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

Boom‐bust dynamics in biological invasions: towards an improved application of the concept

Boom‐bust dynamics – the rise of a population to outbreak levels, followed by a dramatic decline – have been associated with biological invasions and offered as a reason not to manage troublesome invaders. However, boom‐bust dynamics rarely have been critically defined, analyzed, or interpreted. Here, we define boom‐bust dynamics and provide specific suggestions for improving the application of the boom‐bust concept. Boom‐bust dynamics can arise from many causes, some closely associated with invasions, but others occurring across a wide range of ecological settings, especially when environmental conditions are changing rapidly. As a result, it is difficult to infer cause or predict future trajectories merely by observing the dynamic. We use tests with simulated data to show that a common metric for detecting and describing boom‐bust dynamics, decline from an observed peak to a subsequent trough, tends to severely overestimate the frequency and severity of busts, and should be used cautiously if at all. We review and test other metrics that are better suited to describe boom‐bust dynamics. Understanding the frequency and importance of boom‐bust dynamics requires empirical studies of large, representative, long‐term data sets that use clear definitions of boom‐bust, appropriate analytical methods, and careful interpretations

Convection in nanofluids with a particle-concentration-dependent thermal conductivity

Thermal convection in nanofluids is investigated by means of a continuum model for binary-fluid mixtures, with a thermal conductivity depending on the local concentration of colloidal particles. The applied temperature difference between the upper and the lower boundary leads via the Soret effect to a variation of the colloid concentration and therefore to a spatially varying heat conductivity. An increasing difference between the heat conductivity of the mixture near the colder and the warmer boundary results in a shift of the onset of convection to higher values of the Rayleigh number for positive values of the separation ratio psi>0 and to smaller values in the range psi<0. Beyond some critical difference of the thermal conductivity between the two boundaries, we find an oscillatory onset of convection not only for psi<0, but also within a finite range of psi>0. This range can be extended by increasing the difference in the thermal conductivity and it is bounded by two codimension-2 bifurcations.Comment: 13 pages, 11 figures; submitted to Physical Review

Mechanism Design in Social Networks

This paper studies an auction design problem for a seller to sell a commodity in a social network, where each individual (the seller or a buyer) can only communicate with her neighbors. The challenge to the seller is to design a mechanism to incentivize the buyers, who are aware of the auction, to further propagate the information to their neighbors so that more buyers will participate in the auction and hence, the seller will be able to make a higher revenue. We propose a novel auction mechanism, called information diffusion mechanism (IDM), which incentivizes the buyers to not only truthfully report their valuations on the commodity to the seller, but also further propagate the auction information to all their neighbors. In comparison, the direct extension of the well-known Vickrey-Clarke-Groves (VCG) mechanism in social networks can also incentivize the information diffusion, but it will decrease the seller's revenue or even lead to a deficit sometimes. The formalization of the problem has not yet been addressed in the literature of mechanism design and our solution is very significant in the presence of large-scale online social networks.Comment: In The Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, US, 04-09 Feb 201

Hidden Markov Model Variants and their Application

Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way. This approach is based on mutual information, probabilistic measures, hidden Markov models, and other purely statistical inputs. This approach also provides a uniquely common ground for comparative prokaryotic genomics. The approach is an on-going effort by its nature, as a multi-pass learning process, where each round is more informed than the last, and thereby allows a shift to the more powerful methods available for supervised learning at each iteration. It is envisaged that this "bootstrap" learning process will also be useful as a knowledge discovery tool. For such an ab initio prokaryotic gene-finder to work, however, it needs a mechanism to identify critical motif structure, such as those around the start of coding or start of transcription (and then, hopefully more). For eukaryotes, even with better start-of-coding identification, parsing of eukaryotic coding regions by the HMM is still limited by the HMM's single gene assumption, as evidenced by the poor performance in alternatively spliced regions. To address these complications an approach is described to expand the states in a eukaryotic gene-predictor HMM, to operate with two layers of DNA parsing. This extension from the single layer gene prediction parse is indicated after preliminary analysis of the C. elegans alt-splice statistics. State profiles have made use of a novel hash-interpolating MM (hIMM) method. A new implementation for an HMM-with-Duration is also described, with far-reaching application to gene-structure identification and analysis of channel current blockade data

Genetic and environmental influences on stomates of big bluestem (Andropogon gerardii)

Big bluestem (Andropogon gerardii) is a dominant C4 prairie grass that has wide distribution and several genetically distinct ecotypes. Many of the ecotypic adaptations are related to water availability in the native environment. Stomates facilitate photosynthetic gas exchange and regulate water loss from the plant. As such, stomatal size and density represent possible adaptations to conserve water. We hypothesized drought-tolerant ecotypes of big bluestem would have fewer or smaller stomates compared to more mesic ecotypes. Five ecotypes of big bluestem were planted in four common gardens from western Kansas to southern Illinois, USA to determine genetic and environmental influences on stomates. Leaves of all ecotypes of A. gerardii were largely hypostomatous and genetics was a greater influence than environment for stomatal size and density. The drought-tolerant Sand bluestem had larger stomates on abaxial surfaces of leaves, but a lower density compared to most other ecotypes. The most mesic Illinois ecotype and the Kaw cultivar had the greatest density of stomates on abaxial surfaces of leaves. Sand Bluestem had a greater density of stomates on adaxial surfaces of leaves compared to all other ecotypes. Gas exchange measures followed patterns of stomate distribution, where abaxial CO2 uptake rates were greater than adaxial CO2 uptake rates, although differences between leaf surfaces was more pronounced in stomatal density than in CO2 uptake. There were minor differences in size and density of stomates among sites that corresponded with precipitation, although these differences were minor, illustrating the genetic underpinnings of stomates in big bluestem. There is a genetic predisposition for drought-tolerant ecotypes to have fewer stomates, illustrating an evolutionary adaptation to drought tolerance in an important prairie species

Design, Physicochemical Characterization, and Optimization of Organic Solution Advanced Spray-Dried Inhalable Dipalmitoylphosphatidylcholine (DPPC) and Dipalmitoylphosphatidylethanolamine Poly(Ethylene Glycol) (DPPE-PEG) Microparticles and Nanoparticles for Targeted Respiratory Nanomedicine Delivery as Dry Powder Inhalation Aerosols

Novel advanced spray-dried and co-spray-dried inhalable lung surfactant-mimic phospholipid and poly(ethylene glycol) (PEG)ylated lipopolymers as microparticulate/nanoparticulate dry powders of biodegradable biocompatible lipopolymers were rationally formulated via an organic solution advanced spray-drying process in closed mode using various phospholipid formulations and rationally chosen spray-drying pump rates. Ratios of dipalmitoylphosphatidylcholine (DPPC) and dipalmitoylphosphatidylethanolamine PEG (DPPE-PEG) with varying PEG lengths were mixed in a dilute methanol solution. Scanning electron microscopy images showed the smooth, spherical particle morphology of the inhalable particles. The size of the particles was statistically analyzed using the scanning electron micrographs and SigmaScan® software and were determined to be 600 nm to 1.2 μm in diameter, which is optimal for deep-lung alveolar penetration. Differential scanning calorimetry (DSC) and powder X-ray diffraction (PXRD) were performed to analyze solid-state transitions and long-range molecular order, respectively, and allowed for the confirmation of the presence of phospholipid bilayers in the solid state of the particles. The residual water content of the particles was very low, as quantified analytically via Karl Fischer titration. The composition of the particles was confirmed using attenuated total-reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy and confocal Raman microscopy (CRM), and chemical imaging confirmed the chemical homogeneity of the particles. The dry powder aerosol dispersion properties were evaluated using the Next Generation Impactor™ (NGI™) coupled with the HandiHaler® dry powder inhaler device, where the mass median aerodynamic diameter from 2.6 to 4.3 μm with excellent aerosol dispersion performance, as exemplified by high values of emitted dose, fine particle fraction, and respirable fraction. Overall, it was determined that the pump rates defined in the spray-drying process had a significant effect on the solid-state particle properties and that a higher pump rate produced the most optimal system. Advanced dry powder inhalers of inhalable lipopolymers for targeted dry powder inhalation delivery were successfully achieved

Stripe-hexagon competition in forced pattern forming systems with broken up-down symmetry

We investigate the response of two-dimensional pattern forming systems with a broken up-down symmetry, such as chemical reactions, to spatially resonant forcing and propose related experiments. The nonlinear behavior immediately above threshold is analyzed in terms of amplitude equations suggested for a $1:2$ and $1:1$ ratio between the wavelength of the spatial periodic forcing and the wavelength of the pattern of the respective system. Both sets of coupled amplitude equations are derived by a perturbative method from the Lengyel-Epstein model describing a chemical reaction showing Turing patterns, which gives us the opportunity to relate the generic response scenarios to a specific pattern forming system. The nonlinear competition between stripe patterns and distorted hexagons is explored and their range of existence, stability and coexistence is determined. Whereas without modulations hexagonal patterns are always preferred near onset of pattern formation, single mode solutions (stripes) are favored close to threshold for modulation amplitudes beyond some critical value. Hence distorted hexagons only occur in a finite range of the control parameter and their interval of existence shrinks to zero with increasing values of the modulation amplitude. Furthermore depending on the modulation amplitude the transition between stripes and distorted hexagons is either sub- or supercritical.Comment: 10 pages, 12 figures, submitted to Physical Review

NetServ Framework Design and Implementation 1.0

Eyeball ISPs today are under-utilizing an important asset: edge routers. We present NetServ, a programmable node architecture aimed at turning edge routers into distributed service hosting platforms. This allows ISPs to allocate router resources to content publishers and application service pro\-vi\-ders motivated to deploy content and services at the network edge. This model provides important benefits over currently available solutions like CDN. Content and services can be brought closer to end users by dynamically installing and removing custom modules as needed throughout the network. Unlike previous programmable router proposals which focused on customizing features of a router, NetServ focuses on deploying content and services. All our design decisions reflect this change in focus. We set three main design goals: a wide-area deployment, a multi-user execution environment, and a clear economic benefit. We built a prototype using Linux, NSIS signaling, and the Java OSGi framework. We also implemented four prototype applications: ActiveCDN provides publisher-specific content distribution and processing; KeepAlive Responder and Media Relay reduce the infrastructure needs of telephony providers; and Overload Control makes it possible to deploy more flexible algorithms to handle excessive traffic