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The construction of highly coherent x-ray sources has enabled new research opportunities across the scientific landscape. The maximum raw data rate per beamline now exceeds 40 GB/s, posing unprecedented challenges for the online processing and offline storage of the big data. Such challenge is particularly prominent for x-ray photon correlation spectroscopy (XPCS), where real time analyses require simultaneous calculation on all the previously acquired data in the time series. We present a homomorphic compression scheme to effectively reduce the computational time and memory space required for XPCS analysis. Leveraging similarities in the mathematical expression between a matrix-based compression algorithm and the correlation calculation, our approach allows direct operation on the compressed data without their decompression. The lossy compression reduces the computational time by a factor of 10,000, enabling real time calculation of the correlation functions at kHz framerate. Our demonstration of a homomorphic compression of scientific data provides an effective solution to the big data challenge at coherent light sources. Beyond the example shown in this work, the framework can be extended to facilitate real-time operations directly on a compressed data stream for other techniques.

The tilt-to-length coupling during the LISA Pathfinder mission has been numerically and analytically modeled for particular timespans. In this work, we investigate the long-term stability of the coupling coefficients of this noise. We show that they drifted slowly (by 1\,$\mu$m/rad and 6$\times10^{-6}$ in 100 days) and strongly correlated to temperature changes within the satellite (8\,$\mu$m/rad/K and 30$\times10^{-6}$/K). Based on analytical TTL coupling models, we attribute the temperature-driven coupling changes to rotations of the test masses and small distortions in the optical setup. Particularly, we show that LISA Pathfinder's optical baseplate was bent during the cooldown experiment, which started in late 2016 and lasted several months.

A precise characterization of the magnetic properties of LISA Pathfinder free falling test-masses is of special interest for future gravitational wave observatory in space. Magnetic forces have an important impact on the instrument sensitivity in the low frequency regime below the millihertz. In this paper we report on the magnetic injection experiments performed throughout LISA Pathfinder operations. We show how these experiments allowed a high precision estimate of the instrument magnetic parameters. The remanent magnetic moment was found to have a modulus of $(0.245\pm0.081)\,\rm{nAm}^2$, the x-component of the background magnetic field within the test masses position was measured to be $(414 \pm 74)$ nT and its gradient had a value of $(-7.4\pm 2.1)\,\mu$T/m. Finally, we also measured the test mass magnetic susceptibility to be $(-3.35\pm0.15)\times$10$^{-5}$ in the low frequency regime. All results are in agreement with on-ground estimates.

LISA Pathfinder was a mission designed to test key technologies required for gravitational wave detection in space. Magnetically driven forces play a key role in the instrument sensitivity in the low-frequency regime, which corresponds to the measurement band of interest for future space-borne gravitational wave observatories. Magnetic-induced forces couple to the test mass motion, introducing a contribution to the relative acceleration noise between the free falling test masses. In this Letter we present the first complete estimate of this term of the instrument performance model. Our results set the magnetic-induced acceleration noise during the February 2017 noise run of $\rm 0.25_{-0.08}^{+0.15}\,fm\,s^{-2}/\sqrt{Hz}$ at 1 mHz and $\rm 1.01_{-0.24}^{+0.73}\, fm\,s^{-2}/\sqrt{Hz}$ at 0.1 mHz. We also discuss how the non-stationarities of the interplanetary magnetic field can affect these values during extreme space weather conditions.

Scanning X-ray nanodiffraction microscopy is a powerful technique for spatially resolving nanoscale structural morphologies by diffraction contrast. One of the critical challenges in experimental nanodiffraction data analysis is posed by the convergence angle of nanoscale focusing optics which creates simultaneous dependency of the far-field scattering data on three independent components of the local strain tensor - corresponding to dilation and two potential rigid body rotations of the unit cell. All three components are in principle resolvable through a spatially mapped sample tilt series however traditional data analysis is computationally expensive and prone to artifacts. In this study, we implement NanobeamNN, a convolutional neural network specifically tailored to the analysis of scanning probe X-ray microscopy data. NanobeamNN learns lattice strain and rotation angles from simulated diffraction of a focused X-ray nanobeam by an epitaxial thin film and can directly make reasonable predictions on experimental data without the need for additional fine-tuning. We demonstrate that this approach represents a significant advancement in computational speed over conventional methods, as well as a potential improvement in accuracy over the current standard.

Infants learn actively in their environments, shaping their own learning curricula. They learn about their environments' affordances, that is, how local circumstances determine how their behavior can affect the environment. Here we model this type of behavior by means of a deep learning architecture. The architecture mediates between global cognitive map exploration and local affordance learning. Inference processes actively move the simulated agent towards regions where they expect affordance-related knowledge gain. We contrast three measures of uncertainty to guide this exploration: predicted uncertainty of a model, standard deviation between the means of several models (SD), and the Jensen-Shannon Divergence (JSD) between several models. We show that the first measure gets fooled by aleatoric uncertainty inherent in the environment, while the two other measures focus learning on epistemic uncertainty. JSD exhibits the most balanced exploration strategy. From a computational perspective, our model suggests three key ingredients for coordinating the active generation of learning curricula: (1) Navigation behavior needs to be coordinated with local motor behavior for enabling active affordance learning. (2) Affordances need to be encoded locally for acquiring generalized knowledge. (3) Effective active affordance learning mechanisms should use density comparison techniques for estimating expected knowledge gain. Future work may seek collaborations with developmental psychology to model active play in children in more realistic scenarios.

We present an in-depth analysis of the LISA Pathfinder differential acceleration performance over the entire course of its science operations, spanning approximately 500 days. We find that: 1) the evolution of the Brownian noise that dominates the acceleration amplitude spectral density (ASD), for frequencies $f\gtrsim 1\,\text{mHz}$, is consistent with the decaying pressure due to the outgassing of a single gaseous species. 2) between $f=36\,\mu\text{Hz}$ and $1\,\text{mHz}$, the acceleration ASD shows a $1/f$ tail in excess of the Brownian noise of almost constant amplitude, with $\simeq 20\%$ fluctuations over a period of a few days, with no particular time pattern over the course of the mission; 3) at the lowest considered frequency of $f=18\,\mu\text{Hz}$, the ASD significantly deviates from the $1/f$ behavior, because of temperature fluctuations that appear to modulate a quasi-static pressure gradient, sustained by the asymmetries of the outgassing pattern. We also present the results of a projection of the observed acceleration noise on the potential sources for which we had either a direct correlation measurement, or a quantitative estimate from dedicated experiments. These sources account for approximately $40\%$ of the noise power in the $1/f$ tail. Finally, we analyze the possible sources of the remaining unexplained fraction, and identify the possible measures that may be taken to keep those under control in LISA.

Surface acoustic wave devices are key components for processing radio frequency signals in wireless communication because these devices offer simultaneously high performance, compact size and low cost. The optimization of the device structure requires a quantitative understanding of energy conversion and loss mechanisms. Stroboscopic full-field diffraction x-ray microscopy studies of a prototypical one-port resonator device revealed the existence of unanticipated acoustic loss. A non-uniform acoustic excitation in the active area was responsible for the substantial end and side leakages observed at the design frequency. Quantitative analysis of the strain amplitude using a wave decomposition method allowed the determination of several key device parameters. This high-resolution spatiotemporal strain imaging technique is, more generally, suited for studying nanophononics, specifically when the feature size is smaller than optical wavelengths. The strain sensitivity allows precise measurement of acoustic waves with picometer-scale amplitude.

Controlling the Mott transition through strain engineering is crucial for advancing the development and application of memristive and neuromorphic computing devices. Yet, Mott insulators are heterogeneous due to intrinsic phase boundaries and extrinsic defects, posing significant challenges to fully understanding the impact of local microscopic distortions on the local Mott transition. Addressing these challenges demands structural characterizations at the relevant length scale. Here, using a synchrotron-based scanning X-ray nanoprobe, we studied the real-space structural heterogeneity during the structural phase transition in a V2O3 thin film. Through temperature-dependent metal-insulator phase coexistence mapping, we report a variation in the local transition temperature of up to 7 K across the film and the presence of the transition hysteresis at the nanoscale. Furthermore, a detailed quantitative analysis demonstrates that the spatial heterogeneity of the transition is closely tied to the tilting of crystallographic planes in the pure insulating phase. Our work highlights the impact of local heterogeneity on the Mott transition and lays the groundwork for future innovations in harnessing strain heterogeneity within Mott systems for the next-generation computational technologies.

Strongly correlated electron materials are often characterized by competition and interplay of multiple quantum states. For example, in high-temperature cuprate superconductors unconventional superconductivity, spin- and charge-density wave orders coexist. A key question is whether competing states coexist on the atomic scale or if they segregate into distinct 'islands'. Using X-ray diffraction, we investigate the competition between charge order and superconductivity in the archetypal cuprate La(2-x)BaxCuO4, around the x = 1/8-doping, where uniaxial stress restores optimal 3D superconductivity at approximately 0.06 GPa. We find that the charge order peaks and the correlation length along the stripe are strongly reduced up to the critical stress, above which they stay constant. Simultaneously, the charge order onset temperature only shows a modest decrease. Our findings suggest that optimal 3D superconductivity is not linked to the absence of charge stripes but instead requires their arrangement into smaller 'islands'. Our results provide insight into the length scales over which the interplay between superconductivity and charge order takes place.

The great scientific and technological advances that are being carried out in the field of quantum communications, accompanied by large investment programs such as EuroQCI, are driving the deployment of quantum network throughout the world. One of the final long-term objectives is to achieve the development of a quantum internet that provides greater security in its services and new functionalities that the current internet does not have. This article analyzes the possible integration strategies of already deployed networks or in the process of being deployed in order to reach a future global quantum network. Two strategies based on the SDN paradigm are proposed, based on a hierarchical controller scheme and on a distributed model. Each of these approaches shows pros and cons and could be applicable in different use cases. To define these strategies, the most relevant deployments of quantum communications networks carried out to date has been analyzed, as well as the different approaches for a quantum network architecture and topology, and the various proposed definitions of what quantum internet is and what are the components that would make it up in an ideal scenario. Finally, several detected opportunities and challenges regarding security and technological aspects are presented.

Electrostatic force actuation is a key component of the system of geodesic reference test masses (TM) for the LISA orbiting gravitational wave observatory and in particular for performance at low frequencies, below 1 mHz, where the observatory sensitivity is limited by stray force noise. The system needs to apply forces of order 10$^{-9}$ N while limiting fluctuations in the measurement band to levels approaching 10$^{-15}$ N/Hz$^{1/2}$. We present here the LISA actuation system design, based on audio-frequency voltage carrier signals, and results of its in-flight performance test with the LISA Pathfinder test mission. In LISA, TM force actuation is used to align the otherwise free-falling TM to the spacecraft-mounted optical metrology system, without any forcing along the critical gravitational wave-sensitive interferometry axes. In LISA Pathfinder, on the other hand, the actuation was used also to stabilize the TM along the critical $x$ axis joining the two TM, with the commanded actuation force entering directly into the mission's main differential acceleration science observable. The mission allowed demonstration of the full compatibility of the electrostatic actuation system with the LISA observatory requirements, including dedicated measurement campaigns to amplify, isolate, and quantify the two main force noise contributions from the actuation system, from actuator gain noise and from low frequency ``in band'' voltage fluctuations. These campaigns have shown actuation force noise to be a relevant, but not dominant, noise source in LISA Pathfinder and have allowed performance projections for the conditions expected in the LISA mission.

Bulk-mode acoustic waves in a crystalline material exert lattice strain through the thickness of the sample, which couples to the spin Hamiltonian of defect-based qubits such as the nitrogen-vacancy (NV) center defect in diamond. This mechanism has been previously harnessed for unconventional quantum spin control, spin decoherence protection, and quantum sensing. Bulk-mode acoustic wave devices are also important in the microelectronics industry as microwave filters. A key challenge in both applications is a lack of appropriate operando microscopy tools for quantifying and visualizing gigahertz-frequency dynamic strain. In this work, we directly image acoustic strain within NV center-coupled diamond thin-film bulk acoustic wave resonators using stroboscopic scanning hard X-ray diffraction microscopy at the Advanced Photon Source. The far-field scattering patterns of the nano-focused X-ray diffraction encode strain information entirely through the illuminated thickness of the resonator. These patterns have a real-space spatial variation that is consistent with the bulk strain's expected modal distribution and a momentum-space angular variation from which the strain amplitude can be quantitatively deduced. We also perform optical measurements of strain-driven Rabi precession of the NV center spin ensemble, providing an additional quantitative measurement of the strain amplitude. As a result, we directly measure the NV spin-stress coupling parameter $b = 2.73(2)$ MHz/GPa by correlating these measurements at the same spatial position and applied microwave power. Our results demonstrate a unique technique for directly imaging AC lattice strain in micromechanical structures and provide a direct measurement of a fundamental constant for the NV center defect spin Hamiltonian., Comment: 12 pages with appendix

Current quantum key distribution (QKD) networks focus almost exclusively on transporting secret keys with the highest possible rate. Consequently, they are built as mostly fixed, ad hoc, logically, and physically isolated infrastructures designed to avoid any penalty to the quantum channel. This architecture is neither scalable nor cost-effective and future, real-world deployments will differ considerably. The structure of the MadQCI QKD network presented here is based on disaggregated components and modern paradigms especially designed for flexibility, upgradability, and facilitating the integration of QKD in the security and telecommunications-networks ecosystem. These underlying ideas have been tested by deploying many QKD systems from several manufacturers in a real-world, multi-tenant telecommunications network, installed in production facilities and sharing the infrastructure with commercial traffic. Different technologies have been used in different links to address the variety of situations and needs that arise in real networks, exploring a wide range of possibilities. Finally, a set of realistic use cases have been implemented to demonstrate the validity and performance of the network. The testing took place during a period close to three years, where most of the nodes were continuously active.

Current slot-oriented approaches for compositional scene segmentation from images and videos rely on provided background information or slot assignments. We present a segmented location and identity tracking system, Loci-Segmented (Loci-s), which does not require either of this information. It learns to dynamically segment scenes into interpretable background and slot-based object encodings, separating rgb, mask, location, and depth information for each. The results reveal largely superior video decomposition performance in the MOVi datasets and in another established dataset collection targeting scene segmentation. The system's well-interpretable, compositional latent encodings may serve as a foundation model for downstream tasks.

While human infants exhibit knowledge about object permanence from two months of age onwards, deep-learning approaches still largely fail to recognize objects' continued existence. We introduce a slot-based autoregressive deep learning system, the looped location and identity tracking model Loci-Looped, which learns to adaptively fuse latent imaginations with pixel-space observations into consistent latent object-specific what and where encodings over time. The novel loop empowers Loci-Looped to learn the physical concepts of object permanence, directional inertia, and object solidity through observation alone. As a result, Loci-Looped tracks objects through occlusions, anticipates their reappearance, and shows signs of surprise and internal revisions when observing implausible object behavior. Notably, Loci-Looped outperforms state-of-the-art baseline models in handling object occlusions and temporary sensory interruptions while exhibiting more compositional, interpretable internal activity patterns. Our work thus introduces the first self-supervised interpretable learning model that learns about object permanence directly from video data without supervision.

We propose an efficient solution to the state estimation problem in multi-scan multi-sensor multiple extended target sensing scenarios. We first model the measurement process by a doubly inhomogeneous-generalized shot noise Cox process and then estimate the parameters using a jump Markov chain Monte Carlo sampling technique. The proposed approach scales linearly in the number of measurements and can take spatial properties of the sensors into account, herein, sensor noise covariance, detection probability, and resolution. Numerical experiments using radar measurement data suggest that the algorithm offers improvements in high clutter scenarios with closely spaced targets over state-of-the-art clustering techniques used in existing multiple extended target tracking algorithms.

We present a parsimonious deep learning weather prediction model to forecast seven atmospheric variables with 3-h time resolution for up to one-year lead times on a 110-km global mesh using the Hierarchical Equal Area isoLatitude Pixelization (HEALPix). In comparison to state-of-the-art (SOTA) machine learning (ML) weather forecast models, such as Pangu-Weather and GraphCast, our DLWP-HPX model uses coarser resolution and far fewer prognostic variables. Yet, at one-week lead times, its skill is only about one day behind both SOTA ML forecast models and the SOTA numerical weather prediction model from the European Centre for Medium-Range Weather Forecasts. We report several improvements in model design, including switching from the cubed sphere to the HEALPix mesh, inverting the channel depth of the U-Net, and introducing gated recurrent units (GRU) on each level of the U-Net hierarchy. The consistent east-west orientation of all cells on the HEALPix mesh facilitates the development of location-invariant convolution kernels that successfully propagate weather patterns across the globe without requiring separate kernels for the polar and equatorial faces of the cube sphere. Without any loss of spectral power after the first two days, the model can be unrolled autoregressively for hundreds of steps into the future to generate realistic states of the atmosphere that respect seasonal trends, as showcased in one-year simulations., Comment: Submitted to Journal of Advances in Modeling Earth Systems (JAMES)

With their shielded 4f orbitals, rare-earth ions (REIs) offer optical and electron spin transitions with good coherence properties even when embedded in a host crystal matrix, highlighting their utility as promising quantum emitters and memories for quantum information processing. Among REIs, trivalent erbium (Er$^{3+}$) uniquely has an optical transition in the telecom C-band, ideal for transmission over optical fibers, and making it well-suited for applications in quantum communication. The deployment of Er$^{3+}$ emitters into a thin film TiO$_2$ platform has been a promising step towards scalable integration; however, like many solid-state systems, the deterministic spatial placement of quantum emitters remains an open challenge. We investigate laser annealing as a means to locally tune the optical resonance of Er$^{3+}$ emitters in TiO$_2$ thin films on Si. Using both nanoscale X-ray diffraction measurements and cryogenic photoluminescence spectroscopy, we show that tightly focused below-gap laser annealing can induce anatase to rutile phase transitions in a nearly diffraction-limited area of the films and improve local crystallinity through grain growth. As a percentage of the Er:TiO$_2$ is converted to rutile, the Er$^{3+}$ optical transition blueshifts by 13 nm. We explore the effects of changing laser annealing time and show that the amount of optically active Er:rutile increases linearly with laser power. We additionally demonstrate local phase conversion on microfabricated Si structures, which holds significance for quantum photonics., Comment: 7 pages, 4 figures

We present a study of the tilt-to-length coupling noise during the LISA Pathfinder mission and how it depended on the system's alignment. Tilt-to-length coupling noise is the unwanted coupling of angular and lateral spacecraft or test mass motion into the primary interferometric displacement readout. It was one of the major noise sources in the LISA Pathfinder mission and is likewise expected to be a primary noise source in LISA. We demonstrate here that a recently derived and published analytical model describes the dependency of the LISA Pathfinder tilt-to-length coupling noise on the alignment of the two freely falling test masses. This was verified with the data taken before and after the realignments performed in March (engineering days) and June 2016, and during a two-day experiment in February 2017 (long cross-talk experiment). The latter was performed with the explicit goal of testing the tilt-to-length coupling noise dependency on the test mass alignment. Using the analytical model, we show that all realignments performed during the mission were only partially successful and explain the reasons why. In addition to the analytical model, we computed another physical tilt-to-length coupling model via a minimising routine making use of the long cross-talk experiment data. A similar approach could prove useful for the LISA mission.

Relaxor ferroelectrics are a class of materials that are widely perceived as deriving their exotic properties from structural heterogeneities. Understanding the microscopic origin of the superior electromechanical response requires knowledge not only concerning the formation of polar nanodomains (PNDs) built from individual atoms but more importantly the spatial distribution of PNDs over longer distances. The mesoscale PND arrangement is shaped by the interactions between these domains and, in turn, dictates the electric-field driven PND response directly relevant to the macroscopic material properties. Here, we show the emergence of mesoscale lattice order that we name "polar laminates" in the canonical relaxor ferroelectric 0.68PbMg$_{1/3}$Nb$_{2/3}$O$_{3}$-0.32PbTiO$_{3}$ (PMN-0.32PT) using X-ray coherent nano-diffraction. These laminates are nematic with a size of ~350 nm and arise from the staggered arrangement of ~13 nm monoclinic PNDs along the <110> of the pseudocubic lattice. The spatial distribution of c-axis strain is directly correlated with the tilting of the PNDs and is most prominent between the laminates. Further operando nano-diffraction studies demonstrate heterogeneous electric-field-driven responses. The most active regions tend to reside inside the laminates while the spatial pinning centers are between the laminates. This observation reveals the hierarchical assembly of lattice order as a novel form of electron and lattice self-organization in heterogenous materials and establishes the role of such mesoscale spatial arrangement in connecting the nanoscale heterogeneity and macroscopic material properties. These findings provide a guiding principle for the design and optimization of future relaxors and may shed light on the existence of similar behavior in a wide range of quantum and functional materials., Comment: 12 pages, 4 figures

Deep learning has gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based weather prediction (DLWP) models have made significant progress in the last few years, achieving forecast skills comparable to established numerical weather prediction models with comparatively lesser computational costs. In order to train accurate, reliable, and tractable DLWP models with several millions of parameters, the model design needs to incorporate suitable inductive biases that encode structural assumptions about the data and the modelled processes. When chosen appropriately, these biases enable faster learning and better generalisation to unseen data. Although inductive biases play a crucial role in successful DLWP models, they are often not stated explicitly and their contribution to model performance remains unclear. Here, we review and analyse the inductive biases of state-of-the-art DLWP models with respect to five key design elements: data selection, learning objective, loss function, architecture, and optimisation method. We identify the most important inductive biases and highlight potential avenues towards more efficient and probabilistic DLWP models.

A comprehensive summary of the measurements made to characterize test mass charging due to the space environment during the LISA Pathfinder mission is presented. Measurements of the residual charge of the test mass after release by the grabbing and positioning mechanism, show that the initial charge of the test masses was negative after all releases, leaving the test mass with a potential in the range $-12$ mV to $-512$ mV. Variations in the neutral test mass charging rate between $21.7$ e s$^{-1}$ and $30.7$ e s$^{-1}$ were observed over the course of the 17-month science operations produced by cosmic ray flux changes including a Forbush decrease associated with a small solar energetic particle event. A dependence of the cosmic ray charging rate on the test mass potential between $-30.2$ e s$^{-1}$ V$^{-1}$ and $-40.3$ e s$^{-1}$ V$^{-1}$ was observed and this is attributed to a contribution to charging from low-energy electrons emitted from the gold surfaces of the gravitational reference sensor. Data from the on-board particle detector show a reliable correlation with the charging rate and with other environmental monitors of the cosmic ray flux. This correlation is exploited to extrapolate test mass charging rates to a 20-year period giving useful insight into the expected range of charging rate that may be observed in the LISA mission., Comment: 17 pages, 10 figures

Coherent microscopy techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells. Driven by the construction of brighter sources and high-rate detectors, coherent X-ray microscopy methods like ptychography are poised to revolutionize nanoscale materials characterization. However, associated significant increases in data and compute needs mean that conventional approaches no longer suffice for recovering sample images in real-time from high-speed coherent imaging experiments. Here, we demonstrate a workflow that leverages artificial intelligence at the edge and high-performance computing to enable real-time inversion on X-ray ptychography data streamed directly from a detector at up to 2 kHz. The proposed AI-enabled workflow eliminates the sampling constraints imposed by traditional ptychography, allowing low dose imaging using orders of magnitude less data than required by traditional methods.

Superconductivity in binary ruthenium pnictides occurs proximal to and upon suppression of a mysterious non-magnetic ground state, preceded by a pseudogap phase associated with Fermi surface instability, and its critical temperature, T$_{c}$, is maximized around the pseudogap quantum critical point. By analogy with isoelectronic iron based counterparts, antiferromagnetic fluctuations became "usual suspects" as putative mediators of superconducting pairing. Here we report on a high temperature local symmetry breaking in RuP, the parent of the maximum-Tc branch of these novel superconductors, revealed by combined nanostructure-sensitive powder and single crystal X-ray total scattering experiments. Large local Ru$_{6}$ hexamer distortions associated with orbital-charge trimerization form above the two-stage electronic transition in RuP. While hexamer ordering enables the nonmagnetic ground state and presumed complex oligomerization, the relevance of pseudogap fluctuations for superconductivity emerges as a distinct prospect. As a transition metal system in which partial d-manifold filling combined with high crystal symmetry promotes electronic instabilities, this represents a further example of local electronic precursors underpinning the macroscopic collective behavior of quantum materials., Comment: 10 pages 6 figures

According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. Hierarchical reinforcement learning is a promising computational approach that may eventually yield comparable problem-solving behaviour in artificial agents and robots. However, to date the problem-solving abilities of many human and non-human animals are clearly superior to those of artificial systems. Here, we propose steps to integrate biologically inspired hierarchical mechanisms to enable advanced problem-solving skills in artificial agents. Therefore, we first review the literature in cognitive psychology to highlight the importance of compositional abstraction and predictive processing. Then we relate the gained insights with contemporary hierarchical reinforcement learning methods. Interestingly, our results suggest that all identified cognitive mechanisms have been implemented individually in isolated computational architectures, raising the question of why there exists no single unifying architecture that integrates them. As our final contribution, we address this question by providing an integrative perspective on the computational challenges to develop such a unifying architecture. We expect our results to guide the development of more sophisticated cognitively inspired hierarchical machine learning architectures., Comment: Published as accepted article in Nature Machine Intelligence: https://www.nature.com/articles/s42256-021-00433-9. arXiv admin note: substantial text overlap with arXiv:2012.10147

Photonic integrated circuits (PICs) at cryogenic temperatures enable a wide range of applications in scalable classical and quantum systems for computing and sensing. A promising application of cryogenic PICs is to provide optical interconnects by up-converting signals from electrical to optical domain, allowing massive data-transfer from 4 K superconducting (SC) electronics to room temperature environment. Such a solution is central to overcome the major bottleneck in the scalability of cryogenic systems, which currently rely on bulky copper cables that suffer from limited bandwidth, large heat load, and do not show any scalability path. A key element for realizing a cryogenic-to-room temperature optical interconnect is a high-speed electro-optic (EO) modulator operating at 4 K with operation voltage at mV scale, compatible with SC electronics. Although several cryogenic EO modulators have been demonstrated, their driving voltages are significantly large compared to the mV scale voltage required for SC circuits. Here, we demonstrate a cryogenic modulator with ~10 mV peak-to-peak driving voltage and gigabits/sec data rate, with ultra-low electric and optical energy consumptions of ~10.4 atto-joules/bit and ~213 femto-joules/bit, respectively. We achieve this record performance by designing a compact optical ring resonator modulator in a heterogeneous InP-on-Silicon platform, where we optimize a multi-quantum well layer of InAIGaAs to achieve a strong EO effect at 4 K. Unlike other semiconductors such as silicon, our platform benefits from the high-carrier mobility and minimal free carrier freezing of III-V compounds at low temperatures, with moderate doping level and low loss (intrinsic resonator Q~272,000). These modulators can pave the path for complex cryogenic photonic functionalities and massive data transmission between cryogenic and room-temperature electronics., Comment: 18 page manuscript with 5 figures and 1 table, plus 17 page supplementary material

Real-world topological semimetals typically exhibit Dirac and Weyl nodes that coexist with trivial Fermi pockets. This tends to mask the physics of the relativistic quasiparticles. Using the example of ZrTe5, we show that strain provides a powerful tool for in-situ tuning of the band structure such that all trivial pockets are pushed far away from the Fermi energy, but only for a certain range of Van der Waals gaps. Our results naturally reconcile contradicting reports on the presence or absence of additional pockets in ZrTe$_5$, and provide a clear map of where to find a pure three-dimensional Dirac semimetallic phase in the structural parameter space of the material., Comment: 17 pages

Humans can make predictions on various time scales and hierarchical levels. Thereby, the learning of event encodings seems to play a crucial role. In this work we model the development of hierarchical predictions via autonomously learned latent event codes. We present a hierarchical recurrent neural network architecture, whose inductive learning biases foster the development of sparsely changing latent state that compress sensorimotor sequences. A higher level network learns to predict the situations in which the latent states tend to change. Using a simulated robotic manipulator, we demonstrate that the system (i) learns latent states that accurately reflect the event structure of the data, (ii) develops meaningful temporal abstract predictions on the higher level, and (iii) generates goal-anticipatory behavior similar to gaze behavior found in eye-tracking studies with infants. The architecture offers a step towards the autonomous learning of compressed hierarchical encodings of gathered experiences and the exploitation of these encodings to generate adaptive behavior., Comment: accepted at ICDL 2022

To effectively perceive and process observations in our environment, feature binding and perspective taking are crucial cognitive abilities. Feature binding combines observed features into one entity, called a Gestalt. Perspective taking transfers the percept into a canonical, observer-centered frame of reference. Here we propose a recurrent neural network model that solves both challenges. We first train an LSTM to predict 3D motion dynamics from a canonical perspective. We then present similar motion dynamics with novel viewpoints and feature arrangements. Retrospective inference enables the deduction of the canonical perspective. Combined with a robust mutual-exclusive softmax selection scheme, random feature arrangements are reordered and precisely bound into known Gestalt percepts. To corroborate evidence for the architecture's cognitive validity, we examine its behavior on the silhouette illusion, which elicits two competitive Gestalt interpretations of a rotating dancer. Our system flexibly binds the information of the rotating figure into the alternative attractors resolving the illusion's ambiguity and imagining the respective depth interpretation and the corresponding direction of rotation. We finally discuss the potential universality of the proposed mechanisms.

Our brain can almost effortlessly decompose visual data streams into background and salient objects. Moreover, it can anticipate object motion and interactions, which are crucial abilities for conceptual planning and reasoning. Recent object reasoning datasets, such as CATER, have revealed fundamental shortcomings of current vision-based AI systems, particularly when targeting explicit object representations, object permanence, and object reasoning. Here we introduce a self-supervised LOCation and Identity tracking system (Loci), which excels on the CATER tracking challenge. Inspired by the dorsal and ventral pathways in the brain, Loci tackles the binding problem by processing separate, slot-wise encodings of `what' and `where'. Loci's predictive coding-like processing encourages active error minimization, such that individual slots tend to encode individual objects. Interactions between objects and object dynamics are processed in the disentangled latent space. Truncated backpropagation through time combined with forward eligibility accumulation significantly speeds up learning and improves memory efficiency. Besides exhibiting superior performance in current benchmarks, Loci effectively extracts objects from video streams and separates them into location and Gestalt components. We believe that this separation offers a representation that will facilitate effective planning and reasoning on conceptual levels., Comment: Accepted at ICLR 2023

To date, quantum communication widely relies on attenuated lasers for secret key generation. In future quantum networks fundamental limitations resulting from their probabilistic photon distribution must be overcome by using deterministic quantum light sources. Confined excitons in monolayers of transition metal dichalcogenides (TMDCs) constitute an emerging type of emitter for quantum light generation. These atomically-thin solid-state sources show appealing prospects for large-scale and low-cost device integration, meeting the demands of quantum information technologies. Here, we pioneer the practical suitability of TMDC devices in quantum communication. We employ a $\mathrm{WSe}_2$ monolayer single-photon source to emulate the BB84 protocol in a quantum key distribution (QKD) setup and achieve click rates of up to 66.95 kHz and antibunching values down to 0.034 - a performance competitive with QKD experiments using semiconductor quantum dots or color centers in diamond. Our work opens the route towards wider applications of quantum information technologies using TMDC single-photon sources., Comment: Main text: 11 pages (including 4 figures, 1 table, and 58 citations); Supplementary: 5 pages (including 6 figures) Revised version with extended discussions of the results in the main text and extended information in the supplementary

Functional properties of transition-metal oxides strongly depend on crystallographic defects. In transition-metal-oxide electrocatalysts such as SrIrO3 (SIO), crystallographic lattice deviations can affect ionic diffusion and adsorbate binding energies. Scanning x-ray nanodiffraction enables imaging of local structural distortions across an extended spatial region of thin samples. Line defects remain challenging to detect and localize using nanodiffraction, due to their weak diffuse scattering. Here we apply an unsupervised machine learning clustering algorithm to isolate the low-intensity diffuse scattering in as-grown and alkaline-treated thin epitaxially strained SIO films. We pinpoint the defect locations, find additional strain variation in the morphology of electrochemically cycled SIO, and interpret the defect type by analyzing the diffraction profile through clustering. Our findings demonstrate the use of a machine learning clustering algorithm for identifying and characterizing hard-to-find crystallographic defects in thin films of electrocatalysts and highlight the potential to study electrochemical reactions at defect sites in operando experiments., Comment: 11 pages and 4 figures in main text, supporting information included

Next-generation dark matter (DM) detectors searching for weakly interacting massive particles (WIMPs) will be sensitive to coherent scattering from solar neutrinos, demanding an efficient background-signal discrimination tool. Directional detectors improve sensitivity to WIMP DM despite the irreducible neutrino background. Wide-bandgap semiconductors offer a path to directional detection in a high-density target material. A detector of this type operates in a hybrid mode. The WIMP or neutrino-induced nuclear recoil is detected using real-time charge, phonon, or photon collection. The directional signal, however, is imprinted as a durable sub-micron damage track in the lattice structure. This directional signal can be read out by a variety of atomic physics techniques, from point defect quantum sensing to x-ray microscopy. In this white paper, we present the detector principle and review the status of the experimental techniques required for directional readout of nuclear recoil tracks. Specifically, we focus on diamond as a target material; it is both a leading platform for emerging quantum technologies and a promising component of next-generation semiconductor electronics. Based on the development and demonstration of directional readout in diamond over the next decade, a future WIMP detector will leverage or motivate advances in multiple disciplines towards precision dark matter and neutrino physics., Comment: contribution to Snowmass 2021, 30 pages + references, 14 figures; v3: journal format (20 pages + references, 14 figures), journal reference added

The Second Quantum Revolution facilitates the engineering of new classes of sensors, communication technologies, and computers with unprecedented capabilities. Supply chains for quantum technologies are emerging, some focussed on commercially available components for enabling technologies and/or quantum-technologies research infrastructures, others with already higher technology-readiness levels, near to the market. In 2018, the European Commission has launched its large-scale and long-term Quantum Flagship research initiative to support and foster the creation and development of a competitive European quantum technologies industry, as well as the consolidation and expansion of leadership and excellence in European quantum technology research. One of the measures to achieve an accelerated development and uptake has been identified by the Quantum Flagship in its Strategic Research Agenda: the promotion of coordinated, dedicated standardisation and certification efforts. Standardisation is indeed of paramount importance to facilitate the growth of new technologies, and the development of efficient and effective supply chains. The harmonisation of technologies, methodologies, and interfaces enables interoperable products, innovation, and competition, all leading to structuring and hence growth of markets. As quantum technologies are maturing, time has come to start thinking about further standardisation needs. This article presents insights on standardisation for quantum technologies from the perspective of the CEN-CENELEC Focus Group on Quantum Technologies (FGQT), which was established in June 2020 to coordinate and support the development of standards relevant for European industry and research.

Flexible, goal-directed behavior is a fundamental aspect of human life. Based on the free energy minimization principle, the theory of active inference formalizes the generation of such behavior from a computational neuroscience perspective. Based on the theory, we introduce an output-probabilistic, temporally predictive, modular artificial neural network architecture, which processes sensorimotor information, infers behavior-relevant aspects of its world, and invokes highly flexible, goal-directed behavior. We show that our architecture, which is trained end-to-end to minimize an approximation of free energy, develops latent states that can be interpreted as affordance maps. That is, the emerging latent states signal which actions lead to which effects dependent on the local context. In combination with active inference, we show that flexible, goal-directed behavior can be invoked, incorporating the emerging affordance maps. As a result, our simulated agent flexibly steers through continuous spaces, avoids collisions with obstacles, and prefers pathways that lead to the goal with high certainty. Additionally, we show that the learned agent is highly suitable for zero-shot generalization across environments: After training the agent in a handful of fixed environments with obstacles and other terrains affecting its behavior, it performs similarly well in procedurally generated environments containing different amounts of obstacles and terrains of various sizes at different locations., Comment: 26 pages, 12 figures, submitted to Frontiers in Neurorobotics

An approach is described for studying texture in nanostructured materials. The approach implements the real space texture PDF, txPDF, laid out in [Gong and Billinge (2018) arXiv:1805.10342 [cond-mat]]. It is demonstrated on a fiber textured polycrystalline Pt thin film. The approach uses 3D PDF methods to reconstruct the orientation distribution function (ODF) of the powder crystallites from a set of diffraction patterns taken at different tilt angles of the substrate with respect to the incident beam directly from the 3D PDF of the sample. A real space equivalent of the reciprocal space pole figure is defined in terms of interatomic vectors in the PDF and computed for various interatomic vectors in the Pt film. Further, it is shown how a valid isotropic PDF may be obtained from a weighted average over the tilt series, and the measurement conditions for the best approximant to the isotropic PDF from a single exposure, which for the case of the fiber textured film was in a nearly grazing incidence orientation of around 10 degrees. Finally, we describe an open source Python software package, Fourigui, that may be used to help in studies of texture from 3D reciprocal space data, and indeed for Fourier transforming and visualizing 3D PDF data in general.

We introduce a compositional physics-aware FInite volume Neural Network (FINN) for learning spatiotemporal advection-diffusion processes. FINN implements a new way of combining the learning abilities of artificial neural networks with physical and structural knowledge from numerical simulation by modeling the constituents of partial differential equations (PDEs) in a compositional manner. Results on both one- and two-dimensional PDEs (Burgers', diffusion-sorption, diffusion-reaction, Allen--Cahn) demonstrate FINN's superior modeling accuracy and excellent out-of-distribution generalization ability beyond initial and boundary conditions. With only one tenth of the number of parameters on average, FINN outperforms pure machine learning and other state-of-the-art physics-aware models in all cases -- often even by multiple orders of magnitude. Moreover, FINN outperforms a calibrated physical model when approximating sparse real-world data in a diffusion-sorption scenario, confirming its generalization abilities and showing explanatory potential by revealing the unknown retardation factor of the observed process., Comment: Accepted at ICML2022. Code available at https://github.com/CognitiveModeling/finn

A common approach to prediction and planning in partially observable domains is to use recurrent neural networks (RNNs), which ideally develop and maintain a latent memory about hidden, task-relevant factors. We hypothesize that many of these hidden factors in the physical world are constant over time, changing only sparsely. To study this hypothesis, we propose Gated $L_0$ Regularized Dynamics (GateL0RD), a novel recurrent architecture that incorporates the inductive bias to maintain stable, sparsely changing latent states. The bias is implemented by means of a novel internal gating function and a penalty on the $L_0$ norm of latent state changes. We demonstrate that GateL0RD can compete with or outperform state-of-the-art RNNs in a variety of partially observable prediction and control tasks. GateL0RD tends to encode the underlying generative factors of the environment, ignores spurious temporal dependencies, and generalizes better, improving sampling efficiency and overall performance in model-based planning and reinforcement learning tasks. Moreover, we show that the developing latent states can be easily interpreted, which is a step towards better explainability in RNNs., Comment: Accepted at NeurIPS 2021