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Measurement-based quantum computing offers a promising route towards scalable, universal photonic quantum computation. This approach relies on the deterministic and efficient generation of photonic graph states in which many photons are mutually entangled with various topologies. Recently, deterministic sources of graph states have been demonstrated with quantum emitters in both the optical and microwave domains. In this work, we demonstrate deterministic and reconfigurable graph state generation with optical solid-state integrated quantum emitters. Specifically, we use a single semiconductor quantum dot in a cavity to generate caterpillar graph states, the most general type of graph state that can be produced with a single emitter. By using fast detuned optical pulses, we achieve full control over the spin state, enabling us to vary the entanglement topology at will. We perform quantum state tomography of two successive photons, measuring Bell state fidelities up to 0.80$\pm$0.04 and concurrences up to 0.69$\pm$0.09, while maintaining high photon indistinguishability. This simple optical scheme, compatible with commercially available quantum dot-based single photon sources, brings us a step closer to fault-tolerant quantum computing with spins and photons.

Doping a self-assembled InGaAs/GaAs quantum dot (QD) with a single Mn atom, a magnetic acceptor impurity, provides a quantum system with discrete energy levels and original spin-dependent optical selection rules, which thus has large potential in quantum photonics, notably as a source of multi-entangled photons. To investigate this potential further, we perform coherent optical spectroscopy under continuous wave excitation of the 3-level V-like system formed in such a Mn-doped QD when charged by a single hole. In spite of a large inhomogeneous broadening of the optical transitions, we demonstrate Autler-Townes splitting both by resonant Raman scattering and by probe absorption spectroscopy for different saturation powers. Analysing these data with a comprehensive model based on optical Bloch equations, we show evidence for quantum interference within the V-like system and assess the pure dephasing rate between the corresponding spin states., Comment: 10 pages, 6 figures

Let $G$ be an abelian group. The main theorem of this paper asserts that there exists a Cayley graph on $G$ with chromatic number 3 if and only if $G$ is not of exponent 1, 2, or 4. Although motivated by ideas from algebraic topology, our proof may be expressed purely combinatorially. As a by-product, we derive a topological result which is of independent interest. Suppose $X$ is a connected non-bipartite graph, and let $\mathcal N(X)$ denote its neighborhood complex. We show that if the fundamental group $\pi_1(\mathcal N(X))$ or first homology group $H_1(\mathcal N(X))$ is torsion, then the chromatic number of $X$ is at least 4. This strengthens a classical result of Lov\'asz, which derives the same conclusion if $\pi_1(\mathcal N(X))$ is trivial., Comment: 11 pages

The frequency or color of photons is an attractive degree of freedom to encode and distribute the quantum information over long distances. However, the generation of frequency-encoded photonic qubits has so far relied on probabilistic non-linear single-photon sources and inefficient gates. Here, we demonstrate the deterministic generation of photonic qubits hyper-encoded in frequency and polarization based on a semiconductor quantum dot in a cavity. We exploit the double dipole structure of a neutral exciton and demonstrate the generation of any quantum superposition in amplitude and phase, controlled by the polarization of the pump laser pulse. The source generates frequency-polarization single-photon qubits at a rate of 4 MHz corresponding to a generation probability at the first lens of 28 $\pm$ 2%, with a photon number purity > 98%. The photons show an indistinguishability > 91% for each dipole and 88% for a balanced quantum superposition of both. The density matrix of the hyper-encoded photonic state is measured by time-resolved polarization tomography, evidencing a fidelity to the target state of 94 $\pm$ 8% and concurrence of 77 $\pm$ 2%, here limited by frequency overlap in our device. Our approach brings the advantages of quantum dot sources to the field of quantum information processing based on frequency encoding.

Josephson junction spectroscopy is a powerful local microwave spectroscopy technique that has promising potential as a diagnostic tool to probe the microscopic origins of noise in superconducting qubits. We present advancements toward realizing Josephson junction spectroscopy in a scanning geometry, where the Josephson junction is formed between a superconducting sample and a high capacitance superconducting STM tip. Data from planar Nb-based Josephson junction devices first demonstrate the benefits of including a high capacitance shunt across the junction, which decreases linewidth and improves performance at elevated temperatures. We show how an equivalent circuit can be implemented by utilizing a planarized STM tip with local prominences, which are fabricated via electron beam lithography and reactive ion etching, followed by coating with a superconducting layer. Differential conductance measurements on a superconducting NbN surface demonstrate the ability of these high capacitance tips to decrease both thermal noise and P(E)-broadening in comparison to typical wire tips., Comment: 8 pages, 7 figures

A major challenge for transformers is generalizing to sequences longer than those observed during training. While previous works have empirically shown that transformers can either succeed or fail at length generalization depending on the task, theoretical understanding of this phenomenon remains limited. In this work, we introduce a rigorous theoretical framework to analyze length generalization in causal transformers with learnable absolute positional encodings. In particular, we characterize those functions that are identifiable in the limit from sufficiently long inputs with absolute positional encodings under an idealized inference scheme using a norm-based regularizer. This enables us to prove the possibility of length generalization for a rich family of problems. We experimentally validate the theory as a predictor of success and failure of length generalization across a range of algorithmic and formal language tasks. Our theory not only explains a broad set of empirical observations but also opens the way to provably predicting length generalization capabilities in transformers.

Scanning tunneling potentiometry (STP) is used to probe the local, current-induced electrochemical potential of carriers in graphene near circular electrostatic barriers in an out-of-plane magnetic field ranging from 0 to 1.4 T. These measurements provide nanometer-resolved information about the local motion of carriers, revealing significant changes in carrier dynamics with increasing field strength. At low magnetic fields the electrochemical potential displays a spiral-like pattern, while at high fields it exhibits distinct changes at particular radii. We show that the observed behavior indicates a transition from diffusive to ballistic transport. Additionally, the sharp changes in the measured potential profile at high fields result from the `spirograph' motion of carriers, which creates a local enhancement of the Hall field one cyclotron diameter away from the semiclassical turning point near the electrostatic barrier., Comment: 18 pages, 6 figures

We present team semantics for two of the most important linear and branching time specification languages, Linear Temporal Logic (LTL) and Computation Tree Logic (CTL). With team semantics, LTL is able to express hyperproperties, which have in the last decade been identified as a key concept in the verification of information flow properties. We study basic properties of the logic and classify the computational complexity of its satisfiability, path, and model checking problem. Further, we examine how extensions of the basic logic react to adding additional atomic operators. Finally, we compare its expressivity to the one of HyperLTL, another recently introduced logic for hyperproperties. Our results show that LTL with team semantics is a viable alternative to HyperLTL, which complements the expressivity of HyperLTL and has partially better algorithmic properties. For CTL with team semantics, we investigate the computational complexity of the satisfiability and model checking problem. The satisfiability problem is shown to be EXPTIME-complete while we show that model checking is PSPACE-complete.

A key element of many odometry pipelines using spinning frequency-modulated continuous-wave (FMCW) radar is the extraction of a point-cloud from the raw signal. This extraction greatly impacts the overall performance of point-cloud-based odometry. This paper provides a first-of-its-kind, comprehensive comparison of 13 common radar point-cloud extractors for the task of iterative closest point based odometry in autonomous driving environments. Each extractor's parameters are tuned and tested on two FMCW radar datasets using approximately 176km of data from public roads. We find that the simplest, and fastest extractor, K-strongest, is the best overall extractor, consistently outperforming the average by 13.59% and 24.94% on each dataset, respectively. Additionally, we highlight the significance of tuning an extractor and the substantial improvement in odometry accuracy that it yields., Comment: 8 pages, 2 figures, 1 table. Submitted to IEEE International Conference on Robotics and Automation (ICRA)

The digital footprints of today's internet-active individuals are a testament to their lives, and have the potential to become digital legacies once they pass on. Future descendants of those alive today will greatly appreciate the unprecedented insight into the lives of their long since deceased ancestors, but this can only occur if today we have a process for data preservation and handover after death. Many prominent online platforms offer nebulous or altogether absent policies regarding posthumous data handling, and despite recent advances it is currently unclear who the average Australian would like their data to be managed after their death (i.e., social media platforms, a trusted individual, or another digital executor). While at present the management of deceased accounts is largely performed by the platform (e.g., Facebook), it is conceivable that many Australians may not trust such platforms to do so with integrity. This study aims to further the academic conversation around posthumous data by delving deeper into the preferences of the Australian Public regarding the management of their data after death, ultimately to inform future development of research programs and industry solutions. A survey of 1020 Australians revealed that most desired a level of control over how their data is managed after death. Australians currently prefer to entrust the management of their data to a trusted close individual or third party software that they can administrate themselves. As expected, social media companies ranked low regarding both trust and convenience to manage data after death. Future research focus should be to conceptualise and develop a third-party solution that enables these preferences to be realised. Such a solution could interface with the major online vendors (social media, cloud hosting etc.) to action the deceased's will., Comment: Accepted at 18th International Symposium on Human Aspects of Information Security and Assurance

We emphasize the usefulness of treating delta resonances as explicit degrees of freedom in applications of chiral effective field theory (EFT) to parity-violating and time-reversal-violating (PVTV) nuclear interactions. Compared with the delta-less framework, the explicit inclusion of the delta isobar allows one to resum certain types of contributions to the PVTV two-pion exchange two- and three-nucleon potentials without at the same time introducing any unknown parameters up to next-to-next-to-leading order in the EFT expansion. We provide the corresponding expressions for the delta contributions in momentum and coordinate spaces and compare the convergence of the EFT expansion in both formulations., Comment: 11 pages, 4 figures, 1 table

We develop an in-situ index of refraction profile using the transit time of radio signals broadcast from an englacial transmitter to 2-5 km distant radio-frequency receivers, deployed at depths up to 200 m. Maxwell's equations generally admit two ray propagation solutions from a given transmitter, corresponding to a direct path (D) and a refracted path (R); the measured D vs. R (dt(D,R)) timing differences provide constraints on the index of refraction profile near South Pole, where the Askaryan Radio Array (ARA) neutrino observatory is located. We constrain the refractive index profile by simulating D and R ray paths via ray tracing and comparing those to measured dt(D,R) signals. Using previous ice density data as a proxy for n(z), we demonstrate that our data strongly favors a glaciologically-motivated three-phase densification model rather than a single exponential scale height model. Simulations show that the single exponential model overestimates ARA neutrino sensitivity compared to the three-phase model.

Quantum field theory predicts a nonlinear response of the vacuum to strong electromagnetic fields of macroscopic extent. This fundamental tenet has remained experimentally challenging and is yet to be tested in the laboratory. A particularly distinct signature of the resulting optical activity of the quantum vacuum is vacuum birefringence. This offers an excellent opportunity for a precision test of nonlinear quantum electrodynamics in an uncharted parameter regime. Recently, the operation of the high-intensity laser ReLaX provided by the Helmholtz International Beamline for Extreme Fields (HIBEF) has been inaugurated at the High Energy Density (HED) scientific instrument of the European XFEL. We make the case that this worldwide unique combination of an x-ray free-electron laser and an ultra-intense near-infrared laser together with recent advances in high-precision x-ray polarimetry, refinements of prospective discovery scenarios, and progress in their accurate theoretical modelling have set the stage for performing an actual discovery experiment of quantum vacuum nonlinearity., Comment: 34 pages, 21 figures

Across a number of sign languages, temporal and spatial characteristics of dominant hand articulation are used to express semantic and grammatical features. In this study of Austrian Sign Language (\"Osterreichische Geb\"ardensprache, or \"OGS), motion capture data of four Deaf signers is used to quantitatively characterize the kinematic parameters of sign production in verbs and adjectives. We investigate (1) the difference in production between verbs involving a natural endpoint (telic verbs; e.g. arrive) and verbs lacking an endpoint (atelic verbs; e.g. analyze), and (2) adjective signs in intensified vs. non-intensified (plain) forms. Motion capture data analysis using linear-mixed effects models (LME) indicates that both the endpoint marking in verbs, as well as marking of intensification in adjectives, are expressed by movement modulation in \"OGS. While the semantic distinction between verb types (telic/atelic) is marked by higher peak velocity and shorter duration for telic signs compared to atelic ones, the grammatical distinction (intensification) in adjectives is expressed by longer duration for intensified compared to non-intensified adjectives. The observed individual differences of signers might be interpreted as personal signing style., Comment: 10 pages, 7 figures

The Radio Neutrino Observatory - Greenland (RNO-G) seeks discovery of ultra-high energy neutrinos from the cosmos through their interactions in ice. The science program extends beyond particle astrophysics to include radioglaciology and, as we show herein, solar observations, as well. Currently seven of 35 planned radio-receiver stations (24 antennas/station) are operational. These stations are sensitive to impulsive radio signals with frequencies between 80 and 700 MHz and feature a neutrino trigger threshold for recording data close to the thermal floor. RNO-G can also trigger on elevated signals from the Sun, resulting in nanosecond resolution time-domain flare data; such temporal resolution is significantly shorter than from most dedicated solar observatories. In addition to possible RNO-G solar flare polarization measurements, the Sun also represents an extremely useful above-surface calibration source. Using RNO-G data recorded during the summers of 2022 and 2023, we find signal excesses during solar flares reported by the solar-observing Callisto network and also in coincidence with $\sim$2/3 of the brightest excesses recorded by the SWAVES satellite. These observed flares are characterized by significant time-domain impulsivity. Using the known position of the Sun, the flare sample is used to calibrate the RNO-G absolute pointing on the radio signal arrival direction to sub-degree resolution. We thus establish the Sun as a regularly observed astronomical calibration source to provide the accurate absolute pointing required for neutrino astronomy.

In this paper, an Ultra-Wideband (UWB) positioning system is introduced, that leverages six identical custom-designed boards, each featuring an ESP32 microcontroller and a DWM3000 module from Quorvo. The system is capable of achieving localization with an accuracy of up to 10 cm, by utilizing Two-Way-Ranging (TWR) measurements between one designated tag and five anchor devices. The gathered distance measurements are subsequently processed by an Extended Kalman Filter (EKF) running locally on the tag board, enabling it to determine its own position, relying on fixed, a priori known positions of the anchor boards. This paper presents a comprehensive overview of the systems architecture, the key components, and the capabilities it offers for indoor positioning and tracking applications.

Visual imitation learning has achieved impressive progress in learning unimanual manipulation tasks from a small set of visual observations, thanks to the latest advances in computer vision. However, learning bimanual coordination strategies and complex object relations from bimanual visual demonstrations, as well as generalizing them to categorical objects in novel cluttered scenes remain unsolved challenges. In this paper, we extend our previous work on keypoints-based visual imitation learning (\mbox{K-VIL})~\cite{gao_kvil_2023} to bimanual manipulation tasks. The proposed Bi-KVIL jointly extracts so-called \emph{Hybrid Master-Slave Relationships} (HMSR) among objects and hands, bimanual coordination strategies, and sub-symbolic task representations. Our bimanual task representation is object-centric, embodiment-independent, and viewpoint-invariant, thus generalizing well to categorical objects in novel scenes. We evaluate our approach in various real-world applications, showcasing its ability to learn fine-grained bimanual manipulation tasks from a small number of human demonstration videos. Videos and source code are available at https://sites.google.com/view/bi-kvil.

Live programming provides feedback on run-time behavior by visualizing concrete values of expressions close to the source code. When using such a local perspective on run-time behavior, programmers have to mentally reconstruct the control flow if they want to understand the relation between observed values. As this requires complete and correct knowledge of all relevant code, this reconstruction is impractical for larger programs as well as in the case of unexpected program behavior. In turn, cross-cutting perspectives on run-time behavior can visualize the actual control flow directly. At the same time, cross-cutting perspectives are often difficult to navigate due to the large number of run-time events. We propose to integrate cross-cutting perspectives into live programming environments based on local perspectives so that the two complement each other: the cross-cutting perspective provides an overview of the run-time behavior; the local perspective provides detailed feedback as well as points of interest to navigate the cross-cutting perspective. We present a cross-cutting perspective prototype in the form of a call tree browser integrated into the Babylonian/S live programming environment. In an exploratory user study, we observed that programmers found the tool useful for debugging, code comprehension, and navigation. Finally, we discuss how our prototype illustrates how the features of live programming environments may serve as the basis for other powerful dynamic development tools.

A deep understanding of quantum entanglement is vital for advancing quantum technologies. The strength of entanglement can be quantified by counting the degrees of freedom that are entangled, which results in a quantity called Schmidt number. A particular challenge is to identify the strength of entanglement in quantum states which remain positive under partial transpose (PPT), otherwise recognized as undistillable states. Finding PPT states with high Schmidt number has become a mathematical and computational challenge. In this work, we introduce efficient analytical tools for calculating the Schmidt number for a class of bipartite states, called generalized grid states. Our methods improve the best known bounds for PPT states with high Schmidt number. Most notably, we construct a Schmidt number three PPT state in five dimensional systems and a family of states with a Schmidt number of $(d+1)/2$ for odd $d$-dimensional systems, representing the best-known scaling of the Schmidt number in a local dimension. Additionally, these states possess intriguing geometrical properties, which we utilize to construct indecomposable entanglement witnesses., Comment: 14 pages, 6 figures, comments welcome

Spin noise spectroscopy has become a widespread technique to extract information on spin dynamics in atomic and solid-state systems, in a potentially non-invasive way, through the optical probing of spin fluctuations. Here we experimentally demonstrate a new approach in spin noise spectroscopy, based on the detection of single photons. Due to the large spin-dependent polarization rotations provided by a deterministically-coupled quantum dot-micropillar device, giant spin noise signals induced by a single-hole spin are extracted in the form of photon-photon cross-correlations. Ultimately, such a technique can be extended to an ultrafast regime probing mechanisms down to few tens of picoseconds.

Advancements in medical imaging and endovascular grafting have facilitated minimally invasive treatments for aortic diseases. Accurate 3D segmentation of the aorta and its branches is crucial for interventions, as inaccurate segmentation can lead to erroneous surgical planning and endograft construction. Previous methods simplified aortic segmentation as a binary image segmentation problem, overlooking the necessity of distinguishing between individual aortic branches. In this paper, we introduce Context Infused Swin-UNet (CIS-UNet), a deep learning model designed for multi-class segmentation of the aorta and thirteen aortic branches. Combining the strengths of Convolutional Neural Networks (CNNs) and Swin transformers, CIS-UNet adopts a hierarchical encoder-decoder structure comprising a CNN encoder, symmetric decoder, skip connections, and a novel Context-aware Shifted Window Self-Attention (CSW-SA) as the bottleneck block. Notably, CSW-SA introduces a unique utilization of the patch merging layer, distinct from conventional Swin transformers. It efficiently condenses the feature map, providing a global spatial context and enhancing performance when applied at the bottleneck layer, offering superior computational efficiency and segmentation accuracy compared to the Swin transformers. We trained our model on computed tomography (CT) scans from 44 patients and tested it on 15 patients. CIS-UNet outperformed the state-of-the-art SwinUNetR segmentation model, which is solely based on Swin transformers, by achieving a superior mean Dice coefficient of 0.713 compared to 0.697, and a mean surface distance of 2.78 mm compared to 3.39 mm. CIS-UNet's superior 3D aortic segmentation offers improved precision and optimization for planning endovascular treatments. Our dataset and code will be publicly available.

We study two-sample tests for relevant differences in persistence diagrams obtained from $L^p$-$m$-approximable data $(\mathcal{X}_t)_t$ and $(\mathcal{Y}_t)_t$. To this end, we compare variance estimates w.r.t.\ the Wasserstein metrics on the space of persistence diagrams. In detail, we consider two test procedures. The first compares the Fr{\'e}chet variances of the two samples based on estimators for the Fr{\'e}chet mean of the observed persistence diagrams $PD(\mathcal{X}_i)$ ($1\le i\le m$), resp., $PD(\mathcal{Y}_j)$ ($1\le j\le n$) of a given feature dimension. We use classical functional central limit theorems to establish consistency of the testing procedure. The second procedure relies on a comparison of the so-called independent copy variances of the respective samples. Technically, this leads to functional central limit theorems for U-statistics built on $L^p$-$m$-approximable sample data.

Low-energy nuclear structure and reactions can be described in a systematically improvable way using the framework of chiral effective field theory. This requires solving the quantum mechanical many-body problem with regularized nuclear forces and current operators, derived from the most general effective chiral Lagrangian. To maintain the chiral and gauge symmetries, a symmetry preserving cutoff regularization has to be employed when deriving nuclear potentials. Here, we discuss various regularization techniques and show how this task can be accomplished by regularizing the pion field in the effective chiral Lagrangian using the gradient flow method. The actual derivation of the nuclear forces and currents from the regularized effective Lagrangian can be carried out utilizing the novel path-integral approach introduced in our earlier paper., Comment: 19 pages, 4 figures

Movement primitives (MPs) are compact representations of robot skills that can be learned from demonstrations and combined into complex behaviors. However, merely equipping robots with a fixed set of innate MPs is insufficient to deploy them in dynamic and unpredictable environments. Instead, the full potential of MPs remains to be attained via adaptable, large-scale MP libraries. In this paper, we propose a set of seven fundamental operations to incrementally learn, improve, and re-organize MP libraries. To showcase their applicability, we provide explicit formulations of the spatial operations for libraries composed of Via-Point Movement Primitives (VMPs). By building on Riemannian manifold theory, our approach enables the incremental learning of all parameters of position and orientation VMPs within a library. Moreover, our approach stores a fixed number of parameters, thus complying with the essential principles of incremental learning. We evaluate our approach to incrementally learn a VMP library from motion capture data provided sequentially., Comment: This work has been submitted to the IEEE for possible publication. 7 pages, 7 figures and 2 tables