82,922 research outputs found
Bending modes, elastic constants and mechanical stability of graphitic systems
The thermodynamic and mechanical properties of graphitic systems are strongly
dependent on the shear elastic constant C44. Using state-of-the-art density
functional calculations, we provide the first complete determination of their
elastic constants and exfoliation energies. We show that stacking
misorientations lead to a severe lowering of C44 of at least one order of
magnitude. The lower exfoliation energy and the lower C44 (more bending modes)
suggest that flakes with random stacking should be easier to exfoliate than the
ones with perfect or rhombohedral stacking. We also predict ultralow friction
behaviour in turbostratic graphitic systems.Comment: 7 pages, 6 figure
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking
Physical intuition is pivotal for intelligent agents to perform complex
tasks. In this paper we investigate the passive acquisition of an intuitive
understanding of physical principles as well as the active utilisation of this
intuition in the context of generalised object stacking. To this end, we
provide: a simulation-based dataset featuring 20,000 stack configurations
composed of a variety of elementary geometric primitives richly annotated
regarding semantics and structural stability. We train visual classifiers for
binary stability prediction on the ShapeStacks data and scrutinise their
learned physical intuition. Due to the richness of the training data our
approach also generalises favourably to real-world scenarios achieving
state-of-the-art stability prediction on a publicly available benchmark of
block towers. We then leverage the physical intuition learned by our model to
actively construct stable stacks and observe the emergence of an intuitive
notion of stackability - an inherent object affordance - induced by the active
stacking task. Our approach performs well even in challenging conditions where
it considerably exceeds the stack height observed during training or in cases
where initially unstable structures must be stabilised via counterbalancing.Comment: revised version to appear at ECCV 201
Action Tubelet Detector for Spatio-Temporal Action Localization
Current state-of-the-art approaches for spatio-temporal action localization
rely on detections at the frame level that are then linked or tracked across
time. In this paper, we leverage the temporal continuity of videos instead of
operating at the frame level. We propose the ACtion Tubelet detector
(ACT-detector) that takes as input a sequence of frames and outputs tubelets,
i.e., sequences of bounding boxes with associated scores. The same way
state-of-the-art object detectors rely on anchor boxes, our ACT-detector is
based on anchor cuboids. We build upon the SSD framework. Convolutional
features are extracted for each frame, while scores and regressions are based
on the temporal stacking of these features, thus exploiting information from a
sequence. Our experimental results show that leveraging sequences of frames
significantly improves detection performance over using individual frames. The
gain of our tubelet detector can be explained by both more accurate scores and
more precise localization. Our ACT-detector outperforms the state-of-the-art
methods for frame-mAP and video-mAP on the J-HMDB and UCF-101 datasets, in
particular at high overlap thresholds.Comment: 9 page
Disrupted orbital order and the pseudo-gap in layered 1T-TaS
We present a state-of-the-art density functional theory (DFT) study which
models crucial features of the partially disordered orbital order stacking in
the prototypical layered transition metal dichalcogenide 1T-TaS2 . Our results
not only show that DFT models with realistic assumptions about the orbital
order perpendicular to the layers yield band structures which agree remarkably
well with experiments. They also demonstrate that DFT correctly predicts the
formation of an excitation pseudo-gap which is commonly attributed to
Mott-Hubbard type electron-electron correlations. These results highlight the
importance of interlayer interactions in layered transition metal
dichalcogenides and serve as an intriguing example of how disorder within an
electronic crystal can give rise to pseudo-gap features
Anomaly Identification Model for Telecom Users Based on Machine Learning Model Fusion
With the development of economic globalization and modern information and communication technology, the situation of communication fraud is becoming more and more serious. How to identify fraudulent calls accurately and effectively has become an urgent task in current telecommunications operations. Affected by the sample set and the current state of the art, the current machine learning methods used to identify the imbalanced distribution dataset of positive and negative samples have low recognition accuracy. Therefore, in this paper, we propose a new hybrid model solution that uses feature construction, feature selection and imbalanced classes handling. A stacking model fusion algorithm composed of a two-layer stacking framework with several state-of-the-art machine learning classifiers is adopted. The results show that the risk user identification model based on mobile network communication behavior established by our stacking model fusion algorithm can accurately predict the category labels of telecom users and improve the risk of telecom users. The generalization performance of the identification is high, which provides a certain reference for the telecommunications industry to identify risk users based on mobile network communication behaviors
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