Publications

2023

• Combining Diverse Meta-Features to Accurately Identify Recurring Concept Drift in Data Streams
  
  • Halstead Ben
  • Koh Yun Sing
  • Riddle Patricia
  • Pechenizkiy Mykola
  • Bifet Albert
  ACM Transactions on Knowledge Discovery from Data (TKDD), ACM, 2023, 17 (8), pp.107:1--107:36. Learning from streaming data is challenging as the distribution of incoming data may change over time, a phenomenon known as concept drift. The predictive patterns, or experience learned under one distribution may become irrelevant as conditions change under concept drift, but may become relevant once again when conditions reoccur. Adaptive learning methods adapt a classifier to concept drift by identifying which distribution, or concept, is currently present in order to determine which experience is relevant. Identifying a concept requires some representation to be stored for comparison, with the quality of the representation being key to accurate identification. Existing concept representations are based on meta-features, efficient univariate summaries of a concept. However, no single meta-feature can fully represent a concept, leading to severe accuracy loss when existing representations cannot describe concept drift. To avoid these failure cases, we propose the first general framework for combining a diverse range of meta-features into a single representation. We solve two main challenges, first presenting a method of efficiently computing, storing, and querying an arbitrary set of meta-features as a single representation, showing that a combination of meta-features may successfully avoid failure cases seen with existing methods. Second, we present the first method for dynamically learning which meta-features distinguish concepts in any given dataset, significantly improving performance. Our proposed approach enables state-of-the-art feature selection methods, such as mutual information, to be applied to concept representation meta-features for the first time. We investigate tradeoffs between memory budget and classification performance, observing accuracy increases of up to 16% by dynamically weighting the contribution of each meta-feature. (10.1145/3587098)
  DOI : 10.1145/3587098
• Methods for improved brain PET quantification using super-resolution and non-negative matrix factorization
  
  • Chemli Yanis
  , 2023. The advent of radiotracers binding to misfolded proteins such as amyloid and neurofibrillary tangles (tau), has ushered in a new era of PET imaging for neurodegenerative diseases, bringing new requirements for image quantification and processing. In particular, imaging of tau pathology, especially in early disease stages, is fueling a need for improved PET quantification to allow for accurate imaging of more focal tracer uptake patterns and small brain structures, such as the entorhinal cortex. However, this task is usually affected by the poor spatial resolution inherent to PET imaging as well as noise and the partial volume effect induced from tissue fraction effect. To address these issues, this thesis explores different methods for improving quantification, such as super-resolution (SR) and non-negative matrix factorization (NMF).Super-resolution (SR) is a methodology that seeks to improve image resolution by exploiting the increased spatial sampling information obtained from multiple acquisitions of the same target with accurately known sub-resolution shifts. The first contribution of this work aims to study, develop and evaluate an SR estimation framework for brain positron emission tomography (PET), taking advantage of a high-resolution infra-red tracking camera to measure shifts precisely and continuously. Moving phantoms and non-human primate (NHP) experiments were performed on a GE Discovery MI PET/CT scanner (GE Healthcare) using an NDI Polaris Vega (Northern Digital Inc), an external optical motion tracking device. To enable SR, a robust temporal and spatial calibration of the two devices was developed as well as a list-mode Ordered Subset Expectation Maximization (OSEM) PET reconstruction algorithm, incorporating the high-resolution tracking data from the Polaris Vega to correct motion for measured line of responses (LORs) on an event-by-event basis. For both phantoms and NHP studies, the SR reconstruction method yielded PET images with visibly increased spatial resolution compared to standard static acquisitions, allowing improved visualization of small structures. Quantitative analysis in terms of SSIM, CNR and line profiles were conducted and validated our observations. The results demonstrate that SR can be achieved in brain PET by measuring target motion in real-time using a high-resolution infrared tracking camera.The second objective of this thesis was to explore the use of non-negative matrix factorization (NMF) in dynamic PET imaging, specifically in relation to the [18F]MK6240 Tau PET tracer. This tracer has potential clinical limitations, such as off-target binding in dynamic imaging. NMF is a method that can be used to overcome these limitations by accurately separating tau-specific signals, non-specific signals, and off-target signals in the acquired data. In this thesis, the theoretical foundations of NMF are discussed and its practical applications in dynamic PET imaging are examined. To demonstrate the effectiveness of NMF, simulations were applied to a numerical phantom and real dynamic PET images acquired from cognitively normal subjects. The results of the NMF analysis are presented and discussed, highlighting the potential of this method to improve the quantification and interpretation of dynamic PET imaging data in the context of tau pathology.
• Accelerated Dynamic MR Imaging Using Linear And Non-Linear Machine Learning-Based Image Reconstruction Models
  
  • Djebra Yanis
  , 2023. Dynamic Magnetic Resonance (MR) imaging is of high value in medical diagnosis thanks to its contrast versatility, high spatial resolution, high Signal-to-Noise Ratio (SNR), and allows for non-invasive multi-planar images of the body. It can be particularly useful for imaging the brain, heart, spine, and joints, as well as for detecting abnormalities. In addition, the increasing availability of Positron Emission Tomography (PET)/MR machines enables simultaneous acquisition of PET and MR data for better reconstruction and complementary information. However, a key challenge in dynamic MRI is reconstructing high-dimensional images from sparse k-space data sampled below the Nyquist sampling rate. Many methods have been proposed for accelerated imaging with sparse sampling, including parallel imaging and compressed sensing.The first objective of this thesis is to show the potential and usefulness of the linear subspace model for free-breathing MR imaging. Such a model can in principle capture regular respiratory and cardiac motion. However, when dealing with lengthy scans, irregular motion patterns can occur, such as erratic breathing or bulk motion caused by patient discomfort. A first question thus naturally arises: can such a model capture irregular types of motion and, if so, can it reconstruct images from a dynamic MR scan presenting bulk motion and irregular respiratory motion? We demonstrate in this thesis how the subspace model can efficiently reconstruct artifact-free images from highly undersampled k-space data with various motion patterns. A first application is presented where we reconstruct high-resolution, high frame-rate dynamic MR images from a PET/MR scanner and use them to correct motion in PET data, capturing complex motion patterns such as irregular respiratory patterns and bulk motion. A second application on cardiac T1 mapping is presented. Undersampled k-space data were acquired using a free-breathing, ECG-gated inversion recovery sequence, and dynamic 3D MR images of the whole heart were reconstructed leveraging the linear subspace model.The second objective of this thesis is to understand the limits of the linear subspace model and develop a novel dynamic MR reconstruction scheme that palliates these limitations. More specifically, the subspace model assumes that high-dimensional data reside in a low-dimensional linear subspace that captures the spatiotemporal correlations of dynamic MR images. This model relies on a linear dimensionality reduction model and does not account for intrinsic non-linear features of the signal, which may show its limits with higher undersampling rates. Manifold learning-based models have therefore been explored for image reconstruction in dynamic MRI and aim at learning the intrinsic structure of the input data that are embedded in a high-dimensional signal space by solving non-linear dimensionality reduction problems. We present in this thesis an alternative strategy for manifold learning-based MR image reconstruction. The proposed method learns the manifold structure via linear tangent space alignment (LTSA) and can be interpreted as a non-linear generalization of the subspace model. Validation on numerical simulation studies as well as in vivo 2D and 3D cardiac imaging experiments were performed, demonstrating improved performances compared to state-of-the-art techniques.The two first objectives present respectively linear and non-linear models yet both methods use conventional linear optimization techniques to solve the reconstruction problem. In contrast, using deep neural networks for optimization may procure non-linear and better representation power. Early results on deep learning-based approaches are presented in this thesis and state-of-the-art techniques are discussed. The last chapter then presents conclusions, discusses the author's contributions, and considers the potential research perspectives that have been opened up by the work presented in this thesis.
• Fast and accurate nonlinear interference in-band spectrum prediction for sparse channel allocation
  
  • Andrenacci Isaia
  • Lonardi Matteo
  • Ramantanis Petros
  • Awwad Elie
  • Irurozki Ekhine
  • Clémençon Stéphan
  , 2023. We propose and numerically evaluate a machine-learning-based nonlinear interference spectrum estimator for a coherent optical network. The solution shows a root-mean-squared error of about 0.13 dB compared with split-step Fourier simulation when estimating the nonlinear interference variance.
• PointCloudSlicer: Gesture-based segmentation of point clouds
  
  • Gowtham Hari Hara
  • Parakkat Amal Dev
  • Cani Marie-Paule
  , 2023. Segmentation is a fundamental problem in point-cloud processing, addressing points classification into consistent regions, the criteria for consistency being based on the application. In this paper, we introduce a simple, interactive framework enabling the user to quickly segment a point cloud in a few cutting gestures in a perceptually consistent way. As the user perceives the limit of a shape part, they draw a simple separation stroke over the current 2D view. The point cloud is then segmented without needing any intermediate meshing step. Technically, we find an optimal, perceptually consistent cutting plane constrained by user stroke and use it for segmentation while automatically restricting the extent of the cut to the closest shape part from the current viewpoint. This enables users to effortlessly segment complex point clouds from an arbitrary viewpoint with a possibility of handling self-occlusions.
• A Generic Transform from Multi-Round Interactive Proof to NIZK
  
  • Fouque Pierre-Alain
  • Georgescu Adela
  • Qian Chen
  • Roux-Langlois Adeline
  • Wen Weiqiang
  , 2023, 13941, pp.461-481. We present a new generic transform that takes a multi-round interactive proof for the membership of a language L and outputs a non-interactive zero-knowledge proof (not of knowledge) in the common reference string model. Similar to the Fiat-Shamir transform, it requires a hash function H. However, in our transform the zero-knowledge property is in the standard model, and the adaptive soundness is in the non-programmable random oracle model (NPROM). Behind this new generic transform, we build a new generic OR-composition of two multi-round interactive proofs. Note that the two common techniques for building OR-proofs (parallel OR-proof and sequential OR-proof) cannot be naturally extended to the multi-round setting. We also give a proof of security for our OR-proof in the quantum oracle model (QROM), surprisingly the security loss in QROM is independent from the number of rounds. (10.1007/978-3-031-31371-4_16)
  DOI : 10.1007/978-3-031-31371-4_16
• Spectrum-Based Selective Monitoring of Propagation Effects
  
  • Girard-Jollet Joana
  • Antona Jean-Christophe
  • Meseguer A. C.
  • Rekaya-Ben Othman Ghaya
  • Lonardi M.
  , 2023, paper SM3I.4. We propose a machine learning based method to estimate the proportion of inter-channel and intra-channel nonlinear effects. The model is tested with simulations over different power profiles, fiber attenuations, span lengths and chromatic dispersions. (10.1364/CLEO_SI.2023.SM3I.4)
  DOI : 10.1364/CLEO_SI.2023.SM3I.4
• GLADIS: A General and Large Acronym Disambiguation Benchmark
  
  • Chen Lihu
  • Varoquaux Gaël
  • Suchanek Fabian M.
  , 2023. Acronym Disambiguation (AD) is crucial for natural language understanding on various sources, including biomedical reports, scientific papers, and search engine queries. However, existing acronym disambiguation benchmarks and tools are limited to specific domains, and the size of prior benchmarks is rather small. To accelerate the research on acronym disambiguation, we construct a new benchmark named GLADIS with three components: (1) a much larger acronym dictionary with 1.5M acronyms and 6.4M long forms; (2) a pre-training corpus with 160 million sentences; (3) three datasets that cover the general, scientific, and biomedical domains. We then pre-train a language model, AcroBERT, on our constructed corpus for general acronym disambiguation, and show the challenges and values of our new benchmark.
• Unbiased Supervised Contrastive Learning
  
  • Barbano Carlo Alberto
  • Dufumier Benoit
  • Tartaglione Enzo
  • Grangetto Marco
  • Gori Pietro
  , 2023. Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distribution of the data. For this reason, learning unbiased models from biased data has become a very relevant research topic in the last years. In this work, we tackle the problem of learning representations that are robust to biases. We first present a margin-based theoretical framework that allows us to clarify why recent contrastive losses (InfoNCE, SupCon, etc.) can fail when dealing with biased data. Based on that, we derive a novel formulation of the supervised contrastive loss (ϵ-SupInfoNCE), providing more accurate control of the minimal distance between positive and negative samples. Furthermore, thanks to our theoretical framework, we also propose FairKL, a new debiasing regularization loss, that works well even with extremely biased data. We validate the proposed losses on standard vision datasets including CIFAR10, CIFAR100, and ImageNet, and we assess the debiasing capability of FairKL with ϵ-SupInfoNCE, reaching stateof-the-art performance on a number of biased datasets, including real instances of biases "in the wild".
• Complexity of domination problems in graphs
  
  • Hudry Olivier
  , 2023.
• Demonstrating Low Cost SIM Bluetooth Token For Generation Of Ethereum Transactions
  
  • Urien Pascal
  , 2023, pp.1-3. This demonstration presents an original low cost SIM Ethereum Bluetooth token (SIM_ETH_BLE_TOKEN), used from a mobile application, for the generation of Ethereum transaction. The token is based on open hardware (i.e. Arduino) and open source code. The core security is a secure element (i.e. javacard) with SIM card form factor, protected by PIN code, which stores keys and generates transactions. The token has no keypad or screen; it uses a LED and a button for user interface. The mobile application is available on Google Play. It signs files stored in smartphone, thanks to transactions, inserted in the Ethereum ledger (10.1109/ICBC56567.2023.10174874)
  DOI : 10.1109/ICBC56567.2023.10174874
• Inversion alleviation for stable elastic body simulation
  
  • Lee JaeHyun
  • Kim Seung‐wook
  • Um Kiwon
  • Kee Min Hyung
  • Han JungHyun
  Computer Animation and Virtual Worlds, Wiley, 2023, 34 (3-4). In general, it is not easy to simulate an elastic body that undergoes large deformations. Especially when its elements are inverted or tangled, that is, when its vertices penetrate its polygons, simulation often fails. In this paper, we propose a simple yet highly effective method for alleviating the inversion problems of elastic bodies. Our experiments made with typical optimization-based solvers demonstrate that the proposed method successfully stabilizes the solvers and produces visually plausible motions. We believe that our method can be widely adopted by a variety of state-of-the-art elastic-body simulators thanks to its simplicity. (10.1002/cav.2183)
  DOI : 10.1002/cav.2183
• An Optimization‐based SPH Solver for Simulation of Hyperelastic Solids
  
  • Kee Min Hyung
  • Um Kiwon
  • Kang Hyunmo
  • Han Junghyun
  Computer Graphics Forum, Wiley, 2023, 42 (2), pp.225-233. This paper proposes a novel method for simulating hyperelastic solids with Smoothed Particle Hydrodynamics (SPH). The proposed method extends the coverage of the state-of-the-art elastic SPH solid method to include different types of hyperelastic materials, such as the Neo-Hookean and the St. Venant-Kirchoff models. To this end, we reformulate an implicit integration scheme for SPH elastic solids into an optimization problem and solve the problem using a general-purpose quasi-Newton method. Our experiments show that the Limited-memory BFGS (L-BFGS) algorithm can be employed to efficiently solve our optimization problem in the SPH framework and demonstrate its stable and efficient simulations for complex materials in the SPH framework. Thanks to the nature of our unified representation for both solids and fluids, the SPH formulation simplifies coupling between different materials and handling collisions. (10.1111/cgf.14756)
  DOI : 10.1111/cgf.14756
• Packed-Ensembles for Efficient Uncertainty Estimation
  
  • Laurent Olivier
  • Lafage Adrien
  • Tartaglione Enzo
  • Daniel Geoffrey
  • Martinez Jean-Marc
  • Bursuc Andrei
  • Franchi Gianni
  , 2022. Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detection. However, hardware limitations of real-world systems constrain to smaller ensembles and lower-capacity networks, significantlydeteriorating their performance and properties. We introduce Packed-Ensembles(PE), a strategy to design and train lightweight structured ensembles by carefully modulating the dimension of their encoding space. We leverage grouped convolu-tions to parallelize the ensemble into a single shared backbone and forward pass to improve training and inference speeds. PE is designed to operate within the memory limits of a standard neural network. Our extensive research indicates that PE accurately preserves the properties of DE, such as diversity, and performs equally well in terms of accuracy, calibration, out-of-distribution detection, and robustness to distribution shift. We make our code available at github.com/ENSTA-U2IS/torch-uncertainty.
• P122 - Association entre les marqueurs d'utilisation du téléphone dans la vie réelle et les performances cognitives à court terme, le bien-être et le sommeil
  
  • Pujol S.
  • Eeftens M.
  • Klaiber A.
  • Riss A.
  • Smayra F.
  • Flückiger B.
  • Chopard G.
  • Gehin T.
  • Diallo K.
  • Moussounda N.
  • Wiart Joe
  • Mazloum Taghrid
  • Mauny F.
  • Röösli M.
  Epidemiology and Public Health = Revue d'Epidémiologie et de Santé Publique, Elsevier Masson, 2023, 71 (Supplément 2), pp.101770. Les conséquences à court terme de l'exposition aux radiofréquences (RF) issues des champs électromagnétiques (CEM) sur les performances cognitives et la qualité de vie liée à la santé ont été peu étudiées. L'étude SPUTNIC visait à analyser la relation entre le rayonnement des téléphones mobiles et la santé humaine, notamment les capacités cognitives, le sommeil et la qualité de vie liée à la santé. (10.1016/j.respe.2023.101770)
  DOI : 10.1016/j.respe.2023.101770
• Progress in high-speed optical links in the 8-12 microns thermal atmospheric window from the perspective of unipolar quantum technology
  
  • Didier Pierre
  • Dely Hamza
  • Spitz Olivier
  • Bonazzi Thomas
  • Awwad Elie
  • Rodriguez Etienne
  • Vasanelli Angela
  • Sirtori Carlo
  • Grillot Frédéric
  , 2023, pp.10. (10.1117/12.2663847)
  DOI : 10.1117/12.2663847
• Designing smart home services using machine learning and knowledge-based approaches
  
  • Qiu Mingming
  , 2023. The intelligence of a smart home is realized by creating various services. Eachservice tries to adjust one monitored state by controlling related actuators after consideringenvironment states detected by sensors. However, the design of the logic of the services deployedin a smart home faces limitations of either dynamic adaptability (predefined rules) orexplicability (learning techniques). Four proposals that are parts of a hybrid approach combiningpredefined rules and learning techniques aim at mitigating these limitations.The first proposal is to use reinforcement learning to create a dynamic service. The deploymentof this single service includes two phases : pretraining in the simulation and continuous trainingin the real world. Our study only focuses on the simulation part. Extending the first proposal,the second proposal proposes several architectures to create multiple dynamic and conflictfreeservices. However, the created data-driven services are not explicable. Therefore, the thirdproposal aims to extract explicable knowledgebased services from dynamic data-driven services.The fourth proposal attempts to combine the second and third proposals to create dynamicand explicable services. These proposals are evaluated in a simulated environment ontemperature control, light intensity, and air quality services adapted to the activities of the inhabitant.They can be extended according to several perspectives, such as the co-simulation ofphysical phenomena, the dynamic adaptation to various inhabitant profiles, and the energy efficiencyof the deployed services.
• Exploring Physical Latent Spaces for Deep Learning
  
  • Paliard Chloe
  • Thuerey Nils
  • Um Kiwon
  , 2022. We explore training deep neural network models in conjunction with physical simulations via partial differential equations (PDEs), using the simulated degrees of freedom as latent space for the neural network. In contrast to previous work, we do not impose constraints on the simulated space, but rather treat its degrees of freedom purely as tools to be used by the neural network. We demonstrate this concept for learning reduced representations. It is typically extremely challenging for conventional simulations to faithfully preserve the correct solutions over long time-spans with traditional, reduced representations. This problem is particularly pronounced for solutions with large amounts of small scale features. Here, data-driven methods can learn to restore the details as required for accurate solutions of the underlying PDE problem. We explore the use of physical, reduced latent space within this context, and train models such that they can modify the content of physical states as much as needed to best satisfy the learning objective. Surprisingly, this autonomy allows the neural network to discover alternate dynamics that enable a significantly improved performance in the given tasks. We demonstrate this concept for a range of challenging test cases, among others, for Navier-Stokes based turbulence simulations.
• Introducing the 3MT_French Dataset
  
  • Biancardi Beatrice
  • Chollet Mathieu
  • Clavel Chloé
  , 2023.
• Feasibility and Benchmarking of Post-Quantum Cryptography in the Cooperative ITS Ecosystem
  
  • Lonc Brigitte
  • Aubry Alexandre
  • Bakhti Hafeda
  • Christofi Maria
  • Aissaoui Mehrez Hassane
  , 2023. Localized communication between vehicles and their surrounding environment (V2X) is a key technology to enable Cooperative Intelligent Transportation Systems (C-ITS) aiming at road safety, traffic flow and driving comfort. Security services based on Elliptic Curve Cryptography (ECC) for authenticity and confidentiality (mostly application-dependent) have been chosen to meet the hard constraints of low latency safety communications and limited bandwidth radio communication in dense traffic conditions. Due to threats raised by Quantum Computers (QC), the classical asymmetric cryptographic algorithms could be broken impacting the Public Key Infrastructure (PKI)-based security solutions, with negative safety consequences on the (semi)-autonomous vehicles and road users. Our project (TAM: Trusted Autonomous Mobility) [18] is focusing on end-to-end cybersecurity and privacy for innovative services in the field of cooperative, connected and automated mobility (CCAM). One main objective is to find suitable quantum safe schemes to replace the current cryptographic standards based on ECC which are used in V2X communications. After defining the main requirements and key performance indicators for C-ITS, a benchmarking of current NIST pre-standards PQC algorithms was performed to assess the feasibility and performances in C-ITS applications and based on the results a best fit solution is selected.
• Origins of Low-dimensional Adversarial Perturbations
  
  • Dohmatob Elvis
  • Guo Chuan
  • Goibert Morgane
  , 2023. In this paper, we initiate a rigorous study of the phenomenon of low-dimensional adversarial perturbations (LDAPs) in classification. Unlike the classical setting, these perturbations are limited to a subspace of dimension $k$ which is much smaller than the dimension $d$ of the feature space. The case $k=1$ corresponds to so-called universal adversarial perturbations (UAPs; Moosavi-Dezfooli et al., 2017). First, we consider binary classifiers under generic regularity conditions (including ReLU networks) and compute analytical lower-bounds for the fooling rate of any subspace. These bounds explicitly highlight the dependence of the fooling rate on the pointwise margin of the model (i.e., the ratio of the output to its $L_2$ norm of its gradient at a test point), and on the alignment of the given subspace with the gradients of the model w.r.t. inputs. Our results provide a rigorous explanation for the recent success of heuristic methods for efficiently generating low-dimensional adversarial perturbations. Finally, we show that if a decision-region is compact, then it admits a universal adversarial perturbation with $L_2$ norm which is $\sqrt{d}$ times smaller than the typical $L_2$ norm of a data point. Our theoretical results are confirmed by experiments on both synthetic and real data.
• Special Session: Security Verification & Testing for SR-Latch TRNGs
  
  • Bahrami Javad
  • Ebrahimabadi Mohammad
  • Danger Jean-Luc
  • Guilley Sylvain
  • Karimi Naghmeh
  , 2023, pp.1-10. Secure chips implement cryptographic algorithms and protocols to ensure self-protection (e.g., firmware authenticity) as well as user data protection (e.g., encrypted data storage). In turn, cryptography needs to defer to incorruptible sources of entropy to implement their functions according to their mandatory usage guidance. Typically, keys, nonces, initialization vectors, tweaks, etc. shall not be guessed by attackers. In practice, True Random Number Generators (TRNGs) are in charge of producing such sensitive elements. Fully aware of the central role of TRNGs in the proper implementation of security in chips, stakeholders have been formalizing the requirements recently. The methods to strengthen such requirements are manifold. In this paper, we discuss and apply three of them by targeting the Set-Reset Latch TRNG which is an alternative to Ring-Oscillator (RO) TRNGs as it provides faster throughputs. The first method concerns the confidence in the TRNG being random enough. It explores how the TRNG properties can be reliably predicted by simulation, compared to real silicon experiments. The second aspect dealt with in this paper is the assessment of the TRNG properties over time, i.e., considering the impact of aging in the TRNG properties. Such knowledge is important as secure chips are expected to be in service for a long period, and it would be detrimental to the service they render if the quality of the entropy they deliver would be declining over time. Eventually, the third aspect of this paper is the timely detection of unforeseen failures or malevolent attacks. The mitigation lies in leveraging "health tests" launched prior to using random numbers. This paper focuses on a particular type of TRNG that is not prone to biasing by attackers: it is the so-called Set-Reset Latch (SR-latch) TRNG and exploits a race condition in an arbitration gate. Such kind of TRNG is of great practical interest as an alternative design compared to the mainstream "Ring Oscillator" TRNG, and it is also very amenable to analyses by various sorts of simulations aiming at properly characterizing its security in various operational environments. (10.1109/VTS56346.2023.10140057)
  DOI : 10.1109/VTS56346.2023.10140057
• Quantum optics with time-frequency degrees of freedom of single photons
  
  • Fabre Nicolas
  , 2023, 12570 (1257002), pp.3. We discuss the use of time and frequency degrees of freedom of single photons in quantum optics. Such a degree of freedom is generally discretized into modes for experimental reasons, but it is not a physical requirement. The origin of the quantumness of the time and frequency variables can be explained because of the non-commutativity of time and frequency operators - which can be defined properly- when restricted to the one photon per mode subspace. As a consequence, We will show that frequency and time operators can be used to define a universal set of gates in this particular subspace and provide an experimental implementation of such a universal set of gates. (10.1117/12.2665062)
  DOI : 10.1117/12.2665062
• Understanding Physical Breakdowns in Virtual Reality
  
  • Tseng Wen-Jie
  , 2023 (506), pp.1-5. Virtual Reality (VR) moves away from well-controlled laboratory environments into public and personal spaces. As users are visually disconnected from the physical environment, interacting in an uncontrolled space frequently leads to collisions and raises safety concerns. In my thesis, I investigate this phenomenon which I defne as the physical breakdown in VR. The goal is to understand the reasons for physical breakdowns, provide solutions, and explore future mechanisms that could perpetuate safety risks. First, I explored the reasons for physical breakdowns by investigating how people interact with the current VR safety mechanism (e.g., Oculus Guardian). Results show one reason for breaking out of the safety boundary is when interacting with large motions (e.g., swinging arms), the user does not have enough time to react although they see the safety boundary. I proposed a solution, FingerMapper, that maps small-scale finger motions onto virtual arms and hands to enable whole-body virtual arm motions in VR to avoid physical breakdowns. To demonstrate future safety risks, I explored the malicious use of perceptual manipulations (e.g., redirection techniques) in VR, which could deliberately create physical breakdowns without users noticing. Results indicate further open challenges about the cognitive process of how users comprehend their physical environment when they are blindfolded in VR. (10.1145/3544549.3577064)
  DOI : 10.1145/3544549.3577064
• Memory Manipulations in Extended Reality
  
  • Bonnail Elise
  • Lecolinet Eric
  • Tseng Wen-Jie
  • Mcgill Mark
  • Huron Samuel
  • Gugenheimer Jan
  , 2023. Human memory has notable limitations (e.g., forgetting) which have necessitated a variety of memory aids (e.g., calendars). As we grow closer to mass adoption of everyday Extended Reality (XR), which is frequently leveraging perceptual limitations (e.g., redirected walking), it becomes pertinent to consider how XR could leverage memory limitations (forgetting, distorting, persistence) to induce memory manipulations. As memories highly impact our self-perception, social interactions, and behaviors, there is a pressing need to understand XR Memory Manipulations (XRMMs). We ran three speculative design workshops (n=12), with XR and memory researchers creating 48 XRMM scenarios. Through thematic analysis, we define XRMMs, present a framework of their core components and reveal three classes (at encoding, pre-retrieval, at retrieval). Each class differs in terms of technology (AR, VR) and impact on memory (influencing quality of memories, inducing forgetting, distorting memories). We raise ethical concerns and discuss opportunities of perceptual and memory manipulations in XR. (10.1145/3544548.3580988)
  DOI : 10.1145/3544548.3580988