nils-herrmann/clean_scirepeval huggingface dataset:
Quality and denoising in real time functional magnetic resonance imaging neurofeedback: A methods review
Venue: Human brain mapping
Year: 2020

Neurofeedback training using real time functional magnetic resonance imaging (rtfMRI NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI NF studies. We found: (a) that less than a third of the studies reported implementing standard real time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI NF studies: (a) report implementation of a set of standard real time fMRI denoising steps according to a proposed COBIDAS style checklist (https:/osf.io/kjwhf/ (b) ensure the quality of the neurofeedback signal by calculating and reporting community informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open source rtfMRI NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https:/github.com/jsheunis/quality and denoising in rtfmri nf.
Estimating the Statistical Power of the Benjamini Hochberg Procedure
Venue: HPC
Year: 2019

In this work we investigate the properties of the statistical power of the Benjamini Hochberg procedure. That procedure provides a widely used method for controlling the False Discovery Rate in a multiple comparison setup. We show that in many cases the statistical power of the p values adjusted by the Benjamini Hochberg procedure could be approximated by a normal distribution with both its mean and standard deviation having an exponential fit and convergence when the number of tests increase. As a result one could estimate the probability that the power belongs in a predetermined interval in a very computationally efficient way using only simulations for several values of the number of tests parameter. The fit for the mean also supports the conjecture that the power of the test decreases to the limit power, which is known to exist, when increasing the number of tests. The latter is a very favourable observation from practical perspective and we try to offer partially rigorous explanation why the monotonicity is present.
New paradigms in the treatment of small bowel obstruction.
Venue: Current problems in surgery
Year: 2012

urgery in the 21st century has seen the development of a host of exciting echnological and management innovations such as minimally invasive urgery, endovascular treatment in vascular disease, endoscopic proceures that require no incision, and potent and effective new medications hat have altered the natural history of some surgical diseases. One isease that has changed very little in its incidence, natural history, reatment approach, and preventability is small bowel obstruction (SBO) mall bowel obstruction remains a common and difficult problem ncountered by all surgeons who operate in the abdomen. Given its ommon and serious nature, it is surprising that so little progress has been ade in preventing and treating it when it occurs. Nevertheless, over the ast 15 years, some modest progress and advancements have been made n its treatment, which will be the focus of this review. In Wiseman's review of the topic, he noted that Hunter knew about BO in the mid 1700s and described a case associated with adhesions, eritonitis, and infection. In 1842, Bryant reported a fatal case of bowel bstruction caused by adhesions. Both Muller and Malcolm described sing salt solution to "float the intestines" and prevent adhesions in 1886 nd 1889 respectively. By 1932, adhesions accounted for 7% of ntestinal obstructions in the United Kingdom, and by 1934, Moss eported that 27% of intestinal obstructions in the United States were due o adhesions (Table 1)
Socioeconomic inequality in functional deficiencies and chronic diseases among older Indian adults: a sex stratified cross sectional decomposition analysis
Venue: BMJ Open
Year: 2019

Older adults with adverse socioeconomic conditions suffer disproportionately from a poor quality of life. Stratified by sex, income related inequalities have been decomposed for functional deficiencies and chronic diseases among older adults, and the degree to which social and demographic factors contribute to these inequalities was identified in this study.   Data used for this study were retrieved from the WHO Study on Global AGEing and Adult Health Wave 1. A total of 3753 individuals (men: 1979, and women: 1774) aged =60 years were found eligible for the analysis.  Instrumental Activity of Daily Living (IADL) deficiency and presence of chronic diseases.  The decomposition method proposed by Adam Wagstaff and his colleagues was used. The method allows estimating how determinants of health contribute proportionally to inequality in a health variable.  Compared with men, women were disproportionately affected by both functional deficiencies and chronic diseases. The relative contribution of sociodemographic factors to IADL deficiency was highest among those with poor economic status (38.5% followed by those who were illiterate (22.5% which collated to 61% of the total explained inequalities. Similarly, for chronic diseases, about 93% of the relative contribution was shared by those with poor economic status (42.3% rural residence (30.5% and illiteracy (20.3% Significant difference in predictors was evident between men and women in IADL deficiency and chronic illness.  Pro poor intervention strategies could be designed to address functional deficiencies and chronic diseases, with special attention to women.
Hedonic Pricing of Cloud Computing Services
Venue: IEEE Transactions on Cloud Computing
Year: 2021

Cloud service providers (CSP) and cloud consumers often need to forecast the cloud price to optimize their business strategy. However, pricing of cloud services is a challenging task due to its services complexity and dynamic nature of the ever changing environment. Moreover, the cloud pricing based on consumers' willingness to pay (W2P) becomes even more challenging due to the subjectiveness of consumers' experiences and implicit values of some non marketable features, such as burstable CPU, dedicated server, and cloud data center global footprints. Unfortunately, many existing pricing models often cannot support value based pricing. In this paper, we propose a novel solution based on value based pricing, which does not only consider how much does the service cost (or intrinsic values) to a CSP but also how much a customer is willing to pay (or extrinsic values) for the service. We demonstrate that the cloud extrinsic values would not only become one of the competitive advantages for CSPs to lead the cloud market but also increase the profit margin. Our approach is often referred to as a hedonic pricing model. We show that our model can capture the value of non marketable features. This value is about 43.4 percent on average above the baseline, which is often ignored by many traditional cloud pricing models. We also show that Average Annual Growth Rate (AAGR) of Amazon Web Services' (AWS) is about 20.0 percent per annum between 2008 and 2017, ceteris paribus. In comparison with Moore's law 50 percent per annum) it is at a far slower pace. We argue this value is Moore's law equivalent in the cloud. The primary goal of this research is to provide a less biased pricing model for cloud decision makers to develop their optimizing investment strategy.
Corporate Governance in China: Current Practices, Economic Effects and Institutional Determinants
Year: 2006

This article provides a preliminary survey of the burgeoning literature on the Chinese listed firms' corporate governance. We structure the existing research around three themes: (1) What are the current corporate governance practices in China? (2) How do these corporate governance practices affect the Chinese listed firms' valuation and various corporate decisions? (3) How does China's unique institutional setting pre determine the governance model adopted in China? The evidence indicates that the current governance practice adopted in China can be best described as a control based model, which contrasts strikingly with the market oriented model commonly used in the US and UK, and championed by most corporate governance advocates. The evidence also shows that Chinese firms, whose corporate governance practices deviate from the control based model, demonstrate stronger performance, and tend to make decisions in line with the shareholders' interest. The evidence from the literature also suggests that the control based model is rooted in the 'administrative governance' approach adopted by the Chinese regulatory authorities, and is tailed to China's specific institutional setting.
Clinical significance of CD151 overexpression in subtypes of invasive breast cancer
Venue: British Journal of Cancer
Year: 2012

CD151 is a member of the tetraspanin family, which interacts with laminin binding integrins and other tetraspanins. This protein is implicated in motility, invasion, and metastasis of cancer cells, but the prevalence of CD151 expression in subtypes of breast cancers and its influence on clinical outcome remains to be evaluated.The immunohistochemistry based tissue microarray analysis showed that 127 (14.3% cases overexpressed CD151 among 886 breast cancer patients. CD151 overexpression was found to be significantly associated with larger tumour size, higher nodal stage, advanced stage, absence of oestrogen receptor and progesterone receptor, and human epidermal growth factor receptor 2 overexpression. CD151 overexpression resulted in poorer overall survival (OS) (P<0.001) and disease free survival (P=0.02) and stage II and III patients with CD151 overexpression demonstrated substantially poorer OS (P=0.0474 and 0.0169) In the five subtypes analyses, CD151 overexpression retained its adverse impact on OS in the Luminal A (P=0.0105) and quintuple negative breast cancer (QNBC) subtypes, one subgroup of triple negative breast cancer (P=0.0170) Multivariate analysis that included stage, subtype, and adjuvant chemotherapy showed that CD151 overexpression was independently associated with poor OS in invasive breast cancer.CD151 overexpression may be a potential molecular therapeutic target for breast cancer, especially in QNBC subtype and more advanced stages of breast cancer.
StarCraft II: A New Challenge for Reinforcement Learning
Venue: ArXiv
Year: 2017

This paper introduces SC2LE (StarCraft II Learning Environment) a reinforcement learning environment based on the StarCraft II game. This domain poses a new grand challenge for reinforcement learning, representing a more difficult class of problems than considered in most prior work. It is a multi agent problem with multiple players interacting; there is imperfect information due to a partially observed map; it has a large action space involving the selection and control of hundreds of units; it has a large state space that must be observed solely from raw input feature planes; and it has delayed credit assignment requiring long term strategies over thousands of steps. We describe the observation, action, and reward specification for the StarCraft II domain and provide an open source Python based interface for communicating with the game engine. In addition to the main game maps, we provide a suite of mini games focusing on different elements of StarCraft II gameplay. For the main game maps, we also provide an accompanying dataset of game replay data from human expert players. We give initial baseline results for neural networks trained from this data to predict game outcomes and player actions. Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain. On the mini games, these agents learn to achieve a level of play that is comparable to a novice player. However, when trained on the main game, these agents are unable to make significant progress. Thus, SC2LE offers a new and challenging environment for exploring deep reinforcement learning algorithms and architectures.
An integrated MCDM approach considering demands matching for reverse logistics
Year: 2019

Reverse logistics (RL) has been regarded as a key driving force for remanufacturing. However, there are great uncertainties in terms of quality and quantity of used components for RL. There are also complexities in suppliers and operations. These make decision making of RL very complex. In order to identify the best collection mode for used components, a demand matching oriented Multiple Criteria Decision Making (MCDM) method is established. In this method, the damage level and remaining service life are firstly incorporated into the evaluation criteria of reuse modes, then a hybrid method (AHP EW) that integrates Analytic Hierarchy Process (AHP) and Entropy Weight (EW) method is applied to derive criteria weights and the grey Multi Attributive Border Approximation Area Comparison (MABAC) is adopted to rank the collection modes. Finally, sensitivity analysis is implemented to test the stability of the proposed method, and a demands matching method is proposed to validate and evaluate the feasibility of the optimal alternative. The collection of used pressurizers is taken as case study to validate the applicability of the proposed model. The results showed the effectiveness of the proposed method in MCDM of RL.
A Review of Improving Software Quality using Machine Learning Algorithms
Year: 2017

Software is a process and maintains continuous change to improve the functionality and effectiveness of the software quality. During the life cycle of software various problems arises like advanced planning, well documentation and proper process control. This problem may result in not achieving the software quality as desired. With respect to competition in the market it is necessary to remove this problem with the help of software engineering. In this paper software prediction model used to control the classes of software which are often to change. Machine learning algorithms are used for predicting software.
Distributed Optimization Methods for Nonconvex Problems with Inequality Constraints over Time Varying Networks
Venue: Complex.
Year: 2017

Network structured optimization problems are found widely in engineering applications. In this paper, we investigate a nonconvex distributed optimization problem with inequality constraints associated with a time varying multiagent network, in which each agent is allowed to locally access its own cost function and collaboratively minimize a sum of nonconvex cost functions for all the agents in the network. Based on successive convex approximation techniques, we first approximate locally the nonconvex problem by a sequence of strongly convex constrained subproblems. In order to realize distributed computation, we then exploit the exact penalty function method to transform the sequence of convex constrained subproblems into unconstrained ones. Finally, a fully distributed method is designed to solve the unconstrained subproblems. The convergence of the proposed algorithm is rigorously established, which shows that the algorithm can converge asymptotically to a stationary solution of the problem under consideration. Several simulation results are illustrated to show the performance of the proposed method.
Invariant distributions and cohomology for geodesic flows and higher cohomology of higher rank Anosov actions
Year: 2013

We are motivated by a conjecture of A. and S. Katok to study the smooth cohomologies of a family of Weyl chamber flows. The conjecture is a natural generalization of the Livsic Theorem to Anosov actions by higher rank abelian groups; it involves a description of top degree cohomology and a vanishing statement for lower degrees. Our main result, proved in Part II, verifies the conjecture in lower degrees for our systems, and steps in the "correct" direction in top degree. In Part I we study our "base case" geodesic flows of finite volume hyperbolic manifolds. We describe obstructions (invariant distributions) to solving the coboundary equation in unitary representations of the group of orientation preserving isometries of hyperbolic N space, and we study Sobolev regularity of solutions. (One byproduct is a smooth Livsic Theorem for geodesic flows of hyperbolic manifolds with cusps. Part I provides the tools needed in Part II for the main theorem.
Uric acid and cardiovascular disease risk reclassification: Findings from NHANES III
Venue: European journal of preventive cardiology
Year: 2015

The studied associations between serum uric acid (sUA) and cardiovascular disease (CVD) events have been controversial. We sought to evaluate the association between sUA and CVD mortality, including its ability to reclassify risk in a multiethnic nationally representative population free of clinical CVD and diabetes at baseline.  The study cohort included 11,009 adults enrolled as a part of the National Health and Nutrition Examination Survey (NHANES) III. Multivariate Cox proportional hazard analysis was performed to evaluate sUA as a predictor of CVD and coronary heart disease (CHD) mortality. Discriminative and recalibrative properties of sUA for CHD deaths were also assessed over traditional CVD risk factors. Net reclassification index (NRI) was calculated by comparing regression models incorporating traditional CVD risk factors with and without sUA. Results sUA was not predictive of either CVD mortality [model 4: hazards ratio (HR) 1.06, 95% confidence interval (CI) 0.96 1.16, p 0.27] or CHD mortality (model 4: HR 1.06, 95% CI 0.94 1.19, p 0.32) Addition of sUA to traditional CVD risk factors resulted in no significant increment in c statistic, receiver operating characteristics area under curve, absolute NRI (0.5% 95% CI 1.9 to 2.9% p 0.68) or intermediate NRI (2.5% 95% CI 1.6 to 6.6% p 0.24) for prediction of hard CHD deaths. Conclusions sUA was not an independent predictor of both CVD and CHD mortality. Ethnicity did not influence the association of sUA with CVD mortality. Furthermore, sUA did not add to risk assessment beyond traditional CVD risk factors.
Data Preparation for Longitudinal Data Mining: a case study on human ageing
Venue: J. Inf. Data Manag.
Year: 2016

An adequate preparation of a database is essential to the extraction of useful knowledge contained in it. On longitudinal studies, that follow a fixed set of records through a time period, the data preparation process should adapt to the features added to the database by the temporal aspect of data. This article presents the data preparation process of a real longitudinal database, from a human ageing study. The process addresses the conceptual feature selection of the attributes in the database, and its preprocessing, including noisy data elimination, variable merging, discretization, outlier detection, and missing data analysis. The guidelines to the procedures were generalized, allowing their replication on other longitudinal databases, for similar studies.
Impacts of Atmospheric Nitrogen Deposition and Coastal Nitrogen Fluxes on Oxygen Concentrations in Chesapeake Bay
Year: 2018

Although rivers are the primary source of dissolved inorganic nitrogen (DIN) inputs to the Chesapeake Bay, direct atmospheric DIN deposition and coastal DIN concentrations on the continental shelf can also significantly influence hypoxia; however, the relative impact of these additional sources of DIN on Chesapeake Bay hypoxia has not previously been quantified. In this study, the estuarine carbonbiogeochemistry model embedded in the Regional Ocean Modeling System (ChesROMS ECB) is used to examine the relative impact of these three DIN sources. Model simulations highlight that DIN from the atmosphere has roughly the same impact on hypoxia as the same gram for gram change in riverine DIN loading, although their spatial and temporal distributions are distinct. DIN concentrations on the continental shelf have a similar overall impact on hypoxia as DIN from the atmosphere ~0.2 mg L however, atmospheric DIN impacts dissolved oxygen (DO) primarily via the decomposition of autochthonous organic matter, whereas coastal DIN concentrations primarily impact DO via the decomposition of allochthonous organic matter entering the Bay mouth from the shelf. The impacts of atmospheric DIN deposition and coastal DIN concentrations on hypoxia are greatest in summer and occur farther downstream (southern mesohaline) in wet years than in dry years (northern mesohaline) Integrated analyses of the relative contributions of all three DIN sources on summer bottomDO indicate that impacts of atmospheric deposition are largest in the eastern mesohaline shoals, riverine DIN has dominant impacts in the largest tributaries and the oligohaline Bay, while coastal DIN concentrations are most influential in the polyhaline region.  Most organisms living in the Chesapeake Bay, like fish, crabs, and oysters, need adequate oxygen concentrations to survive. However, general increases in the supply of nutrients to estuaries always enhance the production of algae, and the decomposition of these algae takes away oxygen from other organisms, resulting in hypoxic (low oxygen) conditions or what is commonly referred to as a "dead zone." Generally, researchers focus on how terrestrial nutrients entering the bay, for example, from fertilizer, wastewater treatment, or sewer runoff, produce the Chesapeake Bay dead zone, since they account for most of the nutrients entering the bay. However, the atmospheric and oceanic nutrients directly impacting the bay are often not accurately considered. In this study the impacts of nutrients from the atmosphere and the open ocean on Chesapeake Bay hypoxia are quantified via the application of a three dimensional ecosystemmodel. Atmospheric deposition of nitrate is found to have the same gram for gram impact on hypoxia as terrestrial nitrate entering via rivers. Overall, these two sources of nutrients have the greatest impact in the summer and have similar impacts on dissolved oxygen, reducing oxygen concentrations by up to 0.2 mg L 1 in the mid Chesapeake Bay region where oxygen concentrations are lowest.