nils-herrmann/clean_scirepeval huggingface dataset:
TVA based modeling of short term memory capacity, speed of processing and perceptual threshold in chronic stroke patients undergoing cognitive training: case control differences, reliability, and associations with cognitive performance
Venue: PeerJ
Year: 2020

Attentional deficits following stroke are common and pervasive, and are important predictors for functional recovery. Attentional functions comprise a set of specific cognitive processes allowing to attend, filter and select among a continuous stream of stimuli. These mechanisms are fundamental for more complex cognitive functions such as learning, planning and cognitive control, all crucial for daily functioning. The distributed functional neuroanatomy of these processes is a likely explanation for the high prevalence of attentional impairments following stroke, and underscores the importance of a clinical implementation of computational approaches allowing for sensitive and specific modeling of attentional sub processes. The Theory of Visual Attention (TVA) offers a theoretical, computational, neuronal and practical framework to assess the efficiency of visual selection performance and parallel processing of multiple objects. Here, in order to assess the sensitivity and reliability of TVA parameters reflecting short term memory capacity (K) processing speed (C) and perceptual threshold (t0) we used a whole report paradigm in a cross sectional case control comparison and across six repeated assessments over the course of a three week computerized cognitive training (CCT) intervention in chronic stroke patients 6 months since hospital admission, NIHSS 7 at hospital discharge) Cross sectional group comparisons documented lower short term memory capacity, lower processing speed and higher perceptual threshold in patients (n 70) compared to age matched healthy controls (n 140) Further, longitudinal analyses in stroke patients during the course of CCT (n 54) revealed high reliability of the TVA parameters, and higher processing speed at baseline was associated with larger cognitive improvement after the intervention. The results support the feasibility, reliability and sensitivity of TVA based assessment of attentional functions in chronic stroke patients.
LANGUAGE LEARNING STRATEGIES OF UNDERGRADUATE EFL STUDENTS
Year: 2020

This study attempts to explore the language learning strategies used by the students' who take English Literature study programs in English as a foreign language (EFL) setting. This study involves 76 participants asked to fulfill a questionnaire called Strategy Inventory for Language Learning (SILL) developed by Oxford (1990) The result of this research showed that metacognitive has been the most frequently used strategy followed by social and compensation strategies while affective strategies become the least strategy used by the students. This research could be meaningful insight for other researches or the students to analyze the language learning strategies used by the students and be meaningful to know the language learning strategies that appropriate especially in the field of the second language.
OCT intensity and phase fluctuations correlated with activity dependent neuronal calcium dynamics in the Drosophila CNS [Invited]
Venue: Biomedical optics express
Year: 2017

Phase resolved OCT and fluorescence microscopy were used simultaneously to examine stereotypic patterns of neural activity in the isolated Drosophila central nervous system. Both imaging modalities were focused on individually identified bursicon neurons known to be involved in a fixed action pattern initiated by ecdysis triggering hormone. We observed clear correspondence of OCT intensity, phase fluctuations, and activity dependent calcium induced fluorescence.
A Concave Optimization Algorithm for Matching Partially Overlapping Point Sets
Venue: Pattern Recognit.
Year: 2020

Point matching refers to the process of finding spatial transformation and correspondences between two sets of points. In this paper, we focus on the case that there is only partial overlap between two point sets. Following the approach of the robust point matching method, we model point matching as a mixed linear assignment least square problem and show that after eliminating the transformation variable, the resulting problem of minimization with respect to point correspondence is a concave optimization problem. Furthermore, this problem has the property that the objective function can be converted into a form with few nonlinear terms via a linear transformation. Based on these properties, we employ the branch and bound (BnB) algorithm to optimize the resulting problem where the dimension of the search space is small. To further improve efficiency of the BnB algorithm where computation of the lower bound is the bottleneck, we propose a new lower bounding scheme which has a k cardinality linear assignment formulation and can be efficiently solved. Experimental results show that the proposed algorithm outperforms state of the art methods in terms of robustness to disturbances and point matching accuracy.
Music Feature Classification Based on Recurrent Neural Networks with Channel Attention Mechanism
Venue: Mob. Inf. Syst.
Year: 2021

With the advancement of multimedia and digital technologies, music resources are rapidly increasing over the Internet, which changed listeners' habits from hard drives to online music platforms. It has allowed the researchers to use classification technologies for efficient storage, organization, retrieval, and recommendation of music resources. The traditional music classification methods use many artificially designed acoustic features, which require knowledge in the music field. The features of different classification tasks are often not universal. This paper provides a solution to this problem by proposing a novel recurrent neural network method with a channel attention mechanism for music feature classification. The music classification method based on a convolutional neural network ignores the timing characteristics of the audio itself. Therefore, this paper combines convolution structure with the bidirectional recurrent neural network and uses the attention mechanism to assign different attention weights to the output of the recurrent neural network at different times; the weights are assigned for getting a better representation of the overall characteristics of the music. The classification accuracy of the model on the GTZAN data set has increased to 93.1% The AUC on the multilabel labeling data set MagnaTagATune has reached 92.3% surpassing other comparison methods. The labeling of different music labels has been analyzed. This method has good labeling ability for most of the labels of music genres. Also, it has good performance on some labels of musical instruments, singing, and emotion categories.
Physicochemical characterization to assess Ni and Zn incorporation into zeotype SAPO 34 nanoparticles synthesized with different mixing methods through ultrasound promoted crystallization
Year: 2017

The synthesis of transition metal (Me Ni and Zn) containing SAPO 34 nanoparticles was conducted by applying insonation as the precrystallization treatment. The effects of synthesis parameters on the properties of SAPO 34 products and their derivatives in terms of the crystallinity, size and morphology of particles, structure and hydroxyl functional groups, and stereochemistry of metal ions were investigated to elucidate how the isomorphous substitution or the mere presence of transition metal ions was influenced by the crystallization treatment, mixing method, or the amount and type of metal species. The physicochemical properties of the prepared samples were characterized by various techniques including XRD, SEM, TEM, EDX, UV Vis DRS, FTIR, and TGA. The results indicated that sonochemically prepared SAPO 34 samples possessed higher crystallinity and uniform crystals with narrow particle size distribution. The nature of the metal species could affect the physicochemical properties of the synthesized samples and Zn doped SAPO 34 samples showed a lower crystallinity, larger unit cell parameters, and more uniform crystals. Also, it was found that the significant variations in the morphology, size, and uniformity of the metal doped SAPO 34 crystals were acquired based on the different mixing methods. Meanwhile, the mixing methods played a key role in the incorporation of metal ions into the SAPO 34 framework, and mixing method (II) in which the metal sources were added to the precursor gel prior to the addition of the phosphoric acid, had prominent capability to locate the metal ions, in particular nickel ions, in the SAPO 34 skeleton.
A Managerial Analysis of Urban Parcel Delivery: A Lean Business Approach
Venue: Sustainability
Year: 2019

The improper integration of traditional transportation modes with low emissions vehicles can generate a price war that reduces the service quality, undermining the efficiency and the profitability of parcel delivery operators. This paper aims to provide managerial insights to design a win win strategy for the co existence of traditional and green business models. In doing so, we adopt a multi disciplinary approach that integrates a qualitative analysis through a Lean Business methodology, named GUEST, with a quantitative analysis based on simulation optimisation techniques. This kind of holistic vision has received little attention in the literature. The first analysis investigates the parcel delivery industry with an emphasis on the main business models involved, their costs and revenues structures, while the quantitative part aims to simulate the system and extract sustainable policies. In particular, results highlight that in deploying mixed fleet policies, the decision makers have to focus both on the environmental sustainability that benefits from the adoption of low emission vehicles, and on the operational feasibility and economic sustainability of the two services. In this direction, the paper suggests some managerial insights concerning the split of the customer demand between traditional and green operators, according to the classes of parcels and geographical areas of the city.
Review: To be or not to be an identifiable model. Is this a relevant question in animal science modelling?
Venue: Animal an international journal of animal bioscience
Year: 2018

What is a good (useful) mathematical model in animal science? For models constructed for prediction purposes, the question of model adequacy (usefulness) has been traditionally tackled by statistical analysis applied to observed experimental data relative to model predicted variables. However, little attention has been paid to analytic tools that exploit the mathematical properties of the model equations. For example, in the context of model calibration, before attempting a numerical estimation of the model parameters, we might want to know if we have any chance of success in estimating a unique best value of the model parameters from available measurements. This question of uniqueness is referred to as structural identifiability; a mathematical property that is defined on the sole basis of the model structure within a hypothetical ideal experiment determined by a setting of model inputs (stimuli) and observable variables (measurements) Structural identifiability analysis applied to dynamic models described by ordinary differential equations (ODEs) is a common practice in control engineering and system identification. This analysis demands mathematical technicalities that are beyond the academic background of animal science, which might explain the lack of pervasiveness of identifiability analysis in animal science modelling. To fill this gap, in this paper we address the analysis of structural identifiability from a practitioner perspective by capitalizing on the use of dedicated software tools. Our objectives are (i) to provide a comprehensive explanation of the structural identifiability notion for the community of animal science modelling, (ii) to assess the relevance of identifiability analysis in animal science modelling and (iii) to motivate the community to use identifiability analysis in the modelling practice (when the identifiability question is relevant) We focus our study on ODE models. By using illustrative examples that include published mathematical models describing lactation in cattle, we show how structural identifiability analysis can contribute to advancing mathematical modelling in animal science towards the production of useful models and, moreover, highly informative experiments via optimal experiment design. Rather than attempting to impose a systematic identifiability analysis to the modelling community during model developments, we wish to open a window towards the discovery of a powerful tool for model construction and experiment design.
Obstacle avoidance of mobile robots using modified artificial potential field algorithm
Venue: EURASIP J. Wirel. Commun. Netw.
Year: 2019

In recent years, topics related to robotics have become one of the researching fields. In the meantime, intelligent mobile robots have great acceptance, but the control and navigation of these devices are very difficult, and the lack of dealing with fixed obstacles and avoiding them, due to safe and secure routing, is the basic requirement of these systems. In this paper, the modified artificial potential field (APF) method is proposed for that robot avoids collision with fixed obstacles and reaches the target in an optimal path; using this algorithm, the robot can run to the target in optimal environments without any problems by avoiding obstacles, and also using this algorithm, unlike the APF algorithm, the robot does not get stuck in the local minimum. We are looking for an appropriate cost function, with restrictions that we have, and the goal is to avoid obstacles, achieve the target, and do not stop the robot in local minimum. The previous method, APF algorithm, has advantages, such as the use of a simple math model, which is easy to understand and implement. However, this algorithm has many drawbacks; the major drawback of this problem is at the local minimum and the inaccessibility of the target when the obstacles are in the vicinity of the target. Therefore, in order to obtain a better result and to improve the shortcomings of the APF algorithm, this algorithm needs to be improved. Here, the obstacle avoidance planning algorithm is proposed based on the improvement of the artificial potential field algorithm to solve this local minimum problem. In the end, simulation results are evaluated using MATLAB software. The simulation results show that the proposed method is superior to the existing solution.
Notes on De Jong's period=index theorem for central simple algebras over fields of transcendence degree two
Year: 2008

These are notes on de Jong's proof of the period=index theorem over fields of transcendence degree two. They are actually about the simplified proof sketched by de Jong in the last section of his paper. These notes were meant as support for my lectures at the summer school "Central Simple Algebras over Function Fields" at the Universitat Konstanz between August, 26 and September, 1 2007 (other lectures on this subject were given by Philippe Gille, Andrew Kresch, Max Lieblich, Tamas Szamuely and Jan Van Geel) No originality is intended (except perhaps a little in the proof of the Artin splitting theorem) Various sources on which the material is based are indicated in the notes. The reader should be warned that these notes have not been updated to reflect developments in the subject which occurred after the end of the summerschool.
Efficient Third Order Dependency Parsers
Venue: ACL
Year: 2010

We present algorithms for higher order dependency parsing that are "third order" in the sense that they can evaluate substructures containing three dependencies, and "efficient" in the sense that they require only O(n4) time. Importantly, our new parsers can utilize both sibling style and grandchild style interactions. We evaluate our parsers on the Penn Treebank and Prague Dependency Treebank, achieving unlabeled attachment scores of 93.04% and 87.38% respectively.
Efficient two step chemo enzymatic synthesis of all trans retinyl palmitate with high substrate concentration and product yield
Venue: Applied Microbiology and Biotechnology
Year: 2015

A new two step chemo enzymatic approach for highly efficient synthesis of all trans retinyl palmitate is constructed in this study. In the first step, retinyl acetate as starting material was fully hydrolyzed to retinol by potassium hydroxide. In the hydrolysis system, anhydrous ethanol was the best co solvent to increase the solubility of retinyl acetate. The addition amounts of 5 M potassium hydroxide and anhydrous ethanol were 8 and 10 mL against 10 g retinyl acetate, respectively, and 100 hydrolysis rate was obtained. In the second step, esterification was catalyzed by immobilized lipase on macroporous acrylic resin AB 8 using the extracted retinol and palmitic acid as substrates in non aqueous system. After optimization, the parameters of esterification reaction were confirmed as follows: non aqueous solvent was selected as n hexane, washing times of extraction solution was four times, retinol concentration was 300 g/L, substrate molar ratio of retinol to palmitic acid was 1:1.1, the amount of immobilized enzyme was 10 g/L, and the esterification temperature was 30 degC. Under the optimal conditions, this protocol resulted in a 97.5 yield of all trans retinyl palmitate in 700 L reactor. After purification, all trans retinyl palmitate was obtained with above 99 of purity and 88 of total recovery rate. This methodology provides a promising strategy for the large scale production of all trans retinyl palmitate.
One Shot Learning for Semantic Segmentation
Venue: BMVC
Year: 2017

Low shot learning methods for image classification support learning from sparse data. We extend these techniques to support dense semantic image segmentation. Specifically, we train a network that, given a small set of annotated images, produces parameters for a Fully Convolutional Network (FCN) We use this FCN to perform dense pixel level prediction on a test image for the new semantic class. Our architecture shows a 25% relative meanIoU improvement compared to the best baseline methods for one shot segmentation on unseen classes in the PASCAL VOC 2012 dataset and is at least 3 times faster.
The Use of Cronbach's Alpha When Developing and Reporting Research Instruments in Science Education
Year: 2018

Cronbach's alpha is a statistic commonly quoted by authors to demonstrate that tests and scales that have been constructed or adopted for research projects are fit for purpose. Cronbach's alpha is regularly adopted in studies in science education: it was referred to in 69 different papers published in 4 leading science education journals in a single year (2015) usually as a measure of reliability. This article explores how this statistic is used in reporting science education research and what it represents. Authors often cite alpha values with little commentary to explain why they feel this statistic is relevant and seldom interpret the result for readers beyond citing an arbitrary threshold for an acceptable value. Those authors who do offer readers qualitative descriptors interpreting alpha values adopt a diverse and seemingly arbitrary terminology. More seriously, illustrative examples from the science education literature demonstrate that alpha may be acceptable even when there are recognised problems with the scales concerned. Alpha is also sometimes inappropriately used to claim an instrument is unidimensional. It is argued that a high value of alpha offers limited evidence of the reliability of a research instrument, and that indeed a very high value may actually be undesirable when developing a test of scientific knowledge or understanding. Guidance is offered to authors reporting, and readers evaluating, studies that present Cronbach's alpha statistic as evidence of instrument quality.
E Marketing of Library Resources and Patronage by Students in University Libraries of Akwa Ibom State, Nigeria
Year: 2019

Electronic marketing of library resources and students patronage in University libraries of Akwa Ibom State was examined in this study. Five research questions and hypotheses were formulated to guide the study.  Survey research design was employed in the study. The population of the study was two thousand and sixteen (2016) registered library users from four University libraries in Akwa Ibom State out of which six hundred and four (604) registered library users were sampled representing thirty percent (30) of the population. A structured questionnaire was developed and face validated by the researchers before being used to elicit information from the respondents. Four (4) of the questionnaires were invalid. Frequency distribution and percentages were used to analyse the research questions.  University libraries in Akwa Ibom State were yet to effectively explore the opportunities presented by web 2.0 and other emerging technologies to market their information resources. Website, email and Facebook accounts are available in the library but they are not used for marketing of resources.  The inability to explore the unlimited opportunities presented by these technologies in marketing of information resources adversely affects patronage. Several library users were unaware of the library's existing collections.  The results and findings of this study were primarily derived and analysed by the authors. It is an exclusive study in the field of library and information science. The study will help inform management of University libraries in Akwa Ibom State of the need to capitalise on the prospects presented by electronic technologies to market their information resources for improved patronage.
Development and validation of a high sensitivity assay for measuring p217+tau in plasma
Venue: Alzheimer's dementia
Year: 2021

Diagnosis of Alzheimer's disease (AD) based on amyloid beta (A) pathologic tau (T) and neurodegeneration (N) biomarkers in peripheral fluids promises to accelerate clinical trials and intercept disease earlier.  Qualification of a Simoa plasma p217+tau assay was performed, followed by clinical utility evaluation in a cohort of 227 subjects with broad A and T spectrum.  The p217+tau plasma assay was accurate, precise, dilution linear, and highly sensitive. All measured samples were within linear range of the assay, presented higher concentration in AD versus healthy controls (P .0001) and plasma and cerebrospinal fluid levels correlated (r2 0.35) The plasma p217+tau results were predictive of central T and A status (area under the curve 0.90 and 0.90, respectively) with low false rates.  The assay described here exhibits good technical performance and shows potential as a highly accurate peripheral biomarker for A or T status in AD and cognitively normal subjects.
StudentLife: Using Smartphones to Assess Mental Health and Academic Performance of College Students
Venue: Mobile Health Sensors, Analytic Methods, and Applications
Year: 2017

Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day to day and week by week impact of workload on stress, sleep, activity, mood, sociability, mental well being and academic performance of a single class of 48 students across a 10 weeks term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We propose a simple model based on linear regression with lasso regularization that can accurately predict cumulative GPA. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.