Local Search, Semantics, and Genetic Programming:a Global Analysis

Geometric Semantic Geometric Programming (GSGP) is one of the most prominent Genetic Programming (GP) variants, thanks to its solid theoretical background, the excellent performance achieved, and the execution time significantly smaller than standard syntax-based GP. In recent years, a new mutation operator, Geometric Semantic Mutation with Local Search (GSM-LS), has been proposed to include a local search step in the mutation process based on the idea that performing a linear regression during the mutation can allow for a faster convergence to good-quality solutions. While GSM-LS helps the convergence of the evolutionary search, it is prone to overfitting. Thus, it was suggested to use GSM-LS only for a limited number of generations and, subsequently, to switch back to standard geometric semantic mutation. A more recently defined variant of GSGP (called GSGP-reg) also includes a local search step but shares similar strengths and weaknesses with GSM-LS. Here we explore multiple possibilities to limit the overfitting of GSM-LS and GSGP-reg, ranging from adaptive methods to estimate the risk of overfitting at each mutation to a simple regularized regression. The results show that the method used to limit overfitting is not that important: providing that a technique to control overfitting is used, it is possible to consistently outperform standard GSGP on both training and unseen data. The obtained results allow practitioners to better understand the role of local search in GSGP and demonstrate that simple regularization strategies are effective in controlling overfitting

calibration and prediction assessment of different ductile damage models on ti6al4v and 17 4ph additive manufactured alloys

Abstract Nowadays, metal additive manufacturing is becoming always more popular, being able to deliver complex shaped high quality products. Though many studies have been conducted on the high cycle fatigue behavior of these materials, yet ductile failure has still not been completely investigated, to identify the failure limits under static complex stress states. In the present study, the calibration of three ductile damage models on two popular additive manufactured alloys was carried out. The selected alloys were Ti6Al4V, processed via Electron Beam Melting, and 17-4PH fabricated with Selective Laser Melting technology; both broadly used in actual industrial applications. For each material a set of samples, was fabricated to perform a thorough static mechanical characterization, involving tensile tests on round smooth bars, notched bars, tests under plane strain conditions and torsion tests. The stress state in the critical points was retrieved relying on FEM simulations, and the data collected via the hybrid experimental-numerical procedure subsequently used to tune the damage models. Specifically, the selected models are the Rice and Tracey, the Modified Mohr-Coulomb by Wierzbicki and the one proposed by Coppola and Cortese. While the former does not take into account the effect of Lode parameter, the latter two consider its influence on fracture onset. A minimization algorithm was used for their calibration, and different optimization strategies were adopted to check the robustness of identified parameters. The resulting strains to fracture as a function of damage parameters were plotted for each formulation. The failure prediction accuracy of all models was assessed and compared to the others

Tecnologia delle applicazioni digitali per il diabete: benefici, sfide e raccomandazioni. Consensus Report del Diabetes Technology Working Group della European Association for the Study of Diabetes (EASD) e della American Diabetes Association (ADA)

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Relating Implicit Bias and Adversarial Attacks through Intrinsic Dimension

Despite their impressive performance in classification, neural networks are known to be vulnerable to adversarial attacks. These attacks are small perturbations of the input data designed to fool the model. Naturally, a question arises regarding the potential connection between the architecture, settings, or properties of the model and the nature of the attack. In this work, we aim to shed light on this problem by focusing on the implicit bias of the neural network, which refers to its inherent inclination to favor specific patterns or outcomes. Specifically, we investigate one aspect of the implicit bias, which involves the essential Fourier frequencies required for accurate image classification. We conduct tests to assess the statistical relationship between these frequencies and those necessary for a successful attack. To delve into this relationship, we propose a new method that can uncover non-linear correlations between sets of coordinates, which, in our case, are the aforementioned frequencies. By exploiting the entanglement between intrinsic dimension and correlation, we provide empirical evidence that the network bias in Fourier space and the target frequencies of adversarial attacks are closely tied

Applicazione della texture analysis delle immagini TC all'adenocarcinoma duttale del pancreas dopo chemioradioterapia neoadiuvante: correlazione con resecabilità e prognosi

copo Valutare la correlazione tra i risultati della texture analysis, la resecabilità, la sopravvivenza libera da recidiva (RFS) e la sopravvivenza globale (OS) nei pazienti con adenocarcinoma duttale del pancreas (PDAC) localmente avanzato e borderline resectable trattato con chemioradioterapia neoadiuvante

Analisis Penggunaan Google Dan Pengaruhnya Terhadap Kinerja (Studi Pada Mahasiswa S-1 Angkatan 2013-2014 Program Studi Administrasi Bisnis Fakultas Ilmu Administrasi Universitas Brawijaya)

The goal of this study to analyzes influence esay to use Google, usefulness Google, and use Google towards bachelor degree performence. The sample in this study is university student bachelor degree on 2013-2014 level at Administration Business Department, Administration Scients Faculty in Brawijaya University. They are 164 university students using questionnaire as the instrument of this research. The result showed that easy of use Google has positif significant towards usefulness Google, esay of use Google has positif significance towards use Google, usefulness Google has positif significant towards use Google, use Google has positif significant toward university students\u27 performance. Further showed that use Google that used by university student. The used of Goggle is considered easy and useful, in implementation. This research showed a high assessment is easy and usefull Google on its use towards university student\u27s performence

CT Enhancement and 3D Texture Analysis of Pancreatic Neuroendocrine Neoplasms

To evaluate pancreatic neuroendocrine neoplasms (panNENs) grade prediction by means of qualitative and quantitative CT evaluation, and 3D CT-texture analysis. Patients with histopathologically-proven panNEN, availability of Ki67% values and pre-treatment CT were included. CT images were retrospectively reviewed, and qualitative and quantitative images analysis were done; for quantitative analysis four enhancement-ratios and three permeability-ratios were created. 3D CT-texture imaging analysis was done (Mean Value; Variance; Skewness; Kurtosis; Entropy). Subsequently, these features were compared among the three grading (G) groups. 304 patients affected by panNENs were considered, and 100 patients were included. At qualitative evaluation, frequency of irregular margins was significantly different between tumor G groups. At quantitative evaluation, for all ratios, comparisons resulted statistical significant different between G1 and G3 groups and between G2 and G3 groups. At 3D CT-texture analysis, Kurtosis resulted statistical significant different among three G groups and Entropy resulted statistical significant different between G1 and G3 and between G2 and G3 groups. Quantitative CT evaluation of panNENs can predict tumor grade, discerning G1 from G3 and G2 from G3 tumors. CT-texture analysis can predict panNENs tumor grade, distinguishing G1 from G3 and G2 from G3, and G1 from G2 tumors

Termoablazione a radiofrequenze nell'adenocarcinoma pancreatico: studio TC della termolesione e del residuo tumorale tramite texture analysis

Lo scopo di questo lavoro consiste nello studio TC della termoablazione a radiofrequenza applicata all’adenocarcinoma pancreatico e nella valutazione tramite texture analysis delle modificazioni tissutali da essa indotte

Radiogenomica dell'adenocarcinoma duttale del Pancreas

Il tumore del pancreas ha una prognosi molto sfavorevole e il suo istotipo più frequente è l'adenocarcinoma duttale pancreatico (PDAC). Ci sono alcuni geni spesso mutati nella cancerogenesi del PDAC: KRAS, CDKN2a/INK4a, TP53 e DPC4/SMAD4. Lo scopo del nostro studio è trovare correlazioni tra parametri di texture analysis di TC e RM con i diversi geni mutati nel PDAC, in particolare con l'espressione di KRAS e TP53

Neoplasie neuroendocrine del pancreas serotonina positive: correlazione imaging – anatomia patologica

Analisi delle caratteristiche radiologiche delle neoplasie neuroendocrine pancreatiche serotonina positive