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  1. Free, publicly-accessible full text available September 25, 2025
  2. Dynamic spectrum sharing has emerged as a promising solution to address the spectrum scarcity challenge. Currently, the FCC has designated several Spectrum Access Systems (SAS) administrators to deploy their SAS that coordinates the usage of the certificated shared band(s) such as the 3.55-3.7 GHz CBRS band. The SAS ensures that the incumbent’s access to the shared band is guaranteed while also granting commercial users access rights when the incumbents are not present. However, explicitly sharing the spectrum band(s) information among participants raises privacy concerns. Certain participants, such as curious SAS administrators, have the ability to deduce the confidential operational patterns of the incumbents through the Environmental Sensing Capability (ESC) or Incumbent Informing Capability (IIC) notifications. Additionally, a curious SAS administrator may obtain the client’s operational information of other SAS administrators throughout the process of inter-SAS coordination. We propose Pri-Share, a novel privacy-preserving spectrum sharing paradigm that tailors the threshold-based private set union (PSU) and homomorphic encryption (HE) techniques to address the aforementioned privacy problems. Specifically, it enables all parties to jointly compute a unified spectrum allocation plan to resolve the potential conflicts between different parties while safeguarding the confidentiality of each stakeholder’s spectrum requirements and usage. Pri-Share also ensures that while a curious participant might ascertain the usage of a particular spectrum band, they are unable to deduce the precise identity of the party utilizing it. Besides, Pri-Share adheres to the key spectrum allocation regulations outlined by FCC (part 96), such as assurance of access rights for various priority levels. Our implementation result shows that Pri-Share can be achieved with notable computational and communication efficiency, 
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    Free, publicly-accessible full text available May 14, 2025
  3. The eXtreme Multi-label Classification (XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extraction of semantic features from input query text. However, conventional XMC studies usually neglect the side information of instances and labels, which can be of use in many real-world applications such as recommendation systems and e-commerce product search. We propose Predicted Instance Neighborhood Aggregation (PINA), a data enhancement method for the general XMC problem that leverages beneficial side information. Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances. Extensive experimental results demonstrate the consistent gain of PINA on various XMC tasks compared to the state-of-the-art methods: PINA offers a gain in accuracy compared to standard XR-Transformers on five public benchmark datasets. Moreover, PINA achieves a ∼ 5% gain in accuracy on the largest dataset LF-AmazonTitles-1.3M. Our implementation is publicly available https://github.com/amzn/pecos/ tree/mainline/examples/pina. 
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  4. Light Field Networks, the re-formulations of radiance fields to oriented rays, are magnitudes faster than their coordinate network counterparts, and provide higher fidelity with respect to representing 3D structures from 2D observations. They would be well suited for generic scene representation and manipulation, but suffer from one problem: they are limited to holistic and static scenes. In this paper, we propose the Dynamic Light Field Network (DyLiN) method that can handle non-rigid deformations, including topological changes. We learn a deformation field from input rays to canonical rays, and lift them into a higher dimensional space to handle discontinuities. We further introduce CoDyLiN, which augments DyLiN with controllable attribute inputs. We train both models via knowledge distillation from pretrained dynamic radiance fields. We evaluated DyLiN using both synthetic and real world datasets that include various non-rigid deformations. DyLiN qualitatively outperformed and quantitatively matched state-of-the-art methods in terms of visual fidelity, while being 25 - 71x computationally faster. We also tested CoDyLiN on attribute annotated data and it surpassed its teacher model. Project page: https://dylin2023.github.io. 
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  5. Data from the Colorado State University radiosonde systems that were deployed at Hsinchu, Taiwan and Yonaguni, Japan for the PRECIP campaign. Measurements include vertical profiles of temperature, moisture and winds. 
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  6. Abstract

    Unlike PIWI-interacting RNA (piRNA) in other species that mostly target transposable elements (TEs), >80% of piRNAs in adult mammalian testes lack obvious targets. However, mammalian piRNA sequences and piRNA-producing loci evolve more rapidly than the rest of the genome for unknown reasons. Here, through comparative studies of chickens, ducks, mice, and humans, as well as long-read nanopore sequencing on diverse chicken breeds, we find that piRNA loci across amniotes experience: (1) a high local mutation rate of structural variations (SVs, mutations ≥ 50 bp in size); (2) positive selection to suppress young and actively mobilizing TEs commencing at the pachytene stage of meiosis during germ cell development; and (3) negative selection to purge deleterious SV hotspots. Our results indicate that genetic instability at pachytene piRNA loci, while producing certain pathogenic SVs, also protects genome integrity against TE mobilization by driving the formation of rapid-evolving piRNA sequences.

     
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  7. Brehm, Christoph ; Pandya, Shishir (Ed.)
    We have derived a 3-D kinetic-based discrete dynamic system (DDS) from the lattice Boltzmann equation (LBE) for incompressible flows through a Galerkin procedure. Expressed by a poor-man lattice Boltzmann equation (PMLBE), it involves five bifurcation parameters including relaxation time from the LBE, splitting factor of large and sub-grid motion scales, and wavevector components from the Fourier space. Numerical experiments have shown that the DDS can capture laminar behaviors of periodic, subharmonic, n-period, and quasi-periodic and turbulent behaviors of noisy periodic with harmonic, noisy subharmonic, noisy quasi-periodic, and broadband power spectra. In this work, we investigated the effects of bifurcation parameters on the capturing of the laminar and turbulent flows in terms of the convergence of time series and the pattern of power spectra. We have found that the 2nd order and 3rd order PMLBEs are both able to capture laminar and turbulent flow behaviors but the 2nd order DDS performs better with lower computation cost and more flow behaviors captured. With the specified ranges of the bifurcation parameters, we have identified two optimal bifurcation parameter sets for laminar and turbulent behaviors. Beyond this work, we are exploring the regime maps for a deeper understanding of the contributions of the bifurcation parameters to the capturing of laminar and turbulent behaviors. Surrogate models (to replace the PMLBE) are being developed using deep learning techniques to overcome the overwhelming computation cost for the regime maps. Meanwhile, the DDS is being employed in the large eddy simulation of turbulent pulsatile flows to provide dynamic sub-grid scale information. 
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  8. Brehm, Christoph ; Pandya, Shishir (Ed.)
    Computational fluid dynamics (CFD) and its uncertainty quantification are computationally expensive. We use Gaussian Process (GP) methods to demonstrate that machine learning can build efficient and accurate surrogate models to replace CFD simulations with significantly reduced computational cost without compromising the physical accuracy. We also demonstrate that both epistemic uncertainty (machine learning model uncertainty) and aleatory uncertainty (randomness in the inputs of CFD) can be accommodated when the machine learning model is used to reveal fluid dynamics. The demonstration is performed by applying simulation of Hagen-Poiseuille and Womersley flows that involve spatial and spatial-tempo responses, respectively. Training points are generated by using the analytical solutions with evenly discretized spatial or spatial-temporal variables. Then GP surrogate models are built using supervised machine learning regression. The error of the GP model is quantified by the estimated epistemic uncertainty. The results are compared with those from GPU-accelerated volumetric lattice Boltzmann simulations. The results indicate that surrogate models can produce accurate fluid dynamics (without CFD simulations) with quantified uncertainty when both epistemic and aleatory uncertainties exist. 
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  9. We investigate the regularity of the free boundaries in the three elastic membranes problem. We show that the two free boundaries corresponding to the coincidence regions between consecutive membranes are C1,log-hypersurfaces near a regular intersection point. We also study two types of singular intersections. The first type of singular points are locally covered by a C1,alpha-hypersurface. The second type of singular points stratify and each stratum is locally covered by a C1-manifold. 
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