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In a recent paper, PNAS, 118, e1921529118 (2021), it was argued that while the standard definition of conservation laws in quantum mechanics, which is of a statistical character, is perfectly valid, it misses essential features of nature and it can and must be revisited to address the issue of conservation/non-conservation in individual cases. Specifically, in the above paper an experiment was presented in which it can be proven that in some individual cases energy is not conserved, despite being conserved statistically. It was felt however that this is worrisome, and that something must be wrong if there are individual instances in which conservation doesn't hold, even though this is not required by the standard conservation law. Here we revisit that experiment and show that although its results are correct, there is a way to circumvent them and ensure individual case conservation in that situation. The solution is however quite unusual, challenging one of the basic assumptions of quantum mechanics, namely that any quantum state can be prepared, and it involves a time-holistic, double non-conservation effect. Our results bring new light on the role of the preparation stage of the initial state of a particle and on the interplay of conservation laws and frames of reference. We also conjecture that when such a full analysis of any conservation experiment is performed, conservation is obeyed in every individual case.

The high-pressure crystal structure evolution of CH3NH3PbBr3 (MAPbBr3) perovskite has been investigated by single-crystal X-ray diffraction and synchrotron-based powder X-ray diffraction. Single-crystal X-ray diffraction reveals that the crystal structure of MAPbBr3 undergoes two phase transitions following the space-group sequence: Pm-3m-to-Im3-to-Pmn21. The transitions take place at around 0.8 and 1.8 GPa, respectively. This result is contradicting the previously reported phase transition sequence: Pm-3m-to-Im3-to-Pnma. In this work the crystal structures of each of the three phases are determined from single-crystal X-ray diffraction analysis which is later supported by Rietveld refinement of powder X-ray diffraction patterns. The pressure dependence of the crystal lattice parameters and unit-cell volumes are determined from the two aforementioned techniques, as well as the bulk moduli for each phase. The bandgap behaviour of MAPbBr3 has been studied up to around 4 GPa, by the means of single-crystal optical-absorption experiments. The evolution of the bandgap has been well explained using the pressure dependence of the Pb-Br bond distance and Pb-Br-Pb angles as determined from single-crystal X-ray diffraction experiments., Comment: 20 pages, 5 pages

We report a characterization of the high-pressure behavior of zinc-iodate, Zn(IO3)2. By the combination of x-ray diffraction, Raman spectroscopy, and first-principles calculations we have found evidence of two subtle isosymmetric structural phase transitions. We present arguments relating these transitions to a non-linear behavior of phonons and changes induced by pressure on the coordination sphere of the iodine atoms. This fact is explained as a consequence of the formation of metavalent bonding at high-pressure which is favored by the lone-electron pairs of iodine. In addition, the pressure dependence of unit-cell parameters, volume, and bond is reported. An equation of state to describe the pressure dependence of the volume is presented, indicating that Zn(IO3)2 is the most compressible iodate among those studied up to now. Finally, phonon frequencies are reported together with their symmetry assignment and pressure dependence., Comment: 33 pages, 3 tables, 15 figures

We report a study of the high-pressure structural behavior of ZnV2O6, Zn2V2O7, and Zn3V2O8, which has been explored by means of synchrotron powder x-ray diffraction. We found that ZnV2O6 and Zn3V2O8 remain in the ambient-pressure structure up to 15 GPa. In contrast, in the same pressure range, Zn2V2O7 undergoes three phase transitions at 0.7, 3.0, and 10.8 GPa, respectively. Possible crystal structures for the first and second high-pressure phases are proposed. Reasons for the distinctive behavior of Zn2V2O7 are discussed. The compressibility of the different polymorphs has been determined. The response to pressure is found to be anisotropic in all the considered compounds and the room-temperature equations of state have been determined. The bulk moduli of ZnV2O6 (129(2) GPa) and Zn3V2O8 (120(2) GPa) are consistent with a structural framework composed of compressible ZnO6 octahedra and uncompressible VO4 tetrahedra. In contrast, Zn2V2O7 is highly compressible with a bulk modulus of 58(9) GPa, which is almost half of the bulk modulus of the other two vanadates. The large compressibility of Zn2V2O7 and its sequence of structural transitions are related to the fact that this material is less dense than the other zinc vanadates and to the penta-coordination of Zn atoms by oxygen atoms in Zn2V2O7. A comparison to the high-pressure behavior of related compounds is presented., Comment: 35 pages, 15 figures, 2 tables

Neural machine translation systems estimate probabilities of target sentences given source sentences, yet these estimates may not align with human preferences. This work introduces QE-fusion, a method that synthesizes translations using a quality estimation metric (QE), which correlates better with human judgments. QE-fusion leverages a pool of candidates sampled from a model, combining spans from different candidates using a QE metric such as CometKiwi. We compare QE-fusion against beam search and recent reranking techniques, such as Minimum Bayes Risk decoding or QE-reranking. Our method consistently improves translation quality in terms of COMET and BLEURT scores when applied to large language models (LLMs) used for translation (PolyLM, XGLM, Llama2, Mistral, ALMA, and Tower) and to multilingual translation models (NLLB), over five language pairs. Notably, QE-fusion exhibits larger improvements for LLMs due to their ability to generate diverse outputs. We demonstrate that our approach generates novel translations in over half of the cases and consistently outperforms other methods across varying numbers of candidates (5-200). Furthermore, we empirically establish that QE-fusion scales linearly with the number of candidates in the pool., Comment: Accepted at ACL 2024

The Extreme Universe Space Observatory on a Super Pressure Balloon 1 (EUSO-SPB1) was launched in 2017 April from Wanaka, New Zealand. The plan of this mission of opportunity on a NASA super pressure balloon test flight was to circle the southern hemisphere. The primary scientific goal was to make the first observations of ultra-high-energy cosmic-ray extensive air showers (EASs) by looking down on the atmosphere with an ultraviolet (UV) fluorescence telescope from suborbital altitude (33~km). After 12~days and 4~hours aloft, the flight was terminated prematurely in the Pacific Ocean. Before the flight, the instrument was tested extensively in the West Desert of Utah, USA, with UV point sources and lasers. The test results indicated that the instrument had sensitivity to EASs of approximately 3 EeV. Simulations of the telescope system, telescope on time, and realized flight trajectory predicted an observation of about 1 event assuming clear sky conditions. The effects of high clouds were estimated to reduce this value by approximately a factor of 2. A manual search and a machine-learning-based search did not find any EAS signals in these data. Here we review the EUSO-SPB1 instrument and flight and the EAS search., Comment: 18 pages, 19 figures

This study explores non-linear development of the magnetized Rayleigh-Taylor instability (RTI) in a prominence-corona transition region. Using a two-fluid model of a partially ionized plasma, we compare RTI simulations for several different magnetic field configurations. We follow prior descriptions of the numerical prominence model [Popescu Braileanu et al., 2021a,b, 2023] and explore the charged-neutral fluid coupling and plasma heating in a two-dimensional mixing layer for different magnetic field configurations. We also investigate how the shear in magnetic field surrounding a prominence may impact the release of the gravitational potential energy of the prominence material. We show that the flow decoupling is strongest in the plane normal to the direction of the magnetic field, where neutral pressure gradients drive ion-neutral drifts and frictional heating is balanced by adiabatic cooling of the expanding prominence material. We also show that magnetic field within the mixing plane can lead to faster non-linear release of the gravitational energy driving the RTI, while more efficiently heating the plasma via viscous dissipation of associated plasma flows. We relate the computational results to potential observables by highlighting how integrating over under-resolved two-fluid sub-structure may lead to misinterpretation of observational data., Comment: accepted to Philosophical Transactions A

Determining the size distribution of asteroids is key for understanding the collisional history and evolution of the inner Solar System. We aim at improving our knowledge on the size distribution of small asteroids in the Main Belt by determining the parallaxes of newly detected asteroids in the Hubble Space Telescope (HST) Archive and hence their absolute magnitudes and sizes. Asteroids appear as curved trails in HST images due to the parallax induced by the fast orbital motion of the spacecraft. The parallax effect can be computed to obtain the distance to the asteroids by fitting simulated trajectories to the observed trails. Using distance, we can obtain the object's absolute magnitude and size estimation assuming an albedo value, along with some boundaries for its orbital parameters. In this work we analyse a set of 632 serendipitously imaged asteroids found in the ESA HST Archive. An object-detection machine learning algorithm was used to perform this task during previous work. Our raw data consists of 1,031 asteroids trails from unknown objects (not matching any entries in the MPC database). We also found 670 trails from known objects (objects featuring matching entries in the MPC). After an accuracy assessment and filtering process, our analysed HST set consists of 454 unknown objects and 178 known objects. We obtain a sample dominated by potential Main Belt objects featuring absolute magnitudes (H) mostly between 15 and 22 mag. The absolute magnitude cumulative distribution confirms the previously reported slope change for 15 < H < 18, from 0.56 to 0.26, maintained in our case down to absolute magnitudes around H = 20, hence expanding the previous results by approximately two magnitudes. HST archival observations can be used as an asteroid survey since the telescope pointings are statistically randomly oriented in the sky and they cover long periods of time., Comment: Accepted for publication in A&A. 10 pages, 2 tables, 17 figures

As silicon photonics transitions from research to commercial deployment, packaging solutions that efficiently couple light into highly-compact and functional sub-micron silicon waveguides are imperative but remain challenging. The 220 nm silicon-on-insulator (SOI) platform, poised to enable large-scale integration, is the most widely adopted by foundries, resulting in established fabrication processes and extensive photonic component libraries. The development of a highly-efficient, scalable and broadband coupling scheme for this platform is therefore of paramount importance. Leveraging two-photon polymerization (TPP) and a deterministic free-form micro-optics design methodology based on the Fermat's principle, this work demonstrates an ultra-efficient and broadband 3-D coupler interface between standard SMF-28 single-mode fibers and silicon waveguides on the 220 nm SOI platform. The coupler achieves a low coupling loss of 0.8 dB for fundamental TE mode, along with 1-dB bandwidth exceeding 180 nm. The broadband operation enables diverse bandwidth-driven applications ranging from communications to spectroscopy. Furthermore, the 3-D free-form coupler also enables large tolerance to fiber misalignments and manufacturing variability, thereby relaxing packaging requirements towards cost reduction capitalizing on standard electronic packaging process flows.

Programmable and reconfigurable optics hold significant potential for transforming a broad spectrum of applications, spanning space explorations to biomedical imaging, gas sensing, and optical cloaking. The ability to adjust the optical properties of components like filters, lenses, and beam steering devices could result in dramatic reductions in size, weight, and power consumption in future optoelectronic devices. Among the potential candidates for reconfigurable optics, chalcogenide-based phase change materials (PCMs) offer great promise due to their non-volatile and analogue switching characteristics. Although PCM have found widespread use in electronic data storage, these memory devices are deeply sub-micron-sized. To incorporate phase change materials into free-space optical components, it is essential to scale them up to beyond several hundreds of microns while maintaining reliable switching characteristics. This study demonstrated a non-mechanical, non-volatile transmissive filter based on low-loss PCMs with a 200 $\mu$m$ \times $200 $\mu$m switching area. The device/metafilter can be consistently switched between low- and high-transmission states using electrical pulses with a switching contrast ratio of 5.5 dB. The device was reversibly switched for 1250 cycles before accelerated degradation took place. The work represents an important step toward realizing free-space reconfigurable optics based on PCMs.

Suppose $X$ and $Y$ are Banach spaces, $K$ is a compact Hausdorff space, $\Sigma$ is the $\sigma$-algebra of Borel subsets of $K$, $C(K,X)$ is the Banach space of all continuous $X$-valued functions (with the supremum norm), and $T:C(K,X)\to Y$ is a strongly bounded operator with representing measure $m:\Sigma \to L(X,Y)$. We show that if $\hat{T}: B(K, X) \to Y$ is its extension, then $T$ is weak Dunford-Pettis (resp. weak$^*$ Dunford-Pettis, weak $p$-convergent, weak$^*$ $p$-convergent) if and only if $\hat{T}$ has the same property. We prove that if $T:C(K,X)\to Y$ is strongly bounded limited completely continuous (resp. limited $p$-convergent), then $m(A):X\to Y$ is limited completely continuous (resp. limited $p$-convergent) for each $A\in \Sigma$. We also prove that the above implications become equivalences when $K$ is a dispersed compact Hausdorff space.

There are many recent advanced developments for the Text-to-SQL task, where the Picard model is one of the the top performing models as measured by the Spider dataset competition. However, bringing Text-to-SQL systems to realistic use-cases through domain adaptation remains a tough challenge. We analyze how well the base T5 Language Model and Picard perform on query structures different from the Spider dataset, we fine-tuned the base model on the Spider data and on independent databases (DB). To avoid accessing the DB content online during inference, we also present an alternative way to disambiguate the values in an input question using a rule-based approach that relies on an intermediate representation of the semantic concepts of an input question. In our results we show in what cases T5 and Picard can deliver good performance, we share the lessons learned, and discuss current domain adaptation challenges.

The Mancha3D code is a versatile tool for numerical simulations of magnetohydrodynamic processes in solar/stellar atmospheres. The code includes non-ideal physics derived from plasma partial ionization, a realistic equation of state and radiative transfer, which allows performing high quality realistic simulations of magneto-convection, as well as idealized simulations of particular processes, such as wave propagation, instabilities or energetic events. The paper summarizes the equations and methods used in the Mancha3D code. It also describes its numerical stability and parallel performance and efficiency. The code is based on a finite difference discretization and memory-saving Runge-Kutta (RK) scheme. It handles non-ideal effects through super-time stepping and Hall diffusion schemes, and takes into account thermal conduction by solving an additional hyperbolic equation for the heat flux. The code is easily configurable to perform different kinds of simulations. Several examples of the code usage are given. It is demonstrated that splitting variables into equilibrium and perturbation parts is essential for simulations of wave propagation in a static background. A perfectly matched layer (PML) boundary condition built into the code greatly facilitates a non-reflective open boundary implementation. Spatial filtering is an important numerical remedy to eliminate grid-size perturbations enhancing the code stability. Parallel performance analysis reveals that the code is strongly memory bound, which is a natural consequence of the numerical techniques used, such as split variables and PML boundary conditions. Both strong and weak scalings show adequate performance up till several thousands of CPUs.

Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting what was learned in the past. In this work, we take a step back, and ask: "Why should one care about continual learning in the first place?". We set the stage by examining recent continual learning papers published at four major machine learning conferences, and show that memory-constrained settings dominate the field. Then, we discuss five open problems in machine learning, and even though they might seem unrelated to continual learning at first sight, we show that continual learning will inevitably be part of their solution. These problems are model editing, personalization and specialization, on-device learning, faster (re-)training and reinforcement learning. Finally, by comparing the desiderata from these unsolved problems and the current assumptions in continual learning, we highlight and discuss four future directions for continual learning research. We hope that this work offers an interesting perspective on the future of continual learning, while displaying its potential value and the paths we have to pursue in order to make it successful. This work is the result of the many discussions the authors had at the Dagstuhl seminar on Deep Continual Learning, in March 2023.

On 26 September 2022, the Double Asteroid Redirection Test (DART) spacecraft impacted Dimorphos, the satellite of binary near-Earth asteroid (65803) Didymos. This demonstrated the efficacy of a kinetic impactor for planetary defense by changing the orbital period of Dimorphos by 33 minutes (Thomas et al. 2023). Measuring the period change relied heavily on a coordinated campaign of lightcurve photometry designed to detect mutual events (occultations and eclipses) as a direct probe of the satellite's orbital period. A total of 28 telescopes contributed 224 individual lightcurves during the impact apparition from July 2022 to February 2023. We focus here on decomposable lightcurves, i.e. those from which mutual events could be extracted. We describe our process of lightcurve decomposition and use that to release the full data set for future analysis. We leverage these data to place constraints on the post-impact evolution of ejecta. The measured depths of mutual events relative to models showed that the ejecta became optically thin within the first ~1 day after impact, and then faded with a decay time of about 25 days. The bulk magnitude of the system showed that ejecta no longer contributed measurable brightness enhancement after about 20 days post-impact. This bulk photometric behavior was not well represented by an HG photometric model. An HG1G2 model did fit the data well across a wide range of phase angles. Lastly, we note the presence of an ejecta tail through at least March 2023. Its persistence implied ongoing escape of ejecta from the system many months after DART impact., Comment: 52 pages, 5 tables, 9 figures, accepted to PSJ

We present the results of observations of asteroid (248370) QN$_{173}$ obtained during July 2021 - January 2022 with three telescopes. Our analysis revealed the presence of the dust tail for about half of a year. The direct images of the asteroid were obtained with broad-band filters. No emissions were revealed in the spectra, and the spectrum of the asteroid closely matched that of a C-type asteroid. Created color and linear polarization variations along the tail were analyzed. The asteroid demonstrated a redder color compared to the Sun. Dramatic changes in dust productivity obtained in different filters were not detected. The $g-r$ color changes from $0.2^{m}$ to $0.7^{m}$ over the coma, and the linear polarization degree varies from about $1.2$\% to $0.2$\% and from $-0.2$\% to $-1.5$\% at the phase angle of $23.2^{\circ}$ and $8.16^{\circ}$. The total dust mass ejected until the latest observation on October 10 is $4.2 \times 10^7$~kg, with a maximum rate of 2.6~kg\,s$^{-1}$ based on the Monte Carlo modeling of the dust tail. The estimated asteroid size is 1.3~km. It is shown that large particles are concentrated around the nucleus, whereas smaller ones dominate in the tail. The evolution of (248370) QN$_{173}$ orbit and the orbits of the sample of the 464 short-periodic comets were followed. Ten of them approached the asteroid's orbit. These objects are not genetically related, despite the very close distance of their orbits for a relatively long time., Comment: pages 13; 5 tables; figures 12

Conditional independence testing (CIT) is a common task in machine learning, e.g., for variable selection, and a main component of constraint-based causal discovery. While most current CIT approaches assume that all variables are numerical or all variables are categorical, many real-world applications involve mixed-type datasets that include numerical and categorical variables. Non-parametric CIT can be conducted using conditional mutual information (CMI) estimators combined with a local permutation scheme. Recently, two novel CMI estimators for mixed-type datasets based on k-nearest-neighbors (k-NN) have been proposed. As with any k-NN method, these estimators rely on the definition of a distance metric. One approach computes distances by a one-hot encoding of the categorical variables, essentially treating categorical variables as discrete-numerical, while the other expresses CMI by entropy terms where the categorical variables appear as conditions only. In this work, we study these estimators and propose a variation of the former approach that does not treat categorical variables as numeric. Our numerical experiments show that our variant detects dependencies more robustly across different data distributions and preprocessing types.

In order to enable spectrum sharing, spectrum sensing plays a crucial role in wireless communication. The challenges in wireless spectrum require collaboration among stakeholders to devise innovative solutions. This research explores the use of a Cognitive Radio (CR) system that employs a Time-Domain Symbol Cross-correlation (TDSC) based spectrum sensing algorithm. WiMAX and LTE standards are utilized as case studies to demonstrate the efficacy of the TDSC method. The study presents theoretical and simulation results and also suggests future research to investigate the performance of the TDSC method in WiMAX and LTE systems. Additionally, this study compares the spectrum sensing capabilities of WiMAX and LTE., Comment: 10 pages, 12 figures

Lensless coherent x-ray imaging techniques have great potential for high-resolution imaging of magnetic systems with a variety of in-situ perturbations. Despite many investigations of ferromagnets, extending these techniques to the study of other magnetic materials, primarily antiferromagnets, is lacking. Here, we demonstrate the first (to our knowledge) study of an antiferromagnet using holographic imaging through the "holography with extended reference by autocorrelation linear differential operation" technique. Energy-dependent contrast with both linearly and circularly polarised x-rays are demonstrated. Antiferromagnetic domains and topological textures are studied in the presence of applied magnetic fields, demonstrating quasi-cyclic domain reconfiguration up to 500 mT., Comment: 14 pages, 6 figures

Transfer of conserved quantities between two remote regions is generally assumed to be a rather trivial process: a flux of particles carrying the conserved quantities propagates from one region to another. We however demonstrate a flow of angular momentum from one region to another across a region of space in which there is a vanishingly small probability of any particles (or fields) being present. This shows that the usual view of how conservation laws work needs to be revisited., Comment: V4: 8 Pages, 2 Figures, minor improvements

Detecting out-of-distribution (OOD) data is a critical challenge in machine learning due to model overconfidence, often without awareness of their epistemological limits. We hypothesize that ``neural collapse'', a phenomenon affecting in-distribution data for models trained beyond loss convergence, also influences OOD data. To benefit from this interplay, we introduce NECO, a novel post-hoc method for OOD detection, which leverages the geometric properties of ``neural collapse'' and of principal component spaces to identify OOD data. Our extensive experiments demonstrate that NECO achieves state-of-the-art results on both small and large-scale OOD detection tasks while exhibiting strong generalization capabilities across different network architectures. Furthermore, we provide a theoretical explanation for the effectiveness of our method in OOD detection. Code is available at https://gitlab.com/drti/neco, Comment: Accepted to ICLR2024

A quantitative derivation of the intrinsic properties of galaxies related to their fundamental building blocks, gas, dust and stars is essential for our understanding of galaxy evolution. A fully self-consistent derivation of these properties can be achieved with radiative transfer (RT) methods that are constrained by panchromatic imaging observations. Here we present an axi-symmetric RT model of the UV-optical-FIR/submm spectral and spatial energy distribution of the face-on spiral galaxy M51. The model reproduces reasonably well the azimuthally averaged radial profiles derived from the imaging data available for this galaxy, from GALEX, SDSS, 2MASS, Spitzer and Herschel. We model the galaxy with three distinct morphological components: a bulge, an inner disc and a main disc. We derive the length parameters of the stellar emissivity and of the dust distribution. We also derive the intrinsic global and spatially resolved parameters of M51. We find a faint \lq\lq outer disc\rq\rq\ bridging M51 with its companion galaxy M51b. Finally, we present and discuss an alternative model, with dust properties that change within the galaxy., Comment: 22 pages, 14 figures; accepted in MNRAS for publication, Sept 7 2023

We describe the survey design and science goals for ODIN (One-hundred-deg^2 DECam Imaging in Narrowbands), a NOIRLab survey using the Dark Energy Camera (DECam) to obtain deep (AB~25.7) narrow-band images over an unprecedented area of sky. The three custom-built narrow-band filters, N419, N501, and N673, have central wavelengths of 419, 501, and 673 nm and respective full-widthat-half-maxima of 7.2, 7.4, and 9.8 nm, corresponding to Lya at z=2.4, 3.1, and 4.5 and cosmic times of 2.8, 2.1, and 1.4 Gyr, respectively. When combined with even deeper, public broad-band data from Hyper Suprime-Cam, DECam, and in the future, LSST, the ODIN narrow-band images will enable the selection of over 100,000 Lya-emitting (LAE) galaxies at these epochs. ODIN-selected LAEs will identify protoclusters as galaxy overdensities, and the deep narrow-band images enable detection of highly extended Lya blobs (LABs). Primary science goals include measuring the clustering strength and dark matter halo connection of LAEs, LABs, and protoclusters, and their respective relationship to filaments in the cosmic web. The three epochs allow the redshift evolution of these properties to be determined during the period known as Cosmic Noon, where star formation was at its peak. The two narrow-band filter wavelengths are designed to enable interloper rejection and further scientific studies by revealing [O II] and [O III] at z=0.34, Lya and He II 1640 at z=3.1, and Lyman continuum plus Lya at z=4.5. Ancillary science includes similar studies of the lower-redshift emission-line galaxy samples and investigations of nearby star-forming galaxies resolved into numerous [O III] and [S II] emitting regions., Comment: 21 pages, 8 figures, submitted to Astrophysical Journal

Deep learning (DL)-based solutions have been extensively researched in the medical domain in recent years, enhancing the efficacy of diagnosis, planning, and treatment. Since the usage of health-related data is strictly regulated, processing medical records outside the hospital environment for developing and using DL models demands robust data protection measures. At the same time, it can be challenging to guarantee that a DL solution delivers a minimum level of performance when being trained on secured data, without being specifically designed for the given task. Our approach uses singular value decomposition (SVD) and principal component analysis (PCA) to obfuscate the medical images before employing them in the DL analysis. The capability of DL algorithms to extract relevant information from secured data is assessed on a task of angiographic view classification based on obfuscated frames. The security level is probed by simulated artificial intelligence (AI)-based reconstruction attacks, considering two threat actors with different prior knowledge of the targeted data. The degree of privacy is quantitatively measured using similarity indices. Although a trade-off between privacy and accuracy should be considered, the proposed technique allows for training the angiographic view classifier exclusively on secured data with satisfactory performance and with no computational overhead, model adaptation, or hyperparameter tuning. While the obfuscated medical image content is well protected against human perception, the hypothetical reconstruction attack proved that it is also difficult to recover the complete information of the original frames., Comment: 6 pages, 9 figures, Published in: 2023 31st Mediterranean Conference on Control and Automation (MED)

Class-Incremental Learning (CIL) aims to build classification models from data streams. At each step of the CIL process, new classes must be integrated into the model. Due to catastrophic forgetting, CIL is particularly challenging when examples from past classes cannot be stored, the case on which we focus here. To date, most approaches are based exclusively on the target dataset of the CIL process. However, the use of models pre-trained in a self-supervised way on large amounts of data has recently gained momentum. The initial model of the CIL process may only use the first batch of the target dataset, or also use pre-trained weights obtained on an auxiliary dataset. The choice between these two initial learning strategies can significantly influence the performance of the incremental learning model, but has not yet been studied in depth. Performance is also influenced by the choice of the CIL algorithm, the neural architecture, the nature of the target task, the distribution of classes in the stream and the number of examples available for learning. We conduct a comprehensive experimental study to assess the roles of these factors. We present a statistical analysis framework that quantifies the relative contribution of each factor to incremental performance. Our main finding is that the initial training strategy is the dominant factor influencing the average incremental accuracy, but that the choice of CIL algorithm is more important in preventing forgetting. Based on this analysis, we propose practical recommendations for choosing the right initial training strategy for a given incremental learning use case. These recommendations are intended to facilitate the practical deployment of incremental learning.

This work aims at constraining the size, shape, and geometric albedo of the dwarf planet candidate 2002 MS4 through the analysis of nine stellar occultation events. Using multichord detection, we also studied the object's topography by analyzing the obtained limb and the residuals between observed chords and the best-fitted ellipse. We predicted and organized the observational campaigns of nine stellar occultations by 2002 MS4 between 2019 and 2022, resulting in two single-chord events, four double-chord detections, and three events with three to up to sixty-one positive chords. Using 13 selected chords from the 8 August 2020 event, we determined the global elliptical limb of 2002 MS4. The best-fitted ellipse, combined with the object's rotational information from the literature, constrains the object's size, shape, and albedo. Additionally, we developed a new method to characterize topography features on the object's limb. The global limb has a semi-major axis of 412 $\pm$ 10 km, a semi-minor axis of 385 $\pm$ 17 km, and the position angle of the minor axis is 121 $^\circ$ $\pm$ 16$^\circ$. From this instantaneous limb, we obtained 2002 MS4's geometric albedo and the projected area-equivalent diameter. Significant deviations from the fitted ellipse in the northernmost limb are detected from multiple sites highlighting three distinct topographic features: one 11 km depth depression followed by a 25$^{+4}_{-5}$ km height elevation next to a crater-like depression with an extension of 322 $\pm$ 39 km and 45.1 $\pm$ 1.5 km deep. Our results present an object that is $\approx$138 km smaller in diameter than derived from thermal data, possibly indicating the presence of a so-far unknown satellite. However, within the error bars, the geometric albedo in the V-band agrees with the results published in the literature, even with the radiometric-derived albedo.