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

Locally advanced adenocarcinoma and adenosquamous carcinomas of the cervix compared to squamous cell carcinomas of the cervix in gynecologic oncology group trials of cisplatin-based chemoradiation

OBJECTIVE: Conflicting results have been reported for adeno- and adenosquamous carcinomas of the cervix with respect to their response to therapy and prognosis. The current study sought to evaluate impact of adeno- and adenosquamous histology in the randomized trials of primary cisplatin-based chemoradiation for locally advanced cervical cancer. METHODS: Patients with adeno- and adenosquamous cervical carcinomas were retrospectively studied and compared to squamous cell carcinomas in GOG trials of chemoradiation. RESULTS: Among 1671 enrolled in clinical trials of chemoradiation, 182 adeno- and adenosquamous carcinomas were identified (10.9%). A higher percentage of adeno- and adenosquamous carcinomas were stage IB2 (27.5% versus 20.0%) and fewer had stage IIIB (21.4% versus 28.6%). The mean tumor size was larger for squamous than adeno- and adenosquamous. Adeno- and adenosquamous carcinomas were more often poorly differentiated (46.2% versus 26.8%). When treated with radiation therapy alone, the 70 patients with adeno- and adenosquamous carcinoma of the cervix showed a statistically poorer overall survival (p=0.0499) compared to the 647 patients with squamous cell carcinoma of the cervix. However, when treated with radiation therapy with concurrent cisplatin-based chemotherapy, the 112 patients with adeno- and adenosquamous carcinomas had a similar overall survival (p=0.459) compared the 842 patients with squamous cell carcinoma. Adverse effects to treatment were similar across histologies. CONCLUSION: Adeno- and adenosquamous carcinomas of the cervix are associated with worse overall survival when treated with radiation alone but with similar progression-free and overall survival compared to squamous cell carcinomas of the cervix when treated with cisplatin based chemoradiation

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

Is age a prognostic biomarker for survival among women with locally advanced cervical cancer treated with chemoradiation? An NRG Oncology/Gynecologic Oncology Group ancillary data analysis

Objective To determine the effect of age on completion of and toxicities following treatment of local regionally advanced cervical cancer (LACC) on Gynecologic Oncology Group (GOG) Phase I–III trials. Methods An ancillary data analysis of GOG protocols 113, 120, 165, 219 data was performed. Wilcoxon, Pearson, and Kruskal-Wallis tests were used for univariate and multivariate analysis. Log rank tests were used to compare survival lengths. Results One-thousand-three-hundred-nineteen women were included; 60.7% were Caucasian, 15% were age 60–70 years and an additional 5% were >70; 87% had squamous histology, 55% had stage IIB disease and 34% had IIIB disease. Performance status declined with age (p = 0.006). Histology and tumor stage did not significantly differ., Number of cycles of chemotherapy received, radiation treatment time, nor dose modifications varied with age. Notably, radiation protocol deviations and failure to complete brachytherapy (BT) did increase with age (p = 0.022 and p 50 for all-cause mortality (HR 1.02; 95% CI, 1.01–1.04) was found, but no association between age and disease specific mortality was found. Conclusion This represents a large analysis of patients treated for LACC with chemo/radiation, approximately 20% of whom were >60 years of age. Older patients, had higher rates of incomplete brachytherapy which is not explained by collected toxicity data. Age did not adversely impact completion of chemotherapy and radiation or toxicities

Photosynthetic growth despite a broken Q-cycle

Central in respiration or photosynthesis, the cytochrome bc1 and b6f complexes are regarded as functionally similar quinol oxidoreductases. They both catalyse a redox loop, the Q-cycle, which couples electron and proton transfer. This loop involves a bifurcated electron transfer step considered as being mechanistically mandatory, making the Q-cycle indispensable for growth. Attempts to falsify this paradigm in the case of cytochrome bc1 have failed. The rapid proteolytic degradation of b6f complexes bearing mutations aimed at hindering the Q-cycle has precluded so far the experimental assessment of this model in the photosynthetic chain. Here we combine mutations in Chlamydomonas that inactivate the redox loop but preserve high accumulation levels of b6f complexes. The oxidoreductase activity of these crippled complexes is sufficient to sustain photosynthetic growth, which demonstrates that the Q-cycle is dispensable for oxygenic photosynthesis

A Map of Dielectric Heterogeneity in a Membrane Protein: the Hetero-Oligomeric Cytochrome b 6 f Complex

The cytochrome b6f complex, a member of the cytochrome bc family that mediates energy transduction in photosynthetic and respiratory membranes, is a hetero-oligomeric complex that utilizes two pairs of b-hemes in a symmetric dimer to accomplish trans-membrane electron transfer, quinone oxidation–reduction, and generation of a proton electrochemical potential. Analysis of electron storage in this pathway, utilizing simultaneous measurement of heme reduction, and of circular dichroism (CD) spectra, to assay heme–heme interactions, implies a heterogeneous distribution of the dielectric constants that mediate electrostatic interactions between the four hemes in the complex. Crystallographic information was used to determine the identity of the interacting hemes. The Soret band CD signal is dominated by excitonic interaction between the intramonomer b-hemes, bn and bp, on the electrochemically negative and positive sides of the complex. Kinetic data imply that the most probable pathway for transfer of the two electrons needed for quinone oxidation–reduction utilizes this intramonomer heme pair, contradicting the expectation based on heme redox potentials and thermodynamics, that the two higher potential hemes bn on different monomers would be preferentially reduced. Energetically preferred intramonomer electron storage of electrons on the intramonomer b-hemes is found to require heterogeneity of interheme dielectric constants. Relative to the medium separating the two higher potential hemes bn, a relatively large dielectric constant must exist between the intramonomer b-hemes, allowing a smaller electrostatic repulsion between the reduced hemes. Heterogeneity of dielectric constants is an additional structure–function parameter of membrane protein complexes