nils-herrmann/clean_scirepeval huggingface dataset:
Actor Critic Provably Finds Nash Equilibria of Linear Quadratic Mean Field Games
Venue: ICLR
Year: 2020

We study discrete time mean field Markov games with infinite numbers of agents where each agent aims to minimize its ergodic cost. We consider the setting where the agents have identical linear state transitions and quadratic cost functions, while the aggregated effect of the agents is captured by the population mean of their states, namely, the mean field state. For such a game, based on the Nash certainty equivalence principle, we provide sufficient conditions for the existence and uniqueness of its Nash equilibrium. Moreover, to find the Nash equilibrium, we propose a mean field actor critic algorithm with linear function approximation, which does not require knowing the model of dynamics. Specifically, at each iteration of our algorithm, we use the single agent actor critic algorithm to approximately obtain the optimal policy of the each agent given the current mean field state, and then update the mean field state. In particular, we prove that our algorithm converges to the Nash equilibrium at a linear rate. To the best of our knowledge, this is the first success of applying model free reinforcement learning with function approximation to discrete time mean field Markov games with provable non asymptotic global convergence guarantees.
Digital Employer Branding
Year: 2021

Digital transformation has become a key concern for many enterprises that social media now occupy an important place more than ever in the managerial strategies. The human resources (HR) function is considered as one of the organizational dimensions that have taken advantage of these digital platforms, especially for the employer branding purposes. This chapter aims to explore the practices that enterprises in Morocco use to manage their employer brand through social media to attract potential candidates, retain recruits, and limit turnover. To achieve this research objective, a multiple case study was carried out with managers from four customer relation centers through semi structured interviews. The findings highlight the existence of digital ambassadorship and e reputation oversight practices. Finally, this chapter also provides some managerial recommendations and future research directions.
Parallel BLAST Analysis and Performance Evaluation
Venue: BICoB
Year: 2011

BLAST is an efficient heuristic algorithm used for comparing biological sequences, such as amino acids or nucleotides and identifying similarities between them. However, extensive genetic mapping projects feed sequence databases continuously, forcing them to grow steadily, turning execution time into a potential constraint to sequential implementations of the algorithm and driving the search for new approaches, such as parallelism, to reduce it. In this work, parallel implementations of BLAST are evaluated, in particular mpiBLAST, aiming to find the best conditions for executing those applications in high performance parallel systems. Through comparative tests and a time profile of the application, we analyzed its performance variation regarding parameters such as load balancing, advanced load of database fragments in RAM and also employing a high throughput parallel file system.
Deep CNN Based Blind Image Quality Predictor
Venue: IEEE Transactions on Neural Networks and Learning Systems
Year: 2019

Image recognition based on convolutional neural networks (CNNs) has recently been shown to deliver the state of the art performance in various areas of computer vision and image processing. Nevertheless, applying a deep CNN to no reference image quality assessment (NR IQA) remains a challenging task due to critical obstacles, i.e. the lack of a training database. In this paper, we propose a CNN based NR IQA framework that can effectively solve this problem. The proposed method deep image quality assessor (DIQA) separates the training of NR IQA into two stages: 1) an objective distortion part and 2) a human visual system related part. In the first stage, the CNN learns to predict the objective error map, and then the model learns to predict subjective score in the second stage. To complement the inaccuracy of the objective error map prediction on the homogeneous region, we also propose a reliability map. Two simple handcrafted features were additionally employed to further enhance the accuracy. In addition, we propose a way to visualize perceptual error maps to analyze what was learned by the deep CNN model. In the experiments, the DIQA yielded the state of the art accuracy on the various databases.
72 jahrige Patientin mit Pruritus vulvae
Venue: Der Gynakologe
Year: 2020

Eine 72 jahrige Patientin (164 cm, Gewicht 71kg; Multipara) klagt uber einen seit uber einem Jahr bestehenden und an Intensitat zunehmenden Juckreiz im Bereich des ausseren Genitale. Unter den Verdachtsdiagnosen Candidainfektion, Zystitis und Herpes genitalis war die Patientin mit unterschiedlichen Salben (Antimykotika, Kortison, Antibiotika und Virostatika) behandeltworden, ohnedass eine der Therapien einen wesentlichen Erfolg erkennen liess. Es besteht ein leichter, mit einem b Blocker behandelter arterieller Hypertonus sowie ein Diabetes mellitus Typ 2, der mit Metformin gut eingestellt ist. Die ubrige Eigenund Familienanamnese ist blande. Prufungsfragen Welche Untersuchungen wurden Sie bei der Patientin zunachst durchfuhren? Nennen Sie die wichtigsten Differenzialdiagnosen des Pruritus vulvae.  Diagnose ist am wahrscheinlichsten, und wie wurden Sie das Krankheitsbild charakterisieren? Wie wurden Sie dieses Krankheitsbild behandeln? Welche erganzenden Untersuchungen halten Sie fur erforderlich? Erklaren Sie die aktuelle Stadieneinteilung der WHO (World Health Organization) fur die vulvare intraepitheliale Neoplasie (VIN) Wie wurden Sie die unterschiedlichen Entstehungsmechanismen fur ein Vulvakarzinom charakterisieren? Welche grundsatzlichen Therapieziele wurden Sie fur eine VIN benennen? Welche Faktoren sollten bei der Therapiewahl berucksichtigt werden? Welche Optionen bestehen fur die Therapie einer VIN?
Gendered Organizations in the New Economy
Venue: Gender society official publication of Sociologists for Women in Society
Year: 2012

Gender scholars draw on the "theory of gendered organizations" to explain persistent gender inequality in the workplace. This theory argues that gender inequality is built into work organizations in which jobs are characterized by long term security, standardized career ladders and job descriptions, and management controlled evaluations. Over the past few decades, this basic organizational logic has been transformed. In the so called new economy, work is increasingly characterized by job insecurity, teamwork, career maps, and networking. Using a case study of geoscientists in the oil and gas industry, we apply a gender lens to this evolving organization of work. This article extends Acker's theory of gendered organizations by identifying the mechanisms that reproduce gender inequality in the twenty first century workplace, and by suggesting appropriate policy approaches to remedy these disparities.
Canopy structure explains the relationship between photosynthesis and sun induced chlorophyll fluorescence in crops
Year: 2020

Remote sensing of far red sun induced chlorophyll fluorescence (SIF) has emerged as an important tool for studying gross primary productivity (GPP) at the global scale. However, the relationship between SIF and GPP at the canopy scale lacks a clear mechanistic explanation. This is largely due to the poorly characterized role of the relative contributions from canopy structure and leaf physiology to the variability of the top of canopy, observed SIF signal. In particular, the effect of the canopy structure beyond light absorption is that only a fraction (fesc) of the SIF emitted from all leaves in the canopy can escape from the canopy due to the strong scattering of near infrared radiation. We combined rice, wheat and corn canopy level in situ datasets to study how the physiological and structural components of SIF individually relate to measures of photosynthesis. At seasonal time scales, we found a considerably strong positive correlation (R2 0.4 0.6) of fesc to the seasonal dynamics of the photosynthetic light use efficiency (LUEP) while the estimated physiological SIF yield was almost entirely uncorrelated to LUEP both at seasonal and diurnal time scales, with the partial exception of wheat. Consistent with these findings, the canopy structure and radiation component of SIF, defined as the product of APAR and fesc, explained the relationship of observed SIF to GPP and even outperformed GPP estimation based on observed SIF at two of the three sites investigated. These results held for both half hourly and daily mean values. In contrast, the total emitted SIF, obtained by normalizing observed SIF for fesc, improved only the relationship to APAR but considerably decreased the correlation to GPP for all three crops. Our findings demonstrate the dominant role of canopy structure in the SIF GPP relationship and establish a strong, mechanistic link between the near infrared reflectance of vegetation (NIRV) and the relevant canopy structure information contained in the SIF signal. These insights are expected to be useful in improving remote sensing based GPP estimates.
It's All in the Name: Mitigating Gender Bias with Name Based Counterfactual Data Substitution
Venue: EMNLP
Year: 2019

This paper treats gender bias latent in word embeddings. Previous mitigation attempts rely on the operationalisation of gender bias as a projection over a linear subspace. An alternative approach is Counterfactual Data Augmentation (CDA) in which a corpus is duplicated and augmented to remove bias, e.g. by swapping all inherently gendered words in the copy. We perform an empirical comparison of these approaches on the English Gigaword and Wikipedia, and find that whilst both successfully reduce direct bias and perform well in tasks which quantify embedding quality, CDA variants outperform projection based methods at the task of drawing non biased gender analogies by an average of 19% across both corpora. We propose two improvements to CDA: Counterfactual Data Substitution (CDS) a variant of CDA in which potentially biased text is randomly substituted to avoid duplication, and the Names Intervention, a novel name pairing technique that vastly increases the number of words being treated. CDA/S with the Names Intervention is the only approach which is able to mitigate indirect gender bias: following debiasing, previously biased words are significantly less clustered according to gender (cluster purity is reduced by 49% thus improving on the state of the art for bias mitigation.
A Theory of the Stakeholder Corporation
Year: 2015

There is a widely held view within the general public that large corporations should act in the interests of a broader group of agents than just their shareholders (the stakeholder view) This paper presents a framework where this idea can be justified. The point of departure is the observation that a large firm typically faces endogenous risks that may have a significant impact on the workers it employs and the consumers it serves. These risks generate externalities on these stakeholders which are not internalized by shareholders. As a result, in the competitive equilibrium, there is under investment in the prevention of these risks. We suggest that this under investment problem can be alleviated if firms are instructed to maximize the total welfare of their stakeholders rather than shareholder value alone (stakeholder equilibrium) The stakeholder equilibrium can be implemented by introducing new property rights (employee rights and consumer rights) and instructing managers to maximize the total value of the firm (the value of these rights plus shareholder value) If there is only one firm, the stakeholder equilibrium is Pareto optimal. However, this is not true with more than one firm and/or heterogeneous agents, which illustrates some of the limits of the stakeholder model.
A new superhard material: Osmium diboride OsB2
Year: 2006

Superhard materials have many industrial applications, wherever resistance to abrasion and wear are important. The synthesis of new superhard materials is one of the great challenges to scientists. We re examined the phase diagram of the binary osmium boron system and confirmed the existence of two hexagonal phases, OsB1.1, Os2B3, and an orthorhombic phase, OsB2. Almost nothing is known about the physical properties of osmium borides. Microhardness measurements show that OsB2 is extremely hard. Ab initio calculations show that this is due to formation of covalent bonds between boron atoms. OsB2 is also a low compressibility material. It can be used as hard coating.
Design and Investigation of Microstrip Patch Rectangular Antenna Using Two Parasitic Ring Resonators and Partial Ground Plane for Multiband Applications
Venue: 2019 IEEE International Electromagnetics and Antenna Conference (IEMANTENNA)
Year: 2019

In this paper, advanced planar monopole antenna for multi band applications is proposed. The simulated results show that the proposed antenna operates at multiband resonance frequencies, which covers different wireless communication applications. The proposed antenna consists of a quasi modified rectangular radiating patch with a partial ground plane and two parasitic elements (open loop ring resonators) to serve as coupling bridges. A stepped cut at the partial ground plane is used, to achieve the multi band features. The proposed antenna is simulated and optimized using High Frequency Simulation System (HFSS) The antenna topology possesses an area of 40x40 x1.27 mm3. The measured results demonstrate that the proposed antenna has impedance bandwidths for 10 dB return loss which meet the requirements of wireless local area network (WLAN) worldwide interoperability for microwave access (Wi MAX) C (Uplink) X (Uplink) and Ku (Uplink) band applications. An Acceptable agreement is obtained between measurement and simulation results. Experimental results show that the antenna is successfully simulated and measured.
A Compatible Framework for RGB D SLAM in Dynamic Scenes
Venue: IEEE Access
Year: 2019

Localization and mapping in a dynamic scene is a crucial problem for the indoor visual simultaneous localization and mapping (SLAM) system. Most existed visual odometry (VO) or SLAM systems are based on the assumption that the environment is static. The performance of a SLAM system may degenerate when it is operated in a severely dynamic environment. The assumption limits the applications of RGB D SLAM in the dynamic environment. In this paper, we propose a workflow to segment the objects accurately, which will be marked as the potentially dynamic object area based on the semantic information. A novel approach for motion detection and removal from the moving camera is introduced. We integrate the semantics based motion detection and the segmentation approach with an RGB D SLAM system. To evaluate the effectiveness of the proposed approach, we conduct the experiments on the challenging dynamic sequences of TUM RGBD datasets. The experimental results suggest that our approach improves the accuracy of localization and outperforms the state of the art dynamic removal based SLAM system in both severely dynamic and slightly dynamic scenes.
Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
Venue: Remote. Sens.
Year: 2019

This study aims to assess the potential of the LDAS Monde platform, a land data assimilation system developed by Meteo France, to monitor the impact on vegetation state of the 2018 summer heatwave over Western Europe. The LDAS Monde is driven by ECMWF's (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS HRES) used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI) The study of long time series of satellite derived CGLS LAI (2000 2018) and SSM (2008 2018) highlights marked negative anomalies for July 2018 affecting large areas of northwestern Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the domain of interest have never been observed in the LAI product over this 19 year period. LDAS Monde land surface reanalyses were produced at spatial resolutions of 0.25deg x 0.25deg (January 2008 to October 2018) and 0.10deg x 0.10deg (April 2016 to December 2018) Both configurations of LDAS Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5 and IFS HRES driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g. associated to a better representation of the land cover, topography) and using HRES forcing still enhances the skill. While there are advantages in using HRES, there is added value in down scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedback in the model. Finally, we also found added value of initializing 8 day land surface HRES driven forecasts from LDAS Monde analysis when compared with model only initial conditions.
Simultaneous measurement of forward backward asymmetry and top polarization in dilepton final states from tt production at the Tevatron
Year: 2015

We present a simultaneous measurement of the forward backward asymmetry and the top quark polarization in t (t) over bar production in dilepton final states using 9.7 fb( 1) of proton antiproton collisions at root s 1.96 TeV with the D0 detector. To reconstruct the distributions of kinematic observables we employ a matrix element technique that calculates the likelihood of the possible t (t) over bar kinematic configurations. After accounting for the presence of background events and for calibration effects, we obtain a forward backward asymmetry of A(t (t) over bar) (15.0 6.4(stat) 4.9(syst) and a top quark polarization times spin analyzing power in the beam basis of kappa P (7.2 10.5(stat) 4.2(syst) with a correlation of 56% between the measurements. If we constrain the forward backward asymmetry to its expected standard model value, we obtain a measurement of the top polarization of kappa P (11.3 9.1(stat) 1.9(syst) If we constrain the top polarization to its expected standard model value, we measure a forward backward asymmetry of A(t (t) over bar) (17.5 5.6(stat) 3.1(syst) A combination with the D0 A(t (t) over bar) measurement in the lepton jets final state yields an asymmetry of A(t (t) over bar) (11.8 2.5(stat) 1.3(syst) Within their respective uncertainties, all these results are consistent with the standard model expectations.
Immune Thrombocytopenic Purpura (ITP) Triggered by COVID 19 Infection and Vaccination.
Venue: The Israel Medical Association journal IMAJ
Year: 2021

I thrombocytopenia (ITP) is an autoimmune condition caused by the inhibition of platelet production and circulation by autoantibodies. It presents clinically as minor to major bleeding secondary to the thrombocytopenia  The most common form of ITP is idiopatic, but it has been described following various infections and drugs, and most recently, vaccinations  Since the beginning of the coronavirus disease 2019 (COVID 19) pandemic in December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) was postulated as a potential trigger to numerous autoimmune disorders  Specifically ITP has been reported following SARS CoV 2 infection  Different vaccines against the infection were given worldwide in an attempt to control the SARS CoV 2 pandemic, raising the question of whether ITP may also result from COVID 19 vaccination, in addition to the infection.
A Distributed Branch and Bound Algorithm for Computing Optimal Coalition Structures
Venue: SETN
Year: 2006

Coalition formation is an important area of research in multi agent systems. Computing optimal coalition structures for a large number of agents is an important problem in coalition formation but has received little attention in the literature. Previous studies assume that each coalition value is known a priori. This assumption is impractical in real world settings. Furthermore, the problem of finding coalition values become intractable for even a relatively small number of agents. This work proposes a distributed branch and bound algorithm for computing optimal coalition structures in linear production domain, where each coalition value is not known a priori. The common goal of the agents is to maximize the system's profit. In our algorithm, agents perform two tasks: i) deliberate profitable coalitions, and ii) cooperatively compute optimal coalition structures. We show that our algorithm outperforms exhaustive search in generating optimal coalition structure in terms of elapses time and number of coalition structures generated.
Introduccion a las bases de datos NoSQL usando Cassandra
Year: 2019

En este libro se realiza una introduccion a las bases de datos NoSQL utilizando como elemento de ilustracion Cassandra, que pertenece a las bases de datos orientadas a columnas que a su vez se agrupan en las denominadas bases de datos orientadas hacia agregados. El libro se estructura en dos partes, de las cuales la primera es mas teorica y se centra en la descripcion de los conceptos fundamentales de las bases de datos NoSQL, mientras que la segunda es una introduccion a Cassandra. Asi, se explica su instalacion, arquitectura, modelo de datos y lenguaje de consultas CQL. Asimismo, se dedica un capitulo al problema de la modelizacion con Cassandra, para lo cual se resuelven varios casos y se presenta un ejemplo de implementacion con Python.
What About the "Instruction" in Instructional Sensitivity? Raising a Validity Issue in Research on Instructional Sensitivity
Venue: Educational and psychological measurement
Year: 2018

In instructional sensitivity research, it is important to evaluate the validity argument about the extent to which student performance on the assessment can be used to infer differences in instructional experiences. This study examines whether three different measures of mathematics instruction consistently identify mathematics assessments as being sensitive to instruction. Mixed findings across fourth grade (n 8,298) and fifth grade (n 9,336) students and their teachers across three school districts raise questions as to whether different ways of measuring instruction provide similar inferences about the instructional sensitivity of assessments. This raises validity concerns about the quality of inferences based on different measures of instruction.
Analyzing the Relationship between Budget Deficit, Current Account Deficit, and Government Debt Sustainability
Year: 2019

The main purpose of this study is to determine the dynamic relationships between budget deficit, current account deficit, and government debt sustainability during 1974 2015 in the Iranian economy. We used a VAR model with Impulse Functions and Variance Decomposition in our dynamic analysis. The results show that there is a long term stable relationship among the variables of the model suggesting that to improve the government debt sustainability it has to reduce the budget deficit and current account deficit. Since Iran's dependence on oil revenues is the underlying cause of the dependence of the variables on each other, the government needs to reduce the dependence of the current account and the state budget on oil revenues to reduce both types of deficits and government debt sustainability.