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

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

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

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 CO₂Desorption

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