nils-herrmann/clean_scirepeval huggingface dataset:
Investigation of UK Farmer Go/No Go Decisions in Response to Tractor Based Risk Scenarios
Venue: Journal of agromedicine
Year: 2018

Tractors are a source of injury and fatality in agriculture. Despite this farmers continue to engage in risk taking behaviors, including operating tractors without appropriate equipment. In order to change behaviors and attitudes toward safety, it is important to understand how farmers approach different types of risk relevant to tractor use. The current study used a qualitative approach to investigate farmer perception of four types of tractor based risk related to self, equipment, lack of safety features, and environment.  A sample of 148 farmers from the United Kingdom and Ireland was recruited from farming forums to take part in an online scenario based qualitative study. Participants were presented with 12 tractor based scenarios, three from each category of risk, and asked to indicate if they would proceed to use the tractor (go) or not (no go) then report their reasoning. Thematic analysis was used to identify data patterns.  Farmers appeared to assess the consequences of risk in broader terms than the risk of injury to themselves; they also evaluated risk in terms of potential costs (e.g. repair bill) and losses (e.g. delay) Analysis indicated that financial risk was considered with more caution than personal risk in many cases. Farmers also considered usability, such as the functionality of tractor seat belts, indicating the importance of ensuring all safety equipment is fit for purpose.  The management of agricultural risk should consider farmer priorities (such as financial costs) in order to develop effective messaging that focuses on those priorities, with the aim of reducing risk taking behaviors.
The Role of Health Technology and Informatics in a Global Public Health Emergency: Practices and Implications From the COVID 19 Pandemic
Venue: JMIR medical informatics
Year: 2020

At present, the coronavirus disease (COVID 19) is spreading around the world. It is a critical and important task to take thorough efforts to prevent and control the pandemic. Compared with severe acute respiratory syndrome and Middle East Respiratory Syndrome, COVID 19 spreads more rapidly owing to increased globalization, a longer incubation period, and unobvious symptoms. As the coronavirus has the characteristics of strong transmission and weak lethality, and since the large scale increase of infected people may overwhelm health care systems, efforts are needed to treat critical patients, track and manage the health status of residents, and isolate suspected patients. The application of emerging health technologies and digital practices in health care, such as artificial intelligence, telemedicine or telehealth, mobile health, big data, 5G, and the Internet of Things, have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control. Applications and evaluations of all of these technologies, practices, and health delivery services are highlighted in this study.
Understanding procrastination: A motivational approach
Year: 2018

Procrastination is often seen as a self regulatory failure. We want to offer a perspective that shifts the focus from volitional to motivational aspects of procrastination. In an attempt to demystify the phenomenon, we combine two studies exploring the motivational foundations of procrastination with a study aiming to uncover its implicit normative connotations. Study 1 investigated the link between value orientations and procrastination at a general level, showing that people high in procrastination entertain low achievement and high well being value orientations. Study 2 investigated the link between self determination and procrastination within and across daily activities. Low self determination related to low levels of activity completion and to procrastination in general. Finally, Study 3 investigated the link between value as well as political orientations and perceptions of procrastination. Individuals who favored modern, conservative values were more likely to attribute academic procrastination as personal failure, whereas individuals who endorsed post modern, liberal values were more likely to consider situational causes of academic procrastination. Against this background, we argue for a less normative view on procrastination and recommend motivational (e.g. goal selection) rather than volitional (e.g. goal implementation) interventions to prevent procrastination.
Calibration of one factor and two factor Hull White models using swaptions
Venue: Comput. Manag. Sci.
Year: 2019

In this paper, we analize a novel approach for calibrating the one factor and the two factor Hull White models using swaptions under a market consistent framework. The technique is based on the pricing formulas for coupon bond options and swaptions proposed by Russo and Fabozzi ( 2016b; J Fixed Income 27:30 36, 2017b) Under this approach, the volatility of the coupon bond is derived as a function of the stochastic durations. Consequently, the price of coupon bond options and swaptions can be calculated by simply applying standard no arbitrage pricing theory given the equivalence between the price of a coupon bond option and the price of the corresponding swaption. This approach can be adopted to calibrate parameters of the one factor and the two factor Hull White models using swaptions quoted in the market. It represents an alternative with respect to the existing approaches proposed in the literature and currently used by practitioners. Numerical analyses are provided in order to highlight the quality of the calibration results in comparison with existing models, addressing some computational issues related to the optimization model. In particular, calibration results and sensitivities are provided for the one and the two factor models using market data from 2011 to 2016. Finally, an out of sample analysis is performed in order to test the ability of the model in fitting swaption prices different from those used in the calibration process.
A Regularization Approach to Learning Task Relationships in Multitask Learning
Venue: ACM Trans. Knowl. Discov. Data
Year: 2013

Multitask learning is a learning paradigm that seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this article, we propose a regularization approach to learning the relationships between tasks in multitask learning. This approach can be viewed as a novel generalization of the regularized formulation for single task learning. Besides modeling positive task correlation, our approach multitask relationship learning (MTRL) can also describe negative task correlation and identify outlier tasks based on the same underlying principle. By utilizing a matrix variate normal distribution as a prior on the model parameters of all tasks, our MTRL method has a jointly convex objective function. For efficiency, we use an alternating method to learn the optimal model parameters for each task as well as the relationships between tasks. We study MTRL in the symmetric multitask learning setting and then generalize it to the asymmetric setting as well. We also discuss some variants of the regularization approach to demonstrate the use of other matrix variate priors for learning task relationships. Moreover, to gain more insight into our model, we also study the relationships between MTRL and some existing multitask learning methods. Experiments conducted on a toy problem as well as several benchmark datasets demonstrate the effectiveness of MTRL as well as its high interpretability revealed by the task covariance matrix.
Deep learning guided image based droplet sorting for on demand selection and analysis of single cells and 3D cell cultures.
Venue: Lab on a chip
Year: 2020

Uncovering the heterogeneity of cellular populations and multicellular constructs is a long standing goal in fields ranging from antimicrobial resistance to cancer research. Emerging technology platforms such as droplet microfluidics hold the promise to decipher such heterogeneities at ultra high throughput. However, there is a lack of methods able to rapidly identify and isolate single cells or 3D cell cultures. Here we demonstrate that deep neural networks can accurately classify single droplet images in real time based on the presence and number of micro objects including single mammalian cells and multicellular spheroids. This approach also enables the identification of specific objects within mixtures of objects of different types and sizes. The training sets for the neural networks consisted of a few hundred images manually picked and augmented to up to thousands of images per training class. Training required less than 10 minutes using a single GPU, and yielded accuracies of over 90% for single mammalian cell identification. Crucially, the same model could be used to classify different types of objects such as polystyrene spheres, polyacrylamide beads and MCF 7 cells. We applied the developed method for the selection of 3D cell cultures generated with Hek293FT cells encapsulated in agarose gel beads, highlighting the potential of the technology for the selection of objects with a high diversity of visual appearances. The real time sorting of single droplets was in line with droplet generation and occurred at rates up to 40 per second independently of image size up to 480 x 480 pixels. The presented microfluidic device also enabled storage of sorted droplets to allow for downstream analyses.
A Randomized Double blind Clinical Trial of Dentin Surface Treatments for Composite Restorations in Noncarious Cervical Lesions: A 36 month Evaluation.
Venue: Operative dentistry
Year: 2019

This randomized, double blind clinical trial aimed to evaluate the influence of different dentin surface treatments in noncarious cervical lesions (NCCLs)  Twenty nine patients participated in this study. One hundred sixty five NCCLs were selected and randomly assigned to three groups: G0 (control group) with phosphoric acid etching for 15 seconds; G1: phosphoric acid etching for 30 seconds; and G2: ultrasound probe applied for 30 seconds on the dentin surface. Class V composite resin restorations were performed (Z350, 3M ESPE, St Paul, MN, USA) The restorations were evaluated at baseline and at six, 12, 24, and 36 months according to the World Dental Federation criteria. Survival curves were obtained using the Kaplan Meier method and the log rank test. Comparisons between groups and times were performed using the McNemar and Chi square tests (a =0.05)  The presence of failures due to retention was statistically different among the groups p=0.012) and G0 and G2 showed better clinical performance than did G1. Sensitivity decreased over time in all groups. Marginal discoloration, postoperative sensitivity, and marginal adaptation were not different among the groups p>0.05)  The studied dentin surface treatments showed similar clinical performance to the conventional technique at 36 months in terms of marginal discoloration, marginal adaptation, and postoperative sensitivity. In contrast, increased acid etching time resulted in a higher risk of failure due to retention over time in composite Class V restorations.
lncRNA BORG:TRIM28 Complexes Drive Metastatic Progression by Inducing a6 Integrin/CD49f Expression in Breast Cancer Stem Cells.
Venue: Molecular cancer research MCR
Year: 2021

Triple negative breast cancer (TNBC) is the most lethal subtype of breast cancer, with its aggressive phenotype being attributed to chemotherapy resistance, metastatic dissemination, and rapid disease recurrence. Breast cancer stem cells (BCSCs) are significant contributors to tumor initiation, as well as to the acquisition of aggressive tumorigenic phenotypes, namely due to their ability to self replicate and to produce heterogeneous differentiated tumor cells. To elucidate the underlying mechanisms that drive BCSC tumorigenicity in TNBC, we identified the long noncoding RNA BMP/OP Responsive Gene (BORG) as an enhancer of BCSC phenotypes. Indeed, we found BORG expression to (i) correlate with stem cell markers Nanog, Aldh1a3, and Itga6 (alpha6 integrin/CD49f) (ii) enhance stem cell phenotypes in murine and human TNBC cells, and (iii) promote TNBC tumor initiation in mice. Mechanistically, BORG promoted BCSC phenotypes through its ability to interact physically with the E3 SUMO ligase TRIM28. Moreover, TRIM28 binding was observed in the promoter region of Itga6, whose genetic inactivation prevented BORG:TRIM28 complexes from (i) inducing BCSC self renewal and expansion in vitro, and (ii) eliciting BCSC metastatic outgrowth in the lungs of mice. Collectively, these findings implicate BORG:TRIM28 complexes as novel drivers of BCSC phenotypes in developing and progressing TNBCs.  This work establishes the lncRNA BORG as a driver of BCSC phenotypes and the aggressive behaviors of TNBCs, events critically dependent upon the formation of BORG:TRIM28 complexes and expression of alpha6 integrin.
A Model for Implementation and Assessment of ICT adoption by teachers in secondary schools in Kikuyu district in Kenya
Year: 2012

Secondary schools and teachers are under increasing pressure to use Information and Communication Technology to impart to students the knowledge, skills and attitudes they need to survive in the current century. Many schools have embarked on introducing computers and other ICT related tools for management and for teaching Computer Studies a subject that has gained popularity among students. Despite all this effort by schools to introduce computers in their schools, it is a known fact that few teachers have taken the initiative to use and apply computers and other ICT tools in their teaching. The purpose of this study was to establish a model that can be used by schools and teachers to adopt ICT and in particular computers in their subject areas. The design of this study was descriptive survey that was conducted in Kikuyu District of Kiambu County, Kenya. The study targeted 100 teachers in 10 secondary schools. Convenience sampling was used to select a sample of 71 teachers. Questionnaires were used to collect data. Analysis of the data was done by use of descriptive statistics as well as simple correlation. The study found out that nearly all the schools surveyed had embraced ICT; however few teachers use the same to deliver content in class. Secondary school teachers are willing to embrace ICT in their teaching but only if there are policies that will force them to follow. Teachers are also going to embrace ICT in their teaching if there are model schools from which they can imitate. The study therefore recommends that the government works out a policy of ICT adoption by teachers in secondary schools.
Learning Random Kernel Approximations for Object Recognition
Venue: ArXiv
Year: 2012

Approximations based on random Fourier features have recently emerged as an efficient and formally consistent methodology to design large scale kernel machines. By expressing the kernel as a Fourier expansion, features are generated based on a finite set of random basis projections, sampled from the Fourier transform of the kernel, with inner products that are Monte Carlo approximations of the original kernel. Based on the observation that different kernel induced Fourier sampling distributions correspond to different kernel parameters, we show that an optimization process in the Fourier domain can be used to identify the different frequency bands that are useful for prediction on training data. Moreover, the application of group Lasso to random feature vectors corresponding to a linear combination of multiple kernels, leads to efficient and scalable reformulations of the standard multiple kernel learning model \cite{Varma09} In this paper we develop the linear Fourier approximation methodology for both single and multiple gradient based kernel learning and show that it produces fast and accurate predictors on a complex dataset such as the Visual Object Challenge 2011 (VOC2011)
Talent Management, Employee Recognition and Performance in the Research Institutions
Venue: Studies in Business and Economics
Year: 2019

This paper aims to provide insight into talent management and employee recognition variables, in an effort to understand if these management practices are, in a way, implemented in the Research Institutions in Qatar. It also aims to examine the link of these two constructs to employee performance and to suggest a pathway to improve the current practices, if any, in order to enhance the work performance. The sample of the study consisted of 180 full time employees who were randomly selected and surveyed using an online personalized questionnaire. The results of the empirical research uncover that talent management and employee recognition can significantly affect the level of employee performance, as well contributing to the organizational success and positioning. This study also found that talent management and employee recognition are interrelated variables that affect employee performance. Talent Management and employee performance are seen as strategic tools to implement strategic objectives and to enhance employees and organization's performance. The study concludes with a set of recommendations for both practitioners and researchers working in Qatar and beyond. The results of the study emphasize the importance of managing employee recognition in shaping work performance. It seems to be individuals' need for recognition, esteem and social status continues to be a higher order need irrespective of time and context. This paper is the first paper in Qatar and the Middle East that examines the role of talent management and employee recognition in shaping employee performance in the context of research institutions.
Efficient Ray Tracing Through Aspheric Lenses and Imperfect Bokeh Synthesis
Venue: Comput. Graph. Forum
Year: 2016

We present an efficient ray tracing technique to render bokeh effects produced by parametric aspheric lenses. Contrary to conventional spherical lenses, aspheric lenses do generally not permit a simple closed form solution of ray surface intersections. We propose a numerical root finding approach, which uses tight proxy surfaces to ensure a good initialization and convergence behavior. Additionally, we simulate mechanical imperfections resulting from the lens fabrication via a texture based approach. Fractional Fourier transform and spectral dispersion add additional realism to the synthesized bokeh effect. Our approach is well suited for execution on graphics processing units (GPUs) and we demonstrate complex defocus blur and lens flare effects.
Artificial and Internet of Healthcare Things Based Alzheimer Care During COVID 19
Venue: BI
Year: 2020

Alzheimer patient's routine care at the onset of a catastrophe like coronavirus disease 2019 (COVID 19) pandemic is interrupted as healthcare is providing special attention to the patient having severe acute respiratory syndrome coronavirus 2 (SARS COV 2) or COVID 19 infection. In order to decrease the spread of the disease, government has shut down regular services at the hospital, and advised all vulnerable people to stay at home and maintain social distance (of 3 fts) which hampered the routine care and rehabilitation therapy of elderly patient having a chronic disease like Alzheimer. On the other hand, the artificial intelligence (AI) based internet of healthcare things allows clinicians to monitor physiological conditions of patients in real time and machine learning models can able to detect any anomaly in the patient's condition. Besides, the advancement in Information and Communication Technology enable us to provide special distance care (such as medication and therapy) by dedicated medical teams or special therapists. This paper discusses the effect of COVID 19 on patient care of Alzheimer's Disease (AD) and how AI based IoT can help special care of AD patients at home. Finally, we have outlined some recommendations for Family and Caregiver, Volunteer and Social Care which will help to develop the Government policy.
Numerical solution of an optimal control model of dendritic cell treatment of a growing tumour
Year: 2013

A new optimal control model of the interactions between a growing tumour and the host immune system, along with an immunotherapy treatment strategy, is presented. The model is based on an ordinary differential equation model of interactions between the growing tumour and the natural killer, cytotoxic T lymphocyte and dendritic cells of the host immune system, extended through the addition of a control function representing the application of a dendritic cell treatment to the system. The numerical solution of this model, obtained from a multi species Runge Kutta forward backward sweep scheme, is described. We investigate the effects of varying the maximum allowed amount of dendritic cell vaccine administered to the system and find that control of the tumour cell population is best effected via a high initial vaccine level, followed by reduced treatment and finally cessation of treatment. We also found that increasing the strength of the dendritic cell vaccine causes an increase in the number of natural killer cells and lymphocytes, which in turn reduces the growth of the tumour.
Not Guilty as Charged: The Myth of Mens Rea for Defendants with Mental Retardation
Year: 2012

The notion that mens rea is an indicia of culpability runs deep in the American criminal law psyche. For most defendants, a finding that they had the requisite legal intent may be all we need to know to pronounce them morally culpable. This is because most defendants those of average intelligence enjoy a level of socialization, rationality, and agency sufficient to be aware of social norms, make a choice to violate them or not, and to control their own impulses in doing so. But for defendants with mental retardation, the state of mind element fails to accurately signify a "guilty mind." Social science research makes clear (and existing neuroscience research seems to support) that these presumptions of consciousness, choice, and control do not apply to people with mental retardation. In essence, then, for this population, all offenses become strict liability offenses, where an intent inquiry is all but meaningless. While the criminal law does make some allowances for differences in cognitive capacity, it does so only in very limited circumstances, through the doctrines of competency, insanity, and diminished capacity. As a result, litigants must resort to crude perversions of justice to introduce evidence of mental retardation. Finding no valid policy or theoretical justification (apart from incapacitation) for this failure to adequately address the disjuncture between actual culpability and criminal liability, this article offers a new, comprehensive approach to cases charging defendants with mental retardation. Specifically, it proposes a new default rule, where non violent cases against them would be presumptively dismissed. More serious cases charging violent crimes could proceed to trial with the standard mens rea requirements, but would require that any sentence imposed be the least restrictive alternative necessary to accomplish an articulable sentencing goal. This proposal redresses a major flaw in current criminal law doctrine, one which unjustly permits a finding of guilty minds among defendants whose true culpability may not be presumed.