nils-herrmann/clean_scirepeval huggingface dataset:
Reflection and transmission two way structural colors.
Venue: Nanoscale
Year: 2020

Management of reflection and transmission two way structural colors is significant in color displays, projections, and anticounterfeiting. Here, inspired by the Lycurgus Cup, we fabricated photonic crystals with opal and inverse opal structures with controlled thickness, which show reflection and transmission two way structural colors. In order to balance the reflection and transmission intensities, we first studied the effect of the order layer thickness on the reflection and transmission spectra and found that a thickness of about 5 mm can help the structural colors achieve high saturation in both directions. The photonic crystal film built with 295 nm SiO2 spheres shows bright red and green structural colors in the reflection and transmission directions, respectively. These two way colors can be projected onto substrates, similar to a transflective color filter. The color displays can be tuned by adjusting the angle between the incident light and the sample. Furthermore, we also patterned the photonic crystal film with two way structural colors, which shows clear patterns and rich colors in both directions. The photonic crystals assembled on a small wine glass display two way structural colors similar to those of the Lycurgus Cup. More importantly, a flexible inverse opal photonic crystal film with two way structural colors was also fabricated, which can be applied in multimode anticounterfeiting. This work will greatly expand the application field of photonic crystals in double sided displays, transflective color filters and anticounterfeiting.
Artificial Intelligence Applied to Gastrointestinal Diagnostics: A Review.
Venue: Journal of pediatric gastroenterology and nutrition
Year: 2019

Artificial intelligence (AI) a discipline encompassed by data science, has seen recent rapid growth in its application to healthcare and beyond, and is now an integral part of daily life. Uses of AI in gastroenterology include the automated detection of disease and differentiation of pathology subtypes and disease severity. While a majority of AI research in gastroenterology focuses on adult applications, there are a number of pediatric pathologies that could benefit from more research. As new and improved diagnostic tools become available and more information is retrieved from them, AI could provide physicians a method to distill enormous amounts of data into enhanced decision making and cost saving for children with digestive disorders. This review provides a broad overview of AI and examples of its possible applications in pediatric gastroenterology.
Blockchain and Principles of Business Process Re Engineering for Process Innovation
Venue: ArXiv
Year: 2018

Blockchain has emerged as one of the most promising and revolutionary technologies in the past years. Companies are exploring implementation of use cases in hope of significant gains in efficiencies. However, to achieve the impact hoped for, it is not sufficient to merely replace existing technologies. The current business processes must also be redesigned and innovated to enable realization of hoped for benefits. This conceptual paper provides a theoretical contribution on how blockchain technology and smart contracts potentially can, within the framework of the seven principles of business process re engineering (BPR) enable process innovations. In this paper, we analyze the BPR principles in light of their applicability to blockchain based solutions. We find these principles to be applicable and helpful in understanding how blockchain technology could enable transformational redesign of current processes. However, the viewpoint taken, should be expanded from intra to inter organizational processes operating within an ecosystem of separate organizational entities. In such a blockchain powered ecosystem, smart contracts take on a pivotal role, both as repositories of data and executioner of activities.
The tetraspanins CD151 and Tspan8 are essential exosome components for the crosstalk between cancer initiating cells and their surrounding
Venue: Oncotarget
Year: 2015

Tspan8 and CD151 are metastasis promoting tetraspanins and a knockdown (kd) of Tspan8 or CD151 and most pronounced of both tetraspanins affects the metastatic potential of the rat pancreatic adenocarcinoma line ASML. Approaching to elaborate the underlying mechanism, we compared ASMLwt, CD151kd and/or Tspan8kd clones. We focused on tumor exosomes, as exosomes play a major role in tumor progression and tetraspanins are suggested to be engaged in exosome targeting. ASML CD151/Tspan8kd cells poorly metastasize, but regain metastatic capacity, when rats are pretreated with ASMLwt, but not ASML CD151kd and/or Tspan8kd exosomes. Both exosomal CD151 and Tspan8 contribute to host matrix remodelling due to exosomal tetraspanin integrin and tetraspanin protease associations. ASMLwt exosomes also support stroma cell activation with upregulation of cytokines, cytokine receptors and proteases and promote inflammatory cytokine expression in hematopoietic cells. Finally, CD151 /Tspan8 competent exosomes support EMT gene expression in poorly metastatic ASML CD151/Tspan8kd cells. These effects are not seen or are weakened using ASML CD151kd or Tspan8kd exosomes, which is at least partly due to reduced binding/uptake of CD151 and/or Tspan8 deficient exosomes. Thus, CD151 and Tspan8 competent tumor exosomes support matrix degradation, reprogram stroma and hematopoietic cells and drive non metastatic ASML CD151/Tspan8kd cells towards a motile phenotype.
Schein und Sein: Die Informationsdelikte im Kapitalmarktstrafrecht
Year: 2019

Die Informationsdelikte haben im Kapitalmarktstrafrecht eine prominente und sogar noch weiter wachsende Bedeutung. Vor dem Hintergrund des Stellenwerts der Information in unserer Gesellschaft scheint dies auf den ersten Blick auch stimmig zu sein selbst dann, wenn man die Funktionsbedingungen einer Marktwirtschaft zugrunde legt, die zum Teil gerade auf unterschiedlichen Informationsniveaus basieren. Eine sowohl dogmatische als auch empirische Analyse dieser Informationsdelikte fuhrt den Verfasser jedoch zu einem uberraschenden Ergebnis: Die dort genannten Informationsquellen interessieren die Kapitalmarktteilnehmer in aller Regel allenfalls am Rande. Umgekehrt sind die sie interessierenden Medien von den Informationsdelikten nicht erfasst, die daruber hinaus eine ganz andere Zielrichtung hatten. Es gehe bei ihnen um die Sicherung der gesetzlich festgelegten Eintragungsvoraussetzungen. Dies lasse zum einen Zweifel an der Relevanz klassischer Kommunikationsmodelle aufkommen. Zum anderen spricht der Verfasser sich dafur aus, die Stosrichtung der Informationsdelikte auf die staatliche Kontrolle wirtschaftlicher Macht zu konzentrieren und diesen Prozess weiter voranzutreiben. Denn hierin liege der wahre Motor fur eine Stabilisierung des Finanzsystems.
The Impact of Job Stress and Job Satisfaction on Workforce Productivity in an Iranian Petrochemical Industry
Venue: Safety and health at work
Year: 2017

Job stress and job satisfaction are important factors affecting workforce productivity. This study was carried out to investigate the job stress, job satisfaction, and workforce productivity levels, to examine the effects of job stress and job satisfaction on workforce productivity, and to identify factors associated with productivity decrement among employees of an Iranian petrochemical industry.  In this study, 125 randomly selected employees of an Iranian petrochemical company participated. The data were collected using the demographic questionnaire, Osipow occupational stress questionnaire to investigate the level of job stress, Job Descriptive Index to examine job satisfaction, and Hersey and Goldsmith questionnaire to investigate productivity in the study population.  The levels of employees' perceived job stress and job satisfaction were moderate high and moderate, respectively. Also, their productivity was evaluated as moderate. Although the relationship between job stress and productivity indices was not statistically significant, the positive correlation between job satisfaction and productivity indices was statistically significant. The regression modeling demonstrated that productivity was significantly associated with shift schedule, the second and the third dimensions of job stress (role insufficiency and role ambiguity) and the second dimension of job satisfaction (supervision)  Corrective measures are necessary to improve the shift work system. "Role insufficiency" and "role ambiguity" should be improved and supervisor support must be increased to reduce job stress and increase job satisfaction and productivity.
Targeted Therapy as an Alternative to Whole Brain Radiotherapy in EGFR Mutant or ALK Positive Non Small Cell Lung Cancer With Brain Metastases.
Venue: JAMA oncology
Year: 2017

Clinical Question Is up front whole brain radiotherapy required to treat multiple brain metastases from non small cell lung cancer when highly active targeted therapies are available? Clinical Application Patients with EGFR mutant or ALK positive non small cell lung cancer with brain metastases now have the potential to achieve a prolonged survival. Through use of highly active targeted therapies, whole brain radiotherapy can be safely postponed, diminishing toxic effects that could impair quality of life.
Data Transmission Based on Exact Inverse Periodic Nonlinear Fourier Transform, Part II: Waveform Design and Experiment
Venue: Journal of Lightwave Technology
Year: 2020

The nonlinear Fourier transform has the potential to overcome limits on performance and achievable data rates which arise in modern optical fiber communication systems when nonlinear interference is treated as noise. The periodic nonlinear Fourier transform (PNFT) has been much less investigated compared to its counterpart based on vanishing boundary conditions. In this article, we design a first experiment based on the PNFT in which information is encoded in the invariant nonlinear main spectrum. To this end, we propose a method to construct a set of periodic waveforms each having the same fixed period, by employing the exact inverse PNFT algorithm developed in Part I. We demonstrate feasibility of the transmission scheme in experiment in good agreement with simulations and obtain a bit error ratio of $10^ 3} over a distance of 2000 km. It is shown that the transmission reach is significantly longer than expected from a naive estimate based on group velocity dispersion and cyclic prefix length, which is explained through a dominating solitonic component in the transmitted waveform. Our constellation design can be generalized to an arbitrary number of nonlinear degrees of freedom.
Can You Fix My Neural Network? Real Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification
Venue: IEEE INFOCOM 2021 IEEE Conference on Computer Communications
Year: 2021

Due to the sheer scale of the Internet of Things (IoT) and 5G, the wireless spectrum is becoming severely congested. For this reason, wireless devices will need to continuously adapt to current spectrum conditions by changing their communication parameters in real time. Therefore, wireless signal classification (WSC) will become a compelling necessity to decode fast changing signals from dynamic transmitters. Thanks to its capability of classifying complex phenomena without explicit mathematical modeling, deep learning (DL) has been demonstrated to be a key enabler of WSC. Although DL can achieve a very high accuracy under certain conditions, recent research has unveiled that the wireless channel can disrupt the features learned by the DL model during training, thus drastically reducing the classification performance in real world live settings. Since retraining classifiers is cumbersome after deployment, existing work has leveraged the usage of carefully tailored Finite Impulse Response (FIR) filters that, when applied at the transmitter's side, can restore the features that are lost because of the the channel actions, i.e. waveform synthesis. However, these approaches compute FIRs using offline optimization strategies, which limits their efficacy in highly dynamic channel settings. In this paper, we improve the state of the art by proposing Chares, a Deep Reinforcement Learning (DRL) based framework for channel resilient adaptive waveform synthesis. Chares adapts to new and unseen channel conditions by optimally computing through DRL the FIRs in real time. Chares is a DRL agent whose architecture is based upon the Twin Delayed Deep Deterministic Policy Gradients (TD3) which requires minimal feedback from the receiver and explores a continuous action space for best performance. Chares has been extensively evaluated on two well known datasets with an extensive number of channels. We have also evaluated the real time latency of Chares with an implementation on field programmable gate array (FPGA) Results show that Chares increases the accuracy up to 4.1x when no waveform synthesis is performed, by 1.9x with respect to existing work, and can compute new actions within 41us.
A Two Microphone Noise Reduction Method in Highly Non stationary Multiple Noise Source Environments
Venue: IEICE Trans. Fundam. Electron. Commun. Comput. Sci.
Year: 2008

In this paper, we propose a two microphone noise reduction method to deal with non stationary interfering noises in multiple noise source environments in which the traditional two microphone algorithms cannot function well. In the proposed algorithm, multiple interfering noise sources are regarded as one virtually integrated noise source in each subband, and the spectrum of the integrated noise is then estimated using its virtual direction of arrival. To do this, we suggest a direction finder for the integrated noise using only two microphones that performs well even in speech active periods. The noise spectrum estimate is further improved by integrating a single channel noise estimation approach and then subtracted from that of the noisy signal, finally enhancing the desired target signal. The performance of the proposed algorithm is evaluated and compared with the traditional algorithms in various conditions. Experimental results demonstrate that the proposed algorithm outperforms the traditional algorithms in various conditions in terms of objective and subjective speech quality measures.
A New Project Risk Management Model based on Scrum Framework and Prince2 Methodology
Year: 2018

With increasing competition in the software industry, software companies need to effectively manage the risks of software projects with minimal time and cost to deliver high quality products. High frequencies of warning errors and failures in software projects are indicative of human and financial costs in the software projects and teams. One of the reasons for the failure of software projects is the lack of risk management mechanism in the software development process, which can, in case of proper implementation of risk management, increase the success rate of such projects. In most projects, risk management activities are strongly confined to the adopted software methodology. Therefore, is needed a solution or model to overcome this constraint. Scrum is one of the most popular software development methodologies which has recently considered by software teams. This methodology seems not to have paid much attention to risk management. Focusing on this weakness, this research has been trying to provide a model for risk management with the participation of 52 Agile experts from six different countries using the Prince2 project management framework in Scrum methodology. The main goals of this model are to improve the coverage and appropriate risk management mechanism on software projects, increase the project's success rate and to provide a good estimation of the required time, improve product quality and enhance quality parameters, such as the usability, flexibility, efficiency, and reliability.
Conceptualising the Punch Line: An Analysis of Humorous Webcomic Stories with the Help of Blending Theory
Year: 2020

Although research on humour carried out from a cognitive standpoint is well established, it has mainly contributed to the study of single framed cartoons. This paper goes further and offers a cognitive multimodal analysis of so called webcomics, i.e. sequences of multiple cartoon frames published on the Internet. In general, the analysis makes use of Fauconnier and Turner's (2002) Blending Theory and Groensteen's (2007) semiotic approach to comics. These insights equip us with new tools for analysis, enabling us to find out where the punch lines and incongruities reside, as well as explaining how we are able to conceptualize them.
Federated Learning Based Proactive Handover in Millimeter wave Vehicular Networks
Venue: 2020 15th IEEE International Conference on Signal Processing (ICSP)
Year: 2020

Proactive handover can avoid frequent handovers and reduce handover delay, which plays an important role in maintaining the quality of service (QoS) for mobile users in millimeter wave vehicular networks. To reduce the communication cost of training the learning model for proactive handover, we propose a federated learning (FL) framework. The proposed FL framework can accommodate the limited storage capacity of each user, increase the number of users who participate in the FL, and adapt to the dynamic mobility pattern. Simulation results validate the effectiveness of the proposed FL framework. Compared to reactive handover schemes, the proposed handover scheme can reduce the unnecessary handovers and improve the QoS of users simultaneously.
Subcycle time resolved study for efficient terahertz high harmonic generation in Dirac semimetal Cd3As2 (Conference Presentation)
Year: 2020

High harmonic generation in solids attracted great attentions as a new scheme for frequency conversion. We report observation of an extremely efficient terahertz (THz) third and fifth harmonic generation in thin films of Cd3As2, a three dimensional Dirac semimetal with massless electron dispersion, as it is observable with tabletop THz source at room temperature. Our THz pump THz probe study with subcycle time resolution elucidates that the intraband current of coherently accelerated Dirac electrons is the main source of the THz harmonics as expected theoretically. The results pave the way toward novel devices for ultrafast THz electronics and photonics based on topological semimetals.
Side effects from acute myeloid leukemia treatment: results from a national survey
Venue: Current medical research and opinion
Year: 2019

Acute myeloid leukemia (AML) is experiencing a therapeutic renaissance due to the heightened biomedical understanding of AML and patient focused drug development (PFDD) Many AML patients now live long term with the side effects of treatment. This study documents the prevalence and severity of AML treatment related side effects.  A national cross sectional survey designed with the Leukemia Lymphoma Society assessed patients' experiences with short term (nausea/vomiting, diarrhea, hair loss, mouth sores, infection, rash) and long term (organ dysfunction, chemobrain, fatigue, neuropathy) treatment side effects. Patient and caregiver participants rated side effect severity (none severe)  Survey participants (n 1182) were mostly female (65% AML patients (76% and had undergone chemotherapy (94% Eighty seven per cent of participants reported severe short term effects, and 33% reported severe long term effects of treatment. Only 11% of respondents did not have any severe effects. Hair loss and fatigue were the most common severe short and long term side effects (78% 33% There was a moderate correlation between having short and long term adverse effects (r 0.41, p 0.001) Caregivers were more likely than patients to report severe organ dysfunction, fatigue, and neuropathy (p values 0.05)  Survivors experience a high burden of side effects from AML treatments highlighting the need for the development of less toxic therapies. Differences in patients' and caregivers' experiences illustrate the importance of sampling from diverse sources to understand the full burden of AML treatment, and the need for less toxic drugs. This study informs patients, patient advocacy groups, clinicians, and regulators about AML treatment burdens and provides the community with information to inform PFDD.
The real component of virtual learning: motivations for face to face MOOC meetings in developing and industrialised countries
Year: 2015

Massive open online courses (MOOCs) offer the possibility of entirely virtual learning environments, with lectures, discussions, and assignments all distributed via the internet. The virtual nature of MOOCs presents considerable advantages to students in terms of flexibility to learn what they want, when they want. Yet despite their virtual focus, some MOOC users also seek to create face to face communities with students taking similar courses or using similar platforms. This paper aims to assess the learner motivations behind creation of these offline communities. Do these face to face meetings represent an added extra to the learning experience, with students taking advantage of the context of the MOOCs to create new personal and professional connections? Or, are offline meetups filling a gap for students who feel that not all learning can take place online? We also assess the extent to which these patterns vary between developing and industrialised regions, thus testing the claim that MOOCs are helping to democratise access to education around the world. Our research is based on a unique source of socially generated big data, drawn from the website 'meetup.com' which gives us a data set of over 4000 MOOC related events taking place in over 140 countries around the world over a two year period. We apply a mixed methods approach to this data, combining large scale analysis with more in depth thematic hand coding, to more fully explore the reasons why some learners add a 'real' component to their virtual learning experience.
Overcoming catastrophic forgetting in neural networks
Venue: Proceedings of the National Academy of Sciences
Year: 2017

Deep neural networks are currently the most successful machine learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks. This approach, inspired by synaptic consolidation in neuroscience, enables state of the art results on multiple reinforcement learning problems experienced sequentially. The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand written digit dataset and by learning several Atari 2600 games sequentially.