Similarity Analysis of Projectile Penetration into Concrete

This paper presents a dimensionless model for the depth of penetration (DOP) of a projectile penetrating into a concrete target, based on the similarity theory involving intermediate asymptotics, complete similarity, and incomplete similarity. The calculated numerical results are in good agreement with previous experimental data, including two sets of full-scale and twenty-four sets of sub-scale penetration of non-deformable projectiles into concrete targets. Moreover, compared with several empirical and semi-empirical DOP models, the new model is applicable within a relatively broader range, including the penetration of both sub-scale and full-scale projectiles. For the limitations of the validity, dimensionless parameters Π3  = ϕt/ϕ larger than 12, Π4 = (ϕ3fc)/(Mv02) smaller than 0.1, and the initial impact velocity of the projectile less than about 900 to 1000m/s are necessary for the model

Rear-Surface Collapse of Finite Thickness Concrete Targets under Internal Explosion

An experimental investigation on the buried internal explosion in finite thickness concrete targets was carried out, with the aim at developing an available criterion for the critical collapse of rear-surface to determine the critical collapse thickness and the critical amount of explosive charge under different depth of buried. It is found, under a certain density and diameter of explosive charge, the critical collapse thickness increases monotonically with the length-to-diameter ratio or the amount of the explosive charge, but the increasing becomes slower down after the length-to-diameter ratio of the explosive charge is larger than about 5, which implies that the geometry of the explosive charge can have much influence on the damage and failure of concrete targets due to different mechanism of energy dissipation. Moreover, by using the dimensional analysis approach, the function relation between the dimensionless critical collapse thickness and the length-to-diameter ratio was obtained, which shows that the dimensionless critical collapse thickness depends on both the amount and the length-to-diameter ratio of the charge.Defence Science Journal, 2012, 62(5), pp.295-299, DOI:http://dx.doi.org/10.14429/dsj.62.123

Parrot-Trained Adversarial Examples: Pushing the Practicality of Black-Box Audio Attacks against Speaker Recognition Models

Audio adversarial examples (AEs) have posed significant security challenges to real-world speaker recognition systems. Most black-box attacks still require certain information from the speaker recognition model to be effective (e.g., keeping probing and requiring the knowledge of similarity scores). This work aims to push the practicality of the black-box attacks by minimizing the attacker's knowledge about a target speaker recognition model. Although it is not feasible for an attacker to succeed with completely zero knowledge, we assume that the attacker only knows a short (or a few seconds) speech sample of a target speaker. Without any probing to gain further knowledge about the target model, we propose a new mechanism, called parrot training, to generate AEs against the target model. Motivated by recent advancements in voice conversion (VC), we propose to use the one short sentence knowledge to generate more synthetic speech samples that sound like the target speaker, called parrot speech. Then, we use these parrot speech samples to train a parrot-trained(PT) surrogate model for the attacker. Under a joint transferability and perception framework, we investigate different ways to generate AEs on the PT model (called PT-AEs) to ensure the PT-AEs can be generated with high transferability to a black-box target model with good human perceptual quality. Real-world experiments show that the resultant PT-AEs achieve the attack success rates of 45.8% - 80.8% against the open-source models in the digital-line scenario and 47.9% - 58.3% against smart devices, including Apple HomePod (Siri), Amazon Echo, and Google Home, in the over-the-air scenario

Evolution characteristics of the network core in the facebook

Statistical properties of the static networks have been extensively studied. However, online social networks are evolving dynamically, understanding the evolving characteristics of the core is one of major concerns in online social networks. In this paper, we empirically investigate the evolving characteristics of the Facebook core. Firstly, we separate the Facebook-link(FL) and Facebook-wall(FW) datasets into 28 snapshots in terms of timestamps. By employing the k-core decomposition method to identify the core of each snapshot, we find that the core sizes of the FL and FW networks approximately contain about 672 and 373 nodes regardless of the exponential growth of the network sizes. Secondly, we analyze evolving topological properties of the core, including the k-core value, assortative coefficient, clustering coefficient and the average shortest path length. Empirical results show that nodes in the core are getting more interconnected in the evolving process. Thirdly, we investigate the life span of nodes belonging to the core. More than 50% nodes stay in the core for more than one year, and 19% nodes always stay in the core from the first snapshot. Finally, we analyze the connections between the core and the whole network, and find that nodes belonging to the core prefer to connect nodes with high k-core values, rather than the high degrees ones. This work could provide new insights into the online social network analysis

Generation of 3-Dimensional graph state with Josephson charge qubits

On the basis of generations of 1-dimensional and 2-dimensional graph states, we generate a 3-dimensional N3-qubit graph state based on the Josephson charge qubits. Since any two charge qubits can be selectively and effectively coupled by a common inductance, the controlled phase transform between any two-qubit can be performed. Accordingly, we can generate arbitrary multi-qubit graph states corresponding to arbitrary shape graph, which meet the expectations of various quantum information processing schemes. All the devices in the scheme are well within the current technology. It is a simple, scalable and feasible scheme for the generation of various graph states based on the Josephson charge qubits.Comment: 4 pages, 4 figure

Nature, Generation, and Dissipation of Alfvénic Kinks/Switchbacks Observed by Parker Solar Probe and WIND

The discovery of very prominent magnetic kinks/switchbacks in the solar wind within 0.3 au has become a scientific highlight of the Parker Solar Probe (PSP) mission. This discovery points at the promising impact of small-scale solar activity on the inner heliosphere. To address the nature, generation, and dissipation of these kinks, we perform a statistical analysis of the plasma and boundary properties of the kinks using PSP multi-encounter observations and WIND measurements at 1 au. The kinks show strong Alfvénicity and velocity fluctuations of the order of the local Alfvén speed. These findings suggest that the nature of the kinks is consistent with large-amplitude Alfvén pulses, and the steepening of these Alfvén pulses is likely the formation mechanism of these kinks. Based on the angle between the normal direction of the kinks’ boundaries and the background magnetic field vector, PSP kinks and WIND kinks can be divided into two groups: quasi-parallel and quasi-perpendicular kinks. We speculate that quasi-parallel kinks form through the coupling of Alfvén and fast waves as launched from coronal interchange magnetic reconnection. In contrast, quasi-perpendicular kinks may come from the steepening of Alfvén waves launched from both coronal interchange magnetic reconnection and from the more inhomogeneous lower solar atmosphere. We find that the kink velocity perturbation gradually decreases during outward propagation and is much lower than expected from WKB theory, suggesting a progressive dissipation of the kinks. Comparing PSP kinks and WIND kinks, we conjecture that the kinks dissipate through merging with the turbulent energy cascade within 0.25 au

A deep learning algorithm for white matter hyperintensity lesion detection and segmentation

Purpose: White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the clinical management of patients, but existing automated software is often developed for a single type of disease and may not be applicable for clinical scans with thick slices and different scanning protocols. The purpose of the study is to develop and validate an algorithm for automatic quantification of white matter hyperintensity suitable for heterogeneous MRI data with different disease types. / Methods: We developed and evaluated “DeepWML”, a deep learning method for fully automated white matter lesion (WML) segmentation of multicentre FLAIR images. We used MRI from 507 patients, including three distinct white matter diseases, obtained in 9 centres, with a wide range of scanners and acquisition protocols. The automated delineation tool was evaluated through quantitative parameters of Dice similarity, sensitivity and precision compared to manual delineation (gold standard). / Results: The overall median Dice similarity coefficient was 0.78 (range 0.64 ~ 0.86) across the three disease types and multiple centres. The median sensitivity and precision were 0.84 (range 0.67 ~ 0.94) and 0.81 (range 0.64 ~ 0.92), respectively. The tool’s performance increased with larger lesion volumes. / Conclusion: DeepWML was successfully applied to a wide spectrum of MRI data in the three white matter disease types, which has the potential to improve the practical workflow of white matter lesion delineation

Multiple ocular diseases detection by graph regularized multi-label learning

We develop a general framework for multiple ocular diseases diagnosis, based on Graph Regularized Multi-label Learning (GRML). Glaucoma, Pathological Myopia (PM), and Age-related Macular Degeneration (AMD) are three leading ocular diseases in the world. By exploiting the correlations among these three diseases, a novel GRML scheme is investigated for a simultaneous detection of these three leading ocular diseases for a given fundus image. We validate our GRML framework by conducting extensive experiments on SiMES dataset. The results show area under curve (AUC) of the receiver operating characteristic curve in multiple ocular diseases detection are much better than traditional popular algorithms. The method could be used for glaucoma, PM, and AMD diagnosis