pwc-archive/papers-with-abstracts huggingface dataset:
Anchor-points Assisted Uplink Sensing in Perceptive Mobile Networks
Authors: Yanmo Hu, J. Andrew Zhang, Weibo Deng, Y. Jay Guo
Date: 2024-01-17 00:00:00
Tasks: ISAC

Uplink sensing in integrated sensing and communications (ISAC) systems, such as Perceptive Mobile Networks, is challenging due to the clock asynchronism between transmitter and receiver. Existing solutions typically require the presence of a dominating line-of-sight path and the knowledge of transmitter location at the receiver. In this paper, relaxing these requirements, we propose a novel and effective uplink sensing scheme with the assistance of static anchor points. Two major algorithms are proposed in the scheme. The first algorithm estimates the relative timing and carrier frequency offsets due to clock asynchronism, with respect to those at a randomly selected reference snapshot. Theoretical performance analysis is provided for the algorithm. The estimates from the first algorithm are then used to compensate for the offsets and generate the angle-Doppler maps. Using the maps, the second algorithm identifies the anchor points, and then locates the UE and dynamic targets. Feasibility of UE localization is also analyzed. Simulation results are provided and demonstrate the effectiveness of the proposed algorithms.
Multi-target Detection for Reconfigurable Holographic Surfaces Enabled Radar
Authors: XiaoYu Zhang, Haobo Zhang, Ruoqi Deng, Liang Liu, Boya Di
Date: 2024-01-17 00:00:00

Multi-target detection is one of the primary tasks in radar-based localization and sensing, typically built on phased array antennas. However, the bulky hardware in the phased array restricts its potential for enhancing detection accuracy, since the cost and power of the phased array can become unaffordable as its physical aperture scales up to pursue higher beam shaping capabilities. To resolve this issue, we propose a radar system enabled by reconfigurable holographic surfaces (RHSs), a novel meta-surface antenna composed of meta-material elements with cost-effective and power-efficient hardware, which performs multi-target detection in an adaptive manner. Different from the phase-control structure in the phased array, the RHS is able to apply beamforming by controlling the radiation amplitudes of its elements. Consequently, traditional beamforming schemes designed for phased arrays cannot be directly applied to RHSs due to this structural difference. To tackle this challenge, a waveform and amplitude optimization algorithm (WAOA) is designed to jointly optimize the radar waveform and RHS amplitudes in order to improve the detection accuracy. Simulation results reveal that the proposed RHS-enabled radar increases the probability of detection by 0.13 compared to phased array radars when six iterations of adaptive detection are performed given the same hardware cost.
Privacy Protected Contactless Cardio-respiratory Monitoring using Defocused Cameras during Sleep
Authors: Yingen Zhu, Jia Huang, Hongzhou Lu, Wenjin Wang
Date: 2024-01-17 00:00:00
Tasks: Sleep Quality

The monitoring of vital signs such as heart rate (HR) and respiratory rate (RR) during sleep is important for the assessment of sleep quality and detection of sleep disorders. Camera-based HR and RR monitoring gained popularity in sleep monitoring in recent years. However, they are all facing with serious privacy issues when using a video camera in the sleeping scenario. In this paper, we propose to use the defocused camera to measure vital signs from optically blurred images, which can fundamentally eliminate the privacy invasion as face is difficult to be identified in obtained blurry images. A spatial-redundant framework involving living-skin detection is used to extract HR and RR from the defocused camera in NIR, and a motion metric is designed to exclude outliers caused by body motions. In the benchmark, the overall Mean Absolute Error (MAE) for HR measurement is 4.4 bpm, for RR measurement is 5.9 bpm. Both have quality drops as compared to the measurement using a focused camera, but the degradation in HR is much less, i.e. HR measurement has strong correlation with the reference ($R \geq 0.90$). Preliminary experiments suggest that it is feasible to use a defocused camera for cardio-respiratory monitoring while protecting the privacy. Further improvement is needed for robust RR measurement, such as by PPG-modulation based RR extraction.
Graph-based vulnerability assessment of resting-state functional brain networks in full-term neonates
Authors: Mahshid Fouladivanda, Kamran Kazemi, Habibollah Danyali, Ardalan Aarabi
Date: 2024-01-17 00:00:00

Network disruption during early brain development can result in long-term cognitive impairments. In this study, we investigated rich-club organization in resting-state functional brain networks in full-term neonates using a multiscale connectivity analysis. We further identified the most influential nodes, also called spreaders, having higher impacts on the flow of information throughout the network. The network vulnerability to damage to rich-club (RC) connectivity within and between resting-state networks was also assessed using a graph-based vulnerability analysis. Our results revealed a rich club organization and small-world topology for resting-state functional brain networks in full term neonates, regardless of the network size. Interconnected mostly through short-range connections, functional rich-club hubs were confined to sensory-motor, cognitive-attention-salience (CAS), default mode, and language-auditory networks with an average cross-scale overlap of 36%, 20%, 15% and 12%, respectively. The majority of the functional hubs also showed high spreading potential, except for several non-RC spreaders within CAS and temporal networks. The functional networks exhibited high vulnerability to loss of RC nodes within sensorimotor cortices, resulting in a significant increase and decrease in network segregation and integration, respectively. The network vulnerability to damage to RC nodes within the language-auditory, cognitive-attention-salience, and default mode networks was also significant but relatively less prominent. Our findings suggest that the network integration in neonates can be highly compromised by damage to RC connectivity due to brain immaturity.
From Physics to Sentience: Deciphering the Semantics of the Free-Energy Principle and Evaluating its Claims
Authors: Zahra Sheikhbahaee, Adam Safron, Casper Hesp, Guillaume Dumas
Date: 2024-01-16 00:00:00

The Free-Energy Principle (FEP) [1-3] has been adopted in a variety of ambitious proposals that aim to characterize all adaptive, sentient, and cognitive systems within a unifying framework. Judging by the amount of attention it has received from the scientific community, the FEP has gained significant traction in these pursuits. The current target article represents an important iteration of this research paradigm in formally describing emergent dynamics rather than merely (quasi-)steady states. This affords more in-depth considerations of the spatio-temporal complexities of cross-scale causality - as we have encouraged and built towards in previous publications (e.g., [4-9]). In this spirit of constructive feedback, we submit a few technical comments on some of the matters that appear to require further attention, in order to improve the clarity, rigour, and applicability of this framework.
Concept Alignment
Authors: Sunayana Rane, Polyphony J. Bruna, Ilia Sucholutsky, Christopher Kello, Thomas L. Griffiths
Date: 2024-01-09 00:00:00
Tasks: Concept Alignment, Philosophy

Discussion of AI alignment (alignment between humans and AI systems) has focused on value alignment, broadly referring to creating AI systems that share human values. We argue that before we can even attempt to align values, it is imperative that AI systems and humans align the concepts they use to understand the world. We integrate ideas from philosophy, cognitive science, and deep learning to explain the need for concept alignment, not just value alignment, between humans and machines. We summarize existing accounts of how humans and machines currently learn concepts, and we outline opportunities and challenges in the path towards shared concepts. Finally, we explain how we can leverage the tools already being developed in cognitive science and AI research to accelerate progress towards concept alignment.
Validation and Comparison of Non-Stationary Cognitive Models: A Diffusion Model Application
Authors: Lukas Schumacher, Martin Schnuerch, Andreas Voss, Stefan T. Radev
Date: 2023-12-07 00:00:00
Tasks: Decision Making

Cognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such non-stationary dynamics into existing cognitive model classes. In this work, we provide the first experimental validation of superstatistics and formal comparison of four non-stationary diffusion decision models in a specifically designed perceptual decision-making task. Task difficulty and speed-accuracy trade-off were systematically manipulated to induce expected changes in model parameters. To validate our models, we assess whether the inferred parameter trajectories align with the patterns and sequences of the experimental manipulations. To address computational challenges, we present novel deep learning techniques for amortized Bayesian estimation and comparison of models with time-varying parameters. Our findings indicate that transition models incorporating both gradual and abrupt parameter shifts provide the best fit to the empirical data. Moreover, we find that the inferred parameter trajectories closely mirror the sequence of experimental manipulations. Posterior re-simulations further underscore the ability of the models to faithfully reproduce critical data patterns. Accordingly, our results suggest that the inferred non-stationary dynamics may reflect actual changes in the targeted psychological constructs. We argue that our initial experimental validation paves the way for the widespread application of superstatistics in cognitive modeling and beyond.
Dynamic Brain Behaviours in Stroke: A Longitudinal Investigation Based on fMRI Analysis
Authors: Kaichao Wu, Beth Jelfs, Katrina Neville, Qiang Fang
Date: 2023-11-28 00:00:00

Background: The brain's functional network constantly adapts to external changes. However, the mechanisms underlying this dynamic adaptive behavior in stroke patients with motor injuries and its role in post-stroke motor recovery remain poorly understood. Method: This study conducted a long-term investigation involving 15 first-stroke patients. Each participant underwent five fMRI scans distributed equally over a six-month period. Using functional neuroimaging data, time-varying functional modularity in post-stroke patients was detected, and subsequently, the dynamic brain behaviors, including recruitment, integration, and flexibility, along with their longitudinal changes, were assessed. Results: Our findings reveal that stroke lesions lead to significant and enduring alterations in all three dynamic behaviors within functional brain networks. Furthermore, during the six-month recovery period, patients who exhibited good and poor recovery showed notable differences in recruitment and flexibility, indicating distinct recovery trajectories for these groups. Notably, when predicting post-stroke recovery status, whole-brain recruitment emerged as a robust and reliable feature, achieving an AUC of 85.93 Significance: Our study offers a comprehensive depiction of dynamic brain behavior in the post-ischemic-stroke brain, with a focus on longitudinal changes concurrent with functional recovery. These dynamic patterns hold promise as valuable tools for evaluating and predicting motor recovery following stroke.
Long cycles in linear thresholding systems
Authors: Anna Laddach, Michael Shapiro
Date: 2023-11-22 00:00:00

Linear thresholding systems have been used as a model of neural activation and more recently proposed as a model of gene regulation. Here we exhibit linear thresholding systems whose dynamics produce surprisingly long cycles.
From Conceptual Spaces to Quantum Concepts: Formalising and Learning Structured Conceptual Models
Authors: Sean Tull, Razin A. Shaikh, Sara Sabrina Zemljic, Stephen Clark
Date: 2023-11-06 00:00:00

In this article we present a new modelling framework for structured concepts using a category-theoretic generalisation of conceptual spaces, and show how the conceptual representations can be learned automatically from data, using two very different instantiations: one classical and one quantum. A contribution of the work is a thorough category-theoretic formalisation of our framework. We claim that the use of category theory, and in particular the use of string diagrams to describe quantum processes, helps elucidate some of the most important features of our approach. We build upon Gardenfors' classical framework of conceptual spaces, in which cognition is modelled geometrically through the use of convex spaces, which in turn factorise in terms of simpler spaces called domains. We show how concepts from the domains of shape, colour, size and position can be learned from images of simple shapes, where concepts are represented as Gaussians in the classical implementation, and quantum effects in the quantum one. In the classical case we develop a new model which is inspired by the Beta-VAE model of concepts, but is designed to be more closely connected with language, so that the names of concepts form part of the graphical model. In the quantum case, concepts are learned by a hybrid classical-quantum network trained to perform concept classification, where the classical image processing is carried out by a convolutional neural network and the quantum representations are produced by a parameterised quantum circuit. Finally, we consider the question of whether our quantum models of concepts can be considered conceptual spaces in the Gardenfors sense.
Fitting random cash management models to data
Authors: Francisco Salas-Molina
Date: 2024-01-16 00:00:00
Tasks: Management

Organizations use cash management models to control balances to both avoid overdrafts and obtain a profit from short-term investments. Most management models are based on control bounds which are derived from the assumption of a particular cash flow probability distribution. In this paper, we relax this strong assumption to fit cash management models to data by means of stochastic and linear programming. We also introduce ensembles of random cash management models which are built by randomly selecting a subsequence of the original cash flow data set. We illustrate our approach by means of a real case study showing that a small random sample of data is enough to fit sufficiently good bound-based models.
Reinforcement Learning and Deep Stochastic Optimal Control for Final Quadratic Hedging
Authors: Bernhard Hientzsch
Date: 2023-11-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We consider two data driven approaches, Reinforcement Learning (RL) and Deep Trajectory-based Stochastic Optimal Control (DTSOC) for hedging a European call option without and with transaction cost according to a quadratic hedging P&L objective at maturity ("variance-optimal hedging" or "final quadratic hedging"). We study the performance of the two approaches under various market environments (modeled via the Black-Scholes and/or the log-normal SABR model) to understand their advantages and limitations. Without transaction costs and in the Black-Scholes model, both approaches match the performance of the variance-optimal Delta hedge. In the log-normal SABR model without transaction costs, they match the performance of the variance-optimal Barlett's Delta hedge. Agents trained on Black-Scholes trajectories with matching initial volatility but used on SABR trajectories match the performance of Bartlett's Delta hedge in average cost, but show substantially wider variance. To apply RL approaches to these problems, P&L at maturity is written as sum of step-wise contributions and variants of RL algorithms are implemented and used that minimize expectation of second moments of such sums.
Leverage Staking with Liquid Staking Derivatives (LSDs): Opportunities and Risks
Authors: Xihan Xiong, Zhipeng Wang, Xi Chen, William Knottenbelt, Michael Huth
Date: 2023-11-28 00:00:00
Tasks: POS

In the Proof of Stake (PoS) Ethereum ecosystem, users can stake ETH on Lido to receive stETH, a Liquid Staking Derivative (LSD) that represents staked ETH and accrues staking rewards. LSDs improve the liquidity of staked assets by facilitating their use in secondary markets, such as for collateralized borrowing on Aave or asset exchanges on Curve. The composability of Lido, Aave, and Curve enables an emerging strategy known as leverage staking, an iterative process that enhances financial returns while introducing potential risks. This paper establishes a formal framework for leverage staking with stETH and identifies 442 such positions on Ethereum over 963 days. These positions represent a total volume of 537,123 ETH (877m USD). Our data reveal that 81.7% of leverage staking positions achieved an Annual Percentage Rate (APR) higher than conventional staking on Lido. Despite the high returns, we also recognize the potential risks. For example, the Terra crash incident demonstrated that token devaluation can impact the market. Therefore, we conduct stress tests under extreme conditions of significant stETH devaluation to evaluate the associated risks. Our simulations reveal that leverage staking amplifies the risk of cascading liquidations by triggering intensified selling pressure through liquidation and deleveraging processes. Furthermore, this dynamic not only accelerates the decline of stETH prices but also propagates a contagion effect, endangering the stability of both leveraged and ordinary positions.
Forking paths in financial economics
Authors: Guillaume Coqueret
Date: 2023-11-25 00:00:00

We argue that spanning large numbers of degrees of freedom in empirical analysis allows better characterizations of effects and thus improves the trustworthiness of conclusions. Our ideas are illustrated in three studies: equity premium prediction, asset pricing anomalies and risk premia estimation. In the first, we find that each additional degree of freedom in the protocol expands the average range of $t$-statistics by at least 30%. In the second, we show that resorting to forking paths instead of bootstrapping in multiple testing raises the bar of significance for anomalies: at the 5% confidence level, the threshold for bootstrapped statistics is 4.5, whereas with paths, it is at least 8.2, a bar much higher than those currently used in the literature. In our third application, we reveal the importance of particular steps in the estimation of premia. In addition, we use paths to corroborate prior findings in the three topics. We document heterogeneity in our ability to replicate prior studies: some conclusions seem robust, others do not align with the paths we were able to generate.
Neural Hawkes: Non-Parametric Estimation in High Dimension and Causality Analysis in Cryptocurrency Markets
Authors: Timothée Fabre, Ioane Muni Toke
Date: 2024-01-17 00:00:00

We propose a novel approach to marked Hawkes kernel inference which we name the moment-based neural Hawkes estimation method. Hawkes processes are fully characterized by their first and second order statistics through a Fredholm integral equation of the second kind. Using recent advances in solving partial differential equations with physics-informed neural networks, we provide a numerical procedure to solve this integral equation in high dimension. Together with an adapted training pipeline, we give a generic set of hyperparameters that produces robust results across a wide range of kernel shapes. We conduct an extensive numerical validation on simulated data. We finally propose two applications of the method to the analysis of the microstructure of cryptocurrency markets. In a first application we extract the influence of volume on the arrival rate of BTC-USD trades and in a second application we analyze the causality relationships and their directions amongst a universe of 15 cryptocurrency pairs in a centralized exchange.
Spurious Default Probability Projections in Credit Risk Stress Testing Models
Authors: Bernd Engelmann
Date: 2024-01-17 00:00:00
Tasks: Management

Credit risk stress testing has become an important risk management device which is used both by banks internally and by regulators. Stress testing is complex because it essentially means projecting a bank's full balance sheet conditional on a macroeconomic scenario over multiple years. Part of the complexity stems from using a wide range of model parameters for, e.g., rating transition, write-off rules, prepayment, or origination of new loans. A typical parameterization of a credit risk stress test model specifies parameters linked to an average economic, the through-the-cycle, state. These parameters are transformed to a stressed state by utilizing a macroeconomic model. It will be shown that the model parameterization implies a unique through-the-cycle portfolio which is unrelated to a bank's current portfolio. Independent of the stress imposed to the model, the current portfolio will have a tendency to propagate towards the through-the-cycle portfolio. This could create unwanted spurious effects on projected portfolio default rates especially when a stress test model's parameterization is inconsistent with a bank's current portfolio.
A closer look at the chemical potential of an ideal agent system
Authors: Christoph J. Börner, Ingo Hoffmann, John H. Stiebel
Date: 2024-01-17 00:00:00
Tasks: Econometrics

Models for spin systems known from statistical physics are used in econometrics in the form of agent-based models. Econophysics research in econometrics is increasingly developing general market models that describe exchange phenomena and use the chemical potential $\mu$ known from physics in the context of particle number changes. In statistical physics, equations of state are known for the chemical potential, which take into account the respective model framework and the corresponding state variables. A simple transfer of these equations of state to problems in econophysics appears difficult. To the best of our knowledge, the equation of state for the chemical potential is currently missing even for the simplest conceivable model of an ideal agent system. In this paper, this research gap is closed and the equation of state for the chemical potential is derived from the econophysical model assumptions of the ideal agent system. An interpretation of the equation of state leads to fundamental relationships that could also have been guessed, but are shown here by the theory.