470 research outputs found
On the Applicability of Temperature and Precipitation Data from CMIP3 for China
Global Circulation Models (GCMs) contributed to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) and are widely used in global change research. This paper assesses the performance of the AR4 GCMs in simulating precipitation and temperature in China from 1960 to 1999 by comparison with observed data, using system bias (B), root-mean-square error (RMSE), Pearson correlation coefficient (R) and Nash-Sutcliffe model efficiency (E) metrics. Probability density functions (PDFs) are also fitted to the outputs of each model. It is shown that the performance of each GCM varies to different degrees across China. Based on the skill score derived from the four metrics, it is suggested that GCM 15 (ipsl_cm4) and GCM 3 (cccma_cgcm_t63) provide the best representations of temperature and precipitation, respectively, in terms of spatial distribution and trend over 10 years. The results also indicate that users should apply carefully the results of annual precipitation and annual temperature generated by AR4 GCMs in China due to poor performance. At a finer scale, the four metrics are also used to obtain best fit scores for ten river basins covering mainland China. Further research is proposed to improve the simulation accuracy of the AR4 GCMs regarding China
Taylor Genetic Programming for Symbolic Regression
Genetic programming (GP) is a commonly used approach to solve symbolic regression (SR) problems. Compared with the machine learning or deep learning methods that depend on the pre-defined model and the training dataset for solving SR problems, GP is more focused on finding the solution in a search space. Although GP has good performance on large-scale benchmarks, it randomly transforms individuals to search results without taking advantage of the characteristics of the dataset. So, the search process of GP is usually slow, and the final results could be unstable. To guide GP by these characteristics, we propose a new method for SR, called Taylor genetic programming (TaylorGP). TaylorGP leverages a Taylor polynomial to approximate the symbolic equation that fits the dataset. It also utilizes the Taylor polynomial to extract the features of the symbolic equation: low order polynomial discrimination, variable separability, boundary, monotonic, and parity. GP is enhanced by these Taylor polynomial techniques. Experiments are conducted on three kinds of benchmarks: classical SR, machine learning, and physics. The experimental results show that TaylorGP not only has higher accuracy than the nine baseline methods, but also is faster in finding stable results
PointOBB: Learning Oriented Object Detection via Single Point Supervision
Single point-supervised object detection is gaining attention due to its
cost-effectiveness. However, existing approaches focus on generating horizontal
bounding boxes (HBBs) while ignoring oriented bounding boxes (OBBs) commonly
used for objects in aerial images. This paper proposes PointOBB, the first
single Point-based OBB generation method, for oriented object detection.
PointOBB operates through the collaborative utilization of three distinctive
views: an original view, a resized view, and a rotated/flipped (rot/flp) view.
Upon the original view, we leverage the resized and rot/flp views to build a
scale augmentation module and an angle acquisition module, respectively. In the
former module, a Scale-Sensitive Consistency (SSC) loss is designed to enhance
the deep network's ability to perceive the object scale. For accurate object
angle predictions, the latter module incorporates self-supervised learning to
predict angles, which is associated with a scale-guided Dense-to-Sparse (DS)
matching strategy for aggregating dense angles corresponding to sparse objects.
The resized and rot/flp views are switched using a progressive multi-view
switching strategy during training to achieve coupled optimization of scale and
angle. Experimental results on the DIOR-R and DOTA-v1.0 datasets demonstrate
that PointOBB achieves promising performance, and significantly outperforms
potential point-supervised baselines.Comment: 11 pages,5 figures, 6 tables. Code:
https://github.com/Luo-Z13/pointob
Does Graph Distillation See Like Vision Dataset Counterpart?
Training on large-scale graphs has achieved remarkable results in graph
representation learning, but its cost and storage have attracted increasing
concerns. Existing graph condensation methods primarily focus on optimizing the
feature matrices of condensed graphs while overlooking the impact of the
structure information from the original graphs. To investigate the impact of
the structure information, we conduct analysis from the spectral domain and
empirically identify substantial Laplacian Energy Distribution (LED) shifts in
previous works. Such shifts lead to poor performance in cross-architecture
generalization and specific tasks, including anomaly detection and link
prediction. In this paper, we propose a novel Structure-broadcasting Graph
Dataset Distillation (SGDD) scheme for broadcasting the original structure
information to the generation of the synthetic one, which explicitly prevents
overlooking the original structure information. Theoretically, the synthetic
graphs by SGDD are expected to have smaller LED shifts than previous works,
leading to superior performance in both cross-architecture settings and
specific tasks. We validate the proposed SGDD across 9 datasets and achieve
state-of-the-art results on all of them: for example, on the YelpChi dataset,
our approach maintains 98.6% test accuracy of training on the original graph
dataset with 1,000 times saving on the scale of the graph. Moreover, we
empirically evaluate there exist 17.6% ~ 31.4% reductions in LED shift crossing
9 datasets. Extensive experiments and analysis verify the effectiveness and
necessity of the proposed designs. The code is available in the GitHub
repository: https://github.com/RingBDStack/SGDD.Comment: Accepted by NeurIPS 202
Experimental measurement of the quantum geometric tensor using coupled qubits in diamond
Geometry and topology are fundamental concepts, which underlie a wide range
of fascinating physical phenomena such as topological states of matter and
topological defects. In quantum mechanics, the geometry of quantum states is
fully captured by the quantum geometric tensor. Using a qubit formed by an NV
center in diamond, we perform the first experimental measurement of the
complete quantum geometric tensor. Our approach builds on a strong connection
between coherent Rabi oscillations upon parametric modulations and the quantum
geometry of the underlying states. We then apply our method to a system of two
interacting qubits, by exploiting the coupling between the NV center spin and a
neighboring C nuclear spin. Our results establish coherent dynamical
responses as a versatile probe for quantum geometry, and they pave the way for
the detection of novel topological phenomena in solid state
H2RBox-v2: Boosting HBox-supervised Oriented Object Detection via Symmetric Learning
With the increasing demand for oriented object detection e.g. in autonomous
driving and remote sensing, the oriented annotation has become a
labor-intensive work. To make full use of existing horizontally annotated
datasets and reduce the annotation cost, a weakly-supervised detector H2RBox
for learning the rotated box (RBox) from the horizontal box (HBox) has been
proposed and received great attention. This paper presents a new version,
H2RBox-v2, to further bridge the gap between HBox-supervised and
RBox-supervised oriented object detection. While exploiting axisymmetry via
flipping and rotating consistencies is available through our theoretical
analysis, H2RBox-v2, using a weakly-supervised branch similar to H2RBox, is
embedded with a novel self-supervised branch that learns orientations from the
symmetry inherent in the image of objects. Complemented by modules to cope with
peripheral issues, e.g. angular periodicity, a stable and effective solution is
achieved. To our knowledge, H2RBox-v2 is the first symmetry-supervised paradigm
for oriented object detection. Compared to H2RBox, our method is less
susceptible to low annotation quality and insufficient training data, which in
such cases is expected to give a competitive performance much closer to
fully-supervised oriented object detectors. Specifically, the performance
comparison between H2RBox-v2 and Rotated FCOS on DOTA-v1.0/1.5/2.0 is
72.31%/64.76%/50.33% vs. 72.44%/64.53%/51.77%, 89.66% vs. 88.99% on HRSC, and
42.27% vs. 41.25% on FAIR1M.Comment: 13 pages, 4 figures, 7 tables, the source code is available at
https://github.com/open-mmlab/mmrotat
A Carbon Nanotube-based Hundred Watt-level Ka-band Backward Wave Oscillator
Carbon nanotube (CNT) cold-cathodes hold much promise in a variety of millimeter-wave and terahertz vacuum electronic radiation devices due to their inherent near instantaneous temporal turn-on and near-ideal ideal field electron emission performance. Here we report on the development of a CNT cold-cathode Ka -band backward-wave oscillator (BWO). Using a novel beam compression stage, theoretical studies, simulation results, and empirical findings collectively demonstrate that this device affords an unprecedentedly high output power of 230 W at a technologically important operating frequency of 33.65 GHz. The developed magnetic injection electron gun achieves a high emission current of 265.5 mA (emission current density of 188.3 mA/cm 2 ) and a high focused beam current density of 18.5 A/cm 2 , which our studies suggest, is essential to the BWOs high output power
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