A Deep Learning Framework for Predicting Cyber Attacks Rates

Like how useful weather forecasting is, the capability of forecasting or predicting cyber threats can never be overestimated. Previous investigations show that cyber attack data exhibits interesting phenomena, such as long-range dependence and high nonlinearity, which impose a particular challenge on modeling and predicting cyber attack rates. Deviating from the statistical approach that is utilized in the literature, in this paper we develop a deep learning framework by utilizing the bi-directional recurrent neural networks with long short-term memory, dubbed BRNN-LSTM. Empirical study shows that BRNN-LSTM achieves a significantly higher prediction accuracy when compared with the statistical approach

Semidefinite Programming Approximation for A Matrix Optimization Problem over An Uncertain Linear System

A matrix optimization problem over an uncertain linear system on finite horizon (abbreviated as MOPUL) is studied, in which the uncertain transition matrix is regarded as a decision variable. This problem is in general NP-hard. By using the given reference values of system outputs at each stage, we develop a polynomial-time solvable semidefinite programming (SDP) approximation model for the problem. The upper bound of the cumulative error between reference outputs and the optimal outputs of the approximation model is theoretically analyzed. Two special cases associated with specific applications are considered. The quality of the SDP approximate solutions in terms of feasibility and optimality is also analyzed. Results of numerical experiments are presented to show the influences of perturbed noises at reference outputs and control levels on the performance of SDP approximation

Doubly Heavy Baryon Production at A High Luminosity e+e−e^+ e^- Collider

Within the framework of nonrelativistic QCD, we make a detailed discussion on the doubly heavy baryon production through the $e^+ e^-$ annihilation channel, $e^{+}e^{-}\rightarrow\gamma/Z^0 \rightarrow \Xi_{QQ^{\prime}} +\bar{Q} +\bar{Q^{\prime}}$, at a high luminosity $e^{+}e^{-}$ collider. Here $Q^{(\prime)}$ stands for the heavy $b$ or $c$ quark. In addition to the channel through the usually considered diquark state $(QQ^{\prime})[^3S_1]_{\bf\bar{3}}$, contributions from the channels through other same important diquark states such as $(QQ^{\prime})[^1S_0]_{\bf 6}$ have also been discussed. Uncertainties for the total cross sections are predicted by taking $m_c=1.80\pm0.30$ GeV and $m_b=5.10\pm0.40$ GeV. At a super $Z$-factory running around the $Z^0$ mass and with a high luminosity up to ${\cal L} \propto 10^{34}\sim 10^{36}{\rm cm}^{-2} {\rm s}^{-1}$, we estimate that about $1.1\times10^{5 \sim 7}$ $\Xi_{cc}$ events, $2.6\times10^{5 \sim 7}$ $\Xi_{bc}$ events and $1.2\times 10^{4 \sim 6}$ $\Xi_{bb}$ events can be generated in one operation year. Such a $Z$-factory, thus, will provide a good platform for studying the doubly heavy baryons in comparable to the CERN large hadronic collider.Comment: 9 pages, 4 figures. To be published in Phys.Rev.

LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch

Structured pruning is a commonly used convolutional neural network (CNN) compression approach. Pruning rate setting is a fundamental problem in structured pruning. Most existing works introduce too many additional learnable parameters to assign different pruning rates across different layers in CNN or cannot control the compression rate explicitly. Since too narrow network blocks information flow for training, automatic pruning rate setting cannot explore a high pruning rate for a specific layer. To overcome these limitations, we propose a novel framework named Layer Adaptive Progressive Pruning (LAPP), which gradually compresses the network during initial training of a few epochs from scratch. In particular, LAPP designs an effective and efficient pruning strategy that introduces a learnable threshold for each layer and FLOPs constraints for network. Guided by both task loss and FLOPs constraints, the learnable thresholds are dynamically and gradually updated to accommodate changes of importance scores during training. Therefore the pruning strategy can gradually prune the network and automatically determine the appropriate pruning rates for each layer. What's more, in order to maintain the expressive power of the pruned layer, before training starts, we introduce an additional lightweight bypass for each convolutional layer to be pruned, which only adds relatively few additional burdens. Our method demonstrates superior performance gains over previous compression methods on various datasets and backbone architectures. For example, on CIFAR-10, our method compresses ResNet-20 to 40.3% without accuracy drop. 55.6% of FLOPs of ResNet-18 are reduced with 0.21% top-1 accuracy increase and 0.40% top-5 accuracy increase on ImageNet.Comment: 12 pages, 8 tables, 3 figure

Generating scalable entanglement of ultracold bosons in superlattices through resonant shaking

Based on a one-dimensional double-well superlattice with a unit filling of ultracold atoms per site, we propose a scheme to generate scalable entangled states in the superlattice through resonant lattice shakings. Our scheme utilizes periodic lattice modulations to entangle two atoms in each unit cell with respect to their orbital degree of freedom, and the complete atomic system in the superlattice becomes a cluster of bipartite entangled atom pairs. To demonstrate this we perform $ab \ initio$ quantum dynamical simulations using the Multi-Layer Multi-Configuration Time-Dependent Hartree Method for Bosons, which accounts for all correlations among the atoms. The proposed clusters of bipartite entanglements manifest as an essential resource for various quantum applications, such as measurement based quantum computation. The lattice shaking scheme to generate this cluster possesses advantages such as a high scalability, fast processing speed, rich controllability on the target entangled states, and accessibility with current experimental techniques.Comment: 13 pages, 3 figure

Neuroprotective effects of bis(7)-tacrine against glutamate-induced retinal ganglion cells damage

<p>Abstract</p> <p>Background</p> <p>Glutamate-mediated excitotoxicity, primarily through N-methyl-D-aspartate (NMDA) receptors, may be an important cause of retinal ganglion cells (RGCs) death in glaucoma and several other retinal diseases. Bis(7)-tacrine is a noncompetitive NMDA receptors antagonist that can prevent glutamate-induced hippocampal neurons damage. We tested the effects of bis(7)-tacrine against glutamate-induced rat RGCs damage in vitro and in vivo.</p> <p>Results</p> <p>In cultured neonatal rats RGCs, the MTT assay showed that glutamate induced a concentration- and time-dependent toxicity. Bis(7)-tacrine and memantine prevented glutamate-induced cell death in a concentration-dependent manner with IC50 values of 0.028 μM and 0.834 μM, respectively. The anti-apoptosis effects of bis(7)-tacrine were confirmed by annexin V-FITC/PI staining. In vivo, TUNEL analysis and retrograde labeling analysis found that pretreatment with bis(7)-tacrine(0.2 mg/kg) induced a significant neuroprotective effect against glutamate-induced RGCs damage.</p> <p>Conclusions</p> <p>Our results showed that bis(7)-tacrine had neuroprotective effects against glutamate-induced RGCs damage in vitro and in vivo, possibly through the drug's anti-NMDA receptor effects. These findings make bis(7)-tacrine potentially useful for treating a variety of ischemic or traumatic retinopathies inclusive of glaucoma.</p

PB-ACR: Node Payload Balanced Ant Colony Optimal Cooperative Routing for Multi-Hop Underwater Acoustic Sensor Networks

For a given source-destination pair in multi-hop underwater acoustic sensor networks (UASNs), an optimal route is the one with the lowest energy consumptions that usually consists of the same relay nodes even under different transmission tasks. However, this will lead to the unbalanced payload of the relay nodes in the multi-hop UASNs and accelerate the loss of the working ability for the entire system. In this paper, we propose a node payload balanced ant colony optimal cooperative routing (PB-ACR) protocol for multi-hop UASNs, through combining the ant colony algorithm and cooperative transmission. The proposed PB-ACR protocol is a relay node energy consumption balanced scheme, which considers both data priority and residual energy of each relay node, aiming to reduce the occurrence of energy holes and thereby prolong the lifetime of the entire UASNs. We compare the proposed PB-ACR protocol with the existing ant colony algorithm routing (ACAR) protocol to verify its performances in multi-hop UASNs, in terms of network throughput, energy consumption, and algorithm complexity. The simulation results show that the proposed PB-ACR protocol can effectively balance the energy consumption of underwater sensor nodes and hence prolong the network lifetime

Bivariate statistics of floating offshore wind turbine dynamic response under operational conditions

FOWT (Floating offshore wind turbines) belong to the modern offshore wind energy industry generating green renewable energy. Accurate extreme loads and response prediction during power generation is an important design concern. More accurate and reliable estimations of extreme responses are significant for the offshore wind industry as it advances the design, manufacturing and deployment of large FOWTs in the coming decade. In this study, the OpenFAST code was used to model offshore wind turbine mooring line tension force and blade bending moment due to environmental loads, acting on a site-specific FOWT under realistic local environmental conditions. This paper presents an efficient Monte Carlo based method to study bivariate extreme dynamic response statistics. The ACER2D (bivariate average conditional exceedance rate) method is briefly discussed. The ACER2D method enables robust estimation of bivariate statistics, efficiently utilizing available data. Large return period 2D (two-dimensional) probability contours were obtained using the ACER2D method. Based on the studied performance of the presented methodology, it was seen that ACER2D provides accurate and efficient predictions of extreme return period contours. The described approach may be utilized at the design stage, defining optimal FOWT design values to minimize potential structural damage due to extreme environmental loads. It should be noted that the bivariate design point is less conservative than the classic univariate one; therefore, this study advocates a design method leading to lower structural production costs.acceptedVersio