Modeling the flow-level performance of hierarchical modulation in?OFDMA-based networks

Hierarchical Modulation (HM) is a means to enhance the spectral efficiency of a system by superposing, in terms of modulation, an additional stream for a given user with good radio conditions on a basic stream of a user with worse radio conditions. This, in turn, increases the throughput of the former user and hence the overall performance of the whole system. We consider, in this work, such a performance at the flow level, for a realistic dynamic setting where users come to the system and leave it after a finite duration corresponding, for instance, to the completion of a file transfer. We specifically model and quantify, both analytically and via simulations, the gain thus achieved and propose two extensions to the basic HM algorithm: a first one in which a user with bad radio conditions is also superposed on one with better radio conditions and a second one in which a user of one type is further superposed on a user of the same type as well.     

Reversibly crosslinked self‐healing PCL‐based networks

Poly(ε‐caprolactone) (PCL)‐based thermoreversible networks with self‐healing properties were prepared through Diels–Alder (DA) and retro‐DA reactions. Bis‐ or Tris‐maleimide compounds and a series of copolymer(caprolactone‐diene) PCL_XF_Y (X: degree of polymerization and Y: furan‐average functionality) with Y between 2.4 and 4.9 were used. The successive sequences of formation and dissociation of polycaprolactone networks via DA and retro‐DA reactions were observed repeatedly by dynamic mechanical analyses (DMA) and their gel‐temperatures determined. The cross‐linking densities, thermal properties, and thermal reversibility of the PCL_XF_Y/multimaleimide polymers have been modulated by the structure and functionalities of the used diene and dienophile moieties. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Molecular Docking and Biophysical Studies for Antiproliferative Assessment of Synthetic Pyrazolo-Pyrimidinones Tethered with Hydrazide-Hydrazones

Chemotherapy represents the most applied approach to cancer treatment. Owing to the frequent onset of chemoresistance and tumor relapses, there is an urgent need to discover novel and more effective anticancer drugs. In the search for therapeutic alternatives to treat the cancer disease, a series of hybrid pyrazolo[3,4-d]pyrimidin-4(5H)-ones tethered with hydrazide-hydrazones, 5a–h, was synthesized from condensation reaction of pyrazolopyrimidinone-hydrazide 4 with a series of arylaldehydes in ethanol, in acid catalysis. In vitro assessment of antiproliferative effects against MCF-7 breast cancer cells, unveiled that 5a, 5e, 5g, and 5h were the most effective compounds of the series and exerted their cytotoxic activity through apoptosis induction and G0/G1 phase cell-cycle arrest. To explore their mechanism at a molecular level, 5a, 5e, 5g, and 5h were evaluated for their binding interactions with two well-known anticancer targets, namely the epidermal growth factor receptor (EGFR) and the G-quadruplex DNA structures. Molecular docking simulations highlighted high binding affinity of 5a, 5e, 5g, and 5h towards EGFR. Circular dichroism (CD) experiments suggested 5a as a stabilizer agent of the G-quadruplex from the Kirsten ras (KRAS) oncogene promoter. In the light of these findings, we propose the pyrazolo-pyrimidinone scaffold bearing a hydrazide-hydrazone moiety as a lead skeleton for designing novel anticancer compounds.     

Towards Securing Machine Learning Models Against Membership Inference Attacks

From fraud detection to speech recognition, including price prediction, Machine Learning (ML) applications are manifold and can significantly improve different areas. Nevertheless, machine learning models are vulnerable and are exposed to different security and privacy attacks. Hence, these issues should be addressed while using ML models to preserve the security and privacy of the data used. There is a need to secure ML models, especially in the training phase to preserve the privacy of the training datasets and to minimise the information leakage. In this paper, we present an overview of ML threats and vulnerabilities, and we highlight current progress in the research works proposing defence techniques against ML security and privacy attacks. The relevant background for the different attacks occurring in both the training and testing/inferring phases is introduced before presenting a detailed overview of Membership Inference Attacks (MIA) and the related countermeasures. In this paper, we introduce a countermeasure against membership inference attacks (MIA) on Conventional Neural Networks (CNN) based on dropout and L2 regularization. Through experimental analysis, we demonstrate that this defence technique can mitigate the risks of MIA attacks while ensuring an acceptable accuracy of the model. Indeed, using CNN model training on two datasets CIFAR-10 and CIFAR-100, we empirically verify the ability of our defence strategy to decrease the impact of MIA on our model and we compare results of five different classifiers. Moreover, we present a solution to achieve a trade-off between the performance of the model and the mitigation of MIA attack.     

ResLNet: deep residual LSTM network with longer input for action recognition

Action recognition is an important research topic in video analysis that remains very challenging. Effective recognition relies on learning a good representation of both spatial information (for appearance) and temporal information (for motion). These two kinds of information are highly correlated but have quite different properties, leading to unsatisfying results of both connecting independent models (e.g., CNN-LSTM) and direct unbiased co-modeling (e.g., 3DCNN). Besides, a long-lasting tradition on this task with deep learning models is to just use 8 or 16 consecutive frames as input, making it hard to extract discriminative motion features. In this work, we propose a novel network structure called ResLNet (Deep Residual LSTM network), which can take longer inputs (e.g., of 64 frames) and have convolutions collaborate with LSTM more effectively under the residual structure to learn better spatial-temporal representations than ever without the cost of extra computations with the proposed embedded variable stride convolution. The superiority of this proposal and its ablation study are shown on the three most popular benchmark datasets: Kinetics, HMDB51, and UCF101. The proposed network could be adopted for various features, such as RGB and optical flow. Due to the limitation of the computation power of our experiment equipment and the real-time requirement, the proposed network is tested on the RGB only and shows great performance.     

A video prediction approach for animating single face image

Generating dynamic 2D image-based facial expressions is a challenging task for facial animation. Much research work focused on performance-driven facial animation from given videos or images of a target face, while animating a single face image driven by emotion labels is a less explored problem. In this work, we treat the task of animating single face image from emotion labels as a conditional video prediction problem, and propose a novel framework by combining factored conditional restricted boltzmann machines (FCRBM) and reconstruction contractive auto-encoder (RCAE). A modified RCAE with an associated efficient training strategy is used to extract low dimensional features and reconstruct face images. FCRBM is used as animator to predict facial expression sequence in the feature space given discrete emotion labels and a frontal neutral face image as input. Both quantitative and qualitative evaluations on two facial expression databases, and comparison to state-of-the-art showed the effectiveness of our proposed framework for animating frontal neutral face image from given emotion labels.

     

HPL-1焦化汽油加氢精制催化剂的开发和工业应用

介绍了HPL-1型焦化汽油加氢精制催化剂的实验室研究、评价及工业应用情况。研究结果表明HPL-1型加氢精制催化剂加氢脱硫脱氮和烯烃饱和性能极佳，可适用于较苛刻的工艺条件，尤其对于高硫焦化汽油和直馏汽油的加氢效果更佳。HPL-1催化剂活性较高，能使加氢装置入口温度降低，减少能耗。     

Optimal Selective Arbitrary-Spaced Filters Optimization Using GA Synthesis in One-Dimensional Silicon Photonic Crystal

Silicon - The optimization process is a necessary step in the design of optimal optical devices with high performances. In this paper, optimization of unidirectional photonic crystal selective...