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Time-series phylogenetic analysis

Time-series phylogenetic analysis reconstructs the evolutionary history of organisms or genetic variants using sequences sampled at known time points. By incorporating sampling dates directly into the model, it estimates divergence times, substitution rates, and ancestral relationships on an absolute timescale — making

2 källor2000

Time-series proteomics analysis

Time-series proteomics analysis quantifies protein abundance across two or more ordered time points to reveal how the proteome changes dynamically in response to stimuli, developmental stages, or disease progression. By combining mass spectrometry-based protein quantification with statistical models designed for tempor

2 källor2000

Time-series RNA-seq differential expression

Time-series RNA-seq differential expression analysis identifies genes whose expression levels change systematically across ordered time points — such as during development, disease progression, or response to a treatment. Unlike two-condition DE analysis, it explicitly models the temporal structure of the data, capturi

2 källor2006

Time-series single-cell RNA-seq analysis

Time-series single-cell RNA-seq analysis captures gene expression across multiple time points at single-cell resolution to reveal how cell populations emerge, transition, and diverge during dynamic biological processes such as development, differentiation, or disease progression. By combining pseudotime ordering, RNA v

2 källor2014

Time-series variant calling

Time-series variant calling is a bioinformatics pipeline that identifies and tracks genomic variants — typically somatic mutations — across multiple sequencing samples collected from the same subject at different time points. It is most widely applied in cancer genomics to reconstruct tumour evolution, monitor minimal

2 källor2009

TimeGPT

TimeGPT is a time series foundation model introduced by Garza and White in 2023 that unifies forecasting, anomaly detection, and classification in a single pre-trained model. Inspired by large language models, TimeGPT is pre-trained on diverse time series and transfers well to downstream tasks with minimal fine-tuning.

text mining

Timeline Extraction

Timeline extraction is a natural-language-processing task that identifies events mentioned in text, anchors each event to a temporal expression, and arranges them into a chronologically ordered timeline. Formalised through the TempEval shared tasks (Verhagen et al., 2010), it enables automatic reconstruction of histori

TimeMixer

TimeMixer is a decomposition-based, attention-free time-series forecasting architecture introduced by Wang et al. at ICLR 2024. The central idea is to disentangle seasonal and trend components across multiple temporal scales constructed by average pooling, then mix information across those scales using lightweight MLP

1 källa2024

TimesFM

TimesFM is a pre-trained foundation model for univariate time-series forecasting introduced by Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou from Google in 2024. The model adopts a decoder-only transformer architecture, similar in spirit to large language models, and is trained on a large corpus of real-world

1 källa2024

TimesNet

TimesNet is a general-purpose time-series model introduced by Wu et al. at ICLR 2023. Its central idea is that univariate or multivariate time series can be reinterpreted as collections of two-dimensional temporal maps by reshaping the 1D signal according to its dominant periodicities, detected via Fast Fourier Transfo

TiRex

TiRex is a pretrained zero-shot time-series forecasting model introduced in 2025 by the NX-AI xLSTM team (Auer et al.). Built on the Extended Long Short-Term Memory (xLSTM) architecture, TiRex is trained at scale on diverse time-series corpora and can forecast unseen datasets without any fine-tuning. Its core idea is t

1 källa2025

cryptography

TLS Protocol Analysis

The Transport Layer Security (TLS) protocol is the cryptographic standard that secures web communication and email transmission. Evolved from SSL (Secure Sockets Layer), TLS provides authentication, encryption, and integrity protection for data in transit. The protocol combines public-key cryptography (RSA, ECDH) for k

3 källor1994

telecommunications

Token Bucket

Token bucket is a simple and elegant algorithm for traffic shaping and rate limiting. A virtual bucket accumulates tokens at a fixed rate (the committed information rate). Incoming packets consume tokens (one token per byte); packets are transmitted only if sufficient tokens are available. If the bucket is full, excess

2 källor1986

Topic Modeling

Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary —

2 källor1999

text mining

Topic Modeling (LDA)

Latent Dirichlet Allocation (LDA) is a generative probabilistic model introduced by Blei, Ng and Jordan (2003) that extracts the hidden topic distributions underlying a collection of documents. It treats each document as a mixture of latent topics and each topic as a distribution over words, turning an unlabelled corpu

1 källa2003

topology

Topological Deep Learning

Topological Deep Learning (TDL) is a framework that extends deep learning beyond graphs to higher-order topological domains such as simplicial complexes, cell complexes, and hypergraphs. Formalized by Hajij et al. (2023), TDL provides a unified mathematical language for defining message-passing schemes across cells of

reliability engineering

Topology Optimization

Topology Optimization is a computational method for distributing material optimally within a design space to maximize structural performance (strength, stiffness) while minimizing weight or cost. The Solid Isotropic Material with Penalization (SIMP) method, developed by Bendsoe and Kikuchi (1988), iteratively refines a

4 källor1988

civil engineering

Traffic Flow (LWR Model)

The Lighthill-Whitham-Richards (LWR) model is a macroscopic traffic flow model that treats traffic as a compressible fluid, applying conservation of vehicles and a flow-density relationship. Introduced independently by Lighthill and Whitham (1955) and Richards (1956), the model predicts traffic wave propagation, conges

3 källor1955

machine learning

Transfer Learning

Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learnin

Transfer learning GAN

Transfer Learning GAN initialises a Generative Adversarial Network — or both its generator and discriminator — from weights pretrained on a large source dataset, then fine-tunes the network on a smaller target dataset. This approach allows high-quality generative modelling even when target-domain data are scarce, by re

2 källor2014

Transfer learning variational autoencoder

Transfer Learning with a Variational Autoencoder (TL-VAE) reuses an encoder and/or decoder pre-trained on a large source dataset and adapts it to a smaller target domain. By inheriting a rich probabilistic latent space rather than starting from random weights, TL-VAE dramatically reduces the amount of target-domain dat

2 källor2014

Transfer Learning with BERT-based Classification

Transfer Learning with BERT-based Classification adapts a large transformer language model, pre-trained on massive text corpora, to a target classification task by fine-tuning its weights on labeled examples. The pre-trained representations encode rich syntactic and semantic knowledge, enabling high accuracy even when

2 källor2019

Transfer Learning with Convolutional Neural Network

Transfer Learning with CNN reuses a convolutional neural network that has already been trained on a large dataset — most commonly ImageNet — and adapts its learned feature detectors to a new, often smaller target dataset. This lets researchers achieve strong image-recognition performance without the massive compute and

Transfer Learning with Diffusion Model

Transfer Learning with Diffusion Models adapts a large pre-trained diffusion model — such as Stable Diffusion or DALL-E 2 — to a new target domain or task by continuing training on a smaller domain-specific dataset. Rather than learning the full generative process from scratch, practitioners leverage knowledge already

2 källor2020

Transfer Learning with Graph Neural Network

Transfer Learning with Graph Neural Networks (GNNs) adapts a GNN pre-trained on a large source graph dataset to a smaller, often label-scarce target graph task. By reusing learned node and edge representations, this approach achieves strong predictive performance where collecting sufficient labeled graph data is expens

Transfer Learning with Image Classification

Transfer Learning with Image Classification reuses a deep neural network backbone — typically a CNN or Vision Transformer — pretrained on a large dataset such as ImageNet, and adapts it to classify images in a new target domain. By inheriting general visual features from the source task, the approach achieves high accu

Transfer Learning with Instance Segmentation

Transfer learning with instance segmentation reuses a backbone convolutional network pretrained on a large image corpus (typically ImageNet or COCO) as the feature extractor for an instance segmentation model such as Mask R-CNN, then fine-tunes the full pipeline on a smaller target dataset. This approach delivers state

2 källor2017

Transfer Learning with LDA Topic Model

Transfer Learning with LDA Topic Model applies knowledge from a well-studied source domain to guide Latent Dirichlet Allocation inference on a data-scarce target domain. By injecting source-derived topic priors into the Dirichlet hyperparameters, the method produces coherent, domain-relevant topics even when target-dom

2 källor2003

Transfer Learning with LSTM

Transfer Learning with LSTM is a technique in which a Long Short-Term Memory network is first pre-trained on a large source corpus or task, and then its learned weights are transferred and fine-tuned on a smaller target task. This approach, popularized by ULMFiT (Howard & Ruder, 2018), allows LSTM-based models to reach

2 källor2018

Transfer Learning with Named Entity Recognition

Transfer Learning with Named Entity Recognition (NER) adapts a large pretrained language model — such as BERT, RoBERTa, or a domain-specific encoder — to the task of identifying and classifying named entities (persons, locations, organizations, dates, etc.) in text. By reusing rich linguistic representations learned fr

Transfer Learning with NMF Topic Model

Transfer Learning with NMF Topic Model applies knowledge from a labeled or data-rich source domain to improve Non-Negative Matrix Factorization topic discovery in a low-resource target domain. By initializing or constraining the NMF basis matrix with source-domain topics, the model discovers coherent target topics even

Transfer Learning with Object Detection

Transfer learning with object detection starts from a deep neural network pretrained on a large image dataset — typically ImageNet for the backbone or COCO for the full detector — and adapts it to detect objects in a new domain. By reusing learned visual representations, it achieves strong detection accuracy with far f

Transfer Learning with Recurrent Neural Network

Transfer Learning with Recurrent Neural Network (TL-RNN) reuses weights learned by an RNN on a large source task — such as language modelling or sequence prediction — and adapts them to a new, often smaller target task. This strategy lets practitioners obtain strong sequence-modelling performance without the need for m

Transfer Learning with Reinforcement Learning

Transfer Learning with Reinforcement Learning (Transfer RL) is a training paradigm in which knowledge acquired by an agent in one or more source tasks — encoded as policy weights, value functions, or learned representations — is reused to accelerate or improve learning in a related but different target task. It directl

2 källor2009

Transfer Learning with Sentence Embeddings

Transfer Learning with Sentence Embeddings takes a large pre-trained encoder — such as Sentence-BERT or the Universal Sentence Encoder — that already encodes general language knowledge into fixed-length vectors, and adapts it to a new task or domain with little additional labelled data. The pre-trained representations

2 källor2017

Transfer Learning with Text Summarization

Transfer Learning with Text Summarization adapts a large language model pre-trained on broad text corpora — such as T5, BART, or PEGASUS — to the task of condensing documents into shorter, coherent summaries. By reusing learned linguistic knowledge and fine-tuning on domain-specific pairs of source documents and refere

2 källor2019

Transfer Learning with Topic Modeling

Transfer Learning with Topic Modeling adapts topic structures discovered on a large or well-labeled source corpus to a related but distinct target domain where labeled data or large corpora are scarce. By reusing source-domain topic priors or pretrained embeddings as initialization, the approach produces richer, more c

Transfer Learning with Word2Vec

Transfer Learning with Word2Vec uses word embeddings pre-trained on large text corpora via the Skip-gram or CBOW objectives introduced by Mikolov et al. (2013) to initialize the embedding layer of a downstream NLP model. This approach transfers distributional semantic knowledge to tasks where labeled data is scarce, co

2 källor2013

Transformer

The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attent

1 källa2017

astronomy

Transit Photometry

Transit photometry is an observational technique that detects exoplanets by monitoring the periodic dips in stellar brightness as planets cross in front of their host stars. First systematized by William Borucki in 1984, this method became the most successful exoplanet detection technique, with the Kepler space telesco

3 källor1984

genetics

Transmission Disequilibrium Test

The Transmission Disequilibrium Test (TDT) is a family-based statistical method for testing genetic association with disease or traits while inherently controlling for population stratification. Developed by Spielman and Ewens in 1993, the TDT examines whether an allele is preferentially transmitted from heterozygous p

3 källor1993

electrical engineering

Transmission-Line Matrix Method

The Transmission-Line Matrix (TLM) method is a direct discretization of Maxwell equations using an equivalent transmission line network. Introduced by Johns and Beurle in 1971, TLM models electromagnetic fields as voltage and current waves propagating on coupled transmission lines. The method is intuitive, numerically

3 källor1971

TSMixer

TSMixer is a multivariate time-series forecasting model introduced by Si-An Chen and colleagues at Google in 2023. It challenges the prevailing dominance of Transformer-based architectures by demonstrating that a simple stack of interleaved MLP layers — alternating between mixing along the time axis and mixing across f

oceanography

Tsunami Shallow Water Model

The tsunami shallow water model is a numerical method based on shallow water equations that simulates tsunami wave propagation from earthquake source regions to coastal areas. Developed by Kenji Satake and colleagues in the 1990s, this approach provides rapid estimates of tsunami arrival times, wave amplitudes, and inu

2 källor1995

telecommunications

Turbo Code

Turbo codes, introduced by Berrou, Glavieux, and Thitimajshima in 1993, are a landmark in channel coding history. They achieve performance within 0.5 dB of the Shannon limit—the theoretical boundary for reliable communication—a feat previously thought impossible with practical complexity. Turbo codes use concatenated c

2 källor1993

network analysis

Two-mode Network Analysis

Two-mode network analysis examines networks built from two distinct types of nodes — such as actors and events, authors and papers, or companies and board members — connected only across types. By analysing this bipartite structure directly or projecting it onto one-mode networks, researchers uncover affiliation patter

2 källor1974

astronomy

Type Ia SN Light Curve Fitting

Type Ia supernova light curve fitting is a technique for measuring cosmic distances by observing the brightness evolution of thermonuclear explosions in binary star systems. Developed systematically by Mark Phillips in 1993, this method revealed that SNe Ia can be standardized to provide precise distance measurements,

3 källor1993