methods such as wavelet-based denoising and Independent Component Analysis (ICA) based dimensionality reduction approach. To extract the characteristics from the processed data, both signal processing includes Short-Term Fourier Transforms (STFT) and a probabilistic approach such as Gramian Angular field Theory are used. Furthermore, the gathered feature signals are analyzed and converted into noteworthy commands by Deep Learning algorithms, which can be a mix of complicated Deep Learning algorithm families such as CNN and RNN. The Weights of trained model with the particular subject is further used for the multiple subject which shows in the elevation of accuracy rate in translating the Motor Imagery EEG signals into the relevant motor actions
D-TIPO: Deep time-inconsistent portfolio optimization with stocks and options
Authors: Kristoffer Andersson, Cornelis W. Oosterlee
Date: 2023-08-21 00:00:00
Tasks: Portfolio Optimization

In this paper, we propose a machine learning algorithm for time-inconsistent portfolio optimization. The proposed algorithm builds upon neural network based trading schemes, in which the asset allocation at each time point is determined by a a neural network. The loss function is given by an empirical version of the objective function of the portfolio optimization problem. Moreover, various trading constraints are naturally fulfilled by choosing appropriate activation functions in the output layers of the neural networks. Besides this, our main contribution is to add options to the portfolio of risky assets and a risk-free bond and using additional neural networks to determine the amount allocated into the options as well as their strike prices. We consider objective functions more in line with the rational preference of an investor than the classical mean-variance, apply realistic trading constraints and model the assets with a correlated jump-diffusion SDE. With an incomplete market and a more involved objective function, we show that it is beneficial to add options to the portfolio. Moreover, it is shown that adding options leads to a more constant stock allocation with less demand for drastic re-allocations.
To the Moon: Analyzing Collective Trading Events on the Wings of Sentiment Analysis
Authors: Tim Matthies, Thomas Löhden, Stephan Leible, Jun-Patrick Raabe
Date: 2023-08-19 00:00:00
Tasks: Sentiment Analysis

This research investigates the growing trend of retail investors participating in certain stocks by organizing themselves on social media platforms, particularly Reddit. Previous studies have highlighted a notable association between Reddit activity and the volatility of affected stocks. This study seeks to expand the analysis to Twitter, which is among the most impactful social media platforms. To achieve this, we collected relevant tweets and analyzed their sentiment to explore the correlation between Twitter activity, sentiment, and stock volatility. The results reveal a significant relationship between Twitter activity and stock volatility but a weak link between tweet sentiment and stock performance. In general, Twitter activity and sentiment appear to play a less critical role in these events than Reddit activity. These findings offer new theoretical insights into the impact of social media platforms on stock market dynamics, and they may practically assist investors and regulators in comprehending these phenomena better.
Digital Real Estate in the Metaverse: An Empirical Analysis of Retail Investor Motivations
Authors: Lennart Ante, Friedrich-Philipp Wazinski, Aman Saggu
Date: 2023-08-20 00:00:00

This paper investigates retail investor motivations for digital real estate ownership in the crypto-metaverse. Utilizing a detailed financial behavior survey of metaverse landowners' intrinsic and extrinsic motivations, we apply principal components analysis to uncover four distinct motivational groups: (1) Aesthetics and Identity, (2) Social and Community, (3) Speculation and Investment, and (4) Innovation and Technology. Our findings reveal that age, education, investment knowledge, risk-taking, and impulsivity significantly influence investor group membership. This research provides valuable insights to investors and developers, underscoring the potential of a platform to attract retail investors with speculative intentions, engagement longevity, and passive or active trading characteristics, contingent on unique crypto-metaverse attributes.
Analytical valuation of vulnerable derivative claims with bilateral cash flows under credit, funding and wrong-way risk
Authors: Juan Jose Francisco Miguelez, Cristin Buescu
Date: 2023-08-21 00:00:00

We study the problem of valuing and hedging a vulnerable derivative claim with bilateral cash flows between two counterparties in the presence of asymmetric funding costs, defaults and wrong way risk (WWR). We characterize the pre-default claim value as the solution to a non-linear Cauchy problem. We show an explicit stochastic representation of the solution exists under a funding policy which linearises the Cauchy PDE. We apply this framework to the valuation of a vulnerable equity forward and show it can be represented as a portfolio of European options. Despite the complexity of the model, we prove the forward's value admits an analytical formula involving only elementary functions and Gaussian integrals. Based on this explicit formula, numerical analysis demonstrates WWR has a significant impact even under benign assumptions: with a parameter configuration less punitive than that representative of Archegos AM default, we find WWR can shift values for vulnerable forwards by 100bps of notional, while peak exposures increase by 25% of notional. This framework is the first to apply to contracts with bilateral cash flows in the presence of credit, funding and WWR, resulting in a non-linear valuation formula which admits a closed-form solution under a suitable funding policy.
Explicit Computations for Delayed Semistatic Hedging
Authors: Yan Dolinsky, Or Zuk
Date: 2023-08-21 00:00:00

In this work we consider the exponential utility maximization problem in the framework of semistatic hedging.
Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction
Authors: Tao Zou, Le Yu, Leilei Sun, Bowen Du, Deqing Wang, Fuzhen Zhuang
Date: 2023-08-04 00:00:00
Tasks: Classification, Graph Learning, Graph Representation Learning, Patent classification, Representation Learning

Accurate prediction of what types of patents that companies will apply for in the next period of time can figure out their development strategies and help them discover potential partners or competitors in advance. Although important, this problem has been rarely studied in previous research due to the challenges in modelling companies' continuously evolving preferences and capturing the semantic correlations of classification codes. To fill in this gap, we propose an event-based dynamic graph learning framework for patent application trend prediction. In particular, our method is founded on the memorable representations of both companies and patent classification codes. When a new patent is observed, the representations of the related companies and classification codes are updated according to the historical memories and the currently encoded messages. Moreover, a hierarchical message passing mechanism is provided to capture the semantic proximities of patent classification codes by updating their representations along the hierarchical taxonomy. Finally, the patent application trend is predicted by aggregating the representations of the target company and classification codes from static, dynamic, and hierarchical perspectives. Experiments on real-world data demonstrate the effectiveness of our approach under various experimental conditions, and also reveal the abilities of our method in learning semantics of classification codes and tracking technology developing trajectories of companies.