Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Java Microbenchmark Tail Latencies

As Java microbenchmarks are great at profiling the performance of essential code elements, they are widely adopted for Java performance testing. Performance testing using Java microbenchmarks is composed of two phases: the warmup phase and the steady phase. Usually, testing results from the warmup phase are discarded because of the highly fluctuating performance caused by the optimization of Java Virtual Machine. The performance results collected during the steady phase are used for performance evaluation as they are assumed to be more stable. However, according to our study, severe performance fluctuations also occur during the steady phase, which leads to long tail latencies. Long tail latencies constitute a big problem in modern Java systems (of course, other systems and applications) as they hurt the user experience by prolonging the overall execution time. In this paper, we extensively evaluated the long tail performance of 586 Java microbenchmarks from 30 Java systems. The evaluation results show that, for 38% of the benchmarks in steady phase, their 99%tile execution times are over 30% higher than their median execution times. In the worst-case scenario, the 99%ile performance is 659 times higher than the median performance. Furthermore, the 95%ile execution times are above 30% higher than the median execution times for 11% of the steady phase benchmarks.
Searching for the Ground Truth: Assessing the Similarity of Benchmarking Runs

Stable and repeatable measurements are essential for comparing the performance of different systems or applications, and benchmarks are used to ensure accuracy and replication. However, if the corresponding measurements are not stable and repeatable, wrong conclusions can be drawn. To facilitate the task of determining whether the measurements are similar, we used a data set of 586 micro-benchmarks to (i) analyze the data set itself, (ii) examine our previous approach, and (iii) propose and evaluate a heuristic. To evaluate the different approaches, we perform a peer review to assess the dissimilarity of the benchmark runs. Our results show that this task is challenging even for humans and that our heuristic exhibits a sensitivity of 92%.
Ensemble Filtering for Nonlinear Dynamics

Abstract A method for data assimilation currently being developed is the ensemble Kalman filter. This method evolves the statistics of the system by computing an empirical ensemble of sample realizations and incorporates measurements by a linear interpolation between observations and predictions. However, such an interpolation is only justified for linear dynamics and Gaussian statistics, and it is known to produce erroneous results for nonlinear dynamics with far-from-Gaussian statistics. For example, the ensemble Kalman filter method, when used in models with multimodal statistics, fails to track state transitions correctly. Here alternative ensemble methods for data assimilation into nonlinear dynamical systems, in particular, those with a large state space are studied. In these methods conditional probabilities at measurement times are calculated by applying Bayes's rule. These results show that the new methods accurately track the transitions between likely states in a system with bimodal statistics,.
A Case of Multi-Resource Fairness for Serverless Workflows (Work In Progress Paper)

Serverless platforms have exploded in popularity in recent years, but, today, these platforms are still unsuitable for large classes of applications. They perform well for batch-oriented workloads that perform coarse transformations over data asynchronously, but their lack of clear service level agreements (SLAs), high per-invocation overheads, and interference make deploying online applications with stringent response time demands impractical. Our assertion is that beyond the glaring issues like cold start costs, a more fundamental shift is needed in how serverless function invocations are provisioned and scheduled in order to support these more demanding applications. Specifically, we propose a platform that leverages the observability and predictability of serverless functions to enforce multi-resource fairness. We explain why we believe interference across a spectrum of resources (CPU, network, and storage) contributes to lower resource utilization and poor response times for latency-sensitive and high-fanout serverless application patterns. Finally, we propose a new distributed and hierarchical function scheduling architecture that combines lessons from multi-resource fair scheduling, hierarchical scheduling, batch-analytics resource scheduling, and statistics to create an approach that we believe will enable tighter SLAs on serverless platforms than has been possible in the past.
Towards Evaluation/Mitigation Risk of Systemic Failures in a Recoverable Network with Redistributed Elastic Load

While systemic failure/overload in recoverable networks with load redistribution is a common phenomenon, current ability to evaluate and moreover mitigate the corresponding systemic risk is vastly insufficient due to complexity of the problem and relying on oversimplified models. The proposed in this paper framework for systemic risk evaluation relies on approximate dimension reduction at the onset of systemic failure. Assuming a general failure/recovery microscopic model, the macro-level system dynamics is approximated by a 2-state Markov process alternating between systemically operational and failed states.
Uncovering Steady State Executions in Java Microbenchmarking with Call Graph Analysis

Developers often use microbenchmarking tools to evaluate the performance of a Java program. These tools run a small section of code multiple times and measure its performance. However, this process can be problematic as Java execution is traditionally divided into two stages: a warmup stage where the JVM's JIT compiler optimizes frequently used code and a steady stage where performance is stable. Measuring performance before reaching the steady stage can provide an inaccurate representation of the program's efficiency. The challenge comes from determining when a program should be considered as in a steady state. In this paper, we propose that call stack sampling data should be considered when conducting steady state performance evaluations. By analyzing this data, we can generate call graphs for individual microbenchmark executions. Our proposed method of using call stack sampling data and visualizing call graphs intuitively empowers developers to effectively distinguish between warmup and steady state executions. Additionally, by utilizing machine learning classification techniques this method can automate the steady state detection, working towards a more accurate and efficient performance evaluation process.
Transparent Trace Annotation for Performance Debugging in Microservice-oriented Systems (Work In Progress Paper)

Microservices is a cloud-native architecture in which a single application is implemented as a collection of small, independent, and loosely-coupled services. This architecture is gaining popularity in the industry as it promises to make applications more scalable and easier to develop and deploy. Nonetheless, adopting this architecture in practice has raised many concerns, particularly regarding the difficulty of diagnosing performance bugs and explaining abnormal software behaviour. Fortunately, many tools based on distributed tracing were proposed to achieve observability in microservice-oriented systems and address these concerns (e.g., Jaeger). Distributed tracing is a method for tracking user requests as they flow between services. While these tools can identify slow services and detect latency-related problems, they mostly fail to pinpoint the root causes of these issues. This paper presents a new approach for enacting cross-layer tracing of microservice-based applications. It also proposes a framework for annotating traces generated by most distributed tracing tools with relevant tracing data and metrics collected from the kernel. The information added to the traces aims at helping the practitioner get a clear insight into the operations of the application executing user requests. The framework we present is notably efficient in diagnosing the causes of long tail latencies. Unlike other solutions, our approach for annotating traces is completely transparent as it does not require the modification of the application, the tracer, or the operating system. Furthermore, our evaluation shows that this approach incurs low overhead costs.
Determination of kinematic distances of pre-main-sequence stars in star-forming regions

Abstract Many studies of star-forming regions have been carried out since the discovery of compact Hii regions in the late 1960s. The kinematic properties of young stars in the nearest regions with ongoing and recent star formation provide essential tests of their formation mechanisms. The detection of coeval moving groups allows determination of individual distances through the convergent-point method. As a result, the main physical properties of these stars and their early evolutionary stages can be determined if we know how distant they are.
Antimicrobial Resistance and Biofilm Formation of Staphylococcus aureus Isolates from Febrile Cases: Findings from a Rural Cohort of Odisha, India

Abstract This study investigated the major pathogens in fever patients’ blood in a rural cohort and characterized its virulence. A total of 718 blood samples received from IPD/OPD (inpatient department/outpatient department) patients with H/O (history of) fever were cultured, and 73 out of 83 culture-positive samples were identified as Staphylococcus aureus. The isolates showed higher resistance to penicillin, most being multidrug resistant. They formed biofilm in vitro, and 27.4% of the isolates were strong biofilm producers. They were sensitive towards linezolid, gentamicin, and tetracycline. The findings emphasize the necessity of preventing and managing staphylococcal infection and regular antimicrobial surveillance in rural areas.
Dynamic programming and its application in economics andfinance

Multistage decision problems are numerically challenging. Typically the work to solve such problems is an exponential function with respect to both the number of stages and the number of decision parameters. The problems have been researched extensively and a wide variety of methods to solve them have been proposed. Inevitably all methods are limited in the size of problem they can solve. The purpose of our work is to develop a new more efficient algorithm and one that can run on parallel architectures, thereby extending significantly the size of problems that are tractable. We present a numerical dynamic programming algorithm that has three components: optimization, approximation, and integration. A key feature of the approximation methods we use is to preserve mathematical features such as convexity and differentiability, which enables us to use powerful optimization methods. To illustrate the efficiency of the new method we present extensive results on optimal growth models and dynamic portfolio problems, using our implementation of the algorithm on the Condor Master-Worker system.
Silicon doping of HVPE GaN bulk-crystals avoiding tensile strain generation

Doped GaN:Si crystals were grown in a commercially available vertical HVPE reactor. The templates used for the HVPE heteroepitaxy were so-called FACELO seeds, with a starting GaN layer thickness of 3–4 μm. The FWHM of the 0002 and the 303¯2 ? > reflection of the HVPE-grown GaN:Si crystals with a thickness of 3 mm are 31″ ? > and 78″ ? >, respectively, indicating excellent crystal quality. Hall measurements resulted in a charge carrier concentration of 1.5×1018 ? > cm−3, while exhibiting a mobility of 250 cm−2V−1 s−1. These values coincide with the values extracted from FTIR measurements and the lineshape fitting of the A1(LO)/plasmon coupled phonon mode of the confocal Raman measurements. SIMS investigations yielded a silicon atom concentration of 1.8×1018 ? > cm−3. This indicates an activation of the dopant atoms of approximately 90%. The TDD determined by CL dark spot counting was 2×106 ? > cm−2. Within the measurement accuracy, the confocal Raman measurements did not show a tensile strain generation due to the silicon doping with resulting charge carrier concentrations of 1.5×1018 ? > cm−3.
Tight-binding theory of graphene bending

I. Nikiforov,1 E. Dontsova,2 R. D. James,3 and T. Dumitrică1,* 1Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA 2Department of Materials Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4 3Department of Aerospace Engineering and Mechanics, University of Minnesota, Minneapolis, Minnesota 55455, USA (Received 30 January 2014; revised manuscript received 11 April 2014; published 30 April 2014)
Long-term effect of pulmonary rehabilitation in idiopathic pulmonary fibrosis: a randomised controlled trial

Background Idiopathic pulmonary fibrosis (IPF) is characterised by worsening dyspnoea and exercise intolerance. Research question Does a long-term pulmonary rehabilitation improve exercise tolerance in patients with IPF treated with standard antifibrotic drugs, which are expected to reduce disease progression? Methods This open-label randomised controlled trial was performed at 19 institutions. Stable patients receiving nintedanib were randomised into pulmonary rehabilitation and control groups (1:1). The pulmonary rehabilitation group underwent initial rehabilitation which included twice-weekly sessions of monitored exercise training for 12 weeks, followed by an at-home rehabilitation programme for 40 weeks. The control group received usual care only, without pulmonary rehabilitation. Both groups continued to receive nintedanib. The primary and main secondary outcomes were change in 6 min walking distance (6MWD) and change in endurance time (using cycle ergometry) at week 52. Results Eighty-eight patients were randomised into pulmonary rehabilitation (n=45) and control (n=43) groups. Changes in 6MWD were −33 m (95% CI −65 to −1) and −53 m (95% CI −86 to −21) in the pulmonary rehabilitation and control groups, respectively, with no statistically significant difference (mean difference, 21 m (95% CI −25 to 66), p=0.38). Changes in endurance time were significantly better in the pulmonary rehabilitation (64 s, 95% CI −42.3 to 171)) than in the control (−123 s (95% CI −232 to −13)) group (mean difference, 187 s (95% CI 34 to 153), p=0.019). Interpretation Although pulmonary rehabilitation in patients taking nintedanib did not improve 6MWD in the long term, it led to prolonged improvement in endurance time. Trial registration number UMIN000026376.
Outlier rejection with mixture models in alignment

Outliers in measurement often interfere with alignment. They are caused by sudden damages in the alignment mark, and existence of particles, resist damages and so on. In a conventional way to identify outliers, the observations that have larger residual than previously determined threshold are identified as outlier. It works well only with the operator’s labor of adjusting the threshold according to the deviation of ordinaries (non-outliers). However, labor is a problem especially in Small-Quantity Large-Variation fabrication such as for ASIC, System-LSI and so on. A novel method for elimination of the labor has been developed. It utilizes normal mixture models whose number of components is determined based on the Maximum Penalized Likelihood (MPL) method. It can be regarded as an identification method that determines threshold adaptively using ordinaries’ deviation. Simulation results show that the penalty coefficient, the only parameter of the method, can be a constant in the variation of ordinarie's deviation. It also shows that in the absence of outliers, the accuracy of the method is comparable with the maximum likelihood estimation that is commonly considered to be the best method when the observations follow the normal distribution. The method performs better than conventional ones when there are a sufficient number of observations (no less than ten) in the standard Enhanced Global Alignment (EGA). Superiority of the adaptive method is dependent upon the probability of outlier occurrence, variation of the situation, the number of observations and the complexity of the model fitted to the observations.
Quantitative depth‐dependent analysis using the inelastic scattering backgrounds from X‐ray photoelectron spectroscopy and hard X‐ray photoelectron spectroscopy

The inelastic scattering contribution to an X‐ray photoelectron spectrum (XPS) reflects the distribution of elements within the sample depth. Varying the energy of the incident photons changes the inelastic scattering contribution. We present a standardless, automated method that exploits this change to enable compositional analysis within the XPS information depth (also known as amount of substance, AOS3λ). In this method, the photoelectron intensities observed using two or more X‐ray photon energies are normalised using correction factors. These correction factors are derived to determine the AOS3λ from the inelastic scattering signals collected from different samples. The AOS3λ are then compared with the ratio of the elastic XPS peak intensities calculated for different sample depths to find the layer thickness. The method has been applied to analyse spectra collected from thin amorphous carbon films to yield quantitative, standardless and automated XPS analysis.
Early diagnosis of COVID-19 patients using deep learning-based deep forest model

ABSTRACT Coronavirus disease-19 (COVID-19) has rapidly spread all over the world. It is found that the low sensitivity of reverse transcription-polymerase chain reaction (RT-PCR) examinations during the early stage of COVID-19 disease. Thus, efficient models are desirable for early-stage testing of COVID-19 infected patients. Chest X-ray (CXR) images of COVID-19 infected patients have shown some bilateral changes. In this paper, deep transfer learning and a deep forest-based model are proposed to diagnose COVID-19 infection from CXR images. Initially, features of X-ray images are extracted using the well-known deep transfer learning model (i.e., ResNet101), which does not require tuning many parameters compared to the deep convolutional neural network (CNN). After that, the deep forest model is utilised to predict COVID-19 infected patients. The deep forest is based upon ensemble learning and requires a small number of hyper-parameters. Additionally, the proposed model is trained on a multi-class dataset that contains four different classes as COVID-19 (+), pneumonia, tuberculosis, and healthy patients. The comparisons are drawn among the proposed deep transfer learning and deep forest-based models, the competitive models. The obtained results show that the proposed model effectively diagnoses COVID-19 infection with an accuracy of 99.4%.
Microbiome diversity: A barrier to the environmental spread of antimicrobial resistance?

Background In the environment, microbial communities are constantly exposed to invasion by antimicrobial resistant bacteria (ARB) and their associated antimicrobial resistance genes (ARGs) that were enriched in the anthroposphere. A successful invader has to overcome the biotic resilience of the habitat, which is more difficult with increasing biodiversity. The capacity to exploit resources in a given habitat is enhanced when communities exhibit greater diversity, reducing opportunities for invaders, leading to a lower persistence. In the context of antimicrobial resistance (AMR) dissemination, exogenous ARB reaching a natural community may persist longer if the biodiversity of the autochthonous community is low, increasing the chance of ARGs to transfer to community members. Reciprocally, high microbial diversity could serve as a natural long-term barrier towards invasion by ARB and ARGs. Results To test this hypothesis, a sampling campaign across seven European countries was carried out to obtain 172 environmental samples from sites with low anthropogenic impact. Samples were collected from contrasting environments: stationary structured forest soils, or dynamic river biofilms and sediments. Microbial diversity and relative abundance of 27 ARGs and 5 mobile genetic element marker genes were determined. In soils, higher diversity, evenness and richness were all significantly negatively correlated with the relative abundance of the majority (>85%) of ARGs. Furthermore, the number of detected ARGs per sample was inversely correlated with diversity. However, no such effects were found for the more dynamic, regularly mixed rivers. Conclusions: In conclusion, we demonstrate that diversity can serve as barrier towards AMR dissemination in the environment. This effect is mainly observed in stationary, structured environments, where long-term, diversity-based resilience against invasion can evolve. Such barrier effects can in the future be exploited to limit the environmental proliferation of AMR.
Electrophysiological indices of hierarchical speech processing differentially reflect the comprehension of speech in noise

The past few years have seen an increase in the use of encoding models to explain neural responses to natural speech. The goal of these models is to characterize how the human brain converts acoustic speech energy into different linguistic representations that enable everyday speech comprehension. For example, researchers have shown that electroencephalography (EEG) data can be modeled in terms of acoustic features of speech, such as its amplitude envelope or spectrogram, linguistic features such as phonemes and phoneme probability, and higher-level linguistic features like context-based word predictability. However, it is unclear how reliably EEG indices of these different speech representations reflect speech comprehension in different listening conditions. To address this, we recorded EEG from neurotypical adults who listened to segments of an audiobook in different levels of background noise. We modeled how their EEG responses reflected different acoustic and linguistic speech features and how this varied with speech comprehension across noise levels. In line with our hypothesis, EEG signatures of context-based word predictability and phonetic features were more closely correlated with behavioral measures of speech comprehension and percentage of words heard than EEG measures based on low-level acoustic features. EEG markers of the influence of top- down, context-based prediction on bottom-up acoustic processing also correlated with behavior. These findings help characterize the relationship between brain and behavior by comprehensively linking hierarchical indices of neural speech processing to language comprehension metrics. SIGNIFICANCE STATEMENT Acoustic and linguistic features of speech have been shown to be consistently tracked by neural activity even in noisy conditions. However, it is unclear how signatures of low- and high- level features covary with one another and relate to behavior across these listening conditions. Here, we find that categorical phonetic feature processing is more affected by noise than acoustic and word probability-based speech features. We also find that phonetic features and word probability-based features better correlate with measures of intelligibility and comprehension. These results extend our understanding of how various speech features are comparatively reflected in electrical brain activity and how they relate to perception in challenging listening conditions.