Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

The role of architectural and learning constraints in neural network models: A case study on visual space coding

The recent “deep learning revolution” in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems

Extended Adjuvant Endocrine Treatment in Luminal Breast Cancers in the Era of Genomic Tests

In patients with early-stage endocrine receptor-positive (ER+) breast cancer (BC), adjuvant endocrine therapy (ET) for 5 years is the standard of care. However, for some patients, the risk of recurrence remain high for up to 15 years after diagnosis and extended ET beyond 5 years may be a reasonable option. Nevertheless, this strategy significantly increases the occurrence of side effects. Here we summarize the available evidence from randomized clinical trials on the efficacy and safety profile of extended ET and discuss available clinical and genomic tools helpful to select eligible patients in daily clinical practice

Synthesis, size-dependent optoelectronic and charge transport properties of thieno(bis)imide end-substituted molecular semiconductors

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MicroRNAs’ crucial role in salivary gland cancers’ onset and prognosis

Simple Summary: Salivary gland cancers are incredibly heterogeneous, both in the physical onset and in the aggressiveness. Setting up a novel diagnostic and prognostic detection method based on the noninvasive microRNAs’ profiling might represent a goal for the clinical management of those particular malignancies, saving precious time for the patients. Abstract: Salivary gland cancer (SGC) is an uncommon and heterogeneous disease that accounts for around 8.5% of all head and neck cancers. MicroRNAs (miRNAs) consist of a class of highly conserved, short, single-stranded segments (18–25 nucleotides) of noncoding RNA that represent key gene-transcription regulators in physiological and pathological human conditions. However, their role in SGC development and progression is not completely clear. This review aims to compile and summarize the recent findings on the topic, focusing on the prognostic and diagnostic value of the major modulated and validated microRNAs in SGC. Their differential expression could possibly aid the clinician in delivering an early diagnosis, therapeutic strategy and precision medicine

Diagnostic Accuracy of Obstructive Airway Adult Test for Diagnosis of Obstructive Sleep Apnea

Rationale. The gold standard for the diagnosis of Obstructive Sleep Apnea (OSA) is polysomnography, whose access is however reduced by costs and limited availability, so that additional diagnostic tests are needed. Objectives. To analyze the diagnostic accuracy of the Obstructive Airway Adult Test (OAAT) compared to polysomnography for the diagnosis of OSA in adult patients. Methods. Ninety patients affected by OSA verified with polysomnography (AHI ≥ 5) and ten healthy patients, randomly selected, were included and all were interviewed by one blind examiner with OAAT questions. Measurements and Main Results. The Spearman rho, evaluated to measure the correlation between OAAT and polysomnography, was 0.72 ( < 0.01). The area under the ROC curve (95% CI) was the parameter to evaluate the accuracy of the OAAT: it was 0.91 (0.81-1.00) for the diagnosis of OSA (AHI ≥ 5), 0.90 (0.82-0.98) for moderate OSA (AHI ≥ 15), and 0.84 (0.76-0.92) for severe OSA (AHI ≥ 30). Conclusions. The OAAT has shown a high correlation with polysomnography and also a high diagnostic accuracy for the diagnosis of OSA. It has also been shown to be able to discriminate among the different degrees of severity of OSA. Additional large studies aiming to validate this questionnaire as a screening or diagnostic test are needed

Efficacy and safety of T-DM1 in the 'common-practice' of HER2+ advanced breast cancer setting: a multicenter study

Ado-trastuzumab emtansine (T-DM1) is an antibody-drug conjugate approved for the treatment of patients with human epidermal growth factor receptor 2 (HER2)-positive, metastatic breast cancer (mBC). The aim of this 'field-practice' study was to investigate the efficacy and safety of T-DM1, focusing on treatment line, previous lapatinib treatment and patterns of metastasis. Three hundred and three patients with HER2-positive mBC who received T-DM1 were identified by reviewing the medical records of 24 Italian Institutions. One hundred fourty-nine (49%) and 264 (87%) had received prior hormonal treatment and/or anti-HER2 targeted therapy, respectively. Particularly, 149 patients had been previously treated with lapatinib. The objective response rate (ORR) was 36.2%, and 44.5% when T-DM1 was administrated as second-line therapy. Considering only patients with liver metastases, the ORR was 44.4%. The median progression-free survival (PFS) was 7.0 months in the overall population, but it reached 9.0 and 12.0 months when TDM-1 was administered as second- and third-line treatment, respectively.In conclusion, in this 'real-word' study evaluating the effects of T-DM1 in patients with HER2-positive mBC who progressed on prior anti-HER2 therapies, we observed a clinically-relevant benefit in those who had received T-DM1 in early metastatic treatment-line and in subjects previously treated with lapatini

Elastodontic Therapy of Hyperdivergent Class II Patients Using AMCOP® Devices: A Retrospective Study

Background: The management of a hyperdivergent growth pattern is one of the most challenging in orthodontics and different treatments are advocated. The present study analyses the effectiveness of elastodontic therapy with AMCOP® devices in treating children with hyperdivergent class II malocclusion and the effect on the upper airway patency. Methods: The study group included 21 patients (10 males and 11 females, mean age 8.22 ± 1.17 years) with a hyperdivergent growth and a class II malocclusion treated with AMCOP® devices. Cephalometric analysis was performed before treatment (T0) and after treatment (T1). Results: After treatment, the cephalometric analysis revealed a correction of the class II malocclusion and a modification of the growth pattern with a divergence reduction. The improvement of the upper airway space was also observed. Conclusion: The elastodontic therapy effectively corrected hyperdivergent class II malocclusion in growing patients over a short period

Comparison and combination of a hemodynamics/biomarkers-based model with simplified PESI score for prognostic stratification of acute pulmonary embolism: findings from a real world study

Background: Prognostic stratification is of utmost importance for management of acute Pulmonary Embolism (PE) in clinical practice. Many prognostic models have been proposed, but which is the best prognosticator in real life remains unclear. The aim of our study was to compare and combine the predictive values of the hemodynamics/biomarkers based prognostic model proposed by European Society of Cardiology (ESC) in 2008 and simplified PESI score (sPESI).Methods: Data records of 452 patients discharged for acute PE from Internal Medicine wards of Tuscany (Italy) were analysed. The ESC model and sPESI were retrospectively calculated and compared by using Areas under Receiver Operating Characteristics (ROC) Curves (AUCs) and finally the combination of the two models was tested in hemodinamically stable patients. All cause and PE-related in-hospital mortality and fatal or major bleedings were the analyzed endpointsResults: All cause in-hospital mortality was 25% (16.6% PE related) in high risk, 8.7% (4.7%) in intermediate risk and 3.8% (1.2%) in low risk patients according to ESC model. All cause in-hospital mortality was 10.95% (5.75% PE related) in patients with sPESI score ≥1 and 0% (0%) in sPESI score 0. Predictive performance of sPESI was not significantly different compared with 2008 ESC model both for all cause (AUC sPESI 0.711, 95% CI: 0.661-0.758 versus ESC 0.619, 95% CI: 0.567-0.670, difference between AUCs 0.0916, p=0.084) and for PE-related mortality (AUC sPESI 0.764, 95% CI: 0.717-0.808 versus ESC 0.650, 95% CI: 0.598-0.700, difference between AUCs 0.114, p=0.11). Fatal or major bleedings occurred in 4.30% of high risk, 1.60% of intermediate risk and 2.50% of low risk patients according to 2008 ESC model, whereas these occurred in 1.80% of high risk and 1.45% of low risk patients according to sPESI, respectively. Predictive performance for fatal or major bleeding between two models was not significantly different (AUC sPESI 0.658, 95% CI: 0.606-0.707 versus ESC 0.512, 95% CI: 0.459-0.565, difference between AUCs 0.145, p=0.34). In hemodynamically stable patients, the combined endpoint in-hospital PE-related mortality and/or fatal or major bleeding (adverse events) occurred in 0% of patients with low risk ESC model and sPESI score 0, whilst it occurred in 5.5% of patients with low-risk ESC model but sPESI ≥1. In intermediate risk patients according to ESC model, adverse events occurred in 3.6% of patients with sPESI score 0 and 6.65% of patients with sPESI score ≥1.Conclusions: In real world, predictive performance of sPESI and the hemodynamic/biomarkers-based ESC model as prognosticator of in-hospital mortality and bleedings is similar. Combination of sPESI 0 with low risk ESC model may identify patients with very low risk of adverse events and candidate for early hospital discharge or home treatment.