Search

The $t_{2g}$ band-structure of SrTiO$_3$-based two-dimensional electron gasses (2DEGs), have been found to play a role in features such as the superconducting dome, high-mobility transport, and the magnitude of spin-orbit coupling. This adds to the already very diverse range of phenomena, including magnetism and extreme magnetoresistance, exhibited by this particular material platform. Tuning and/or combining these intriguing attributes could yield significant progress within quantum and spintronics technologies. Doing so demands precise control of the parameters, which requires a better understanding of the factors that affect them. Here we present effects of the $t_{2g}$ band-order inversion, stemming from the growth of spinel-structured $\gamma$-Al$_2$O$_3$ onto perovskite SrTiO$_3$. Electronic transport measurements show that with LaAlO$_3$/SrTiO$_3$ as the reference, the carrier density and electron mobility are enhanced, and the sample displays a reshaping of the superconducting dome. Additionally, unpaired spins are evidenced by increasing Anomalous Hall Effect with decreasing temperature, entering the same temperature range as the superconducting transition, and a Kondo-like upturn in the sheet resistance. Finally, it is argued that the high-mobility $d_{xz/yz}$-band is more likely than the $d_{xy}$-band to host the supercurrent., Comment: 10 pages, 5 figures

Deep learning-based single-channel speaker separation has improved significantly in recent years largely due to the introduction of the transformer-based attention mechanism. However, these improvements come at the expense of intense computational demands, precluding their use in many practical applications. As a computationally efficient alternative with similar modeling capabilities, Mamba was recently introduced. We propose SepMamba, a U-Net-based architecture composed primarily of bidirectional Mamba layers. We find that our approach outperforms similarly-sized prominent models - including transformer-based models - on the WSJ0 2-speaker dataset while enjoying a significant reduction in computational cost, memory usage, and forward pass time. We additionally report strong results for causal variants of SepMamba. Our approach provides a computationally favorable alternative to transformer-based architectures for deep speech separation.

Accurate time series forecasting is a highly valuable endeavour with applications across many industries. Despite recent deep learning advancements, increased model complexity, and larger model sizes, many state-of-the-art models often perform worse or on par with simpler models. One of those cases is a recently proposed model, FITS, claiming competitive performance with significantly reduced parameter counts. By training a one-layer neural network in the complex frequency domain, we are able to replicate these results. Our experiments on a wide range of real-world datasets further reveal that FITS especially excels at capturing periodic and seasonal patterns, but struggles with trending, non-periodic, or random-resembling behavior. With our two novel hybrid approaches, where we attempt to remedy the weaknesses of FITS by combining it with DLinear, we achieve the best results of any known open-source model on multivariate regression and promising results in multiple/linear regression on price datasets, on top of vastly improving upon what FITS achieves as a standalone model., Comment: arXiv:2307.03756v3 45 pages, 36 figures

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

While numerous indices of inter-coder reliability exist, Krippendorff's {\alpha} and Cohen's \{kappa} have long dominated in communication studies and other fields, respectively. The near consensus, however, may be near the end. Recent theoretical and mathematical analyses reveal that these indices assume intentional and maximal random coding, leading to paradoxes and inaccuracies. A controlled experiment with one-way golden standard and Monte Carlo simulations supports these findings, showing that \{kappa} and {\alpha} are poor predictors and approximators of true intercoder reliability. As consensus on a perfect index remains elusive, more authors recommend selecting the best available index for specific situations (BAFS). To make informed choices, researchers, reviewers, and educators need to understand the liberal-conservative hierarchy of indices, i.e., which indices produce higher or lower scores. This study extends previous efforts by expanding the math-based hierarchies to include 23 indices and constructing six additional hierarchies using Monte Carlo simulations. These simulations account for factors like the number of categories and distribution skew. The resulting eight hierarchies display a consistent pattern and reveal a previously undetected paradox in the Ir index., Comment: 30 pages

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

Background: Voxel-based analysis (VBA) for population level radiotherapy (RT) outcomes modeling requires topology preserving inter-patient deformable image registration (DIR) that preserves tumors on moving images while avoiding unrealistic deformations due to tumors occurring on fixed images. Purpose: We developed a tumor-aware recurrent registration (TRACER) deep learning (DL) method and evaluated its suitability for VBA. Methods: TRACER consists of encoder layers implemented with stacked 3D convolutional long short term memory network (3D-CLSTM) followed by decoder and spatial transform layers to compute dense deformation vector field (DVF). Multiple CLSTM steps are used to compute a progressive sequence of deformations. Input conditioning was applied by including tumor segmentations with 3D image pairs as input channels. Bidirectional tumor rigidity, image similarity, and deformation smoothness losses were used to optimize the network in an unsupervised manner. TRACER and multiple DL methods were trained with 204 3D CT image pairs from patients with lung cancers (LC) and evaluated using (a) Dataset I (N = 308 pairs) with DL segmented LCs, (b) Dataset II (N = 765 pairs) with manually delineated LCs, and (c) Dataset III with 42 LC patients treated with RT. Results: TRACER accurately aligned normal tissues. It best preserved tumors, blackindicated by the smallest tumor volume difference of 0.24\%, 0.40\%, and 0.13 \% and mean square error in CT intensities of 0.005, 0.005, 0.004, computed between original and resampled moving image tumors, for Datasets I, II, and III, respectively. It resulted in the smallest planned RT tumor dose difference computed between original and resampled moving images of 0.01 Gy and 0.013 Gy when using a female and a male reference., Comment: Minor revision under the journal of Medical Physics

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

Motivated by the need for realistic, dynamically self-consistent, evolving galaxy models that avoid the complexity of full, and zoom-in, cosmological simulations, we have developed NEXUS, an integral framework to create and evolve synthetic galaxies made of collisionless and gaseous components. NEXUS leverages the power of publicly available, tried-and-tested packages: the stellar-dynamics, action-based library AGAMA; and the adaptive mesh refinement, N-body/hydrodynamical code RAMSES, modified to meet our needs. In addition, we make use of a proprietary module to account for galaxy formation physics, including gas cooling and heating, star formation, stellar feedback, and chemical enrichment. NEXUS' basic functionality consists in the generation of bespoke initial conditions (ICs) for a diversity of galaxy models, which are advanced in time to simulate the galaxy's evolution. The fully self-consistent ICs are generated with a distribution-function based approach, as implemented in the galaxy modelling module of AGAMA -- up to now restricted to collisionless components, extended in this work to treat two types of gaseous configurations: hot halos and gas discs. NEXUS allows constructing equilibrium models with disc gas fractions $0~\leq~f_{\rm gas}~\leq~1$, appropriate to model both low- and high-redshift galaxies. Similarly, the framework is ideally suited to the study of galactic ecology, i.e. the dynamical interplay between stars and gas over billions of years. As a validation and illustration of our framework, we reproduce several isolated galaxy model setups reported in earlier studies, and present a new, `nested bar' galaxy simulation. Future upgrades of NEXUS will include magneto-hydrodynamics and highly energetic particle (`cosmic ray') heating., Comment: MNRAS accepted

Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requires flexible initial state preparation, precise time evolution, and extensive probes for final state characterization. We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. Emulating a two-dimensional (2D) XY quantum magnet, we leverage a wide range of measurement techniques to study quantum states after ramps from an antiferromagnetic initial state. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions attributed to the interplay between quantum and classical coarsening of the correlated domains. This interpretation is corroborated by injecting variable energy density into the initial state, which enables studying the effects of the eigenstate thermalization hypothesis (ETH) in targeted parts of the eigenspectrum. Finally, we digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization. These results establish the efficacy of superconducting analog-digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

Deep learning-based probabilistic models of musical data are producing increasingly realistic results and promise to enter creative workflows of many kinds. Yet they have been little-studied in a performance setting, where the results of user actions typically ought to feel instantaneous. To enable such study, we designed Notochord, a deep probabilistic model for sequences of structured events, and trained an instance of it on the Lakh MIDI dataset. Our probabilistic formulation allows interpretable interventions at a sub-event level, which enables one model to act as a backbone for diverse interactive musical functions including steerable generation, harmonization, machine improvisation, and likelihood-based interfaces. Notochord can generate polyphonic and multi-track MIDI, and respond to inputs with latency below ten milliseconds. Training code, model checkpoints and interactive examples are provided as open source software., Comment: 12 pages, 6 figures. Proceedings of the 3rd Conference on AI Music Creativity (2022, September 17)

Recent observations of high-redshift galaxies ($z \lesssim 7$) reveal that a substantial fraction have turbulent, gas-rich disks with well-ordered rotation and elevated levels of star formation. In some instances, disks show evidence of spiral arms, with bar-like structures. These remarkable observations have encouraged us to explore a new class of dynamically self-consistent models using our hydrodynamic N-body simulation framework that mimic a plausible progenitor of the Milky Way at high redshift. We explore disk gas fractions of $f_{\rm gas} = 0, 20, 40, 60, 80, 100\%$ and track the creation of stars and metals. The high gas surface densities encourage vigorous star formation, which in turn couples with the gas to drive turbulence. We explore three distinct histories: (i) there is no ongoing accretion and the gas is used up by the star formation; (ii) the star-forming gas is replenished by cooling in the hot halo gas; (iii) in a companion paper, we revisit the models in the presence of a strong perturbing force. At low $f_{\rm disk}$ ($<0.3$), where $f_{\rm disk}$ is the mass fraction of stars relative to dark matter within 2.2 $R_{\rm disk}$, a bar does not form in a stellar disk; this remains true even when gas dominates the inner disk potential. For a dominant baryon disk ($f_{\rm disk} \gtrsim 0.5$) at all gas fractions, the turbulent gas forms a strong "radial shear flow" that leads to an intermittent star-forming bar within about 500 Myr; turbulent gas speeds up the formation of bars compared to gas-free models. For $f_{\rm gas} \lesssim 60\%$, all bars survive, but for higher gas fractions, the bar devolves into a central bulge after 1 Gyr. The star-forming bars are reminiscent of recent discoveries in high-redshift ALMA observations of gaseous disks., Comment: 40 pages, 21 figures, ApJ, in press (April 2024) - simulation movies at http://www.physics.usyd.edu.au/turbo_disks

We present a novel, deep-learning based method -- dubbed Galactic-Seismology Substructures and Streams Hunter, or GS$^{3}$ Hunter for short, to search for substructures and streams in stellar kinematics data. GS$^{3}$ Hunter relies on a combined application of Siamese Neural Networks to transform the phase space information and the K-means algorithm for the clustering. As a validation test, we apply GS$^{3}$ Hunter to a subset of the Feedback in Realistic Environments (FIRE) cosmological simulations. The stellar streams and substructures thus identified are in good agreement with corresponding results reported earlier by the FIRE team. In the same vein, we apply our method to a subset of local halo stars from the Gaia Early Data Release 3 and GALAH DR3 datasets, and recover several, previously known dynamical groups, such as Thamnos 1+2, Hot Thick Disk, ED-1, L-RL3, Helmi 1+2, and Gaia-Sausage-Enceladus, Sequoia, VRM, Cronus, Nereus. Finally, we apply our method without fine-tuning to a subset of K-giant stars located in the inner halo region, obtained from the LAMOST Data Release 5 (DR5) dataset. We recover three, previously known structures (Sagittarius, Hercules-Aquila Cloud, and the Virgo Overdensity), but we also discover a number of new substructures. We anticipate that GS$^{3}$ Hunter will become a useful tool for the community dedicated to the search of stellar streams and structures in the Milky Way (MW) and the Local group, thus helping advance our understanding of the stellar inner and outer halos, and of the assembly and tidal stripping history in and around the MW., Comment: 41 pages, 34 figures, 3 tables. Accepted for Publication in the Astrophysical Journal

Histogramming is often taken for granted, but the power and compactness of partially aggregated, multidimensional summary statistics, and their fundamental connection to differential and integral calculus make them formidable statistical objects, especially when very large data volumes are involved. But expressing these concepts robustly and efficiently in high-dimensional parameter spaces and for large data samples is a highly non-trivial challenge -- doubly so if the resulting library is to remain usable by scientists as opposed to software engineers. In this paper we summarise the core principles required for consistent generalised histogramming, and use them to motivate the design principles and implementation mechanics of the re-engineered YODA histogramming library, a key component of physics data-model comparison and statistical interpretation in collider physics., Comment: 22 pages, 5 figures

In this work we study the phenomenon of self-testing from the first principles, aiming to place this versatile concept on a rigorous mathematical footing. Self-testing allows a classical verifier to infer a quantum mechanical description of untrusted quantum devices that she interacts with in a black-box manner. Somewhat contrary to the black-box paradigm, existing self-testing results tend to presuppose conditions that constrain the operation of the untrusted devices. A common assumption is that these devices perform a projective measurement of a pure quantum state. Naturally, in the absence of any prior knowledge it would be appropriate to model these devices as measuring a mixed state using POVM measurements, since the purifying/dilating spaces could be held by the environment or an adversary. We prove a general theorem allowing to remove these assumptions, thereby promoting most existing self-testing results to their assumption-free variants. On the other hand, we pin-point situations where assumptions cannot be lifted without loss of generality. As a key (counter)example we identify a quantum correlation which is a self-test only if certain assumptions are made. Remarkably, this is also the first example of a correlation that cannot be implemented using projective measurements on a bipartite state of full Schmidt rank. Finally, we compare existing self-testing definitions, establishing many equivalences as well as identifying subtle differences., Comment: 43 pages, 2 figures