arXiv subjects

Nicolas Michel

Publications and source records attributed to Nicolas Michel.

At least 19 recordsLinked to original sources

arXiv2026

Gamow shell model description of the hypernuclei

Hypernuclear physics studies baryon interactions and the structure of exotic atomic nuclei, where strangeness plays a key role in dense matter. High-resolution $\gamma$-ray spectroscopy (e.g., Hyperball at BNL/KEK) and upcoming facilities (J-PARC, JLab) have provided precise data on $p$-shell hypernuclei, constraining $YN$ potentials. We applied the Gamow shell model to $p$-shell hypernuclei to systematically investigate the role of the $\Lambda N$ interaction and its components, and how it affects the mean field of nucleons. The Gamow shell model extends the standard shell model by replacing the harmonic oscillator basis with the Berggren ensemble, treating bound, resonant, and continuum states on equal footing. The effective Hamiltonian includes Woods-Saxon core potentials and two-body interactions (central, spin-orbit, tensor) optimized to experimental data. Our calculations reproduce and predict binding energies, excitation spectra, and densities for hypernuclei from ${}^{5}_{\Lambda}\mathrm{He}$ to ${}^{16}_{\Lambda}\mathrm{O}$ using the same $\Lambda N$ interaction. The tensor force significantly impacts excited-state splittings in bound systems, while its effect is suppressed in unbound cases due to the continuum coupling. The Gamow shell model provides a unified framework for hypernuclei, capturing the interplay between bound, resonant, and continuum states. This work lays the foundation for extending the model to heavier and multi-strange systems.

nucl-th↗

arXiv2026

Search for the Double Poles of the Scattering Matrix in Light Nuclei

Exceptional points (EPs) are non-Hermitian degeneracies at which two eigenvalues and their eigenvectors coalesce, producing a defective Hamiltonian and a double pole of the $S$-matrix. Using the coupled-channel Gamow Shell Model with the $\ell=1$ spin-orbit strengths as control parameters, we locate and characterize EPs in $^6\text{Li}$, $^7\text{Li}$, $^7\text{Be}$, and $^8\text{Be}$, and analyze their imprint on energies, widths, phase rigidity, spectroscopic factors, elastic cross sections, survival probabilities, and spectral functions. The signatures of the EP in scattering and time-domain observables are found to be strongly channel-dependent, and threshold effects play a decisive role in determining the accessibility of the EP in parameter space.

nucl-th↗

arXiv2026

Synesthesia via Direct Latent Augmentation:Bypassing the Decode-Encode Loop for Cross-Modal Distillation

While multimodal integration significantly improves computer vision models, deploying them incurs prohibitive inference costs and requires scarce, perfectly paired datasets. Recent methods address this data bottleneck by synthesizing missing modalities via generative AI, yet they introduce a severe inefficiency: the Decode-Encode Loop. Specifically, information-rich generative latents are decoded into noisy raw signals, forcing the downstream classifier to waste capacity re-encoding them. To bypass this bottleneck, we propose Direct Latent Augmentation (DLA), utilizing undecoded generative latents directly as privileged information. Furthermore, to transfer this dense knowledge to a purely visual student, we introduce Multilayer Explicit Simulated Synesthesia (MESSy). Instead of enforcing rigid representation matching, which forces the student to distort its native visual features to accommodate complex multimodal topologies, MESSy uses a predictive objective to safely internalize these physical priors. Empirical results demonstrate that our framework significantly outperforms raw data augmentation and traditional distillation. Ultimately, our approach yields highly accurate unimodal students with "synesthetic" latent structures that are inherently aligned with modalities they have never directly observed.

cs.CV↗

arXiv2026

Continual Distillation of Teachers from Different Domains

Deep learning models continue to scale, with some requiring more storage than many large-scale datasets. Thus, we introduce a new paradigm: Continual Distillation (CD), where a student learns sequentially from a stream of teacher models without retaining access to earlier teachers. CD faces two challenges: teacher training data is unavailable, and teachers have varying expertise. We show that external unlabeled data enables Unseen Knowledge Transfer (UKT), allowing the student to acquire information from domains not present in the training data, while known to the teacher. We also show that sequential distillation causes Unseen Knowledge Forgetting (UKF) when transferred knowledge is lost after training on later teachers. To better trade off between UKT and UKF, we propose Self External Data Distillation (SE2D), a method that preserves logits on external data to stabilize learning across heterogeneous teachers. Experiments on multiple benchmarks show that SE2D reduces UKF and improves cross-domain generalization. The code and implementation for this work are publicly available at: https://github.com/Nicolas1203/continual_distillation.

cs.LG↗

arXiv2025

Complex-energy eigenvector continuation for nuclear many-body broad resonances

Broad resonances are a unique phenomenon in nuclear many-body systems. Theoretical studies usually involve the continuum degree of freedom, which drastically increases the model space of calculations, and may lead to non-convergence or instability of computations. In this paper, we present the extension of the eigenvector continuation (EC) method to the complex-energy space to treat the broad resonances of open quantum systems of nuclei. EC provides an efficient method to predict the solution of a large-space many-body problem within a small subspace. Using only a few bound and narrow resonance solutions as input in EC, we can obtain the solution of a broad resonance. We have applied the complex-energy EC to the broad resonances of $^4$H, four-neutron $^4n$, $^6$He and $^7$He systems.

nucl-th↗

arXiv2025

Double pole $S$-matrix singularity in the continuum of $^7$Be

The double pole singularity of the $S$-matrix, the so-called exceptional point, associated with the $5/2^-$ doublet of resonances in the spectrum of $^{7}$Be has been identified in the framework of the Gamow shell model. The exceptional point singularity is demonstrated by the coalescence of wave functions and spectral functions of the two resonances, as well as by the singular behavior of spectroscopic factors and electromagnetic transitions.

nucl-th↗

arXiv2025

Gamow shell model description of exceptional point in $^7$Li

We report the first identification of an exceptional point (EP) within the Gamow Shell Model in the Coupled Channels representation (GSM-CC). In the spectrum of $^{7}$Li, an EP is found for the $5/2^-$ doublet, where the two states coalesce in both energy and width, the phase rigidity vanishes, and the S-matrix develops a double pole. These features manifest directly in observables: the elastic cross section acquires a split-peak structure, and the phase shift shows a single $2\pi$ jump. This work demonstrates that GSM-CC provides a powerful framework to explore EP phenomena in nuclei and their experimental signatures.

nucl-th↗

arXiv2025

From Offline to Online Memory-Free and Task-Free Continual Learning via Fine-Grained Hypergradients

Continual Learning (CL) aims to learn from a non-stationary data stream where the underlying distribution changes over time. While recent advances have produced efficient memory-free methods in the offline CL (offCL) setting, where tasks are known in advance and data can be revisited, online CL (onCL) remains dominated by memory-based approaches. The transition from offCL to onCL is challenging, as many offline methods rely on (1) prior knowledge of task boundaries and (2) sophisticated scheduling or optimization schemes, both of which are unavailable when data arrives sequentially and can be seen only once. In this paper, we investigate the adaptation of state-of-the-art memory-free offCL methods to the online setting. We first show that augmenting these methods with lightweight prototypes significantly improves performance, albeit at the cost of increased Gradient Imbalance, resulting in a biased learning towards earlier tasks. To address this issue, we introduce Fine-Grained Hypergradients, an online mechanism for rebalancing gradient updates during training. Our experiments demonstrate that the synergy between prototype memory and hypergradient reweighting substantially enhances the performance of memory-free methods in onCL and surpasses onCL baselines. Code will be released upon acceptance.

cs.LG↗

arXiv2025

Enhancing Multirotor Drone Efficiency: Exploring Minimum Energy Consumption Rate of Forward Flight under Varying Payload

Multirotor unmanned aerial vehicle is a prevailing type of aircraft with wide real-world applications. Energy efficiency is a critical aspect of its performance, determining the range and duration of the missions that can be performed. In this study, we show both analytically and numerically that the optimum of a key energy efficiency index in forward flight, namely energy per meter traveled per unit mass, is a constant under different vehicle mass (including payload). Note that this relationship is only true under the optimal forward velocity that minimizes the energy consumption (under different mass), but not under arbitrary velocity. The study is based on a previously developed model capturing the first-principle energy dynamics of the multirotor, and a key step is to prove that the pitch angle under optimal velocity is a constant. By employing both analytical derivation and validation studies, the research provides critical insights into the optimization of multirotor energy efficiency, and facilitate the development of flight control strategies to extend mission duration and range.

cs.RO↗

arXiv2024

Dealing with Synthetic Data Contamination in Online Continual Learning

Image generation has shown remarkable results in generating high-fidelity realistic images, in particular with the advancement of diffusion-based models. However, the prevalence of AI-generated images may have side effects for the machine learning community that are not clearly identified. Meanwhile, the success of deep learning in computer vision is driven by the massive dataset collected on the Internet. The extensive quantity of synthetic data being added to the Internet would become an obstacle for future researchers to collect "clean" datasets without AI-generated content. Prior research has shown that using datasets contaminated by synthetic images may result in performance degradation when used for training. In this paper, we investigate the potential impact of contaminated datasets on Online Continual Learning (CL) research. We experimentally show that contaminated datasets might hinder the training of existing online CL methods. Also, we propose Entropy Selection with Real-synthetic similarity Maximization (ESRM), a method to alleviate the performance deterioration caused by synthetic images when training online CL models. Experiments show that our method can significantly alleviate performance deterioration, especially when the contamination is severe. For reproducibility, the source code of our work is available at https://github.com/maorong-wang/ESRM.

cs.CV↗

arXiv2024

Energy-Optimal Planning of Waypoint-Based UAV Missions -- Does Minimum Distance Mean Minimum Energy?

Multirotor unmanned aerial vehicle is a prevailing type of aerial robots with wide real-world applications. The energy efficiency of the robot is a critical aspect of its performance, determining the range and duration of the missions that can be performed. This paper studies the energy-optimal planning of the multirotor, which aims at finding the optimal ordering of waypoints with the minimum energy consumption for missions in 3D space. The study is performed based on a previously developed model capturing first-principle energy dynamics of the multirotor. We found that in majority of the cases (up to 95%) the solutions of the energy-optimal planning are different from those of the traditional traveling salesman problem which minimizes the total distance. The difference can be as high as 14.9%, with the average at 1.6%-3.3% and 90th percentile at 3.7%-6.5% depending on the range and number of waypoints in the mission. We then identified and explained the key features of the minimum-energy order by correlating to the underlying flight energy dynamics. It is shown that instead of minimizing the distance, coordination of vertical and horizontal motion to promote aerodynamic efficiency is the key to optimizing energy consumption.

cs.RO↗

arXiv2024

ammBoost: State Growth Control for AMMs

Automated market makers (AMMs) are a prime example of Web 3.0 applications. Their popularity and high trading activity led to serious scalability issues in terms of throughput and state size. In this paper, we address these challenges by utilizing a new sidechain architecture, building a system called ammBoost. ammBoost reduces the amount of on-chain transactions, boosts throughput, and supports blockchain pruning. We devise several techniques to enable layer 2 processing for AMMs, including a functionality-split and layer 2 traffic summarization paradigm, an epoch-based deposit mechanism, and pool snapshot-based and delayed token-payout trading. We also build a proof-of-concept for a Uniswap-inspired use case to empirically evaluate performance. Our experiments show that ammBoost decreases the gas cost by 96.05% and the chain growth by at least 93.42%, and that it can support up to 500x of the daily traffic volume of Uniswap. We also compare ammBoost to an Optimism-inspired solution showing a 99.94% reduction in transaction finality.

cs.CR↗

arXiv2024

Rethinking Momentum Knowledge Distillation in Online Continual Learning

Online Continual Learning (OCL) addresses the problem of training neural networks on a continuous data stream where multiple classification tasks emerge in sequence. In contrast to offline Continual Learning, data can be seen only once in OCL, which is a very severe constraint. In this context, replay-based strategies have achieved impressive results and most state-of-the-art approaches heavily depend on them. While Knowledge Distillation (KD) has been extensively used in offline Continual Learning, it remains under-exploited in OCL, despite its high potential. In this paper, we analyze the challenges in applying KD to OCL and give empirical justifications. We introduce a direct yet effective methodology for applying Momentum Knowledge Distillation (MKD) to many flagship OCL methods and demonstrate its capabilities to enhance existing approaches. In addition to improving existing state-of-the-art accuracy by more than $10\%$ points on ImageNet100, we shed light on MKD internal mechanics and impacts during training in OCL. We argue that similar to replay, MKD should be considered a central component of OCL. The code is available at \url{https://github.com/Nicolas1203/mkd_ocl}.

cs.LG↗

arXiv2024

Improving Plasticity in Online Continual Learning via Collaborative Learning

Online Continual Learning (CL) solves the problem of learning the ever-emerging new classification tasks from a continuous data stream. Unlike its offline counterpart, in online CL, the training data can only be seen once. Most existing online CL research regards catastrophic forgetting (i.e., model stability) as almost the only challenge. In this paper, we argue that the model's capability to acquire new knowledge (i.e., model plasticity) is another challenge in online CL. While replay-based strategies have been shown to be effective in alleviating catastrophic forgetting, there is a notable gap in research attention toward improving model plasticity. To this end, we propose Collaborative Continual Learning (CCL), a collaborative learning based strategy to improve the model's capability in acquiring new concepts. Additionally, we introduce Distillation Chain (DC), a collaborative learning scheme to boost the training of the models. We adapt CCL-DC to existing representative online CL works. Extensive experiments demonstrate that even if the learners are well-trained with state-of-the-art online CL methods, our strategy can still improve model plasticity dramatically, and thereby improve the overall performance by a large margin. The source code of our work is available at https://github.com/maorong-wang/CCL-DC.

cs.LG↗

arXiv2024

Direct Extraction of Nuclear Structure Information Using Precision Lithium-Ion Spectroscopy

Accurately describing nuclear interactions within atomic nuclei remains a challenge, which hinders our exploration of new physics beyond the Standard Model. However, these nuclear interactions can be characterized by nuclear parameters such as the Zemach radius and the electric quadrupole moment, which are reflected in atomic spectra. Our work has achieved high-precision measurements of lithium ion hyperfine splittings at the level of $10$~kHz, and directly extracted these important nuclear structure parameters. We observed significant discrepancies between our results and both nuclear theory and molecular spectra regarding the electric quadrupole moment. The result for $^7$Li deviated by $2.3\sigma$ from the currently recommended value, whereas the result for $^6$Li deviated by up to $6.2\sigma$ from the recommended value determined by molecular spectroscopy. These discrepancies motivated us to conduct independent calculations based on nuclear structure theory, which provided support for the results obtained from ion spectroscopy. Our results provide valuable information for characterizing nuclear forces, serve as sensitive benchmarks for testing nuclear structure theories, and enable critical comparisons with both electron-nuclear scattering and molecular spectroscopy.

physics.atom-ph↗

arXiv2024

Learning Representations on the Unit Sphere: Investigating Angular Gaussian and von Mises-Fisher Distributions for Online Continual Learning

We use the maximum a posteriori estimation principle for learning representations distributed on the unit sphere. We propose to use the angular Gaussian distribution, which corresponds to a Gaussian projected on the unit-sphere and derive the associated loss function. We also consider the von Mises-Fisher distribution, which is the conditional of a Gaussian in the unit-sphere. The learned representations are pushed toward fixed directions, which are the prior means of the Gaussians; allowing for a learning strategy that is resilient to data drift. This makes it suitable for online continual learning, which is the problem of training neural networks on a continuous data stream, where multiple classification tasks are presented sequentially so that data from past tasks are no longer accessible, and data from the current task can be seen only once. To address this challenging scenario, we propose a memory-based representation learning technique equipped with our new loss functions. Our approach does not require negative data or knowledge of task boundaries and performs well with smaller batch sizes while being computationally efficient. We demonstrate with extensive experiments that the proposed method outperforms the current state-of-the-art methods on both standard evaluation scenarios and realistic scenarios with blurry task boundaries. For reproducibility, we use the same training pipeline for every compared method and share the code at https://github.com/Nicolas1203/ocl-fd.

cs.LG↗

arXiv2023

Domain-Aware Augmentations for Unsupervised Online General Continual Learning

Continual Learning has been challenging, especially when dealing with unsupervised scenarios such as Unsupervised Online General Continual Learning (UOGCL), where the learning agent has no prior knowledge of class boundaries or task change information. While previous research has focused on reducing forgetting in supervised setups, recent studies have shown that self-supervised learners are more resilient to forgetting. This paper proposes a novel approach that enhances memory usage for contrastive learning in UOGCL by defining and using stream-dependent data augmentations together with some implementation tricks. Our proposed method is simple yet effective, achieves state-of-the-art results compared to other unsupervised approaches in all considered setups, and reduces the gap between supervised and unsupervised continual learning. Our domain-aware augmentation procedure can be adapted to other replay-based methods, making it a promising strategy for continual learning.

cs.LG↗

arXiv2023

New metrics for analyzing continual learners

Deep neural networks have shown remarkable performance when trained on independent and identically distributed data from a fixed set of classes. However, in real-world scenarios, it can be desirable to train models on a continuous stream of data where multiple classification tasks are presented sequentially. This scenario, known as Continual Learning (CL) poses challenges to standard learning algorithms which struggle to maintain knowledge of old tasks while learning new ones. This stability-plasticity dilemma remains central to CL and multiple metrics have been proposed to adequately measure stability and plasticity separately. However, none considers the increasing difficulty of the classification task, which inherently results in performance loss for any model. In that sense, we analyze some limitations of current metrics and identify the presence of setup-induced forgetting. Therefore, we propose new metrics that account for the task's increasing difficulty. Through experiments on benchmark datasets, we demonstrate that our proposed metrics can provide new insights into the stability-plasticity trade-off achieved by models in the continual learning environment.

cs.LG↗