Redshift drift exploration for interacting dark energy

By detecting redshift drift in the spectra of Lyman-$\alpha$ forest of distant quasars, Sandage-Loeb (SL) test directly measures the expansion of the universe, covering the "redshift desert" of $2 \lesssim z \lesssim5$. Thus this method is definitely an important supplement to the other geometric measurements and will play a crucial role in cosmological constraints. In this paper, we quantify the ability of SL test signal by a CODEX-like spectrograph for constraining interacting dark energy. Four typical interacting dark energy models are considered: (i) $Q=\gamma H\rho_c$, (ii) $Q=\gamma H\rho_{de}$, (iii) $Q=\gamma H_0\rho_c$, and (iv) $Q=\gamma H_0\rho_{de}$. The results show that for all the considered interacting dark energy models, relative to the current joint SN+BAO+CMB+$H_0$ observations, the constraints on $\Omega_m$ and $H_0$ would be improved by about 60\% and 30--40\%, while the constraints on $w$ and $\gamma$ would be slightly improved, with a 30-yr observation of SL test. We also explore the impact of SL test on future joint geometric observations. In this analysis, we take the model with $Q=\gamma H\rho_c$ as an example, and simulate future SN and BAO data based on the space-based project WFIRST. We find that in the future geometric constraints, the redshift drift observations would help break the geometric degeneracies in a meaningful way, thus the measurement precisions of $\Omega_m$, $H_0$, $w$, and $\gamma$ could be substantially improved using future probes.Comment: 6 pages, 5 figures; accepted for publication in EPJC. arXiv admin note: text overlap with arXiv:1407.712

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts

Non-classical non-Gaussian state of a mechanical resonator via selectively incoherent damping in three-mode optomechanical systems

We theoretically propose a scheme for the generation of a non-classical single-mode motional state of a mechanical resonator (MR) in the three-mode optomechanical systems, in which two optical modes of the cavities are linearly coupled to each other and one mechanical mode of the MR is optomechanically coupled to the two optical modes with the same coupling strength simultaneously. One cavity is driven by a coherent laser light. By properly tuning the frequency of the weak driving field, we obtain engineered Liouvillian superoperator via engineering the selective interaction Hamiltonian confined to the Fock subspaces. In this case, the motional state of the MR can be prepared into a non-Gaussian state, which possesses the sub-Poisson statistics although its Wigner function is positive.Comment: 6 pages, 5 figure

A method based on hierarchical spatiotemporal features for trojan traffic detection

Trojans are one of the most threatening network attacks currently. HTTP-based Trojan, in particular, accounts for a considerable proportion of them. Moreover, as the network environment becomes more complex, HTTP-based Trojan is more concealed than others. At present, many intrusion detection systems (IDSs) are increasingly difficult to effectively detect such Trojan traffic due to the inherent shortcomings of the methods used and the backwardness of training data. Classical anomaly detection and traditional machine learning-based (TML-based) anomaly detection are highly dependent on expert knowledge to extract features artificially, which is difficult to implement in HTTP-based Trojan traffic detection. Deep learning-based (DL-based) anomaly detection has been locally applied to IDSs, but it cannot be transplanted to HTTP-based Trojan traffic detection directly. To solve this problem, in this paper, we propose a neural network detection model (HSTF-Model) based on hierarchical spatiotemporal features of traffic. Meanwhile, we combine deep learning algorithms with expert knowledge through feature encoders and statistical characteristics to improve the self-learning ability of the model. Experiments indicate that F1 of HSTF-Model can reach 99.4% in real traffic. In addition, we present a dataset BTHT consisting of HTTP-based benign and Trojan traffic to facilitate related research in the field.Comment: 8 pages, 7 figure

Excited Heavy Quarkonium Production at the LHC through WW-Boson Decays

Sizable amount of heavy-quarkonium events can be produced through $W$-boson decays at the LHC. Such channels will provide a suitable platform to study the heavy-quarkonium properties. The "improved trace technology", which disposes the amplitude ${\cal M}$ at the amplitude-level, is helpful for deriving compact analytical results for complex processes. As an important new application, in addition to the production of the lower-level Fock states $|(Q\bar{Q'})[1S]>$ and $|(Q\bar{Q'})[1P]>$, we make a further study on the production of higher-excited $|(Q\bar{Q'})>$-quarkonium Fock states $|(Q\bar{Q'})[2S]>$, $|(Q\bar{Q'})[3S]>$ and $|(Q\bar{Q'})[2P]>$. Here $|(Q\bar{Q'})>$ stands for the $|(c\bar{c})>$-charmonium, $|(c\bar{b})>$-quarkonium and $|(b\bar{b})>$-bottomonium respectively. We show that sizable amount of events for those higher-excited states can also be produced at the LHC. Therefore, we need to take them into consideration for a sound estimation.Comment: 7 pages, 9 figures and 6 tables. Typo errors are corrected, more discussions and two new figures have been adde

PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks

Machine Learning (ML) techniques can facilitate the automation of malicious software (malware for short) detection, but suffer from evasion attacks. Many studies counter such attacks in heuristic manners, lacking theoretical guarantees and defense effectiveness. In this paper, we propose a new adversarial training framework, termed Principled Adversarial Malware Detection (PAD), which offers convergence guarantees for robust optimization methods. PAD lays on a learnable convex measurement that quantifies distribution-wise discrete perturbations to protect malware detectors from adversaries, whereby for smooth detectors, adversarial training can be performed with theoretical treatments. To promote defense effectiveness, we propose a new mixture of attacks to instantiate PAD to enhance deep neural network-based measurements and malware detectors. Experimental results on two Android malware datasets demonstrate: (i) the proposed method significantly outperforms the state-of-the-art defenses; (ii) it can harden ML-based malware detection against 27 evasion attacks with detection accuracies greater than 83.45%, at the price of suffering an accuracy decrease smaller than 2.16% in the absence of attacks; (iii) it matches or outperforms many anti-malware scanners in VirusTotal against realistic adversarial malware.Comment: Accepted by IEEE Transactions on Dependable and Secure Computing; To appea

A study on the preparation and characterization of plasmid DNA and drug-containing magnetic nanoliposomes for the treatment of tumors

Zi-Yu Wang1,2, Li Wang1, Jia Zhang1, Yun-Tao Li1, Dong-Sheng Zhang11School of Medicine, Southeast University, Nanjing, China; 2School of Basic Medical Sciences, Nanjing University of Traditional Chinese Medicine, Nanjing, ChinaPurpose: To explore the preparation and characterization of a novel nanosized magnetic liposome containing the PEI-As2O3/Mn0.5Zn0.5Fe2O4 complex.Methods: Mn0.5Zn0.5Fe2O4 and As2O3/Mn0.5Zn0.5Fe2O4 nanoparticles were prepared by chemical coprecipitation and loaded with PEI. The PEI-As2O3/Mn0.5Zn0.5Fe2O4 complex was characterized using transmission electron and scanning electron microscopy, X-ray diffraction, energy dispersive spectrometry, and Fourier transform infrared spectroscopy. Cell transfection experiments were performed to evaluate the transfect efficiency. Magnetic nanoliposomes were prepared by rotatory evaporation and their shape, diameter, and thermodynamic characteristics were observed.Results: Mn0.5Zn0.5Fe2O4 and PEI-As2O3/Mn0.5Zn0.5Fe2O4 nanoparticles were spherical, with an average diameter of 20–40 nm. PEI-As2O3/Mn0.5Zn0.5Fe2O4 was an appropriate carrier for the delivery of a foreign gene to HepG2 cells. Energy dispersive spectrometry results confirmed the presence of the elements nitrogen and arsenic. Nanoliposomes of approximately 100 nm were observed under a transmission electron microscope. Upon exposure to an alternating magnetic field, they also had good magnetic responsiveness, even though Mn0.5Zn0.5Fe2O4 was modified by PEI and encased in liposomes. Temperatures increased to 37°C–54°C depending on different concentrations of PEI-As2O3/Mn0.5Zn0.5Fe2O4 and remained stable thereafter.Conclusion: Our results suggest that PEI-As2O3/Mn0.5Zn0.5Fe2O4 magnetic nanoliposomes are an excellent biomaterial, which has multiple benefits in tumor thermotherapy, gene therapy, and chemotherapy.Keywords: nanoliposomes, magnetic fluid hyperthermia, As2O3, DN