Miracle-dz/UniFAR-FTUs huggingface dataset:
The ADIS study: A large-scale correspondence test on labor market discrimination in Germany - Technical Report

Das Arbeitsmarktdiskriminierung "ADIS" Projekt ist eine grosangelegte Korrespondenzteststudie, die deutschlandweit zwischen Oktober 2014 und April 2016 durchgefuhrt wurde. Wahrend in der Vergangenheit mit Hilfe von Korrespondenzteststudien bereits eindrucklich nachgewiesen werden konnte, dass Bewerber mit Migrationshintergrund auf dem deutschen Arbeitsmarkt benachteiligt werden, geben diese Studien wenig Einblick in die zugrundeliegenden Ursachen und Mechanismen. Ziel der Studie war es daher, das Auftreten und das Ausmas von Diskriminierung gegenuber qualifizierten Migranten zweiter Generation zu untersuchen und hierbei insbesondere zwei Fragestellungen tiefergehend zu erforschen: Erstens sollen zugrundeliegende Mechanismen ethnischer Diskriminierung auf dem Arbeitsmarkt betrachtet werden. Hierbei wird die Rolle von Faktoren wie ethnischer Gruppe, Religion oder Phanotyp beleuchtet, die eventuell ethnische Hierarchien erklaren konnen. Zum anderen sollen Annahmen zweier unterschiedlicher okonomischer Erklarungsmodelle fur diskriminierende Entscheidungen (praferenzbasierte und statistische Diskriminierung) untersucht werden. Fur die ADIS-Studie nutzen wir ein multidimensionales Forschungsdesign und messen das Ausmas von Diskriminierung fur insgesamt 35 verschiedene ethnische Gruppen, fur die sowohl der Phanotyp und die Religionszugehorigkeit, als auch produktivitatsrelevante Indikatoren wie Zeugnisnoten und Referenzschreiben variiert werden. Die Studie basiert auf rund 7,557 Beobachtungen von mannlichen und weiblichen Kandidaten, die sich deutschlandweit auf insgesamt acht verschiedene Berufe beworben haben. Im folgenden Bericht diskutieren wir das Forschungsdesign und geben eine detaillierte Beschreibung der Studienumsetzung sowie der Herausforderungen bei der Datenerhebung und der Operationalisierung der einzelnen Treatments.
Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains

In the future, with the advent of the smart factory era, manufacturing and logistics processes will become more complex, and the complexity and criticality of traceability will further increase. This research aims at developing a performance assessment method to verify scalability when implementing traceability systems based on key technologies for smart factories, such as Internet of Things (IoT) and BigData. To this end, based on existing research, we analyzed traceability requirements and an event schema for storing traceability data in MongoDB, a document-based Not Only SQL (NoSQL) database. Next, we analyzed the algorithm of the most representative traceability query and defined a query-level performance model, which is composed of response times for the components of the traceability query algorithm. Next, this performance model was solidified as a linear regression model because the response times increase linearly by a benchmark test. Finally, for a case analysis, we applied the performance model to a virtual automobile parts logistics. As a result of the case study, we verified the scalability of a MongoDB-based traceability system and predicted the point when data node servers should be expanded in this case. The traceability system performance assessment method proposed in this research can be used as a decision-making tool for hardware capacity planning during the initial stage of construction of traceability systems and during their operational phase.
Framing the Game: Assessing the Impact of Cultural Representations on Consumer Perceptions of Legitimacy

The purpose of this article is to understand how media frames affect consumer judgments of legitimacy. Because frames exist on the sociocultural and individual level, our research takes a multimethod approach to this question. On the sociocultural level, we conduct a content analysis of operant media frames for discussing online gambling and perform an event analysis, finding that a shift in consumer judgments follows an abrupt shift in frame. Then, on the individual level, the causal mechanism for these shifts is investigated in an experimental setting using the Implicit Association Test (IAT). These experiments show that framing affects normative legitimacy judgments by changing implicit associations. Further, users and nonusers respond differently to frame elements, with users favoring an established frame and nonusers favoring a novel, legitimating frame. This suggests that media frames play a critical role in establishing legitimacy at the sociocultural level and that framing potentially bridges cognitive and normative legitimacy.
How Advice and Its Source Characteristics Prompts Changes in Investment Decisions

People are using the Internet for financial planning assistance. Yet those seeking advice on the Internet rarely tend to question the advice source. Little research has examined the unique aspects of online financial advice taking. Online advice offers a unique setting which does not mirror “offline advice”. This paper addresses the research questions (1) What kinds of people are more likely to change their investment decisions given different online source characteristics, (2) How do people change their investment decisions given the disclosure of human vs. computer advice sources, and (3) How do people change their investment decisions given the disclosure of source credibility? This study finds that users with higher levels of task-specific self-efficacy are less likely to take advice and certain online design features influence changes in investment advice taking.
Visuospatial Function and Language Function in Healthy Senior and Mild Cognitive Impairment

The aim of the present study was to investigate the characteristics of visuospatial functions and language functions of healthy elderly and the elderly with mild cognitive impairment (MCI). The subjects of study were 174 healthy elderly and 71 elderly with MCI over the age of 65. As the result of Independent t-test, healthy elderly group had significantly higher mean value of RCFT-copy and K-BNT than MCI group. There was no significant difference, however, in reading and writing ability. In this study, there were significant differences in the performance of visuospatial functions and confrontational naming ability between healthy elderly and the elderly with MCI.
The Development of the Journal Environment of Leonardo

Abstract The authors present animations based on the aggregated journal-journal citations of Leonardo during the period 1974–2008. Leonardo is mainly cited by journals outside the arts domain for cultural reasons, for example, in neuropsychology and physics. Articles in Leonardo itself cite a large number of journals, but with a focus on the arts. Animations at this level of aggregation enable us to show the history of the journal from a network perspective.
Infection by a symbiotic polydnavirus induces wasting and inhibits metamorphosis of the moth Pseudoplusia includens

SUMMARY Insect pathogens and parasites often affect the growth and development of their hosts, but understanding of these processes is fragmentary. Among the most species-rich and important mortality agents of insects are parasitoid wasps that carry symbiotic polydnaviruses (PDVs). Like many PDV-carrying wasps, Microplitis demolitor inhibits growth and pupation of its lepidopteran host, Pseudoplusia includens, by causing host hemolymph juvenile hormone (JH) titers to remain elevated and preventing ecdysteroid titers from rising. Here we report these alterations only occurred if P. includens was parasitized prior to achieving critical weight, and were fully mimicked by infection with only M. demolitor bracovirus (MdBV). Metabolic assays revealed that MdBV infection of pre-critical weight larvae caused a rapid and persistent state of hyperglycemia and reduced nutrient stores. In vitro ecdysteroid assays further indicated that prothoracic glands from larvae infected prior to achieving critical weight remained in a refractory state of ecdysteroid release, whereas infection of post-critical weight larvae had little or no effect on ecdysteroid release by prothoracic glands. Taken together, our results suggest MdBV causes alterations in metabolic physiology, which prevent the host from achieving critical weight. This in turn inhibits the endocrine events that normally trigger metamorphosis.
Radiation-induced soft errors in digital circuits - A literature survey

The current technical note gives an overview of the available literature on the soft error rate (SER) of semiconductor devices at sea level. The main focus is on SRAM circuits, but other memories and logic are considered also. The radiation sources causing ionizing particles in semiconductors are discussed and the physical mechanisms of the upset of data bits are treated. Reported work on soft error modeling and simulation is reviewed and test methods are summarized. The impact of technology scaling on SER is discussed. Several techniques are treated that were proposed to improve the SER sensitivity of circuit designs. The report includes a discussion about the impact of the SER issue on deep-submicron design in coming years. Conclusions: The soft error issue is increasingly important in deep-submicron design. Nowadays, the topic is particularly relevant for memories, but in the near future it will also have to be addressed in logic design. The radiation sources inducing soft errors are alpha particles (from wafer and package materials) and cosmic neutrons. The two types of particles require their own measurement techniques. Different modeling and simulation methods have been developed at both the device and the circuit level. Several solutions have been proposed to improve the SER sensitivity of devices. Eventually, the application of error detection and correction methodologies will be necessary to obtain sufficient reliability. Technology scaling increases both the total SER and the percentage of multiple-bit errors. c ©Philips Electronics Nederland BV 2002 iii 2002/828 Unclassified Report iv c ©Philips Electronics Nederland BV 2002 Unclassified Report 2002/828
Factors affecting the acceptability of grammatical features of Hong Kong English

Although a large number of varieties of English in Asia have gained recognition as independent varieties, this has not been the case for Hong Kong English (HKE) (Jenkins, 2015: 162). The city has a low level of affiliation towards HKE (Jenkins, 2015: 167) and often laments its ever-falling standard of English (Leung, 2015). There exists a phenomenon of ‘linguistic schizophrenia’ – the community may recognise that a local variety of English exists and conform to its features in practice, but it still looks to native varieties as the norm and views local features as evidence of deteriorating language standards (Kachru, 1983: 118).
Balancing the Interactions of Ions, Water, and DNA in the Drude Polarizable Force Field

Recently we presented a first-generation all-atom Drude polarizable force field for DNA based on the classical Drude oscillator model, focusing on optimization of key dihedral angles followed by extensive validation of the force field parameters. Presently, we describe the procedure for balancing the electrostatic interactions between ions, water, and DNA as required for development of the Drude force field for DNA. The proper balance of these interactions is shown to impact DNA stability and subtler conformational properties, including the conformational equilibrium between the BI and BII states, and the A and B forms of DNA. The parametrization efforts were simultaneously guided by gas-phase quantum mechanics (QM) data on small model compounds and condensed-phase experimental data on the hydration and osmotic properties of biologically relevant ions and their solutions, as well as theoretical predictions for ionic distribution around DNA oligomer. In addition, fine-tuning of the internal base parameters was performed to obtain the final DNA model. Notably, the Drude model is shown to more accurately reproduce counterion condensation theory predictions of DNA charge neutralization by the condensed ions as compared to the CHARMM36 additive DNA force field, indicating an improved physical description of the forces dictating the ionic solvation of DNA due to the explicit treatment of electronic polarizability. In combination with the polarizable DNA force field, the availability of Drude polarizable parameters for proteins, lipids, and carbohydrates will allow for simulation studies of heterogeneous biological systems.
Ocean acidification impacts on coastal ecosystem services due to habitat degradation

Abstract The oceanic uptake of anthropogenic carbon dioxide emissions is changing seawater chemistry in a process known as ocean acidification. The chemistry of this rapid change in surface waters is well understood and readily detectable in oceanic observations, yet there is uncertainty about the effects of ocean acidification on society since it is difficult to scale-up from laboratory and mesocosm tests. Here, we provide a synthesis of the likely effects of ocean acidification on ecosystem properties, functions and services based on observations along natural gradients in pCO2. Studies at CO2 seeps worldwide show that biogenic habitats are particularly sensitive to ocean acidification and that their degradation results in less coastal protection and less habitat provisioning for fisheries. The risks to marine goods and services amplify with increasing acidification causing shifts to macroalgal dominance, habitat degradation and a loss of biodiversity at seep sites in the tropics, the sub-tropics and on temperate coasts. Based on this empirical evidence, we expect ocean acidification to have serious consequences for the millions of people who are dependent on coastal protection, fisheries and aquaculture. If humanity is able to make cuts in fossil fuel emissions, this will reduce costs to society and avoid the changes in coastal ecosystems seen in areas with projected pCO2 levels. A binding international agreement for the oceans should build on the United Nations Sustainable Development Goal to ‘minimise and address the impacts of ocean acidification’.
Study of Automatic Text Summarization Based on Natural Language Understanding

In this paper, we present an automatic text summarization method based on natural language understanding by using RST (Rhetorical Structure Theory) and CIT (Comprehensive Information Theory). RST is an analytic framework designed to account for text structure at the clause level. CIT is an integrated concept of syntactic, semantic and pragmatic information. The system extracts the rhetorical structure of text and the compound of the rhetorical relations between sentences, and then cuts out less important parts from the extracted structure. Finally it analyses the sentences in the extracted structure to generate text summarization by using CIT.
Continuation method for total variation denoising problems

The denoising problem can be solved by posing it as a constrained minimization problem. The objective function is the TV norm of the denoised image whereas the constraint is the requirement that the denoised image does not deviate too much from the observed image. The Euler-Lagrangian equation corresponding to the minimization problem is a nonlinear equation. The Newton method for such equation is known to have a very small domain of convergence. In this paper, we propose to couple the Newton method with the continuation method. Using the Newton-Kantorovich theorem, we give a bound on the domain of convergence. Numerical results are given to illustrate the convergence.
The Negative Impact of Missing Value Imputation in Classification of Diabetes Dataset and Solution for Improvement

The common problem for data quality is missing data. The real datasets have lot of missing values. Missing values imputation is a challenging issue in machine learning and data mining. Missing data should be carefully handled; otherwise it affects the quality of the mining process or the performance of classification algorithms. Mean method of imputation is the most common method to replace the missing values. In this paper, we address the negative impact of missing value imputation and solution for improvement while evaluating the performance of kNN algorithm for classification of Diabetes data. We selected diabetes dataset because it contains lot of missing values and the impact of imputation is very obvious. To measure the performance, we used Accuracy and Error rate as the metrics.
Adaptive collective decision-making in limited robot swarms without communication

In this work we investigate spatial collective decision-making in a swarm of microrobots, inspired by the thermotactic aggregation behavior of honeybees. The sensing and navigation capabilities of these robots are intentionally limited; no digital sensor data processing and no direct communication are allowed. In this way, we can approximate the features of smaller mesoscopic-scale systems and demonstrate that even such a limited swarm is nonetheless able to exhibit simple forms of intelligent and adaptive collective behavior.
A Comparison of Two Methods Employing Inductive Logic Programming for Corpus-based Parser Construction

This paper presents results from recent experiments with Chill, a corpus-based parser acquisition system. Chill treats grammar acquisition as the learning of search-control rules within a logic program. Unlike many current corpus-based approaches that use propositional or probabilistic learning algorithms, Chill uses techniques from inductive logic programming (ILP) to learn relational representations. The reported experiments compare Chill's performance to that of a more naive application of ILP to parser acquisition. The results show that ILP techniques, as employed in Chill, are a viable alternative to propositional methods and that the control-rule framework is fundamental to Chill's success.
ESL Power Estimation: A Design Case Study for Early Design Phases

Electronic System Level (ESL) power estimation is executed in early design phases to avoid expensive redesigns. In this paper we concentrate on the evaluation of an existing ESL power estimation methodology for the modeling of future platforms, with respect to applicability, ﬂexibility and reusability. Two industrial design cases are investigated. In the ﬁrst one, an existing mobile communication platform supporting GSM paging burst is discussed to elaborate the modeling approach in detail. Based on this, a novel platform for paging burst is modeled and analyzed. The highly ﬂexible and reusable modeling capabilities are validated. This shows that the methodology is applicable to the what-if analysis of HW/SW architecture in early design phases.
From Cancer Patient to Cancer Survivor: Lost in Translation

As a 20-plus-year cancer survivor, I have been heartened to see the number of cancer survivors increase (currently estimated at well over 10 million Americans), and new attention paid to the unique, wide-ranging, and long-term issues that follow the diagnosis and treatment of cancer. This volume reflects that trend. It reports the work of an Institute of Medicine (IOM) and National Research Council (NRC) of the National Academies "Committee on Cancer Survivorship: Improving Care and Quality of Life. "
Very short feature vector for music genre classiciation based on distance metric lerning

In our study, a very short feature vector, obtained from low dimensional projection and already developed audio features, is used for music genre classification problem. A long feature vector based on the concatenation of various features is generally used in music genre classification system. Our objective is to find a short feature vector, and we applied a distance metric learning algorithm in order to reduce the dimensionality of feature vector with a little performance degradation. In our experiments based on two widely-used dataset, dimension reduction based on distance metric learning is very effective, and we can get over 80% of accuracy with only 10-dimensional feature vector.
Automatic Oil Spill Detection Based on Envisat, RADARSAT and ERS Images (10)

In this paper, we present algorithms for automatic detection of oil spills in SAR images. The algorithms have been trained on a large number of ERS, RADARSAT and ENVISAT images. The performance of the algorithms are compared to manual and semi-automatic approaches in a benchmark study involving 32 RADARSAT images and 28 ENVISAT ASAR images from 2003. Our algorithm performed well both on RADARSAT and ENVISAT images, and can be considered a good alternative to manual inspection if large ocean areas are to be inspected.
Toward Efficient and Scalable Wireless Network Simulation

The broadcast nature of the wireless medium and its susceptibility to interference not only affects the capacity of wireless networks but also has an impact on the fidelity, accuracy and efficiency of wireless network simulation. This is because transmission of a wireless signal has to be received and processed by a potentially large number of nodes that operate on the same channel. How to faithfully and yet effectively carry out simulation has thus been an important issue to realizing real-time, large scale wireless network simulation. In this paper, we take a systematic approach and investigate the process of signal transmission in the various stages: signal propagation, signal interference, and interaction with the PHY/MAC layers. We identify the key components that affect the simulation performance and quantify their impacts whenever possible. Specifically, we first investigate the impact of the signal propagation limit on the accuracy and efficiency of simulation. Second, we devise a simple computation model to capture the effect of the grid size — the parameter that characterizes how nodes that are subject to interference are being located — on simulation efficiency in a concave manner and derive its optimal value. Third, we make a key observation on whether or not, and when, a node needs to respond to a signal arrival event from the perspective of the MAC layer, and propose a reactive channel model (RCM) which can effectively reduce the large number of events related to signal transmissions by an order of magnitude. The simulation results have demonstrated that RCM, with the appropriate choices of the signal propagation limit and grid size, gives an order of magnitude improvement in the execution time. This, coupled with the fact that RCM can be implemented in a modular manner, requires only modest changes to existing PHY/MAC layers in the simulator, and does not incur extra memory consumption, suggests that RCM is a promising approach to enabling large-scale wireless network simulation.
2016 Ieee International Conference on Big Data (big Data) Predicting Statistics of Asynchronous Sgd Parameters for a Large-scale Distributed Deep Learning System on Gpu Supercomputers

—Many studies have shown that Deep Convolutional Neural Networks (DCNNs) exhibit great accuracies given large training datasets in image recognition tasks. Optimization technique known as asynchronous mini-batch Stochastic Gradient Descent (SGD) is widely used for deep learning because it gives fast training speed and good recognition accuracies, while it may increases generalization error if training parameters are in inappropriate ranges. We propose a performance model of a distributed DCNN training system called SPRINT that uses asyn-chronous GPU processing based on mini-batch SGD. The model considers the probability distribution of mini-batch size and gradient staleness that are the core parameters of asynchronous SGD training. Our performance model takes DCNN architecture and machine specifications as input parameters, and predicts time to sweep entire dataset, mini-batch size and staleness with 5%, 9% and 19% error in average respectively on several supercomputers with up to thousands of GPUs. Experimental results on two different supercomputers show that our model can steadily choose the fastest machine configuration that nearly meets a target mini-batch size.