107 research outputs found
Stratified Rule-Aware Network for Abstract Visual Reasoning
Abstract reasoning refers to the ability to analyze information, discover
rules at an intangible level, and solve problems in innovative ways. Raven's
Progressive Matrices (RPM) test is typically used to examine the capability of
abstract reasoning. The subject is asked to identify the correct choice from
the answer set to fill the missing panel at the bottom right of RPM (e.g., a
33 matrix), following the underlying rules inside the matrix. Recent
studies, taking advantage of Convolutional Neural Networks (CNNs), have
achieved encouraging progress to accomplish the RPM test. However, they partly
ignore necessary inductive biases of RPM solver, such as order sensitivity
within each row/column and incremental rule induction. To address this problem,
in this paper we propose a Stratified Rule-Aware Network (SRAN) to generate the
rule embeddings for two input sequences. Our SRAN learns multiple granularity
rule embeddings at different levels, and incrementally integrates the
stratified embedding flows through a gated fusion module. With the help of
embeddings, a rule similarity metric is applied to guarantee that SRAN can not
only be trained using a tuplet loss but also infer the best answer efficiently.
We further point out the severe defects existing in the popular RAVEN dataset
for RPM test, which prevent from the fair evaluation of the abstract reasoning
ability. To fix the defects, we propose an answer set generation algorithm
called Attribute Bisection Tree (ABT), forming an improved dataset named
Impartial-RAVEN (I-RAVEN for short). Extensive experiments are conducted on
both PGM and I-RAVEN datasets, showing that our SRAN outperforms the
state-of-the-art models by a considerable margin.Comment: AAAI 2021 paper. Code: https://github.com/husheng12345/SRA
AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design
Metaheuristics are prominent gradient-free optimizers for solving hard
problems that do not meet the rigorous mathematical assumptions of analytical
solvers. The canonical manual optimizer design could be laborious, untraceable
and error-prone, let alone human experts are not always available. This arises
increasing interest and demand in automating the optimizer design process. In
response, this paper proposes AutoOptLib, the first platform for accessible
automated design of metaheuristic optimizers. AutoOptLib leverages computing
resources to conceive, build up, and verify the design choices of the
optimizers. It requires much less labor resources and expertise than manual
design, democratizing satisfactory metaheuristic optimizers to a much broader
range of researchers and practitioners. Furthermore, by fully exploring the
design choices with computing resources, AutoOptLib has the potential to
surpass human experience, subsequently gaining enhanced performance compared
with human problem-solving. To realize the automated design, AutoOptLib
provides 1) a rich library of metaheuristic components for continuous,
discrete, and permutation problems; 2) a flexible algorithm representation for
evolving diverse algorithm structures; 3) different design objectives and
techniques for different optimization scenarios; and 4) a graphic user
interface for accessibility and practicability. AutoOptLib is fully written in
Matlab/Octave; its source code and documentation are available at
https://github.com/qz89/AutoOpt and https://AutoOpt.readthedocs.io/,
respectively
Adversarial Examples in the Physical World: A Survey
Deep neural networks (DNNs) have demonstrated high vulnerability to
adversarial examples. Besides the attacks in the digital world, the practical
implications of adversarial examples in the physical world present significant
challenges and safety concerns. However, current research on physical
adversarial examples (PAEs) lacks a comprehensive understanding of their unique
characteristics, leading to limited significance and understanding. In this
paper, we address this gap by thoroughly examining the characteristics of PAEs
within a practical workflow encompassing training, manufacturing, and
re-sampling processes. By analyzing the links between physical adversarial
attacks, we identify manufacturing and re-sampling as the primary sources of
distinct attributes and particularities in PAEs. Leveraging this knowledge, we
develop a comprehensive analysis and classification framework for PAEs based on
their specific characteristics, covering over 100 studies on physical-world
adversarial examples. Furthermore, we investigate defense strategies against
PAEs and identify open challenges and opportunities for future research. We aim
to provide a fresh, thorough, and systematic understanding of PAEs, thereby
promoting the development of robust adversarial learning and its application in
open-world scenarios.Comment: Adversarial examples, physical-world scenarios, attacks and defense
A new improved Kurtogram and its application to planetary gearbox degradation feature analysis
Because of various advantages of planetary transmission system, it has been widely used in modern industry. And study on planetary gearbox degradation feature analysis method has important significance for mechanical system prognostics and health management (PHM). In order to analysis the degradation characteristic of planetary gearbox, Energram is proposed in this paper based on Kurtogram. Kurtogram is used for finding the optimal frequency band to rotating machinery fault diagnosis by calculating kurtosis. Similarly, Energram is used to show the energy trend of each frequency band by calculating energy, and arithmetic Energram is used to show the change of frequency band energy. The principle and application of Energram and arithmetic Energram are described by experimental data examples in this paper. A detailed study of planetary gearbox degradation characteristics is expressed in case study, which including Energram, arithmetic Energram and four particular comparative analyses. And the conclusions of each comparative analysis are given
The Ginger-shaped Asteroid 4179 Toutatis: New Observations from a Successful Flyby of Chang'e-2
On 13 December 2012, Chang'e-2 conducted a successful flyby of the near-Earth
asteroid 4179 Toutatis at a closest distance of 770 120 meters from the
asteroid's surface. The highest-resolution image, with a resolution of better
than 3 meters, reveals new discoveries on the asteroid, e.g., a giant basin at
the big end, a sharply perpendicular silhouette near the neck region, and
direct evidence of boulders and regolith, which suggests that Toutatis may bear
a rubble-pile structure. Toutatis' maximum physical length and width are (4.75
1.95 km) 10, respectively, and the direction of the + axis
is estimated to be (2505, 635) with respect to the
J2000 ecliptic coordinate system. The bifurcated configuration is indicative of
a contact binary origin for Toutatis, which is composed of two lobes (head and
body). Chang'e-2 observations have significantly improved our understanding of
the characteristics, formation, and evolution of asteroids in general.Comment: 21 pages, 3 figures, 1 tabl
Optical Cherenkov radiation in an As<sub>2</sub>S<sub>3</sub> slot waveguide with four zero-dispersion wavelengths
Solid polymer electrolytes: Ion conduction mechanisms and enhancement strategies
Solid polymer electrolytes (SPEs) possess comprehensive advantages such as high flexibility, low interfacial resistance with the electrodes, excellent film-forming ability, and low price, however, their applications in solid-state batteries are mainly hindered by the insufficient ionic conductivity especially below the melting temperatures, etc. To improve the ion conduction capability and other properties, a variety of modification strategies have been exploited. In this review article, we scrutinize the structure characteristics and the ion transfer behaviors of the SPEs (and their composites) and then disclose the ion conduction mechanisms. The ion transport involves the ion hopping and the polymer segmental motion, and the improvement in the ionic conductivity is mainly attributed to the increase of the concentration and mobility of the charge carriers and the construction of fast-ion pathways. Furthermore, the recent advances on the modification strategies of the SPEs to enhance the ion conduction from copolymer structure design to lithium salt exploitation, additive engineering, and electrolyte micromorphology adjustion are summarized. This article intends to give a comprehensive, systemic, and profound understanding of the ion conduction and enhancement mechanisms of the SPEs for their viable applications in solid-state batteries with high safety and energy density
Comorbid depressive symptoms can aggravate the functional changes of the pain matrix in patients with chronic back pain: A resting-state fMRI study
ObjectiveThe purposes of this study are to explore (1) whether comorbid depressive symptoms in patients with chronic back pain (CBP) affect the pain matrix. And (2) whether the interaction of depression and CBP exacerbates impaired brain function.MethodsThirty-two patients with CBP without comorbid depressive symptoms and thirty patients with CBP with comorbid depressive symptoms were recruited. All subjects underwent functional magnetic resonance imaging (fMRI) scans. The graph theory analysis, mediation analysis, and functional connectivity (FC) analysis were included in this study. All subjects received the detection of clinical depressive symptoms and pain-related manifestations.ResultCompared with the CBP group, subjects in the CBP with comorbid depressive symptoms (CBP-D) group had significantly increased FC in the left medial prefrontal cortex and several parietal cortical regions. The results of the graph theory analyses showed that the area under the curve of small-world property (t = −2.175, p = 0.034), gamma (t = −2.332, p = 0.023), and local efficiency (t = −2.461, p = 0.017) in the CBP-D group were significantly lower. The nodal efficiency in the ventral posterior insula (VPI) (t = −3.581, p = 0.0007), and the network efficiency values (t = −2.758, p = 0.008) in the pain matrix were significantly lower in the CBP-D group. Both the topological properties and the FC values of these brain regions were significantly correlated with self-rating depression scale (SDS) scores (all FDR corrected) but not with pain intensity. Further mediation analyses demonstrated that pain intensity had a mediating effect on the relationship between SDS scores and Pain Disability Index scores. Likewise, the SDS scores mediated the relationship between pain intensity and PDI scores.ConclusionOur study found that comorbid depressive symptoms can aggravate the impairment of pain matrix function of CBP, but this impairment cannot directly lead to the increase of pain intensity, which may be because some brain regions of the pain matrix are the common neural basis of depression and CBP
Effects of grain boundaries in oxide scale on tribological properties of nanoparticles lubrication
Terephthalonitrile-derived nitrogen-rich networks for high performance supercapacitors
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