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Tactile hand gesture recognition is a crucial task for user control in the automotive sector, where Human-Machine Interactions (HMI) demand low latency and high energy efficiency. This study addresses the challenges of power-constrained edge training and inference by utilizing analog Resistive Random Access Memory (ReRAM) technology in conjunction with a real tactile hand gesture dataset. By optimizing the input space through a feature engineering strategy, we avoid relying on large-scale crossbar arrays, making the system more suitable for edge deployment. Through realistic hardware-aware simulations that account for device non-idealities derived from experimental data, we demonstrate the functionalities of our analog ReRAM-based analog in-memory computing for on-chip training, utilizing the state-of-the-art Tiki-Taka algorithm. Furthermore, we validate the classification accuracy of approximately 91.4% for post-deployment inference of hand gestures. The results highlight the potential of analog ReRAM technology and crossbar architecture with fully parallelized matrix computations for real-time HMI systems at the Edge., Comment: Accepted in IEEE ISCAS 2025

Pruning offers a promising solution to mitigate the associated costs and environmental impact of deploying large deep neural networks (DNNs). Traditional approaches rely on computationally expensive trained models or time-consuming iterative prune-retrain cycles, undermining their utility in resource-constrained settings. To address this issue, we build upon the established principles of saliency (LeCun et al., 1989) and connection sensitivity (Lee et al., 2018) to tackle the challenging problem of one-shot pruning neural networks (NNs) before training (PBT) at initialization. We introduce Fisher-Taylor Sensitivity (FTS), a computationally cheap and efficient pruning criterion based on the empirical Fisher Information Matrix (FIM) diagonal, offering a viable alternative for integrating first- and second-order information to identify a model's structurally important parameters. Although the FIM-Hessian equivalency only holds for convergent models that maximize the likelihood, recent studies (Karakida et al., 2019) suggest that, even at initialization, the FIM captures essential geometric information of parameters in overparameterized NNs, providing the basis for our method. Finally, we demonstrate empirically that layer collapse, a critical limitation of data-dependent pruning methodologies, is easily overcome by pruning within a single training epoch after initialization. We perform experiments on ResNet18 and VGG19 with CIFAR-10 and CIFAR-100, widely used benchmarks in pruning research. Our method achieves competitive performance against state-of-the-art techniques for one-shot PBT, even under extreme sparsity conditions. Our code is made available to the public., Comment: 8 pages of main content (excluding references), 2 figures, 2 tables, 1 algorithm, and 11 pages of appendix. Code available at https://github.com/Gollini/Fisher_Taylor_Sensitivity

Strong Differential Privacy (DP) and Optimization guarantees are two desirable properties for a method in Federated Learning (FL). However, existing algorithms do not achieve both properties at once: they either have optimal DP guarantees but rely on restrictive assumptions such as bounded gradients/bounded data heterogeneity, or they ensure strong optimization performance but lack DP guarantees. To address this gap in the literature, we propose and analyze a new method called Clip21-SGD2M based on a novel combination of clipping, heavy-ball momentum, and Error Feedback. In particular, for non-convex smooth distributed problems with clients having arbitrarily heterogeneous data, we prove that Clip21-SGD2M has optimal convergence rate and also near optimal (local-)DP neighborhood. Our numerical experiments on non-convex logistic regression and training of neural networks highlight the superiority of Clip21-SGD2M over baselines in terms of the optimization performance for a given DP-budget.

A five-dimensional Kaluza-Klein spacetime model is considered, with one extra compactified spatial dimension. The equation of state of an electrically neutral, zero-temperature Fermi gas with a repulsive linear potential is described. From the equation of state, the speed of sound squared is calculated and shown for different model parameters. Its properties are studied from lower energies up to the conformal limit., Comment: 7 pages, 1 figure

Collaborative learning enables multiple participants to learn a single global model by exchanging focused updates instead of sharing data. One of the core challenges in collaborative learning is ensuring that participants are rewarded fairly for their contributions, which entails two key sub-problems: contribution assessment and reward allocation. This work focuses on fair reward allocation, where the participants are incentivized through model rewards - differentiated final models whose performance is commensurate with the contribution. In this work, we leverage the concept of slimmable neural networks to collaboratively learn a shared global model whose performance degrades gracefully with a reduction in model width. We also propose a post-training fair allocation algorithm that determines the model width for each participant based on their contributions. We theoretically study the convergence of our proposed approach and empirically validate it using extensive experiments on different datasets and architectures. We also extend our approach to enable training-time model reward allocation.

A dedicated search for upward-going air showers at zenith angles exceeding $110^\circ$ and energies $E>0.1$ EeV has been performed using the Fluorescence Detector of the Pierre Auger Observatory. The search is motivated by two "anomalous" radio pulses observed by the ANITA flights I and III which appear inconsistent with the Standard Model of particle physics. Using simulations of both regular cosmic ray showers and upward-going events, a selection procedure has been defined to separate potential upward-going candidate events and the corresponding exposure has been calculated in the energy range [0.1-33] EeV. One event has been found in the search period between 1 Jan 2004 and 31 Dec 2018, consistent with an expected background of $0.27 \pm 0.12$ events from mis-reconstructed cosmic ray showers. This translates to an upper bound on the integral flux of $(7.2 \pm 0.2) \times 10^{-21}$ cm$^{-2}$ sr$^{-1}$ y$^{-1}$ and $(3.6 \pm 0.2) \times 10^{-20}$ cm$^{-2}$ sr$^{-1}$ y$^{-1}$ for an $E^{-1}$ and $E^{-2}$ spectrum, respectively. An upward-going flux of showers normalized to the ANITA observations is shown to predict over 34 events for an $E^{-3}$ spectrum and over 8.1 events for a conservative $E^{-5}$ spectrum, in strong disagreement with the interpretation of the anomalous events as upward-going showers., Comment: 10+5 Pages of Manuscript plus Supplemental Material, 3+8 figures, Accepted for publication in Physical Review Letters

Diffuse photons of energy above 0.1 PeV, produced through the interactions between cosmic rays and either interstellar matter or background radiation fields, are powerful tracers of the distribution of cosmic rays in the Galaxy. Furthermore, the measurement of a diffuse photon flux would be an important probe to test models of super-heavy dark matter decaying into gamma-rays. In this work, we search for a diffuse photon flux in the energy range between 50 PeV and 200 PeV using data from the Pierre Auger Observatory. For the first time, we combine the air-shower measurements from a 2 km$^2$ surface array consisting of 19 water-Cherenkov surface detectors, spaced at 433 m, with the muon measurements from an array of buried scintillators placed in the same area. Using 15 months of data, collected while the array was still under construction, we derive upper limits to the integral photon flux ranging from 13.3 to 13.8 km$^{-2}$ sr$^{-1}$ yr$^{-1}$ above tens of PeV. We extend the Pierre Auger Observatory photon search program towards lower energies, covering more than three decades of cosmic-ray energy. This work lays the foundation for future diffuse photon searches: with the data from the next 10 years of operation of the Observatory, this limit is expected to improve by a factor of $\sim$20., Comment: Submitted to JCAP

We report the discovery of PSR J0031$-$5726 in the GaLactic and Extragalactic All-sky MWA eXtended imaging survey at a Galactic latitude of $b \approx -60^\circ$. The pulsar exhibits both sporadic, extremely bright pulses reminiscent of rotating radio transients (RRATs) as well as a persistent, dimmer pulses. The bright pulses tend to arrive at later rotation phases than their dimmer counterparts, and have dramatically varying polarization angle curves, such that the integrated profile appears almost completely depolarized down to the system noise level. The rotation measure of individual pulses was found to sometimes vary by up to ${\sim}0.8\,$rad/m$^2$, but was otherwise generally consistent with its average (ionosphere-corrected) value of $10.0 \pm 0.1\,$rad/m$^2$. We surmise that J0031$-$5726 may represent a class of pulsar that is intermediate between normal pulsars and RRATs., Comment: 16 pages, 6 figures, accepted for publication in ApJ

This article aims to study the class of strongly self-dual polytopes (ssd-polytopes for short), defined in a paper by Lov\'asz \cite{lovasz}. He described a series of such polytopes (called $L$-type polytopes), which he used to solve a combinatorial problem. From a geometrical point of view, there are interesting questions: what additional elements of this class exist, and are there any with a different structure from the $L$-type ones? We show that in dimension three, one of their faces defines $L$-type polyhedra. Illustrating the algorithm of the proof, we present an ssd-polytope of 23 vertices whose combinatorial structure differ from those of $L$-type ones. Finally, with an elementary discussion, we prove that for fewer than nine vertices, there are only fifth one ssd-polyhedra, four of them can be constructed by Lov\'asz's method, and we can find the fifth one with "the diameter gradient flow algorithm" of Katz, Memoli and Wang \cite{katz-memoli-wang}., Comment: 21 pages, 19 figures

Neural networks have become a fundamental component of numerous practical applications, and their implementations, which are often accelerated by hardware, are integrated into all types of real-world physical devices. User interactions with neural networks on hardware accelerators are commonly considered privacy-sensitive. Substantial efforts have been made to uncover vulnerabilities and enhance privacy protection at the level of machine learning algorithms, including membership inference attacks, differential privacy, and federated learning. However, neural networks are ultimately implemented and deployed on physical devices, and current research pays comparatively less attention to privacy protection at the implementation level. In this paper, we introduce a generic physical side-channel attack, ScaAR, that extracts user interactions with neural networks by leveraging electromagnetic (EM) emissions of physical devices. Our proposed attack is implementation-agnostic, meaning it does not require the adversary to possess detailed knowledge of the hardware or software implementations, thanks to the capabilities of deep learning-based side-channel analysis (DLSCA). Experimental results demonstrate that, through the EM side channel, ScaAR can effectively extract the class label of user interactions with neural classifiers, including inputs and outputs, on the AMD-Xilinx MPSoC ZCU104 FPGA and Raspberry Pi 3 B. In addition, for the first time, we provide side-channel analysis on edge Large Language Model (LLM) implementations on the Raspberry Pi 5, showing that EM side channel leaks interaction data, and different LLM tokens can be distinguishable from the EM traces.

Collaborative learning (CL) enables multiple participants to jointly train machine learning (ML) models on decentralized data sources without raw data sharing. While the primary goal of CL is to maximize the expected accuracy gain for each participant, it is also important to ensure that the gains are fairly distributed. Specifically, no client should be negatively impacted by the collaboration, and the individual gains must ideally be commensurate with the contributions. Most existing CL algorithms require central coordination and focus on the gain maximization objective while ignoring collaborative fairness. In this work, we first show that the existing measure of collaborative fairness based on the correlation between accuracy values without and with collaboration has drawbacks because it does not account for negative collaboration gain. We argue that maximizing mean collaboration gain (MCG) while simultaneously minimizing the collaboration gain spread (CGS) is a fairer alternative. Next, we propose the CYCle protocol that enables individual participants in a private decentralized learning (PDL) framework to achieve this objective through a novel reputation scoring method based on gradient alignment between the local cross-entropy and distillation losses. Experiments on the CIFAR-10, CIFAR-100, and Fed-ISIC2019 datasets empirically demonstrate the effectiveness of the CYCle protocol to ensure positive and fair collaboration gain for all participants, even in cases where the data distributions of participants are highly skewed. For the simple mean estimation problem with two participants, we also theoretically show that CYCle performs better than standard FedAvg, especially when there is large statistical heterogeneity.

LocalSGD and SCAFFOLD are widely used methods in distributed stochastic optimization, with numerous applications in machine learning, large-scale data processing, and federated learning. However, rigorously establishing their theoretical advantages over simpler methods, such as minibatch SGD (MbSGD), has proven challenging, as existing analyses often rely on strong assumptions, unrealistic premises, or overly restrictive scenarios. In this work, we revisit the convergence properties of LocalSGD and SCAFFOLD under a variety of existing or weaker conditions, including gradient similarity, Hessian similarity, weak convexity, and Lipschitz continuity of the Hessian. Our analysis shows that (i) LocalSGD achieves faster convergence compared to MbSGD for weakly convex functions without requiring stronger gradient similarity assumptions; (ii) LocalSGD benefits significantly from higher-order similarity and smoothness; and (iii) SCAFFOLD demonstrates faster convergence than MbSGD for a broader class of non-quadratic functions. These theoretical insights provide a clearer understanding of the conditions under which LocalSGD and SCAFFOLD outperform MbSGD.

Recent innovations in light sheet microscopy, paired with developments in tissue clearing techniques, enable the 3D imaging of large mammalian tissues with cellular resolution. Combined with the progress in large-scale data analysis, driven by deep learning, these innovations empower researchers to rapidly investigate the morphological and functional properties of diverse biological samples. Segmentation, a crucial preliminary step in the analysis process, can be automated using domain-specific deep learning models with expert-level performance. However, these models exhibit high sensitivity to domain shifts, leading to a significant drop in accuracy when applied to data outside their training distribution. To address this limitation, and inspired by the recent success of self-supervised learning in training generalizable models, we organized the SELMA3D Challenge during the MICCAI 2024 conference. SELMA3D provides a vast collection of light-sheet images from cleared mice and human brains, comprising 35 large 3D images-each with over 1000^3 voxels-and 315 annotated small patches for finetuning, preliminary testing and final testing. The dataset encompasses diverse biological structures, including vessel-like and spot-like structures. Five teams participated in all phases of the challenge, and their proposed methods are reviewed in this paper. Quantitative and qualitative results from most participating teams demonstrate that self-supervised learning on large datasets improves segmentation model performance and generalization. We will continue to support and extend SELMA3D as an inaugural MICCAI challenge focused on self-supervised learning for 3D microscopy image segmentation., Comment: 2st version

This work addresses the challenge of optimal energy management in microgrids through a collaborative and privacy-preserving framework. We propose the FedTRPO methodology, which integrates Federated Learning (FL) and Trust Region Policy Optimization (TRPO) to manage distributed energy resources (DERs) efficiently. Using a customized version of the CityLearn environment and synthetically generated data, we simulate designed net-zero energy scenarios for microgrids composed of multiple buildings. Our approach emphasizes reducing energy costs and carbon emissions while ensuring privacy. Experimental results demonstrate that FedTRPO is comparable with state-of-the-art federated RL methodologies without hyperparameter tunning. The proposed framework highlights the feasibility of collaborative learning for achieving optimal control policies in energy systems, advancing the goals of sustainable and efficient smart grids., Comment: Submitted to Environmental Data Science Journal from Cambridge University Press

Anyons are low-dimensional quasiparticles that obey fractional statistics, hence interpolating between bosons and fermions. In two dimensions, they exist as elementary excitations of fractional quantum Hall states and they are believed to enable topological quantum computing. One-dimensional (1D) anyons have been theoretically proposed, but their experimental realization has proven to be difficult. Here, we observe anyonic correlations, which emerge through the phenomenon of spin-charge separation, in a 1D strongly-interacting quantum gas. The required spin degree of freedom is provided by a mobile impurity, whose effective anyonic correlations are associated with an experimentally tunable statistical angle. These anyonic correlations are measured by monitoring the impurity momentum distribution, whose asymmetric feature demonstrates the transmutation of bosons via anyons to fermions. Going beyond equilibrium conditions, we study the dynamical properties of the anyonic correlations via dynamical fermionization of the anyons. Our work opens up the door to the exploration of non-equilibrium anyonic phenomena in a highly controllable setting.

Temporal forward-tracking has been the dominant approach for multi-object segmentation and tracking (MOTS). However, a novel time-symmetric tracking methodology has recently been introduced for the detection, segmentation, and tracking of budding yeast cells in pre-recorded samples. Although this architecture has demonstrated a unique perspective on stable and consistent tracking, as well as missed instance re-interpolation, its evaluation has so far been largely confined to settings related to videomicroscopic environments. In this work, we aim to reveal the broader capabilities, advantages, and potential challenges of this architecture across various specifically designed scenarios, including a pedestrian tracking dataset. We also conduct an ablation study comparing the model against its restricted variants and the widely used Kalman filter. Furthermore, we present an attention analysis of the tracking architecture for both pretrained and non-pretrained models

Non-convex Machine Learning problems typically do not adhere to the standard smoothness assumption. Based on empirical findings, Zhang et al. (2020b) proposed a more realistic generalized $(L_0, L_1)$-smoothness assumption, though it remains largely unexplored. Many existing algorithms designed for standard smooth problems need to be revised. However, in the context of Federated Learning, only a few works address this problem but rely on additional limiting assumptions. In this paper, we address this gap in the literature: we propose and analyze new methods with local steps, partial participation of clients, and Random Reshuffling without extra restrictive assumptions beyond generalized smoothness. The proposed methods are based on the proper interplay between clients' and server's stepsizes and gradient clipping. Furthermore, we perform the first analysis of these methods under the Polyak-{\L} ojasiewicz condition. Our theory is consistent with the known results for standard smooth problems, and our experimental results support the theoretical insights.

Low-rank adapters have become standard for efficiently fine-tuning large language models (LLMs), but they often fall short of achieving the performance of full fine-tuning. We propose a method, LoRA Silver Bullet or LoRA-SB, that approximates full fine-tuning within low-rank subspaces using a carefully designed initialization strategy. We theoretically demonstrate that the architecture of LoRA-XS, which inserts a learnable (r x r) matrix between B and A while keeping other matrices fixed, provides the precise conditions needed for this approximation. We leverage its constrained update space to achieve optimal scaling for high-rank gradient updates while removing the need for hyperparameter tuning. We prove that our initialization offers an optimal low-rank approximation of the initial gradient and preserves update directions throughout training. Extensive experiments across mathematical reasoning, commonsense reasoning, and language understanding tasks demonstrate that our approach exceeds the performance of standard LoRA while using \textbf{27-90} times fewer learnable parameters, and comprehensively outperforms LoRA-XS. Our findings establish that it is possible to simulate full fine-tuning in low-rank subspaces, and achieve significant efficiency gains without sacrificing performance. Our code is publicly available at https://github.com/RaghavSinghal10/lora-sb., Comment: Kaustubh Ponkshe and Raghav Singhal contributed equally to this work

In a recent article we introduced a new class of shapes, called \emph{soft cells} which fill space as \emph{soft tilings} without gaps and overlaps while minimizing the number of sharp corners. We defined the \textit{edge bending algorithm} deforming a polyhedral tiling into a soft tiling and we proved that by this algorithm an infinite class of polyhedral tilings can be smoothly deformed into soft tilings. Using the algorithm we constructed the soft versions of all Dirichlet-Voronoi cells associated with point lattices in two and three dimensions; for brevity we will refer to the soft cells produced by the edge bending algorithm as the \textit{standard} soft cells. Here we show that the edge bending algorithm has at least one alternative which is specific to one particular polyhedral tiling. By using the Dirichlet-Voronoi tiling based on the $bcc$ lattice, we demonstrate a new type of soft cell which is non-standard, i.e. it can not be produced by the universal edge bending algorithm and it shows a marked difference compared to the standard soft cell with identical combinatorial properties. We use the new, tiling-specific algorithm to construct a one parameter family of space-filling cells, the end members of which are soft cells and one member of which is the Kelvin cell. By using the same, tiling-specific algorithm we point out the connection of the new, non-standard soft cells to Schwarz minimal surfaces. Our findings indicate that soft cells may be identified by algorithms which are specific for the tiling. Also, results show that not only soft cells themselves, but also the geometric constructions resulting in soft cells could be of immediate interest when studying natural shapes., Comment: 12 pages, 7 figures

The statistics of fluctuations on large regions of space encodes universal properties of many-body systems. At equilibrium, it is described by thermodynamics. However, away from equilibrium such as after quantum quenches, the fundamental principles are more nebulous. In particular, although exact results have been conjectured in integrable models, a correct understanding of the physics is largely missing. In this letter, we explain these principles, taking the example of the number of particles lying on a large interval in one-dimensional interacting systems. These are based on simple hydrodynamic arguments from the theory of ballistically transported fluctuations, and in particular the Euler-scale transport of long-range correlations. Using these principles, we obtain the full counting statistics (FCS) in terms of thermodynamic and hydrodynamic quantities, whose validity depends on the structure of hydrodynamic modes. In fermionic-statistics interacting integrable models with a continuum of hydrodynamic modes, such as the Lieb-Liniger model for cold atomic gases, the formula reproduces previous conjectures, but is in fact not exact: it gives the correct cumulants up to, including, order 5, while long-range correlations modify higher cumulants. In integrable and non-integrable models with two or less hydrodynamic modes, the formula is expected to give all cumulants., Comment: 6+14 pages, 3 figures

We propose a new method for converting single microwave photons to single optical sideband photons based on spinful impurities in magnetic materials. This hybrid system is advantageous over previous proposals because (i) the implementation allows much higher transduction rates ($10^{3}$ times faster at the same optical pump Rabi frequency) than state-of the art devices, (ii) high-efficiency transduction is found to happen in a significantly larger space of device parameters (in particular, over $1$ GHz microwave detuning), and (iii) it does not require mode volume matching between optical and microwave resonators. We identify the needed magnetic interactions as well as potential materials systems to enable this speed-up using erbium dopants for telecom compatibility. This is an important step towards realizing high-fidelity entangling operations between remote qubits and will provide additional control of the transduction through perturbation of the magnet., Comment: 3 figures, 12 pages including supplementary material

In dynamic vehicle routing problems (DVRPs), some part of the information is revealed or changed on the fly, and the decision maker has the opportunity to re-plan the vehicle routes during their execution, reflecting on the changes. Accordingly, the solution to a DVRP is a flexible policy rather than a set of fixed routes. A policy is basically a problem-specific algorithm that is invoked at various decision points in the planning horizon and returns a decision according to the current state. Since DVRPs involve dynamic decision making, a simulator is an essential tool for dynamically testing and evaluating the policies. Despite this, there are few tools available that are specifically designed for this purpose. To fill this gap, we have developed a simulation framework that is suitable for a wide range of dynamic vehicle routing problems and allows to dynamically test different policies for the given problem. In this paper, we present the background of this simulation tool, for which we proposed a general modeling framework suitable for formalizing DVRPs independently of simulation purposes. Our open source simulation tool is already available, easy to use, and easily customizable, making it a useful tool for the research community.

With the increase in the number of parameters in large language models, the process of pre-training and fine-tuning increasingly demands larger volumes of GPU memory. A significant portion of this memory is typically consumed by the optimizer state. To overcome this challenge, recent approaches such as low-rank adaptation (LoRA (Hu et al., 2021)), low-rank gradient projection (GaLore (Zhao et al., 2024)), and blockwise optimization (BAdam (Luo et al., 2024)) have been proposed. However, in all these algorithms, the $\textit{effective rank of the weight updates remains low-rank}$, which can lead to a substantial loss of information from the gradient. This loss can be critically important, especially during the pre-training stage. In this paper, we introduce $\texttt{FRUGAL}$ ($\textbf{F}$ull-$\textbf{R}$ank $\textbf{U}$pdates with $\textbf{G}$r$\textbf{A}$dient sp$\textbf{L}$itting), a new memory-efficient optimization framework. $\texttt{FRUGAL}$ leverages gradient splitting to perform low-dimensional updates using advanced algorithms (such as Adam), while updates along the remaining directions are executed via state-free methods like SGD or signSGD (Bernstein et al., 2018). Our framework can be integrated with various low-rank update selection techniques, including GaLore and BAdam. We provide theoretical convergence guarantees for our framework when using SGDM for low-dimensional updates and SGD for state-free updates. Additionally, our method consistently outperforms concurrent approaches across various fixed memory budgets, achieving state-of-the-art results in pre-training and fine-tuning tasks while balancing memory efficiency and performance metrics.

We present a novel 4.5B parameter small language model that can handle multiple input and output modalities, including text, images, videos, and audio. Despite its small size, the model achieves near state-of-the-art performance on a variety of tasks, demonstrating the potential of multi-modal models to tackle complex real-world problems. Our approach leverages recent advancements in language modeling and multi-task learning to create a versatile and high-performing model that can even be deployed for edge inference. Experimental results show the model's strong performance across multiple benchmarks, paving the way for further progress in multi-modal artificial intelligence.

The measurement and description of the charge-carrier lifetime (tauc) is crucial for the wide-ranging applications of lead-halide perovskites. We present time-resolved microwave-detected photoconductivity decay (TRMCD) measurements and a detailed analysis of the possible recombination mechanisms including trap-assisted, radiative, and Auger recombination. We prove that performing injection-dependent measurement is crucial in identifying the recombination mechanism. We present temperature and injection level dependent measurements in CsPbBr_3, which is an inorganic lead-halide perovskite. In this material, we observe the dominance of charge-carrier trapping, which results in ultra-long charge-carrier lifetimes. Although charge trapping can limit the effectiveness of materials in photovoltaic applications, it also offers significant advantages for various alternative uses, including delayed and persistent photodetection, charge-trap memory, afterglow light-emitting diodes, quantum information storage, and photocatalytic activity.

Stable diffusion models have ushered in a new era of advancements in image generation, currently reigning as the state-of-the-art approach, exhibiting unparalleled performance. The process of diffusion, accompanied by denoising through iterative convolutional or transformer network steps, stands at the core of their implementation. Neural networks operating in continuous time naturally embrace the concept of diffusion, this way they could enable more accurate and energy efficient implementation. Within the confines of this paper, my focus delves into an exploration and demonstration of the potential of celllular neural networks in image generation. I will demonstrate their superiority in performance, showcasing their adeptness in producing higher quality images and achieving quicker training times in comparison to their discrete-time counterparts on the commonly cited MNIST dataset.

Binary population synthesis (BPS) is an essential tool for extracting information about massive binary evolution from gravitational-wave (GW) detections of compact object mergers. It has been successfully used to constrain the most likely permutations of evolution models among hundreds of alternatives, while initial condition models, in contrast, have not yet received the same level of attention. Here, we introduce BOSSA, a detailed initial sampling code including a set of 192 initial condition permutations for BPS that capture both "invariant" and "varying" models, the latter accounting for a possible metallicity- and star formation rate (SFR)-dependence of the initial mass function (IMF); as well as correlations between the initial primary mass, orbital period, mass ratio and eccentricity of binaries. We include 24 metallicity-specific cosmic star formation history (cSFH) models and propose two alternate models for the mass-dependent binary fraction. We build a detailed pipeline for time-evolving BPS, such that each binary has well-defined initial conditions, and we are able to distinguish the contributions from populations of different ages. We discuss the meaning of the IMF for binaries and introduce a refined initial sampling procedure for component masses. We also discuss the treatment of higher-order multiple systems when normalizing a binary sample. In particular, we argue for how a consistent interpretation of the IMF implies that this is not the distribution from which any set of component masses should be independently drawn, and show how the individual IMF of primaries and companions is expected to deviate from the full IMF., Comment: 23 pages, 16 figures, 2 tables. Submitted to MNRAS. Repository (under construction) at https://github.com/lmdesa/BOSSA

Compact stars in the Kaluza-Klein space-time are investigated, with multiple additional compactified spatial dimensions ($d$). Within the extended phenomenological model, a static, spherically symmetric solution is considered, with the equation of state provided by a zero temperature, interacting multi-dimensional Fermi gas. The maximal masses of compact stars are calculated for different model parameters. We investigated the effect of the existence of multiple extra compactified dimensions within the Kaluza-Klein compact star structure. We found that the number of extra dimensions plays a similar role, and to a similar order, as the excitation number: increasing their number, $d$, reduces the maximal mass by a few percent., Comment: 12 pages, 4 figures, submitted to Zim\'anyi School 2023

Distribution Regression (DR) on stochastic processes describes the learning task of regression on collections of time series. Path signatures, a technique prevalent in stochastic analysis, have been used to solve the DR problem. Recent works have demonstrated the ability of such solutions to leverage the information encoded in paths via signature-based features. However, current state of the art DR solutions are memory intensive and incur a high computation cost. This leads to a trade-off between path length and the number of paths considered. This computational bottleneck limits the application to small sample sizes which consequently introduces estimation uncertainty. In this paper, we present a methodology for addressing the above issues; resolving estimation uncertainties whilst also proposing a pipeline that enables us to use DR for a wide variety of learning tasks. Integral to our approach is our novel distance approximator. This allows us to seamlessly apply our methodology across different application domains, sampling rates, and stochastic process dimensions. We show that our model performs well in applications related to estimation theory, quantitative finance, and physical sciences. We demonstrate that our model generalises well, not only to unseen data within a given distribution, but also under unseen regimes (unseen classes of stochastic models)., Comment: 24 pages, 4 figures

Federated learning is increasingly popular as it enables multiple parties with limited datasets and resources to train a high-performing machine learning model collaboratively. However, similarly to other collaborative systems, federated learning is vulnerable to free-riders -- participants who do not contribute to the training but still benefit from the shared model. Free-riders not only compromise the integrity of the learning process but also slow down the convergence of the global model, resulting in increased costs for the honest participants. To address this challenge, we propose FRIDA: free-rider detection using privacy attacks, a framework that leverages inference attacks to detect free-riders. Unlike traditional methods that only capture the implicit effects of free-riding, FRIDA directly infers details of the underlying training datasets, revealing characteristics that indicate free-rider behaviour. Through extensive experiments, we demonstrate that membership and property inference attacks are effective for this purpose. Our evaluation shows that FRIDA outperforms state-of-the-art methods, especially in non-IID settings.

Non-iid data is prevalent in real-world federated learning problems. Data heterogeneity can come in different types in terms of distribution shifts. In this work, we are interested in the heterogeneity that comes from concept shifts, i.e., shifts in the prediction across clients. In particular, we consider multi-task learning, where we want the model to adapt to the task of the client. We propose a parameter-efficient framework to tackle this issue, where each client learns to mix between parameter-efficient adaptors according to its task. We use Low-Rank Adaptors (LoRAs) as the backbone and extend its concept to other types of layers. We call our framework Federated Low-Rank Adaptive Learning (FLoRAL). This framework is not an algorithm but rather a model parameterization for a multi-task learning objective, so it can work on top of any algorithm that optimizes this objective, which includes many algorithms from the literature. FLoRAL is memory-efficient, and clients are personalized with small states (e.g., one number per adaptor) as the adaptors themselves are federated. Hence, personalization is--in this sense--federated as well. Even though clients can personalize more freely by training an adaptor locally, we show that collaborative and efficient training of adaptors is possible and performs better. We also show that FLoRAL can outperform an ensemble of full models with optimal cluster assignment, which demonstrates the benefits of federated personalization and the robustness of FLoRAL to overfitting. We show promising experimental results on synthetic datasets, real-world federated multi-task problems such as MNIST, CIFAR-10, and CIFAR-100. We also provide a theoretical analysis of local SGD on a relaxed objective and discuss the effects of aggregation mismatch on convergence., Comment: 36 pages, 10 figures

Statistical data heterogeneity is a significant barrier to convergence in federated learning (FL). While prior work has advanced heterogeneous FL through better optimization objectives, these methods fall short when there is extreme data heterogeneity among collaborating participants. We hypothesize that convergence under extreme data heterogeneity is primarily hindered due to the aggregation of conflicting updates from the participants in the initial collaboration rounds. To overcome this problem, we propose a warmup phase where each participant learns a personalized mask and updates only a subnetwork of the full model. This personalized warmup allows the participants to focus initially on learning specific subnetworks tailored to the heterogeneity of their data. After the warmup phase, the participants revert to standard federated optimization, where all parameters are communicated. We empirically demonstrate that the proposed personalized warmup via subnetworks (FedPeWS) approach improves accuracy and convergence speed over standard federated optimization methods.

We perform a first study of the impact of varying two components of the initial conditions in binary population synthesis of compact binary mergers - the initial mass function, which is made metallicity- and star formation rate-dependent, and the orbital parameter (orbital period, mass ratio and eccentricity) distributions, which are assumed to be correlated - within a larger grid of initial condition models also including alternatives for the primary mass-dependent binary fraction and the metallicity-specific cosmic star formation history. We generate the initial populations with the sampling code BOSSA and evolve them with the rapid population synthesis code COMPAS. We find strong suggestions that the main role of initial conditions models is to set the relative weights of key features defined by the evolution models. In the two models we compare, black hole-black hole (BHBH) mergers are the most strongly affected, which we connect to a shift from the common envelope to the stable Roche lobe overflow formation channels with decreasing redshift. We also characterize variations in the black hole-neutron star (BHNS) and neutron star-neutron star (NSNS) final parameter distributions. We obtain the merger rate evolution for BHBH, BHNS and NSNS mergers up to $z=10$, and find a variation by a factor of $\sim50-60$ in the local BHBH and BHNS merger rates, suggesting a more important contribution from initial conditions than previously thought, and calling for a complete exploration of the initial conditions model permutations., Comment: 28 pages, 14 figures, 3 tables. Accepted for publication in MNRAS. Code/data to be made publicly available

Considering potentials defined by Bessel kernel with Bessel convolution a Kerman-Sawyer type characterization of trace inequality is given. As an application an estimate on the least eigenvalue of Schr\"odinger-Bessel operators is derived.

Due to the non-smoothness of optimization problems in Machine Learning, generalized smoothness assumptions have been gaining a lot of attention in recent years. One of the most popular assumptions of this type is $(L_0,L_1)$-smoothness (Zhang et al., 2020). In this paper, we focus on the class of (strongly) convex $(L_0,L_1)$-smooth functions and derive new convergence guarantees for several existing methods. In particular, we derive improved convergence rates for Gradient Descent with (Smoothed) Gradient Clipping and for Gradient Descent with Polyak Stepsizes. In contrast to the existing results, our rates do not rely on the standard smoothness assumption and do not suffer from the exponential dependency from the initial distance to the solution. We also extend these results to the stochastic case under the over-parameterization assumption, propose a new accelerated method for convex $(L_0,L_1)$-smooth optimization, and derive new convergence rates for Adaptive Gradient Descent (Malitsky and Mishchenko, 2020)., Comment: 58 pages, 3 figures. Changes in V2: improved results for AdGD, more discussion of the related work, and new experiments

A theoretical framework for calculating the mass-radius curve of compact stars in the Kaluza-Klein space-time is introduced, with one additional compact spatial dimension. Static, spherically symmetric solutions are considered, with the equation of state provided by a zero temperature, interacting multidimensional Fermi gas. To model the strong force between baryons, a repulsive potential is introduced, which is linear in the particle number density. The maximal mass of compact stars is calculated for different model parameters, and with a physical parameter choice, it satisfies observational data, meaning that it is possible to model simple, realistic objects within this framework. Based on this comparison, a limiting size for the observational regime of extra dimensions in compact stars is provided, with $r_c \gtrsim 0.2$~fm., Comment: 11 pages, 8 figures

We present a long-period radio transient (GLEAM-X J0704-37) discovered to have an optical counterpart, consistent with a cool main sequence star of spectral type M3. The radio pulsations occur at the longest period yet found, 2.9 hours, and were discovered in archival low-frequency data from the Murchison Widefield Array (MWA). High time resolution observations from MeerKAT show that pulsations from the source display complex microstructure and high linear polarisation, suggesting a pulsar-like emission mechanism occurring due to strong, ordered magnetic fields. The timing residuals, measured over more than a decade, show tentative evidence of a ~6-yr modulation. The high Galactic latitude of the system and the M-dwarf star excludes a magnetar interpretation, suggesting a more likely M-dwarf / white dwarf binary scenario for this system., Comment: 11 pages, 4 figures, accepted by ApJL 19th October 2024

We study the effects of integrability breaking on the relaxation dynamics of the (double) sine-Gordon model. Compared to previous studies, we apply an alternative viewpoint motivated by open-system physics by separating the phase field into homogeneous and inhomogeneous parts, describing a quantum pendulum (subsystem) and an interacting phononic bath (environment). To study the relaxation dynamics in the model, we perform quantum quenches using the mini-superspace-based truncated Hamiltonian approach developed recently and simulate the real-time evolution of various entanglement measures and the energy transfer between the subsystem and its environment. Our findings demonstrate that in the presence of integrability-breaking perturbations, the relaxation dynamics is substantially faster, signalled by the increase of entanglement and energy transfer between the quantum pendulum and the phonon bath., Comment: 10 pages, 28 figures

CRISPR (clustered regularly interspaced short palindromic repeat)- based diagnostics are at the forefront of rapid detection platforms of infectious diseases. The integration of reverse transcription-loop-mediated isothermal amplification (RT-LAMP) with CRISPR-Cas protein systems has led to the creation of advanced one-pot assays. The sensitivity of these assays has been bolstered by the utilization of a thermophilic Cas12 protein, BrCas12b, and its engineered variant, which exhibits enhanced thermal stability and allows for broader operation temperatures of the assay. Here, we perform all-atom molecular dynamics (MD) simulations on wild-type and mutant BrCas12b to reveal the mechanism of stabilization conferred by the mutation. High-temperature simulations reveal a small structural change along with greater flexibility in the PAM-interacting domain of the mutant BrCas12b, with marginal structural and flexibility changes in the other mutated domains. Comparative essential dynamics analysis between the wild-type and mutant BrCas12b at both ambient and elevated temperatures provides insights into the stabilizing effects of the mutations. Our findings not only offer a comprehensive insight into the dynamic alterations induced by mutations but reveal important motions in BrCas12b, important for the rational design of diagnostic and therapeutic platforms of Cas12 proteins.

std::string view is a reference-like data structure in the C++ Standard Template Library (STL) that enables fast and cheap processing of read-only strings. Due to its wide applicability and performance enhancing power, std::string view has been very popular since its introduction in the C++17 standard. However, its careless use can lead to serious memory management bugs. As the lifetime of a std::string view is not tied to the lifetime of the referenced string in any way, it is the user's responsibility to ensure that the view is only used while the viewed string is live and its buffer is not reallocated. This paper describes a static analysis tool that finds programming errors caused by the incorrect use of std::string view. Our work included modeling std::string view operations in the analysis, defining steps to detect lifetime errors, constructing user-friendly diagnostic messages, and performing an evaluation of the checker.

Static analysis is a method of analyzing source code without executing it. It is widely used to find bugs and code smells in industrial software. Besides other methods, the most important techniques are those based on the abstract syntax tree and those performing symbolic execution. Both of these methods found their role in modern software development as they have different advantages and limitations. In this tutorial, we present two problems from the C++ programming language: the elimination of redundant pointers, and the reporting of dangling pointers originating from incorrect use of the std::string class. These two issues have different theoretical backgrounds and finding them requires different implementation techniques. We will provide a step-by-step guide to implement the checkers (software to identify the aforementioned problems) - one based on the abstract syntax analysis method, the other exploring the possibilities of symbolic execution. The methods are explained in great detail and supported by code examples. The intended audience for this tutorial are both architects of static analysis tools and developers who want to understand the advantages and constraints of the different methods.

Results are presented for the measurement of large-scale anisotropies in the arrival directions of ultra-high-energy cosmic rays detected at the Pierre Auger Observatory during 19 years of operation, prior to AugerPrime, the upgrade of the Observatory. The 3D dipole amplitude and direction are reconstructed above $4\,$EeV in four energy bins. Besides the established dipolar anisotropy in right ascension above $8\,$EeV, the Fourier amplitude of the $8$ to $16\,$EeV energy bin is now also above the $5\sigma$ discovery level. No time variation of the dipole moment above $8\,$EeV is found, setting an upper limit to the rate of change of such variations of $0.3\%$ per year at the $95\%$ confidence level. Additionally, the results for the angular power spectrum are shown, demonstrating no other statistically significant multipoles. The results for the equatorial dipole component down to $0.03\,$EeV are presented, using for the first time a data set obtained with a trigger that has been optimized for lower energies. Finally, model predictions are discussed and compared with observations, based on two source emission scenarios obtained in the combined fit of spectrum and composition above $0.6\,$EeV., Comment: Minor typo in table 4 corrected

CodeChecker is an open source project that integrates different static analysis tools such as the Clang Static Analyzer and Clang-Tidy into the build systems, continuous integration loops, and development workflows of C++ programmers. It has a powerful issue management system to make it easier to evaluate the reports of the static analysis tools. This document was handed out as supportive material for a code analysis lecture at the 2018 3COWS conference in Kosice, Slovakia.