pwc-archive/papers-with-abstracts huggingface dataset:
Legal Entity Extraction using a Pointer Generator Network
Authors: Stavroula Skylaki, Ali Oskooei, Omar Bari, Nadja Herger, Zac Kriegman
Date: 2022-01-20 00:00:00
Proceeding: International Conference on Data Mining Workshops (ICDMW) 2022 1
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Optical Character Recognition (OCR)

Named Entity Recognition (NER) is the task of
identifying and classifying named entities in unstructured text.
In the legal domain, named entities of interest may include the
case parties, judges, names of courts, case numbers, references
to laws, etc. We study the problem of legal named entity
extraction from noisy text extracted from PDF files of filed court
cases from US courts. The “gold standard” training data for
classical NER systems provide annotation for each token of the
text with the corresponding entity or non-entity label. We work
with only partially complete training data, which differ from the
gold standard NER data in that the exact location of the entities
in the text is unknown and the entities may contain typos and/or
OCR mistakes. To overcome the challenges of our noisy training
data, e.g., text extraction errors and/or typos and unknown label
indices, we frame the NER task as a sequence generation task
(seq2seq) and train a pointer generator network to generate the
entities in the document rather than label them. We attempt to
create a NER gold standard dataset via sequence matching and
use this dataset to train classical NER baselines and compare
them with our seq2seq approach for Named Entity (NE)
extraction. We show that the seq2seq approach can effectively
extract legal named entities, in the absence of gold standard
data, and outperform the common neural network architectures
for NER in long legal documents.
Data-Driven Cooperative Adaptive Cruise Control for Unknown Nonlinear Vehicle Platoons
Authors: Jianglin Lan
Date: 2023-07-21 00:00:00

This paper studies cooperative adaptive cruise control (CACC) for vehicle platoons with consideration of the unknown nonlinear vehicle dynamics that are normally ignored in the literature. A unified data-driven CACC design is proposed for platoons of pure automated vehicles (AVs) or of mixed AVs and human-driven vehicles (HVs). The CACC leverages online-collected sufficient data samples of vehicle accelerations, spacing and relative velocities. The data-driven control design is formulated as a semidefinite program (SDP) that can be solved efficiently using off-the-shelf solvers. The efficacy and advantage of the proposed CACC are demonstrated through a comparison with the classic adaptive cruise control (ACC) method on a platoon of pure AVs and a mixed platoon under a representative aggressive driving profile.
Data-driven dual-loop control for platooning mixed human-driven and automated vehicles
Authors: Jianglin Lan
Date: 2023-07-21 00:00:00
Tasks: Model Predictive Control

This paper considers controlling automated vehicles (AVs) to form a platoon with human-driven vehicles (HVs) under consideration of unknown HV model parameters and propulsion time constants. The proposed design is a data-driven dual-loop control strategy for the ego AVs, where the inner loop controller ensures platoon stability and the outer loop controller keeps a safe inter-vehicular spacing under control input limits. The inner loop controller is a constant-gain state feedback controller solved from a semidefinite program (SDP) using the online collected data of platooning errors. The outer loop is a model predictive control (MPC) that embeds a data-driven internal model to predict the future platooning error evolution. The proposed design is evaluated on a mixed platoon with a representative aggressive reference velocity profile, the SFTP-US06 Drive Cycle. The results confirm efficacy of the design and its advantages over the existing single loop data-driven MPC in terms of platoon stability and computational cost.
Data-based system representations from irregularly measured data
Authors: Mohammad Alsalti, Ivan Markovsky, Victor G. Lopez, Matthias A. Müller
Date: 2023-07-21 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

Non-parametric representations of dynamical systems based on the image of a Hankel matrix of data are extensively used for data-driven control. However, if samples of data are missing, obtaining such representations becomes a difficult task. By exploiting the kernel structure of Hankel matrices of irregularly measured data generated by a linear time-invariant system, we provide computational methods for which any complete finite-length behavior of the system can be obtained. For the special case of periodically missing outputs, we provide conditions on the input such that the former result is guaranteed. In the presence of noise in the data, our method returns an approximate finite-length behavior of the system. We illustrate our result with several examples, including its use for approximate data completion in real-world applications and compare it to alternative methods.
Unambiguous Interpretation of IEC 60848 GRAFCET based on a Literature Review
Authors: Robin Mroß, Aron Schnakenbeck, Marcus Völker, Alexander Fay, Stefan Kowalewski
Date: 2023-07-21 00:00:00

IEC 60848 GRAFCET is a standardized, graphical specification language for control functions. Because of the semiformal nature of IEC 60848, the details of specifications created with GRAFCET can be interpreted in different ways, possibly leading to faulty implementations. These ambiguities have been partially addressed in existing literature, but solved in different manners. Based on a literature review, this work aims at providing an overview of existing interpretations and, based on that, proposes a comprehensive interpretation algorithm for IEC 60848, which takes all relevant ambiguities from the literature review into account.
An Extended Nonlinear Stability Assessment Methodology For Type-4 Wind Turbines via Time Reversal Trajectory
Authors: Sujay Ghosh, Mohammad Kazem Bakhshizadeh, Guangya Yang, Łukasz Kocewiak
Date: 2023-07-21 00:00:00

As the integration of renewable energy generation increases and as conventional generation is phased out, there is a gradual decline in the grid's strength and resilience at the connection point of wind turbines (WTs). Previous studies have shown that traditional grid-following controlled converters exhibit deteriorating dynamic characteristics and may result in an unstable system when connected to a weak grid. Due to the limitations of linear analysis, transient stability investigations are necessary. However, existing methods, such as standalone time-domain simulations or analytical Lyapunov stability criteria, have drawbacks, including computational intensity or excessive conservatism. Our prior research proposed an innovative approach to estimate the system boundary - a time-limited region of attraction (TLRoA), using a hybrid linearised Lyapunov function-based method and the time-reversal technique to compensate for the known limitations. However, in that work, the accuracy of the estimated TLRoA was not investigated, i.e. the TLRoA was not compared against a forward simulated region of attraction, and the sensitivity of the system parameters on the TLRoA was not explored. Moreover, the framework did not consider nonlinear control elements such as PLL saturation. In this paper, we not only build upon our previous work and propose directions that address these gaps but also enhance its effectiveness by introducing optimal sampling to improve further the speed of estimating the TLRoA. Furthermore, the stability boundary is verified using time-domain simulation studies in PSCAD.
Analysis of potential lifetime extension through dynamic battery reconfiguration
Authors: Albert Skegro, Changfu Zou, Torsten Wik
Date: 2023-07-21 00:00:00

Growing demands for electrification result in increasingly larger battery packs. Due to factors such as cell position in the pack and variations in the manufacturing process, the packs exhibit variations in the performance of their constituent cells. Moreover, due to the fixed cell configuration, the weakest cell renders the pack highly susceptible to these variations. Reconfigurable battery pack systems, which have increased control flexibility due to additional power electronics, present a promising solution for these issues. Nevertheless, to what extent they can prolong the battery lifetime has not been investigated. This simulation study analyzes the potential of dynamic reconfiguration for extending battery lifetime w.r.t. several parameters. Results indicate that the lifetime extension is larger for series than for parallel configurations. For the latter, the dominant factor is equivalent full cycles spread at the end of life, but resistance increase with age and the number of cells in parallel are also influential. Finally, for the former, the number of series-connected elements amplifies these effects.
Overcoming voltage fluctuation in electric vehicles by considering Al electrolytic capacitor-based voltage stabilizer
Authors: Ida Hamidah, Doni Fajar Ramadhan, Ramdhani Ramdhani, Budi Mulyanti, Roer Eka Pawinanto, Lilik Hasanah, Asep Bayu Dani Nandiyanto, Jumril Yunas, Andrivo Rusydi
Date: 2023-07-21 00:00:00

Battery pack used in electrical vehicles (EVs) have developed fastly nowadays and are important due to its capability to stores electric charge. However, it is still found that there is an electrical voltage fluctuations due to sudden load changes or faults in the power system. To overcome this problem, the conventional strategy focuses on using voltage stabilizer with inductor-capacitor resonant circuits, leaving the problem of using capacitors at cryogenic temperatures. Herewith, we design a vehicle automatic voltage stabilizer (VAVS) operating at room temperature. The VAVS consists of nine main components, including resistors, capacitors, transistors, and diodes arranged with a specific composition to stabilize the voltage. By considering Al electrolytic capacitor, type of diode, resistor, and transistor, the VAVS device stabilizes the input voltage from 10-15V to obtain an output voltage of 12V. Then, by varying the engine rotation speed of vehicle, VAVS applied in vehicle battery supplies a constant voltage of about 12.1 V. The lowest line regulation analysis reveals a regulated voltage fluctuation of 0.17%/V. Our result shows that VAVS design works at room temperature, overcomes the voltage fluctuation and complies with the standard regulation of voltage stabilizers.
BLISS: Interplanetary Exploration with Swarms of Low-Cost Spacecraft
Authors: Alexander N. Alvara, Lydia Lee, Emmanuel Sin, Nathan Lambert, Andrew J. Westphal, Kristofer S. J. Pister
Date: 2023-07-20 00:00:00

Leveraging advancements in micro-scale technology, we propose a fleet of autonomous, low-cost, small solar sails for interplanetary exploration. The Berkeley Low-cost Interplanetary Solar Sail (BLISS) project aims to utilize small-scale technologies to create a fleet of tiny interplanetary femto-spacecraft for rapid, low-cost exploration of the inner solar system. This paper describes the hardware required to build a nearly 10 g spacecraft using a 1 m2 solar sail steered by micro-electromechanical systems (MEMS) inchworm actuators. The trajectory control to a NEO, here 101955 Bennu, is detailed along with the low-level actuation control of the solar sail and the specifications of proposed onboard communication and computation. Two other applications are also shortly considered: sample return from dozens of Jupiter-family comets and interstellar comet rendezvous and imaging. The paper concludes by discussing the fundamental scaling limits and future directions for steerable autonomous miniature solar sails with onboard custom computers and sensors.
Functional Differencing in Networks
Authors: Stéphane Bonhomme, Kevin Dano
Date: 2023-07-21 00:00:00
Tasks: Form, valid

Economic interactions often occur in networks where heterogeneous agents (such as workers or firms) sort and produce. However, most existing estimation approaches either require the network to be dense, which is at odds with many empirical networks, or they require restricting the form of heterogeneity and the network formation process. We show how the functional differencing approach introduced by Bonhomme (2012) in the context of panel data, can be applied in network settings to derive moment restrictions on model parameters and average effects. Those restrictions are valid irrespective of the form of heterogeneity, and they hold in both dense and sparse networks. We illustrate the analysis with linear and nonlinear models of matched employer-employee data, in the spirit of the model introduced by Abowd, Kramarz, and Margolis (1999).
Deep Learning Hyperspectral Pansharpening on large scale PRISMA dataset
Authors: Simone Zini, Mirko Paolo Barbato, Flavio Piccoli, Paolo Napoletano
Date: 2023-07-21 00:00:00
Tasks: Deep Learning, Pansharpening

In this work, we assess several deep learning strategies for hyperspectral pansharpening. First, we present a new dataset with a greater extent than any other in the state of the art. This dataset, collected using the ASI PRISMA satellite, covers about 262200 km2, and its heterogeneity is granted by randomly sampling the Earth's soil. Second, we adapted several state of the art approaches based on deep learning to fit PRISMA hyperspectral data and then assessed, quantitatively and qualitatively, the performance in this new scenario. The investigation has included two settings: Reduced Resolution (RR) to evaluate the techniques in a supervised environment and Full Resolution (FR) for a real-world evaluation. The main purpose is the evaluation of the reconstruction fidelity of the considered methods. In both scenarios, for the sake of completeness, we also included machine-learning-free approaches. From this extensive analysis has emerged that data-driven neural network methods outperform machine-learning-free approaches and adapt better to the task of hyperspectral pansharpening, both in RR and FR protocols.
Computational Image Formation: Simulators in the Deep Learning Era
Authors: Stanley H. Chan
Date: 2023-07-21 00:00:00
Tasks: Deep Learning, Image Reconstruction

At the pinnacle of computational imaging is the co-optimization of camera and algorithm. This, however, is not the only form of computational imaging. In problems such as imaging through adverse weather, the bigger challenge is how to accurately simulate the forward degradation process so that we can synthesize data to train reconstruction models and/or integrating the forward model as part of the reconstruction algorithm. This article introduces the concept of computational image formation (CIF). Compared to the standard inverse problems where the goal is to recover the latent image $x$ from the observation $y = G(x)$, CIF shifts the focus to designing an approximate mapping $H$ such that $H \approx G$ while giving a good image reconstruction result. The word "computational" highlights the fact that the image formation is now replaced by a numerical simulator. While matching the mother nature remains an important goal, CIF pays even greater attention on strategically choosing an $H$ so that the reconstruction performance is maximized. The goal of this article is to conceptualize the idea of CIF by elaborating on its meaning and implications. The first part of the article is a discussion on the four attributes of a CIF simulator: accurate enough to mimic $G$, fast enough to be integrated as part of the reconstruction, provides a well-posed inverse problem when plugged into the reconstruction, and differentiable to allow backpropagation. The second part of the article is a detailed case study based on imaging through atmospheric turbulence. A plethora of simulators, old and new ones, are discussed. The third part of the article is a collection of other examples that fall into the category of CIF, including imaging through bad weather, dynamic vision sensors, and differentiable optics. Finally, thoughts about the future direction and recommendations to the community are shared.
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction
Authors: Ruoyou Wu, Cheng Li, Juan Zou, Shanshan Wang
Date: 2023-07-21 00:00:00
Tasks: Federated Learning, Image Reconstruction, Neural Architecture Search

Centralized training methods have shown promising results in MR image reconstruction, but privacy concerns arise when gathering data from multiple institutions. Federated learning, a distributed collaborative training scheme, can utilize multi-center data without the need to transfer data between institutions. However, existing federated learning MR image reconstruction methods rely on manually designed models which have extensive parameters and suffer from performance degradation when facing heterogeneous data distributions. To this end, this paper proposes a novel FederAted neUral archiTecture search approach fOr MR Image reconstruction (FedAutoMRI). The proposed method utilizes differentiable architecture search to automatically find the optimal network architecture. In addition, an exponential moving average method is introduced to improve the robustness of the client model to address the data heterogeneity issue. To the best of our knowledge, this is the first work to use federated neural architecture search for MR image reconstruction. Experimental results demonstrate that our proposed FedAutoMRI can achieve promising performances while utilizing a lightweight model with only a small number of model parameters compared to the classical federated learning methods.
Prediction of sunflower leaf area at vegetative stage by image analysis and application to the estimation of water stress response parameters in post-registration varieties
Authors: Pierre Casadebaig, Nicolas Blanchet, Nicolas Bernard Langlade
Date: 2023-07-20 00:00:00

The automatic measurement of developmental and physiological responses of sunflowers to water stress represents an applied challenge for a better knowledge of the varieties available to growers, but also a fundamental one for identifying the biological, genetic and molecular bases of plant response to their environment.On INRAE Toulouse's Heliaphen high-throughput phenotyping platform, we set up two experiments, each with 8 varieties (2*96 plants), and acquired images of plants subjected or not to water stress, using a light barrier on a daily basis. At the same time, we manually measured the leaf surfaces of these plants every other day for the duration of the stress, which lasted around ten days. The images were analyzed to extract morphological characteristics of the segmented plants and different models were evaluated to estimate total plant leaf areas using these data.A linear model with a posteriori smoothing was used to estimate total leaf area with a relative squared error of 11% and an efficiency of 93%. Leaf areas estimated conventionally or with the developed model were used to calculate the leaf expansion and transpiration responses (LER and TR) used in the SUNFLO crop model for 8 sunflower varieties studied. Correlation coefficients of 0.61 and 0.81 for LER and TR respectively validate the use of image-based leaf area estimation. However, the estimated values for LER are lower than for the manual method on Heliaphen, but closer overall to the manual method on greenhouse-grown plants, potentially suggesting an overestimation of stress sensitivity.It can be concluded that the LE and TR parameter estimates can be used for simulations. The low cost of this method (compared with manual measurements), the possibility of parallelizing and repeating measurements on the Heliaphen platform, and of benefiting from the Heliaphen platform's data management, are major improvements for valorizing the SUNFLO model and characterizing the drought sensitivity of cultivated varieties.
Amplitude-Based Sequential Optimization of Energy Harvesting with Reconfigurable Intelligent Surfaces
Authors: Morteza Tavana, Meysam Masoudi, Emil Björnson
Date: 2023-07-21 00:00:00

Reconfigurable Intelligent Surfaces (RISs) have gained immense popularity in recent years because of their ability to improve wireless coverage and their flexibility to adapt to the changes in a wireless environment. These advantages are due to RISs' ability to control and manipulate radio frequency (RF) wave propagation. RISs may be deployed in inaccessible locations where it is difficult or expensive to connect to the power grid. Energy harvesting can enable the RIS to self-sustain its operations without relying on external power sources. In this paper, we consider the problem of energy harvesting for RISs in the absence of coordination with the ambient RF source. We consider both direct and indirect energy harvesting scenarios and show that the same mathematical model applies to them. We propose a sequential phase-alignment algorithm that maximizes the received power based on only power measurements. We prove the convergence of the proposed algorithm to the optimal value under specific circumstances. Our simulation results show that the proposed algorithm converges to the optimal solution in a few iterations and outperforms the random phase update method in terms of the number of required measurements.
On Optimality in ROVir
Authors: Justin P. Haldar
Date: 2023-07-20 00:00:00
Tasks: Dimensionality Reduction

We recently published an approach named ROVir (Region-Optimized Virtual coils) that uses the beamforming capabilities of a multichannel magnetic resonance imaging (MRI) receiver array to achieve coil compression (reducing an original set of receiver channels into a much smaller number of virtual channels for the purposes of dimensionality reduction), while simultaneously preserving the MRI signal from desired spatial regions and suppressing the MRI signal arising from unwanted spatial regions. The original ROVir procedure is computationally-simple to implement (involving just a single small generalized eigendecomposition), and its signal-suppression capabilities have proven useful in an increasingly wide range of MRI applications. Our original paper made claims about the theoretical optimality of this generalized eigendecomposition procedure, but did not present the details. The purpose of this write-up is to elaborate on these mathematical details, and to introduce a new greedy iterative ROVir algorithm that enjoys certain advantages over the original ROVir calculation approach. This discussion is largely academic, with implications that we suspect will be minor for practical applications -- we have only observed small improvements to ROVir performance in the cases we have tried, and it would have been safe in these cases to still use the simpler original calculation procedure with negligible practical impact on the final imaging results.
Bayesian Linear Regression with Cauchy Prior and Its Application in Sparse MIMO Radar
Authors: Jun Li, Ryan Wu, I-Tai Lu, Dongyin Ren
Date: 2023-07-20 00:00:00
Tasks: regression, Single Particle Analysis

In this paper, a sparse signal recovery algorithm using Bayesian linear regression with Cauchy prior (BLRC) is proposed. Utilizing an approximate expectation maximization(AEM) scheme, a systematic hyper-parameter updating strategy is developed to make BLRC practical in highly dynamic scenarios. Remarkably, with a more compact latent space, BLRC not only possesses essential features of the well-known sparse Bayesian learning (SBL) and iterative reweighted l2 (IR-l2) algorithms but also outperforms them. Using sparse array (SPA) and coprime array (CPA), numerical analyses are first performed to show the superior performance of BLRC under various noise levels, array sizes, and sparsity levels. Applications of BLRC to sparse multiple-input and multiple-output (MIMO) radar array signal processing are then carried out to show that the proposed BLRC can efficiently produce high-resolution images of the targets.