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Magnetometry using Nitrogen-Vacancy (NV) color centers in diamond predominantly relies on microwave spectroscopy. However, microwaves may hinder certain studies involving biological systems or thin conductive samples. This work demonstrates a wide-field, microwave-free imaging magnetometer utilizing NV centers in nanodiamonds by exploiting the cross-relaxation feature near zero magnetic fields under ambient conditions without applying microwaves. For this purpose, we measure the center shift, contrast, and linewidth of the zero-field cross-relaxation in 140 nm nanodiamonds drop-cast on a current-carrying conductive pattern while scanning a background magnetic field, achieving a sensitivity of 4.5 $\mathrm{\mu T/\sqrt{Hz}}$. Our work allows for applying the NV zero-field feature in nanodiamonds for magnetic field sensing in the zero and low-field regimes and highlights the potential for microwave-free all-optical wide-field magnetometry based on nanodiamonds., Comment: 8 pages, 5 figures

We perform a numerical study of the kinetic Blume-Capel (BC) model to find if it exhibits the metamagnetic anomalies previously observed in the kinetic Ising model for supercritical periods. We employ a heat-bath Monte Carlo (MC) algorithm on a square lattice in which spins can take values of $\pm 1, 0$, with a non-zero crystal field, subjected to a sinusoidal oscillating field in conjunction with a constant bias. In the ordered region, we find an equivalent hysteretic response of the order parameters with its respective conjugate fields between the kinetic and the equilibrium model. In the disordered region (supercritical periods), we observed two peaks, symmetrical with respect to zero bias, in the susceptibility and scaled variance curves, consistent with the numerical and experimental findings on the kinetic Ising model. This behavior does not have a counterpart in the equilibrium model. Furthermore, we find that the peaks occur at higher values of the bias field and become progressively smaller as the density of zeros, or the amplitude of the oscillating field, increases. Using nucleation theory, we demonstrate that these fluctuations, as in the Ising model, are not a critical phenomenon, but that they are associated with a crossover between a single-droplet (SD) and a multi-droplet (MD) magnetization switching mechanism. For strong (weak) bias, the SD (MD) mechanism dominates. We also found that the zeros concentrate on the droplets' surfaces, which may cause a reduced interface tension in comparison with the Ising model . Our results suggest that metamagnetic anomalies are not particular to the kinetic Ising model, but rather are a general characteristic of spin kinetic models, and provide further evidence that the equivalence between dynamical phase transitions and equilibrium ones is only valid near the critical point., Comment: 34 pages, 11 figures

We introduce a framework for the study of multiparty entanglement by analyzing the behavior of collective variables. Throughout the manuscript, we explore a specific type of multiparty entanglement which can be detected through the fluctuation of a collective observable. We thoroughly analyze its properties and how it can be extended to mixed states while placing it within the context of the existing literature. The novelty of our approach also lies in the fact that we present a graphical point of view. This is done by introducing a spectral space on which the various properties of our entanglement quantifier have a direct pictorial interpretation. Notably, this approach proves particularly effective for assessing $k$-entanglement, as we show its ability to extend previously established inequalities. To enhance understanding, we also demonstrate how this framework applies to specific scenarios, encompassing both finite-dimensional cases and infinite-dimensional systems, the latter being exemplified by the time-frequency modal degree of freedom of co-propagating single photons., Comment: 15 pages, 5 figures

This paper proposes a two-level restricted additive Schwarz (RAS) method for multiscale PDEs, built on top of a multiscale spectral generalized finite element method (MS-GFEM). The method uses coarse spaces constructed from optimal local approximation spaces, which are based on local eigenproblems posed on (discrete) harmonic spaces. We rigorously prove that the method, used as an iterative solver or as a preconditioner for GMRES, converges at a rate of $\Lambda$, where $\Lambda$ represents the error of the underlying MS-GFEM. The exponential convergence property of MS-GFEM, which is indepdendent of the fine mesh size $h$ even for highly oscillatory and high contrast coefficients, thus guarantees convergence in a few iterations with a small coarse space. We develop the theory in an abstract framework, and demonstrate its generality by applying it to various elliptic problems with highly heterogeneous coefficients, including $H({\rm curl})$ elliptic problems. The performance of the proposed method is systematically evaluated and illustrated via applications to two and three dimensional heterogeneous PDEs, including challenging elasticity problems in realistic composite aero-structures.

Emergence of universal collective behaviour from interactions in a sufficiently large group of elementary constituents is a fundamental scientific paradigm. In physics, correlations in fluctuating microscopic observables can provide key information about collective states of matter such as deconfined quark-gluon plasma in heavy-ion collisions or expanding quantum degenerate gases. Two-particle correlations in mesoscopic colliders have provided smoking-gun evidence on the nature of exotic electronic excitations such as fractional charges, levitons and anyon statistics. Yet the gap between two-particle collisions and the emergence of collectivity as the number of interacting particles grows is hard to address at the microscopic level. Here, we demonstrate all-body correlations in the partitioning of up to $N = 5$ electron droplets driven by a moving potential well through a Y-junction in a semiconductor. We show that the measured multivariate cumulants (of order $k = 2$ to $N$) of the electron droplet are accurately described by $k$-spin correlation functions of an effective Ising model below the N\'eel temperature and can be interpreted as a Coulomb liquid in the thermodynamic limit. Finite size scaling of high-order correlation functions provides otherwise inaccessible fingerprints of emerging order. Our demonstration of emergence in a simple correlated electron collider opens a new way to study engineered states of matter.

In the most interesting case of safe prime powers $q$, G\'omez and Winterhof showed that a subfamily of the family of Golomb Costas permutations of $\{1,2,\ldots,q-2\}$ of size $\varphi(q-1)$ has maximal cross-correlation of order of magnitude at most $q^{1/2}$. In this paper we study a larger family of Golomb Costas permutations and prove a weaker bound on its maximal cross-correlation. Considering the whole family of Golomb Costas permutations we show that large cross-correlations are very rare. Finally, we collect several conditions for a small cross-correlation of two Costas permutations. Our main tools are the Weil bound and the Szemer\'edi-Trotter theorem for finite fields.

Electronic flying qubits offer an interesting alternative to photonic qubits: electrons propagate slower, hence easier to control in real time, and Coulomb interaction enables direct entanglement between different qubits. While their coherence time is limited, picosecond-scale control would make them competitive in terms of number of possible coherent operations. The key challenge lies in achieving the dynamical regime, where the injected plasmonic pulse width is shorter than the quantum device dimensions. Here we reach this new regime in a quantum nanoelectronic system by injecting ultrashort single electron plasmonic pulses into a 14-micrometer-long Mach-Zehnder interferometer. Our findings reveal that quantum coherence is preserved for ultrashort plasmonic pulses, exhibiting enhanced contrast of coherent oscillations compared to the DC regime. Moreover, this coherence remains robust even under large bias voltages. This milestone demonstrates the feasibility of flying qubits as a promising alternative to localized qubit architectures, offering reduced hardware footprint, increased connectivity, and potential for scalable quantum information processing.

We present a detailed investigation of the near-field optical response of core-shell nanoparticles using Fourier-demodulated full-wave simulations, revealing significant modifications to established contrast mechanisms in scattering-type scanning near-field optical microscopy (s-SNOM). Our work examines the complex interplay of geometrical and optical resonances within core-shell structures. Using a finite element method (FEM) simulation closely aligned with the actual s-SNOM measurement processes, we capture the specific near-field responses in these nanostructures. Our findings show that core-shell nanoparticles exhibit unexpected distinct resonance shifts and massively enhanced scattering driven by both core and shell properties. This investigation not only advances the understanding of near-field interactions in complex nanosystems but also provides a refined theoretical framework to accurately predict the optical signatures of nanostructures with internal heterogeneity., Comment: 18 pages, 5 figures

Abstract argumentation is a popular toolkit for modeling, evaluating, and comparing arguments. Relationships between arguments are specified in argumentation frameworks (AFs), and conditions are placed on sets (extensions) of arguments that allow AFs to be evaluated. For more expressiveness, AFs are augmented with \emph{acceptance conditions} on directly interacting arguments or a constraint on the admissible sets of arguments, resulting in dialectic frameworks or constrained argumentation frameworks. In this paper, we consider flexible conditions for \emph{rejecting} an argument from an extension, which we call rejection conditions (RCs). On the technical level, we associate each argument with a specific logic program. We analyze the resulting complexity, including the structural parameter treewidth. Rejection AFs are highly expressive, giving rise to natural problems on higher levels of the polynomial hierarchy., Comment: accepted version as ECAI24

We introduce necklicial nerve functors from enriched categories to simplicial sets, which include Cordier's homotopy coherent, Lurie's differential graded and Le Grignou's cubical nerves. It is shown that every necklicial nerve can be lifted to the templicial objects of arXiv:2302.02484v2. Building on the work of Dugger and Spivak, we give sufficient conditions under which the left-adjoint of a necklicial nerve can be described more explicitly. As an application, we obtain novel and simple expressions for the left-adjoints of the dg-nerve and cubical nerve., Comment: 49 pages, no figures, v2: updated acknowledgements

We investigate algorithmic approaches for targeted drug delivery in a complex, maze-like environment, such as a vascular system. The basic scenario is given by a large swarm of micro-scale particles (''agents'') and a particular target region (''tumor'') within a system of passageways. Agents are too small to contain on-board power or computation and are instead controlled by a global external force that acts uniformly on all particles, such as an applied fluidic flow or electromagnetic field. The challenge is to deliver all agents to the target region with a minimum number of actuation steps. We provide a number of results for this challenge. We show that the underlying problem is NP-complete, which explains why previous work did not provide provably efficient algorithms. We also develop several algorithmic approaches that greatly improve the worst-case guarantees for the number of required actuation steps. We evaluate our algorithmic approaches by numerous simulations, both for deterministic algorithms and searches supported by deep learning, which show that the performance is practically promising., Comment: 21 pages, 11 figures; full version of an extended abstract that appeared in the proceedings of the 37th IEEE International Conference on Robotics and Automation (ICRA 2020)

Attosecond X-ray pulses are the key to studying electron dynamics at their natural timescale in specifically targeted electronic states. They promise to build the conceptual bridge between physical and chemical photo-reaction processes. Free-electron lasers (FELs) have demonstrated their capability of generating intense attosecond X-ray pulses. The use of SASE-based FELs for time-resolving experiments and investigations of nonlinear X-ray absorption mechanisms, however, necessitates their full pulse-to-pulse characterisation which remains a cutting-edge challenge. We have characterised X-ray pulses with durations of down to 600 attoseconds and peak powers up to 200 GW at ~1 keV photon energy via angular streaking at the Small Quantum Systems instrument of the European XFEL in Germany. As a direct application, we present results of nonlinear X-ray--matter interaction via time-resolved electron spectroscopy on a transient system, observing single- and double-core-hole generation in neon atoms. Using the derived temporal information about each single X-ray pulse, we reveal an otherwise hidden peak-intensity dependence of the probability for formation of double-core vacancies in neon after primary K-shell ionisation. Our results advance the field of attosecond science with highly intense and fully characterised X-ray pulses to the state-specific investigation of electronic motion in non-stationary media.

Axion-like particles (ALPs) arise from well-motivated extensions to the Standard Model and could account for dark matter. ALP dark matter would manifest as a field oscillating at an (as of yet) unknown frequency. The frequency depends linearly on the ALP mass and plausibly ranges from $10^{-22}$ to $10$ eV/$c^2$. This motivates broadband search approaches. We report on a direct search for ALP dark matter with an interferometer composed of two atomic K-Rb-$^3$He comagnetometers, one situated in Mainz, Germany, and the other in Krak\'ow, Poland. We leverage the anticipated spatio-temporal coherence properties of the ALP field and probe all ALP-gradient-spin interactions covering a mass range of nine orders of magnitude. No significant evidence of an ALP signal is found. We thus place new upper limits on the ALP-neutron, ALP-proton and ALP-electron couplings reaching below $g_{aNN}<10^{-9}$ GeV$^{-1}$, $g_{aPP}<10^{-7}$ GeV$^{-1}$ and $g_{aee}<10^{-6}$ GeV$^{-1}$, respectively. These limits improve upon previous laboratory constraints for neutron and proton couplings by up to three orders of magnitude.

We study how to infer new choices from prior choices using the framework of choice functions, a unifying mathematical framework for decision-making based on sets of preference orders. In particular, we define the natural (most conservative) extension of a given choice assessment to a coherent choice function -- whenever possible -- and use this natural extension to make new choices. We provide a practical algorithm for computing this natural extension and various ways to improve scalability. Finally, we test these algorithms for different types of choice assessments., Comment: 40 pages, 8 figures, pre-print for International Journal of Approximate Reasoning

High-dimensional networks play a key role in understanding complex relationships. These relationships are often dynamic in nature and can change with multiple external factors (e.g., time and groups). Methods for estimating graphical models are often restricted to static graphs or graphs that can change with a single covariate (e.g., time). We propose a novel class of graphical models, the covariate-varying network (CVN), that can change with multiple external covariates. In order to introduce sparsity, we apply a $L_1$-penalty to the precision matrices of $m \geq 2$ graphs we want to estimate. These graphs often show a level of similarity. In order to model this 'smoothness', we introduce the concept of a 'meta-graph' where each node in the meta-graph corresponds to an individual graph in the CVN. The (weighted) adjacency matrix of the meta-graph represents the strength with which similarity is enforced between the $m$ graphs. The resulting optimization problem is solved by employing an alternating direction method of multipliers. We test our method using a simulation study and we show its applicability by applying it to a real-world data set, the gene expression networks from the study 'German Cancer in childhood and molecular-epidemiology' (KiKme). An implementation of the algorithm in R is publicly available under https://github.com/bips-hb/cvn

A spin-photon interface is one of the key components of a quantum network. Physical platforms under investigation span the range of modern experimental physics, from ultra-cold atoms and ions to a variety of solid-state systems. Each system has its strengths and weaknesses, typically with a trade-off between spin properties and photonic properties. Currently, the best deterministic single-photon sources use a semiconductor quantum dot embedded in an optical microcavity. However, coherent spin control has not yet been integrated with a state-of-the-art single-photon source, and the magnetic noise from host nuclear spins in the semiconductor environment has placed strong limitations on the spin coherence. Here, we combine high-fidelity all-optical spin control with a quantum dot in an open microcavity, currently the most efficient single-photon source platform available. By imprinting a microwave signal onto a red-detuned optical field, a Raman process, we demonstrate coherent rotations of a hole spin around an arbitrary axis of the Bloch sphere, achieving a maximum {\pi}-pulse fidelity of 98.6%. The cavity enhances the Raman process, enabling ultra-fast Rabi frequencies above 1 GHz. We use our flexible spin control to perform optical cooling of the nuclear spins in the host material via the central hole spin, extending the hole-spin coherence time T2* from 28 ns to 535 ns. Hahn echo preserves the spin coherence on a timescale of 20 {\mu}s, and dynamical decoupling extends the coherence close to the relaxation limit. Both the spin T2* and spin rotation time are much larger than the Purcell-enhanced radiative recombination time, 50 ps, enabling many spin-photon pairs to be created before the spin loses its coherence., Comment: 15 pages, 3 main figures, 6 supplementary figures

Autonomous cleaning robots for public spaces have potential for addressing current societal challenges, such as labor shortages and cleanliness in public spaces. Other application domains like autonomous driving, bin picking, or search and rescue have shown that benchmarking platforms and approaches in competitive settings can advance their respective research fields, resulting in more applicable systems under real-world conditions. For this paper, we analyzed seven semi-structured, qualitative stakeholder interviews about outdoor cleaning, identified current needs as well as limitations, and considered those results for the development of a benchmarking scenario based on the previous observations., Comment: 12 pages, 3 figures, 4 tables, RoboCup Symposium 2024

Superior computational power promised by quantum computers utilises the fundamental quantum mechanical principle of entanglement. However, achieving entanglement and verifying that the generated state does not follow the principle of local causality has proven difficult for spin qubits in gate-defined quantum dots, as it requires simultaneously high concurrence values and readout fidelities to break the classical bound imposed by Bell's inequality. Here we employ heralded initialization and calibration via gate set tomography (GST), to reduce all relevant errors and push the fidelities of the full 2-qubit gate set above 99 %, including state preparation and measurement (SPAM). We demonstrate a 97.17 % Bell state fidelity without correcting for readout errors and violate Bell's inequality with a Bell signal of S = 2.731 close to the theoretical maximum of $2\sqrt{2}$. Our measurements exceed the classical limit even at elevated temperatures of 1.1 K or entanglement lifetimes of 100 $\mu s$., Comment: 19 pages, 5 main figures, 9 extended data figures

A key virtue of spin qubits is their sub-micron footprint, enabling a single silicon chip to host the millions of qubits required to execute useful quantum algorithms with error correction. With each physical qubit needing multiple control lines however, a fundamental barrier to scale is the extreme density of connections that bridge quantum devices to their external control and readout hardware. A promising solution is to co-locate the control system proximal to the qubit platform at milli-kelvin temperatures, wired-up via miniaturized interconnects. Even so, heat and crosstalk from closely integrated control have potential to degrade qubit performance, particularly for two-qubit entangling gates based on exchange coupling that are sensitive to electrical noise. Here, we benchmark silicon MOS-style electron spin qubits controlled via heterogeneously-integrated cryo-CMOS circuits with a low enough power density to enable scale-up. Demonstrating that cryo-CMOS can efficiently enable universal logic operations for spin qubits, we go on to show that mill-kelvin control has little impact on the performance of single- and two-qubit gates. Given the complexity of our milli-kelvin CMOS platform, with some 100-thousand transistors, these results open the prospect of scalable control based on the tight packaging of spin qubits with a chiplet style control architecture.

We present the generalization of the CNC formalism, based on closed and noncontextual sets of Pauli observables, to the setting of odd-prime-dimensional qudits. By introducing new CNC-type phase space point operators, we construct a quasiprobability representation for quantum computation which is covariant with respect to the Clifford group and positivity preserving under Pauli measurements, and whose nonnegative sector strictly contains the subtheory of quantum theory described by nonnegative Wigner functions. This allows for a broader class of magic state quantum circuits to be efficiently classically simulated than those covered by the stabilizer formalism and Wigner function methods., Comment: 25 pages, 4 figure, 2 algorithms

The Einstein Probe (EP) achieved its first detection and localization of a bright X-ray flare, EP240219a, on 2024 February 19, during its commissioning phase. Subsequent targeted searches triggered by the EP240219a alert identified a faint, untriggered gamma-ray burst (GRB) in the archived data of Fermi Gamma-ray Burst Monitor (GBM), Swift Burst Alert Telescope (BAT), and Insight-HXMT/HE. The EP Wide-field X-ray Telescope (WXT) light curve reveals a long duration of approximately 160 s with a slow decay, whereas the Fermi/GBM light curve shows a total duration of approximately 70 s. The peak in the Fermi/GBM light curve occurs slightly later with respect to the peak seen in the EP/WXT light curve. Our spectral analysis shows that a single cutoff power-law (PL) model effectively describes the joint EP/WXT--Fermi/GBM spectra in general, indicating coherent broad emission typical of GRBs. The model yielded a photon index of $\sim -1.70 \pm 0.05$ and a peak energy of $\sim 257 \pm 134$ keV. After detection of GRB 240219A, long-term observations identified several candidates in optical and radio wavelengths, none of which was confirmed as the afterglow counterpart during subsequent optical and near-infrared follow-ups. The analysis of GRB 240219A classifies it as an X-ray rich GRB (XRR) with a high peak energy, presenting both challenges and opportunities for studying the physical origins of X-ray flashes, XRRs, and classical GRBs. Furthermore, linking the cutoff PL component to nonthermal synchrotron radiation suggests that the burst is driven by a Poynting flux-dominated outflow., Comment: 15 pages, 8 figures, 3 tables

Template-free SMILES-to-SMILES translation models for reaction prediction and single-step retrosynthesis are of interest for industrial applications in computer-aided synthesis planning systems due to their state-of-the-art accuracy. However, they suffer from slow inference speed. We present a method to accelerate inference in autoregressive SMILES generators through speculative decoding by copying query string subsequences into target strings in the right places. We apply our method to the molecular transformer implemented in Pytorch Lightning and achieve over 3X faster inference in reaction prediction and single-step retrosynthesis, with no loss in accuracy., Comment: 8 pages, 3 figures

In the fiber melt spinning of semi-crystalline polymers, the degree of crystallization can be non-homogeneous over the cross-section of the fiber, affecting the properties of the end product. For simulation-based process design, the question arises as to which fiber quantities and hence model equations must be resolved in radial direction to capture all practically relevant effects and at the same time imply a model that can be computed with reasonable effort. In this paper, we present a hierarchy of viscoelastic two-phase fiber models ranging from a complex, fully resolved and highly expensive three-dimensional description to a cross-sectionally averaged, cheap-to-evaluate one-dimensional model. In particular, we propose a novel stress-averaged one-two-dimensional fiber model, which circumvents additional assumptions on the inlet profiles needed in the established stress-resolved fiber model by Doufas et al.\ (2001). Simulation results demonstrate the performance and application regime of the dimensionally reduced models. The novel stress-averaged variant provides fast and reliable results, especially in the regime of low flow-enhanced crystallization.

We present a method to systematically identify and classify quantum optical non-classical states as classical/non-classical based on the resources they create on a bosonic quantum computer. This is achieved by converting arbitrary bosonic states into multiple modes, each occupied by a single photon, thereby defining qubits of a bosonic quantum computer. Starting from a bosonic classical-like state in a representation that explicitly respects particle number super-selection rules, we apply universal gates to create arbitrary superpositions of states with the same total particle number. The non-classicality of the corresponding states can then be associated to the operations they induce in the quantum computer. We also provide a correspondence between the adopted representation and the more conventional one in quantum optics, where superpositions of Fock states describe quantum optical states, and we identify how multi-mode states can lead to quantum advantage. Our work contributes to establish a seamless transition from continuous to discrete properties of quantum optics while laying the grounds for a description of non-classicality and quantum computational advantage that is applicable to spin systems as well.

The Goldman-Iwahori space of seminorms on a finite-dimensional vector space over a non-Archimedean field is a non-Archimedean analogue of a symmetric space. If, in addition, $K$ is real closed, we define a signed analogue of the Goldman-Iwahori space consisting of signed seminorms. This new space can be seen as the linear algebraic version of the real analytification of projective space over $K$. We study this space with methods from real tropical geometry by constructing natural real tropicalization maps from the signed Goldman-Iwahori space to all real tropicalized linear spaces. We prove that this space is the limit of all real tropicalized linear embeddings. We give a combinatorial interpretation of this result by showing that the signed Goldman-Iwahori space is the real tropical linear space associated to the universal realizable oriented matroid. In the constant coefficient case for $K = \mathbb{R}$, we describe this space explicitly and relate it to real Bergman fans., Comment: 36 pages, 3 figures, comments very welcome!

Rapidly Exploring Random Tree (RRT) algorithms, notably used for nonholonomic vehicle navigation in complex environments, are often not thoroughly evaluated for their specific challenges. This paper presents a first such comparison study of the variants Potential-Quick RRT* (PQ-RRT*), Informed RRT* (IRRT*), RRT*, and RRT, in maritime single-query nonholonomic motion planning. Additionally, the practicalities of using these algorithms in maritime environments are discussed and outlined. We also contend that these algorithms are beneficial not only for trajectory planning in Collision Avoidance Systems (CAS) but also for CAS verification when used as vessel behavior generators. Optimal RRT variants tend to produce more distance-optimal paths but require more computational time due to complex tree wiring and nearest neighbor searches. Our findings, supported by Welch`s t-test at a significance level of Alpha = 0.05, indicate that PQ-RRT* slightly outperform IRRT* and RRT* in achieving shorter trajectory length but at the expense of higher tuning complexity and longer run-times. Based on the results, we argue that these RRT algorithms are better suited for smaller-scale problems or environments with low obstacle congestion ratio. This is attributed to the curse of dimensionality, and trade-off with available memory and computational resources.

We study the orbital Edelstein effect (OEE) that originates from a particular inversion symmetry breaking mechanism: an asymmetric scalar potential. We compute OEE of this kind with the help of the Kubo formula in the diffusive regime for a parabolic band Hamiltonian. We also present a qualitative derivation of the effect. Both approaches give the same result. This result does not rely on spin-orbit coupling (SOC) and scales as a cube of the momentum relaxation time. In sufficiently clean large systems with weak SOC, OEE of this nature should exceed the spin Edelstein effect by orders of magnitude. It may also provide an alternative interpretation for some experiments concerning the spin Hall effect., Comment: Submission to SciPost

If an algorithm is to be counted as a practically working solution to a decision problem, then the algorithm must must verifiable in some constructed and ``trusted'' theory such as PA or ZF. In this paper, a class of decision problems related to inconsistency proofs for a general class of formal theories is used to demonstrate that under this constructibility restriction, an unformalizable yet arguably convincing argument can be given for the existence of decision problems for which there exists an explicitly constructible algorithm recognizing correct solutions in polynomial time, but for which there exists no explicitly constructible solution algorithm. While these results do not solve the P versus NP problem in the classical sense of supplying a proof one way or the other in a ``trusted'' formal theory, arguably they resolve the natural constructive version of the problem., Comment: Clarifications and improvements of the text, mostly on pages 8 and 9. Some misprints corrected

The impact of transformer networks is booming, yet, they come with significant computational complexity. It is therefore essential to understand how to optimally map and execute these networks on modern neural processor hardware. So far, literature on transformer scheduling optimization has been focusing on deployment on GPU and specific ASICs. This work enables extensive hardware/mapping exploration by extending the DSE framework Stream towards support for transformers across a wide variety of hardware architectures and different execution schedules. After validation, we explore the optimal schedule for transformer layers/attention heads and investigate whether layer fusion is beneficial to improve latency, energy or memory requirements. Our study shows that the memory requirements for active feature data can be drastically reduced, by adapting the execution schedule based on the size of the input of the attention head., Comment: Accepted to ISQED2024

This paper introduces a novel method for predicting tool wear in CNC turning operations, combining ultrasonic microphone arrays and convolutional neural networks (CNNs). High-frequency acoustic emissions between 0 kHz and 60 kHz are enhanced using beamforming techniques to improve the signal- to-noise ratio. The processed acoustic data is then analyzed by a CNN, which predicts the Remaining Useful Life (RUL) of cutting tools. Trained on data from 350 workpieces machined with a single carbide insert, the model can accurately predict the RUL of the carbide insert. Our results demonstrate the potential gained by integrating advanced ultrasonic sensors with deep learning for accurate predictive maintenance tasks in CNC machining.

In light of recent advancements in AI capabilities and the increasingly widespread integration of AI systems into society, governments worldwide are actively seeking to mitigate the potential harms and risks associated with these technologies through regulation and other governance tools. However, there exist significant gaps between governance aspirations and the current state of the technical tooling necessary for their realisation. In this position paper, we survey policy documents published by public-sector institutions in the EU, US, and China to highlight specific areas of disconnect between the technical requirements necessary for enacting proposed policy actions, and the current technical state of the art. Our analysis motivates a call for tighter integration of the AI/ML research community within AI governance in order to i) catalyse technical research aimed at bridging the gap between current and supposed technical underpinnings of regulatory action, as well as ii) increase the level of technical expertise within governing institutions so as to inform and guide effective governance of AI., Comment: 9 pages, 3 figures, Proceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024

Because of the speed of its development, broad scope of application, and its ability to augment human performance, generative AI challenges the very notions of governance, trust, and human agency. The technology's capacity to mimic human knowledge work, feedback loops including significant uptick in users, research, investor, policy, and media attention, data and compute resources, all lead to rapidly increasing capabilities. For those reasons, adaptive governance, where AI governance and AI co-evolve, is essential for governing generative AI. In sharp contrast to traditional governance's regulatory regimes that are based on a mix of rigid one-and-done provisions for disclosure, registration and risk management, which in the case of AI carry the potential for regulatory misalignment, this paper argues that generative AI calls for adaptive governance. We define adaptive governance in the context of AI and outline an adaptive AI governance framework. We outline actors, roles, as well as both shared and actors-specific policy activities. We further provide examples of how the framework could be operationalized in practice. We then explain that the adaptive AI governance stance is not without its risks and limitations, such as insufficient oversight, insufficient depth, regulatory uncertainty, and regulatory capture, and provide potential approaches to fix these shortcomings.

We construct flexible spatio-temporal models through stochastic partial differential equations (SPDEs) where both diffusion and advection can be spatially varying. Computations are done through a Gaussian Markov random field approximation of the solution of the SPDE, which is constructed through a finite volume method. The new flexible non-separable model is compared to a flexible separable model both for reconstruction and forecasting, and evaluated in terms of root mean square errors and continuous rank probability scores. A simulation study demonstrates that the non-separable model performs better when the data is simulated from a non-separable model with diffusion and advection. Further, we estimate surrogate models for emulating the output of a ocean model in Trondheimsfjorden, Norway, and simulate observations of autonomous underwater vehicles. The results show that the flexible non-separable model outperforms the flexible separable model for real-time prediction of unobserved locations.

Tsetlin machines (TMs) have been successful in several application domains, operating with high efficiency on Boolean representations of the input data. However, Booleanizing complex data structures such as sequences, graphs, images, signal spectra, chemical compounds, and natural language is not trivial. In this paper, we propose a hypervector (HV) based method for expressing arbitrarily large sets of concepts associated with any input data. Using a hyperdimensional space to build vectors drastically expands the capacity and flexibility of the TM. We demonstrate how images, chemical compounds, and natural language text are encoded according to the proposed method, and how the resulting HV-powered TM can achieve significantly higher accuracy and faster learning on well-known benchmarks. Our results open up a new research direction for TMs, namely how to expand and exploit the benefits of operating in hyperspace, including new booleanization strategies, optimization of TM inference and learning, as well as new TM applications., Comment: 9 pages, 17 figures

This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.

So far, flamelet theory has treated curvature as an independent parameter requiring specific means for closure. In this work, it is shown how the adoption of a two-dimensional orthogonal composition space allows obtaining formal mathematical relations between the flame curvatures and the gradients of the conditioning scalars (also called flamelet coordinates). With these, both curvatures become a flame response to the underlying flow, which conveniently allows removing them from the corresponding set of flamelet equations. While the demonstration is performed in the context of partially premixed flames, the approach is general and applicable to any orthogonal coordinate system.

The lack of interactions between single photons prohibits direct nonlinear operations in quantum optical circuits, representing a central obstacle in photonic quantum technologies. Here, we demonstrate multi-mode nonlinear photonic circuits where both linear and direct nonlinear operations can be programmed with high precision at the single-photon level. Deterministic nonlinear interaction is realized with a tunable quantum dot embedded in a nanophotonic waveguide mediating interactions between individual photons within a temporal linear optical interferometer. We demonstrate the capability to reprogram the nonlinear photonic circuits and implement protocols where strong nonlinearities are required, in particular for quantum simulation of anharmonic molecular dynamics, thereby showcasing the new key functionalities enabled by our technology., Comment: 8 pages, 3 figures

We describe the symmetry group of the stabilizer polytope for any number $n$ of systems and any prime local dimension $d$. In the qubit case, the symmetry group coincides with the linear and anti-linear Clifford operations. In the case of qudits, the structure is somewhat richer: for $n=1$, it is a wreath product of permutations of bases and permutations of the elements within each basis. For $n>1$, the symmetries are given by affine symplectic similitudes. These are the affine maps that preserve the symplectic form of the underlying discrete phase space up to a non-zero multiplier. We phrase these results with respect to a number of a priori different notions of "symmetry'', including Kadison symmetries (bijections that are compatible with convex combinations), Wigner symmetries (bijections that preserve inner products), and symmetries realized by an action on Hilbert space. Going beyond stabilizer states, we extend an observation of Heinrich and Gross (Ref. [25]) and show that the symmetries of fairly general sets of Hermitian operators are constrained by certain moments. In particular: the symmetries of a set that behaves like a 3-design preserve Jordan products and are therefore realized by conjugation with unitaries or anti-unitaries. (The structure constants of the Jordan algebra are encoded in an order-three tensor, which we connect to the third moments of a design). This generalizes Kadison's formulation of the classic Wigner Theorem on quantum mechanical symmetries., Comment: 21 pages, v2: minor notation changes and references added

New unconventional compensated magnets with a $p$-wave spin polarization protected by a composite time-reversal translation symmetry have been proposed in the wake of altermagnets. To facilitate the experimental discovery and applications of these unconventional magnets, we construct an effective analytical model. The effective model is based on a minimal tight-binding model for unconventional $p$-wave magnets that clarifies the relation to other magnets with $p$-wave spin-polarized bands. One of the most prominent advantages of our analytical model is the possibility to employ various analytical approaches while capturing essential features of $p$-wave magnets. We illustrate the effective model by evaluating the tunneling conductance in junctions with $p$-wave magnets, revealing a large magnetoresistance, spin filtering, and anisotropic bulk spin conductivity beyond linear response despite the absence of a net magnetization. These results show that unconventional $p$-wave magnets offer several useful functionalities, broadening the material selection for spintronics devices., Comment: 7+2+15 pages, 4+9 figures; close to published version

The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design. Here, we propose an in-silico approach for the $\textit{de novo}$ generation of 3D ligand structures using the equivariant diffusion model PILOT, combining pocket conditioning with a large-scale pre-training and property guidance. Its multi-objective trajectory-based importance sampling strategy is designed to direct the model towards molecules that not only exhibit desired characteristics such as increased binding affinity for a given protein pocket but also maintains high synthetic accessibility. This ensures the practicality of sampled molecules, thus maximizing their potential for the drug discovery pipeline. PILOT significantly outperforms existing methods across various metrics on the common benchmark dataset CrossDocked2020. Moreover, we employ PILOT to generate novel ligands for unseen protein pockets from the Kinodata-3D dataset, which encompasses a substantial portion of the human kinome. The generated structures exhibit predicted $IC_{50}$ values indicative of potent biological activity, which highlights the potential of PILOT as a powerful tool for structure-based drug design.

Semantic Image Segmentation facilitates a multitude of real-world applications ranging from autonomous driving over industrial process supervision to vision aids for human beings. These models are usually trained in a supervised fashion using example inputs. Distribution Shifts between these examples and the inputs in operation may cause erroneous segmentations. The robustness of semantic segmentation models against distribution shifts caused by differing camera or lighting setups, lens distortions, adversarial inputs and image corruptions has been topic of recent research. However, robustness against spatially varying radial distortion effects that can be caused by uneven glass structures (e.g. windows) or the chaotic refraction in heated air has not been addressed by the research community yet. We propose a method to synthetically augment existing datasets with spatially varying distortions. Our experiments show, that these distortion effects degrade the performance of state-of-the-art segmentation models. Pretraining and enlarged model capacities proof to be suitable strategies for mitigating performance degradation to some degree, while fine-tuning on distorted images only leads to marginal performance improvements.

Computer vision techniques are on the rise for industrial applications, like process supervision and autonomous agents, e.g., in the healthcare domain and dangerous environments. While the general usability of these techniques is high, there are still challenging real-world use-cases. Especially transparent structures, which can appear in the form of glass doors, protective casings or everyday objects like glasses, pose a challenge for computer vision methods. This paper evaluates the combination of transparent objects in conjunction with (naturally occurring) contamination through environmental effects like hazing. We introduce a novel publicly available dataset containing 489 images incorporating three grades of water droplet contamination on transparent structures and examine the resulting influence on transparency handling. Our findings show, that contaminated transparent objects are easier to segment and that we are able to distinguish between different severity levels of contamination with a current state-of-the art machine-learning model. This in turn opens up the possibility to enhance computer vision systems regarding resilience against, e.g., datashifts through contaminated protection casings or implement an automated cleaning alert.

X-ray absorption spectroscopy is a crucial experimental technique for elucidating the mechanisms of structural degradation in battery materials. However, extracting information from the measured spectrum is challenging without high-quality simulations. In this work, we propose simulating near-edge X-ray absorption spectra as a promising application for quantum computing. It is attractive due to the ultralocal nature of X-ray absorption that significantly reduces the sizes of problems to be simulated, and because of the classical hardness of simulating spectra. We describe three quantum algorithms to compute the X-ray absorption spectrum and provide their asymptotic cost. One of these is a Monte-Carlo based time-domain algorithm, which is cost-friendly to early fault-tolerant quantum computers. We then apply the framework to an industrially relevant example, a CAS(22e,18o) active space for an O-Mn cluster in a Li-excess battery cathode, showing that practically useful simulations could be obtained with much fewer qubits and gates than ground-state energy estimation of the same material.

The objective of Information Extraction (IE) is to derive structured representations from unstructured or semi-structured documents. However, developing IE models is complex due to the need of integrating several subtasks. Additionally, representation of data among varied tasks and transforming datasets into task-specific model inputs presents further challenges. To streamline this undertaking for researchers, we introduce PyTorch-IE, a deep-learning-based framework uniquely designed to enable swift, reproducible, and reusable implementations of IE models. PyTorch-IE offers a flexible data model capable of creating complex data structures by integrating interdependent layers of annotations derived from various data types, like plain text or semi-structured text, and even images. We propose task modules to decouple the concerns of data representation and model-specific representations, thereby fostering greater flexibility and reusability of code. PyTorch-IE also extends support for widely used libraries such as PyTorch-Lightning for training, HuggingFace datasets for dataset reading, and Hydra for experiment configuration. Supplementary libraries and GitHub templates for the easy setup of new projects are also provided. By ensuring functionality and versatility, PyTorch-IE provides vital support to the research community engaged in Information Extraction.

To solve problems in domains such as filtering, optimization, and posterior sampling, interacting-particle methods have recently received much attention. These parallelizable and often gradient-free algorithms use an ensemble of particles that evolve in time, based on a combination of well-chosen dynamics and interaction between the particles. For computationally expensive dynamics -- for example, dynamics that solve inverse problems with an expensive forward model -- the cost of attaining a high accuracy quickly becomes prohibitive. We exploit a hierarchy of approximations to this forward model and apply multilevel Monte Carlo (MLMC) techniques, improving the asymptotic cost-to-error relation. More specifically, we use MLMC at each time step to estimate the interaction term within a single, globally-coupled ensemble. This technique was proposed by Hoel et al. in the context of the ensemble Kalman filter; the goal of the present paper is to study its applicability to a general framework of interacting-particle methods. After extending the algorithm and its analysis to a broad set of methods with fixed numbers of time steps, we motivate the application of the method to the class of algorithms with an infinite time horizon, which includes popular methods such as ensemble Kalman algorithms for optimization and sampling. Numerical tests confirm the improved asymptotic scaling of the multilevel approach.

Masers once represented the state-of-the-art in low noise microwave amplification technology, but eventually became obsolete due to their need for cryogenic cooling. Masers based on solid-state spin systems perform most effectively as amplifiers, since they provide a large density of spins and can therefore operate at relatively high powers. Whilst solid-state masers oscillators have been demonstrated at room temperature, continuous-wave amplification in these systems has only ever been realized at cryogenic temperatures. Here we report on a continuous-wave solid-state maser amplifier operating at room temperature. We achieve this feat using a practical setup that includes an ensemble of nitrogen-vacancy center spins in a diamond crystal, a strong permanent magnet and simple laser diode. We describe important amplifier characteristics including gain, bandwidth, compression power and noise temperature and discuss the prospects of realizing a room-temperature near-quantum-noise-limited amplifier with this system. Finally, we show that in a different mode of operation the spins can be used to cool the system noise in an external circuit to cryogenic levels, all without the requirement for physical cooling.

The interaction between two impurity spins provides vital information about the host system and has been suggested to form a building block in quantum computation and spintronic devices. We here determine this Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction in recently discovered altermagnetic materials, including the presence of a Zeeman field, in a two- and three-dimensional altermagnet. In two dimensions, we also study the effect of Rashba spin-orbit coupling. Our results reveal that the momentum-resolved spin-polarization of the itinerant carriers in the altermagnet changes the RKKY interaction qualitatively from the isotropic spin-splitting in a ferromagnet. The Ising-contribution directed parallel with the altermagnetism is found to exhibit a beating pattern reflecting the shape of the Fermi surface of the system and may thus be further influenced by an out-of-plane Zeeman field. However, the exchange interaction for in-plane impurity spins deviates only slightly from that of a normal metal. On shorter length scales, we find that the Ising, the Dzyaloshinskii-Moriya terms, and other non-collinear interaction terms in the RKKY interaction acquire a rapidly oscillating behavior as a function of the relative angle between the impurity spins, which is not present in the ferromagnetic case. We determine how this new length scale depends on the system parameters analytically. Our results show that the RKKY interaction in altermagnets is qualitatively different from that of ferromagnets despite both breaking time-reversal symmetry., Comment: 7 pages, 4 figures

We propose a circuit architecture for a dissipatively error-corrected GKP qubit. The device consists of a high-impedance LC circuit coupled to a Josephson junction and a resistor via a controllable switch. When the switch is activated via a particular family of stepwise protocols, the resistor absorbs all noise-induced entropy, resulting in dissipative error correction of both phase and amplitude errors. This leads to an exponential increase of qubit lifetime, reaching beyond 10ms in simulations with near-feasible parameters. We show that the lifetime remains exponentially long in the presence of extrinsic noise and device/control imperfections (e.g., due to parasitics and finite control bandwidth) under specific thresholds. In this regime, lifetime is likely only limited by phase slips and quasiparticle tunneling. We show that the qubit can be read out and initialized via measurement of the supercurrent in the Josephson junction. We finally show that the qubit supports native self-correcting single-qubit Clifford gates, where dissipative error-correction of control noise leads to exponential suppression of gate infidelity., Comment: 12 pages + 8 figures in the main text

Efficient, reliable and easy-to-use structure recognition of atomic environments is essential for the analysis of atomic scale computer simulations. In this work, we train two neuronal network (NN) architectures, namely PointNet and dynamic graph convolutional NN (DG-CNN) using different hyperparameters and training regimes to assess their performance in structure identification tasks of atomistic structure data. We show benchmarks on simple crystal structures, where we can compare against established methods. The approach is subsequently extended to structurally more complex SiO$_2$ phases. By making use of this structure recognition tool, we are able to achieve a deeper understanding of the crystallization process in amorphous SiO$_2$ under shock compression. Lastly, we show how the NN based structure identification workflows can be integrated into OVITO using its python interface.

Anonymizing the GPS locations of observations can bias a spatial model's parameter estimates and attenuate spatial predictions when improperly accounted for, and is relevant in applications from public health to paleoseismology. In this work, we demonstrate that a newly introduced method for geostatistical modeling in the presence of anonymized point locations can be extended to account for more general kinds of positional uncertainty due to location anonymization, including both jittering (a form of random perturbations of GPS coordinates) and geomasking (reporting only the name of the area containing the true GPS coordinates). We further provide a numerical integration scheme that flexibly accounts for the positional uncertainty as well as spatial and covariate information. We apply the method to women's secondary education completion data in the 2018 Nigeria demographic and health survey (NDHS) containing jittered point locations, and the 2016 Nigeria multiple indicator cluster survey (NMICS) containing geomasked locations. We show that accounting for the positional uncertainty in the surveys can improve predictions in terms of their continuous rank probability score., Comment: main manuscript: 31 pages, 6 figures, 2 tables; supplemental materials: 10 pages, 4 figures, 7 tables