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![2: Drawing of different neurons stained with the Golgi method. Shape and extension of axons and dendritic tree show a considerable variation across species. (From Rosenzweig et al. [1998].)](profile/Jan-Eichhorn/publication/307962039/figure/fig3/AS:648988751577089@1531742384144/Drawing-of-different-neurons-stained-with-the-Golgi-method-Shape-and-extension-of.png)
2: Drawing of different neurons stained with the Golgi method. Shape and extension of axons and dendritic tree show a considerable variation across species. (From Rosenzweig et al. [1998].)
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Die vorliegende Dissertation behandelt die Anwendung von Methoden des überwachten Lernens auf zwei Probleme recht unterschiedlicher Natur, die aus dem Bereich der Neurowissenschaft sowie aus der computergestützten Bildverarbeitung stammen. Die dabei zur Lösung von Klassifikationsproblemen eingesetzten Kernalgorithmen erlauben auch die Behandlung ko...
Citations
... Schematic View of Human Visual System [4] Figure. Specialized Cells) [5] Figure. ...
... Ganglion cell axons pass the electric signals from the eyes to various parts of the brain for further processing. [2] Figure 2.1 Schematic View of Human Visual System [4] ...
... . The irregular wavelength columns are converted to extrapolated into integers.3. Eliminate duplicate Full wavelength.4. Sort and filter out the range between 380-780 nm. ...
Only in recent years, scientists have uncovered the importance of lighting design, beyond facilitating vision. Human eyes function in a dual manner, and the second function is to facilitate healthy circadian rhythms. The photobiological research is still evolving, but preliminary findings show that light- sensing opsins within the retina interact with genes oscillating to circadian rhythms. Photoreceptors photopsin (OPN1), melanopsin (ONP4) and neuropsin (OPN5) send information that impacts health, vision, and circadian rhythms.
Research in neonatal intensive units (NICU) shows that circadian light regimes can exert a positive influence on a baby's brain and eye development, and metabolic body functions. It is necessary to design, control, and manage the intensity and spectra of light in NICU settings to support the healthy development for premature babies. Currently, design guidelines for circadian lighting in healthcare settings are not well established; and there are not any tools that can simulate the neuropic light levels in built environments. Hence, this thesis addresses to a need for a tool that can predict the visual and non-visual effects of lighting decisions within a design workflow.
LARK Multi-Spectral Lighting simulation tool was developed in 2015 as a Rhino Grasshopper plugin to simulate the non-visual effects of lighting. The objectives of this research are i) to further develop LARK to quantify the recently discovered non-visual opsin neuropsin along with photopsin and melanopsin, and ii) to demonstrate simulation workflows for NICU settings to perform robust and accurate daylighting and electric lighting analyses for occupants including patients, clinicians, and patient families.
Sample workflows are exemplified to study the role of daylight and electric lighting in a NICU setting with the goal of improving design decisions. Different date, time, and weather conditions, spectral properties of glazing, surface materials, and electric light sources are simulated, and the resulting photopic, melanopic, and neuropic light levels are analyzed. The results of this thesis show that healthy lighting recipes, which satisfy the criteria for all three opsins, can be prescribed through dynamic commissioning practices for shading and tunable electric lighting systems, in addition to thoughtful design decisions such as appropriate glazing and material selections.
... Unfortunately, the Irredundant Class (the name we will use for the general method in the rest of the article) seems to lack the positive-definiteness property, and therefore it must be treated as an indefinite kernel. In particular, following the work of Eichhorn (2007) for indefinite kernels applied to SVMs, we have that the Irredundant Class is in the case of weak non-positivity, and thus we need only to force the SVM optimizer to stop after a maximum number of iterations. ...
The automatic classification of protein sequences into families is of great help for the functional prediction and annotation of new proteins. In this article, we present a method called Irredundant Class that address the remote homology detection problem. The best performing methods that solve this problem are string kernels, that compute a similarity function between pairs of proteins based on their subsequence composition. We provide evidence that almost all string kernels are based on patterns that are not independent, and therefore the associated similarity scores are obtained using a set of redundant features, overestimating the similarity between the proteins. To specifically address this issue, we introduce the class of irredundant common patterns. Loosely speaking, the set of irredundant common patterns is the smallest class of independent patterns that can describe all common patterns in a pair of sequences. We present a classification method based on the statistics of these patterns, named Irredundant Class. Results on benchmark data show that the Irredundant Class outperforms most of the string kernels previously proposed, and it achieves results as good as the current state-of-the-art method Local Alignment, but using the same pairwise information only once.
... where k is the length of p. Unfortunately the Irredundant Class, the name as we will call the general method in the rest of the paper, seems to lack the positive-definiteness property , and therefore it must be treated as an indefinite kernel. In particular, following the work of (Eichhorn, 2007) for indefinite kernels applied to SVMs, we have that the Irredundant Class is in the case of weak non-positivity, and thus we need only to force the SVM optimizer to stop after a maximum number of iterations. ...
The automatic classification of protein sequences into families is of great help for the functional prediction and annotation of new proteins. In the paper we present a method called Irredundant Class that address the remote homology detection problem. The best performing methods that solve this problem are string kernels, that compute a similarity function between pairs of proteins based on their subsequence composition. We provide evidence that almost all string kernels are based on patterns that are not independent, and therefore the associated similarity scores are obtained using a set of redundant features, overestimating the similarity between the proteins. To specifically address this issue, we introduce the class of irredundant common patterns. Loosely speaking the set of irredundant common patterns is the smallest class of independent patterns that can describe all common patterns in a pair of sequences. We present a classification method based on the statistics of these patterns, named Irredundant Class. Results on benchmark data show that the Irredundant Class outperforms most of the string kernels previously proposed, and it achieves results as good as the current state-of-the-art method Local Alignment, but using the same pairwise information only once.
... Devido ao fato de descritores locais possuírem funções de distância complexas o classificador SVM, por exemplo, precisa de "kernels" muito específicos, como os descritos no trabalho de Eichhorn [23], que consigam lidar com descritores locais. Outros classificadores baseados em distâncias explícitas como o algoritmo K Vizinhos Mais Próximos (KNN) [45], utilizam-se de medidas de distância especiais como a Earth Mover's Distance (EMD) [73] para tratar este tipo de descritores. ...
The computational methods development can helps specialists of several areas in your works is focus of many studies. In health area the premature diagnosis of diseases is very important to improve the patient’s life quality. To ophthalmologists who treat patients with diabetics, a reliable method to anomalies detects in eye fundus images is important to a premature diagnosis, avoiding appear of retina complications. Such complications can
cause blindness. Hard Exudates is one of more common anomalies found at retina, being your detection is the focus of many kinds of approaches in literature. This master’s thesis presents a new and efficient approach for detection of exsudates at eye fundus images. This approach uses computer vision and artificial inteligence techniques like visiual dictionaries, clustering and pattern recognition to detect hard exsudates in images
... Score(s i , s j ) with 1 ≤ i, j ≤ N. The result of this process is a learning distance function that can be treated as an indefinite kernel. When applying SVMs with this kernel, we therefore have to resort to one of the workarounds discussed in [28]. In particular, in case of weak non-positivity of the learning function, we force the optimizer to stop after a maximum number of iterations. ...
The classification of protein sequences using string algorithms provides valuable insights for protein function prediction. Several methods, based on a variety of different patterns, have been previously proposed. Almost all string-based approaches discover patterns that are not "independent, " and therefore the associated scores overcount, a multiple number of times, the contribution of patterns that cover the same region of a sequence.
In this paper we use a class of patterns, called irredundant, that is specifically designed to address this issue. Loosely speaking the set of irredundant patterns is the smallest class of "independent" patterns that can describe all common patterns in two sequences, thus they avoid overcounting. We present a novel discriminative method, called Irredundant Class, based on the statistics of irredundant patterns combined with the power of support vector machines.
Tests on benchmark data show that Irredundant Class outperforms most of the string algorithms previously proposed, and it achieves results as good as current state-of-the-art methods. Moreover the footprints of the most discriminative irredundant patterns can be used to guide the identification of functional regions in protein sequences.
