Tagging Schemes Research Articles

Progress with Repository-based Annotation Infrastructure for Biodiversity Applications

Progress with Repository-based Annotation Infrastructure for Biodiversity Applications

STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research, aiming to extract triplets of the aspect term, its corresponding opinion term, and its associated sentiment polarity from a given sentence. Recently, many neural networks based models with different tagging schemes have been proposed, but almost all of them have their limitations: heavily relying on 1) prior assumption that each word is only associated with a single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level interactions and treating each opinion/aspect as a set of independent words. Hence, they perform poorly on the complex ASTE task, such as a word associated with multiple roles or an aspect/opinion term with multiple words. Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously. To this end, this paper formulates the ASTE task as a multi-class span classification problem. Specifically, STAGE generates more accurate aspect sentiment triplet extractions via exploring span-level information and constraints, which consists of two components, namely, span tagging scheme and greedy inference strategy. The former tag all possible candidate spans based on a newly-defined tagging set. The latter retrieves the aspect/opinion term with the maximum length from the candidate sentiment snippet to output sentiment triplets. Furthermore, we propose a simple but effective model based on the STAGE, which outperforms the state-of-the-arts by a large margin on four widely-used datasets. Moreover, our STAGE can be easily generalized to other pair/triplet extraction tasks, which also demonstrates the superiority of the proposed scheme STAGE.

Automatic sentence segmentation for classical Chinese: The Spring and Autumn Annals as an example

Abstract There exists no sentence boundary in most classical Chinese literature texts. Since it is difficult to read literature of this kind, experts in literature or linguistics would segment the sentence manually. This article explores the effectiveness of classical Chinese sentence segmentation method so as to provide a reference for classical Chinese punctuation. On the basis of the machine learning methods, we chose three components of machine learning, namely models, tagging schemes, and features, to compare the learning results. The models include conditional random field (CRF) models, long short term memory (LSTM) models, BiLSTM–CRF models, and three Bidirectional Encoder Representation from Transformers (BERT) models. There are five tagging schemes in this article and three features including the statistical feature, Guangyun, and Fanqie. Finally, the performance of the combined feature template is evaluated by ten-fold cross-validation on four classical Chinese texts in different genres. The SikuBERT model is proved to be the most effective model for sentence segmentation at present. Different tagging schemes and various features are introduced. The results show that 5-tag-J tagging schemes can improve performance. Statistical feature, as an important clue for classical Chinese sentence segmentation, is useful in related tasks, but Guangyun and Fanqie have little impact. Other important factors of sentence segmentation are genres and writing styles.

Pronounce differently, mean differently: A multi-tagging-scheme learning method for Chinese NER integrated with lexicon and phonetic features

Named Entity Recognition (NER) aims to automatically extract specific entities from the unstructured text. Compared with performing NER in English, Chinese NER is more challenging in recognizing entity boundaries because there are no explicit delimiters between Chinese characters. However, most previous researches focused on the semantic information of the Chinese language on the character level but ignored the importance of the phonetic characteristics. To address these issues, we integrated phonetic features of Chinese characters with the lexicon information to help disambiguate the entity boundary recognition by fully exploring the potential of Chinese as a pictophonetic language. In addition, a novel multi-tagging-scheme learning method was proposed, based on the multi-task learning paradigm, to alleviate the data sparsity and error propagation problems that occurred in the previous tagging schemes, by separately annotating the segmentation information of entities and their corresponding entity types. Extensive experiments performed on four Chinese NER benchmark datasets: OntoNotes4.0, MSRA, Resume, and Weibo, show that our proposed method consistently outperforms the existing state-of-the-art baseline models. The ablation experiments further demonstrated that the introduction of the phonetic feature and the multi-tagging-scheme has a significant positive effect on the improvement of the Chinese NER task.

Gene replacement strategies validate the use of functional tags on centromeric chromatin and invalidate an essential role for CENP-AK124ub.

SUMMARYFunctional tags are ubiquitous in cell biology, and for studies of one chromosomal locus, the centromere, tags have been remarkably useful. The centromere directs chromosome inheritance at cell division. The location of the centromere is defined by a histone H3 variant, CENP-A. The regulation of the chromatin assembly pathway essential for centromere inheritance and function includes posttranslational modification (PTM) of key components, including CENP-A itself. Others have recently called into question the use of functional tags, with the claim that at least two widely used tags obscured the essentiality of one particular PTM, CENP-AK124 ubiquitination (ub). Here, we employ three independent gene replacement strategies that eliminate large, lysine-containing tags to interrogate these claims. Using these approaches, we find no evidence to support an essential function of CENP-AK124ub. Our general methodology will be useful to validate discoveries permitted by powerful functional tagging schemes at the centromere and other cellular locations.

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. In this article, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. To facilitate this investigation, we compile a comprehensive biomedical NLP benchmark from publicly available datasets. Our experiments show that domain-specific pretraining serves as a solid foundation for a wide range of biomedical NLP tasks, leading to new state-of-the-art results across the board. Further, in conducting a thorough evaluation of modeling choices, both for pretraining and task-specific fine-tuning, we discover that some common practices are unnecessary with BERT models, such as using complex tagging schemes in named entity recognition. To help accelerate research in biomedical NLP, we have released our state-of-the-art pretrained and task-specific models for the community, and created a leaderboard featuring our BLURB benchmark (short for Biomedical Language Understanding & Reasoning Benchmark) at https://aka.ms/BLURB .

Femtosecond laser activation and sensing of hydroxyl for velocimetry in reacting flows.

A molecular tagging method for velocity measurements in reacting environments such as propulsion devices and high-temperature combustion-assisted wind tunnels is described. The method employs a femtosecond (write) laser to photodissociate H2O, a common combustion product, into a locally high concentration of OH radicals. These radicals are tracked by planar laser-induced fluorescence (PLIF) from the A2Σ-X2Π (1-0) vibrational band excited by a time-delayed 284 nm (read) laser sheet. As a variant of hydroxyl tagging velocimetry, the source laser can also be used to dissociate nitrogen for femtosecond laser electronic excitation tagging velocimetry to mark the time-zero location of the write laser for velocimetry in non-reacting regions using the same imaging system without OH PLIF. The OH tracer lifetime is studied in a hydrogen-air Hencken burner operating at Φ=0.5-1.8 to evaluate the tracking capability for velocimetry over a range of conditions. Effects of changing read laser wavelength, excitation energy, and influence of background flame emission are also studied. The data processing methodology and results are described for tracking displacements with 9-25 µm uncertainty in a hydrogen diffusion flame. This method presents several advantages in operational convenience and availability of laser sources, and it provides an avenue for improvements in the repetition rate, precision, and applicability over previously demonstrated hydroxyl tagging schemes.

Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes

Time expressions and named entities play important roles in data mining, information retrieval, and natural language processing. However, the conventional position-based tagging schemes (e.g., the BIO and BILOU schemes) that previous research used to model time expressions and named entities suffer from the problem of inconsistent tag assignment. To overcome the problem of inconsistent tag assignment, we designed a new type of tagging schemes to model time expressions and named entities based on their constituents. Specifically, to model time expressions, we defined a constituent-based tagging scheme termed TOMN scheme with four tags, namely T, O, M, and N, indicating the defined constituents of time expressions, namely time token, modifier, numeral, and the words outside time expressions. To model named entities, we defined a constituent-based tagging scheme termed UGTO scheme with four tags, namely U, G, T, and O, indicating the defined constituents of named entities, namely uncommon word, general modifier, trigger word, and the words outside named entities. In modeling, our TOMN and UGTO schemes model time expressions and named entities under conditional random fields with minimal features according to an in-depth analysis for the characteristics of time expressions and named entities. Experiments on diverse datasets demonstrate that our proposed methods perform equally with or more effectively than representative state-of-the-art methods on both time expression extraction and named entity extraction.

Looking into the Operational Modalities Adopted in Some of the POS Tagging Tools in Identification of Contextual Part-of-Speech of Words in Texts

Part-of-speech (POS) tagging is an indispensable method of text processing. The main aim is to assign part-of-speech to words after considering their actual contextual syntactic-cum-semantic roles in a piece of text where they occur (Siemund & Claridge 1997). This is a useful strategy in language processing, language technology, machine learning, machine translation, and computational linguistics as it generates a kind of output that enables a system to work with natural language texts with greater accuracy and success. Part-of-speech tagging is also known as ‘grammatical annotation’ and ‘word category disambiguation’ in some area of linguistics where analysis of form and function of words are important avenues for better comprehension and application of texts. Since the primary task of POS tagging involves a process of assigning a tag to each word, manually or automatically, in a piece of natural language text, it has to pay adequate attention to the contexts where words are used. This is a tough challenge for a system as it normally fails to know how word carries specific linguistic information in a text and what kind of larger syntactic frames it requires for its operation. The present paper takes up this issue into consideration and tries to critically explore how some of the well-known POS tagging systems are capable of handling this kind of challenge and if these POS tagging systems are at all successful in assigning appropriate POS tags to words without accessing information from extratextual domains. The novelty of the paper lies in its attempt for looking into some of the POS tagging schemes proposed so far to see if the systems are actually successful in dealing with the complexities involved in tagging words in texts. It also checks if the performance of these systems is better than manual POS tagging and verifies if information and insights gathered from such enterprises are at all useful for enhancing our understanding about identity and function of words used in texts. All these are addressed in this paper with reference to some of the POS taggers available to us. Moreover, the paper tries to see how a POS tagged text is useful in various applications thereby creating a sense of awareness about multifunctionality of tagged texts among language users.

Identifying bioethanol technology generations from the patent data

Reliable search results are necessary in the empirical analyses utilizing patent data. In this study, we evaluate the tagging schemes for patent documents related to low carbon technologies and provide a patent analysis of bioethanol with PCT patent publications between 2010 and 2016. The classification-based queries were optimized to find grain bioethanol (1G), cellulosic bioethanol (2G) and patents belonging to grain and cellulosic bioethanol (1G AND 2G) trajectories. The results reveal that based on original search strategy, only second-generation cellulosic bioethanol patents could be identified with a good match. Through use of co-classification and content analysis of patents we expand understanding of bioethanol inventions and potential future technology generations in the field.

Unraveling the couplings of a Drell-Yan produced Z′ with heavy-flavor tagging

Despite no new physics so far at the LHC, a $Z'$ boson with $m_{Z'} \sim 100$ GeV could still emerge via Drell-Yan (DY) production, $q \bar q \to Z' \to \mu^+ \mu^-$, in the next few years. To unravel the nature of the $Z'$ coupling, we utilize the $c$- and $b$-tagging algorithms developed by ATLAS and CMS to investigate $cg \to c Z'$ at 14 TeV LHC. While light-jet contamination can be eliminated, mistagged $b$-jets cannot be rejected in any of the tagging schemes we adopt. On the other hand, for nonzero $bbZ'$ coupling, far superior $b$-tagging could discover the $bg \to b Z'$ process, where again light-jet mistag can be ruled out, but mistagged $c$-jets cannot yet be excluded. Provided that DY production is discovered soon enough, we find that a simultaneous search for $c g \to c Z'$ and $b g \to b Z'$ can conclusively discern the nature of $Z'$ couplings involved.

American and British English: Divided by a Common Language? by Paul Baker, and: The Prodigal Tongue: The Love-Hate Relationship Between American and British English by Lynne Murphy

Reviewed by: American and British English: Divided by a Common Language? by Paul Baker, and: The Prodigal Tongue: The Love-Hate Relationship Between American and British English by Lynne Murphy Orin Hargraves (bio) American and British English: Divided by a Common Language?, by Paul Baker. Cambridge: Cambridge University Press, 2017. Pp. xiii + 264. $24.99 (paperback). ISBN: 978-1-107-46088-1. The Prodigal Tongue: The Love-Hate Relationship Between American and British English, by Lynne Murphy. New York: Penguin Books, 2018. Pp. 360. $17.00 (paperback). ISBN: 978-0-1431-3110-6 (also published in the U.K. by Oneworld Publications). The differences between the varieties of English spoken in the United States and the United Kingdom have been remarked on since colonial times. In public discourse, it’s a regular trope of journalists and columnists to remark on differences, often as a pretext for some other agenda. At the scholarly level, articles, papers, and books have been written to address diverse aspects of these dialectal differences and what they mean or portend. Many past and current DSNA members are among the contributors to this genre: John Algeo, Richard Bailey, Carol-June Cassidy, Edward Finegan, Paul Heacock, and myself, to name a few. Now two new books have appeared in this genre, and they are as different as chalk and cheese, to use a decidedly British expression. STILL DIFFERENT AFTER ALL THESE YEARS Paul Baker, a professor at Lancaster University in England, has written American and British English: Divided by a Common Language? It is a much more technically oriented book than the title might suggest; those who geek out on corpus statistics and the arcane ways in which they can be wrung from data will find much to examine in his book. Baker sets out to determine “to what extent are British and American English different, in what ways, and how have these differences altered over the last 100 years” (3). His approach to these questions is constrained by the data he examines, which is limited to the Brown family of corpora—a set of eight corpora comprising published material in separate collections of British and American English drawn from writing at different time periods. Baker’s claim is that comparisons between them have “high validity” (6) because they were all built using the same sampling frame. The corpora offer a view into the state of the dialects in 1931, 1961, 1991, and 2006. [End Page 115] To give a flavor of the highly technical analysis that Baker undertakes, you will be a third of the way through the book before there is much discussion of words and what they mean in one dialect or the other. Up to that point, his discussion centers on a more atomic view of the dialects’ components: spelling differences, morphemes, affixes, letter sequences. The results are laid out in tables, charts, and graphs, with accompanying interpretive discussion. Baker’s sophisticated analysis of the corpus data is far beyond what a casual corpus user might discover. It is based on algorithmic queries of the corpora that make a simple wildcard search feel like child’s play. You will learn, for example, that in the Brown corpora, mean sentence length in British English began a slow increase in 1961 that did not begin to taper off until 1991, but even in 2006 it remained significantly longer than sentence length in American English. Americans, for their part, come out on top of the “standardised type token ratios” (22; a measure of lexical diversity), an achievement in which the data suggests that they surpassed the British in about 1980. For the most part, Baker is examining corpus language through a more abstract lens than the one that lexicographers use. He generates statistics about language based on changing frequencies of semantic categories, syntactic functions, and parts of speech, as indicated in the corpora by computer-driven tagging schemes. The findings in Baker’s book will be of interest mainly to corpus linguists, who are also the readers most likely to find fault with them. His analysis of the Brown corpora are novel and ingenious, and his interpretations of his results often show great imagination. But are they...

Contribution of emissions to concentrations: the TAGGING 1.0 submodel based on the Modular Earth Submodel System (MESSy 2.52)

Abstract. Questions such as what is the contribution of road traffic emissions to climate change? or what is the impact of shipping emissions on local air quality? require a quantification of the contribution of specific emissions sectors to the concentration of radiatively active species and air-quality-related species, respectively. Here, we present a diagnostics package, implemented in the Modular Earth Submodel System (MESSy), which keeps track of the contribution of source categories (mainly emission sectors) to various concentrations. The diagnostics package is implemented as a submodel (TAGGING) of EMAC (European Centre for Medium-Range Weather Forecasts – Hamburg (ECHAM)/MESSy Atmospheric Chemistry). It determines the contributions of 10 different source categories to the concentration of ozone, nitrogen oxides, peroxyacytyl nitrate, carbon monoxide, non-methane hydrocarbons, hydroxyl, and hydroperoxyl radicals ( = tagged tracers). The source categories are mainly emission sectors and some other sources for completeness. As emission sectors, road traffic, shipping, air traffic, anthropogenic non-traffic, biogenic, biomass burning, and lightning are considered. The submodel obtains information on the chemical reaction rates, online emissions, such as lightning, and wash-out rates. It then solves differential equations for the contribution of a source category to each of the seven tracers. This diagnostics package does not feed back to any other part of the model. For the first time, it takes into account chemically competing effects: for example, the competition between NOx, CO, and non-methane hydrocarbons (NMHCs) in the production and destruction of ozone. We show that the results are in-line with results from other tagging schemes and provide plausibility checks for concentrations of trace gases, such as OH and HO2, which have not previously been tagged. The budgets of the tagged tracers, i.e. the contribution from individual source categories (mainly emission sectors) to, e.g., ozone, are only marginally sensitive to changes in model resolution, though the level of detail increases. A reduction in road traffic emissions by 5 % shows that road traffic global tropospheric ozone is reduced by 4 % only, because the net ozone productivity increases. This 4 % reduction in road traffic tropospheric ozone corresponds to a reduction in total tropospheric ozone by ≈ 0.3 %, which is compensated by an increase in tropospheric ozone from other sources by 0.1 %, resulting in a reduction in total tropospheric ozone of ≈ 0.2 %. This compensating effect compares well with previous findings. The computational costs of the TAGGING submodel are low with respect to computing time, but a large number of additional tracers are required. The advantage of the tagging scheme is that in one simulation and at every time step and grid point, information is available on the contribution of different emission sectors to the ozone budget, which then can be further used in upcoming studies to calculate the respective radiative forcing simultaneously.

Bisazide Cyanine Dyes as Fluorogenic Probes for Bis-Cyclooctynylated Peptide Tags and as Fluorogenic Cross-Linkers of Cyclooctynylated Proteins.

Herein we present the synthesis and fluorogenic characterization of a series of double-quenched bisazide cyanine probes with emission maxima between 565 and 580 nm that can participate in covalent, two-point binding bioorthogonal tagging schemes in combination with bis-cyclooctynylated peptides. Compared to other fluorogenic cyanines, these double-quenched systems showed remarkable fluorescence intensity increase upon formation of cyclic dye-peptide conjugates. Furthermore, we also demonstrated that these bisazides are useful fluorogenic cross-linking platforms that are able to form a covalent linkage between monocyclooctynylated proteins.

Particle tagging and its implications for stellar population dynamics

We establish a controlled comparison between the properties of galactic stellar halos obtained with hydrodynamical simulations and with `particle tagging'. Tagging is a fast way to obtain stellar population dynamics: instead of tracking gas and star formation, it `paints' stars directly onto a suitably defined subset of dark matter particles in a collisionless, dark-matter-only simulation.Our study shows that there are conditions under which particle tagging generates good fits to the hydrodynamical stellar density profiles of a central Milky-Way-like galaxy and its most prominent substructure. Phase-space diffusion processes are crucial to reshaping the distribution of stars in infalling spheroidal systems and hence the final stellar halo. We conclude that the success of any particular tagging scheme hinges on this diffusion being taken into account, at a minimum by making use of `live' tagging schemes, in which particles are regularly tagged throughout the evolution of a galaxy.

Named Entity Recognition System for Postpositional Languages: Urdu as a Case Study

Named Entity Recognition and Classification is the process of identifying named entities and classifying them into one of the classes like person name, organization name, location name, etc. In this paper, we propose a tagging scheme Begin Inside Last -2 (BIL2) for the Subject Object Verb (SOV) languages that contain postposition. We use the Urdu language as a case study. We compare the F-measure values obtained for the tagging schemes IO, BIO2, BILOU and BIL2 using Hidden Markov Model (HMM) and Conditional Random Field (CRF). The BIL2 tagging scheme results are better than the other three tagging schemes using the same parameters including bigram and context window. With HMM, the F-measure values for IO, BIO2, BILOU, and BIL2 are 44.87%, 44.88%, 45.14%, and 45.88%, respectively. With CRF, the F-measure values for IO, BIO2, BILOU, and BIL2 are 35.13%, 35.90%, 37.85%, and 38.39%, respectively. The F-measure values for BIL2 are better than those of previously reported techniques

Abstract B31: Development of fucose based pancreatic cancer biomarkers using modified lectins

Abstract Pancreatic cancer has poor prognosis because of ineffective and delayed diagnosis as well as treatment. Accurately identifying molecular changes could help with early detection and evaluation of treatment efficacy. Glycoprotein fucosylation is associated with various cancers. Interestingly, information at structural level such as diversity in fucose linkages can provide extensive details of disease state. But, lack of diagnostic reagents has impeded the research in identifying and interpreting these changes. This study is aimed to develop fucose based cancer biomarker reagents to identify important fucose linkages associated with cancer progression. We hypothesized that a panel of lectins, each specific for a particular type of fucose presentation, could identify differences between glycoproteins that are not discernable using any individual lectin or measurements of total fucose. Using a database of glycan array data, we identified lectins with high specificity for diverse linkages of fucose. We selected three for further testing: CGL2 for detecting fucose in a 2’ linkage, CCL2 for detecting fucose in a 3’ linkage, and RSL for detecting fucose in all linkages. To improve quality, production consistency, and test various modes of detection the lectins were recombinantly expressed with avitag (biotin) and poly-histidine fusion tags. The choice of terminus as well as the type of fusion tag could affect the molecular architecture and thereby lectin-glycan interaction. Therefore, two variants for each lectin (N terminus biotin and C terminus histidine and vice-versa) were expressed. The initial purification by size-exclusion chromatography showed similar results between the two tagging schemes for each protein. To test the accessibility of fusion tags and lectin multimerization with secondary detections (streptavidin or anti-poly-histindine), the two were pre-incubated prior to separation on an analytical scale size exclusion column resulting in protein elution that was entirely shifted to a higher molecular weight. Specificity for recombinant lectins to their respective linkages was performed using thermostability assay that resulted in a stability shift of lectins with respective glycan target but not with off-target glycan. To examine if the use of particular tag or secondary reagent was optimal for detection we performed competitive assay. Lectin binding to spotted glycoproteins in microarray assay was reduced upon pre-incubation with a competing sugar but not upon pre-incubation with a non-specific sugar. This suggests that recombinant lectins with fusion tags were functional. Lectins produced by recombinant expression showed specific glycan-binding using either a biotin or poly-histidine fusion tag at either terminus, but detection using the C-terminus tag was more reliable in each case. We probed the glycans on various glycoproteins with each lectin and predicted that the pattern of lectin binding would perform better than individual lectin measurements for identifying differences in fucose presentation. We tested this prediction using monoclonal antibodies against selected fucose-bearing glycans. Based on the known specificities of the antibodies, we could confirm differences in glycans predicted by the pattern of lectin binding but not by individual lectin measurements, thus confirming the ability to distinguish fucose presentations using a panel of lectins. This technique promises to help uncover details about the presentation of fucose on specific glycoproteins. The ability of these lectins to distinguish subtle changes in glycan presentation with enough precision at early stage and enable comparisons between tumor grades could prove valuable diagnostic reagents for pancreatic cancer. Citation Format: Sudhir Singh, Kuntal Pal, Elliot Ensink, Jessica Yadav, Doron Kletter, Marshall Bern, Anand Mehta, Karsten Melcher, Brian B. Haab. Development of fucose based pancreatic cancer biomarkers using modified lectins. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Innovations in Research and Treatment; May 18-21, 2014; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2015;75(13 Suppl):Abstract nr B31.

Synthesis and Evaluation of Nicotinic Acid Derived Tetrazines for Bioorthogonal Labeling

A set of nicotinic acid derived tetrazines were synthesized and evaluated for activity in inverse-electron-demand Diels–Alder (IEDDA­) reactions with various dienophiles. It was found that the performance of these tetrazines is governed by two factors. Theoretical and experimental investigations showed that steric effects may override the energetically predicted order of reactivity. Making a compromise between reactivity and stability, a selected tetrazine was incorporated into a deoxynucleotide to afford a bioorthogonalized building block enabling IEDDA-based tagging schemes of nucleic acids.

Correcting Mutual Coupling and Poor Isolation for Real-Time 2-D Microwave Imaging Systems

Recent technological advancements have made it possible to produce synthetic aperture radar (SAR) based microwave images in real time by using a variety of microwave imaging (array) systems. However, depending on the imaging array construction and the data collection scheme, the pertinent data of interest may be corrupted by undesired signals resulting from array element mutual coupling and overall poor isolation. Poor isolation associated with array elements may result from imperfect RF switching, internal coupling mechanisms inherent in a measurement system, or imperfect signal tagging schemes (i.e., multiple elements modulating when using the modulated scatterer technique). Images produced from such corrupted data are blurred or have artifacts that tend to mask the desired indications. This paper demonstrates the extension of a well-known correlation canceling technique for the purpose of preprocessing the data to remove such undesired coupling effects. To demonstrate its effectiveness, it is applied to a recently developed 2-D high-resolution and real-time microwave imaging system (camera). This camera is composed of 576 array elements, which are susceptible to the type of signal degradation mentioned above. Three correction estimates of the preprocessor are performed and compared. Two of the correction estimates directly address coupling, and the third, which does not consider coupling, is used for comparison purposes. Simulation results show the efficacy of this method, which is then corroborated by experiments.

HARDWARE SECURITY TAGS FOR ENHANCED OPERATING SYSTEM SECURITY

Most modern software is vulnerable to attack from a wide range of sources. To assist the system developer, researchers are looking at hardware-based security tagging schemes to enhance system security. This paper addresses the design and implementation of a new tagging scheme for access control and information flow; specifically the implementation at the assembly language level for a zero-kernel operating system. We also discuss key lessons learned that we have not seen addressed in related literature.