20,991 research outputs found
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions
We present the first -differentially private, computationally
efficient algorithm that estimates the means of product distributions over
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
Rank-based linkage is a new tool for summarizing a collection 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 . Rank-based linkage is applied
to the -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
-nearest neighbor graph on . In steps it builds an
edge-weighted linkage graph where
is called the in-sway between objects and . Take to be
the links whose in-sway is at least , and partition into components of
the graph , for varying . 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
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 H, H, and all of the
[OIII] and [OII] lines targeted by ALMA and the JWST at . We illustrate
the power of this approach by applying our line emission model to the publicly
available FIRE high- simulation suite and perform a detailed comparison with
current observations. We show that the FIRE mass--metallicity relation is in
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 H
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
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
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 1--2 solar. The /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
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
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
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 by deep Magellan/MagE
spectroscopy for faint {\sc[Oiii]}4363 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 (), we find no significant ()
difference between these two CCFs. We also compare mass-metallicity relations
(MZRs) of the EMPGs and those of galaxies at 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- EMPG search, we select 17 candidates of
4--5 EMPGs with the deep ( mag) near-infrared JWST/NIRCam
images obtained by ERO and ERS programs. We find galaxy candidates with
negligible {\sc[Oiii]}4959,5007 emission weaker than the local
EMPGs and known high- galaxies, suggesting that some of these candidates may
fall in 0--0.01 , which potentially break the lowest metallicity limit
known to date
Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency
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
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
, 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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