pwc-archive/papers-with-abstracts huggingface dataset:
Tracking Control of Optical Beam Transceivers using Mean Field Models
Authors: Ibrahima N'Doye, Taous-Meriem Laleg-Kirati
Date: 2023-01-26 00:00:00

This paper proposes mean field models to maintain an accurate line-of-sight and tracking between transceivers mounted in mobile unmanned aerial vehicles (UAVs) platforms in the presence of underlying mechanical vibration effects. We describe a two-way optical link beam tracking control that coordinates mobile UAVs in a network architecture to provide reliable network structure, distributed connectivity, and communicability, enhancing terrestrial public safety communication systems. We derive the optical transceiver trajectory tracking problem in which each agent dynamic and cost function is coupled with other optical beam transceiver agent states via a mean field term. We propose two optimal mean field beam tracking control frameworks through decentralized and centralized strategies in which the optical transceivers compete to reach a Nash equilibrium and cooperate to attain a social optimum, respectively. The solutions of these strategies are derived from forward-backward ordinary differential equations and rely on the linearity Hamilton-Jacobi-Bellman Fokker-Planck (HJB-FP) equations and stochastic maximum principle. Moreover, we numerically compute the solution pair of the resulting joint equations using Newton and fixed point iteration methods to verify the existence and uniqueness of the equilibrium and social optimum.
Multi-compartment poroelastic models of perfused biological soft tissues: implementation in FEniCSx
Authors: Thomas Lavigne, Stéphane Urcun, Pierre-Yves Rohan, Giuseppe Sciumè, Davide Baroli, Stéphane P. A. Bordas
Date: 2023-01-26 00:00:00

Soft biological tissues demonstrate strong time-dependent and strain-rate mechanical behavior, arising from their intrinsic visco-elasticity and fluid-solid interactions (especially at sufficiently large time scales). The time-dependent mechanical properties of soft tissues influence their physiological functions and are linked to several pathological processes. Poro-elastic modeling represents a promising approach because it allows the integration of multiscale/multiphysics data to probe biologically relevant phenomena at a smaller scale and embeds the relevant mechanisms at the larger scale. The implementation of multi-phasic flow poro-elastic models however is a complex undertaking, requiring extensive knowledge. The open-source software FEniCSx Project provides a novel tool for the automated solution of partial differential equations by the finite element method. This paper aims to provide the required tools to model the mixed formulation of poro-elasticity, from the theory to the implementation, within FEniCSx. Several benchmark cases are studied. A column under confined compression conditions is compared to the Terzaghi analytical solution, using the L2-norm. An implementation of poro-hyper-elasticity is proposed. A bi-compartment column is compared to previously published results (Cast3m implementation). For all cases, accurate results are obtained in terms of a normalized Root Mean Square Error (RMSE). Furthermore, the FEniCSx computation is found three times faster than the legacy FEniCS one. The benefits of parallel computation are also highlighted.
Inflation targeting strategy and its credibility
Authors: Carlos Esteban Posada
Date: 2023-01-26 00:00:00

The money supply is endogenous if the monetary policy strategy is the so called Inflation and Interest Rate Targeting, IRT. With that and perfect credibility, the theory of the price level and inflation only needs the Fisher equation, but it interprets causality in a new sense: if the monetary authority raises the policy rate, it will raise the inflation target, and vice versa, given the natural interest rate. If credibility is not perfect or if expectations are not completely rational, the theory needs something more. Here I present a model corresponding to this theory that includes both the steady state case and the recovery dynamics after a supply shock, with and without policy reactions to such a shock. But, under the finite horizon assumption for IRT, at some future point in time the money supply must become exogenous. This creates the incentive for agents to examine, as of today, statistics on monetary aggregates and form their forecasts of money supply growth and inflation rates. Additionally, inflation models of the small open economy allow us to deduce that the IRT in this case is much more powerful than otherwise, and for the same degree of credibility. But things are not necessarily easier for the monetary authority: it must monitor not only internal indicators, but also external inflation and its determinants, and it must, in certain circumstances, make more intense adjustments to the interest rate.
Adults in the room? The auditor and dividends in small firms: Evidence from a natural experiment
Authors: Hakim Lyngstadås, Johannes Mauritzen
Date: 2023-01-26 00:00:00
Tasks: regression

We examine the effect of auditing on dividends in small private firms. We hypothesize that auditing can constrain dividends by way of promoting accounting conservatism. We use register data on private Norwegian firms and random variation induced by the introduction of a policy allowing small private firms to forgo the use of an auditor to estimate the effect of auditing on dividend payout. Identification is obtained by a regression discontinuity around the arbitrary thresholds for the policy. Propensity score matching is used to create a balanced synthetic control. We consistently find that forgoing auditing led to a significant increase in dividends in small private firms.
JointNET: A Deep Model for Predicting Active Sacroiliitis from Sacroiliac Joint Radiography
Authors: Sevcan Turk, Ahmet Demirkaya, M Yigit Turali, Cenk Hepdurgun, Salman UH Dar, Ahmet K Karabulut, Aynur Azizova, Mehmet Orman, Ipek Tamsel, Ustun Aydingoz, Mehmet Argin, Tolga Cukur
Date: 2023-01-25 00:00:00
Tasks: Specificity

Purpose: To develop a deep learning model that predicts active inflammation from sacroiliac joint radiographs and to compare the success with radiologists. Materials and Methods: A total of 1,537 (augmented 1752) grade 0 SIJs of 768 patients were retrospectively analyzed. Gold-standard MRI exams showed active inflammation in 330 joints according to ASAS criteria. A convolutional neural network model (JointNET) was developed to detect MRI-based active inflammation labels solely based on radiographs. Two radiologists blindly evaluated the radiographs for comparison. Python, PyTorch, and SPSS were used for analyses. P<0.05 was considered statistically significant. Results: JointNET differentiated active inflammation from radiographs with a mean AUROC of 89.2 (95% CI:86.8%, 91.7%). The sensitivity was 69.0% (95% CI:65.3%, 72.7%) and specificity 90.4% (95% CI:87.8 % 92.9%). The mean accuracy was 90.2% (95% CI: 87.6%, 92.8%). The positive predictive value was 74.6% (95% CI: 72.5%, 76.7%) and negative predictive value was 87.9% (95% CI: 85.4%, 90.5%) when prevalence was considered 1%. Statistical analyses showed a significant difference between active inflammation and healthy groups (p<0.05). Radiologists accuracies were less than 65% to discriminate active inflammation from sacroiliac joint radiographs. Conclusion: JointNET successfully predicts active inflammation from sacroiliac joint radiographs, with superior performance to human observers.
SNR/CRB-Constrained Joint Beamforming and Reflection Designs for RIS-ISAC Systems
Authors: Rang Liu, Ming Li, Qian Liu, A. Lee Swindlehurst
Date: 2023-01-26 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

In this paper, we investigate the integration of integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RIS) for providing wide-coverage and ultra-reliable communication and high-accuracy sensing functions. In particular, we consider an RIS-assisted ISAC system in which a multi-antenna base station (BS) simultaneously performs multi-user multi-input single-output (MU-MISO) communications and radar sensing with the assistance of an RIS. We focus on both target detection and parameter estimation performance in terms of the signal-to-noise ratio (SNR) and Cramer-Rao bound (CRB), respectively. Two optimization problems are formulated for maximizing the achievable sum-rate of the multi-user communications under an SNR constraint for target detection or a CRB constraint for parameter estimation, the transmit power budget, and the unit-modulus constraint of the RIS reflection coefficients. Efficient algorithms are developed to solve these two complicated non-convex problems. Extensive simulation results demonstrate the advantages of the proposed joint beamforming and reflection designs compared with other schemes. In addition, it is shown that more RIS reflection elements bring larger performance gains for direct-of-arrival (DoA) estimation than for target detection.
Joint data rate and EMF exposure analysis in Manhattan environments: stochastic geometry and ray tracing approaches
Authors: Charles Wiame, Simon Demey, Luc Vandendorpe, Philippe De Doncker, Claude Oestges
Date: 2023-01-26 00:00:00

The objective of this study is to jointly analyze the data rate and electromagnetic field (EMF) exposure in urban environments. Capitalizing on stochastic geometry (SG), a network level analysis is performed by modelling these environments via Manhattan Poisson line processes (MPLP). Using this framework, a number of performance metrics are derived: first moments, marginal distributions and joint distributions of the data rate and exposure. In addition, the original Manhattan model is generalized to include advanced features: corner diffraction, presence of potential blockages in streets, and users positioned at crossroads. As a second approach, deterministic ray tracing (RT) is utilized to compute the same metrics. The two methods are shown to provide close results on the condition that the model parameters are coherently selected. Furthermore, the numerical results enable to gain insight into several aspects: the role of the propagation mechanisms in the performance metrics, existing trade-offs between the rate and exposure requirements, as well as the impact of the user location (at a crossroad or in a single street). (This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible)
Sherman-Morrison Regularization for ELAA Iterative Linear Precoding
Authors: Jinfei Wang, Yi Ma, Na Yi, Rahim Tafazolli
Date: 2023-01-26 00:00:00

The design of iterative linear precoding is recently challenged by extremely large aperture array (ELAA) systems, where conventional preconditioning techniques could hardly improve the channel condition. In this paper, it is proposed to regularize the extreme singular values to improve the channel condition by deducting a rank-one matrix from the Wishart matrix of the channel. Our analysis proves the feasibility to reduce the largest singular value or to increase multiple small singular values with a rank-one matrix when the singular value decomposition of the channel is available. Knowing the feasibility, we propose a low-complexity approach where an approximation of the regularization matrix can be obtained based on the statistical property of the channel. It is demonstrated, through simulation results, that the proposed low-complexity approach significantly outperforms current preconditioning techniques in terms of reduced iteration number for more than $10\%$ in both ELAA systems as well as symmetric multi-antenna (i.e., MIMO) systems when the channel is i.i.d. Rayleigh fading.
Scheduling Policy for Value-of-Information (VoI) in Trajectory Estimation for Digital Twins
Authors: Van-Phuc Bui, Shashi Raj Pandey, Federico Chiariotti, Petar Popovski
Date: 2023-01-26 00:00:00
Tasks: Scheduling, State Estimation

This paper presents an approach to schedule observations from different sensors in an environment to ensure their timely delivery and build a digital twin (DT) model of the system dynamics. At the cloud platform, DT models estimate and predict the system's state, then compute the optimal scheduling policy and resource allocation strategy to be executed in the physical world. However, given limited network resources, partial state vector information, and measurement errors at the distributed sensing agents, the acquisition of data (i.e., observations) for efficient state estimation of system dynamics is a non-trivial problem. We propose a Value of Information (VoI)-based algorithm that provides a polynomial-time solution for selecting the most informative subset of sensing agents to improve confidence in the state estimation of DT models. Numerical results confirm that the proposed method outperforms other benchmarks, reducing the communication overhead by half while maintaining the required estimation accuracy.
Human-machine Hierarchical Networks for Decision Making under Byzantine Attacks
Authors: Chen Quan, Baocheng Geng, Yunghsiang S. Han, Pramod K. Varshney
Date: 2023-01-25 00:00:00
Tasks: Decision Making

This paper proposes a belief-updating scheme in a human-machine collaborative decision-making network to combat Byzantine attacks. A hierarchical framework is used to realize the network where local decisions from physical sensors act as reference decisions to improve the quality of human sensor decisions. During the decision-making process, the belief that each physical sensor is malicious is updated. The case when humans have side information available is investigated, and its impact is analyzed. Simulation results substantiate that the proposed scheme can significantly improve the quality of human sensor decisions, even when most physical sensors are malicious. Moreover, the performance of the proposed method does not necessarily depend on the knowledge of the actual fraction of malicious physical sensors. Consequently, the proposed scheme can effectively defend against Byzantine attacks and improve the quality of human sensors' decisions so that the performance of the human-machine collaborative system is enhanced.
Optimal Complex-Valued Prototype Filter Design for GFDM Systems
Authors: Behzad Mozaffari Tazehkand, Mohammad Reza Ghavidel Aghdam, Reza Abdolee
Date: 2023-01-25 00:00:00

One of the main challenges with generalized frequency division multiplexing (GFDM) systems is prototype filter design. A poorly designed filter increases inherent and out-of-band (OOB) interferences. In this paper, we introduce a novel optimal prototype filter for GFDM systems that eliminates the negative effects of intrinsic interferences. We introduce a complex-valued pulse shape similar to a single-sideband (SSB) modulation scheme, which significantly improves bandwidth efficiency. Specifically, we introduce an optimization problem to design an optimal pulse shape filter to reduce all intrinsic interference to zero. We derive analytical expressions to evaluate the bit error rate (BER) of the system and show how the designed optimal prototype filter outperforms its current counterparts.
Effect of sonication time and surfactant concentration on improving the bio-accessibility of lycopene synthesized in poly-lactic co-glycolic acid nanoparticles
Authors: Mohammad Anwar Ul Alam, Aschalew Kassu, Lamin Kassama
Date: 2023-01-25 00:00:00

The use of biodegradable polymers simplifies the development of therapeutic devices with regards to transient implants and three-dimensional platform suitable for tissue engineering. Further advances have also occurred in the controlled released mechanism of bioactive compounds encapsulated in biodegradable polymers. This application requires the understanding of the physicochemical properties of the polymeric materials and their inherent impact on the delivery of encapsulated bioactive. Hence, the objective of this study was to evaluate the effect of surfactant and sonication time on the bio-accessibility of lycopene encapsulated polymeric nanoparticles. The emulsion evaporation method was used to encapsulate lycopene in poly-lactic co-glycolic acid (PLGA) with surfactant concentration, sonication time and polymer concentration as independent variables. Physicochemical and morphological characteristics were measured with a zetasizer and SEM, while the encapsulation efficiency and controlled release kinetics with spectrophotometric, and the dialysis method was used to estimate bioaccessibility. The results have shown sonication time to have significantly (p < 0.05) influenced the encapsulation efficiency. Hence, the sonication time of 4 min yield an encapsulation efficiency of 78% and increased to 97% with increase sonication time (6 min). Increased sonication time had a decreasing effect on the hydrodynamic diameter and stability of the encapsulated nanoparticles. The slow release of lycopene was observed during the first 12 days, followed by a burst release of about 44% on the 13th day in-vitro. The study will have significant impact on the manufacturing of functional food with encapsulated ingredients and provide an understanding of their inherent control release mechanism in the GIT.
Music Enhances Activity in the Hypothalamus, Brainstem, and Anterior Cerebellum during Script-Driven Imagery of Affective Scenes
Authors: Chia-Wei Li, Tzu-Han Cheng, Chen-Gia Tsai
Date: 2023-01-26 00:00:00
Tasks: Hippocampus

Music is frequently used to establish atmosphere and to enhance/alter emotion in dramas and films. During music listening, visual imagery is a common mechanism underlying emotion induction. The present functional magnetic resonance imaging (fMRI) study examined the neural substrates of the emotional processing of music and imagined scene. A factorial design was used with factors emotion valence (positive; negative) and music (withoutMUSIC: script-driven imagery of emotional scenes; withMUSIC: script-driven imagery of emotional scenes and simultaneously listening to affectively congruent music). The baseline condition was imagery of neutral scenes in the absence of music. Eleven females and five males participated in this fMRI study. The contrasts of positive and negative withoutMUSIC conditions minus the baseline (imagery of neutral scenes) showed no significant activation. When comparing the withMUSIC to withoutMUSIC conditions, activity in a number of emotion-related regions was observed, including the temporal pole (TP), amygdala, hippocampus, hypothalamus, anterior ventral tegmental area (VTA), locus coeruleus, and anterior cerebellum. We hypothesized that the TP may integrate music and the imagined scene to extract socioemotional significance, initiating the subcortical structures to generate subjective feelings and bodily responses. For the withMUSIC conditions, negative emotions were associated with enhanced activation in the posterior VTA compared to positive emotions. Our findings replicated and extended previous research which suggests that different subregions of the VTA are sensitive to rewarding and aversive stimuli. Taken together, this study suggests that emotional music embedded in an imagined scenario is a salient social signal that prompts preparation of approach/avoidance behaviours and emotional responses in listeners.
Can linking the recall system to addiction enable a better understanding of the dopaminergic pathway?
Authors: Dozie Iwuh
Date: 2022-12-15 00:00:00

Human addiction, as a learned behaviour, has and is constantly being treated psychologically, with specific and timely interventions from Neuroscience. We endorse that human addiction can receive further boost as regards treatment, when we firmly understand how it works from a quantum scale. This is majorly because the dopaminergic pathway (DP) that is well elaborated in the brain of every addict is connected to the memory pathway. This further implies that the recall process in the brain of the addict, as regards his/her addiction is fully functional in line with the pleasure that arises from the element of his/her addiction. This dopamine-led pathway shows itself as prominent in what pertains to addiction, this is because of the role it plays in reward. As a neurotransmitter, dopamine flickers when reward is in the offing. It should be noted that a full understanding of the dimensions of addiction in the human person has not be attained to, therefore, we seek to add to this ongoing research, by considering excerpts arising from Quantum Field Theory. We are introducing excerpts from QFT, because DP, is an attendant element in the process of reward and motivation. In clear terms, we are alluding that it all begins with the memory.
Functional Differences of MIP (Lens Fiber Major Intrinsic Protein) Between Animals and Birds
Authors: J. C. Phillips
Date: 2023-01-24 00:00:00

The major intrinsic protein (MIP) of the lens fiber cell membrane plays a role in lens biogenesis and maintenance. Its polypeptide chains span the membrane six times, and the protein is naturally divided into two halves. We use modern sequence analysis to identify differences between halves for humans (common to animals) and chickens (common to birds).
Three facets of mathematical cancer biology research
Authors: Yue Wang
Date: 2023-01-24 00:00:00

Cancer, as the uncontrollable cell growth, is related to many branches of biology. In this review, we will discuss three mathematical approaches for studying cancer biology: population dynamics, gene regulation, and developmental biology. If we understand all biochemical mechanisms of cancer cells, we can directly calculate how the cancer cell population behaves. Inversely, just from the cell count data, we can use population dynamics to infer the mechanisms. Cancer cells emerge from certain genetic mutations, which affect the expression of other genes through gene regulation. Therefore, knowledge of gene regulation can help with cancer prevention and treatment. Developmental biology studies acquisition and maintenance of normal cellular function, which is inspiring to cancer biology in the opposite direction. Besides, cancer cells implanted into an embryo can differentiate into normal tissues, which provides a possible approach of curing cancer. This review illustrates the role of mathematics in these three fields: what mathematical models are used, what data analysis tools are applied, and what mathematical theorems need to be proved. We hope that applied mathematicians and even pure mathematicians can find meaningful mathematical problems related to cancer biology.