179 research outputs found
Semi-supervised Text Regression with Conditional Generative Adversarial Networks
Enormous online textual information provides intriguing opportunities for
understandings of social and economic semantics. In this paper, we propose a
novel text regression model based on a conditional generative adversarial
network (GAN), with an attempt to associate textual data and social outcomes in
a semi-supervised manner. Besides promising potential of predicting
capabilities, our superiorities are twofold: (i) the model works with
unbalanced datasets of limited labelled data, which align with real-world
scenarios; and (ii) predictions are obtained by an end-to-end framework,
without explicitly selecting high-level representations. Finally we point out
related datasets for experiments and future research directions
EdgeYOLO: An Edge-Real-Time Object Detector
This paper proposes an efficient, low-complexity and anchor-free object
detector based on the state-of-the-art YOLO framework, which can be implemented
in real time on edge computing platforms. We develop an enhanced data
augmentation method to effectively suppress overfitting during training, and
design a hybrid random loss function to improve the detection accuracy of small
objects. Inspired by FCOS, a lighter and more efficient decoupled head is
proposed, and its inference speed can be improved with little loss of
precision. Our baseline model can reach the accuracy of 50.6% AP50:95 and 69.8%
AP50 in MS COCO2017 dataset, 26.4% AP50:95 and 44.8% AP50 in VisDrone2019-DET
dataset, and it meets real-time requirements (FPS>=30) on edge-computing device
Nvidia Jetson AGX Xavier. We also designed lighter models with less parameters
for edge computing devices with lower computing power, which also show better
performances. Our source code, hyper-parameters and model weights are all
available at https://github.com/LSH9832/edgeyolo
Experimental quantum key distribution with source flaws
Decoy-state quantum key distribution (QKD) is a standard technique in current
quantum cryptographic implementations. Unfortunately, existing experiments have
two important drawbacks: the state preparation is assumed to be perfect without
errors and the employed security proofs do not fully consider the finite-key
effects for general attacks. These two drawbacks mean that existing experiments
are not guaranteed to be secure in practice. Here, we perform an experiment
that for the first time shows secure QKD with imperfect state preparations over
long distances and achieves rigorous finite-key security bounds for decoy-state
QKD against coherent attacks in the universally composable framework. We
quantify the source flaws experimentally and demonstrate a QKD implementation
that is tolerant to channel loss despite the source flaws. Our implementation
considers more real-world problems than most previous experiments and our
theory can be applied to general QKD systems. These features constitute a step
towards secure QKD with imperfect devices.Comment: 12 pages, 4 figures, updated experiment and theor
Semi-supervised Text Regression with Conditional Generative Adversarial Networks
Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt to associate textual data and social outcomes in a semi-supervised manner. Besides promising potential of predicting capabilities, our superiorities are twofold: (i) the model works with unbalanced datasets of limited labelled data, which align with real-world scenarios; and (ii) predictions are obtained by an end-to-end framework, without explicitly selecting high-level representations. Finally we point out related datasets for experiments and future research directions
A Blockchain Based Certificate Revocation Scheme For Vehicular Communication Systems
Both the academy and industry believe that Intelligent Transportation System (ITS) would be achievable in one decade since modern vehicle and communication technologies advanced apace. Vehicular Communication System (VCS) introduces information technology to the ITS and aims to improve road safety and traffic efficiency. In recent year, security and privacy schemes in VCS are becoming important. However, recovery mechanisms to eliminate the negative effect of security and privacy attacks are still an important topic for research. Therefore, the certificate revocation scheme is considered as a feasible technique to prevent the system from potential attacks. The major challenge of the certificate revocation scheme is to achieve low-cost operation since the communication resources must be capable of carrying various applications apart from the security and privacy purposes. In this paper, we propose an efficient certificate revocation scheme in VCS. The Blockchain concept is introduced to simplify the network structure and distributed maintenance of the Certificate Revocation List (CRL). The proposed scheme embeds part of the certificate revocation functions within the security and privacy applications, aiming to reduce the communication overhead and shorten the processing time cost. Extensive simulations and analysis show the effectiveness and efficiency of the proposed scheme, in which the Blockchain structure costs fewer network resources and gives a more economic solution to against further cybercrime attacks
Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks
Machine learning is gaining growing momentum in various recent models for the
dynamic analysis of information flows in data communications networks. These
preliminary models often rely on off-the-shelf learning models to predict from
historical statistics while disregarding the physics governing the generating
behaviors of these flows. This paper instead introduces Flow Neural Network
(FlowNN) to improve the feature representation with learned physical bias. This
is implemented by an induction layer, working upon the embedding layer, to
impose the physics connected data correlations, and a self-supervised learning
strategy with stop-gradient to make the learned physics universal. For the
short-timescale network prediction tasks, FlowNN achieves 17% - 71% of loss
decrease than the state-of-the-art baselines on both synthetic and real-world
networking datasets, which shows the strength of this new approach. Code will
be made available.Comment: re-organize the presentatio
Molecular-size dependence of glycogen enzymatic degradation and its importance for diabetes
Glycogen, a hyperbranched glucose polymer, is the blood-sugar reservoir in animals. Liver glycogen comprises small β particles, which can join together as large composite α particles. It had been shown that the binding between β in α particles in the liver of diabetic mice is more fragile than in healthy mice. This could be linked to the loss of blood-sugar control characteristic of diabetes if the rate per monomer unit of the enzymatic degradation to glucose of α particles were significantly slower than that of β particles. This is tested here by examining the in vitro time evolution of the molecular size distribution of glycogen from the livers of healthy and diabetic mice and rats, containing distinct components of both α and β particles; this treatment is analogous to the “competitive growth” method used to explore mechanisms in emulsion polymerization. Simulations for the time evolution of the molecular size distribution were also performed. It is found that the degradation rate per monomer unit is indeed faster for the smaller particles, supporting the hypothesis of a causal link between chemical fragility of glycogen from diabetic liver with poor control of blood-sugar release. Comparison between simulations and experiment indicate that α and β particles have significant structural differences
MOF-Derived Robust and Synergetic Acid Sites Inducing C-N Bond Disruption for Energy-Efficient CO<sub>2</sub>Desorption
Amine-based scrubbing technique is recognized as a promising method of capturing CO2 to alleviate climate change. However, the less stability and poor acidity of solid acid catalysts (SACs) limit their potential to further improve amine regeneration activity and reduce the energy penalty. To address these challenges, here, we introduce two-dimensional (2D) cobalt-nitrogen-doped carbon nanoflakes (Co-N-C NSs) driven by a layered metal-organic framework that work as SACs. The designed 2D Co-N-C SACs can exhibit promising stability, superhydrophilic surface, and acidity. Such 2D structure also contains well-confined Co-N4 Lewis acid sites and -OH Brønsted acid sites to have a synergetic effect on C-N bond disruption and significantly increase CO2 desorption rate by 281% and reduce the reaction temperatures to 88 °C, minimizing water evaporation by 20.3% and subsequent regeneration energy penalty by 71.7% compared to the noncatalysis.</p
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