nils-herrmann/clean_scirepeval huggingface dataset:
An Empirical Study of Multi Objective Algorithms for Stock Ranking
Year: 2007

Quantitative models for stock selection and portfolio management face the challenge of determining the most efficacious factors, and how they interact, from large amounts of financial data. Genetic programming using "simple objective" fitness functions has been shown to be an effective technique for selecting factors and constructing multi factor models for ranking stocks, but the resulted models can be somewhat unbalanced in satisfying the multiple objectives that portfolio managers seek: large excess returns that are consistent across time and the cross sectional dimensions of the investment universe. In this study, we implement and evaluate three multi objective algorithms to simultaneously optimize the information ratio, information coefficient, and intra fractile hit rate of a portfolio. These algorithms the constrained fitness function, sequential algorithm, and parallel algorithm take widely different approaches to combine these different portfolio metrics. The results show that the multi objective algorithms do produce well balanced portfolio performance, with the constrained fitness function performing much better than the sequential and parallel multi objective algorithms. Moreover, this algorithm generalizes to the held out test data set much better than any of the single fitness algorithms.
Modification of polydopamine coated Fe3O4 nanoparticles with multi walled carbon nanotubes for magnetic m dispersive solid phase extraction of antiepileptic drugs in biological matrices
Venue: Analytical and Bioanalytical Chemistry
Year: 2018

AbstractIn this study, multi walled carbon nanotubes were coated on the surface of magnetic nanoparticles modified by polydopamine. The synthesized composite was characterized and applied to magnetic m dispersive solid phase extraction of oxcarbazepine (OXC) phenytoin (PHT) and carbamazepine (CBZ) from human plasma, urine, and cerebrospinal fluid samples prior to analysis by a high performance liquid chromatography photodiode array detector. The extraction parameters were investigated and the optimum condition was obtained when the variables were set to the following: sorbent type, Fe3O4@polyDA MWCNTs (length 2 mm) sample pH, 6; amount of sorbent, 15 mg; sorption time, 1.5 min at room temperature; type and volume of the eluent, 2.5 mL methanol; and salt content, none added. Under the optimized conditions, the calibration curves are linear in the concentration range 2 2000 ng/mL, the limits of detection are in the range 0.4 3.1 ng/mL, and the relative standard deviations and relative recoveries of plasma (spiked at 200 ng/mL) and CSF (spiked at 50 ng/mL) are in the ranges 1.4 8.2% and 92.8 96.5% respectively. The applicability of the method was successfully confirmed by extraction and determination of OXC, PHT, and CBZ in biological matrices. Graphical abstractMagnetic multi walled carbon nanotube core shell composites were applied as magnetic m dispersive solid phase extraction adsorbents for determination of antiepileptic drugs in biological matrices.
Sense of Abundance is Associated with Momentary Positive and Negative Affect: An Experience Sampling Study of Trait Gratitude in Daily Life
Venue: Journal of Happiness Studies
Year: 2019

Results are reported from a study examining the association between subdimensions of trait gratitude (appreciation of others, sense of abundance, and simple appreciation, SGRAT, Thomas and Watkins 2003) and daily life affective processing, in order to respond to the acknowledged need for further research on how trait gratitude may contribute to our well being. Using experience sampling methodology actual momentary experiences of positive and negative affect were measured on 7 consecutive days in a sample of 106 respondents (63 women and 43 men, varying in age from 18 to 65 years) Multilevel regression analyses revealed that only sense of abundance was significantly associated with momentary experiences of positive affect (positive association) and negative affect (negative association) in daily life. Our findings add to the theoretical understanding of the underlying pathway of the association between trait gratitude and well being and provide a practical starting point for gratitude interventions.
Geriatric assessment among older adults receiving intensive therapy for acute myeloid leukemia: Report of CALGB 361006 (Alliance)
Venue: Journal of geriatric oncology
Year: 2019

To demonstrate feasibility of performing geriatric assessment (GA) in the National Clinical Trials Network (NCTN) and to explore the utility of GA to characterize treatment tolerance.  We conducted a multisite companion study (CALGB 361006) to CALGB 11001, a phase 2 trial of adults =60 years old with newly diagnosed FLT3 mutated AML, testing the efficacy of adding sorafenib to intensive chemotherapy. On 361006, a GA was administered prior to induction and prior to post remission therapy. The GA is divided into items requiring administration by a health care professional (HCP) and patient self administered questionnaires. Feasibility outcomes were recruitment rate, time to GA completion, difficulty with GA administration, percent of patients requiring assistance, and satisfaction. Change in GA measures pre and post induction were compared using Wilcoxon signed rank test and McNemar's tests.  The recruitment rate was 80% (N 43, median age 68 years) Median completion time of the GA was 30 min; (10 and 21 min for HCP and patients, respectively) HCP reported no difficulty completing assessments (100% Most patients completed questionnaires without assistance (77% and were satisfied with the length (89% Self reported physical function, mental health, social activity and nutritional parameters worsened after induction.  GA is feasible to administer in the setting of intensive induction for older adults with AML in the NCTN and provides evidence of the impact of induction therapy on physical and emotional health.
Modeling Measures of Float Allocation for Quantitative Risk Management of Construction Projects
Year: 2015

Risk is ever present and manifests itself in different ways in construction projects of all sizes and complexities. Its most recognizable and quantifiable form is schedule risk, which is related to float within a project, i.e. its flexibility to absorb delays. Allocating, owning, valuing, and expending such float in network schedules has been debated since the inception of the critical path method. This research investigates the first element of a three part approach that models how float may be mitigated by allocating, correlating, and valuing it so that it may eventually become traded as a commodity, an unrequited concept in construction engineering and management. Mathematical voting models that are based on the power of a single entity to change the outcome of an election are explored as analogies for the interactions of participants in construction projects: Disparate entities, here subcontractors, participate in a complex decision making process subject to constraints and uncertainties as to whether the project will be on time. Risk measurement is explored through the Penrose Banzhaf 'square root law' which allocates voting power based on the square root of the population where voting rights are not equally divided. It newly adapts this theory to allocate float among participants and their activities, who likewise have unequal individual durations, values, and positions in the network, based on the square root of their respective population elements: Schedule duration or contract value. Recognizing the impact of each entity on the project outcome represents the first element of the model allocating tradable float.
Depression, relationship quality, and couples' demand/withdraw and demand/submit sequential interactions.
Venue: Journal of counseling psychology
Year: 2014

This study investigated the associations among depression, relationship quality, and demand/withdraw and demand/submit behavior in couples' conflict interactions. Two 10 min conflict interactions were coded for each couple (N 97) using Structural Analysis of Social Behavior (SASB; Benjamin, 1979a, 1987, 2000a) Depression was assessed categorically (via the presence of depressive disorders) and dimensionally (via symptom reports) Results revealed that relationship quality was negatively associated with demanding behavior, as well as receiving submissive or withdrawing behavior from one's partner. Relationship quality was positively associated with withdrawal. Demanding behavior was positively associated with women's depression symptoms but negatively associated with men's depression symptoms. Sequential analysis revealed couples' behavior was highly stable across time. Initiation of demand/withdraw and demand/submit sequences were negatively associated with partners' relationship adjustment. Female demand/male withdraw was positively associated with men's depression diagnosis. Results underscore the importance of sequential analysis when investigating associations among depression, relationship quality, and couples' interpersonal behavior.
BLNet: A Fast Deep Learning Framework for Low Light Image Enhancement with Noise Removal and Color Restoration
Venue: ArXiv
Year: 2021

Images obtained in real world low light conditions are not only low in brightness, but they also suffer from many other types of degradation, such as color bias, unknown noise, detail loss and halo artifacts. In this paper, we propose a very fast deep learning framework called Bringing the Lightness (denoted as BLNet) that consists of two U Nets with a series of welldesigned loss functions to tackle all of the above degradations. Based on Retinex Theory, the decomposition net in our model can decompose low light images into reflectance and illumination and remove noise in the reflectance during the decomposition phase. We propose a Noise and Color Bias Control module (NCBC Module) that contains a convolutional neural network and two loss functions (noise loss and color loss) This module is only used to calculate the loss functions during the training phase, so our method is very fast during the test phase. This module can smooth the reflectance to achieve the purpose of noise removal while preserving details and edge information and controlling color bias. We propose a network that can be trained to learn the mapping between low light and normallight illumination and enhance the brightness of images taken in low light illumination. We train and evaluate the performance of our proposed model over the real world Low Light (LOL) dataset) and we also test our model over several other frequently used datasets (LIME, DICM and MEF datasets) We conduct extensive experiments to demonstrate that our approach achieves a promising effect with good rubustness and generalization and outperforms many other state of the art methods qualitatively and quantitatively. Our method achieves high speed because we use loss functions instead of introducing additional denoisers for noise removal and color correction. The code and model are available at https:/github.com/weixinxu666/BLNet.
A study on frequency of proteinuria and its determinants among type 2 diabetes mellitus patients presenting at tertiary care hospital of North India
Year: 2018

Type 2 diabetes mellitus is the disease which has reached epidemic proportion worldwide. Diabetic kidney disease (DKD) is an important and irreversible microvascular complication of diabetes mellitus which is characterized by persistent proteinuria, hypertension and progressive irreversible decline in renalfunction. The present study was carried out to stratify proportion of diabetic nephropathy as reflected by proteinuria, according to duration of diabetes mellitus as well as to identify the possible demographic, anthropometric and hemodynamic risk factors for the development of proteinuria. This crosssectional study was conducted on 74 patients of type 2 diabetes mellitus. Age, sex, height, weight, systolic and diastolic blood pressure and body mass index was recorded. 4ml of fasting venous blood was collected under aseptic precaution. Proteinuria was assessed semi quantitatively using dipsticks. All data is presented as Mean+ SD. Unpaired student t test was used to compare mean values of continuous variable. Total 37 out of 74 participants showed FPG 126 or more mg/dl out of which 22/37(59.5% showed no proteinuria but 15/37(40.5% showed proteinuria. Total 14(18.9% participants out of 74 were obese out of which 57.1% (8/14) did not have proteinuria while 42.9% (6/14) hadproteinuria. There was clear association seen in frequency of proteinuria with the duration of diabetes. 45% of patients who had diabetes for more than 10 years developed proteinuria as compared to 35.7% and 10% patients who had proteinuria for 5 10 years and less than 5 years respectively. Frequency of proteinuria in type 2 DM patients was 24.32% Predictors were BMI, FBG and duration of diabetes mellitus. Early measurements should be taken to control obesity, blood pressure and blood glucose to reduce the occurrence of diabetic nephropathy.  Proteinuria, Type 2 Diabetes Mellitus, Diabetic nephropathy
Product Audit as a continuous improvement instrument for Perceived Quality on vehicles at Automobili Lamborghini SpA
Year: 2019

The following thesis is about product Audit and its involvement in the continuous improvement processes of Automobili Lamborghini products. The first task will be to contextualize the company by describing its history, analysing main phases and management changes which have characterized it. Then, company's actual product offer, its competitors and its targeted customers will be studied, with a particular focus about the tough segment in which Automobili Lamborghini works: super sport cars and super SUV. After, Quality will be defined, described and analysed in all its facets, starting from the beginning, up to present days. The following task will be to understand what is vehicle Audit and why it is generally seen as an important instrument for evaluating level of quality perceived by customers, especially explaining its central role in Volkswagen Group. Therefore, it will be described how product Audit is organized in Automobili Lamborghini and how it relates with other departments such as Production, Research Development, Supplier Quality and many others. In particular, it will be discussed the method used in order to assess the Quality Level of vehicles produced by the company, with a particular attention about defects classification and investigation. Moreover, it will be exposed in detail the whole process that must be followed in order to perform a product Audit session, starting from the dynamic and static inspection of the vehicle till the filling of the Quality Class report. Furthermore, monitoring instruments, which are daily used by Audit team, will be described and their functions will be analysed. It will be also clarified the role of Audit as continuous improvement instrument for those models already in production phase. After, it will be described the method for analysing and monitoring continuous improvement methodologies based on Defect modules. Finally, it will be defined the importance of Audit in phases preceding SOP (Start of Production) and how it is seen as an improvement instrument which guarantees conformity of production and high quality perceived.
Data management in bioengineering systems
Venue: 2015 IEEE 9th International Symposium on Intelligent Signal Processing (WISP) Proceedings
Year: 2015

Effective data management and processing belongs to one of the most important tasks of the development of current information and database systems. It is necessary to store not only actual valid data objects, but also the complex record of changes during the whole life cycle of the object historical values, actual states, but also data valid in the future based on actual decisions. Existing temporal solutions are inadequate in terms of performance effectiveness of the whole system, which is manifested by the size of the required data and processing time. Moreover, bioengineering systems highlight the reliability of processed data. This paper deals with the magnetic resonance imaging result processing based on tumour markers with emphasis on temporal data management and changes monitoring based on Epsilon definition.
Multinomial logistic regression and product unit neural network models: Application of a new hybrid methodology for solving a classification problem in the livestock sector
Venue: Expert Syst. Appl.
Year: 2009

This work presents a new approach for multi class pattern recognition based on the hybridization of a linear and nonlinear model. We propose multinomial logistic regression where some new covariates are defined by a product unit neural network, where in turn, the nonlinear basis functions are constructed with the product of the inputs raised to arbitrary powers. The application of this methodology involves, first of all, training the coefficients and the basis structure of product unit models using techniques based on artificial neural networks and evolutionary algorithms, followed by the application of multinomial logistic regression to both the new derived features and the original ones. To evaluate the efficacy of our technique we pose a difficult problem, the classification of sheep with respect to their milk production in different lactations, using covariates that only involve the first weeks of lactation. This enables the productive capacity of the animal to be identified more rapidly and leads to a faster selection process in determining the best producers. The results obtained with our approach are compared to other classification methodologies. Although several of these methodologies offer good results, the percentage of cases correctly classified was higher with our approach, which shows how instrumental the potential use of this methodology is for decision making in livestock enterprises, a sector relatively untouched by the technological innovations in business management that have been appearing in the last few years.
Population age structure and consumption growth: evidence from National Transfer Accounts
Year: 2017

We assess the effects of the population age structure and the population dynamics on economic growth. Following recent research, we focus on the generational turnover effect to characterize the influence of birth and death rates, depending on the age profile of individual consumption, the extent of annuity market imperfections, and the willingness of households to shift consumption over time. Using data from the National Transfer Accounts on age profiles of consumption for a number of different countries, we assess in a comparative way the sign and the magnitude of the generational turnover effect and its impact on economic growth. We find considerable cross country differences and trace them back to the underlying variation in demography and in the age structure of consumption.
El uso e interpretacion de fracciones en la solucion de problemas en los contextos de medicion, conteo, variacion y aleatoriedad, en estudiantes de basica primaria
Year: 2019

Este trabajo de investigacion se realizo con el proposito de utilizar las interpretaciones de una fraccion, sus diferentes representaciones y significados, en la solucion de problemas en contextos de medicion, conteo, variacion y aleatoriedad a traves de un trabajo de aula que contemplo el diseno de talleres basados en problemas y la aplicacion de estos en un grupo de estudiantes de grado quinto de la Institucion Educativa Claretiano Gustavo Torres Parra de Neiva Huila. En la metodologia, se establecio que esta propuesta estaria enmarcada dentro del paradigma cualitativo de caracter descriptivo y se tomo como principal fuente de informacion la produccion escrita de los estudiantes al resolver los instrumentos metodologicos: taller diagnostico, de afianzamiento y taller final, y establecio los parametros para el analisis de los resultados. La relevancia de este proyecto se cimento en la determinacion de los avances y/o debilidades que presentaron los estudiantes al interpretar la fraccion en conjuntos continuos y en conjuntos discretos, como cociente, operador, probabilidad, porcentaje y razon, durante la solucion de los problemas propuestos. Ademas de ampliar notoriamente el rango de utilidad de las fracciones, se logro fortalecer una cantidad importante de conocimientos y procesos definidos por el ministerio de educacion nacional para el grado quinto de primaria a traves de los estandares basicos de competencia y los derechos basicos de aprendizaje
Maximum Likelihood Estimation of a Structured Covariance Matrix With a Condition Number Constraint
Venue: IEEE Transactions on Signal Processing
Year: 2012

In this paper, we deal with the problem of estimating the disturbance covariance matrix for radar signal processing applications, when a limited number of training data is present. We determine the maximum likelihood (ML) estimator of the covariance matrix starting from a set of secondary data, assuming a special covariance structure (i.e. the sum of a positive semi definite matrix plus a term proportional to the identity) and a condition number upper bound constraint. We show that the formulated constrained optimization problem falls within the class of MAXDET problems and develop an efficient procedure for its solution in closed form. Remarkably, the computational complexity of the algorithm is of the same order as the eigenvalue decomposition of the sample covariance matrix. At the analysis stage, we assess the performance of the proposed algorithm in terms of achievable signal to interference plus noise ratio (SINR) both for a spatial and a Doppler processing. The results show that interesting SINR improvements, with respect to some existing covariance matrix estimation techniques, can be achieved.
Dynamics of learning complex motor skills in preschool children
Year: 2017

The process of motor learning is generally defined by four basic characteristics: 1) a set of inherent events, circumstances or changes in performing certain motor skills; 2) a direct result of exercise or experience; 3) not directly visible but estimated through performance of learned skills; 4) causes a relatively permanent change in the capacity of learned behaviours. At the beginning of artistic gymnastics programmes, a large number of structurally different movement skills are learned. One of them is the cartwheel. The objectives of this study were to determine the dynamics of the learning process and the differences in levels of cartwheel performance in beginner gymnasts both with the right and the left side. Objectivity and sensitivity of the judges have both been analysed; the average score has been determined by calculating the mean value; two factor 2x6 ANOVA with repeated measures has analysed the significance of the differences in the levels of analysed motor skills in certain points of the treatment. Research showed that the three month treatment is too short to identify significant improvements in the performance of these skills.