Tracking Target Signal Strengths on a Grid using Sparsity

Multi-target tracking is mainly challenged by the nonlinearity present in the measurement equation, and the difficulty in fast and accurate data association. To overcome these challenges, the present paper introduces a grid-based model in which the state captures target signal strengths on a known spatial grid (TSSG). This model leads to \emph{linear} state and measurement equations, which bypass data association and can afford state estimation via sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of the novel model, two types of sparsity-cognizant TSSG-KF trackers are developed: one effects sparsity through $\ell_1$-norm regularization, and the other invokes sparsity as an extra measurement. Iterative extended KF and Gauss-Newton algorithms are developed for reduced-complexity tracking, along with accurate error covariance updates for assessing performance of the resultant sparsity-aware state estimators. Based on TSSG state estimates, more informative target position and track estimates can be obtained in a follow-up step, ensuring that track association and position estimation errors do not propagate back into TSSG state estimates. The novel TSSG trackers do not require knowing the number of targets or their signal strengths, and exhibit considerably lower complexity than the benchmark hidden Markov model filter, especially for a large number of targets. Numerical simulations demonstrate that sparsity-cognizant trackers enjoy improved root mean-square error performance at reduced complexity when compared to their sparsity-agnostic counterparts.Comment: Submitted to IEEE Trans. on Signal Processin

TANGO: Performance and Fault Management in Cellular Networks through Cooperation between Devices and Edge Computing Nodes

Cellular networks have become an essential part of our lives. With increasing demands on its available bandwidth, we are seeing failures and performance degradations for data and voice traffic on the rise. In this paper, we propose the view that fog computing, integrated in the edge components of cellular networks, can partially alleviate this situation. In our vision, some data gathering and data analytics capability will be developed at the edge of the cellular network and client devices and the network using this edge capability will coordinate to reduce failures and performance degradations. We also envisage proactive management of disruptions including prediction of impending events of interest (such as, congestion or call drop) and deployment of appropriate mitigation actions. We show that a simple streaming media pre-caching service built using such device-fog cooperation significantly expands the number of streaming video users that can be supported in a nominal cellular network of today

Refinement and standardization of storage procedures for clonal crops. Global Public Goods Phase 2: Part 1. Project landscape and general status of clonal crop in vitro conservation technologies

Among the collective actions of the World Bank-funded Global Public Goods Phase II Project (GPG2), the following collaborative activity: “Refinement and standardization of storage procedures for clonal crops” was given to the CGIAR’s In Vitro Genebanks, represented by the Clonal Crop Task Force (CCTF) composed of genetic resources research staff from the four centres: Bioversity International, CIAT, CIP and IITA. These hold the in trust collections of Musa, cassava, potato, sweetpotato, yam and Andean root and tuber crops (ARTCs). The overarching aims of this activity were to: (1) review the status of vitro conservation in the context of the GPG2 project with an emphasis on the mandated clonal crops; (2) survey the facilities, storage protocols and practices of CGIAR’s clonal crop genebanks; (3) collate and review this information with a view to developing quality and risk management systems to support the production and validation of multi-crop best practice guidelines. Outputs from this activity are designated as a three part ‘trilogy’: Part I, entitled “Project landscape and general status of clonal crop in vitro conservation technologies” introduces the GPG2 project within the CGIAR landscape and overviews the status of in vitro plant conservation in the wider conservation community of practice. This part describes the role of risk and quality management for the effective maintenance of in vitro genebanks in the context of research and the development and validation of best practices

Refinement and standardization of storage procedures for clonal crops. Global Public Goods Phase 2. Part 2: Status of in vitro conservation technologies for: Andean root and tuber crops, cassava, Musa, potato, sweetpotato and yam

Among the collective actions of the World Bank-funded Global Public Goods Phase II Project (GPG2), the following collaborative activity: “Refinement and standardization of storage procedures for clonal crops” was given to the CGIAR’s In Vitro Genebanks, represented by the Clonal Crop Task Force (CCTF) composed of genetic resources research staff from the four centres: Bioversity International, CIAT, CIP and IITA. These hold the in trust collections of Musa, cassava, potato, sweetpotato, yam and Andean root and tuber crops (ARTCs). The overarching aims of this activity were to: (1) review the status of vitro conservation in the context of the GPG2 project with an emphasis on the mandated clonal crops; (2) survey the facilities, storage protocols and practices of CGIAR’s clonal crop genebanks; (3) collate and review this information with a view to developing quality and risk management systems to support the production and validation of multi-crop best practice guidelines. Outputs from this activity are designated as a three part ‘trilogy’: Part II, “Status of in vitro conservation technologies for Andean root and tuber crops, cassava, Musa, potato, sweetpotato and yam” provides a status update on the mandate clonal crops. As tasked by GPG2, it includes lessons learnt, critical point analyses and the priority research needs of CGIAR’s in vitro genebanks

Refinement and standardization of storage procedures for clonal crops. Global Public Goods Phase 2. Part 3: Multi-crop guidelines for developing in vitro conservation best practices for clonal crops

Among the collective actions of the World Bank-funded Global Public Goods Phase II Project (GPG2), the following collaborative activity: “Refinement and standardization of storage procedures for clonal crops” was given to the CGIAR’s In Vitro Genebanks, represented by the Clonal Crop Task Force (CCTF) composed of genetic resources research staff from the four centres: Bioversity International, CIAT, CIP and IITA. These hold the in trust collections of Musa, cassava, potato, sweetpotato, yam and Andean root and tuber crops (ARTCs). The overarching aims of this activity were to: (1) review the status of vitro conservation in the context of the GPG2 project with an emphasis on the mandated clonal crops; (2) survey the facilities, storage protocols and practices of CGIAR’s clonal crop genebanks; (3) collate and review this information with a view to developing quality and risk management systems to support the production and validation of multi-crop best practice guidelines. Outputs from this activity are designated as a three part ‘trilogy’: Part III, “Multi-crop guidelines for developing in vitro conservation best practices for clonal crops” is a compilation of quality and risk management best practices and guidelines from both plant and other bioresources communities. This collective knowledge provided the foundation for developing the GPG2 multi-crop best practice guidelines. They are compiled in two parts. Section I comprises general operational guidelines for quality and risk management in in vitro plant genebanks. Section II provides generic, multi-crop technical guidelines for the medium-term (slow growth) and long-term (cryopreservation) storage of crop germplasm held in In vitro active genebanks (IVAGs) and In vitro base genebanks (IVBGs) respectively

OCTOPUS database (v.2)

OCTOPUS v.2 is an Open Geospatial Consortium (OGC) compliant web-enabled database that allows users to visualise, query, and download cosmogenic radionuclide, luminescence, and radiocarbon ages and denudation rates associated with erosional landscapes, Quaternary depositional landforms, and archaeological records, along with ancillary geospatial (vector and raster) data layers. The database follows the FAIR (Findability, Accessibility, Interoperability, and Reuse) data principles and is based on open-source software deployed on the Google Cloud Platform. Data stored in the database can be accessed via a custom-built web interface and via desktop geographic information system (GIS) applications that support OGC data access protocols. OCTOPUS v.2 hosts five major data collections. CRN Denudation and ExpAge consist of published cosmogenic 10Be and 26Al measurements in modern fluvial sediment and glacial samples respectively. Both collections have a global extent; however, in addition to geospatial vector layers, CRN Denudation also incorporates raster layers, including a digital elevation model, gradient raster, flow direction and flow accumulation rasters, atmospheric pressure raster, and CRN production scaling and topographic shielding factor rasters. SahulSed consists of published optically stimulated luminescence (OSL) and thermoluminescence (TL) ages for fluvial, aeolian, and lacustrine sedimentary records across the Australian mainland and Tasmania. SahulArch consists of published OSL, TL, and radiocarbon ages for archaeological records, and FosSahul consists of published late-Quaternary records of direct and indirect non-human vertebrate (mega)fauna fossil ages that have been systematically quality rated. Supporting data are comprehensive and include bibliographic, contextual, and sample-preparation- and measurement-related information. In the case of cosmogenic radionuclide data, OCTOPUS also includes all necessary information and input files for the recalculation of denudation rates using the open-source program CAIRN. OCTOPUS v.2 and its associated data curation framework allow for valuable legacy data to be harnessed that would otherwise be lost to the research community. The database can be accessed at https://octopusdata.org (last access: 1 July 2022). The individual data collections can also be accessed via their respective digital object identifiers (DOIs) (see Table 1)

Development of a clinical prediction model for the onset of functional decline in people aged 65-75 years: Pooled analysis of four European cohort studies

Background: Identifying those people at increased risk of early functional decline in activities of daily living (ADL) is essential for initiating preventive interventions. The aim of this study is to develop and validate a clinical prediction model for onset of functional decline in ADL in three years of follow-up in older people of 65-75 years old. Methods: Four population-based cohort studies were pooled for the analysis: ActiFE-ULM (Germany), ELSA (United Kingdom), InCHIANTI (Italy), LASA (Netherlands). Included participants were 65-75 years old at baseline and reported no limitations in functional ability in ADL at baseline. Functional decline was assessed with two items on basic ADL and three items on instrumental ADL. Participants who reported at least some limitations at three-year follow-up on any of the five items were classified as experiencing functional decline. Multiple logistic regression analysis was used to develop a prediction model, with subsequent bootstrapping for optimism-correction. We applied internal-external cross-validation by alternating the data from the four cohort studies to assess the discrimination and calibration across the cohorts. Results: Two thousand five hundred sixty community-dwelling people were included in the analyses (mean age 69.7 ± 3.0 years old, 47.4% female) of whom 572 (22.3%) reported functional decline at three-year follow-up. The final prediction model included 10 out of 22 predictors: age, handgrip strength, gait speed, five-repeated chair stands time (non-linear association), body mass index, cardiovascular disease, diabetes, chronic obstructive pulmonary disease, arthritis, and depressive symptoms. The optimism-corrected model showed good discrimination with a C statistic of 0.72. The calibration intercept was 0.06 and the calibration slope was 1.05. Internal-external cross-validation showed consistent performance of the model across the four cohorts. Conclusions: Based on pooled cohort data analyses we were able to show that the onset of functional decline in ADL in three years in older people aged 65-75 years can be predicted by specific physical performance measures, age, body mass index, presence of depressive symptoms, and chronic conditions. The prediction model showed good discrimination and calibration, which remained stable across the four cohorts, supporting external validity of our findings

MARS spectral molecular imaging of lamb tissue: data collection and image analysis

Spectral molecular imaging is a new imaging technique able to discriminate and quantify different components of tissue simultaneously at high spatial and high energy resolution. Our MARS scanner is an x-ray based small animal CT system designed to be used in the diagnostic energy range (20 to 140 keV). In this paper, we demonstrate the use of the MARS scanner, equipped with the Medipix3RX spectroscopic photon-processing detector, to discriminate fat, calcium, and water in tissue. We present data collected from a sample of lamb meat including bone as an illustrative example of human tissue imaging. The data is analyzed using our 3D Algebraic Reconstruction Algorithm (MARS-ART) and by material decomposition based on a constrained linear least squares algorithm. The results presented here clearly show the quantification of lipid-like, water-like and bone-like components of tissue. However, it is also clear to us that better algorithms could extract more information of clinical interest from our data. Because we are one of the first to present data from multi-energy photon-processing small animal CT systems, we make the raw, partial and fully processed data available with the intention that others can analyze it using their familiar routines. The raw, partially processed and fully processed data of lamb tissue along with the phantom calibration data can be found at [http://hdl.handle.net/10092/8531].Comment: 11 pages, 6 fig