pwc-archive/papers-with-abstracts huggingface dataset:
Learning stability guarantees for constrained switching linear systems from noisy observations
Authors: Adrien Banse, Zheming Wang, Raphaël M. Jungers
Date: 2023-02-10 00:00:00

We present a data-driven framework based on Lyapunov theory to provide stability guarantees for a family of hybrid systems. In particular, we are interested in the asymptotic stability of switching linear systems whose switching sequence is constrained by labeled graphs, namely constrained switching linear systems. In order to do so, we provide chance-constrained bounds on stability guarantees, that can be obtained from a finite number of noisy observations. We first present a method providing stability guarantees from sampled trajectories in the hybrid state-space of the system. We then study the harder situation where one only observes the continuous part of the hybrid states. We show that in this case, one may still obtain formal chance-constrained stability guarantees. For this latter result we provide a new upper bound of general interest, also for model-based stability analysis
Target Controllability of Multiagent Systems under Directed Weighted Topology
Authors: Yanan Ji, Zhijian Ji, Yungang Liu, Chong Lin
Date: 2023-02-10 00:00:00

In this paper, the target controllability of multiagent systems under directed weighted topology is studied. A graph partition is constructed, in which part of the nodes are divided into different cells, which are selected as leaders. The remaining nodes are divided by maximum equitable partition. By taking the advantage of reachable nodes and the graph partition, we provide a necessary and sufficient condition for the target controllability of a first-order multiagent system. It is shown that the system is target controllable if and only if each cell contains no more than one target node and there are no unreachable target nodes, with $\delta-$reachable nodes belonging to the same cell in the above graph partition. By means of controllability decomposition, a necessary and sufficient condition for the target controllability of the system is given, as well as a target node selection method to ensure the target controllability. In a high-order multiagent system, once the topology, leaders, and target nodes are fixed, the target controllability of the high-order multiagent system is shown to be the same to the first-order one. This paper also considers a general linear system. If there is an independent strongly connected component that contains only target nodes and the general linear system is target controllable, then graph $\mathcal{G}$ is leader-target follower connected.
Output tracking based on extended observer for nonlinear uncertain systems
Authors: Xinhua Wang, Zengqiang Chen, Zhuzhi Yuan
Date: 2023-02-10 00:00:00

A high-gain extended observer is designed for a class of nonlinear uncertain systems. This observer has the ability of estimating system uncertainty, and it can be used to estimate the derivatives of signal up to order n. The controller based on this extended observer can make the tracking error and its derivatives converge to zero rapidly even when uncertainties and disturbances exist. The result of simulation indicates that this method has satisfactory control performance for nonlinear uncertain systems.
Design of the Reverse Logistics System for Medical Waste Recycling Part I: System Architecture and Disposal Site Selection Algorithm
Authors: Chaozhong Xue, Yongqi Dong, Jiaqi Liu, Yijun Liao, Lingbo Li
Date: 2023-02-09 00:00:00

With social progress and the development of modern medical technology, the amount of medical waste generated is increasing dramatically. The problem of medical waste recycling and treatment has gradually drawn concerns from the whole society. The sudden outbreak of the COVID-19 epidemic further brought new challenges. To tackle the challenges, this study proposes a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module. This overall solution design won the Grand Prize of the "YUNFENG CUP" China National Contest on Green Supply and Reverse Logistics Design ranking 1st. This paper focuses on the description of architectural design and the module on site selection. Specifically, regarding system architecture, a framework diagram is provided, together with brief descriptions of the three proposed modules and a case study under the COVID-19 epidemic with the customized model. Regarding the disposal site selection module, a multi-objective optimization model is developed, and considering different types of waste collection sites (i.e., prioritized large collection sites and common collection sites), a hierarchical solution method is developed employing linear programming and K-means clustering algorithms sequentially. The proposed site selection method is verified with a case study using real-world data, and compared with the baseline, it can immensely reduce the daily operational costs and working time. Limited by length, detailed descriptions of the whole system as well as the remaining medical waste classification & monitoring module and route optimization module can be found at https://shorturl.at/cdY59.
Algebraic structure of hierarchic first-order reaction networks applicable to models of clone size distribution and stochastic gene expression
Authors: Ximo Pechuan-Jorge, Raymond S. Puzio, Cameron Smith
Date: 2023-02-02 00:00:00

In biology, stochastic branching processes with a two-stage, hierarchical structure arise in the study of population dynamics, gene expression, and phylogenetic inference. These models have been commonly analyzed using generating functions, the method of characteristics and various perturbative approximations. Here we describe a general method for analyzing hierarchic first-order reaction networks using Lie theory. Crucially, we identify the fact that the Lie group associated to hierarchic reaction networks decomposes as a wreath product of the groups associated to the subnetworks of the independent and dependent types. After explaining the general method, we illustrate it on a model of population dynamics and the so-called two-state or telegraph model of single-gene transcription. Solutions to such processes provide essential input to downstream methods designed to attempt to infer parameters of these and related models.
Policy Learning with Rare Outcomes
Authors: Julia Hatamyar, Noemi Kreif
Date: 2023-02-10 00:00:00

Machine learning (ML) estimates of conditional average treatment effects (CATE) can guide policy decisions, either by allowing targeting of individuals with beneficial CATE estimates, or as inputs to decision trees that optimise overall outcomes. There is limited information available regarding how well these algorithms perform in real-world policy evaluation scenarios. Using synthetic data, we compare the finite sample performance of different policy learning algorithms, machine learning techniques employed during their learning phases, and methods for presenting estimated policy values. For each algorithm, we assess the resulting treatment allocation by measuring deviation from the ideal ("oracle") policy. Our main finding is that policy trees based on estimated CATEs outperform trees learned from doubly-robust scores. Across settings, Causal Forests and the Normalised Double-Robust Learner perform consistently well, while Bayesian Additive Regression Trees perform poorly. These methods are then applied to a case study targeting optimal allocation of subsidised health insurance, with the goal of reducing infant mortality in Indonesia.
Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates
Authors: Christis Katsouris
Date: 2023-02-10 00:00:00
Tasks: Management, regression, Time Series, Time Series Analysis, Unity

We propose an econometric environment for structural break detection in nonstationary quantile predictive regressions. We establish the limit distributions for a class of Wald and fluctuation type statistics based on both the ordinary least squares estimator and the endogenous instrumental regression estimator proposed by Phillips and Magdalinos (2009a, Econometric Inference in the Vicinity of Unity. Working paper, Singapore Management University). Although the asymptotic distribution of these test statistics appears to depend on the chosen estimator, the IVX based tests are shown to be asymptotically nuisance parameter-free regardless of the degree of persistence and consistent under local alternatives. The finite-sample performance of both tests is evaluated via simulation experiments. An empirical application to house pricing index returns demonstrates the practicality of the proposed break tests for regression quantiles of nonstationary time series data.
On semiparametric estimation of the intercept of the sample selection model: a kernel approach
Authors: Zhewen Pan
Date: 2023-02-10 00:00:00
Tasks: regression

This paper presents a new perspective on the identification at infinity for the intercept of the sample selection model as identification at the boundary via a transformation of the selection index. This perspective suggests generalizations of estimation at infinity to kernel regression estimation at the boundary and further to local linear estimation at the boundary. The proposed kernel-type estimators with an estimated transformation are proven to be nonparametric-rate consistent and asymptotically normal under mild regularity conditions. A fully data-driven method of selecting the optimal bandwidths for the estimators is developed. The Monte Carlo simulation shows the desirable finite sample properties of the proposed estimators and bandwidth selection procedures.
A CT-based deep learning system for automatic assessment of aortic root morphology for TAVI planning
Authors: Simone Saitta, Francesco Sturla, Riccardo Gorla, Omar A. Oliva, Emiliano Votta, Francesco Bedogni, Alberto Redaelli
Date: 2023-02-10 00:00:00
Tasks: Anatomy, Computed Tomography (CT)

Accurate planning of transcatheter aortic implantation (TAVI) is important to minimize complications, and it requires anatomic evaluation of the aortic root (AR), commonly done through 3D computed tomography (CT) image analysis. Currently, there is no standard automated solution for this process. Two convolutional neural networks (CNNs) with 3D U-Net architectures (model 1 and model 2) were trained on 310 CT scans for AR analysis. Model 1 performed AR segmentation and model 2 identified the aortic annulus and sinotubular junction (STJ) contours. Results were validated against manual measurements of 178 TAVI candidates. After training, the two models were integrated into a fully automated pipeline for geometric analysis of the AR. The trained CNNs effectively segmented the AR, annulus and STJ, resulting in mean Dice scores of 0.93 for the AR, and mean surface distances of 1.16 mm and 1.30 mm for the annulus and STJ, respectively. Automatic measurements were in good agreement with manual annotations, yielding annulus diameters that differed by 0.52 [-2.96, 4.00] mm (bias and 95% limits of agreement for manual minus algorithm). Evaluating the area-derived diameter, bias and limits of agreement were 0.07 [-0.25, 0.39] mm. STJ and sinuses diameters computed by the automatic method yielded differences of 0.16 [-2.03, 2.34] and 0.1 [-2.93, 3.13] mm, respectively. The proposed tool is a fully automatic solution to quantify morphological biomarkers for pre-TAVI planning. The method was validated against manual annotation from clinical experts and showed to be quick and effective in assessing AR anatomy, with potential for time and cost savings.
The Neural Networks Based Needle Detection for Medical Retinal Surgery
Authors: Jidong Xu, Jinglun Yu, Jianing Yao, Rendong Zhang
Date: 2023-02-10 00:00:00
Tasks: Position, Segmentation

In recent years, deep learning technology has developed rapidly, and the application of deep neural networks in the medical image processing field has become the focus of the spotlight. This paper aims to achieve needle position detection in medical retinal surgery by adopting the target detection algorithm based on YOLOv5 as the basic deep neural network model. The state-of-the-art needle detection approaches for medical surgery mainly focus on needle structure segmentation. Instead of the needle segmentation, the proposed method in this paper contains the angle examination during the needle detection process. This approach also adopts a novel classification method based on the different positions of the needle to improve the model. The experiments demonstrate that the proposed network can accurately detect the needle position and measure the needle angle. The performance test of the proposed method achieves 4.80 for the average Euclidean distance between the detected tip position and the actual tip position. It also obtains an average error of 0.85 degrees for the tip angle across all test sets.
Ringing Artifact Reduction Method for Ultrasound Reconstruction Using Multi-Agent Consensus Equilibrium
Authors: Abdulrahman M. Alanazi, Singanallur Venkatakrishnan, Gregery T. Buzzard, Charles A. Bouman
Date: 2023-02-09 00:00:00
Tasks: Image Reconstruction

Non-destructive characterization of multi-layered structures that can be accessed from only a single side is important for applications such as well-bore integrity inspection. Existing methods related to Synthetic Aperture Focusing Technique (SAFT) rapidly produce acceptable results but with significant artifacts. Recently, ultrasound model-based iterative reconstruction (UMBIR) approaches have shown significant improvements over SAFT. However, even these methods produce ringing artifacts due to the high fractional-bandwidth of the excitation signal. In this paper, we propose a ringing artifact reduction method for ultrasound image reconstruction that uses a multi-agent consensus equilibrium (RARE-MACE) framework. Our approach integrates a physics-based forward model that accounts for the propagation of a collimated ultrasonic beam in multi-layered media, a spatially varying image prior, and a denoiser designed to suppress the ringing artifacts that are characteristic of reconstructions from high-fractional bandwidth ultrasound sensor data. We test our method on simulated and experimental measurements and show substantial improvements in image quality compared to SAFT and UMBIR.
Globally Optimized TDOA High Frequency Source Localization Based on Quasi-Parabolic Ionosphere Modeling and Collaborative Gradient Projection
Authors: Wenxin Xiong, Christian Schindelhauer, Hing Cheung So
Date: 2023-02-10 00:00:00

We investigate the problem of high frequency (HF) source localization using the time-difference-of-arrival (TDOA) observations of ionosphere-refracted radio rays based on quasi-parabolic (QP) modeling. An unresolved but pertinent issue in such a field is that the existing gradient-type scheme can easily get trapped in local optima for practical use. This will lead to the difficulty in initializing the algorithm and finally degraded positioning performance if the starting point is inappropriately selected. In this paper, we develop a collaborative gradient projection (GP) algorithm in order to globally solve the highly nonconvex QP-based TDOA HF localization problem. The metaheuristic of particle swarm optimization (PSO) is exploited for information sharing among multiple GP models, each of which is guaranteed to work out a critical point solution to the simplified maximum likelihood formulation. Random mutations are incorporated to avoid the early convergence of PSO. Rather than treating the geolocation of HF transmitter as a pure optimization problem, we further provide workarounds for addressing the possible impairments and challenges when the proposed technique is applied in practice. Numerical results demonstrate the effectiveness of our PSO-assisted re-initialization strategy in achieving the global optimality, and the superiority of our method over its competitor in terms of positioning accuracy.
RIS-Assisted Jamming Rejection and Path Planning for UAV-Borne IoT Platform: A New Deep Reinforcement Learning Framework
Authors: Shuyan Hu, Xin Yuan, Wei Ni, Xin Wang, Abbas Jamalipour
Date: 2023-02-10 00:00:00
Tasks: Deep Reinforcement Learning, Trajectory Planning

This paper presents a new deep reinforcement learning (DRL)-based approach to the trajectory planning and jamming rejection of an unmanned aerial vehicle (UAV) for the Internet-of-Things (IoT) applications. Jamming can prevent timely delivery of sensing data and reception of operation instructions. With the assistance of a reconfigurable intelligent surface (RIS), we propose to augment the radio environment, suppress jamming signals, and enhance the desired signals. The UAV is designed to learn its trajectory and the RIS configuration based solely on changes in its received data rate, using the latest deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) models. Simulations show that the proposed DRL algorithms give the UAV with strong resistance against jamming and that the TD3 algorithm exhibits faster and smoother convergence than the DDPG algorithm, and suits better for larger RISs. This DRL-based approach eliminates the need for knowledge of the channels involving the RIS and jammer, thereby offering significant practical value.
NR Conformance Testing of Analog Radio-over-LWIR FSO Fronthaul link for 6G Distributed MIMO Networks
Authors: Rafael Puerta, Mengyao Han, Mahdieh Joharifar, Richard Schatz, Yan-Ting Sun, Yuchuan Fan, Anders Djupsjöbacka, Grégory Maisons, Johan Abautret, Roland Teissier, Lu Zhang, Sandis Spolitis, Muguang Wang, Vjaceslavs Bobrovs, Sebastian Lourdudoss, Xianbin Yu, Sergei Popov, Oskars Ozolins, Xiaodan Pang
Date: 2023-02-09 00:00:00

We experimentally test the compliance with 5G/NR 3GPP technical specifications of an analog radio-over-FSO link at 9 {\mu}m. The ACLR and EVM transmitter requirements are fulfilled validating the suitability of LWIR FSO for 6G fronthaul.
NeSe: Near-Sensor Event-Driven Scheme for Low Power Energy Harvesting Sensors
Authors: Sepehr Tabrizchi, Mehrdad Morsali, Shaahin Angizi, Arman Roohi
Date: 2023-02-07 00:00:00
Tasks: Event Detection

Digital technologies have made it possible to deploy visual sensor nodes capable of detecting motion events in the coverage area cost-effectively. However, background subtraction, as a widely used approach, remains an intractable task due to its inability to achieve competitive accuracy and reduced computation cost simultaneously. In this paper, an effective background subtraction approach, namely NeSe, for tiny energy-harvested sensors is proposed leveraging non-volatile memory (NVM). Using the developed software/hardware method, the accuracy and efficiency of event detection can be adjusted at runtime by changing the precision depending on the application's needs. Due to the near-sensor implementation of background subtraction and NVM usage, the proposed design reduces the data movement overhead while ensuring intermittent resiliency. The background is stored for a specific time interval within NVMs and compared with the next frame. If the power is cut, the background remains unchanged and is updated after the interval passes. Once the moving object is detected, the device switches to the high-powered sensor mode to capture the image.
Some asymptotics for short maturity Asian options
Authors: Humayra Shoshi, Indranil SenGupta
Date: 2023-02-10 00:00:00

Most of the existing methods for pricing Asian options are less efficient in the limit of small maturities and small volatilities. In this paper, we use the large deviations theory for the analysis of short-maturity Asian options. We present a local volatility model for the underlying market that incorporates a jump term in addition to the drift and diffusion terms. We estimate the asymptotics for the out-of-the-money, in-the-money, and at-the-money short-maturity Asian call and put options. Under appropriate assumptions, we show that the asymptotics for out-of-the-money Asian call and put options are governed by rare events. For the at-the-money Asian options, the result is more involved and in that case, we find the upper and lower bounds of the asymptotics of the Asian option price.
Initial validation of a soil-based mass-balance approach for empirical monitoring of enhanced rock weathering rates
Authors: Tom Reershemius, Mike E. Kelland, Jacob S. Jordan, Isabelle R. Davis, Rocco D'Ascanio, Boriana Kalderon-Asael, Dan Asael, T. Jesper Suhrhoff, Dimitar Z. Epihov, David J. Beerling, Christopher T. Reinhard, Noah J. Planavsky
Date: 2023-02-10 00:00:00

Enhanced Rock Weathering (ERW) is a promising scalable and cost-effective Carbon Dioxide Removal (CDR) strategy with significant environmental and agronomic co-benefits. A major barrier to large-scale implementation of ERW is a robust Monitoring, Reporting, and Verification (MRV) framework. To successfully quantify the amount of carbon dioxide removed by ERW, MRV must be accurate, precise, and cost-effective. Here, we outline a mass-balance-based method where analysis of the chemical composition of soil samples is used to track in-situ silicate rock weathering. We show that signal-to-noise issues of in-situ soil analysis can be mitigated by using isotope-dilution mass spectrometry to reduce analytical error. We implement a proof-of-concept experiment demonstrating the method in controlled mesocosms. In our experiment, basalt rock feedstock is added to soil columns containing the cereal crop Sorghum bicolor at a rate equivalent to 50 t ha$^{-1}$. Using our approach, we calculate rock weathering corresponding to an average initial CDR value of 1.44 +/- 0.27 tCO$_2$eq ha$^{-1}$ from our experiments after 235 days, within error of an independent estimate calculated using conventional elemental budgeting of reaction products. Our method provides a robust time-integrated estimate of initial CDR, to feed into models that track and validate large-scale carbon removal through ERW.
Characterizing contaminant noise in barcoded perturbation experiments
Authors: Forrest Sheldon
Date: 2023-02-10 00:00:00

Bursting cells lead to ambient RNA that contaminates sequencing data. This process is especially problematic in perturbation experiments where transcription factors are implanted into cells to determine their effects. The presence of contaminants makes it difficult to determine whether a factor is truly expressed in the cell. This paper studies the properties of contaminant noise from an analytical perspective, showing that the cell bursting process constrains the form of the noise distribution across factors. These constraints can be leveraged to improve decontamination by removing counts that are more likely the result of noise than expression. In two biological replicates of a perturbation experiment, run across two sequencing protocols, decontaminated counts agree with bulk genomic measurements of the transduction rate and are automatically corrected for differences in sequencing.