nils-herrmann/clean_scirepeval huggingface dataset:
Bitcoin and entrepreneurship: breaking the network effect
Year: 2017

This paper explores the question of whether the market process is capable of bringing about a spontaneous monetary switch to a new currency in the presence of strong network effects of the incumbent currency as well as the absence of contingencies such as extreme inflation or political instability. It does so by examining current happenings around Bitcoin. It finds that two mechanisms stand out: the coordinating efforts of the profit maximizing entrepreneur as well as the ability to use the old and the new currency simultaneously. Specifically, it finds that marginal decisions made by rational agents merely seeking to maximize net private benefit irrespective of the network effect, be it entrepreneurs or users of the new currency, are capable of setting in motion a switch to a new currency. Whether or not these mechanisms play out fully in the case of Bitcoin still remains to be seen.
Dynamic Pricing of New Products in Competitive Markets: A Mean Field Game Approach
Venue: Dyn. Games Appl.
Year: 2021

Dynamic pricing of new products has been extensively studied in monopolistic and oligopolistic markets. But, the optimal control and differential game tools used to investigate the pricing behavior on markets with a finite number of firms are not well suited to model competitive markets with an infinity of firms. Using a mean field games approach, this paper examines dynamic pricing policies in competitive markets, where no firm exerts market power. The theoretical setting is based on a diffusion modei a la Bass. We prove both the existence and the uniqueness of a mean field game equilibrium, and we investigate mean tendencies and firms dispersion in the market. Numerical simulations show that the competitive market splits into two separate groups of firms depending on their production experience. The two groups differ in price and profit. Thus, high prices and profits do not have to signal anticompetitive practices, stimulating the debate on market regulation.
Face masks: benefits and risks during the COVID 19 crisis
Venue: European Journal of Medical Research
Year: 2020

The German government has made it mandatory to wear respiratory masks covering mouth and nose (MNC) as an effective strategy to fight SARS CoV 2 infections. In many countries, this directive has been extended on shopping malls or public transportation. The aim of this paper is to critically analyze the statutory regulation to wear protective masks during the COVID 19 crisis from a medical standpoint.  We performed an extensive query of the most recent publications addressing the prevention of viral infections including the use of face masks in the community as a method to prevent the spread of the infection. We addressed the issues of practicability, professional use, and acceptability based on the community and the environment where the user resided.  Upon our critical review of the available literature, we found only weak evidence for wearing a face mask as an efficient hygienic tool to prevent the spread of a viral infection. However, the use of MNC seems to be linked to relevant protection during close contact scenarios by limiting pathogen containing aerosol and liquid droplet dissemination. Importantly, we found evidence for significant respiratory compromise in patients with severe obstructive pulmonary disease, secondary to the development of hypercapnia. This could also happen in patients with lung infections, with or without SARS CoV 2.  Epidemiologists currently emphasize that wearing MNC will effectively interrupt airborne infections in the community. The government and the politicians have followed these recommendations and used them to both advise and, in some cases, mandate the general population to wear MNC in public locations. Overall, the results seem to suggest that there are some clinically relevant scenarios where the use of MNC necessitates more defined recommendations. Our critical evaluation of the literature both highlights the protective effects of certain types of face masks in defined risk groups, and emphasizes their potential risks.
Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection
Venue: IET Circuits Devices Syst.
Year: 2018

An automatic detection system for distinguishing healthy, ictal, and inter ictal EEG signals plays an important role in medical practice. This paper presents a very large scale integration (VLSI) architecture of three class classification for epilepsy and seizure detection. In order to find out the most efficient three class classification scheme for hardware implementation, several multiclass non linear support vector machine (NLSVM) classifiers are compared and validated using software implementation. Finally, the one against one (OAO) multiclass NLSVM is selected due to its highest accuracy. The designed system consists of a discrete wavelet transform (DWT) based feature extraction module, a modified sequential minimal optimization (MSMO) training module, and an OAO multiclass classification module. A lifting structure of Daubechies order 4 wavelet is introduced in three level DWT to save circuit area and speed up the computational time. The MSMO is used for on chip training. The circuit of the largest absolute value decision is designed to avoid the unclassifiable problem in the OAO multiclass classification. The designed system is implemented on a field programmable gate array (FPGA) platform and evaluated using the publicly available epilepsy dataset. The experimental results demonstrate that the designed system achieves high accuracy with low dimensional feature vectors.
The moderating role of person environment fit on the relationship between perceived workload and work engagement among hospital nurses
Year: 2020

The study examines the moderating roles of person environment fit on the relationship between perceived workload and employee work engagement. The participants of the study comprised of 216 hospital nurses sampled from public hospitals in southeast, Nigeria. The results of the regression analyses showed that perceived high workload was significantly and negatively related to work engagement. The results of the moderated hierarchical regression analyses showed that person job fit had a moderating effect on the relationship between perceived high workload and work engagement. The moderating effect of person organization fit in the relation between perceived high workload and work engagement was not significant. The implications of the findings to the job demands resources theory and employee selection and development were discussed.
Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug Induced Liver Injury in Advance
Venue: J. Chem. Inf. Model.
Year: 2019

Drug induced liver injury (DILI) one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most of the cases, showing an emerging challenge that is to accurately predict DILI in early stage. Recently, the vast amount of gene expression data provides us valuable information for distinguishing DILI on a genomic scale. Moreover, deep learning algorithm is a powerful strategy to automatically learn important features from raw and noisy data and shows great success in the field of medical diagnosis. In this study, a gene expression data based deep learning model was developed to predict DILI in advance by using gene expression data associated with DILI collected from ArrayExpress, and then optimized by feature gene selection and parameters optimization. In addition, the previous machine learning algorithm support vector machine (SVM) was also used to construct another prediction model based on the same datasets, comparing the model performance with the optimal DL model. Finally, the evaluation test using 198 randomly selected samples showed that the optimal DL model achieved 97.1% accuracy, 97.4% sensitivity, 96.8% specificity, 0.942 matthews correlation coefficient and 0.989 area under the ROC curve, while the performance of SVM model only reached 88.9% accuracy, 78.8% sensitivity, 99.0% specificity, 0.794 matthews correlation coefficient and 0.901 area under the ROC curve. Furthermore, external data sets verification and animal experiments were conducted to assess the optimal DL model performance. Finally, the predicted results of the optimal DL model were almost consistent with experiment results. These results indicated that our gene expression data based deep learning model could systematically and accurately predict DILI in advance. It could be a useful tool to provide safety information for drug discovery and clinical rational drug use in early stage, and become an important part of drug safety assessment.
Pilot survey of oncologists regarding treatment related infertility and fertility preservation in female cancer patients.
Venue: The Journal of reproductive medicine
Year: 2009

To conduct a quantitative survey that focuses on oncologists' practice patterns and attitudes surrounding treatment related infertility and fertility preservation, specifically among women of reproductive age.  A 19 item survey was emailed to medical, pediatric, radiation and surgical oncologists at Duke University. Descriptive statistics were used.  Most oncologists (61% who responded always or usually discuss the impact treatment will have on fertility. Nearly half (45% never refer women to reproductive specialists. Respondents who attended an educational session on fertility preservation were more likely to consider a patient's desire for fertility when planning her treatment than those who did not attend (45% vs. 33% More than half (55% of attendees were willing to consider a less aggressive regimen to preserve fertility, compared with 29% of those who did not attend.  While most oncologists recognize the importance of discussing infertility risks, many do not discuss fertility preservation routinely. Reasons for this discrepancy included poor prognosis and emergent need to start therapy. Increasing awareness through educational events may influence current practice patterns and increase collaboration between reproductive endocrinologists and oncologists.
NDN Based Pub/Sub System for Scalable IoT Cloud
Venue: 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)
Year: 2016

The Internet of Things (IoT) has recently become mainstream, different from the incomplete realization of ubiquitous computing, sensor networks, and others in past that commonly shared the notion of visionary hyper connectivity, i.e. everything is connected. Among many, one significant reason for this achievement of IoT is the nowadays availability of cloud computing which can cost effectively deal with the sheer number of globally distributed devices and data generated from those devices. In this paper, we address the issue of IoT scalability in cloud environments by proposing the unique integration scheme between pub/sub systems and Named Data Networking (NDN) While the pub/sub architecture has been applied in modern IoT cloud platforms, its implementation and deployment practice have not been fully studied for large scale IoT applications. Our approach concentrates on how to leverage the essential scalability of NDN for building pub/sub systems, thereby achieving scalable IoT cloud services.
Executive function is an important consideration for coping strategy use in people with multiple sclerosis
Venue: Journal of clinical and experimental neuropsychology
Year: 2017

Executive function deficits are prevalent in people with multiple sclerosis (PwMS) and PwMS use less adaptive coping than healthy controls. This cross sectional study assessed whether there is a relationship between executive function and coping in PwMS.  One hundred and seven participants with relapsing remitting or secondary progressive MS (n 83 and 24, respectively; age M 48.8 11.1 years) completed measures of coping and executive function.  A positive relationship was found between verbal fluency and use of active, emotional, and instrumental social support coping, and total executive function and substance abuse coping. There was a negative relationship between coping strategies and core (social support, acceptance, religion, restraint, and total coping) higher order (denial and humor) and total executive function indices (acceptance, religion, behavioral disengagement, denial, and total coping)  These directional differences provide support for the importance of specific executive functions in coping strategy utilization. Understanding these relationships will assist psychologists and neuropsychologists with patient psychoeducation, adaptive coping strategy intervention and management for PwMS with reduced executive function ability.
Advances in Solar Photovoltaic Grid Parity
Venue: 2019 7th International Renewable and Sustainable Energy Conference (IRSEC)
Year: 2019

The paper briefly considers the most recent literature on solar photovoitaic grid parity with inference to the market price phenomenon for costs and future success of the technology proliferation. The resolution to populate the energy sector with green energy initiatives, and the preparedness to invest in the sustainable pathways to mitigate the ill effects caused by the use of fossil based fuels and its dedicated technologies calls for worldwide collaboration/participation, in addition, to evaluate and track achievements' The present exploratory study extrapolates the cost of PV modules, progress in electricity prices, environmental impact, and cost consideration in operations, and grid integration to have an effect on grid parity. The authors identified different analytical tools used by researchers to evaluate and determine solar PV market prices. A discussion of researchers' resolves on the year to reach grid parity is presented according to findings from literature, based on performances, and compared to the residential PV price parity. The present article is cogent to guide future research in Solar grid parity, and support key players involved in making future techno innovation decisions on Solar power.
An Increase in the Omega 6/Omega 3 Fatty Acid Ratio Increases the Risk for Obesity
Venue: Nutrients
Year: 2016

In the past three decades, total fat and saturated fat intake as a percentage of total calories has continuously decreased in Western diets, while the intake of omega 6 fatty acid increased and the omega 3 fatty acid decreased, resulting in a large increase in the omega 6/omega 3 ratio from 1:1 during evolution to 20:1 today or even higher. This change in the composition of fatty acids parallels a significant increase in the prevalence of overweight and obesity. Experimental studies have suggested that omega 6 and omega 3 fatty acids elicit divergent effects on body fat gain through mechanisms of adipogenesis, browning of adipose tissue, lipid homeostasis, brain gut adipose tissue axis, and most importantly systemic inflammation. Prospective studies clearly show an increase in the risk of obesity as the level of omega 6 fatty acids and the omega 6/omega 3 ratio increase in red blood cell (RBC) membrane phospholipids, whereas high omega 3 RBC membrane phospholipids decrease the risk of obesity. Recent studies in humans show that in addition to absolute amounts of omega 6 and omega 3 fatty acid intake, the omega 6/omega 3 ratio plays an important role in increasing the development of obesity via both AA eicosanoid metabolites and hyperactivity of the cannabinoid system, which can be reversed with increased intake of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) A balanced omega 6/omega 3 ratio is important for health and in the prevention and management of obesity.
Design high bandwidth density, low latency and energy efficient on chip interconnect
Venue: 2017 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)
Year: 2017

For future high performance computing chips, on chip interconnect requires large bandwidth density, low latency, and high energy efficiency, which pose significant design challenges. This paper presents a design space exploration of transmission line based on chip interconnect. First, we conduct an optimization of on chip transmission lines to minimize the size, channel loss and inter symbol interference (ISI) hence to maximize the bandwidth density. Based on the result, differential coplanar waveguide (CPW) with 55 um pitch size is chosen as the transmission line topology. Next, channel capacities of channel lengths from 2 to 8 cm are characterized based on time domain pulse responses. Various equalizers are studied, which are used to increase the ISI limited channel capacity. To make better use of the large equalized channel capacity, pulse amplitude modulation (PAM) is employed instead of traditional non return to zero (NRZ) signaling. A link budget analysis is then conducted to find the optimal modulation format for each channel. To verify our analyses, several transceivers are designed in 28 nm CMOS technology. An 84 Gb/s PAM 8 transceiver achieves 1.5 Gb/s/mm bandwidth density over a 4 cm channel. The unrepeated bandwidth density is 6.1 Gb/s/mm*cm, which is almost 10 times larger compared to prior work.
The Review of Published Articles on Mobile Learning Area in EBSCO Database
Year: 2015

The distance learning is an instruction system which has various and independent instruction environment, customized lecture design, method and techniques. Distance learning has been improving from letter learning to mobile learning. That's why the aim of this research is to lead for new researchers on mobile learning area. Also we will find out new trends on mobile learning through this research. This research has limited with 2008 2013 years and it has been used EBSCO as a database. The sample of this study consist 67 articles. The criteria of this research is reported according groups of publication dates, countries, research areas, methods, education levels, researching methods, used mobile devices.
Wasserstein Adversarial Autoencoders for Knowledge Graph Embedding based Drug Drug Interaction Prediction
Venue: ArXiv
Year: 2020

Interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug drug interactions (DDI) is one of the key tasks in public health and drug development. Recently, several knowledge graph embedding approaches have received increasing attention in the DDI domain due to their capability of projecting drugs and interactions into a low dimensional feature space for predicting links and classifying triplets. However, existing methods only apply a uniformly random mode to construct negative samples. As a consequence, these samples are often too simplistic to train an effective model. In this paper, we propose a new knowledge graph embedding framework by introducing adversarial autoencoders (AAE) based on Wasserstein distances and Gumbel Softmax relaxation for drug drug interactions tasks. In our framework, the autoencoder is employed to generate high quality negative samples and the hidden vector of the autoencoder is regarded as a plausible drug candidate. Afterwards, the discriminator learns the embeddings of drugs and interactions based on both positive and negative triplets. Meanwhile, in order to solve vanishing gradient problems on the discrete representation an inherent flaw in traditional generative models we utilize the Gumbel Softmax relaxation and the Wasserstein distance to train the embedding model steadily. We empirically evaluate our method on two tasks, link prediction and DDI classification. The experimental results show that our framework can attain significant improvements and noticeably outperform competitive baselines.
Cognitive robotics using vision and mapping systems with Soar
Venue: Defense Commercial Sensing
Year: 2010

The Cognitive Robotic System (CRS) has been developed to use the Soar cognitive architecture for the control of unmanned vehicles and has been tested on two heterogeneous ground robots: a six legged robot (hexapod) and a wheeled robot. The CRS has been used to demonstrate the applicability of Soar for unmanned vehicles by using a Soar agent to control a robot to navigate to a target location in the presence of a cul de sac obstacle. Current work on the CRS has focused on the development of computer vision, additional sensors, and map generating systems that are capable of generating high level information from the environment that will be useful for reasoning in Soar. The scalability of Soar allows us to add more sensors and behaviors quite easily.
Nonlinear pulse width modulation control device of single phase current source grid connected inverter
Year: 2009

The invention relates to a nonlinear pulse width modulation control device of a single phase current source grid connected inverter. The device can be used for adjusting duty ratio in time and outputting high quality power grid current by feedback of modulation current, so as to effectively overcome the native defect of serious aberration of power grid current wave form when the traditional linear PWM control strategy is adopted; a control circuit of the device comprises an integrating circuit having high frequency resetting function, an absolute value circuit I, a comparison circuit, a RS trigger, a combinational logic circuit as well as a reference sinusoidal current circuit, an absolute value circuit II, a zero crossing comparison circuit and a constant frequency clock circuit that have fixed phase relationship with the power grid voltage. The nonlinear pulse width modulation control device can lead unstable DC voltage to be converted into stable grid connected current with the needed magnitude and high quality, and has the advantages of single stage power conversion, high conversion efficiency, small harmonic wave of the grid connected current, rapid dynamic response, high reliability when in overloading, low cost and the like, so as to establish key technology for a renewable energy source grid connected power generation system.
Settler Modernity's Spatial Exceptions: The US POW Camp, Metapolitical Authority, and Ha Jin's War Trash
Year: 2017

This essay provides a critical genealogy of the United States' militarized relation with China through an analysis of Ha Jin's novel War Trash. This vexed relation takes form in the novel as a self described "documentary style" memoir of a Chinese POW who survives the brutality of a UN US camp during the Korean War. War Trash gestures to the significance of China in the consolidation of the United States as an increasingly militarized national security and permanent warfare state from World War II and into the twenty first century. Focusing on how the novel renders the POW camp as a particular kind of spatial exception, in which the biopolitical space and the geopolitical territory coconstitute each other, the essay demonstrates how the nexus of US militarism, imperialism, and settler colonialism an ensemble theorized as settler modernity in Asia and the Pacific is largely structured and reproduced through such exceptions. War Trash's rendering of the Korean War POW camp thus generates broader questions about US settler modernity's attempt to seize metapolitical authority through spatial exceptions extending beyond what became the fifty states (or incorporated territories) constituting the United States in an era when the settler state also becomes a military empire.