No abstract

Selfish mining attacks sabotage the blockchain systems by utilizing the vulnerabilities of consensus mechanism. The attackers' main target is to obtain higher revenues compared with honest parties. More specifically, the essence of selfish mining is to waste the power of honest parties by generating a private chain. However, these attacks are not practical due to high forking rate. The honest parties may quit the blockchain system once they detect the abnormal forking rate, which impairs their revenues. While selfish mining attacks make no sense anymore with the honest parties' departure. Therefore, selfish miners need to restrain when launch selfish mining attacks such that the forking rate is not preposterously higher than normal level. The crux is how to illustrate the attacks toward the view of honest parties, who are blind to the private chain. Generally, previous works, especially those using Markov decision processes, stress on the increment of attackers' revenues, while overlooking the detection on forking rate. In this paper, we propose, to maintain the benefit from selfish mining, an improved selfish mining based on hidden Markov decision processes (SMHMDP). To reduce the forking rate, we also relax the behaviors of selfish miners (also known as semi-selfish miners), who mine on the private chain, to mine on public chain with a small

Five aluminoborates Al[B(5)O(10)] x H(2)dab x 2 H(2)O (1), [Al(B(4)O(9))(BO)] x H(2)en (2), [Al(B(4)O(9))(BO)] x H(2)dap (3), K(2)Al[B(5)O(10)] x 4 H(2)O (4), and (NH(4))(2)Al[B(5)O(10)] x 4 H(2)O (5) have been synthesized under hydrothermal conditions and characterized by means of elemental analysis, IR, TG analysis, MAS NMR, UV-vis and fluorescence spectroscopy, powder X-ray diffraction, single-crystal X-ray diffraction, and NLO determination. The structure 1 comprises of AlO(4) tetrahedra and B(5)O(10) clusters and contains 12-, 11-, and 8-MR 3D-intersecting channels with a CrB(4) topological net. Structures 2 and 3 are both constructed from the same AlB(5)O(13) clusters and exhibit a six-connected framework with 4(12)6(3) topology. Structures 4 and 5 are isomorphous and are composed of AlO(4) tetrahedra and B(5)O(10) clusters that possess odd 11-MR channels and 8-MR helical channels. Structures 2-5 crystallized in an acentric structure and presented good SHG properties. UV-vis spectral investigation indicates that they are wide-band-gap semiconductors. The electronic structure calculations for the compounds also have been performed.

A new approach to prepare heterometallic cluster organic frameworks has been developed. The method was employed to link Anderson-type polyoxometalate (POM) clusters and transition-metal clusters by using a designed rigid tris(alkoxo) ligand containing a pyridyl group to form a three-fold interpenetrated anionic diamondoid structure and a 2D anionic layer, respectively. This technique facilitates the integration of the unique inherent properties of Anderson-type POM clusters and cuprous iodide clusters into one cluster organic framework.

Multi-modal pre-training models have been intensively explored to bridge vision and language in recent years. However, most of them explicitly model the cross-modal interaction between image-text pairs, by assuming that there exists strong semantic correlation between the text and image modalities. Since this strong assumption is often invalid in real-world scenarios, we choose to implicitly model the cross-modal correlation for large-scale multi-modal pretraining, which is the focus of the Chinese project 'Wen-Lan' led by our team. Specifically, with the weak correlation assumption over image-text pairs, we propose a twotower pre-training model called BriVL within the crossmodal contrastive learning framework. Unlike OpenAI CLIP that adopts a simple contrastive learning method, we devise a more advanced algorithm by adapting the latest method MoCo into the cross-modal scenario. By building a large queue-based dictionary, our BriVL can incorporate more negative samples in limited GPU resources. We further construct a large Chinese multi-source imagetext dataset called RUC-CAS-WenLan for pre-training our BriVL model. Extensive experiments demonstrate that the pre-trained BriVL model outperforms both UNITER and OpenAI CLIP on various downstream tasks.

A new inorganic-organic hybrid solid, [Zn(dap)2 ][AlB5 O10 ], combining the structural features of 3D open-framework inorganic solids and 2D metal-organic coordination polymers has been synthesized under solvothermal conditions. The compound displays extensive luminescence and moderate second-harmonic-generation efficiency.

Retinoschisin (RS1) is a cell-surface adhesion molecule expressed by photoreceptor and bipolar cells of the retina. The 24-kDa protein encodes two conserved sequence motifs: the initial signal sequence targets the protein for secretion while the larger discoidin domain is implicated in cell adhesion. RS1 helps to maintain the structural organization of the retinal cell layers and promotes visual signal transduction. RS1 gene mutations cause X-linked retinoschisis disease (XLRS) in males, characterized by early-onset central vision loss. We analyzed the biochemical consequences of several RS1 signal-sequence mutants (c.1A>T, c.35T>A, c.38T>C, and c. 52G>A) found in our subjects. Expression analysis in COS-7 cells demonstrates that they affect RS1 biosynthesis and result in an RS1 null phenotype by several different mechanisms. By comparison, discoidin-domain mutations generally lead to nonfunctional conformational variants that remain trapped inside the cell. XLRS disease has a broad heterogeneity in general, but subjects with the RS1 null-protein signal-sequence mutations are on the more severe end of the clinical phenotype. Results from the signal-sequence mutants are discussed in the context of the discoidin-domain mutations, clinical phenotypes, genotype-phenotype correlations, and implications for RS1 gene replacement therapy.

Die direkte Kombination von {Ni6PW9}‐Baugruppen mit starren Carboxylatbrücken unter Hydrothermalbedingungen führt zu Polyoxometallat‐organischen Netzwerken (POMOFs) mit 1D‐, 2D‐ und 3D‐Strukturen auf der Grundlage von Monomeren, Dimeren oder helikalen Ketten (siehe Bild einer 3D‐Struktur; WO6 rot, NiO6 grün, PO4 gelb, 1,2,4‐Benzoltricarboxylat gold).