Fast and simple decycling and dismantling of networks

Decycling and dismantling of complex networks are underlying many important applications in network science. Recently these two closely related problems were tackled by several heuristic algorithms, simple and considerably sub-optimal, on the one hand, and time-consuming message-passing ones that evaluate single-node marginal probabilities, on the other hand. In this paper we propose a simple and extremely fast algorithm, CoreHD, which recursively removes nodes of the highest degree from the $2$-core of the network. CoreHD performs much better than all existing simple algorithms. When applied on real-world networks, it achieves equally good solutions as those obtained by the state-of-art iterative message-passing algorithms at greatly reduced computational cost, suggesting that CoreHD should be the algorithm of choice for many practical purposes

Clustering Gene Expression Data Based on Predicted Differential Effects of GV Interaction

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation

Modeling and Hydrodynamic Characteristics of Bionic Undulate Fin Propeller Driven by Hydraulic

The bionic undulating propeller driven by hydraulic system has different structure, kinematic and dynamic characteristics than that of the common bionic undulating propellers driven by other sources. This paper highlights firstly the structure and driving mechanism of bionic undulating propeller with a hydraulic system, and then setup its kinematic model, based on ruled-surface equation. Changing rules of dynamic mesh for bionic fin is designed based on kinetic model. Later on, the changing courses of hydrodynamic force caused by the bionic undulating fin are calculated and studied with the CFD (Computational Fluid Dynamics) method, as well as the changing characteristics of the fluid pressure field. The analysis showed that while driven by hydraulic system, the bionic propeller could produce full-baseline undulating motion, and has flexible start-up process, as well as doubled-frequency character. The bionic undulating fin driven by hydraulic system puts up flexible characters on both kinematic and dynamics. Key Words: Hydraulic Driven; Undulating Fin; Bionic Propeller; Dynamic Mesh; Hydrodynamic

1,2-Bis(1H-tetra­zol-5-yl)benzene dihydrate

The asymmetric unit of the title compound, C8H6N8·2H2O, contains one half-mol­ecule, with the benzene ring on a centre of symmetry, and two uncoordinated water mol­ecules. The benzene ring is oriented at a dihedral angle of 34.43 (12)° with respect to the tetra­zole ring. Strong O—H⋯N hydrogen bonds link the water mol­ecules to the N atoms of the tetra­zole ring. In the crystal structure, strong inter­molecular O—H⋯O and O—H⋯N hydrogen bonds link the mol­ecules into a network. One of the water H atoms is disordered over two positions and was refined with occupancies of 0.50

Red Teaming Game: A Game-Theoretic Framework for Red Teaming Language Models

Deployable Large Language Models (LLMs) must conform to the criterion of helpfulness and harmlessness, thereby achieving consistency between LLMs outputs and human values. Red-teaming techniques constitute a critical way towards this criterion. Existing work rely solely on manual red team designs and heuristic adversarial prompts for vulnerability detection and optimization. These approaches lack rigorous mathematical formulation, thus limiting the exploration of diverse attack strategy within quantifiable measure and optimization of LLMs under convergence guarantees. In this paper, we present Red-teaming Game (RTG), a general game-theoretic framework without manual annotation. RTG is designed for analyzing the multi-turn attack and defense interactions between Red-team language Models (RLMs) and Blue-team Language Model (BLM). Within the RTG, we propose Gamified Red-teaming Solver (GRTS) with diversity measure of the semantic space. GRTS is an automated red teaming technique to solve RTG towards Nash equilibrium through meta-game analysis, which corresponds to the theoretically guaranteed optimization direction of both RLMs and BLM. Empirical results in multi-turn attacks with RLMs show that GRTS autonomously discovered diverse attack strategies and effectively improved security of LLMs, outperforming existing heuristic red-team designs. Overall, RTG has established a foundational framework for red teaming tasks and constructed a new scalable oversight technique for alignment

Association between TGFBR1*6A and osteosarcoma: A Chinese case-control study

<p>Abstract</p> <p>Background</p> <p>TGFBR1*6A is a common hypomorphic variant of transforming growth factor β receptor 1 (TGFBR1). TGFBR1*6A is associated with an increased cancer risk, but the association of this polymorphism with osteosarcoma remains unknown. We have measured the frequency of TGFBR1*6A variants in osteosarcoma cases and controls.</p> <p>Methods</p> <p>Our case-control study is based on 168 osteosarcoma patients and 168 age- and gender-matched controls. Blood samples were obtained and the TGFBR1*6A variant determined by PCR amplification and DNA sequencing. The odds ratio (OR) and 95% confidence interval (95% CI) for the TGFBR1*6A polymorphism were calculated by unconditional logistic regression, adjusted for both age and gender. Three models - dominant, additive and recessive - were used to analyze the contribution of the TGFBR1*6A variant to osteosarcoma susceptibility.</p> <p>Results</p> <p>Heterozygotic and homozygotic TGFBR1*6A variants represented 50.4% and 6.0% of the 168 cases, whereas the controls had 18. 5% and 1.3%, respectively. ORs for homozygosity and heterozygosity of the TGFBR1*6A allele were 4.6 [95% CI, 2.33-7.97] and 2.9 [95% CI, 1.59-5.34] in the additive model. There were significant increases in the TGFBR1*6A variants in osteosarcoma cases compared to control in all 3 models. Further analysis showed that TGFBR1*6A genotypes were not associated with gender, age, or tumor location. However, TGFBR1*6A was significantly associated with less metastasis.</p> <p>Conclusions</p> <p>TGFBR1*6A, a dominant polymorphism of TGFBR1, is associated with increased susceptibility and metastasis spread of osteosarcoma.</p

Ultraviolet photon-counting single-pixel imaging

We demonstrate photon-counting single-pixel imaging in the ultraviolet region. Toward this target, we develop a high-performance compact single-photon detector based on a 4H-SiC single-photon avalanche diode (SPAD), where a tailored readout circuit with active hold-off time is designed to restrain detector noise and operate the SPAD in free-running mode. We use structured illumination to reconstruct 192$\times$192 compressed images at a 4 fps frame rate. To show the superior capability of ultraviolet characteristics, we use our single-pixel imaging system to identify and distinguish different transparent objects under low-intensity irradiation, and image ultraviolet light sources. The results provide a practical solution for general ultraviolet imaging applications.Comment: 5 pages, 5 figures, accepted for publication in Applied Physics Letter