Biblio

Filters: Author is Liao, J.  [Clear All Filters]
2021-03-09
Liao, Q., Gu, Y., Liao, J., Li, W..  2020.  Abnormal transaction detection of Bitcoin network based on feature fusion. 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). 9:542—549.

Anomaly detection is one of the research hotspots in Bitcoin transaction data analysis. In view of the existing research that only considers the transaction as an isolated node when extracting features, but has not yet used the network structure to dig deep into the node information, a bitcoin abnormal transaction detection method that combines the node’s own features and the neighborhood features is proposed. Based on the formation mechanism of the interactive relationship in the transaction network, first of all, according to a certain path selection probability, the features of the neighbohood nodes are extracted by way of random walk, and then the node’s own features and the neighboring features are fused to use the network structure to mine potential node information. Finally, an unsupervised detection algorithm is used to rank the transaction points on the constructed feature set to find abnormal transactions. Experimental results show that, compared with the existing feature extraction methods, feature fusion improves the ability to detect abnormal transactions.

2018-05-16
Liao, J., Vallobra, P., Petit, D., Vemulkar, T., O'Brien, L., Malinowski, G., Hehn, M., Mangin, S., Cowburn, R..  2017.  All-optical switching behaviours in synthetic ferrimagnetic heterostructures with different ferromagnetic-layer Curie temperatures. 2017 IEEE International Magnetics Conference (INTERMAG). :1–1.
Summary form only given. All-optical switching (AOS) has been observed in ferromagnetic (FM) layers and synthetic ferrimagnetic heterostructures [1-4]. In this work, we use anomalous Hall effect (AHE) measurements to demonstrate controlled helicity-dependent switching in synthetic ferrimagnetic heterostructures. The two FM layers are engineered to have different Curie temperatures Tc1 (fixed) and Tc2 (variable). We show that irrespective of whether Tc2 is higher or lower than Tc1, the final magnetic configuration of the heterostructure is controlled by using the laser polarization to set the magnetic state of the FM layer with the highest Tc. All samples were grown on glass substrates at room temperature by DC magnetron sputtering. Two sets of samples were prepared. The first set are single FM layers with layer composition Ta (3 nm)/Pt (4 nm)/FM1(2)/Pt capping (4 nm), where FM1 = Co (0.6 nm) is a Co layer and FM2 = CoFeB (tCoFeB)/Pt(0.4 nm)/ CoFeB (tCoFeB) (0.2 ≤ tCoFeB ≤ 0.6 nm) is a composite CoFeB layer where both CoFeB layers are ferromagnetically coupled and act as a single layer. FM1 and FM2 were used to produce the second set of synthetic ferrimagnetic samples with layer structure Ta (3 nm)/Pt (4 nm)/FM1/Pt (0.4 nm)/Ru (0.9 nm)/Pt (0.4 nm)/FM2/Pt capping (4 nm). The Ru layer provides the antiferromagnetic RKKY interlayer exchange coupling between the adjacent FM1 and FM2 layers while the Pt layers on either side of the Ru layer can tune the strength of the coupling and stabilize their perpendicular anisotropy [5]. To study the AOS, we use a Ti: sapphire fs-laser with a wavelength of 800 nm and a pulse duration of 43 fs. A quarter-wave plate is used to create a circularly polarized [right(σ+) and left-handed (σ-)] beam. We first measured the magnetic properties of the FM1 and FM2 layers using vibrating sample magnetometry (VSM). All FM samples show full remanence in perpendicular hyst- resis loops at room temperature (not shown). The temperature-dependent magnetization scans (not shown) give a Curie temperature Tc1 of 524 K for FM1. For FM2, increasing tCoFeB increases its Curie temperatureTc2. At tCoFeB = 0.5 nm, Tc2 - Tc1. Hall crosses are patterned by optical lithography and ion milling. The width of the current carrying wire is - 5 um, giving a DC current density of - 6 x 109 A/m2 during the measurement. Figure 1(a) shows the resulting perpendicular Hall hysteresis loop of the synthetic ferrimagnetic sample with tCoFeB = 0.2 nm. At remanence, the stable magnetic configurations are the two antiparallel orientations of FM1 and FM2 [State I and II in Fig. 1(a)]. To study the AOS, we swept the laser beam with a power of 0.45 mW and a speed of 1 μm/sec across the Hall cross, and the corresponding Hall voltage was constantly monitored. In Fig. 1(b), we show the normalized Hall voltage, VHall, as a function of the laser beam position x for both beam polarizations σ+ and σ-. The initial magnetic configuration was State I. When the beam is at the center of the cross (position B), both beam polarizations give VHall - 0. As the beam leaves the cross (position C), the σbeam changes the magnetic configurations from State I to State II (FM1 magnetization pointing down), while the system reverts to State I using the σ+ beam. Changing the initial configuration from State I to State II results in the same final magnetic configurations, determined by the laser beam polarizations (not shown). Similar results (not shown) were obtained for samples with tCoFeB ≤ 0.4 nm. However, at tCoFeB = 0.6 nm, the σbeam results in the final magnetic configurations with FM2 magnetization pointing down (State I) and the σ+ beam results in the State II configuration, suggesting that the final state is determined by the beam polar
2018-11-19
Chen, D., Liao, J., Yuan, L., Yu, N., Hua, G..  2017.  Coherent Online Video Style Transfer. 2017 IEEE International Conference on Computer Vision (ICCV). :1114–1123.

Training a feed-forward network for the fast neural style transfer of images has proven successful, but the naive extension of processing videos frame by frame is prone to producing flickering results. We propose the first end-to-end network for online video style transfer, which generates temporally coherent stylized video sequences in near realtime. Two key ideas include an efficient network by incorporating short-term coherence, and propagating short-term coherence to long-term, which ensures consistency over a longer period of time. Our network can incorporate different image stylization networks and clearly outperforms the per-frame baseline both qualitatively and quantitatively. Moreover, it can achieve visually comparable coherence to optimization-based video style transfer, but is three orders of magnitude faster.