IronForge: An Open, Secure, Fair, Decentralized Federated Learning.

Federated learning (FL) offers an effective learning architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, overdependence on a central coordinator, and lack of an open and fair incentive mechanism collectively hinder FL's further development. We propose IronForge, a new generation of FL framework, that features a directed acyclic graph (DAG)-based structure, where nodes represent uploaded models, and referencing relationships between models form the DAG that guides the aggregation process. This design eliminates the need for central coordinators to achieve fully decentralized operations. IronForge runs in a public and open network and launches a fair incentive mechanism by enabling state consistency in the DAG. Hence, the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of IronForge. Experimental results based on a newly developed test bed FLSim highlight the superiority of IronForge to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, IronForge is the first secure and fully decentralized FL (DFL) framework that can be applied in open networks with realistic network and training settings.

Doubly Constrained Waveform Optimization for Integrated Sensing and Communications.

This paper investigates threshold-constrained joint waveform optimization for an integrated sensing and communication (ISAC) system. Unlike existing studies, we employ mutual information (MI) and sum rate (SR) as sensing and communication metrics, respectively, and optimize the waveform under constraints to both metrics simultaneously. This provides significant flexibility in meeting system performance. We formulate three different optimization problems that constrain the radar performance only, the communication performance only, and the ISAC performance, respectively. New techniques are developed to solve the original problems, which are NP-hard and cannot be directly solved by conventional semi-definite programming (SDP) techniques. Novel gradient descent methods are developed to solve the first two problems. For the third non-convex optimization problem, we transform it into a convex problem and solve it via convex toolboxes. We also disclose the connections between three optimizations using numerical results. Finally, simulation results are provided and validate the proposed optimization solutions.

Novel Graph Topology Learning for Spatio-Temporal Analysis of COVID-19 Spread.

This article presents a new graph-learning technique to accurately infer the graph structure of COVID-19 data, helping to reveal the correlation of pandemic dynamics among different countries and identify influential countries for pandemic response analysis. The new technique estimates the graph Laplacian of the COVID-19 data by first deriving analytically its precise eigenvectors, also known as graph Fourier transform (GFT) basis. Given the eigenvectors, the eigenvalues of the graph Laplacian are readily estimated using convex optimization. With the graph Laplacian, we analyze the confirmed cases of different COVID-19 variants among European countries based on centrality measures and identify a different set of the most influential and representative countries from the current techniques. The accuracy of the new method is validated by repurposing part of COVID-19 data to be the test data and gauging the capability of the method to recover missing test data, showing 33.3% better in root mean squared error (RMSE) and 11.11% better in correlation of determination than existing techniques. The set of identified influential countries by the method is anticipated to be meaningful and contribute to the study of COVID-19 spread.

Unified Fine-Grained Access Control for Personal Health Records in Cloud Computing.

Attribute-based encryption has been a promising encryption technology to secure personal health records (PHRs) sharing in cloud computing. PHRs consist of the patient data often collected from various sources including hospitals and general practice centres. Different patients' access policies have a common access sub-policy. In this paper, we propose a novel attribute-based encryption scheme for fine-grained and flexible access control to PHRs data in cloud computing. The scheme generates shared information by the common access sub-policy, which is based on different patients' access policies. Then, the scheme combines the encryption of PHRs from different patients. Therefore, both time consumption of encryption and decryption can be reduced. Medical staff require varying levels of access to PHRs. The proposed scheme can also support multi-privilege access control so that medical staff can access the required level of information while maximizing patient privacy. Through implementation and simulation, we demonstrate that the proposed scheme is efficient in terms of time. Moreover, we prove the security of the proposed scheme based on security of the ciphertext-policy attribute-based encryption scheme.

Gene-gene interaction of GJB2, SOD2, and CAT on occupational noise-induced hearing loss in Chinese Han population.

The effects of genetic factors on the noise-induced hearing loss (NIHL) are still unclear. In the present study, eight single-nucleotide polymorphisms (SNPs) included rs1227049 and rs3802711 (CDH23), rs1695 (GSTP1), rs137852540 (GJB2), rs2289274 (PMCA2), rs4880 (SOD2), rs7943316, and rs769214 within CAT that might associated with NIHL were further validated in Chinese workers. The results showed that the carriers of the T allele (AT+TT) of rs7943316 and A allele (GA+AA) of rs769214, were significantly associated with an increased risk of NIHL compared to those with AA genotype (P<0.05) and GG genotype (P<0.05). Moreover, a significant three-locus model (P=0.0107) involving rs2016520, rs9794, and rs1805192 were observed that might associated with NIHL, with 53.95% of testing accuracy. Thus, our present study provided the evidence that GJB2, SOD2, and CAT genes might account for the NIHL development in independently and/or in an interactive manner.

Effect of hybrid circle reservoir injected with wavelet-neurons on performance of echo state network.

The Echo State Network (ESN) has attracted wide attention for its superior performance in chaos time-series prediction. However, the complicated ESN topologies and the random reservoirs are difficult to implement in practice. We propose a hybrid circle reservoir (HCR) ESN architecture that comprises the following features: (1) built with low complexity circle reservoir; (2) partly injected with wavelet-neurons; (3) uses fixed connection weights in both input matrix and dynamic reservoir matrix. The HCR model has been successfully applied to solve six application problems, and the results are used to compare with the existing low complexity simple circle reservoir (SCR) ESN. Furthermore, we analyze the performance of the new model under different ratios of wavelet-neurons, different circle distributions and different input sign patterns. Simulation results show that the HCR model achieves significantly better performance in prediction accuracy than the SCR model. Additionally, the HCR model has similar low complexity as the SCR. Moreover, the short-term memory capacity (MC) in the HCR is close to the theoretical optimal MC value.

[Advances in the study of inhibitors of kinases and nuclear factors for treating allergic asthma].

Currently, about 300 million people worldwide are affected by asthma. Most of these sufferers inhale immunosuppressants (ie corticosteroids) and beta-adrenergic receptor agonists for their asthma treatment. However, about 5%-10% of patients of asthma have poor response to such treatment. Investigation of kinase signaling pathway and nuclear transcription factor as a target molecule in the treatment of allergic asthma has been the concern of scholars home and abroad. This paper reviewed inhibitors of kinase signaling pathway and nuclear transcription factors for the treatment of asthma.

The chemical and oxidation characteristics of semi-dry flue gas desulfurization ash from a steel factory.

Some samples of semi-dry flue gas desulfurization (FGD) ash were taken from sinter gas of a steel factory. Scanning electron microscope (SEM) and X-ray diffraction (XRD) analyses were employed to identify the samples in order to investigate their physical and chemical characteristics. The results show that semi-dry FGD ash from a steel factory is stable under atmospheric conditions. It has irregular shape, a smooth surface and loose construction. The size of FGD ash particles is around 0.5-25 µm, the average size is about 5 µm and the median diameter is 4.18 µm. Semi-dry FGD ash from a steel factory consists of CaSO3, CaSO4, CaCO3, some amorphous vitreous material and unburned carbon. An experimental method was found to study the oxidation characteristics of ash. A prediction model of the oxidation efficiency was obtained based on response surface methodology. The results show that not only the temperature, but also gas:solid ratio, play an important role in influencing the oxidation efficiency. The interactions of the gas:solid ratio with temperature play an essential role. An improved response surface model was obtained which can be helpful to describe the degree of oxidation efficiency of semi-dry FGD ash.