Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Subsequently to the publication of this paper, the authors have noticed that Fig. 5 was published with inadvertent errors; essentially, the y‑axis label in Fig. 5D should have read "TNF‑α" instead of "IL‑1β" and conversely, in Fig. 5E, the y‑axis label should have read "IL‑1β" instead of "TNF‑α". The revised version of Fig. 5, with Figs. 5D and E now labelled correctly, is shown below. Note that these errors did not significantly affect either the results or the conclusions reported in this paper, and all the authors agree to this corrigendum. Furthermore, the authors thank the Editor of Molecular Medicine Reports for allowing them the opportunity to publish this corrigendum, and apologize to the readership for any inconvenience caused. [Molecular Medicine Reports 24: 795, 2021; DOI: 10.3892/mmr.2021.12435].

/pdf/exosomal-mir-1184-derived-from-mesenchymal-stem-cells-2ocmzw72.pdf

Exosomal-miR-1184 derived from mesenchymal stem cells alleviates cisplatin-associated acute kidney injury

Knowledge Management via Human-Centric, Domain-Specific Visual Languages for Data-Intensive Software Systems

Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients’ local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds; 2) it does not recognize or utilize model diversity when producing a global model; and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × −3.20 ×, and improves model accuracy by 2.14% - 6.63%.

FedEdge: Accelerating Edge-Assisted Federated Learning

The invention relates to the technical field of magnetic resonance imaging, and discloses a rapid magnetic resonance variable-resolution imaging method and system and a readable medium. The main technical scheme of the invention is as follows: the method comprises the following steps of: acquiring data by adopting a parallel imaging acquisition method and a variable density Cartesian sampling method by combining parallel imaging and the compressed sensing technology to obtain three frequency band parts, namely a low-frequency part, an intermediate-frequency part and a high-frequency part; and performing convolution on data of different frequency bands by respectively adopting three different convolution cores with different sizes to reconstruct an image. The implementation of the invention has the following beneficial effects: the noise amplification phenomenon caused by the increase of the acceleration times in parallel imaging can be well inhibited, imaging can be performed under higher acceleration times, and the imaging speed is improved; and moreover, the obtained images can have different resolutions, and the resolution of the interested area is higher than that of other places, so that the image quality is better.

Rapid magnetic resonance variable-resolution imaging method and system and readable medium

A dual-nuclear radio frequency (RF) coil device includes a first RF coil and a second RF coil. The first RF coil includes at least one adjustment capacitor, the first RF coil is configured to generate a first magnetic field, and a direction of a primary magnetic field of the first magnetic field is a first direction. The second RF coil includes an electric dipole and a tuning and matching circuit connected between two conductors of the electric dipole. The second RF coil is configured to generate a second magnetic field and a direction of a primary magnetic field of the second magnetic field is a second direction; the electric dipole is disposed in a center line of the first RF coil and an insulating layer is disposed between the electric dipole and the first RF coil; and the first direction is perpendicular to the second direction.

Qiang He

Papers

Exosomal-miR-1184 derived from mesenchymal stem cells alleviates cisplatin-associated acute kidney injury

Knowledge Management via Human-Centric, Domain-Specific Visual Languages for Data-Intensive Software Systems

FedEdge: Accelerating Edge-Assisted Federated Learning

Rapid magnetic resonance variable-resolution imaging method and system and readable medium

Dual-nuclear RF coil device and dual-nuclear RF coil array device