Miracle-dz/UniFAR-FTUs huggingface dataset:
A hybrid collaborative filtering recommendation model-based on complex attribute of goods

Collaborative filtering as the most widely used, the most recommendation algorithm, the shortcomings inherent in the data sparse, cold start and others has been greatly improved, but few studies based on commodity price to improve the prediction accuracy. At the same time, facing the full e-commerce market network Navy, the ratings and reviews also indirectly led to reducing the accuracy of prediction. Therefore, considering comprehensively the subjective scoring of users and the objective scoring of products, the paper puts forward a hybrid collaborative filtering recommendation model by combing situational pre-filtering, social network theories and experts' opinions. And through experiments, the model has higher forecast accuracy than the traditional collaborative filtering, and is more suitable for the commodity with complex attributes.
Applicability of Clinical Trials in an Unselected Cohort of Patients With Intracerebral Hemorrhage

Background and Purpose— Patient selection in clinical trials on intracerebral hemorrhage (ICH) affects overall applicability of results. We estimated eligibility for completed, ongoing, and planned clinical trials in an unselected cohort of patients with ICH. Methods— Large clinical ICH trials were identified using trial registration databases. Each trial’s inclusion criteria were applied to a consecutive group of patients with ICH from the prospective hospital-based Lund Stroke Register. Survival status was obtained from the National Census Office and 90-day poor functional outcome (modified Rankin Scale ≥4) from the Swedish Stroke Register or medical files. Results— Among 253 patients with ICH, estimated eligibility proportions ranged between 2% and 36% for the 11 identified clinical trials. Patients not eligible for any trial (n=96) had more intraventricular hemorrhage, lower baseline level of consciousness, higher rates of cerebellar ICH, and lower rates of lobar ICH (P⩽0.001). Thirty-day case fatality for noneligible patients was 54% versus 18% among patients eligible in ≥1 trial (95% confidence interval, 44%–64% versus 13%–25%; P<0.001). Noneligible ICH patients more frequently had poor functional outcome (75% versus 48%; 95% confidence interval, 65%–83% versus 40%–56%; P<0.001). Conclusions— There is large variation in proportions of patients with ICH eligible for inclusion in clinical trials and over a third of patients with ICH are not eligible for any trial.
Assessment of child feeding practices using a summary index: stability over time and association with child growth in urban Madagascar.

BACKGROUND Previous studies investigating the association between an infant and child feeding index (ICFI) and length-for-age were based on a cross-sectional design and on the assumption that data collected with brief recalls could provide information about more enduring processes. OBJECTIVES The objectives were to test the stability of the individual ICFI values over time and to investigate how they relate to length-for-age z score (LAZ) and weight-for-length z score (WLZ) at the end of the study. DESIGN This prospective cohort study included 363 children aged 6-17 mo who were visited 3 times over 6 mo. A cross-sectional ICFI (CS-ICFI) was constructed for each visit by using data on feeding practices and data from quantitative 24-h recalls. A longitudinal ICFI (L-ICFI) was constructed with use of the 3 CS-ICFIs. The stability of the CS-ICFI was assessed by using the variance of the repeatability coefficient (s(2)r). RESULTS Stability of the CS-ICFI was shown by the value of 0.704 (95% CI: 0.625, 0.805) of the s(2)r, which differed significantly from 1 (P < 0.0001). There was no significant association between the CS-ICFIs and LAZ or WLZ at visit 3. In contrast, when moving from low to high L-ICFI, there was a highly significant 0.5 z score difference in mean LAZ at visit 3 (P = 0.0008). The L-ICFI was not associated with WLZ. CONCLUSIONS The ICFI constructed by using data collected with brief recalls can provide information about feeding in the long term. However, the absence of association with LAZ suggests a lack of precision that can be reduced by using an ICFI based on repeated measurements.
Graph SLAM Sparsification With Populated Topologies Using Factor Descent Optimization

Current solutions to the simultaneous localization and mapping (SLAM) problem approach it as the optimization of a graph of geometric constraints. Scalability is achieved by reducing the size of the graph, usually in two phases. First, some selected nodes in the graph are marginalized and then, the dense and nonrelinearizable result is sparsified. The sparsified network has a new set of relinearizable factors and is an approximation to the original dense one. Sparsification is typically approached as a Kullback–Liebler divergence (KLD) minimization between the dense marginalization result and the new set of factors. For a simple topology of the new factors, such as a tree, there is a closed form optimal solution. However, more populated topologies can achieve a much better approximation because more information can be encoded, although in that case iterative optimization is needed to solve the KLD minimization. Iterative optimization methods proposed by the state-of-art sparsification require parameter tuning that strongly affects their convergence. In this letter, we propose factor descent and noncyclic factor descent, two simple algorithms for SLAM sparsification that match the state-of-art methods without any parameters to be tuned. The proposed methods are compared against the state of the art with regard to accuracy and CPU time, in both synthetic and real world datasets.
Objectifying Tactics: Athlete and Race Variability in Elite Short-Track Speed Skating.

PURPOSE To objectively capture and understand tactical considerations in a race, the authors explored whether race-to-race variation of an athlete and the variation of competitors within a race could provide insight into how and when athletes modify their pacing decisions in response to other competitors. METHODS Lap times of elite 500-, 1000-, and 1500-m short-track speed-skating competitions from 2011 to 2016 (N = 6965 races) were collected. Log-transformed lap and finishing times were analyzed with mixed linear models. To determine within-athlete race-to-race variability, athlete identity (between-athletes differences) and the residual (within-athlete race-to-race variation) were added as random effects. To determine race variability, race identity (between-races differences) and the residual (within-race variation) were added as random effects. Separate analyses were performed for each event. RESULTS Within-athlete race-to-race variability of the finishing times increased with prolonged distance of the event (500-m, CV = 1.6%; 1000-m, CV = 2.8%; 1500-m, CV = 4.1%), mainly due to higher within-athlete race-to-race variability in the initial phase of 1000-m (3.3-6.9%) and 1500-m competitions (8.7-12.2%). During these early stages, within-race variability is relatively low in 1000-m (1.1-1.4%) and 1500-m (1.3-2.8%) competitions. CONCLUSION The present study demonstrated how analyses of athlete and race variability could provide insight into tactical pacing decisions in sports where finishing position is emphasized over finishing time. The high variability of short-track skaters is a result of the decision to alter initial pacing behavior based on the behavior of other competitors in their race, emphasizing the importance of athlete-environment interactions in the context of pacing.
Mining Probabilistically Frequent Sequential Patterns in Large Uncertain Databases

Data uncertainty is inherent in many real-world applications such as environmental surveillance and mobile tracking. Mining sequential patterns from inaccurate data, such as those data arising from sensor readings and GPS trajectories, is important for discovering hidden knowledge in such applications. In this paper, we propose to measure pattern frequentness based on the possible world semantics. We establish two uncertain sequence data models abstracted from many real-life applications involving uncertain sequence data, and formulate the problem of mining probabilistically frequent sequential patterns (or p-FSPs) from data that conform to our models. However, the number of possible worlds is extremely large, which makes the mining prohibitively expensive. Inspired by the famous PrefixSpan algorithm, we develop two new algorithms, collectively called U-PrefixSpan, for p-FSP mining. U-PrefixSpan effectively avoids the problem of “possible worlds explosion”, and when combined with our four pruning and validating methods, achieves even better performance. We also propose a fast validating method to further speed up our U-PrefixSpan algorithm. The efficiency and effectiveness of U-PrefixSpan are verified through extensive experiments on both real and synthetic datasets.
Variability of Metabolic Power Data in Elite Soccer Players During Pre-Season Matches

Abstract This study aimed to determine the within-subject variability of GPS-derived metabolic power data in elite soccer players across several pre-season matches and compare the variability of high metabolic power, velocity, acceleration and deceleration running. Additionally, differences in metabolic power data among playing positions and relationships with various physical abilities were also investigated. Metabolic power data from 12 outfield starting players competing in the German Bundesliga were collected during five pre-season matches using GPS-technology (10 Hz). The players were also tested for speed, agility, power and intermittent endurance. Variability of global metabolic power data such as energy expenditure (CV = 2.2-7.0%) was lower than that for high-intensity including time ≥20 W·kg-1 (CV = 14.0-26.2%). Variability of high metabolic power (≥20 W·kg-1; CV = 14.1 ± 3.5%) was comparable to that of high velocity (≥15.5 km·h-1; CV = 17.0 ± 6.2%), acceleration (≥3 m·s-2; CV = 11.1 ± 5.1%) and deceleration running (≤-3 m·s-2; CV = 11.9 ± 4.5%) (p > 0.05, ES < 0.2). Defenders had a largely higher overall energy expenditure than midfielders and attackers (p < 0.01, ES > 0.6). Overall energy expenditure and cost were largely to very largely correlated with 5 m speed and 22 m agility sprint time and counter movement jump height (r = -0.70-0.69, p < 0.05). The detected variability indicates that global GPS-derived metabolic power data in elite soccer players from a single preseason match should be preferably used for practical applications. Contrary, high-intensity indicators should be interpreted cautiously and repeated match observations are recommended to establish meaningful high-intensity profiles of the players. Differences among playing positions and relationships with explosive physical abilities indicate that metabolic power analyses can provide new insights into the mechanics and energetics of soccer.
nalyzing respiratory effort amplitude for automated sleep stage lassification

Respiratory effort has been widely used for objective analysis of human sleep during bedtime. Several features extracted from respiratory effort signal have succeeded in automated sleep stage classification throughout the night such as variability of respiratory frequency, spectral powers in different frequency bands, respiratory regularity and self-similarity. In regard to the respiratory amplitude, it has been found that the respiratory depth is more irregular and the tidal volume is smaller during rapid-eye-movement (REM) sleep than during non-REM (NREM) sleep. However, these physiological properties have not been explicitly elaborated for sleep stage classification. By analyzing the respiratory effort amplitude, we propose a set of 12 novel features that should reflect respiratory depth and volume, respectively. They are expected to help classify sleep stages. Experiments were conducted with a data set of 48 sleepers using a linear discriminant (LD) classifier and classification performance was evaluated by overall accuracy and Cohen’s Kappa coefficient of agreement. Cross validations (10-fold) show that adding the new features into the existing feature set achieved significantly improved results in classifying wake, REM sleep, light sleep and deep sleep (Kappa of 0.38 and accuracy of 63.8%) and in classifying wake, REM sleep and NREM sleep (Kappa of 0.45 and accuracy of 76.2%). In particular, the incorporation of these new features can help improve deep sleep detection to more extent (with a Kappa coefficient increasing from 0.33 to 0.43). We also revealed that calibrating the respiratory effort signals by means of body movements and performing subject-specific feature normalization can ultimately yield enhanced classification performance.
Metabolic Syndrome in Hispanic Youth: Results from the Hispanic Community Children's Health Study/Study of Latino Youth.

BACKGROUND Metabolic syndrome (MetS), a cluster of cardiovascular risk factors, is being diagnosed in youth. Specific diagnostic criteria used to define MetS influence prevalence estimates and populations considered at risk for cardiovascular disease. The National Cholesterol Education Program's Adult Treatment Panel III (ATP), the World Health Organization (WHO), and the International Diabetes Federation (IDF) provide three MetS definitions used in medical research. This study examined concordance among these definitions in 1137 children 10-16 years of age, who participated in the Hispanic Community Children's Health Study/Study of Latino Youth. METHODS Prevalence of MetS and of individual components was estimated using SAS. Mplus was used to test a single-factor model of MetS components (triglycerides, high-density lipoprotein cholesterol, systolic and diastolic blood pressure, waist circumference, and fasting glucose). RESULTS The ATP definition identified most MetS cases in 10-15 (N = 19, 4.7%) and 16-year-old girls (N = 3, 7.3%). The IDF definition identified most cases of MetS in 10-15 (N = 16, 3.1%) and 16-year-old boys (N = 2, 2.8%). Fewest cases of MetS were identified with the WHO definition across age and sex groups. CONCLUSION Only one participant was classified as having MetS across all three definitions. Confirmatory factor analysis indicated fasting glucose and systolic blood pressure did not reliably cluster with other risk factors that define MetS in Hispanic/Latino adolescents. We conclude that prevalence estimates of MetS in youth are unstable across current criteria, calling into question the accuracy of defining and diagnosing MetS in youth.
Preventing and detecting cache side-channel attacks in cloud computing

Cloud computing offers new cost effective services on-demand such as Software as a service (SaaS), Infrastructure as a service (IaaS) and Platform as a service (PaaS). However, with all of these services promising facilities and benefits, there are still a number of challenges associated with utilizing cloud computing such as data security, cyber-attacks, and multi-tenancy. Multi-tenancy in cloud computing has unique vulnerabilities, one particular issue involves virtual machines physical co-residency, which has been exploited to leak sensitive information and extract sensitive data using hardware side-channels. Side-channel attacks are classified according to the hardware medium they target and exploit, for instance, cache side-channel attacks, which are one of the most common hardware devices targeted by adversaries due to their high-rate interactions and sharing between processes. There are a number of proposed solutions to detect and prevent cache side-channel attacks, which failed due to the deceived normal behavior by cache side-channel in one hand. In the other hand, these solutions mainly rely on attached software or applications to detect any abnormal behavior on the CPU cache. These applications and software will slow down the CPU operations and introduce unwanted overload, which will affect the CPU performance. This paper presents a detailed study and analysis to cache side-channel attacks in cloud computing. It surveys and reports the important directions utilized to detect and prevent them. It also identifies important gaps, which are not fulfilled by the proposed solutions.
Flexible photodetectors based on phase dependent PbI2 single crystals

As a precursor of perovskites, lead iodide (PbI2) is a typical layered material with a direct bandgap. Perovskites are widely utilized in highly efficient photovoltaics, but the low-dimensional PbI2 nanostructures and their (opto)electronic properties are rarely reported. Herein, single-crystalline PbI2 nanosheets (phase I) and nanowires (phase II) are controllably synthesized via a facile physical vapor deposition method. Their different crystal morphology and crystallographic symmetry show obvious phase dependence. The corresponding photodetectors on both SiO2/Si and flexible polyethylene terephthalate (PET) substrates are investigated systematically. Compared with PbI2 nanowire based photodetectors, PbI2 nanosheet based photodetectors exhibit a relatively high sensitivity (with a high photoresponsivity of 147.6 A W−1 and fast response time) to the 450 nm laser. Both the PbI2 nanosheet and nanowire devices with flexible PET substrates exhibit comparable performance to their photodetectors fabricated on SiO2/Si, and also show excellent mechanical stability and durability. At the same time, the photoelectric properties vary greatly with different bending angles for such flexible PbI2 photodetectors. By modeling the band structures under different compressive strains, the theoretical simulations fit very well with experimental results. These findings provide a scientific basis for exploiting high-performance flexible photodetectors based on low-dimensional PbI2 single crystals.
Fast implementation of image mosaicing on GPU

Image mosaicing has been studied and widely used in many fields of computer science, but there exists a huge amount of computations involved in steps of feature matching, warping and blending. And thus it could not meet the real-time demands of some applications. Fortunately, some related parallel operations which can speed up the process of mosaicing have been developed and implemented on the Graphics Processor Unit (GPU). In this paper, we present a parallel implementation of image mosaicing based on GPU using the Compute Unified Device Architecture (CUDA). We obtain better results in terms of execution time than that of implementation on the central processing unit (CPU). When an integrated GPU GTX745 was used in the experiment, we achieved a speedup ratio up to 27.6 times for large input images.
Convergence Analysis and Design of Multi-block ADMM via Switched Control Theory

We consider three challenges in multi-block Alternating Direction Method of Multipliers (ADMM): building convergence conditions for ADMM with any block (variable) sequence, finding available block sequences to be fit for ADMM, and designing useful parameter controllers for ADMM with unfixed parameters. To address these challenges, we develop a switched control framework for studying multi-block ADMM. First, since ADMM recursively and alternately updates the block-variables, it is converted into a discrete-time switched dynamical system. Second, we study exponential stability and stabilizability of the switched system for linear convergence analysis and design of ADMM by employing switched Lyapunov functions. Moreover, linear matrix inequalities conditions are proposed to ensure convergence of ADMM under arbitrary sequence, to find convergent sequences, and to design the fixed parameters. These conditions are checked and solved by employing semidefinite programming. Numerical experiments further verify the effectiveness of our proposed theories.
QoE-aware Edge Computing for Complex IoT Event Processing

Computing offloading is a promising technique for Internet of Things (IoT) to handle complex IoT events processing, e.g., voice recognition. Existing work mainly focuses on offloading in mobile networks, while little addresses the problems of heterogeneity and quality of experience (QoE) on resource constrained devices in edge networks. In this paper, we present a framework of QoE-aware edge computing for complex IoT event processing. Developers can offload their existing methods in edge networks with little modifications. We design a scheduling algorithm to minimize the offloading latency given user specified QoE. In addition, we present a scheduler selection algorithm to select the most suitable scheduler at runtime. We implement the above algorithms and a real-world RPC function on heterogeneous devices. We evaluate our framework and results show that our framework can save up to 91.64% of the execution time in a small edge network.
On parallelizing graph theoretical approaches for identifying causal genes and pathways from very large biological networks

The time taken to compute meaningful information from biological networks is very high for most of the applications and therefore these networks are very hard to process. This work focuses on improving the speed of processing biological networks, in particular, faster traversal of genomes which have been mapped into a network for the detection of causal genes and associated pathways. Inference of disease causing genes and their pathways has achieved a crucial role in computational biology because of its practicality in understanding the major causal genes and their interactions that lead to a disease state, and suggesting new drug targets. In this work, Hadoop's distributed storage system has been used to store the molecular interaction network. Graph parallel processing techniques of Hadoop MapReduce, in conjunction with graph theoretical approaches have been utilized to improve the accuracy of results and execution time on benchmark data.