20,991 research outputs found

    A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions

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    We present the first ε\varepsilon-differentially private, computationally efficient algorithm that estimates the means of product distributions over {0,1}d\{0,1\}^d accurately in total-variation distance, whilst attaining the optimal sample complexity to within polylogarithmic factors. The prior work had either solved this problem efficiently and optimally under weaker notions of privacy, or had solved it optimally while having exponential running times

    Rank-based linkage I: triplet comparisons and oriented simplicial complexes

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    Rank-based linkage is a new tool for summarizing a collection SS of objects according to their relationships. These objects are not mapped to vectors, and ``similarity'' between objects need be neither numerical nor symmetrical. All an object needs to do is rank nearby objects by similarity to itself, using a Comparator which is transitive, but need not be consistent with any metric on the whole set. Call this a ranking system on SS. Rank-based linkage is applied to the KK-nearest neighbor digraph derived from a ranking system. Computations occur on a 2-dimensional abstract oriented simplicial complex whose faces are among the points, edges, and triangles of the line graph of the undirected KK-nearest neighbor graph on SS. In ∣S∣K2|S| K^2 steps it builds an edge-weighted linkage graph (S,L,σ)(S, \mathcal{L}, \sigma) where σ({x,y})\sigma(\{x, y\}) is called the in-sway between objects xx and yy. Take Lt\mathcal{L}_t to be the links whose in-sway is at least tt, and partition SS into components of the graph (S,Lt)(S, \mathcal{L}_t), for varying tt. Rank-based linkage is a functor from a category of out-ordered digraphs to a category of partitioned sets, with the practical consequence that augmenting the set of objects in a rank-respectful way gives a fresh clustering which does not ``rip apart`` the previous one. The same holds for single linkage clustering in the metric space context, but not for typical optimization-based methods. Open combinatorial problems are presented in the last section.Comment: 37 pages, 12 figure

    Efficient simulations of ionized ISM emission lines: A detailed comparison between the FIRE high-redshift suite and observations

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    The Atacama Large Millimeter/Submillimeter Array (ALMA) in the sub-millimeter and the James Webb Space Telescope (JWST) in the infrared have achieved robust spectroscopic detections of emission lines from the interstellar medium (ISM) in some of the first galaxies. These unprecedented measurements provide valuable information regarding the ISM properties, stellar populations, galaxy morphologies, and kinematics in these high-redshift galaxies and, in principle, offer powerful tests of state-of-the-art galaxy formation models, as implemented in hydrodynamical simulations. To facilitate direct comparisons between simulations and observations, we develop a fast post-processing pipeline for predicting the line emission from the HII regions around simulated star particles, accounting for spatial variations in the surrounding gas density, metallicity, temperature, and incident radiation spectrum. Our ISM line emission model currently captures Hα\alpha, Hβ\beta, and all of the [OIII] and [OII] lines targeted by ALMA and the JWST at z>6z>6. We illustrate the power of this approach by applying our line emission model to the publicly available FIRE high-zz simulation suite and perform a detailed comparison with current observations. We show that the FIRE mass--metallicity relation is in 1σ1\sigma agreement with ALMA/JWST measurements after accounting for the inhomogeneities in ISM properties. We also quantitatively validate the one-zone model description, which is widely used for interpreting [OIII] and Hβ\beta line luminosity measurements. This model is publicly available and can be implemented on top of a broad range of galaxy formation simulations for comparison with JWST and ALMA measurements.Comment: 15 pages, 13 figure

    Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-free Massive Random Access

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    In the massive machine-type communication (mMTC) scenario, a large number of devices with sporadic traffic need to access the network on limited radio resources. While grant-free random access has emerged as a promising mechanism for massive access, its potential has not been fully unleashed. In particular, the common sparsity pattern in the received pilot and data signal has been ignored in most existing studies, and auxiliary information of channel decoding has not been utilized for user activity detection. This paper endeavors to develop advanced receivers in a holistic manner for joint activity detection, channel estimation, and data decoding. In particular, a turbo receiver based on the bilinear generalized approximate message passing (BiG-AMP) algorithm is developed. In this receiver, all the received symbols will be utilized to jointly estimate the channel state, user activity, and soft data symbols, which effectively exploits the common sparsity pattern. Meanwhile, the extrinsic information from the channel decoder will assist the joint channel estimation and data detection. To reduce the complexity, a low-cost side information-aided receiver is also proposed, where the channel decoder provides side information to update the estimates on whether a user is active or not. Simulation results show that the turbo receiver is able to reduce the activity detection, channel estimation, and data decoding errors effectively, while the side information-aided receiver notably outperforms the conventional method with a relatively low complexity

    Chandra X-ray Measurement of Gas-phase Heavy Element Abundances in the Central Parsec of the Galaxy

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    Elemental abundances are key to our understanding of star formation and evolution in the Galactic center. Previous work on this topic has been based on infrared (IR) observations, but X-ray observations have the potential of constraining the abundance of heavy elements, mainly through their K-shell emission lines. Using 5.7 Ms Chandra observations, we provide the first abundance measurement of Si, S, Ar, Ca and Fe, in four prominent diffuse X-ray features located in the central parsec of the Galaxy, which are the manifestation of shock-heated hot gas. A two-temperature, non-equilibrium ionization spectral model is employed to derive the abundances of these five elements. In this procedure, a degeneracy is introduced due to uncertainties in the composition of light elements, in particular, H, C and N. Assuming that the hot gas is H-depleted but C- and N-enriched, as would be expected for a standard scenario in which the hot gas is dominated by Wolf-Rayet star winds, the spectral fit finds a generally subsolar abundance for the heavy elements. If, instead, the light elements had a solar-like abundance, the heavy elements have a fitted abundance of ∼\sim1--2 solar. The α\alpha/Fe abundance ratio, on the other hand, is mostly supersolar and insensitive to the exact composition of the light elements. These results are robust against potential biases due to either a moderate spectral S/N or the presence of non-thermal components. Implications of the measured abundances for the Galactic center environment are addressed.Comment: 13 pages, 6 figures, Accepted for publication in MNRA

    Concept Graph Neural Networks for Surgical Video Understanding

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    We constantly integrate our knowledge and understanding of the world to enhance our interpretation of what we see. This ability is crucial in application domains which entail reasoning about multiple entities and concepts, such as AI-augmented surgery. In this paper, we propose a novel way of integrating conceptual knowledge into temporal analysis tasks via temporal concept graph networks. In the proposed networks, a global knowledge graph is incorporated into the temporal analysis of surgical instances, learning the meaning of concepts and relations as they apply to the data. We demonstrate our results in surgical video data for tasks such as verification of critical view of safety, as well as estimation of Parkland grading scale. The results show that our method improves the recognition and detection of complex benchmarks as well as enables other analytic applications of interest

    Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules

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    We target the problem of automatically synthesizing proofs of semantic equivalence between two programs made of sequences of statements. We represent programs using abstract syntax trees (AST), where a given set of semantics-preserving rewrite rules can be applied on a specific AST pattern to generate a transformed and semantically equivalent program. In our system, two programs are equivalent if there exists a sequence of application of these rewrite rules that leads to rewriting one program into the other. We propose a neural network architecture based on a transformer model to generate proofs of equivalence between program pairs. The system outputs a sequence of rewrites, and the validity of the sequence is simply checked by verifying it can be applied. If no valid sequence is produced by the neural network, the system reports the programs as non-equivalent, ensuring by design no programs may be incorrectly reported as equivalent. Our system is fully implemented for a given grammar which can represent straight-line programs with function calls and multiple types. To efficiently train the system to generate such sequences, we develop an original incremental training technique, named self-supervised sample selection. We extensively study the effectiveness of this novel training approach on proofs of increasing complexity and length. Our system, S4Eq, achieves 97% proof success on a curated dataset of 10,000 pairs of equivalent programsComment: 30 pages including appendi

    EMPRESS. XI. SDSS and JWST Search for Local and z~4-5 Extremely Metal-Poor Galaxies (EMPGs): Clustering and Chemical Properties of Local EMPGs

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    We search for local extremely metal-poor galaxies (EMPGs), selecting photometric candidates by broadband color excess and machine-learning techniques with the SDSS photometric data. After removing stellar contaminants by shallow spectroscopy with Seimei and Nayuta telescopes, we confirm that three candidates are EMPGs with 0.05--0.1 Z⊙Z_\odot by deep Magellan/MagE spectroscopy for faint {\sc[Oiii]}λ\lambda4363 lines. Using a statistical sample consisting of 105 spectroscopically-confirmed EMPGs taken from our study and the literature, we calculate cross-correlation function (CCF) of the EMPGs and all SDSS galaxies to quantify environments of EMPGs. Comparing another CCF of all SDSS galaxies and comparison SDSS galaxies in the same stellar mass range (107.0−108.4M⊙10^{7.0}-10^{8.4} M_\odot), we find no significant (>1σ>1\sigma) difference between these two CCFs. We also compare mass-metallicity relations (MZRs) of the EMPGs and those of galaxies at z∼z\sim 0--4 with a steady chemical evolution model and find that the EMPG MZR is comparable with the model prediction on average. These clustering and chemical properties of EMPGs are explained by a scenario of stochastic metal-poor gas accretion on metal-rich galaxies showing metal-poor star formation. Extending the broadband color-excess technique to a high-zz EMPG search, we select 17 candidates of z∼z\sim 4--5 EMPGs with the deep (≃30\simeq30 mag) near-infrared JWST/NIRCam images obtained by ERO and ERS programs. We find galaxy candidates with negligible {\sc[Oiii]}λλ\lambda\lambda4959,5007 emission weaker than the local EMPGs and known high-zz galaxies, suggesting that some of these candidates may fall in 0--0.01 Z⊙Z_\odot, which potentially break the lowest metallicity limit known to date

    Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency

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    Visual simultaneous localization and mapping (SLAM) systems face challenges in detecting loop closure under the circumstance of large viewpoint changes. In this paper, we present an object-based loop closure detection method based on the spatial layout and semanic consistency of the 3D scene graph. Firstly, we propose an object-level data association approach based on the semantic information from semantic labels, intersection over union (IoU), object color, and object embedding. Subsequently, multi-view bundle adjustment with the associated objects is utilized to jointly optimize the poses of objects and cameras. We represent the refined objects as a 3D spatial graph with semantics and topology. Then, we propose a graph matching approach to select correspondence objects based on the structure layout and semantic property similarity of vertices' neighbors. Finally, we jointly optimize camera trajectories and object poses in an object-level pose graph optimization, which results in a globally consistent map. Experimental results demonstrate that our proposed data association approach can construct more accurate 3D semantic maps, and our loop closure method is more robust than point-based and object-based methods in circumstances with large viewpoint changes

    Bright X-ray pulsars as sources of MeV neutrinos in the sky

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    High mass accretion rate onto strongly magnetised neutron stars results in the appearance of accretion columns supported by the radiation pressure and confined by the strong magnetic field of a star. At mass accretion rates above ∼1019 g s−1\sim 10^{19}\,{\rm g\,s^{-1}}, accretion columns are expected to be advective. Under such conditions, a noticeable part of the total energy release can be carried away by neutrinos of a MeV energy range. Relying on a simple model of the neutrino luminosity of accreting strongly magnetised neutron stars, we estimate the neutrino energy fluxes expected from six ULX pulsars known up to date and three brightest Be X-ray transits hosting magnetised neutron stars. Despite the large neutrino luminosity expected in ULX pulsars, the neutrino energy flux from the Be X-ray transients of our Galaxy, SMC and LMC is dominant. However, the neutrino flux from the brightest X-ray transients is estimated to be below the isotropic background by two orders of magnitude at least, which makes impossible direct registration of neutrino emission from accreting strongly magnetised neutron stars nowadays.Comment: 7 pages, 6 figures, submitted to MNRA
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