Spectral Compressive Sensing with Model Selection

The performance of existing approaches to the recovery of frequency-sparse signals from compressed measurements is limited by the coherence of required sparsity dictionaries and the discretization of frequency parameter space. In this paper, we adopt a parametric joint recovery-estimation method based on model selection in spectral compressive sensing. Numerical experiments show that our approach outperforms most state-of-the-art spectral CS recovery approaches in fidelity, tolerance to noise and computation efficiency.Comment: 5 pages, 2 figures, 1 table, published in ICASSP 201

Assessing the causality between thyroid and breast neoplasms: A bidirectional Mendelian randomization study

AimThis study aimed to evaluate the association between thyroid neoplasms (TN) and the risk of developing breast neoplasms (BN) by assessing data on single nucleotide polymorphisms (SNPs) obtained from the Deutsches Krebsforschungszentrum (DKFZ) and Breast Cancer Association (BCAC).MethodsData on SNPs associated with TN and BN were obtained from DKFZ and BCAC, respectively. Secondary data analysis of all pooled data from genome-wide association studies (GWAS) was performed to identify the genetic loci closely associated with TN or BN as instrumental variables (IVs). To evaluate the causal relationship between TN and BN, a bidirectional Mendelian randomization (MR) analysis was performed using MR Egger regression, weighted median, inverse variance weighted (IVW) random effects model, simple mode, weighted mode, maximum likelihood, penalized weighted median, IVW radial, IVW fixed effects, and robust adjusted profile scores (RAPS) method.ResultsThe MR in this study demonstrated a modest reverse causal relationship between TN and BN but a significant positive causal relationship between BN and TN.ConclusionsThe MR of this study provided genetic evidence suggesting an association between BN and TN; however, further research is warranted to explore the potential mechanism of interaction between these two malignancies. Moreover, general breast screening should be performed in individuals with TN, but TN screening should be reinforced in individuals with BN

Community Detection in Complex Networks

Network science plays a central role in understanding and modeling complex systems in many disciplines, including physics, sociology, biology, computer science, economics, politics, and neuroscience. By studying networks, we can gain a deep understanding of the behavior of the systems they represent. Many networks exhibit community structure, i.e., they have clusters of nodes that are locally densely interconnected. These communities manifest the hierarchical organization of the objects in systems, and detecting communities greatly facilitates the study of the organization and structure of complex systems. Most existing community-detection methods consider low-order connection patterns, at the level of individual links. But high-order connection patterns, at the level of small sub-networks, are generally not considered. This work starts by developing a novel community-detection method based on cliques, i.e., locally complete subnetworks. The proposed method overcomes the deficiencies of previous similar community-detection methods by considering the mathematical properties of cliques. We apply the proposed method to computer-generated graphs and real-world network datasets. When applied to networks with known community structure (but not known to our method), the proposed method detects the structure with high fidelity and sensitivity. Moreover, our method yields insightful results revealing the organization of real-world complex networks, which we have no a~priori information regarding their community structure. We also show that the proposed method guarantees near-optimal performance in identifying clusters in the bipartition case. Detecting communities in a whole network presents inherent problems because massive graphs incur a prohibitively large computational load. Moreover, in many cases, we are interested only in a local cluster near a given node. We next present a local graph clustering method that uses heat kernel PageRank to efficiently find a local cluster in massive graphs. The heat kernel PageRank provides a quantitative ranking of nodes, and a local cluster is then found by performing a sweep over the heat kernel PageRank vector. But computing an exact heat kernel PageRank vector may be expensive, so approximate algorithms are often used instead. Most approximate algorithms compute the heat kernel PageRank vector on the whole graph, and thus are dependent on global structures. We develop an algorithm for approximating the heat kernel PageRank on a local subgraph. Moreover, we show that the number of computations required by the proposed algorithm is sublinear in terms of the expected size of the local cluster of interest, and that it provides a good approximation of the heat kernel PageRank, with approximation errors bounded by a probabilistic guarantee. Numerical experiments verify that the local clustering algorithm using our approximate heat kernel PageRank achieves state-of-the-art performance. Local clustering is particularly important in the study of spreading phenomena in networks, notably in studying the spread of disease, a topic of considerable interest in the network science research community. In the third part of the dissertation, we show that the outbreak of an epidemic can be effectively contained and suppressed in a small subnetwork by a combination of two measures: antidote distribution, providing antidotes to nodes in the subnetwork, and partial quarantine, which is equivalent to removing part of the boundary edges of the subnetwork. Our containment strategy improves over existing antidote distribution schemes based on personalized PageRank in two ways. First, we replace the constraint on the topology of this subnetwork described by Chung et al., that a large fraction of the value of the personalized PageRank vector must be contained in the local cluster, with a partial quarantine scheme. We obtain this result by using the properties of personalized PageRank to devise the partial quarantine scheme, which optimizes the tradeoff between successful containment and interference with the network topology. Second, we derive a new lower bound on the amount of antidote required to achieve successful containment. We prove that, under our antidote distribution scheme, the probability of the infection spreading to the whole network is bounded, and that the infection inside the subnetwork will disappear after a period that is proportional to the logarithm of the number of initially infected nodes. Thus, we find that an epidemic can be effectively contained and suppressed in the small subnetwork in which the outbreak was detected, under weaker conditions than those proposed in earlier research, after our containment strategy is implemented. We demonstrate the effectiveness of our strategy with numerical simulations of epidemics on benchmark networks. We also test our strategy on two examples of epidemics in real-world networks, one in a network of YouTube users, and another in a network of autonomous systems in the Internet. Our strategy is dependent only on the rate of infection, the rate of recovery, and the topology around the initially infected nodes, and is independent of the rest of the network

Improved successive approximation control for formation flying at libration points of solar-earth system

In deep space exploration, the libration points (especially L2 point) of solar-earth system is a re-search hotspot in recent years. Space station and telescope can be arranged at this point, and it does not need too much kinetic energy. Therefore, it is of great significance to arrange flight formation on the libration point of solar-earth for scientific research. However, the flight keeping control technology of flight formation on the solar-earth libration points (also called Lagrange points) is one of the key problems to be solved urgently. Based on the nonlinear dynamic model of formation flying, the improved successive approximation algorithm is used to achieve formation keeping con-trol. Compared with the control algorithm based on orbital elements, this control algorithm has the advantages of high control accuracy and short control time in formation keeping control of solar-earth libration points. The disadvantage is that the calculation is complicated. But, with the devel-opment of computer technology, the computational load is gradually increasing, and there will be more extensive application value in the future. Finally, the error and control simulations of the formation flying of the spacecraft with the libration points of the solar-earth system are carried out for two days. The simulation results show that the method can quickly achieve the requirements of high-precision control

Study on the propagation characteristics of methane-air explosion under the promotion of crushed gangue

Abstract Methane-air explosion is one of the major disasters in industrial process. The explosion strength could be influenced by the crushed coal gangue, which is widely distributed in coal mine gob and roadway. To understand the influence of the coal gangue on gas explosion, an experimental system with a 0.2 × 0.2 × 3.0 m3 pipeline was designed and explosion experiments of coal gangue with 5 blockage length-diameter ratios (ratio of axial blockage length to pipeline equivalent diameter) were carried out. The results show that coal gangue can cause significant disturbances to the flame front, resulting in a violent acceleration of the explosion flame. The overpressure ratio presents a negative exponential function distribution with the blockage length-diameter ratio. The influence range increases with the blockage length-diameter ratio under the condition of rich fuel, and reaches the maximum when equivalent ratio is 1.237. The explosion intensity is more sensitive to the blockage length-diameter ratio for the equivalent ratio equals 1.0 and 1.237. The formation of high-intensity explosion should be avoided by controlling the accumulation state of the overlying rock in coal mining

a new variant of time memory trade-off on the improvement of thing and ying's attack

In this paper, we present a rigorous evaluation of Thing and Ying's attack (TY attack) [11] along with practical implementations. We find that the cryptanalysis time of their attack is too high to be practical. We also propose a more general time memory trade-off by combining the distinguished points strategy with TY attack. Both theoretical analysis and experimental results show that our new design can save about 53.7% cryptanalysis time compared to TY attack and can reduce about 35.2% storage requirement compared to the original rainbow attack. © 2012 Springer-Verlag.In this paper, we present a rigorous evaluation of Thing and Ying's attack (TY attack) [11] along with practical implementations. We find that the cryptanalysis time of their attack is too high to be practical. We also propose a more general time memory trade-off by combining the distinguished points strategy with TY attack. Both theoretical analysis and experimental results show that our new design can save about 53.7% cryptanalysis time compared to TY attack and can reduce about 35.2% storage requirement compared to the original rainbow attack. © 2012 Springer-Verlag

applying time-memory-data trade-off to plaintext recovery attack

In this paper, we propose a new attack for block ciphers by applying the well known time-memory-data (TMD) trade-off to plaintext recovery attack (PRA), thus creating two new schemes: TMD-PRA-I and TMD-PRA-II. Compared with the traditional trade-off attacks, these two schemes possess several robust properties which can greatly increase the success probability and enhance the process of analysis. We also evaluate the performance of our schemes by applying them to several block ciphers like DES, Triple-DES, Skipjack and AES. Results show that they have favourable performance especially when the key size is larger than the block size, which gives us a reminder that PRA based on TMD trade-off should be considered when designing a new cryptographic scheme. © 2012 Springer-Verlag.In this paper, we propose a new attack for block ciphers by applying the well known time-memory-data (TMD) trade-off to plaintext recovery attack (PRA), thus creating two new schemes: TMD-PRA-I and TMD-PRA-II. Compared with the traditional trade-off attacks, these two schemes possess several robust properties which can greatly increase the success probability and enhance the process of analysis. We also evaluate the performance of our schemes by applying them to several block ciphers like DES, Triple-DES, Skipjack and AES. Results show that they have favourable performance especially when the key size is larger than the block size, which gives us a reminder that PRA based on TMD trade-off should be considered when designing a new cryptographic scheme. © 2012 Springer-Verlag