403 research outputs found
Erratum: The role of visual preferences in architecture views
No full textThe article “The role of visual preferences in architecture views” by Ali Akbar Amini, Bahman Adibzadeh, published on 24 September 2020 in the Journal of Architecture and Urbanism, 44(2), 122–127, https://doi.org/10.3846/jau.2020.12582 contained a following errors on:
122 p. The source is incorrectly cited in the text. The correct citation is:
(de la Fuente Suárez, 2016)
126 p. The references incorrectly indicate author name, lastname and title of article. The correct citation is:
de la Fuente Suárez, L. A. (2016). Towards experiential representation in architecture. Journal of Architecture and Urbanism, 40(1), 47–58. https://doi.org/10.3846/20297955.2016.1163243
Corrected version of the article is available online.
The publisher apologises for this error
Sedimentation Processes in the Tinto and Odiel Salt Marshes in Huelva, Spain
No full textGlobal warming is a key factor to take into account when a study is conducted on tidal
wetlands. Both Odiel and Tinto salt marshes are the major wetlands in Andalusia (Spain).
From the mid-1950s to date, the land use changes (LUC) have caused a great landscape
alteration that along with the effects of climatic variables and sea wave energy have given
rise to a hard impact on the environment. The advent of new image processing procedures and use of high-resolution images from satellites gave precise patterns of erosion.
In this work, a new method patented by the author is presented and used to obtain the
total cubic meters of eroded soil in both salt marshes. Moreover, the different factors that
begin this phenomenon as well as the influence of intertidal processes are discussed. The
results show how the greater integration of remote sensing and geographical information
systems (GIS) technologies, with regression model, was most useful to describe, analyze
and predict the volumetric change process in both salt marshes
Freeway traffic control strategies using fuzzy system-based solutions toward congestion risk mitigation
No full textImplementation of complete ASIC design using low power methodology
No full textIn today's technological advancements in VLSI industry, the limits of ASICs/FPGA chips in terms of area, power and speed are constantly shrinking. The end user requirements are also influencing these limits and pushing them to a new level on top of all these technological advancements. The effects of nanometer technologies on congestion, signal integrity, crosstalk etc. are becoming more significant as the technology sizes of semiconductor devices continue to decrease. All of these factors are affecting and forcing various technological methodologies throughout the design flow to constantly fight and keep updating the EDA tools to cop-up with these issues. Thus, there is always a need of constant learning and exposure to new advanced EDA tools like Synopsys Design Compiler, IC Compiler, PrimeTime, TetraMax etc. The aim of this project is to successfully complete the ASIC design flow with low power techniques, using the advance industry level tools. This project provides a solid base and practical hands-on experience of these advanced tools. It also provides an overview of types of ASICs, detailed ASIC standard design flow and Synopsys IC compiler flow. Along with this, the analysis of various design factors affecting the performance of the final chip such as power, area and timing is also performed. In this project, a RISC CHIP from Synopsys will be used to perform ASIC design flow and low power methodology.Includes bibliographical references (pages 51)California State University, Northridge. Department of Electrical and Computer Engineering
„A Voice for Iran” – animacja jako forma protestu. Irańska rewolucja „Woman, Life, Freedom”
No full textThe article focuses on animations as artistic forms that can be classified as part of the cultural resistance repertoire strongly present during the protests in Iran following the death of Mahsa Jina Amini in September 2022. Two collections of short animated films are analysed – Mehran Sanei’s Instagram Onimations and the digital archive – Archive of Defiance, curated by Shahrzad Mojab and Afsaneh Hojabri. Symbolic communication, represented by both collections, is an integral element of any revolution. The aims of the article are to analyse the techniques, forms and specific features of animations used in creating artistic works for political purposes, to determine the function of animation as a form of protest communication, to define the role of both collections in resistance communication, and finally, to define the essence of social media action in transnational activism and in creating international support groups.The article focuses on animations as artistic forms that can be classified as part of the cultural resistance repertoire strongly present during the protests in Iran following the death of Mahsa Jina Amini in September 2022. Two collections of short animated films are analysed – Mehran Sanei’s Instagram Onimations and the digital archive – Archive of Defiance, curated by Shahrzad Mojab and Afsaneh Hojabri. Symbolic communication, represented by both collections, is an integral element of any revolution. The aims of the article are to analyse the techniques, forms and specific features of animations used in creating artistic works for political purposes, to determine the function of animation as a form of protest communication, to define the role of both collections in resistance communication, and finally, to define the essence of social media action in transnational activism and in creating international support groups
LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks
No full textThe use of learning curves for decision making in supervised machine learning is standard practice, yet understanding of their behavior is rather limited. To facilitate a deepening of our knowledge, we introduce the Learning Curve Database (LCDB), which contains empirical learning curves of 20 classification algorithms on 246 datasets. One of the LCDB’s unique strength is that it contains all (probabilistic) predictions, which allows for building learning curves of arbitrary metrics. Moreover, it unifies the properties of similar high quality databases in that it (i) defines clean splits between training, validation, and test data, (ii) provides training times, and (iii) provides an API for convenient access (pip install lcdb). We demonstrate the utility of LCDB by analyzing some learning curve phenomena, such as convexity, monotonicity, peaking, and curve shapes. Improving our understanding of these matters is essential for efficient use of learning curves for model selection, speeding up model training, and to determine the value of more training data.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Pattern Recognition and Bioinformatic
Adversarially Robust Decision Tree Relabeling
No full textDecision trees are popular models for their interpretation properties and their success in ensemble models for structured data. However, common decision tree learning algorithms produce models that suffer from adversarial examples. Recent work on robust decision tree learning mitigates this issue by taking adversarial perturbations into account during training. While these methods generate robust shallow trees, their relative quality reduces when training deeper trees due the methods being greedy. In this work we propose robust relabeling, a post-learning procedure that optimally changes the prediction labels of decision tree leaves to maximize adversarial robustness. We show this can be achieved in polynomial time in terms of the number of samples and leaves. Our results on 10 datasets show a significant improvement in adversarial accuracy both for single decision trees and tree ensembles. Decision trees and random forests trained with a state-of-the-art robust learning algorithm also benefited from robust relabeling.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit
Implementation of complete ASIC design using low power methodology
No full textIncludes bibliographical references (pages 51)In today???s technological advancements in VLSI industry, the limits of ASICs/FPGA chips in terms of area, power and speed are constantly shrinking. The end user requirements are also influencing these limits and pushing them to a new level on top of all these technological advancements. The effects of nanometer technologies on congestion, signal integrity, crosstalk etc. are becoming more significant as the technology sizes of semiconductor devices continue to decrease. All of these factors are affecting and forcing various technological methodologies throughout the design flow to constantly fight and keep updating the EDA tools to cop-up with these issues. \ud
Thus, there is always a need of constant learning and exposure to new advanced EDA tools like Synopsys Design Compiler, IC Compiler, PrimeTime, TetraMax etc. The aim of this project is to successfully complete the ASIC design flow with low power techniques, using the advance industry level tools. This project provides a solid base and practical hands-on experience of these advanced tools. It also provides an overview of types of ASICs, detailed ASIC standard design flow and Synopsys IC compiler flow. Along with this, the analysis of various design factors affecting the performance of the final chip such as power, area and timing is also performed.\ud
In this project, a RISC CHIP from Synopsys will be used to perform ASIC design flow and low power methodology
Penalized FTRL with Time-Varying Constraints
No full textIn this paper we extend the classical Follow-The-Regularized-Leader (FTRL) algorithm to encompass time-varying constraints, through adaptive penalization. We establish sufficient conditions for the proposed Penalized FTRL algorithm to achieve O(t) regret and violation with respect to a strong benchmark X^tmax. Lacking prior knowledge of the constraints, this is probably the largest benchmark set that we can reasonably hope for. Our sufficient conditions are necessary in the sense that when they are violated there exist examples where O(t) regret and violation is not achieved. Compared to the best existing primal-dual algorithms, Penalized FTRL substantially extends the class of problems for which O(t) regret and violation performance is achievable.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Networked System
SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting
No full textSequence clustering in a streaming environment is challenging because it is computationally expensive, and the sequences may evolve over time. K-medoids or Partitioning Around Medoids (PAM) is commonly used to cluster sequences since it supports alignment-based distances, and the k-centers being actual data items helps with cluster interpretability. However, offline k-medoids has no support for concept drift, while also being prohibitively expensive for clustering data streams. We therefore propose SECLEDS, a streaming variant of the k-medoids algorithm with constant memory footprint. SECLEDS has two unique properties: i) it uses multiple medoids per cluster, producing stable highquality clusters, and ii) it handles concept drift using an intuitive Medoid Voting scheme for approximating cluster distances. Unlike existing adaptive algorithms that create new clusters for new concepts, SECLEDS follows a fundamentally different approach, where the clusters themselves evolve with an evolving stream. Using real and synthetic datasets, we empirically demonstrate that SECLEDS produces high-quality clusters regardless of drift, stream size, data dimensionality, and number of clusters. We compare against three popular stream and batch clustering algorithms. The state-of-the-art BanditPAM is used as an offline benchmark. SECLEDS achieves comparable F1 score to BanditPAM while reducing the number of required distance computations by 83.7%. Importantly, SECLEDS outperforms all baselines by 138.7% when the stream contains drift. We also cluster real network traffic, and provide evidence that SECLEDS can support network bandwidths of up to 1.08 Gbps while using the (expensive) dynamic time warping distance.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit
- …
