Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure

Diversity represents an important aspect of genetic programming, being directly correlated with search performance. When considered at the genotype level, diversity often requires expensive tree distance measures which have a negative impact on the algorithm's runtime performance. In this work we introduce a fast, hash-based tree distance measure to massively speed-up the calculation of population diversity during the algorithmic run. We combine this measure with the standard GA and the NSGA-II genetic algorithms to steer the search towards higher diversity. We validate the approach on a collection of benchmark problems for symbolic regression where our method consistently outperforms the standard GA as well as NSGA-II configurations with different secondary objectives.Comment: 8 pages, conference, submitted to congress on evolutionary computatio

The Influence of Covid-19 Pandemy on Financial Fraud Risk Assessment

In today's economic environment, the many harms caused by financial fraud have attracted increased attention from academia and regulatory bodies alike. In the last two decades, marked by great crises, pandemics, financial fraud has affected to the global economy, being a threat for stability of the capital markets. The purpose of this study is to highlight the main aspects of the specialized literature on the subject of fraud risk, for its analysis and assessment. This study aims to obtain a financial profile of entities at risk of being subject to financial reporting fraud or asset misappropriation. The sample is represented by the entities listed on the BSE regulated market in the period 2020-2022. According to the object of activity, the analyzed entities are grouped as follows: chemical-pharmaceutical, tourism and services. In the study it is proposed the option of analyzing and assessment of fraud risk using quantitative and qualitative methods

Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data

Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which allow to calculate the potential energy of an atomic system as a function of atomic coordinates and potentially other properties. First-principles-based ab initio potentials can reach arbitrary levels of accuracy, however their aplicability is limited by their high computational cost. Machine learning (ML) has recently emerged as an effective way to offset the high computational costs of ab initio atomic potentials by replacing expensive models with highly efficient surrogates trained on electronic structure data. Among a plethora of current methods, symbolic regression (SR) is gaining traction as a powerful "white-box" approach for discovering functional forms of interatomic potentials. This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results. A genetic programming-based approach for modeling atomic potentials from raw data (consisting of snapshots of atomic positions and associated potential energy) is presented and empirically validated on ab initio electronic structure data.Comment: Submitted to the GPTP XIX Workshop, June 2-4 2022, University of Michigan, Ann Arbor, Michiga

Naşterea prematură: protocol clinic naţional PCN-185

Protocolul clinic naţional este elaborat în conformitate cu ghidurile internaţionale actuale şi experienţa autorilor acumulată în domeniul prematurităţii. Acesta va servi la elaborarea protocoalelor clinice instituţionale, în baza posibilităţilor reale ale fiecărei instituţii în anul curent. La recomandarea MS pentru monitorizarea protocoalelor clinice instituţionale pot fi folosite formulare suplimentare, care nu sunt incluse în protocolul clinic naţional

Regulation of cellular sterol homeostasis by the oxygen responsive noncoding RNA lincNORS

We hereby provide the initial portrait of lincNORS, a spliced lincRNA generated by the MIR193BHG locus, entirely distinct from the previously described miR-193b-365a tandem. While inducible by low O2 in a variety of cells and associated with hypoxia in vivo, our studies show that lincNORS is subject to multiple regulatory inputs, including estrogen signals. Biochemically, this lincRNA fine-tunes cellular sterol/steroid biosynthesis by repressing the expression of multiple pathway components. Mechanistically, the function of lincNORS requires the presence of RALY, an RNA-binding protein recently found to be implicated in cholesterol homeostasis. We also noticed the proximity between this locus and naturally occurring genetic variations highly significant for sterol/steroid-related phenotypes, in particular the age of sexual maturation. An integrative analysis of these variants provided a more formal link between these phenotypes and lincNORS, further strengthening the case for its biological relevance

Tracing of Evolutionary Search Trajectories in Complex Hypothesis Spaces

Understanding the internal functioning of evolutionary algorithms is an essential requirement for improving their performance and reliability. Increased computational resources available in current mainstream computers make it possible for new previously infeasible research directions to be explored. Therefore, a comprehensive theoretical analysis of their mechanisms and dynamics using modern tools becomes possible. Recent algorithmic achievements like offspring selection in combination with linear scaling have enabled genetic programming (GP) to achieve high quality results in system identification in less than 50 generations using populations of only several hundred individuals. Therefore, the active gene pool of evolutionary search remains manageable and may be subjected to new theoretical investigations closely related to genetic programming schema theories, building block hypotheses and bloat theories. Genetic algorithms emulate emergent systems in which complex patterns are formed from an initially simple and random pool of elementary structures. In GP, complexity emerges under the influence of stabilizing selection which preserves the useful genetic variation created by recombination and mutation. The mapping between the structures used for solution representation and the ones used for the evaluation of fitness has a major influence on algorithm behavior. Population-wide effects concerning building blocks, genetic diversity and bloat can be conceptually seen as results of the complex interaction between phenotypic operators (selection) and genotypic operators (mutation and recombination). This coupling known as the “variation-selection loop” is the main engine for GP emergent behavior and constitutes the main topic of this research. This thesis aims to provide a unified theoretical framework which can explain GP evolution. To this end, it explores the way in which the genotype-phenotype map, in relation with the evolutionary operators (selection, recombination, mutation) determines algorithmic behaviour. As the title suggests, the main contribution consists of a novel “tracing” framework that makes it possible to determine the exact patterns of building block and gene propagation through the generations and the way smaller elements are gradually assembled into more complex structures by the evolutionary algorithm.Um die Leistung und Zuverlässigkeit von evolutionären Algorithmen zu verbessern, ist es notwendig deren interne Funktionsweise zu verstehen. Die hohe Rechenleistung aktueller Mainstream-Computer erlaubt neue Forschungsrichtungen zu erkunden, welche früher, aufgrund fehlender Rechenleistung, nicht möglich waren. Für evolutionäre Algorithmen wird es dadurch möglich, deren interne Mechaniken und Dynamiken umfangreich zu analysieren. Aktuelle algorithmische Fortschritte, wie Nachkommensselektion (Offspring Selection) in Kombination mit linearer Skalierung, ermöglicht Genetischer Programmierung (GP) hochqualitative Ergebnisse in der Systemidentifikation zu erreichen, in weniger als 50 Generationen bei einer Populationsgröße von nur wenigen hunderten Individuen. Der dadurch überschaubare aktive Genpool der evolutionären Suche ermöglicht neue theoretische Untersuchungen zum GP Schema Theorem, zur Baustein Hypothese und zu Bloat-Theorien. Genetische Algorithmen emulieren emergente Systeme in denen komplexe Muster geformt werden, basierend auf einer initialen, zufällig generiertem Menge an elementaren Strukturen. In GP entsteht die Komplexität durch den Einfluss der stabilisierenden Selektion, welche nützliche genetische Variation erhält die von Rekombination und Mutation erzeugt werden. Die Zuordnung zwischen Strukturen zur Lösungsrepräsentation (Genotyp) und Fitnessevaluierung (Phänotyp) beeinflusst das algorithmische Verhalten stark. Populationsweite Auswirkungen betreffend Bausteine, genetischer Diversität und Bloat entstehen durch das komplexe Zusammenwirken phänotypischen Operatoren (Selektion) und genotypischen Operatoren (Rekombination und Mutation). Dieser Mechanismus, bekannt als „Variation-Selektion Schleife”, ist die treibende Kraft des emergente Verhalten von GP und bildet das Hauptforschungsthema dieser Arbeit. Diese Arbeit zielt darauf ab, einen einheitlichen, theoretischen Rauem zu schaffen welcher Evolution in GP erklären kann. Dafür wird der Einfluss von auf das algorithmische Verhalten untersucht, basierend auf die Zuordnung von Genotyp und Phänotyp, unter Berücksichtigung der evolutionärer Operatoren (Selektion, Rekombination, Mutation). Wie der Titel der Arbeit bereits andeutet, besteht der wichtigste Beitrag aus einem neuartigen System zur Überwachung und Rückverfolgung von Genen über mehrere Generationen hinweg. Dies ermöglicht es das Verhalten von Bausteinen zu erforschen, sowie zu erkunden wie bei evolutionären Algorithmen aus kleinen Elementen nach und nach komplexere Strukturen gebildet werden.eingereicht von Bogdan BurlacuUniversität Linz, Dissertation, 2017OeBB(VLID)224637

Dual Stem Cell Therapy Improves the Myocardial Recovery Post-Infarction through Reciprocal Modulation of Cell Functions

Mesenchymal stromal cells (MSC) are promising candidates for regenerative therapy of the infarcted heart. However, poor cell retention within the transplantation site limits their potential. We hypothesized that MSC benefits could be enhanced through a dual-cell approach using jointly endothelial colony forming cells (ECFC) and MSC. To assess this, we comparatively evaluated the effects of the therapy with MSC and ECFC versus MSC-only in a mouse model of myocardial infarction. Heart function was assessed by echocardiography, and the molecular crosstalk between MSC and ECFC was evaluated in vitro through direct or indirect co-culture systems. We found that dual-cell therapy improved cardiac function in terms of ejection fraction and stroke volume. In vitro experiments showed that ECFC augmented MSC effector properties by increasing Connexin 43 and Integrin alpha-5 and the secretion of healing-associated molecules. Moreover, MSC prompted the organization of ECFC into vascular networks. This indicated a reciprocal modulation in the functionality of MSC and ECFC. In conclusion, the crosstalk between MSC and ECFC augments the therapeutic properties of MSC and enhances the angiogenic properties of ECFC. Our data consolidate the dual-cell therapy as a step forward for the development of effective treatments for patients affected by myocardial infarction

Peculiarities and Consequences of Different Angiographic Patterns of STEMI Patients Receiving Coronary Angiography Only: Data from a Large Primary PCI Registry

Background. Inappropriate cardiac catheterization lab activation together with false-positive angiographies and no-culprit found coronary interventions are now reported as costly to the medical system, influencing STEMI process efficiency. We aimed to analyze data from a high-volume interventional centre (>1000 primary PCIs/year) exploring etiologies and reporting characteristics from all “blank” coronary angiographies in STEMI. Methods. In this retrospective observational single-centre cohort study, we reported two-year data from a primary PCI registry (2035 patients). “Angio-only” cases were assigned to one of these categories: (a) Takotsubo syndrome; (b) coronary embolisation; (c) myocardial infarction with nonobstructive coronary arteries; (d) myocarditis; (e) CABG-referred; (f) normal coronary arteries (mostly diagnostic errors); and (g)others (refusals and death prior angioplasty). Univariate analysis assessed correlations between each category and cardiovascular risk factors. Results. 412 STEMI patients received coronary angiography “only,” accounting for 20.2% of cath lab activations. Barely 77 patients had diagnostic errors (3.8% from all patients) implying false-activations. 40% of “angio-only” patients (n = 165) were referred to surgery due to severe atherosclerosis or mechanical complications. Patients with diagnostic errors and normal arteries displayed strong correlations with all cardiovascular risk factors. Probably, numerous risk factors “convinced” emergency department staff to call for an angio. Conclusions. STEMI network professionals often confront with coronary angiography “only” situations. We propose a classification according to etiologies. Next, STEMI guidelines should include audit recommendations and specific thresholds regarding “angio-only” patients, with specific focus on MINOCA, CABG referrals, and diagnostic errors. These measures will have a double impact: a better management of the patient, and a clearer perception about the usefulness of the investments