Optimal Distributed Resource Allocation for Decode-and-Forward Relay Networks

This paper presents a distributed resource allocation algorithm to jointly optimize the power allocation, channel allocation and relay selection for decode-and-forward (DF) relay networks with a large number of sources, relays, and destinations. The well-known dual decomposition technique cannot directly be applied to resolve this problem, because the achievable data rate of DF relaying is not strictly concave, and thus the local resource allocation subproblem may have non-unique solutions. We resolve this non-strict concavity problem by using the idea of the proximal point method, which adds quadratic terms to make the objective function strictly concave. However, the proximal solution adds an extra layer of iterations over typical duality based approaches, which can significantly slow down the speed of convergence. To address this key weakness, we devise a fast algorithm without the need for this additional layer of iterations, which converges to the optimal solution. Our algorithm only needs local information exchange, and can easily adapt to variations of network size and topology. We prove that our distributed resource allocation algorithm converges to the optimal solution. A channel resource adjustment method is further developed to provide more channel resources to the bottleneck links and realize traffic load balance. Numerical results are provided to illustrate the benefits of our algorithm

Self-assembly of noble metal nanoparticles into sub-100 nm colloidosomes with collective optical and catalytic properties.

Self-assembly at the nanoscale represents a powerful tool for creating materials with new structures and intriguing collective properties. Here, we report a novel strategy to synthesize nanoscale colloidosomes of noble metals by assembling primary metal nanoparticles at the interface of emulsion droplets formed by their capping agent. This strategy produces noble metal colloidosomes of unprecedentedly small sizes (<100 nm) in high yield and uniformity, which is highly desirable for practical applications. In addition, it enables the high tunability of the composition, producing a diversity of monometallic and bimetallic alloy colloidosomes. The colloidosomes exhibit interesting collective properties that are different from those of individual colloidal nanoparticles. Specifically, we demonstrate Au colloidosomes with well-controlled interparticle plasmon coupling and Au-Pd alloy colloidosomes with superior electrocatalytic performance, both thanks to the special structural features that arise from the assembly. We believe this strategy provides a general platform for producing a rich class of miniature colloidosomes that may have fascinating collective properties for a broad range of applications

Correlated metallic state in honeycomb lattice: Orthogonal Dirac semimetal

A novel gapped metallic state coined orthogonal Dirac semimetal is proposed in the honeycomb lattice in terms of $Z_{2}$ slave-spin representation of Hubbard model. This state corresponds to the disordered phase of slave-spin and has the same thermaldynamical and transport properties as usual Dirac semimetal but its singe-particle excitation is gapped and has nontrivial topological order due to the $Z_{2}$ gauge structure. The quantum phase transition from this orthogonal Dirac semimetal to usual Dirac semimetal is described by a mean-field decoupling with complementary fluctuation analysis and its criticality falls into the universality class of 2+1D Ising model while a large anomalous dimension for the physical electron is found at quantum critical point (QCP), which could be considered as a fingerprint of our fractionalized theory when compared to other non-fractionalized approaches. As byproducts, a path integral formalism for the $Z_{2}$ slave-spin representation of Hubbard model is constructed and possible relations to other approaches and the sublattice pairing states, which has been argued to be a promising candidate for gapped spin liquid state found in the numerical simulation, are briefly discussed. Additionally, when spin-orbit coupling is considered, the instability of orthogonal Dirac semimetal to the fractionalized quantum spin Hall insulator (fractionalized topological insulator) is also expected. We hope the present work may be helpful for future studies in $Z_{2}$ slave-spin theory and related non-Fermi liquid phases in honeycomb lattice.Comment: 12 pages,no figures, more discussions added. arXiv admin note: text overlap with arXiv:1203.063

Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation

Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) to comprehend instructions and generate appropriate responses. Existing methods either manually annotate or employ LLM (e.g., GPT-series) to generate data for instruction tuning. However, they often overlook associating instructions with existing annotated datasets. In this paper, we propose Dynosaur, a dynamic growth paradigm for the automatic curation of instruction-tuning data. Based on the metadata of existing datasets, we use LLMs to automatically construct instruction-tuning data by identifying relevant data fields and generating appropriate instructions. By leveraging the existing annotated datasets, Dynosaur offers several advantages: 1) it reduces the API cost for generating instructions (e.g., it costs less than $12 USD by calling GPT-3.5-turbo for generating 800K instruction tuning samples; 2) it provides high-quality data for instruction tuning (e.g., it performs better than Alpaca and Flan on Super-NI and Longform with comparable data sizes); and 3) it supports the continuous improvement of models by generating instruction-tuning data when a new annotated dataset becomes available. We further investigate a continual learning scheme for learning with the ever-growing instruction-tuning dataset, and demonstrate that replaying tasks with diverse instruction embeddings not only helps mitigate forgetting issues but generalizes to unseen tasks better. Code and data are available at https://github.com/WadeYin9712/Dynosaur.Comment: EMNLP 2023. Code and data are available at https://github.com/WadeYin9712/Dynosau

Influence of “Internet plus” based continuous nursing intervention on hemodialysis self-management ability of patients with uremia and its countermeasures

Objective: To analyze the impact of “Internet Plus”oriented continuous nursing intervention on hemodialysis self-management ability (HSMA) of patients with uremia and its countermeasures.Methods: 60 uremia patients admitted to hemodialysis in the hospital from January to December 2018 were selected as the control group (using routine continuous nursing intervention); 60 uremia patients admitted to hemodialysis from January to December 2019 were also selected as the observation group (using "Internet Plus"oriented continuous nursing intervention); the changes in the score values of the two groups of patients according to the self-management scale (SMSH) and chronic disease health literacy after intervention respectively. Results: After intervention，the self-management score of the patients in the observation group in terms of problem solving，emotional processing and self-care was higher than that of the control group (P<0.05)，and the score value in terms of information acquisition ability，improvement of health willingness, communication and interaction ability was higher than that of the control group (P<0).0.05).Conclusion:the continuity of nursing intervention based on “Internet plus”self-management enhances the self-management ability of patients with uremic hemodialysis and improves their health literacy