pwc-archive/papers-with-abstracts huggingface dataset:
Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models
Authors: Haibo Lu, Shihua Li, Minna Ma, Vladislav Bastrikov, Xiuzhi Chen, Philippe Ciais, Yongjiu Dai, Akihiko Ito, Weimin Ju, Sebastian Lienert, Danica Lombardozzi, Xingjie Lu, Fabienne Maignan, Mahdi Nakhavali, Timothy Quine, Andreas Schindlbacher, Jun Wang, Yingping Wang, David Wårlind, Shupeng Zhang and Wenping Yuan
Date: 2021-05-05 00:00:00
Proceeding: Environmental Research Letters 2021 5

The CO2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively derived global benchmark values are warranted for constraining the various ecosystem model output. In this study, we developed models based on the global soil respiration database (version 5.0), using the random forest (RF) method to generate the global benchmark distribution of total SR and its components. Benchmark values were then compared with the output of ten different global terrestrial ecosystem models. Our observationally derived global mean annual benchmark rates were 85.5 ± 40.4 (SD) Pg C yr−1 for SR, 50.3 ± 25.0 (SD) Pg C yr−1 for HR and 35.2 Pg C yr−1 for AR during 1982–2012, respectively. Evaluating against the observations, the RF models showed better performance in both of SR and HR simulations than all investigated terrestrial ecosystem models. Large divergences in simulating SR and its components were observed among the terrestrial ecosystem models. The estimated global SR and HR by the ecosystem models ranged from 61.4 to 91.7 Pg C yr−1 and 39.8 to 61.7 Pg C yr−1, respectively. The most discrepancy lays in the estimation of AR, the difference (12.0–42.3 Pg C yr−1) of estimates among the ecosystem models was up to 3.5 times. The contribution of AR to SR highly varied among the ecosystem models ranging from 18% to 48%, which differed with the estimate by RF (41%). This study generated global SR and its components (HR and AR) fluxes, which are useful benchmarks to constrain the performance of terrestrial ecosystem models.
Step-GRAND: A Low Latency Universal Soft-input Decoder
Authors: Syed Mohsin Abbas, Marwan Jalaleddine, Chi-Ying Tsui, Warren J. Gross
Date: 2023-07-14 00:00:00
Tasks: Decoder

GRAND features both soft-input and hard-input variants that are well suited to efficient hardware implementations that can be characterized with achievable average and worst-case decoding latency. This paper introduces step-GRAND, a soft-input variant of GRAND that, in addition to achieving appealing average decoding latency, also reduces the worst-case decoding latency of the corresponding hardware implementation. The hardware implementation results demonstrate that the proposed step-GRAND can decode CA-polar code $(128,105+11)$ with an average information throughput of $47.7$ Gbps at the target FER of $\leq10^{-7}$. Furthermore, the proposed step-GRAND hardware is $10\times$ more area efficient than the previous soft-input ORBGRAND hardware implementation, and its worst-case latency is $\frac{1}{6.8}\times$ that of the previous ORBGRAND hardware.
Polarization-Based Security: Safeguarding Wireless Communications at the Physical Layer
Authors: Pol Henarejos, Ana I. Pérez-Neira
Date: 2023-07-14 00:00:00

Physical layer security is a field of study that continues to gain importance over time. It encompasses a range of algorithms applicable to various aspects of communication systems. While research in the physical layer has predominantly focused on secrecy capacity, which involves logical and digital manipulations to achieve secure communication, there is limited exploration of directly manipulating electromagnetic fields to enhance security against eavesdroppers. In this paper, we propose a novel system that utilizes the Mueller calculation to establish a theoretical framework for manipulating electromagnetic fields in the context of physical layer security. We develop fundamental expressions and introduce new metrics to analyze the system's performance analytically. Additionally, we present three techniques that leverage polarization to enhance physical layer security.
Antenna Selection With Beam Squint Compensation for Integrated Sensing and Communications
Authors: Ahmet M. Elbir, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil
Date: 2023-07-14 00:00:00
Tasks: ISAC

Next-generation wireless networks strive for higher communication rates, ultra-low latency, seamless connectivity, and high-resolution sensing capabilities. To meet these demands, terahertz (THz)-band signal processing is envisioned as a key technology offering wide bandwidth and sub-millimeter wavelength. Furthermore, THz integrated sensing and communications (ISAC) paradigm has emerged jointly access spectrum and reduced hardware costs through a unified platform. To address the challenges in THz propagation, THz-ISAC systems employ extremely large antenna arrays to improve the beamforming gain for communications with high data rates and sensing with high resolution. However, the cost and power consumption of implementing fully digital beamformers are prohibitive. While hybrid analog/digital beamforming can be a potential solution, the use of subcarrier-independent analog beamformers leads to the beam-squint phenomenon where different subcarriers observe distinct directions because of adopting the same analog beamformer across all subcarriers. In this paper, we develop a sparse array architecture for THz-ISAC with hybrid beamforming to provide a cost-effective solution. We analyze the antenna selection problem under beam-squint influence and introduce a manifold optimization approach for hybrid beamforming design. To reduce computational and memory costs, we propose novel algorithms leveraging grouped subarrays, quantized performance metrics, and sequential optimization. These approaches yield a significant reduction in the number of possible subarray configurations, which enables us to devise a neural network with classification model to accurately perform antenna selection.
Mega-TTS 2: Boosting Prompting Mechanisms for Zero-Shot Speech Synthesis
Authors: Ziyue Jiang, Jinglin Liu, Yi Ren, Jinzheng He, Zhenhui Ye, Shengpeng Ji, Qian Yang, Chen Zhang, Pengfei Wei, Chunfeng Wang, Xiang Yin, Zejun Ma, Zhou Zhao
Date: 2023-07-14 00:00:00
Tasks: In-Context Learning, Language Modelling, Sentence, Speech Synthesis, text-to-speech, Text to Speech, Voice Cloning

Zero-shot text-to-speech (TTS) aims to synthesize voices with unseen speech prompts, which significantly reduces the data and computation requirements for voice cloning by skipping the fine-tuning process. However, the prompting mechanisms of zero-shot TTS still face challenges in the following aspects: 1) previous works of zero-shot TTS are typically trained with single-sentence prompts, which significantly restricts their performance when the data is relatively sufficient during the inference stage. 2) The prosodic information in prompts is highly coupled with timbre, making it untransferable to each other. This paper introduces Mega-TTS 2, a generic prompting mechanism for zero-shot TTS, to tackle the aforementioned challenges. Specifically, we design a powerful acoustic autoencoder that separately encodes the prosody and timbre information into the compressed latent space while providing high-quality reconstructions. Then, we propose a multi-reference timbre encoder and a prosody latent language model (P-LLM) to extract useful information from multi-sentence prompts. We further leverage the probabilities derived from multiple P-LLM outputs to produce transferable and controllable prosody. Experimental results demonstrate that Mega-TTS 2 could not only synthesize identity-preserving speech with a short prompt of an unseen speaker from arbitrary sources but consistently outperform the fine-tuning method when the volume of data ranges from 10 seconds to 5 minutes. Furthermore, our method enables to transfer various speaking styles to the target timbre in a fine-grained and controlled manner. Audio samples can be found in https://boostprompt.github.io/boostprompt/.
Low Rank Properties for Estimating Microphones Start Time and Sources Emission Time
Authors: Faxian Cao, Yongqiang Cheng, Adil Mehmood Khan, Zhijing Yang, S. M. Ahsan Kazmiand Yingxiu Chang
Date: 2023-07-14 00:00:00

Uncertainty in timing information pertaining to the start time of microphone recordings and sources' emission time pose significant challenges in various applications, such as joint microphones and sources localization. Traditional optimization methods, which directly estimate this unknown timing information (UTIm), often fall short compared to approaches exploiting the low-rank property (LRP). LRP encompasses an additional low-rank structure, facilitating a linear constraint on UTIm to help formulate related low-rank structure information. This method allows us to attain globally optimal solutions for UTIm, given proper initialization. However, the initialization process often involves randomness, leading to suboptimal, local minimum values. This paper presents a novel, combined low-rank approximation (CLRA) method designed to mitigate the effects of this random initialization. We introduce three new LRP variants, underpinned by mathematical proof, which allow the UTIm to draw on a richer pool of low-rank structural information. Utilizing this augmented low-rank structural information from both LRP and the proposed variants, we formulate four linear constraints on the UTIm. Employing the proposed CLRA algorithm, we derive global optimal solutions for the UTIm via these four linear constraints.Experimental results highlight the superior performance of our method over existing state-of-the-art approaches, measured in terms of both the recovery number and reduced estimation errors of UTIm.
An Embedded Auto-Calibrated Offset Current Compensation Technique for PPG/fNIRS System
Authors: Sadan Saquib Khan, Sumit Kumar, Benish Jan, Laxmeesha Somappa, Shahid Malik
Date: 2023-07-14 00:00:00
Tasks: Photoplethysmography (PPG)

Usually, the current generated by the photodiode proportional to the oxygenated blood in the photoplethysmography (PPG) and functional infrared spectroscopy (fNIRS) based recording systems is small as compared to the offset-current. The offset current is the combination of the dark current of the photodiode, the current due to ambient light, and the current due to the reflected light from fat and skull . The relatively large value of the offset current limits the amplification of the signal current and affects the overall performance of the PPG/fNIRS recording systems. In this paper, we present a mixed-signal auto-calibrated offset current compensation technique for PPG and fNIRS recording systems. The system auto-calibrates the offset current, compensates using a dual discrete loop technique, and amplifies the signal current. Thanks to the amplification, the system provides better sensitivity. A prototype of the system is built and tested for PPG signal recording. The prototype is developed for a 3.3 V single supply. The results show that the proposed system is able to effectively compensate for the offset current.
Vehicle-to-grid plug-in forecasting for participation in ancillary services markets
Authors: Jemima Graham, Fei Teng
Date: 2023-07-14 00:00:00

Electric vehicle (EV) charge points (CPs) can be used by aggregators to provide frequency response (FR) services. Aggregators must have day-ahead half-hourly forecasts of minimum aggregate vehicle-to-grid (V2G) plug-in to produce meaningful bids for the day-ahead ancillary services market. However, there is a lack of understanding on what features should be considered and how complex the forecasting model should be. This paper explores the dependency of aggregate V2G plug-in on historic plug-in levels, calendar variables, and weather conditions. These investigations are used to develop three day-ahead forecasts of minimum aggregate V2G plug-in during 30-minute window. A neural network that considers previous V2G plug-in values the day before, three days before, and seven days before, in addition to day of the week, month, and hour, is found to be the most accurate.
Benchmarking Explanatory Models for Inertia Forecasting using Public Data of the Nordic Area
Authors: Jemima Graham, Evelyn Heylen, Yuankai Bian, Fei Teng
Date: 2023-07-14 00:00:00
Tasks: Benchmarking, Time Series

This paper investigates the performance of a day-ahead explanatory model for inertia forecasting based on field data in the Nordic system, which achieves a 43% reduction in mean absolute percentage error (MAPE) against a state-of-the-art time-series forecast model. The generalizability of the explanatory model is verified by its consistent performance on Nordic and Great Britain datasets. Also, it appears that a long duration of training data is not required to obtain accurate results with this model, but taking a more spatially granular approach reduces the MAPE by 3.6%. Finally, two further model enhancements are studied considering the specific features in Nordic system: (i) a monthly interaction variable applied to the day-ahead national demand forecast feature, reducing the MAPE by up to 18%; and (ii) a feature based on the inertia from hydropower, although this has a negligible impact. The field dataset used for benchmarking is also made publicly available.
An Online Learning Analysis of Minimax Adaptive Control
Authors: Venkatraman Renganathan, Andrea Iannelli, Anders Rantzer
Date: 2023-07-14 00:00:00

We present an online learning analysis of minimax adaptive control for the case where the uncertainty includes a finite set of linear dynamical systems. Precisely, for each system inside the uncertainty set, we define the model-based regret by comparing the state and input trajectories from the minimax adaptive controller against that of an optimal controller in hindsight that knows the true dynamics. We then define the total regret as the worst case model-based regret with respect to all models in the considered uncertainty set. We study how the total regret accumulates over time and its effect on the adaptation mechanism employed by the controller. Moreover, we investigate the effect of the disturbance on the growth of the regret over time and draw connections between robustness of the controller and the associated regret rate.
Risk-Constrained Control of Mean-Field Linear Quadratic Systems
Authors: Masoud Roudneshin, Saba Sanami, Amir G. Aghdam
Date: 2023-07-14 00:00:00

The risk-neutral LQR controller is optimal for stochastic linear dynamical systems. However, the classical optimal controller performs inefficiently in the presence of low-probability yet statistically significant (risky) events. The present research focuses on infinite-horizon risk-constrained linear quadratic regulators in a mean-field setting. We address the risk constraint by bounding the cumulative one-stage variance of the state penalty of all players. It is shown that the optimal controller is affine in the state of each player with an additive term that controls the risk constraint. In addition, we propose a solution independent of the number of players. Finally, simulations are presented to verify the theoretical findings.
Data-driven Polytopic Output Synchronization of Heterogeneous Multi-agent Systems from Noisy Data
Authors: Yifei Li, Wenjie Liu, Jian Sun, Gang Wang, Lihua Xie, Jie Chen
Date: 2023-07-14 00:00:00

This paper proposes a novel approach to addressing the output synchronization problem in unknown heterogeneous multi-agent systems (MASs) using noisy data. Unlike existing studies that focus on noiseless data, we introduce a distributed data-driven controller that enables all heterogeneous followers to synchronize with a leader's trajectory. To handle the noise in the state-input-output data, we develop a data-based polytopic representation for the MAS. We tackle the issue of infeasibility in the set of output regulator equations caused by the noise by seeking approximate solutions via constrained fitting error minimization. This method utilizes measured data and a noise-matrix polytope to ensure near-optimal output synchronization. Stability conditions in the form of data-dependent semidefinite programs are derived, providing stabilizing controller gains for each follower. The proposed distributed data-driven control protocol achieves near-optimal output synchronization by ensuring the convergence of the tracking error to a bounded polytope, with the polytope size positively correlated with the noise bound. Numerical tests validate the practical merits of the proposed data-driven design and theory.
State-Robust Observability Measures for Sensor Selection in Nonlinear Dynamic Systems
Authors: Mohamad H. Kazma, Sebastian A. Nugroho, Aleksandar Haber, Ahmad F. Taha
Date: 2023-07-13 00:00:00

This paper explores the problem of selecting sensor nodes for a general class of nonlinear dynamical networks. In particular, we study the problem by utilizing altered definitions of observability and open-loop lifted observers. The approach is performed by discretizing the system's dynamics using the implicit Runge-Kutta method and by introducing a state-averaged observability measure. The observability measure is computed for a number of perturbed initial states in the vicinity of the system's true initial state. The sensor node selection problem is revealed to retain the submodular and modular properties of the original problem. This allows the problem to be solved efficiently using a greedy algorithm with a guaranteed performance bound while showing an augmented robustness to unknown or uncertain initial conditions. The validity of this approach is numerically demonstrated on a $H_{2}/O_{2}$ combustion reaction network.
Coordinated Path Following of UAVs using Event-Triggered Communication over Time-Varying Networks with Digraph Topologies
Authors: Hyungsoo Kang, Isaac Kaminer, Venanzio Cichella, Naira Hovakimyan
Date: 2023-07-13 00:00:00

In this article, a novel time-coordination algorithm based on event-triggered communications is proposed to achieve coordinated path-following of UAVs. To be specific, in the approach adopted a UAV transmits its progression information over a time-varying network to its neighbors only when a decentralized trigger condition is satisfied, thereby significantly reducing the volume of inter-vehicle communications required when compared with the existing algorithms based on continuous communications. Using such intermittent communications, it is shown that a decentralized coordination controller guarantees exponential convergence of the coordination error to a neighborhood of zero. Also, a lower bound on the interval between two consecutive event-triggered times is provided showing that the chattering issue does not arise with the proposed algorithm. Finally, simulation results validate the efficacy of the proposed algorithm.
Complete characterization of robust perfect adaptation in biochemical reaction networks
Authors: Yuji Hirono, Ankit Gupta, Mustafa Khammash
Date: 2023-07-14 00:00:00

Perfect adaptation is a phenomenon whereby the output variables of a system can maintain certain values despite external disturbances. Robust perfect adaptation (RPA) refers to an adaptation property that does not require fine-tuning of system parameters. RPA plays a vital role for the survival of living systems in unpredictable environments. However, complex interaction patterns in biochemical systems pose a significant challenge in identifying RPA and associated regulatory mechanisms. The goal of this paper is to present a novel approach for finding all RPA properties that are realized for a generic choice of kinetics for general deterministic chemical reaction systems. This is accomplished by proving that an RPA property is represented by a subnetwork with certain topological features. This connection is exploited to show that these structures generate all kinetics-independent RPA properties, allowing us to systematically identify all RPA properties by enumerating these subnetworks. An efficient method is developed for this enumeration, and we provide a computational package for this purpose. We pinpoint the integral feedback controllers that work in concert to realize each RPA property, casting our results into the familiar control-theoretic paradigm of the Internal Model Principle. We further generalize the regulation problem to the multi-output scenario where the target values belong to a manifold of nonzero dimension, and provide a sufficient condition for this. The present work significantly advances our understanding of regulatory mechanisms that lead to RPA in endogenous biochemical systems, and it also provides rational design principles for synthetic controllers. The present results indicate that an RPA property is essentially equivalent to the existence of a "topological invariant", which is an instance of what we call the "Robust Adaptation is Topological"(RAT) principle.
Probabilistic Measures for Biological Adaptation and Resilience
Authors: Jorge M. Ramirez, Juan M. Restrepo, Valerio Lucarini, David Weston
Date: 2023-07-13 00:00:00

This paper introduces a novel approach to quantifying ecological resilience in biological systems, particularly focusing on noisy systems responding to episodic disturbances with sudden adaptations. Incorporating concepts from non-equilibrium statistical mechanics, we propose a measure termed `ecological resilience through adaptation,' specifically tailored to noisy, forced systems that undergo physiological adaptation in the face of stressful environmental changes. Randomness plays a key role, accounting for model uncertainty and the inherent variability in the dynamical response among components of biological systems. Our measure of resilience is rooted in the probabilistic description of states within these systems, and is defined in terms of the dynamics of the ensemble average of a model-specific observable quantifying success or well-being. Our approach utilizes stochastic linear response theory to compute how the expected success of a system, originally in statistical equilibrium, dynamically changes in response to a environmental perturbation and a subsequent adaptation. The resulting mathematical derivations allow for the estimation of resilience in terms of ensemble averages of simulated or experimental data. Finally, through a simple but clear conceptual example, we illustrate how our resilience measure can be interpreted and compared to other existing frameworks in the literature. The methodology is general but inspired by applications in plant systems, with the potential for broader application to complex biological processes.
A Hamiltonian Approach to Barrier Option Pricing Under Vasicek Model
Authors: Chao Guo, Ning Yao
Date: 2023-07-14 00:00:00

In this paper, we study option pricing under Vasicek Model by a Hamiltonian approach. Since the interest rate changes with time, we split the time to maturity into infinite steps, and the matrix element during each step could be calculated by quantum mechanics methods. Using completeness condition, the pricing kernel and the integral expression of option price could also be derived. Numerical results of option prices as functions of underlying asset price, floating rate and regression rate are also shown.