nils-herrmann/clean_scirepeval huggingface dataset:
Ultrafast Fiber Laser Technology
Venue: IEEE Journal of Selected Topics in Quantum Electronics
Year: 2009

In this paper, a review of fiber laser technology as relevant for applications in ultrafast optics is given. We discuss core enabling fiber technologies, such as fiber amplifiers, all fiber dispersion control, and highly nonlinear and large core fibers. We concentrate on systems built around passively mode locked fiber lasers and fiber frequency combs, which are further amplified in large core fiber amplifiers. Our review further encompasses coherent supercontinuum generation and techniques for absolute phase control of fiber lasers and amplifiers. Applications concerned with spectral generation in the range from the vacuum UV to the terahertz range are also described.
When Bonding Fails: Audit Firm Oversight of US Listed Chinese Companies
Year: 2014

Although "good" companies have incentives to signal their types by listing in the strict regulatory environment of the US, there have been an unprecedented number of recent accounting frauds by US listed Chinese companies. We argue that the traditional bonding argument failed for US listed Chinese companies due to a lack of audit quality and audit firm oversight. We find: 1) that US listed Chinese companies were more likely than US listed companies from other countries to avoid hiring high quality annually inspected US audit firms, and 2) that investors reacted negatively to news that US regulators would be unable to provide oversight of Chinese auditors.
Emerging Trends in Polymerization Induced Self Assembly
Venue: ACS Macro Letters
Year: 2019

In this Perspective, we summarize recent progress in polymerization induced self assembly (PISA) for the rational synthesis of block copolymer nanoparticles with various morphologies. Much of the PISA literature has been based on thermally initiated reversible addition fragmentation chain transfer (RAFT) polymerization. Herein, we pay particular attention to alternative PISA protocols, which allow the preparation of nanoparticles with improved control over copolymer morphology and functionality. For example, initiation based on visible light, redox chemistry, or enzymes enables the incorporation of sensitive monomers and fragile biomolecules into block copolymer nanoparticles. Furthermore, PISA syntheses and postfunctionalization of the resulting nanoparticles (e.g. cross linking) can be conducted sequentially without intermediate purification by using various external stimuli. Finally, PISA formulations have been optimized via high throughput polymerization and recently evaluated within flow reactors fo.
Blockchain challenges and opportunities: a survey
Venue: Int. J. Web Grid Serv.
Year: 2018

Blockchain has numerous benefits such as decentralisation, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, internet of things (IoT) to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology.
Fiber optic sensors for high throughput screening of pathogens
Year: 2015

A large group of fiber optic biosensors are also known as evanescent wave fluoroimmunosensors. When incoming light interacts with fluorophore bound molecules on the surface of the waveguide, it generates an evanescent wave that undergoes total internal reflection and is detected in real time. This sensor has been used for detection of toxins, microorganisms (bacteria and virus) spores, proteins, and other small molecules from air, water, environment (soil) food, and clinical specimens. The most significant advantage of this sensor is its speed in assessing the presence of contaminants in a sample. The planar array waveguide format detects multiple pathogens or toxins at a time and thus is amenable to high throughput screening.
Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques.
Venue: Accident; analysis and prevention
Year: 2020

Lane change has been recognized as a challenging driving maneuver and a significant component of traffic safety research. Developing a real time continuous lane change detection system can assist drivers to perform and deal with complex driving tasks or provide assistance when it is needed the most. This study proposed trajectory level lane change detection models based on features from vehicle kinematics, machine vision, roadway characteristics, and driver demographics under different weather conditions. To develop the models, the SHRP2 Naturalistic Driving Study (NDS) and Roadway Information Database (RID) datasets were utilized. Initially, descriptive statistics were utilized to investigate the lane change behavior, which revealed significant differences among different weather conditions for most of the parameters. Six data fusion categories were introduced for the first time, considering different data availability. In order to select relevant features in each category, Boruta, a wrapper based algorithm was employed. The lane change detection models were trained, validated, and comparatively evaluated using four Machine Learning algorithms including Random Forest (RF) Support Vector Machine (SVM) Artificial Neural Network (ANN) and eXtrem Gradient Boosting (XGBoost) The results revealed that the highest overall detection accuracy was found to be 95.9 using the XGBoost model when all the features were included in the model. Moreover, the highest overall detection accuracy of 81.9 using the RF model was achieved considering only vehicle kinematics based features, indicating that the proposed model could be utilized when other data are not available. Furthermore, the analysis of the impact of weather conditions on lane change detection suggested that incorporating weather could improve the accuracy of lane change detection. In addition, the analysis of early lane change detection indicated that the proposed algorithm could predict the lane changes within 5 s before the vehicles cross the lane line. The developed detection models could be used to monitor and control driver behavior in a Cooperative Automated Vehicle environment.
Weibo Authorship Identification based on Wasserstein generative adversarial networks
Venue: 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)
Year: 2019

During the past years, authorship identification has played a significant role in the public security area. Recently, deep learning based approaches have been used in authorship identification. However, all approaches based on deep learning require a large amount of original data, while the author only posts a limited number of texts to be used as positive samples. As such, the issue of data missing and class imbalance arises, resulting in the classifier overfitting on small data. Furthermore, this approach can be biased by prior of the data. To address these issues, in this paper, we use Wasserstein generative adversarial networks (WGANs) to generate samples for the positive class which lacks of data and present a novel data augmentation framework for authorship identification. To form an augmented training dataset, we mix the generated new samples with the original features in the dataset. As such, the classifier trained on it will not suffer from overfitting and class imbalance, and thus has substantially improved performance. Using crawled Sina Weibo dataset, we empirically evaluate this data augmentation. The experimental results show that our method has a significant improvement of accuracy by 14% compared with powerful baselines. We further validate its effectiveness via a set of comparison experiments.
A Convergence Proof of MLEM and MLEM 3 With Fixed Background
Venue: IEEE Transactions on Medical Imaging
Year: 2019

Maximum likelihood expectation maximization (MLEM) is a popular algorithm to reconstruct the activity image in positron emission tomography. This paper introduces a "fundamental equality" for the MLEM complete data from which two key properties easily follow that allows us to: 1) prove in an elegant and compact way the convergence of MLEM for a forward model with fixed background (i.e. counts such as random and scatter coincidences) and 2) generalize this proof for the MLEM 3 algorithm. Moreover, we give necessary and sufficient conditions for the solution to be unique.
DeepCOVID: An Operational Deep Learning driven Framework for Explainable Real time COVID 19 Forecasting
Venue: medRxiv
Year: 2020

How do we forecast an emerging pandemic in real time in a purely data driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DeepCOVID, an operational deep learning framework designed for real time COVID 19 forecasting. DeepCOVID works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The framework also consists of modules for both real time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID 19 Forecast Hub, especially for short term predictions.
Discrimination of viable and dead Vibrio parahaemolyticus subjected to low temperatures using Propidium Monoazide Quantitative loop mediated isothermal amplification (PMA qLAMP) and PMA qPCR.
Venue: Microbial pathogenesis
Year: 2019

The aim of this study was to determine the effect of cold (4 degC) and subzero 18 degC, 45 degC) temperatures on the occurrence time of membrane damage to provide Propidium Monoazide (PMA) penetration of Vibrio parahaemolyticus inoculated to the sea bass. Direct plate counting (DPC) and PMA based quantitative loop mediated isothermal amplification (qLAMP) and qPCR was utilized for discrimination of dead and live bacteria on the designated storage days (1, 3, 7, and 14) The optimum amount of PMA was 50 mM for inhibition of amplification derived from dead cells in spiked samples. The number of live V. parahaemolyticus was detectable at the end of the 14. day using PMA qLAMP and PMA qPCR at all the temperatures. On the 7th day, culturability has lost at any of the storage temperatures and DPCs at 18 degC and 45 degC revealed a difference of about 1 log10 CFU/ml between 1st and 3rd days. The same difference was also observed in PMA qLAMP and PMA qPCR on the same days (0.59 0.95 log10 CFU/ml) Subzero temperatures have the highest rate of viability while causing the fastest decrease in culturability in sample groups as a result of the higher level of transition to VBNC state. qLAMP and qPCR methods in the PMA treated and nontreated groups on the storage days at all temperatures gave similar results (p 0.05)
2D image to 3D range registration in urban environments via scene categorization and combination of similarity measurements
Venue: 2016 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2016

Statistical similarity measurements, such as mutual information (MI) and normalized mutual information (NMI) show potential in the registration of 2D image to 3D range scans collected in urban environments. However 2D 3D registration with these measurements are of limited usage in urban sensing applications because: 1) it relies on the diversity and dependency between pre defined pair of 2D 3D attributes, such as the intensity from images and reflectivity from range scans, in the urban sensing environment, and 2) it requires high end range sensors with strong abilities to capture reflectivity in urban scenarios. In this paper, we propose a robust way of estimating statistical similarity measurements for 2D 3D data that are collected in various urban scenes with both low cost and high end range sensors. Rather than estimate the similarity of 2D 3D data on specific pair of 2D 3D attributes, we compute similarity measurements between a set of 2D 3D attribute pairs that could be dominant in the category of sensed urban scene and combine them into a reliable similarity measurement. By applying the combined similarity measurement to the common framework of statistical 2D 3D registration, we get superior results when compared with state of art similarity measurements (MI and NMI) in terms of registration accuracy and robustness to initial condition, as indicated by experiments conducted on two datasets that are collected in various urban scenes, with low cost and high end sensors.
COVID 19 under the SARS Cloud: Mental Health Nursing during the Pandemic in Hong Kong
Venue: Journal of psychiatric and mental health nursing
Year: 2020

This editorial presents a commentary on COVID 19 and mental health in Hong Kong. We outline the current measures being used to contain the outbreak and how the experience of the SARS epidemic may have influenced the response in Hong Kong. We also discuss the potential mental health ill effects of the pandemic and its impact on mental health nursing locally.
What's Good for the Goose? Examining the Impact of Gender neutral and Gender specific Factors in the Assessment and Treatment of Female and Male Justice involved Youth
Year: 2016

In response to female youths' increased visibility in the legal system, more attention has been paid to understanding girls' pathways to justice system involvement, risk for re offending, and rehabilitative needs. Widely used risk assessment and case management tools based on the Risk Need Responsivity (RNR) framework are largely gender neutral. Gender responsive scholars have long advocated for the importance of additional gender specific factors in guiding the assessment and treatment of female justice involved youth. The dissertation is comprised of two papers which examine the contribution of proposed gender specific factors alongside well established RNR factors in the prediction of recidivism, and how service provision aimed at intervening with these factors impacts recidivism for both male and female justice involved youth. Paper 1 explores the relationship between trauma, criminogenic needs and recidivism. I first sought to define the distinct constructs often referred to under the umbrella term 'trauma' PTSD symptomology, maltreatment, and childhood adversity. The relationships between these factors, well established criminogenic needs, and recidivism were examined and compared in a matched sample of 50 female and 50 male justice involved youth. Females were significantly iii more likely than males to have experienced multiple forms of maltreatment. Several maltreatment and childhood adversity factors were significantly and positively related to criminogenic needs. PTSD symptomology and childhood adversity were not significant predictors of recidivism; however, maltreatment was the strongest predictor of recidivism for both males and females in a model that included well established risk factors. Gender was not found to be moderating the relationship between maltreatment and recidivism. Implications of the findings for theory and practice are discussed. Paper 2 examines the contribution of both criminogenic needs and several additional proposed 'female' gender specific factors to risk assessment and rehabilitative treatment. Female youth were more likely than male youth to have proposed 'female' gender specific needs but these needs alone did not predict recidivism. Successfully matching services to youths' criminogenic needs predicted reduced recidivism for both male and female youth. For youth who had 'female' gender specific needs, successful matching of services to these needs also predicted reduced recidivism for both genders. Theoretical and practice implications of these results are discussed.
Machine Learning and Feature Selection Applied to SEER Data to Reliably Assess Thyroid Cancer Prognosis
Venue: Scientific Reports
Year: 2020

Utilizing historical clinical datasets to guide future treatment choices is beneficial for patients and physicians. Machine learning and feature selection algorithms (namely, Fisher's discriminant ratio, Kruskal Wallis' analysis, and Relief F) have been combined in this research to analyse a SEER database containing clinical features from de identified thyroid cancer patients. The data covered 34 unique clinical variables such as patients' age at diagnosis or information regarding lymph nodes, which were employed to build various novel classifiers to distinguish patients that lived for over 10 years since diagnosis, from those who did not survive at least five years. By properly optimizing supervised neural networks, specifically multilayer perceptrons, using data from large groups of thyroid cancer patients (between 6,756 and 20,344 for different models) we demonstrate that unspecialized and existing medical recording can be reliably turned into power of prediction to help doctors make informed and optimized treatment decisions, as distinguishing patients in terms of prognosis has been achieved with 94.5% accuracy. We also envisage the potential of applying our machine learning strategy to other diseases and purposes such as in designing clinical trials for unmasking the maximum benefits and minimizing risks associated with new drug candidates on given populations.
Simulation based medical education is no better than problem based discussions and induces misjudgment in self assessment
Venue: Advances in health sciences education theory and practice
Year: 2009

Simulation based teaching (SBT) is increasingly used in medical education. As an alternative to other teaching methods there is a lack of evidence concerning its efficacy. The aim of this study was to evaluate the potency of SBT in anesthesia in comparison to problem based discussion (PBD) with students in a randomized controlled setting. Thirty three fourth year medical students attending a curricular anesthesiology course were randomly allocated to either a session of SBT or a session of PBD on an emergency induction method. Ten days later all students underwent examination in a simulator. The performance of each student was evaluated by weighted tasks, established according to a modified Delphi process. Confidence and a multiple choice questionnaire were additionally performed pre and post intervention. A total of 32 students completed the study. Participants in the SBT group presented with significantly higher self assessment scores after the intervention than students in the PBD group. However, students in the SBT group achieved only slightly and statistically insignificantly higher scores in the theoretical and simulator examination (p 0.05) with only a moderate effect size of d 0.52. The current study demonstrates that both PBD and SBT lead to comparable short term outcomes in theoretical knowledge and clinical skills. However, undesirably, SBT students overrated their anticipated clinical abilities and knowledge improvement.
Treatment of Love and Sex in Walt Whitman's " Song of Myself"
Year: 2020

This paper focuses on Walt Whitman's treatment of love and sex in his "Song of Myself." Whitman is often discussed either as a poet of body or of soul. But the fact is that there is interdependence of body and spirit in his poetry. Both are supplementary and complementary to each other in his scheme of things. Whitman denounces both the extremes the puritanic obsession with spirituality as well as immersion in carnality. Whitman while singing of the spirit does not close his eyes to the needs flesh and regards sexual urge not merely as lust but as the procreant urge of the world which perpetuates all life on this earth. He makes a powerful plea for the grant of parity to sexual love with spiritual love.
Gift policy, bribes and corruption
Venue: European Journal of Law and Economics
Year: 2019

A ban on gifts in public offices partially deters bribery but may induce undeterred bribers to switch from gifts to money. The rise in the use of a more liquid instrument (money) increases the bribe acceptance rate and, possibly, the measure of unqualified applications approved by the office. A gift ban may thus amplify the social costs of corruption by allocating public resources to the wrong people. It is shown that the optimal limited gift ban with a value cap binding on "expensive" gifts dominates the free gifts policy. The limited ban has weaker deterrence than the complete ban but it may reduce the social costs of corruption by minimizing the switch from gifts to money.
Autonomous Agents and Ethical Decision Making
Venue: Cognitive Computation
Year: 2015

Machine ethics, also known as artificial morality, is a newly emerging field concerned with ensuring appropriate behavior of machines toward humans and other machines. In this article, we discuss the importance of machine ethics and present a computational model of ethical decision making for autonomous agents. The proposed model implements a mechanism for integrating the results of diverse assessments into a unique cue, and takes into account the agent's preferences, good and bad past experiences, ethical rules, and current emotional state as the main factors involved in choosing the most appropriate option. The design of the model is based on theories and models developed in fields such as neuroscience, psychology, artificial intelligence, and cognitive informatics. In particular, the model attempts to emulate neural mechanisms of the human brain involved in ethical decision making.
Spherical Hands: Toward Underactuated, In Hand Manipulation Invariant to Object Size and Grasp Location
Year: 2016

Minimalist, underactuated hand designs can be modified to produce useful, dexterous, in hand capabilities without sacrificing their passive adaptability in power grasping. Incorporating insight from studies in parallel mechanisms, we implement and investigate the "spherical hand" morphologies: novel, hand topologies with two fingers configured such that the instantaneous screw axes, describing the displacement of the grasped object, always intersect at the same point relative to the palm. This produces the same instantaneous motion about a common point for any object geometry in a stable grasp. Various rotary fingertip designs are also implemented to help maintain stable contact conditions and minimize slip, in order to prove the feasibility of this design in physical hand implementations. The achievable precision manipulation workspaces of the proposed morphologies are evaluated and compared to prior human manipulation data as well as manipulation results with traditional three finger hand topologies. Experiments suggest that the spherical hands' design modifications can make the system's passive reconfiguration more easily predictable, providing insight into the expected object workspace while minimizing the dependence on accurate object and contact modeling. We believe this design can significantly reduce the complexity of planning and executing dexterous manipulation movements in unstructured environments with underactuated hands.