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The square root of the threetangle is calculated for the transverse XY-model with an integrability-breaking in-plane field component. To be in a regime of quasi-solvability of the convex roof, here we concentrate here on a 4-site model Hamiltonian. In general, the field and hence a mixing of the odd/even sectors, has a detrimental effect on the threetangle, as expected. Only in a particular spot of models with no or weak inhomogeneity $\gamma$ does a finite value of the tangle prevail in a broad maximum region of the field strength $h\approx 0.3\pm 0.1$. There, the threetangle is basically independent of the non-zero angle $\alpha$. This system could be experimentally used as a quasi-pure source of threetangled states or as an entanglement triggered switch depending on the experimental error in the field orientation., Comment: 10 pages, 7 figures, submitted for publication

The mechanism for generating directed and elliptic flow in heavy-ion collisions is investigated and quantified for the SIS18 and SIS100 energy regimes. The observed negative elliptic flow $v_2$, at midrapidity has been explained either via (in-plane) shadowing or via (out-of-plane) squeeze-out. To settle this question, we employ the Ultra-relativistic Quantum Molecular Dynamics model (UrQMD) to calculate Au+Au collisions at E$_\mathrm{lab}=0.6A$ GeV, E$_\mathrm{lab}=1.23A$ GeV and $\sqrt{s_\mathrm{NN}}=3.0$ GeV using a hard Skyrme type Equation-of-State to calculate the time evolution and generation of directed flow and elliptic flow. We quantitatively distinguish the impact of collisions and of the potential on $v_1$ and $v_2$ during the evolution of the system. These calculations reveal that in this energy regime the generation of $v_1$ and $v_2$ follows from a highly intricate interplay of different processes and is created late, after the system has reached its highest density and has created a matter bridge between projectile and target remnant, which later breaks. Initially, we find a strong out-of-plane pressure. Then follows a strong stopping and the built up of an in-plane pressure. The $v_2$, created by both processes, compensate to a large extend. The finally observed $v_2$ is caused by the potential, reflects the freeze-out geometry and can neither be associated to squeeze-out nor to shadowing. The results are highly relevant for experiments at GSI, RHIC-FXT and the upcoming FAIR facility, but also for experiments at FRIB, and strengthens understanding on the Equation-of-State at large baryon densities., Comment: 19 pages, 18 figures

Linear Recurrent Neural Networks (LRNNs) such as Mamba, RWKV, GLA, mLSTM, and DeltaNet have emerged as efficient alternatives to Transformers in large language modeling, offering linear scaling with sequence length and improved training efficiency. However, LRNNs struggle to perform state-tracking which may impair performance in tasks such as code evaluation or tracking a chess game. Even parity, the simplest state-tracking task, which non-linear RNNs like LSTM handle effectively, cannot be solved by current LRNNs. Recently, Sarrof et al. (2024) demonstrated that the failure of LRNNs like Mamba to solve parity stems from restricting the value range of their diagonal state-transition matrices to $[0, 1]$ and that incorporating negative values can resolve this issue. We extend this result to non-diagonal LRNNs, which have recently shown promise in models such as DeltaNet. We prove that finite precision LRNNs with state-transition matrices having only positive eigenvalues cannot solve parity, while complex eigenvalues are needed to count modulo $3$. Notably, we also prove that LRNNs can learn any regular language when their state-transition matrices are products of identity minus vector outer product matrices, each with eigenvalues in the range $[-1, 1]$. Our empirical results confirm that extending the eigenvalue range of models like Mamba and DeltaNet to include negative values not only enables them to solve parity but consistently improves their performance on state-tracking tasks. Furthermore, pre-training LRNNs with an extended eigenvalue range for language modeling achieves comparable performance and stability while showing promise on code and math data. Our work enhances the expressivity of modern LRNNs, broadening their applicability without changing the cost of training or inference.

The field of hydride superconductivity has recently been mired in a controversy that might divert attention from the question of central importance: do hydrides support genuine superconductivity or not? We examine some key papers from the field, and conclude that hydride superconductivity is real.

Integrating sensing functionality into 6G communication networks requires some changes to existing components as well as new entities processing the radar sensing signals received by the communication antennas. This whitepaper provides a comprehensive overview of the 6G design proposal for ISaC (Integrated Sensing and Communication). The whitepaper has been created by the architecture group of the KOMSENS-6G project. It represents an intermediate state of the work, as the KOMSENS-6G project is still ongoing. The proposal should serve as a basis for further discussions and alignment across innovative 6G projects., Comment: 13 pages, 14 figures, 1 table

In machine learning and data mining, outliers are data points that significantly differ from the dataset and often introduce irrelevant information that can induce bias in its statistics and models. Therefore, unsupervised methods are crucial to detect outliers if there is limited or no information about them. Global-Local Outlier Scores based on Hierarchies (GLOSH) is an unsupervised outlier detection method within HDBSCAN*, a state-of-the-art hierarchical clustering method. GLOSH estimates outlier scores for each data point by comparing its density to the highest density of the region they reside in the HDBSCAN* hierarchy. GLOSH may be sensitive to HDBSCAN*'s minpts parameter that influences density estimation. With limited knowledge about the data, choosing an appropriate minpts value beforehand is challenging as one or some minpts values may better represent the underlying cluster structure than others. Additionally, in the process of searching for ``potential outliers'', one has to define the number of outliers n a dataset has, which may be impractical and is often unknown. In this paper, we propose an unsupervised strategy to find the ``best'' minpts value, leveraging the range of GLOSH scores across minpts values to identify the value for which GLOSH scores can best identify outliers from the rest of the dataset. Moreover, we propose an unsupervised strategy to estimate a threshold for classifying points into inliers and (potential) outliers without the need to pre-define any value. Our experiments show that our strategies can automatically find the minpts value and threshold that yield the best or near best outlier detection results using GLOSH., Comment: Accepted at IEEE International Conference on Big Data, IEEE BigData 2024

In recent years, Large Language Models (LLMs) through Transformer structures have dominated many machine learning tasks, especially text processing. However, these models require massive amounts of data for training and induce high resource requirements, particularly in terms of the large number of Floating Point Operations (FLOPs) and the high amounts of memory needed. To fine-tune such a model in a parameter-efficient way, techniques like Adapter or LoRA have been developed. However, we observe that the application of LoRA, when used in federated learning (FL), while still being parameter-efficient, is memory and FLOP inefficient. Based on that observation, we develop a novel layer finetuning scheme that allows devices in cross-device FL to make use of pretrained neural networks (NNs) while adhering to given resource constraints. We show that our presented scheme outperforms the current state of the art when dealing with homogeneous or heterogeneous computation and memory constraints and is on par with LoRA regarding limited communication, thereby achieving significantly higher accuracies in FL training.

Xenoscope is a demonstrator for a next-generation xenon-based observatory for astroparticle physics, as proposed by the XLZD (XENON-LUX-ZEPLIN-DARWIN) collaboration. It houses a 2.6 m tall, two-phase xenon time projection chamber (TPC), in a cryostat filled with $\sim$ 360 kg of liquid xenon. The main goals of the facility are to demonstrate electron drift in liquid xenon over this distance, to measure the electron cloud transversal and longitudinal diffusion, as well as the optical properties of the medium. In this work, we describe in detail the construction and commissioning of the TPC and report on the observation of light and charge signals with cosmic muons.

We consider the problem of collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. In particular, we provide a method based on hypothesis testing coupled with differential privacy and data variance estimation. Two privacy mechanisms and two data variance estimation schemes are proposed, and we provide a theoretical convergence analysis of the proposed algorithm for any bounded unknown distributions on the agents' data, showing that collaboration provides faster convergence than a fully local approach where agents do not share data. Moreover, we provide analytical performance curves for the case with an oracle class estimator, i.e., the class structure of the agents, where agents receiving data from distributions with the same mean are considered to be in the same class, is known. The theoretical faster-than-local convergence guarantee is backed up by extensive numerical results showing that for a considered scenario the proposed approach indeed converges much faster than a fully local approach, and performs comparably to ideal performance where all data is public. This illustrates the benefit of private collaboration in an online setting., Comment: Presented in part at the 2023 IEEE International Symposium on Information Theory (ISIT)

Ab initio techniques have revolutionized the way how theory can help practitioners to explore critical mechanisms and devise new strategies in discovering and designing materials. Yet, their application to electrochemical systems is still limited. A well known example how novel concepts can boost our ability to perform such studies is the introduction of temperature control into ab initio simulations. The analogous technique to model electrochemical systems -- potential control -- is just emerging. In this review, we critically discuss state-of-the-art approaches to describe electrified interfaces between a solid electrode and a liquid electrolyte in realistic environments. By exchanging energy, electronic charge and ions with their environment, electrochemical interfaces are thermodynamically open systems. In addition, on the time and length scales relevant for chemical reactions, large fluctuations of the electrostatic potential and field occur. We systematically discuss the key challenges in including these features in realistic ab initio simulations, as well as the available techniques and approaches to overcome them, in order to facilitate the development and use of these novel techniques to the wider community. These methodological developments provide researchers with a new level of realism to explore fundamental electrochemical mechanisms and reactions from first principles.

We investigate the influence of the equation-of-state (EoS) of nuclear matter on collective observables, the directed ($v_1$) and the elliptic flow ($v_2$) of nucleons and light clusters in heavy-ion collisions at GeV beam energies employing the Parton-Hadron-Quantum-Molecular Dynamics (PHQMD) microscopic transport approach. Here the clusters are formed dynamically during the entire heavy-ion collision by potential interaction between nucleons, including additionally deuteron production by hadronic kinetic reactions. We employ three different EoS - realized via potential interactions: two static EoS, dubbed 'soft' and 'hard', which differ in the compressibility modulus, as well as a soft momentum dependent EoS, adjusted to pA elastic scattering data. We find that the momentum dependent potential has different consequences for rapidity and transverse momentum spectra than for flow coefficients. We obtain the best description of the HADES and FOPI data on the directed and elliptic flow coefficients of protons and light clusters applying a momentum dependent EoS. Moreover, we observe a scaling behavior of $v_2$ versus $p_T$ with atomic number $A$. Finally we demonstrate that flow observables can help to identify the cluster production mechanisms.

We investigate light cluster and anti-cluster production in heavy-ion collisions from SIS to RHIC energies within the Parton-Hadron-Quantum-Molecular Dynamics (PHQMD) microscopic transport approach which propagates (anti-)baryons using n-body QMD dynamics. In PHQMD the clusters are formed dynamically by potential interactions between baryons - and recognized by the Minimum Spanning Tree (MST) algorithm - as well as by kinetic reactions in case of deuterons. We present the novel PHQMD results for different observables such as excitation functions of the multiplicity of deuterons, anti-deuterons and tritons, as well as their transverse momentum spectra. Moreover, we investigate the system size dependence of proton and deuteron production in p+A collisions and show the PHQMD results for p+A collisions (A = Be, Al, Cu, Au) at 14 AGeV/c, as well as for asymmetric Au+A collisions (A = Al, Cu, Pb) at a bombarding energy of about 10 AGeV., Comment: Contribution to: QNP2024

Integrated Sensing and Communication (ISAC) is a technology paradigm that combines sensing capabilities with communication functionalities in a single device or system. In vehicle-to-everything (V2X) sidelink, ISAC can provide enhanced safety by allowing vehicles to not only communicate with one another but also sense the surrounding environment by using sidelink signals. In ISAC-capable V2X sidelink, the random resource allocation results in an unstructured and sparse distribution of time and frequency resources in the received orthogonal frequency division multiplexing (OFDM) grid, leading to degraded radar detection performance when processed using the conventional 2D-FFT method. To address this challenge, this paper proposes a high-resolution off-grid radar target detection algorithm irrespective of the OFDM grid structure. The proposed method utilizes the Newtonized orthogonal matching pursuit (NOMP) algorithm to effectively detect weak targets masked by the sidelobes of stronger ones and accurately estimates off-grid range and velocity parameters with minimal resources through Newton refinements. Simulation results demonstrate the superior performance of the proposed NOMP-based target detection algorithm compared to existing compressed sensing (CS) methods in terms of detection probability, resolution, and accuracy. Additionally, experimental validation is performed using a bi-static radar setup in a semi-anechoic chamber. The measurement results validate the simulation findings, showing that the proposed algorithm significantly enhances target detection and parameter estimation accuracy in realistic scenarios.

Reverberation may severely degrade the quality of speech signals recorded using microphones in a room. For compact microphone arrays, the choice of the reference microphone for multi-microphone dereverberation typically does not have a large influence on the dereverberation performance. In contrast, when the microphones are spatially distributed, the choice of the reference microphone may significantly contribute to the dereverberation performance. In this paper, we propose to perform reference microphone selection for the weighted prediction error (WPE) dereverberation algorithm based on the normalized $\ell_p$-norm of the dereverberated output signal. Experimental results for different source positions in a reverberant laboratory show that the proposed method yields a better dereverberation performance than reference microphone selection based on the early-to-late reverberation ratio or signal power.

The eXTP mission is a major project of the Chinese Academy of Sciences (CAS), with a large involvement of Europe. Its scientific payload includes four instruments: SFA, PFA, LAD and WFM. They offer an unprecedented simultaneous wide-band Xray timing and polarimetry sensitivity. A large European consortium is contributing to the eXTP study, both for the science and the instrumentation. Europe is expected to provide two of the four instruments: LAD and WFM; the LAD is led by Italy and the WFM by Spain. The WFM for eXTP is based on the design originally proposed for the LOFT ESA M3 mission, that underwent a Phase A feasibility study. It will be a wide field of view X-ray monitor instrument working in the 2-50 keV energy range, achieved with large-area Silicon Drift Detectors (SDDs), similar to the ones used for the LAD but with better spatial resolution. The WFM will consist of 3 pairs of coded mask cameras with a total combined field of view (FoV) of 90x180 degrees at zero response and a source localisation accuracy of ~1 arc min. The main goal of the WFM is to provide triggers for the target of opportunity observations of the SFA, PFA and LAD, in order to perform the core science programme, dedicated to the study of matter under extreme conditions of density, gravity and magnetism. In addition, the unprecedented combination of large field of view and imaging capability, down to 2 keV, of the WFM will allow eXTP to make important discoveries of the variable and transient X-ray sky, and provide X-ray coverage of a broad range of astrophysical objects covered under 'observatory science', such as gamma-ray bursts, fast radio bursts, gravitational wave electromagnetic counterparts. In this paper we provide an overview of the WFM instrument, explaining its design, configuration, and anticipated performance., Comment: 15 pages, 13 figures, Proceedings of SPIE 13093, Space Telescopes and Instrumentation 2024: Ultraviolet to Gamma Ray; Proceedings Volume 13093, Space Telescopes and Instrumentation 2024: Ultraviolet to Gamma Ray; 130931Y (2024); doi: 10.1117/12.3020020

We propose an analytical thermodynamic model for describing defect phase transformations, which we term the statistical phase evaluation approach (SPEA). The SPEA model assumes a Boltzmann distribution of finite size phase fractions and calculates their statistical average. To benchmark the performance of the model, we apply it to construct binary surface phase diagrams of metal alloys. Two alloy systems are considered: a Mg surface with Ca substitutions and a Ni surface with Nb substitutions. To construct a firm basis against which the performance of the analytical model can be leveled, we first perform Monte Carlo (MC) simulations coupled with cluster expansion of density functional theory dataset. We then demonstrate the SPEA model to reproduce the MC results accurately. Specifically, it correctly predicts the surface order-disorder transitions as well as the coexistence of the 1/3 ordered phase and the disordered phase. Finally, we compare the SPEA method to the sublattice model commonly used in the CALPHAD approach to describe ordered and random solution phases and their transitions. The proposed SPEA model provides a highly efficient approach for modeling defect phase transformations., Comment: Main: 10 pagers, 9 figures, SI: 2 pages, 5 figures

We report the results of a spectroscopic survey of candidate T subdwarfs identified by the Backyard Worlds: Planet 9 program. Near-infrared spectra of 31 sources with red $J-W2$ colors and large $J$-band reduced proper motions show varying signatures of subsolar metallicity, including strong collision-induced H$_2$ absorption, obscured methane and water features, and weak K I absorption. These metallicity signatures are supported by spectral model fits and 3D velocities, indicating thick disk and halo population membership for several sources. We identify three new metal-poor T subdwarfs ([M/H] $\lesssim$ $-$0.5), CWISE J062316.19+071505.6, WISEA J152443.14$-$262001.8, and CWISE J211250.11-052925.2; and 19 new "mild" subdwarfs with modest metal deficiency ([M/H] $\lesssim$ $-$0.25). We also identify three metal-rich brown dwarfs with thick disk kinematics. We provide kinematic evidence that the extreme L subdwarf 2MASS J053253.46+824646.5 and the mild T subdwarf CWISE J113010.07+313944.7 may be part of the Thamnos population, while the T subdwarf CWISE J155349.96+693355.2 may be part of the Helmi stream. We define a metallicity classification system for T dwarfs that adds mild subdwarfs (d/sdT), subdwarfs (sdT), and extreme subdwarfs (esdT) to the existing dwarf sequence. We also define a metallicity spectral index that correlates with metallicities inferred from spectral model fits and iron abundances from stellar primaries of benchmark T dwarf companions. This expansion of the T dwarf classification system supports investigations of ancient, metal-poor brown dwarfs now being uncovered in deep imaging and spectroscopic surveys., Comment: 82 pages, 19 figures, accepted to ApJS

As the landscape of large language models expands, efficiently finetuning for specific tasks becomes increasingly crucial. At the same time, the landscape of parameter-efficient finetuning methods rapidly expands. Consequently, practitioners face a multitude of complex choices when searching for an optimal finetuning pipeline for large language models. To reduce the complexity for practitioners, we investigate transfer learning for finetuning large language models and aim to transfer knowledge about configurations from related finetuning tasks to a new task. In this work, we transfer learn finetuning by meta-learning performance and cost surrogate models for grey-box meta-optimization from a new meta-dataset. Counter-intuitively, we propose to rely only on transfer learning for new datasets. Thus, we do not use task-specific Bayesian optimization but prioritize knowledge transferred from related tasks over task-specific feedback. We evaluate our method on eight synthetic question-answer datasets and a meta-dataset consisting of 1,800 runs of finetuning Microsoft's Phi-3. Our transfer learning is superior to zero-shot, default finetuning, and meta-optimization baselines. Our results demonstrate the transferability of finetuning to adapt large language models more effectively., Comment: Accepted at NeurIPS 2024 Workshop on Adaptive Foundation Models

In this paper we define three different notions of tensor products for Leibniz bimodules. The ``natural" tensor product of Leibniz bimodules is not always a Leibniz bimodule. In order to fix this, we introduce the notion of a weak Leibniz bimodule and show that the ``natural" tensor product of weak bimodules is again a weak bimodule. Moreover, it turns out that weak Leibniz bimodules are modules over a cocommutative Hopf algebra canonically associated to the Leibniz algebra. Therefore, the category of all weak Leibniz bimodules is symmetric monoidal and the full subcategory of finite-dimensional weak Leibniz bimodules is rigid and pivotal. On the other hand, we introduce two truncated tensor products of Leibniz bimodules which are again Leibniz bimodules. These tensor products induce a non-associative multiplication on the Grothendieck group of the category of finite-dimensional Leibniz bimodules. In particular, we prove that in characteristic zero for a finite-dimensional solvable Leibniz algebra this Grothendieck ring is an alternative power-associative commutative Jordan ring, but for a finite-dimensional non-zero semi-simple Leibniz algebra it is neither alternative nor a Jordan ring., Comment: 37 pages

The sustainable production of many bulk chemicals relies on heterogeneous catalysis. The rational design or improvement of the required catalysts critically depends on insights into the underlying mechanisms at the atomic scale. In recent years, substantial progress has been made in applying advanced experimental techniques to complex catalytic reactions in operando, but in order to achieve a comprehensive understanding, additional information from computer simulations is indispensable in many cases. In particular, ab initio molecular dynamics (AIMD) has become an important tool to explicitly address the atomistic level structure, dynamics, and reactivity of interfacial systems, but the high computational costs limit applications to systems consisting of at most a few hundred atoms for simulation times of up to tens of picoseconds. Rapid advances in the development of modern machine learning potentials (MLP) now offer a new approach to bridge this gap, enabling simulations of complex catalytic reactions with ab initio accuracy at a small fraction of the computational costs. In this perspective, we provide an overview of the current state of the art of applying MLPs to systems relevant for heterogeneous catalysis along with a discussion of the prospects for the use of MLPs in catalysis science in the years to come.

Wave-like dark matter may feature quadratic couplings to ordinary matter. This carries profound consequences for the phenomenologies of such models. It changes the dark matter density around dense objects made from ordinary matter such as planets and stars, thereby changing the sensitivity of direct detection experiments on Earth as well as implying forces on other ordinary matter objects in the vicinity. In this note we study the time dependence of the dark matter field around spherical objects of ordinary matter. This work indicates the time-scale on which accelerating objects settle into a stationary state and delineates the applicability of stationary solutions for experimental dark matter tests. We also use this to understand (and effectively eliminate) the infinities in energies, forces, and pressures that appear when naively comparing the total energy around objects with different size but the same total number of ordinary matter particles., Comment: 31 pages, 10 figures

Spurious measurements in surface data are common in technical surfaces. Excluding or ignoring these spurious points may lead to incorrect surface characterization if these points inherit features of the surface. Therefore, data imputation must be applied to ensure that the estimated data points at spurious measurements do not strongly deviate from the true surface and its characteristics. Traditional surface data imputation methods rely on simple assumptions and ignore existing knowledge of the surface, yielding in suboptimal estimates. In this paper, we propose using stochastic processes for data imputation. This approach, which originates from surface simulation, allows for the straightforward integration of a priori knowledge. We employ Gaussian processes for surface data imputation with artificially generated missing features. Both the surfaces and the missing features are generated artificially. Our results demonstrate that the proposed method fills the missing values and interpolates data points with better alignment to the measured surface compared to traditional approaches, particularly when surface features are missing.

How many permutations are needed so that every infinite-coinfinite set of natural numbers with asymptotic density can be rearranged to no longer have the same density? We prove that the density number $\mathfrak{dd}$, which answers this question, is equal to the least size of a non-meager set of reals, $\mathsf{non} (\mathcal{M})$. The same argument shows that a slight modification of the rearrangement number $\mathfrak{rr}$ of~\cite{BBBHHL20} is equal to $\mathsf{non} (\mathcal{M})$, and similarly for a cardinal invariant related to large-scale topology introduced by Banakh~\cite{Ba23}, thus answering a question of the latter. We then consider variants of $\mathfrak{dd}$ given by restricting the possible densities of the original set and / or of the permuted set, providing lower and upper bounds for these cardinals and proving consistency of strict inequalities. We finally look at cardinals defined in terms of relative density and of asymptotic mean, and relate them to the rearrangement numbers of~\cite{BBBHHL20}.

In this paper, we consider the monoids of all partial endomorphisms, of all partial weak endomorphisms, of all injective partial endomorphisms, of all partial strong endomorphisms and of all partial strong weak endomorphisms of a star graph with a finite number of vertices. Our main objective is to exhibit a presentation for each of them.

The STS detector in the CBM experiment delivers data via multiple E-Links connected to GBTX ASICs. In the process of data aggregation, that data must be received, combined into a smaller number of streams, and packed into so-called microslices containing data from specific periods. The aggregation must consider data randomization due to amplitude-dependent processing time in the FEE ASICs and different occupancy of individual E-Links. During the development of the STS readout, the continued progress in the available technology affected the requirements for data aggregation, its architecture, and algorithms. The contribution presents considered solutions and discusses their properties., Comment: TWEPP 2024 Topical Workshop on Electronics for Particle Physics

We investigate the combinatorial structure of the set of maximal antichains in a Boolean algebra ordered by almost refinement. We also consider the reaping relation and its associated cardinal invariants, focusing in particular on reduced powers of Boolean algebras. As an application, we obtain that, on the one hand, the ultrafilter number of the Cohen algebra is greater than or equal to the cofinality of the meagre ideal and, on the other hand, a suitable parametrized diamond principle implies that the ultrafilter number of the Cohen algebra is equal to $\aleph_1$.

Refrigeration limits are of fundamental and practical importance. We here show that quantum systems can be cooled below existing incoherent cooling bounds by employing coherent virtual qubits, even if the amount of coherence is incompletely known. Virtual subsystems, that do not necessarily correspond to a natural eigensubspace of a system, are a key conceptual tool in quantum information science and quantum thermodynamics. We derive universal coherent cooling limits and introduce specific protocols to reach them. As an illustration, we propose a generalized algorithmic cooling protocol that outperforms its current incoherent counterpart. Our results provide a general framework to investigate the performance of coherent refrigeration processes., Comment: 6+8 pages, 3+6 figures

Nonlinear electron transport induced by polarized terahertz radiation is studied in two-dimensional tellurene at room temperature. A direct current, quadratic in the radiation's electric field, is observed. Contributions sensitive to radiation helicity, polarization orientation as well as polarization independent current are found. We show that these contributions can be modified by the magnitude of the external gate potential. We demonstrate that this terahertz-driven electric current arises from the Berry curvature dipole and the side-jump microscopic mechanisms.Nonlinear electron transport induced by polarized terahertz radiation is studied in two-dimensional tellurene at room temperature. A direct current, quadratic in the radiation's electric field, is observed. Contributions sensitive to radiation helicity, polarization orientation as well as polarization independent current are found. We show that these contributions can be modified by the magnitude of the external gate potential. We demonstrate that this terahertz-driven electric current arises from the Berry curvature dipole and the side-jump microscopic mechanisms., Comment: 6 pages and 5 figures

Accurate charge densities are essential for reliable electronic structure calculations because they significantly impact predictions of various chemical properties and in particular, according to the Hellmann-Feynman theorem, atomic forces. This study examines the accuracy of charge densities obtained from different DFT exchange-correlation functionals in comparison with coupled cluster calculations with single and double excitations. We find that modern DFT functionals can provide highly accurate charge densities, particularly in case of meta-GGA and hybrid functionals. In connection with Gaussian basis sets, it is necessary to use the largest basis sets available to obtain densitites that are nearly basis set error free. These findings highlight the importance of selecting appropriate computational methods for generating high-precision charge densities, which are for instance needed to generate reference data for training modern machine learned potentials.

Isotopic substitution, which can be realized both in experiment and computer simulations, is a direct approach to assess the role of nuclear quantum effects on the structure and dynamics of matter. Yet, the impact of nuclear quantum effects on the structure of liquid water as probed in experiment by comparing normal to heavy water has remained controversial. To settle this issue, we employ a highly accurate machine-learned high-dimensional neural network potential to perform converged coupled cluster-quality path integral simulations of liquid H$_2$O versus D$_2$O at ambient conditions. We find substantial H/D quantum effects on the rotational and translational dynamics of water, in close agreement with the experimental benchmarks. However, in stark contrast to the role for dynamics, H/D quantum effects turn out to be unexpectedly small, on the order of 1/1000 \r{A}, on both intramolecular and H-bonding structure of water. The most probable structure of water remains nearly unaffected by nuclear quantum effects, but effects on fluctuations away from average are appreciable, rendering H$_2$O substantially more "liquid" than D$_2$O.

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

Knowledge transfer in multi-task learning is typically viewed as a dichotomy; positive transfer, which improves the performance of all tasks, or negative transfer, which hinders the performance of all tasks. In this paper, we investigate the understudied problem of asymmetric task relationships, where knowledge transfer aids the learning of certain tasks while hindering the learning of others. We propose an optimisation strategy that includes additional cloned tasks named self-auxiliaries into the learning process to flexibly transfer knowledge between tasks asymmetrically. Our method can exploit asymmetric task relationships, benefiting from the positive transfer component while avoiding the negative transfer component. We demonstrate that asymmetric knowledge transfer provides substantial improvements in performance compared to existing multi-task optimisation strategies on benchmark computer vision problems.

We present a method to rapidly approximate convolution quadrature (CQ) approximations, based on a piecewise polynomial interpolation of the Laplace domain operator, which we call the \emph{parsimonious} convolution quadrature method. For implicit Euler and second order backward difference formula based discretizations, we require $O(\sqrt{N}\log N)$ evaluations in the Laplace domain to approximate $N$ time steps of the convolution quadrature method to satisfactory accuracy. The methodology proposed here differentiates from the well-understood fast and oblivious convolution quadrature \cite{SLL06}, since it is applicable to Laplace domain operator families that are only defined and polynomially bounded on a positive half space, which includes acoustic and electromagnetic wave scattering problems. The methods is applicable to linear and nonlinear integral equations. To elucidate the core idea, we give a complete and extensive analysis of the simplest case and derive worst-case estimates for the performance of parsimonious CQ based on the implicit Euler method. For sectorial Laplace transforms, we obtain methods that require $O(\log^2 N)$ Laplace domain evaluations on the complex right-half space. We present different implementation strategies, which only differ slightly from the classical realization of CQ methods. Numerical experiments demonstrate the use of the method with a time-dependent acoustic scattering problem, which was discretized by the boundary element method in space., Comment: 19 pages, 5 figures

Pre-trained Language Models (PLMs) encode various facts about the world at their pre-training phase as they are trained to predict the next or missing word in a sentence. There has a been an interest in quantifying and improving the amount of facts that can be extracted from PLMs, as they have been envisioned to act as soft knowledge bases, which can be queried in natural language. Different approaches exist to enhance fact retrieval from PLM. Recent work shows that the hidden states of PLMs can be leveraged to determine the truthfulness of the PLMs' inputs. Leveraging this finding to improve factual knowledge retrieval remains unexplored. In this work, we investigate the use of a helper model to improve fact retrieval. The helper model assesses the truthfulness of an input based on the corresponding hidden states representations from the PLMs. We evaluate this approach on several masked PLMs and show that it enhances fact retrieval by up to 33\%. Our findings highlight the potential of hidden states representations from PLMs in improving their factual knowledge retrieval.

A pseudodifferential calculus for parameter-dependent operators on smooth manifolds with boundary in the spirit of Boutet de Monvel's algebra is constructed. The calculus contains, in particular, the resolvents of realizations of differential operators subject to global projection boundary conditions (spectral boundary conditions are a particular example); resolvent trace asymptotics are easily derived. The calculus is related to but different from the calculi developed by Grubb and Grubb-Seeley. We use ideas from the theory of pseudodifferential operators on manifolds with edges due to Schulze, in particular the concept of operator-valued symbols twisted by a group-action. Parameter-ellipticity in the calculus is characterized by the invertibility of three principal symbols: the homogeneous principal symbol, the principal boundary symbol, and the so-called principal limit symbol. The principal boundary symbol has, in general, a singularity in the co-variable/parameter space, the principal limit symbol is a new ingredient of our calculus., Comment: 75 pages

In-context knowledge editing (IKE) enables efficient modification of large language model (LLM) outputs without parameter changes and at zero-cost. However, it can be misused to manipulate responses opaquely, e.g., insert misinformation or offensive content. Such malicious interventions could be incorporated into high-level wrapped APIs where the final input prompt is not shown to end-users. To address this issue, we investigate the detection and reversal of IKE-edits. First, we demonstrate that IKE-edits can be detected with high accuracy (F1 > 80\%) using only the top-10 output probabilities of the next token, even in a black-box setting, e.g. proprietary LLMs with limited output information. Further, we introduce the novel task of reversing IKE-edits using specially tuned reversal tokens. We explore using both continuous and discrete reversal tokens, achieving over 80\% accuracy in recovering original, unedited outputs across multiple LLMs. Our continuous reversal tokens prove particularly effective, with minimal impact on unedited prompts. Through analysis of output distributions, attention patterns, and token rankings, we provide insights into IKE's effects on LLMs and how reversal tokens mitigate them. This work represents a significant step towards enhancing LLM resilience against potential misuse of in-context editing, improving their transparency and trustworthiness.

The use of computer simulations to model physical systems has gained significant traction in recent years. A key factor in ensuring the accuracy of these models is the proper calibration of model parameters based on real-world observations or experimental data. Inevitably, uncertainties arise, and Bayesian methods provide a robust framework for quantifying and propagating these uncertainties to model predictions. However, predictions can become inaccurate if model errors are neglected. A promising approach to address this issue involves embedding a bias term in the inference parameters, allowing the quantified bias to influence non-observed Quantities of Interest (QoIs). This paper introduces a more interpretable framework for bias embedding compared to existing methods. Current likelihood formulations that incorporate embedded bias often fail when measurement noise is present. To overcome these limitations, we adapt the existing likelihood models to properly account for noise and propose two new formulations designed to address the shortcomings of the previous approaches. Moreover, we evaluate the performance of this bias-embedding approach in the presence of discrepancies between measurements and model predictions, including noise and outliers. Particular attention is given to how the uncertainty associated with the bias term propagates to the QoIs, enabling a more comprehensive statistical analysis of prediction reliability. Finally, the proposed embedded bias model is applied to estimate the uncertainty in the predicted heat flux from a transient thermal simulation, using temperature observations to illustrate its effectiveness., Comment: 37 pages, 24 figures, 5 tables

This paper investigates acoustic wave scattering from materials with periodic time-modulated material parameters. We consider the basic case of a single connected domain where absorbing or Neumann boundary conditions are enforced. Energy estimates limit the exponential growth of solutions to the initial value problem, thereby confining Floquet exponents to a complex half-space under absorbing boundary conditions and to a strip near the real axis for Neumann conditions. We introduce a system of coupled harmonics and establish a Fredholm alternative result using Riesz-Schauder theory. For a finite set of harmonics, we show that the spectrum remains discrete. Different eigenvalue formulations for the Floquet exponents are formulated and connected to these results. Employing a space discretization to the system of coupled harmonics filters spatially oscillating modes, which is shown to imply a localization result for the temporal spectrum of the fully discrete coupled harmonics. Such a localization result is the key to further analysis, since the truncation of the coupled harmonics is critically affected for non-localized modes. We use the localization result to show that, when enough harmonics are included, the approximated Floquet exponents exhibit the same limitations as their continuous counterparts. Moreover, the approximated modes are shown to satisfy the defining properties of Bloch modes, with a defect that vanishes as the number of harmonics approaches infinity. Numerical experiments demonstrate the effectiveness of the proposed approach and illustrate the theoretical findings., Comment: 33 pages, 7 figures

Variational Autoencoders (VAEs) are a powerful framework for learning compact latent representations, while NeuralODEs excel in learning transient system dynamics. This work combines the strengths of both to create fast surrogate models with adjustable complexity. By leveraging the VAE's dimensionality reduction using a non-hierarchical prior, our method adaptively assigns stochastic noise, naturally complementing known NeuralODE training enhancements and enabling probabilistic time series modeling. We show that standard Latent ODEs struggle with dimensionality reduction in systems with time-varying inputs. Our approach mitigates this by continuously propagating variational parameters through time, establishing fixed information channels in latent space. This results in a flexible and robust method that can learn different system complexities, e.g. deep neural networks or linear matrices. Hereby, it enables efficient approximation of the Koopman operator without the need for predefining its dimensionality. As our method balances dimensionality reduction and reconstruction accuracy, we call it Balanced Neural ODE (B-NODE). We demonstrate the effectiveness of this method on academic test cases and apply it to a real-world example of a thermal power plant., Comment: Conference paper under review

Training accurate machine learning potentials requires electronic structure data comprehensively covering the configurational space of the system of interest. As the construction of this data is computationally demanding, many schemes for identifying the most important structures have been proposed. Here, we compare the performance of high-dimensional neural network potentials (HDNNPs) for quantum liquid water at ambient conditions trained to data sets constructed using random sampling as well as various flavors of active learning based on query by committee. Contrary to the common understanding of active learning, we find that for a given data set size, random sampling leads to smaller test errors for structures not included in the training process. In our analysis we show that this can be related to small energy offsets caused by a bias in structures added in active learning, which can be overcome by using instead energy correlations as an error measure that is invariant to such shifts. Still, all HDNNPs yield very similar and accurate structural properties of quantum liquid water, which demonstrates the robustness of the training procedure with respect to the training set construction algorithm even when trained to as few as 200 structures. However, we find that for active learning based on preliminary potentials, a reasonable initial data set is important to avoid an unnecessary extension of the covered configuration space to less relevant regions.

We study the dynamics and decay of quantum phase coherence for Bose-Einstein condensates in tunnel-coupled quantum wires. The two elongated Bose-Einstein condensates exhibit a wide variety of dynamic phenomena where quantum fluctuations can lead to a rapid loss of phase coherence. We investigate the phenomenon of self-trapping in the relative population imbalance of the two condensates, particularly $\pi$-trapped oscillations that occur when also the relative phase is trapped. Though this state appears stable in mean-field descriptions, the $\pi$-trapped state becomes dynamically unstable due to quantum fluctuations. Nonequilibrium instabilities result in the generation of pairs excited from the condensate to higher momentum modes. We identify tachyonic instabilities, which are associated with imaginary parts of the dispersion relation, and parametric resonance instabilities that are triggered by oscillations of the relative phase and populations. At early times, we compute the instability chart of the characteristic modes through a linearized analysis and identify the underlying physical process. At later times, the primary instabilities trigger secondary instabilities due to the build-up of non-linearities. We perform numerical simulations in the Truncated Wigner approximation in order to observe the dynamics also in this non-linear regime. Furthermore, we discuss realistic parameters for experimental realizations of the $\pi$-mode in ultracold atom setups., Comment: 17 pages, 10 figures

We consider smooth Gowdy-symmetric generalised Taub-NUT solutions, a class of inhomogeneous cosmological models with spatial three-sphere topology. They are characterised by existence of a smooth past Cauchy horizon and, with the exception of certain singular cases, they also develop a regular future Cauchy horizon. Several examples of exact solutions were previously constructed, where the initial data (in form of the initial Ernst potentials) are polynomials of low degree. Here, we generalise to polynomial initial data of arbitrary degree. Utilising methods from soliton theory, we obtain a simple algorithm that allows us to construct the resulting Ernst potential with purely algebraic calculations. We also derive an explicit formula in terms of determinants, and we illustrate the method with two examples., Comment: 23 pages, 4 figures

Nanoelectrical and photonic integration of quantum optical components is crucial for scalable solid-state quantum technologies. Silicon carbide stands out as a material with mature quantum defects and a wide variety of applications in semiconductor industry. Here, we study the behaviour of single silicon vacancy (V2) colour centres in a metal-semiconductor (Au/Ti/4H-SiC) epitaxial wafer device, operating in a Schottky diode configuration. We explore the depletion of free carriers in the vicinity of the defect, as well as electrical tuning of the defect optical transition lines. By detecting single charge traps, we investigate their impact on V2 optical line width. Additionally, we investigate the charge-photon-dynamics of the V2 centre and find its dominating photon-ionisation processes characteristic rate and wavelength dependence. Finally, we probe the spin coherence properties of the V2 system in the junction and demonstrate several key protocols for quantum network applications. Our work shows the first demonstration of low temperature integration of a Schottky device with optical microstructures for quantum applications and paves the way towards fundamentally scalable and reproducible optical spin defect centres in solids.

Understanding protein motion within the cell is crucial for predicting reaction rates and macromolecular transport in the cytoplasm. A key question is how crowded environments affect protein dynamics through hydrodynamic and direct interactions at molecular length scales. Using megahertz X-ray Photon Correlation Spectroscopy (MHz-XPCS) at the European X-ray Free Electron Laser (EuXFEL), we investigate ferritin diffusion at microsecond time scales. Our results reveal anomalous diffusion, indicated by the non-exponential decay of the intensity autocorrelation function $g_2(q,t)$ at high concentrations. This behavior is consistent with the presence of cage-trapping in between the short- and long-time protein diffusion regimes. Modeling with the $\delta\gamma$-theory of hydrodynamically interacting colloidal spheres successfully reproduces the experimental data by including a scaling factor linked to the protein direct interactions. These findings offer new insights into the complex molecular motion in crowded protein solutions, with potential applications for optimizing ferritin-based drug delivery, where protein diffusion is the rate-limiting step.

This paper presents a comprehensive overview of the data preparation pipeline developed for the OpenGPT-X project, a large-scale initiative aimed at creating open and high-performance multilingual large language models (LLMs). The project goal is to deliver models that cover all major European languages, with a particular focus on real-world applications within the European Union. We explain all data processing steps, starting with the data selection and requirement definition to the preparation of the final datasets for model training. We distinguish between curated data and web data, as each of these categories is handled by distinct pipelines, with curated data undergoing minimal filtering and web data requiring extensive filtering and deduplication. This distinction guided the development of specialized algorithmic solutions for both pipelines. In addition to describing the processing methodologies, we provide an in-depth analysis of the datasets, increasing transparency and alignment with European data regulations. Finally, we share key insights and challenges faced during the project, offering recommendations for future endeavors in large-scale multilingual data preparation for LLMs.

Non-homogeneous self-similar measures are generically absolute continuous in the domain of parameters for which the similarity dimension is larger than one, see \cite{[SSS]}. Using certain algebraic curves we construct here exceptional singular non-homogeneous self-similar measures in this domain.

We evaluate the impact of pretraining Graph Transformer architectures on atom-level quantum-mechanical features for the modeling of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. We compare this pretraining strategy with two others: one based on molecular quantum properties (specifically the HOMO-LUMO gap) and one using a self-supervised atom masking technique. After fine-tuning on Therapeutic Data Commons ADMET datasets, we evaluate the performance improvement in the different models observing that models pretrained with atomic quantum mechanical properties produce in general better results. We then analyse the latent representations and observe that the supervised strategies preserve the pretraining information after finetuning and that different pretrainings produce different trends in latent expressivity across layers. Furthermore, we find that models pretrained on atomic quantum mechanical properties capture more low-frequency laplacian eigenmodes of the input graph via the attention weights and produce better representations of atomic environments within the molecule. Application of the analysis to a much larger non-public dataset for microsomal clearance illustrates generalizability of the studied indicators. In this case the performances of the models are in accordance with the representation analysis and highlight, especially for the case of masking pretraining and atom-level quantum property pretraining, how model types with similar performance on public benchmarks can have different performances on large scale pharmaceutical data.

This paper presents a novel graphical approach that counts the number of global 8-cycles on the SCRAM three-layer Tanner graph. SCRAM, which stands for Slotted Coded Random Access Multiplexing, is a joint decoder that is meets challenging requirements of 6G. At the transmitter side, the data of the accommodated users is encoded by Low Density Parity Check (LDPC) codes, and the codewords are transmitted over the shared channel by means of Slotted ALOHA. Unlike the state-of-the-art sequential decoders, the SCRAM decoder jointly resolves collisions and decodes the LDPC codewords, in a similar analogy to Belief Propagation on a three-layer Tanner graph. By leveraging the analogy between the two-layer Tanner graph of conventional LDPC codes and the three-layer Tanner graph of SCRAM, the well-developed analysis tools of classical LDPC codes could be utilized to enhance the performance of SCRAM. In essence, the contribution of this paper is three-fold; First it proposes the methodology to utilize these tools to assess the performance of SCRAM. Second, it derives a lower bound on the shortest cycle length of an arbitrary SCRAM Tanner graph. Finally, the paper presents a novel graphical method that counts the number of cycles of length that corresponds to the girth., Comment: 12 pages, 10 figures, 3 tables, 1 Algorithm, Submitted to IEEE Internet of Things Journal