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NN

A neural network (NN), or artificial neural network (ANN), is a machine learning paradigm comprising interconnected computational units called neurons, arranged in layers that process input data through weighted connections and activation functions to produce outputs, fundamentally modeling statistical patterns rather than biological fidelity to brain processes.^[1]^[2] Inspired by simplified abstractions of neuronal signaling, NNs adjust parameters via backpropagation to minimize prediction errors on training datasets, enabling applications in classification, regression, and generation tasks.^[3] Originating from mid-20th-century efforts to formalize neuron-like computation—such as McCulloch-Pitts models in 1943 and the perceptron algorithm in 1958—NNs faced "AI winters" due to limitations in scalability and theory, but revived in the 2010s through computational advances like GPUs and large datasets, culminating in deep learning architectures that dominate modern AI.^[4]^[3] Key achievements include surpassing human performance in image recognition (e.g., convolutional NNs on ImageNet benchmarks), natural language processing via transformers, and strategic games like Go through reinforcement learning integrations, driving empirical breakthroughs in fields from computer vision to protein folding predictions.^[5]^[6] Despite these successes, NNs exhibit defining limitations and controversies, including opacity as "black boxes" where internal decision mechanisms evade causal interpretation, vulnerability to adversarial perturbations that exploit superficial correlations, and amplification of biases embedded in training data—often reflecting systemic skews in academic and media-sourced datasets toward particular ideological framings.^[7]^[8] Empirical evidence underscores their reliability issues, such as overfitting to noise and poor extrapolation beyond distribution shifts, necessitating hybrid approaches with explicit causal modeling for robust real-world deployment.^[9]^[7]

Science and technology

Neural networks

Neural networks, also known as artificial neural networks (ANNs), are computational models composed of interconnected nodes organized in layers that process input data to produce outputs, mimicking the signal-processing capabilities of biological neurons. Each node, or artificial neuron, receives inputs, applies weights to them, adds a bias, and passes the result through a nonlinear activation function to determine its output, enabling the network to approximate complex functions. These models excel in tasks requiring pattern recognition when trained on large datasets but rely fundamentally on mathematical optimization rather than true biological replication.^[10]^[11] The foundational mathematical model of a neuron was proposed by Warren McCulloch and Walter Pitts in 1943, representing neurons as binary threshold units capable of logical operations like AND and OR, demonstrating that networks of such units could compute any computable function. This abstract framework laid the groundwork for viewing brain-like computation as Turing-complete. In 1958, Frank Rosenblatt introduced the perceptron, a single-layer network trainable via a rule that adjusts weights based on classification errors, capable of linearly separating data but limited to simple patterns.^[12]^[13] A major advance occurred in 1986 when David Rumelhart, Geoffrey Hinton, and Ronald Williams popularized backpropagation, an efficient algorithm for computing gradients in multilayer networks using the chain rule, allowing error signals to propagate backward from output to input layers to update weights via gradient descent. This enabled training of deeper architectures, though early implementations faced challenges like vanishing gradients. The field surged in 2012 with AlexNet, a convolutional neural network by Alex Krizhevsky, Ilya Sutskever, and Hinton, which achieved a top-5 error rate of 15.3% on the ImageNet dataset—10 percentage points better than competitors—leveraging GPUs for training on 1.2 million images and introducing ReLU activations to mitigate gradient issues.^[14]^[15] Basic architectures consist of an input layer receiving data, one or more hidden layers performing transformations, and an output layer producing predictions; feedforward networks process data unidirectionally without loops, suitable for static inputs. Activation functions such as sigmoid (output 0-1), tanh (output -1 to 1), or ReLU (output max(0, x)) introduce nonlinearity, preventing the network from collapsing to a linear model regardless of depth. Convolutional neural networks (CNNs) incorporate filters to detect local patterns like edges in images, reducing parameters via shared weights and pooling. Recurrent neural networks (RNNs) include loops to maintain state, handling sequential data like time series, though variants like LSTMs address long-term dependencies.^[11]^[16] Training typically involves supervised learning, where the network minimizes a loss function (e.g., mean squared error for regression or cross-entropy for classification) by iteratively adjusting millions of parameters through stochastic gradient descent; unsupervised variants like autoencoders learn representations by reconstructing inputs. Modern networks can have billions of parameters, trained on datasets exceeding terabytes, requiring specialized hardware.^[14] Applications span computer vision (e.g., CNNs classifying medical images with accuracies over 95% in controlled studies), natural language processing (RNNs or transformers generating text), and predictive modeling (e.g., stock forecasting with historical data). In autonomous vehicles, neural networks process sensor inputs for object detection in real-time.^[2] Despite efficacy, neural networks demand vast labeled data—often millions of examples—for generalization, exhibit poor interpretability as "black boxes" where internal decisions resist causal explanation, and risk overfitting without regularization like dropout. They are computationally expensive, with training AlexNet-scale models requiring weeks on multi-GPU clusters, and susceptible to adversarial attacks where minor input perturbations cause errors. Empirical evidence shows performance degrades on out-of-distribution data, underscoring reliance on inductive biases rather than robust causal understanding.^[17]

Nearest-neighbor methods

Nearest-neighbor methods constitute a class of non-parametric algorithms in statistics and machine learning that perform prediction by identifying the most similar instances from a reference dataset, typically using a distance metric such as Euclidean or Manhattan distance.^[18] These methods are instance-based, storing the entire training data and deferring computation until query time, which distinguishes them from parametric models that learn a fixed function during training.^[19] The core idea relies on the assumption that nearby points in the feature space share similar outcomes, enabling tasks like classification—via majority voting among neighbors—and regression—via averaging their target values.^[18] The k-nearest neighbors (k-NN) algorithm exemplifies these methods, selecting the k closest training samples to a query point for prediction.^[19] Introduced in 1951 by Evelyn Fix and Joseph L. Hodges, Jr., in their work on nonparametric discriminatory analysis, the approach demonstrated consistency properties under certain conditions, proving that as sample size grows, predictions converge to the true conditional expectation.^[20] Thomas M. Cover extended and generalized the framework in 1967, providing theoretical bounds on error rates and emphasizing its utility in pattern recognition.^[21] Parameter k controls model flexibility: low values capture local patterns but amplify noise sensitivity, while higher values promote smoother decisions at the risk of underfitting.^[19] Implementation involves preprocessing for efficiency, such as feature scaling to normalize distances, and selection of appropriate metrics; for instance, Minkowski distance generalizes Euclidean (p=2) and Manhattan (p=1) cases.^[18] Despite their intuitiveness and lack of distributional assumptions—making them robust to non-linear relationships—nearest-neighbor methods face scalability challenges, requiring O(n time per query for n training points, which becomes prohibitive for large datasets.^[19] They are also vulnerable to the curse of dimensionality, where in high-dimensional spaces (e.g., beyond 10–20 features), points become equidistant, eroding meaningful similarity.^[19] Irrelevant features and outliers further degrade performance by skewing neighbor selection.^[21] To mitigate these limitations, variants employ data structures like kd-trees for exact queries in low dimensions or approximate methods such as locality-sensitive hashing (LSH) for high-dimensional, large-scale applications, trading minor accuracy loss for logarithmic speedups.^[22] Applications span recommendation systems, where user-item similarities drive suggestions; bioinformatics, for classifying protein structures; and signal processing, including 5G network optimization via neighbor-based classification of channel states.^[23] Recent advancements integrate k-NN with deep learning for uncertainty estimation in neural predictions, leveraging neighbor distances to quantify confidence.^[24] Empirical evaluations consistently show k-NN's effectiveness on small-to-medium datasets with clear cluster structures, though it underperforms parametric alternatives like support vector machines on noisy or sparse data without preprocessing.^[21]

Businesses and organizations

NN, Inc.

NN, Inc. is a publicly traded diversified industrial manufacturer (NASDAQ: NNBR) specializing in high-precision metal, plastic, and rubber components for mission-critical applications in industries including automotive, aerospace, electronics, and medical devices.^[25] ^[26] The company combines advanced engineering, close-tolerance manufacturing, and materials science to produce items such as bearing components, industrial plastics, and precision metal assemblies via processes like machining, stamping, and precious metal plating.^[27] Its operational philosophy emphasizes quality systems, customer co-design, continuous improvement, and employee empowerment, drawing from W. Edwards Deming's 14 Points for management.^[25] Founded in 1980 as Nonnenmann, Inc. by Richard Ennen in Knoxville, Tennessee, the company initially produced balls and rollers for the oil and gas sector but shifted focus amid the early 1980s recession toward broader precision manufacturing.^[28] ^[29] Over decades, NN expanded through acquisitions and organic growth, establishing a global footprint; by the 2010s, it had integrated operations like Autocam for precision metal components.^[30] Headquartered today in Charlotte, North Carolina, NN operates 27 facilities employing about 3,000 people across North America, South America, Europe, and Asia.^[31] ^[25] NN aggregates its business into three reportable segments: Metal Bearing Components (high-precision rollers, balls, and retainers); Plastic and Rubber Components (custom-molded products for seals and vibration dampening); and Autocam Precision Components (complex machined metal parts for automotive and fluid power markets).^[32] These segments serve end markets demanding reliability under extreme conditions, with emphasis on in-house tooling and quality controls to meet ISO and industry standards.^[25] In recent financial performance, NN reported second-quarter 2025 net sales of $107.9 million, a 2.4% decline on a pro forma basis from the prior year, with adjusted gross margin at 16.9%.^[33] First-quarter 2025 adjusted operating income stood at $2.0 million, reflecting operational efficiencies amid market headwinds.^[34] The company maintains a focus on cost discipline and strategic growth, with full-year 2025 revenue projections around $439 million.^[35]

NN Group

NN Group N.V. is a Dutch multinational financial services company focused on insurance, pensions, and asset management, headquartered in The Hague.^[36] It serves approximately 19 million customers across Europe through retirement services, life and non-life insurance, banking products, and investments, employing around 16,000 people.^[37] The company is listed on Euronext Amsterdam as an AEX index component under the ticker NN.^[38] The entity's roots trace to 1845 with the founding of the Netherlands Fire Insurance Company in Zutphen, which evolved through mergers and expansions into a broader insurer.^[39] By the late 20th century, its operations were integrated into ING Group following the 1991 merger of Nationale-Nederlanden with Postbank and subsequent banking consolidations.^[40] As ING Insurance Topholding N.V., it managed ING's global insurance and investment activities until regulatory mandates post-2008 financial crisis required divestitures to repay Dutch state aid.^[41] NN Group separated from ING in 2011 operationally, becoming the legal successor to ING Insurance in March 2014 and renaming accordingly.^[40] ^[42] It launched its initial public offering on June 27, 2014, and began trading as a standalone entity on July 2, 2014, with ING retaining an initial stake that was fully divested by April 2016 through share sales totaling 45.7 million shares at €30.15 each.^[43] ^[40] Under CEO David Knibbe since 2020, the firm has emphasized growth in core European markets like the Netherlands, Belgium, and Hungary while streamlining non-core assets.^[38] Operations center on pensions and life insurance in the Netherlands via Nationale-Nederlanden, with international presence in eight countries including banking in Greece and property-casualty insurance in Romania.^[36] NN Investment Partners, its asset management arm, oversees €200 billion in assets under management as of 2023, focusing on sustainable and fixed-income strategies.^[37] The company reported operating profit of €2.1 billion in 2023, driven by higher investment returns and premium growth amid rising interest rates.^[44] It maintains a dividend policy targeting 40-50% of operating profit, with a 2025 payout of €1.38 per share announced on August 8, 2025.^[45] As of September 30, 2025, NN Group had 269 million issued shares, with 5.47 million treasury shares, resulting in 263.53 million outstanding.^[44]

Languages

Nynorsk

Nynorsk is one of two official written standards for the Norwegian language, the other being Bokmål, and translates to "New Norwegian."^[46] It emerged as an effort to standardize a form closer to rural Norwegian dialects, contrasting with the urban, Danish-influenced written tradition prevalent at the time.^[47] Unlike Bokmål, which evolved from Dano-Norwegian, Nynorsk prioritizes phonetic consistency with spoken variants from western and central regions, incorporating elements like distinct verb conjugations and vocabulary drawn from dialects.^[46] The standard was constructed by philologist Ivar Aasen (1813–1896), who conducted extensive fieldwork across Norway in the 1830s and 1840s to document rural speech patterns.^[48] Initially termed Landsmål, Aasen's system sought to foster national linguistic independence following Norway's separation from Denmark in 1814, rejecting the dominance of Riksmål.^[49] Landsmål gained official parallel status to Riksmål on May 12, 1885, through parliamentary decree, enabling its use in schools and administration.^[46] It was rebranded Nynorsk in 1929 to reflect its modern construction while broadening its base to include more dialectal influences.^[46] Usage of Nynorsk remains concentrated in western Norway, particularly around Bergen and in rural valleys, where it aligns more closely with local spoken forms.^[50] Nationwide, it accounts for 10–15% of written Norwegian, with Bokmål dominating at 85–90%.^[51] In 2023, Statistics Norway reported that 11% of primary and lower secondary pupils were instructed primarily in Nynorsk, compared to 87% in Bokmål.^[50] User estimates range from 550,000 to 640,000 individuals, representing about 10–12% of Norway's 5.5 million population, though its share has declined from over 30% in the early 20th century due to urbanization and educational preferences.^[52]^[53] Norwegian law mandates equal status for both standards in public documents, media, and education, with municipalities able to designate Nynorsk as the primary form.^[50] Despite this, practical adoption often favors Bokmål in higher education and urban professions, prompting debates on preservation efforts.^[53]

Places

Nanortalik Heliport

Nanortalik Heliport (IATA: JNN, ICAO: BGNN) is a heliport situated in the eastern part of Nanortalik, the southernmost town in Greenland, within the Kujalleq municipality.^[54] Located at coordinates 60°08′24″N 045°13′54″W and an elevation of 17 feet above sea level, it provides essential air access to the remote coastal settlement amid challenging Arctic terrain and weather.^[54] The facility supports helicopter operations only, with no fixed-wing runway, reflecting the region's reliance on rotorcraft for connectivity in fjord-dotted landscapes.^[55] Operated primarily by Air Greenland, the national flag carrier, the heliport functions as a year-round hub for scheduled passenger and cargo services, including government contract flights to nearby villages in the Nanortalik area.^[56] ^[57] Air Greenland deploys helicopters such as the Eurocopter EC155 and AS350/355 for routes linking to Narsarsuaq Airport and other regional points, with flight durations around 40 minutes to key destinations.^[58] ^[59] These operations are critical for supplying isolated communities, though they face risks from strong winds, as evidenced by a 2024 incident where an EC155 helicopter with 10 passengers made an emergency landing 100 meters from the heliport during takeoff.^[60] The heliport's role underscores Greenland's dependence on air links for logistics in areas lacking road or sea alternatives year-round, facilitating transport of passengers, freight, and emergency services despite frequent adverse weather.^[56] No major expansions or historical establishment dates are publicly detailed in aviation records, but it integrates into Air Greenland's broader network serving South Greenland's sparse population.^[57]

Norfolk (postcode NN)

Norfolk Street is a residential street in Northampton, Northamptonshire, England, falling within the NN2 postcode district of the Northampton postcode area (NN).^[61] The street lies in the St. George's ward of West Northamptonshire unitary authority, characterized by terraced and semi-detached housing typical of early 20th-century urban development in the area.^[62] It features approximately 40-50 properties, primarily two-storey homes with gardens or yards, situated near local amenities including schools, shops, and transport links to Northampton town centre.^[63] Property values on Norfolk Street reflect modest affordability in Northampton's housing market; for instance, a terraced house sold for £180,000 in December 2023, while a semi-detached property fetched £238,000 in January 2023.^[64] The area has a population density aligned with Northampton's urban profile, with residents engaged in diverse occupations including professional services, retail, and manufacturing, per local demographic data.^[63] No major historical landmarks or events are associated with the street, which serves as a typical suburban thoroughfare in a post-industrial town with a population of around 225,000 as of the 2021 census.

Other uses

No name

Nomen nescio, abbreviated as NN or N.N., is a Latin phrase translating to "I do not know the name," employed to designate an anonymous or unnamed individual.^[65] The term derives from nomen ("name") and nescio ("I do not know").^[66] It signifies a person whose identity remains unknown, often appearing in contexts requiring notation of incomplete information.^[67] This abbreviation finds frequent use in genealogy and historical documentation to mark entries for unidentified persons, such as in family trees or archival records where names are absent.^[68] In chess notation, NN represents an anonymous opponent, historically denoting casual or unrecorded games against unknown players.^[69] Beyond these, it appears in legal, medical, or artistic contexts to anonymize subjects without implying deliberate pseudonymity.^[70] The convention underscores a factual gap in knowledge rather than intentional obscurity, distinguishing it from placeholders like "John Doe."^[71]

Night, night

"Night, night" is a colloquial English expression used as an affectionate variant of "good night," particularly in bedtime contexts with children.^[72]^[73] The phrase conveys warmth and familiarity, often employed by parents or caregivers to signal the end of the day and encourage sleep.^[74] Its repetitive structure mimics childlike speech patterns, fostering a soothing, rhythmic quality suitable for lullabies or tucking-in rituals.^[75] Etymologically, "night-night" emerged as nursery talk for "good-night" around 1896, with the variant "nighty-night" documented as early as 1876.^[76] This reduplication reflects a broader linguistic tendency in English to soften farewells through repetition, similar to "bye-bye" or "ta-ta," enhancing endearment without altering core meaning.^[76] Historical usage appears in children's literature and family correspondence from the late 19th century onward, emphasizing its roots in domestic, informal settings rather than formal discourse.^[75] In contemporary informal communication, particularly online and via text messaging, "night night" is frequently abbreviated as "NN."^[77] This shorthand signifies signing off for the evening, akin to "GN" for "good night," and is common in casual digital exchanges among friends or family. While primarily affectionate and non-sexual, its application can extend to adults in close relationships, though overuse by mature individuals may evoke perceptions of immaturity or undue familiarity.^[78] The abbreviation's prevalence underscores evolving language adaptation to brevity in electronic media, without evidence of broader cultural or institutional endorsement beyond everyday parlance.^[77]

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Apr 25, 2024 · Air Greenland GL382 H155 helicopter with 10 passengers on board had to land 100 metres from Nanortalik heliport due to strong wind conditions during takeoff.Missing: operations | Show results with:operations
[61]
https://www.doogal.co.uk/UKPostcodes?Search=NN2%206HR
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NN2 6HR maps, stats, and open data - GetTheData.com
NN2 6HR lies on Norfolk Street in Northampton. NN2 6HR is located in the St. George electoral ward, within the unitary authority of West Northamptonshire and ...
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Area Information for Norfolk Street, Northampton, NN2 6HR
View information about Norfolk Street, Northampton, NN26HR postcode, including population, age, housing, relationships, broadband, religion and employment.
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House Prices in Norfolk Street, Northampton, West ... - Rightmove
Recent sales on Norfolk Street include a terraced house for £180,000 (Dec 2023), a semi-detached for £238,000 (Jan 2023), and a terraced for £152,500 (Jun 2020 ...
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Nomen nescio - Registry of Pseudonyms
Nomen nescio, abbreviated to NN, is used to signify an anonymous or unnamed person. From the Latin 'nomen' [name] and 'nescio' [I do not know], it literally ...
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nomen nescio (N.N.) - Latin is Simple Online Dictionary
nomen nescio (N.N.) Phrase. Meaning: I do not know the name. Comment. Thus, the name or person in question is unknown. Word-for-word analysis: Nomen · nomen ...Missing: acronym | Show results with:acronym
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nn - "nomen nescio (not named - Acronyms and Slang
What is the meaning of nn abbreviation? The meaning of nn abbreviation is "nomen nescio (not named; name unknown". What does nn mean?
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What does nn in front of a deceased persons name in an obituary ...
Jan 28, 2007 · 'NN' before a deceased person's name in an obituary typically stands for "Nomen Nescio," a Latin term meaning "name unknown." It is used when ...
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Who is N.N? - Chess Forums - Page 2
Dec 5, 2007 · Although NN is sometimes believed to stand for "no name", that is a misconception: it is in fact an abbreviation of the Latin nomen nescio which ...
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Nomen Nescio - Olympedia
Nomen Nescio stands for an unnamed person, the used abbreviation is N.N. It comes from Latin vocabularies nomen, “name”, and nescio, “I do not know” (from ...
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N.N. - Daniel Wirtberg
N.N. is an abbreviation of the latin Nomen Nescio, used to signify an anonymous or unnamed person. Mixed media (visuals, audio, installation, objects).<|control11|><|separator|>
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NIGHT-NIGHT | definition in the Cambridge English Dictionary
used as another way of saying good night, usually by or to children: "Night-night, Daddy." "Good night, honey. Sleep well!" "Night-night, Jacob. Sweet dreams."
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https://www.dictionary.com/browse/night-night
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Exploring the Meaning and Use of the Phrase 'Night Night'
Jan 19, 2024 · "Night Night" is generally more affectionate and conveys warmth and closeness. It's often used among friends, family members, or romantic partners.When can we say nighty night?Which is correct "I work nights" or "I work at night"More results from ell.stackexchange.com
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NIGHT-NIGHT definition in American English - Collins Dictionary
Bedtimes are up for negotiation, with 16 per cent of under-11s choosing what time they say night-night. ... meanings and uses in English. Read more.
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Night-night - Etymology, Origin & Meaning
night-night. nursery talk, "good-night," 1896; form nighty-night is attested from 1876.
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"NN" Meaning, Origin and Examples - 7ESL
Nov 9, 2024 · NN stands for “night night,” a friendly and affectionate expression often used by or to children as a way of saying “good night.”
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Why do grown adults say “nite nite”? - Quora
Sep 6, 2022 · Night night, or nighty night, is an affectionate way to say good night. It's often used to tuck a child into bed and may be followed by 'sleep ...What does, 'night night', mean? - QuoraWhat is meant by the phrase 'Nighty Nite' and at what level would it ...More results from www.quora.com