Bitcoin Double-Spending Attack Detection using Graph Neural Network

Bitcoin transactions include unspent transaction outputs (UTXOs) as their inputs and generate one or more newly owned UTXOs at specified addresses. Each UTXO can only be used as an input in a transaction once, and using it in two or more different transactions is referred to as a double-spending attack. Ultimately, due to the characteristics of the Bitcoin protocol, double-spending is impossible. However, problems may arise when a transaction is considered final even though its finality has not been fully guaranteed in order to achieve fast payment. In this paper, we propose an approach to detecting Bitcoin double-spending attacks using a graph neural network (GNN). This model predicts whether all nodes in the network contain a given payment transaction in their own memory pool (mempool) using information only obtained from some observer nodes in the network. Our experiment shows that the proposed model can detect double-spending with an accuracy of at least 0.95 when more than about 1% of the entire nodes in the network are observer nodes.Comment: 3 pages, 1 table, Accepted as poster at IEEE ICBC 202

Low energy proton-proton scattering in effective field theory

Low energy proton-proton scattering is studied in pionless effective field theory. Employing the dimensional regularization and MS-bar and power divergence subtraction schemes for loop calculation, we calculate the scattering amplitude in 1S0 channel up to next-to-next-to leading order and fix low-energy constants that appear in the amplitude by effective range parameters. We study regularization scheme and scale dependence in separation of Coulomb interaction from the scattering length and effective range for the S-wave proton-proton scattering.Comment: 23 pages, 6 eps figures, revised considerably, accepted for publication in Phys. Rev.

Regularization in neural network optimization via trimmed stochastic gradient descent with noisy label

Regularization is essential for avoiding over-fitting to training data in neural network optimization, leading to better generalization of the trained networks. The label noise provides a strong implicit regularization by replacing the target ground truth labels of training examples by uniform random labels. However, it may also cause undesirable misleading gradients due to the large loss associated with incorrect labels. We propose a first-order optimization method (Label-Noised Trim-SGD) which combines the label noise with the example trimming in order to remove the outliers. The proposed algorithm enables us to impose a large label noise and obtain a better regularization effect than the original methods. The quantitative analysis is performed by comparing the behavior of the label noise, the example trimming, and the proposed algorithm. We also present empirical results that demonstrate the effectiveness of our algorithm using the major benchmarks and the fundamental networks, where our method has successfully outperformed the state-of-the-art optimization methods

Development of the MICROMEGAS Detector for Measuring the Energy Spectrum of Alpha Particles by using a 241-Am Source

We have developed MICROMEGAS (MICRO MEsh GASeous) detectors for detecting {\alpha} particles emitted from an 241-Am standard source. The voltage applied to the ionization region of the detector is optimized for stable operation at room temperature and atmospheric pressure. The energy of {\alpha} particles from the 241-Am source can be varied by changing the flight path of the {\alpha} particle from the 241 Am source. The channel numbers of the experimentally-measured pulse peak positions for different energies of the {\alpha} particles are associated with the energies deposited by the alpha particles in the ionization region of the detector as calculated by using GEANT4 simulations; thus, the energy calibration of the MICROMEGAS detector for {\alpha} particles is done. For the energy calibration, the thickness of the ionization region is adjusted so that {\alpha} particles may completely stop in the ionization region and their kinetic energies are fully deposited in the region. The efficiency of our MICROMEGAS detector for {\alpha} particles under the present conditions is found to be ~ 97.3 %

Diosgenin Induces Apoptosis in HepG2 Cells through Generation of Reactive Oxygen Species and Mitochondrial Pathway

Diosgenin, a naturally occurring steroid saponin found abundantly in legumes and yams, is a precursor of various synthetic steroidal drugs. Diosgenin is studied for the mechanism of its action in apoptotic pathway in human hepatocellular carcinoma cells. Based on DAPI staining, diosgenin-treated cells manifested nuclear shrinkage, condensation, and fragmentation. Treatment of HepG2 cells with 40 μM diosgenin resulted in activation of the caspase-3, -8, -9 and cleavage of poly-ADP-ribose polymerase (PARP) and the release of cytochrome c. In the upstream, diosgenin increased the expression of Bax, decreased the expression of Bid and Bcl-2, and augmented the Bax/Bcl-2 ratio. Diosgenin-induced, dose-dependent induction of apoptosis was accompanied by sustained phosphorylation of JNK, p38 MAPK and apoptosis signal-regulating kinase (ASK)-1, as well as generation of the ROS. NAC administration, a scavenger of ROS, reversed diosgene-induced cell death. These results suggest that diosgenin-induced apoptosis in HepG2 cells through Bcl-2 protein family-mediated mitochndria/caspase-3-dependent pathway. Also, diosgenin strongly generated ROS and this oxidative stress might induce apoptosis through activation of ASK1, which are critical upstream signals for JNK/p38 MAPK activation in HepG2 cancer cells