XPySom: High-performance self-organizing maps

In this paper, we introduce XPySom, a new open-source Python implementation of the well-known Self-Organizing Maps (SOM) technique. It is designed to achieve high performance on a single node, exploiting widely available Python libraries for vector processing on multi-core CPUs and GP-GPUs. We present results from an extensive experimental evaluation of XPySom in comparison to widely used open-source SOM implementations, showing that it outperforms the other available alternatives. Indeed, our experimentation carried out using the Extended MNIST open data set shows a speed-up of about 7x and 100x when compared to the best open-source multi-core implementations we could find with multi-core and GP-GPU acceleration, respectively, achieving the same accuracy levels in terms of quantization error

Analyzing Declarative Deployment Code with Large Language Models

In the cloud-native era, developers have at their disposal an unprecedented landscape of services to build scalable distributed systems. The DevOps paradigm emerged as a response to the increasing necessity of better automations, capable of dealing with the complexity of modern cloud systems. For instance, Infrastructure-as-Code tools provide a declarative way to define, track, and automate changes to the infrastructure underlying a cloud application. Assuring the quality of this part of a code base is of utmost importance. However, learning to produce robust deployment specifications is not an easy feat, and for the domain experts it is time-consuming to conduct code-reviews and transfer the appropriate knowledge to novice members of the team. Given the abundance of data generated throughout the DevOps cycle, machine learning (ML) techniques seem a promising way to tackle this problem. In this work, we propose an approach based on Large Language Models to analyze declarative deployment code and automatically provide QA-related recommendations to developers, such that they can benefit of established best practices and design patterns. We developed a prototype of our proposed ML pipeline, and empirically evaluated our approach on a collection of Kubernetes manifests exported from a repository of internal projects at Nokia Bell Labs

Predictive auto-scaling with OpenStack Monasca

Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a predefined threshold. Tuning these rules becomes particularly cumbersome when scaling-up a cluster involves non-negligible times to bootstrap new instances, as it happens frequently in production cloud services. To deal with this problem, we propose an architecture for auto-scaling cloud services based on the status in which the system is expected to evolve in the near future. Our approach leverages on time-series forecasting techniques, like those based on machine learning and artificial neural networks, to predict the future dynamics of key metrics, e.g., resource consumption metrics, and apply a threshold-based scaling policy on them. The result is a predictive automation policy that is able, for instance, to automatically anticipate peaks in the load of a cloud application and trigger ahead of time appropriate scaling actions to accommodate the expected increase in traffic. We prototyped our approach as an open-source OpenStack component, which relies on, and extends, the monitoring capabilities offered by Monasca, resulting in the addition of predictive metrics that can be leveraged by orchestration components like Heat or Senlin. We show experimental results using a recurrent neural network and a multi-layer perceptron as predictor, which are compared with a simple linear regression and a traditional non-predictive auto-scaling policy. However, the proposed framework allows for the easy customization of the prediction policy as needed

Behavioral analysis for virtualized network functions: A som-based approach

In this paper, we tackle the problem of detecting anomalous behaviors in a virtualized infrastructure for network function virtualization, proposing to use self-organizing maps for analyzing historical data available through a data center. We propose a joint analysis of system-level metrics, mostly related to resource consumption patterns of the hosted virtual machines, as available through the virtualized infrastructure monitoring system, and the application-level metrics published by individual virtualized network functions through their own monitoring subsystems. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, show that our technique is able to identify specific points in space and time of the recent evolution of the monitored infrastructure that are worth to be investigated by a human operator in order to keep the system running under expected conditions

Using Self-Organizing Maps for the Behavioral Analysis of Virtualized Network Functions

Detecting anomalous behaviors in a network function virtualization infrastructure is of the utmost importance for network operators. In this paper, we propose a technique, based on Self-Organizing Maps, to address such problem by leveraging on the massive amount of historical system data that is typically available in these infrastructures. Indeed, our method consists of a joint analysis of system-level metrics, provided by the virtualized infrastructure monitoring system and referring to resource consumption patterns of the physical hosts and the virtual machines (or containers) that run on top of them, and application-level metrics, provided by the individual virtualized network functions monitoring subsystems and related to the performance levels of the individual applications. The implementation of our approach has been validated on real data coming from a subset of the Vodafone infrastructure for network function virtualization, where it is currently employed to support the decisions of data center operators. Experimental results show that our technique is capable of identifying specific points in space (i.e., components of the infrastructure) and time of the recent evolution of the monitored infrastructure that are worth to be investigated by human operators in order to keep the system running under expected conditions

Self-reported face recognition abilities moderately predict face-learning skills: Evidence from Italian samples

Face Recognition Ability (FRA) varies widely throughout the population. Previous research highlights a positive relationship between self-perceived and objectively measured FRA in the healthy population, suggesting that people do have insight into their FRA. Given that this relationship has not been investigated in Italian samples yet, the main aim of the present work was to develop an Italian translation of the Prosopagnosia Index-20 (PI-20), a self-report measure of FRA, to investigate the relationship between PI-20 performances and an objective assessment given by the Cambridge Face Memory Test Long Form (CFMT+) in the Italian population. A sample of 553 participants filled in the PI-20 Italian version 1 or 2 (PI-20_GE or PI-20_BA) and completed the CFMT+. Results showed a negative correlation between both versions of the Italian PI-20 and CFMT+ scores, meaning that the more self-evaluations were negative, the worse they objectively performed. The same results applied to the extreme limits of the distribution (i.e., 10% of the highest and lowest PI-20 scores). Furthermore, both age and administration order of the tests were predictor variables of CFMT+ scores. Overall, our results suggest that people possess insight, although relatively limited, into their FRA

Preparation and characterization of in situ polymerized cyclic butylene terephthalate/graphene nanocomposites

Graphene reinforced cyclic butylene terephthalate (CBT) matrix nanocomposites were prepared and characterized by mechanical and thermal methods. These nanocomposites containing different amounts of graphene (up to 5 wt%) were prepared by melt mixing with CBT that was polymerized in situ during a subsequent hot pressing. The nanocomposites and the neat polymerized CBT (pCBT) as reference material were subjected to differential scanning calorimetry (DSC), dynamical mechanical analysis (DMA), thermogravimetrical analysis (TGA) and heat conductivity measurements. The dispersion of the grapheme nanoplatelets was characterized by transmission electron microscopy (TEM). It was established that the partly exfoliated graphene worked as nucleating agent for crystallization, acted as very efficient reinforcing agent (the storage modulus at room temperature was increased by 39 and 89% by incorporating 1 and 5 wt.% graphene, respectively). Graphene incorporation markedly enhanced the heat conductivity but did not influence the TGA behaviour due to the not proper exfoliation except the ash content

Treatment of squamous cell carcinoma of the uterine cervix with radiation therapy alone: long-term survival, late complications, and incidence of second cancers

The objective of this retrospective study was to determine the survival rate, incidence of late complications, and incidence of second cancers when radiation therapy alone is used for carcinoma of the uterine cervix. Between 1971 and 1995, 1495 patients with squamous cell carcinoma of the uterine cervix (stages I–IV) were treated with radiation therapy alone in our hospital. Radiation therapy consisted of a combination of high-dose-rate intracavitary brachytherapy and external beam radiotherapy. The cumulative 5-year survival rates for stages Ib, II, and III/IVa carcinoma were 93.5, 77.0, and 60.3%, respectively, and the 10-year survival rates were 90.9, 74.5, and 56.1%, respectively. Local control rates for stages Ib, II, and III/IVa carcinoma were 92.0, 79.4 and 64.2%, respectively. Eighty-two (5.5%) patients suffered grade III/IV or V (fatal) complications. A second cancer developed in 13 (0.87%) patients. Second cancers were observed most frequently in the rectum (five cases), colon (three cases), and uterine body (two cases). Long-term follow-up data revealed that our method of radiation therapy alone for locally advanced carcinoma of the uterine cervix is effective, with low incidences of late complications and second cancers

Brachytherapy for cervix cancer: low-dose rate or high-dose rate brachytherapy – a meta-analysis of clinical trials

<p>Abstract</p> <p>Background</p> <p>The literature supporting high-dose rate brachytherapy (HDR) in the treatment of cervical carcinoma derives primarily from retrospective series. However, controversy still persists regarding the efficacy and safety of HDR brachytherapy compared to low-dose rate (LDR) brachytherapy, in particular, due to inadequate tumor coverage for stage III patients. Whether LDR or HDR brachytherapy produces better results for these patients in terms of survival rate, local control rate and the treatment complications remain controversial.</p> <p>Methods</p> <p>A meta-analysis of RCT was performed comparing LDR to HDR brachytherapy for cervix cancer treated for radiotherapy alone. The MEDLINE, EMBASE, CANCERLIT and Cochrane Library databases, as well as abstracts published in the annual proceedings were systematically searched. We assessed methodological quality for each outcome by grading the quality of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. We used "recommend" for strong recommendations, and "suggest" for weak recommendations.</p> <p>Results</p> <p>Pooled results from five randomized trials (2,065 patients) of HDR brachytherapy in cervix cancer showed no significant increase of mortality (p = 0.52), local recurrence (p = 0.68), or late complications (rectal; p = 0.7, bladder; p = 0.95 or small intestine; p = 0.06) rates as compared to LDR brachytherapy. In the subgroup analysis no difference was observed for overall mortality and local recurrence in patients with clinical stages I, II and III. The quality of evidence was low for mortality and local recurrence in patients with clinical stage I, and moderate for other clinical stages.</p> <p>Conclusion</p> <p>Our meta-analysis shows that there are no differences between HDR and LDR for overall survival, local recurrence and late complications for clinical stages I, II and III. By means of the GRADE system, we recommend the use of HDR for all clinical stages of cervix cancer.</p

Identification of molecular markers for the early detection of human squamous cell carcinoma of the uterine cervix

To identify novel cellular genes that could potentially act as predictive molecular markers for human cervical cancer, we employed RT–PCR differential display, reverse Northern and Northern blot analysis to compare the gene expression profiles between squamous cell carcinoma biopsies and adjacent histo-pathological normal epithelium tissues. Twenty-eight cDNA clones were isolated that were demonstrated to be consistently over-expressed in squamous cell cervical cancer biopsies of FIGO stages 1B to 3B. Most importantly, it was observed that, in addition to their over-expression in cancer lesions, some of these genes are upregulated in the presumably histo-pathological normal adjacent tissues. Of particular interest is clone G30CC that has been identified to be the gene that encodes S12 ribosomal protein. When employed for RNA–RNA in situ hybridization experiments, expression of G30CC could be detected in the immature basal epithelial cells of histo-pathological normal tissues collected from cervical cancer patients of early FIGO stages. In comparison, the expression of G30CC was not detected in cervical tissues collected from patients admitted for surgery of non-malignant conditions. These results allow the distinct possibility of employing the ribosomal protein S12 gene as an early molecular diagnostic identifier for the screening of human cervical cancer and a potential target employed for cancer gene therapy trials