Miracle-dz/UniFAR-FTUs huggingface dataset:
Deadline and Reliability Aware Multiserver Configuration Optimization for Maximizing Profit

Maximizing profit is a key goal for cloud service providers in the modern cloud business market. Service revenue and business cost are two major factors in determining profit and highly depend on multiserver configuration. Understanding the relationship between multiserver configuration and profit is important to service providers. Although existing papers have explored this issue, few of them consider deadline miss rate and soft error reliability of cloud services in multiserver configuration for profit maximization. Since deadline misses violate cloud services' real-time requirements and soft error prevents successful processing of cloud services, it is necessary to consider the impact of deadline miss rate and soft error reliability on service providers' profits when configuring the multiserver. This paper introduces a deadline miss rate and soft error reliability aware multiserver configuration scheme for maximizing cloud service providers' profit. Specifically, we derive the deadline miss rate considering the heterogeneity of cloud service requests and propose an analytical method to calculate the soft error reliability of multiserver systems. Based on the new deadline miss rate and soft error reliability models, we formulate the multiserver configuration optimization problem and introduce an augmented Lagrange multiplier-based iterative method to find the optimal multiserver configuration. Extensive experiments evaluate the efficacy of the proposed multiserver configuration approach. Compared with the two state-of-the-art methods, the profit gained by our scheme can be up to 11.92% higher.
Online Thread Auto-Tuning for Performance Improvement and Resource Saving

Multi-threading is a common way for programs to benefit from the multi/many-core design. However, the performance of some parallel programs does not increase/even decrease as the number of cores/threads increases. Our study shows that the performance of a parallel program is impacted by the number of cores/threads, the thread placement, the inputs of the program. It is nontrivial to identify the optimal number of cores and the corresponding thread placement to maximize the performance, when the input of a program is determined online and the workload of different iterations may not be identical. To resolve the above problem, we propose Otter, a thread auto-tuning system at runtime for iterative parallel programs. Otter collects the runtime information in the first few iterations and makes decisions on the number of threads and thread placement policy to achieve the goal of improving performance or saving resources. It considers the characteristics of dynamic workload in the iteration process and reduces the time overhead through a migration method. Experiments on a 96-core machine show that Otter improves the performance of the benchmarks by 20.7% and reduces core hours by 51.3% on average compared to the case of running them with all the CPU cores.
Relationship between eruptions of active-region filaments and associated flares and CMEs

To better understand the dynamical process of active-region filament eruptions and associated flares and coronal mass ejections (CMEs), we carried out a statistical study of 120 events observed by Big Bear Solar Observatory (BBSO), Transition Region and Coronal Explorer (TRACE) and the Extreme-ultraviolet Imaging Telescope (EIT) on board Solar and Heliospheric Observatory (SOHO) from 1998 to 2007. We combined filament observations with the NOAA's flare reports, Michelson Doppler Imager (MDI) magnetograms and Large Angle and Spectrometric Coronagraph (LASCO) data, to investigate the relationship between active-region filament eruptions and other solar activities. We found that 115 out of 120 (about 96 per cent) filament eruptions are associated with flares. 56 out of 105 (about 53 per cent) filament eruptions are found to be associated with CMEs except for 15 events without corresponding LASCO data. We note the limitation of coronagraphs duo to geometry or sensitivity, leading to many smaller CMEs that are Earth-directed or well out of the plane of sky not being detected by near-Earth spacecraft. Excluding those without corresponding LASCO data, the CME association rate of active-region filament eruptions clearly increases with X-ray flare class from about 32 per cent for C-class flares to 100 per cent for X-class flares. We also found that the eruptions of active-region filaments associated with halo CMEs are often accompanied by large flares (18 out of 20 events; >= M1.0). About 92 per cent events (11 out of 12) associated with X-class flare are associated with halo CMEs. Such a result is due to the fact that the Earth-directed CMEs detected as halo CMEs are often the larger CMEs and many of the smaller ones are not detected because of the geometry and low intensity. The average speed of the associated CMEs of filament eruptions increases with X-ray flare size from 563.7 kms(-1) for C-class flares to 1506.6 kms(-1) for X-class flares. Excluding the active region located in the area more than 50. from the solar centre and five without corresponding MDI data, the beta magnetic field configuration (about 47 per cent; 36 out of 77) is more likely to form eruptive filaments than the other ones and there are 33 filament eruptions associated with magnetic flux cancellation, 42 events associated with magnetic flux emergence and two events without variation of magnetic field. The average area of emergence regions is 855.9 arcsec(2). These findings may be instructive not only in respect to the modelling of active-region filament eruptions but also in predicting flares and CMEs.
Automated Extraction and Checking of Property Models from Source Code for Robot Swarms

As robots become a common presence in our everyday lives, ensuring the security and safety of robotic systems becomes an increasingly important and urgent challenge. Multi-robot systems, in particular, have the potential to revolutionize multiple industries-such as transportation and home care-where safety guarantees are a primary requirement. A known challenge for swarms and multi-robot systems is the gap between requirements and design, due to the need to translate swarm-level objectives into robot-level behaviors. In this paper, we focus on a less studied problem-the gap between requirements and implementation. As a case study, we use Buzz, that is a dynamic programming language designed for swarm robotics applications. Similarly to Python, Lua, and JavaScript, Buzz does not natively offer formal guarantees of correctness or safety. We propose an approach to automatically extract” as-implemented” models from Buzz programs, whose properties can then be formally analyzed and verified. Results obtained from the experiments performed on two medium-size open-source production-level systems for robotics research have also been reported. Our results show that the approach is feasible and is scalable to larger systems.
Determination of Glycosyltransferase Speciﬁcities for the Escherichia coli O111 O Antigen by a Generic Approach (cid:1) †

We describe a bacterial strain developed to facilitate the determination of glycosyltransferase (GT) speci- ﬁcities for O antigens of known structure and gene cluster sequence. For proof of principle for the approach, the strain was used to determine the speciﬁcity of the Escherichia coli O111 O-antigen GT genes. The O antigen is a repeat unit polysaccharide that is part of the lipopolysaccharide (LPS). It is very diverse, with about 190 forms known for Escherichia coli (including Shigella strains) (5) and 46 for Salmonella enterica (19). The genes responsible for each O-antigen form are generally in a gene cluster that maps to a speciﬁc locus, which is between galF and gnd for E. coli (22). The structure and O-antigen gene cluster of E. coli O111 are shown in Fig. 1.
An signal associated with desire for is suppressed in recently abstinent alcoholics.

Background— Alcoholics report persistent alcohol craving that is heightened by cognitive cues, stressful situations, and abstinence. The role of endogenous cannabinoids in human alcohol craving —though long suspected—remains elusive. Materials and methods— We employed laboratory exposure to stress, alcohol cue, and neutral relaxed situations through guided imagery procedures to evoke alcohol desire and craving in healthy social drinkers ( n =11) and in treatment-engaged, recently abstinent alcoholic subjects ( n =12) and assessed alcohol craving, heart rate, and changes in circulating endocannabinoid levels. Subjective anxiety was also measured as a manipulation check for the procedures. Results— In healthy social drinkers, alcohol cue imagery increased circulating levels of the endocannabinoid anandamide, whereas neutral and stress-related imagery had no such effect. Notably, baseline and response anandamide levels in these subjects were negatively and positively correlated with self-reported alcohol craving scores, respectively. Cue-induced increases in heart rate were also correlated with anandamide responses. By contrast, no imagery-induced anandamide mobilization was observed in alcoholics, whose baseline anandamide levels were markedly reduced compared to healthy drinkers and were uncorrelated to either alcohol craving or heart rate. Conclusions— The results suggest that plasma anandamide levels provide a marker of the desire for alcohol in social drinkers, which is suppressed in recently abstinent
Ultrahigh magnetic field sensitivity in laminates of TERFENOL-D and Pb(Mg1/3Nb2/3)O3–PbTiO3 crystals

It has been found that laminate composites of longitudinally magnetized magnetostrictive TERFENOL-D and a transversely poled piezoelectric Pb(Mg1/3Nb2/3)O3–PbTiO3 crystal have extremely high magnetic field sensitivity. At room temperature, an output voltage with an exceptionally good linear response to an ac magnetic field Hac was found over the range of 10−11
Resting metabolic rate, plasma leptin concentrations, leptin receptor expression, and adipose tissue measured by whole-body magnetic resonance imaging in women with Prader-Willi syndrome 1–3

Background: Obesity in Prader-Willi syndrome (PWS) may be related to abnormalities in the adipocyte-leptin-hypothalamic pathway and may be exacerbated by reductions in the resting metabolic rate (RMR). Objective: We compared body composition, body-composition– adjusted RMR, and adiposity-adjusted plasma leptin between women with PWS and control women. We also examined leptin receptor expression in the PWS group. Design: We studied body composition using whole-body magnetic resonance imaging and measured plasma leptin by radioim-munoassay in 45 control women aged 18–56 y and in 13 women with PWS aged 20–38 y. RMR was measured by indirect calorimetry in 41 control women and in 8 women with PWS. Age, body composition, and regional adipose tissue (AT) depots were corrected for by multiple regression analysis. Messenger RNA expression of the leptin receptor was examined by reverse transcriptase–polymerase chain reaction in lymphocytes. Results: In the PWS group, fat mass was greater after correction for fat-free mass, and RMR was normal after correction for both fat-free mass and fat mass. Leptin was influenced primarily by subcutaneous AT volume in both subject groups. Leptin concentrations were not significantly different between the 2 groups after adjustment for age and AT content or distribution. Full-length leptin receptor messenger RNA was expressed in the lymphocytes of the PWS group. Conclusions: Differences in RMR in women with PWS are explained by abnormal body composition, suggesting that energy expenditure is normal at the tissue level in PWS. There is no evidence that defective leptin production causes obesity in PWS, and leptin receptor deficiency is not a primary consequence of the gene defects leading to leptin resistance.
The E cacy of Moxibustion and Acupuncture Therapy for Ankylosing Spondylitis: A Protocol for an Overview of Systematic Reviews and Meta- analyses

Jun Chen Jiangxi University of Traditional Chinese Medicine Jun Xiong (  xiongjun196071@163.com ) A liated Hospital of Jiangxi University of TCM https://orcid.org/0000-0002-4196-3351 Lunbin Lu Jiangxi University of Traditional Chinese Medicine Siyuan Zhu Jiangxi University of Traditional Chinese Medicine Zhiying Zhong Jiangxi University of Traditional Chinese Medicine Genhua Tang Jiangxi University of Traditional Chinese Medicine Xingchen Zhou A liated Hospital of JiangXi University of TCM Han Guo A liated Hospital of JiangXi University of TCM
A Note on Estimating Proportions by Linear Regression

Abstract : The estimation of the parameters of a linear regression model in which the dependent variable is a fraction or proportion frequently occurs in statistical analyses. An example of such an analysis has been performed by McCall in studying the movements of people into and/or out of various income classes over time. In the note the author shows that the estimated proportions sum to one and no constraint is needed on the proportions if the parameter estimates are BLUE. If the parameters are not, then a constraint can be constructed using Zellner's technique of Seemingly Unrelated Least Squares (SULS) to ensure that the estimated proportions sum to one.
SciSpot: Scientific Computing On Temporally Constrained Cloud Preemptible VMs

Scientific computing applications are being increasingly deployed on cloud computing platforms. Transient servers such as EC2 spot instances and Google Preemptible VMs, can be used to lower the costs of running applications on the cloud by up to $10\times$. However, the frequent preemptions and resource heterogeneity of these transient servers introduces many challenges in their effective and efficient use. In this paper, we develop techniques for modeling and mitigating preemptions of transient servers, and present SciSpot, a software framework that enables low-cost scientific computing on the cloud. SciSpot deploys applications on Google Cloud Preemptible Virtual Machines that exhibit temporally constrained preemptions: VMs are always preempted in a 24 hour interval. Our empirical analysis shows that the preemption rate is bathtub shaped, which raises multiple fundamental challenges in performance modeling and policy design. We develop a new reliability model for temporally constrained preemptions, and use statistical mechanics to show why the bathtub shape is a fundamental characteristic. SciSpot's design is guided by our observation that many emerging scientific computing applications that integrate machine learning with simulations, can be deployed as ‘`bags’' of jobs, which represent multiple instantiations of the same computation with different physical model parameters. For a bag of jobs, SciSpot finds the optimal transient server on-the-fly, by taking into account the price, performance, and preemption rates of different servers. SciSpot reduces costs by $5\times$ compared to conventional cloud deployments, and reduces makespans by up to $10\times$ compared to conventional high performance computing clusters.
Private Regression In Multiple Outcomes (PRIMO)

We introduce a new differentially private regression setting we call Private Regression in Multiple Outcomes (PRIMO) inspired the common situation in the social and biomedical sciences where a data analyst wants to perform a set of l regressions while preserving privacy, where in each of the regressions the covariates X are shared, and each regression i has a different vector of outcomes y i . We show that while taking any one of the number of existing private linear regression techniques and naively applying it l times leads to an increase in error of √ l for PRIMO over the standard linear regression setting, techniques based on sufficient statistics perturbation (SSP) Wang [2018] can be modified to yield greatly improved dependence on l in a range of common parameter regimes. Our first key insight is that the data covariance matrix X T X is shared across the regressions, so when privately computing this covariance matrix is the main source of error we obtain PRIMO with no dependence on l in the asymptotic error. In Section 4.2 via an equivalence from privately computing the X T Y term to the problem of private query release with low l 22 error, we adapt the geometric projection-based methods of Nikolov et al. [2013] for query release to the PRIMO setting. Under the assumption the labels Y are public, the projection gives improved results over the Gaussian mechanism when n < l √ d . In order to give high probability bounds that are required for analyzing the error of the regression, we give a high probability analysis of the error of the projection mechanism using a variant of the Hanson-Wright Inequality. To our knowledge this is the first application of query release algorithms to private regression. In Section 4.3 we analyze the complexity of our proposed algorithm, introduced several algorithmic tricks based on matrix decomposition that decrease the dependence of the running time on l . Finally in Section 4.4 we aim to speed up our method via sub-sampling, giving an analysis of sub-sampled SSP that makes heavy use of Matrix Chernoff bounds for sampling without replacement.
E ﬀ ect of Cleaning Multiple-Funnel Traps on Captures of Bark and Woodboring Beetles in Northeastern United States

Simple Summary: Semiochemical-baited traps are used to survey insect communities and detect invasive species. These traps are left in the ﬁeld during the growing season where large amounts of pollen and other debris can build up on smooth trapping surfaces. There was a concern this buildup would provide an escape route for some insects and interfere with trapping results. We tested the e ﬀ ects of this pollen buildup on captures of bark and woodboring beetles in northeastern forests in two experiments. While many beetles did not respond to treatments, we found a positive e ﬀ ect of trap cleaning for three bark beetles and one cerambycid species. The response of other species was more nuanced. Abstract: Two experiments were conducted in mixed hardwood-conifer forests in the northeastern United States to test the e ﬀ ects of cleaning surfactant and non-surfactant treated multiple-funnel traps used to catch bark and woodboring beetles. Large amounts of pollen and other debris often form a crust on the interior of traps (personal observations). Such surface deposits may provide footholds for beetles to escape capture in traps. In one experiment, we tested cleaned surfactant and non-surfactant traps against non-cleaned surfactant and non-surfactant traps. In a second experiment, we tested ﬁeld cleaning of modiﬁed multiple-funnel traps as an alternative to substituting clean traps on each collection visit. There was no e ﬀ ect of surfactant treated traps, cleaned or not, on total beetles or individual bark beetle species captured. However, in situ cleaned traps were statistically better at capturing total beetles, total bark beetles, and several bark beetle species than non-cleaned control traps. Surfactant-treated non-modiﬁed traps and cleaned modiﬁed traps had higher species richness and abundance than other treatments at the site level. Our results suggest that cleaning traps to remove accumulated pollen and debris may be helpful for some species but would have limited beneﬁt for broad-scale trapping of bark and woodboring beetles in northeastern forests.
Identiﬁcation of the 3-lncRNA Signature as a Prognostic Biomarker for Colorectal Cancer

: Colorectal cancer (CRC) is one of the most common malignant carcinomas in the world, and metastasis is the main cause of CRC-related death. However, the molecular network involved in CRC metastasis remains poorly understood. Long noncoding RNA (lncRNA) plays a vital role in tumorigenesis and may act as a competing endogenous RNA (ceRNA) to a ﬀ ect the expression of mRNA by suppressing miRNA function. In this study, we identiﬁed 628 mRNAs, 144 lncRNAs, and 25 miRNAs that are di ﬀ erentially expressed (DE) in metastatic CRC patients compared with nonmetastatic CRC patients from the Cancer Genome Atlas (TCGA) database. Functional enrichment analyses conﬁrmed that the identiﬁed DE mRNAs are extensively involved in CRC tumorigenesis and migration. By bioinformatics analysis, we constructed a metastasis-associated ceRNA network for CRC that includes 28 mRNAs, 12 lncRNAs, and 15 miRNAs. We then performed multivariate Cox regression analysis on the ceRNA-related DE lncRNAs and identiﬁed a 3-lncRNA signature (LINC00114, LINC00261, and HOTAIR) with the greatest prognostic value for CRC. Clinical feature analysis and functional enrichment analysis further proved that these three lncRNAs are involved in CRC tumorigenesis. Finally, we used Transwell, Cell Counting Kit (CCK)-8, and colony formation assays to clarify that the inhibition of LINC00114 promotes the migratory, invasive, and proliferative abilities of CRC cells. The results of the luciferase assay suggest that LINC00114 is the direct target of miR-135a, which also veriﬁed the ceRNA network. In summary, this study provides a metastasis-associated ceRNA network for and suggests that the 3-lncRNA signature may be a useful candidate for the diagnosis and prognosis of CRC.
Rhetorical Structure Approach for Online Deception Detection: A Survey

Most information is passed on in the form of language. Therefore, research on how people use language to inform and misinform, and how this knowledge may be automatically extracted from large amounts of text is surely relevant. This survey provides first-hand experiences and a comprehensive review of rhetorical-level structure analysis for online deception detection. We systematically analyze how discourse structure, aligned or not with other approaches, is applied to automatic fake news and fake reviews detection on the web and social media. Moreover, we categorize discourse-tagged corpora along with results, hence offering a summary and accessible introductions to new researchers.
ISSN 2320-5407 International Journal of Advanced Research (2016), Volume 4, Issue 1, 840- 845

Jagdish Chandra Nagar. The present work was carried out to study the effect of roots of Erythrina variegata and leaves of Breynia vitis-idaea on antioxidant enzymes levels in Alloxan induced diabetic rats. Alloxan (120 mg/kg, i.p) induced diabetic rats were treated with alcohol and aqueous extract at a dose levels of 300 and 600 mg/kg for 21 days. Antioxidant enzymes levels viz. Lipid peroxidation (LPO), Superoxide dismutase (SOD), Catalase (CAT) and Glutathione (GSH) were measured in liver homogenate. After 21 days of experimental period the level of reduced glutathione (GSH), superoxide dismutase (SOD) and catalase (CAT) significantly increase, while elevated level of lipid peroxidation (LPO) significantly decrease. It suggests that because of its antioxidant effects its administration may be useful in controlling the diabetic complications in experimental diabetic rats. So, it can be concluded that both the extracts of Erythrina variegata and Breynia vitis-idaea are very promising candidate for the design of new drugs based on its pharmacological effects of antioxidant adequacy.