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We investigate Riemannian quantum-geometric structures in semiclassical transport features of two-dimensional multigap topological phases. In particular, we study nonlinear Hall-like bulk electric current responses and, accordingly, semiclassical equations of motion induced by the presence of a topological Euler invariant. We provide analytic understanding of these quantities by phrasing them in terms of momentum-space geodesics and geodesic deviation equations and further corroborate these insights with numerical solutions. Within this framework, we moreover uncover anomalous bulk dynamics associated with the second- and third-order nonlinear Hall conductivities induced by a patch Euler invariant. As a main finding, our results show how one can reconstruct the Euler invariant on coupling to electric fields at nonlinear order and from the gradients of the electric fields. Furthermore, we comment on the possibility of deducing the non-trivial non-Abelian Euler class invariant specifically in second-order nonlinear ballistic conductance measurements within a triple-contact setup, which was recently proposed to probe the Euler characteristics of more general Fermi surfaces. Generally, our results provide a route for deducing the topology in real materials that exhibit the Euler invariant by analyzing bulk electrical currents., Comment: 6+12 pages, 3+3 figures

Deep learning architectures based on convolutional neural networks tend to rely on continuous, smooth features. While this characteristics provides significant robustness and proves useful in many real-world tasks, it is strikingly incompatible with the physical characteristic of the world, which, at the scale in which humans operate, comprises crisp objects, typically representing well-defined categories. This study proposes a class of neurosymbolic systems that learn by reconstructing the observed images in terms of visual primitives and are thus forced to form high-level, structural explanations of them. When applied to the task of diagnosing abnormalities in histological imaging, the method proved superior to a conventional deep learning architecture in terms of classification accuracy, while being more transparent., Comment: 16 pages, 9 figures

Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing the capabilities of robots. When deploying learning-based approaches on real robots, extra effort is required to address the challenges posed by various real-world factors. To investigate the key factors influencing real-world deployment and to encourage original solutions from different researchers, we organized the Robot Air Hockey Challenge at the NeurIPS 2023 conference. We selected the air hockey task as a benchmark, encompassing low-level robotics problems and high-level tactics. Different from other machine learning-centric benchmarks, participants need to tackle practical challenges in robotics, such as the sim-to-real gap, low-level control issues, safety problems, real-time requirements, and the limited availability of real-world data. Furthermore, we focus on a dynamic environment, removing the typical assumption of quasi-static motions of other real-world benchmarks. The competition's results show that solutions combining learning-based approaches with prior knowledge outperform those relying solely on data when real-world deployment is challenging. Our ablation study reveals which real-world factors may be overlooked when building a learning-based solution. The successful real-world air hockey deployment of best-performing agents sets the foundation for future competitions and follow-up research directions., Comment: Accept at NeurIPS 2024 Dataset and Benchmark Track

The Fourier Transform (FT) is a fundamental tool that permeates modern science and technology. While chemistry undergraduates encounter the FT as early as second year, their courses often only mention it in passing because computers frequently perform it automatically behind the scenes. Although this automation enables students to focus on `the chemistry', students miss out on an opportunity to understand and use one of the most powerful tools in the scientific arsenal capable of revealing how time-dependent signals encode chemical structure. Although many educational resources introduce chemists to the FT, they often require familiarity with sophisticated mathematical and computational concepts. Here, we present a series of three self-contained, Python-based laboratory activities for undergraduates to understand the FT and apply it to analyze audio signals, an infrared (IR) spectroscopy interferogram, and a nuclear magnetic resonance (NMR) free induction decay (FID). In these activities, students observe how the FT reveals and quantifies the contribution of each frequency present in a temporal signal and how decay timescales dictate signal broadening. Our activities empower students with the tools to transform their own temporal datasets (e.g., FID) to a frequency spectrum. To ensure accessibility of the activities and lower the barrier to implementation, we utilize Google Colab's open-source, cloud-based platform to run Jupyter notebooks. We also offer a pre-laboratory activity that introduces students to the basics of Python and the Colab platform, and reviews the math and programming skills needed to complete the lab activities. These lab activities help students build a qualitative, quantitative, and practical understanding of the FT., Comment: 11 pages, 5 figures

Excitons dominate the optoelectronic response of many materials. Depending on the timescale and host material, excitons can exhibit free diffusion, phonon-limited diffusion, or polaronic diffusion, and exciton transport often limits the efficiency of optoelectronic devices such as solar cells or photodetectors. We demonstrate that topological excitons exhibit enhanced diffusion in all transport regimes. Appealing to notions of quantum geometry, we find that topological excitons are generically larger and more dispersive than their trivial counterparts, promoting their diffusion. We apply this general theory to a range of organic polyacene semiconductors, and show that exciton transport increases four-fold when topological excitons are present. We also propose that non-uniform electric fields can be used to directly probe the quantum metric of excitons, providing an experimental window into a basic geometric feature of quantum states. Our results provide a new avenue to enhance exciton transport and reveal that the mathematical ideas of topology and quantum geometry can be important ingredients in designing next-generation optoelectronic technologies.

This paper presents a conceptual exploration of how Digital Learning Platforms (DLPs) can be utilized to investigate the impact of language clarity, precision, engagement, and contextual relevance on mathematics learning from word problems. Focusing on three distinct DLPs--ASSISTments/E-TRIALS, MATHia/UpGrade, and Canvas/Terracotta--we propose hypothetical studies aimed at uncovering how nuanced language modifications can enhance mathematical understanding and engagement. While these studies are illustrative in nature, they provide a blueprint for researchers interested in leveraging DLPs for empirical investigation so that future investigators gain a better understanding of the emerging infrastructure for research in DLPs and the opportunities provided by them. In highlighting three distinct implementations of the same core research question, we reveal both commonalities as well as differences in how different educational technologies might build evidence, offering a unique opportunity to advance the field of math education and other education research fields.

$\beta$-NMR is an advancing technique that enables measurements relevant to various fields of research, ranging from physics to chemistry and biology. Among the recent achievements of the $\beta$-NMR setup located at the ISOLDE facility at CERN is the determination of the magnetic moment of a shortlived nucleus with a part-per-million accuracy. Presented here are major upgrades and extensions of that $\beta$-NMR setup. The most important advancement is the installation of a 4.7 T superconducting solenoidal magnet, leading to sub-ppm spatial homogeneity and temporal stability of the magnetic field. A detector array optimised for such magnetic field has also been implemented and a more powerful, time-resolved, fully-digital data acquisition system has been deployed. To commission the upgraded beamline, $\beta$-NMR resonances of short-lived 26Na were recorded in solid and liquid samples. These showed 3-fold narrower linewidths and 15-fold higher resolving power than using the previous setup. Hence, the improvements achieved here permit more accurate bio-$\beta$-NMR studies, investigating, e.g., the interaction of metal ions with biomolecules, such as DNA. They also pave the way for the first studies of the distribution of the magnetisation inside short-lived nuclei.

We report on the discovery and spectroscopic confirmation of TOI-2458 b, a transiting mini-Neptune around an F-type star leaving the main-sequence with a mass of $M_\star=1.05 \pm 0.03$ M$_{\odot}$, a radius of $R_\star=1.31 \pm 0.03$ R$_{\odot}$, an effective temperature of $T_{\rm eff}=6005\pm50$ K, and a metallicity of $-0.10\pm0.05$ dex. By combining TESS photometry with high-resolution spectra acquired with the HARPS spectrograph, we found that the transiting planet has an orbital period of $\sim$3.74 days, a mass of $M_p=13.31\pm0.99$ M$_{\oplus}$ and a radius of $R_p=2.83\pm0.20$ R$_{\oplus}$. The host star TOI-2458 shows a short activity cycle of $\sim$54 days revealed in the HARPS S-index and H$\alpha$ times series. We took the opportunity to investigate other F stars showing activity cycle periods comparable to that of TOI-2458 and found that they have shorter rotation periods than would be expected based on the gyrochronology predictions. In addition, we determined TOI-2458's stellar inclination angle to be $i_\star\,=\,10.6_{-10.6}^{+13.3}$ degrees. We discuss that both phenomena (fast stellar rotation and planet orbit inclination) could be explained by in situ formation of a hot Jupiter interior to TOI-2458 b. It is plausible that this hot Jupiter was recently engulfed by the star. Analysis of HARPS spectra has identified the presence of another planet with a period of $P\,=\,16.55\pm0.06$ days and a minimum mass of $M_p \sin i=10.22\pm1.90$ M$_{\oplus}$. Using dynamical stability analysis, we constrained the mass of this planet to the range $M_{c} \simeq (10, 25)$ M$_{\oplus}$., Comment: 25 pages, 20 figures, accepted for publication in Astronomy & Astrophysics

The exoplanet sub-Neptune population currently poses a conundrum, as to whether small-size planets are volatile-rich cores without an atmosphere, or rocky cores surrounded by a H-He envelope. To test the different hypotheses from an observational point of view, a large sample of small-size planets with precise mass and radius measurements is the first step. On top of that, much more information will likely be needed, including atmospheric characterisation and a demographic perspective on their bulk properties. We present here the concept and strategy of the THIRSTEE project, which aims to shed light on the composition of the sub-Neptune population across stellar types by increasing their number and improving the accuracy of bulk density measurements, as well as investigating their atmospheres and performing statistical, demographic analysis. We report the first results of the program, characterising a new two-planet system around the M-dwarf TOI-406. We analyse TESS and ground-based photometry, together with ESPRESSO and NIRPS/HARPS RVs to derive the orbital parameters and investigate the internal composition of the 2 planets orbiting TOI-406, which have radii and masses of $R_c = 1.32 \pm 0.12 R_{\oplus}$, $M_c = 2.08_{-0.22}^{+0.23} M_{\oplus}$ and $R_b = 2.08_{-0.15}^{+0.16} R_{\oplus}$, $M_b = 6.57_{-0.90}^{+1.00} M_{\oplus}$, and periods of $3.3$ and $13.2$ days, respectively. Planet c is consistent with an Earth-like composition, while planet b is compatible with multiple internal composition models, including volatile-rich planets without H/He atmospheres. The 2 planets are located in 2 distinct regions in the mass-density diagram, supporting the existence of a density gap among small exoplanets around M dwarfs. With an T$_{\rm eq}$ of only 368 K, TOI-406 b stands up as a particularly interesting target for atmospheric characterisation with JWST in the low-temperature regime., Comment: 22 pages, 19 figures. Accepted for publication in A&A. Please note: Planet lettering changed with respect to submitted version

We report observations of the highly active FRB 20240114A with MeerKAT using the Ultra-High Frequency (UHF; $544\text{--}1088$ MHz) and L-band ($856\text{--}1712$ MHz) receivers. A total of 62 bursts were detected in coherent tied-array beams using the MeerTRAP real-time transient detection pipeline. We measure a structure-optimising dispersion measure of $527.65\pm0.01\,\text{pc}\,\text{cm}^{-3}$ using the brightest burst in the sample. We find the bursts of FRB 20240114A are generally detected in part of the broad band of MeerKAT, $\sim40\%$ in the UHF and $\sim30\%$ in the L-band, indicating the band limited nature. We analyse the fluence distribution of the 44 bursts detected at UHF, constraining the fluence completeness limit to $\sim1\,$Jy ms, above which the cumulative burst rate follows a power law $R (>F)\propto (F/1\,\text{Jy}\,\text{ms})^\gamma$ with $\gamma=-1.8\pm0.2$. Using channelised telescope data captured in our transient buffer we localise FRB 20240114A in the image domain to RA = 21h27m39.86s, Dec = +04d19m45.01s with an uncertainty of 1.4 arcsec. This localisation allows us to confidently identify the host galaxy of FRB 20240114A. Also using the transient buffer data we perform a polarimetric study and demonstrate that most of the bursts have $\sim100\%$ linear polarisation fractions and up to $\sim20\%$ circular polarisation fractions. Finally, we predict the flux density of a potential persistent radio source (PRS) associated with FRB 20240114A is $\backsimeq[0.6\text{--}60]\,\mu\text{Jy}$ based on the simple relation between the luminosity of the PRS and the rotation measure arising from the FRB local environment., Comment: Accepted for publication in MNRAS

Following the recent interest in van der Waals (vdW) ferroelectrics, topologically nontrivial polar structures have been predicted to form in twisted bilayers. Due to the unconventional nature of vdW ferroelectricity, these topological polar structures have proven difficult to observe experimentally. Here we propose that these textures may be probed optically by showing that topological polarization textures result in exotic nonlinear optical responses. We derive this relationship analytically using non-Abelian Berry connections and a quantum-geometric framework, supported by tight-binding and first-principles calculations. For the case of moir\'e materials without centrosymmetry, which form networks of polar merons and antimerons, the shift photoconductivity forms a vortex-like structure in real space. For a range of frequencies where transitions occur at the Brillouin zone edge, the shift photocurrents are anti-parallel to the in-plane electronic polarization field. Our findings highlight the interplay between complex polarization textures and nonlinear optical responses in vdW materials and provide a sought-after strategy for the experimental detection of topological polarization structures., Comment: 9+14 pages, 4+10 figures

We report on a class of gapped projected entangled pair states (PEPS) with non-trivial Euler topology motivated by recent progress in band geometry. In the non-interacting limit, these systems have optimal conditions relating to saturation of quantum geometrical bounds, allowing for parent Hamiltonians whose lowest bands are completely flat and which have the PEPS as unique ground states. Protected by crystalline symmetries, these states evade restrictions on capturing tenfold-way topological features with gapped PEPS. These PEPS thus form the first tensor network representative of a non-interacting, gapped two-dimensional topological phase, similar to the Kitaev chain in one dimension. Using unitary circuits, we then formulate interacting variants of these PEPS and corresponding gapped parent Hamiltonians. We reveal characteristic entanglement features shared between the free-fermionc and interacting states with Euler topology. Our results hence provide a rich platform of PEPS models that have, unexpectedly, a finite topological invariant, providing a platform for new spin liquids, quantum Hall physics, and quantum information pursuits., Comment: 5+6 pages, 4+1 figures

Aims. We aim to elucidate the pulsar radio emission by studying several single-pulse phenomena, how they relate to each other, and how they evolve with observing frequency. We intend to inspire models for the pulsar radio emission and fast radio bursts. Methods. We set up an observing programme called the SUSPECT project running at the Nancay Radio Observatory telescopes in France (10 - 85 MHz, 110 - 240 MHz, 1 - 3.5 GHz) and the upgraded Giant Metrewave Radio Telescope (uGMRT) in India. In this first paper, we focus on high-sensitivity data obtained of PSR B1822-09 with the uGMRT between 550 and 750 MHz. The pulsar has precursor (PC), main pulse (MP), and interpulse (IP) emission, and exhibits mode-switching. We present its single-pulse stacks, investigate its mode-switching using a hidden Markov switching model, and analyse its single-pulse morphology. Results. PSR B1822-09's pulse profile decomposes into seven components. We show that its mode-switching is well described using a hidden Markov switching model. The pulsar exhibits at least three stable emission modes, one of which is a newly discovered bright flaring mode. We confirm that the PC and MP switch synchronously to each other, and both asynchronously to the IP, indicating information transfer between the polar caps. Additionally, we performed a fluctuation spectral analysis and discovered three fluctuation features in its quiescent Q-mode emission, one of which is well known. We conclude that it is longitude-stationary amplitude modulation. Finally, we visually classified the single-pulses into four categories. We found extensive 0.2 - 0.4 ms microstructure in the PC with a typical quasi-periodicity of 0.8 ms. There is low-level PC activity during the Q-mode, indicating mode mixing. We discovered low-intensity square-like pulses and extremely bright pulses in the MP, which suggest bursting., Comment: 19 pages, 19 figures, 2 tables. Submitted to A&A. We appreciate comments and questions

Planning robot contact often requires reasoning over a horizon to anticipate outcomes, making such planning problems computationally expensive. In this letter, we propose a learning framework for efficient contact planning in real-time subject to uncertain contact dynamics. We implement our approach for the example task of robot air hockey. Based on a learned stochastic model of puck dynamics, we formulate contact planning for shooting actions as a stochastic optimal control problem with a chance constraint on hitting the goal. To achieve online re-planning capabilities, we propose to train an energy-based model to generate optimal shooting plans in real time. The performance of the trained policy is validated %in experiments both in simulation and on a real-robot setup. Furthermore, our approach was tested in a competitive setting as part of the NeurIPS 2023 Robot Air Hockey Challenge., Comment: This work has been submitted to the IEEE Robotics & Automation Letters for possible publication

Localisation of fast radio bursts (FRBs) to arcsecond and sub-arcsecond precision maximizes their potential as cosmological probes. To that end, FRB detection instruments are deploying triggered complex-voltage capture systems to localize FRBs, identify their host galaxy and measure a redshift. Here, we report the discovery and localisation of two FRBs (20220717A and 20220905A) that were captured by the transient buffer system deployed by the MeerTRAP instrument at the MeerKAT telescope in South Africa. We were able to localize the FRBs to a precision of $\sim$1 arc-second that allowed us to unambiguously identify the host galaxy for FRB 20220717A (posterior probability$\sim$0.97). FRB 20220905A lies in a crowded region of the sky with a tentative identification of a host galaxy but the faintness and the difficulty in obtaining an optical spectrum preclude a conclusive association. The bursts show low linear polarization fractions (10--17$\%$) that conform to the large diversity in the polarization fraction observed in apparently non-repeating FRBs akin to single pulses from neutron stars. We also show that the host galaxy of FRB 20220717A contributes roughly 15$\%$ of the total dispersion measure (DM), indicating that it is located in a plasma-rich part of the host galaxy which can explain the large rotation measure. The scattering in FRB 20220717A can be mostly attributed to the host galaxy and the intervening medium and is consistent with what is seen in the wider FRB population., Comment: 13 pages, 8 figures, 1 Appendix, revised version after addressing the reviewer's comments

Excitons drive the optoelectronic properties of organic semiconductors which underpin devices including solar cells and light-emitting diodes. We demonstrate that excitons can exhibit topologically non-trivial states and identify a family of organic semiconductors exhibiting the predicted excitonic topological phases. We also show that the topological phase can be controlled through chemical functionalisation and through the dielectric environment of the material. Appealing to quantum Riemannian geometry, we predict that topologically non-trivial excitons have a lower bound on their centre-of-mass spatial spread, which can significantly exceed the size of a unit cell. The discovery of excitonic topology and excitonic Riemannian geometry in organic materials brings together two mature fields and suggests many new possibilities for a range of future optoelectronic applications., Comment: 8+8 pages, 5+3 figures

Graph Neural Networks (GNNs) have excelled in predicting graph properties in various applications ranging from identifying trends in social networks to drug discovery and malware detection. With the abundance of new architectures and increased complexity, GNNs are becoming highly specialized when tested on a few well-known datasets. However, how the performance of GNNs depends on the topological and features properties of graphs is still an open question. In this work, we introduce a comprehensive benchmarking framework for graph machine learning, focusing on the performance of GNNs across varied network structures. Utilizing the geometric soft configuration model in hyperbolic space, we generate synthetic networks with realistic topological properties and node feature vectors. This approach enables us to assess the impact of network properties, such as topology-feature correlation, degree distributions, local density of triangles (or clustering), and homophily, on the effectiveness of different GNN architectures. Our results highlight the dependency of model performance on the interplay between network structure and node features, providing insights for model selection in various scenarios. This study contributes to the field by offering a versatile tool for evaluating GNNs, thereby assisting in developing and selecting suitable models based on specific data characteristics.

We discuss a class of three-band non-Abelian topological insulators in three dimensions that carry a single bulk Hopf index protected by spatiotemporal ($\mathcal{PT}$) inversion symmetry. These phases may also host subdimensional topological invariants given by the Euler characteristic class, resulting in real Hopf-Euler insulators. Such systems naturally realize helical nodal structures in the three-dimensional Brillouin zone, providing a physical manifestation of the linking number described by the Hopf invariant. We show that, by opening a gap between the valence bands of these systems, one finds a fully-gapped ``flag'' phase, which displays a three-band multi-gap Pontryagin invariant. Unlike the previously reported $\mathcal{PT}$-symmetric four-band real Hopf insulator, which hosts a $\mathbb{Z} \oplus \mathbb{Z}$ invariant, these phases are not unitarily equivalent to two copies of a complex two-band Hopf insulator. We show that such uncharted phases can be obtained through dimensional extension of two-dimensional Euler insulators, and that they support (i) an optical bulk integrated circular shift effect quantized by the Hopf invariant, (ii) quantum-geometric breathing in the real space Wannier functions, and (iii) surface Euler topology on boundaries. Consequently, our findings pave the way for novel experimental realizations of real-space quantum-geometry, as these systems may be directly simulated by utilizing synthetic dimensions in metamaterials or ultracold atoms., Comment: 16+11 pages, 5+6 figures

Sample efficiency in Reinforcement Learning (RL) has traditionally been driven by algorithmic enhancements. In this work, we demonstrate that scaling can also lead to substantial improvements. We conduct a thorough investigation into the interplay of scaling model capacity and domain-specific RL enhancements. These empirical findings inform the design choices underlying our proposed BRO (Bigger, Regularized, Optimistic) algorithm. The key innovation behind BRO is that strong regularization allows for effective scaling of the critic networks, which, paired with optimistic exploration, leads to superior performance. BRO achieves state-of-the-art results, significantly outperforming the leading model-based and model-free algorithms across 40 complex tasks from the DeepMind Control, MetaWorld, and MyoSuite benchmarks. BRO is the first model-free algorithm to achieve near-optimal policies in the notoriously challenging Dog and Humanoid tasks., Comment: NeurIPS 2024 Spotlight

We investigate a new mode-locking regime in the singly-resonant OPO employing simultaneous amplitude- and frequency-modulation of the intracavity field. This OPO exhibits deterministic, "turn-key" formation of a stable, broadband, chirped frequency comb with high conversion efficiency. Comb-forming dynamics follow a simple phase-space dynamical model, governed by cavity dispersion and modulator chirp, which agrees closely with full numerical simulations. The comb exhibits fast, mode-hop-free tuning over the full gain window of the OPA crystal, controlled by the modulator frequency. Conditions for comb stability, and techniques to enhance comb bandwidth through intentional phase-mismatch and chirping, are investigated., Comment: 20 pages, 15 figures, 4 tables. Appendix: 10 pages, 5 figures

Liquid metal catalysts (LMCats), primarily molten copper, have demonstrated their efficiency in the chemical vapour deposition (CVD) approach for synthesising high-quality, large-area graphene. However, their high melting temperatures limit broader applications. Reducing the temperature of graphene production on LMCats would lead to a more efficient and cost-effective process. Here, we investigated the effects of alloying copper with a low-melting temperature metal on graphene growth in real-time. We examined a set of liquid copper-gallium alloy systems using two complementary in situ techniques: radiation-mode optical microscopy and synchrotron X-ray reflectivity (XRR). Microscopy observations revealed reduced catalytic activity and graphene quality degradation in compositions with gallium domination. The XRR confirmed the formation of single-layer graphene on alloys with up to 60 wt% of gallium. Additionally, we detected a systematic increase in adsorption height on the alloys' surface, suggesting a weaker graphene adhesion on gallium. These findings propose a trade-off between layer quality and production cost reduction is feasible. Our results offer insights into the CVD synthesis of graphene on bimetallic liquid surfaces and underscore the potential of gallium-copper alloys for enabling the direct transfer of graphene from a liquid substrate, thereby addressing the limitations imposed by high melting temperatures of conventional LMCats.

Two-dimensional materials are a highly tunable platform for studying the momentum space topology of the electronic wavefunctions and real space topology in terms of skyrmions, merons, and vortices of an order parameter. Such textures for electronic polarization can exist in moir\'e heterostructures. A quantum-mechanical definition of local polarization textures in insulating supercells was recently proposed. Here, we propose a definition for local polarization that is also valid for systems with topologically non-trivial bands. We introduce semilocal hybrid polarizations, which are valid even when the Wannier functions in a system cannot be made exponentially localized in all dimensions. We use this definition to explicitly show that nontrivial real-space polarization textures can exist in topologically non-trivial systems with non-zero Chern number under (1) an external superlattice potential, and (2) under a stacking-induced moir\'e potential. In the latter, we find that while the magnitude of the local polarization decreases discontinuously across a topological phase transition from trivial to topologically nontrivial, the polarization does not completely vanish. Our findings suggest that band topology and real-space polar topology may coexist in real materials., Comment: 7+7 pages, 3+2 figures

Non-prehensile manipulation such as pushing is typically subject to uncertain, non-smooth dynamics. However, modeling the uncertainty of the dynamics typically results in intractable belief dynamics, making data-efficient planning under uncertainty difficult. This article focuses on the problem of efficiently generating robust open-loop pushing plans. First, we investigate how the belief over object configurations propagates through quasi-static contact dynamics. We exploit the simplified dynamics to predict the variance of the object configuration without sampling from a perturbation distribution. In a sampling-based trajectory optimization algorithm, the gain of the variance is constrained in order to enforce robustness of the plan. Second, we propose an informed trajectory sampling mechanism for drawing robot trajectories that are likely to make contact with the object. This sampling mechanism is shown to significantly improve chances of finding robust solutions, especially when making-and-breaking contacts is required. We demonstrate that the proposed approach is able to synthesize bi-manual pushing trajectories, resulting in successful long-horizon pushing maneuvers without exteroceptive feedback such as vision or tactile feedback. We furthermore deploy the proposed approach in a model-predictive control scheme, demonstrating additional robustness against unmodeled perturbations., Comment: submitted to the International Journal of Robotics Research (IJRR)

Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications. The Segment Anything Model is one of the first and most well-known foundation models for computer vision segmentation tasks. This work presents a multi-faceted red-teaming analysis that tests the Segment Anything Model against challenging tasks: (1) We analyze the impact of style transfer on segmentation masks, demonstrating that applying adverse weather conditions and raindrops to dashboard images of city roads significantly distorts generated masks. (2) We focus on assessing whether the model can be used for attacks on privacy, such as recognizing celebrities' faces, and show that the model possesses some undesired knowledge in this task. (3) Finally, we check how robust the model is to adversarial attacks on segmentation masks under text prompts. We not only show the effectiveness of popular white-box attacks and resistance to black-box attacks but also introduce a novel approach - Focused Iterative Gradient Attack (FIGA) that combines white-box approaches to construct an efficient attack resulting in a smaller number of modified pixels. All of our testing methods and analyses indicate a need for enhanced safety measures in foundation models for image segmentation., Comment: CVPR 2024 - The 4th Workshop of Adversarial Machine Learning on Computer Vision: Robustness of Foundation Models

Interaction with others influences our opinions and behaviours. Our activities within various social circles lead to different opinions expressed in various situations, groups, and ways of communication. Earlier studies on agent-based modelling of conformism within networks were based on a single-layer approach. Contrary to that, in this work, we propose a model incorporating conformism in which a person can share different continuous opinions on different layers depending on the social circle. Afterwards, we extend the model with more components that are known to influence opinions, e.g. authority or openness to new views. These two models are then compared to show that only sole conformism leads to opinion convergence., Comment: ICCS 2023 - 23rd International Conference on Computational Science

The hydrogen deuteride (HD) molecule is an important deuterium tracer in astrophysical studies. The atmospheres of gas giants are dominated by molecular hydrogen, and simultaneous observation of H$_2$ and HD lines provides reliable information on the D/H ratios on these planets. The reference spectroscopic parameters play a crucial role in such studies. Under thermodynamic conditions encountered in these atmospheres, the spectroscopic studies of HD require not only the knowledge of line intensities and positions but also accurate reference data on pressure-induced line shapes and shifts. Our aim is to provide accurate collision-induced line-shape parameters for HD lines that cover any thermodynamic conditions relevant to the atmospheres of giant planets, i.e., any relevant temperature, pressure, and perturbing gas (the H$_2$/He mixture) composition. We perform quantum-scattering calculations on a new highly accurate ab initio potential energy surface, and we use scattering S-matrices obtained this way to determine the collision-induced line-shape parameters. We use the cavity ring-down spectroscopy for validation of our theoretical methodology. We report accurate collision-induced line-shape parameters for the pure rotational R(0), R(1), and R(2) lines, the most relevant HD lines for the investigations of atmospheres of the giant planets. Besides the basic Voigt-profile collisional parameters (i.e. the broadening and shift parameters), we also report their speed dependences and the complex Dicke parameter, which can influence the effective width and height of the HD lines up to almost a factor of 2 for giant planet conditions. The sub-percent-level accuracy, reached in this work, considerably improves the previously available data. All the reported parameters are consistent with the HITRAN database format, hence allowing for the use of HAPI for generating the beyond-Voigt spectra of HD.

On-chip photonic processors for neural networks have potential benefits in both speed and energy efficiency but have not yet reached the scale at which they can outperform electronic processors. The dominant paradigm for designing on-chip photonics is to make networks of relatively bulky discrete components connected by one-dimensional waveguides. A far more compact alternative is to avoid explicitly defining any components and instead sculpt the continuous substrate of the photonic processor to directly perform the computation using waves freely propagating in two dimensions. We propose and demonstrate a device whose refractive index as a function of space, $n(x,z)$, can be rapidly reprogrammed, allowing arbitrary control over the wave propagation in the device. Our device, a 2D-programmable waveguide, combines photoconductive gain with the electro-optic effect to achieve massively parallel modulation of the refractive index of a slab waveguide, with an index modulation depth of $10^{-3}$ and approximately $10^4$ programmable degrees of freedom. We used a prototype device with a functional area of $12\,\text{mm}^2$ to perform neural-network inference with up to 49-dimensional input vectors in a single pass, achieving 96% accuracy on vowel classification and 86% accuracy on $7 \times 7$-pixel MNIST handwritten-digit classification. This is a scale beyond that of previous photonic chips relying on discrete components, illustrating the benefit of the continuous-waves paradigm. In principle, with large enough chip area, the reprogrammability of the device's refractive index distribution enables the reconfigurable realization of any passive, linear photonic circuit or device. This promises the development of more compact and versatile photonic systems for a wide range of applications, including optical processing, smart sensing, spectroscopy, and optical communications.

We show that certain three-dimensional multigap topological insulators can host quantized integrated shift photoconductivities due to bulk invariants that are defined under reality conditions imposed by additional symmetries. We recast the quantization in terms of the integrated torsion tensor and the non-Abelian Berry connection constituting Chern-Simons forms. Physically, we recognize that the topological quantization emerges purely from virtual transitions contributing to the optical response. Our findings provide another quantized electromagnetic dc response due to the nontrivial band topology, beyond the quantum anomalous Hall effect of Chern insulators and quantized circular photogalvanic effect found in Weyl semimetals., Comment: 8+13 pages; 3+4 figures

Safety in the face of uncertainty is a key challenge in robotics. We introduce a real-time capable framework to generate safe and task-efficient robot motions for stochastic control problems. We frame this as a chance-constrained optimisation problem constraining the probability of the controlled system to violate a safety constraint to be below a set threshold. To estimate this probability we propose a Monte--Carlo approximation. We suggest several ways to construct the problem given a fixed number of uncertainty samples, such that it is a reliable over-approximation of the original problem, i.e. any solution to the sample-based problem adheres to the original chance-constraint with high confidence. To solve the resulting problem, we integrate it into our motion planner VP-STO and name the enhanced framework Chance-Constrained (CC)-VPSTO. The strengths of our approach lie in i) its generality, without assumptions on the underlying uncertainty distribution, system dynamics, cost function, or the form of inequality constraints; and ii) its applicability to MPC-settings. We demonstrate the validity and efficiency of our approach on both simulation and real-world robot experiments., Comment: 17 pages, 11 figures, submitted to IEEE Transactions on Robotics

Air hockey is a highly reactive game which requires the player to quickly reason over stochastic puck and contact dynamics. We implement a hierarchical framework which combines stochastic optimal control for planning shooting angles and sampling-based model-predictive control for continuously generating constrained mallet trajectories. Our agent was deployed and evaluated in simulation and on a physical setup as part of the Robot Air-Hockey challenge competition at NeurIPS 2023.

In existing models and embedding methods of networked systems, node features describing their qualities are usually overlooked in favor of focusing solely on node connectivity. This study introduces $FiD$-Mercator, a model-based ultra-low dimensional reduction technique that integrates node features with network structure to create $D$-dimensional maps of complex networks in a hyperbolic space. This embedding method efficiently uses features as an initial condition, guiding the search of nodes' coordinates towards an optimal solution. The research reveals that downstream task performance improves with the correlation between network connectivity and features, emphasizing the importance of such correlation for enhancing the description and predictability of real networks. Simultaneously, hyperbolic embedding's ability to reproduce local network properties remains unaffected by the inclusion of features. The findings highlight the necessity for developing network embedding techniques capable of exploiting such correlations to optimize both network structure and feature association jointly in the future.

Photonic integrated circuits with second-order ($\chi^{(2)}$) nonlinearities are rapidly scaling to remarkably low powers. At this time, state-of-the-art devices achieve saturated nonlinear interactions with thousands of photons when driven by continuous-wave lasers, and further reductions in these energy requirements enabled by the use of ultrafast pulses may soon push nonlinear optics into the realm of single-photon nonlinearities. This tutorial reviews these recent developments in ultrafast nonlinear photonics, discusses design strategies for realizing few-photon nonlinear interactions, and presents a unified treatment of ultrafast quantum nonlinear optics using a framework that smoothly interpolates from classical behaviors to the few-photon scale. These emerging platforms for quantum optics fundamentally differ from typical realizations in cavity quantum electrodynamics due to the large number of coupled optical modes. Classically, multimode behaviors have been well studied in nonlinear optics, with famous examples including soliton formation and supercontinuum generation. In contrast, multimode quantum systems exhibit a far greater variety of behaviors, and yet closed-form solutions are even sparser than their classical counterparts. In developing a framework for ultrafast quantum optics, we will identify what behaviors carry over from classical to quantum devices, what intuition must be abandoned, and what new opportunities exist at the intersection of ultrafast and quantum nonlinear optics. While this article focuses on establishing connections between the classical and quantum behaviors of devices with $\chi^{(2)}$ nonlinearities, the frameworks developed here are general and are readily extended to the description of dynamical processes based on third-order ($\chi^{(3)}$) nonlinearities., Comment: The first two authors contributed equally to this work; 183 pages, 55 figures

Young terrestrial worlds are critical test beds to constrain prevailing theories of planetary formation and evolution. We present the discovery of HD 63433d - a nearby (22pc), Earth-sized planet transiting a young sunlike star (TOI-1726, HD 63433). HD 63433d is the third planet detected in this multiplanet system. The kinematic, rotational, and abundance properties of the host star indicate that it belongs to the young (414 $\pm$ 23 Myr) Ursa Major moving group, whose membership we update using new data from Gaia DR3 and TESS. Our transit analysis of the TESS light curves indicates that HD 63433 d has a radius of 1.1 $R_\oplus$ and closely orbits its host star with a period of 4.2 days. To date, HD 63433 d is the smallest confirmed exoplanet with an age less than 500 Myr, and the nearest young Earth-sized planet. Furthermore, the apparent brightness of the stellar host (V $\approx$ 6.9 mag) makes this transiting multiplanet system favorable to further investigations, including spectroscopic follow-up to probe atmospheric loss in a young Earth-sized world., Comment: 24 pages, 7 figures, 6 tables. Accepted for publication in AJ. The first two authors contributed equally to the manuscript