Uniqueness of isometric immersions with the same mean curvature

Motivated by the quasi-local mass problem in general relativity, we study the rigidity of isometric immersions with the same mean curvature into a warped product space. As a corollary of our main result, two star-shaped hypersurfaces in a spatial Schwarzschild or AdS-Schwarzschild manifold with nonzero mass differ only by a rotation if they are isometric and have the same mean curvature. We also give similar results if the mean curvature condition is replaced by an $\sigma_2$-curvature condition.Comment: 23 pages, introduction revised, references update

PPBFL: A Privacy Protected Blockchain-based Federated Learning Model

With the rapid development of machine learning and a growing concern for data privacy, federated learning has become a focal point of attention. However, attacks on model parameters and a lack of incentive mechanisms hinder the effectiveness of federated learning. Therefore, we propose A Privacy Protected Blockchain-based Federated Learning Model (PPBFL) to enhance the security of federated learning and encourage active participation of nodes in model training. Blockchain technology ensures the integrity of model parameters stored in the InterPlanetary File System (IPFS), providing protection against tampering. Within the blockchain, we introduce a Proof of Training Work (PoTW) consensus algorithm tailored for federated learning, aiming to incentive training nodes. This algorithm rewards nodes with greater computational power, promoting increased participation and effort in the federated learning process. A novel adaptive differential privacy algorithm is simultaneously applied to local and global models. This safeguards the privacy of local data at training clients, preventing malicious nodes from launching inference attacks. Additionally, it enhances the security of the global model, preventing potential security degradation resulting from the combination of numerous local models. The possibility of security degradation is derived from the composition theorem. By introducing reverse noise in the global model, a zero-bias estimate of differential privacy noise between local and global models is achieved. Furthermore, we propose a new mix transactions mechanism utilizing ring signature technology to better protect the identity privacy of local training clients. Security analysis and experimental results demonstrate that PPBFL, compared to baseline methods, not only exhibits superior model performance but also achieves higher security

Quantifying energy landscape of oscillatory systems: Explosion, pre-solution, and diffusion decomposition

The energy landscape theory finds its both extensive and intensive application in studying stochastic dynamics of physical and biological systems. Although the weighted summation of the Gaussian approximation (WSGA) approach has been proposed for quantifying the energy landscape in multistable systems by solving the diffusion equation approximately from moment equations, we are still lacking an accurate approach for quantifying the energy landscape of the periodic oscillatory systems. To address this challenge, we propose an approach, called the diffusion decomposition of the Gaussian approximation (DDGA). Using typical oscillatory systems as examples, we demonstrate the efficacy of the proposed DDGA in quantifying the energy landscape of oscillatory systems and corresponding stochastic dynamics, in comparison with existing approaches. By further applying the DDGA to a high-dimensional cell cycle network, we are able to uncover more intricate biological mechanisms in cell cycle, which cannot be discerned using previously developed approaches.Comment: 13 pages, 4 figure

HL-DPoS: An Enhanced Anti-Long-Range Attack DPoS Algorithm

The consensus algorithm is crucial in blockchain for ensuring the validity and security of transactions across the decentralized network. However, achieving consensus among nodes and packaging blocks in blockchain networks is a complex task that requires efficient and secure consensus algorithms. The DPoS consensus algorithm has emerged as a popular choice due to its fast transaction processing and high throughput. Despite these advantages, the algorithm still suffers from weaknesses such as centralization and vulnerability to long-range attacks, which can compromise the integrity of the blockchain network. To combat these problems, we developed an Enhanced Anti-Long-Range Attack DPoS algorithm (HL-DPoS). First, we split nodes into pieces to reduce centralization issues while giving witness nodes the power to report and benefit from malicious node's reports, maintaining high efficiency and high security. Second, we propose a validation method in HL-DPoS that compares consensuses transactions with the longest chain to detect long-range attacks. Algorithm analysis and simulation experiment results demonstrate that our HL-DPoS consensus algorithm improves security while achieving better consensus performance

Configured Quantum Reservoir Computing for Multi-Task Machine Learning

Amidst the rapid advancements in experimental technology, noise-intermediate-scale quantum (NISQ) devices have become increasingly programmable, offering versatile opportunities to leverage quantum computational advantage. Here we explore the intricate dynamics of programmable NISQ devices for quantum reservoir computing. Using a genetic algorithm to configure the quantum reservoir dynamics, we systematically enhance the learning performance. Remarkably, a single configured quantum reservoir can simultaneously learn multiple tasks, including a synthetic oscillatory network of transcriptional regulators, chaotic motifs in gene regulatory networks, and the fractional-order Chua's circuit. Our configured quantum reservoir computing yields highly precise predictions for these learning tasks, outperforming classical reservoir computing. We also test the configured quantum reservoir computing in foreign exchange (FX) market applications and demonstrate its capability to capture the stochastic evolution of the exchange rates with significantly greater accuracy than classical reservoir computing approaches. Through comparison with classical reservoir computing, we highlight the unique role of quantum coherence in the quantum reservoir, which underpins its exceptional learning performance. Our findings suggest the exciting potential of configured quantum reservoir computing for exploiting the quantum computation power of NISQ devices in developing artificial general intelligence

A New Method of RNA Secondary Structure Prediction Based on Convolutional Neural Network and Dynamic Programming

In recent years, obtaining RNA secondary structure information has played an important role in RNA and gene function research. Although some RNA secondary structures can be gained experimentally, in most cases, efficient, and accurate computational methods are still needed to predict RNA secondary structure. Current RNA secondary structure prediction methods are mainly based on the minimum free energy algorithm, which finds the optimal folding state of RNA in vivo using an iterative method to meet the minimum energy or other constraints. However, due to the complexity of biotic environment, a true RNA structure always keeps the balance of biological potential energy status, rather than the optimal folding status that meets the minimum energy. For short sequence RNA its equilibrium energy status for the RNA folding organism is close to the minimum free energy status; therefore, the minimum free energy algorithm for predicting RNA secondary structure has higher accuracy. Nevertheless, in a longer sequence RNA, constant folding causes its biopotential energy balance to deviate far from the minimum free energy status. This deviation is because of its complex structure and results in a serious decline in the prediction accuracy of its secondary structure. In this paper, we propose a novel RNA secondary structure prediction algorithm using a convolutional neural network model combined with a dynamic programming method to improve the accuracy with large-scale RNA sequence and structure data. We analyze current experimental RNA sequences and structure data to construct a deep convolutional network model, and then we extract implicit features of an effective classification from large-scale data to predict the pairing probability of each base in an RNA sequence. For the obtained probabilities of RNA sequence base pairing, an enhanced dynamic programming method is applied to obtain the optimal RNA secondary structure. Results indicate that our proposed method is superior to the common RNA secondary structure prediction algorithms in predicting three benchmark RNA families. Based on the characteristics of deep learning algorithm, it can be inferred that the method proposed in this paper has a 30% higher prediction success rate when compared with other algorithms, which will be needed as the amount of real RNA structure data increases in the future

Construction and Evaluation of the Brucella Double Gene Knock-out Vaccine Strain MB6 Δbp26ΔwboA (RM6)

Brucellosis is a serious zoonotic infection worldwide. To date, vaccination is the most effective measure against brucellosis. This study was aimed at obtaining a vaccine strain that has high protective efficacy and low toxicity, and allows vaccination to be differentiated from infection. Using homologous recombination, we constructed a double gene-deletion Brucella strain MB6 Δbp26ΔwboA (RM6) and evaluated its characteristics, safety and efficacy. The RM6 strain had good proliferative ability and stable biological characteristics in vivo and in vitro. Moreover, it had a favorable safety profile and elicited specific immune responses in mice and sheep. The RM6 strain may have substantial practical application value

Development and Efficacy Evaluation of an SP01-adjuvanted Inactivated Escherichia Coli Mutant Vaccine Against Bovine Coliform Mastitis

Escherichia coli ( E. coli ) is one of the most common pathogens causing clinical mastitis in cattle, but no vaccine is available to prevent this disease in China. Therefore, development of an E. coli vaccine against bovine clinical mastitis is urgently needed. The candidate vaccine (Ch-O111-1) and challenge (LZ06) strains were screened from milk samples of cows with clinical mastitis. To extend the cross-protection of the Ch-O111-1 strain, we deleted the galE gene fragment of the Ch-O111-1 strain through homologous recombination between the Ch-O111-1 strain and pCVD442/ΔgalE plasmid, which was identified through conventional methods, including PCR, SDS-PAGE and sequencing. The Ch-O111-1/ΔgalE (Z9) strain was characterized by extensive cross-reactivity and attenuated virulence. We prepared inactivated Z9 vaccines with different adjuvants. Immunization of inactivated Z9 antigen induced adjuvant-, dosage- and inoculation time-dependent antibody titers in cows and mice. Furthermore, immunization with SP01-adjuvanted inactivated Z9 vaccine protected cows against severe clinical mastitis caused by LZ06 and protected mice against death due to LZ06. An SP01-adjuvanted inactivated Z9 vaccine was successfully developed and found to protect cows against severe mastitis caused by Escherichia coli