Bundle recommendation aims to recommend a bundle of items for a user to consume as a whole. Existing solutions integrate user-item interaction modeling into bundle recommendation by sharing model parameters or learning in a multi-task manner, which cannot explicitly model the affiliation between items and bundles, and fail to explore the decision-making when a user chooses bundles. In this work, we propose a graph neural network model named BGCN (short for extit{ extBF{B}undle extBF{G}raph extBF{C}onvolutional extBF{N}etwork}) for bundle recommendation. BGCN unifies user-item interaction, user-bundle interaction and bundle-item affiliation into a heterogeneous graph. With item nodes as the bridge, graph convolutional propagation between user and bundle nodes makes the learned representations capture the item level semantics. Through training based on hard-negative sampler, the user's fine-grained preferences for similar bundles are further distinguished. Empirical results on two real-world datasets demonstrate the strong performance gains of BGCN, which outperforms the state-of-the-art baselines by 10.77\% to 23.18\%.

We show that combining a prediction model (based on neural networks), with a new method of test pooling (better than the original Dorfman method, and better than double-pooling) called 'Grid', we can reduce the number of Covid-19 tests by 73%.

Rescue vessels are the main actors in maritime safety and rescue operations. At the same time, aerial drones bring a significant advantage into this scenario. This paper presents the research directions of the AutoSOS project, where we work in the development of an autonomous multi-robot search and rescue assistance platform capable of sensor fusion and object detection in embedded devices using novel lightweight AI models. The platform is meant to perform reconnaissance missions for initial assessment of the environment using novel adaptive deep learning algorithms that efficiently use the available sensors and computational resources on drones and rescue vessel. When drones find potential objects, they will send their sensor data to the vessel to verity the findings with increased accuracy. The actual rescue and treatment operation are left as the responsibility of the rescue personnel. The drones will autonomously reconfigure their spatial distribution to enable multi-hop communication, when a direct connection between a drone transmitting information and the vessel is unavailable.

The paper develops a robotic manipulation system to treat the pressing needs for handling a large number of test tubes in clinical examination and replace or reduce human labor. It presents the technical details of the system, which separates and arranges test tubes in racks with the help of 3D vision and artificial intelligence (AI) reasoning/planning. The developed system only requires a person to put a rack with mixed and non-arranged tubes in front of a robot. The robot autonomously performs recognition, reasoning, planning, manipulation, etc., and returns a rack with separated and arranged tubes. The system is simple-to-use, and there are no requests for expert knowledge in robotics. We expect such a system to play an important role in helping managing public health and hope similar systems could be extended to other clinical manipulation like handling mixers and pipettes in the future.

Network security has become an area of significant importance more than ever as highlighted by the eye-opening numbers of data breaches, attacks on critical infrastructure, and malware/ransomware/cryptojacker attacks that are reported almost every day. Increasingly, we are relying on networked infrastructure and with the advent of IoT, billions of devices will be connected to the internet, providing attackers with more opportunities to exploit. Traditional machine learning methods have been frequently used in the context of network security. However, such methods are more based on statistical features extracted from sources such as binaries, emails, and packet flows.

On the other hand, recent years witnessed a phenomenal growth in computer vision mainly driven by the advances in the area of convolutional neural networks. At a glance, it is not trivial to see how computer vision methods are related to network security. Nonetheless, there is a significant amount of work that highlighted how methods from computer vision can be applied in network security for detecting attacks or building security solutions. In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection. Next, we review a set of such commercial products for which public information is available and explore how computer vision methods are effectively used in those products. Finally, we discuss existing research gaps and future research directions, especially focusing on how network security research community and the industry can leverage the exponential growth of computer vision methods to build much secure networked systems.

This paper investigates the convergence of the randomized Kaczmarz algorithm for the problem of phase retrieval of complex-valued objects. While this algorithm has been studied for the real-valued case}, its generalization to the complex-valued case is nontrivial and has been left as a conjecture. This paper establishes the connection between the convergence of the algorithm and the convexity of an objective function. Based on the connection, it demonstrates that when the sensing vectors are sampled uniformly from a unit sphere and the number of sensing vectors $m$ satisfies $m>O(nlog n)$ as $n, m ightarrowinfty$, then this algorithm with a good initialization achieves linear convergence to the solution with high probability.

Over the years, performance evaluation has become essential in computer vision, enabling tangible progress in many sub-fields. While talking-head video generation has become an emerging research topic, existing evaluations on this topic present many limitations. For example, most approaches use human subjects (e.g., via Amazon MTurk) to evaluate their research claims directly. This subjective evaluation is cumbersome, unreproducible, and may impend the evolution of new research. In this work, we present a carefully-designed benchmark for evaluating talking-head video generation with standardized dataset pre-processing strategies. As for evaluation, we either propose new metrics or select the most appropriate ones to evaluate results in what we consider as desired properties for a good talking-head video, namely, identity preserving, lip synchronization, high video quality, and natural-spontaneous motion. By conducting a thoughtful analysis across several state-of-the-art talking-head generation approaches, we aim to uncover the merits and drawbacks of current methods and point out promising directions for future work. All the evaluation code is available at: https://github.com/lelechen63/talking-head-generation-survey.

We analyze the computational complexity of motion planning through local "input/output" gadgets with separate entrances and exits, and a subset of allowed traversals from entrances to exits, each of which changes the state of the gadget and thereby the allowed traversals. We study such gadgets in the 0-, 1-, and 2-player settings, in particular extending past motion-planning-through-gadgets work to 0-player games for the first time, by considering "branchless" connections between gadgets that route every gadget's exit to a unique gadget's entrance. Our complexity results include containment in L, NL, P, NP, and PSPACE; as well as hardness for NL, P, NP, and PSPACE. We apply these results to show PSPACE-completeness for certain mechanics in Factorio, [the Sequence], and a restricted version of Trainyard, improving prior results. This work strengthens prior results on switching graphs and reachability switching games.

Most optimization-based community detection approaches formulate the problem in a single or bi-objective framework. In this paper, we propose two variants of a three-objective formulation using a customized non-dominated sorting genetic algorithm III (NSGA-III) to find community structures in a network. In the first variant, named NSGA-III-KRM, we considered Kernel k means, Ratio cut, and Modularity, as the three objectives, whereas the second variant, named NSGA-III-CCM, considers Community score, Community fitness and Modularity, as three objective functions. Experiments are conducted on four benchmark network datasets. Comparison with state-of-the-art approaches along with decomposition-based multi-objective evolutionary algorithm variants (MOEA/D-KRM and MOEA/D-CCM) indicates that the proposed variants yield comparable or better results. This is particularly significant because the addition of the third objective does not worsen the results of the other two objectives. We also propose a simple method to rank the Pareto solutions so obtained by proposing a new measure, namely the ratio of the hyper-volume and inverted generational distance (IGD). The higher the ratio, the better is the Pareto set. This strategy is particularly useful in the absence of empirical attainment function in the multi-objective framework, where the number of objectives is more than two.

Several nonlinear model reduction techniques are compared for the three cases of the non-parallel version of the Kuramoto-Sivashinsky equation, the transient regime of flow past a cylinder at $Re=100$ and fully developed flow past a cylinder at the same Reynolds number. The linear terms of the governing equations are reduced by Galerkin projection onto a POD basis of the flow state, while the reduced nonlinear convection terms are obtained either by a Galerkin projection onto the same state basis, by a Galerkin projection onto a POD basis representing the nonlinearities or by applying the Discrete Empirical Interpolation Method (DEIM) to a POD basis of the nonlinearities. The quality of the reduced order models is assessed as to their stability, accuracy and robustness, and appropriate quantitative measures are introduced and compared. In particular, the properties of the reduced linear terms are compared to those of the full-scale terms, and the structure of the nonlinear quadratic terms is analyzed as to the conservation of kinetic energy. It is shown that all three reduction techniques provide excellent and similar results for the cases of the Kuramoto-Sivashinsky equation and the limit-cycle cylinder flow. For the case of the transient regime of flow past a cylinder, only the pure Galerkin techniques are successful, while the DEIM technique produces reduced-order models that diverge in finite time.

We propose an augmented Lagrangian preconditioner for a three-field stress-velocity-pressure discretization of stationary non-Newtonian incompressible flow with an implicit constitutive relation of power-law type. The discretization employed makes use of the divergence-free Scott-Vogelius pair for the velocity and pressure. The preconditioner builds on the work [P. E. Farrell, L. Mitchell, and F. Wechsung, SIAM J. Sci. Comput., 41 (2019), pp. A3073-A3096], where a Reynolds-robust preconditioner for the three-dimensional Newtonian system was introduced. The preconditioner employs a specialized multigrid method for the stress-velocity block that involves a divergence-capturing space decomposition and a custom prolongation operator. The solver exhibits excellent robustness with respect to the parameters arising in the constitutive relation, allowing for the simulation of a wide range of materials.

In this technical report we give an elementary introduction to Quantum Computing for non-physicists. In this introduction we describe in detail some of the foundational Quantum Algorithms including: the Deutsch-Jozsa Algorithm, Shor's Algorithm, Grocer Search, and Quantum Counting Algorithm and briefly the Harrow-Lloyd Algorithm. Additionally we give an introduction to Solomonoff Induction, a theoretically optimal method for prediction. We then attempt to use Quantum computing to find better algorithms for the approximation of Solomonoff Induction. This is done by using techniques from other Quantum computing algorithms to achieve a speedup in computing the speed prior, which is an approximation of Solomonoff's prior, a key part of Solomonoff Induction. The major limiting factors are that the probabilities being computed are often so small that without a sufficient (often large) amount of trials, the error may be larger than the result. If a substantial speedup in the computation of an approximation of Solomonoff Induction can be achieved through quantum computing, then this can be applied to the field of intelligent agents as a key part of an approximation of the agent AIXI.

Coordinating the operation of combined heat and power plants (CHPs) and heat pumps (HPs) at the interface between heat and power systems is essential to achieve a cost-effective and efficient operation of the overall energy system. Indeed, in the current sequential market practice, the heat market has no insight into the impacts of heat dispatch on the electricity market. While preserving this sequential practice, this paper introduces an electricity-aware heat unit commitment model. Coordination is achieved through bid validity constraints, which embed the techno-economic linkage between heat and electricity outputs and costs of CHPs and HPs. This approach constitutes a novel market mechanism for the coordination of heat and power systems, defining heat bids conditionally on electricity market prices. The resulting model is a trilevel optimization problem, which we recast as a mixed-integer linear program using a lexicographic function. We use a realistic case study based on the Danish power and heat system, and show that the proposed model yields a 4.5% reduction in total operating cost of heat and power systems compared to a traditional decoupled unit commitment model, while reducing the financial losses of each CHP and HP due to invalid bids by up-to 20.3 million euros.

Making changes to a program to optimize its performance is an unscalable task that relies entirely upon human intuition and experience. In addition, companies operating at large scale are at a stage where no single individual understands the code controlling its systems, and for this reason, making changes to improve performance can become intractably difficult. In this paper, a learning system is introduced that provides AI assistance for finding recommended changes to a program. Specifically, it is shown how the evaluative feedback, delayed-reward performance programming domain can be effectively formulated via the Monte Carlo tree search (MCTS) framework. It is then shown that established methods from computational games for using learning to expedite tree-search computation can be adapted to speed up computing recommended program alterations. Estimates of expected utility from MCTS trees built for previous problems are used to learn a sampling policy that remains effective across new problems, thus demonstrating transferability of optimization knowledge. This formulation is applied to the Apache Spark distributed computing environment, and a preliminary result is observed that the time required to build a search tree for finding recommendations is reduced by up to a factor of 10x.

Homomorphic encryption (HE) allows direct computations on encrypted data. Despite numerous research efforts, the practicality of HE schemes remains to be demonstrated. In this regard, the enormous size of ciphertexts involved in HE computations degrades computational efficiency. Near-memory Processing (NMP) and Computing-in-memory (CiM) - paradigms where computation is done within the memory boundaries - represent architectural solutions for reducing latency and energy associated with data transfers in data-intensive applications such as HE. This paper introduces CiM-HE, a Computing-in-memory (CiM) architecture that can support operations for the B/FV scheme, a somewhat homomorphic encryption scheme for general computation. CiM-HE hardware consists of customized peripherals such as sense amplifiers, adders, bit-shifters, and sequencing circuits. The peripherals are based on CMOS technology, and could support computations with memory cells of different technologies. Circuit-level simulations are used to evaluate our CiM-HE framework assuming a 6T-SRAM memory. We compare our CiM-HE implementation against (i) two optimized CPU HE implementations, and (ii) an FPGA-based HE accelerator implementation. When compared to a CPU solution, CiM-HE obtains speedups between 4.6x and 9.1x, and energy savings between 266.4x and 532.8x for homomorphic multiplications (the most expensive HE operation). Also, a set of four end-to-end tasks, i.e., mean, variance, linear regression, and inference are up to 1.1x, 7.7x, 7.1x, and 7.5x faster (and 301.1x, 404.6x, 532.3x, and 532.8x more energy efficient). Compared to CPU-based HE in a previous work, CiM-HE obtain 14.3x speed-up and >2600x energy savings. Finally, our design offers 2.2x speed-up with 88.1x energy savings compared to a state-of-the-art FPGA-based accelerator.

Did you know that breaking the Americans with Disabilities Act (ADA) can result in a six-figure fine? For every violation, companies can receive a $150,000 fine — and if you have a WordPress site, you could be liable. While WordPress aims to ensure website accessibility, it cannot guarantee it since every site owner customizes the […]

The post Is My WordPress Site ADA Compliant? 3+ Plugins for Finding Out! appeared first on WebFX Blog.

What do Amazon, Hershey’s, and The Wall Street Journal have in common? They’ve all gotten named in lawsuits related to website accessibility and the Americans with Disabilities Act (ADA). They aren’t alone, either. In 2018, more than 2000 website accessibility lawsuits (a 177% increase from 2017) got filed, emphasizing the increased importance and focus on […]

The post Is My Website ADA Compliant? How to Check (and Update) Your Site appeared first on WebFX Blog.

As a small or midsize business (SMB), your company website is often the first touchpoint for potential clients — and you want it to make a great first impression. The secret to hitting home with your audience is to have a sophisticated and lively website design that’s aesthetically pleasing and provides great user experience (UX). […]

The post 20 Company Website Designs to Inspire Your Small Business appeared first on WebFX Blog.

Are you looking to build a new site for your business? If so, company website design templates can help you create the website you want. With so many templates available, how do you determine which one is best for your business? On this page, we’ll provide you with the top six company website design templates, […]

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By Timothy Schoechle Courtesy of Solar Today A Colorado community is developing a community-based clean energy economy. Boulder, Colorado, sits at the foot of the Rocky Mountains a half-hour drive northwest of Denver. It is a city, but still small … Continue reading →

Welcome back! In the first part of this series, we looked at a very "low-level" way to interact with a relational database by sending it raw SQL strings and retrieving the results. We created a very simple Express application that we can use as an example and deployed it on Heroku with a Postgres database.

In this part, we're going to examine a few libraries which build on top of that foundation, adding layers of abstraction that let you read and manipulate database data in a more "JavaScript-like" way.

Node.js 14 is available now. We wanted to get more context and details about the state of Node, and why developers should care about Node.js 14. We talked with Bethany Griggs, Node.js Technical Steering Committee member and Open-source Engineer at IBM, to find out more. 

Bethany has been a Node Core Collaborator for over two years. She contributes to the open-source Node.js runtime and is a member of the Node.js Release Working Group where she is involved with auditing commits for the long-term support (LTS) release lines and the creation of releases. 

There’s a cliche that always follows a big tragedy — something we say after natural disasters, economic collapses, school shootings, acts of terrorisms.…

[IMAGE-1] Noah Baumbach's Marriage Story begins as its central marriage is coming to an end. Our two protagonists are fiercely independent, articulate, opinionated creative types: Charlie (Adam Driver) is the director of an avant-garde theater troupe in New York City; Nicole (Scarlett Johansson) is an actress and one of his primary collaborators.…

Ever since Ghostbusters, the go-to tactic for supernatural comedy is to show characters experiencing remarkable, seemingly impossible things and yet reacting with the kind of mild bemusement you get watching someone successfully parallel park.…

As COVID-19 fears grew, public officials and sports execs contemplated health risks — and debated a PR message — but let 33,000 fans into a Seattle Sounders soccer match, emails show. By Ken Armstrong, ProPublica, and David Gutman and Lewis Kamb, The Seattle Times On March 6, at 2:43 p.m., the health officer for Public Health — Seattle & King County, the hardest-hit region in the first state to be slammed by COVID-19, sent an email to a half-dozen colleagues, saying, “I want to cancel large group gatherings now.”…

When he was putting together the latest compilation CD for his label CorpoRAT Records, Kris Martin had intended to hand out promotional discs at Boise's Treefort Music Festival, where several artists from the Spokane label were scheduled to perform, and then officially release the album in April for Record Store Day.…

The Spokane Comedy Club might be quiet right now, but there are still laughs to be had on Zoom, and not just from watching your co-workers try to navigate the online meeting platform. Saturday night, and again next Saturday, the comedy club is hosting Comedians Doing Comedy: A Virtual Comedy Show.…

Noting that safety of its fans and vendors is always a priority, the folks behind Lilac City Comicon announced Friday that this year's event would move from its original June dates to Oct. 11-12 at the Spokane Convention Center. Anyone who bought tickets for the original June 6-7 comicon will be able to use those tickets at the rescheduled event, or refunds will be available from the businesses where tickets were bought, once those businesses are able to reopen.…

In times of trouble, escaping into the art that calms you is key to keeping your sanity.…

Drink Local For the majority of regional craft breweries, most revenue comes from two avenues: direct-to-consumer sales out of a tasting room and selling beer to local bars and restaurants.…

At this point in our locked-down lives, it’s entirely possible many of us have exhausted our Netflix queue, completed every puzzle in our houses and perfected our sourdough loaves. OK, probably not.…

It's all right there in the name: "Community Pint."…

One great thing about seeing "oldies" acts on tour is the vivid reminder you get that groups in the old days really knew how to serve their fans. Take the Commodores, for example, a group with a 52-year-history that swung by Northern Quest Resort & Casino Thursday night.…

Every serious pinball player remembers their first machine.…

As soon as state Rep. Marcus Riccelli returned home from Olympia, he jumpstarted a community-wide effort to feed Spokane constituents deeply affected by the COVID-19 crisis.…

Disclosed herein is a method for producing an alcohol and an amine from an amide under an atmosphere of hydrogen with the use of, as a catalyst, a ruthenium complex that is easily prepared, easy to handle, and relatively cheaply obtained. Specifically, the method is a method for producing an alcohol and/or an amine from an amide compound under an atmosphere of hydrogen with the use of as a catalyst, a ruthenium carbonyl complex represented by the following general formula (1): RuXY(CO)(L) (1) wherein X and Y may be the same or different from each other and each represents an anionic ligand and L represents a tridentate aminodiphosphine ligand containing two phosphino groups and a —NH— group.

A method for preparation of an aryl poly(oxalkyl) quaternary ammonium compound is provided, said method comprising steps of: 1) reacting a phenol with a dihalopolyalkylene ether under the action of a phase transfer catalyst, to obtain an arylpoly(oxalkyl) halide; 2) reacting said arylpoly(oxalkyl) halide with an amination reagent under the action of a phase transfer catalyst, to obtain an arylpoly(oxalkyl) amine; 3) reacting said arylpoly(oxalkyl) amine with an alkylation reagent, to obtain an aryl poly(oxalkyl) quaternary ammonium compound; wherein R1 is H or a C1 to C16 alkyl group, located in the ortho, meta or para position; n is an integer of 2 to 6; R2 is H or a C1 to C16 alkyl group; R3 is H or a C1 to C16 alkyl group; R4 is a C1 to C16 alkyl group; X1 is Br or Cl; X is Cl, Br, or I. The preparation method according to the present invention requires low temperature and low pressure, the reaction time is short, and an overall yield can reach 75%. The operation is simple, the cost is low, and the product can be separated easily and have a purity of pharmaceutical grade, thereby facilitating the large-scale production.

A library of macrocyclic compounds of the formula (I) where part (A) is a bivalent radical, a —(CH2)y— bivalent radical or a covalent bond;where part (B) is a bivalent radical, a —(CH2)z— bivalent radical, or a covalent bond;where part (C) is a bivalent radical, a —(CH2)t— bivalent radical, or a covalent bond; andwhere part (T) is a —Y-L-Z— radical wherein Y is CH2 or CO, Z is NH or O and L is a bivalent radical. These compounds are useful for carrying out screening assays or as intermediates for the synthesis of other compounds of pharmaceutical interest. A process for the preparation of these compounds in a combinatorial manner, is also disclosed.

The invention relates to protein binding interacting/binding compounds and methods of identifying and using them. The invention further relates to pharmaceutical compositions and methods for treating 5-HT2C and/or RSK disorders, including diseases and disorders mediated by GPCRs and/or RSKs.

A compound represented by Formula 1 below and an organic light-emitting device including an organic layer containing the compound of Formula 1: wherein R1 to R4, X and Y, a and b, and m and n are defined as in the specification.

A branched hetero polyfunctional polyoxyalkylene compound represented by the following formula (1): wherein Z represents a hydroxyl group-removed residue of pentaerythritol or dipentaerythritol, OA1 and OA2 represent an oxyalkylene group having 2 to 4 carbon atoms, L1, L2 and L3 represent an alkylene group or an alkylene group that contains an ester bond, a urethane bond, an amide bond, an ether bond, a carbonate bond, a secondary amino group or a urea bond, X and Y are different from each other and represent a functional group capable of a chemical reaction; m and n are an average number of moles of the oxyalkylene group added, m represents 5 to 1,000, n represents 0 to 1,000, and p, q and r represent 0 or 1; and s1 is an integer of 2 or more and s1+s2=4 or 6.

The present invention provides a pest control agent, acaricide or fungicide that contains, as the active ingredient thereof, at least one type of compound selected from the aryloxyurea compounds represented by formula (V) (wherein R1 to R5 each independently represents an alkyl group or the like, X is a halogen atom or the like, n is an integer of 0 to 5, and Z is an oxygen atom or sulfur atom) or salts thereof.

Biocidal compositions and their use in aqueous media, such as metalworking fluids, the compositions comprising a biocidal agent; and a non-biocidal primary amino alcohol compound of the formula (I); wherein R1, R2, R3, R4, and R5 are as defined herein.

Disclosed is a process for preparing an aryloxyalkylene amine compound via an aminoethylation reaction comprising: a) reacting an aromatic hydroxyl compound in the presence of a basic catalyst with a 2-oxazolidinone compound of the formula II to form an intermediate reaction product; wherein R3 is selected from the group consisting of hydrogen or lower alkyl having 1 to 6 carbon atoms, R4 is selected from the group consisting of hydrogen, straight or branched chain alkyl having from one to six carbon atoms, phenyl, alkaryl, or arylalkyl; and b) reacting the intermediate product of step a) with a polyalkylene polyamine.

An improved process for making ethoxylated amine compounds such as ethanolamines. The improvement comprises the addition of an acid to the amine compound prior to the addition of ethylene oxide to a reactor wherein the ethoxylated amine compound is prepared. The improvement reduces the concentration of undesirable glycol ether and/or vinyl ether ethoxylate byproducts which may contribute to undesirable properties, such as color and foaming, of the ethoxylated amine compounds.

Embodiments of the invention provide systems and methods for analyzing sequential data. The sequential data can comprise a sequence of data points arranged in a particular order and thus representing a sequence. A number of such sequences can be analyzed, for example, to identify patterns or commonalities within the sequences or portions of sequences represented by the data. According to one embodiment, a method of identifying patterns in sequences of data points can comprise reading a set of sequential data. The sequential data can comprises a plurality of sequences and each of the plurality of sequences can represent an ordered sequence of tokens. A dotplot representing matches between each sequence of the plurality sequences can be generated. One or more patterns within the sequential data can then be identified based on the dotplot.

A “Wearable Electromyography-Based Controller” includes a plurality of Electromyography (EMG) sensors and provides a wired or wireless human-computer interface (HCI) for interacting with computing systems and attached devices via electrical signals generated by specific movement of the user's muscles. Following initial automated self-calibration and positional localization processes, measurement and interpretation of muscle generated electrical signals is accomplished by sampling signals from the EMG sensors of the Wearable Electromyography-Based Controller. In operation, the Wearable Electromyography-Based Controller is donned by the user and placed into a coarsely approximate position on the surface of the user's skin. Automated cues or instructions are then provided to the user for fine-tuning placement of the Wearable Electromyography-Based Controller. Examples of Wearable Electromyography-Based Controllers include articles of manufacture, such as an armband, wristwatch, or article of clothing having a plurality of integrated EMG-based sensor nodes and associated electronics.

Various embodiments provide lossless compression of an enumeration space for genetic founder lines. In one embodiment, an input comprising a set of genetic founder lines and a maximum number of generations G is obtained. A set of genetic crossing templates of a height h is generated. A determination is made if at least a first genetic crossing template in the set of genetic crossing templates is redundant with respect to a second genetic crossing template in the set of genetic crossing templates. Based on the at least first genetic crossing template being redundant is redundant with respect to the second genetic crossing template, the at least first genetic crossing template is removed from the set of genetic crossing templates. This process of removing the at least first genetic crossing template from the set of genetic crossing templates the redundant creates an updated set of genetic crossing templates.

Systems and methods are disclosed that may be implemented for network management system (NMS) configuration management support for network devices using a learning and natural language processing application to capture the usage and behavior of the Command Line Interface (CLI) of a network device with the aid of a CLI knowledge model, which in one example may be ontology-based.