Miracle-dz/UniFAR-FTUs huggingface dataset:
SPECnet: Predicting SPEC Scores using Deep Learning

In this work we show how to build a deep neural network (DNN) to predict SPEC® scores - called the SPECnet. More than ten years have passed since the introduction of the SPEC CPU2006 suite (retired in January 2018) and thousands of submissions are available for CPU2006 integer and floating point benchmarks. We build a DNN which inputs hardware and software features from these submissions and is subsequently trained on the corresponding reported SPEC scores. We then use the trained DNN to predict scores for upcoming machine configurations. We achieve 5%-7% training and dev/test errors pointing to pretty high accuracy rates (93%-95%) for prediction. Such a prediction rate is very comparable to expected human-level accuracy of 97%-98% achieved via careful performance modelling of the core and un-core system components. In addition to the CPU2006 suite, we also apply SPECnet to SPEComp2012 and SPECjbb2015. Though the reported submissions for these benchmark suites number in hundreds only, we show that such a DNN is able to predict for these benchmarks reasonably well (~85% accuracy) too. Our SPECnet implementation uses state-of-the-art Tensorflow infrastructure and is extremely flexible and extensible.
Smartphone APPlications in the clinical care and management of Rheumatic Diseases

Background: Rheumatic diseases (RDs) are the most common cause of severe long-term pain and physical disability, affecting hundreds of millions of people around the world. Smartphones technology have the potential to become an important tool that rheumatologist can employ in the clinical care management of RD. Methods: Research of the published literature on the principle electronic databases available as Ovid MEDLINE, Health Technology Assessment Database, Embase, and PsycINFO was conducted, and the studies evaluated eligible were reviewed. Results: Our search produced 120 results from which 47 eligible articles were identified reporting studies of smartphone apps for patients with RD. All examined feasibility and five assessed the efficacy of a smartphone intervention for clinical care management. Conclusions: It has been demonstrated a strong evidence for the feasibility of using smartphone to enhance care of patients with RD. Based on the available literature and our personal experiences, we consider useful the development of some mobile phone apps, to simplify and assist the rheumatologist during his clinical practice. Still remains limited data on the efficacy of such interventions. (www.actabiomedica.it)
Bubble Execution: Resource-aware Reliable Analytics at Cloud Scale

Enabling interactive data exploration at cloud scale requires minimizing end-to-end query execution latency, while guaranteeing fault tolerance, and query execution under resource-constraints. Typically, such a query execution involves orchestrating the execution of hundreds or thousands of related tasks on cloud scale clusters. Without any resource constraints, all query tasks can be scheduled to execute simultaneously (gang scheduling) while connected tasks stream data between them. When the data size referenced by a query increases, gang scheduling may be resource-wasteful or un-satisfiable with a limited, per-query resource budget. This paper introduces Bubble Execution, a new query processing framework for interactive workloads at cloud scale, that balances cost-based query optimization, fault tolerance, optimal resource management, and execution orchestration. Bubble execution involves dividing a query execution graph into a collection of query sub-graphs (bubbles), and scheduling them within a per-query resource budget. The query operators (tasks) inside a bubble stream data between them while fault tolerance is handled by persisting temporary results at bubble boundaries. Our implementation enhances our JetScope service, for interactive workloads, deployed in production clusters at Microsoft. Experiments with TPC-H queries show that bubble execution can reduce resource usage significantly in the presence of failures while maintaining performance competitive with gang execution.
Elastic Characterization of SWNT-Reinforced Polymer Thin Films Using a Nanoindener-Based Bending Test

Carbon nanotube-reinforced polymer thin films are being investigated for applications in micromechanical resonators and actuators, damping layers, and wear resistant films. An accurate characterization of the mechanical properties of nanocomposite thin films is necessary to evaluate their suitability for these different applications. This paper presents a nanoindenter-based bending test to characterize the elastic properties of polymer thin films reinforced with carbon nanotubes. The underlying mechanics is based on the bending deformation of a clamped circular plate loaded at its center with a spherical indenter. The response consists of plate deformation and a local contact indentation. Two different approaches for estimating the latter, including the use of the method of finite elements, are developed and critically evaluated. Results are presented for the synthesis and elastic characterization of epoxy-matrix and vinyl ester epoxy-matrix nanocomposites films (ranging from 50 to 70 μm in thickness) reinforced with single-walled carbon nanotubes.
Impact of hypoglycaemia on neurodevelopmental outcomes in hypoxic ischaemic encephalopathy: a retrospective cohort study

Background Low blood glucose levels (BGLs) in infants are known to adversely affect neurodevelopmental outcomes. However, this risk is not well explored in infants with hypoxic ischaemic encephalopathy (HIE) that receive therapeutic hypothermia (TH). Additionally, little information is available on the optimal BGLs to target in infants with HIE. Aim To explore the association between hypoglycaemia and neurodevelopmental outcomes at different BGL thresholds (2.6 and 3.0 mmol/L) in neonates with HIE treated with TH. Methods Retrospective cohort study. Clinical information and 2-year neurodevelopmental data using Bayley Scales of Infant Development, third edition (BSID-III) and disabilities were recorded for infants born in Western Australia with HIE and treated with TH between February 2008 and February 2012. Multivariable logistic regression models explored the association between hypoglycaemia and neurodevelopmental outcomes. Results 122 infants underwent a total of 1616 BGL estimations before and during 72 hours of TH. Hypoglycaemia (BGL<2.6 mmol/L) occurred in 38/122 (31%) infants and 11/122 (9%) had recurrent hypoglycaemia (three or more episodes). Infants with recurrent hypoglycaemia (<2.6 mmol/L) had significantly lower mean BSID-III cognitive, language and socioemotional subscale scores. On multivariable analysis, recurrent hypoglycaemia (<2.6 mmol/L) was associated with increased odds of death or disability (adjusted OR 8.15; 95% CI 1.31 to 50.58; p=0.024). Recurrent hypoglycaemia (<3.0 mmol/L) during the first 12 hours of life was also associated with severe disability among survivors (adjusted OR 11.13; 95% CI 2.06 to 59.89; p=0.005). Conclusions Early recurrent hypoglycaemia was associated with increased risk of death or severe disability in neonates undergoing TH for HIE. Prospective studies are needed to identify the ideal target BGL in this population.
Role of the glass/TCO substrate in thin film silicon solar cells

This paper addresses the influence of the glass/TCO-substrates on the performance of p-i-n thin film silicon solar cells. We investigate various commercially available glass/SnO/sub 2/-substrates as well as magnetron-sputtered and chemically textured ZnO prepared in-house. In detail, we study differences in type and thickness of the glass substrate, the influence of material and light scattering properties of the TCO as well as measurement aspects when assessing and separating the individual effects.
Rheology and microscopy of low calorie mayonnaise-type emulsions made with stearic acid- modified maize and teff starches

Modification of teff and maize starches with stearic acid, a naturally occurring fatty acid, has resulted in a paste with reduced gelling ability and higher viscosity. These modified teff and maize starches were proposed as potential fat replacers. The objective of this study was to determine the effect of modified teff and maize starches on the rheological properties, microstructure and freeze-thaw and high temperature storage stability of low-calorie mayonnaise type emulsion (LCMTE). Starch suspensions (10% w/v of starch in water) containing stearic acid (1.5%) were pasted for an extended holding time of 2 hr at 91 °C in a Rapid Visco Analyser (RVA) and used to prepare LCMTE with the oil replaced at 50%, 80% and 100% in comparison to a full fat mayonnaise. Flow property measurements showed a decrease in yield stress and viscosity values of LCMTE when using modified teff and maize starches to replace oil compared to LCMTE with unmodified teff and maize starches. Increasing level of oil replacement (from 50% to 100%) increased the viscosity and LCMTE with maize starch were found to have higher yield stress and viscosity values than LCMTE with teff starch. All samples were found to be shear thinning with n<1. Optical and confocal laser scanning microscopy showed LCMTE with modified starches to have large size oil droplets compared to LCMTE with unmodified starches. The smaller size oil droplets of LCMTE with unmodified starches are related to the higher yield stress and viscosity values compared to the LCMTE with modified starches which have larger size oil droplets. LCMTE with maize starch were found to have smaller size oil droplets than LCMTE with teff starch. All mayonnaise samples were found to be more solid like with storage modules G' to be greater than loss modules G" and tanδ < 1. All the LCMTE were found to be more stable to freeze-thaw cycles and high temperature storage. At 50% oil replacement unmodified and modified teff and maize starch with stearic acid could produce LCMTE. When the oil content was further decreased to 80% and 100% only the LCMTE with modified starches were similar to the full fat. © University of Pretoria
Scaling of field-measured soil-water properties: I. Methodology

Concepts of similar soil and scaling are applied to investigate the spatial variability of the field-measured soil-water properties, soil-water pressure head, hydraulic conductivity, and soil-water diffusivity associated with unsaturated flow. The classical, analytical aspects of scale factors as regards the invariance of the flow equations expressed in terms of “reduced variables” are reviewed and extended by considering stochastic aspects of random variations in soil-water properties. It is demonstrated that scaling can best be achieved when soil-water properties are represented by a set of related model functions. The scale distribution is obtained from soil-water pressure head and water content measurements for soil sampling locations 30, 60, 120, 180, 240, and 300 cm below the surface in 12 plots planted to corn. Scale factors are found to have an approximate log-normal distribution. Methods of computing scale factors directly from soil-water pressure head and hydraulic conductivity measurements and indirectly from soil-water content profiles for a drainage experiment are derived. Improved methods for estimating unsaturated hydraulic conductivity are also presented. Stochastic behavior of flux and cumulative seepage as random functions of the scale factors for a similar soil is described for a simple drainage experiment.
Trace-based performance analysis of scheduling bags of tasks in grids

Grid computing promises large scale computing facilities based on distributed systems. Much research has been done on the subject of increasing the performance of grids. We believe that an adequate performance analysis of grids requires knowledge of the workload and the architecture of the grid. Currently, researchers assume that grids are similar to other distributed systems, such as massively parallel computers. However, workloads in grids differ from other distributed systems, because they consist for a significant part of bags-of-tasks. This research presents a method to model the workload of grids realistically, which enables us to analyze the performance of those systems. We have created a flexible workload model that is specifically tailed for grids. The model explicitly handles bag-of-tasks, which comprise the majority of grid workloads. This workload model has been built using a vast amount of workload trace data from seven real-world grids. The workload model enables us to conduct a performance analysis in which we analyze the impact of several workload characteristics, task selection and scheduling policies, and resource management architectures on system performance. We use simulations to systematically and realistically investigate the system performance in various scenarios. This research has resulted in a grid performance analysis toolbox, a software package that allows researchers to analyze, model, and generate workloads of grids. In addition, we have contributed trace data and analysis to the community by means of the Grid Workloads Archive, an on-line archive of trace data analyzed with our toolbox.
Recommending source code locations for system specific transformations

From time to time, developers perform sequences of code transformations in a systematic and repetitive way. This may happen, for example, when introducing a design pattern in a legacy system: similar classes have to be introduced, containing similar methods that are called in a similar way. Automation of these sequences of transformations has been proposed in the literature to avoid errors due to their repetitive nature. However, developers still need support to identify all the relevant code locations that are candidate for transformation. Past research showed that these kinds of transformation can lag for years with forgotten instances popping out from time to time as other evolutions bring them into light. In this paper, we evaluate three distinct code search approaches (“structural”, based on Information Retrieval, and AST based algorithm) to find code locations that would require similar transformations. We validate the resulting candidate locations from these approaches on real cases identified previously in literature. The results show that looking for code with similar roles, e.g., classes in the same hierarchy, provides interesting results with an average recall of 87% and in some cases the precision up to 70%.
Exploring the potential of nanofillers for advanced thin film nanocomposite forward osmosis membranes fabrication

Novel and promising forward osmosis (FO) is a membrane-based separation with significant potentials for the desalination process. While this technology offers various benefits, overcoming its internal concentration polarization (ICP) and membrane fouling in polyamide (PA) skin layer remain as a challenge. In this study, three types of novel thin film nanocomposite (TFN) membranes were synthesized by either coating a typical PA film over the surface of substrate made of polysulfonehalloysite nanotubes (HNTs) or embedding HNTs and titanium dioxide (TiO2)/HNTs nanocomposites into PA thin layer formed over a typical polysulfone (PSF) substrate. These approaches aim to reduce membrane fouling and/or ICP during FO applications. In the first stage of this study, both hydrophilicity and porosity of the substrate were increased using HNTs. The results obtained from filtration experiments showed that the TFN membrane prepared with incorporation of 0.5 wt% HNTs (TFN 0.5) demonstrated the most satisfactory results by exhibiting high water permeability and low reverse solute flux in both FO and pressure retarded osmosis (PRO) configurations. This improvement can be ascribed to the fact that the structural parameter (S value) of TFN membrane is much lower compared to that of control thin film composite (TFC) membrane (0.37 vs 0.95 mm), leading to reduced ICP effect. In the second stage of this study, both hydrophilicity and surface roughness of TFN membranes increased with incorporation of HNTs into PA layer. In the FO mode, the fabricated TFN FO membrane in this study exhibited significantly higher fouling resistance compared to the control TFC membrane. As an indication to reversibility of fouling in TFN FO membrane, it was also found that more than 96% permeate flux could be recovered after a simple water rinsing process. In the third stage of this study, TiO2/HNTs nanocomposites synthesized via one-step solvothermal method were used as nanofillers in the preparation of TFN membranes for the FO application. With respect to separation performance, it was discovered that the TFN membrane incorporated with 0.05% (w/v) TiO2/HNTs (TFN 0.05) exhibited the best performance due to its high water permeability and low reverse solute flux when tested using 10 mM sodium chloride (NaCl) feed solution and 2.0 M NaCl draw solution under two different membrane configurations. Compared to the control membrane (without TiO2/HNTs incorporation), the fabricated TFN 0.05 membrane could offer up to 90% higher water flux and exhibited significantly better antifouling affinity against bovine serum albumin (BSA). The results revealed that fouling in the TFN 0.05 membrane was completely reversible. As a conclusion, it was found that modifying the PA skin layer of composite membrane using TiO2/HNTs as nanofillers could give the most promising results, improving not only membrane permeability and selectivity but also its antifouling property.
Restoration of fMRI Decodability Does Not Imply Latent Working Memory States

Recent imaging studies have challenged the prevailing view that working memory is mediated by sustained neural activity. Using machine learning methods to reconstruct memory content, these studies found that previously diminished representations can be restored by retrospective cueing or other forms of stimulation. These findings have been interpreted as evidence for an activity-silent working memory state that can be reactivated dependent on task demands. Here, we test the validity of this conclusion by formulating a neural process model of working memory based on sustained activity and using this model to emulate a spatial recall task with retro-cueing. The simulation reproduces both behavioral and fMRI results previously taken as evidence for latent states, in particular the restoration of spatial reconstruction quality following an informative cue. Our results demonstrate that recovery of the decodability of an imaging signal does not provide compelling evidence for an activity-silent working memory state.
A Case Study on a Specification Approach Using Activity Diagrams in Requirements Documents

Rising complexity of systems has long been a major challenge in requirements engineering. This manifests in more extensive and harder to understand requirements documents. At the Daimler AG, an approach is applied that combines the use of activity diagrams with natural language specifications to specify system functions. The approach starts with an activity diagram that is created to get an early overview. The contained information is then transferred to a textual requirements document, where details are added and the behavior is refined. While the approach aims to reduce efforts needed to understand a system's behavior, the application of the approach itself causes new challenges on its own. By examining existing specifications at Daimler, we identified nine categories of inconsistencies and deviations between activity diagrams and their textual representations. In a case study, we examined one system in detail to assess how often these occur. In a follow-up survey, we presented instances of the categories to different stakeholders of the system and let them asses the categories regarding their severity. Our analysis indicates that a coexistence of textual and graphical representations of models without proper tool support results in inconsistencies and deviations that may cause severe maintenance costs or even provoke faults in subsequent development steps.
DENA: A DVFS-Capable Heterogeneous NoC Architecture

The current design drivers for multi-cores, namely performance per watt, scalability and flexibility, make the Networks-on-Chip (NoCs) the de-facto on-chip interconnect. State of the art NoCs can exploit heterogeneous solutions and complex DVFS techniques to fulfill also the variability of the application requirements. Relevant showstoppers to the design of a truly flexible NoC fitting all the possible traffic conditions, are the burstiness of the traffic generated by modern applications magnified by the unbalanced usage of the interconnect resources due to the implemented coherence protocol. This paper presents DENA, a DVFS-capable, heterogeneous NoC design, encompassing the coherence protocol, the application behavior and the need to minimize the energy budget. Simulation results on a 64-core, 2D-mesh architecture executing the SPLASH2 benchmark suite, testify the advantages of DENA from both the performance and energy viewpoints with an average 34.3% energy-performance improvement against the state of the art DVFS-capable NoC design.
Quantitative Microbial Risk Assessment for Spray Irrigation of Dairy Manure Based on an Empirical Fate and Transport Model

Background: Spray irrigation for land-applying livestock manure is increasing in the United States as farms become larger and economies of scale make manure irrigation affordable. Human health risks from exposure to zoonotic pathogens aerosolized during manure irrigation are not well understood. Objectives: We aimed to a) estimate human health risks due to aerosolized zoonotic pathogens downwind of spray-irrigated dairy manure; and b) determine which factors (e.g., distance, weather conditions) have the greatest influence on risk estimates. Methods: We sampled downwind air concentrations of manure-borne fecal indicators and zoonotic pathogens during 21 full-scale dairy manure irrigation events at three farms. We fit these data to hierarchical empirical models and used model outputs in a quantitative microbial risk assessment (QMRA) to estimate risk [probability of acute gastrointestinal illness (AGI)] for individuals exposed to spray-irrigated dairy manure containing Campylobacter jejuni, enterohemorrhagic Escherichia coli (EHEC), or Salmonella spp. Results: Median risk estimates from Monte Carlo simulations ranged from 10−5 to 10−2 and decreased with distance from the source. Risk estimates for Salmonella or EHEC-related AGI were most sensitive to the assumed level of pathogen prevalence in dairy manure, while risk estimates for C. jejuni were not sensitive to any single variable. Airborne microbe concentrations were negatively associated with distance and positively associated with wind speed, both of which were retained in models as a significant predictor more often than relative humidity, solar irradiation, or temperature. Conclusions: Our model-based estimates suggest that reducing pathogen prevalence and concentration in source manure would reduce the risk of AGI from exposure to manure irrigation, and that increasing the distance from irrigated manure (i.e., setbacks) and limiting irrigation to times of low wind speed may also reduce risk. https://doi.org/10.1289/EHP283