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Recently, masked image modeling (MIM), an important self-supervised learning (SSL) method, has drawn attention for its effectiveness in learning data representation from unlabeled data. Numerous studies underscore the advantages of MIM, highlighting how models pretrained on extensive datasets can enhance the performance of downstream tasks. However, the high computational demands of pretraining pose significant challenges, particularly within academic environments, thereby impeding the SSL research progress. In this study, we propose efficient training recipes for MIM based SSL that focuses on mitigating data loading bottlenecks and employing progressive training techniques and other tricks to closely maintain pretraining performance. Our library enables the training of a MAE-Base/16 model on the ImageNet 1K dataset for 800 epochs within just 18 hours, using a single machine equipped with 8 A100 GPUs. By achieving speed gains of up to 5.8 times, this work not only demonstrates the feasibility of conducting high-efficiency SSL training but also paves the way for broader accessibility and promotes advancement in SSL research particularly for prototyping and initial testing of SSL ideas. The code is available in https://github.com/erow/FastSSL.

One major challenge of disentanglement learning with variational autoencoders is the trade-off between disentanglement and reconstruction fidelity. Previous studies, which increase the information bottleneck during training, tend to lose the constraint of disentanglement, leading to the information diffusion problem. In this paper, we present a novel framework for disentangled representation learning, DeVAE, which utilizes hierarchical latent spaces with decreasing information bottlenecks across these spaces. The key innovation of our approach lies in connecting the hierarchical latent spaces through disentanglement-invariant transformations, allowing the sharing of disentanglement properties among spaces while maintaining an acceptable level of reconstruction performance. We demonstrate the effectiveness of DeVAE in achieving a balance between disentanglement and reconstruction through a series of experiments and ablation studies on dSprites and Shapes3D datasets. Code is available at https://github.com/erow/disentanglement_lib/tree/pytorch#devae.

Recently, self-supervised metric learning has raised attention for the potential to learn a generic distance function. It overcomes the limitations of conventional supervised one, e.g., scalability and label biases. Despite progress in this domain, current benchmarks, incorporating a narrow scope of classes, stop the nuanced evaluation of semantic representations. To bridge this gap, we introduce a large-scale benchmark with diversity and granularity of classes, Statistical Metric Learning Benchmark (SMLB) built upon ImageNet-21K and WordNet. SMLB is designed to rigorously evaluate the discriminative discernment and generalizability across more than 14M images, 20K classes, and 16K taxonomic nodes. Alongside, we propose novel evaluation metrics -- `overlap' for separability and `aSTD' for consistency -- to measure distance statistical information, which are efficient and robust to the change of class number. Our benchmark offers a novel perspective of evaluating the quality of representations beyond accuracy. Our findings reveal the limitations of supervised learning and the class bias inherent in SSL models, offering insights into potential areas for future model enhancement.

Self-supervised pretraining (SSP) has emerged as a popular technique in machine learning, enabling the extraction of meaningful feature representations without labelled data. In the realm of computer vision, pretrained vision transformers (ViTs) have played a pivotal role in advancing transfer learning. Nonetheless, the escalating cost of finetuning these large models has posed a challenge due to the explosion of model size. This study endeavours to evaluate the effectiveness of pure self-supervised learning (SSL) techniques in computer vision tasks, obviating the need for finetuning, with the intention of emulating human-like capabilities in generalisation and recognition of unseen objects. To this end, we propose an evaluation protocol for zero-shot segmentation based on a prompting patch. Given a point on the target object as a prompt, the algorithm calculates the similarity map between the selected patch and other patches, upon that, a simple thresholding is applied to segment the target. Another evaluation is intra-object and inter-object similarity to gauge discriminatory ability of SSP ViTs. Insights from zero-shot segmentation from prompting and discriminatory abilities of SSP led to the design of a simple SSP approach, termed MMC. This approaches combines Masked image modelling for encouraging similarity of local features, Momentum based self-distillation for transferring semantics from global to local features, and global Contrast for promoting semantics of global features, to enhance discriminative representations of SSP ViTs. Consequently, our proposed method significantly reduces the overlap of intra-object and inter-object similarities, thereby facilitating effective object segmentation within an image. Our experiments reveal that MMC delivers top-tier results in zero-shot semantic segmentation across various datasets.

Owing to the continuous increase of traffic loads, bitumen modification has been manifested as an efficient methodology to enhance asphaltic pavement performance. Currently, the modification index, defined as the ratio of mechanical properties (e.g., complex modulus) before and after bitumen modification, is extensively adopted to evaluate the modification degree. However, bituminous materials behave as temperature-dependent, which indicates that the mechanical property varies with measured temperatures. As a result, the calculated modification index also shows temperature-dependent property, which inhibits the use of modification index. For this reason, this study introduced a method to eliminate the temperature-dependency of the modification index. In specific, a mathematical model considering the properties of modifiers was firstly established to predict the modification index-temperature curve (MI-T curve). In what follows, the temperature-dependency of modification index was analyzed to verify the proposed model on three types of modifiers, which were graphene, Styrene-Butadiene-Styrene (SBS), and Ethyl-Vinyl-Acetate (EVA), respectively. The results indicated that the developed model could efficiently predict the MI-T curves. Besides, the effective modification area (EMA) and optimal modification index (OMI) were two reasonable indicators that evaluate the bitumen modification without considering the temperature-dependency., Pavement Engineering

Climate science has become more ambitious in recent years as global awareness about the environment has grown. To better understand climate, historical climate (e.g. archived meteorological variables such as temperature, wind, water, etc.) and climate-related data (e.g. geographical features and human activities) are widely used by today's climate research to derive models for an explainable climate change and its effects. However, such data sources are often dispersed across a multitude of disconnected data silos on the Web. Moreover, there is a lack of advanced climate data platforms to enable multi-source heterogeneous climate data analysis, therefore, researchers must face a stern challenge in collecting and analyzing multi-source data. In this paper, we address this problem by proposing a climate knowledge graph for the integration of multiple climate data and other data sources into one service, leveraging Web technologies (e.g. HTTP) for multi-source climate data analysis. The proposed knowledge graph is primarily composed of data from the National Oceanic and Atmospheric Administration's daily climate summaries, OpenStreetMap, and Wikidata, and it supports joint data queries on these widely used databases. This paper shows, with a use case in Ireland and the United Kingdom, how climate researchers could benefit from this platform as it allows them to easily integrate datasets from different domains and geographical locations., Comment: Published in Computers and Geosciences, 2022

In a decentralized household energy system comprised of various devices such as home appliances, electric vehicles, and solar panels, end-users are able to dig deeper into the system's details and further achieve energy sustainability if they are presented with data on the electric energy consumption and production at the granularity of the device. However, many databases in this field are siloed from other domains, including solely information pertaining to energy. This may result in the loss of information (e.g. weather) on each device's energy use. Meanwhile, a large number of these datasets have been extensively used in computational modeling techniques such as machine learning models. While such computational approaches achieve great accuracy and performance by concentrating only on a local view of datasets, model reliability cannot be guaranteed since such models are very vulnerable to data input fluctuations when information omission is taken into account. This article tackles the data isolation issue in the field of smart energy systems by examining Semantic Web methods on top of a household energy system. We offer an ontology-based approach for managing decentralized data at the device-level resolution in a system. As a consequence, the scope of the data associated with each device may easily be expanded in an interoperable manner throughout the Web, and additional information, such as weather, can be obtained from the Web, provided that the data is organized according to W3C standards., Comment: Published in Sustainable Energy, Grids and Networks (SEGAN) 2022

In a decentralized household energy system comprised of various devices such as home appliances, electric vehicles, and solar panels, end-users are able to dig deeper into the system's details and further achieve energy sustainability if they are presented with data on the electric energy consumption and production at the granularity of the device. However, many databases in this field are siloed from other domains, including solely information pertaining to energy. This may result in the loss of information (e.g. weather) on each device's energy use. Meanwhile, a large number of these datasets have been extensively used in computational modeling techniques such as machine learning models. While such computational approaches achieve great accuracy and performance by concentrating only on a local view of datasets, model reliability cannot be guaranteed since such models are very vulnerable to data input fluctuations when information omission is taken into account. This article tackles the data isolation issue in the field of smart energy systems by examining Semantic Web methods on top of a household energy system. We offer an ontology-based approach for managing decentralized data at the device-level resolution in a system. As a consequence, the scope of the data associated with each device may easily be expanded in an interoperable manner throughout the Web, and additional information, such as weather, can be obtained from the Web, provided that the data is organized according to W3C standards.

Self-supervised pre-training for images without labels has recently achieved promising performance in image classification. The success of transformer-based methods, ViT and MAE, draws the community's attention to the design of backbone architecture and self-supervised task. In this work, we show that current masked image encoding models learn the underlying relationship between all objects in the whole scene, instead of a single object representation. Therefore, those methods bring a lot of compute time for self-supervised pre-training. To solve this issue, we introduce a novel object selection and division strategy to drop non-object patches for learning object-wise representations by selective reconstruction with interested region masks. We refer to this method ObjMAE. Extensive experiments on four commonly-used datasets demonstrate the effectiveness of our model in reducing the compute cost by 72% while achieving competitive performance. Furthermore, we investigate the inter-object and intra-object relationship and find that the latter is crucial for self-supervised pre-training.

Owing to the continuous increase of traffic loads, bitumen modification has been manifested as an efficient methodology to enhance asphaltic pavement performance. Currently, the modification index, defined as the ratio of mechanical properties (e.g., complex modulus) before and after bitumen modification, is extensively adopted to evaluate the modification degree. However, bituminous materials behave as temperature-dependent, which indicates that the mechanical property varies with measured temperatures. As a result, the calculated modification index also shows temperature-dependent property, which inhibits the use of modification index. For this reason, this study introduced a method to eliminate the temperature-dependency of the modification index. In specific, a mathematical model considering the properties of modifiers was firstly established to predict the modification index-temperature curve (MI-T curve). In what follows, the temperature-dependency of modification index was analyzed to verify the proposed model on three types of modifiers, which were graphene, Styrene-Butadiene-Styrene (SBS), and Ethyl-Vinyl-Acetate (EVA), respectively. The results indicated that the developed model could efficiently predict the MI-T curves. Besides, the effective modification area (EMA) and optimal modification index (OMI) were two reasonable indicators that evaluate the bitumen modification without considering the temperature-dependency., Pavement Engineering

This work proposes an open interoperable data portal that offers access to a Web-wide climate domain knowledge graph created for Ireland and England's NOAA climate daily data. There are three main components contributing to this data portal: the first is the upper layer schema of the knowledge graph -- the climate analysis (CA) ontology -- the second is an ad hoc SPARQL server by which to store the graph data and provide public Web access, the last is a dereferencing engine deployed to resolve URIs for entity information. Our knowledge graph form of NOAA climate data facilitates the supply of semantic climate information to researchers and offers a variety of semantic applications that can be built on top of it., Comment: Accepted in Proc. IEEE AP-S Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2021

The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are fundamental for climate researchers and all stakeholders in the current digital ecosystem. In this paper, we demonstrate how relational climate data can be "FAIR" and modeled using RDF, in line with Semantic Web technologies and our Climate Analysis ontology. Thus, heterogeneous climate data can be stored in graph databases and offered as Linked Data on the Web. As a result, climate researchers will be able to use the standard SPARQL query language to query these sources directly on the Web. In this paper, we demonstrate the usefulness of our SPARQL endpoint for automated climate analytics. We illustrate two sample use cases that establish the advantage of representing climate data as knowledge graphs., Comment: Accepted in Proc. IEEE AP-S Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2021

In a decentralized household energy system consisting of various devices such as washing machines, heat pumps, and solar panels, understanding the electric energy consumption and production data at the granularity of the device helps end-users be closer to the system and further achieve the sustainability of energy use. However, many datasets in this area are isolated from other domains with records of only energy-related data. This may raise a loss of information (e.g. weather) that is relevant to the energy use of each device. A noticeable disadvantage is that many of those datasets have to be used in computational modeling approaches such as machine learning models, which are vulnerable to the data feed, to advance the understanding of energy consumption and production. Although such computational methods have achieved a high benchmark merely through a local view of datasets, the reusability cannot be firmly guaranteed when the information omission is taken into account. This paper addresses the data isolation problem in the smart energy systems area by exploring Semantic Web techniques on top of a household energy system. We propose an ontology modeling solution for the management of decentralized data at the resolution of a device in the system. As a result, the scope of the data concerning each device can be easily extended to be wider across the web and more information that may be of interest such as weather can be retrieved from the Web if the data are structured by the ontology., Comment: Published in IEEE International Conference on Smart Energy Systems and Technologies (SEST), 2021

Recently ontologies have been exploited in a wide range of research areas for data modeling and data management. They greatly assists in defining the semantic model of the underlying data combined with domain knowledge. In this paper, we propose the Climate Analysis (CA) Ontology to model climate datasets used by remote sensing analysts. We use the data published by National Oceanic and Atmospheric Administration (NOAA) to further explore how ontology modeling can be used to facilitate the field of climatic data processing. The idea of this work is to convert relational climate data to the Resource Description Framework (RDF) data model, so that it can be stored in a graph database and easily accessed through the Web as Linked Data. Typically, this provides climate researchers, who are interested in datasets such as NOAA, with the potential of enriching and interlinking with other databases. As a result, our approach facilitates data integration and analysis of diverse climatic data sources and allows researchers to interrogate these sources directly on the Web using the standard SPARQL query language., Comment: Published in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2021

Disentanglement is a highly desirable property of representation owing to its similarity to human understanding and reasoning. Many works achieve disentanglement upon information bottlenecks (IB). Despite their elegant mathematical foundations, the IB branch usually exhibits lower performance. In order to provide an insight into the problem, we develop an annealing test to calculate the information freezing point (IFP), which is a transition state to freeze information into the latent variables. We also explore these clues or inductive biases for separating the entangled factors according to the differences in the IFP distributions. We found the existing approaches suffer from the information diffusion problem, according to which the increased information diffuses in all latent variables. Based on this insight, we propose a novel disentanglement framework, termed the distilling entangled factor (DEFT), to address the information diffusion problem by scaling backward information. DEFT applies a multistage training strategy, including multigroup encoders with different learning rates and piecewise disentanglement pressure, to disentangle the factors stage by stage. We evaluate DEFT on three variants of dSprite and SmallNORB, which show low-variance and high-level disentanglement scores. Furthermore, the experiment under the correlative factors shows incapable of TC-based approaches. DEFT also exhibits a competitive performance in the unsupervised setting.

Disentanglement is a highly desirable property of representation due to its similarity with human's understanding and reasoning. This improves interpretability, enables the performance of down-stream tasks, and enables controllable generative models. However, this domain is challenged by the abstract notion and incomplete theories to support unsupervised disentanglement learning. We demonstrate the data itself, such as the orientation of images, plays a crucial role in disentanglement and instead of the factors, and the disentangled representations align the latent variables with the action sequences. We further introduce the concept of disentangling action sequences which facilitates the description of the behaviours of the existing disentangling approaches. An analogy for this process is to discover the commonality between the things and categorizing them. Furthermore, we analyze the inductive biases on the data and find that the latent information thresholds are correlated with the significance of the actions. For the supervised and unsupervised settings, we respectively introduce two methods to measure the thresholds. We further propose a novel framework, fractional variational autoencoder (FVAE), to disentangle the action sequences with different significance step-by-step. Experimental results on dSprites and 3D Chairs show that FVAE improves the stability of disentanglement.

Sewage sludge is one of the final products from wastewater treatment processes. Handling and disposal of sewage sludge has become one of the major economic and environmental concerns. Instead of simply treating and disposing, high temperature melting treatment is considered as one of the effective alternative efforts to utilize sewage sludge as reusable materials. High temperature melting is defined as the method of melting sewage sludge at temperature of about 1500°C before cooling the molten sludge to become solidified materials, known as molten slag. Past studies have shown that high temperature melting treatment could convert sewage sludge into reusable products without adverse environmental effects. The objective of this study is to evaluate the possible utilization of sewage sludge molten slag as aggregate substitute in asphalt mixtures for pavement construction applications. The whole concept of this study was designed to assess the materials characteristics and suitability, and thus to evaluate the performance of asphalt mixtures with sewage sludge molten slag as aggregate substitute in terms of Marshall stability and flow tests. Based on the conducted study, it can be concluded that sewage sludge molten slag fulfilled almost all the standard requirements for aggregates in asphalt mixtures and can be utilized as partial replacement for aggregates. Sewage sludge molten slag has shown to be able to resist wear, polishing effect and weathering actions.

Sewage sludge is one of the final products from wastewater treatment processes. Handling and disposal of sewage sludge has become one of the major economic and environmental concerns. Instead of simply treating and disposing, high temperature melting treatment is considered as one of the effective alternative efforts to utilize sewage sludge as reusable materials. High temperature melting is defined as the method of melting sewage sludge at temperature of about 1500°C before cooling the molten sludge to become solidified materials, known as molten slag. Past studies have shown that high temperature melting treatment could convert sewage sludge into reusable products without adverse environmental effects. The objective of this study is to evaluate the possible utilization of sewage sludge molten slag as aggregate substitute in asphalt mixtures for pavement construction applications. The whole concept of this study was designed to assess the materials characteristics and suitability, and thus to evaluate the performance of asphalt mixtures with sewage sludge molten slag as aggregate substitute in terms of Marshall stability and flow tests. Based on the conducted study, it can be concluded that sewage sludge molten slag fulfilled almost all the standard requirements for aggregates in asphalt mixtures and can be utilized as partial replacement for aggregates. Sewage sludge molten slag has shown to be able to resist wear, polishing effect and weathering actions.

The dynamic shear rheometer (DSR) is one of the most complex and powerful instruments currently used to characterise the linear viscoelastic (LVE) rheological properties of bituminous binders. However, the DSR also has its limitations in that the measured complex modulus data are exposed to errors, known as compliance (testing) errors, particularly at low temperatures and/or high frequencies. This study was conducted to investigate the validity of equations developed by Schröter and associates on complex modulus data collected using the DSR. The equations used were originally developed based on the calibration of the advanced rheometric expanse system (ARES) rheometer. It was found that those equations are able to satisfactorily correct the data on unmodified bitumens and unaged bitumen-filler mastics, including unaged and aged samples. Similar results were also observed for storage and loss moduli master curves. Finally, the 2S2P1D Model was used to calibrate the corrected complex moduli data and it was found that the model satisfactorily simulates the rheological properties of tested samples.

The dynamic shear rheometer (DSR) is one of the most complex and powerful instruments currently used to characterise the linear viscoelastic (LVE) rheological properties of bituminous binders. However, the DSR also has its limitations in that the measured complex modulus data are exposed to errors, known as compliance (testing) errors, particularly at low temperatures and/or high frequencies. This study was conducted to investigate the validity of equations developed by Schröter and associates on complex modulus data collected using the DSR. The equations used were originally developed based on the calibration of the advanced rheometric expanse system (ARES) rheometer. It was found that those equations are able to satisfactorily correct the data on unmodified bitumens and unaged bitumen-filler mastics, including unaged and aged samples. Similar results were also observed for storage and loss moduli master curves. Finally, the 2S2P1D Model was used to calibrate the corrected complex moduli data and it was found that the model satisfactorily simulates the rheological properties of tested samples.

Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

High-throughput sequencing technology enables population-level surveys of human genomic variation. Here, we examine the joint allele frequency distributions across continental human populations and present an approach for combining complementary aspects of whole-genome, low-coverage data and targeted high-coverage data. We apply this approach to data generated by the pilot phase of the Thousand Genomes Project, including whole-genome 2–4× coverage data for 179 samples from HapMap European, Asian, and African panels as well as high-coverage target sequencing of the exons of 800 genes from 697 individuals in seven populations. We use the site frequency spectra obtained from these data to infer demographic parameters for an Out-of-Africa model for populations of African, European, and Asian descent and to predict, by a jackknife-based approach, the amount of genetic diversity that will be discovered as sample sizes are increased. We predict that the number of discovered nonsynonymous coding variants will reach 100,000 in each population after ∼1,000 sequenced chromosomes per population, whereas ∼2,500 chromosomes will be needed for the same number of synonymous variants. Beyond this point, the number of segregating sites in the European and Asian panel populations is expected to overcome that of the African panel because of faster recent population growth. Overall, we find that the majority of human genomic variable sites are rare and exhibit little sharing among diverged populations. Our results emphasize that replication of disease association for specific rare genetic variants across diverged populations must overcome both reduced statistical power because of rarity and higher population divergence.