Search

Time series classification (TSC) is fundamental in numerous domains, including finance, healthcare, and environmental monitoring. However, traditional TSC methods often struggle with the inherent complexity and variability of time series data. Building on our previous work with the linear law-based transformation (LLT) - which improved classification accuracy by transforming the feature space based on key data patterns - we introduce adaptive law-based transformation (ALT). ALT enhances LLT by incorporating variable-length shifted time windows, enabling it to capture distinguishing patterns of various lengths and thereby handle complex time series more effectively. By mapping features into a linearly separable space, ALT provides a fast, robust, and transparent solution that achieves state-of-the-art performance with only a few hyperparameters., Comment: 8 pages, 1 figure, 5 tables

Artificial intelligence has significantly advanced skin cancer diagnosis by enabling rapid and accurate detection of malignant lesions. In this domain, most publicly available image datasets consist of single, isolated skin lesions positioned at the center of the image. While these lesion-centric datasets have been fundamental for developing diagnostic algorithms, they lack the context of the surrounding skin, which is critical for improving lesion detection. The iToBoS dataset was created to address this challenge. It includes 16,954 images of skin regions from 100 participants, captured using 3D total body photography. Each image roughly corresponds to a $7 \times 9$ cm section of skin with all suspicious lesions annotated using bounding boxes. Additionally, the dataset provides metadata such as anatomical location, age group, and sun damage score for each image. This dataset aims to facilitate training and benchmarking of algorithms, with the goal of enabling early detection of skin cancer and deployment of this technology in non-clinical environments., Comment: Article Submitted to Scientific Data

Our goal is to provide simple and practical algorithms in higher-order Fourier analysis which are based on spectral decompositions of operators. We propose a general framework for such algorithms and provide a detailed analysis of the quadratic case. Our results reveal new spectral aspects of the theory underlying higher-order Fourier analysis. Along these lines, we prove new inverse and regularity theorems for the Gowers norms based on higher-order character decompositions. Using these results, we prove a spectral inverse theorem and a spectral regularity theorem in quadratic Fourier analysis., Comment: 71 pages

In this paper, we investigate the dimension theory of the one parameter family of Okamoto's function. We compute the Hausdorff, box-counting and Assouad dimensions of the graph for a typical choice of parameter. Furthermore, we study the dimension of the level sets. We give an upper bound on the dimension of every level set and we show that for a typical choice of parameters this value is attained for Lebesgue almost every level sets.

We present DeepSSM, an open-source code powered by neural networks (NNs) to emulate gravitational wave (GW) spectra produced by sound waves during cosmological first-order phase transitions in the radiation-dominated era. The training data is obtained from an enhanced version of the Sound Shell Model (SSM), which accounts for the effects of cosmic expansion and yields more accurate spectra in the infrared regime. The emulator enables instantaneous predictions of GW spectra given the phase transition parameters, while achieving agreement with the enhanced SSM model within 10\% accuracy in the worst-case scenarios. The emulator is highly computationally efficient and fully differentiable, making it particularly suitable for direct Bayesian inference on phase transition parameters without relying on empirical templates, such as broken power-law models. We demonstrate this capability by successfully reconstructing phase transition parameters and their degeneracies from mock LISA observations using a Hamiltonian Monte Carlo sampler. The code is available at: https://github.com/ctian282/DeepSSM., Comment: 20 pages, 5 figures, 2 tables

We give a self-contained proof of a recently established $\mathcal{B}(\mathcal{H})$-valued version of Jaffards Lemma. That is, we show that the Jaffard algebra of $\mathcal{B}(\mathcal{H})$-valued matrices, whose operator norms of their respective entries decay polynomially off the diagonal, is a Banach algebra which is inverse-closed in the Banach algebra $\mathcal{B}(\ell^2(X;\mathcal{H}))$ of all bounded linear operators on $\ell^2(X;\mathcal{H})$, the Bochner-space of square-summable $\mathcal{H}$-valued sequences.

An ab initio scheme based on the linear response theory of exchange torque correlation is presented to calculate intrinsic Gilbert damping parameters in magnets of reduced dimensions. The method implemented into the real-space Korringa-Kohn-Rostoker (RS-KKR) Greens' function framework enables to obtain diagonal elements of the atomic-site-dependent on-site and non-local Gilbert damping tensor. Going from the 3D bulk and surfaces of iron and cobalt ferromagnets addressed in our previous work [Phys. Rev. B 109, 094417 (2024)], in the present paper monolayers of Fe and Co on (001)- and (111)-oriented Cu, Ag, and Au substrates are studied, and particularly the substrate-dependent trends are compared. Furthermore, the Gilbert damping parameters are calculated for Fe and Co adatoms and dimers on (001)-oriented substrates. It is investigated how the damping parameter of single adatoms depends on their vertical position. This dependence is quantified in relation to the adatoms' density of states at the Fermi energy showing a non-monotonic behavior. By rotating the spin moment of the adatoms and collinear magnetic dimers, an anisotropic behavior of the damping is revealed. Finally, a significant, three- to ten-times increase of the on-site Gilbert damping is found in antiferromagnetic dimers in comparison to the ferromagnetic ones, whilst the inter-site damping is even more enhanced., Comment: 12 pages, 6 figures, 8 tables, submitted to Phys. Rev. B

Spiking Neural Networks (SNNs) are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks. Spiking neurons are stateful and intrinsically recurrent, making them well-suited for spatio-temporal tasks. However, this intrinsic memory is limited by synaptic and membrane time constants. A powerful additional mechanism are delays. In this paper, we propose a novel event-based training method for SNNs with delays, grounded in the EventProp formalism and enabling the calculation of exact gradients with respect to weights and delays. Our method supports multiple spikes per neuron and, to our best knowledge, is the first delay learning algorithm to be applied to recurrent SNNs. We evaluate our method on a simple sequence detection task, and the Yin-Yang, Spiking Heidelberg Digits and Spiking Speech Commands datasets, demonstrating that our algorithm can optimize delays from suboptimal initial conditions and enhance classification accuracy compared to architectures without delays. Finally, we show that our approach uses less than half the memory of the current state-of-the-art delay-learning method and is up to 26x faster.

For a family $\mathcal{F}$ of sets and a disjoint pair $A,B$ we let $\mathcal{F}(A,\overline{B})=\{F\in \mathcal{F}: A\subseteq F, ~B\cap F=\emptyset\}$. The \textbf{$(p,q)$-d\"omd\"od\"om} of a family $\mathcal{F}\subseteq 2^{[n]}$ is $\beta_{p,q}(\mathcal{F})=\min\{|\mathcal{F}(A,\overline{B})|:|A|=p,|B|=q, A\cap B=\emptyset, A,B\subseteq [n]\} $. This definition encompasses size, diversity, minimum degree, and sturdiness as special cases. We investigate the maximum possible value $\beta_{p,q}(n,k)$ of $\beta_{p,q}(\mathcal{F})$ over all $k$-uniform intersecting families $\mathcal{F}\subset 2^{[n]}$. We determine the order of magnitude of $\beta_{p,q}(n,k)$ for all fixed $p,q,k$. We relate the asymptotics of $\beta_{p,q}(n,k)$ to the constant value of $\beta_{0,q}(n,q+1)$ and establish $\beta_{p,1}(n,k)=\binom{n-3-p}{k-2-p}$ and $\beta_{p,2}(n,k)=2\binom{n-5}{k-3-p}-\binom{n-7}{k-5-p}$ if $n$ is large enough., Comment: 6 pages

This paper presents a robust position controller for electric power assisted steering and steer-by-wire force-feedback systems. A position controller is required in steering systems for haptic feedback control, advanced driver assistance systems and automated driving. However, the driver's \textit{physical} arm impedance causes an inertial uncertainty during coupling. Consequently, a typical position controller, i.e., based on single variable, becomes less robust and suffers tracking performance loss. Therefore, a robust position controller is investigated. The proposed solution is based on the multi-variable concept such that the sensed driver torque signal is also included in the position controller. The subsequent solution is obtained by solving the LMI$-H_{\infty}$ optimization problem. As a result, the desired loop gain shape is achieved, i.e., large gain at low frequencies for performance and small gain at high frequencies for robustness. Finally, frequency response comparison of different position controllers on real hardware is presented. Experiments and simulation results clearly illustrate the improvements in reference tracking and robustness with the proposed $H_\infty$ controller., Comment: 11 pages, 9 figures, journal paper

Searches for annihilating dark matter are often designed with a specific dark matter candidate in mind. However, the space of potential dark matter models is vast, which raises the question: how can we search for dark matter without making strong assumptions about unknown physics. We present a model-independent approach for measuring dark matter annihilation ratios and branching fractions with $\gamma$-ray event data. By parameterizing the annihilation ratios for seven different channels, we obviate the need to search for a specific dark matter candidate. To demonstrate our approach, we analyse simulated data using the \texttt{GammaBayes} pipeline. Given a 5$\sigma$ signal, we reconstruct the annihilation ratios for five dominant channels to within 95% credibility. This allows us to reconstruct dark matter annihilation/decay channels without presuming any particular model, thus offering a model-independent approach to indirect dark matter searches in $\gamma$-ray astronomy. This approach shows that for masses between 0.3-5 TeV we can probe values below the thermal relic velocity annihilation weighted cross-section allowing a 2$\sigma$ detection for 525 hours of simulated observation data by the Cherenkov Telescope Array Observatory of the Galactic Centre., Comment: 19 pages, 14 figures

A magnetic impurity in a BCS superconductor induces the formation of a Shiba state and drives a local quantum phase transition. We generalize this concept to a one-dimensional superconductor with fractionalized excitations, where the dominant instability is superconducting. In this framework, conduction electrons fractionalize into gapless charge and gapped spin excitations. We show that magnetic impurity interacts exclusively with the spin degrees of freedom and induces a quantum phase transition. Furthermore, charge excitations influence dynamical observables, giving rise to the phenomenon we term the fractionalized Shiba state. At zero temperature, the tunneling spectrum exhibits universal power-law scaling with an exponent of $-1/2$ at half filling, stemming from the gapless charge modes that form a standard Luttinger liquid. Extending this analysis to finite temperatures reveals that the spectral features retain universal behavior at the critical point., Comment: 6 pages, 4 figures

We consider spin chain models with local Hamiltonians that display weak ergodicity breaking. In these models, the majority of the eigenstates are thermal, but there is a distinguished subspace of the Hilbert space in which ergodicity is broken. We achieve such a weak breaking by embedding selected integrable models into larger Hilbert spaces of otherwise chaotic models. The integrable subspaces do not have a tensor product structure with respect to any spatial bipartition, therefore our constructions differ from certain trivial embeddings. We consider multiple mechanisms for such an embedding, and we also review previous examples in the literature. Curiously, all our examples can be seen as perturbations of models with Hilbert space fragmentation, such that the perturbed models are not fragmented anymore., Comment: 23 pages, 12 figures

Stabilizer entropies and quantum magic have been extensively explored in real-space formulations of quantum systems within the framework of resource theory. However, interesting and transparent physics often emerges in momentum space, such as Cooper pairing. Motivated by this, we investigate the momentum-space structure of Pauli strings and stabilizer entropies in the one-dimensional transverse-field quantum Ising model. By mapping the Ising chain onto momentum-space qubits, where the stabilizer state corresponds to the paramagnetic state, we analyze the evolution of the Pauli string distribution. In the ferromagnetic phase, the distribution is broad, whereas in the paramagnetic phase, it develops a two-peaked structure. We demonstrate that all ferromagnetic states possess the same degree of magic in the thermodynamic limit, while stabilizer entropies are non-analytic at the critical point and vanish with increasing transverse field. The momentum-space approach to quantum magic not only complements its real-space counterpart but also provides advantages in terms of analyzing nonstabilizerness and classical simulability., Comment: 5 pages, 3 figures

In 1972, Branko Gr\"unbaum conjectured that any arrangement of $n>2$ pairwise crossing pseudocircles in the plane can have at most $2n-2$ digons (regions enclosed by exactly two pseudoarcs), with the bound being tight. While this conjecture has been confirmed for cylindrical arrangements of pseudocircles and more recently for geometric circles, we extend these results to any simple arrangement of pairwise intersecting pseudocircles. Using techniques from the above-mentioned special cases, we provide a complete proof of Gr\"unbaum's conjecture that has stood open for over five decades.

We introduce and study a new random permutation model that generalizes the $k$-card minimum model defined by Travers and the Mallows model. We calculate the permuton limit of such a sequence of random permutations. As a corollary, we deduce the law of large numbers for pattern densities. Moreover, we prove a universality result about the band structure of the limiting permuton, confirming a conjecture of Travers about the $k$-card minimum model. More specifically, we show that if a certain model parameter goes to infinity then the appropriately scaled restriction of the permuton measure to a line that intersects the diagonal perpendicularly converges weakly to the logistic distribution., Comment: 12 pages, 1 figure

Recent space-borne and ground-based observations provide photometric measurements as time series. The effect of interstellar dust extinction in the near-infrared range is only 10% of that measured in the V band. However, the sensitivity of the light curve shape to the physical parameters in the near-infrared is much lower. So, interpreting these types of data sets requires new approaches like the different large-scale surveys, which create similar problems with big data. Using a selected data set, we provide a method for applying routines implemented in R to extract most information of measurements to determine physical parameters, which can also be used in automatic classification schemes and pipeline processing. We made a multivariate classification of 131 Cepheid light curves (LC) in J, H, and K colors, where all the LCs were represented in 20D parameter space in these colors separately. Performing a Principal Component Analysis (PCA), we got an orthogonal coordinate system and squared Euclidean distances between LCs, with 6 significant eigenvalues, reducing the 20-dimension to 6. We also estimated the optimal number of partitions of similar objects and found it to be equal to 7 in each color; their dependence on the period, absolute magnitude, amplitude, and metallicity are also discussed. We computed the Spearman rank correlations, showing that periods and absolute magnitudes correlate with the first three PCs significantly. The first two PC are also found to have a relationship with the amplitude, but the metallicity effects are only marginal. The method shown can be generalized and implemented in unsupervised classification schemes and analysis of mixed and biased samples. The analysis of our Classical Cepheid near-infrared LC sample showed that the J, H, K curves are insufficient for determination of stellar metallicity, with mass being the key factor shaping them., Comment: Accepted in New Astronomy, 16 Pages, 18 Figures

In this paper, we study the smoothness of the density function of absolutely continuous measures supported on random self-similar sets on the line. We show that the natural projection of a measure with symbolic local dimension greater than 1 at every point is absolutely continuous with H\"older continuous density almost surely. In particular, if the similarity dimension is greater than 1 then the random self-similar set on the line contains an interior point almost surely.

In recent years, the prospect of detecting gravitational waves sourced from a strongly first-order cosmological phase transition has emerged as one of the most exciting frontiers of gravitational wave astronomy. Cosmological phase transitions are an essential ingredient in the Standard Model of particle cosmology, and help explain the mechanism for creation of matter in the early Universe, provide insights into fundamental theories of physics, and shed light on the nature of dark matter. This underscores the significance of developing robust end-to-end tools for determining the resulting gravitational waves from these phase transitions. In this article we present PhaseTracer2, an improved version of the C++ software package PhaseTracer, designed for mapping cosmological phases and transitions in Standard Model extensions of multiple scalar fields. Building on the robust framework of its predecessor, PhaseTracer2 extends its capabilities by including new features crucial for a more comprehensive analysis of cosmological phase transitions. It can calculate more complex properties, such as the bounce action through the path deformation method or an interface with BubbleProfiler, thermodynamic parameters, and gravitational wave spectra. Its applicability has also been broadened via incorporating the dimensionally reduced effective potential for models obtained from DRalgo, as well as calculations in the MSbar and OS-like renormalisation schemes. This modular, flexible, and practical upgrade retains the speed and stability of the original PhaseTracer, while significantly expanding its utility., Comment: 32 pages, 12 figures

We prove a universal projection theorem, giving conditions on a parametrized family of maps $\Pi_\lambda : X \to \mathbb{R}^d$ and a collection $M$ of measures on $X$ under which for almost every $\lambda$ equality $\mathrm{dim}_H \Pi_\lambda \mu = \min\{d, \mathrm{dim}_H \mu\}$ holds for all measures $\mu \in M$ simultaneously (i.e. on a full-measure set of $\lambda$'s independent of $\mu$). We require $\Pi_\lambda$ to satisfy a transversality condition and $M$ to satisfy a new condition called relative dimension separability. We also prove that if the Assouad dimension of $X$ is smaller than d, then for almost every $\lambda$, projection $\Pi_\lambda$ is nearly bi-Lipschitz at $\mu$-a.e. $x$, for all measures $\mu \in M$ simultaneously. Our setting can include families of orthogonal projections, natural projections for conformal, non-autonomous or random iterated functions systems. As an application, we prove that for a parametrized family of contracting conformal IFS with natural projections $\Pi_\lambda$ satisfying the transversality condition, for almost every parameter $\lambda$ one has $\mathrm{dim}_H \Pi_\lambda \mu = \min\{ d, \frac{h(\mu)}{\chi(\lambda, \mu)} \}$ for all ergodic shift-invariant measures simultaneously. We also prove that for self-similar systems on the line with similarity dimension smaller than one, for Lebesgue almost every choice of translations the multifractal formalism holds simultaneously on the full spectrum interval $\left[\min \frac{\log p_i}{\log|\lambda_i|},\max \frac{\log p_i}{\log|\lambda_i|}\right]$ for every self-similar measure. We prove that the dimension part of the Marstrand-Mattila projection theorem holds simultaneously for the collection of all ergodic measures on a self-conformal set with the strong separation condition and without any separation for the collection of all Gibbs measures.

The cactus group acts combinatorially on crystals via partial Sch\"utzenberger involutions. This action has been studied extensively in type $A$ and described via Bender-Knuth involutions. We prove an analogous result for the family of crystals $B(n\varpi_1)$ in type $D$. Our main tools are combinatorial toggles acting on reverse plane partitions of height $n$. As a corollary, we show that the length one and two subdiagram elements generate the full cactus action, addressing conjectures of Dranowski, the second author, Kamnitzer, and Morton-Ferguson., Comment: 12 pages

We investigate the behavior of countable Borel equivalence relations (CBERs) on topological Ramsey spaces. First, we give a simple proof of the fact that every CBER on $[\mathbb{N}]^{\mathbb{N}}$ is hyperfinite on some set of the form $[A]^{\mathbb{N}}$. Using the idea behind the proof, we show the analogous result for every topological Ramsey space.

Recently, samplets have been introduced as localized discrete signed measures which are tailored to an underlying data set. Samplets exhibit vanishing moments, i.e., their measure integrals vanish for all polynomials up to a certain degree, which allows for feature detection and data compression. In the present article, we extend the different construction steps of samplets to functionals in Banach spaces more general than point evaluations. To obtain stable representations, we assume that these functionals form frames with square-summable coefficients or even Riesz bases with square-summable coefficients. In either case, the corresponding analysis operator is injective and we obtain samplet bases with the desired properties by means of constructing an isometry of the analysis operator's image. Making the assumption that the dual of the Banach space under consideration is imbedded into the space of compactly supported distributions, the multilevel hierarchy for the generalized samplet construction is obtained by spectral clustering of a similarity graph for the functionals' supports. Based on this multilevel hierarchy, generalized samplets exhibit vanishing moments with respect to a given set of primitives within the Banach space. We derive an abstract localization result for the generalized samplet coefficients with respect to the samplets' support sizes and the approximability of the Banach space elements by the chosen primitives. Finally, we present three examples showcasing the generalized samplet framework.

We present the results of a novel type of numerical simulation that realizes a rotating Universe with a shear-free, rigid body rotation inspired by a G\"{o}del-like metric. We run cosmological simulations of unperturbed glasses with various degrees of rotation in the Einstein-de Sitter and the $\Lambda$CDM cosmologies. To achieve this, we use the StePS N-body code capable of simulating the infinite Universe, overcoming the technical obstacles of classical toroidal (periodic) topologies that would otherwise prevent us from running such simulations. Results show a clear anisotropy between the polar and equatorial expansion rates with more than $1\%$ deviation from the isotropic case for maximal rotation without closed timeline curves within the horizon, $\omega_{0} \approx 10^{-3}$ Gyr$^{-1}$; a considerable effect in the era of precision cosmology.

We study the number of queries needed to identify a monotone Boolean function $f:\{0,1\}^n \rightarrow \{0,1\}$. A query consists of a 0-1-sequence, and the answer is the value of $f$ on that sequence. It is well-known that the number of queries needed is $\binom{n}{\lfloor n/2\rfloor}+\binom{n}{\lfloor n/2\rfloor+1}$ in general. Here we study a variant where $f$ has $k$ ``reasons'' to be 1, i.e., its disjunctive normal form has $k$ conjunctions if the redundant conjunctions are deleted. This problem is equivalent to identifying an upfamily in $2^{[n]}$ that has exactly $k$ minimal members. We find the asymptotics on the number of queries needed for fixed $k$. We also study the non-adaptive version of the problem, where the queries are asked at the same time, and determine the exact number of queries for most values of $k$ and $n$.

We report the generation of a multi-octave supercontinuum spanning from 350 nm to 1700 nm with exceptional spectral flatness and high conversion efficiency to both the visible and near infrared region, by pumping a methane-filled hollow-core antiresonant fiber with 1030 nm laser pulses. The dynamics exhibited signs of both modulational instability and stimulated Raman scattering. Fiber lengths ranging from 15 to 200~cm were investigated along with gas pressures up to 50 bar and pump pulse durations from 220~fs up to 10~ps. The best supercontinuum, in terms of spectral width and flatness, was achieved with 220~fs pulses, 25~bar filling pressure, and 60~cm propagation length. Comparison with argon-filled fiber with matched nonlinearity and dispersion showed that the Raman contribution enhances the supercontinuum generation process compared to a pure modulational instability-based process. The average power was scaled up by increasing the pulse repetition rate to 50~kHz, but further scaling was hindered by linear and nonlinear absorption leading to fiber damage.

We consider the preparation of all the eigenstates of spin chains using quantum circuits. It is known that generic eigenstates of free-fermionic spin chains can be prepared with circuits whose depth grows only polynomially with the length of the chain and the number of particles. We show that the polynomial growth is also achievable for selected interacting models where the interaction between the particles is sufficiently simple. Our working example is the folded XXZ model, an integrable spin chain that exhibits Hilbert space fragmentation. We present the explicit quantum circuits that prepare arbitrary eigenstates of this model on an open chain efficiently. We perform error-mitigated noisy simulations with circuits of up to 13 qubits and different connectivities between qubits, achieving a relative error below 5%. As a byproduct, we extend a recent reformulation of the Bethe ansatz as a quantum circuit from closed to open boundary conditions., Comment: 42 pages, 19 figures

Superconducting nanowire single-photon detectors (SNSPDs) were integrated with plasmonic nanostructures to enhance the absorption efficiency of superconducting BSCCO stripes. A numerical investigation of optimized nanocavity array (NCAI) and nanocavity-trench-array (NCTAI) SNSPDs has revealed that more than one order of magnitude larger absorptance can be achieved at perpendicular incidence, when compared to the corresponding meandered BSCCO pattern in a resonant optical cavity. The SNSPDs were considerably improved either via first and third quarter cavity resonances, as evidenced by the near-field maps and validated by the standard retrieval method. Although, NCAI-SNSPD exhibits slightly larger absorptance, NCTAI-SNSPD remains competitive due to its larger period and significantly smaller filling factor, thereby allowing for quicker electric response., Comment: 6 pages, 4 Figures

Considering a Markov chain defined on a cycle, near-quadratic improvement of mixing is shown when only a subtle perturbation is introduced to the structure and non-reversible transition probabilities are used. More precisely, a mixing time of $O(n^{\frac{k+2}{k+1}})$ can be achieved by adding $k$ random edges to the cycle, keeping $k$ fixed while $n\to\infty$. The construction builds upon a biased random walk along the cycle., Comment: 19 pages

Dark matter is a fundamental constituent of the universe, which is needed to explain a wide variety of astrophysical and cosmological observations. Although the existence of dark matter was first postulated nearly a century ago and its abundance is precisely measured, approximately five times larger than that of ordinary matter, its underlying identity remains a mystery. A leading hypothesis is that it is composed of new elementary particles, which are predicted to exist in many extensions of the Standard Model of particle physics. In this article we review the basic evidence for dark matter and the role it plays in cosmology and astrophysics, and discuss experimental searches and potential candidates. Rather than targeting researchers in the field, we aim to provide an accessible and concise summary of the most important ideas and results, which can serve as a first entry point for advanced undergraduate students of physics or astronomy., Comment: 15 pages, 5 figures. Preprint of a chapter for the 'Encyclopedia of Astrophysics' (Editor-in-Chief Ilya Mandel, Section Editor Cullan Howlett) to be published by Elsevier as a Reference Module

We introduce Agent K v1.0, an end-to-end autonomous data science agent designed to automate, optimise, and generalise across diverse data science tasks. Fully automated, Agent K v1.0 manages the entire data science life cycle by learning from experience. It leverages a highly flexible structured reasoning framework to enable it to dynamically process memory in a nested structure, effectively learning from accumulated experience stored to handle complex reasoning tasks. It optimises long- and short-term memory by selectively storing and retrieving key information, guiding future decisions based on environmental rewards. This iterative approach allows it to refine decisions without fine-tuning or backpropagation, achieving continuous improvement through experiential learning. We evaluate our agent's apabilities using Kaggle competitions as a case study. Following a fully automated protocol, Agent K v1.0 systematically addresses complex and multimodal data science tasks, employing Bayesian optimisation for hyperparameter tuning and feature engineering. Our new evaluation framework rigorously assesses Agent K v1.0's end-to-end capabilities to generate and send submissions starting from a Kaggle competition URL. Results demonstrate that Agent K v1.0 achieves a 92.5\% success rate across tasks, spanning tabular, computer vision, NLP, and multimodal domains. When benchmarking against 5,856 human Kaggle competitors by calculating Elo-MMR scores for each, Agent K v1.0 ranks in the top 38\%, demonstrating an overall skill level comparable to Expert-level users. Notably, its Elo-MMR score falls between the first and third quartiles of scores achieved by human Grandmasters. Furthermore, our results indicate that Agent K v1.0 has reached a performance level equivalent to Kaggle Grandmaster, with a record of 6 gold, 3 silver, and 7 bronze medals, as defined by Kaggle's progression system.

The Bethe Ansatz is a method for constructing exact eigenstates of quantum-integrable spin chains. Recently, deterministic quantum algorithms, referred to as "algebraic Bethe circuits", have been developed to prepare Bethe states for the spin-1/2 XXZ model. These circuits represent a unitary formulation of the standard algebraic Bethe Ansatz, expressed using matrix-product states that act on both the spin chain and an auxiliary space. In this work, we systematize these previous results, and show that algebraic Bethe circuits can be derived by a change of basis in the auxiliary space. The new basis, identical to the "F-basis" known from the theory of quantum-integrable models, generates the linear superpositions of plane waves that are characteristic of the coordinate Bethe Ansatz. We explain this connection, highlighting that certain properties of the F-basis (namely, the exchange symmetry of the spins) are crucial for the construction of algebraic Bethe circuits. We demonstrate our approach by presenting new quantum circuits for the inhomogeneous spin-1/2 XXZ model., Comment: 38 pages, 18 figures

The study of matroid products traces back to the 1970s, when Lov\'asz and Mason studied the existence of various types of matroid products with different strengths. Among these, the tensor product is arguably the most important, which can be considered as an extension of the tensor product from linear algebra. However, Las Vergnas showed that the tensor product of two matroids does not always exist. Over the following four decades, matroid products remained surprisingly underexplored, regaining attention only in recent years due to applications in tropical geometry and the limit theory of matroids. In this paper, inspired by the concept of coupling in probability theory, we introduce the notion of coupling for matroids -- or, more generally, for submodular set functions. This operation can be viewed as a relaxation of the tensor product. Unlike the tensor product, however, we prove that a coupling always exists for any two submodular functions and can be chosen to be increasing if the original functions are increasing. As a corollary, we show that two matroids always admit a matroid coupling, leading to a novel operation on matroids. Our construction is algorithmic, providing an oracle for the coupling matroid through a polynomial number of oracle calls to the original matroids. We apply this construction to derive new necessary conditions for matroid representability and establish connection between tensor products and Ingleton's inequality. Additionally, we verify the existence of set functions that are universal with respect to a given property, meaning any set function over a finite domain with that property can be obtained as a quotient., Comment: 24 pages

It is well known that silicon photomultipliers (SiPMs) are prone to radiation damage. With the increasing popularity of SiPMs among new spaceborne missions, especially on CubeSats, it is of paramount importance to characterize their performance in space environment. In this work, we report the in-orbit ageing of SiPM arrays, so-called multi-pixel photon counters (MPPCs), using measurements acquired by the GRBAlpha and VZLUSAT-2 CubeSats at low Earth orbit (LEO) spanning over three years, which in duration is unique. GRBAlpha is a 1U CubeSat launched on March 22, 2021, to a 550 km altitude sun-synchronous polar orbit (SSO) carrying on board a gamma-ray detector based on CsI(Tl) scintillator readout by eight MPPCs and regularly detecting gamma-ray transients such as gamma-ray bursts and solar flares in the energy range of ~30-900 keV. VZLUSAT-2 is a 3U CubeSat launched on January 13, 2022 also to a 550 km altitude SSO carrying on board, among other payloads, two gamma-ray detectors similar to the one on GRBAlpha. We have flight-proven the Hamamatsu MPPCs S13360-3050 PE and demonstrated that MPPCs, shielded by 2.5 mm of PbSb alloy, can be used in an LEO environment on a scientific mission lasting beyond three years. This manifests the potential of MPPCs being employed in future satellites., Comment: Submitted to Nuclear Instruments and Methods in Physics Research Section A, 13 pages, 14 figures

We present statistical analysis of video meteor observations for the Perseid and Geminid showers taken with two camera systems operating in Hungary from the end of 2019 through 2023. Zenithal hourly rates (ZHR) and meteor fluxes, determined by MetRec-based analog video cameras HUKON, HUPIS and HUHOD, are inferred and compared with detections of slow fireballs measured at the same sites by a system consisting of automated DSLR cameras (the KoMON system)., Comment: 7 pages, accepted for publication in WGN, Journal of the International Meteor Organization (IMO)

A strong first-order electroweak phase transition (SFOEWPT) is essential for explaining baryogenesis and for potentially generating observable gravitational waves. This study investigates the potential of a high-energy muon collider to examine the occurrence of SFOEWPT within the context of a Standard Model extended by a real scalar singlet (xSM). We analyzed all possible decay modes of the singlet to constrain the valid parameter space of SFOEWPT, which was extracted numerically at different renormalization scales to account for theoretical uncertainties, thereby determining the sensitivity of a muon collider to the production and decay channels of novel heavy scalar particles that emerge in the xSM. The findings demonstrate that a 3 TeV muon collider can directly examine the nature of electroweak symmetry breaking by efficiently detecting novel scalar particles associated with a first-order electroweak phase transition through jet-rich final states, thus complementing the indirect constraints from gravitational wave experiments.

Perceiving the environment via cameras is crucial for Reinforcement Learning (RL) in robotics. While images are a convenient form of representation, they often complicate extracting important geometric details, especially with varying geometries or deformable objects. In contrast, point clouds naturally represent this geometry and easily integrate color and positional data from multiple camera views. However, while deep learning on point clouds has seen many recent successes, RL on point clouds is under-researched, with only the simplest encoder architecture considered in the literature. We introduce PointPatchRL (PPRL), a method for RL on point clouds that builds on the common paradigm of dividing point clouds into overlapping patches, tokenizing them, and processing the tokens with transformers. PPRL provides significant improvements compared with other point-cloud processing architectures previously used for RL. We then complement PPRL with masked reconstruction for representation learning and show that our method outperforms strong model-free and model-based baselines on image observations in complex manipulation tasks containing deformable objects and variations in target object geometry. Videos and code are available at https://alrhub.github.io/pprl-website, Comment: 18 pages, 15 figures, accepted for publication at the 8th Conference on Robot Learning (CoRL 2024)

The ubiquitous effects of the environment on quantum-mechanical systems generally cause temporally correlated fluctuations. This particularly holds for systems of interest for quantum computation where such effects lead to correlated errors. The Markovian approximation neglects these correlations and thus fails to accurately describe open-system dynamics where these correlations become relevant. In driven open systems, yet another approximation is persistently used, often unknowingly, in which one describes the decay effects independently from the time-dependent controlling fields acting on the system, thereby ignoring further temporally correlated effects. To overcome these shortcomings, we develop a quantum master equation for driven systems weakly coupled to quantum environments that avoids the aforementioned field-independent approximation, as well as the Markovian approximation. Our method makes it possible to track all occurring decay channels and their time-dependent generalized rates which we illustrate in the example of a generally driven two-level system. We also demonstrate that correlated and field-dependent dissipative effects can lead to an increase in the performance of single-qubit gate operations., Comment: 18 pages, 3 figures

We review the relationship between discrete groups of symmetries of Euclidean three-space, constructions in algebraic geometry around Kleinian singularities including versions of Hilbert and Quot schemes, and their relationship to finite-dimensional and affine Lie algebras via the McKay correspondence. We focus on combinatorial aspects, such as the enumeration of certain types of partition-like objects, reviewing in particular a recently developed root-of-unity-substitution calculus. While the most complete results are in type A, we also develop aspects of the theory in type D, and end with some questions about the exceptional type E cases., Comment: 27 pages, one figure

We investigate the critical behavior of the Kondo compensation in the presence of a power-law pseudogap in the density of states, $\varrho(\omega)\sim |\omega|^\epsilon$. For $\epsilon<1$, this model exhibits a quantum phase transition from a partially screened doublet ground state to a fully screened many-body singlet ground state with increasing Kondo coupling, $j$. At the critical point, $j_c$, the Kondo compensation is found to scale as $\kappa(j

We study the non-equilibrium dynamics of the superconducting order parameter in the Hatsugai-Kohmoto (HK) model. In the absence of superconductivity, its ground state is a non-Fermi liquid, whose properties are controlled by the HK interaction. Our protocol involves quantum quenching the HK interaction but leaving the interaction responsible for superconductivity unchanged. We map out the non-equilibrium dynamical phase diagram of the interacting model which contains three phases where, at long times, the order parameter amplitude vanishes, approaches a constant value or persistently oscillates. We also investigate the Loschmidt echo in searching for dynamical quantum phase transition, and find no significant correlation between the three phases and the non-analytic temporal behaviour of the Loschmidt echo. The momentum space entanglement entropy between positive and negative momentum modes, relevant for Coopair pairing, is calculated. Counterintuitively, this momentum space entanglement does not change significantly during the quench dynamics and its value remains reasonably large even for vanishing superconducting order parameter. Nevertheless, its derivative with respect to the HK interaction signals the dynamical phase transition associated to the late time vanishing of superconductivity., Comment: 10 pages, 8 figures

Regulatory frameworks for the use of AI are emerging. However, they trail behind the fast-evolving malicious AI technologies that can quickly cause lasting societal damage. In response, we introduce a pioneering Assistive AI framework designed to enhance human decision-making capabilities. This framework aims to establish a trust network across various fields, especially within legal contexts, serving as a proactive complement to ongoing regulatory efforts. Central to our framework are the principles of privacy, accountability, and credibility. In our methodology, the foundation of reliability of information and information sources is built upon the ability to uphold accountability, enhance security, and protect privacy. This approach supports, filters, and potentially guides communication, thereby empowering individuals and communities to make well-informed decisions based on cutting-edge advancements in AI. Our framework uses the concept of Boards as proxies to collectively ensure that AI-assisted decisions are reliable, accountable, and in alignment with societal values and legal standards. Through a detailed exploration of our framework, including its main components, operations, and sample use cases, the paper shows how AI can assist in the complex process of decision-making while maintaining human oversight. The proposed framework not only extends regulatory landscapes but also highlights the synergy between AI technology and human judgement, underscoring the potential of AI to serve as a vital instrument in discerning reality from fiction and thus enhancing the decision-making process. Furthermore, we provide domain-specific use cases to highlight the applicability of our framework., Comment: 37 pages, 6 figures; Changes: Revised references (k-anonimity)

The emerging zero-shot capabilities of Large Language Models (LLMs) have led to their applications in areas extending well beyond natural language processing tasks. In reinforcement learning, while LLMs have been extensively used in text-based environments, their integration with continuous state spaces remains understudied. In this paper, we investigate how pre-trained LLMs can be leveraged to predict in context the dynamics of continuous Markov decision processes. We identify handling multivariate data and incorporating the control signal as key challenges that limit the potential of LLMs' deployment in this setup and propose Disentangled In-Context Learning (DICL) to address them. We present proof-of-concept applications in two reinforcement learning settings: model-based policy evaluation and data-augmented off-policy reinforcement learning, supported by theoretical analysis of the proposed methods. Our experiments further demonstrate that our approach produces well-calibrated uncertainty estimates. We release the code at https://github.com/abenechehab/dicl.

Calibrating verbalized probabilities presents a novel approach for reliably assessing and leveraging outputs from black-box Large Language Models (LLMs). Recent methods have demonstrated improved calibration by applying techniques like Platt scaling or temperature scaling to the confidence scores generated by LLMs. In this paper, we explore the calibration of verbalized probability distributions for discriminative tasks. First, we investigate the capability of LLMs to generate probability distributions over categorical labels. We theoretically and empirically identify the issue of re-softmax arising from the scaling of verbalized probabilities, and propose using the invert softmax trick to approximate the "logit" by inverting verbalized probabilities. Through extensive evaluation on three public datasets, we demonstrate: (1) the robust capability of LLMs in generating class distributions, and (2) the effectiveness of the invert softmax trick in estimating logits, which, in turn, facilitates post-calibration adjustments., Comment: 21 pages

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.

Ensemble data assimilation techniques form an indispensable part of numerical weather prediction. As the ensemble size grows and model resolution increases, the amount of required storage becomes a major issue. Data compression schemes may come to the rescue not only for operational weather prediction, but also for weather history archives. In this paper, we present the design and implementation of an easy-to-use framework for evaluating the impact of lossy data compression in large scale ensemble data assimilation. The framework leverages robust statistical qualifiers to determine which compression parameters can be safely applied to the climate variables. Furthermore, our proposal can be used to apply the best parameters during operation, while monitoring data integrity. We perform an exemplary study on the Lorenz96 model to identify viable compression parameters and achieve a 1/3 saving in storage space and an effective speedup of 6% per assimilation cycle, while monitoring the state integrity., Comment: 12 pages, 6 figures

Studying structural brain networks has witnessed significant advancement in recent decades. Findings have revealed a geometric principle, the exponential distance rule (EDR) showing that the number of neurons decreases exponentially with the length of their axons. An EDR based network model explained various characteristics of inter-areal cortical networks in macaques, mice, and rats. The complete connectome of the Drosophila fruit fly has recently been mapped at the neuronal level. Our study demonstrates that the EDR holds true in Drosophila, and the EDR model effectively accounts for numerous binary and weighted properties of neuropil networks, also called projectome. Our study illustrates that the EDR model is a suitable null model for analyzing networks of brain regions, as it captures geometric and physical constraints in very different species. The importance of the null model lies in its ability to facilitate the identification of functionally significant features that are not caused by inevitable geometric constraints, as we illustrate with the pronounced asymmetry of connection weights important for functional hierarchy., Comment: 40 pages in total (34 main article + 4 pages the SI); 8 figures in the main text and 7 figures in the supplementary information

Maintaining numerical stability in machine learning models is crucial for their reliability and performance. One approach to maintain stability of a network layer is to integrate the condition number of the weight matrix as a regularizing term into the optimization algorithm. However, due to its discontinuous nature and lack of differentiability the condition number is not suitable for a gradient descent approach. This paper introduces a novel regularizer that is provably differentiable almost everywhere and promotes matrices with low condition numbers. In particular, we derive a formula for the gradient of this regularizer which can be easily implemented and integrated into existing optimization algorithms. We show the advantages of this approach for noisy classification and denoising of MNIST images., Comment: Accepted at ICML24 Workshop: Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators

For several decades now, Bayesian inference techniques have been applied to theories of particle physics, cosmology and astrophysics to obtain the probability density functions of their free parameters. In this study, we review and compare a wide range of Markov Chain Monte Carlo (MCMC) and nested sampling techniques to determine their relative efficacy on functions that resemble those encountered most frequently in the particle astrophysics literature. Our first series of tests explores a series of high-dimensional analytic test functions that exemplify particular challenges, for example highly multimodal posteriors or posteriors with curving degeneracies. We then investigate two real physics examples, the first being a global fit of the $\Lambda$CDM model using cosmic microwave background data from the Planck experiment, and the second being a global fit of the Minimal Supersymmetric Standard Model using a wide variety of collider and astrophysics data. We show that several examples widely thought to be most easily solved using nested sampling approaches can in fact be more efficiently solved using modern MCMC algorithms, but the details of the implementation matter. Furthermore, we also provide a series of useful insights for practitioners of particle astrophysics and cosmology., Comment: 45 pages, 8 figures, 20 tables