94 research outputs found
Preparation and properties of asphalt binders modified by THFS extracted from direct coal liquefaction residue
This paper aims to study the preparation and viscoelastic properties of asphalt binder modified by tetrahydrofuran soluble fraction (THFS) extracted from direct coal liquefaction residue. The modified asphalt binders, which blended with SK-90 (control asphalt binder) and 4%, 6%, 8% and 10% THFS (by weight of SK-90), were fabricated. The preparation process for asphalt binder was optimized in terms of the orthogonal array test strategy and gray correlation analysis results. The properties of asphalt binder were measured by applying Penetration performance grade and Superpave performance grade specifications. In addition, the temperature step and frequency sweep test in Dynamic Shear Rheometer were conducted to predict the rheological behavior, temperature and frequency susceptibility of asphalt binder. The test results suggested the optimal preparation process, such as 150 °C shearing temperature, 45 min shearing time and 4000 rpm shearing rate. Subsequently, the addition of THFS was beneficial in increasing the high-temperature properties but decreased the low-temperature properties and resistance to fatigue. The content analysis of THFS showed the percentage of 4~6% achieved a balance in the high-and-low temperature properties of asphalt binder. The asphalt binder with higher THFS content exhibited higher resistance to rutting and less sensitivity to frequency and temperature
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
Transferable adversarial attacks against Deep neural networks (DNNs) have
received broad attention in recent years. An adversarial example can be crafted
by a surrogate model and then attack the unknown target model successfully,
which brings a severe threat to DNNs. The exact underlying reasons for the
transferability are still not completely understood. Previous work mostly
explores the causes from the model perspective, e.g., decision boundary, model
architecture, and model capacity. adversarial attacks against Deep neural
networks (DNNs) have received broad attention in recent years. An adversarial
example can be crafted by a surrogate model and then attack the unknown target
model successfully, which brings a severe threat to DNNs. The exact underlying
reasons for the transferability are still not completely understood. Previous
work mostly explores the causes from the model perspective. Here, we
investigate the transferability from the data distribution perspective and
hypothesize that pushing the image away from its original distribution can
enhance the adversarial transferability. To be specific, moving the image out
of its original distribution makes different models hardly classify the image
correctly, which benefits the untargeted attack, and dragging the image into
the target distribution misleads the models to classify the image as the target
class, which benefits the targeted attack. Towards this end, we propose a novel
method that crafts adversarial examples by manipulating the distribution of the
image. We conduct comprehensive transferable attacks against multiple DNNs to
demonstrate the effectiveness of the proposed method. Our method can
significantly improve the transferability of the crafted attacks and achieves
state-of-the-art performance in both untargeted and targeted scenarios,
surpassing the previous best method by up to 40 in some cases.Comment: \copyright 20XX IEEE. Personal use of this material is permitted.
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this work in other work
Boosting Out-of-distribution Detection with Typical Features
Out-of-distribution (OOD) detection is a critical task for ensuring the
reliability and safety of deep neural networks in real-world scenarios.
Different from most previous OOD detection methods that focus on designing OOD
scores or introducing diverse outlier examples to retrain the model, we delve
into the obstacle factors in OOD detection from the perspective of typicality
and regard the feature's high-probability region of the deep model as the
feature's typical set. We propose to rectify the feature into its typical set
and calculate the OOD score with the typical features to achieve reliable
uncertainty estimation. The feature rectification can be conducted as a
{plug-and-play} module with various OOD scores. We evaluate the superiority of
our method on both the commonly used benchmark (CIFAR) and the more challenging
high-resolution benchmark with large label space (ImageNet). Notably, our
approach outperforms state-of-the-art methods by up to 5.11 in the average
FPR95 on the ImageNet benchmark
Remote sensing inversion of soil organic matter in cropland combining topographic factors with spectral parameters
Remote sensing has become an effective way for regional soil organic matter (SOM) quantitative analysis. Topographic factors affect SOM content and distribution, also influence the accuracy of SOM remote sensing inversion. In large region with complex topographic conditions, characteristic topographic factors of SOM in different topographic regions are unknown, and the effect of combining characteristic topographic factors with spectral parameters on improving SOM inversion accuracy remains to be further studied. Three typical topographic regions of Shandong Province in China, namely Western plain region (WPR), Central and southern mountain region (CSMR), Eastern hilly region (EHR), were selected. Topographic factors, namely Elevation, Slope, Aspect and Relief Amplitude, were introduced. Respectively, the characteristic topographic factors and spectral parameters of SOM in each region were identified. The SOM inversion models were built separately for each region by integrating spectral parameters with topographic factors. The results revealed that as for the characteristic topographic factors of SOM, none was in the WPR, E, RA, and S were in the CSMR, E and RA were in the EHR. In combination with characteristic topographic factors, the accuracy of SOM spectral inversion models improved, the calibration R2 increased by 0.075–0.102, the RMSE (Root mean square error) decreased by 0.162–0.171 g/kg, the validation R2 increased by 0.067–0.095, the RMSE decreased by 0.236–0.238 g/kg, and RPD (Relative prediction deviation) increased by 0.129–0.169. The most significant improvement was observed in the CSMR with the calibration R2 of 0.725, the validation R2 of 0.713 and the RPD of 1.852, followed by the EHR. This study not only contributes to the advancement of soil quantitative remote sensing theory but also offers more precise data support for the development of green, low-carbon, and precision agriculture
Flexoelectricity-stabilized ferroelectric phase with enhanced reliability in ultrathin La:HfO2 films
Doped HfO2 thin films exhibit robust ferroelectric properties even for
nanometric thicknesses, are compatible with current Si technology and thus have
great potential for the revival of integrated ferroelectrics. Phase control and
reliability are core issues for their applications. Here we show that, in
(111)-oriented 5%La:HfO2 (HLO) epitaxial thin films deposited on
(La0.3Sr0.7)(Al0.65Ta0.35)O3 substrates, the flexoelectric effect, arising from
the strain gradient along the films normal, induces a rhombohedral distortion
in the otherwise Pca21 orthorhombic structure. Density functional calculations
reveal that the distorted structure is indeed more stable than the pure Pca21
structure, when applying an electric field mimicking the flexoelectric field.
This rhombohedral distortion greatly improves the fatigue endurance of HLO thin
films by further stabilizing the metastable ferroelectric phase against the
transition to the thermodynamically stable non-polar monoclinic phase during
repetitive cycling. Our results demonstrate that the flexoelectric effect,
though negligibly weak in bulk, is crucial to optimize the structure and
properties of doped HfO2 thin films with nanometric thicknesses for integrated
ferroelectric applications
Electronic bandstructure of in-plane ferroelectric van der Waals
Layered indium selenides () have recently been discovered to
host robust out-of-plane and in-plane ferroelectricity in the and
' phases, respectively. In this work, we utilise angle-resolved
photoelectron spectroscopy to directly measure the electronic bandstructure of
, and compare to hybrid density functional theory (DFT)
calculations. In agreement with DFT, we find the band structure is highly
two-dimensional, with negligible dispersion along the c-axis. Due to n-type
doping we are able to observe the conduction band minima, and directly measure
the minimum indirect (0.97 eV) and direct (1.46 eV) bandgaps. We find the Fermi
surface in the conduction band is characterized by anisotropic electron pockets
with sharp in-plane dispersion about the points, yielding
effective masses of 0.21 along and 0.33 along
. The measured band structure is well supported by hybrid
density functional theory calculations. The highly two-dimensional (2D)
bandstructure with moderate bandgap and small effective mass suggest that
is a potentially useful new van der Waals semiconductor.
This together with its ferroelectricity makes it a viable material for
high-mobility ferroelectric-photovoltaic devices, with applications in
non-volatile memory switching and renewable energy technologies.Comment: 19 pages, 4 + 1 figures; typos corrected;added references; updated
figures & discussion to reflect changes in mode
On minimizing coding operations in network coding based multicast: an evolutionary algorithm
In telecommunications networks, to enable a valid data transmission based on network coding, any intermediate node within a given network is allowed, if necessary, to perform coding operations. The more coding operations needed, the more coding resources consumed and thus the more computational overhead and transmission delay incurred. This paper investigates an efficient evolutionary algorithm to minimize the amount of coding operations required in network coding based multicast. Based on genetic algorithms, we adapt two extensions in the proposed evolutionary algorithm, namely a new crossover operator and a neighbourhood search operator, to effectively solve the highly complex problem being concerned. The new crossover is based on logic OR operations to each pair of selected parent individuals, and the resulting offspring are more likely to become feasible. The aim of this operator is to intensify the search in regions with plenty of feasible individuals. The neighbourhood search consists of two moves which are based on greedy link removal and path reconstruction, respectively. Due to the specific problem feature, it is possible that each feasible individual corresponds to a number of, rather than a single, valid network coding based routing subgraphs. The neighbourhood search is applied to each feasible individual to find a better routing subgraph that consumes less coding resource. This operator not only improves solution quality but also accelerates the convergence. Experiments have been carried out on a number of fixed and randomly generated benchmark networks. The results demonstrate that with the two extensions, our evolutionary algorithm is effective and outperforms a number of state-of-the-art algorithms in terms of the ability of finding optimal solutions
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