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The chemistry within a protoplanetary disk is greatly affected by external radiation from the local stellar environment. Previous work has focused on extreme radiation fields, representative of the center of something like the Orion Nebula Cluster. However, even in such environments, many disks exist at the edges of a cluster where the lower stellar density leads to radiation fields weaker by orders of magnitude compared to the center. We present new chemical models of a T-Tauri disk in the presence of a moderately increased interstellar radiation field (ISRF). Such an environment has a background UV strength of 10 to 100 times higher than the galactic average ISRF. Moderate radiation fields are among the most prevalent disk-harboring environments and have interesting implications for the chemistry of the outer disk radii. We find that the external UV radiation creates an outer ionization front that impacts the cold disk chemistry to varying degrees, depending on outer disk structure. Certain molecules like C$^+$, N$_2$H$^+$, C, and CS are more strongly impacted by the ISRF in their abundance, column density, and observable emission. Other abundant species like HCO$^+$ and CO are less affected by the external UV flux in the outer disk under such moderate UV conditions. Further, we demonstrate that the chemistry occurring in the inner tens of au is relatively unchanged, which suggests that even in moderately externally irradiated disks, the inner disk chemistry may be more similar to isolated disks like those in, e.g., the Taurus and Lupus star-forming regions.

Rare-earth spin ensembles are a promising platform for microwave quantum memory applications due to their hyperfine transitions, which can exhibit exceptionally long coherence times when using an operation point with zero first-order Zeeman (ZEFOZ) shift. In this work, we use broadband electron paramagnetic resonance (EPR) spectroscopy on $^{167}$Er:$^{7}$LiYF$_4$ single crystals at sub-Kelvin temperatures. By fitting the spin Hamiltonian to the zero-field spectrum, we obtain refined parameters of the magnetic field-independent interactions, such as the hyperfine and quadrupole interaction. We also study the influence of the quadrupole interaction on the hyperfine splitting in the zero and low magnetic field range by analyzing EPR-spectra between 0 mT and 50 mT. Our findings highlight the broadband EPR spectroscopy approach as a powerful tool for the precise determination of the spin Hamiltonian parameters and for the characterization of hyperfine transitions in terms of their selection rules and linewidth., Comment: 12 pages, 6 figures

Ultra-wideband (UWB) is a state-of-the-art technology designed for applications requiring centimeter-level localization. Its widespread adoption by smartphone manufacturer naturally raises security and privacy concerns. Successfully implementing Radio Frequency Fingerprinting (RFF) to UWB could enable physical layer security, but might also allow undesired tracking of the devices. The scope of this paper is to explore the feasibility of applying RFF to UWB and investigates how well this technique generalizes across different environments. We collected a realistic dataset using off-the-shelf UWB devices with controlled variation in device positioning. Moreover, we developed an improved deep learning pipeline to extract the hardware signature from the signal data. In stable conditions, the extracted RFF achieves over 99% accuracy. While the accuracy decreases in more changing environments, we still obtain up to 76% accuracy in untrained locations., Comment: conference ICNC'25, 7 pages, 7 figures

Deep reinforcement learning (RL) policies can demonstrate unsafe behaviors and are challenging to interpret. To address these challenges, we combine RL policy model checking--a technique for determining whether RL policies exhibit unsafe behaviors--with co-activation graph analysis--a method that maps neural network inner workings by analyzing neuron activation patterns--to gain insight into the safe RL policy's sequential decision-making. This combination lets us interpret the RL policy's inner workings for safe decision-making. We demonstrate its applicability in various experiments.

In this paper, we propose a novel approach for verifying the compliance of turn-based multi-agent reinforcement learning (TMARL) agents with complex requirements in stochastic multiplayer games. Our method overcomes the limitations of existing verification approaches, which are inadequate for dealing with TMARL agents and not scalable to large games with multiple agents. Our approach relies on tight integration of TMARL and a verification technique referred to as model checking. We demonstrate the effectiveness and scalability of our technique through experiments in different types of environments. Our experiments show that our method is suited to verify TMARL agents and scales better than naive monolithic model checking.

Augmented reality applications are bitrate intensive, delay-sensitive, and computationally demanding. To support them, mobile edge computing systems need to carefully manage both their networking and computing resources. To this end, we present a proof of concept resource management scheme that adapts the bandwidth at the base station and the GPU frequency at the edge to efficiently fulfill roundtrip delay constrains. Resource adaptation is performed using a Multi-Armed Bandit algorithm that accounts for the monotonic relationship between allocated resources and performance. We evaluate our scheme by experimentation on an OpenAirInterface 5G testbed where the considered application is OpenRTiST. The results indicate that our resource management scheme can substantially reduce both bandwidth usage and power consumption while delivering high quality of service. Overall, this work demonstrates that intelligent resource control can potentially establish systems that are not only more efficient but also more sustainable.

We report on magnetic anisotropy modulation in Bismuth substituted Yttrium Iron Garnet (Bi-YIG) thin films and mesoscale patterned structures deposited on a PMN-PT substrate with the application of voltage-induced strain. The Bi content is selected for low coercivity and higher magnetostriction than that of YIG, yielding significant changes in the hysteresis loops through the magnetoelastic effect. The piezoelectric substrate is poled along its thickness, which is the [011] direction, by applying a voltage across the PMN-PT/SiO2/Bi-YIG/Pt heterostructure. In-situ magneto-optical Kerr effect microscopy (MOKE) shows the modulation of magnetic anisotropy with voltage-induced strain. Furthermore, voltage control of the magnetic domain state of the Bi-YIG film at a fixed magnetic field produces a 90{\deg} switching of the magnetization easy axis above a threshold voltage. The magnetoelectric coefficient of the heterostructure is 1.05x10^(-7)s/m which is competitive with that of other ferromagnetic oxide films on ferroelectric substrates such as La0.67Sr0.33MnO3/PMNPT and YIG/PMN-PZT. Voltage-control of magnetization reversal fields in 5-30 microns wide dots and racetracks of Bi-YIG show potential for energy efficient non-volatile memory and neuromorphic computing devices.

In the context of a gaseous-based DHCAL, we investigate the potential of using Graph Neural Networks (GNN) for hadron energy reconstruction and particle identification (PID) in future collider experiments. For PID, we demonstrate an accuracy of approximately 90% for neutrons and pions, and near 100% for neutral kaons and protons. The energy resolution for these hadrons is studied up to 50 GeV, with an additional investigation into the resolution as a function of the incoming particle angle and readout granularity using pions. Compared to traditional analysis methods, our results suggest that improved performance could be achieved even with coarser detector granularity, potentially making future DHCAL systems more cost-effective., Comment: 15 pages, 10 figures

In this paper, we detail recent and current work that is being carried out to fabricate and advance novel SiC UV instrumentation that is aimed at enabling more sensitive measurements across numerous disciplines, with a short discussion of the promise such detectors may hold for the Habitable Worlds Observatory. We discuss SiC instrument development progress that is being carried out under multiple NASA grants, including several PICASSO and SBIR grants, as well as an ECI grant. Testing of pixel design, properties and layout as well as maturation of the integration scheme developed through these efforts provide key technology and engineering advancement for potential HWO detectors. Achieving desired noise characteristics, responsivity, and validating operation of SiC detectors using standard read out techniques offers a compelling platform for operation of denser and higher dimensionality SiC photodiode arrays of interest for use in potential HWO Coronagraph, Spectrograph, and High Resolution Imaging Instruments. We incorporate these SiC detector properties into a simulation of potential NUV exoplanet observations by HWO using SiC detectors and also discuss potential application to HWO., Comment: 8 figures, Submitted to JATIS

A Schr\"odinger operator that is bounded below and has a unique positive ground state can be transformed into a Dirichlet form operator by the ground state transformation. If the resulting Dirichlet form operator is hypercontractive, Davies and Simon call the Schr\"odinger operator ``intrinsically hypercontractive". I will show that if one adds a suitable potential onto an intrinsically hypercontractive Schr\"odinger operator it remains intrinsically hypercontractive. The proof uses a fortuitous relation between the WKB equation and logarithmic Sobolev inequalities. All bounds are dimension independent. The main theorem will be applied to several examples., Comment: 123 pages

A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has uncovered new codes and new code implementations. In this work, we experimentally demonstrate three time-dynamic implementations of the surface code, each offering a unique solution to hardware design challenges and introducing flexibility in surface code realization. First, we embed the surface code on a hexagonal lattice, reducing the necessary couplings per qubit from four to three. Second, we walk a surface code, swapping the role of data and measure qubits each round, achieving error correction with built-in removal of accumulated non-computational errors. Finally, we realize the surface code using iSWAP gates instead of the traditional CNOT, extending the set of viable gates for error correction without additional overhead. We measure the error suppression factor when scaling from distance-3 to distance-5 codes of $\Lambda_{35,\text{hex}} = 2.15(2)$, $\Lambda_{35,\text{walk}} = 1.69(6)$, and $\Lambda_{35,\text{iSWAP}} = 1.56(2)$, achieving state-of-the-art error suppression for each. With detailed error budgeting, we explore their performance trade-offs and implications for hardware design. This work demonstrates that dynamic circuit approaches satisfy the demands for fault-tolerance and opens new alternative avenues for scalable hardware design., Comment: 11 pages, 5 figures, Supplementary Information

Quantum error correction is essential for bridging the gap between the error rates of physical devices and the extremely low logical error rates required for quantum algorithms. Recent error-correction demonstrations on superconducting processors have focused primarily on the surface code, which offers a high error threshold but poses limitations for logical operations. In contrast, the color code enables much more efficient logic, although it requires more complex stabilizer measurements and decoding techniques. Measuring these stabilizers in planar architectures such as superconducting qubits is challenging, and so far, realizations of color codes have not addressed performance scaling with code size on any platform. Here, we present a comprehensive demonstration of the color code on a superconducting processor, achieving logical error suppression and performing logical operations. Scaling the code distance from three to five suppresses logical errors by a factor of $\Lambda_{3/5}$ = 1.56(4). Simulations indicate this performance is below the threshold of the color code, and furthermore that the color code may be more efficient than the surface code with modest device improvements. Using logical randomized benchmarking, we find that transversal Clifford gates add an error of only 0.0027(3), which is substantially less than the error of an idling error correction cycle. We inject magic states, a key resource for universal computation, achieving fidelities exceeding 99% with post-selection (retaining about 75% of the data). Finally, we successfully teleport logical states between distance-three color codes using lattice surgery, with teleported state fidelities between 86.5(1)% and 90.7(1)%. This work establishes the color code as a compelling research direction to realize fault-tolerant quantum computation on superconducting processors in the near future.

We present a new method for reconstructing the appearance properties of human faces from a lightweight capture procedure in an unconstrained environment. Our method recovers the surface geometry, diffuse albedo, specular intensity and specular roughness from a monocular video containing a simple head rotation in-the-wild. Notably, we make no simplifying assumptions on the environment lighting, and we explicitly take visibility and occlusions into account. As a result, our method can produce facial appearance maps that approach the fidelity of studio-based multi-view captures, but with a far easier and cheaper procedure.

Engineering sublattice imbalance is an intuitive way to induce high-spin ground states in bipartite polycyclic conjugated hydrocarbons (PCHs). Such high-spin molecules can be employed as building blocks of quantum spin chains, which are outstanding platforms to study many-body physics and fundamental models in quantum magnetism. Recent reports on the bottom-up synthesis of antiferromagnetic molecular spin chains provided insights into paradigmatic quantum phenomena such as fractionalization. In contrast to antiferromagnetism, demonstration of ferromagnetic coupling between PCHs has been scarce. Previous attempts in this direction were limited by the formation of non-benzenoid rings leading to spin quenching, or the use of spacer motifs that considerably weaken the magnitude of ferromagnetic exchange. Here, we demonstrate the on-surface synthesis of short ferromagnetic spin chains based on dibenzotriangulene (DBT), a PCH with a triplet ground state. Our synthetic strategy centers on achieving a direct (that is, without a spacer motif) majority-minority sublattice coupling between adjacent units. This leads to a global sublattice imbalance in spin chains scaling with the chain length, and therefore a ferromagnetic ground state with a strong intermolecular ferromagnetic exchange. By means of scanning probe measurements and multiconfigurational quantum chemistry calculations, we analyze the electronic and magnetic properties of ferromagnetic dimers and trimers of DBT, and confirm their quintet and septet ground states, respectively, with an intermolecular ferromagnetic exchange of 7 meV. Furthermore, we elucidate the role of sublattice coupling on magnetism through complementary experiments on antiferromagnetic DBT dimers with majority-majority and minority-minority couplings. We expect our proof-of-principle study to provide impetus for the design of purely organic ferromagnetic materials., Comment: Manuscript: 11 pages and 5 figures. Supporting information: 8 pages and 8 figures. The abstract has been slightly shortened to fit the arXiv guidelines, the full abstract is provided in the manuscript

Superconducting circuits incorporating Josephson elements represent a promising hardware platform for quantum technologies. Potential applications include scalable quantum computing, microwave quantum networks, and quantum-limited amplifiers. However, progress in Josephson junction-based quantum technologies is facing the ongoing challenge of minimizing loss channels. This is also true for parametric superconducting devices based on nonlinear Josephson resonators. In this work, we report on the fabrication and characterization of low-loss Josephson parametric devices operated in the GHz frequency range, showing record internal quality factors. Specifically, we achieve internal quality factors significantly above $10^5$ for both Josephson parametric converters and the Josephson parametric amplifiers at low microwave power ranging in the single-photon regime. These low-loss devices mark a significant step forward in realizing high-performance quantum circuits, enabling further advancements in superconducting quantum technologies.

Precision measurement of hadronic final states presents complex experimental challenges. The study explores the concept of a gaseous Digital Hadronic Calorimeter (DHCAL) and discusses the potential benefits of employing Graph Neural Network (GNN) methods for future collider experiments. In particular, we use GNN to describe calorimeter clusters as point clouds or a collection of data points representing a three-dimensional object in space. Combined with Graph Attention Transformers (GATs) and DeepSets algorithms, this results in an improvement over existing baseline techniques for particle identification and energy resolution. We discuss the challenges encountered in implementing GNN methods for energy measurement in digital calorimeters, e.g., the large variety of hadronic shower shapes and the hyper-parameter optimization. We also discuss the dependency of the measured performance on the angle of the incoming particle and on the detector granularity. Finally, we highlight potential future directions and applications of these techniques., Comment: 4 pages, CALOR 2024, the 20th International Conference on Calorimetry in Particle Physics

We define a Grothendieck ring of varieties for log schemes. It is generated by one additional class ``$P$'' over the usual Grothendieck ring. We show the na\"ive definition of log Hodge numbers does not make sense for all log schemes. We offer an alternative that does., Comment: 19 pages. Comments welcome

A key unresolved question in microbial ecology is how the extraordinary diversity of microbiomes emerges from the behaviour of individual populations. This process is driven by the cross-feeding networks that structure these communities, but are hard to untangle due to their inherent complexity. We address this problem using the tools of network science to develop a model of microbial community structure. We discover tipping points at which diversity abruptly declines due to the catastrophic collapse of cross-feeding networks. Our results are a rare example of an ecological tipping point in diversity and provide insight into the fundamental processes shaping microbiota and their robustness. We illustrate this by showing how the unculturability of microbial diversity emerges as an inherent property of their microbial cross-feeding networks.

We examine images of the protoplanetary disk 114--426 with JWST/NIRCam in 12 bands. This large disk is oriented edge-on with a dark midplane flanked by lobes of scattered light. The outer edges of the midplane are seen in silhouette against the Orion Nebula, providing a unique opportunity to study planet-forming material in absorption. We discover a dip in the scattered light of the disk at 3\,$\micron$ -- compelling evidence for the presence of water ice. The 3\,$\micron$ dip is also seen in the silhouette of the disk, where we quantify the ice abundance with models of pure absorption and avoid the complications of disk scattering effects. We find grain ice-to-refractory mass ratios of up to $\sim$0.2, maximum grain sizes of 0.25 to 5\,$\micron$, and a total dust plus ice mass of 0.46\,$M_\oplus$ in the silhouette region. We also discover excess absorption in the NIRCam bands that include the Paschen $\alpha$ line, suggesting there may be excited atomic hydrogen in the disk. Examining the morphology of the scattered light lobes reveals that they are laterally offset from each other and exhibit a brightness asymmetry that flips with wavelength -- both evidence for a tilted inner disk in this system., Comment: 17 pages, 12 figures, accepted for publication in ApJ

Using the time-of-flight technique, we measured the beta-delayed neutron emission of $^{132}$Cd. From our large-scale shell model (LSSM) calculation using the N$^3$LO interaction [Z.Y. Xu et al., Phys. Rev. Lett. 131, 022501 (2023)], we suggest the decay is dominated by the transformation of a neutron in the $g_{7/2}$ orbital, deep below the Fermi surface, into a proton in the $g_{9/2}$ orbital. We compare the beta-decay half-lives and neutron branching ratios of nuclei with $Z<50$ and $N\geq82$ obtained with our LSSM with those of leading "global" models. Our calculations match known half-lives and neutron branching ratios well and suggest that current leading models overestimate the yet-to-be-measured half-lives. Our model, backed by the $^{132}$Cd decay data presented here, offers robust predictive power for nuclei of astrophysical interest such as $r$-process waiting points., Comment: 7 pages, 5 figures

The goal of mechanistic interpretability is discovering simpler, low-rank algorithms implemented by models. While we can compress activations into features, compressing nonlinear feature-maps -- like MLP layers -- is an open problem. In this work, we present the first case study in rigorously compressing nonlinear feature-maps, which are the leading asymptotic bottleneck to compressing small transformer models. We work in the classic setting of the modular addition models, and target a non-vacuous bound on the behaviour of the ReLU MLP in time linear in the parameter-count of the circuit. To study the ReLU MLP analytically, we use the infinite-width lens, which turns post-activation matrix multiplications into approximate integrals. We discover a novel interpretation of} the MLP layer in one-layer transformers implementing the ``pizza'' algorithm: the MLP can be understood as evaluating a quadrature scheme, where each neuron computes the area of a rectangle under the curve of a trigonometric integral identity. Our code is available at https://tinyurl.com/mod-add-integration.

Under suitable conditions on a family of logarithmic curves, we endow the tropicalization of the family with an affine structure in a neighborhood of the sections in such a way that the tropical $\psi$ classes from \cite{psi-classes} arise as tropicalizations of algebraic $\psi$ classes.

We propose that neutrons may be generated in high-energy, high-flux photon environments via photo-induced reactions on pre-existing baryons. These photo-hadronic interactions are expected to occur in astrophysical jets and surrounding material. Historically, these reactions have been attributed to the production of high-energy cosmic rays and neutrinos. We estimate the photoproduction off of protons in the context of gamma-ray bursts, where it is expected there will be sufficient baryonic material that may be encompassing or entrained in the jet. We show that typical stellar baryonic material, even material completely devoid of neutrons, can become inundated with neutrons in situ via hadronic photoproduction. Consequently, this mechanism provides a means for collapsars and other astrophysical sites containing substantial flux of high-energy photons to be favorable for neutron-capture nucleosynthesis., Comment: 21 pages; 7 figures; comments welcome!

In adaptive dynamical networks, the dynamics of the nodes and the edges influence each other. We show that we can treat such systems as a closed feedback loop between edge and node dynamics. Using recent advances on the stability of feedback systems from control theory, we derive local, sufficient conditions for steady states of such systems to be linearly stable. These conditions are local in the sense that they are written entirely in terms of the (linearized) behavior of the edges and nodes. We apply these conditions to the Kuramoto model with inertia written in adaptive form, and the adaptive Kuramoto model. For the former we recover a classic result, for the latter we show that our sufficient conditions match necessary conditions where the latter are available, thus completely settling the question of linear stability in this setting. The method we introduce can be readily applied to a vast class of systems. It enables straightforward evaluation of stability in highly heterogeneous systems.

Rydberg dressing traditionally refers to a technique where interactions between cold atoms are imprinted through the far off-resonant continuous-wave excitation of high-lying Rydberg states. Dipolar interactions between these electronic states are then translated into effective interactions among ground state atoms. Motivated by recent experiments, we investigate two dressing protocols, in which Rydberg atoms are resonantly excited in a stroboscopic fashion. The first one is non-adiabatic, meaning Rydberg states are excited by fast pulses. In this case, mechanical forces among Rydberg atoms result in electron-motion coupling, which generates effective multi-body interactions. In the second, adiabatic protocol, Rydberg states are excited by smoothly varying laser pulses. We show that also in this protocol substantial multi-body interactions emerge., Comment: 7 pages, 3 figures and Supplemental Material

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably., Comment: Accepted in IEEE CCNC 2025

In this paper, we analyze power-limiting grid-forming droop control used for grid-connected power converters. Compared to conventional grid-forming droop control, power-limiting droop control explicitly accounts for active power limits of the generation (e.g., renewables) interfaced by the converter. While power-limiting droop control has been demonstrated to work well in simulation and experiment, analytical results are not readily available. To address this gap, we first reformulate the dynamics of a power system comprised of converters using power-limiting droop controller as a projected dynamical system in nodal coordinates. Next, we change coordinates from nodal coordinates to edge coordinates to interpret the resulting dynamics as primal-dual dynamics associated with a constrained power flow problem. Leveraging convergence results for primal-dual dynamics, we show that, under mild feasibility assumptions, the frequency dynamics of a power system comprised of converters using power-limiting droop control are globally asymptotically stable with respect to the set of optimizers of its associated constrained power flow problem. Moreover, we show that (i) the converter frequencies synchronize to a common synchronous frequency for each grid-forming converter, and (ii) characterize the synchronous frequency in the case of converters operating at their power limit. Specifically, this result establishes that power-limiting droop control exhibits properties similar to so-called power-sharing in conventional unconstrained droop control.

Generating high output powers while achieving narrow line single mode lasing are often mutual exclusive properties of commercial laser diodes. For this reason, efficient and scalable amplification of narrow line laser light is still a major driving point in modern laser system designs. Commonly, injection locking of high-power semiconductor laser diodes are used for this purpose. However, for many laser diodes it is very challenging to achieve stable operation of the injection locked state due to a complex interplay of non-linearities and thermal effects. Different approaches of active or passive stabilization usually require a large overhead of optical and electrical equipment and are not generally applicable. In our work we present a passive stabilization scheme, that is generally applicable, technically easy to implement and extremely cost-effective. It is based on the externally synchronized automatic acquisition of the optimal injection state. Central to our simple but powerful scheme is the management of thermalization effects during lock acquisition. By periodical relocking, spectrally pure amplified light is maintained in a quasi-CW manner over long timescales. We characterize the performance of our method for laser diodes amplifying 671 nm light and demonstrate the general applicability by confirming the method to work also for laser diodes at 401 nm, 461 nm and 689 nm. Our scheme enables the scaled operation of injection locks, even in cascaded setups, for the distributed amplification of single frequency laser light.

We calculate the gravitational waves (GWs) produced by primordial black holes (PBHs) in the presence of the inflaton condensate in the early Universe. Combining the GW production from the evaporation process, the gravitational scattering of the inflaton itself, and the density fluctuations due to the inhomogeneous distribution of PBHs, we propose for the first time a complete coherent analysis of the spectrum, revealing three peaks, one for each source. Three frequency ranges ($\sim$ kHz, GHz, and PHz, respectively) are expected, each giving rise to a similar GW peak amplitude $\Omega_{\rm GW}$. We also compare our predictions with current and future GWs detection experiments., Comment: 21 pages, 10 figures

In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been explored in the context of single jets. In this paper, we expand the scope to full proton-proton and electron-positron collision events and study reconstruction quality using metrics at the particle, jet, and event levels. Rather than operating on the entire event in a single pass, we train HGPflow on smaller partitions to avoid potentially learning long-range correlations related to the physics process. We demonstrate that this approach is feasible and that on most metrics, HGPflow outperforms both traditional particle flow algorithms and a machine learning-based benchmark model.

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space., Comment: 204 pages, 100+ figures, 30+ tables

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Stacking van der Waals magnets holds promise for creating new hybrid materials with properties that do not exist in bulk materials. Here we investigate orthogonally twisted stacks of the van der Waals antiferromagnet CrSBr, aiming to exploit an extreme misalignment of magnetic anisotropy across the twisted interface.Using nitrogen-vacancy centre microscopy, we construct vector maps of the magnetisation, and track their evolution under an external field, in a range of twisted compensated and uncompensated configurations differing by the number of layers. We show that twisted stacking consistently modifies the local magnetic switching behaviour of constituent flakes, and that these modifications are spatially non-uniform. In the case of compensated component flakes (even number of layers), we demonstrate that the combination of dipolar coupling and stacking-induced strain can reduce the switching field by over an order of magnitude. Conversely, in uncompensated component flakes (odd number of layers), we observe indications of a non-zero interlayer exchange interaction between twisted flakes during magnetization reversal, which can persistently modify magnetic order. This work highlights the importance of spatial imaging in investigating stacking-induced magnetic effects, particularly in the case of twistronics where spatial variation is expected and can be conflated with structural imperfections., Comment: 6 main text pages, 4 figures plus 8 supplementary information pages, 9 figures

This paper studies how groups of robots can effectively navigate through a crowd of agents. It quantifies the performance of platooning and less constrained, greedy strategies, and the extent to which these strategies disrupt the crowd agents. Three scenarios are considered: (i) passive crowds, (ii) counter-flow crowds, and (iii) perpendicular-flow crowds. Through simulations consisting of up to 200 robots, we show that for navigating passive and counter-flow crowds, the platooning strategy is less disruptive and more effective in dense crowds than the greedy strategy, whereas for navigating perpendicular-flow crowds, the greedy strategy outperforms the platooning strategy in either aspect. Moreover, we propose an adaptive strategy that can switch between platooning and greedy behavioral states, and demonstrate that it combines the strengths of both strategies in all the scenarios considered., Comment: 14 pages, 7 figures, to be published in DARS 2024

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.

A recent line of work in mechanistic interpretability has focused on reverse-engineering the computation performed by neural networks trained on the binary operation of finite groups. We investigate the internals of one-hidden-layer neural networks trained on this task, revealing previously unidentified structure and producing a more complete description of such models that unifies the explanations of previous works. Notably, these models approximate equivariance in each input argument. We verify that our explanation applies to a large fraction of networks trained on this task by translating it into a compact proof of model performance, a quantitative evaluation of model understanding. In particular, our explanation yields a guarantee of model accuracy that runs in 30% the time of brute force and gives a >=95% accuracy bound for 45% of the models we trained. We were unable to obtain nontrivial non-vacuous accuracy bounds using only explanations from previous works., Comment: 23 pages, 4 figures

Log-linear exponential random graph models are a specific class of statistical network models that have a log-linear representation. This class includes many stochastic blockmodel variants. In this paper, we focus on $\beta$-stochastic blockmodels, which combine the $\beta$-model with a stochastic blockmodel. Here, using recent results by Almendra-Hern\'{a}ndez, De Loera, and Petrovi\'{c}, which describe a Markov basis for $\beta$-stochastic block model, we give a closed form formula for the maximum likelihood degree of a $\beta$-stochastic blockmodel. The maximum likelihood degree is the number of complex solutions to the likelihood equations. In the case of the $\beta$-stochastic blockmodel, the maximum likelihood degree factors into a product of Eulerian numbers.

Phylogenetic networks describe the evolution of a set of taxa for which reticulate events have occurred at some point in their evolutionary history. Of particular interest is when the evolutionary history between a set of just three taxa has a reticulate event. In molecular phylogenetics, substitution models can model the process of evolution at the genetic level, and the case of three taxa with a reticulate event can be modelled using a substitution model on a mixed graph called a 3-sunlet. We investigate a class of substitution models called group-based phylogenetic models on 3-sunlet networks. In particular, we investigate the discrete geometry of the parameter space and how this relates to the dimension of the phylogenetic variety associated to the model. This enables us to give a dimension formula for this variety for general group-based models when the order of the group is odd.

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.

We present a novel strategy for obtaining optimal probe states and measurement schemes in a class of noiseless multiparameter estimation problems with symmetry among the generators. The key to the framework is the introduction of a set of quantum metrology conditions, analogous to the quantum error correction conditions of Knill and Laflamme, which are utilized to identify probe states that saturate the multiparameter quantum Cram\'{e}r-Rao bound. Similar to finding two-dimensional irreps for encoding a logical qubit in error correction, we identify trivial irreps of finite groups that guarantee the satisfaction of the quantum metrology conditions. To demonstrate our framework, we analyze the SU(2) estimation with symmetric states in which three parameters define a global rotation of an ensemble of $N$ qubits. For even $N$, we find that tetrahedral symmetry and, with fine-tuning, $S_{3}$ symmetry, are minimal symmetry groups providing optimal probe states for SU(2) estimation, but that the quantum metrology conditions can also be satisfied in an entanglement-assisted setting by using a maximally entangled state of two spin-$N/2$ representations for any $N$. By extending the multiparameter method of moments to non-commuting observables, we use the quantum metrology conditions to construct a measurement scheme that saturates the multiparameter quantum Cram\'{e}r-Rao bound for small rotation angles., Comment: Comments are Welcome!

Dual Active Galactic Nuclei (dual AGNs), a phase in some galaxy mergers during which both central supermassive black holes (SMBHs) are active, are expected to be a key observable stage leading up to SMBH mergers. Constraining the population of dual AGNs in both the nearby and high-z universe has proven to be elusive until very recently. We present a multi-wavelength follow-up campaign to confirm the nature of a sample of 20 candidate dual AGNs at cosmic noon (z~2) from the VODKA sample. Through a combination of Hubble Space Telescope (HST) and Very Large Array (VLA) imaging, we refute the possibility of gravitational lensing in all but one target. We find evidence of dual AGNs in four systems, while seven exhibit single AGN in galaxy pairs, either through strong radio emission or ancillary emission line data. The remaining systems are either confirmed as quasar-star superpositions (six) or non-lensed pairs (two) that require further investigations to establish AGN activity. Among the systems with radio detections, we find a variety of radio spectral slopes and UV/optical colors suggesting that our sample contains a range of AGN properties, from obscured radio-quiet objects to those with powerful synchrotron-emitting jets. This study presents one of the largest dedicated multi-wavelength follow-up campaigns to date searching for dual AGNs at high redshift. We confirm several of the highest-z systems at small physical separations, thus representing some of the most evolved dual AGN systems at the epoch of peak quasar activity known to date., Comment: 26 pages, 8 figures, submitted to ApJ, includes appendix of 9 additional figures and 18 tables

We present Gemini/GMOS and HST/STIS optical spectra for 27 dual quasar candidates selected based on their variability-induced astrometric noise or double detections in Gaia (the VODKA project). From this follow-up, we spectroscopically identify 10 star superpositions and 8 dual/lensed quasars. Among the remaining targets, 2 are likely dual/lensed quasars based on additional radio imaging, while the rest are quasars with unknown companions. Notably, WISE J1649+0812 is a newly confirmed dual quasar with a projected separation of 5 kpc at $z=1.39$ and a significant velocity offset of 183$\pm$76 km/s, highlighting the utility of narrow emission lines in identifying genuine dual quasars. Without prior photometric or spectroscopic selection, we find the star contamination rate to be 37-63%, while the dual/lensed quasar fraction is $\gtrsim$ 30% in the follow-up VODKA sample. However, when combined with existing unresolved spectra and spatially-resolved two-band color cuts, the dual/lensed quasar fraction can be increased to $\gtrsim$ 67%. High signal-to-noise ratio spectra ($\gtrsim$ 20 per spectral element) with adequate spectral resolution ($R \gtrsim$ 1000) are essential for identifying faint absorption lines in foreground stars and detecting dual quasars through velocity offsets., Comment: 14 pages, 6 figures, 4 tables, submitted to ApJ, comments are welcome

Accurately reconstructing particles from detector data is a critical challenge in experimental particle physics, where the spatial resolution of calorimeters has a crucial impact. This study explores the integration of super-resolution techniques into an LHC-like reconstruction pipeline to effectively enhance the granularity of calorimeter data and suppress noise. We find that this software preprocessing step can significantly improve reconstruction quality without physical changes to detectors. To demonstrate the impact of our approach, we propose a novel particle flow model that offers enhanced particle reconstruction quality and interpretability. These advancements underline the potential of super-resolution to impact both current and future particle physics experiments.

Natural environments pose significant challenges for autonomous robot navigation, particularly due to their unstructured and ever-changing nature. Hiking trails, with their dynamic conditions influenced by weather, vegetation, and human traffic, represent one such challenge. This work introduces a novel approach to autonomous hiking trail navigation that balances trail adherence with the flexibility to adapt to off-trail routes when necessary. The solution is a Traversability Analysis module that integrates semantic data from camera images with geometric information from LiDAR to create a comprehensive understanding of the surrounding terrain. A planner uses this traversability map to navigate safely, adhering to trails while allowing off-trail movement when necessary to avoid on-trail hazards or for safe off-trail shortcuts. The method is evaluated through simulation to determine the balance between semantic and geometric information in traversability estimation. These simulations tested various weights to assess their impact on navigation performance across different trail scenarios. Weights were then validated through field tests at the West Virginia University Core Arboretum, demonstrating the method's effectiveness in a real-world environment.