pwc-archive/papers-with-abstracts huggingface dataset:
Seamless Integration and Implementation of Distributed Contact and Contactless Vital Sign Monitoring
Authors: Dingding Liang, Yang Chen, Jiawei Gao, Taixia Shi, Jianping Yao
Date: 2024-05-24 00:00:00

Real-time vital sign monitoring is gaining immense significance not only in the medical field but also in personal health management. Facing the needs of different application scenarios of the smart and healthy city in the future, the low-cost, large-scale, scalable, and distributed vital sign monitoring system is of great significance. In this work, a seamlessly integrated contact and contactless vital sign monitoring system, which can simultaneously implement respiration and heartbeat monitoring, is proposed. In contact vital sign monitoring, the chest wall movement due to respiration and heartbeat is translated into changes in the optical output intensity of a fiber Bragg grating (FBG). The FBG is also an important part of radar signal generation for contactless vital sign monitoring, in which the chest wall movement is translated into phase changes of the radar de-chirped signal. By analyzing the intensity of the FBG output and phase of the radar de-chirped signal, real-time respiration and heartbeat monitoring are realized. In addition, due to the distributed structure of the system and its good integration with the wavelength-division multiplexing optical network, it can be massively scaled by employing more wavelengths. A proof-of-concept experiment is carried out. Contact and contactless respiration and heartbeat monitoring of three people are simultaneously realized. During a monitoring time of 60 s, the maximum absolute measurement errors of respiration and heartbeat rates are 1.6 respirations per minute and 2.3 beats per minute, respectively. The measurement error does not have an obvious change even when the monitoring time is decreased to 5 s.
Distributed Adaptive Control of Disturbed Interconnected Systems with High-Order Tuners
Authors: Moh. Kamalul Wafi, Milad Siami
Date: 2024-05-24 00:00:00

This paper addresses the challenge of network synchronization under limited communication, involving heterogeneous agents with different dynamics and various network topologies, to achieve consensus. We investigate the distributed adaptive control for interconnected unknown linear subsystems with a leader and followers, in the presence of input-output disturbance. We enhance the communication within multi-agent systems to achieve consensus under the leadership's guidance. While the measured variable is similar among the followers, the incoming measurements are weighted and constructed based on their proximity to the leader. We also explore the convergence rates across various balanced topologies (Star-like, Cyclic-like, Path, Random), featuring different numbers of agents, using three distributed algorithms, ranging from first- to high-order tuners to effectively address time-varying regressors. The mathematical foundation is rigorously presented from the network designs of the unknown agents following a leader, to the distributed methods. Moreover, we conduct several numerical simulations across various networks, agents and tuners to evaluate the effects of sparsity in the interaction between subsystems using the $L_2-$norm and $L_\infty-$norm. Some networks exhibit a trend where an increasing number of agents results in smaller errors, although this is not universally the case. Additionally, patterns observed at initial times may not reliably predict overall performance across different networks. Finally, we demonstrate that the proposed modified high-order tuner outperforms its counterparts, and we provide related insights along with our conclusions.
Leveraging Gated Recurrent Units for Iterative Online Precise Attitude Control for Geodetic Missions
Authors: Vrushabh Zinage, Shrenik Zinage, Srinivas Bettadpur, Efstathios Bakolas
Date: 2024-05-24 00:00:00

In this paper, we consider the problem of precise attitude control for geodetic missions, such as the GRACE Follow-on (GRACE-FO) mission. Traditional and well-established control methods, such as Proportional-Integral-Derivative (PID) controllers, have been the standard in attitude control for most space missions, including the GRACE-FO mission. Instead of significantly modifying (or replacing) the original PID controllers that are being used for these missions, we introduce an iterative modification to the PID controller that ensures improved attitude control precision (i.e., reduction in attitude error). The proposed modification leverages Gated Recurrent Units (GRU) to learn and predict external disturbance trends derived from incoming attitude measurements from the GRACE satellites. Our analysis has revealed a distinct trend in the external disturbance time-series data, suggesting the potential utility of GRU's to predict future disturbances acting on the system. The learned GRU model compensates for these disturbances within the standard PID control loop in real time via an additive correction term which is updated at regular time intervals. The simulation results verify the significant reduction in attitude error, verifying the efficacy of our proposed approach.
Stability analysis of nonlinear stochastic flexibility function in smart energy systems
Authors: Seyed Shahabaldin Tohidi, Tobias K. S. Ritschel, Georgios Tsaousoglou, Uffe Høgsbro Thygesen, Henrik Madsen
Date: 2024-05-23 00:00:00
Tasks: Decision Making, Management

Demand-side management provides a great potential for improving the efficiency and reliability of energy systems. This requires a mechanism to connect the market level and the demand side. The flexibility function is a novel approach that bridges the gap between the markets and the dynamics of physical assets at the lower levels of the energy systems and activates demand-side flexibility with the purpose of decision-making as well as for offering a new framework for balancing and grid services. Employing this function as a key for many decision-making and control algorithms reveals that a mathematically rigorous stability analysis is required for it. In this paper, we investigate the stability properties of two nonlinear flexibility functions, as a dynamic mapping between electricity price and power consumption. Specifically, we analyze the stability of a deterministic flexibility function and an It\^{o} stochastic flexibility function. Simulation results are also provided to demonstrate the dynamics of the flexibility functions and to show that the analytical results hold.
Black Start Operation of Grid-Forming Converters Based on Generalized Three-phase Droop Control Under Unbalanced Conditions
Authors: Zexian Zeng, Prajwal Bhagwat, Maryam Saeedifard, Dominic Groß
Date: 2024-05-23 00:00:00

This paper focuses on the challenging task of bottom-up restoration in a complete blackout system using Grid-forming (GFM) converters. Challenges arise due to the limited current capability of power converters, resulting in distinct dynamic responses and fault current characteristics compared to synchronous generators. Additionally, GFM control needs to address the presence of unbalanced conditions commonly found in distribution systems. To address these challenges, this paper explores the black start capability of GFM converters with a generalized three-phase GFM droop control. This approach integrates GFM controls individually for each phase, incorporating phase-balancing feedback and enabling current limiting for each phase during unbalanced faults or overloading. The introduction of a phase-balancing gain provides flexibility to trade-off between voltage and power imbalances. The study further investigates bottom-up black start operations using GFM converters, incorporating advanced load relays into breakers for gradual load energization without central coordination. The effectiveness of bottom-up black start operations with GFM converters, utilizing the generalized three-phase GFM droop, is evaluated through electromagnetic transient (EMT) simulations in MATLAB/Simulink. The results confirm the performance and effectiveness of this approach in achieving successful black start operations under unbalanced conditions.
Concurrent Multiphysics and Multiscale Topology Optimization for Lightweight Laser-Driven Porous Actuator Systems
Authors: Musaddiq Al Ali, Masatoshi Shimoda
Date: 2024-05-23 00:00:00

In this research, multi-physics topology optimization is employed to achieve the detailed design of a lightweight porous linear actuation mechanism that harnesses energy through laser activation. A multiscale topology optimization methodology is introduced for micro- and macroscale design, considering energy dissipation via heat convection and radiation. This investigation meticulously considers the impact of heat dissipation mechanisms, including thermal conduction, convection, and radiation. Through various numerical cases, we systematically explore the influence of micro-scale considerations on porous design and understand the effects on the topology optimization process by incorporating various microstructural systems. The results demonstrate that porous actuator designs exhibit superior performance compared to solid actuator designs. This study contributes to advancing the understanding of multiscale effects in topology optimization, paving the way for more efficient and lightweight designs in the field of laser-activated porous actuators.
Metric structural human connectomes: localization and multifractality of eigenmodes
Authors: Anna Bobyleva, Alexander Gorsky, Sergei Nechaev, Olga Valba, Nikita Pospelov
Date: 2024-05-27 00:00:00

In this study, we explore the fundamental principles behind the architecture of the human brain's structural connectome, from the perspective of spectral analysis of Laplacian and adjacency matrices. Building on the idea that the brain strikes a balance between efficient information processing and minimizing wiring costs, we aim to understand the impact of the metric properties of the connectome and how they relate to the existence of an inherent scale. We demonstrate that a simple generative model, combining nonlinear preferential attachment with an exponential penalty for spatial distance between nodes, can effectively reproduce several key characteristics of the human connectome, including spectral density, edge length distribution, eigenmode localization and local clustering properties. We also delve into the finer spectral properties of the human structural connectomes by evaluating the inverse participation ratios ($\text{IPR}_q$) across various parts of the spectrum. Our analysis reveals that the level statistics in the soft cluster region of the Laplacian spectrum deviate from a purely Poisson distribution due to interactions between clusters. Additionally, we identified scar-like localized modes with large IPR values in the continuum spectrum. We identify multiple fractal eigenmodes distributed across different parts of the spectrum, evaluate their fractal dimensions and find a power-law relationship in the return probability, which is a hallmark of critical behavior. We discuss the conjectures that a brain operates in the Griffiths or multifractal phases.
How Do Transformers "Do" Physics? Investigating the Simple Harmonic Oscillator
Authors: Subhash Kantamneni, Ziming Liu, Max Tegmark
Date: 2024-05-23 00:00:00

How do transformers model physics? Do transformers model systems with interpretable analytical solutions, or do they create "alien physics" that are difficult for humans to decipher? We take a step in demystifying this larger puzzle by investigating the simple harmonic oscillator (SHO), $\ddot{x}+2\gamma \dot{x}+\omega_0^2x=0$, one of the most fundamental systems in physics. Our goal is to identify the methods transformers use to model the SHO, and to do so we hypothesize and evaluate possible methods by analyzing the encoding of these methods' intermediates. We develop four criteria for the use of a method within the simple testbed of linear regression, where our method is $y = wx$ and our intermediate is $w$: (1) Can the intermediate be predicted from hidden states? (2) Is the intermediate's encoding quality correlated with model performance? (3) Can the majority of variance in hidden states be explained by the intermediate? (4) Can we intervene on hidden states to produce predictable outcomes? Armed with these two correlational (1,2), weak causal (3) and strong causal (4) criteria, we determine that transformers use known numerical methods to model trajectories of the simple harmonic oscillator, specifically the matrix exponential method. Our analysis framework can conveniently extend to high-dimensional linear systems and nonlinear systems, which we hope will help reveal the "world model" hidden in transformers.
Active gel model for one-dimensional cell migration coupling actin flow and adhesion dynamics
Authors: Valentin Wössner, Oliver M. Drozdowski, Falko Ziebert, Ulrich S. Schwarz
Date: 2024-05-27 00:00:00

Migration of animal cells is based on the interplay between actin polymerization at the front, adhesion along the cell-substrate interface, and actomyosin contractility at the back. Active gel theory has been used before to demonstrate that actomyosin contractility is sufficient for polarization and self-sustained cell migration in the absence of external cues, but did not consider the dynamics of adhesion. Likewise, migration models based on the mechanosensitive dynamics of adhesion receptors usually do not include the global dynamics of intracellular flow. Here we show that both aspects can be combined in a minimal active gel model for one-dimensional cell migration with dynamic adhesion. This model demonstrates that load sharing between the adhesion receptors leads to symmetry breaking, with stronger adhesion at the front, and that bistability of migration arises for intermediate adhesiveness. Local variations in adhesiveness are sufficient to switch between sessile and motile states, in qualitative agreement with experiments.
Gears in Chemical Reaction Networks: Optimizing Energy Transduction Efficiency
Authors: Massimo Bilancioni, Massimiliano Esposito
Date: 2024-05-28 00:00:00

Similarly to gear systems in vehicles, most chemical reaction networks (CRNs) involved in energy transduction have at their disposal multiple transduction pathways, each characterized by distinct efficiencies. We conceptualize these pathways as `chemical gears' and demonstrate their role in refining the second law of thermodynamics. This allows us to determine the optimal efficiency of a CRN, and the gear enabling it, solely based on its topology and operating conditions, defined by the chemical potentials of its input and output species. By suitably tuning reaction kinetics, a CRN can be engineered to self-regulate its gear settings, maintaining optimal efficiency under varying external conditions. We demonstrate this principle in a biological context with a CRN where enzymes function as gear shifters, autonomously adapting the system to achieve near-optimal efficiency across changing environments. Additionally, we examine artificial molecular motors and reveal that their gearing mechanisms are far from optimally regulated.
Phase transitions in debt recycling
Authors: Sabrina Aufiero, Preben Forer, Pierpaolo Vivo, Fabio Caccioli, Silvia Bartolucci
Date: 2024-05-29 00:00:00

Debt recycling is an aggressive equity extraction strategy that potentially permits faster repayment of a mortgage. While equity progressively builds up as the mortgage is repaid monthly, mortgage holders may obtain another loan they could use to invest on a risky asset. The wealth produced by a successful investment is then used to repay the mortgage faster. The strategy is riskier than a standard repayment plan since fluctuations in the house market and investment's volatility may also lead to a fast default, as both the mortgage and the liquidity loan are secured against the same good. The general conditions of the mortgage holder and the outside market under which debt recycling may be recommended or discouraged have not been fully investigated. In this paper, to evaluate the effectiveness of traditional monthly mortgage repayment versus debt recycling strategies, we build a dynamical model of debt recycling and study the time evolution of equity and mortgage balance as a function of loan-to-value ratio, house market performance, and return of the risky investment. We find that the model has a rich behavior as a function of its main parameters, showing strongly and weakly successful phases - where the mortgage is eventually repaid faster and slower than the standard monthly repayment strategy, respectively - a default phase where the equity locked in the house vanishes before the mortgage is repaid, signalling a failure of the debt recycling strategy, and a permanent re-mortgaging phase - where further investment funds from the lender are continuously secured, but the mortgage is never fully repaid. The strategy's effectiveness is found to be highly sensitive to the initial mortgage-to-equity ratio, the monthly amount of scheduled repayments, and the economic parameters at the outset. The analytical results are corroborated with numerical simulations with excellent agreement.
Spatial-temporal analysis of neural desynchronization in sleep-like states reveals critical dynamics
Authors: Davor Curic, Surjeet Singh, Mojtaba Nazari, Majid H. Mohajerani, Joern Davidsen
Date: 2024-05-28 00:00:00

Sleep is characterized by non-rapid eye movement (nREM) sleep, originating from widespread neuronal synchrony, and REM sleep, with neuronal desynchronization akin to waking behavior. While these were thought to be global brain states, recent research suggests otherwise. Using time-frequency analysis of mesoscopic voltage-sensitive dye recordings of mice in a urethane-anesthetized model of sleep, we find transient neural desynchronization occurring heterogeneously across the cortex within a background of synchronized neural activity, in a manner reminiscent of a critical spreading process and indicative of an "edge-of-synchronization phase" transition.
Identifying Extreme Events in the Stock Market: A Topological Data Analysis
Authors: Anish rai, Buddha Nath Sharma, Salam Rabindrajit Luwang, Md. Nurujjaman, Sushovan Majhi
Date: 2024-05-25 00:00:00
Tasks: Topological Data Analysis

This paper employs Topological Data Analysis (TDA) to detect extreme events (EEs) in the stock market at a continental level. Previous approaches, which analyzed stock indices separately, could not detect EEs for multiple time series in one go. TDA provides a robust framework for such analysis and identifies the EEs during the crashes for different indices. The TDA analysis shows that $L^1$, $L^2$ norms and Wasserstein distance ($W_D$) of the world leading indices rise abruptly during the crashes, surpassing a threshold of $\mu+4*\sigma$ where $\mu$ and $\sigma$ are the mean and the standard deviation of norm or $W_D$, respectively. Our study identified the stock index crashes of the 2008 financial crisis and the COVID-19 pandemic across continents as EEs. Given that different sectors in an index behave differently, a sector-wise analysis was conducted during the COVID-19 pandemic for the Indian stock market. The sector-wise results show that after the occurrence of EE, we have observed strong crashes surpassing $\mu+2*\sigma$ for an extended period for the banking sector. While for the pharmaceutical sector, no significant spikes were noted. Hence, TDA also proves successful in identifying the duration of shocks after the occurrence of EEs. This also indicates that the Banking sector continued to face stress and remained volatile even after the crash. This study gives us the applicability of TDA as a powerful analytical tool to study EEs in various fields.
Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
Authors: Shengchao Chen, Guodong Long, Jing Jiang, Chengqi Zhang
Date: 2024-05-24 00:00:00
Tasks: Imputation

This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variables modeling. We present LM-Weather, a generic approach to taming PLMs, that have learned massive sequential knowledge from the universe of natural language databases, to acquire an immediate capability to obtain highly customized models for heterogeneous meteorological data on devices while keeping high efficiency. Concretely, we introduce a lightweight personalized adapter into PLMs and endows it with weather pattern awareness. During communication between clients and the server, low-rank-based transmission is performed to effectively fuse the global knowledge among devices while maintaining high communication efficiency and ensuring privacy. Experiments on real-wold dataset show that LM-Weather outperforms the state-of-the-art results by a large margin across various tasks (e.g., forecasting and imputation at different scales). We provide extensive and in-depth analyses experiments, which verify that LM-Weather can (1) indeed leverage sequential knowledge from natural language to accurately handle meteorological sequence, (2) allows each devices obtain highly customized models under significant heterogeneity, and (3) generalize under data-limited and out-of-distribution (OOD) scenarios.
Encryption in ghost imaging with Kronecker products of random matrices
Authors: Yi-Ning Zhao, Lin-Shan Chen, Lingxin Kong, Chong Wang, Cheng Ren, De-Zhong Cao
Date: 2024-05-30 00:00:00

By forming measurement matrices with the Kronecker product of two random matrices, image encryption in computational ghost imaging is investigated. The two-dimensional images are conveniently reconstructed with the pseudo-inverse matrices of the two random matrices. To suppress the noise, the method of truncated singular value decomposition can be applied to either or both of the two pseudo-inverse matrices. Further, our proposal facilitates for image encryption since more matrices can be involved in forming the measurement matrix. Two permutation matrices are inserted into the matrix sequence. The image information can only be reconstructed with the correct permutation matrices and the matrix sequence in image decryption. The experimental results show the facilitations our proposal. The technique paves the way for the practicality and flexibility of computational ghost imaging.