Geometric interpretation for A-fidelity and its relation with Bures fidelity

A geometric interpretation for the A-fidelity between two states of a qubit system is presented, which leads to an upper bound of the Bures fidelity. The metrics defined based on the A-fidelity are studied by numerical method. An alternative generalization of the A-fidelity, which has the same geometric picture, to a $N$-state quantum system is also discussed.Comment: 4 pages, 1 figure. Phys. Rev.

Anchor Sampling for Federated Learning with Partial Client Participation

Compared with full client participation, partial client participation is a more practical scenario in federated learning, but it may amplify some challenges in federated learning, such as data heterogeneity. The lack of inactive clients' updates in partial client participation makes it more likely for the model aggregation to deviate from the aggregation based on full client participation. Training with large batches on individual clients is proposed to address data heterogeneity in general, but their effectiveness under partial client participation is not clear. Motivated by these challenges, we propose to develop a novel federated learning framework, referred to as FedAMD, for partial client participation. The core idea is anchor sampling, which separates partial participants into anchor and miner groups. Each client in the anchor group aims at the local bullseye with the gradient computation using a large batch. Guided by the bullseyes, clients in the miner group steer multiple near-optimal local updates using small batches and update the global model. By integrating the results of the two groups, FedAMD is able to accelerate the training process and improve the model performance. Measured by $\epsilon$-approximation and compared to the state-of-the-art methods, FedAMD achieves the convergence by up to $O(1/\epsilon)$ fewer communication rounds under non-convex objectives. Empirical studies on real-world datasets validate the effectiveness of FedAMD and demonstrate the superiority of the proposed algorithm: Not only does it considerably save computation and communication costs, but also the test accuracy significantly improves.Comment: ICML 202

Activity-assisted barrier-crossing of self-propelled colloids over parallel microgrooves

We report a systematic study of the dynamics of self-propelled particles (SPPs) over a one-dimensional periodic potential landscape, which is fabricated on a microgroove-patterned polydimethylsiloxane (PDMS) substrate. From the measured non-equilibrium probability density function of the SPPs, we find that the escape dynamics of the slow-rotating SPPs across the potential landscape can be described by an effective potential, once the self-propulsion force is included into the potential under the fixed angle approximation. This work demonstrates that the parallel microgrooves provide a versatile platform for a quantitative understanding of the interplay among the self-propulsion force, spatial confinement by the potential landscape, and thermal noise, as well as its effects on activity-assisted escape dynamics and transport of the SPPs

Enhancing pentachlorophenol degradation by vermicomposting associated bioremediation

Vermicomposting is an effective and environmentally friendly approach for soil organic contamination clean-up. This study investigated the roles and mechanisms of earthworm (Eisenia foetida) on soil pentachlorophenol (PCP) degradation with sterile and non-sterile soil-compost treatment. Limited soil PCP degradation was observed in the control and sterile compost treatments, whereas the synergetic effects of earthworm and compost contributed to the PCP biodegradation acceleration by significantly improving microbial biomass and activities. Sequence analysis and phylogentic classification of soil bacterial and fungal community structure after 42 days treatment identified the dominancy of indigenous bacterial families Pseudomonadaceae, Sphingobacteriaceae and Xanthomonadaceae, and fungal family Trichocomaceae, which were responsible for PCP biodegradation and stimulated by vermicomposting. Further investigation revealed the dominant roles of sterile compost during PCP biodegradation as the formation of humus-PCP in soil rather than neutralizing soil pH and increasing PCP availability. The mechanisms of vermicomposting include humus-PCP complex degradation, humus consumption and soil pH neutralization. This study provides a comprehensive understanding of the synergetic effect of vermicomposting on microbial community functions and PCP degradation enhancement in soils

The impact on the soil microbial community and enzyme activity of two earthworm species during the bioremediation of pentachlorophenol-contaminated soils

The ecological effect of earthworms on the fate of soil pentachlorophenol (PCP) differs with species. This study addressed the roles and mechanisms by which two earthworm species (epigeic Eisenia fetida and endogeic Amynthas robustus E. Perrier) affect the soil microbial community and enzyme activity during the bioremediation of PCP-contaminated soils. A. robustus removed more soil PCP than did E. foetida. A. robustus improved nitrogen utilisation efficiency and soil oxidation more than did E. foetida, whereas the latter promoted the organic matter cycle in the soil. Both earthworm species significantly increased the amount of cultivable bacteria and actinomyces in soils, enhancing the utilisation rate of the carbon source (i.e. carbohydrates, carboxyl acids, and amino acids) and improving the richness and evenness of the soil microbial community. Additionally, earthworm treatment optimized the soil microbial community and increased the amount of the PCP-4-monooxygenase gene. Phylogenic classification revealed stimulation of indigenous PCP bacterial degraders, as assigned to the families Flavobacteriaceae, Pseudomonadaceae and Sphingobacteriacea, by both earthworms. A. robustus and E. foetida specifically promoted Comamonadaceae and Moraxellaceae PCP degraders, respectively

Advances in 3D Generation: A Survey

Generating 3D models lies at the core of computer graphics and has been the focus of decades of research. With the emergence of advanced neural representations and generative models, the field of 3D content generation is developing rapidly, enabling the creation of increasingly high-quality and diverse 3D models. The rapid growth of this field makes it difficult to stay abreast of all recent developments. In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications. Specifically, we introduce the 3D representations that serve as the backbone for 3D generation. Furthermore, we provide a comprehensive overview of the rapidly growing literature on generation methods, categorized by the type of algorithmic paradigms, including feedforward generation, optimization-based generation, procedural generation, and generative novel view synthesis. Lastly, we discuss available datasets, applications, and open challenges. We hope this survey will help readers explore this exciting topic and foster further advancements in the field of 3D content generation.Comment: 33 pages, 12 figure