nils-herrmann/clean_scirepeval huggingface dataset:
Infection and cancer suppress pDC derived IFN I.
Venue: Current opinion in immunology
Year: 2020

Plasmacytoid dendritic cells (pDCs) are specialized producers of Type I interferon (IFN I) that promote anti viral and anti tumor immunity. However, chronic infections and cancer inhibit pDC derived IFN I. While the mechanisms of this inhibition are multifarious they can be classified broadly into two categories: i) reduction or ablation of pDC IFN I production capacity (functional exhaustion) and/or ii) decrease in pDC numbers (altered population dynamics) Recent work has identified many processes that contribute to suppression of pDC derived IFN I during chronic infections and cancer, including sustained stimulation through Toll Like Receptors (TLRs) inhibitory microenvironments, inhibitory receptor ligation, and reduced development from bone marrow progenitors and apoptosis. Emerging success leveraging pDCs in treatment of disease through TLR activation illustrates the therapeutic potential of targeting pDCs. Deeper understanding of the systems that limit pDC derived IFN I has the potential to improve these emerging therapies as well as help devising new approaches that harness the outstanding IFN I production capacity of pDCs.
System regression test planning with a fuzzy expert system
Venue: Inf. Sci.
Year: 2014

An effective system testing activity for consecutive releases of very large software systems depends considerably on the selection of test cases for execution. From a practical point of view, it is not feasible to run all possible test cases because of the real world constraints of limited time, human and monetary resources. While existing techniques for test case selection provide methods to limit the growth of the number of test cases with software evolution, many of them are based on the assumption that source code analysis is available. Very few works have explored the problem of test case selection given the constraint that source code analysis is not available. We propose fuzzy expert systems as an effective solution to the problem. Fuzzy expert systems have the ability to emulate fuzzy human reasoning and judgment processes. The proposed fuzzy expert system identifies potentially critical test cases for system test by correlating knowledge represented by one or more of the following: customer profile, analysis of past test case results, system failure rate, and change in system architecture. We piloted this fuzzy expert system in a large telecommunications system and the results show that test effectiveness and efficiency is significantly improved.
Investigating the Relationship between Organizational Culture and Quality of Work Life from the Perspective of Nurses Working in the Hospitals of Shahrekord University of Medical Sciences in 2015
Year: 2017

This paper should be cited as: Torkan B, Razavi Vanani SM [Investigating the Relationship between Organizational Culture and Quality of Work Life from the Perspective of Nurses Working in the Hospitals of Shahrekord University of Medical Sciences in 2015]  2017;14(2):29 35. Investigating the Relationship between Organizational Culture and Quality of Work Life from the Perspective of Nurses Working in the Hospitals of Shahrekord University of Medical Sciences in 2015
Investigating the Effects of Brainstem Neuronal Adaptation on Cardiovascular Homeostasis
Venue: Frontiers in Neuroscience
Year: 2020

Central coordination of cardiovascular function is accomplished, in part, by the baroreceptor reflex, a multi input multi output physiological control system that regulates the activity of the parasympathetic and sympathetic nervous systems via interactions among multiple brainstem nuclei. Recent single cell analyses within the brain revealed that individual neurons within and across brain nuclei exhibit distinct transcriptional states contributing to neuronal function. Such transcriptional heterogeneity complicates the task of understanding how neurons within and across brain nuclei organize and function to process multiple inputs and coordinate cardiovascular functions within the larger context of the baroreceptor reflex. However, prior analysis of brainstem neurons revealed that single neuron transcriptional heterogeneity reflects an adaptive response to synaptic inputs and that neurons organize into distinct subtypes with respect to synaptic inputs received. Based on these results, we hypothesize that adaptation of neuronal subtypes support robust biological function through graded cellular responses. We test this hypothesis by examining the functional impact of neuronal adaptation on parasympathetic activity within the context of short term baroreceptor reflex regulation. In this work, we extend existing quantitative closed loop models of the baroreceptor reflex by incorporating into the model distinct input driven neuronal subtypes and neuroanatomical groups that modulate parasympathetic activity. We then use this extended model to investigate, via simulation, the functional role of neuronal adaptation under conditions of health and systolic heart failure. Simulation results suggest that parasympathetic activity can be modulated appropriately by the coordination of distinct neuronal subtypes to maintain normal cardiovascular functions under systolic heart failure conditions. Moreover, differing degrees of adaptation of these neuronal subtypes contribute to cardiovascular behaviors corresponding to distinct clinical phenotypes of heart failure, such as exercise intolerance. Further, our results suggest that an imbalance between sympathetic and parasympathetic activity regulating ventricular contractility contributes to exercise intolerance in systolic heart failure patients, and restoring this balance can improve the short term cardiovascular performance of these patients.
Desarrollo de competencias investigativas en estudiantes de las Ciencias de la Salud. Sistematizacion de experiencias
Year: 2020

Desarrollar competencias investigativas en un estudiante implica conocer el uso del conocimiento de forma adecuada; esto permite que se fortalezcan las habilidades para la observacion, argumentacion, sistematizacion y cuestionamiento a fin de que se pueda crear y/o gestionar un conocimiento. Para el desarrollo de las competencias en investigacion se han planteado distintas estrategias didacticas: semilleros de investigacion, aprendizaje cooperativo, metodos por proyectos, aprendizaje basado en problemas, uso de mapas conceptuales, estudios de casos, seminarios, redaccion de ensayos y elaboracion de talleres. En la presente revision sistematica se analizo los principales programas que se han implementado en las universidades para mejorar las competencias investigativas de estudiantes de las Ciencias de la Salud. Se destaca a los programas de verano y el trabajo colaborativo con mentores como las intervenciones mas comunes y eficaces para el desarrollo de tales competencias.
Financial accountability, ethical issues and organizational citizenship behavior in fragile economic systems: The case of the Nigerian economy
Year: 2018

Received November 1, 2017 Received in revised format November 11 2017 Accepted January7 2018 Available online January82018 The general objective of this study is to examine the relationship among financial accountability, ethical issues and organizational citizenship behaviour, in a national economic system. The null hypothesis of the study examined the inverted trajectories of the variables. The work is situated within the context of Nigeria's national economy, which typifies a fragile economic system. We have argued in the work that the fragility of the Nigerian national economic system (which is representative of similar economies among developing nations) is a function of its input output processes. The study essentially finds that to reverse the trend of fragility in an economic system (a national economic system) in the context of developing economies, the issues of financial accountability, ethical questions and great organizational citizenship behaviour must in their combinatory format, be elevated to the status of a national imperative. We found in the work, that ethical regeneration is antecedent to institutional reform, in the schemata of mechanisms, for reversing the tendency of fragility, in troubled economic systems, classifiable as developing or underdeveloped.
Lifelong Zero Shot Learning by Bridging Prediction and Explanation
Year: 2017

Lifelong learning is a learning paradigm in which learning agents receive new tasks sequentially, in a lifelong manner. When a new task arrives, the knowledge from all previous tasks is leveraged in order to positively bias the learning of the new task. This is an attractive ability for intelligent agents to have as it mitigates the problem of having to learn new tasks from scratch each time, as well as alleviating the need for large labelled datasets that are both scarce and of high cost. Another attractive ability is that of zero shot learning, where a task is solved without ever being explicitly trained to do so. Zero shot learning is complemented by learning since lifelong learning is concerned with accumulating prior knowledge over time resulting in strong priors and inductive biases that can aid in the goal of zero shot learning. We propose to explore a novel framework for lifelong zero shot learning with the aim of learning how to utilize arbitrary prior knowledge.
ANALISIS KADAR ASAM CUKA DARI FERMENTASI MENGGUNAKAN Saccharomyces cerevisiae DAN Acetobacter aceti PADA BONGGOL PISANG (Musa paradisiaca L. VARIETAS AMBON NANGKA, AMBON BAWEN DAN AMBON WULUNG YANG HIDUP DI JALUR PANTAI SELATAN DESA TEGAL KAMULYAN CILACAP
Year: 2015

Penelitian ini bertujuan untuk mengetahui perbandingan kadar asam cuka dari fermentasi menggunakan Saccharomyces cerevisiae dan Acetobacter aceti pada bonggol pisang varietas ambon bawen, ambon wulung dan ambon nangka yang hidup di jalur pantai selatan Desa Tegal Kamulyan Cilacap. Metode penelitian yang digunakan adalah metode eksperimen menggunakan Rancangan Acak Lengkap (RAL) pola faktorial dengan 3 kali Ulangan pada ketiga varietas. Hasil penelitian menunjukan nilai Fhitung=259.692 Ftabel=5.14 (kadar glukosa) Fhitung=48.654 Ftabel=5.14 (kadar etanol) dan Fhitung=48.654 Ftabel=5.14 (Kadar asam cuka) pada taraf signifikasi 5% sehingga berbeda nyata. Hasil uji DMRT menunjukan ketiga varietas bnggol pisang memiliki kadar glukosa dan etanol yang berbeda beda, sehingga menghasilkan kadar asam cuka yang berbeda. Kadar glukosa varietas ambon bawen, ambon wulung dan ambon nangka yaitu: 4% 2,17% dan 2% Kadar etanol varietas ambon bawen, ambon wulung dan ambon nangka yaitu: 14,18% 10,50% dan 6,99% Kadar asam cuka varietas ambon bawen, ambon wulung dan ambon nangka yaitu: 1,38% 1,12% dan 1,09% Berdasarkan analisis data tersebut menunjukkan kadar glukosa suatu bahan akan menentukan kadar etanol hasil fermentasi secara anaerob, sedangkan Kadar etanol akan menentukan kadar asam cuka hasil fermentasi secara aerob. Perbedaan kadar glukosa disebabkan oleh kandungan pati pada ketiga varietas berbeda, sedangkan perbedaan kadar etanol disebabkan kadar glukosa hasil fermentasi berbeda. Kadar glukosa dan kadar etanol suatu bahan akan menentukan kadar asam cuka hasil fermentasi secara aerob.
Latent Space Cartography: Visual Analysis of Vector Space Embeddings
Venue: Comput. Graph. Forum
Year: 2019

Latent spaces reduced dimensionality vector space embeddings of data, fit via machine learning have been shown to capture interesting semantic properties and support data analysis and synthesis within a domain. Interpretation of latent spaces is challenging because prior knowledge, sometimes subtle and implicit, is essential to the process. We contribute methods for "latent space cartography" the process of mapping and comparing meaningful semantic dimensions within latent spaces. We first perform a literature survey of relevant machine learning, natural language processing, and scientific research to distill common tasks and propose a workflow process. Next, we present an integrated visual analysis system for supporting this workflow, enabling users to discover, define, and verify meaningful relationships among data points, encoded within latent space dimensions. Three case studies demonstrate how users of our system can compare latent space variants in image generation, challenge existing findings on cancer transcriptomes, and assess a word embedding benchmark.
Construction of Hybrid English Courses Based on Mobile Software Assisted Teaching in Chinese Universities
Year: 2020

Developing "Internet Education" and promoting the sharing of high quality educational resources are the major trends of national educational reformation and development in China. As a compulsory course for undergraduates, it is necessary to reform the traditional mode of English teaching in order to better meet the needs of cultivating internationalized and practical talents in the Internet era. Therefore, based on the project of "Seminar Course" in one Chinese university, this paper explores the construction of Hybrid English Courses by employing Superstar Platform which is a mobile learning software. It aims to establish a new teaching mode which could improve undergraduates' language use ability in higher education in China.
Technological Aids for Dyscalculic Children
Year: 2020

Dyscalculia is associated with the child's inability to understand the basic mathematical concepts. Dyscalculia covers a variety of challenges in mathematics that include understanding the numbers which are the root of mathematics to the inability to grasp the mathematical language. Hence, for such kid's dealing with mathematics even in day to day life is a challenging aspect and even more difficult if they have no tools that would be an aid to their disability. Although there are many interactive learning techniques, most of the time students tend to lose interest so learning in the form of game can be helpful and can enhance learner's motivation. The process of learning through games is known as gamification. The main aim of gamification is providing a technological aid to support students suffering from dyscalculia. Understanding of gamification is an important task as playing video games and games are considered as tasks for entertainment purposes, so it is not considered as an aid in learning. So, gamification awareness is very important, to be considered as an aid to teach children suffering from dyscalculia.
Insight into the Mechanism of Antibacterial Activity of ZnO: Surface Defects Mediated Reactive Oxygen Species Even in the Dark.
Venue: Langmuir the ACS journal of surfaces and colloids
Year: 2015

A systematic and complete antibacterial study on well designed and well characterized microparticle (micro) nanoparticle (nano) and capped nano ZnO has been carried out in both dark and light conditions with the objective of arriving at the mechanism of the antibacterial activity of ZnO, particularly in the dark. The present systematic study has conclusively proved that reactive oxygen species (ROS) such as )OH, )O2( and H2O2 are significantly produced from aqueous suspension of ZnO even in the dark and are mainly responsible for the activity in the dark up to 17% rather than Zn(2+ ion leaching as proposed earlier. This work further confirms that surface defects play a major role in the production of ROS both in the presence and absence of light. In the dark, superoxide )O2( radical mediated ROS generation through singly ionized oxygen vacancy is proposed for the first time, and it is confirmed by EPR and scavenger studies. ROS such as )O2( H2O2, and )OH have been estimated by UV visible spectroscopy using nitro blue tetrazolium (NBT) KMnO4 titrations, and fluorescence spectroscopy, respectively. These are correlated to the antibacterial activity of ZnO in the dark and light. The activity is found to be highest for nano ZnO and least for micro ZnO, with capped ZnO between the two, highlighting the important role of surface defects in generation of ROS. The surface charge density of ZnO in dark and light has been estimated for the first time to the best of our knowledge, and it can influence antibacterial activity. Our work proposes a new mechanism mediated by superoxide species, for antibacterial activity of ZnO especially in the dark.
VISION KG: Topic centric Visualization System for Summarizing Knowledge Graph
Venue: WSDM
Year: 2020

Large scale knowledge graph (KG) has attracted wide attentions in both academia and industry recently. However, due to the complexity of SPARQL syntax and massive volume of real KG, it remains difficult for ordinary users to access KG. In this demo, we present VISION KG, a topic centric visualization system to help users navigate KG easily via entity summarization and entity clustering. Given a query entity v0, VISION KG summarizes the induced subgraph of v0's neighbor nodes via our proposed facts ranking method that measures importance, relatedness and diversity. Moreover, to achieve conciseness, we split the summarized graph into several topic centric summarized subgraph according to semantic and structural similarities among entities. We will demonstrate how VISION KG provides a user friendly visualization interface for navigating KG.
Fighting misinformation on social media using crowdsourced judgments of news source quality
Venue: Proceedings of the National Academy of Sciences
Year: 2019

Many people consume news via social media. It is therefore desirable to reduce social media users' exposure to low quality news content. One possible intervention is for social media ranking algorithms to show relatively less content from sources that users deem to be untrustworthy. But are laypeople's judgments reliable indicators of quality, or are they corrupted by either partisan bias or lack of information? Perhaps surprisingly, we find that laypeople on average are quite good at distinguishing between lower and higher quality sources. These results indicate that incorporating the trust ratings of laypeople into social media ranking algorithms may prove an effective intervention against misinformation, fake news, and news content with heavy political bias. Reducing the spread of misinformation, especially on social media, is a major challenge. We investigate one potential approach: having social media platform algorithms preferentially display content from news sources that users rate as trustworthy. To do so, we ask whether crowdsourced trust ratings can effectively differentiate more versus less reliable sources. We ran two preregistered experiments (n 1,010 from Mechanical Turk and n 970 from Lucid) where individuals rated familiarity with, and trust in, 60 news sources from three categories: (i) mainstream media outlets, (ii) hyperpartisan websites, and (iii) websites that produce blatantly false content "fake news" Despite substantial partisan differences, we find that laypeople across the political spectrum rated mainstream sources as far more trustworthy than either hyperpartisan or fake news sources. Although this difference was larger for Democrats than Republicans mostly due to distrust of mainstream sources by Republicans every mainstream source (with one exception) was rated as more trustworthy than every hyperpartisan or fake news source across both studies when equally weighting ratings of Democrats and Republicans. Furthermore, politically balanced layperson ratings were strongly correlated (r 0.90) with ratings provided by professional fact checkers. We also found that, particularly among liberals, individuals higher in cognitive reflection were better able to discern between low and high quality sources. Finally, we found that excluding ratings from participants who were not familiar with a given news source dramatically reduced the effectiveness of the crowd. Our findings indicate that having algorithms up rank content from trusted media outlets may be a promising approach for fighting the spread of misinformation on social media.
Metal Organic Frameworks in Heterogeneous Catalysis: Recent Progress, New Trends, and Future Perspectives.
Venue: Chemical reviews
Year: 2020

More than 95% (in volume) of all of today's chemical products are manufactured through catalytic processes, making research into more efficient catalytic materials a thrilling and very dynamic research field. In this regard, metal organic frameworks (MOFs) offer great opportunities for the rational design of new catalytic solids, as highlighted by the unprecedented number of publications appearing over the past decade. In this review, the recent advances in the application of MOFs in heterogeneous catalysis are discussed. MOFs with intrinsic thermocatalytic activity, as hosts for the incorporation of metal nanoparticles, as precursors for the manufacture of composite catalysts and those active in photo and electrocatalytic processes are critically reviewed. The review is wrapped up with our personal view on future research directions.
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
Venue: ICLR
Year: 2017

We introduce Deep Variational Bayes Filters (DVBF) a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in Stochastic Gradient Variational Bayes, DVBF can overcome intractable inference distributions via variational inference. Thus, it can handle highly nonlinear input data with temporal and spatial dependencies such as image sequences without domain knowledge. Our experiments show that enabling backpropagation through transitions enforces state space assumptions and significantly improves information content of the latent embedding. This also enables realistic long term prediction.
Multiobjective Optimization for Green Network Routing in Game Theoretical Perspective
Venue: IEEE Journal on Selected Areas in Communications
Year: 2015

In this paper, we study the multiobjective optimization problem for green network routing. Although traditional commonly used multiobjective optimization methods can yield a Pareto efficient solution, they need to construct an aggregate objective function (AOF) or model one objective as a constraint in the optimization problem formulation. As a result, it is difficult to achieve a fair tradeoff among all objectives. Accordingly, we induce a Nash bargaining framework, which treats the two objectives as two virtual players in a game theoretic model, who negotiate how traffic should be routed to optimize both objectives. During the negotiation, each of them announces its performance threat value to reduce its cost, so the model is regarded as a threat value game. Our analysis shows that no agreement can be achieved if each player sets its threat value selfishly. To avoid such a negotiation break down, we modify the threat value game to have a repeated process and design a mechanism to not only guarantee an agreement, but also generate a fair solution. Finally, to evaluate the efficiency of our proposed framework, we implement it into two multiobjective optimization cases for network green routing. The first case is load balancing and energy efficiency optimization for intradomain routing, and the second one is the energy efficiency optimization of two domains for interdomain routing.
Creativity: Generating Diverse Questions Using Variational Autoencoders
Venue: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2017

Generating diverse questions for given images is an important task for computational education, entertainment and AI assistants. Different from many conventional prediction techniques is the need for algorithms to generate a diverse set of plausible questions, which we refer to as creativity. In this paper we propose a creative algorithm for visual question generation which combines the advantages of variational autoencoders with long short term memory networks. We demonstrate that our framework is able to generate a large set of varying questions given a single input image.