Siamese network based trackers formulate tracking as convolutional feature cross-correlation between a target template and a search region. However, Siamese trackers still have an accuracy gap compared with state-of-theart algorithms and they cannot take advantage of features from deep networks, such as ResNet-50 or deeper. In this work we prove the core reason comes from the lack of strict translation invariance. By comprehensive theoretical analysis and experimental validations, we break this restriction through a simple yet effective spatial aware sampling strategy and successfully train a ResNet-driven Siamese tracker with significant performance gain. Moreover, we propose a new model architecture to perform layer-wise and depthwise aggregations, which not only further improves the accuracy but also reduces the model size. We conduct extensive ablation studies to demonstrate the effectiveness of the proposed tracker, which obtains currently the best results on five large tracking benchmarks, including OTB2015, VOT2018, UAV123, LaSOT, and TrackingNet. Our model will be released to facilitate further researches. * The first three authors contributed equally. Work done at SenseTime. Project page: https://lb1100.github.io/SiamRPN++. Recently, the Siamese network based trackers [40,1,15,42,41,24,43,52,44] have drawn much attention in the community. These Siamese trackers formulate the visual object tracking problem as learning a general similarity map by cross-correlation between the feature representations learned for the target template and the search region. To ensure tracking efficiency, the offline learned Siamese similarity function is often fixed during the running time [40,1,15]. The CFNet tracker [41] and DSiam tracker [11] update the tracking model via a running average template and a fast transformation module, respectively. The SiamRNN tracker [24] introduces the region proposal network [24] after the Siamese network and performs joint classification and regression for tracking. The DaSiamRPN tracker [52] further introduces a distractor-aware module and improves the discrimination power of the model.

CONTENTS 1. Introduction and Scope of Review 4124 2. Synthesis of LDH Nanosheets 4125 2.1. Delamination or Top Down Methods 4125 2.1.1. Delamination in Butanol 4125 2.1.2. Delamination in Acrylates 4126 2.1.3. Delamination in CCl 4 and Toluene 4126 2.1.4. Delamination in Formamide 4127 2.1.5. Delamination in N,N-Dimethylformamide−Ethanol Mixture 4130 2.1.6. Delamination in Water 4130 2.1.7. Partial Delamination in Dimethyl Sulfoxide and N-Methylpyrrolidone 4131 2.2. Controlled Nucleation or "Bottom Up" Methods 4132 3. Practical Applications of Delaminated LDHs 4132 3.1. Synthesis of Polymer/LDH Nanocomposites 4133 3.1.1. Intercalation of the Monomers and in Situ Polymerization 4133 3.1.2. Direct Intercalation of Extended Polymer Chains 4136 3.1.3. Pre-exfoliation Followed by Mixing with Polymer 4137 3.2. Synthesis of Core/Shell Multifunctional Materials 4140 3.3. Synthesis of Thin Films 4140 3.4. Synthesis of Catalysts 4145 3.5. Synthesis of Electrode Materials 4146 3.5.1. Application in Supercapacitors 4147 3.5.2. Application in Lithium Ion Batteries 4147 3.5.3.Application in Dye-Sensitized Solar Cells 4148 3.6. Synthesis of Hybrid Magnets 4148 3.7. Synthesis of Bioinorganic Hybrid Materials 4150 4. Conclusions 4151 Author Information 4152 Corresponding Author 4152 Notes 4152 Biographies 4152 Acknowledgments 4152 References 4152

In vitro, β-amyloid (Aβ) peptides form polymorphic fibrils, with molecular structures that depend on growth conditions, plus various oligomeric and protofibrillar aggregates. Detailed structural information about Aβ assemblies in the human brain has been lacking. Here, we investigate structures of brain-derived Aβ fibrils, using seeded fibril growth from brain extract and data from solid state nuclear magnetic resonance and electron microscopy. Experiments on tissue from two Alzheimer’s disease (AD) patients with distinct clinical histories indicate a single predominant 40-residue Aβ (Aβ40) fibril structure in each patient, but different structures in the two patients. A molecular structural model developed for Aβ40 fibrils from one patient reveals features that distinguish in vivo from in vitro fibrils. The data suggest that fibrils in the brain may spread from a single nucleation site, that structural variations may correlate with variations in AD, and that structure-specific amyloid imaging agents may be an important future goal.

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.