nils-herrmann/clean_scirepeval huggingface dataset:
Board level codetermination: A driving force for corporate social responsibility in German companies?
Venue: European Journal of Industrial Relations
Year: 2019

We examine the relationship between board level codetermination and corporate social responsibility in German companies, engaging with two distinct literatures. Most quantitative studies of codetermination focus on its economic impact, with little attention to other outcomes. Studies of corporate social responsibility rarely consider the role of worker representatives. Our new measure of the strength of codetermination, the Mitbestimmungsindex (MB ix) shows a positive relationship with 'substantive' policies such as the adoption of targets for reducing pollution, but not with 'symbolic' policies, such as membership of the UN Global Compact. We therefore shed new light on the role of codetermination and provide a more differentiated view of the spread of what has been termed 'explicit' corporate social responsibility in Germany.
Model uncertainty, political contestation, and public trust in science: Evidence from the COVID 19 pandemic
Venue: Science Advances
Year: 2020

Communications engaging uncertainty in COVID 19 research affect public trust in science and support for science based policy. While scientific uncertainty always invites the risk of politicization and raises questions of how to communicate about science, this risk is magnified for COVID 19. The limited data and accelerated research timelines mean that some prominent models or findings inevitably will be overturned or retracted. In this research, we examine the attitudes of more than 6000 Americans across five different survey experiments to understand how the cue giver and cue given about scientific uncertainty regarding COVID 19 affect public trust in science and support for science based policy. Criticism from Democratic political elites undermines trust more than criticism from Republicans. Emphasizing uncertainty in projections can erode public trust in some contexts. Downplaying uncertainty can raise support in the short term, but reversals in projections may temper these effects or even reduce scientific trust. Careful science communication is critical to maintaining public support for science based policies as the scientific consensus shifts over time.
Critical approach to PLE technique application in the analysis of secondary metabolites in plants
Venue: TrAC Trends in Analytical Chemistry
Year: 2019

The aim of this paper is to present in the most comprehensive way the facts about the application of PLE in the analysis of secondary metabolites in plants. It is true that due to its specific extraction conditions, PLE is one of the most effective and versatile techniques of plant constituents isolation. It is also true that it has popularized the use of aqueous organic mixtures, and even water alone, for the extraction of nonpolar compounds. Yet, the same specific extraction conditions can lead to erroneous results of plant constituent content analysis. Disclosure of new facts about the application of PLE in the analysis of secondary plant metabolites provides an insight into an often overlooked aspect of extraction of active compounds from plants.
Directing the self assembly of supra biomolecular nanotubes using entropic forces.
Venue: Soft matter
Year: 2014

Peptide self assembly, ubiquitous in biology, is one of the most promising 'bottom up' approaches for the generation of synthetic supramolecular architectures. However, directing the self assembly of functional peptides into predictable ordered structures most often requires precise tuning of weak intermolecular forces. Existing strategies are generally based on specific interactions between molecular mediators that require complex chemical synthesis pathways and elaborated design rules. Here we establish a theoretical framework that delineates a generic route towards directing the self assembly of small peptides by simply using entropic forces generated by the polymer chains attached to the peptides. We demonstrate the viability of this concept for polymer conjugated peptide nanotubes using coarse grained molecular dynamics (CGMD) simulations combined with theoretical calculations. We show that conjugated polymer chains create an entropic penalty due to chain confinement upon assembly, and illustrate that the self assembly process can be directed by merely varying the degree of polymer conjugation. Specifically, the entropic penalty, and consequently, the binding energy between peptides can be greatly varied by changing the length and the number of conjugated polymers. Extending this concept for peptides with different degrees of conjugation reveals a path towards controlling the stacking sequence of binary mixtures. Remarkably, we find that a large disparity in the conjugation degree of the two peptides results in a preference towards alternating mixed sequences that minimize the entropic penalty of confinement in the thermodynamic limit. Our study explains recent experiments on polymer peptide conjugates and sets the stage for utilizing entropic forces to guide the stacking sequence of functional macrocycles in tubular assemblies.
Online Similarity Learning for Big Data with Overfitting
Venue: IEEE Transactions on Big Data
Year: 2018

In this paper, we propose a general model to address the overfitting problem in online similarity learning for big data, which is generally generated by two kinds of redundancies: 1) feature redundancy, that is there exists redundant (irrelevant) features in the training data; 2) rank redundancy, that is non redundant (or relevant) features lie in a low rank space. To overcome these, our model is designed to obtain a simple and robust metric matrix through detecting the redundant rows and columns in the metric matrix and constraining the remaining matrix to a low rank space. To reduce feature redundancy, we employ the group sparsity regularization, i.e. the <inline formula> <tex math notation="LaTeX"\ell _{2,1}/tex math><alternatives><inline graphic xlink:href="cong ieq1 2688360.gif" /alternatives>/inline formula> norm, to encourage a sparse feature set. To address rank redundancy, we adopt the low rank regularization, the <italic>m</italic>ax norm, instead of calculating the SVD as in traditional models using the nuclear norm. Therefore, our model can not only generate a low rank metric matrix to avoid overfitting, but also achieves feature selection simultaneously. For model optimization, an online algorithm based on the stochastic proximal method is derived to solve this problem efficiently with the complexity of <inline formula> <tex math notation="LaTeX"$O(d^2)/tex math><alternatives><inline graphic xlink:href="cong ieq2 2688360.gif" /alternatives>/inline formula> To validate the effectiveness and efficiency of our algorithms, we apply our model to online scene categorization and synthesized data and conduct experiments on various benchmark datasets with comparisons to several state of the art methods. Our model is as efficient as the fastest online similarity learning model OASIS, while performing generally as well as the accurate model OMLLR. Moreover, our model can exclude irrelevant redundant feature dimension simultaneously.
A time of change: Behavioral and neural correlates of adolescent sensitivity to appetitive and aversive environmental cues
Venue: Brain and Cognition
Year: 2010

Adolescence is a developmental period that entails substantial changes in affective and incentive seeking behavior relative to both childhood and adulthood, including a heightened propensity to engage in risky behaviors and experience persistent negative and labile mood states. This review discusses the emotional and incentive driven behavioral changes in adolescents and their associated neural mechanisms, focusing on the dynamic interactions between the amygdala, ventral striatum, and prefrontal cortex. Common behavioral changes during adolescence may be associated with a heightened responsiveness to incentives and emotional cues while the capacity to effectively engage in cognitive and emotion regulation is still relatively immature. We highlight empirical work in humans and animals that addresses the interactions between these neural systems in adolescents relative to children and adults, and propose a neurobiological model that may account for the nonlinear changes in adolescent behavior. Finally, we discuss other influences that may contribute to exaggerated reward and emotion processing associated with adolescence, including hormonal fluctuations and the role of the social environment.
Accumulated occupational class and self rated health. Can information on previous experience of class further our understanding of the social gradient in health?
Venue: Social science medicine
Year: 2013

Previous research has shown a social gradient in health with better health for people in more advantaged positions in society. This research has mainly been on the relationship between current position and health, or social position in childhood and health, but less is known about the potential accumulative impact of positions held in adulthood. In this paper I use the economic activity histories from the Swedish Level of Living survey to examine the relationship between accumulated occupational class positions and health. Step wise linear probability models are used to investigate how to best capture the potential association between class experience and self rated health (SRH) and whether the effect of current class is modified when measures of accumulated class are included. I then further test the potentially lasting association between previous exposure to the health risk of working class by analysing only individuals currently in higher or intermediate level service class; the classes under least exposure. I find a positive association between accumulated experiences of working class and less than good SRH. Furthermore, even for employees currently in non manual positions the risk for less than good SRH increases with each added year of previous experience within working class. This suggests that the social gradient can be both accumulative and lasting, and that more information on the mechanisms of health disparities can be found by taking detailed information on peoples' pasts into account. Although gender differences in health are not a focus in this paper, results also indicate that the influence of class experiences on health might differ between men and women.
An Experimental mmWave Channel Model for UAV to UAV Communications
Venue: ArXiv
Year: 2020

Unmanned Aerial Vehicle (UAV) networks can provide a resilient communication infrastructure to enhance terrestrial networks in case of traffic spikes or disaster scenarios. However, to be able to do so, they need to be based on high bandwidth wireless technologies for both radio access and backhaul. With this respect, the millimeter wave (mmWave) spectrum represents an enticing solution, since it provides large chunks of untapped spectrum that can enable ultra high data rates for aerial platforms. Aerial mmWave channels, however, experience characteristics that are significantly different from terrestrial deployments in the same frequency bands. As of today, mmWave aerial channels have not been extensively studied and modeled. Specifically, the combination of UAV micro mobility (because of imprecisions in the control loop, and external factors including wind) and the highly directional mmWave transmissions require ad hoc models to accurately capture the performance of UAV deployments. To fill this gap, we propose an empirical propagation loss model for UAV to UAV communications at 60 GHz, based on an extensive aerial measurement campaign conducted with the Facebook Terragraph channel sounders. We compare it with 3GPP channel models and make the measurement dataset publicly available.
On flames established with air jet in cross flow of fuel rich combustion products
Year: 2015

Advances in combustor technologies are driving aircraft gas turbine engines to operate at higher pressures, temperatures and equivalence ratios. A viable approach for protecting the combustor from the high temperature environment is to inject air through the holes drilled on the surfaces. However, it is possible that the air intended for cooling purposes may react with fuel rich combustion products and may increase heat flux. Air Force Research Laboratory (AFRL) has developed an experimental rig for studying the flames formed between the injected cold air and the cross flow of combustion products. Laser based OH measurements revealed an upstream shift for the flames when the air injection velocity was increased and downstream shift when the fuel content in the cross flow was increased. As conventional understanding of the flame stability does not explain such shifts in flame anchoring location, a time dependent, detailed chemistry computational fluid dynamics model is used for identifying the mechanisms that are responsible. Combustion of propane fuel with air is modeled using a chemical kinetics mechanism involving 52 species and 544 reactions. Calculations reveled that the flames in the film cooling experiment are formed through autoignition process. Simulations have reproduced the various flame characteristics observed in the experiments. Numerical results are used for explaining the non intuitive shifts in flame anchoring location to the changes in blowing ratio and equivalence ratio. The higher diffusive mass transfer rate of hydrogen in comparison to the local heat transport enhances H2 O2 mixing compared to thermal dissipation rate, which, in turn, affects the autoignition process. While increasing the blowing ratio abates the differences resulting from non equal mass and heat transport rates, higher concentrations of hydrogen in the fuel rich cross flows accelerate those differences.
Gully erosion susceptibility assessment and management of hazard prone areas in India using different machine learning algorithms.
Venue: The Science of the total environment
Year: 2019

Gully erosion is one of the most effective drivers of sediment removal and runoff from highland areas to valley floors and stable channels, where continued off site effects of water erosion occur. Gully initiation and development is a natural process that greatly impacts natural resources, agricultural activities, and environmental quality as it promotes land and water degradation, ecosystem disruption, and intensification of hazards. In this research, an attempt is made to produce gully erosion susceptibility maps for the management of hazard prone areas in the Pathro River Basin of India using four well known machine learning models, namely, multivariate additive regression splines (MARS) flexible discriminant analysis (FDA) random forest (RF) and support vector machine (SVM) To support this effort, observations from 174 gully erosion sites were made using field surveys. Of the 174 observations, 70% were randomly split into a training data set to build susceptibility models and the remaining 30% were used to validate the newly built models. Twelve gully erosion conditioning factors were assessed to find the areas most susceptible to gully erosion. The predisposing factors were slope gradient, altitude, plan curvature, slope aspect, land use, slope length (LS) topographical wetness index (TWI) drainage density, soil type, distance from the river, distance from the lineament, and distance from the road. Finally, the results from the four applied models were validated with the help of ROC (Receiver Operating Characteristics) curves. The AUC value for the RF model was calculated to be 96.2% whereas for those for the FDA, MARS, and SVM models were 84.2% 91.4% and 88.3% respectively. The AUC results indicated that the random forest model had the highest prediction accuracy, followed by the MARS, SVM, and FDA models. However, it could be concluded that all the machine learning models performed well according to their prediction accuracy. The produced GESMs can be very useful for land managers and policy makers as they can be used to initiate remedial measures and erosion hazard mitigation in prioritized areas.
Better and Faster: Knowledge Transfer from Multiple Self supervised Learning Tasks via Graph Distillation for Video Classification
Venue: IJCAI
Year: 2018

Video representation learning is a vital problem for classification task. Recently, a promising unsupervised paradigm termed self supervised learning has emerged, which explores inherent supervisory signals implied in massive data for feature learning via solving auxiliary tasks. However, existing methods in this regard suffer from two limitations when extended to video classification. First, they focus only on a single task, whereas ignoring complementarity among different task specific features and thus resulting in suboptimal video representation. Second, high computational and memory cost hinders their application in real world scenarios. In this paper, we propose a graph based distillation framework to address these problems: (1) We propose logits graph and representation graph to transfer knowledge from multiple self supervised tasks, where the former distills classifier level knowledge by solving a multi distribution joint matching problem, and the latter distills internal feature knowledge from pairwise ensembled representations with tackling the challenge of heterogeneity among different features; (2) The proposal that adopts a teacher student framework can reduce the redundancy of knowledge learnt from teachers dramatically, leading to a lighter student model that solves classification task more efficiently. Experimental results on 3 video datasets validate that our proposal not only helps learn better video representation but also compress model for faster inference.
Investment Valuation Model for Renewable Energy Systems in Buildings
Year: 2015

AbstractTransformative technologies for producing renewable energy have promising features for substantial reductions in carbon emissions and environmental footprints of the building sector. Investments in renewable energy systems require substantial implementation costs followed by a long period of recovering the invested capital through savings in utilities bills. Favorable timing of renewable energy system implementation can reduce capital expenses and enhance returns on investments. An appropriate investment valuation method is needed to identify the appropriate time to implement any given renewable energy system and to find the values of properly scheduled investments. Real options analysis provides the ability to cope with investment timing under uncertainty. However, current real options models are theoretically limited in the context of decision making about investments in building energy improvements. In this paper, the writers address the theoretical limitations of current real options models an.
Overexpression of GSDMC is a prognostic factor for predicting a poor outcome in lung adenocarcinoma
Venue: Molecular medicine reports
Year: 2019

The gasdermin (GSDM) superfamily has been demonstrated to consist of several important molecules that modulate multifunctional signal processes, such as cell pyroptosis. In this research, the roles of the GSDM superfamily on the occurrence and prognosis of lung adenocarcinoma (LUAD) were evaluated using integrative bioinformatic analyses and in vitro methods. Here, data from several bioinformatic platforms revealed that GSDMC is significantly upregulated in LUAD tissues and cell lines. Real time fluorescence quantitative PCR (qPCR) demonstrated that GSDMC was obviously upregulated in radio resistant LUAD cells, compared with their parental cells. Moreover, upregulated GSDMC expression was confirmed to be an independent indicator of poor first progression (FP) and overall survival (OS) in LUAD patients. DNA methylation analysis showed an evidently negative correlation between GSDMC expression and methylation status of one CpG site (cg05316065) in its DNA sequence. Patients with high methylation values had significantly higher Karnofsky performance scores (KPSs) and prolonged OS rates. Together, we confirmed that overexpression of GSDMC acts as a promising predictive factor for the poor prognosis of LUAD patients.
MixMatch Domain Adaptaion: Prize winning solution for both tracks of VisDA 2019 challenge
Venue: ArXiv
Year: 2019

We present a domain adaptation (DA) system that can be used in multi source and semi supervised settings. Using the proposed method we achieved 2nd place on multi source track and 3rd place on semi supervised track of the VisDA 2019 challenge (this http URL) The source code of the method is available at this https URL.
Ambige Sexisierungspraktiken und die Dimension der Sprache im Kontext von antimuslimischem Rassismus
Year: 2019

,Burkas? Wir steh'n auf Bikinis" (AfD 2017) lautete der Slogan eines Wahlkampfplakates der Alternative fur Deutschland (AfD) 2017, der den Ausgang unserer Diskussion darstellt. Neben vielem anderen ist er Teil einer Konjunktur von Rassismus und im speziellen von Antimuslimischem Rassismus (im Weiteren mit AMR abgekurzt) Unser Fokus ist auf den Slogan gerichtet, nicht so sehr auf dessen Verfasser_innen, denn: mit Blick auf europaische Gesellschaften hat nicht nur die kritische Rassismusforschung bereits mehrfach herausgestellt, dass Rassismus, in dem Verstandnis, dem wir hier folgen, nicht bloses Phanomen der extremen Rechten oder ,ungebildeter' Menschen sei. Den Slogan und nicht die Verfasser_innen fokussieren zu wollen, geht auch damit einher, Rassismus eben nicht als ,Problem' einzelner Personen(gruppen) zu verstehen, sondern damit ein gesellschaftliches Verhaltnis in den Blick zu nehmen, das von Macht und Ungleichheit strukturiert ist.
The Research on the Problems of Smart Old Age Care in the Background of Smart City Construction
Venue: 2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS)
Year: 2019

According Since 2012, China has started to build a "smart city" which is to build a digital city with informatization and intelligence. Smartness is an important direction for future urban construction to push forward. At the same time, with the progress and development of society and the development of medical science and technology, the continuous improvement of medical standards and national living standards has made the average life expectancy of Chinese nationals prolonged and the aging of the population has become increasingly prominent. The problem of old age care has become an important issue that cannot be avoided in building a smart city. "Smart old age care" will become a new type of old age care service model. It makes us ponder on the development system of smart pension in different aspects such as concept, technology application and functional thinking, providing ideas and references for the development of old age care in smart city.