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Motivated by recent work on the use of topological methods to study collections of rings between an integral domain and its quotient field, we examine spaces of subrings of a commutative ring, where these spaces are endowed with the Zariski or patch topologies. We introduce three notions to study such a space $X$: patch bundles, patch presheaves and patch algebras. When $X$ is compact and Hausdorff, patch bundles give a way to approximate $X$ with topologically more tractable spaces, namely Stone spaces. Patch presheaves encode the space $X$ into stalks of a presheaf of rings over a Boolean algebra, thus giving a more geometrical setting for studying $X$. To both objects, a patch bundle and a patch presheaf, we associate what we call a patch algebra, a commutative ring that efficiently realizes the rings in $X$ as factor rings, or even localizations, and whose structure reflects various properties of the rings in $X$.

Co-based honeycomb magnets have been actively studied recently for the potential realization of emergent quantum magnetism therein such as the Kitaev spin liquid. Here we employ density functional and dynamical mean-field theory methods to examine a family of the Kitaev magnet candidates BaCo$_2$($X$O$_4$)$_2$ ($X$ = P, As, Sb), where the compound with $X$ = Sb being not synthesized yet. Our study confirms the formation of Mott insulating phase and the $J_{\rm eff}$ = 1/2 spin moments at Co$^{2+}$ sites despite the presence of a sizable amount of trigonal crystal field in all three compounds. The pnictogen substitution from phosphorus to antimony significantly changes the in-plane lattice parameters and direct overlap integral between the neighboring Co ions, leading to the suppression of the Heisenberg interaction. More interestingly, the marginal antiferromagnetic nearest-neighbor Kitaev term changes sign into a ferromagnetic one and becomes sizable at the $X$ = Sb limit. Our study suggests that the pnictogen substitution can be a viable route to continuously tune magnetic exchange interactions and to promote magnetic frustration for the realization of potential spin liquid phases in BaCo$_2$($X$O$_4$)$_2$., Comment: 8 pages, 4 figures

We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology produces actual numerical solutions instead of bounds on them. We extend the original BootSTOP algorithm by adding multiple constraints derived from domain-specific knowledge, even in integral form, to improve the accuracy of the solution. We investigate a particular equation in a one-dimensional Conformal Field Theory., Comment: ICLR 2024 Workshop on AI4DifferentialEquations In Science

Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowledge, thus often underperforming when compared with an offline model trained jointly on the entire data stream. Given that any CL model will eventually make mistakes, it is of crucial importance to build calibrated CL models: models that can reliably tell their confidence when making a prediction. Model calibration is an active research topic in machine learning, yet to be properly investigated in CL. We provide the first empirical study of the behavior of calibration approaches in CL, showing that CL strategies do not inherently learn calibrated models. To mitigate this issue, we design a continual calibration approach that improves the performance of post-processing calibration methods over a wide range of different benchmarks and CL strategies. CL does not necessarily need perfect predictive models, but rather it can benefit from reliable predictive models. We believe our study on continual calibration represents a first step towards this direction., Comment: Accepted at CLVISION workshop, CVPR 2024

We employ ab-initio electronic structure calculations to investigate the charge-density waves and periodic lattice distortions in bilayer 2H-NbSe$_2$. We demonstrate that the vertical stacking can give rise to a variety of patterns that may lower the symmetry of the CDW exhibited separately by the two composing 1H-NbSe$_2$ monolayers. The general tendency to a spontaneous symmetry breaking observed in the ground state and the first excited states is shown to originate from a non-negligible inter-layer coupling. Simulated images for scanning tunnelling microscopy (STM) as well as diffraction/scattering patterns show signatures of the different stacking orders. This may not only be useful to reinterpret past experiments on surfaces and thin films, but may also be exploited to devise ad-hoc experiments for the investigation of the stacking order in 2H-NbSe$_2$. We anticipate that our analysis does not only apply to the 2H-NbSe$_2$ bilayer, but is also relevant for thin films and bulk, whose smallest centro-symmetric component is indeed the bilayer. Finally, our results illustrate clearly that the vertical stacking is not only important for 1T structures, as exemplified by the metal-to-insulator transition observed in 1T-TaS$_2$, but seems to be a general feature of metallic layered transition metal dichalcogenides as well., Comment: 11 pages, 5 figures

We explore the inelastic spectra of electrons impinging in a magnetic system. The methodology here presented is intended to highlight the charge-dependent interaction of the electron beam in a STEM-EELS experiment, and the local vector potential generated by the magnetic lattice. This interaction shows an intensity $10^{-2}$ smaller than the purely spin interaction, which is taken to be functionally the same as in the inelastic neutron experiment. On the other hand, it shows a strong scattering vector dependence ($\kappa^{-4}$) and a dependence with the relative orientation between the probe wavevector and the local magnetic moments of the solid. We present YIG as a case study due to its high interest by the community.

As highlighted in a series of recent papers by Tringali and the author, fundamental aspects of the classical theory of factorization can be significantly generalized by blending the languages of monoids and preorders. Specifically, the definition of a suitable preorder on a monoid allows for the exploration of decompositions of its elements into (more or less) arbitrary factors. We provide an overview of the principal existence theorems in this new theoretical framework. Furthermore, we showcase additional applications beyond classical factorization, emphasizing its generality. In particular, we recover and refine a classical result by Howie on idempotent factorizations in the full transformation monoid of a finite set.

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.

Using first-principles techniques, we study the structural, magnetic and electronic properties of (111)-oriented (LaMnO$_3$)$_{2n}$$\vert$(SrMnO$_3$)$_{n}$ superlattices of varying thickness ($n=2,4,6$). We find that the properties of the thinnest superlattice ($n=2$) are similar to the celebrated half-metallic ferromagnetic alloy La$_{2/3}$Sr$_{1/3}$MnO$_3$, with quenched Jahn-Teller distortions. At intermediate thickness ($n=4$), the $a^{-}a^{-}a^{-}$ tilting pattern transitions to the $a^{-}a^{-}c^{+}$ tilting pattern, driven by the lattice degrees of freedom in the LaMnO$_3$ region. The emergence of the Jahn-Teller modes and the spatial extent needed for their development play a key role in this structural transition. For the largest thickness considered ($n=6$), we unveil an emergent separation of Jahn-Teller and volume-breathing orders in the ground-state structure with the $a^{-}a^{-}c^{+}$ tilting pattern, whereas it vanishes in the antiferromagnetic configurations. The ground state of all superlattices is half-metallic ferromagnetic, not affected by the underlying series of structural transitions. Overall, these results outline a thickness-induced crossover between the physical properties of bulk La$_{2/3}$Sr$_{1/3}$MnO$_3$ and bulk LaMnO$_3$., Comment: 7 pages excluding references, 8 figures

Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evaluate various methods from the literature that tackle online continual learning. More specifically, we focus on the class-incremental setting in the context of image classification, where the learner must learn new classes incrementally from a stream of data. We compare these methods on the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average accuracy, forgetting, stability, and quality of the representations, to evaluate various aspects of the algorithm at the end but also during the whole training period. We find that most methods suffer from stability and underfitting issues. However, the learned representations are comparable to i.i.d. training under the same computational budget. No clear winner emerges from the results and basic experience replay, when properly tuned and implemented, is a very strong baseline. We release our modular and extensible codebase at https://github.com/AlbinSou/ocl_survey based on the avalanche framework to reproduce our results and encourage future research., Comment: ICCV Visual Continual Learning Workshop 2023 accepted paper

NEWFOCUS is an EU COST Action targeted at exploring radical solutions that could influence the design of future wireless networks. The project aims to address some of the challenges associated with optical wireless communication (OWC) and to establish it as a complementary technology to the radio frequency (RF)-based wireless systems in order to meet the demanding requirements of the fifth generation (5G) and the future sixth generation (6G) backhaul and access networks. Only 6G will be able to widely serve the exponential growth in connected devices (i.e., more than 500 billion) in 2030, real-time holographic communication, future virtual reality, etc. Space is emerging as the new frontier in 5 and 6G and beyond communication networks, where it offers high-speed wireless coverage to remote areas both in lands and sees. This activity is supported by the recent development of low-altitude Earth orbit satellite mega-constellations. The focus of this 2nd White Paper is on the use of OWC as an enabling technology for medium- and long-range links for deployment in (i) smart-cities and intelligent transportation systems; (ii) first- and last-mile access and backhaul/fronthaul wireless networks; (iii) hybrid free-space optics/RF adaptive wireless connections; (iv) space-to-ground, inter-satellite, ground-to-air, and air-to-air communications; and (v) underwater communications.

Continual Learning trains models on a stream of data, with the aim of learning new information without forgetting previous knowledge. Given the dynamic nature of such environments, explaining the predictions of these models can be challenging. We study the behavior of SHAP values explanations in Continual Learning and propose an evaluation protocol to robustly assess the change of explanations in Class-Incremental scenarios. We observed that, while Replay strategies enforce the stability of SHAP values in feedforward/convolutional models, they are not able to do the same with fully-trained recurrent models. We show that alternative recurrent approaches, like randomized recurrent models, are more effective in keeping the explanations stable over time., Comment: ESANN 2023, 6 pages, added link to code

The influence of stable and unstable stratification on the amplification of coherent structures in turbulent channel flows is investigated by computing the linear response to stochastic forcing. The responses to momentum and thermal forcing are considered separately. Consistently with results of previous direct numerical simulations, the influence of the mean flow stratification on stochastic forcing amplifications is found to be non-negligible only for streamwise-elongated large-scale structures. Unstable stratification is found to enhance the peak variance of the response, except for the velocity response to thermal forcing, and to increase the spanwise wavelength of the most amplified structures. Stable stratification induces opposite effects. The spanwise wavelengths maximizing the different types of variance amplifications, all converge to approximately 6h when approaching the linear instability threshold where large-scale coherent rolls become linearly unstable. In the presence of even moderately unstable stratification, the profiles of turbulent buoyancy and momentum fluxes and of rms vertical velocity of all types of most amplified stochastic responses are nearly indistinguishable from those of the critical mode becoming unstable at the critical Richardson number. The two most energetic POD modes always contribute to more than 90% of the variance of the response, except for the thermal response to thermal forcing. We conclude that the same mechanism underlies the onset of the instability of coherent large-scale rolls at the critical Richardson number and the amplification of coherent large-scale structures at subcritical Richardson numbers., Comment: accepted for publication in Phys. Rev. Fluids

Distributed learning on the edge often comprises self-centered devices (SCD) which learn local tasks independently and are unwilling to contribute to the performance of other SDCs. How do we achieve forward transfer at zero cost for the single SCDs? We formalize this problem as a Distributed Continual Learning scenario, where SCD adapt to local tasks and a CL model consolidates the knowledge from the resulting stream of models without looking at the SCD's private data. Unfortunately, current CL methods are not directly applicable to this scenario. We propose Data-Agnostic Consolidation (DAC), a novel double knowledge distillation method that consolidates the stream of SC models without using the original data. DAC performs distillation in the latent space via a novel Projected Latent Distillation loss. Experimental results show that DAC enables forward transfer between SCDs and reaches state-of-the-art accuracy on Split CIFAR100, CORe50 and Split TinyImageNet, both in reharsal-free and distributed CL scenarios. Somewhat surprisingly, even a single out-of-distribution image is sufficient as the only source of data during consolidation.

Continual learning is the problem of learning from a nonstationary stream of data, a fundamental issue for sustainable and efficient training of deep neural networks over time. Unfortunately, deep learning libraries only provide primitives for offline training, assuming that model's architecture and data are fixed. Avalanche is an open source library maintained by the ContinualAI non-profit organization that extends PyTorch by providing first-class support for dynamic architectures, streams of datasets, and incremental training and evaluation methods. Avalanche provides a large set of predefined benchmarks and training algorithms and it is easy to extend and modular while supporting a wide range of continual learning scenarios. Documentation is available at \url{https://avalanche.continualai.org}.

Real-world data streams naturally include the repetition of previous concepts. From a Continual Learning (CL) perspective, repetition is a property of the environment and, unlike replay, cannot be controlled by the agent. Nowadays, the Class-Incremental (CI) scenario represents the leading test-bed for assessing and comparing CL strategies. This scenario type is very easy to use, but it never allows revisiting previously seen classes, thus completely neglecting the role of repetition. We focus on the family of Class-Incremental with Repetition (CIR) scenario, where repetition is embedded in the definition of the stream. We propose two stochastic stream generators that produce a wide range of CIR streams starting from a single dataset and a few interpretable control parameters. We conduct the first comprehensive evaluation of repetition in CL by studying the behavior of existing CL strategies under different CIR streams. We then present a novel replay strategy that exploits repetition and counteracts the natural imbalance present in the stream. On both CIFAR100 and TinyImageNet, our strategy outperforms other replay approaches, which are not designed for environments with repetition., Comment: Accepted to the 2nd Conference on Lifelong Learning Agents (CoLLAs), 2023 19 pages

This paper describes the fundamental timing properties of a single-pixel sensor for charged particle detection based on the 3D-trench silicon structure. We derive the results both analytically and numerically by considering a simple ideal sensor and the corresponding fast front-end electronics in two different case scenarios: ideal integrator and real fast electronics (trans-impedance amplifier). The particular shape of the Time of Arrival (TOA) distribution is examined and the relation between the time resolution and the spread of intrinsic charge collection time is discussed, by varying electronics parameters and discrimination thresholds. The results are obtained with and without simulated electronics noise. We show that the 3D-trench sensors are characterized by a $synchronous~region$, i.e. a portion of the active volume which leads to the same TOA values when charged particles cross it. The synchronous region size is dependent on the front-end electronics and discrimination threshold, and the phenomenon represents an intrinsic physical effect that leads to the excellent time resolution of these sensors. Moreover, we show that the TOA distribution is characterized by an intrinsic asymmetry, due to the 3D geometry only, that becomes negligible in case of significant electronics jitter.

Detectors based on pixels with timing capabilities are gaining increasing importance in the last years. Next-to-come high-energy physics experiments at colliders require the use of time information in tracking, due to the expected levels of track densities in the foreseen experimental conditions. A promising solution to gain high-resolution performance at the sensor level is given by so-called 3D silicon sensors. The excellent intrinsic time resolution of a special case of 3D sensors, the trench type, is limited by residual non-uniformities in the duration of the induced currents. The intrinsic contribution of the sensor to the total time resolution of the system, when the detector is coupled to a front-end electronics, depends on the characteristics of the electronics itself and can be minimized with a proper design. This paper aims to analyze the possible performance in the timing of a typically-used front-end circuit, the Trans-Impedance Amplifier, considering different possible configurations. Evidence of the preferred modes of operation in sensor read-out for timing measurement will be given., Comment: arXiv admin note: text overlap with arXiv:2008.09867

Let $H$ be a monoid, $\mathscr F(X)$ be the free monoid on a set $X$, and $\pi_H$ be the unique extension of the identity map on $H$ to a monoid homomorphism $\mathscr F(H) \to H$. Given $A \subseteq H$, an $A$-word $\mathfrak z$ (i.e., an element of $\mathscr F(A)$) is minimal if $\pi_H(\mathfrak z) \ne \pi_H(\mathfrak z')$ for every permutation $\mathfrak z'$ of a proper subword of $\mathfrak z$. The minimal $A$-elasticity of $H$ is then the supremum of all rational numbers $m/n$ with $m, n \in \mathbb N^+$ such that there exist minimal $A$-words $\mathfrak a$ and $\mathfrak b$ of length $m$ and $n$, resp., with $\pi_H(\mathfrak a) = \pi_H(\mathfrak b)$. Among other things, we show that if $H$ is commutative and $A$ is finite, then the minimal $A$-elasticity of $H$ is finite. This yields a non-trivial generalization of the finiteness part of a classical theorem of Anderson et al. from the case where $H$ is cancellative, commutative, and finitely generated (f.g.) modulo units and $A$ is the set $\mathscr A(H)$ of its atoms. We also check that commutativity is somewhat essential here, by proving the existence of an atomic, cancellative, f.g. monoid with trivial group of units whose minimal $\mathscr A(H)$-elasticity is infinite., Comment: 13 pages, no figures. To appear in Arkiv f\"or Matematik