Daniel Feuerriegel Persons related to Daniel Feuerriegel
Daniel Gregory Feuerriegel ist ein australischer Schauspieler, der in Los Angeles, Kalifornien, USA, lebt. Dan Feuerriegel - When you face challenges in life just remember how far you have come, how strong you can be and how you can overcome the hardest of. Daniel Feuerriegel (* Oktober in Sydney, Australien) ist ein australischer Schauspieler. 57 Tsd. Abonnenten, folgen, Beiträge - Sieh dir Instagram-Fotos und -Videos von Dan Feuerriegel (@thedanfeuerriegel) an. Entdecke alle Serien und Filme von Daniel Feuerriegel. Von den Anfängen seiner Karriere bis zu geplanten Projekten. Dan Feuerriegel ist Aaron, einer der drei Anführer der Rebellion in Spartacus. Er ist ein hitzköpfiger Kämpfer und steht Spartacus loyal zur Seite. Hier findet Ihr alle Infos und Bilder zu Schauspieler Dan Feuerriegel und seiner Rolle des Agron in der US-Erfolgs-Serie Spartacus.
My favorite male actors. The misty Outlander Brianna ball: Efficient Hannah Quinlivan of privacy-sensitive attributes in predictive analytics. In detail, we develop a simple feedback mechanism that allows users to express whether a question was answered satisfactorily or whether a different answer is needed. Cost minimization of large-scale infrastructure for Luisa Wöllisch generation and transmission. Stupid Stupid Man. The marked point process is modulated by a continuous-time Markov process in order to account for different latent shopping phases. 
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Wenn Sie dazu noch das Geschlecht einschräänken, bekommen Sie auch nur Frauen oder männer angezeigt. Agron trug auch entscheidend dazu bei Glabers Armee am Vesuvius zu schlagen. Dan Feuerriegel ist bei Facebook. Melde dich an oder erstelle ein Konto, um dich mit Dan Feuerriegel zu verbinden. Anmelden. oder. Dan Feuerriegel as Agron (Spartacus War of the Damned) Spartacus Tv, Cool and utter sexual frustration caused by the magnificent Aussie, Dan Feuerriegel. Finden Sie perfekte Stock-Fotos zum Thema Daniel Feuerriegel sowie redaktionelle Newsbilder von Getty Images. Wählen Sie aus 89 erstklassigen Inhalten. daniel feuerriegel wikipedia. Dan Feuerriegel as Agron (Spartacus War of the Damned) Spartacus Tv, Cool and utter sexual frustration caused by the magnificent Aussie, Dan Feuerriegel.We find that the image content describes a large portion of the variance in prices, even when controlling for location and common characteristics of apartments.
A one standard deviation in the image variable is associated with a increase in price. By utilizing a carefully designed instrumental variables estimation, we further set out to obtain causal estimates.
Our empirical findings contribute to theory by quantifying the hedonic value of images and thus establishing a causal link between visual appearance and product pricing.
Even though a positive relationship seems intuitive, we provide for the first time an empirical confirmation. Based on our large-scale computational study, we further yield evidence of a picture superiority effect: simply put, a beneficial image corresponds to the same price change as In sum, images capture valuable information for users that goes beyond narrative explanations.
As a direct implication, we aid online platforms and their users in assessing and improving the multi-modal presentation of product offerings.
Finally, we contribute to web mining by highlighting the importance of visual information. Points-of-interest POIs; i. The conventional modeling approach relies upon feature engineering, yet it ignores the spatial structure among POIs.
In order to overcome this shortcoming, the present paper proposes a novel spatial model for explaining spatial distributions based on web-mined POIs.
Our key contributions are: 1 We present a rigorous yet highly interpretable formalization in order to model the influence of POIs on a given outcome variable.
Specifically, we accommodate the spatial distributions of both the outcome and POIs. In our case, this modeled by the sum of latent Gaussian processes.
For this purpose, we derive a tailored evidence lower bound ELBO and, for appropriate likelihoods, we even show that an analytical expression can be obtained.
This allows fast and accurate computation of the ELBO. Finally, the value of our approach for web mining is demonstrated in two real-world case studies.
Our findings provide substantial improvements over state-of-the-art baselines with regard to both predictive and, in particular, explanatory performance.
Altogether, this yields a novel spatial model for leveraging web-mined POIs. Within the context of location-based social networks, it promises an extensive range of new insights and use cases.
Business analytics refers to methods and practices that create value through data for individuals, firms, and organizations.
This field is currently experiencing a radical shift due to the advent of deep learning: deep neural networks promise improvements in prediction performance as compared to models from traditional machine learning.
However, our research into the existing body of literature reveals a scarcity of research works utilizing deep learning in our discipline.
Accordingly, the objectives of this overview article are as follows: 1 we review research on deep learning for business analytics from an operational point of view.
All such cases demonstrate improvements in operational performance over traditional machine learning and thus direct value gains.
Recent research has found the language sentiment in financial news to be a substantial driver of prices in financial markets, though there are two diametrically opposed interpretations for this: either markets perceive news sentiment as fundamental information thus leading to changes in the valuation of assets or news sentiment conveys a noise signal thus contributing to the stochastic component of prices.
The opposite roles are resolved in the context of crude oil prices by decomposing price movements into two components referring to fundamental and noise trading.
Contrary to theoretical arguments in prior literature, we find empirical results supporting both interpretations. However, postprandial glycaemic control remains challenging.
TIT was lower and the mean sensor glucose slightly higher, after breakfast compared with lunch and dinner, whereas the insulin dose was higher.
Across meals, when carbohydrates were replaced by fat, or to a lesser extent by protein, postprandial glucose control improved.
For breakfast, a 3. Improvements were slightly lower during lunch and dinner 3. During hypoglycaemia cognitive, executive and psychomotor abilities significantly deteriorate.
Accordingly, hypoglycaemia has consistently been shown to be associated with an increased risk of driving accidents and is, therefore, regarded as one of the relevant factors in traffic safety.
This may allow for an alternative approach to the problem of hypoglycaemia during driving. Using artificial intelligence constantly analyzing driving behavior it may be possible to timely detect changes in driving pattern characteristic for driving in hypoglycaemia.
Based on these alterations in driving variables we aim at establishing algorithms capable of discriminating eu- and hypoglycemic driving patterns using artificial intelligence.
Methods: In a proof of principle study we compared data regrading driving behavior of 5 individuals 3 non- diabetic and 2 with type 1 diabetes tracking measurements in eu- and hypoglycemic condition while driving on a predefined route using a professional driving simulator Carnetsoft BV.
Over 60 driving parameters were assessed at a sampling rate of 30 Hz. Time series of car-based sensor data was then sliced into 5 minute windows and random forest machine learning classifier as well as deep neural networks were applied to build a system detecting hypoglycemia within 5 minute frames.
Results: Car-based data provided 73' measurements in hypoglycemic condition Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement.
This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce costs. Here a common baseline may be derived by fitting a probability density function to past lifetimes and then utilizing the conditional expected remaining useful life as a prognostic.
This approach finds widespread use in practice because of its high explanatory power. A more accurate alternative is promised by machine learning, where forecasts incorporate deterioration processes and environmental variables through sensor data.
However, machine learning largely functions as a black-box method and its forecasts thus forfeit most of the desired interpretability.
As our primary contribution, we propose a structured-effect neural network for predicting the remaining useful life which combines the favorable properties of both approaches: its key innovation is that it offers both a high accountability and the flexibility of deep learning.
The parameters are estimated via variational Bayesian inferences. The different approaches are compared based on the actual time-to-failure for aircraft engines.
This demonstrates the performance and superior interpretability of our method, while we finally discuss implications for decision support.
Accordingly, the costs of emission allowances are part of power generation and, by extension, the price of electricity.
Theoretical works thus suggest a positive relationship between the price of emission allowances and electricity. However, this has not been validated empirically for phase III of the Emissions Trading System in the short run as part of the price setting mechanism of electricity producers.
We further test for a potentially asymmetric influence with the help of quantile regressions. Altogether, the outcome has implications for policy-makers and calls for further attention by academics and policy-makers in the future design of the Emissions Trading System, especially under larger amount of renewables in the electricity system.
Negation scope detection is widely performed as a supervised learning task which relies upon negation labels at word level.
This suffers from two key drawbacks: 1 such granular annotations are costly and 2 highly subjective, since, due to the absence of explicit linguistic resolution rules, human annotators often disagree in the perceived negation scopes.
To the best of our knowledge, our work presents the first approach that eliminates the need for world-level negation labels, replacing it instead with document-level sentiment annotations.
For this, we present a novel strategy for learning fully interpretable negation rules via weak supervision: we apply reinforcement learning to find a policy that reconstructs negation rules from sentiment predictions at document level.
Our experiments demonstrate that our approach for weak supervision can effectively learn negation rules. Furthermore, an out-of-sample evaluation via sentiment analysis reveals consistent improvements of up to 4.
Moreover, the inferred negation rules are fully interpretable. The conventional paradigm in neural question answering QA for narrative content is limited to a two-stage process: first, relevant text passages are retrieved and, subsequently, a neural network for machine comprehension extracts the likeliest answer.
However, both stages are largely isolated in the status quo and, hence, information from the two phases is never properly fused.
In contrast, this work proposes RankQA: RankQA extends the conventional two-stage process in neural QA with a third stage that performs an additional answer re-ranking.
The re-ranking leverages different features that are directly extracted from the QA pipeline, i. While our intentionally simple design allows for an efficient, data-sparse estimation, it nevertheless outperforms more complex QA systems by a significant margin: in fact, RankQA achieves state-of-the-art performance on 3 out of 4 benchmark datasets.
Furthermore, its performance is especially superior in settings where the size of the corpus is dynamic. Here the answer re-ranking provides an effective remedy against the underlying noise-information trade-off due to a variable corpus size.
As a consequence, RankQA represents a novel, powerful, and thus challenging baseline for future research in content-based QA. Question answering promises a means of efficiently searching web-based content repositories such as Wikipedia.
However, the systems of this type most prevalent today merely conduct their learning once in an offline training phase while, afterwards, all parameters remain static.
Thus, the possibility of improvement over time is precluded. As a consequence of this shortcoming, question answering is not currently taking advantage of the wealth of feedback mechanisms that have become prominent on the web e.
This is the first work that introduces a question-answering system for web-based content repositories with an on-line mechanism for user feedback.
Our efforts have resulted in QApedia - a framework for on-line improvement based on shallow user feedback. In detail, we develop a simple feedback mechanism that allows users to express whether a question was answered satisfactorily or whether a different answer is needed.
Even for this simple mechanism, the implementation represents a daunting undertaking due to the complex, multi-staged operations that underlie state-of-the-art systems for neural questions answering.
Another challenge with regard to web-based use is that feedback is limited and possibly even noisy , as the true labels remain unknown.
We thus address these challenges through a novel combination of neural question answering and a dynamic process based on distant supervision, asynchronous updates, and an automatic validation of feedback credibility in order to mine high-quality training samples from the web for the purpose of achieving continuous improvement over time.
Our QApedia framework is the first question-answering system that continuously refines its capabilities by improving its now dynamic content repository and thus the underlying answer extraction.
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