Publications

2023

• Towards Autocomplete Strategies for Visualization Construction
  
  • Wei Wei
  • Huron Samuel
  • Jansen Yvonne
  , 2023, pp.141--145. Constructive visualization uses physical data units - tokens - to enable non-experts to create personalized visualizations engagingly. However, its physical nature limits efficiency and scalability. One potential solution to address this issue is autocomplete. By providing automated suggestions while still allowing for manual intervention, autocomplete can expedite visualization construction while maintaining expressivity. We conduct a speculative design study to examine how people would like to interact with a visualization authoring system that supports autocomplete. Our study identifies three types of autocomplete strategies and gains insights for designing future visualization authoring tools with autocomplete functionality. A free copy of this paper and all supplemental materials are available on our online repository: https://osf.io/nu4z3. (10.1109/vis54172.2023.00037)
  DOI : 10.1109/vis54172.2023.00037
• Locality and Centrality: The Variety ZG
  
  • Amarilli Antoine
  • Paperman Charles
  Logical Methods in Computer Science, Logical Methods in Computer Science Association, 2023, 19 (4). We study the variety ZG of monoids where the elements that belong to a group are central, i.e., commute with all other elements. We show that ZG is local, that is, the semidirect product ZG * D of ZG by definite semigroups is equal to LZG, the variety of semigroups where all local monoids are in ZG. Our main result is thus: ZG * D = LZG. We prove this result using Straubing's delay theorem, by considering paths in the category of idempotents. In the process, we obtain the characterization ZG = MNil \vee Com, and also characterize the ZG languages, i.e., the languages whose syntactic monoid is in ZG: they are precisely the languages that are finite unions of disjoint shuffles of singleton languages and regular commutative languages. (10.46298/lmcs-19(4:4)2023)
  DOI : 10.46298/lmcs-19(4:4)2023
• Towards a Research Agenda for Understanding and ManagingUncertainty in Self-Adaptive Systems
  
  • Weyns Danny
  • Calinescu Radu
  • Mirandola Raffaela
  • Tei Kenji
  • Acosta Maribel
  • Bennaceur Amel
  • Boltz Nicolas
  • Bures Tomas
  • Camara Javier
  • Diaconescu Ada
  • Engels Gregor
  • Gerasimou Simos
  • Gerostathopoulos Ilias
  • Getir Yaman Sinem
  • Grassi Vincenzo
  • Hahner Sebastian
  • Letier Emmanuel
  • Litoiu Marin
  • Marsso Lina
  • Musil Angelika
  • Musil Juergen
  • Nunes Rodrigues Genaina
  • Perez-Palacin Diego
  • Quin Federico
  • Scandurra Patrizia
  • Vallecillo Antonio
  • Zisman Andrea
  Software Engineering Notes, Association for Computing Machinery, 2023, 48 (4), pp.20-36. Despite considerable research efforts on handling uncertainty in self-adaptive systems, a comprehensive understanding of the precise nature of uncertainty is still lacking. This paper summarises the findings of the 2023 Bertinoro Seminar on Uncertainty in Self- Adaptive Systems, which aimed at thoroughly investigating the notion of uncertainty, and outlining open challenges associated with its handling in self-adaptive systems. The seminar discussions were centered around five core topics: (1) agile end-toend handling of uncertainties in goal-oriented self-adaptive systems, (2) managing uncertainty risks for self-adaptive systems, (3) uncertainty propagation and interaction, (4) uncertainty in self-adaptive machine learning systems, and (5) human empowerment under uncertainty. Building on the insights from these discussions, we propose a research agenda listing key open challenges, and a possible way forward for addressing them in the coming years. (10.1145/3617946.3617951)
  DOI : 10.1145/3617946.3617951
• Towards systems that dynamically change and evaluate abstractions
  
  • Diaconescu Ada
  • King David
  • Bellman Kirstie
  • Landauer Christopher
  • Nelson Phyllis
  , 2023.
• Characterizing and Interpreting Music Expressivity through Rhythm and Loudness Simplices
  
  • Lascabettes Paul
  • Chew Elaine
  • Bloch Isabelle
  , 2023, pp.110-117. Characterizing and interpreting expressivity in performed music remains an open problem. In this paper, we explore the novel representation of recorded performances of triple time music using a 2-simplex, a graphical representation used to visualize three-interval rhythms. We analyze the MazurkaBL dataset, which contains beat-level tempo and loudness data of over 2000 recorded performances of 46 Chopin Mazurkas. Mazurkas' triple time lends themselves well to the 2-simplex; the expressive features of each three-beat bar map directly to unique points in the 2-simplex. We extend the rhythm simplex designed for beat durations to the representation of loudness. Each recorded performance is thus reduced to a set of points in 2-simplices based on beat-level duration or loudness. We provide the transformation to convert three-interval information to points in the 2-simplex; prove that inter-beat intervals and tempo representations in the 2-simplex are equivalent when timing variations are small; and, explain how smoothing the data impacts the coordinates of the points in the simplex. We demonstrate that the use of simplices can facilitate the analysis and interpretation of expressive music features; the method enables the identifying of bars with notable expressive variations such as temporal suspensions that form tipping points, and characterizing of performance regularity.
• Sustained Feedback-Induced Oscillations in a Hybrid Single Mode Semiconductor Plasmonic Laser
  
  • Cui Di
  • Chen J
  • Bousseskou A
  • Huang Heming
  • Grillot F
  IEEE Photonics Technology Letters, Institute of Electrical and Electronics Engineers, 2023, 35 (20), pp.1090 - 1093. This work investigates the response to external optical feedback of a hybrid plasmonic semiconductor laser. Intensity noise measurements reveale the relaxation oscillation frequency whereas tabletop feedback experiments unveil a stronger reflection immunity, with a margin of 8 dB in comparison with a standard semiconductor laser made with a dielectric waveguide. On the top of that, the hybrid plasmonic laser does not exhibit a typical route to chaos but rather sustained induced-feedback oscillations instead. These results unlock new properties of hybrid plasmonic semiconductor lasers and provide guidelines in designing laser sources for on-chip hybrid plasmonic integrated platform. (10.1109/lpt.2023.3296962)
  DOI : 10.1109/lpt.2023.3296962
• Disentangling private classes through regularization
  
  • Tartaglione Enzo
  • Gennari Francesca
  • Quétu Victor
  • Grangetto Marco
  Neurocomputing, Elsevier, 2023, 554, pp.126612. Deep learning models are nowadays broadly deployed to solve an incredibly large variety of tasks. However, little attention has been devoted to connected legal aspects. In 2016, the European Union approved the General Data Protection Regulation which entered into force in 2018. Its main rationale was to protect the privacy and data protection of its citizens by the way of operating the so-called “Data Economy”. As data is the fuel of modern Artificial Intelligence, it is argued that the GDPR can be partly applicable to a series of algorithmic decision-making tasks before a more structured AI Regulation enters into force. In the meantime, AI should not allow undesired information leakage deviating from the purpose for which is created. In this work, we propose DisP, an approach for deep learning models disentangling the information related to some classes we desire to keep private, from the data processed by AI. In particular, DisP is a regularization strategy de-correlating the features belonging to the same private class at training time, hiding the information about private class membership. Our experiments on state-of-the-art deep learning models show the effectiveness of DisP, minimizing the risk of extraction for the classes we desire to keep private. (10.1016/j.neucom.2023.126612)
  DOI : 10.1016/j.neucom.2023.126612
• Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer
  
  • Vétil Rebeca
  • Abi Nader Clément
  • Bône Alexandre
  • Vullierme Marie-Pierre
  • Rohé Marc-Michel
  • Gori Pietro
  • Bloch Isabelle
  , 2023, 14295, pp.68-82. We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR). To do so, we extract DLR features using a VAE while enforcing their independence with HCR features by minimizing their mutual information. The resulting DLR features can be combined with hand-crafted ones and leveraged by a classifier to predict early markers of cancer. We illustrate our method on four early markers of pancreatic cancer and validate it on a large independent test set. Our results highlight the value of combining non-redundant DLR and HCR features, as evidenced by an improvement in the Area Under the Curve compared to baseline methods that do not address redundancy or solely rely on HCR features. (10.1007/978-3-031-45350-2_6)
  DOI : 10.1007/978-3-031-45350-2_6
• Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer
  
  • Vétil Rebeca
  • Abi Nader Clément
  • Bône Alexandre
  • Vullierme Marie-Pierre
  • Rohe Marc-Michel
  • Gori Pietro
  • Bloch Isabelle
  , 2023. We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR). To do so, we extract DLR features using a VAE while enforcing their independence with HCR features by minimizing their mutual information. The resulting DLR features can be combined with hand-crafted ones and leveraged by a classifier to predict early markers of cancer. We illustrate our method on four early markers of pancreatic cancer and validate it on a large independent test set. Our results highlight the value of combining non-redundant DLR and HCR features, as evidenced by an improvement in the Area Under the Curve compared to baseline methods that do not address redundancy or solely rely on HCR features.
• Apprendre à diagnostiquer la cirrhose à l'aide de méthodes de pré-entraînement faiblement et auto-supervisées
  
  • Sarfati Emma
  • Bône Alexandre
  • Rohé Marc-Michel
  • Gori Pietro
  • Bloch Isabelle
  , 2023.
• Designing a unique revision loop updating courses simultaneously on different MOOC platforms
  
  • Hamonic Ella
  • Sharrock Rémi
  • Bonfert-Taylor Petra
  • Goudzwaard Michael
  • Memmi Gérard
  • Chow Catherine
  • Meise Josh
  , 2023, pp.1-6. This study introduces a content revision loop for simultaneous course updates across MOOC platforms. It uses a single source of truth and iterative, data-driven methodologies, enabling efficient dissemination of updates and assessment improvement. The approach provides a model for instructors managing courses on multiple platforms. (10.1109/LWMOOCS58322.2023.10305900)
  DOI : 10.1109/LWMOOCS58322.2023.10305900
• Novel mid-infrared quantum cascade devices for applications in free-space optics, data security and microwave photonics
  
  • Didier Pierre
  , 2023. This doctoral thesis focuses on free-space optical (FSO) transmission in the mid-infrared region covering three main aspects: high-speed FSO transmission, private communication through chaotic synchronization, and integration of FSO-to-RF communication systems. In the field of high-speed FSO transmission, the research optimizes modulation schemes, equlaization for high speed data transmission in the mid-infrared spectrum. Cascade devices such as interband cascade lasers (ICLs) and quantum cascade lasers (QCLs) are utilized. Data rates of up to 14 Gbps are achieved using ICL and interband cascade interband photodetectors (ICIPs). QCLs and external modulators based on electrically modulated absorption are also implemented, achieving data rates up to 68 Gbps. These devices high modulated power, making them suitable for long-distance transmissions. The thesis also explores the application of chaos synchronization techniques for private communication over FSO links. Chaotic properties of light sources in the mid-infrared are leveraged, enabling the concealment of messages within chaotic signals. The complexity of the generated chaos allows for private transmission, with legitimate users achieving low error rates while non-legitimate users experience a higher error rate. Furthermore, the thesis investigates the integration of FSO and RF communication systems. The aim is to create a conversion between high-speed FSO links and RF links. Heterodyne beating techniques are utilized, combining quantum cascade lasers to generate beat signals. This approach enables the transmission of FSO signals over the Ka band through a QWIP detector. The research demonstrates the feasibility of FSO-to-RF integration, opening possibilities for combining the advantages of both communication systems. Overall, this thesis presents advancements in high-speed FSO transmission, secure communication using chaotic synchronization, and integration of FSO and RF systems. The research findings have implications for various fields, including telecommunications, satellite communication, and secure data transmission.
• Real-time Traffic Classification for 5G NSA Encrypted Data Flows With Physical Channel Records
  
  • Fei Xiao
  • Martins Philippe
  • Lu Jialiang
  , 2023. The classification of fifth-generation New-Radio (5G-NR) mobile network traffic is an emerging topic in the field of telecommunications. It can be utilized for quality of service (QoS) management and dynamic resource allocation. However, traditional approaches such as Deep Packet Inspection (DPI) can not be directly applied to encrypted data flows. Therefore, new real-time encrypted traffic classification algorithms need to be investigated to handle dynamic transmission. In this study, we examine the real-time encrypted 5G Non-Standalone (NSA) application-level traffic classification using physical channel records. Due to the vastness of their features, decision-tree-based gradient boosting algorithms are a viable approach for classification. We generate a noise-limited 5G NSA trace dataset with traffic from multiple applications. We develop a new pipeline to convert sequences of physical channel records into numerical vectors. A set of machine learning models are tested, and we propose our solution based on Light Gradient Boosting Machine (LGBM) due to its advantages in fast parallel training and low computational burden in practical scenarios. Our experiments demonstrate that our algorithm can achieve 95% accuracy on the classification task with a state-of-the-art response time as quick as 10ms. (10.48550/arXiv.2307.07756)
  DOI : 10.48550/arXiv.2307.07756
• Feature-Sized Sampling for Vector Line Art
  
  • Ohrhallinger Stefan
  • Parakkat Amal Dev
  • Memari Pooran
  , 2023. By introducing a first-of-its-kind quantifiable sampling algorithm based on feature size, we present a fresh perspective on the practical aspects of planar curve sampling. Following the footsteps of ε-sampling, which was originally proposed in the context of curve reconstruction to offer provable topological guarantees [ABE98] under quantifiable bounds, we propose an arbitrarily precise ε-sampling algorithm for sampling smooth planar curves (with a prior bound on the minimum feature size of the curve). This paper not only introduces the first such algorithm which provides user-control and quantifiable precision but also highlights the importance of such a sampling process under two key contexts: 1) To conduct a first study comparing theoretical sampling conditions with practical sampling requirements for reconstruction guarantees that can further be used for analysing the upper bounds of ε for various reconstruction algorithms with or without proofs, 2) As a feature-aware sampling of vector line art that can be used for applications such as coloring and meshing.
• Insights Into the Importance of Linguistic Textual Features on the Persuasiveness of Public Speaking
  
  • Barkar Alisa
  • Chollet Mathieu
  • Biancardi Beatrice
  • Clavel Chloe
  , 2023, pp.51-55. There is a growing need for public speaking skills in both professional and private life. With this background, our research project's long-term aims are to develop tools that can analyse public speeches and provide helpful feedback. The impact of audio and visual characteristics on the automatic analysis of speech quality has been widely explored in the existing literature. However, only a few studies have focused on textual features. In response to this shortcoming, this paper investigates the importance of textual content for the automatic analysis of public speaking. We created an open-source Python library of textual features and integrated them as inputs of simple machine-learning models for automatic public-speaking analysis, and persuasiveness prediction, in particular. The best result (accuracy of 61%) is obtained using a logistic regression. We then evaluated the impact of these features on persuasiveness prediction using both correlation analysis and Explainable AI methods. This evaluation was conducted on the French data set 3MT_French, including student performances in the "Ma Thèse en 180 Secondes" competition. CCS CONCEPTS • Computing methodologies → Classifcation and regression trees; Modeling and simulation. (10.1145/3610661.3617161)
  DOI : 10.1145/3610661.3617161
• Predictive Coding for Animation-Based Video Compression
  
  • Konuko Goluck
  • Lathuilière Stéphane
  • Valenzise Giuseppe
  , 2023. We address the problem of efficiently compressing video for conferencing-type applications. We build on recent approaches based on image animation, which can achieve good reconstruction quality at very low bitrate by representing face motions with a compact set of sparse keypoints. However, these methods encode video in a frame-by-frame fashion, i.e., each frame is reconstructed from a reference frame, which limits the reconstruction quality when the bandwidth is larger. Instead, we propose a predictive coding scheme which uses image animation as a predictor, and codes the residual with respect to the actual target frame. The residuals can be in turn coded in a predictive manner, thus removing efficiently temporal dependencies. Our experiments indicate a significant bitrate gain, in excess of 70% compared to the HEVC video standard and over 30% compared to VVC, on a dataset of talking-head videos. Our code is available at github.com/animation-based-codecs. (10.1109/icip49359.2023.10222205)
  DOI : 10.1109/icip49359.2023.10222205
• Dodging the Double Descent in Deep Neural Networks
  
  • Quétu Victor
  • Tartaglione Enzo
  , 2023, pp.1625-1629. Finding the optimal size of deep learning models is very actual and of broad impact, especially in energy-saving schemes. Very recently, an unexpected phenomenon, the "double descent", has caught the attention of the deep learning community. As the model’s size grows, the performance gets first worse and then goes back to improving. It raises serious questions about the optimal model’s size to maintain high generalization: the model needs to be sufficiently over-parametrized, but adding too many parameters wastes training resources. Is it possible to find, in an efficient way, the best trade-off?Our work shows that the double descent phenomenon is potentially avoidable with proper conditioning of the learning problem, but a final answer is yet to be found. We empirically observe that there is hope to dodge the double descent in complex scenarios with proper regularization, as a simple ℓ 2 regularization is already positively contributing to such a perspective. (10.1109/ICIP49359.2023.10222624)
  DOI : 10.1109/ICIP49359.2023.10222624
• Introducing A Framework for Single-Human Tracking Using Event-Based Cameras
  
  • Eisl Dominik
  • Herzog Fabian
  • Dugelay Jean-Luc
  • Apvrille Ludovic
  • Rigoll Gerhard
  , 2023, pp.3269-3273. Event cameras generate data based on the amount of motion present in the captured scene, making them attractive sensors for solving ob- ject tracking tasks. In this paper, we present a framework for tracking humans using a single event camera which consists of three compo- nents. First, we train a Graph Neural Network (GNN) to recognize a person within the stream of events. Batches of events are represented as spatio-temporal graphs in order to preserve the sparse nature of events and retain their high temporal resolution. Subsequently, the person is localized in a weakly-supervised manner by adopting the well established method of Class Activation Maps (CAM) for our graph-based classification model. Our approach does not require the ground truth position of humans during training. Finally, a Kalman filter is deployed for tracking, which uses the predicted bounding box surrounding the human as measurement. We demonstrate that our approach achieves robust tracking results on test sequences from the Gait3 database, paving the way for further privacy-preserving methods in event-based human tracking. Code, pre-trained models and datasets of our research are publicly available (10.1109/ICIP49359.2023.10222777)
  DOI : 10.1109/ICIP49359.2023.10222777
• Weakly-supervised positional contrastive learning: application to cirrhosis classification
  
  • Sarfati Emma
  • Bône Alexandre
  • Rohé Marc-Michel
  • Gori Pietro
  • Bloch Isabelle
  , 2023. Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e.g., radiological scores). Access to high-confidence labels, such as histology-based diagnoses, is rare and costly. Pretraining strategies, like contrastive learning (CL) methods, can leverage unlabeled or weakly-annotated datasets. These methods typically require large batch sizes, which poses a difficulty in the case of large 3D images at full resolution, due to limited GPU memory. Nevertheless, volumetric positional information about the spatial context of each 2D slice can be very important for some medical applications. In this work, we propose an efficient weakly-supervised positional (WSP) contrastive learning strategy where we integrate both the spatial context of each 2D slice and a weak label via a generic kernel-based loss function. We illustrate our method on cirrhosis prediction using a large volume of weakly-labeled images, namely radiological low-confidence annotations, and small strongly-labeled (i.e., high-confidence) datasets. The proposed model improves the classification AUC by 5% with respect to a baseline model on our internal dataset, and by 26% on the public LIHC dataset from the Cancer Genome Atlas. The code is available at: https://github.com/Guerbet-AI/wsp-contrastive.
• Inductive Graph Neural Networks for Moving Object Segmentation
  
  • Prummel Wieke
  • Giraldo Jhony H.
  • Zakharova Anastasia
  • Bouwmans Thierry
  , 2023, pp.2730-2734. Moving Object Segmentation (MOS) is a challenging problem in computer vision, particularly in scenarios with dynamic backgrounds, abrupt lighting changes, shadows, camouflage, and moving cameras. While graph-based methods have shown promising results in MOS, they have mainly relied on transductive learning which assumes access to the entire training and testing data for evaluation. However, this assumption is not realistic in real-world applications where the system needs to handle new data during deployment. In this paper, we propose a novel Graph Inductive Moving Object Segmentation (GraphIMOS) algorithm based on a Graph Neural Network (GNN) architecture. Our approach builds a generic model capable of performing prediction on newly added data frames using the already trained model. GraphI-MOS outperforms previous inductive learning methods and is more generic than previous transductive techniques. Our proposed algorithm enables the deployment of graph-based MOS models in real-world applications (10.1109/ICIP49359.2023.10222668)
  DOI : 10.1109/ICIP49359.2023.10222668
• Decoupled conditional contrastive learning with variable metadata for prostate lesion detection
  
  • Ruppli Camille
  • Gori Pietro
  • Ardon Roberto
  • Bloch Isabelle
  , 2023. Early diagnosis of prostate cancer is crucial for efficient treatment. Multi-parametric Magnetic Resonance Images (mp-MRI) are widely used for lesion detection. The Prostate Imaging Reporting and Data System (PI-RADS) has standardized interpretation of prostate MRI by defining a score for lesion malignancy. PI-RADS data is readily available from radiology reports but is subject to high inter-reports variability. We propose a new contrastive loss function that leverages weak metadata with multiple annotators per sample and takes advantage of inter-reports variability by defining metadata confidence. By combining metadata of varying confidence with unannotated data into a single conditional contrastive loss function, we report a 3% AUC increase on lesion detection on the public PI-CAI challenge dataset.
• Impact of external carrier noise on the linewidth enhancement factor of a quantum dot distributed feedback laser
  
  • Ding Shihao
  • Zhao Shiyuan
  • Huang Heming
  • Grillot Frédéric
  Optics Express, Optical Society of America - OSA Publishing, 2023, 31 (21), pp.35343-35353. This paper demonstrates that the linewidth enhancement factor of quantum dot lasers is influenced by the external carrier transport issued from different external current sources. A model combining the rate equation and semi-classical carrier noise is used to investigate the different mechanisms leading to the above phenomenon in the context of a quantum dot distributed feedback laser. Meanwhile, the linewidth enhancement factor extracted from the optical phase modulation method shows dramatic differences when the quantum dot laser is driven by different noise-level pumps. Furthermore, the influence of external carrier noise on the frequency noise in the vicinity of the laser’s threshold current directly affects the magnitude of the linewidth enhancement factor. Simulations also investigate how the external carrier transport impacts the frequency noise and the spectral linewidth of the QD laser. Overall, we believe that these results are of paramount importance for the development of on-chip integrated ultra-low noise oscillators producing light at or below the shot-noise level. (10.1364/oe.496131)
  DOI : 10.1364/oe.496131
• Delfines: Detecting Laser Fault Injection Attacks Via Digital Sensors
  
  • Ebrahimabadi Mohammad
  • Mehjabin Suhee Sanjana
  • Viera Raphael
  • Guilley Sylvain
  • Danger Jean-Luc
  • Dutertre Jean-Max
  • Karimi Naghmeh
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, IEEE, 2023, pp.1-1. Laser Fault Injection Attacks (LFIA) are a major concern in physical security of electronic circuits as they allow an attacker to inject a fault with a very high spatial accuracy. They are also often considered by Information Technology Security Evaluation Facilities (ITSEFs) to deliver security certification, as Common Criteria, of embedded systems. Time or spatial redundancy can be foreseen as protection methods but they are costly and do not ensure immunity against multiple laser injections. The detection would be efficient if the detecting sensors meet enough density and sensitivity to cover the functional blocks being protected. Most sensors rely on analog and specific technology. In this paper, we propose a method to detect LFIAs via a fully digital sensor based on a Time to Digital Converter (TDC) and show its efficacy in detecting such faults in various conditions related to the current induced by the laser, the characteristics of the Power Grid Network (PGN) of the circuit and the environmental variables (voltage, temperature). The simulation results obtained using a 45nm Nangate technology confirms the high efficiency of the proposed scheme in detecting LFIAs in a large range of such conditions. (10.1109/TCAD.2023.3322623)
  DOI : 10.1109/TCAD.2023.3322623
• Editorial: Seventh special issue on Knowledge Discovery and Business Intelligence
  
  • Cortez Paulo
  • Bifet Albert
  Expert Systems, Wiley, 2023, 40 (10), pp.e13466/1-2. (10.1111/EXSY.13466)
  DOI : 10.1111/EXSY.13466
• Collision Free Simplification for 2D Multi-Layered Shapes
  
  • Gong Xianjin
  • Parakkat Amal Dev
  • Rohmer Damien
  , 2023. We propose a simplification-aware untangling algorithm for 2D layered shapes stacked on each other. While the shape undergoes simplification, our approach adjusts the vertex positions to prevent collision with other layers while simultaneously maintaining the correct relative ordering and offsets between the layers. The method features a field-based representation of the shapes and extends the concept of "implicit untangling" by incorporating interleaved shape preservation through a parameterized shape-matching technique. Our approach can be plugged on top of any existing vertex-decimation approach, leveraging its localized nature to accelerate the field evaluation. Furthermore, our method can seamlessly handle an arbitrary number of stacked layers, making it a versatile solution for stacked garment simplification.