Relevant bibliographies by topics / Partial Labeling

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Partial Labeling'

Author: Grafiati
Published: 1 July 2021
Last updated: 12 February 2022

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Journal articles on the topic "Partial Labeling"

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Moorhoff, Cornelis M., and Wayne D. Cook. "Partial Deuterium Labeling of Dimethacrylate Monomers." Monatshefte für Chemie - Chemical Monthly 137, no. 4 (April 2006): 449–54. http://dx.doi.org/10.1007/s00706-005-0453-1.

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Arabmakki, Elaheh, and Mehmed Kantardzic. "SOM-based partial labeling of imbalanced data stream." Neurocomputing 262 (November 2017): 120–33. http://dx.doi.org/10.1016/j.neucom.2016.11.088.

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Saber, Eli, Yaowu Xu, and A. Murat Tekalp. "Partial shape recognition by sub-matrix matching for partial matching guided image labeling." Pattern Recognition 38, no. 10 (October 2005): 1560–73. http://dx.doi.org/10.1016/j.patcog.2005.03.027.

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Feng, Lei, and Bo An. "Partial Label Learning with Self-Guided Retraining." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3542–49. http://dx.doi.org/10.1609/aaai.v33i01.33013542.

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Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly. For pseudo-labeling, unlike traditional self-training that manually differentiates the ground-truth label with enough high confidence, we introduce the maximum infinity norm regularization on the modeling outputs to automatically achieve this consideratum, which results in a convex-concave optimization problem. We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems. By proposing an upper-bound surrogate objective function, we turn to solving only one QP problem for improving the optimization efficiency. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches.
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Welply, J. K., P. Shenbagamurthi, F. Naider, H. R. Park, and W. J. Lennarz. "Active site-directed photoaffinity labeling and partial characterization of oligosaccharyltransferase." Journal of Biological Chemistry 260, no. 10 (May 1985): 6459–65. http://dx.doi.org/10.1016/s0021-9258(18)88994-3.

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Narayanan, Lata, and Sunil Shende. "Partial characterizations of networks supporting shortest path interval labeling schemes." Networks 32, no. 2 (September 1998): 103–13. http://dx.doi.org/10.1002/(sici)1097-0037(199809)32:2<103::aid-net3>3.0.co;2-f.

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Chee, Yeow Meng, Charles J. Colbourn, Hoang Dau, Ryan Gabrys, Alan C. H. Ling, Dylan Lusi, and Olgica Milenkovic. "Access balancing in storage systems by labeling partial Steiner systems." Designs, Codes and Cryptography 88, no. 11 (August 14, 2020): 2361–76. http://dx.doi.org/10.1007/s10623-020-00786-z.

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Fang, Jun-Peng, and Min-Ling Zhang. "Partial Multi-Label Learning via Credible Label Elicitation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3518–25. http://dx.doi.org/10.1609/aaai.v33i01.33013518.

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In partial multi-label learning (PML), each training example is associated with multiple candidate labels which are only partially valid. The task of PML naturally arises in learning scenarios with inaccurate supervision, and the goal is to induce a multi-label predictor which can assign a set of proper labels for unseen instance. To learn from PML training examples, the training procedure is prone to be misled by the false positive labels concealed in candidate label set. In light of this major difficulty, a novel two-stage PML approach is proposed which works by eliciting credible labels from the candidate label set for model induction. In this way, most false positive labels are expected to be excluded from the training procedure. Specifically, in the first stage, the labeling confidence of candidate label for each PML training example is estimated via iterative label propagation. In the second stage, by utilizing credible labels with high labeling confidence, multi-label predictor is induced via pairwise label ranking with virtual label splitting or maximum a posteriori (MAP) reasoning. Extensive experiments on synthetic as well as real-world data sets clearly validate the effectiveness of credible label elicitation in learning from PML examples.
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Post, William, and Eric Jones. "TETRACYCLINE LABELING AS AN AID TO EXCISION OF PARTIAL PHYSEAL ARREST." Southern Medical Journal 84, Supplement (September 1991): 58. http://dx.doi.org/10.1097/00007611-199109001-00207.

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Bača, Martin, Nurdin Hinding, Aisha Javed, and Andrea Semaničová-Feňovčíková. "H-Irregularity Strengths of Plane Graphs." Symmetry 13, no. 2 (January 30, 2021): 229. http://dx.doi.org/10.3390/sym13020229.

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Graph labeling is the mapping of elements of a graph (which can be vertices, edges, faces or a combination) to a set of numbers. The mapping usually produces partial sums (weights) of the labeled elements of the graph, and they often have an asymmetrical distribution. In this paper, we study vertex–face and edge–face labelings of two-connected plane graphs. We introduce two new graph characteristics, namely the vertex–face H-irregularity strength and edge–face H-irregularity strength of plane graphs. Estimations of these characteristics are obtained, and exact values for two families of graphs are determined.
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Dissertations / Theses on the topic "Partial Labeling"

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Smyth, Patrick. "Studying the Temporal Dynamics of the Gut Microbiota Using Metabolic Stable Isotope Labeling and Metaproteomics." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42344.

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The gut microbiome and its metabolic processes are dynamic systems. Surprisingly, our understanding of gut microbiome dynamics is limited. Here we report a metaproteomic workflow that involves protein stable isotope probing (protein-SIP) and identification/quantification of partially labeled peptides. We also developed a package, which we call MetaProfiler, that corrects for false identifications and performs phylogenetic and time series analysis for the study of microbiome dynamics. From the stool sample of five mice that were fed with 15-N hydrolysate from Ralstonia eutropha, we identified 15,297 non-redundant unlabeled peptides of which 10,839 of their heavy counterparts were quantified. These peptides revealed incorporation profiles over time that were different between and within taxa, as well as between and within clusters of orthologous groups (COGs). Our study helps unravel the complex dynamics of protein synthesis and bacterial dynamics in the mouse gut microbiome.
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Meadows, Adam M. "Decompositions of Mixed Graphs with Partial Orientations of the P4." Digital Commons @ East Tennessee State University, 2009. https://dc.etsu.edu/etd/1870.

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A decomposition D of a graph H by a graph G is a partition of the edge set of H such that the subgraph induced by the edges in each part of the partition is isomorphic to G. A mixed graph on V vertices is an ordered pair (V,C), where V is a set of vertices, |V| = v, and C is a set of ordered and unordered pairs, denoted (x, y) and [x, y] respectively, of elements of V [8]. An ordered pair (x, y) ∈ C is called an arc of (V,C) and an unordered pair [x, y] ∈ C is called an edge of graph (V,C). A path on n vertices is denoted as Pn. A partial orientation on G is obtained by replacing each edge [x, y] ∈ E(G) with either (x, y), (y, x), or [x, y] in such a way that there are twice as many arcs as edges. The complete mixed graph on v vertices, denoted Mv, is the mixed graph (V,C) where for every pair of distinct vertices v1, v2 ∈ V , we have {(v1, v2), (v2, v1), [v1, v2]} ⊂ C. The goal of this thesis is to establish necessary and sufficient conditions for decomposition of Mv by all possible partial orientations of P4.
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Wu, Qinyi. "Partial persistent sequences and their applications to collaborative text document editing and processing." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/44916.

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In a variety of text document editing and processing applications, it is necessary to keep track of the revision history of text documents by recording changes and the metadata of those changes (e.g., user names and modification timestamps). The recent Web 2.0 document editing and processing applications, such as real-time collaborative note taking and wikis, require fine-grained shared access to collaborative text documents as well as efficient retrieval of metadata associated with different parts of collaborative text documents. Current revision control techniques only support coarse-grained shared access and are inefficient to retrieve metadata of changes at the sub-document granularity. In this dissertation, we design and implement partial persistent sequences (PPSs) to support real-time collaborations and manage metadata of changes at fine granularities for collaborative text document editing and processing applications. As a persistent data structure, PPSs have two important features. First, items in the data structure are never removed. We maintain necessary timestamp information to keep track of both inserted and deleted items and use the timestamp information to reconstruct the state of a document at any point in time. Second, PPSs create unique, persistent, and ordered identifiers for items of a document at fine granularities (e.g., a word or a sentence). As a result, we are able to support consistent and fine-grained shared access to collaborative text documents by detecting and resolving editing conflicts based on the revision history as well as to efficiently index and retrieve metadata associated with different parts of collaborative text documents. We demonstrate the capabilities of PPSs through two important problems in collaborative text document editing and processing applications: data consistency control and fine-grained document provenance management. The first problem studies how to detect and resolve editing conflicts in collaborative text document editing systems. We approach this problem in two steps. In the first step, we use PPSs to capture data dependencies between different editing operations and define a consistency model more suitable for real-time collaborative editing systems. In the second step, we extend our work to the entire spectrum of collaborations and adapt transactional techniques to build a flexible framework for the development of various collaborative editing systems. The generality of this framework is demonstrated by its capabilities to specify three different types of collaborations as exemplified in the systems of RCS, MediaWiki, and Google Docs respectively. We precisely specify the programming interfaces of this framework and describe a prototype implementation over Oracle Berkeley DB High Availability, a replicated database management engine. The second problem of fine-grained document provenance management studies how to efficiently index and retrieve fine-grained metadata for different parts of collaborative text documents. We use PPSs to design both disk-economic and computation-efficient techniques to index provenance data for millions of Wikipedia articles. Our approach is disk economic because we only save a few full versions of a document and only keep delta changes between those full versions. Our approach is also computation-efficient because we avoid the necessity of parsing the revision history of collaborative documents to retrieve fine-grained metadata. Compared to MediaWiki, the revision control system for Wikipedia, our system uses less than 10% of disk space and achieves at least an order of magnitude speed-up to retrieve fine-grained metadata for documents with thousands of revisions.
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Oliver, R. A. "Improved quantification of arterial spin labelling images using partial volume correction techniques." Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1473304/.

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Arterial Spin Labelling (ASL) MRI suffers from a phenomenon known as the partial volume effect (PVE), which causes a degradation in accuracy of quantitative perfusion estimates. The effect is caused by inadequate spatial resolution of the imaging system. Resolution of the system is determined by point spread function (PSF) of the imaging process and the voxel grid on which the image is sampled. ASL voxels are comparatively large, which leads to tissue signal mixing within an individual voxel that results in an underestimation of grey matter (GM) and overestimation of white matter (WM) perfusion. PV correction of ASL images is not routinely applied. When PVC is applied, it usually takes the form of correcting for tissue fraction only, often by masking voxels with a partial volume fraction below a certain threshold. There are recent efforts to correct for tissue fraction effect through the use of linear regression or Bayesian inferencing using high resolution tissue posterior probability maps to estimate tissue concentration. This thesis reports an investigation into techniques for PVC of ASL images. An extension to the linear regression method is described, using a 3D kernel to reduce the inherent blurring of this method and preserve spatial detail. An investigation into the application of a Bayesian inferencing toolkit (BASIL) to single timepoint ASL data to estimate GM and WM perfusion in the absence of kinetic information is described. BASIL is found to rely heavily on the spatial prior for perfusion when the number of signal averages is less than three, and is outperformed by linear regression in terms of spatial smoothing until five or more averages are used. An existing method of creating partial volumes estimates from low resolution data is modified to use a voxelwise estimation for the longitudinal relaxation of GM, which improves segmentation estimates in the deep GM structures and improves GM perfusion estimates. An estimate for the width of the PSF for the 3D GRASE imaging sequence used in these studies is made and incorporated into a complete solution for PVC of ASL data, which deblurs the data through the process of deconvolution of the PSF, prior to a correction for the tissue fraction effect. This is found to elevate GM and reduce WM perfusion to a greater extent than correcting for tissue fraction alone, even in the case of a segmented acquisition. The new method for PVC is applied to two clinical cohorts; a Frontal Temporal Dementia and Posterior Cortical Atrophy groups. These two populations exhibit differential patterns of cortical atrophy and reduced tissue metabolism, which remains after PV correction.
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McNamara, Peter 1978. "Edge labellings of partially ordered sets." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/16919.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2003.
Includes bibliographical references (p. 81-84) and index.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
It is well known that if a finite graded lattice of rank n is supersolvable, then it has an EL-labelling where the labels along any maximal chain form a permutation of [1, 2,..., n]. We call such a labelling an Sn EL-labelling and we show that a finite graded lattice of rank n is supersolvable if and only if it has such a labelling. This result can be used to show that a graded lattice is supersolvable if and only if it has a maximal chain of left modular elements. We next study finite graded bounded posets that have Sn EL-labellings and describe a type A 0-Hecke algebra action on their maximal chains. This action is local and the resulting representation of these Hecke algebras is closely related to the flag h-vector. We show that finite graded lattices of rank n, in particular, have such an action if and only if they have an Sn EL-labelling. Our next goal is to extend these equivalences to lattices that need not be graded and, furthermore, to bounded posets that need not be lattices. In joint work with Hugh Thomas, we define left modularity in this setting, as well as a natural extension of Sn EL-labellings, known as interpolating labellings. We also suitably extend the definition of lattice supersolvability to arbitrary bounded graded posets. We show that these extended definitions preserve the appropriate equivalences. Finally, we move to the study of P-partitions. Here, edges are labelled as either "strict" or "weak" depending on an underlying labelling of the elements of the poset. A well-known conjecture of R. Stanley states that the quasisymmetric generating function for P-partitions is symmetric if and only if P is isomorphic to a Schur labelled skew shape poset.
(cont.) In characterizing these skew shape posets in terms of their local structure, C. Malvenuto made significant progress on this conjecture. We generalize the definition of P-partitions by letting the set of strict edges be arbitrary. Using cylindric diagrams, we extend Stanley's conjecture and Malvenuto's characterization to this setting. We conclude by proving both conjectures for large classes of posets.
by Peter McNamara.
Ph.D.
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Van, Heerden Michael Rudi. "Improving the selectivity of the radio-labelling of ion exchange resin tracers for positron emission particle tracking." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/24310.

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Positron emission particle tracking (PEPT) is a technique which non-invasively tracks tracer particles labelled with a positron emitting radionuclide in a system of flow. The tracers are tracked through the detection in a PET scanner, of the two nearly collinear 511 keV gamma rays resulting from the annihilations of the positrons. For this technique to be effective, the tracer must be representative of the media in the system under study, and labelled with a sufficient activity of radionuclides. Organic ion exchange resins are suitable tracer materials for PEPT experiments, and are usually labelled with ⁶⁸Ga at the laboratories of PEPT Cape Town. The labelling performance relies on the chemical and physical properties of organic strongly acidic cation exchange resins and the nuclear chemistry of ⁶⁸Ga. The objective of this study is to obtain consistent tracer labelling throughout, or even beyond, the lifespan of the SnO₂ ⁶⁸Ge/⁶⁸Ga generator which degrades over time. The objective 1s achieved by integrating a purification technique into a "standard" radiolabelling method used at iThemba LABS. A small 0.5 ml Amberchrom CG-71 column is loaded with the ⁶⁸Ga generator eluent in 7 M HCl, then rinsed of most the contaminants before eluting the product with distilled water and used in the radiolabelling method. Using a 1-year-old 30 mCi SnO₂ ⁶⁸Ge/⁶⁸Ga generator eluent that has been purified by this method improved the radiolabelling performance by an average of at least 10% when compared to the performance of the un-purified product. Purifying the generator eluent will enable PEPT experiments of longer duration, and in highly shielded systems where tracers with high activity are required, such as granular and fluid flow in engineering applications.
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Kerr, Samantha Louise. "Enhancing nucleic acid detection using inductively coupled plasma mass spectrometry, by means of metal and nano-particle labelling." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/4641.

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The application of ICP-MS to the fields of proteomics and genomics has arisen in part due to its ability to detect and quantify trace levels of S and P, which are major constituents in proteins and nucleic acids respectively. The development of collision/reaction cell technology and high resolution instruments has enabled these biologically important elements to be measured and quantified at the pg - ng ml-1 level. Despite these advances, the detection limits of P and S are still inferior compared to other elements. Oligonucleotides containing biotin functionality were labelled with Au nano-particles attached to a streptavidin protein to achieve site specific labelling, with 100% labelling efficiency. Each nano-particle contained ~86 Au atoms, resulting in an 882 fold signal enhancement for 24 base length oligonucleotides. However, this enhancement factor was only observed when one oligonucleotide bound to one nano-particle in a 1:1 ratio. Much lower Au labelling efficiencies and signal enhancements were observed when thiolated oligonucleotides were labelled with maleimide functionalised gold nano-particles. This was attributed to the extensive and difficult sample preparation steps that were required prior to labelling. The detection and quantification of adducts formed between DNA and the Pt anti-cancer drugs cisplatin and oxaliplatin were also investigated with ICP-MS. Acid digestion of the carbon based DNA matrix enabled Pt adducts to be quantified at low dose rates of 1 Pt atom per 1 500 000 nucleotides in ~12 μg DNA. Such sensitive mass spectrometric determinations could be employed in clinical tests to detect and quantify low level adducts formed in patients in-vivo. To complement ICP-MS analysis, electrospray ionisation linear ion trap mass spectrometry was employed to study the interaction of oxaliplatin with the four DNA nucleobases. Multiple stage mass spectrometry enabled detailed Pt-nucleobase adduct fragmentation pathways to be established. The method of DNA detection using P in conjunction with the collision cell, or cool plasma to form PO+ was also demonstrated and the limitations of the method, namely, polyatomic interferences and severe matrix effects were highlighted.
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Kim, Kilsun. "Investigations of Electronic Cigarette Chemistry: 1. Formation Pathways for Degradation Products Using Isotopic Labeling; and 2. Gas/Particle Partitioning of Nicotine and Flavor Related Chemicals in Electronic Cigarette Fluids." PDXScholar, 2017. https://pdxscholar.library.pdx.edu/open_access_etds/3944.

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Use of electronic cigarettes (e-cigarettes) is rapidly growing around the world. E-cigarettes are commonly used as an alternative nicotine delivery system, and have been advocated as generating lower levels of harmful chemicals compared to conventional cigarettes. Cigarette smoke-like aerosols are generated when e-cigarettes heat e-liquids. The main components of e-cigarette liquids are propylene glycol (PG) and glycerol (GL) in a varying ratio, plus nicotine and flavor chemicals. Both PG and GL are considered safe to ingest in foods and beverages, but the toxicity of these chemicals in aerosols is unknown. Current studies of e-cigarettes have mainly focused on dehydration and oxidation products of PG and GL. In this study, the other degradation products that can be generated during the vaping process are discussed. In addition, the gas/particle partitioning of chemicals in vaping aerosols is determined. This work finds that the formation of benzene in electronic cigarettes depends on the wattage, types of coils, and devices. To simulate commerical e-cigarette liquids, mixtures containing equal parts of PG and GL by volume were made with the following added components: benzoic acid (BA), benzoic acid with nicotine (Nic), benzaldehyde (BZ), band enzaldehyde with nicotine. PG only, GL only, and PG and GL mixtures were also made for comparison. The data presented here demonstrate that more benzene is generated as the wattage of a device increases. The results also seem to support the importance of ventilation in the generation of benzene. More benzene is generated from the mixtures containing benzoic acid when using the EVOD device with a smaller vent. However, benzaldehyde yields more benzene when using the Subtank Nano device with a larger vent. Findings also indicate that more benzene is produced from GL rather than PG. This thesis also addresses the chemical formation pathways of degradation compounds found in the aerosols formed from isotopically labeled e-cigarette liquids. Mixtures of both 13C-labeled and unlabeled PG as well as GL were made. The mixtures were vaped and gas-phase samples were collected to determine which chemicals were in the gas-phase portion of the aerosols. With the use of GC/MS methods, these isotopic labeling experiments provided evidence that the majority of the benzene, acetaldehyde, 2,3-butanedione, toluene, xylene, acrolein, and furan found in e-cigarette aerosols originates from GL in the PG plus GL mixtures. It was also shown that the majority of propanal is derived from PG: while hydroxyacetone can be formed from both PG and GL. Possible mechanisms for the formation of acetaldehyde, benzene, 2,3-butanedione, toluene, and xylene formation are proposed. Last, this study investigated the gas/particle partitioning of nicotine and flavor-related chemicals in e-cigarette fluids. The gas/particle partitioning behavior of chemicals in e-cigarettes fluids is highly dependent on the chemical volatility. A total of 37 compounds were examined. The target compounds were divided into 3 groups based on their vapor pressures: high, medium, and low. Headspace gas samples were collected and analyzed to determine the concentration of a compound in equilibrium with the liquid phase. The gas and liquid concentrations were used to calculate the gas/particle partitioning constant (Kp) for each compound. In an e-cigarette aerosol, volatile compounds have smaller Kp values and tend to be found in greater proportion in the gas-phase, whereas the less volatile compounds are likely to stay in the particle phase. General agreement with theory was found for compounds with known activity coefficients in PG and GL, indicating that theory can be used to predict Kp values for other compounds.
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Rojas, Lizana Claudia Nicol, and Salazar Christopher Alvaro Collins. "Factores que influyeron en las exportaciones de la leche evaporada con partida arancelaria 0402911000 hacia el mercado de los Estados Unidos durante el periodo 2008-2018." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/653715.

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La presente investigación tuvo como objetivo principal determinar los factores que influyeron en las exportaciones de la leche evaporada con partida arancelaria 0402911000 hacia el mercado de los Estados Unidos durante el periodo 2008-2018. La metodología que se usó para el estudio fue de tipo descriptiva, con diseño no experimental, con un corte longitudinal y con un enfoque mixto al intervenir variables cualitativas y cuantitativas, tales como las medidas sanitarias y fitosanitarias, el valor FOB y la producción. El principal resultado en el enfoque cualitativo fue el grado de influencia de las exigentes medidas sanitarias que impactó en el desempeño de las exportaciones al mercado estadounidense, debido a que, las normas norteamericanas difieren de las normas internacionales. Por otro lado, en el enfoque cuantitativo, el hallazgo en la competitividad del valor FOB ha favorecido las exportaciones hacia el mercado estadounidense. Sin embargo, en la producción, pese a que se cuenta con la materia prima necesaria para abastecer mercados internacionales, la falta de apoyo por parte del Estado para orientar a los productores es la causa de fondo del porque no se ha llegado a la competitividad esperada. La población de estudio está conformada por 16 gerentes y jefes de las empresas Gloria y Nestlé; En donde se aplicó entrevistas semiestructuradas a expertos como instrumento de recolección de datos. El estudio pretende responder si las normas de etiquetado son una barrera al comercio, y si la competitividad del valor FOB y la producción contribuye a las exportaciones.
The main objective of this investigation was to determine the factors that influenced the evaporated milk exports tariff heading 0402911000 to the United States market during the period 2008-2018. The methodology used for the study was descriptive, with a non-experimental design, with a longitudinal section and with a mixed approach, involving qualitative and quantitative variables, such as sanitary and phytosanitary measures, FOB value production. The main result in the qualitative approach was the degree of influence of the demanding sanitary measures that had and impact of the performance of exports to the US market, because the North American standards differ from the international standards. On the other hand, in the quantitative approach, the finding in the competitiveness of the FOB value has favored exports to the US market. However, in production, despite having the raw material necessary to supply international markets, the lack of support from the State to guide producers is the root cause of why the expected competitiveness has not been reached. The study population is made up of 16 managers and heads of the companies Gloria and Nestle; Where semi-structure interviews with experts were applied as an instrument for data collection. The study aims to answer whether labeling rules are a barrier to trade, and whether the competitiveness of FOB value and production contributes to exports.
Tesis
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Meftah, Sara. "Neural Transfer Learning for Domain Adaptation in Natural Language Processing." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG021.

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Les méthodes d’apprentissage automatique qui reposent sur les Réseaux de Neurones (RNs) ont démontré des performances de prédiction qui s'approchent de plus en plus de la performance humaine dans plusieurs applications du Traitement Automatique de la Langue (TAL) qui bénéficient de la capacité des différentes architectures des RNs à généraliser à partir des régularités apprises à partir d'exemples d'apprentissage. Toutefois, ces modèles sont limités par leur dépendance aux données annotées. En effet, pour être performants, ces modèles neuronaux ont besoin de corpus annotés de taille importante. Par conséquent, uniquement les langues bien dotées peuvent bénéficier directement de l'avancée apportée par les RNs, comme par exemple les formes formelles des langues. Dans le cadre de cette thèse, nous proposons des méthodes d'apprentissage par transfert neuronal pour la construction d'outils de TAL pour les langues peu dotées en exploitant leurs similarités avec des langues bien dotées. Précisément, nous expérimentons nos approches pour le transfert à partir du domaine source des textes formels vers le domaine cible des textes informels (langue utilisée dans les réseaux sociaux). Tout au long de cette thèse nous proposons différentes contributions. Tout d'abord, nous proposons deux approches pour le transfert des connaissances encodées dans les représentations neuronales d'un modèle source, pré-entraîné sur les données annotées du domaine source, vers un modèle cible, adapté par la suite sur quelques exemples annotés du domaine cible. La première méthode transfère des représentations contextuelles pré-entraînées sur le domaine source. Tandis que la deuxième méthode utilise des poids pré-entraînés pour initialiser les paramètres du modèle cible. Ensuite, nous effectuons une série d'analyses pour repérer les limites des méthodes proposées ci-dessus. Nous constatons que, même si l'approche d'apprentissage par transfert proposée améliore les résultats du domaine cible, un transfert négatif « dissimulé » peut atténuer le gain final apporté par l'apprentissage par transfert. De plus, une analyse interprétative du modèle pré-entraîné, montre que les neurones pré-entraînés peuvent être biaisés par ce qu'ils ont appris du domaine source, et donc peuvent avoir des difficultés à apprendre des « patterns » spécifiques au domaine cible. Issu de notre analyse, nous proposons un nouveau schéma d'adaptation qui augmente le modèle cible avec des neurones normalisés, pondérés et initialisés aléatoirement qui permettent une meilleure adaptation au domaine cible tout en conservant les connaissances apprises du domaine source. Enfin, nous proposons une approche d’apprentissage par transfert qui permet de profiter des similarités entre différentes tâches, en plus des connaissances pré-apprises du domaine source
Recent approaches based on end-to-end deep neural networks have revolutionised Natural Language Processing (NLP), achieving remarkable results in several tasks and languages. Nevertheless, these approaches are limited with their "gluttony" in terms of annotated data, since they rely on a supervised training paradigm, i.e. training from scratch on large amounts of annotated data. Therefore, there is a wide gap between NLP technologies capabilities for high-resource languages compared to the long tail of low-resourced languages. Moreover, NLP researchers have focused much of their effort on training NLP models on the news domain, due to the availability of training data. However, many research works have highlighted that models trained on news fail to work efficiently on out-of-domain data, due to their lack of robustness against domain shifts. This thesis presents a study of transfer learning approaches, through which we propose different methods to take benefit from the pre-learned knowledge on the high-resourced domain to enhance the performance of neural NLP models in low-resourced settings. Precisely, we apply our approaches to transfer from the news domain to the social media domain. Indeed, despite the importance of its valuable content for a variety of applications (e.g. public security, health monitoring, or trends highlight), this domain is still poor in terms of annotated data. We present different contributions. First, we propose two methods to transfer the knowledge encoded in the neural representations of a source model pretrained on large labelled datasets from the source domain to the target model, further adapted by a fine-tuning on few annotated examples from the target domain. The first transfers contextualised supervisedly pretrained representations, while the second method transfers pretrained weights, used to initialise the target model's parameters. Second, we perform a series of analysis to spot the limits of the above-mentioned proposed methods. We find that even if the proposed transfer learning approach enhances the performance on social media domain, a hidden negative transfer may mitigate the final gain brought by transfer learning. In addition, an interpretive analysis of the pretrained model, show that pretrained neurons may be biased by what they have learned from the source domain, thus struggle with learning uncommon target-specific patterns. Third, stemming from our analysis, we propose a new adaptation scheme which augments the target model with normalised, weighted and randomly initialised neurons that beget a better adaptation while maintaining the valuable source knowledge. Finally, we propose a model, that in addition to the pre-learned knowledge from the high-resource source-domain, takes advantage of various supervised NLP tasks
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Books on the topic "Partial Labeling"

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Chappell, Michael, Bradley MacIntosh, and Thomas Okell. Partial Volume Effects. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793816.003.0006.

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Partial volume effects are present in all medical imaging methods, and they play a specific role in arterial spin labeling (ASL) MRI measurements of perfusion. This chapter demonstrates how differences in the perfusion properties of gray matter, white matter, and cerebrospinal fluid give rise to the distinctive visual appearance of cerebral perfusion images. The implications of this for quantification of perfusion in gray matter are discussed and solutions to correct for partial volume effects presented.
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Book chapters on the topic "Partial Labeling"

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Bistarelli, Stefano, Philippe Codognet, Yan Georget, and Francesca Rossi. "Labeling and Partial Local Consistency for Soft Constraint Programming." In Practical Aspects of Declarative Languages, 230–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46584-7_16.

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Chen, Jiangning, Zhibo Dai, Juntao Duan, Qianli Hu, Ruilin Li, Heinrich Matzinger, Ionel Popescu, and Haoyan Zhai. "A Cost-Reducing Partial Labeling Estimator in Text Classification Problem." In Advances in Intelligent Systems and Computing, 494–511. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39442-4_37.

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Kovtun, Ivan. "Partial Optimal Labeling Search for a NP-Hard Subclass of (max,+) Problems." In Lecture Notes in Computer Science, 402–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45243-0_52.

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Ambarki, K., J. Petr, A. Wåhlin, R. Wirestam, L. Zarrinkoob, J. Malm, and A. Eklund. "Partial Volume Correction of Cerebral Perfusion Estimates Obtained by Arterial Spin Labeling." In IFMBE Proceedings, 17–19. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12967-9_5.

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Castellano, G., A. M. Fanelli, and M. A. Torsello. "Fuzzy Image Labeling by Partially Supervised Shape Clustering." In Knowlege-Based and Intelligent Information and Engineering Systems, 84–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23863-5_9.

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Neele, Thomas, Antti Valmari, and Tim A. C. Willemse. "The Inconsistent Labelling Problem of Stutter-Preserving Partial-Order Reduction." In Lecture Notes in Computer Science, 482–501. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_25.

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AbstractIn model checking, partial-order reduction (POR) is an effective technique to reduce the size of the state space. Stubborn sets are an established variant of POR and have seen many applications over the past 31 years. One of the early works on stubborn sets shows that a combination of several conditions on the reduction is sufficient to preserve stutter-trace equivalence, making stubborn sets suitable for model checking of linear-time properties. In this paper, we identify a flaw in the reasoning and show with a counter-example that stutter-trace equivalence is not necessarily preserved. We propose a solution together with an updated correctness proof. Furthermore, we analyse in which formalisms this problem may occur. The impact on practical implementations is limited, since they all compute a correct approximation of the theory.
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Consoli, S., J. A. Moreno Pérez, K. Darby-Dowman, and N. Mladenović. "Discrete Particle Swarm Optimization for the Minimum Labelling Steiner Tree Problem." In Nature Inspired Cooperative Strategies for Optimization (NICSO 2007), 313–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78987-1_28.

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Koch, Lisa M., Martin Rajchl, Tong Tong, Jonathan Passerat-Palmbach, Paul Aljabar, and Daniel Rueckert. "Multi-atlas Segmentation as a Graph Labelling Problem: Application to Partially Annotated Atlas Data." In Lecture Notes in Computer Science, 221–32. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19992-4_17.

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Wojtczyk, Hanne, Julian Haegele, Mandy Grüttner, Wiebke Tenner, Gael Bringout, Matthias Graeser, Florian M. Vogt, Jörg Barkhausen, and Thorsten M. Buzug. "Visualization of Instruments in interventional Magnetic Particle Imaging (iMPI): A Simulation Study on SPIO Labelings." In Springer Proceedings in Physics, 167–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24133-8_27.

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Bouchachia, Abdelhamid. "Learning with Partial Supervision." In Machine Learning, 1880–88. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch801.

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Recently the field of machine learning, pattern recognition, and data mining has witnessed a new research stream that is <i>learning with partial supervisio</i>n -LPS- (known also as <i>semi-supervised learning</i>). This learning scheme is motivated by the fact that the process of acquiring the labeling information of data could be quite costly and sometimes prone to mislabeling. The general spectrum of learning from data is envisioned in Figure 1. As shown, in many situations, the data is neither perfectly nor completely labeled.<div><br></div><div>LPS aims at using available labeled samples in order to guide the process of building classification and clustering machineries and help boost their accuracy. Basically, LPS is a combination of two learning paradigms: supervised and unsupervised where the former deals exclusively with labeled data and the latter is concerned with unlabeled data. Hence, the following questions:</div><div><br></div><div><ul><li>Can we improve supervised learning with unlabeled data?&nbsp;<br></li><li>Can we guide unsupervised learning by incorporating few labeled samples?<br></li></ul></div><div><br></div><div>Typical LPS applications are medical diagnosis (Bouchachia &amp; Pedrycz, 2006a), facial expression recognition (Cohen et al., 2004), text classification (Nigam et al., 2000), protein classification (Weston et al., 2003), and several natural language processing applications such as word sense disambiguation (Niu et al., 2005), and text chunking (Ando &amp; Zhangz, 2005).</div><div><br></div><div>Because LPS is still a young but active research field, it lacks a survey outlining the existing approaches and research trends. In this chapter, we will take a step towards an overview. We will discuss (i) the background of LPS, (iii) the main focus of our LPS research and explain the underlying assumptions behind LPS, and (iv) future directions and challenges of LPS research. </div>
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Conference papers on the topic "Partial Labeling"

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Arabmakki, Elaheh, Mehmed Kantardzic, and Tegjyot Singh Sethi. "Ensemble Classifier for Imbalanced Streaming Data Using Partial Labeling." In 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI). IEEE, 2016. http://dx.doi.org/10.1109/iri.2016.40.

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Lin, Lu, Zheng Luo, Dezhi Hong, and Hongning Wang. "Sequential Learning with Active Partial Labeling for Building Metadata." In BuildSys '19: The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3360322.3360866.

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Petr, Jan, Jean-Christophe Ferre, Jean-Yves Gauvrit, and Christian Barillot. "Denoising arterial spin labeling MRI using tissue partial volume." In SPIE Medical Imaging, edited by Benoit M. Dawant and David R. Haynor. SPIE, 2010. http://dx.doi.org/10.1117/12.844443.

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Arabmakki, Elaheh, Mehmed Kantardzic, and Tegjyot Singh Sethi. "A partial labeling framework for multi-class imbalanced streaming data." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7965964.

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Chee, Y. M., C. J. Colbourn, H. Dau, R. Gabrys, A. C. H. Ling, D. Lusi, and O. Milenkovic. "Access Balancing in Storage Systems by Labeling Partial Steiner Systems." In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174154.

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Tian, Yuandong, Wei Liu, Rong Xiao, Fang Wen, and Xiaoou Tang. "A Face Annotation Framework with Partial Clustering and Interactive Labeling." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383282.

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Feng, Lei, and Bo An. "Leveraging Latent Label Distributions for Partial Label Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/291.

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In partial label learning, each training example is assigned a set of candidate labels, only one of which is the ground-truth label. Existing partial label learning frameworks either assume each candidate label of equal confidence or consider the ground-truth label as a latent variable hidden in the indiscriminate candidate label set, while the different labeling confidence levels of the candidate labels are regrettably ignored. In this paper, we formalize the different labeling confidence levels as the latent label distributions, and propose a novel unified framework to estimate the latent label distributions while training the model simultaneously. Specifically, we present a biconvex formulation with constrained local consistency and adopt an alternating method to solve this optimization problem. The process of alternating optimization exactly facilitates the mutual adaption of the model training and the constrained label propagation. Extensive experimental results on controlled UCI datasets as well as real-world datasets clearly show the effectiveness of the proposed approach.
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Rehbein, Ines, Josef Ruppenhofer, and Caroline Sporleder. "Assessing the benefits of partial automatic pre-labeling for frame-semantic annotation." In the Third Linguistic Annotation Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1698381.1698384.

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Liu, Yang, Baojuan Li, Xi Zhang, Linchuan Zhang, Zhengrong Liang, and Hongbing Lu. "Partial volume correction for arterial spin labeling data using spatial-temporal information." In SPIE Medical Imaging, edited by Sébastien Ourselin and Martin A. Styner. SPIE, 2015. http://dx.doi.org/10.1117/12.2081923.

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Wang, Qian-Wei, Yu-Feng Li, and Zhi-Hua Zhou. "Partial Label Learning with Unlabeled Data." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/521.

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Partial label learning deals with training examples each associated with a set of candidate labels, among which only one label is valid. Previous studies typically assume that the candidate label sets are provided for all training examples. In many real-world applications such as video character classification, however, it is generally difficult to label a large number of instances and there exists much data left to be unlabeled. We call this kind of problem semi-supervised partial label learning. In this paper, we propose the SSPL method to address this problem. Specifically, an iterative label propagation procedure between partial label examples and unlabeled instances is employed to disambiguate the candidate label sets of partial label examples as well as assign valid labels to unlabeled instances. The importance of unlabeled instances increases adaptively as the number of iteration increases, since they carry richer labeling information. Finally, unseen instances are classified based on the minimum reconstruction error on both partial label and unlabeled instances. Experiments on real-world data sets clearly validate the effectiveness of the proposed SSPL method.
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Reports on the topic "Partial Labeling"

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Salter, R., Quyen Dong, Cody Coleman, Maria Seale, Alicia Ruvinsky, LaKenya Walker, and W. Bond. Data Lake Ecosystem Workflow. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40203.

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The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.
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Kim, Kilsun. Investigations of Electronic Cigarette Chemistry: 1. Formation Pathways for Degradation Products using Isotopic Labeling; and 2. Gas/Particle Partitioning of Nicotine and Flavor Related Chemicals in Electronic Cigarette Fluids. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5828.

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