Nonlinear dimensionality reduction

Other_algorithms Diffusion maps leverages the relationship between heat diffusion and a random walk (Markov Chain); an analogy is drawn between the diffusion operator on a manifold and a Markov transition matrix operating on functions defined on the graph whose nodes were sampled from the manifold.  In particular, let a data set be represented by X = [ x 1 , x 2 , ��� , x n ] ��� �� ��� R D {\displaystyle \mathbf {X} =[x_{1},x_{2},\ldots ,x_{n}]\in \Omega \subset \mathbf {R^{D}} } .  The underlying assumption of diffusion map is that the high-dimensional data lies on a low-dimensional manifold of dimension d {\displaystyle \mathbf {d} } .  Let X represent the data set and �� {\displaystyle \mu } represent the distribution of the data points on X.  Further, define a kernel which represents some notion of affinity of the points in X.  The kernel k {\displaystyle {\mathit {k}}} has the following properties k is symmetric k is positivity preserving Thus one can think of the individual data points as the nodes of a graph and the kernel k as defining some sort of affinity on that graph.  The graph is symmetric by construction since the kernel is symmetric.  It is easy to see here that from the tuple (X,k) one can construct a reversible Markov Chain.  This technique is common to a variety of fields and is known as the graph Laplacian.  For example, the graph K = (X,E) can be constructed using a Gaussian kernel.  In the above equation, x i ��� x j {\displaystyle x_{i}\sim x_{j}} denotes that x i {\displaystyle x_{i}} is a nearest neighbor of x j {\displaystyle x_{j}} .  Properly, Geodesic distance should be used to actually measure distances on the manifold.  Since the exact structure of the manifold is not available, for the nearest neighbors the geodesic distance is approximated by euclidean distance.  The choice �� {\displaystyle \sigma } modulates our notion of proximity in the sense that if ��� x i ��� x j ��� 2 ��� �� {\displaystyle \|x_{i}-x_{j}\|_{2}\gg \sigma } then K i j = 0 {\displaystyle K_{ij}=0} and if ��� x i ��� x j ��� 2 ��� �� {\displaystyle \|x_{i}-x_{j}\|_{2}\ll \sigma } then K i j = 1 {\displaystyle K_{ij}=1} .  The former means that very little diffusion has taken place while the latter implies that the diffusion process is nearly complete.  Different strategies to choose �� {\displaystyle \sigma } can be found in.  In order to faithfully represent a Markov matrix, K {\displaystyle K} must be normalized by the corresponding degree matrix D {\displaystyle D} : P {\displaystyle P} now represents a Markov chain.  P ( x i , x j ) {\displaystyle P(x_{i},x_{j})} is the probability of transitioning from x i {\displaystyle x_{i}} to x j {\displaystyle x_{j}} in one time step.  Similarly the probability of transitioning from x i {\displaystyle x_{i}} to x j {\displaystyle x_{j}} in t time steps is given by P t ( x i , x j ) {\displaystyle P^{t}(x_{i},x_{j})} .


Nonlinear dimensionality reduction

Other_algorithms Here P t {\displaystyle P^{t}} is the matrix P {\displaystyle P} multiplied by itself t times.  The Markov matrix P {\displaystyle P} constitutes some notion of local geometry of the data set X.  The major difference between diffusion maps and principal component analysis is that only local features of the data are considered in diffusion maps as opposed to taking correlations of the entire data set.  K {\displaystyle K} defines a random walk on the data set which means that the kernel captures some local geometry of data set.  The Markov chain defines fast and slow directions of propagation through the kernel values.  As the walk propagates forward in time, the local geometry information aggregates in the same way as local transitions (defined by differential equations) of the dynamical system.  The metaphor of diffusion arises from the definition of a family diffusion distance { D t } t ��� N {\displaystyle \{D_{t}\}_{t\in N}} For fixed t, D t {\displaystyle D_{t}} defines a distance between any two points of the data set based on path connectivity: the value of D t ( x , y ) {\displaystyle D_{t}(x,y)} will be smaller the more paths that connect x to y and vice versa.  Because the quantity D t ( x , y ) {\displaystyle D_{t}(x,y)} involves a sum over of all paths of length t, D t {\displaystyle D_{t}} is much more robust to noise in the data than geodesic distance.  D t {\displaystyle D_{t}} takes into account all the relation between points x and y while calculating the distance and serves as a better notion of proximity than just Euclidean distance or even geodesic distance.  Local Multidimensional Scaling performs multidimensional scaling in local regions, and then uses convex optimization to fit all the pieces together.  Nonlinear PCA (NLPCA) uses backpropagation to train a multi-layer perceptron (MLP) to fit to a manifold.  Unlike typical MLP training, which only updates the weights, NLPCA updates both the weights and the inputs.  That is, both the weights and inputs are treated as latent values.  After training, the latent inputs are a low-dimensional representation of the observed vectors, and the MLP maps from that low-dimensional representation to the high-dimensional observation space.  Data-driven high-dimensional scaling (DD-HDS) is closely related to Sammon's mapping and curvilinear component analysis except that (1) it simultaneously penalizes false neighborhoods and tears by focusing on small distances in both original and output space, and that (2) it accounts for concentration of measure phenomenon by adapting the weighting function to the distance distribution.  Manifold Sculpting uses graduated optimization to find an embedding.  Like other algorithms, it computes the k-nearest neighbors and tries to seek an embedding that preserves relationships in local neighborhoods.  It slowly scales variance out of higher dimensions, while simultaneously adjusting points in lower dimensions to preserve those relationships.  If the rate of scaling is small, it can find very precise embeddings.  It boasts higher empirical accuracy than other algorithms with several problems.


Nonlinear dimensionality reduction

Other_algorithms It can also be used to refine the results from other manifold learning algorithms.  It struggles to unfold some manifolds, however, unless a very slow scaling rate is used.  It has no model.  RankVisu is designed to preserve rank of neighborhood rather than distance.  RankVisu is especially useful on difficult tasks (when the preservation of distance cannot be achieved satisfyingly).  Indeed, the rank of neighborhood is less informative than distance (ranks can be deduced from distances but distances cannot be deduced from ranks) and its preservation is thus easier.  Topologically constrained isometric embedding (TCIE) is an algorithm based on approximating geodesic distances after filtering geodesics inconsistent with the Euclidean metric.  Aimed at correcting the distortions caused when Isomap is used to map intrinsically non-convex data, TCIE uses weight least-squares MDS in order to obtain a more accurate mapping.  The TCIE algorithm first detects possible boundary points in the data, and during computation of the geodesic length marks inconsistent geodesics, to be given a small weight in the weighted stress majorization that follows.  Uniform manifold approximation and projection (UMAP) is a nonlinear dimensionality reduction technique.  Visually, it is similar to t-SNE, but it assumes that the data is uniformly distributed on a locally connected Riemannian manifold and that the Riemannian metric is locally constant or approximately locally constant.


Researcher

Definitions Research has been defined in a number of different ways, and while there are similarities, there does not appear to be a single, all-encompassing definition that is embraced by all who engage in it. Research in simplest terms is searching for knowledge and searching for truth. In a formal sense, it is a systematic study of a problem attacked by a deliberately chosen strategy which starts with choosing an approach to preparing a blueprint (design) and acting upon it in terms of designing research hypotheses, choosing methods and techniques, selecting or developing data collection tools, processing the data, interpretation and ends with presenting solution/s of the problem. Another definition of research is given by John W. Creswell, who states that "research is a process of steps used to collect and analyze information to increase our understanding of a topic or issue". It consists of three steps: pose a question, collect data to answer the question, and present an answer to the question. The Merriam-Webster Online Dictionary defines research more generally to also include studying already existing knowledge: "studious inquiry or examination; especially: investigation or experimentation aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts, or practical application of such new or revised theories or laws"


Researcher

Forms_of_research Original research, also called primary research, is research that is not exclusively based on a summary, review, or synthesis of earlier publications on the subject of research.  This material is of a primary-source character.  The purpose of the original research is to produce new knowledge, rather than to present the existing knowledge in a new form (e. g. , summarized or classified).  Original research can be in various forms, depending on the discipline it pertains to.  In experimental work, it typically involves direct or indirect observation of the researched subject(s), e. g. , in the laboratory or in the field, documents the methodology, results, and conclusions of an experiment or set of experiments, or offers a novel interpretation of previous results.  In analytical work, there are typically some new (for example) mathematical results produced, or a new way of approaching an existing problem.  In some subjects which do not typically carry out experimentation or analysis of this kind, the originality is in the particular way existing understanding is changed or re-interpreted based on the outcome of the work of the researcher.  The degree of originality of the research is among major criteria for articles to be published in academic journals and usually established by means of peer review.  Graduate students are commonly required to perform original research as part of a dissertation.  Scientific research is a systematic way of gathering data and harnessing curiosity.  This research provides scientific information and theories for the explanation of the nature and the properties of the world.  It makes practical applications possible.  Scientific research is funded by public authorities, by charitable organizations and by private groups, including many companies.  Scientific research can be subdivided into different classifications according to their academic and application disciplines.  Scientific research is a widely used criterion for judging the standing of an academic institution, but some argue that such is an inaccurate assessment of the institution, because the quality of research does not tell about the quality of teaching (these do not necessarily correlate).  Generally, research is understood to follow a certain structural process.  Though step order may vary depending on the subject matter and researcher, the following steps are usually part of most formal research, both basic and applied: Observations and formation of the topic: Consists of the subject area of one's interest and following that subject area to conduct subject-related research.  The subject area should not be randomly chosen since it requires reading a vast amount of literature on the topic to determine the gap in the literature the researcher intends to narrow.  A keen interest in the chosen subject area is advisable.  The research will have to be justified by linking its importance to already existing knowledge about the topic.  Hypothesis: A testable prediction which designates the relationship between two or more variables.  Conceptual definition: Description of a concept by relating it to other concepts.  Operational definition: Details in regards to defining the variables and how they will be measured/assessed in the study.


Researcher

Forms_of_research Gathering of data: Consists of identifying a population and selecting samples, gathering information from or about these samples by using specific research instruments.  The instruments used for data collection must be valid and reliable.  Analysis of data: Involves breaking down the individual pieces of data to draw conclusions about it.  Data Interpretation: This can be represented through tables, figures, and pictures, and then described in words.  Test, revising of hypothesis Conclusion, reiteration if necessaryA common misconception is that a hypothesis will be proven (see, rather, null hypothesis).  Generally, a hypothesis is used to make predictions that can be tested by observing the outcome of an experiment.  If the outcome is inconsistent with the hypothesis, then the hypothesis is rejected (see falsifiability).  However, if the outcome is consistent with the hypothesis, the experiment is said to support the hypothesis.  This careful language is used because researchers recognize that alternative hypotheses may also be consistent with the observations.  In this sense, a hypothesis can never be proven, but rather only supported by surviving rounds of scientific testing and, eventually, becoming widely thought of as true.  A useful hypothesis allows prediction and within the accuracy of observation of the time, the prediction will be verified.  As the accuracy of observation improves with time, the hypothesis may no longer provide an accurate prediction.  In this case, a new hypothesis will arise to challenge the old, and to the extent that the new hypothesis makes more accurate predictions than the old, the new will supplant it.  Researchers can also use a null hypothesis, which states no relationship or difference between the independent or dependent variables.  Research in the humanities involves different methods such as for example hermeneutics and semiotics.  Humanities scholars usually do not search for the ultimate correct answer to a question, but instead, explore the issues and details that surround it.  Context is always important, and context can be social, historical, political, cultural, or ethnic.  An example of research in the humanities is historical research, which is embodied in historical method.  Historians use primary sources and other evidence to systematically investigate a topic, and then to write histories in the form of accounts of the past.  Other studies aim to merely examine the occurrence of behaviours in societies and communities, without particularly looking for reasons or motivations to explain these.  These studies may be qualitative or quantitative, and can use a variety of approaches, such as queer theory or feminist theory.  Artistic research, also seen as 'practice-based research', can take form when creative works are considered both the research and the object of research itself.  It is the debatable body of thought which offers an alternative to purely scientific methods in research in its search for knowledge and truth.  The controversial trend of artistic teaching becoming more academics-oriented is leading to artistic research being accepted as the primary mode of enquiry in art as in the case of other disciplines.


Researcher

Forms_of_research One of the characteristics of artistic research is that it must accept subjectivity as opposed to the classical scientific methods.  As such, it is similar to the social sciences in using qualitative research and intersubjectivity as tools to apply measurement and critical analysis.  Artistic research has been defined by the School of Dance and Circus (Dans och Cirkush��gskolan, DOCH), Stockholm in the following manner ��� "Artistic research is to investigate and test with the purpose of gaining knowledge within and for our artistic disciplines.  It is based on artistic practices, methods, and criticality.  Through presented documentation, the insights gained shall be placed in a context. " Artistic research aims to enhance knowledge and understanding with presentation of the arts.  A simpler understanding by Julian Klein defines artistic research as any kind of research employing the artistic mode of perception.  For a survey of the central problematics of today's artistic research, see Giaco Schiesser.  According to artist Hakan Topal, in artistic research, "perhaps more so than other disciplines, intuition is utilized as a method to identify a wide range of new and unexpected productive modalities".  Most writers, whether of fiction or non-fiction books, also have to do research to support their creative work.  This may be factual, historical, or background research.  Background research could include, for example, geographical or procedural research.  The Society for Artistic Research (SAR) publishes the triannual Journal for Artistic Research (JAR), an international, online, open access, and peer-reviewed journal for the identification, publication, and dissemination of artistic research and its methodologies, from all arts disciplines and it runs the Research Catalogue (RC), a searchable, documentary database of artistic research, to which anyone can contribute.  Patricia Leavy addresses eight arts-based research (ABR) genres: narrative inquiry, fiction-based research, poetry, music, dance, theatre, film, and visual art.  In 2016, the European League of Institutes of the Arts launched The Florence Principles' on the Doctorate in the Arts.  The Florence Principles relating to the Salzburg Principles and the Salzburg Recommendations of the European University Association name seven points of attention to specify the Doctorate / PhD in the Arts compared to a scientific doctorate / PhD.  The Florence Principles have been endorsed and are supported also by AEC, CILECT, CUMULUS and SAR.  The historical method comprises the techniques and guidelines by which historians use historical sources and other evidence to research and then to write history.  There are various history guidelines that are commonly used by historians in their work, under the headings of external criticism, internal criticism, and synthesis.  This includes lower criticism and sensual criticism.


Researcher

Steps_in_conducting_research Research is often conducted using the hourglass model structure of research. The hourglass model starts with a broad spectrum for research, focusing in on the required information through the method of the project (like the neck of the hourglass), then expands the research in the form of discussion and results. The major steps in conducting research are: Identification of research problem Literature review Specifying the purpose of research Determining specific research questions Specification of a conceptual framework, sometimes including a set of hypotheses Choice of a methodology (for data collection) Data collection Verifying data Analyzing and interpreting the data Reporting and evaluating research Communicating the research findings and, possibly, recommendationsThe steps generally represent the overall process; however, they should be viewed as an ever-changing iterative process rather than a fixed set of steps. Most research begins with a general statement of the problem, or rather, the purpose for engaging in the study. The literature review identifies flaws or holes in previous research which provides justification for the study. Often, a literature review is conducted in a given subject area before a research question is identified. A gap in the current literature, as identified by a researcher, then engenders a research question. The research question may be parallel to the hypothesis. The hypothesis is the supposition to be tested. The researcher(s) collects data to test the hypothesis. The researcher(s) then analyzes and interprets the data via a variety of statistical methods, engaging in what is known as empirical research. The results of the data analysis in rejecting or failing to reject the null hypothesis are then reported and evaluated. At the end, the researcher may discuss avenues for further research. However, some researchers advocate for the reverse approach: starting with articulating findings and discussion of them, moving "up" to identification of a research problem that emerges in the findings and literature review. The reverse approach is justified by the transactional nature of the research endeavor where research inquiry, research questions, research method, relevant research literature, and so on are not fully known until the findings have fully emerged and been interpreted. Rudolph Rummel says, "... no researcher should accept any one or two tests as definitive. It is only when a range of tests are consistent over many kinds of data, researchers, and methods can one have confidence in the results." Plato in Meno talks about an inherent difficulty, if not a paradox, of doing research that can be paraphrased in the following way, "If you know what you're searching for, why do you search for it?! [i.e., you have already found it] If you don't know what you're searching for, what are you searching for?!"


Researcher

Research_methods The goal of the research process is to produce new knowledge or deepen understanding of a topic or issue.  This process takes three main forms (although, as previously discussed, the boundaries between them may be obscure): Exploratory research, which helps to identify and define a problem or question.  Constructive research, which tests theories and proposes solutions to a problem or question.  Empirical research, which tests the feasibility of a solution using empirical evidence. There are two major types of empirical research design: qualitative research and quantitative research.  Researchers choose qualitative or quantitative methods according to the nature of the research topic they want to investigate and the research questions they aim to answer: Qualitative research refers to much more subjective non- quantitative, use different methods of collecting data, analyzing data, interpreting data for meanings, definitions, characteristics, symbols metaphors of things. Qualitative research further classified into following types: Ethnography: This research mainly focus on culture of group of people which includes share attributes, language, practices, structure, value, norms and material things, evaluate human lifestyle.  Ethno: people, Grapho: to write, this disciple may include ethnic groups, ethno genesis, composition, resettlement and social welfare characteristics.  Phenomenology: It is very powerful strategy for demonstrating methodology to health professions education as well as best suited for exploring challenging problems in health professions educations.  The quantitative data collection methods rely on random sampling and structured data collection instruments that fit diverse experiences into predetermined response categories.  These methods produce results that can be summarized, compared, and generalized to larger populations if the data are collected using proper sampling and data collection strategies.  Quantitative research is concerned with testing hypotheses derived from theory or being able to estimate the size of a phenomenon of interest.  If the research question is about people, participants may be randomly assigned to different treatments (this is the only way that a quantitative study can be considered a true experiment). [citation needed] If this is not feasible, the researcher may collect data on participant and situational characteristics to statistically control for their influence on the dependent, or outcome, variable.  If the intent is to generalize from the research participants to a larger population, the researcher will employ probability sampling to select participants.  In either qualitative or quantitative research, the researcher(s) may collect primary or secondary data.  Primary data is data collected specifically for the research, such as through interviews or questionnaires.  Secondary data is data that already exists, such as census data, which can be re-used for the research.  It is good ethical research practice to use secondary data wherever possible.  Mixed-method research, i. e.  research that includes qualitative and quantitative elements, using both primary and secondary data, is becoming more common.  This method has benefits that using one method alone cannot offer.  For example, a researcher may choose to conduct a qualitative study and follow it up with a quantitative study to gain additional insights.


Researcher

Research_methods Big data has brought big impacts on research methods so that now many researchers do not put much effort into data collection; furthermore, methods to analyze easily available huge amounts of data have also been developed.  Types of Research Method 1.  Observatory Research Method 2.  Correlation Research Method Non-empirical (theoretical) research is an approach that involves the development of theory as opposed to using observation and experimentation.  As such, non-empirical research seeks solutions to problems using existing knowledge as its source.  This, however, does not mean that new ideas and innovations cannot be found within the pool of existing and established knowledge.  Non-empirical research is not an absolute alternative to empirical research because they may be used together to strengthen a research approach.  Neither one is less effective than the other since they have their particular purpose in science.  Typically empirical research produces observations that need to be explained; then theoretical research tries to explain them, and in so doing generates empirically testable hypotheses; these hypotheses are then tested empirically, giving more observations that may need further explanation; and so on.  See Scientific method.  A simple example of a non-empirical task is the prototyping of a new drug using a differentiated application of existing knowledge; another is the development of a business process in the form of a flow chart and texts where all the ingredients are from established knowledge.  Much of cosmological research is theoretical in nature.  Mathematics research does not rely on externally available data; rather, it seeks to prove theorems about mathematical objects.


Researcher

Problems_in_research Meta-research is the study of research through the use of research methods.  Also known as "research on research", it aims to reduce waste and increase the quality of research in all fields.  Meta-research concerns itself with the detection of bias, methodological flaws, and other errors and inefficiencies.  Among the finding of meta-research is a low rates of reproducibility across a large number of fields.  This widespread difficulty in reproducing research has been termed the "replication crisis. " In many disciplines, Western methods of conducting research are predominant.  Researchers are overwhelmingly taught Western methods of data collection and study.  The increasing participation of indigenous peoples as researchers has brought increased attention to the scientific lacuna in culturally sensitive methods of data collection.  Western methods of data collection may not be the most accurate or relevant for research on non-Western societies.  For example, "Hua Oranga" was created as a criterion for psychological evaluation in M��ori populations, and is based on dimensions of mental health important to the M��ori people ��� "taha wairua (the spiritual dimension), taha hinengaro (the mental dimension), taha tinana (the physical dimension), and taha whanau (the family dimension)".  Research is often biased in the languages that are preferred (linguicism) and the geographic locations where research occurs.  Periphery scholars face the challenges of exclusion and linguicism in research and academic publication.  As the great majority of mainstream academic journals are written in English, multilingual periphery scholars often must translate their work to be accepted to elite Western-dominated journals.  Multilingual scholars' influences from their native communicative styles can be assumed to be incompetence instead of difference.  For comparative politics, Western countries are over-represented in single-country studies, with heavy emphasis on Western Europe, Canada, Australia, and New Zealand.  Since 2000, Latin American countries have become more popular in single-country studies.  In contrast, countries in Oceania and the Caribbean are the focus of very few studies.  Patterns of geographic bias also show a relationship with linguicism: countries whose official languages are French or Arabic are far less likely to be the focus of single-country studies than countries with different official languages.  Within Africa, English-speaking countries are more represented than other countries.  Generalization is the process of more broadly applying the valid results of one study.  Studies with a narrow scope can result in a lack of generalizability, meaning that the results may not be applicable to other populations or regions.  In comparative politics, this can result from using a single-country study, rather than a study design that uses data from multiple countries.  Despite the issue of generalizability, single-country studies have risen in prevalence since the late 2000s.  Peer review is a form of self-regulation by qualified members of a profession within the relevant field.  Peer review methods are employed to maintain standards of quality, improve performance, and provide credibility.  In academia, scholarly peer review is often used to determine an academic paper's suitability for publication.


Researcher

Problems_in_research Usually, the peer review process involves experts in the same field who are consulted by editors to give a review of the scholarly works produced by a colleague of theirs from an unbiased and impartial point of view, and this is usually done free of charge.  The tradition of peer reviews being done for free has however brought many pitfalls which are also indicative of why most peer reviewers decline many invitations to review.  It was observed that publications from periphery countries rarely rise to the same elite status as those of North America and Europe, because limitations on the availability of resources including high-quality paper and sophisticated image-rendering software and printing tools render these publications less able to satisfy standards currently carrying formal or informal authority in the publishing industry.  These limitations in turn result in the under-representation of scholars from periphery nations among the set of publications holding prestige status relative to the quantity and quality of those scholars' research efforts, and this under-representation in turn results in disproportionately reduced acceptance of the results of their efforts as contributions to the body of knowledge available worldwide.  The open access movement assumes that all information generally deemed useful should be free and belongs to a "public domain", that of "humanity".  This idea gained prevalence as a result of Western colonial history and ignores alternative conceptions of knowledge circulation.  For instance, most indigenous communities consider that access to certain information proper to the group should be determined by relationships.  There is alleged to be a double standard in the Western knowledge system.  On the one hand, "digital right management" used to restrict access to personal information on social networking platforms is celebrated as a protection of privacy, while simultaneously when similar functions are used by cultural groups (i. e.  indigenous communities) this is denounced as "access control" and reprehended as censorship.  Even though Western dominance seems to be prominent in research, some scholars, such as Simon Marginson, argue for "the need [for] a plural university world".  Marginson argues that the East Asian Confucian model could take over the Western model.  This could be due to changes in funding for research both in the East and the West.  Focused on emphasizing educational achievement, East Asian cultures, mainly in China and South Korea, have encouraged the increase of funding for research expansion.  In contrast, in the Western academic world, notably in the United Kingdom as well as in some state governments in the United States, funding cuts for university research have occurred, which some[who? ] say may lead to the future decline of Western dominance in research.  Neo-colonial research or neo-colonial science, frequently described as helicopter research, parachute science or research, parasitic research, or safari study, is when researchers from wealthier countries go to a developing country, collect information, travel back to their country, analyze the data and samples, and publish the results with no or little involvement of local researchers.


Researcher

Problems_in_research A 2003 study by the Hungarian academy of sciences found that 70% of articles in a random sample of publications about least-developed countries did not include a local research co-author.  Frequently, during this kind of research, the local colleagues might be used to provide logistics support as fixers but are not engaged for their expertise or given credit for their participation in the research.  Scientific publications resulting from parachute science frequently only contribute to the career of the scientists from rich countries, thus limiting the development of local science capacity (such as funded research centers) and the careers of local scientists.  This form of "colonial" science has reverberations of 19th century scientific practices of treating non-Western participants as "others" in order to advance colonialism���and critics call for the end of these extractivist practices in order to decolonize knowledge.


Spark NLP

Usage_in_healthcare Spark NLP for Healthcare is a commercial extension of Spark NLP for clinical and biomedical text mining. It provides healthcare-specific annotators, pipelines, models, and embeddings for clinical entity recognition, clinical entity linking, entity normalization, assertion status detection, de-identification, relation extraction, and spell checking and correction. The library offers access to several clinical and biomedical transformers: JSL-BERT-Clinical, BioBERT, ClinicalBERT, GloVe-Med, GloVe-ICD-O. It also includes over 50 pre-trained healthcare models, that can recognize the entities such as clinical, drugs, risk factors, anatomy, demographics, and sensitive data.