58 research outputs found
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation
To enable video models to be applied seamlessly across video tasks in
different environments, various Video Unsupervised Domain Adaptation (VUDA)
methods have been proposed to improve the robustness and transferability of
video models. Despite improvements made in model robustness, these VUDA methods
require access to both source data and source model parameters for adaptation,
raising serious data privacy and model portability issues. To cope with the
above concerns, this paper firstly formulates Black-box Video Domain Adaptation
(BVDA) as a more realistic yet challenging scenario where the source video
model is provided only as a black-box predictor. While a few methods for
Black-box Domain Adaptation (BDA) are proposed in image domain, these methods
cannot apply to video domain since video modality has more complicated temporal
features that are harder to align. To address BVDA, we propose a novel Endo and
eXo-TEmporal Regularized Network (EXTERN) by applying mask-to-mix strategies
and video-tailored regularizations: endo-temporal regularization and
exo-temporal regularization, performed across both clip and temporal features,
while distilling knowledge from the predictions obtained from the black-box
predictor. Empirical results demonstrate the state-of-the-art performance of
EXTERN across various cross-domain closed-set and partial-set action
recognition benchmarks, which even surpassed most existing video domain
adaptation methods with source data accessibility.Comment: 9 pages, 4 figures, and 4 table
Fully-Connected Spatial-Temporal Graph for Multivariate Time Series Data
Multivariate Time-Series (MTS) data is crucial in various application fields.
With its sequential and multi-source (multiple sensors) properties, MTS data
inherently exhibits Spatial-Temporal (ST) dependencies, involving temporal
correlations between timestamps and spatial correlations between sensors in
each timestamp. To effectively leverage this information, Graph Neural
Network-based methods (GNNs) have been widely adopted. However, existing
approaches separately capture spatial dependency and temporal dependency and
fail to capture the correlations between Different sEnsors at Different
Timestamps (DEDT). Overlooking such correlations hinders the comprehensive
modelling of ST dependencies within MTS data, thus restricting existing GNNs
from learning effective representations. To address this limitation, we propose
a novel method called Fully-Connected Spatial-Temporal Graph Neural Network
(FC-STGNN), including two key components namely FC graph construction and FC
graph convolution. For graph construction, we design a decay graph to connect
sensors across all timestamps based on their temporal distances, enabling us to
fully model the ST dependencies by considering the correlations between DEDT.
Further, we devise FC graph convolution with a moving-pooling GNN layer to
effectively capture the ST dependencies for learning effective representations.
Extensive experiments show the effectiveness of FC-STGNN on multiple MTS
datasets compared to SOTA methods.Comment: 9 pages, 8 figure
Graph Contextual Contrasting for Multivariate Time Series Classification
Contrastive learning, as a self-supervised learning paradigm, becomes popular
for Multivariate Time-Series (MTS) classification. It ensures the consistency
across different views of unlabeled samples and then learns effective
representations for these samples. Existing contrastive learning methods mainly
focus on achieving temporal consistency with temporal augmentation and
contrasting techniques, aiming to preserve temporal patterns against
perturbations for MTS data. However, they overlook spatial consistency that
requires the stability of individual sensors and their correlations. As MTS
data typically originate from multiple sensors, ensuring spatial consistency
becomes essential for the overall performance of contrastive learning on MTS
data. Thus, we propose Graph Contextual Contrasting (GCC) for spatial
consistency across MTS data. Specifically, we propose graph augmentations
including node and edge augmentations to preserve the stability of sensors and
their correlations, followed by graph contrasting with both node- and
graph-level contrasting to extract robust sensor- and global-level features. We
further introduce multi-window temporal contrasting to ensure temporal
consistency in the data for each sensor. Extensive experiments demonstrate that
our proposed GCC achieves state-of-the-art performance on various MTS
classification tasks.Comment: 9 pages, 5 figure
SEA++: Multi-Graph-based High-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA) methods have been successful in reducing
label dependency by minimizing the domain discrepancy between a labeled source
domain and an unlabeled target domain. However, these methods face challenges
when dealing with Multivariate Time-Series (MTS) data. MTS data typically
consist of multiple sensors, each with its own unique distribution. This
characteristic makes it hard to adapt existing UDA methods, which mainly focus
on aligning global features while overlooking the distribution discrepancies at
the sensor level, to reduce domain discrepancies for MTS data. To address this
issue, a practical domain adaptation scenario is formulated as Multivariate
Time-Series Unsupervised Domain Adaptation (MTS-UDA). In this paper, we propose
SEnsor Alignment (SEA) for MTS-UDA, aiming to reduce domain discrepancy at both
the local and global sensor levels. At the local sensor level, we design
endo-feature alignment, which aligns sensor features and their correlations
across domains. To reduce domain discrepancy at the global sensor level, we
design exo-feature alignment that enforces restrictions on global sensor
features. We further extend SEA to SEA++ by enhancing the endo-feature
alignment. Particularly, we incorporate multi-graph-based high-order alignment
for both sensor features and their correlations. Extensive empirical results
have demonstrated the state-of-the-art performance of our SEA and SEA++ on
public MTS datasets for MTS-UDA
Absence of near-ambient superconductivity in LuHN
Recently near-ambient superconductivity was claimed in N-doped lutetium
hydride (ref. 1). This induces a worldwide fanaticism about the dream of room
temperature superconductivity under low pressures. By using a high pressure and
high temperature synthesis technique, we have successfully obtained the
nitrogen doped lutetium hydride (LuHN) with a dark-bluish
color and a structure with the space group of evidenced by x-ray
diffraction. This structure is the same as that reported in ref. 1. The energy
dispersive X-ray spectroscopy (EDS) confirmed the existence of nitrogen in some
areas of the samples. At ambient pressure, we witness a kink of resistivity and
magnetization at about 300 K, which may correspond to a rearrangement of
hydrogen/nitrogen atoms, namely a structural transition. However, by applying a
pressure from 1 GPa to 6 GPa, we have seen a progressively optimized metallic
behavior without showing superconductivity down to 10 K. Temperature dependence
of magnetization shows a roughly flat feature between 100 and 320 K, and the
magnetization increases with magnetic field at 100 K, all these are not
expected for superconductivity at 100 K. Thus, we conclude the absence of
near-ambient superconductivity in this nitrogen-doped lutetium hydride under
pressures below 6 GPa.Comment: An updated version with more data was published in Nature online on
11th May 2023 as an Accelerated Article Preview for
Electronic correlations and energy gap in the bilayer nickelate LaNiO
The discovery of superconductivity with a critical temperature of 80~K in
LaNiO under pressure has received enormous attention.
LaNiO is not superconducting under ambient pressure but
exhibits a density-wave-like transition at ~K.
Understanding the electronic correlations, charge dynamics and dominant
orbitals are important steps towards the mechanism of superconductivity and
other instabilities. Here, our optical study shows that LaNiO
features strong electronic correlations which significantly reduce the
electron's kinetic energy and place it in the proximity of the Mott phase. The
low-frequency optical conductivity reveals two Drude components arising from
multiple bands dominated by the Ni- and Ni-
orbitals at the Fermi level. Above , the scattering rates for both
Drude components vary linearly with temperature, indicating non-Fermi-liquid
behavior which may be associated with spin-fluctuation scattering. Below
, a gap opens in the Ni- orbital, suggesting the
importance of the Ni- orbital in the density-wave-like
instability. Our experimental results provide key insights into the mechanism
of the density-wave-like order and superconductivity in
LaNiO.Comment: 26 pages, 4 figures, Comments are welcome and appreciate
Pressure induced color change and evolution of metallic behavior in nitrogen-doped lutetium hydride
By applying pressures up to 42 GPa on the nitrogen-doped lutetium hydride
(LuHN), we have found a gradual change of color from
dark-blue to pink-violet in the pressure region of about 12 GPa to 21 GPa. The
temperature dependence of resistivity under pressures up to 50.5 GPa shows
progressively optimized metallic behavior with pressure. Interestingly, in the
pressure region for the color change, a clear decrease of resistivity is
observed with the increase of pressure, which is accompanied by a clear
increase of the residual resistivity ratio (RRR). Fitting to the low
temperature resistivity gives exponents of about 2, suggesting a Fermi liquid
behavior in low temperature region. The general behavior in wide temperature
region suggests that the electron-phonon scattering is still the dominant one.
The magnetoresistance up to 9 tesla in the state under a pressure of 50.5 GPa
shows an almost negligible effect, which suggests that the electric conduction
in the pink-violet state is dominated by a single band. It is highly desired to
have theoretical efforts in understanding the evolution of color and
resistivity in this interesting system.Comment: 15 pages, 4 figure
Pollen source areas of lakes with inflowing rivers: modern pollen influx data from Lake Baiyangdian, China
Comparing pollen influx recorded in traps above the surface and below the surface of Lake Baiyangdian in northern China shows that the average pollen influx in the traps above the surface is much lower, at 1210 grains cm−2 a−1 (varying from 550 to 2770 grains cm−2 a−1), than in the traps below the surface which average 8990 grains cm−2 a−1 (ranging from 430 to 22310 grains cm−2 a−1). This suggests that about 12% of the total pollen influx is transported by air, and 88% via inflowing water. If hydrophyte pollen types are not included, the mean pollen influx in the traps above the surface decreases to 470 grains cm−2 a−1 (varying from 170 to 910 grains cm−2 a−1) and to 5470 grains cm−2 a−1 in the traps below the surface (ranging from 270 to 12820 grains cm−2 a−1), suggesting that the contribution of waterborne pollen to the non-hydrophyte pollen assemblages in Lake Baiyangdian is about 92%. When trap assemblages are compared with sediment–water interface samples from the same location, the differences between pollen assemblages collected using different methods are more significant than differences between assemblages collected at different sample sites in the lake using the same trapping methods. We compare the ratios of terrestrial pollen and aquicolous pollen types (T/A) between traps in the water and aerial traps, and examine pollen assemblages to determine whether proportions of long-distance taxa (i.e. those known to only grow beyond the estimated aerial source radius); these data suggest that the pollen source area of this lake is composed of three parts, an aerial component mainly carried by wind, a fluvial catchment component transported by rivers and another waterborne component transported by surface wash. Where the overall vegetation composition within the ‘aerial catchment’ is different from that of the hydrological catchment, the ratio between aerial and waterborne pollen influx offers a method for estimating the relative importance of these two sources, and therefore a starting point for defining a pollen source area for a lake with inflowing rivers
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