Explainable AI using expressive Boolean formulas

We propose and implement an interpretable machine learning classification model for Explainable AI (XAI) based on expressive Boolean formulas. Potential applications include credit scoring and diagnosis of medical conditions. The Boolean formula defines a rule with tunable complexity (or interpretability), according to which input data are classified. Such a formula can include any operator that can be applied to one or more Boolean variables, thus providing higher expressivity compared to more rigid rule-based and tree-based approaches. The classifier is trained using native local optimization techniques, efficiently searching the space of feasible formulas. Shallow rules can be determined by fast Integer Linear Programming (ILP) or Quadratic Unconstrained Binary Optimization (QUBO) solvers, potentially powered by special purpose hardware or quantum devices. We combine the expressivity and efficiency of the native local optimizer with the fast operation of these devices by executing non-local moves that optimize over subtrees of the full Boolean formula. We provide extensive numerical benchmarking results featuring several baselines on well-known public datasets. Based on the results, we find that the native local rule classifier is generally competitive with the other classifiers. The addition of non-local moves achieves similar results with fewer iterations, and therefore using specialized or quantum hardware could lead to a speedup by fast proposal of non-local moves.Comment: 28 pages, 16 figures, 4 table

The Changing Waves of Migration from the Balkans to Turkey: A Historical Account

Ahmet İçduygu and Deniz Sert tell the history of migration from the Balkans to Turkey from the end of the nineteenth century to the present. They relate this history to nation-building, but also to economic conditions and specific Turkish concerns, such as the perceived need for immigration to compensate for a declining population at that time. They also demonstrate that after 1990, ethnic migration decreased and irregular labour migration became more important

Fatigue Precracking Time Estimates for Three-Point Bending Specimens

Specimens containing sharp cracks are needed in certain types of mechanical tests, first and foremost for fracture toughness measurement of materials. Their use, however, is not just limited to this type of test. Another category of experiments deals with characterizing nonlinear vibrations of beams containing breathing cracks. To produce cracked beam specimens, fatigue cracks can be grown ahead of sharp notches under controlled loading. ASTM E399 and ASTM E1820 standards provide guidance on such procedures for preparation of fracture toughness test specimens. However, certain issues which might become important in testing of vibrations of cracked beams, such as the time required for specimen preparation is not addressed in these standards. In this article, both low cycle fatigue methods and linear elastic fracture mechanics methods are used to estimate the number of loading cycles required to have a crack of desired length at the notch tip. Calculation results are compared with experimental ones, and the effects of various factors influencing the required number of loading cycles for a certain crack size are discussed

Critical crack size investigation method for a land launcher

Structural integrity evaluation of military systems is vital in such applications as rocket launchers. In safe life design, for a certain operational life the system is ensured to function well and no failure would occur even in the presence of some imperfections or flaws. This paper considers the fracture analysis as part of the safe life design approach that is used in the design of a rocket launcher. A methodology based on sub modelling technique is introduced. It was made sure that unstable crack growth would not occur upto certain crack sizes. Subsequently, after actual manufacturing of the launcher, critical locations of welds in the system were checked for presence of any cracks after repeated firing loads. After these controls no cracks were detected due to operational conditions until the time when this document was prepared

Development of molecular detection methods for early warning of the hazelnut powdery mildew pathogen Erysiphe corylacearum

Powdery mildews are an agronomically important group of fungal plant pathogens, which cause serious diseases on numerous crop species. European hazel (Corylus avellana L.) has long been known to be colonized by the mildew fungus Phyllactinia guttata, producing relatively mild symptoms late in the growing season. However, a new and more destructive powdery mildew disease was first observed in orchards in eastern Turkey in 2013, and was subsequently found to be caused by Erysiphe corylacearum, a fungus previously observed only on wild relatives of C. avellana. Mildew disease caused by E. corylacearum develops early in the growing season, reducing yields and quality by causing premature leaf and nut fall. Since its first appearance the disease has become endemic throughout Turkey and other hazelnut growing areas around the Black Sea, and also spread to most of Europe. Protection of affected orchards requires regular fungicide treatment, ideally starting before the first symptoms appear. Therefore, we aimed to develop rapid molecular methods for the early detection of this pathogen to facilitate efficient treatment. The internal transcribed spacer (ITS) regions of the rRNA are commonly used for discrimination between fungal species by the ‘DNA barcoding’ method. E. corylacearum fungi were collected from several heavily mildewed orchards and their ITS regions sequenced. These were then compared with ITS regions from other Erysiphe species, and the most diverse segments used to design species-specific quantitative PCR assays. Using this approach, the presence of the pathogen could be specifically detected from ~50 genome copies, approximately 100 times fewer than when symptoms become visible to the naked eye. This technique was validated by testing samples collected in multiple orchards early in the growing season, before and during the development of disease symptoms