Rapid Constrained Object Motion Estimation based on Centroid Localization of Semantically Labeled Objects
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A state in a quantum system with a given global symmetry, G, can be sensitive to the presence of boundaries, which may either preserve or break this symmetry. In this work, we investigate how conformal invariant boundary conditions influence the G-symmetry breaking through the lens of the entanglement asymmetry, a quantifier of the “distance” between a symmetry-broken state and its symmetrized counterpart. By leveraging 2D boundary conformal field theory (BCFT), we investigate the symmetry breaking for both finite and compact Lie groups. Beyond the leading order term, we also compute the subleading corrections in the subsystem size, highlighting their dependence on the symmetry group G and the BCFT operator content. We further explore the entanglement asymmetry following a global quantum quench, where a symmetry-broken state evolves under a symmetry-restoring Hamiltonian. In this dynamical setting, we compute the entanglement asymmetry by extending the method of images to a BCFT with non-local objects such as invertible symmetry defects.
Conventional tomographic reconstruction typically depends on centralized servers for both data storage and computation, leading to concerns about memory limitations and data privacy. Distributed reconstruction algorithms mitigate these issues by partitioning data across multiple nodes, reducing server load and enhancing privacy. However, these algorithms often encounter challenges related to memory constraints and communication overhead between nodes. In this paper, we introduce a decentralized Alternating Directions Method of Multipliers (ADMM) with configurable quantization. By distributing local objectives across nodes, our approach is highly scalable and can efficiently reconstruct images while adapting to available resources. To overcome communication bottlenecks, we propose two quantization techniques based on K-means clustering and JPEG compression. Numerical experiments with benchmark images illustrate the tradeoffs between communication efficiency, memory use, and reconstruction accuracy.
Inverse problems in imaging are commonly solved by optimization or learned surrogates that return a single reconstruction, while uncertainty information is often unavailable. In many experimental settings, however, uncertainty is required to assess reliability, guide downstream analysis, and prioritize additional measurements. In this note, we present a compact uncertainty-quantification framework based on local objective curvature, and then specialize it to ptychographic reconstruction. We further show how repeated reconstructions can be aggregated in a statistically principled way, including a practical implementation path for PtychoNN.
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Changes in technology, customer expectations, and business and regulatory environments are rapidly evolving causing fundamental changes in the nation’s electrical infrastructure. Nowhere is this more apparent that at the “grid edge”, where there is an increasing number of new devices and systems, as well as complex new interactions between them. This is leading to the traditional relationship between the end-use customers and their utilities being expanded by an increasing number of stakeholders, each with their own operational and financial objectives, governed by regulatory policy. While there are concerns about the rapidly increasing complexity negatively impacting reliable and resilience of the electrical infrastructure, these changes are also bringing new resources and opportunities that hold great potential if they can be properly coordinated. This white paper outlines the considerations for the coordination of multi-stakeholder objectives with electric utility requirements using the concept of grid services. Describing a framework that enables new stakeholders to achieve their local technical and economic objectives, while simultaneously delivering operational benefits to the electrical infrastructure. The concepts of grid architecture are presented as a tool to evaluate how stakeholders might participate in, and benefit from, services, and how utilities can make decision on the reliance on services to ensure reliability and resilience, translating abstract concepts into actionable information for utilities and grid edge stakeholders. The end result of proper coordination, informed by grid architecture, will be a range of new devices and systems, operated by new stakeholders, achieving their local objectives while also increasing the reliability, resilience, security, and affordability of the nation’s critical electrical infrastructure.
Changes in technology, customer expectations, and business and regulatory environments are rapidly evolving causing fundamental changes in the nation’s electrical infrastructure. Nowhere is this more apparent that at the “grid edge”, where there is an increasing number of new devices and systems, as well as complex new interactions between them. This is leading to the traditional relationship between the end-use customers and their utilities being expanded by an increasing number of stakeholders, each with their own operational and financial objectives, governed by regulatory policy. While there are concerns about the rapidly increasing complexity negatively impacting reliable and resilience of the electrical infrastructure, these changes are also bringing new resources and opportunities that hold great potential if they can be properly coordinated. This white paper outlines the considerations for the coordination of multi-stakeholder objectives with electric utility requirements using the concept of grid services. Describing a framework that enables new stakeholders to achieve their local technical and economic objectives, while simultaneously delivering operational benefits to the electrical infrastructure. The concepts of grid architecture are presented as a tool to evaluate how stakeholders might participate in, and benefit from, services, and how utilities can make decision on the reliance on services to ensure reliability and resilience, translating abstract concepts into actionable information for utilities and grid edge stakeholders. The end result of proper coordination, informed by grid architecture, will be a range of new devices and systems, operated by new stakeholders, achieving their local objectives while also increasing the reliability, resilience, security, and affordability of the nation’s critical electrical infrastructure.
We demonstrate that enhanced early galaxy formation can generically arise in axion-like particle (ALP) dark matter (DM) models with a delayed onset of axion field oscillation. In these models, the formation of localized massive objects enhances structure formation, potentially addressing the excess recently observed by the James Webb Space Telescope (JWST), while remaining consistent with existing constraints. We identify viable parameter space with the ALP mass in the range of 10 −22 eV m a < 10 –19 eV. In addition, we show that the ALP parameter regions of interest can lead to intriguing complementary signatures in the small scale structure of DM halos and existing experimental searches for ALPs.
Imaging and sensing of moving objects through opaque scattering media is a challenging but important problem in a variety of applications, including environmental sensing, biomedical imaging, and material inspection. We have previously demonstrated a technique to coherently image a moving object through thick, heavily scattering random media using correlations of speckle images as a function of the object’s spatial translation. Here, we demonstrate that this technique can be combined with localization to achieve imaging without prior knowledge of the object’s motion, greatly extending the application domain. This method is effective beyond the thin or weakly scattering regime and, rather than motion being deleterious, exploits the information available when the hidden object is moving, as could be the case in a cluttered terrestrial environment or through substantial levels of biological tissue scatter.
Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.
High-power, low-voltage DC (LVDC) loads, such as AI data centers, electric vehicle fast-charging hubs, and hydrogen electrolyzers, are accelerating the deployment of solid-state transformers (SSTs) that directly interface medium-voltage AC (MVAC) to LVDC, streamlining power delivery. This paper proposes a submodule-level decentralized control strategy for an SST comprising a cascaded H-bridge (CHB) active rectifier on the MVAC side and per-module quadruple-active-bridge (QAB) converters on the LVDC side with local controllers. The proposed strategy eliminates the need for high-fidelity, real-time cross-isolation communication by synchronizing all active bridges to a common heartbeat signal and enabling the primary- and secondary-side controllers to generate independent local control signals (phase shifts). The resultant phase shifts naturally govern average and double-line frequency power transfer between active bridges, enabling each side to meet its control objectives using only local measurements. Detailed controller design considerations are presented based on a stability analysis. Comprehensive electromagnetic-transient simulations demonstrate tight DC-link voltage regulation, high-quality grid currents, and robust operation under load transients and grid disturbances, without real-time communication among the submodule controllers. These results indicate that the proposed scheme can significantly enhance the reliability, scalability, and flexibility of cascaded bridge-based medium-voltage converters.
High-power, low-voltage DC (LVDC) loads, such as AI data centers, electric vehicle fast-charging hubs, and hydrogen electrolyzers, are accelerating the deployment of solid-state transformers (SSTs) that directly interface medium-voltage AC (MVAC) to LVDC, streamlining power delivery. This paper proposes a submodule-level decentralized control strategy for an SST comprising a cascaded H-bridge (CHB) active rectifier on the MVAC side and per-module quadruple-active-bridge (QAB) converters on the LVDC side with local controllers. The proposed strategy eliminates the need for high-fidelity, real-time cross-isolation communication by synchronizing all active bridges to a common heartbeat signal and enabling the primary- and secondary-side controllers to generate independent local control signals (phase shifts). The resultant phase shifts naturally govern average and double-line frequency power transfer between active bridges, enabling each side to meet its control objectives using only local measurements. Detailed controller design considerations are presented based on a stability analysis. Comprehensive electromagnetic-transient simulations demonstrate tight DC-link voltage regulation, high-quality grid currents, and robust operation under load transients and grid disturbances, without real-time communication among the submodule controllers. These results indicate that the proposed scheme can significantly enhance the reliability, scalability, and flexibility of cascaded bridge-based medium-voltage converters.
We present a materials generation framework that couples a symmetry-conditioned variational autoencoder with a differentiable SO(3) power spectrum objective to steer candidates toward a specified local environment under the crystallographic constraints. In particular, we implement a fully differentiable pipeline that performs batch-wise optimization on both direct and latent crystallographic representations. Using the GPU acceleration, the implementation achieves about fivefold speed compared to our previous CPU workflow, while yielding comparable outcomes. In addition, we introduce the optimization strategy that alternatively performs optimization on the direct and latent crystal representations. This dual-level relaxation approach can effectively escape local minima defined by different objective gradients, thus increasing the success rate of generating complex structures satisfying the target local environments. This framework can be extended to systems consisting of multi-components and multi-environments, providing a scalable route to generate material structures with the target local environment.
An examination of 109,276 spectra of low-mass stars in the Sloan Digital Sky Survey (SDSS) data archive, collected pre-2010, provides a broad collection of K/M (sub)dwarfs tracing the local (d ≲ 250 pc) population of the thin-disk, thick-disk, and halo, based on 3D kinematics. These populations have distinct metallicities and kinematics, which should be reflected in lower-mass members. One complication is measuring metallicities of M dwarfs from low-resolution spectroscopy or photometry alone remains a challenge, even with the availability of Gaia data. To better characterize physical parameters of low-mass stars observed by SDSS, we define a set of 536 improved, empirical K/M dwarf classification templates to more accurately determine spectral subtypes (K5.5–M8.5) and morphology classes (MCs) (0.5–12.5). We select the best-fit template to every archival SDSS spectrum in the range 5000–8000 Å based on minimum χ 2 value. We then use secondary metallicity estimators to calibrate the most likely [Fe/H] values corresponding to the assigned MC. We confirm that the most metal-rich K/M dwarfs have thin-disk kinematics, and we observe the known effect of Galactic radial metallicity migration, which we confirm is strongly imprinted in the chemodynamics of the local disk M dwarfs. Confirmation of these chemodynamic trends validates the precision of our improved templates for estimating [Fe/H] for M (sub)dwarfs, notably at the low-metallicity end (–3 < [Fe/H] < –1). Substructure in the chemodynamics of the most metal-poor K/M subdwarfs begins to distinguish between local Gaia-Enceladus and local in situ halo objects. This methodology opens the door to use the ubiquitous, local K/M (sub)dwarfs to trace local Galactic chemodynamics with the highest possible resolution.
The quantum spin liquid is a state manifesting extraordinary many-body entanglement, and the material NaYbSe 2 is thought to be one of the most promising candidates for its realization. Through low-temperature heat capacity and thermal conductivity measurements, we identify an apparent contradiction familiar to many quantum spin liquid candidates: While entropy is stored by apparently gapless excitations, the itinerant carriers of entropy are gapped. By studying the compositional series NaYb 𝑥 Lu 1−𝑥 Se 2 across a percolation transition of the magnetic lattice, we suggest that this contradiction can be resolved by the presence of entanglement scales of random sizes. Moreover, as we truncate the scale of entanglement by magnetic dilution, we show that the itinerant magnetic entropy carrier in NaYbSe 2 does not arise from a uniform globally entangled spin ground state but rather materializes through the stochastic propagation of boundaries between locally entangled spin objects.
Hydrodynamic shielding is a theoretically well-established but observationally elusive and experimentally difficult-to-replicate phenomenon with implications that extend far beyond meteor physics. Rare earthgrazing meteoroids with infrasound signatures that penetrate to the ground can be used to probe hydrodynamic shielding that leads to strong shock formation at high altitude. Here, we report the first coordinated optical and multi-station infrasound observations of a centimeter-scale earthgrazing fireball that generated sustained cylindrical line shock at thermospheric altitudes near 92 km. The event was recorded by numerous optical stations and three infrasound arrays, allowing trajectory reconstruction, ablation behavior, acoustic source localization, and shock characteristics. Optical observations indicate early mechanical erosion and ablation/evaporation at exceptionally low dynamic pressure, consistent with a cometary or a porous, volatile-bearing CM chondritic object. Independent infrasound detections localize shock generation to multiple points along a 164 km trajectory segment near perigee. Weak-shock modeling yields a consistent blast radius of ∼30 m, implying an acoustic-equivalent source size far exceeding the physical dimensions of the ∼45 g nucleus. We demonstrate that classical gas dynamics and ablation-driven hydrodynamic shielding alone cannot account for these observations under ambient thermospheric conditions. We show that volatile release provides the additional flow-field density enhancement required to amplify hydrodynamic shielding, reduce the effective local Knudsen number, and sustain a shock envelope capable of radiating detectable infrasound. Furthermore, these results demonstrate that small, volatile-rich meteoroids can transiently establish continuum-like flow in rarefied environments.
Density functional tight binding (DFTB) models for f-element species are challenging to parametrize owing to the large number of adjustable parameters. The explicit optimization of the terms entering the semiempirical DFTB Hamiltonian related to f orbitals is crucial to generating a reliable parametrization for f-block elements, because they play import roles in bonding interactions. However, since the number of parameters grows quadratically with the number of orbitals, the computational cost for parameter optimization is much more expensive for the f-elements than for the main group elements. In this work we present a set of efficient approaches for mitigating the hurdle imposed by the large size of the parameter space. A novel group-by-orbital correction functions for two-center bond integrals was developed. With this approach the number of parameters is reduced, and it grows linearly with the number of elements, maintaining the accuracy and the number of parameters, in the case of f elements, by more than 40%. The parameter optimization step was accelerated by means of the mini-batch BFGS method. This method allows parameter optimizations with much larger training sets than other single batch methods. A stochastic optimizer was employed that helped overcome shallow local minima in the objective function. The proposed algorithm was used to parametrize the DFTB Hamiltonian for the Th–O system, which was subsequently applied to the study of ThO 2 nanoparticles. The training set consisted of 6322 unique structures, which is barely feasible with conventional optimization methods. The optimized parameter set, LANL-ThO, displays good agreement with DFT-calculated properties such as energies, forces, and structures for both clusters and bulk ThO 2 . Benefiting from the fewer number of parameters and lower computational costs for objective function evaluations, this new approach shows its potential applications in DFTB parametrization for elements with high angular momentum, which present a challenge to conventional methods.