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Several emission features mark semiconductor quantum dots as promising non-classical light sources for prospective quantum implementations. For long-distance transmission [1] and Si-based on-chip processing[2, 3], the possibility to match the telecom C-band [4] stands out, while source brightness and high single-photon purity are key features in virtually any quantum implementation [5, 6]. Here we present an InAs/InGaAs/GaAs quantum dot emitting in the telecom C-band coupled to a circular Bragg grating. The Purcell enhancement of the emission enables a simultaneously high brightness with a fiber-coupled single-photon count rate of 13.9MHz for an excitation repetition rate of 228MHz (first-lens collection efficiency ca. 17% for NA = 0.6), while maintaining a low multi-photon contribution of g(2)(0) = 0.0052. Moreover, the compatibility with temperatures of up to 40K attainable with compact cryo coolers, further underlines the suitability for out-of-the-lab implementations.

The potential spin-triplet heavy-fermion superconductor UTe$_2$ exhibits signatures of multiple distinct superconducting phases. For field aligned along the $b$ axis, a metamagnetic transition occurs at $\mu_0 H_\mathrm{m}\approx35\,$T. It is associated with magnetic fluctuations that may be beneficial for the field-reinforced superconductivity surviving up to $H_\mathrm{m}$. Once the field is tilted away from the $b$ towards the $c$ axis, a reentrant superconducting phase emerges just above $H_\mathrm{m}$. In order to better understand this remarkably field-resistant superconducting phase, we conducted magnetic-torque and magnetotransport measurements in pulsed magnetic fields. We determine the record-breaking upper critical field of $\mu_0 H_\mathrm{c2}\approx 73\,$T and its evolution with angle. Furthermore, the normal-state Hall effect experiences a drastic suppression indicative of a reduced band polarization above $H_\mathrm{m}$ in the angular range around $30^\circ$ caused by a partial compensation between the applied field and an exchange field. This promotes the Jaccarino-Peter effect as a likely mechanism for the reentrant superconductivity above $H_\mathrm{m}$., Comment: Main text: 29 pages, 4 figure; Supplement: 12 pages, 6 figures

The emergence of neural networks has revolutionized the field of motion synthesis. Yet, learning to unconditionally synthesize motions from a given distribution remains challenging, especially when the motions are highly diverse. In this work, we present MoDi -- a generative model trained in an unsupervised setting from an extremely diverse, unstructured and unlabeled dataset. During inference, MoDi can synthesize high-quality, diverse motions. Despite the lack of any structure in the dataset, our model yields a well-behaved and highly structured latent space, which can be semantically clustered, constituting a strong motion prior that facilitates various applications including semantic editing and crowd simulation. In addition, we present an encoder that inverts real motions into MoDi's natural motion manifold, issuing solutions to various ill-posed challenges such as completion from prefix and spatial editing. Our qualitative and quantitative experiments achieve state-of-the-art results that outperform recent SOTA techniques. Code and trained models are available at https://sigal-raab.github.io/MoDi., Comment: Video: https://youtu.be/O1sVzwrsNUg, Project page: https://sigal-raab.github.io/MoDi, Code: https://github.com/sigal-raab/MoDi

Hypothesis testing of random forest (RF) variable importance measures (VIMP) remains the subject of ongoing research. Among recent developments, heuristic approaches to parametric testing have been proposed whose distributional assumptions are based on empirical evidence. Other formal tests under regularity conditions were derived analytically. However, these approaches can be computationally expensive or even practically infeasible. This problem also occurs with non-parametric permutation tests, which are, however, distribution-free and can generically be applied to any type of RF and VIMP. Embracing this advantage, it is proposed here to use sequential permutation tests and sequential p-value estimation to reduce the high computational costs associated with conventional permutation tests. The popular and widely used permutation VIMP serves as a practical and relevant application example. The results of simulation studies confirm that the theoretical properties of the sequential tests apply, that is, the type-I error probability is controlled at a nominal level and a high power is maintained with considerably fewer permutations needed in comparison to conventional permutation testing. The numerical stability of the methods is investigated in two additional application studies. In summary, theoretically sound sequential permutation testing of VIMP is possible at greatly reduced computational costs. Recommendations for application are given. A respective implementation is provided through the accompanying R package $rfvimptest$. The approach can also be easily applied to any kind of prediction model.

We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements of the original motion, while simultaneously synthesizing novel and diverse movements. Existing data-driven techniques for motion synthesis require a large motion dataset which contains the desired and specific skeletal structure. By contrast, GANimator only requires training on a single motion sequence, enabling novel motion synthesis for a variety of skeletal structures e.g., bipeds, quadropeds, hexapeds, and more. Our framework contains a series of generative and adversarial neural networks, each responsible for generating motions in a specific frame rate. The framework progressively learns to synthesize motion from random noise, enabling hierarchical control over the generated motion content across varying levels of detail. We show a number of applications, including crowd simulation, key-frame editing, style transfer, and interactive control, which all learn from a single input sequence. Code and data for this paper are at https://peizhuoli.github.io/ganimator., Comment: SIGGRAPH 2022. Project page: https://peizhuoli.github.io/ganimator/ , Video: https://www.youtube.com/watch?v=OV9VoHMEeyI

Nuclear properties across the chart of nuclides are key to improving and validating our understanding of the strong interaction in nuclear physics. We present high-precision mass measurements of neutron-rich Fe isotopes performed at the TITAN facility. The multiple-reflection time-of-flight mass spectrometer (MR-ToF-MS), achieving a resolving power greater than $600\,000$ for the first time, enabled the measurement of $^{63-70}$Fe, including first-time high-precision direct measurements ($\delta m/m \sim 10^{-7}$) of $^{68-70}$Fe, as well as the discovery of a long-lived isomeric state in $^{69}$Fe. These measurements are accompanied by both mean-field and ab initio calculations using the most recent realizations which enable theoretical assignment of the spin-parities of the $^{69}$Fe ground and isomeric states. Together with mean-field calculations of quadrupole deformation parameters for the Fe isotope chain, these results benchmark a maximum of deformation in the $N = 40$ island of inversion in Fe, and shed light on trends in level densities indicated in the newly-refined mass surface., Comment: 9 pages, 6 figures

We propose a method for computing a sewing pattern of a given 3D garment model. Our algorithm segments an input 3D garment shape into patches and computes their 2D parameterization, resulting in pattern pieces that can be cut out of fabric and sewn together to manufacture the garment. Unlike the general state-of-the-art approaches for surface cutting and flattening, our method explicitly targets garment fabrication. It accounts for the unique properties and constraints of tailoring, such as seam symmetry, the usage of darts, fabric grain alignment, and a flattening distortion measure that models woven fabric deformation, respecting its anisotropic behavior. We bootstrap a recent patch layout approach developed for quadrilateral remeshing and adapt it to the purpose of computational pattern making, ensuring that the deformation of each pattern piece stays within prescribed bounds of cloth stress. While our algorithm can automatically produce the sewing patterns, it is fast enough to admit user input to creatively iterate on the pattern design. Our method can take several target poses of the 3D garment into account and integrate them into the sewing pattern design. We demonstrate results on both skintight and loose garments, showcasing the versatile application possibilities of our approach., Comment: Live pattern making session: https://www.youtube.com/watch?v=7b27T8rh7SU 13 pages, 24 figures

Multi-layer perceptrons (MLP) have proven to be effective scene encoders when combined with higher-dimensional projections of the input, commonly referred to as \textit{positional encoding}. However, scenes with a wide frequency spectrum remain a challenge: choosing high frequencies for positional encoding introduces noise in low structure areas, while low frequencies result in poor fitting of detailed regions. To address this, we propose a progressive positional encoding, exposing a hierarchical MLP structure to incremental sets of frequency encodings. Our model accurately reconstructs scenes with wide frequency bands and learns a scene representation at progressive level of detail \textit{without explicit per-level supervision}. The architecture is modular: each level encodes a continuous implicit representation that can be leveraged separately for its respective resolution, meaning a smaller network for coarser reconstructions. Experiments on several 2D and 3D datasets show improvements in reconstruction accuracy, representational capacity and training speed compared to baselines., Comment: ICML 2022 (spotlight)

Neural implicit fields are quickly emerging as an attractive representation for learning based techniques. However, adopting them for 3D shape modeling and editing is challenging. We introduce a method for $\mathbf{E}$diting $\mathbf{I}$mplicit $\mathbf{S}$hapes $\mathbf{T}$hrough $\mathbf{P}$art $\mathbf{A}$ware $\mathbf{G}$enera$\mathbf{T}$ion, permuted in short as SPAGHETTI. Our architecture allows for manipulation of implicit shapes by means of transforming, interpolating and combining shape segments together, without requiring explicit part supervision. SPAGHETTI disentangles shape part representation into extrinsic and intrinsic geometric information. This characteristic enables a generative framework with part-level control. The modeling capabilities of SPAGHETTI are demonstrated using an interactive graphical interface, where users can directly edit neural implicit shapes.

We report precision mass measurements of neutron-deficient gallium isotopes approaching the proton drip line. The measurements of $^{60-63}$Ga performed with the TITAN multiple-reflection time-of-flight mass spectrometer provide a more than threefold improvement over the current literature mass uncertainty of $^{61}$Ga and mark the first direct mass measurement of $^{60}$Ga. The improved precision of the $^{61}$Ga mass has important implications for the astrophysical rp process, as it constrains essential reaction Q-values near the $^{60}$Zn waiting point. Based on calculations with a one-zone model, we demonstrate the impact of the improved mass data on prediction uncertainties of X-ray burst models. The first-time measurement of the $^{60}$Ga ground-state mass establishes the proton-bound nature of this nuclide; thus, constraining the location of the proton drip line along this isotopic chain. Including the measured mass of $^{60}$Ga further enables us to extend the evaluated $T=1$ isobaric multiplet mass equation up to $A=60$., Comment: 16 pages, 6 figures

It has been proved by the authors that the (extended) Sadowsky functional can be deduced as the Gamma-limit of the Kirchhoff energy on a rectangular strip of height $\varepsilon$, as $\varepsilon$ tends to 0. In this paper we show that this Gamma-convergence result is stable when affine boundary conditions are prescribed on the short sides of the strip. These boundary conditions include those corresponding to a M\"obius band.

The properties of laser-accelerated ion bunches are demanding and require development of suitable beam diagnostics. In particular, the short and intense particle bunches with a broad energy spectrum emitted in conjunction with a strong electromagnetic pulse (EMP) are challenging for conventional and well established monitoring systems. An approach based on measuring the acoustic signals of particles depositing their energy in water, referred to as ionoacoustics [https://doi.org/10.1118/1.4905047] was recently developed into Ion-Bunch Energy Acoustic Tracing (I-BEAT)\cite{haffa_i-beat:_2019}. I-BEAT allows online detection of single proton bunches while being cost effective and EMP resistant. A simple water phantom equipped with only one ultrasound transducer positioned on the ion axis allows for reconstructing a rather complex energy spectrum that is typical for (manipulated) laser-accelerated ion bunches. To deduce the lateral bunch properties, additional transducers can be added, for example perpendicular to the ion beam axis. This established setup has been adapted for use closely behind the laser target and tested at the PHELIX laser at GSI. The capability of the system to retrieve information about the broad proton spectrum close to the source despite the harsh conditions has been demonstrated. Future improvements are required, most importantly the increase of dynamic range. Nevertheless, I-BEAT holds promise to evolve into an online diagnostic tool particularly suited for laser-driven source development and optimization at high repetition rates.

We consider curves $\gamma : [0, 1]\to\mathbb{R}^3$ endowed with an adapted orthonormal frame $r : [0, 1]\to SO(3)$. We are interested in the cases where the frame is constrained, in the sense that one of its `curvatures' (i.e., off-diagonal elements of $r'r^T$) is prescribed. One example is the Fr\'enet frame. In order to deform such constrained framed curves without spoiling the constraint, we proceed in two stages. First we deform the frame $r$ in a way that is naturally compatible with the differential constraint, by interpreting it in terms of parallel transport on the sphere. The deformation of the base curve $\gamma$ is achieved in a second step, by means of a suitable reparametrization of the frame. We illustrate this deformation procedure by providing some applications.