244 research outputs found
Collaborative Inference of Coexisting Information Diffusions
Recently, \textit{diffusion history inference} has become an emerging
research topic due to its great benefits for various applications, whose
purpose is to reconstruct the missing histories of information diffusion traces
according to incomplete observations. The existing methods, however, often
focus only on single information diffusion trace, while in a real-world social
network, there often coexist multiple information diffusions over the same
network. In this paper, we propose a novel approach called Collaborative
Inference Model (CIM) for the problem of the inference of coexisting
information diffusions. By exploiting the synergism between the coexisting
information diffusions, CIM holistically models multiple information diffusions
as a sparse 4th-order tensor called Coexisting Diffusions Tensor (CDT) without
any prior assumption of diffusion models, and collaboratively infers the
histories of the coexisting information diffusions via a low-rank approximation
of CDT with a fusion of heterogeneous constraints generated from additional
data sources. To improve the efficiency, we further propose an optimal
algorithm called Time Window based Parallel Decomposition Algorithm (TWPDA),
which can speed up the inference without compromise on the accuracy by
utilizing the temporal locality of information diffusions. The extensive
experiments conducted on real world datasets and synthetic datasets verify the
effectiveness and efficiency of CIM and TWPDA
Coulomb-coupled quantum-dot thermal transistors
A quantum-dot thermal transistor consisting of three Coulomb-coupled quantum
dots coupled to respective electronic reservoirs by tunnel contacts is
established. The heat flows through the collector and emitter can be controlled
by the temperature of the base. It is found that a small change in the base
heat flow can induce a large heat flow change in the collector and emitter. The
huge amplification factor can be obtained by optimizing the Coulomb interaction
between the collector and the emitter or by decreasing the energy-dependent
tunneling rate at the base. The proposed quantum-dot thermal transistor may
open up potential applications in low-temperature solid-state thermal circuits
at the nanoscale.Comment: 14 pages, 6 figure
Provable Unrestricted Adversarial Training without Compromise with Generalizability
Adversarial training (AT) is widely considered as the most promising strategy
to defend against adversarial attacks and has drawn increasing interest from
researchers. However, the existing AT methods still suffer from two challenges.
First, they are unable to handle unrestricted adversarial examples (UAEs),
which are built from scratch, as opposed to restricted adversarial examples
(RAEs), which are created by adding perturbations bound by an norm to
observed examples. Second, the existing AT methods often achieve adversarial
robustness at the expense of standard generalizability (i.e., the accuracy on
natural examples) because they make a tradeoff between them. To overcome these
challenges, we propose a unique viewpoint that understands UAEs as
imperceptibly perturbed unobserved examples. Also, we find that the tradeoff
results from the separation of the distributions of adversarial examples and
natural examples. Based on these ideas, we propose a novel AT approach called
Provable Unrestricted Adversarial Training (PUAT), which can provide a target
classifier with comprehensive adversarial robustness against both UAE and RAE,
and simultaneously improve its standard generalizability. Particularly, PUAT
utilizes partially labeled data to achieve effective UAE generation by
accurately capturing the natural data distribution through a novel augmented
triple-GAN. At the same time, PUAT extends the traditional AT by introducing
the supervised loss of the target classifier into the adversarial loss and
achieves the alignment between the UAE distribution, the natural data
distribution, and the distribution learned by the classifier, with the
collaboration of the augmented triple-GAN. Finally, the solid theoretical
analysis and extensive experiments conducted on widely-used benchmarks
demonstrate the superiority of PUAT
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots
We introduce a method to segment the visual field into independently moving
regions, trained with no ground truth or supervision. It consists of an
adversarial conditional encoder-decoder architecture based on Slot Attention,
modified to use the image as context to decode optical flow without attempting
to reconstruct the image itself. In the resulting multi-modal representation,
one modality (flow) feeds the encoder to produce separate latent codes (slots),
whereas the other modality (image) conditions the decoder to generate the first
(flow) from the slots. This design frees the representation from having to
encode complex nuisance variability in the image due to, for instance,
illumination and reflectance properties of the scene. Since customary
autoencoding based on minimizing the reconstruction error does not preclude the
entire flow from being encoded into a single slot, we modify the loss to an
adversarial criterion based on Contextual Information Separation. The resulting
min-max optimization fosters the separation of objects and their assignment to
different attention slots, leading to Divided Attention, or DivA. DivA
outperforms recent unsupervised multi-object motion segmentation methods while
tripling run-time speed up to 104FPS and reducing the performance gap from
supervised methods to 12% or less. DivA can handle different numbers of objects
and different image sizes at training and test time, is invariant to
permutation of object labels, and does not require explicit regularization
Compensating the Degradation of Near-Infrared Absorption of Black Silicon Caused by Thermal Annealing
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