ANINet: a deep neural network for skull ancestry estimation

Abstract Background Ancestry estimation of skulls is under a wide range of applications in forensic science, anthropology, and facial reconstruction. This study aims to avoid defects in traditional skull ancestry estimation methods, such as time-consuming and labor-intensive manual calibration of feature points, and subjective results. Results This paper uses the skull depth image as input, based on AlexNet, introduces the Wide module and SE-block to improve the network, designs and proposes ANINet, and realizes the ancestry classification. Such a unified model architecture of ANINet overcomes the subjectivity of manually calibrating feature points, of which the accuracy and efficiency are improved. We use depth projection to obtain the local depth image and the global depth image of the skull, take the skull depth image as the object, use global, local, and local + global methods respectively to experiment on the 95 cases of Han skull and 110 cases of Uyghur skull data sets, and perform cross-validation. The experimental results show that the accuracies of the three methods for skull ancestry estimation reached 98.21%, 98.04% and 99.03%, respectively. Compared with the classic networks AlexNet, Vgg-16, GoogLenet, ResNet-50, DenseNet-121, and SqueezeNet, the network proposed in this paper has the advantages of high accuracy and small parameters; compared with state-of-the-art methods, the method in this paper has a higher learning rate and better ability to estimate. Conclusions In summary, skull depth images have an excellent performance in estimation, and ANINet is an effective approach for skull ancestry estimation.

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A Novel Monocular Visual Odometer Method Based on Kinect and Improved SURF Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.4081 ◽

2014 ◽

Vol 556-562 ◽

pp. 4081-4084

Author(s):

Li Jun Zhang ◽

Fei Chen

Keyword(s):

Dynamic Environment ◽

Mean Value ◽

Color Images ◽

Least Square ◽

Depth Image ◽

Mean Value Theorem ◽

Feature Points ◽

Kinect Sensor ◽

Depth Images ◽

Visual Odometer

The paper proposes a novel monocular visual odometer method based on Kinect sensor made by Microsoft and the improved SURF algorithm. Firstly the Kinect sensor capture color images and depth images of the surrounding environment, then we use the improved SURF algorithm to extract feature points of the color images and match for them. At last, map what we get with the depth image and estimate the path information of the robot by doing 3D reconstruction and using the the least square mean value theorem. Experimental results show that by using this new method, the average matching accuracy reaches 92.6%. And even in a dynamic environment, it shows good robustness, so it comes down to the conclusion that the combination of the Kinect sensor and the improved SURF algorithm applied to visual odometer is a simple and effective method.

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mtDNAcombine: tools to combine sequences from multiple studies

BMC Bioinformatics ◽

10.1186/s12859-021-04048-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Eleanor F. Miller ◽

Andrea Manica

Keyword(s):

Sequence Data ◽

Data Extraction ◽

Bayesian Skyline Plot ◽

Model Organisms ◽

Data Sets ◽

Data Handling ◽

Online Database ◽

Genetic Studies ◽

Wide Range ◽

Existing Data

Abstract Background Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. Results Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing sequence data for Bayesian skyline plot demographic reconstructions. Conclusions There is now more genetic information available than ever before and large meta-data sets offer great opportunities to explore new and exciting avenues of research. However, compiling multi-study datasets still remains a technically challenging prospect. The mtDNAcombine package provides a pipeline to streamline the process of downloading, curating, and analysing sequence data, guiding the process of compiling data sets from the online database GenBank.

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RobotP: A Benchmark Dataset for 6D Object Pose Estimation

Sensors ◽

10.3390/s21041299 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1299

Author(s):

Honglin Yuan ◽

Tim Hoogenkamp ◽

Remco C. Veltkamp

Keyword(s):

Pose Estimation ◽

Ground Truth ◽

3D Models ◽

Depth Image ◽

Great Success ◽

Estimation Algorithms ◽

Depth Images ◽

Object Pose Estimation ◽

Image Pairs ◽

Bounding Boxes

Deep learning has achieved great success on robotic vision tasks. However, when compared with other vision-based tasks, it is difficult to collect a representative and sufficiently large training set for six-dimensional (6D) object pose estimation, due to the inherent difficulty of data collection. In this paper, we propose the RobotP dataset consisting of commonly used objects for benchmarking in 6D object pose estimation. To create the dataset, we apply a 3D reconstruction pipeline to produce high-quality depth images, ground truth poses, and 3D models for well-selected objects. Subsequently, based on the generated data, we produce object segmentation masks and two-dimensional (2D) bounding boxes automatically. To further enrich the data, we synthesize a large number of photo-realistic color-and-depth image pairs with ground truth 6D poses. Our dataset is freely distributed to research groups by the Shape Retrieval Challenge benchmark on 6D pose estimation. Based on our benchmark, different learning-based approaches are trained and tested by the unified dataset. The evaluation results indicate that there is considerable room for improvement in 6D object pose estimation, particularly for objects with dark colors, and photo-realistic images are helpful in increasing the performance of pose estimation algorithms.

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HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints

Sensors ◽

10.3390/s21041356 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1356

Author(s):

Linda Christin Büker ◽

Finnja Zuber ◽

Andreas Hein ◽

Sebastian Fudickar

Keyword(s):

Color Images ◽

Depth Image ◽

Accuracy Evaluation ◽

Timed Up And Go ◽

Position Errors ◽

Depth Images ◽

Upper And Lower Extremities ◽

Rgb Images ◽

Human Joints ◽

Body Joints

With approaches for the detection of joint positions in color images such as HRNet and OpenPose being available, consideration of corresponding approaches for depth images is limited even though depth images have several advantages over color images like robustness to light variation or color- and texture invariance. Correspondingly, we introduce High- Resolution Depth Net (HRDepthNet)—a machine learning driven approach to detect human joints (body, head, and upper and lower extremities) in purely depth images. HRDepthNet retrains the original HRNet for depth images. Therefore, a dataset is created holding depth (and RGB) images recorded with subjects conducting the timed up and go test—an established geriatric assessment. The images were manually annotated RGB images. The training and evaluation were conducted with this dataset. For accuracy evaluation, detection of body joints was evaluated via COCO’s evaluation metrics and indicated that the resulting depth image-based model achieved better results than the HRNet trained and applied on corresponding RGB images. An additional evaluation of the position errors showed a median deviation of 1.619 cm (x-axis), 2.342 cm (y-axis) and 2.4 cm (z-axis).

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An alternative distribution to Lindley and Power Lindley distributions with characterizations, different estimation methods and data applications

Mathematica Slovaca ◽

10.1515/ms-2017-0406 ◽

2020 ◽

Vol 70 (4) ◽

pp. 953-978

Author(s):

Mustafa Ç. Korkmaz ◽

G. G. Hamedani

Keyword(s):

Hazard Function ◽

Mixture Distribution ◽

Real Data ◽

Quantile Function ◽

Estimation Methods ◽

Data Sets ◽

Unknown Parameters ◽

Lorenz Curves ◽

Proposed Model ◽

New Distribution

AbstractThis paper proposes a new extended Lindley distribution, which has a more flexible density and hazard rate shapes than the Lindley and Power Lindley distributions, based on the mixture distribution structure in order to model with new distribution characteristics real data phenomena. Its some distributional properties such as the shapes, moments, quantile function, Bonferonni and Lorenz curves, mean deviations and order statistics have been obtained. Characterizations based on two truncated moments, conditional expectation as well as in terms of the hazard function are presented. Different estimation procedures have been employed to estimate the unknown parameters and their performances are compared via Monte Carlo simulations. The flexibility and importance of the proposed model are illustrated by two real data sets.

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MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

BMC Bioinformatics ◽

10.1186/s12859-021-04288-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yance Feng ◽

Lei M. Li

Keyword(s):

Biological Significance ◽

Housekeeping Genes ◽

R Package ◽

Data Sets ◽

Statistical Regression ◽

Rna Seq ◽

Least Trimmed Squares ◽

Standard Data ◽

Wide Range ◽

Multiple References

Abstract Background Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable. Results We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren. Conclusions MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

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A Visual and VAE Based Hierarchical Indoor Localization Method

Sensors ◽

10.3390/s21103406 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3406

Author(s):

Jie Jiang ◽

Yin Zou ◽

Lidong Chen ◽

Yujie Fang

Keyword(s):

Image Retrieval ◽

Indoor Localization ◽

Data Sets ◽

Indoor Environments ◽

Global Features ◽

Data Set ◽

Data Annotation ◽

Wide Range ◽

Annotation Costs ◽

Global And Local

Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4° in the 7-Scenes data sets and 32.8% within 5 m and 20° in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods.

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An Interoperability Platform Enabling Reuse of Electronic Health Records for Signal Verification Studies

BioMed Research International ◽

10.1155/2016/6741418 ◽

2016 ◽

Vol 2016 ◽

pp. 1-18 ◽

Cited By ~ 5

Author(s):

Mustafa Yuksel ◽

Suat Gonul ◽

Gokce Banu Laleci Erturkmen ◽

Ali Anil Sinaci ◽

Paolo Invernizzi ◽

...

Keyword(s):

Real Life ◽

Case Series ◽

Background Information ◽

Data Sets ◽

Local Data ◽

Lombardy Region ◽

Common Information ◽

Spontaneous Reports ◽

Wide Range ◽

Common Information Model

Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while structural interoperability and Semantic Interoperability are handled through rule-based reasoning on formal representations of different models and terminology systems maintained in the SALUS Semantic Resource Set. SALUS Common Information Model at the core of this set acts as the common mediator. We demonstrate the capabilities of our framework through one of the SALUS safety analysis tools, namely, the Case Series Characterization Tool, which have been deployed on top of regional EHR Data Warehouse of the Lombardy Region containing about 1 billion records from 16 million patients and validated by several pharmacovigilance researchers with real-life cases. The results confirm significant improvements in signal detection and evaluation compared to traditional methods with the missing background information.

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Moulting growth of the Australian giant crab, Pseudocarcinus gigas

Marine and Freshwater Research ◽

10.1071/mf00074 ◽

2002 ◽

Vol 53 (5) ◽

pp. 869 ◽

Cited By ~ 5

Author(s):

Richard McGarvey ◽

Andrew H. Levings ◽

Janet M. Matthews

Keyword(s):

South Australia ◽

Growth Increment ◽

Likelihood Method ◽

Estimation Methods ◽

Most Probable Number ◽

Minimum Length ◽

Data Sets ◽

Probable Number ◽

Commercial Harvest ◽

Female Data

The growth of Australian giant crabs, Pseudocarcinus gigas, has not been previously studied. A tagging program was undertaken in four Australian states where the species is subject to commercial exploitation. Fishers reported a recapture sample of 1372 females and 383 males from commercial harvest, of which 190 females and 160 males had moulted at least once. Broad-scale modes of growth increment were readily identified and interpreted as 0 , 1 and 2 moults during time at large. Single-moult increments were normally distributed for six of seven data sets. Moult increments were constant with length for males and declined slowly for three of four female data sets. Seasonality of moulting in South Australia was inferred from monthly proportions captured with newly moulted shells. Female moulting peaked strongly in winter (June and July). Males moult in summer (November and December). Intermoult period estimates for P. gigas varied from 3 to 4 years for juvenile males and females (80–120 mm carapace length, CL), with rapid lengthening in time between moulting events to approximately seven years for females and four and a half years for males at legal minimum length of 150 mm CL. New moulting growth estimation methods include a generalization of the anniversary method for estimating intermoult period that uses (rather than rejects) most capture–recapture data and a multiple likelihood method for assigning recaptures to their most probable number of moults during time at large.

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Considerations of the Scale of Radiocarbon Offsets in the East Mediterranean, and Considering a Case for the Latest (Most Recent) Likely Date for the Santorini Eruption

Radiocarbon ◽

10.1017/s0033822200047202 ◽

2012 ◽

Vol 54 (3-4) ◽

pp. 449-474 ◽

Cited By ~ 13

Author(s):

Sturt W Manning ◽

Bernd Kromer

Keyword(s):

Time Horizon ◽

Weighted Average ◽

17Th Century ◽

Data Sets ◽

East Mediterranean ◽

Average Value ◽

Accuracy And Precision ◽

Wide Range ◽

The Impact ◽

East Mediterranean Region

The debate over the dating of the Santorini (Thera) volcanic eruption has seen sustained efforts to criticize or challenge the radiocarbon dating of this time horizon. We consider some of the relevant areas of possible movement in the14C dating—and, in particular, any plausible mechanisms to support as late (most recent) a date as possible. First, we report and analyze data investigating the scale of apparent possible14C offsets (growing season related) in the Aegean-Anatolia-east Mediterranean region (excluding the southern Levant and especially pre-modern, pre-dam Egypt, which is a distinct case), and find no evidence for more than very small possible offsets from several cases. This topic is thus not an explanation for current differences in dating in the Aegean and at best provides only a few years of latitude. Second, we consider some aspects of the accuracy and precision of14C dating with respect to the Santorini case. While the existing data appear robust, we nonetheless speculate that examination of the frequency distribution of the14C data on short-lived samples from the volcanic destruction level at Akrotiri on Santorini (Thera) may indicate that the average value of the overall data sets is not necessarily the most appropriate14C age to use for dating this time horizon. We note the recent paper of Soter (2011), which suggests that in such a volcanic context some (small) age increment may be possible from diffuse CO2emissions (the effect is hypothetical at this stage and hasnotbeen observed in the field), and that "if short-lived samples from the same stratigraphic horizon yield a wide range of14C ages, the lower values may be the least altered by old CO2." In this context, it might be argued that a substantive “low” grouping of14C ages observable within the overall14C data sets on short-lived samples from the Thera volcanic destruction level centered about 3326–3328 BP is perhaps more representative of the contemporary atmospheric14C age (without any volcanic CO2contamination). This is a subjective argument (since, in statistical terms, the existing studies using the weighted average remain valid) that looks to support as late a date as reasonable from the14C data. The impact of employing this revised14C age is discussed. In general, a late 17th century BC date range is found (to remain) to be most likelyeven ifsuch a late-dating strategy is followed—a late 17th century BC date range is thus a robust finding from the14C evidence even allowing for various possible variation factors. However, the possibility of a mid-16th century BC date (within ∼1593–1530 cal BC) is increased when compared against previous analyses if the Santorini data are considered in isolation.

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