8 research outputs found
Transcendental philosophy within perspectives of the romantic fragmentariness
No full textThe relation of Jena romantics to Kant’s transcendental philosophy could be
considered from the point of view of the romantic theory of the fragment. The
author claims that fragmentariness had a transcendental character in the
philosophical ref lections of Friedrich Schlegel and Novalis. That is the
reason that they have acquired the opportunity of approaching to the immanent
tension of Kant’s philosophical project. The problem of ref lection of
relation between systematicity and incompleteness of knowledge and of man’s
theoretical and practical side is among the most important. The author tries
to evaluate the importance of Fichte’s version of critical idealism for
romantics, considering the crucial romanticists’ intention of historization
of transcendental idealism with the help of the fragment. Final chapter
refers to recent interpretations of the romantic fragment which tend to
ignore this intention
Schelling and Peirce’s philosophy of mind
No full textThe crucial thesis of Schelling?s philosophy of nature, according to which
the matter could be understood as the ?extinct mind?, Peirce understands as
the only reasonable theory concerning the solution of the problem of the
relation between mind and matter and considers it as the center of his
synechism. American philosopher develops his synechistical standpoint within
the series of articles which he wrote for the journal The Monist and defines
synechism as the tendency to conceive every being as something continuous.
The author interprets Peirce?s project as the part of the discussion about
the mind-body problem which characterizes the so-called contemporary
philosophy of mind, but by investigation of its Schellingian motives he tries
to explain the comprehensive meaning of Peirce?s attempt. The last chapter of
the paper aims to approach Schelling?s and Peirce?s consideration of the
mind-body relation from the perspective which finds in them attempts of
philosophical integration of the un-consciousness. Two idealistic strivings
are implicitly demarcated with the regard to the mode of defining the place
of the concept of self-consciousness.</jats:p
Microcystis aeruginosa removal by dissolved air flotation (DAF): Options for enhanched process operation and kinetic modelling
No full textAbstract not availableApplied Science
asl-epfl/sml_icassp2021: Network Classifiers Based on Social Learning
No full text<p>This code can be used to generate simulations similar to Fig. 2 in the following paper:</p>
<p>Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, and Ali H. Sayed, "Network Classifiers Based on Social Learning,'' in Proc. IEEE ICASSP, Toronto, Canada, May 2021. DOI : <a href="https://doi.org/10.1109/ICASSP39728.2021.9414126">10.1109/ICASSP39728.2021.9414126</a></p>
<p>The three panels in Fig. 2 are generated executing file 'main.py'.</p>
<p>Please note that the code is not generally perfected for performance, but is rather meant to illustrate certain results from the paper. The code is provided as-is without guarantees.</p>
<p>March 2022 (Author: Virginia Bordignon)</p>
Direct filtration of Biesbosch water and Algae and water treatment in the Netherlands: 3rd Direct Filtration Seminar
No full textThis presentation summarises basic information on direct filtration, and demonstrates the main research findings, related to the performance of simple in-line direct filtration. The results reported are part of a comprehensive ongoing research programm "Direct filtration of Biesbosch water" undertaken jointly by Delft University of Technology and International Institute for Infrastructural, Hydraulic and Environmental Engineering (IHE Delft). The main research goal of the study is to assess the applicability of direct filtration to water from Biesbosch reservoirs. The key issue addressed in this study is coagulation of algae and other particles and their subsequent removal in rapid sand filters.Water ManagementCivil Engineering and Geoscience
asl-epfl/hmm_over_graphs_dslw2022: Hidden Markov Modeling Over Graphs
No full text<p>This code can be used to generate simulations similar to Figs. 1, 2 and 3 in the following paper:</p>
<p>M. Kayaalp, V. Bordignon, S. Vlaski and A. H. Sayed, "Hidden Markov Modeling Over Graphs," 2022 IEEE Data Science and Learning Workshop (DSLW), 2022, pp. 1-6. DOI: 10.1109/DSLW53931.2022.9820077</p>
<p>Figs. 1, 2 and 3 are generated executing file 'main.py'.</p>
<p>Please note that the code is not generally perfected for performance, but is rather meant to illustrate certain results from the paper. The code is provided as-is without guarantees.</p>
<p>November 2022 (Author: Virginia Bordignon)</p>
asl-epfl/sml_it_2023: Learning from Heterogeneous Data Based on Social Interactions over Graphs (IT 2023)
No full text<p>This code can be used to generate simulations similar to Figs. 3-11 in the following paper:</p>
<p>Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, and Ali H. Sayed, "Learning from Heterogeneous Data Based on Social Interactions over Graphs,'' in IEEE Transactions on Information Theory, 2023. (DOI:<a href="https://doi.org/10.1109/TIT.2022.3232368">10.1109/TIT.2022.3232368</a>)</p>
<p>Figs. 3 to 7 are generated executing file 'figures_3_7.py'.</p>
<p>Fig. 8 is generated executing file 'figure_8.py'.</p>
<p>Fig. 9 is generated executing file 'figure_9.py'.</p>
<p>Fig. 10 is generated executing file 'figure_10.py'.</p>
<p>Fig. 11 is generated executing file 'figure_11.py'.</p>
<p>Please note that the code is not generally perfected for performance, but is rather meant to illustrate certain results from the paper. The code is provided as-is without guarantees.</p>
<p>April 2023 (Author: Virginia Bordignon)</p>
asl-epfl/social_learning_under_inferential_attacks_ICASSP2021: Social Learning under Inferential Attacks
No full text<p>This is the code that corresponds to the simulations performed in the paper:</p>
<p>K. Ntemos, V. Bordignon, S. Vlaski and A. H. Sayed, “Social Learning Under Inferential Attacks,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 5479-5483, doi: 10.1109/ICASSP39728.2021.9413642.</p>
<p>Description of the files</p>
<p>batch_file.py: This is the function that runs the various experiments and outputs the respective figures that appear in the paper.</p>
<p>NOTE: the final results may vary with the results presented in the paper due to the randomness in the network construction. This affects the agents’ centrality and as a result the results might differ slightly. For example, maybe more time instants are needed for the beliefs to converge to their limiting values. This can be easily adjusted by increasing the value of the variable times in sl_maliciousfunction.py.</p>
<p>sl_maliciousfunction.py: This file runs an experiment for social learning with adversaries.</p>
<p>fun.py: This file contains various auxiliary functions.</p>
<p>Please note that the code is not generally perfected for performance, but is rather meant to illustrate certain results from the paper. The code is provided as-is without guarantees.</p>
<p>Author: Konstantinos Ntemos.</p>
