In the new perspective of spatial quantization, this article systematically studies the advantages of reconfigurable reflectarray (RRA) designed with closely spaced elements in terms of sidelobe level (SLL), scanning accuracy and scan loss, including theoretical analysis and simulation verification. This article sequentially studies RRAs with element periods of {\lambda}/2, {\lambda}/4 and {\lambda}/8. Both theoretical and simulation results show that under the condition of the same aperture size, with the number of spatial quantization bits increasing, the SLL performance of 1bit RRA using closely spaced structure will have a improvement of about 5dB. The scanning accuracy at 60° is improved from 54.52° at {\lambda}/2 to 57.97° at {\lambda}/8, while the scan loss is improved from 5.02dB at {\lambda}/2 to 2.85dB at {\lambda}/8. This study has an important reference value for reconfigurable reflectarray design, communication system and radar design.

Topological Hall effect (THE) is a hallmark of scalar spin chirality, which is found in static skyrmion lattices. Recent theoretical works have shown that scalar spin chirality could also emerge dynamically from thermal spin fluctuations. Evidence of such a mechanism was found in the kagome magnet YMn6Sn6 where fluctuations arise from frustrated exchange interactions between Mn kagome layers. In YMn6Sn6, the rare-earth ion Y3+ is non-magnetic. When it is replaced by a magnetic ion (Gd3+-Ho3+), the intrinsically antiferromagnetic Mn-Mn interlayer coupling is overwhelmed by the indirect ferromagnetic Mn-R-Mn one, relieving frustration. This generates interesting anomalous Hall conductivity, but not THE. Here we show that Er lies in an intermediate regime where direct and indirect interactions closely compete, so that ErMn6Sn6 can switch from one regime to the other by temperature, i.e., from a collinear ferrimagnetic ground state to a spiral antiferromagnet at 78 K. The AFM phase forms a dome in the temperature-field phase diagram. Close to the boundary of this dome, we find a sizable fluctuations-driven THE, thus underscoring the universality of this chiral fluctuation mechanism for generating non-zero scalar spin chirality.

We present component-separated polarization maps of the cosmic microwave background (CMB) and Galactic thermal dust emission, derived using data from the BICEP/Keck experiments through the 2018 observing season and Planck. By employing a maximum-likelihood method that utilizes observing matrices, we produce unbiased maps of the CMB and dust signals. We outline the computational challenges and demonstrate an efficient implementation of the component map estimator. We show methods to compute and characterize power spectra of these maps, opening up an alternative way to infer the tensor-to-scalar ratio from our data. We compare the results of this map-based separation method with the baseline BICEP/Keck analysis. Our analysis demonstrates consistency between the two methods, finding an 84% correlation between the pipelines.

Standard evaluation protocols in robotic manipulation typically assess policy performance over curated, in-distribution test sets, offering limited insight into how systems fail under plausible variation. We introduce Geometric Red-Teaming (GRT), a red-teaming framework that probes robustness through object-centric geometric perturbations, automatically generating CrashShapes -- structurally valid, user-constrained mesh deformations that trigger catastrophic failures in pre-trained manipulation policies. The method integrates a Jacobian field-based deformation model with a gradient-free, simulator-in-the-loop optimization strategy. Across insertion, articulation, and grasping tasks, GRT consistently discovers deformations that collapse policy performance, revealing brittle failure modes missed by static benchmarks. By combining task-level policy rollouts with constraint-aware shape exploration, we aim to build a general purpose framework for structured, object-centric robustness evaluation in robotic manipulation. We additionally show that fine-tuning on individual CrashShapes, a process we refer to as blue-teaming, improves task success by up to 60 percentage points on those shapes, while preserving performance on the original object, demonstrating the utility of red-teamed geometries for targeted policy refinement. Finally, we validate both red-teaming and blue-teaming results with a real robotic arm, observing that simulated CrashShapes reduce task success from 90% to as low as 22.5%, and that blue-teaming recovers performance to up to 90% on the corresponding real-world geometry -- closely matching simulation outcomes. Videos and code can be found on our project website: this https URL .

Recent advances in machine learning (ML) have accelerated progress in calibrating and operating quantum dot (QD) devices. However, most ML approaches rely on access to large, high-quality labeled datasets for training, benchmarking, and validation, with labels capturing key features in the data. Obtaining such datasets experimentally is challenging due to limited data availability and the labor-intensive nature of labeling. QDFlow is an open-source physics simulator for multi-QD arrays that generates realistic synthetic data with ground-truth labels. QDFlow combines a self-consistent Thomas-Fermi solver, a dynamic capacitance model, and flexible noise modules to produce charge stability diagrams and ray-based data closely resembling experiments. With extensive tunable parameters and customizable noise models, QDFlow supports the creation of large, diverse datasets for ML development, benchmarking, and quantum device research.

Supermassive black hole binary systems (SMBHBs) are thought to emit the recently discovered nHz gravitational wave background; however, not a single individual nHz source has been confirmed to date. Long-term radio-monitoring at the Owens Valley Radio Observatory has revealed two potential SMBHB candidates: blazars PKS 2131-021 and PKS J0805-0111. These sources show periodic flux density variations across the electromagnetic spectrum, signaling the presence of a good clock. To explain the emission, we propose a generalizable jet model, where a mildly relativistic wind creates an outward-moving helical channel, along which the ultra-relativistic jet propagates. The observed flux variation from the jet is mostly due to aberration. The emission at lower frequency arises at larger radius and its variation is consequently delayed, as observed. Our model reproduces the main observable features of both sources and can be applied to other sources as they are discovered. We make predictions for radio polarization, direct imaging, and emission line variation, which can be tested with forthcoming observations. Our results motivate future numerical simulations of jetted SMBHB systems and have implications for the fueling, structure, and evolution of blazar jets.

The rapid adoption of generative artificial intelligence (GenAI) in the biosciences is transforming biotechnology, medicine, and synthetic biology. Yet this advancement is intrinsically linked to new vulnerabilities, as GenAI lowers the barrier to misuse and introduces novel biosecurity threats, such as generating synthetic viral proteins or toxins. These dual-use risks are often overlooked, as existing safety guardrails remain fragile and can be circumvented through deceptive prompts or jailbreak techniques. In this Perspective, we first outline the current state of GenAI in the biosciences and emerging threat vectors ranging from jailbreak attacks and privacy risks to the dual-use challenges posed by autonomous AI agents. We then examine urgent gaps in regulation and oversight, drawing on insights from 130 expert interviews across academia, government, industry, and policy. A large majority ($\approx 76$\%) expressed concern over AI misuse in biology, and 74\% called for the development of new governance frameworks. Finally, we explore technical pathways to mitigation, advocating a multi-layered approach to GenAI safety. These defenses include rigorous data filtering, alignment with ethical principles during development, and real-time monitoring to block harmful requests. Together, these strategies provide a blueprint for embedding security throughout the GenAI lifecycle. As GenAI becomes integrated into the biosciences, safeguarding this frontier requires an immediate commitment to both adaptive governance and secure-by-design technologies.

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at this https URL

Neutral atoms in optical tweezer arrays possess broad applicability for quantum information science, in computing, simulation, and metrology. Among atomic species, Ytterbium-171 is unique as it hosts multiple qubits, each of which is impactful for these distinct applications. Consequently, this atom is an ideal candidate to bridge multiple disciplines, which, more broadly, has been an increasingly effective strategy within the field of quantum science. Realizing the full potential of this synergy requires high-fidelity generation and transfer of many-particle entanglement between these distinct qubit degrees of freedom, and thus between these distinct applications. Here we demonstrate the creation and coherent mapping of entangled quantum states across multiple qubits in Ytterbium-171 tweezer arrays. We map entangled states onto the optical clock qubit from the nuclear spin qubit or the Rydberg qubit. We coherently transfer up to 20 atoms of a $Z_2$-ordered Greenberger-Horne-Zeilinger (GHZ) state from the interacting Rydberg manifold to the metastable nuclear spin manifold. The many-body state is generated via a novel disorder-robust pulse in a two-dimensional ladder geometry. We further find that clock-qubit-based spin detection applied to Rydberg and nuclear spin qubits facilitates atom-loss-detectable qubit measurements and $>90\%$ Rydberg decay detection. This enables mid-circuit and delayed erasure detection, yielding an error-detected two-qubit gate fidelity of $99.78(4)\%$ in the metastable qubits as well as enhanced GHZ state fidelities in analog preparation. These results establish a versatile architecture that advances multiple fields of quantum information science while also establishing bridges between them.

A significant fraction of stars experience close interactions, including collisions resulting from gravitational encounters and mergers within close binary systems. These processes can produce more massive stars that may give rise to relatively rare objects such as blue stragglers. Distinguishing the outcomes of collisions and mergers is challenging yet essential for interpreting observations. This study utilizes the magnetohydrodynamics code AREPO to simulate collisions and mergers of $5$ to $10 \,\mathrm{M}_{\odot}$ main-sequence stars, systematically comparing the properties of the resulting products. Both collisions and mergers yield more massive, strongly magnetized, rapidly and differentially rotating stars with cores enriched in hydrogen, but notable quantitative differences emerge. Merger products exhibit core hydrogen fractions up to $10\%$ higher than those of collision products. In both scenarios, turbulent mixing amplifies magnetic field energies by $9$ to $12$ orders of magnitude. However, magnetic fields in small-impact-parameter collision products display small-scale reversals that may dissipate over time, whereas merger products and large-impact-parameter collision products develop large-scale ordered, potentially long-lived magnetic fields. Additionally, only merger products display magnetically driven, bipolar outflows with radial velocities exceeding $300$ to $400 \,\mathrm{km}\,\mathrm{s}^{-1}$. These distinctions may result in divergent long-term evolutionary outcomes, which warrant further investigation in future studies.

Understanding doped Mott insulators is a fundamental goal in condensed matter physics, with relevance to cuprate superconductors and other quantum materials. The doped Hubbard model minimally describes such systems, and has explicated some of their complex behavior. However, many open questions remain concerning the anomalous metallic states which emerge at low temperatures and intermediate doping and which, in cuprates, give rise to high-temperature superconductivity upon cooling. Here we observe a crossover between a normal metal and a pseudogapped metal in the Hubbard model by performing thermodynamic and spectroscopic measurements in a cold atom quantum simulator, leveraging a recent several-fold reduction in experimentally achievable temperatures. Measurements of the compressibility show a maximum versus doping that develops upon cooling, signaling an inflection point in the equation of state. We track this maximum versus interaction strength, revealing a line of thermodynamic anomalies in the phase diagram that separates an underdoped from an overdoped metal at large interactions. Lattice modulation spectroscopy shows a loss of spectral weight at low energies in the underdoped regime which is non-uniform in the Brillouin zone, indicating the formation of a pseudogap. We use this signal to establish a pseudogap phase diagram as a function of interactions and doping. Our results experimentally demonstrate the existence of a pseudogapped metal in the Hubbard model, partially characterize the pseudogap regime, and suggest a link between the pseudogap and charge order which can be probed in future work. Furthermore, this work demonstrates the utility of quantum simulation in addressing frontier problems in correlated electron physics.

We present a rigorous identification of candidates for dormant black holes (BHs) and neutron stars (NSs) in binaries using summary statistics from Gaia DR3, rather than full orbital solutions. Although Gaia astrometric orbits have already revealed a small sample of compact object binaries, many systems remain undetected due to stringent quality cuts imposed on the published orbits. Using a forward-modelling framework that simulates Gaia observables, in particular the renormalised unit weight error (ruwe) and radial velocity (RV) scatter, we infer posterior distributions for companion mass and orbital period via MCMC sampling, marginalising over nuisance orbital parameters. We validate our approach by comparing the predicted masses and periods against full orbit solutions from DR3, and by successfully recovering known compact object binaries as promising candidates. The method is best suited for systems with red giant primaries, which have more reliable Gaia RV scatter and a light centroid more likely dominated by one component, compared to main-sequence stars. And they are less likely to be triples with short-period inner binaries, which produce confounding signatures. We apply the method to three million giants and identify 556 systems with best-fit companion masses $\gtrsim 3\,M_\odot$. Recovery simulations suggest our selection method is substantially more sensitive than the DR3 non-single-star catalogue, particularly for binaries with periods below 1 year and above $\sim 6$ years. These candidates represent promising targets for spectroscopic follow-up and Gaia DR4 analysis to confirm the presence of compact objects. Candidate main-sequence stars with massive companions face a larger set of confounding effects. Therefore, we present an analogous catalogue of 279 additional `main sequence' candidates only as an appendix.

As quantum error correction (QEC) experiments continue to make rapid progress, there is increased interest in designing experiments with guarantees of logical performance. At present, one difficulty is the lack of a clear connection between logical performance and the low-level error models. In this work, we take an important step toward addressing this issue by proving that consecutive QEC cycles of a stabilizer code with Pauli stochastic noise and with a single-cycle infidelity $\epsilon_1 \leq 1/64$ admit an approximate logical Markovian model, meaning that consecutive noisy QEC cycles can be modeled by a memoryless error process acting only on the logical subsystem. The approximate logical Markovian model can be computed from the low-level error model, and the deviations from the true behavior are exponentially suppressed in the number of QEC cycles. Consequently, we expect that the approximate logical Markovian model will be both a useful tool for logical characterization and an aid for designing stabilizer-code implementations with guarantees of logical performance.

We describe updated scientific goals for the wide-field, millimeter-wave survey that will be produced by the Simons Observatory (SO). Significant upgrades to the 6-meter SO Large Aperture Telescope (LAT) are expected to be complete by 2028, and will include a doubled mapping speed with 30,000 new detectors and an automated data reduction pipeline. In addition, a new photovoltaic array will supply most of the observatory's power. The LAT survey will cover about 60% of the sky at a regular observing cadence, with five times the angular resolution and ten times the map depth of Planck. The science goals are to: (1) determine the physical conditions in the early universe and constrain the existence of new light particles; (2) measure the integrated distribution of mass, electron pressure, and electron momentum in the late-time universe, and, in combination with optical surveys, determine the neutrino mass and the effects of dark energy via tomographic measurements of the growth of structure at z < 3; (3) measure the distribution of electron density and pressure around galaxy groups and clusters, and calibrate the effects of energy input from galaxy formation on the surrounding environment; (4) produce a sample of more than 30,000 galaxy clusters, and more than 100,000 extragalactic millimeter sources, including regularly sampled AGN light-curves, to study these sources and their emission physics; (5) measure the polarized emission from magnetically aligned dust grains in our Galaxy, to study the properties of dust and the role of magnetic fields in star formation; (6) constrain asteroid regoliths, search for Trans-Neptunian Objects, and either detect or eliminate large portions of the phase space in the search for Planet 9; and (7) provide a powerful new window into the transient universe on time scales of minutes to years, concurrent with observations from Rubin of overlapping sky.

We present results from an analysis of all data taken by the BICEP2, Keck Array and BICEP3 CMB polarization experiments up to and including the 2018 observing season. We add additional Keck Array observations at 220 GHz and BICEP3 observations at 95 GHz to the previous 95/150/220 GHz data set. The $Q/U$ maps now reach depths of 2.8, 2.8 and 8.8 $\mu{\mathrm K}_{cmb}$ arcmin at 95, 150 and 220 GHz respectively over an effective area of $\approx 600$ square degrees at 95 GHz and $\approx 400$ square degrees at 150 & 220 GHz. The 220 GHz maps now achieve a signal-to-noise on polarized dust emission exceeding that of Planck at 353 GHz. We take auto- and cross-spectra between these maps and publicly available WMAP and Planck maps at frequencies from 23 to 353 GHz and evaluate the joint likelihood of the spectra versus a multicomponent model of lensed-$\Lambda$CDM+$r$+dust+synchrotron+noise. The foreground model has seven parameters, and no longer requires a prior on the frequency spectral index of the dust emission taken from measurements on other regions of the sky. This model is an adequate description of the data at the current noise levels. The likelihood analysis yields the constraint r_{0.05}<0.036 at 95% confidence. Running maximum likelihood search on simulations we obtain unbiased results and find that $\sigma(r)=0.009$. These are the strongest constraints to date on primordial gravitational waves.

Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models. While most existing GNN models for atomistic predictions are based on atomic distance information, they do not explicitly incorporate bond angles, which are critical for distinguishing many atomic structures. Furthermore, many material properties are known to be sensitive to slight changes in bond angles. We present an Atomistic Line Graph Neural Network (ALIGNN), a GNN architecture that performs message passing on both the interatomic bond graph and its line graph corresponding to bond angles. We demonstrate that angle information can be explicitly and efficiently included, leading to improved performance on multiple atomistic prediction tasks. We ALIGNN models for predicting 52 solid-state and molecular properties available in the JARVIS-DFT, Materials project, and QM9 databases. ALIGNN can outperform some previously reported GNN models on atomistic prediction tasks by up to 85% in accuracy with better or comparable model training speed.