515 research outputs found
Neighborhood Matching Network for Entity Alignment
Structural heterogeneity between knowledge graphs is an outstanding challenge
for entity alignment. This paper presents Neighborhood Matching Network (NMN),
a novel entity alignment framework for tackling the structural heterogeneity
challenge. NMN estimates the similarities between entities to capture both the
topological structure and the neighborhood difference. It provides two
innovative components for better learning representations for entity alignment.
It first uses a novel graph sampling method to distill a discriminative
neighborhood for each entity. It then adopts a cross-graph neighborhood
matching module to jointly encode the neighborhood difference for a given
entity pair. Such strategies allow NMN to effectively construct
matching-oriented entity representations while ignoring noisy neighbors that
have a negative impact on the alignment task. Extensive experiments performed
on three entity alignment datasets show that NMN can well estimate the
neighborhood similarity in more tough cases and significantly outperforms 12
previous state-of-the-art methods.Comment: 11 pages, accepted by ACL 202
A Mesh-free Particle Method for Simulation of Flow over Rectangular Weir of Finite Crest Length
Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive
AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation
This paper presents a method that can quickly adapt dynamic 3D avatars to
arbitrary text descriptions of novel styles. Among existing approaches for
avatar stylization, direct optimization methods can produce excellent results
for arbitrary styles but they are unpleasantly slow. Furthermore, they require
redoing the optimization process from scratch for every new input. Fast
approximation methods using feed-forward networks trained on a large dataset of
style images can generate results for new inputs quickly, but tend not to
generalize well to novel styles and fall short in quality. We therefore
investigate a new approach, AlteredAvatar, that combines those two approaches
using the meta-learning framework. In the inner loop, the model learns to
optimize to match a single target style well; while in the outer loop, the
model learns to stylize efficiently across many styles. After training,
AlteredAvatar learns an initialization that can quickly adapt within a small
number of update steps to a novel style, which can be given using texts, a
reference image, or a combination of both. We show that AlteredAvatar can
achieve a good balance between speed, flexibility and quality, while
maintaining consistency across a wide range of novel views and facial
expressions.Comment: 10 main pages, 14 figures. Project page:
https://alteredavatar.github.i
Cosmological constraints from the redshift dependence of the Alcock-Paczynski effect: Dynamical dark energy
We perform an anisotropic clustering analysis of 1,133,326 galaxies from the
Sloan Digital Sky Survey (SDSS-III) Baryon Oscillation Spectroscopic Survey
(BOSS) Data Release (DR) 12 covering the redshift range . The
geometrical distortions of the galaxy positions, caused by incorrect
cosmological model assumptions, are captured in the anisotropic two-point
correlation function on scales 6 -- 40 . The redshift evolution
of this anisotropic clustering is used to place constraints on the cosmological
parameters. We improve the methodology of Li et al. 2016, to enable efficient
exploration of high dimensional cosmological parameter spaces, and apply it to
the Chevallier-Polarski-Linder parametrization of dark energy,
. In combination with the CMB, BAO, SNIa and from
Cepheid data, we obtain $\Omega_m = 0.301 \pm 0.008,\ w_0 = -1.042 \pm 0.067,\
w_a = -0.07 \pm 0.29\sim\sim$2. We check the robustness
of the results using realistic mock galaxy catalogues.Comment: 12 pages, 9 figures, accepted to Ap
Amodal Segmentation Based on Visible Region Segmentation and Shape Prior
Almost all existing amodal segmentation methods make the inferences of
occluded regions by using features corresponding to the whole image. This is
against the human's amodal perception, where human uses the visible part and
the shape prior knowledge of the target to infer the occluded region. To mimic
the behavior of human and solve the ambiguity in the learning, we propose a
framework, it firstly estimates a coarse visible mask and a coarse amodal mask.
Then based on the coarse prediction, our model infers the amodal mask by
concentrating on the visible region and utilizing the shape prior in the
memory. In this way, features corresponding to background and occlusion can be
suppressed for amodal mask estimation. Consequently, the amodal mask would not
be affected by what the occlusion is given the same visible regions. The
leverage of shape prior makes the amodal mask estimation more robust and
reasonable. Our proposed model is evaluated on three datasets. Experiments show
that our proposed model outperforms existing state-of-the-art methods. The
visualization of shape prior indicates that the category-specific feature in
the codebook has certain interpretability.Comment: Accepted by AAAI 202
Semi-Honest 2-Party Faithful Truncation from Two-Bit Extraction
As a fundamental operation in fixed-point arithmetic, truncation can bring the product of two fixed-point integers back to the fixed-point representation. In large-scale applications like privacy-preserving machine learning, it is essential to have faithful truncation that accurately eliminates both big and small errors. In this work, we improve and extend the results of the oblivious transfer based faithful truncation protocols initialized by Cryptflow2 (Rathee et al., CCS 2020). Specifically, we propose a new notion of two-bit extraction that is tailored for faithful truncation and demonstrate how it can be used to construct an efficient faithful truncation protocol. Benefiting from our efficient construction for two-bit extraction, our faithful truncation protocol reduces the communication complexity of Cryptflow2 from growing linearly with the fixed-point precision to logarithmic complexity.
This efficiency improvement is due to the fact that we reuse the intermediate results of eliminating the big error to further eliminate the small error. Our reuse strategy is effective, as it shows that while eliminating the big error, it is possible to further eliminate the small error at a minimal cost, e.g., as low as communicating only an additional 160 bits in one round
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Assessing agricultural drought in summer over Oklahoma Mesonet sites using the water-related vegetation index from MODIS.
Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113 Oklahoma Mesonet stations comprising various land cover and soil types in Oklahoma. Drought duration in a year was determined by the number of days with LSWI <0 (DNLSWI) during summer months (June-August). Summer rainfall anomalies and LSWI anomalies followed a similar seasonal dynamics and showed strong correlations (r 2 = 0.62-0.73) during drought years (2001, 2006, 2011, and 2012). The DNLSWI tracked the east-west gradient of summer rainfall in Oklahoma. Drought intensity increased with increasing duration of DNLSWI, and the intensity increased rapidly when DNLSWI was more than 48 days. The comparison between LSWI and the US Drought Monitor (USDM) showed a strong linear negative relationship; i.e., higher drought intensity tends to have lower LSWI values and vice versa. However, the agreement between LSWI-based algorithm and USDM indicators varied substantially from 32 % (D 2 class, moderate drought) to 77 % (0 and D 0 class, no drought) for different drought intensity classes and varied from ∼30 % (western Oklahoma) to >80 % (eastern Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions
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