Miracle-dz/UniFAR-FTUs huggingface dataset:
Objectively Measured Physical Activity of Vietnamese Adults With Type 2 Diabetes: Opportunities to Intervene

Objectives To objectively determine and compare the physical activity (PA) levels of adults newly diagnosed with type 2 diabetes (T2D) and adults without T2D in Vietnam using an accelerometer. Methods A total of 120 participants with newly diagnosed T2D and 120 adults without T2D were recruited from a large hospital in Hanoi, the capital city of Vietnam. All participants wore an ActiGraph GT3X accelerometer for at least 5 days, including 1 weekend day. Freedson cut-off points were used to estimate different intensities of PA. In addition, comparisons between groups were made with respect to achieving the World Health Organization (WHO) and International Diabetes Federation (IDF) recommended PA guidelines. Results Men with T2D had significantly lower levels of PA than men without T2D. The respective multivariable-adjusted mean values of daily step count, daily light-intensity, moderate-intensity, and moderate-to-vigorous-intensity PA were approximately 14%, 19%, and 22% lower in the men with T2D than in their non-T2D counterparts. However, women with T2D accumulated a greater number of steps per day than women without T2D. Only 59.2% of the adults with T2D met the minimum recommended level of PA (WHO and IDF), compared to 74.2% of adults without T2D (p<0.05). After adjusting for potential confounders, participants with T2D experienced 50.0% significantly lower odds of achieving PA recommendations. Conclusions Vietnamese men with T2D were less physically active than those without T2D, and adults with T2D were less likely to meet PA guidelines. The results suggest a need for integrating PA into the self-management of this chronic condition.
CCHL: Compression-Consolidation Hardware Logging for Efficient Failure-Atomic Persistent Memory Updates

Non-volatile memory (NVM) is emerging as a fast byte-addressable persistent memory (PM) that promises data persistence at the main memory level. One of the common choices for providing failure-atomic updates in PM is the write-ahead logging (WAL) technique. To mitigate logging overhead, recent studies propose WAL-based hardware logging designs that overlap log writes with transaction execution. However, existing hardware logging designs incur a large number of unnecessary log writes. Many log writes are still performed in the critical path, which causes high performance overhead, particularly for the multi-core systems with many threads. In this paper, we propose a Compression-Consolidation Hardware Logging (CCHL) design that removes log writes from the critical path by eliminating unnecessary log writes and enabling delayed durable transaction commits. To reduce log writes, we design two log optimizations: intra-tx log compression and inter-tx log consolidation. The first optimization compresses log entries by logging only the data whose values are changed, while the second one consolidates log entries by combining multiple transactions. To enable delayed durability, CCHL exploits a small region of DRAM as a cache for log writes. Log entries are first buffered in the DRAM cache and then written back to PM by hardware, which allows transactions to commit without waiting for persisting log entries. Our evaluation shows that CCHL improves throughput by 47.4%, reduces PM write traffic by 36.1%, and reduces memory dynamic energy by 18.7% compared with the state-of-the-art design.
GOSH: Embedding Big Graphs on Small Hardware

In graph embedding, the connectivity information of a graph is used to represent each vertex as a point in a d-dimensional space. Unlike the original, irregular structural information, such a representation can be used for a multitude of machine learning tasks. Although the process is extremely useful in practice, it is indeed expensive and unfortunately, the graphs are becoming larger and harder to embed. Attempts at scaling up the process to larger graphs have been successful but often at a steep price in hardware requirements. We present Gosh, an approach for embedding graphs of arbitrary sizes on a single GPU with minimum constraints. Gosh utilizes a novel graph coarsening approach to compress the graph and minimize the work required for embedding, delivering high-quality embeddings at a fraction of the time compared to the state-of-the-art. In addition to this, it incorporates a decomposition schema that enables any arbitrarily large graph to be embedded using a single GPU with minimum constraints on the memory size. With these techniques, Gosh is able to embed a graph with over 65 million vertices and 1.8 billion edges in less than an hour on a single GPU and obtains a 93% AUCROC for link-prediction which can be increased to 95% by running the tool for 80 minutes.
DIESEL: A Dataset-Based Distributed Storage and Caching System for Large-Scale Deep Learning Training

We observe three problems in existing storage and caching systems for deep-learning training (DLT) tasks: (1) accessing a dataset containing a large number of small files takes a long time, (2) global in-memory caching systems are vulnerable to node failures and slow to recover, and (3) repeatedly reading a dataset of files in shuffled orders is inefficient when the dataset is too large to be cached in memory. Therefore, we propose DIESEL, a dataset-based distributed storage and caching system for DLT tasks. Our approach is via a storage-caching system co-design. Firstly, since accessing small files is a metadata-intensive operation, DIESEL decouples the metadata processing from metadata storage, and introduces metadata snapshot mechanisms for each dataset. This approach speeds up metadata access significantly. Secondly, DIESEL deploys a task-grained distributed cache across the worker nodes of a DLT task. This way node failures are contained within each DLT task. Furthermore, the files are grouped into large chunks in storage, so the recovery time of the caching system is reduced greatly. Thirdly, DIESEL provides chunk-based shuffle so that the performance of random file access is improved without sacrificing training accuracy. Our experiments show that DIESEL achieves a linear speedup on metadata access, and outperforms an existing distributed caching system in both file caching and file reading. In real DLT tasks, DIESEL halves the data access time of an existing storage system, and reduces the training time by hours without changing any training code.
Collaborative Data Scheduling With Joint Forward and Backward Induction in Small Satellite Networks

Small satellite networks (SSNs) have attracted intensive research interest recently and have been regarded as an emerging architecture to accommodate the ever-increasing space data transmission demand. However, the limited number of on-board transceivers restricts the number of feasible contacts (i.e., an opportunity to transmit data over a communication link), which can be established concurrently by a satellite for data scheduling. Furthermore, limited battery space, storage space, and stochastic data arrivals can further exacerbate the difficulty of the efficient data scheduling design to well match the limited network resources and random data demands, so as to the long-term payoff. Based on the above motivation and specific characteristics of SSNs, in this paper, we extend the traditional dynamic programming algorithms and propose a finite-embedded-infinite two-level dynamic programming framework for optimal data scheduling under a stochastic data arrival SSN environment with joint consideration of contact selection, battery management, and buffer management while taking into account the impact of current decisions on the infinite future. We further formulate this stochastic data scheduling optimization problem as an infinite-horizon discrete Markov decision process (MDP) and propose a joint forward and backward induction algorithm framework to achieve the optimal solution of the infinite MDP. Simulations have been conducted to demonstrate the significant gains of the proposed algorithms in the amount of downloaded data and to evaluate the impact of various network parameters on the algorithm performance.
Revisiting Sparse Dynamic Programming for the 0/1 Knapsack Problem

The 0/1-Knapsack Problem is a classic NP-hard problem. There are two common approaches to obtain the exact solution: branch-and-bound (BB) and dynamic programming (DP). A so-called, “sparse” DP algorithm (SKPDP) that performs fewer operations than the standard algorithm (KPDP) is well known. To the best of our knowledge, there has been no quantitative analysis of the benefits of sparsity. We provide a careful empirical evaluation of SKPDP and observe that for a “large enough” capacity, C, the number of operations performed by SKPDP is invariant with respect to C for many problem instances. This leads to the possibility of an exponential improvement over the conventional KPDP. We experimentally explore SKPDP over a large range of knapsack problem instances and provide a detailed study of the attributes that impact the performance. DP algorithms have a nice regular structure and are amenable to highly parallel implementations. However, due to the dependence structure, parallelizing SKPDP is challenging. We propose two parallelization strategies (fine-grain and coarse-grain) for SKPDP on modern multi-core processors and demonstrate a scalable improvement in the performance. We also compare SKPDP with Branch-and-Bound algorithm.
Counting the cost of preventable diabetes-related lower limb amputations at a single district hospital in KwaZulu-Natal: what does this mean, what can be done?

Background: Healthcare policy decisions are driven by many factors, including cost, hence the need to show costs of diabetes mellitus-related lower limb amputations (DMLLA) to inform amendments to health care. Substantial decreases in amputation rates are associated with specialised podiatry foot clinics and ongoing foot education, as per national guidelines on the multidisciplinary team approach (MDTA) to diabetes health care. There are only two podiatry posts in KwaZulu-Natal (KZN) state health department (DoH). Objectives: Aims were to draft the medical costs for 660 DMLLA at Greys Hospital for the period 2013–2017; to extrapolate costs on annual DMLLA in KZN; to outline socio-economic costs for future investigation; to present evidence that podiatry in the MDTA can decrease numbers of DMLLA. Methods: A retrospective review on clinical data captured in real time and maintained by the Pietermaritzburg Metropolitan Trauma Service (PMTS) and Surgical Service (PMSS) was performed. Costs were analysed on data for 660 patients’ DMLLA at Greys Hospital between 2013 and 2017, and psychological and socio-economic costs via literature review. Results: Medical care at Greys Hospital for 660 DMLLA in the five years cost in excess of ZAR 213 million. Extrapolated to the 1 231 diabetic amputations (2014) equals an annual cost to KZN DoH in excess of ZAR 398 million. Personal, family loss and socio-economic costs are estimated in excess of ZAR five million per amputee, resulting in further cost of ZAR 6.155 billion per annum to KZN. Extrapolation across 11 provinces signifies a national cost of at least ZAR 68 billion. Conclusions: We present a gauge of the cost of DMLLA to KZN and national health. Substantial possible socio-economic losses compound these. The role of podiatrists within MDTA teams has an evidence base to prevent DMLLA.
URSA: Precise Capacity Planning and Fair Scheduling based on Low-level Statistics for Public Clouds

Database platform-as-a-service (dbPaaS) is developing rapidly and a large number of databases have been migrated to run on the Clouds for the low cost and flexibility. Emerging Clouds rely on the tenants to provide the resource specification for their database workloads. However, they tend to over-estimate the resource requirement of their databases, resulting in the unnecessarily high cost and low Cloud utilization. A methodology that automatically suggests the “just-enough” resource specification that fulfills the performance requirement of every database workload is profitable. To this end, we propose URSA, a capacity planning and fair scheduling system that is comprised of an online capacity planner, a performance interference estimator, and a contention-aware scheduling engine. The capacity planner identifies the most cost-efficient resource specification for a database workload to achieve the required performance online. The interference estimator quantifies the pressure on the shared resource and the tolerance to the shared resource contention of each workload. The scheduling engine schedules the workloads across Cloud nodes carefully to eliminate unfair performance interference between the co-located workloads. Experimental results show that URSA reduces up to 25.9% of CPU usage, 53.4% of memory and reduces the performance unfairness between the co-located workloads by 47.6% usage compared to the prior works without hurting their performance.
Efficient Block Algorithms for Parallel Sparse Triangular Solve

The sparse triangular solve (SpTRSV) kernel is an important building block for a number of linear algebra routines such as sparse direct and iterative solvers. The major challenge of accelerating SpTRSV lies in the difficulties of finding higher parallelism. Existing work mainly focuses on reducing dependencies and synchronizations in the level-set methods. However, the 2D block layout of the input matrix has been largely ignored in designing more efficient SpTRSV algorithms. In this paper, we implement three block algorithms, i.e., column block, row block and recursive block algorithms, for parallel SpTRSV on modern GPUs, and propose an adaptive approach that can automatically select the best kernels according to input sparsity structures. By testing 159 sparse matrices on two high-end NVIDIA GPUs, the experimental results demonstrate that the recursive block algorithm has the best performance among the three block algorithms, and it is on average 4.72x (up to 72.03x) and 9.95x (up to 61.08x) faster than cuSPARSE v2 and Sync-free methods, respectively. Besides, our method merely needs moderate cost for preprocessing the input matrix, thus is highly efficient for multiple right-hand sides and iterative scenarios.
Robustness of the Young/Daly formula for stochastic iterative applications

The Young/Daly formula for periodic checkpointing is known to hold for a divisible load application where one can checkpoint at any time-step. In an nutshell, the optimal period is where μf is the Mean Time Between Failures (MTBF) and C is the checkpoint time. This paper assesses the accuracy of the formula for applications decomposed into computational iterations where: (i) the duration of an iteration is stochastic, i.e., obeys a probability distribution law of mean ; and (ii) one can checkpoint only at the end of an iteration. We first consider static strategies where checkpoints are taken after a given number of iterations k and provide a closed-form, asymptotically optimal, formula for k, valid for any distribution . We then show that using the Young/Daly formula to compute k (as ) is a first order approximation of this formula. We also consider dynamic strategies where one decides to checkpoint at the end of an iteration only if the total amount of work since the last checkpoint exceeds a threshold Wth , and otherwise proceed to the next iteration. Similarly, we provide a closed-form formula for this threshold and show that is a first-order approximation of Wth . Finally, we provide an extensive set of simulations where is either Uniform, Gamma or truncated Normal, which shows the global accuracy of the Young/Daly formula, even when the distribution had a large standard deviation (and when one cannot use a first-order approximation). Hence we establish that the relevance of the formula goes well beyond its original framework.
Graffenrieda emarginata (Melastomataceae) forms mycorrhizas with Glomeromycota and with a member of the Hymenoscyphus ericae aggregate in the organic soil of a neotropical mountain rain forest

Graffenrieda emarginata (Ruiz & Pav.) Triana (Melastomataceae) was found to be the most frequent tree on the nutrient-poor organic soil in a species-rich tropical mountain rain forest of southern Ecuador. Our studies revealed that the rootlets are symbiotically associated with arbuscular mycorrhizal fungi and simultaneously form ectomy corrhizas displaying a superficial Hartig net but no hyphal mantle. Molecular phylogeny revealed a multitude of previously unknown glomeromycete sequence types. Ultrastructure of the ectomycorrhiza-forming fungus indicated an ascomycete, and molecular phylogenetic studies identified the fungal symbiont as a new taxon within the Hymenoscy phus ericae (Read) Korf & Kernan aggregate (Ascomycota). The mycorrhizal state of G. emarginata is considered crucial for its competitiveness and abundance in these sites.Key words: arbuscular mycorrhiza, ectomycorrhiza, Glomeromycota, Hymenoscyphus ericae aggregate, neotropical mountain rain forest, Ecuador.
Economic losses caused by weed competition in Manitoba grain fields. I. Weed species, their relative abundance and their effect on crop yields.

Economic losses caused by weed competition in Manitoba grain crops were studied on 142 farm fields during a 3-year period, 1956 to 1958 inclusive. Fields were located at intervals of 6 miles along main roads and were all within a 60-mile radius of Winnipeg. Immediately after seeding, ten paired plots, each 4 feet square, were staked in each field. One member of each pair of plots was kept free of weeds by hand pulling at weekly intervals. The species of weeds present and the relative numbers were recorded. As the crops reached maturity a square-yard sample from each plot was harvested for yield determinations.Twenty-eight species of weeds were found growing in the fields. Weed counts ranged from 0 in the cleanest field to 2,143 weeds per square yard in the weediest field, with an over-all recorded average of 224 weeds per square yard. Reductions in crop yields due to weed competition in individual fields ranged from no apparent decrease to 61.5 per cent. The average reduction in crop yields for all fields.
Comparison of immunofluorescence staining and indirect isolation for the detection of Corynebacterium flaccumfaciens in bean seeds

Corynebacterium flaccumfaciens, causing bacterial wilt in bean, is a quarantine organism for Europe. This paper compares two methods for detecting C. flaccumfaciens in seed lots on the market. Experimentally contaminated bean seeds were homogenized and a standard extraction and concentration procedure was used. The final pellets were used for immunofluorescence staining and indirect isolation (through bean seedlings). The immunofluorescence staining sensitivity threshold was 1.23 × 107, corresponding to 1.82 × 104 fluorescent cells per ml of final concentrate. The risk of false negative reactions to immunofluorescence staining caused by serologically unrelated C. flaccumfaciens strains was also examined. The indirect isolation sensitivity threshold was 1.23 × 106 producing 43% symptomatic plants and 53% positive reisolation. There was no serious interference between C. flaccumfaciens and Pseudomonas syringae pv. phaseolicola in mixed inoculations and both pathogens were successfully reisolated. The comparative results are discussed in relation to the applicability of the methods to commercial lots of bean seeds.
Characteristics of woods used recently and historically by Lesser Spotted Woodpeckers Dendrocopos minor in England.

Lesser Spotted Woodpecker Dendrocopos minor numbers have declined greatly in England since the early 1980s for reasons that are not yet fully understood. It has been suggested that the species’ decline may be linked to the increase in Great Spotted Woodpeckers Dendrocopos major, changes in woodland habitat quality (such as deadwood abundance) and landscape-scale changes in tree abundance. We tested some of these hypotheses by comparing the characteristics of woods in southern England where the species is still relatively numerous with those of woods used in the 1980s before the major decline. In each time period, habitat, predator and landscape information from woods known to be occupied by Lesser Spotted Woodpeckers was compared with those found to be unoccupied during surveys. Before the main period of decline, Lesser Spotted Woodpeckers used oak-dominated, mature, open woods with a large amount of standing deadwood. Habitat use assessed from recent data was very similar, the species being present in mature, open, oak-dominated woodlands. There was a strong relationship between wood use probability and the extent of woodland within a 3-km radius, suggesting selection for more heavily wooded landscapes. In recent surveys, there was no difference in deadwood abundance or potential predator densities between occupied and unoccupied woods. Habitat management should focus on creating and maintaining networks of connected woodlands in areas of mature, open woods. Finer-scale habitat selection by Lesser Spotted Woodpecker within woodlands should be assessed to aid development of beneficial management actions.
Estimation of genetic parameters for body weight traits and litter size of Moghani sheep, using a Bayesian approach via Gibbs sampling

SUMMARY The objective of the present study was to estimate genetic parameters for body weights at different ages and litter size (LS) in Moghani sheep. Traits included were birth weight (BW), 3 months weight (3MW), 6 months weight (6MW), 9 months weight (9MW), yearling weight (YW) and LS. The data of 6659 lambing records and pedigree information used in the current programme were collected at the Breeding Station of Moghani sheep (Ardebil province, Iran) during 1987–2005. Different linear and threshold animal models with additive genetic, maternal genetic, maternal permanent environmental and residual effects were implemented by Gibbs sampling (GS) methodology. A single GS chain with 150 000 rounds was generated by the MTGSAM program. The posterior means of genetic parameters were estimated based on the 1300 samples that were left after elimination of 20 000 rounds in the burn-in period and 100 rounds of each thinning interval. Posterior mean estimates of direct heritability were 0·29, 0·13, 0·14, 0·10, 0·31 and 0·10, while those of maternal heritabilities were 0·29, 0·08, 0·11, 0·06, 0·10 and 0·17 for BW, 3MW, 6MW, 9MW, YW and LS, respectively. Genetic correlations among the growth traits and LS were negative for direct genetic and maternal genetic effects. Therefore, selection for increased growth or LS may have a negative genetic effect on the other traits. The medium to high negative estimates of direct–maternal correlations for body weight traits or LS suggest that it would be difficult to improve direct and maternal growth ability jointly for Moghani sheep.