Artificial immune systems

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm

Adaptive Conjoint Wavelet-Support Vector Classifiers

Combined wavelet - large margin classifiers succeed in solving difficult signal classification problems in cases where solely using a large margin classifier like, e.g., the Support Vector Machine may fail. This thesis investigates the problem of conjointly designing both classifier stages to achieve a most effective classifier architecture. Particularly, the wavelet features should be adapted to the Support Vector classifier and the specific classification problem. Three different approaches to achieve this goal are considered: The classifier performance is seriously affected by the wavelet or filter used for feature extraction. To optimally choose this wavelet with respect to the subsequent Support Vector classification, appropriate criteria may be used. The radius - margin Support Vector Machine error bound is proven to be computable by two standard Support Vector problems. Criteria which are computationally still more efficient may be sufficient for filter adaptation. For the classification by a Support Vector Machine, several criteria are examined rating feature sets obtained from various orthogonal filter banks. An adaptive search algorithm is devised that, once the criterion is fixed, efficiently finds the optimal wavelet filter. To extract shift invariant wavelet features, Kingsbury's dual-tree complex wavelet transform is examined. The dual-tree filter bank construction leads to wavelets with vanishing negative frequency parts. An enhanced transform is established in the frequency domain for standard wavelet filters without special filter design. The translation and rotational invariance is improved compared with the common wavelet transform as shown for various standard wavelet filters. So the framework well applies to adapted signal classification. Wavelet adaptation for signal classification is a special case of feature selection. Feature selection is an important combinatorial optimisation problem in the context of supervised pattern classification. Four novel continuous feature selection approaches directly minimising the classifier performance are presented. In particular, they include linear and nonlinear Support Vector classifiers. The key ideas of the approaches are additional regularisation and embedded nonlinear feature selection. To solve the optimisation problems, difference of convex functions programming which is a general framework for non-convex continuous optimisation is applied. This optimisation framework may also be interesting for other applications and succeeds in robustly solving the problems, and hence, building more powerful feature selection methods

Multiple Biolgical Sequence Alignment: Scoring Functions, Algorithms, and Evaluations

Aligning multiple biological sequences such as protein sequences or DNA/RNA sequences is a fundamental task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences\u27 structures, determine the evolutionary relationships between them, or discover drug-like compounds that can bind to the sequences. Unfortunately, multiple sequence alignment (MSA) is NP-Complete. In addition, the lack of a reliable scoring method makes it very hard to align the sequences reliably and to evaluate the alignment outcomes. In this dissertation, we have designed a new scoring method for use in multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. This new technique provides a reliable scoring scheme with low computational complexity. In addition to the new scoring scheme, we have designed an overlapping sequence clustering algorithm to use in our new three multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. Other two algorithms utilize sequence knowledge-bases and sequence consistency to produce biological meaningful sequence alignments. To improve the speed of the multiple sequence alignment, we have developed a parallel algorithm that can be deployed on reconfigurable computer models. Analytically, our parallel algorithm is the fastest progressive multiple sequence alignment algorithm

Entwicklung einer Fully-Convolutional-Netzwerkarchitektur für die Detektion von defekten LED-Chips in Photolumineszenzbildern

Nowadays, light-emitting diodes (LEDs) can be found in a large variety of applications, from standard LEDs in domestic lighting solutions to advanced chip designs in automobiles, smart watches and video walls. The advances in chip design also affect the test processes, where the execution of certain contact measurements is exacerbated by ever decreasing chip dimensions or even rendered impossible due to the chip design. As an instance, wafer probing determines the electrical and optical properties of all LED chips on a wafer by contacting each and every chip with a prober needle. Chip designs without a contact pad on the surface, however, elude wafer probing and while electrical and optical properties can be determined by sample measurements, defective LED chips are distributed randomly over the wafer. Here, advanced data analysis methods provide a new approach to gather defect information from already available non-contact measurements. Photoluminescence measurements, for example, record a brightness image of an LED wafer, where conspicuous brightness values indicate defective chips. To extract these defect information from photoluminescence images, a computer-vision algorithm is required that transforms photoluminescence images into defect maps. In other words, each and every pixel of a photoluminescence image must be classifed into a class category via semantic segmentation, where so-called fully-convolutional-network algorithms represent the state-of-the-art method. However, the aforementioned task poses several challenges: on the one hand, each pixel in a photoluminescence image represents an LED chip and thus, pixel-fine output resolution is required. On the other hand, photoluminescence images show a variety of brightness values from wafer to wafer in addition to local areas of differing brightness. Additionally, clusters of defective chips assume various shapes, sizes and brightness gradients and thus, the algorithm must reliably recognise objects at multiple scales. Finally, not all salient brightness values correspond to defective LED chips, requiring the algorithm to distinguish salient brightness values corresponding to measurement artefacts, non-defect structures and defects, respectively. In this dissertation, a novel fully-convolutional-network architecture was developed that allows the accurate segmentation of defective LED chips in highly variable photoluminescence wafer images. For this purpose, the basic fully-convolutional-network architecture was modifed with regard to the given application and advanced architectural concepts were incorporated so as to enable a pixel-fine output resolution and a reliable segmentation of multiple scaled defect structures. Altogether, the developed dense ASPP Vaughan architecture achieved a pixel accuracy of 97.5 %, mean pixel accuracy of 96.2% and defect-class accuracy of 92.0 %, trained on a dataset of 136 input-label pairs and hereby showed that fully-convolutional-network algorithms can be a valuable contribution to data analysis in industrial manufacturing.Leuchtdioden (LEDs) werden heutzutage in einer Vielzahl von Anwendungen verbaut, angefangen bei Standard-LEDs in der Hausbeleuchtung bis hin zu technisch fortgeschrittenen Chip-Designs in Automobilen, Smartwatches und Videowänden. Die Weiterentwicklungen im Chip-Design beeinflussen auch die Testprozesse: Hierbei wird die Durchführung bestimmter Kontaktmessungen durch zunehmend verringerte Chip-Dimensionen entweder erschwert oder ist aufgrund des Chip-Designs unmöglich. Die sogenannteWafer-Prober-Messung beispielsweise ermittelt die elektrischen und optischen Eigenschaften aller LED-Chips auf einem Wafer, indem jeder einzelne Chip mit einer Messnadel kontaktiert und vermessen wird; Chip-Designs ohne Kontaktpad auf der Oberfläche können daher nicht durch die Wafer-Prober-Messung charakterisiert werden. Während die elektrischen und optischen Chip-Eigenschaften auch mittels Stichprobenmessungen bestimmt werden können, verteilen sich defekte LED-Chips zufällig über die Waferfläche. Fortgeschrittene Datenanalysemethoden ermöglichen hierbei einen neuen Ansatz, Defektinformationen aus bereits vorhandenen, berührungslosen Messungen zu gewinnen. Photolumineszenzmessungen, beispielsweise, erfassen ein Helligkeitsbild des LEDWafers, in dem auffällige Helligkeitswerte auf defekte LED-Chips hinweisen. Ein Bildverarbeitungsalgorithmus, der diese Defektinformationen aus Photolumineszenzbildern extrahiert und ein Defektabbild erstellt, muss hierzu jeden einzelnen Bildpunkt mittels semantischer Segmentation klassifizieren, eine Technik bei der sogenannte Fully-Convolutional-Netzwerke den Stand der Technik darstellen. Die beschriebene Aufgabe wird jedoch durch mehrere Faktoren erschwert: Einerseits entspricht jeder Bildpunkt eines Photolumineszenzbildes einem LED-Chip, so dass eine bildpunktfeine Auflösung der Netzwerkausgabe notwendig ist. Andererseits weisen Photolumineszenzbilder sowohl stark variierende Helligkeitswerte von Wafer zu Wafer als auch lokal begrenzte Helligkeitsabweichungen auf. Zusätzlich nehmen Defektanhäufungen unterschiedliche Formen, Größen und Helligkeitsgradienten an, weswegen der Algorithmus Objekte verschiedener Abmessungen zuverlässig erkennen können muss. Schlussendlich weisen nicht alle auffälligen Helligkeitswerte auf defekte LED-Chips hin, so dass der Algorithmus in der Lage sein muss zu unterscheiden, ob auffällige Helligkeitswerte mit Messartefakten, defekten LED-Chips oder defektfreien Strukturen korrelieren. In dieser Dissertation wurde eine neuartige Fully-Convolutional-Netzwerkarchitektur entwickelt, die die akkurate Segmentierung defekter LED-Chips in stark variierenden Photolumineszenzbildern von LED-Wafern ermöglicht. Zu diesem Zweck wurde die klassische Fully-Convolutional-Netzwerkarchitektur hinsichtlich der beschriebenen Anwendung angepasst und fortgeschrittene architektonische Konzepte eingearbeitet, um eine bildpunktfeine Ausgabeauflösung und eine zuverlässige Sementierung verschieden großer Defektstrukturen umzusetzen. Insgesamt erzielt die entwickelte dense-ASPP-Vaughan-Architektur eine Pixelgenauigkeit von 97,5 %, durchschnittliche Pixelgenauigkeit von 96,2% und eine Defektklassengenauigkeit von 92,0 %, trainiert mit einem Datensatz von 136 Bildern. Hiermit konnte gezeigt werden, dass Fully-Convolutional-Netzwerke eine wertvolle Erweiterung der Datenanalysemethoden sein können, die in der industriellen Fertigung eingesetzt werden

Supervised Classification and Mathematical Optimization

Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data

Towards More Nuanced Patient Management: Decomposing Readmission Risk with Survival Models

Unplanned hospital readmissions are costly and associated with poorer patient outcomes. Overall readmission rates have also come to be used as performance metrics in reimbursement in healthcare policy, further motivating hospitals to identify and manage high-risk patients. Many models predicting readmission risk have been developed to facilitate the equitable measurement of readmission rates and to support hospital decision-makers in prioritising patients for interventions. However, these models consider the overall risk of readmission and are often restricted to a single time point. This work aims to develop the use of survival models to better support hospital decision-makers in managing readmission risk. First, semi-parametric statistical and nonparametric machine learning models are applied to adult patients admitted via the emergency department at Gold Coast University Hospital (n = 46,659) and Robina Hospital (n = 23,976) in Queensland, Australia. Overall model performance is assessed based on discrimination and calibration, as measured by time-dependent concordance and D-calibration. Second, a framework based on iterative hypothesis development and model fitting is proposed for decomposing readmission risk into persistent, patient-specific baselines and transient, care-related components using a sum of exponential hazards structure. Third, criteria for patient prioritisation based on the duration and magnitude of care-related risk components are developed. The extensibility of the framework and subsequent prioritisation criteria are considered for alternative populations, such as outpatient admissions and specific diagnosis groups, and different modelling techniques. Time-to-event models have rarely been applied for readmission modelling but can provide a rich description of the evolution of readmission risk post-discharge and support more nuanced patient management decisions than simple classification models

Primary vertex reconstruction using GPUs for the upgrade of the Inner Tracking System of the ALICE experiment at LHC

L'abstract è presente nell'allegato / the abstract is in the attachmen

International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more